ml-finance-python

Blog_Post_Zipline_SigOpt.ipynb

(1471688B)

 {
  "cells": [
   {
    "cell_type": "markdown",
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    "source": [
     "# Bayesian Optimization of a Technical Trading Algorithm with SigOpt\n",
     "\n",
     "Authors: Justin Lent (Quantopian), Thomas Wiecki (Quantopian), [Scott Clark](https://sigopt.com/about#scott) ([SigOpt](https://sigopt.com))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Parameter optimization of trading algorithms is quite different from most other optimization problems. Specifically, the optimization problem is non-convex, non-linear, stochastic, and can include a mix of integers, floats and enums as parameters. Moreover, most optimizers assume that the objective function is quick to evaluate which is definitely not the case for a trading algorithm run over multiple years of financial data. Immediately that disqualifies 95% of optimizers including those offered by `scipy` or `cvxopt`. At Quantopian we have long been interested in parameter optimization of trading algorithms (some prior blog posts on this topic can be found [here](), [here](), and [here]()). \n",
     "\n",
     "**Bayesian optimization** is a rather novel approach to the selection of parameters that is very well suited to optimize trading algorithms. This blog post will first provide a short introduction to Bayesian optimization with a focus on why it is well suited for quantitative finance. We will then show how you can use [SigOpt](http://sigopt.com/) to perform Bayesian optimization on your own trading algorithms running with [`zipline`](https://github.com/quantopian/zipline).\n",
     "\n",
     "This blog post originally resulted from a collaboration with Alex Wiltschko where we used Whetlab for Bayesian optimization. Whetlab, however, has since been acquired by Twitter and the Whetlab service was discontinued. Scott Clark from SigOpt helped in porting the code to their service which is comparable in functionality and API. Scott also co-wrote the blog post.\n",
     "\n",
     "## Introduction to Bayesian optimization (Scott)\n",
     "\n",
     "[Bayesian Optimization](https://en.wikipedia.org/wiki/Bayesian_optimization) is a powerful tool that is particularly useful when optimizing anything that is both time consuming and expensive to evaluate (like trading algorithms). At the core, Bayesian Optimization attempts to leverage historical observations to make optimal suggestions on the best variation of parameters to sample (maximizing some objective like expected returns). This field has been actively studied in academia for decades, from the seminal paper [\"Efficient Global Optimization of Expensive Black-Box Functions\"](http://www.ressources-actuarielles.net/EXT/ISFA/1226.nsf/0/f84f7ac703bf5862c12576d8002f5259/$FILE/Jones98.pdf) by Jones et al. in 1998 to more recent contributions like [\"Practical Bayesian Optimization of Machine Learning Algorithms\"](http://papers.nips.cc/paper/4522-practical-bayesian-optimization-of-machine-learning-algorithms.pdf) by Snoek et al. in 2012.\n",
     "\n",
     "Many of these approaches take a similar route to solving the problem: they map the observed evaluations onto a [Gaussian Process](http://blog.sigopt.com/post/130275376068/sigopt-fundamentals-intuition-behind-gaussian) (GP), [fit the best possible GP model](http://blog.sigopt.com/post/131498069303/sigopt-fundamentals-intuition-behind-covariance), perform optimization on this model, then return a new set of suggestions for the user to evaluate. At the core, these methods balance the tradeoff between exploration (learning more about the model, the length scales over which they vary, and how they combine to influence the overall objective) and exploitation (using the knowledge already gained to return the best possible expected result). By efficiently and automatically making these tradeoffs, Bayesian Optimization techniques can quickly find the global optima of these difficult to optimize problems, often much faster than traditional methods like brute force or localized search.\n",
     "\n",
     "At every Bayesian Optimization iteration, the point of highest Expected Improvement is returned; this is the set of parameters that, in expectation, will most improve upon the best objective value observed thus far. SigOpt wraps these powerful optimization techniques behind a simple API so that any expert in any field can optimize their models without resorting to expensive trial and error. More information about how SigOpt works, as well as other examples of using Bayesian Optimization to perform parameter optimization, can be found on our [research page](https://sigopt.com/research)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## A Generalized Framework for Evaluating Trading Signals (Justin)\n",
     "\n",
     "Historically quant traders have used many price based signals to define an investment strategy. Many of these signals have been implemented into the popular [TA-lib library](http://ta-lib.org/), with an available Python library [here](https://github.com/mrjbq7/ta-lib).  Typically, a price based signal takes in historical prices as input to compute the signal's value.  For example, RSI [(Relative Strength Index)](http://www.investopedia.com/terms/r/rsi.asp), is commonly used for mean-reversion and momentum trading.  To compute RSI one must first choose the number of pricing days over which to compute the signal. Then, the trader chooses a range of values for determining when to enter a trade. The valid values for RSI is between 0 to 100. If a stock has undergone a sharp selloff recently, RSI values will trend towards 0, and after a strong rally, trend towards 100.  A common trading strategy is going long a stock when RSI is below 20, betting on mean-reversion.  Similarly, to go short when RSI is above 80.  However, other groups of traders have found success betting on persistent momentum in the stock (rather than mean-reversion) when RSI reaches an extreme reading. In which case, the trader will go long when RSI is above 80 betting that the stock will continue going higher.  \n",
     "\n",
     "This poses several questions:\n",
     "* So which is right? Should we use RSI as a _mean-reversion_ indicator or a _momentum_ indicator?  \n",
     "  * What range of RSI values shall we choose to determine if the momentum or mean-reversion condition is met?\n",
     "  * How many lookback days of prices should we use to compute the stock's RSI?\n",
     "* Besides RSI, can I integrate a second indicator into my investment strategy?\n",
     "\n",
     "Each decision made regarding specifying our trading signal (e.g. RSI) or how to interpret the signal's value for making a trading decision is effectively a free parameter in our system, and depending upon the range of reasonable values each free parameter can take, it can quickly explode the total combinations of possible settings. Each signal added to the strategy can quickly increase the total combinations possible into the many millions and even _billions_, even with just 2 signals, as we'll see in the below example. A discussion of strategy model overfitting, and evaluating how overfit a trading backtest may be, will not be addressed here, but will be the topic of a future blog post.  An excellent introduction for addressing trading strategy overfitting can be found here:  .\n",
     "\n",
     "The large parameter space of millions, or billions, of combinations over which our strategy will need to be tested in order to determine the subspace where the global maximum is likely located is why Bayesian Optimization can be so effective at quickly evaluating potentially profitable trading strategies.  Brute force grid-search over a billion combination parameter space is often intractable, even if each combination only takes 30-seconds to complete.  Bayesian optimization decreases the evaluation of the model over the global search space by an order of magnitude, as described in the previous section.\n",
     "\n",
     "The trading algorithm we will implement below will create a simple structure for passing in the free parameters of any number simple price based trading signals (simplified to work more easily with ta-lib functions). Then each signal is evaluated each trading day, when _all_ the conditions are true, a trade is entered, and held until the next signal evaluation period. \n",
     "\n",
     "For the purposes of this optimization our objective function will be [Sharpe Ratio](http://www.investopedia.com/terms/s/sharperatio.asp) of the strategy. A broadly accepted metric from industry for evaluating trading strategy performance.  However, the framework implemented below allows for ease of swapping in any objective function desired by the analyst.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Example Zipline algorithm\n",
     "\n",
     "To illustrate how you can use Bayesian optimization on your [zipline](https://github.com/quantopian/zipline) trading algorithms and how it compares to other naive approaches (i.e. grid search) we will use a rather simple algorithm comprised of a trading trigger based on two commonly used signals from the ta-lib library, RSI [(Relative Strength Index)](http://www.investopedia.com/terms/r/rsi.asp) and ROC [(Rate of Change)](http://www.investopedia.com/terms/p/pricerateofchange.asp). \n",
     "\n",
     "The trading algorithm will search for trades across these Select Sector SPDR ETF's: \n",
     "* XLV, XLF, XLP, XLE, XLK, XLB, XLU, XLI. \n",
     "\n",
     "By running the trading logic across all of these ETF's we will be implementing a simple sector-rotation strategy. \n",
     "\n",
     "#####-----------------------------------------------------------------------------------------------------\n",
     "#####Strategy Specification\n",
     "*Buy the ETF only if it meets _both_ the RSI signal and ROC signal criteria.  When an ETF is bought long, then an equivalent dollar amount of SPY will be shorted, creating a hedged, dollar-neutral portfolio.*\n",
     "#####-----------------------------------------------------------------------------------------------------\n",
     "\n",
     "By hedging all of our trades, it serves to \"tease-apart\" the actual usefulness provided by these signals (RSI, ROC) since it extracts upward movement in the stock simply occurring because the rest of the stock market is going up.  As a result, the profit achieved by this hedged strategy can be perceived as more \"pure alpha,\" rather than highly-correlated to the direction of the broad stock market.\n",
     "\n",
     "The 7 free parameters of our trading strategy are as follows:\n",
     "* RSI\n",
     "  * **(1)** Lookback window for # of prices used in the RSI calculation\n",
     "  * **(2)** Lower_bound value defining the trade entry condition\n",
     "  * **(3)** Range_width, which will be added to the Lower-bound\n",
     "    * Lower_bound + Range_width is the range of values over which our RSI signal will be considered `True`\n",
     "* ROC\n",
     "  * **(4)** Lookback window for # of prices used in the ROC calculation\n",
     "  * **(5)** Lower_bound value defining the trade entry condition\n",
     "  * **(6)** Range_width, which will be added to the Lower-bound\n",
     "    * Lower_bound + Range_width is the range of values over which our ROC signal will be considered `True`\n",
     "* Signal evaluation frequency\n",
     "  * **(7)** Number of days we wait between evaluating if our signals are valid\n",
     "    * Do we evaluate them every day, every week, every month, etc.\n",
     "\n",
     "It's worth noting that even with just 2 price based signals, we have 7 free parameters in this system!\n",
     "\n",
     "Reasonable Ranges for each of the 7 free parameters above (assuming each is an integer, with integer steps):\n",
     "1. 115 values: 5 to 120 \n",
     "2. 90 values: 0 to 90\n",
     "3. 20 values: 10 to 30\n",
     "4. 61 values: 2 to 63\n",
     "5. 30 values: 0 to 30\n",
     "6. 195 values: 5 to 200\n",
     "7. 18 values: 3 to 21\n",
     "\n",
     "Multiplying the valid ranges of each yields a total combination count of:\n",
     "* **1,329,623,100,000** theoretical combinations \n",
     "  * 115 x 90 x 20 x 61 x 30 x 195 x 18 \n",
     "(TODO: check with Thomas on this math)\n",
     "  * Imagine how many combinations are possible if 3, 4, 5... 10 signals are added to a strategy\n",
     "\n",
     "Obviously grid-searching through all those combinations is unreasonable, though a skilled practitioner can prune the search space significantly by only grid-searching across each parameter using wider steps based on their intuition of the model they are building.  But even if the skilled practitioner can reduce the grid-search to 10,000 combinations even that number of combinations may be unwieldy if the objective function (e.g. trading strategy) takes 1-minute to evaluate, which is quite frequently the case with trading strategies.  This is where the benefit of having access to a Bayesian optimizer becomes _extremely_ helpful.  \n",
     "\n",
     "The ipython notebook below compares SigOpt's Bayesian Optimizer's results from 3 independent experiments, of only 300 trials each determined intelligently by their optimizer, to a \"smart\" grid-search of approximately 3500 combinations chosen intuitively from a reasonable interpretations of sensible RSI and ROC values.  Only 3500 trials were chosen for the grid-search approach, because even that few combinations took 48 hours to evaluate. 300 trials were chosen for the SigOpt approach because in practice the SigOpt optimizer is able to find good optima within 10 to 20 times the dimensionality of the parameter space being optimized. This linear number of evaluations with respect to the number of parameters allows for optimization of algorithms that would otherwise be intractable using standard methods like grid search, which grow exponentially with the number of parameters.\n",
     " "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Conclusion\n",
     "### *The SigOpt Bayesian Optimizer discovered a better global maximum when testing only over only 300 combinations, than what was discovered by the ~3500 combination grid search.*  \n",
     "####(This was seen in 4 out of 5 runs with SigOpt, and the 1 that returned a worse value was only minor shortfall)   \n",
     "\n",
     "*One Example of how quickly SigOpt discovered a maximum is shown below*\n",
     "![alt text](SigOpt_max_over_time.png)\n",
     "![alt text](SigOpt_vs_GridSearch_sorted_value_comparison.png)\n",
     "![alt text](SigOpt_vs_GridSearch_time_comparison.png)\n",
     "\n",
     "**Reducing model evaluation by 10x, and achieving more accurate results, while vetting the merits of a trading algorithm is beneficial on many levels, both from CPU time spent, but also with regard to human time not being wasted waiting for results during the researh process.  Since so few trading hypotheses end up being viable, the faster a researcher can discard the bad ideas and spend time diving more deeply in the good ideas with further techniques (e.g. model cross-validation, out-of-sample testing, robustness and overfitting checks, etc).** \n",
     "\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## -----------------------------------------------------------------------------------------"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Implementing our Trading Algo & Running Optimizations\n",
     "We start by importing our tools. Also note that if you want to run this yourself you need to create a file in this folder called `creds.py` where you place your SigOpt credentials (a free trial is avaible if you go to [SigOpt.com](https://sigopt.com))."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "\"\"\"\n",
     "    This cell is for importing all our necessary libraries\n",
     "\"\"\"\n",
     "%matplotlib inline\n",
     "import matplotlib\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "import talib\n",
     "import seaborn as sns\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "from collections import OrderedDict\n",
     "from collections import defaultdict\n",
     "import itertools\n",
     "\n",
     "import zipline\n",
     "from zipline.utils.factory import load_bars_from_yahoo\n",
     "\n",
     "try:\n",
     "    import pyfolio as pf\n",
     "except ImportError:\n",
     "    print('Pyfolio not installed. Pyfolio specific plots will not be available in this notebook.')\n",
     "    \n",
     "try:\n",
     "    import creds\n",
     "except ImportError:\n",
     "    raise ValueError('creds.py not found. Please create it and set user_token, client_token and client_id from https://sigopt.com/user/profile')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Next we will load in pricing data via Yahoo! finance."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "XLV\n",
       "XLF\n",
       "XLP\n",
       "XLE\n",
       "XLK\n",
       "XLB\n",
       "XLU\n",
       "XLI\n",
       "SPY\n",
       "XLV\n",
       "XLF\n",
       "XLP\n",
       "XLE\n",
       "XLK\n",
       "XLB\n",
       "XLU\n",
       "XLI\n",
       "SPY\n",
       "XLV\n",
       "XLF\n",
       "XLP\n",
       "XLE\n",
       "XLK\n",
       "XLB\n",
       "XLU\n",
       "XLI\n",
       "SPY\n"
      ]
     }
    ],
    "source": [
     "#: Get pricing for the same dates in daily mode\n",
     "data = load_bars_from_yahoo(stocks=['XLV', 'XLF', 'XLP','XLE', 'XLK', 'XLB', 'XLU', 'XLI','SPY'\n",
     "                                    ],\n",
     "                            start=pd.Timestamp('2004-01-01', tz='utc'),\n",
     "                            end=pd.Timestamp('2010-01-01', tz='utc')\n",
     "                           )\n",
     "\n",
     "data_heldout = load_bars_from_yahoo(stocks=[ 'XLV', 'XLF', 'XLP','XLE', 'XLK', 'XLB', 'XLU', 'XLI', 'SPY'\n",
     "                                            ],\n",
     "                                    start=pd.Timestamp('2010-01-10', tz='utc'),\n",
     "                                    end=pd.Timestamp('2016-1-7', tz='utc')\n",
     "                                   )\n",
     "\n",
     "data_all = load_bars_from_yahoo(stocks=[ 'XLV', 'XLF', 'XLP','XLE', 'XLK', 'XLB', 'XLU', 'XLI', 'SPY'\n",
     "                                        ],\n",
     "                                start=pd.Timestamp('2004-01-01', tz='utc'),\n",
     "                                end=pd.Timestamp('2016-1-7', tz='utc')\n",
     "                               )"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Trading Algo:  Sector Rotation, using Sector SPDR ETF's, with Hedging"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "import matplotlib\n",
     "import pytz\n",
     "from datetime import datetime, timedelta\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "import talib\n",
     "import seaborn as sns\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "# Zipline imports\n",
     "from zipline.utils.factory import load_bars_from_yahoo\n",
     "from zipline import TradingAlgorithm\n",
     "from zipline.api import (\n",
     "    order_target, \n",
     "    record, \n",
     "    symbol,\n",
     "    get_datetime, \n",
     "    order_target_percent\n",
     ")\n",
     "from zipline.api import order_target_percent, record, symbol, symbols, history, add_history\n",
     "from collections import OrderedDict\n",
     "\n",
     "\n",
     "def initialize_local_sigopt_rsi_roc(context, signals_input=None, \n",
     "                            rsi_timeperiod=-1, rsi_lower=29, rsi_width=27,\n",
     "                            roc_timeperiod=13, roc_lower=0.00, roc_width=2.00, \n",
     "                            rebalance_freq=21, \n",
     "                            max_pos_exp=0.3, min_stocks_for_full_exp=3, \n",
     "                            use_hedge=True, \n",
     "                            debug=False):\n",
     "    \n",
     "    context.debug = debug\n",
     "    context.use_hedge = use_hedge\n",
     "    context.rebalance_freq = rebalance_freq\n",
     "    \n",
     "    context.max_pos_exp = max_pos_exp\n",
     "    context.min_stocks_for_full_exp = min_stocks_for_full_exp\n",
     "    \n",
     "    # A code simplification to make the downstream optimization analysis more straigtforward\n",
     "    if rsi_timeperiod > 0:\n",
     "        context.signals = OrderedDict([\n",
     "                                        ( 'rsi', {'func': talib.RSI, 'func_kwargs': {'timeperiod': rsi_timeperiod}, \n",
     "                                                       'lower': rsi_lower,  'width': rsi_width} ),\n",
     "                                        ( 'roc', {'func': talib.ROCP, 'func_kwargs': {'timeperiod': roc_timeperiod }, \n",
     "                                                       'lower': roc_lower, 'width': roc_width, } )\n",
     "                                    ])\n",
     "    else:\n",
     "        if signals_input is None:\n",
     "            context.signals = signals\n",
     "        else:\n",
     "            context.signals = signals_input\n",
     "    \n",
     "    print \"rebal freq: \" + str(context.rebalance_freq)\n",
     "    print context.signals\n",
     "    \n",
     "    context.max_timeperiod = 0\n",
     "    for signal_name in context.signals.keys():\n",
     "        timeperiod = context.signals[signal_name]['func_kwargs']['timeperiod']\n",
     "        if timeperiod > context.max_timeperiod:\n",
     "            context.max_timeperiod = timeperiod\n",
     "    context.max_timeperiod+=2\n",
     "    \n",
     "    add_history(context.max_timeperiod, '1d', 'price')\n",
     "    \n",
     "    # Declare the stocks we want to trade\n",
     "    context.stocks = symbols('XLV', 'XLF', 'XLP','XLE', 'XLK', 'XLB', 'XLU', 'XLI')\n",
     "    if context.debug: print \"(symbol,sid)\" + str( [ (i.symbol, i.sid) for i in context.stocks ] )\n",
     "        \n",
     "    if context.use_hedge:\n",
     "        context.hedge = symbol('SPY')\n",
     "    \n",
     "    context.i = 0\n",
     "\n",
     "    \n",
     "def handle_data_local_sigopt_rsi_roc(context, data):\n",
     "    \"\"\"\n",
     "    handle_data() is what will be called in every iteration of our algorithm. So every bar we run, will end up running\n",
     "    handle_data(). This is where orders should be placed\n",
     "    \"\"\"\n",
     "    # Skip first N days to get full/valid windows of data for history() below\n",
     "    context.i += 1\n",
     "    if context.i < (context.max_timeperiod + 5):\n",
     "        return\n",
     "    \n",
     "    # only run logic every N trading days\n",
     "    if (context.i % context.rebalance_freq) > 0:\n",
     "        return\n",
     "    \n",
     "    if context.debug:\n",
     "        pv = context.portfolio.portfolio_value\n",
     "        port_exp = context.portfolio.positions_value\n",
     "        print \"port value= \" + str(pv)\n",
     "        print \" port net $ exposure= \" + str(port_exp)\n",
     "        print \"leverage= \" + str( context.account.leverage )\n",
     "        print \"current port:\" + str( context.portfolio.positions.keys() )\n",
     "\n",
     "    max_history = history(context.max_timeperiod, '1d', 'price')\n",
     "    \n",
     "    stocks_to_hold = []\n",
     "    stocks_to_not_hold = []\n",
     "    \n",
     "    for stock in context.stocks:\n",
     "        #print stock.symbol\n",
     "        close_prices = max_history[stock].values\n",
     "        \n",
     "        trade_ok = False\n",
     "        for signal_name in context.signals.keys():\n",
     "            signal_kwargs = context.signals[signal_name]['func_kwargs']\n",
     "            signal_func = context.signals[signal_name]['func']\n",
     "\n",
     "            signal_output_ts = signal_func(close_prices, **signal_kwargs)\n",
     "            signal_output = signal_output_ts[-1]\n",
     "\n",
     "            lower_bound = context.signals[signal_name]['lower']\n",
     "            upper_bound = lower_bound + context.signals[signal_name]['width']\n",
     "            \n",
     "            if lower_bound > upper_bound:\n",
     "                temp_bound = upper_bound\n",
     "                upper_bound = lower_bound\n",
     "                lower_bound = temp_bound\n",
     "            \n",
     "            if signal_output > lower_bound and signal_output < upper_bound:\n",
     "                trade_ok = True\n",
     "            else:\n",
     "                trade_ok = False\n",
     "                break\n",
     "        \n",
     "        if trade_ok:\n",
     "            stocks_to_hold.append(stock)\n",
     "        else:\n",
     "            stocks_to_not_hold.append(stock)\n",
     "    \n",
     "    if context.debug: print \"stock to hold: \" + str( [ (i.symbol, i.sid) for i in stocks_to_hold ] )\n",
     "    if context.debug: print \"stock to NOT hold: \" + str( [ (i.symbol, i.sid) for i in stocks_to_not_hold ] )\n",
     "    \n",
     "    for stock in stocks_to_not_hold:\n",
     "        order_target_percent(stock, 0.0)\n",
     "        if context.debug: print str(max_history.index[-1]) + \" \" + stock.symbol + \",\" + str(stock.sid) + \" Exposure %: 0.0 \"\n",
     "        \n",
     "    for stock in stocks_to_hold:\n",
     "        if len(stocks_to_hold) >= context.min_stocks_for_full_exp:\n",
     "            exposure = 1.0 / len(stocks_to_hold)\n",
     "        else:\n",
     "            exposure = context.max_pos_exp    \n",
     "        order_target_percent(stock, exposure)\n",
     "        if context.debug: print str(max_history.index[-1]) + ' ** Entry: ' + stock.symbol + \",\" + str(stock.sid) + \" Exposure %: \" +  str(exposure) + \" total stocks:\" + str(len(stocks_to_hold))\n",
     "        \n",
     "    if context.use_hedge:\n",
     "        if len(stocks_to_hold) > 0:\n",
     "            hedge_exposure = -1.0 * exposure * len(stocks_to_hold)\n",
     "        else:\n",
     "            hedge_exposure = 0.0\n",
     "        order_target_percent(context.hedge, hedge_exposure)\n",
     "        if context.debug: print str(max_history.index[-1]) + ' ** HEDGE: ' + context.hedge.symbol + \",\" + str(context.hedge.sid) + \" Exposure %: \" + str(hedge_exposure) + \" total stocks:\" + str(len(stocks_to_hold))\n",
     "            "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Setup for the Grid-Search Optimization"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "# this just declares the 'signals' variable and populates with some dummy values\n",
     "signals = OrderedDict([\n",
     "    ( 'rsi', {'func': talib.RSI, 'func_kwargs': {'timeperiod': 20}, \n",
     "                   'lower': 70,  'width': 20} ),\n",
     "    ( 'roc', {'func': talib.ROCP, 'func_kwargs': {'timeperiod': 5 }, \n",
     "                   'lower': 0.70, 'width': 0.20} )\n",
     "    \n",
     "    \n",
     "    ])\n",
     "\n",
     "# This defines all the ta-lib signal hyperparameters used in the grid-search\n",
     "# Same idea as the signal definition above, just in the format required for the grid-search\n",
     "hypers = OrderedDict([\n",
     "    ( 'rsi', {'timeperiod': {'min': 5, 'max': 100,  'step': 30},\n",
     "                   'lower':      {'min': 10,  'max': 91,  'step': 30 }, \n",
     "                   'width':      {'min': 10, 'max': 31, 'step': 10}} ), \n",
     "        \n",
     "    ( 'roc', {'timeperiod': {'min': 2,  'max': 64,   'step': 20},\n",
     "                   'lower':      {'min': 0.01,  'max': 0.31,   'step': 0.10},\n",
     "                   'width':      {'min': 0.05, 'max': 1.06,  'step': 0.30}} ) \n",
     "    \n",
     "    ])\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "rsi\n",
       "<type 'int'>\n",
       "roc\n",
       "<type 'float'>\n",
       "84\n"
      ]
     }
    ],
    "source": [
     "# pre-compute all realistic combos defined in 'hypers'\n",
     "count = 0\n",
     "\n",
     "all_combos = OrderedDict()\n",
     "for combo in hypers:\n",
     "    all_combos[combo] = []\n",
     "\n",
     "for combo in hypers:\n",
     "    print combo\n",
     "    \n",
     "    timeperiod_min = hypers[combo]['timeperiod']['min']\n",
     "    timeperiod_max = hypers[combo]['timeperiod']['max']\n",
     "    timeperiod_step = hypers[combo]['timeperiod']['step']\n",
     "    \n",
     "    lower_min = hypers[combo]['lower']['min']\n",
     "    lower_max = hypers[combo]['lower']['max']\n",
     "    lower_step = hypers[combo]['lower']['step']\n",
     "    \n",
     "    print type(lower_step)\n",
     "    \n",
     "    width_min = hypers[combo]['width']['min']\n",
     "    width_max = hypers[combo]['width']['max']\n",
     "    width_step = hypers[combo]['width']['step']\n",
     "    \n",
     "    for timeperiod in range(timeperiod_min, timeperiod_max, timeperiod_step):\n",
     "        \n",
     "        if isinstance(lower_min, float):\n",
     "            t_lower_min = int(lower_min * 100)\n",
     "            t_lower_max = int(lower_max * 100)\n",
     "            t_lower_step = int(lower_step * 100)\n",
     "            \n",
     "            t_width_min = int(width_min * 100)\n",
     "            t_width_max = int(width_max * 100)\n",
     "            t_width_step = int(width_step * 100)\n",
     "        else:\n",
     "            t_lower_min = lower_min\n",
     "            t_lower_max = lower_max\n",
     "            t_lower_step = lower_step\n",
     "            \n",
     "            t_width_min = width_min\n",
     "            t_width_max = width_max\n",
     "            t_width_step = width_step\n",
     "            \n",
     "        for lower in range(t_lower_min, t_lower_max, t_lower_step):    \n",
     "        \n",
     "            for width in range(t_width_min, t_width_max, t_width_step):\n",
     "                signals[combo]['func_kwargs']['timeperiod'] = timeperiod\n",
     "                \n",
     "                if isinstance(lower_min, float):\n",
     "                    signals[combo]['lower'] = lower / 100.0\n",
     "                    signals[combo]['width'] = width / 100.0\n",
     "                    combo_tup = (timeperiod, lower / 100.0, width / 100.0)\n",
     "                else:\n",
     "                    signals[combo]['lower'] = lower\n",
     "                    signals[combo]['width'] = width\n",
     "                    combo_tup = (timeperiod, lower, width)\n",
     "                \n",
     "                all_combos[combo].append( combo_tup )\n",
     "\n",
     "                count += 1\n",
     "                                      \n",
     "print count           "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "[5, 15]"
       ]
      },
      "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# only test 5-day and 15-day rebalance frequency, rather than all days from 3-days to 21-days\n",
     "all_combos['rebal'] = [5, 15]\n",
     "all_combos['rebal']"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "3456\n"
      ]
     }
    ],
    "source": [
     "# Print out total # of parameters that we'll test in our grid-search\n",
     "count = 0\n",
     "for i in itertools.product(*all_combos.values()):\n",
     "    count = count + 1\n",
     "print count"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
    "source": [
     "# Run this cell if you want to print out and view every parameter combination\n",
     "print len(list(itertools.product(*all_combos.values())) )\n",
     "list(itertools.product(*all_combos.values()))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Run the Grid-Search optimization"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
    "source": [
     "# This runs the grid-search \n",
     "# WARNING: It will take approx 48 hours if run over ~3500 combinations! (on a Macbook Air)\n",
     "\n",
     "all_combos_list = list( itertools.product(*all_combos.values()) )\n",
     "print \"Total combos:  \" + str( len(all_combos_list) )\n",
     "print hypers.keys()\n",
     "\n",
     "all_results = {}\n",
     "count = 0\n",
     "\n",
     "for combo in all_combos_list:\n",
     "    for signal_i in range(0, len(combo)-1):\n",
     "        signal_name = hypers.keys()[signal_i]\n",
     "        signals[signal_name]['func_kwargs']['timeperiod'] = combo[signal_i][0]\n",
     "        signals[signal_name]['lower'] = combo[signal_i][1]\n",
     "        signals[signal_name]['width'] = combo[signal_i][2]\n",
     "    \n",
     "    temp_rebal = combo[-1]\n",
     "    input_params = {}\n",
     "    input_params['rebalance_freq'] = temp_rebal\n",
     "    input_params['signals_input'] = signals\n",
     "    \n",
     "    algo_obj = zipline.TradingAlgorithm(\n",
     "        initialize=initialize_local_sigopt_rsi_roc, \n",
     "        handle_data=handle_data_local_sigopt_rsi_roc,\n",
     "        **input_params\n",
     "    )\n",
     "\n",
     "    # Run algorithm\n",
     "    perf_manual = algo_obj.run(data)\n",
     "    perf_manual.index = perf_manual.index.normalize()\n",
     "    if perf_manual.index.tzinfo is None:\n",
     "        perf_manual.index = perf_manual.index.tz_localize('UTC')\n",
     "    returns = perf_manual.returns\n",
     "    returns_stats = pf.timeseries.perf_stats(returns)\n",
     "    all_results[combo] = returns_stats\n",
     "    \n",
     "    print str(count) + \":  \" + str(combo) + \"  Sharpe: \" + str(returns_stats.sharpe_ratio) + \"  %_return: \" + str(returns_stats.annual_return)\n",
     "    \n",
     "    count += 1\n",
     "    "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 29,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "3456"
       ]
      },
      "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Confirm how many results were computed in the grid-search\n",
     "len(all_results)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 32,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
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        "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th colspan=\"10\" halign=\"left\">(5, 10, 10)</th>\n",
        "      <th>...</th>\n",
        "      <th colspan=\"10\" halign=\"left\">(95, 70, 30)</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">(2, 0.01, 0.05)</th>\n",
        "      <th colspan=\"2\" halign=\"left\">(2, 0.01, 0.35)</th>\n",
        "      <th colspan=\"2\" halign=\"left\">(2, 0.01, 0.65)</th>\n",
        "      <th colspan=\"2\" halign=\"left\">(2, 0.01, 0.95)</th>\n",
        "      <th colspan=\"2\" halign=\"left\">(2, 0.11, 0.05)</th>\n",
        "      <th>...</th>\n",
        "      <th colspan=\"2\" halign=\"left\">(62, 0.11, 0.95)</th>\n",
        "      <th colspan=\"2\" halign=\"left\">(62, 0.21, 0.05)</th>\n",
        "      <th colspan=\"2\" halign=\"left\">(62, 0.21, 0.35)</th>\n",
        "      <th colspan=\"2\" halign=\"left\">(62, 0.21, 0.65)</th>\n",
        "      <th colspan=\"2\" halign=\"left\">(62, 0.21, 0.95)</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>5</th>\n",
        "      <th>15</th>\n",
        "      <th>5</th>\n",
        "      <th>15</th>\n",
        "      <th>5</th>\n",
        "      <th>15</th>\n",
        "      <th>5</th>\n",
        "      <th>15</th>\n",
        "      <th>5</th>\n",
        "      <th>15</th>\n",
        "      <th>...</th>\n",
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        "      <th>15</th>\n",
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        "      <th>15</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>annual_return</th>\n",
        "      <td>0.002098</td>\n",
        "      <td>-0.000396</td>\n",
        "      <td>0.002098</td>\n",
        "      <td>-0.000396</td>\n",
        "      <td>0.002098</td>\n",
        "      <td>-0.000396</td>\n",
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        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>annual_volatility</th>\n",
        "      <td>0.006387</td>\n",
        "      <td>0.006964</td>\n",
        "      <td>0.006387</td>\n",
        "      <td>0.006964</td>\n",
        "      <td>0.006387</td>\n",
        "      <td>0.006964</td>\n",
        "      <td>0.006387</td>\n",
        "      <td>0.006964</td>\n",
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        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sharpe_ratio</th>\n",
        "      <td>0.331424</td>\n",
        "      <td>-0.053408</td>\n",
        "      <td>0.331424</td>\n",
        "      <td>-0.053408</td>\n",
        "      <td>0.331424</td>\n",
        "      <td>-0.053408</td>\n",
        "      <td>0.331424</td>\n",
        "      <td>-0.053408</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>calmar_ratio</th>\n",
        "      <td>0.199049</td>\n",
        "      <td>-0.017505</td>\n",
        "      <td>0.199049</td>\n",
        "      <td>-0.017505</td>\n",
        "      <td>0.199049</td>\n",
        "      <td>-0.017505</td>\n",
        "      <td>0.199049</td>\n",
        "      <td>-0.017505</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>stability_of_timeseries</th>\n",
        "      <td>0.716698</td>\n",
        "      <td>-0.751867</td>\n",
        "      <td>0.716698</td>\n",
        "      <td>-0.751867</td>\n",
        "      <td>0.716698</td>\n",
        "      <td>-0.751867</td>\n",
        "      <td>0.716698</td>\n",
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        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows × 3456 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "                            (5, 10, 10)                                      \\\n",
        "                        (2, 0.01, 0.05)           (2, 0.01, 0.35)             \n",
        "                                     5         15              5         15   \n",
        "annual_return                  0.002098 -0.000396        0.002098 -0.000396   \n",
        "annual_volatility              0.006387  0.006964        0.006387  0.006964   \n",
        "sharpe_ratio                   0.331424 -0.053408        0.331424 -0.053408   \n",
        "calmar_ratio                   0.199049 -0.017505        0.199049 -0.017505   \n",
        "stability_of_timeseries        0.716698 -0.751867        0.716698 -0.751867   \n",
        "\n",
        "                                                                             \\\n",
        "                        (2, 0.01, 0.65)           (2, 0.01, 0.95)             \n",
        "                                     5         15              5         15   \n",
        "annual_return                  0.002098 -0.000396        0.002098 -0.000396   \n",
        "annual_volatility              0.006387  0.006964        0.006387  0.006964   \n",
        "sharpe_ratio                   0.331424 -0.053408        0.331424 -0.053408   \n",
        "calmar_ratio                   0.199049 -0.017505        0.199049 -0.017505   \n",
        "stability_of_timeseries        0.716698 -0.751867        0.716698 -0.751867   \n",
        "\n",
        "                                            ...     (95, 70, 30)      \\\n",
        "                        (2, 0.11, 0.05)     ... (62, 0.11, 0.95)       \n",
        "                                     5   15 ...               5   15   \n",
        "annual_return                         0   0 ...                0   0   \n",
        "annual_volatility                     0   0 ...                0   0   \n",
        "sharpe_ratio                        NaN NaN ...              NaN NaN   \n",
        "calmar_ratio                        NaN NaN ...              NaN NaN   \n",
        "stability_of_timeseries               0   0 ...                0   0   \n",
        "\n",
        "                                                                   \\\n",
        "                        (62, 0.21, 0.05)     (62, 0.21, 0.35)       \n",
        "                                      5   15               5   15   \n",
        "annual_return                          0   0                0   0   \n",
        "annual_volatility                      0   0                0   0   \n",
        "sharpe_ratio                         NaN NaN              NaN NaN   \n",
        "calmar_ratio                         NaN NaN              NaN NaN   \n",
        "stability_of_timeseries                0   0                0   0   \n",
        "\n",
        "                                                                   \n",
        "                        (62, 0.21, 0.65)     (62, 0.21, 0.95)      \n",
        "                                      5   15               5   15  \n",
        "annual_return                          0   0                0   0  \n",
        "annual_volatility                      0   0                0   0  \n",
        "sharpe_ratio                         NaN NaN              NaN NaN  \n",
        "calmar_ratio                         NaN NaN              NaN NaN  \n",
        "stability_of_timeseries                0   0                0   0  \n",
        "\n",
        "[5 rows x 3456 columns]"
       ]
      },
      "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Get a look at the structure of the grid-search results\n",
     "all_results_df = pd.DataFrame(all_results)\n",
     "all_results_df.head()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 36,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
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        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>annual_return</th>\n",
        "      <th>annual_volatility</th>\n",
        "      <th>sharpe_ratio</th>\n",
        "      <th>calmar_ratio</th>\n",
        "      <th>stability_of_timeseries</th>\n",
        "      <th>max_drawdown</th>\n",
        "      <th>omega_ratio</th>\n",
        "      <th>sortino_ratio</th>\n",
        "      <th>skew</th>\n",
        "      <th>kurtosis</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"5\" valign=\"top\">(5, 10, 10)</th>\n",
        "      <th rowspan=\"2\" valign=\"top\">(2, 0.01, 0.05)</th>\n",
        "      <th>5</th>\n",
        "      <td>0.002098</td>\n",
        "      <td>0.006387</td>\n",
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        "      <td>-0.010539</td>\n",
        "      <td>1.554529</td>\n",
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        "      <td>2.000573</td>\n",
        "      <td>183.259788</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>-0.000396</td>\n",
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        "      <td>0.925752</td>\n",
        "      <td>-0.075801</td>\n",
        "      <td>1.017988</td>\n",
        "      <td>171.597375</td>\n",
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        "      <th rowspan=\"2\" valign=\"top\">(2, 0.01, 0.35)</th>\n",
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        "      <td>0.532182</td>\n",
        "      <td>2.000573</td>\n",
        "      <td>183.259788</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>-0.000396</td>\n",
        "      <td>0.006964</td>\n",
        "      <td>-0.053408</td>\n",
        "      <td>-0.017505</td>\n",
        "      <td>-0.751867</td>\n",
        "      <td>-0.022620</td>\n",
        "      <td>0.925752</td>\n",
        "      <td>-0.075801</td>\n",
        "      <td>1.017988</td>\n",
        "      <td>171.597375</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(2, 0.01, 0.65)</th>\n",
        "      <th>5</th>\n",
        "      <td>0.002098</td>\n",
        "      <td>0.006387</td>\n",
        "      <td>0.331424</td>\n",
        "      <td>0.199049</td>\n",
        "      <td>0.716698</td>\n",
        "      <td>-0.010539</td>\n",
        "      <td>1.554529</td>\n",
        "      <td>0.532182</td>\n",
        "      <td>2.000573</td>\n",
        "      <td>183.259788</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "                                annual_return  annual_volatility  \\\n",
        "(5, 10, 10) (2, 0.01, 0.05) 5        0.002098           0.006387   \n",
        "                            15      -0.000396           0.006964   \n",
        "            (2, 0.01, 0.35) 5        0.002098           0.006387   \n",
        "                            15      -0.000396           0.006964   \n",
        "            (2, 0.01, 0.65) 5        0.002098           0.006387   \n",
        "\n",
        "                                sharpe_ratio  calmar_ratio  \\\n",
        "(5, 10, 10) (2, 0.01, 0.05) 5       0.331424      0.199049   \n",
        "                            15     -0.053408     -0.017505   \n",
        "            (2, 0.01, 0.35) 5       0.331424      0.199049   \n",
        "                            15     -0.053408     -0.017505   \n",
        "            (2, 0.01, 0.65) 5       0.331424      0.199049   \n",
        "\n",
        "                                stability_of_timeseries  max_drawdown  \\\n",
        "(5, 10, 10) (2, 0.01, 0.05) 5                  0.716698     -0.010539   \n",
        "                            15                -0.751867     -0.022620   \n",
        "            (2, 0.01, 0.35) 5                  0.716698     -0.010539   \n",
        "                            15                -0.751867     -0.022620   \n",
        "            (2, 0.01, 0.65) 5                  0.716698     -0.010539   \n",
        "\n",
        "                                omega_ratio  sortino_ratio      skew  \\\n",
        "(5, 10, 10) (2, 0.01, 0.05) 5      1.554529       0.532182  2.000573   \n",
        "                            15     0.925752      -0.075801  1.017988   \n",
        "            (2, 0.01, 0.35) 5      1.554529       0.532182  2.000573   \n",
        "                            15     0.925752      -0.075801  1.017988   \n",
        "            (2, 0.01, 0.65) 5      1.554529       0.532182  2.000573   \n",
        "\n",
        "                                  kurtosis  \n",
        "(5, 10, 10) (2, 0.01, 0.05) 5   183.259788  \n",
        "                            15  171.597375  \n",
        "            (2, 0.01, 0.35) 5   183.259788  \n",
        "                            15  171.597375  \n",
        "            (2, 0.01, 0.65) 5   183.259788  "
       ]
      },
      "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Transponse the dataframe. \n",
     "# Makes it easier to manipulate after saving to CSV and opening in Excel\n",
     "\n",
     "all_results_df_T = pd.DataFrame(all_results).T\n",
     "all_results_df_T.head()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 37,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
    "source": [
     "# Save results to a csv\n",
     "all_results_df_T.to_csv('grid_search_all_results_export_T.csv')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 34,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
    "source": [
     "# Pickle results\n",
     "pd.to_pickle(all_results_df, 'grid_search_all_results_pickle.pkl')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 44,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
    "source": [
     "# If you previously exported a CSV of your grid-search results\n",
     "# you can read the results in like this. \n",
     "# After I exported to CSV I took the liberty to break out all the parameters into seperate columns\n",
     "# and then resaved as a cleaner looking CSV\n",
     "\n",
     "grid_df = pd.read_csv('grid_search_all_results_with_labels.csv')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 45,
    "metadata": {
     "collapsed": false,
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>rsi_timeperiod</th>\n",
        "      <th>rsi_lower</th>\n",
        "      <th>rsi_width</th>\n",
        "      <th>roc_timeperiod</th>\n",
        "      <th>roc_lower</th>\n",
        "      <th>roc_width</th>\n",
        "      <th>rebalance_freq</th>\n",
        "      <th>rsi</th>\n",
        "      <th>roc</th>\n",
        "      <th>freq</th>\n",
        "      <th>value</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>5</td>\n",
        "      <td>40</td>\n",
        "      <td>10</td>\n",
        "      <td>42</td>\n",
        "      <td>0.01</td>\n",
        "      <td>0.05</td>\n",
        "      <td>5</td>\n",
        "      <td>(5, 40, 10)</td>\n",
        "      <td>(42, 0.01, 0.05)</td>\n",
        "      <td>5</td>\n",
        "      <td>0.331424</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>65</td>\n",
        "      <td>40</td>\n",
        "      <td>30</td>\n",
        "      <td>42</td>\n",
        "      <td>0.01</td>\n",
        "      <td>0.05</td>\n",
        "      <td>15</td>\n",
        "      <td>(65, 40, 30)</td>\n",
        "      <td>(42, 0.01, 0.05)</td>\n",
        "      <td>15</td>\n",
        "      <td>-0.053408</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>95</td>\n",
        "      <td>40</td>\n",
        "      <td>30</td>\n",
        "      <td>42</td>\n",
        "      <td>0.01</td>\n",
        "      <td>0.05</td>\n",
        "      <td>5</td>\n",
        "      <td>(95, 40, 30)</td>\n",
        "      <td>(42, 0.01, 0.05)</td>\n",
        "      <td>5</td>\n",
        "      <td>0.331424</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>65</td>\n",
        "      <td>40</td>\n",
        "      <td>10</td>\n",
        "      <td>62</td>\n",
        "      <td>0.11</td>\n",
        "      <td>0.05</td>\n",
        "      <td>15</td>\n",
        "      <td>(65, 40, 10)</td>\n",
        "      <td>(62, 0.11, 0.05)</td>\n",
        "      <td>15</td>\n",
        "      <td>-0.053408</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>65</td>\n",
        "      <td>40</td>\n",
        "      <td>10</td>\n",
        "      <td>62</td>\n",
        "      <td>0.11</td>\n",
        "      <td>0.35</td>\n",
        "      <td>5</td>\n",
        "      <td>(65, 40, 10)</td>\n",
        "      <td>(62, 0.11, 0.35)</td>\n",
        "      <td>5</td>\n",
        "      <td>0.331424</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "   rsi_timeperiod  rsi_lower  rsi_width  roc_timeperiod  roc_lower  roc_width  \\\n",
        "0               5         40         10              42       0.01       0.05   \n",
        "1              65         40         30              42       0.01       0.05   \n",
        "2              95         40         30              42       0.01       0.05   \n",
        "3              65         40         10              62       0.11       0.05   \n",
        "4              65         40         10              62       0.11       0.35   \n",
        "\n",
        "   rebalance_freq           rsi               roc  freq     value  \n",
        "0               5   (5, 40, 10)  (42, 0.01, 0.05)     5  0.331424  \n",
        "1              15  (65, 40, 30)  (42, 0.01, 0.05)    15 -0.053408  \n",
        "2               5  (95, 40, 30)  (42, 0.01, 0.05)     5  0.331424  \n",
        "3              15  (65, 40, 10)  (62, 0.11, 0.05)    15 -0.053408  \n",
        "4               5  (65, 40, 10)  (62, 0.11, 0.35)     5  0.331424  "
       ]
      },
      "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "grid_df.head()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Look at the distribution of the the Objective Function, 'value', discovered from the grid search"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 46,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "<matplotlib.legend.Legend at 0x114493610>"
       ]
      },
      "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
       "image/png": 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aAACDsZgMgK3R6xtOR1ADsDV6fcPpmPoGAMBgBDUAAAYjqAEAMFjKa9RHjx7V7bffrrff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\n",
       "text/plain": [
        "<matplotlib.figure.Figure at 0x114584d90>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "grid_df.value.hist()\n",
     "plt.axvline(np.mean(grid_df.value), color=\"orange\", alpha=0.8, ls='--', lw=3, label='mean')\n",
     "plt.axvline(0.0, color=\"black\", alpha=0.8, ls='--', label='0.0')\n",
     "plt.legend()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Look at the distribution of each of the parameters' values tested."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 47,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "image/png": 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4mFatWpGYmEhRUREJCQkUFRXRoUOHej1HSYl3jwYdOXrMq+PV+tl9/IiXt+s1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Pv27aqpqdG//Mu/yDAMHTx4UN3d3WpsbNT+/fv1T//0T9q1a9egO2cCAAAAAIDfS6pA37lzpyoq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\noAMAAAAAYAL/D9QpSgHtCxAMAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<matplotlib.figure.Figure at 0x11462c490>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "_=grid_df.hist(figsize=(17,10))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Grid-Search: Run the best parameter combo from through the algorithm and plot the performance"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
    "source": [
     "# Specify the values of the best parameter combo in the signals dict, and rebalance frequency\n",
     "\n",
     "input_params = {}\n",
     "\n",
     "input_params['rebalance_freq'] = 5\n",
     "\n",
     "signals = OrderedDict([\n",
     "    ( 'rsi', {'func': talib.RSI, 'func_kwargs': {'timeperiod': 5}, \n",
     "                   'lower': 40,  'width': 10} ),\n",
     "    ( 'roc', {'func': talib.ROCP, 'func_kwargs': {'timeperiod': 42 }, \n",
     "                   'lower': 0.01, 'width': 0.05} )  \n",
     "    ])\n",
     "\n",
     "input_params['signals_input'] = signals"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
     "collapsed": false,
     "scrolled": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "rebal freq: 5\n",
       "OrderedDict([('rsi', {'width': 10, 'lower': 40, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 5}}), ('roc', {'width': 0.05, 'lower': 0.01, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 42}})])\n"
      ]
     }
    ],
    "source": [
     "algo_obj = zipline.TradingAlgorithm(\n",
     "    initialize=initialize_local_sigopt_rsi_roc,\n",
     "    handle_data=handle_data_local_sigopt_rsi_roc,\n",
     "    **input_params\n",
     ")\n",
     "\n",
     "# Run algorithm using the training data\n",
     "perf_manual = algo_obj.run(data)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "0.820374098135\n"
      ]
     },
     {
      "data": {
       "text/plain": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x111dc9a90>"
       ]
      },
      "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
       "image/png": 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4cVgsFuTm5mLr1q0oKyvD0qVL8fnnn3v2yomIWqjVGfDK336Cpt7otH3Gjf3w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\nxb3W4X2jW+17+mKtfWEbT5S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RaHDmzBkMGjQIaWlpyMrKAgBkZWUhPT0dKpUKcrkcxcXF\nEAQB2dnZSE9Pd++VEhER+SmJIAiCKwfU1NTg6aefRkNDAxQKBV577TXU19fj1VdfhUwmQ3x8PH7/\n+99DqVRi69at+Oyzz2CxWLB48WLcfvvtaGxsxLJly1BRUYHg4GCsWbMGMTExOHz4MN544w2YzWZk\nZGTgt7/9raeumYiIyK+4HOZERETkXThpDBERkY9jmBMREfk4hjkREZGPY5gTUZs4nIbIdzDM/UR9\nfb3Ts/rUNrPZzJASoaamBpWVld1dDCISSbpy5cqV3V0I6poNGzbgr3/9K5KTk9GrV6/uLo7XWrdu\nHbZv3w7AOhUxte2LL77AU089BYlEgnHjxnV3cbzahg0bcOjQIYSGhiI2Nra7i+O1PvnkExw4cAAW\ni4V/ozyELXMfduXKFdx5552oqqrCH/7wB4wePdr+HlufzQwGA1atWoXa2lo8+uijaGhosNcP66lZ\nXl4eFi5ciPz8fIwYMQIZGRkAAIvF0s0l8z5arRZLly7FsWPHAADvv/8+CgsLu7lU3ker1eJXv/oV\nCgsL0aNHD7z11lv47rvvAPBz5W4uT+dK3c9sNkMqlSImJgb9+/dHcnIy1q5di9raWkREROCFF16A\nRCLp7mJ2O1s9BQcHQ6/X49Zbb8XGjRthNptRUlKCRYsWsZ7QXE+lpaV4/PHHMWHCBHz00Uc4ffo0\n0tLSEBTE7/w2jp+piIgI/OY3v0FcXBxeeeUVxMTEdHfxvIatnsxmM1QqFZ5//nlERkaivr4er7/+\nOu644w5+rtyM3ew+RK/X480338ShQ4dw+fJlDBkyBFqtFhs3bkRGRgbmzZuHTz75BJcuXcKYMWNg\nsVgCMqwc66m6uhrJycn473//C51Oh+TkZMycORPvv/8+ysrKMHbs2ICvp4MHD6Kurg5Tp05FYmIi\nTCYTtm3bhjFjxiAxMTFg68eR42eqpqYGycnJOHToELKzs5GVlYVvvvkGOp0Op0+fxqhRowK2zhzr\nqa6uDrGxsdixYwdGjhyJ6OhoGAwG7NixAzKZDKmpqRAEISDryRP41chHNDY24s9//jNCQkJwxx13\n4MMPP0R2djZSUlLwyCOPYPr06YiJicGKFSvw/fffQ6/XB+Q335b19N5776GgoADBwcHYvXs3Bg4c\niNjYWKxcuZL11FRPd911F9atW4c9e/ZAq9VCJpMhJSUF33zzDQAEZP04avmZWrduHfLz8/HYY48h\nOjoaFRUVyM7OxowZM/Dhhx+ioaEhIOusZT395S9/QUlJCRISEvDxxx9j1apV2LRpEx5++GEcOnQI\nRqORQe5GgfeJ8zEVFRUAAJlMhp9//hn33Xcfhg0bhgULFmDnzp1Qq9W45557UFdXBwC4ePEibr75\nZigUiu4s9jXXWT3dcMMNiI6OxsmTJ+3d7OPHj2c9NdXT448/jh9++AGlpaUAgAkTJiAiIgLl5eXd\nWdxu1V5dLVy4ENu3b0ddXR1kMhmmTp0KuVwOjUaD2267DVKptJtLfm119N/e999/j7vvvhtPPvkk\nEhIS8MILLyAhIQHXX3895HJ5N5fcv7Cb3UuVlZVh9erV+Prrr6HVahETEwOJRIKTJ08iPT0dQ4YM\nwa5duyCXy2E2m7F27Vps3rwZBQUFmDZtGhITE7v7Eq4JMfX03XffITk5Genp6cjJycHGjRvx008/\nYcaMGUhKSuruS7gmOqunwYMHY8+ePQCAYcOGoaysDPv378fAgQMRHx/fzaW/tsR8pnbv3o3o6GgY\njUbk5eXh888/x759+3DfffehX79+3X0J14SYetqxYweCg4MxbNgwlJeXY+PGjdi/fz+mTZvGUe1u\nxjD3Up988glCQkLw5JNPIj8/H3v37kVycjIuX76M4OBg9OrVC4Ig4LPPPsOiRYtw0003IT4+HkuX\nLg2YIAfE1RMA/P3vf8evf/1r3HzzzUhOTsavfvWrgAlyQFw9SSQSbNiwATNmzEBCQgIiIiIwcuTI\n7i76NSf2v70NGzbglVdewciRIxEVFYVnnnkGycnJ3V38a0bsZ2rTpk2YPXs2oqKiIJPJsHz5cga5\nBzDMvci2bdvw8ccfo7CwEBcvXsQjjzyCxMRExMXFoaioCJcvX8aAAQPwxRdfYOrUqSgoKIBCocDo\n0aMRHBwcMOF0NfUUEhKC0aNHQyqVIiEhobsv4ZpwtZ4OHz6MsLAwez317t27uy/hmrmaz5RcLsfo\n0aMRHh4eMK3xq/lMKRQKpKenQ61WY/Dgwd19CX6LYe4FBEHAmjVr8PPPP2PBggX47rvv8PXXX0Mu\nl2PixIkIDQ0FAFy4cAF33303zpw5g88//xy5ublYtGhRwHSDsp7E6Uo9PfHEEwFTT0DX6urJJ58M\nmLrif3vej8+ZewGJRIK6ujrMmjULw4cPx7x58xAfH4+vvvoK06ZNw7BhwxAVFQWdTocePXrg2Wef\nRZXPX4MAAAFMSURBVE1NDeLi4rq76NcU60kc1pN4rCtxWE/ej6PZvYDFYsEdd9yB1NRUAMD27dsx\nefJk/PKXv8Qbb7yBs2fPYt++faitrUVDQwPkcnlA/kfCehKH9SQe60oc1pP3kwicz9JrCIIAnU6H\nRx55BOvWrUN8fDzWrVuHmpoaXLlyBS+88AK7q8B6Eov1JB7rShzWk/diN7sXkUgkKC8vxw033ACN\nRoNVq1Zh0KBBeO655/hMpgPWkzisJ/FYV+KwnrwXw9zL7N+/H+vXr8exY8dwzz33YPr06d1dJK/E\nehKH9SQe60oc1pN3Yje7l9m2bRsuX76Mxx9/nN90O8B6Eof1JB7rShzWk3dimHsZLjwgDutJHNaT\neKwrcVhP3olhTkRE5OP4aBoREZGPY5gTERH5OIY5ERGRj2OYExER+TiGORERkY9jmBMREfm4/w9E\ndR/k9P5NsgAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<matplotlib.figure.Figure at 0x1029af210>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "daily_rets = perf_manual.portfolio_value.pct_change().dropna()\n",
     "sharpe_ratio_rets = daily_rets.mean() / daily_rets.std() * np.sqrt(252)\n",
     "print sharpe_ratio_rets\n",
     "\n",
     "perf_manual.portfolio_value.plot()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Now see how well the strategy performs out-of-sample using the heldout data from 2010-2016"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "-0.857501142839\n"
      ]
     },
     {
      "data": {
       "text/plain": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x10d74f1d0>"
       ]
      },
      "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
       "image/png": 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n9cwd+T5mRAwOHD+PbQeqfXpt5964u3XqfSka4w1pdzVOgCOiAGKYU58oPfTM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\nxzAHACBwDHMAAAL3A/Z9kbUje6jEAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<matplotlib.figure.Figure at 0x10d74fbd0>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "# Run algorithm using the training data\n",
     "perf_manual = algo_obj.run(data_heldout)\n",
     "\n",
     "daily_rets = perf_manual.portfolio_value.pct_change().dropna()\n",
     "sharpe_ratio_rets = daily_rets.mean() / daily_rets.std() * np.sqrt(252)\n",
     "print sharpe_ratio_rets\n",
     "\n",
     "perf_manual.portfolio_value.plot()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# (Above) Seeing how poorly the strategy performs out-of-sample shows how important it is to perform additional analysis (cross-validation, out-of-sample testing, etc) after using parameter optimization to discover a global maximum"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "#Using SigOpt for Optimization"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 45,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
    "source": [
     "def run_sigopt_local_rsi_roc(samples=50, experiment_id=None,\n",
     "                    client_token=None, **algo_descr):\n",
     "\n",
     "    import sigopt.interface\n",
     "\n",
     "    if client_token is None:\n",
     "        raise ValueError('No sigopt credentials passed, find them at https://sigopt.com/user/profile')\n",
     "\n",
     "    conn = sigopt.interface.Connection(client_token=client_token)\n",
     "\n",
     "    if experiment_id != None:\n",
     "        experiment = conn.experiments(experiment_id).fetch()\n",
     "    else:\n",
     "        experiment = conn.experiments().create(\n",
     "            name='Quantopian_POC_talib_5_hedge_width',\n",
     "            parameters=[\n",
     "                {\n",
     "                    'name': 'rsi_timeperiod',\n",
     "                    'type': 'int',\n",
     "                    'bounds': { 'min': 5, 'max': 126 }\n",
     "                },\n",
     "                {\n",
     "                    'name': 'rsi_lower',\n",
     "                    'type': 'int',\n",
     "                    'bounds': { 'min': 0, 'max': 90 }\n",
     "                },\n",
     "                {\n",
     "                    'name': 'rsi_width',\n",
     "                    'type': 'int',\n",
     "                    'bounds': { 'min': 10, 'max': 30 }\n",
     "                },\n",
     "                {\n",
     "                    'name': 'roc_timeperiod',\n",
     "                    'type': 'int',\n",
     "                    'bounds': { 'min': 2, 'max': 63 }\n",
     "                },\n",
     "                {\n",
     "                    'name': 'roc_lower',\n",
     "                    'type': 'double',\n",
     "                    'bounds': { 'min': 0.00, 'max': 0.30 }\n",
     "                },\n",
     "                {\n",
     "                    'name': 'roc_width',\n",
     "                    'type': 'double',\n",
     "                    'bounds': { 'min': 0.05, 'max': 2.00 }\n",
     "                },\n",
     "                {\n",
     "                    'name': 'rebalance_freq',\n",
     "                    'type': 'int',\n",
     "                    'bounds': { 'min': 3, 'max': 21 }\n",
     "                },\n",
     "            ],\n",
     "        )\n",
     "\n",
     "    for trial in range(samples):\n",
     "        print(\"running trial: {0}\".format(trial))\n",
     "        suggestion = conn.experiments(experiment.id).suggestions().create()\n",
     "\n",
     "        # Run the Zipline Algo with the SigOpt suggestion\n",
     "        params_sugg = suggestion.assignments.to_json()\n",
     "        print params_sugg\n",
     "        \n",
     "        signals = OrderedDict([\n",
     "            ( 'rsi', {'func': talib.RSI, 'func_kwargs': {'timeperiod': params_sugg['rsi_timeperiod'] }, \n",
     "                           'lower': params_sugg['rsi_lower'],  'width': params_sugg['rsi_width']} ),\n",
     "            ( 'roc', {'func': talib.ROCP, 'func_kwargs': {'timeperiod': params_sugg['roc_timeperiod'] }, \n",
     "                           'lower': params_sugg['roc_lower'], 'width': params_sugg['roc_width']} )  \n",
     "            ])\n",
     "        \n",
     "        params_sugg['signals_input'] = signals\n",
     "        algo_run = TradingAlgorithm(initialize=initialize_local_sigopt_rsi_roc, \n",
     "                                    handle_data=handle_data_local_sigopt_rsi_roc,\n",
     "                                    **params_sugg\n",
     "                                    )\n",
     "        \n",
     "        perf = algo_run.run(data)\n",
     "        daily_rets = perf.portfolio_value.pct_change().dropna()\n",
     "        \n",
     "        if daily_rets.std() > 0:\n",
     "            sharpe_ratio_calc = daily_rets.mean() / daily_rets.std() * np.sqrt(252)\n",
     "        else:\n",
     "            sharpe_ratio_calc = -999\n",
     "        obj = sharpe_ratio_calc\n",
     "        print obj      \n",
     "\n",
     "        if obj < -900:\n",
     "            print \"*** Flagging as failed\"\n",
     "            conn.experiments(experiment.id).observations().create(\n",
     "              assignments=suggestion.assignments,\n",
     "              failed=True,\n",
     "            )\n",
     "        else:\n",
     "            conn.experiments(experiment.id).observations().create(\n",
     "              assignments=suggestion.assignments,\n",
     "              value=obj,\n",
     "            )\n",
     "\n",
     "    exp_hist = conn.experiments(experiment.id).observations().fetch()\n",
     "    exp_data = exp_hist.to_json()['data']\n",
     "    exp_data.reverse() # start at the beginning\n",
     "\n",
     "    # load up dataframe\n",
     "    from collections import defaultdict\n",
     "    sigopt_df_dict = defaultdict(list)\n",
     "    for data_point in exp_data:\n",
     "        for k, v in data_point['assignments'].iteritems():\n",
     "            sigopt_df_dict[k].append(v)\n",
     "\n",
     "        try:\n",
     "            sigopt_df_dict['failed'].append(data_point['failed'])\n",
     "        except:\n",
     "            sigopt_df_dict['failed'].append(False)\n",
     "\n",
     "        try:\n",
     "            sigopt_df_dict['objective'].append(data_point['value'])\n",
     "        except:\n",
     "            sigopt_df_dict['objective'].append(-999)\n",
     "\n",
     "    sigopt_df = pd.DataFrame(sigopt_df_dict)\n",
     "\n",
     "    return sigopt_df"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 58,
    "metadata": {
     "collapsed": true
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "running trial: 0\n",
       "{u'roc_timeperiod': 32, u'roc_lower': 0.1148092670590023, u'rsi_width': 29, u'rsi_timeperiod': 90, u'roc_width': 1.4373803958710658, u'rsi_lower': 16, u'rebalance_freq': 17}\n",
       "rebal freq: 17\n",
       "OrderedDict([('rsi', {'width': 29, 'lower': 16, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 90}}), ('roc', {'width': 1.4373803958710658, 'lower': 0.1148092670590023, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 32}})])\n",
       "-999\n",
       "*** Flagging as failed\n",
       "running trial: 1\n",
       "{u'roc_timeperiod': 61, u'roc_lower': 0.0844961145944925, u'rsi_width': 18, u'rsi_timeperiod': 62, u'roc_width': 1.1383156724686605, u'rsi_lower': 36, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 36, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 62}}), ('roc', {'width': 1.1383156724686605, 'lower': 0.0844961145944925, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 61}})])\n",
       "0.296384194657\n",
       "running trial: 2\n",
       "{u'roc_timeperiod': 22, u'roc_lower': 0.22803406563195833, u'rsi_width': 12, u'rsi_timeperiod': 73, u'roc_width': 0.7191135230847453, u'rsi_lower': 65, u'rebalance_freq': 7}\n",
       "rebal freq: 7\n",
       "OrderedDict([('rsi', {'width': 12, 'lower': 65, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 73}}), ('roc', {'width': 0.7191135230847453, 'lower': 0.22803406563195833, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 22}})])\n",
       "-999\n",
       "*** Flagging as failed\n",
       "running trial: 3\n",
       "{u'roc_timeperiod': 2, u'roc_lower': 0.2731378920604743, u'rsi_width': 24, u'rsi_timeperiod': 98, u'roc_width': 1.978242597438841, u'rsi_lower': 23, u'rebalance_freq': 9}\n",
       "rebal freq: 9\n",
       "OrderedDict([('rsi', {'width': 24, 'lower': 23, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 98}}), ('roc', {'width': 1.978242597438841, 'lower': 0.2731378920604743, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 2}})])\n",
       "-999\n",
       "*** Flagging as failed\n",
       "running trial: 4\n",
       "{u'roc_timeperiod': 52, u'roc_lower': 0.00922338485484142, u'rsi_width': 25, u'rsi_timeperiod': 23, u'roc_width': 0.5269149378791749, u'rsi_lower': 74, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 25, 'lower': 74, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 23}}), ('roc', {'width': 0.5269149378791749, 'lower': 0.00922338485484142, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 52}})])\n",
       "-999\n",
       "*** Flagging as failed\n",
       "running trial: 5\n",
       "{u'roc_timeperiod': 17, u'roc_lower': 0.19331486969842795, u'rsi_width': 14, u'rsi_timeperiod': 39, u'roc_width': 1.5503892413558846, u'rsi_lower': 81, u'rebalance_freq': 5}\n",
       "rebal freq: 5\n",
       "OrderedDict([('rsi', {'width': 14, 'lower': 81, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 39}}), ('roc', {'width': 1.5503892413558846, 'lower': 0.19331486969842795, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 17}})])\n",
       "-999\n",
       "*** Flagging as failed\n",
       "running trial: 6\n",
       "{u'roc_timeperiod': 41, u'roc_lower': 0.1719860762888669, u'rsi_width': 20, u'rsi_timeperiod': 18, u'roc_width': 0.11655086301768874, u'rsi_lower': 10, u'rebalance_freq': 19}\n",
       "rebal freq: 19\n",
       "OrderedDict([('rsi', {'width': 20, 'lower': 10, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 18}}), ('roc', {'width': 0.11655086301768874, 'lower': 0.1719860762888669, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 41}})])\n",
       "-999\n",
       "*** Flagging as failed\n",
       "running trial: 7\n",
       "{u'roc_timeperiod': 35, u'roc_lower': 0.0628082531371578, u'rsi_width': 15, u'rsi_timeperiod': 121, u'roc_width': 0.9736839670494162, u'rsi_lower': 53, u'rebalance_freq': 10}\n",
       "rebal freq: 10\n",
       "OrderedDict([('rsi', {'width': 15, 'lower': 53, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 121}}), ('roc', {'width': 0.9736839670494162, 'lower': 0.0628082531371578, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 35}})])\n",
       "-0.322010383406\n",
       "running trial: 8\n",
       "{u'roc_timeperiod': 56, u'roc_lower': 0.05890419535760426, u'rsi_width': 21, u'rsi_timeperiod': 84, u'roc_width': 1.2953802802619307, u'rsi_lower': 52, u'rebalance_freq': 9}\n",
       "rebal freq: 9\n",
       "OrderedDict([('rsi', {'width': 21, 'lower': 52, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 84}}), ('roc', {'width': 1.2953802802619307, 'lower': 0.05890419535760426, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 56}})])\n",
       "-0.286484323504\n",
       "running trial: 9\n",
       "{u'roc_timeperiod': 62, u'roc_lower': 0.0950192172004, u'rsi_width': 18, u'rsi_timeperiod': 48, u'roc_width': 1.28594968173, u'rsi_lower': 36, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 36, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 48}}), ('roc', {'width': 1.28594968173, 'lower': 0.0950192172004, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 62}})])\n",
       "0.0431522015222\n",
       "running trial: 10\n",
       "{u'roc_timeperiod': 62, u'roc_lower': 0.0905251058487, u'rsi_width': 19, u'rsi_timeperiod': 65, u'roc_width': 1.00334681373, u'rsi_lower': 36, u'rebalance_freq': 16}\n",
       "rebal freq: 16\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 36, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 65}}), ('roc', {'width': 1.00334681373, 'lower': 0.0905251058487, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 62}})])\n",
       "0.00341989407519\n",
       "running trial: 11\n",
       "{u'roc_timeperiod': 60, u'roc_lower': 0.0742466839925, u'rsi_width': 17, u'rsi_timeperiod': 66, u'roc_width': 1.19222664123, u'rsi_lower': 36, u'rebalance_freq': 12}\n",
       "rebal freq: 12\n",
       "OrderedDict([('rsi', {'width': 17, 'lower': 36, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 66}}), ('roc', {'width': 1.19222664123, 'lower': 0.0742466839925, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 60}})])\n",
       "0.275186103768\n",
       "running trial: 12\n",
       "{u'roc_timeperiod': 60, u'roc_lower': 0.0855485670482, u'rsi_width': 20, u'rsi_timeperiod': 64, u'roc_width': 1.19797579465, u'rsi_lower': 43, u'rebalance_freq': 13}\n",
       "rebal freq: 13\n",
       "OrderedDict([('rsi', {'width': 20, 'lower': 43, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 64}}), ('roc', {'width': 1.19797579465, 'lower': 0.0855485670482, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 60}})])\n",
       "-0.208707587868\n",
       "running trial: 13\n",
       "{u'roc_timeperiod': 62, u'roc_lower': 0.0882125300343, u'rsi_width': 16, u'rsi_timeperiod': 61, u'roc_width': 1.08598524161, u'rsi_lower': 31, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 16, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 61}}), ('roc', {'width': 1.08598524161, 'lower': 0.0882125300343, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 62}})])\n",
       "-999\n",
       "*** Flagging as failed\n",
       "running trial: 14\n",
       "{u'roc_timeperiod': 59, u'roc_lower': 0.055349445266, u'rsi_width': 18, u'rsi_timeperiod': 66, u'roc_width': 1.29903729658, u'rsi_lower': 36, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 36, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 66}}), ('roc', {'width': 1.29903729658, 'lower': 0.055349445266, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 59}})])\n",
       "-0.0856342869641\n",
       "running trial: 15\n",
       "{u'roc_timeperiod': 63, u'roc_lower': 0.0770284678528, u'rsi_width': 19, u'rsi_timeperiod': 58, u'roc_width': 1.09110259946, u'rsi_lower': 34, u'rebalance_freq': 13}\n",
       "rebal freq: 13\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 34, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 58}}), ('roc', {'width': 1.09110259946, 'lower': 0.0770284678528, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 63}})])\n",
       "0.113588625349\n",
       "running trial: 16\n",
       "{u'roc_timeperiod': 56, u'roc_lower': 0.0800060615509, u'rsi_width': 18, u'rsi_timeperiod': 72, u'roc_width': 1.10097040991, u'rsi_lower': 38, u'rebalance_freq': 13}\n",
       "rebal freq: 13\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 38, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 72}}), ('roc', {'width': 1.10097040991, 'lower': 0.0800060615509, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 56}})])\n",
       "0.0423878834545\n",
       "running trial: 17\n",
       "{u'roc_timeperiod': 63, u'roc_lower': 0.0967364895581, u'rsi_width': 18, u'rsi_timeperiod': 69, u'roc_width': 1.25240989165, u'rsi_lower': 38, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 38, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 69}}), ('roc', {'width': 1.25240989165, 'lower': 0.0967364895581, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 63}})])\n",
       "-0.152850457939\n",
       "running trial: 18\n",
       "{u'roc_timeperiod': 59, u'roc_lower': 0.0764049154489, u'rsi_width': 18, u'rsi_timeperiod': 56, u'roc_width': 1.1009945256, u'rsi_lower': 43, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 43, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 56}}), ('roc', {'width': 1.1009945256, 'lower': 0.0764049154489, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 59}})])\n",
       "-0.275611570227\n",
       "running trial: 19\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0924615435964, u'rsi_width': 19, u'rsi_timeperiod': 60, u'roc_width': 1.17692712608, u'rsi_lower': 31, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 60}}), ('roc', {'width': 1.17692712608, 'lower': 0.0924615435964, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.776285891713\n",
       "running trial: 20\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.296181645068, u'rsi_width': 11, u'rsi_timeperiod': 56, u'roc_width': 0.73437937273, u'rsi_lower': 85, u'rebalance_freq': 19}\n",
       "rebal freq: 19\n",
       "OrderedDict([('rsi', {'width': 11, 'lower': 85, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 56}}), ('roc', {'width': 0.73437937273, 'lower': 0.296181645068, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "-999\n",
       "*** Flagging as failed\n",
       "running trial: 21\n",
       "{u'roc_timeperiod': 59, u'roc_lower': 0.0677838140476, u'rsi_width': 16, u'rsi_timeperiod': 66, u'roc_width': 1.29421194311, u'rsi_lower': 36, u'rebalance_freq': 9}\n",
       "rebal freq: 9\n",
       "OrderedDict([('rsi', {'width': 16, 'lower': 36, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 66}}), ('roc', {'width': 1.29421194311, 'lower': 0.0677838140476, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 59}})])\n",
       "-0.243354754021\n",
       "running trial: 22\n",
       "{u'roc_timeperiod': 60, u'roc_lower': 0.0766231698502, u'rsi_width': 20, u'rsi_timeperiod': 66, u'roc_width': 1.16773641765, u'rsi_lower': 23, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 20, 'lower': 23, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 66}}), ('roc', {'width': 1.16773641765, 'lower': 0.0766231698502, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 60}})])\n",
       "-999\n",
       "*** Flagging as failed\n",
       "running trial: 23\n",
       "{u'roc_timeperiod': 25, u'roc_lower': 0.131899720829, u'rsi_width': 23, u'rsi_timeperiod': 61, u'roc_width': 1.50351608871, u'rsi_lower': 89, u'rebalance_freq': 20}\n",
       "rebal freq: 20\n",
       "OrderedDict([('rsi', {'width': 23, 'lower': 89, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 61}}), ('roc', {'width': 1.50351608871, 'lower': 0.131899720829, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 25}})])\n",
       "-999\n",
       "*** Flagging as failed\n",
       "running trial: 24\n",
       "{u'roc_timeperiod': 57, u'roc_lower': 0.119191132405, u'rsi_width': 19, u'rsi_timeperiod': 53, u'roc_width': 1.11991952096, u'rsi_lower': 28, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 28, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 53}}), ('roc', {'width': 1.11991952096, 'lower': 0.119191132405, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 57}})])\n",
       "-999\n",
       "*** Flagging as failed\n",
       "running trial: 25\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0805957842667, u'rsi_width': 18, u'rsi_timeperiod': 73, u'roc_width': 0.734623188307, u'rsi_lower': 30, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 73}}), ('roc', {'width': 0.734623188307, 'lower': 0.0805957842667, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.736090359224\n",
       "running trial: 26\n",
       "{u'roc_timeperiod': 56, u'roc_lower': 0.0245196081598, u'rsi_width': 19, u'rsi_timeperiod': 58, u'roc_width': 1.2124074084, u'rsi_lower': 31, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 58}}), ('roc', {'width': 1.2124074084, 'lower': 0.0245196081598, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 56}})])\n",
       "-0.407345982519\n",
       "running trial: 27\n",
       "{u'roc_timeperiod': 61, u'roc_lower': 0.116215396462, u'rsi_width': 17, u'rsi_timeperiod': 51, u'roc_width': 1.35711067723, u'rsi_lower': 40, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 17, 'lower': 40, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 51}}), ('roc', {'width': 1.35711067723, 'lower': 0.116215396462, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 61}})])\n",
       "0.170172543718\n",
       "running trial: 28\n",
       "{u'roc_timeperiod': 14, u'roc_lower': 0.1980846975761057, u'rsi_width': 12, u'rsi_timeperiod': 73, u'roc_width': 0.7040273801421166, u'rsi_lower': 21, u'rebalance_freq': 13}\n",
       "rebal freq: 13\n",
       "OrderedDict([('rsi', {'width': 12, 'lower': 21, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 73}}), ('roc', {'width': 0.7040273801421166, 'lower': 0.1980846975761057, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 14}})])\n",
       "-999\n",
       "*** Flagging as failed\n",
       "running trial: 29\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0916536280798, u'rsi_width': 18, u'rsi_timeperiod': 48, u'roc_width': 1.45593195219, u'rsi_lower': 33, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 33, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 48}}), ('roc', {'width': 1.45593195219, 'lower': 0.0916536280798, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.224192253805\n",
       "running trial: 30\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.081574629044, u'rsi_width': 19, u'rsi_timeperiod': 73, u'roc_width': 0.866592367621, u'rsi_lower': 30, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 73}}), ('roc', {'width': 0.866592367621, 'lower': 0.081574629044, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.79336563351\n",
       "running trial: 31\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0686352155935, u'rsi_width': 18, u'rsi_timeperiod': 74, u'roc_width': 0.764300465235, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 74}}), ('roc', {'width': 0.764300465235, 'lower': 0.0686352155935, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.832788193734\n",
       "running trial: 32\n",
       "{u'roc_timeperiod': 59, u'roc_lower': 0.070224720576, u'rsi_width': 19, u'rsi_timeperiod': 87, u'roc_width': 0.569830838899, u'rsi_lower': 31, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 87}}), ('roc', {'width': 0.569830838899, 'lower': 0.070224720576, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 59}})])\n",
       "0.260122344497\n",
       "running trial: 33\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0409559045131, u'rsi_width': 18, u'rsi_timeperiod': 109, u'roc_width': 0.05, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 109}}), ('roc', {'width': 0.05, 'lower': 0.0409559045131, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.52516582284\n",
       "running trial: 34\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0909249473008, u'rsi_width': 19, u'rsi_timeperiod': 60, u'roc_width': 1.25027890837, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 60}}), ('roc', {'width': 1.25027890837, 'lower': 0.0909249473008, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.55669350398\n",
       "running trial: 35\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0655476515413, u'rsi_width': 19, u'rsi_timeperiod': 89, u'roc_width': 0.05, u'rsi_lower': 30, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 89}}), ('roc', {'width': 0.05, 'lower': 0.0655476515413, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.603638724703\n",
       "running trial: 36\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0565723203443, u'rsi_width': 19, u'rsi_timeperiod': 75, u'roc_width': 0.857076361146, u'rsi_lower': 32, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 0.857076361146, 'lower': 0.0565723203443, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "-0.0109758081186\n",
       "running trial: 37\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0527568819909, u'rsi_width': 17, u'rsi_timeperiod': 78, u'roc_width': 0.821388502928, u'rsi_lower': 32, u'rebalance_freq': 16}\n",
       "rebal freq: 16\n",
       "OrderedDict([('rsi', {'width': 17, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 78}}), ('roc', {'width': 0.821388502928, 'lower': 0.0527568819909, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.54248401227\n",
       "running trial: 38\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.114539516759, u'rsi_width': 20, u'rsi_timeperiod': 49, u'roc_width': 1.44626902496, u'rsi_lower': 31, u'rebalance_freq': 13}\n",
       "rebal freq: 13\n",
       "OrderedDict([('rsi', {'width': 20, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 49}}), ('roc', {'width': 1.44626902496, 'lower': 0.114539516759, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "-999\n",
       "*** Flagging as failed\n",
       "running trial: 39\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0794466423658, u'rsi_width': 18, u'rsi_timeperiod': 68, u'roc_width': 0.818887402435, u'rsi_lower': 31, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 68}}), ('roc', {'width': 0.818887402435, 'lower': 0.0794466423658, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.407699598033\n",
       "running trial: 40\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0, u'rsi_width': 18, u'rsi_timeperiod': 123, u'roc_width': 0.233668991614, u'rsi_lower': 32, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 123}}), ('roc', {'width': 0.233668991614, 'lower': 0.0, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.483018277599\n",
       "running trial: 41\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0069189560716, u'rsi_width': 17, u'rsi_timeperiod': 101, u'roc_width': 0.386671984684, u'rsi_lower': 31, u'rebalance_freq': 16}\n",
       "rebal freq: 16\n",
       "OrderedDict([('rsi', {'width': 17, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 101}}), ('roc', {'width': 0.386671984684, 'lower': 0.0069189560716, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.375750915892\n",
       "running trial: 42\n",
       "{u'roc_timeperiod': 51, u'roc_lower': 0.0678721090036, u'rsi_width': 19, u'rsi_timeperiod': 78, u'roc_width': 0.24939843343, u'rsi_lower': 30, u'rebalance_freq': 16}\n",
       "rebal freq: 16\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 78}}), ('roc', {'width': 0.24939843343, 'lower': 0.0678721090036, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 51}})])\n",
       "0.247676980999\n",
       "running trial: 43\n",
       "{u'roc_timeperiod': 63, u'roc_lower': 0.0695424924533, u'rsi_width': 18, u'rsi_timeperiod': 75, u'roc_width': 1.3824812369, u'rsi_lower': 30, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 1.3824812369, 'lower': 0.0695424924533, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 63}})])\n",
       "0.146287449802\n",
       "running trial: 44\n",
       "{u'roc_timeperiod': 61, u'roc_lower': 0.0579682803646, u'rsi_width': 18, u'rsi_timeperiod': 97, u'roc_width': 0.05, u'rsi_lower': 26, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 26, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 97}}), ('roc', {'width': 0.05, 'lower': 0.0579682803646, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 61}})])\n",
       "-999\n",
       "*** Flagging as failed\n",
       "running trial: 45\n",
       "{u'roc_timeperiod': 40, u'roc_lower': 0.0264310293806, u'rsi_width': 19, u'rsi_timeperiod': 99, u'roc_width': 0.05, u'rsi_lower': 36, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 36, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 99}}), ('roc', {'width': 0.05, 'lower': 0.0264310293806, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 40}})])\n",
       "0.164613445289\n",
       "running trial: 46\n",
       "{u'roc_timeperiod': 59, u'roc_lower': 0.0659192212594, u'rsi_width': 19, u'rsi_timeperiod': 90, u'roc_width': 0.05, u'rsi_lower': 34, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 34, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 90}}), ('roc', {'width': 0.05, 'lower': 0.0659192212594, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 59}})])\n",
       "0.394021263036\n",
       "running trial: 47\n",
       "{u'roc_timeperiod': 53, u'roc_lower': 0.0705666802398, u'rsi_width': 18, u'rsi_timeperiod': 79, u'roc_width': 0.777379970463, u'rsi_lower': 31, u'rebalance_freq': 16}\n",
       "rebal freq: 16\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 79}}), ('roc', {'width': 0.777379970463, 'lower': 0.0705666802398, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 53}})])\n",
       "0.479706684697\n",
       "running trial: 48\n",
       "{u'roc_timeperiod': 63, u'roc_lower': 0.0603829272553, u'rsi_width': 17, u'rsi_timeperiod': 23, u'roc_width': 0.680558720451, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 17, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 23}}), ('roc', {'width': 0.680558720451, 'lower': 0.0603829272553, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 63}})])\n",
       "0.124628141681\n",
       "running trial: 49\n",
       "{u'roc_timeperiod': 60, u'roc_lower': 0.0693311961775, u'rsi_width': 18, u'rsi_timeperiod': 126, u'roc_width': 0.879559784199, u'rsi_lower': 32, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 126}}), ('roc', {'width': 0.879559784199, 'lower': 0.0693311961775, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 60}})])\n",
       "0.537205409492\n",
       "running trial: 50\n",
       "{u'roc_timeperiod': 42, u'roc_lower': 0.0672724053413, u'rsi_width': 19, u'rsi_timeperiod': 5, u'roc_width': 0.751149995649, u'rsi_lower': 30, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 5}}), ('roc', {'width': 0.751149995649, 'lower': 0.0672724053413, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 42}})])\n",
       "0.000686877417427\n",
       "running trial: 51\n",
       "{u'roc_timeperiod': 63, u'roc_lower': 0.0688373310734, u'rsi_width': 18, u'rsi_timeperiod': 126, u'roc_width': 0.545278693489, u'rsi_lower': 33, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 33, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 126}}), ('roc', {'width': 0.545278693489, 'lower': 0.0688373310734, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 63}})])\n",
       "0.192052955408\n",
       "running trial: 52\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0698533936418, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 0.67370087461, u'rsi_lower': 31, u'rebalance_freq': 16}\n",
       "rebal freq: 16\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.67370087461, 'lower': 0.0698533936418, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.591806367702\n",
       "running trial: 53\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.09448302614, u'rsi_width': 20, u'rsi_timeperiod': 69, u'roc_width': 1.40689422113, u'rsi_lower': 29, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 20, 'lower': 29, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 69}}), ('roc', {'width': 1.40689422113, 'lower': 0.09448302614, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.407699598033\n",
       "running trial: 54\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0665925207623, u'rsi_width': 18, u'rsi_timeperiod': 59, u'roc_width': 1.38844087554, u'rsi_lower': 33, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 33, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 59}}), ('roc', {'width': 1.38844087554, 'lower': 0.0665925207623, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.710104028809\n",
       "running trial: 55\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.075277651721, u'rsi_width': 19, u'rsi_timeperiod': 97, u'roc_width': 0.05, u'rsi_lower': 30, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 97}}), ('roc', {'width': 0.05, 'lower': 0.075277651721, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.741515118654\n",
       "running trial: 56\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0286104144892, u'rsi_width': 18, u'rsi_timeperiod': 73, u'roc_width': 1.44615164547, u'rsi_lower': 34, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 34, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 73}}), ('roc', {'width': 1.44615164547, 'lower': 0.0286104144892, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.404309187699\n",
       "running trial: 57\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0910081523135, u'rsi_width': 18, u'rsi_timeperiod': 77, u'roc_width': 0.05, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 77}}), ('roc', {'width': 0.05, 'lower': 0.0910081523135, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.939148134103\n",
       "running trial: 58\n",
       "{u'roc_timeperiod': 57, u'roc_lower': 0.0858646784314, u'rsi_width': 18, u'rsi_timeperiod': 81, u'roc_width': 0.05, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 81}}), ('roc', {'width': 0.05, 'lower': 0.0858646784314, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 57}})])\n",
       "0.316099374723\n",
       "running trial: 59\n",
       "{u'roc_timeperiod': 59, u'roc_lower': 0.0808951838129, u'rsi_width': 18, u'rsi_timeperiod': 126, u'roc_width': 1.76890061664, u'rsi_lower': 31, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 126}}), ('roc', {'width': 1.76890061664, 'lower': 0.0808951838129, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 59}})])\n",
       "0.218707858988\n",
       "running trial: 60\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0, u'rsi_width': 18, u'rsi_timeperiod': 80, u'roc_width': 0.05, u'rsi_lower': 30, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 80}}), ('roc', {'width': 0.05, 'lower': 0.0, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "-0.212176701333\n",
       "running trial: 61\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0773600381305, u'rsi_width': 18, u'rsi_timeperiod': 79, u'roc_width': 0.05, u'rsi_lower': 33, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 33, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 79}}), ('roc', {'width': 0.05, 'lower': 0.0773600381305, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.482453578553\n",
       "running trial: 62\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0859374989484, u'rsi_width': 17, u'rsi_timeperiod': 77, u'roc_width': 0.05, u'rsi_lower': 26, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 17, 'lower': 26, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 77}}), ('roc', {'width': 0.05, 'lower': 0.0859374989484, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "-999\n",
       "*** Flagging as failed\n",
       "running trial: 63\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.13119630017, u'rsi_width': 19, u'rsi_timeperiod': 77, u'roc_width': 0.05, u'rsi_lower': 55, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 55, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 77}}), ('roc', {'width': 0.05, 'lower': 0.13119630017, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "-0.24407328919\n",
       "running trial: 64\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0301304523525, u'rsi_width': 17, u'rsi_timeperiod': 79, u'roc_width': 1.9973571249, u'rsi_lower': 49, u'rebalance_freq': 17}\n",
       "rebal freq: 17\n",
       "OrderedDict([('rsi', {'width': 17, 'lower': 49, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 79}}), ('roc', {'width': 1.9973571249, 'lower': 0.0301304523525, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.173230027371\n",
       "running trial: 65\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.095488678804, u'rsi_width': 19, u'rsi_timeperiod': 77, u'roc_width': 0.05, u'rsi_lower': 37, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 37, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 77}}), ('roc', {'width': 0.05, 'lower': 0.095488678804, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.538566915691\n",
       "running trial: 66\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.104218405558, u'rsi_width': 19, u'rsi_timeperiod': 59, u'roc_width': 1.05829612948, u'rsi_lower': 32, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 59}}), ('roc', {'width': 1.05829612948, 'lower': 0.104218405558, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.776285891713\n",
       "running trial: 67\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0874261690672, u'rsi_width': 18, u'rsi_timeperiod': 89, u'roc_width': 0.05, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 89}}), ('roc', {'width': 0.05, 'lower': 0.0874261690672, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.32930683396\n",
       "running trial: 68\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0970812025412, u'rsi_width': 17, u'rsi_timeperiod': 56, u'roc_width': 0.05, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 17, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 56}}), ('roc', {'width': 0.05, 'lower': 0.0970812025412, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "-999\n",
       "*** Flagging as failed\n",
       "running trial: 69\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.110391959253, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 0.0582654631143, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.0582654631143, 'lower': 0.110391959253, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.736090359224\n",
       "running trial: 70\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0916711899834, u'rsi_width': 18, u'rsi_timeperiod': 62, u'roc_width': 1.14735666032, u'rsi_lower': 34, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 34, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 62}}), ('roc', {'width': 1.14735666032, 'lower': 0.0916711899834, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.197190987875\n",
       "running trial: 71\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.100551033614, u'rsi_width': 18, u'rsi_timeperiod': 75, u'roc_width': 0.05, u'rsi_lower': 33, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 33, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 0.05, 'lower': 0.100551033614, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.883286455707\n",
       "running trial: 72\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0860966801832, u'rsi_width': 20, u'rsi_timeperiod': 78, u'roc_width': 0.05, u'rsi_lower': 30, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 20, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 78}}), ('roc', {'width': 0.05, 'lower': 0.0860966801832, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.502251565998\n",
       "running trial: 73\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0645758848505, u'rsi_width': 18, u'rsi_timeperiod': 70, u'roc_width': 1.08184684035, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 70}}), ('roc', {'width': 1.08184684035, 'lower': 0.0645758848505, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.832788193734\n",
       "running trial: 74\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0579134258975, u'rsi_width': 19, u'rsi_timeperiod': 97, u'roc_width': 0.05, u'rsi_lower': 29, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 29, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 97}}), ('roc', {'width': 0.05, 'lower': 0.0579134258975, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.626398515563\n",
       "running trial: 75\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0963138227125, u'rsi_width': 18, u'rsi_timeperiod': 78, u'roc_width': 0.0646342962157, u'rsi_lower': 32, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 78}}), ('roc', {'width': 0.0646342962157, 'lower': 0.0963138227125, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.502251565998\n",
       "running trial: 76\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0882768317903, u'rsi_width': 18, u'rsi_timeperiod': 70, u'roc_width': 0.05, u'rsi_lower': 32, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 70}}), ('roc', {'width': 0.05, 'lower': 0.0882768317903, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.32930683396\n",
       "running trial: 77\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0769041765886, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 0.109920094361, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.109920094361, 'lower': 0.0769041765886, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.983439526744\n",
       "running trial: 78\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.061580098722, u'rsi_width': 19, u'rsi_timeperiod': 72, u'roc_width': 2.0, u'rsi_lower': 30, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 72}}), ('roc', {'width': 2.0, 'lower': 0.061580098722, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.653842012422\n",
       "running trial: 79\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0869078977485, u'rsi_width': 18, u'rsi_timeperiod': 76, u'roc_width': 0.408048768236, u'rsi_lower': 33, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 33, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.408048768236, 'lower': 0.0869078977485, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.930664518778\n",
       "running trial: 80\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0468924997288, u'rsi_width': 17, u'rsi_timeperiod': 69, u'roc_width': 1.03126770215, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 17, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 69}}), ('roc', {'width': 1.03126770215, 'lower': 0.0468924997288, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.578854816386\n",
       "running trial: 81\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.060767816319, u'rsi_width': 19, u'rsi_timeperiod': 73, u'roc_width': 0.895795018615, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 73}}), ('roc', {'width': 0.895795018615, 'lower': 0.060767816319, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.729319631006\n",
       "running trial: 82\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0842705124788, u'rsi_width': 20, u'rsi_timeperiod': 59, u'roc_width': 1.6477725208, u'rsi_lower': 30, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 20, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 59}}), ('roc', {'width': 1.6477725208, 'lower': 0.0842705124788, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.224192253805\n",
       "running trial: 83\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0824060575488, u'rsi_width': 18, u'rsi_timeperiod': 77, u'roc_width': 0.05, u'rsi_lower': 33, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 33, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 77}}), ('roc', {'width': 0.05, 'lower': 0.0824060575488, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.580406092501\n",
       "running trial: 84\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0735975100807, u'rsi_width': 18, u'rsi_timeperiod': 74, u'roc_width': 1.34296813626, u'rsi_lower': 32, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 74}}), ('roc', {'width': 1.34296813626, 'lower': 0.0735975100807, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.84451677802\n",
       "running trial: 85\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0871458384877, u'rsi_width': 18, u'rsi_timeperiod': 76, u'roc_width': 0.523767527677, u'rsi_lower': 32, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.523767527677, 'lower': 0.0871458384877, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.938258190484\n",
       "running trial: 86\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0915056052511, u'rsi_width': 19, u'rsi_timeperiod': 75, u'roc_width': 0.05, u'rsi_lower': 34, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 34, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 0.05, 'lower': 0.0915056052511, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.582542829275\n",
       "running trial: 87\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0826347068017, u'rsi_width': 18, u'rsi_timeperiod': 76, u'roc_width': 0.05, u'rsi_lower': 30, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.05, 'lower': 0.0826347068017, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.822930306749\n",
       "running trial: 88\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0939266841426, u'rsi_width': 18, u'rsi_timeperiod': 60, u'roc_width': 0.462572461365, u'rsi_lower': 32, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 60}}), ('roc', {'width': 0.462572461365, 'lower': 0.0939266841426, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.776285891713\n",
       "running trial: 89\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0750950300876, u'rsi_width': 19, u'rsi_timeperiod': 77, u'roc_width': 0.05, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 77}}), ('roc', {'width': 0.05, 'lower': 0.0750950300876, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.608132497431\n",
       "running trial: 90\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.083606079985, u'rsi_width': 20, u'rsi_timeperiod': 60, u'roc_width': 2.0, u'rsi_lower': 32, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 20, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 60}}), ('roc', {'width': 2.0, 'lower': 0.083606079985, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.460022295691\n",
       "running trial: 91\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0854272437141, u'rsi_width': 18, u'rsi_timeperiod': 75, u'roc_width': 0.660548427464, u'rsi_lower': 33, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 33, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 0.660548427464, 'lower': 0.0854272437141, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.950974399658\n",
       "running trial: 92\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0719150664579, u'rsi_width': 18, u'rsi_timeperiod': 59, u'roc_width': 0.682600648675, u'rsi_lower': 32, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 59}}), ('roc', {'width': 0.682600648675, 'lower': 0.0719150664579, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.68936355513\n",
       "running trial: 93\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0842306234264, u'rsi_width': 19, u'rsi_timeperiod': 75, u'roc_width': 0.226494262634, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 0.226494262634, 'lower': 0.0842306234264, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.863114101646\n",
       "running trial: 94\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0572981029053, u'rsi_width': 19, u'rsi_timeperiod': 75, u'roc_width': 0.332202842837, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 0.332202842837, 'lower': 0.0572981029053, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.76867616353\n",
       "running trial: 95\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0731437790397, u'rsi_width': 19, u'rsi_timeperiod': 75, u'roc_width': 0.05, u'rsi_lower': 30, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 0.05, 'lower': 0.0731437790397, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.834307203277\n",
       "running trial: 96\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.112653775495, u'rsi_width': 19, u'rsi_timeperiod': 60, u'roc_width': 1.58890514008, u'rsi_lower': 31, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 60}}), ('roc', {'width': 1.58890514008, 'lower': 0.112653775495, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.776285891713\n",
       "running trial: 97\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.102380087565, u'rsi_width': 19, u'rsi_timeperiod': 77, u'roc_width': 0.05, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 77}}), ('roc', {'width': 0.05, 'lower': 0.102380087565, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.888949502771\n",
       "running trial: 98\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.045790026739, u'rsi_width': 18, u'rsi_timeperiod': 70, u'roc_width': 2.0, u'rsi_lower': 30, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 70}}), ('roc', {'width': 2.0, 'lower': 0.045790026739, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.578854816386\n",
       "running trial: 99\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0825583378435, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 0.252452451944, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.252452451944, 'lower': 0.0825583378435, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.938258190484\n",
       "running trial: 100\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0593584409278, u'rsi_width': 20, u'rsi_timeperiod': 76, u'roc_width': 1.62440791181, u'rsi_lower': 30, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 20, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 1.62440791181, 'lower': 0.0593584409278, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.843567336358\n",
       "running trial: 101\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.069683349804, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 0.05, u'rsi_lower': 32, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.05, 'lower': 0.069683349804, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.608604874683\n",
       "running trial: 102\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0727224038191, u'rsi_width': 17, u'rsi_timeperiod': 97, u'roc_width': 1.3631015076, u'rsi_lower': 30, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 17, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 97}}), ('roc', {'width': 1.3631015076, 'lower': 0.0727224038191, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.741150977102\n",
       "running trial: 103\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0785929426029, u'rsi_width': 19, u'rsi_timeperiod': 72, u'roc_width': 1.78561555634, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 72}}), ('roc', {'width': 1.78561555634, 'lower': 0.0785929426029, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.654304125492\n",
       "running trial: 104\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0611766316331, u'rsi_width': 19, u'rsi_timeperiod': 90, u'roc_width': 1.77483401216, u'rsi_lower': 31, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 90}}), ('roc', {'width': 1.77483401216, 'lower': 0.0611766316331, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.437929798306\n",
       "running trial: 105\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0718023953391, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 0.05, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.05, 'lower': 0.0718023953391, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.71723729937\n",
       "running trial: 106\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0743056423866, u'rsi_width': 19, u'rsi_timeperiod': 90, u'roc_width': 0.839894103673, u'rsi_lower': 29, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 29, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 90}}), ('roc', {'width': 0.839894103673, 'lower': 0.0743056423866, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.78677114826\n",
       "running trial: 107\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.102083544948, u'rsi_width': 20, u'rsi_timeperiod': 60, u'roc_width': 1.30472201501, u'rsi_lower': 32, u'rebalance_freq': 13}\n",
       "rebal freq: 13\n",
       "OrderedDict([('rsi', {'width': 20, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 60}}), ('roc', {'width': 1.30472201501, 'lower': 0.102083544948, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.795865439658\n",
       "running trial: 108\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0608123899645, u'rsi_width': 17, u'rsi_timeperiod': 74, u'roc_width': 0.05, u'rsi_lower': 33, u'rebalance_freq': 16}\n",
       "rebal freq: 16\n",
       "OrderedDict([('rsi', {'width': 17, 'lower': 33, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 74}}), ('roc', {'width': 0.05, 'lower': 0.0608123899645, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.186531754032\n",
       "running trial: 109\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.104987013208, u'rsi_width': 18, u'rsi_timeperiod': 76, u'roc_width': 0.0504366133898, u'rsi_lower': 24, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 24, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.0504366133898, 'lower': 0.104987013208, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "-999\n",
       "*** Flagging as failed\n",
       "running trial: 110\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0618219098434, u'rsi_width': 18, u'rsi_timeperiod': 69, u'roc_width': 1.00928983769, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 69}}), ('roc', {'width': 1.00928983769, 'lower': 0.0618219098434, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.381240225866\n",
       "running trial: 111\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0792153660947, u'rsi_width': 18, u'rsi_timeperiod': 75, u'roc_width': 0.369516137993, u'rsi_lower': 32, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 0.369516137993, 'lower': 0.0792153660947, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.909541954276\n",
       "running trial: 112\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0665653892923, u'rsi_width': 18, u'rsi_timeperiod': 71, u'roc_width': 0.230140750794, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 71}}), ('roc', {'width': 0.230140750794, 'lower': 0.0665653892923, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.832788193734\n",
       "running trial: 113\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.102126136713, u'rsi_width': 19, u'rsi_timeperiod': 60, u'roc_width': 1.18047954644, u'rsi_lower': 31, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 60}}), ('roc', {'width': 1.18047954644, 'lower': 0.102126136713, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.776285891713\n",
       "running trial: 114\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0952665526919, u'rsi_width': 18, u'rsi_timeperiod': 76, u'roc_width': 0.05, u'rsi_lower': 32, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.05, 'lower': 0.0952665526919, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.888949502771\n",
       "running trial: 115\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0782244616309, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 0.431218579947, u'rsi_lower': 29, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 29, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.431218579947, 'lower': 0.0782244616309, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.909541954276\n",
       "running trial: 116\n",
       "{u'roc_timeperiod': 57, u'roc_lower': 0.0668687510866, u'rsi_width': 19, u'rsi_timeperiod': 97, u'roc_width': 0.50855637589, u'rsi_lower': 30, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 97}}), ('roc', {'width': 0.50855637589, 'lower': 0.0668687510866, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 57}})])\n",
       "0.652192991559\n",
       "running trial: 117\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0786026114365, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 0.05, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.05, 'lower': 0.0786026114365, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.914477152282\n",
       "running trial: 118\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.105338463307, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 0.05, u'rsi_lower': 34, u'rebalance_freq': 15}\n",
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       "OrderedDict([('rsi', {'width': 19, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 70}}), ('roc', {'width': 0.669913471368, 'lower': 0.0659413605762, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.832788193734\n",
       "running trial: 157\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0980253189888, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 0.05, u'rsi_lower': 33, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 33, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.05, 'lower': 0.0980253189888, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.633582805152\n",
       "running trial: 158\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0771768495774, u'rsi_width': 18, u'rsi_timeperiod': 76, u'roc_width': 1.59684793037, u'rsi_lower': 32, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 1.59684793037, 'lower': 0.0771768495774, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.983439526744\n",
       "running trial: 159\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0846313775317, u'rsi_width': 19, u'rsi_timeperiod': 75, u'roc_width': 2.0, u'rsi_lower': 33, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 33, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 2.0, 'lower': 0.0846313775317, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.922558146524\n",
       "running trial: 160\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0593720338155, u'rsi_width': 19, u'rsi_timeperiod': 75, u'roc_width': 0.520524506989, u'rsi_lower': 30, u'rebalance_freq': 16}\n",
       "rebal freq: 16\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 0.520524506989, 'lower': 0.0593720338155, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.638228322522\n",
       "running trial: 161\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.105399423956, u'rsi_width': 20, u'rsi_timeperiod': 59, u'roc_width': 2.0, u'rsi_lower': 31, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 20, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 59}}), ('roc', {'width': 2.0, 'lower': 0.105399423956, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.776285891713\n",
       "running trial: 162\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0705692616012, u'rsi_width': 20, u'rsi_timeperiod': 90, u'roc_width': 0.586243719584, u'rsi_lower': 29, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 20, 'lower': 29, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 90}}), ('roc', {'width': 0.586243719584, 'lower': 0.0705692616012, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.760232147599\n",
       "running trial: 163\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0919389761527, u'rsi_width': 18, u'rsi_timeperiod': 76, u'roc_width': 0.05, u'rsi_lower': 32, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.05, 'lower': 0.0919389761527, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.888949502771\n",
       "running trial: 164\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0826919772764, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 2.0, u'rsi_lower': 30, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 2.0, 'lower': 0.0826919772764, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.863114101646\n",
       "running trial: 165\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.10523301419, u'rsi_width': 19, u'rsi_timeperiod': 60, u'roc_width': 1.57648591155, u'rsi_lower': 31, u'rebalance_freq': 13}\n",
       "rebal freq: 13\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 60}}), ('roc', {'width': 1.57648591155, 'lower': 0.10523301419, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.916075747319\n",
       "running trial: 166\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.059731662535, u'rsi_width': 18, u'rsi_timeperiod': 75, u'roc_width': 0.05, u'rsi_lower': 30, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 0.05, 'lower': 0.059731662535, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.511631176995\n",
       "running trial: 167\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.102746878192, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 0.781870275373, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.781870275373, 'lower': 0.102746878192, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.938258190484\n",
       "running trial: 168\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.108415970608, u'rsi_width': 19, u'rsi_timeperiod': 59, u'roc_width': 0.680890902834, u'rsi_lower': 31, u'rebalance_freq': 13}\n",
       "rebal freq: 13\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 59}}), ('roc', {'width': 0.680890902834, 'lower': 0.108415970608, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.916075747319\n",
       "running trial: 169\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.105001648483, u'rsi_width': 18, u'rsi_timeperiod': 76, u'roc_width': 0.05, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.05, 'lower': 0.105001648483, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.822930306749\n",
       "running trial: 170\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0660465725456, u'rsi_width': 18, u'rsi_timeperiod': 70, u'roc_width': 2.0, u'rsi_lower': 30, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 70}}), ('roc', {'width': 2.0, 'lower': 0.0660465725456, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.526118179776\n",
       "running trial: 171\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0595962782797, u'rsi_width': 20, u'rsi_timeperiod': 75, u'roc_width': 0.962787477979, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 20, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 0.962787477979, 'lower': 0.0595962782797, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.665552050278\n",
       "running trial: 172\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.101720769934, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 0.05, u'rsi_lower': 31, u'rebalance_freq': 16}\n",
       "rebal freq: 16\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.05, 'lower': 0.101720769934, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.357431157949\n",
       "running trial: 173\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.101932094686, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 0.206638623991, u'rsi_lower': 31, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.206638623991, 'lower': 0.101932094686, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.407699598033\n",
       "running trial: 174\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.104836899906, u'rsi_width': 19, u'rsi_timeperiod': 77, u'roc_width': 2.0, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 77}}), ('roc', {'width': 2.0, 'lower': 0.104836899906, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.938258190484\n",
       "running trial: 175\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.06581128707, u'rsi_width': 19, u'rsi_timeperiod': 75, u'roc_width': 1.58184926397, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 1.58184926397, 'lower': 0.06581128707, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.909541954276\n",
       "running trial: 176\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0927159354337, u'rsi_width': 18, u'rsi_timeperiod': 77, u'roc_width': 2.0, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 77}}), ('roc', {'width': 2.0, 'lower': 0.0927159354337, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.981029981719\n",
       "running trial: 177\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0713020752223, u'rsi_width': 18, u'rsi_timeperiod': 89, u'roc_width': 0.370904876564, u'rsi_lower': 29, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 29, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 89}}), ('roc', {'width': 0.370904876564, 'lower': 0.0713020752223, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.761181371291\n",
       "running trial: 178\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.106517415593, u'rsi_width': 19, u'rsi_timeperiod': 59, u'roc_width': 0.557230713299, u'rsi_lower': 31, u'rebalance_freq': 13}\n",
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       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 59}}), ('roc', {'width': 0.557230713299, 'lower': 0.106517415593, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.916075747319\n",
       "running trial: 179\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0636716826365, u'rsi_width': 20, u'rsi_timeperiod': 75, u'roc_width': 1.51419631313, u'rsi_lower': 30, u'rebalance_freq': 15}\n",
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       "OrderedDict([('rsi', {'width': 20, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 1.51419631313, 'lower': 0.0636716826365, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.860058310989\n",
       "running trial: 180\n",
       "{u'roc_timeperiod': 57, u'roc_lower': 0.0737407396175, u'rsi_width': 18, u'rsi_timeperiod': 97, u'roc_width': 1.08756441351, u'rsi_lower': 30, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 97}}), ('roc', {'width': 1.08756441351, 'lower': 0.0737407396175, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 57}})])\n",
       "0.743113155741\n",
       "running trial: 181\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0761091726953, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 0.05, u'rsi_lower': 30, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.05, 'lower': 0.0761091726953, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.834307203277\n",
       "running trial: 182\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0790052650866, u'rsi_width': 18, u'rsi_timeperiod': 76, u'roc_width': 0.726709806868, u'rsi_lower': 28, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 28, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.726709806868, 'lower': 0.0790052650866, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.698861750531\n",
       "running trial: 183\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0851507301763, u'rsi_width': 18, u'rsi_timeperiod': 76, u'roc_width': 2.0, u'rsi_lower': 33, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 33, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 2.0, 'lower': 0.0851507301763, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.930664518778\n",
       "running trial: 184\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.106284676773, u'rsi_width': 21, u'rsi_timeperiod': 76, u'roc_width': 1.04550620872, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 21, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 1.04550620872, 'lower': 0.106284676773, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.45747729645\n",
       "running trial: 185\n",
       "{u'roc_timeperiod': 57, u'roc_lower': 0.0717706309688, u'rsi_width': 19, u'rsi_timeperiod': 90, u'roc_width': 0.05, u'rsi_lower': 30, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 90}}), ('roc', {'width': 0.05, 'lower': 0.0717706309688, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 57}})])\n",
       "-0.0589865526629\n",
       "running trial: 186\n",
       "{u'roc_timeperiod': 59, u'roc_lower': 0.0660132901116, u'rsi_width': 18, u'rsi_timeperiod': 70, u'roc_width': 0.998528803548, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 70}}), ('roc', {'width': 0.998528803548, 'lower': 0.0660132901116, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 59}})])\n",
       "0.254922697689\n",
       "running trial: 187\n",
       "{u'roc_timeperiod': 59, u'roc_lower': 0.0710302722614, u'rsi_width': 18, u'rsi_timeperiod': 90, u'roc_width': 1.51440163603, u'rsi_lower': 29, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 29, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 90}}), ('roc', {'width': 1.51440163603, 'lower': 0.0710302722614, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 59}})])\n",
       "0.502742669448\n",
       "running trial: 188\n",
       "{u'roc_timeperiod': 57, u'roc_lower': 0.0653822198111, u'rsi_width': 19, u'rsi_timeperiod': 70, u'roc_width': 1.64144483319, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 70}}), ('roc', {'width': 1.64144483319, 'lower': 0.0653822198111, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 57}})])\n",
       "0.259222667574\n",
       "running trial: 189\n",
       "{u'roc_timeperiod': 57, u'roc_lower': 0.0763872106218, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 0.818372940469, u'rsi_lower': 32, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.818372940469, 'lower': 0.0763872106218, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 57}})])\n",
       "0.589473210771\n",
       "running trial: 190\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0775934469102, u'rsi_width': 18, u'rsi_timeperiod': 75, u'roc_width': 0.05, u'rsi_lower': 29, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 29, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 0.05, 'lower': 0.0775934469102, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.698861750531\n",
       "running trial: 191\n",
       "{u'roc_timeperiod': 59, u'roc_lower': 0.0657235656326, u'rsi_width': 19, u'rsi_timeperiod': 75, u'roc_width': 0.05, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 0.05, 'lower': 0.0657235656326, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 59}})])\n",
       "-999\n",
       "*** Flagging as failed\n",
       "running trial: 192\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0789409236942, u'rsi_width': 20, u'rsi_timeperiod': 76, u'roc_width': 0.05, u'rsi_lower': 28, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 20, 'lower': 28, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.05, 'lower': 0.0789409236942, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.834307203277\n",
       "running trial: 193\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0829431720388, u'rsi_width': 18, u'rsi_timeperiod': 72, u'roc_width': 1.9794732304, u'rsi_lower': 30, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 72}}), ('roc', {'width': 1.9794732304, 'lower': 0.0829431720388, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.79336563351\n",
       "running trial: 194\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0973750984149, u'rsi_width': 19, u'rsi_timeperiod': 60, u'roc_width': 1.11585469428, u'rsi_lower': 31, u'rebalance_freq': 13}\n",
       "rebal freq: 13\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 60}}), ('roc', {'width': 1.11585469428, 'lower': 0.0973750984149, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.916075747319\n",
       "running trial: 195\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0713305988334, u'rsi_width': 18, u'rsi_timeperiod': 90, u'roc_width': 0.05, u'rsi_lower': 29, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 29, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 90}}), ('roc', {'width': 0.05, 'lower': 0.0713305988334, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.727344883271\n",
       "running trial: 196\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0969987185826, u'rsi_width': 18, u'rsi_timeperiod': 60, u'roc_width': 1.24297906844, u'rsi_lower': 31, u'rebalance_freq': 13}\n",
       "rebal freq: 13\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 60}}), ('roc', {'width': 1.24297906844, 'lower': 0.0969987185826, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.672412822696\n",
       "running trial: 197\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0720354217877, u'rsi_width': 19, u'rsi_timeperiod': 89, u'roc_width': 1.84065050499, u'rsi_lower': 30, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 89}}), ('roc', {'width': 1.84065050499, 'lower': 0.0720354217877, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.67084327966\n",
       "running trial: 198\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.107611037588, u'rsi_width': 19, u'rsi_timeperiod': 60, u'roc_width': 2.0, u'rsi_lower': 31, u'rebalance_freq': 13}\n",
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       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 60}}), ('roc', {'width': 2.0, 'lower': 0.107611037588, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.916075747319\n",
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       "{u'roc_timeperiod': 58, u'roc_lower': 0.0998526131822, u'rsi_width': 16, u'rsi_timeperiod': 75, u'roc_width': 0.05, u'rsi_lower': 33, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 16, 'lower': 33, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 0.05, 'lower': 0.0998526131822, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.822930306749\n",
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       "{u'roc_timeperiod': 58, u'roc_lower': 0.0703840385559, u'rsi_width': 19, u'rsi_timeperiod': 60, u'roc_width': 0.474440172532, u'rsi_lower': 33, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 33, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 60}}), ('roc', {'width': 0.474440172532, 'lower': 0.0703840385559, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.550399319648\n",
       "running trial: 201\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0765205965727, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 0.398186198873, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.398186198873, 'lower': 0.0765205965727, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.983439526744\n",
       "running trial: 202\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.073140686667, u'rsi_width': 19, u'rsi_timeperiod': 75, u'roc_width': 2.0, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 2.0, 'lower': 0.073140686667, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.909541954276\n",
       "running trial: 203\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0995454796295, u'rsi_width': 17, u'rsi_timeperiod': 75, u'roc_width': 2.0, u'rsi_lower': 33, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 17, 'lower': 33, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 2.0, 'lower': 0.0995454796295, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.863114101646\n",
       "running trial: 204\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0976195314358, u'rsi_width': 20, u'rsi_timeperiod': 60, u'roc_width': 0.66552691568, u'rsi_lower': 31, u'rebalance_freq': 13}\n",
       "rebal freq: 13\n",
       "OrderedDict([('rsi', {'width': 20, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 60}}), ('roc', {'width': 0.66552691568, 'lower': 0.0976195314358, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.916075747319\n",
       "running trial: 205\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0428726821706, u'rsi_width': 18, u'rsi_timeperiod': 69, u'roc_width': 1.92813649904, u'rsi_lower': 32, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 69}}), ('roc', {'width': 1.92813649904, 'lower': 0.0428726821706, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.387316090622\n",
       "running trial: 206\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0644432255557, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 2.0, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 2.0, 'lower': 0.0644432255557, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.887341435167\n",
       "running trial: 207\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0843447928944, u'rsi_width': 18, u'rsi_timeperiod': 75, u'roc_width': 2.0, u'rsi_lower': 32, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 2.0, 'lower': 0.0843447928944, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.863114101646\n",
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       "{u'roc_timeperiod': 58, u'roc_lower': 0.0995459376687, u'rsi_width': 19, u'rsi_timeperiod': 60, u'roc_width': 2.0, u'rsi_lower': 31, u'rebalance_freq': 13}\n",
       "rebal freq: 13\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 60}}), ('roc', {'width': 2.0, 'lower': 0.0995459376687, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.916075747319\n",
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       "{u'roc_timeperiod': 58, u'roc_lower': 0.0686759578989, u'rsi_width': 17, u'rsi_timeperiod': 59, u'roc_width': 1.82285049453, u'rsi_lower': 32, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 17, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 59}}), ('roc', {'width': 1.82285049453, 'lower': 0.0686759578989, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "-999\n",
       "*** Flagging as failed\n",
       "running trial: 210\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0650856374484, u'rsi_width': 19, u'rsi_timeperiod': 59, u'roc_width': 0.939899684771, u'rsi_lower': 34, u'rebalance_freq': 14}\n",
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       "OrderedDict([('rsi', {'width': 19, 'lower': 34, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 59}}), ('roc', {'width': 0.939899684771, 'lower': 0.0650856374484, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.540812205357\n",
       "running trial: 211\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0663698500526, u'rsi_width': 18, u'rsi_timeperiod': 70, u'roc_width': 2.0, u'rsi_lower': 31, u'rebalance_freq': 16}\n",
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       "OrderedDict([('rsi', {'width': 18, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 70}}), ('roc', {'width': 2.0, 'lower': 0.0663698500526, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.854836229399\n",
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       "{u'roc_timeperiod': 58, u'roc_lower': 0.0737395017542, u'rsi_width': 19, u'rsi_timeperiod': 59, u'roc_width': 0.135608415199, u'rsi_lower': 32, u'rebalance_freq': 14}\n",
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       "OrderedDict([('rsi', {'width': 19, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 59}}), ('roc', {'width': 0.135608415199, 'lower': 0.0737395017542, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.710104028809\n",
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       "{u'roc_timeperiod': 58, u'roc_lower': 0.0763235119514, u'rsi_width': 18, u'rsi_timeperiod': 76, u'roc_width': 2.0, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
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       "OrderedDict([('rsi', {'width': 18, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 2.0, 'lower': 0.0763235119514, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.909541954276\n",
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       "{u'roc_timeperiod': 58, u'roc_lower': 0.101574585492, u'rsi_width': 19, u'rsi_timeperiod': 77, u'roc_width': 1.9999993802, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
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       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 77}}), ('roc', {'width': 1.9999993802, 'lower': 0.101574585492, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.938258190484\n",
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       "{u'roc_timeperiod': 58, u'roc_lower': 0.0762260573038, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 0.05, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
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       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.05, 'lower': 0.0762260573038, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.914477152282\n",
       "running trial: 216\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0705866013688, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 0.91939361166, u'rsi_lower': 30, u'rebalance_freq': 15}\n",
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       "OrderedDict([('rsi', {'width': 19, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.91939361166, 'lower': 0.0705866013688, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.909541954276\n",
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       "{u'roc_timeperiod': 58, u'roc_lower': 0.0820878892536, u'rsi_width': 17, u'rsi_timeperiod': 76, u'roc_width': 2.0, u'rsi_lower': 32, u'rebalance_freq': 15}\n",
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       "OrderedDict([('rsi', {'width': 17, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 2.0, 'lower': 0.0820878892536, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.863114101646\n",
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       "{u'roc_timeperiod': 58, u'roc_lower': 0.104988716423, u'rsi_width': 18, u'rsi_timeperiod': 76, u'roc_width': 0.110871931072, u'rsi_lower': 79, u'rebalance_freq': 15}\n",
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       "OrderedDict([('rsi', {'width': 18, 'lower': 79, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.110871931072, 'lower': 0.104988716423, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "-999\n",
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       "{u'roc_timeperiod': 53, u'roc_lower': 0.0578259212625, u'rsi_width': 21, u'rsi_timeperiod': 97, u'roc_width': 0.05, u'rsi_lower': 34, u'rebalance_freq': 14}\n",
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       "OrderedDict([('rsi', {'width': 21, 'lower': 34, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 97}}), ('roc', {'width': 0.05, 'lower': 0.0578259212625, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 53}})])\n",
       "0.471356037963\n",
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       "{u'roc_timeperiod': 58, u'roc_lower': 0.0778730524215, u'rsi_width': 18, u'rsi_timeperiod': 75, u'roc_width': 1.19909942391, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
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       "OrderedDict([('rsi', {'width': 18, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 1.19909942391, 'lower': 0.0778730524215, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
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       "{u'roc_timeperiod': 58, u'roc_lower': 0.0759293093881, u'rsi_width': 19, u'rsi_timeperiod': 75, u'roc_width': 0.244295128123, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
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       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 0.244295128123, 'lower': 0.0759293093881, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
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       "{u'roc_timeperiod': 58, u'roc_lower': 0.0767457896943, u'rsi_width': 18, u'rsi_timeperiod': 76, u'roc_width': 0.212595882485, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
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       "OrderedDict([('rsi', {'width': 18, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.212595882485, 'lower': 0.0767457896943, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
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       "{u'roc_timeperiod': 58, u'roc_lower': 0.0767748689838, u'rsi_width': 19, u'rsi_timeperiod': 75, u'roc_width': 1.22027180526, u'rsi_lower': 32, u'rebalance_freq': 15}\n",
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       "OrderedDict([('rsi', {'width': 19, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 1.22027180526, 'lower': 0.0767748689838, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.976425219121\n",
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       "{u'roc_timeperiod': 58, u'roc_lower': 0.100125957637, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 0.423899168687, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
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       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.423899168687, 'lower': 0.100125957637, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.938258190484\n",
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       "{u'roc_timeperiod': 58, u'roc_lower': 0.0765040524729, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 0.243799018108, u'rsi_lower': 32, u'rebalance_freq': 15}\n",
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       "OrderedDict([('rsi', {'width': 19, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.243799018108, 'lower': 0.0765040524729, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.978776031181\n",
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       "OrderedDict([('rsi', {'width': 19, 'lower': 33, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.908866858882, 'lower': 0.0731650700784, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.424220011945\n",
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       "{u'roc_timeperiod': 58, u'roc_lower': 0.101700712457, u'rsi_width': 19, u'rsi_timeperiod': 59, u'roc_width': 0.86484767427, u'rsi_lower': 31, u'rebalance_freq': 13}\n",
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       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 59}}), ('roc', {'width': 0.86484767427, 'lower': 0.101700712457, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.916075747319\n",
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       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.220618623863, 'lower': 0.0773386691467, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
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       "OrderedDict([('rsi', {'width': 20, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 77}}), ('roc', {'width': 0.788752204212, 'lower': 0.080806761324, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
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       "0.938258190484\n",
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       "OrderedDict([('rsi', {'width': 19, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.616310571021, 'lower': 0.0750027090238, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
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       "{u'roc_timeperiod': 58, u'roc_lower': 0.0830681742033, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 1.94622801128, u'rsi_lower': 32, u'rebalance_freq': 15}\n",
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       "{u'roc_timeperiod': 58, u'roc_lower': 0.102027446628, u'rsi_width': 18, u'rsi_timeperiod': 76, u'roc_width': 0.265371486889, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.265371486889, 'lower': 0.102027446628, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.863114101646\n",
       "running trial: 271\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0861563986373, u'rsi_width': 19, u'rsi_timeperiod': 77, u'roc_width': 1.67270300725, u'rsi_lower': 32, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 77}}), ('roc', {'width': 1.67270300725, 'lower': 0.0861563986373, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.74145151814\n",
       "running trial: 272\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0726096216059, u'rsi_width': 18, u'rsi_timeperiod': 74, u'roc_width': 0.509083636561, u'rsi_lower': 30, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 74}}), ('roc', {'width': 0.509083636561, 'lower': 0.0726096216059, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.791627217325\n",
       "running trial: 273\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0833192909004, u'rsi_width': 18, u'rsi_timeperiod': 75, u'roc_width': 1.75778210596, u'rsi_lower': 34, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 34, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 1.75778210596, 'lower': 0.0833192909004, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.922558146524\n",
       "running trial: 274\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0993305190699, u'rsi_width': 19, u'rsi_timeperiod': 60, u'roc_width': 1.16189748532, u'rsi_lower': 31, u'rebalance_freq': 13}\n",
       "rebal freq: 13\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 60}}), ('roc', {'width': 1.16189748532, 'lower': 0.0993305190699, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.916075747319\n",
       "running trial: 275\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0686686674087, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 1.35654710783, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 1.35654710783, 'lower': 0.0686686674087, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.887341435167\n",
       "running trial: 276\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.107500192165, u'rsi_width': 19, u'rsi_timeperiod': 59, u'roc_width': 1.22679360271, u'rsi_lower': 31, u'rebalance_freq': 13}\n",
       "rebal freq: 13\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 59}}), ('roc', {'width': 1.22679360271, 'lower': 0.107500192165, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.916075747319\n",
       "running trial: 277\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0987279618458, u'rsi_width': 19, u'rsi_timeperiod': 59, u'roc_width': 0.667859071745, u'rsi_lower': 31, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 59}}), ('roc', {'width': 0.667859071745, 'lower': 0.0987279618458, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.224192253805\n",
       "running trial: 278\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0819832821089, u'rsi_width': 18, u'rsi_timeperiod': 75, u'roc_width': 1.75061582311, u'rsi_lower': 33, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 33, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 1.75061582311, 'lower': 0.0819832821089, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.950974399658\n",
       "running trial: 279\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0818105867791, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 0.511580127315, u'rsi_lower': 32, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.511580127315, 'lower': 0.0818105867791, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.930664518778\n",
       "running trial: 280\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0865040981205, u'rsi_width': 18, u'rsi_timeperiod': 77, u'roc_width': 2.0, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 77}}), ('roc', {'width': 2.0, 'lower': 0.0865040981205, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.981029981719\n",
       "running trial: 281\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0641383598287, u'rsi_width': 18, u'rsi_timeperiod': 71, u'roc_width': 1.19298490589, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 71}}), ('roc', {'width': 1.19298490589, 'lower': 0.0641383598287, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.832788193734\n",
       "running trial: 282\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0753699044248, u'rsi_width': 18, u'rsi_timeperiod': 76, u'roc_width': 0.475928995262, u'rsi_lower': 32, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.475928995262, 'lower': 0.0753699044248, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.983439526744\n",
       "running trial: 283\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0757938775802, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 0.507359818264, u'rsi_lower': 32, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.507359818264, 'lower': 0.0757938775802, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.978776031181\n",
       "running trial: 284\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0691362494393, u'rsi_width': 18, u'rsi_timeperiod': 59, u'roc_width': 1.13013083081, u'rsi_lower': 32, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 59}}), ('roc', {'width': 1.13013083081, 'lower': 0.0691362494393, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.68936355513\n",
       "running trial: 285\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0761291701225, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 0.124363137169, u'rsi_lower': 32, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.124363137169, 'lower': 0.0761291701225, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.978776031181\n",
       "running trial: 286\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0722475896471, u'rsi_width': 19, u'rsi_timeperiod': 75, u'roc_width': 1.107715067, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 1.107715067, 'lower': 0.0722475896471, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.909541954276\n",
       "running trial: 287\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.066138662478, u'rsi_width': 19, u'rsi_timeperiod': 71, u'roc_width': 0.992343474249, u'rsi_lower': 30, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 71}}), ('roc', {'width': 0.992343474249, 'lower': 0.066138662478, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.832788193734\n",
       "running trial: 288\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0636867897542, u'rsi_width': 19, u'rsi_timeperiod': 75, u'roc_width': 0.986328610714, u'rsi_lower': 30, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 0.986328610714, 'lower': 0.0636867897542, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.909541954276\n",
       "running trial: 289\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0897953615282, u'rsi_width': 18, u'rsi_timeperiod': 77, u'roc_width': 1.93994434442, u'rsi_lower': 32, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 77}}), ('roc', {'width': 1.93994434442, 'lower': 0.0897953615282, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.981029981719\n",
       "running trial: 290\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0771954869554, u'rsi_width': 18, u'rsi_timeperiod': 76, u'roc_width': 1.7761340404, u'rsi_lower': 32, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 1.7761340404, 'lower': 0.0771954869554, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.983439526744\n",
       "running trial: 291\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0875796076303, u'rsi_width': 20, u'rsi_timeperiod': 75, u'roc_width': 1.31451545139, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 20, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 1.31451545139, 'lower': 0.0875796076303, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.950974399658\n",
       "running trial: 292\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0738845980468, u'rsi_width': 19, u'rsi_timeperiod': 90, u'roc_width': 0.617891940696, u'rsi_lower': 30, u'rebalance_freq': 14}\n",
       "rebal freq: 14\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 90}}), ('roc', {'width': 0.617891940696, 'lower': 0.0738845980468, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.773011242219\n",
       "running trial: 293\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0767056075541, u'rsi_width': 18, u'rsi_timeperiod': 76, u'roc_width': 0.58186987918, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.58186987918, 'lower': 0.0767056075541, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.909541954276\n",
       "running trial: 294\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0628668987083, u'rsi_width': 19, u'rsi_timeperiod': 75, u'roc_width': 1.79588870087, u'rsi_lower': 31, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 31, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 1.79588870087, 'lower': 0.0628668987083, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.860058310989\n",
       "running trial: 295\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0949810019766, u'rsi_width': 18, u'rsi_timeperiod': 75, u'roc_width': 0.32217597336, u'rsi_lower': 33, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 33, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 0.32217597336, 'lower': 0.0949810019766, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.950974399658\n",
       "running trial: 296\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0629964160787, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 1.35320417538, u'rsi_lower': 29, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 29, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 1.35320417538, 'lower': 0.0629964160787, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.909541954276\n",
       "running trial: 297\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.06160033075, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 1.43667774554, u'rsi_lower': 30, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 30, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 1.43667774554, 'lower': 0.06160033075, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.909541954276\n",
       "running trial: 298\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0942001973876, u'rsi_width': 19, u'rsi_timeperiod': 76, u'roc_width': 0.406773739917, u'rsi_lower': 32, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 19, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 76}}), ('roc', {'width': 0.406773739917, 'lower': 0.0942001973876, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.930664518778\n",
       "running trial: 299\n",
       "{u'roc_timeperiod': 58, u'roc_lower': 0.0794787244906, u'rsi_width': 18, u'rsi_timeperiod': 75, u'roc_width': 0.614159079508, u'rsi_lower': 32, u'rebalance_freq': 15}\n",
       "rebal freq: 15\n",
       "OrderedDict([('rsi', {'width': 18, 'lower': 32, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 75}}), ('roc', {'width': 0.614159079508, 'lower': 0.0794787244906, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 58}})])\n",
       "0.863114101646\n"
      ]
     }
    ],
    "source": [
     "sigopt_df = run_sigopt_local_rsi_roc(samples=300,\n",
     "                             experiment_id=None,\n",
     "                             user_token=creds.user_token, \n",
     "                             client_token=creds.client_token, \n",
     "                             client_id=creds.client_id \n",
     "                            )"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>failed</th>\n",
        "      <th>objective</th>\n",
        "      <th>rebalance_freq</th>\n",
        "      <th>roc_lower</th>\n",
        "      <th>roc_timeperiod</th>\n",
        "      <th>roc_width</th>\n",
        "      <th>rsi_lower</th>\n",
        "      <th>rsi_timeperiod</th>\n",
        "      <th>rsi_width</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>False</td>\n",
        "      <td>-0.054690</td>\n",
        "      <td>3</td>\n",
        "      <td>0.168719</td>\n",
        "      <td>17</td>\n",
        "      <td>1.834614</td>\n",
        "      <td>33</td>\n",
        "      <td>75</td>\n",
        "      <td>28</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>False</td>\n",
        "      <td>-0.073903</td>\n",
        "      <td>17</td>\n",
        "      <td>0.205710</td>\n",
        "      <td>29</td>\n",
        "      <td>1.186417</td>\n",
        "      <td>64</td>\n",
        "      <td>8</td>\n",
        "      <td>18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>False</td>\n",
        "      <td>-0.250872</td>\n",
        "      <td>20</td>\n",
        "      <td>0.014307</td>\n",
        "      <td>21</td>\n",
        "      <td>0.238028</td>\n",
        "      <td>35</td>\n",
        "      <td>95</td>\n",
        "      <td>23</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>True</td>\n",
        "      <td>-999.000000</td>\n",
        "      <td>8</td>\n",
        "      <td>0.109979</td>\n",
        "      <td>63</td>\n",
        "      <td>0.678920</td>\n",
        "      <td>82</td>\n",
        "      <td>44</td>\n",
        "      <td>11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>True</td>\n",
        "      <td>-999.000000</td>\n",
        "      <td>10</td>\n",
        "      <td>0.236430</td>\n",
        "      <td>3</td>\n",
        "      <td>0.853963</td>\n",
        "      <td>70</td>\n",
        "      <td>107</td>\n",
        "      <td>26</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "  failed   objective  rebalance_freq  roc_lower  roc_timeperiod  roc_width  \\\n",
        "4  False   -0.054690               3   0.168719              17   1.834614   \n",
        "3  False   -0.073903              17   0.205710              29   1.186417   \n",
        "1  False   -0.250872              20   0.014307              21   0.238028   \n",
        "0   True -999.000000               8   0.109979              63   0.678920   \n",
        "2   True -999.000000              10   0.236430               3   0.853963   \n",
        "\n",
        "   rsi_lower  rsi_timeperiod  rsi_width  \n",
        "4         33              75         28  \n",
        "3         64               8         18  \n",
        "1         35              95         23  \n",
        "0         82              44         11  \n",
        "2         70             107         26  "
       ]
      },
      "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "sigopt_df.sort('objective', ascending=False).head()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 41,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "# If you exported a CSV of your SigOpt results from your account page on the SigOpt website,\n",
     "# you can read the results in like this. \n",
     "# To make it 'pandas friendly' you might want to replace 'None' with 'NA' prior to reading it in\n",
     "\n",
     "sigopt_df = pd.read_csv('SigOpt_Experiment_good_2.csv')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 42,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>rsi_timeperiod</th>\n",
        "      <th>rsi_lower</th>\n",
        "      <th>rsi_width</th>\n",
        "      <th>roc_timeperiod</th>\n",
        "      <th>roc_lower</th>\n",
        "      <th>roc_width</th>\n",
        "      <th>rebalance_freq</th>\n",
        "      <th>value</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>27</td>\n",
        "      <td>55</td>\n",
        "      <td>17</td>\n",
        "      <td>16</td>\n",
        "      <td>0.132454</td>\n",
        "      <td>1.943223</td>\n",
        "      <td>7</td>\n",
        "      <td>0.337197</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>38</td>\n",
        "      <td>44</td>\n",
        "      <td>18</td>\n",
        "      <td>46</td>\n",
        "      <td>0.000729</td>\n",
        "      <td>0.812360</td>\n",
        "      <td>9</td>\n",
        "      <td>0.425559</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>66</td>\n",
        "      <td>89</td>\n",
        "      <td>13</td>\n",
        "      <td>4</td>\n",
        "      <td>0.168258</td>\n",
        "      <td>1.104381</td>\n",
        "      <td>16</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>119</td>\n",
        "      <td>76</td>\n",
        "      <td>21</td>\n",
        "      <td>35</td>\n",
        "      <td>0.057774</td>\n",
        "      <td>0.265620</td>\n",
        "      <td>3</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>86</td>\n",
        "      <td>21</td>\n",
        "      <td>12</td>\n",
        "      <td>51</td>\n",
        "      <td>0.250782</td>\n",
        "      <td>1.664795</td>\n",
        "      <td>11</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "   rsi_timeperiod  rsi_lower  rsi_width  roc_timeperiod  roc_lower  roc_width  \\\n",
        "0              27         55         17              16   0.132454   1.943223   \n",
        "1              38         44         18              46   0.000729   0.812360   \n",
        "2              66         89         13               4   0.168258   1.104381   \n",
        "3             119         76         21              35   0.057774   0.265620   \n",
        "4              86         21         12              51   0.250782   1.664795   \n",
        "\n",
        "   rebalance_freq     value  \n",
        "0               7  0.337197  \n",
        "1               9  0.425559  \n",
        "2              16       NaN  \n",
        "3               3       NaN  \n",
        "4              11       NaN  "
       ]
      },
      "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "sigopt_df.head()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 43,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>rsi_timeperiod</th>\n",
        "      <th>rsi_lower</th>\n",
        "      <th>rsi_width</th>\n",
        "      <th>roc_timeperiod</th>\n",
        "      <th>roc_lower</th>\n",
        "      <th>roc_width</th>\n",
        "      <th>rebalance_freq</th>\n",
        "      <th>value</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>226</th>\n",
        "      <td>33</td>\n",
        "      <td>43</td>\n",
        "      <td>17</td>\n",
        "      <td>54</td>\n",
        "      <td>0</td>\n",
        "      <td>0.568999</td>\n",
        "      <td>12</td>\n",
        "      <td>1.009966</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>223</th>\n",
        "      <td>33</td>\n",
        "      <td>43</td>\n",
        "      <td>17</td>\n",
        "      <td>54</td>\n",
        "      <td>0</td>\n",
        "      <td>0.608607</td>\n",
        "      <td>12</td>\n",
        "      <td>1.009966</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>247</th>\n",
        "      <td>33</td>\n",
        "      <td>44</td>\n",
        "      <td>16</td>\n",
        "      <td>54</td>\n",
        "      <td>0</td>\n",
        "      <td>0.534478</td>\n",
        "      <td>12</td>\n",
        "      <td>1.009966</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>148</th>\n",
        "      <td>33</td>\n",
        "      <td>42</td>\n",
        "      <td>18</td>\n",
        "      <td>54</td>\n",
        "      <td>0</td>\n",
        "      <td>1.488573</td>\n",
        "      <td>12</td>\n",
        "      <td>1.009709</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>141</th>\n",
        "      <td>33</td>\n",
        "      <td>42</td>\n",
        "      <td>18</td>\n",
        "      <td>54</td>\n",
        "      <td>0</td>\n",
        "      <td>1.012120</td>\n",
        "      <td>12</td>\n",
        "      <td>1.009709</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>115</th>\n",
        "      <td>33</td>\n",
        "      <td>42</td>\n",
        "      <td>18</td>\n",
        "      <td>54</td>\n",
        "      <td>0</td>\n",
        "      <td>0.921427</td>\n",
        "      <td>12</td>\n",
        "      <td>1.009709</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>139</th>\n",
        "      <td>33</td>\n",
        "      <td>42</td>\n",
        "      <td>18</td>\n",
        "      <td>54</td>\n",
        "      <td>0</td>\n",
        "      <td>1.114055</td>\n",
        "      <td>12</td>\n",
        "      <td>1.009709</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>153</th>\n",
        "      <td>33</td>\n",
        "      <td>42</td>\n",
        "      <td>18</td>\n",
        "      <td>54</td>\n",
        "      <td>0</td>\n",
        "      <td>1.405196</td>\n",
        "      <td>12</td>\n",
        "      <td>1.009709</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>245</th>\n",
        "      <td>34</td>\n",
        "      <td>44</td>\n",
        "      <td>16</td>\n",
        "      <td>54</td>\n",
        "      <td>0</td>\n",
        "      <td>0.452321</td>\n",
        "      <td>12</td>\n",
        "      <td>1.009289</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>293</th>\n",
        "      <td>34</td>\n",
        "      <td>44</td>\n",
        "      <td>16</td>\n",
        "      <td>54</td>\n",
        "      <td>0</td>\n",
        "      <td>0.531977</td>\n",
        "      <td>12</td>\n",
        "      <td>0.990455</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "     rsi_timeperiod  rsi_lower  rsi_width  roc_timeperiod  roc_lower  \\\n",
        "226              33         43         17              54          0   \n",
        "223              33         43         17              54          0   \n",
        "247              33         44         16              54          0   \n",
        "148              33         42         18              54          0   \n",
        "141              33         42         18              54          0   \n",
        "115              33         42         18              54          0   \n",
        "139              33         42         18              54          0   \n",
        "153              33         42         18              54          0   \n",
        "245              34         44         16              54          0   \n",
        "293              34         44         16              54          0   \n",
        "\n",
        "     roc_width  rebalance_freq     value  \n",
        "226   0.568999              12  1.009966  \n",
        "223   0.608607              12  1.009966  \n",
        "247   0.534478              12  1.009966  \n",
        "148   1.488573              12  1.009709  \n",
        "141   1.012120              12  1.009709  \n",
        "115   0.921427              12  1.009709  \n",
        "139   1.114055              12  1.009709  \n",
        "153   1.405196              12  1.009709  \n",
        "245   0.452321              12  1.009289  \n",
        "293   0.531977              12  0.990455  "
       ]
      },
      "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "sigopt_df.sort('value', ascending=False).head(10)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## View the distribution of all the values attempted by the SigOpt Bayesian Optimizer for each parameter in the trading model "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 30,
    "metadata": {
     "collapsed": false,
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
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TvXr1ANiyZQuLFi3iD3/4g+qqiPiF635F2ZEjRxg8eDC9evWiZ8+eBAT831PsdjuRkZGE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\nLDc3V7m5uWZHAwCfVovwcyNNPv7445LOTvWQnZ0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rQv1Mf0s6OtaAmeNTBFptratdnz0c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\ngg==\n",
       "text/plain": [
        "<matplotlib.figure.Figure at 0x10de95e90>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "_=sigopt_df.hist(figsize=(17,10))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Diagonal: Visualize the distribution of each parameter attempted by the SigOpt optimizer\n",
     "### Scatterplots: Each individual parameter attempted vs. each of the others"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 31,
    "metadata": {
     "collapsed": false,
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "<seaborn.axisgrid.PairGrid at 0x114613f50>"
       ]
      },
      "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
       "image/png": 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EAAAg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\n0pEhcz6vXLlSl112mbZs2aIXXnhBl19+ub75zW9GXW7RokX66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gAA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ZojbDt0qjfifSIieWHymYiIiIiIiIgoAYlnPqclawEAlaU5UCgmt53uGMLQqCkaXSMimWDy\nmWSlrlKPRaJfacuLslhrOErqKvUoL8oSbpcXZaG+tjiKPSKSB0/HFsc5SnT1tcU8LsgjjpkkNcYU\nyc2oOPmcMjnzOT1Fi9KCTACAHcAR0dXFRETBYs1nkhWNWoXHv7ocf9naDIALgESTRq3C+lurnBZj\n0Wr4XhCFytOxxXGOEp1Ww+OCPOOYSVJjTJHcjE04l90YH50AACyZl4tT7QYAwOFTvfyRhYimjcln\nkh2tRoVVNYXR7gZh8uSc7wWR9HhsEbnjcUHeMDZIaowpkgubzY7xCSsAQAEgRTeVfK6al4s33m8B\nABw72w+zxQaNmhfPE1HwOHIQERERERERESWYsYmpkhu6JDWUSoVwW5+TgvysZADAhMmKptaBiPeP\niOSByWciIiIiIiIiogQzZpwquZGqc74wXqFQoGpernD70KneiPWLiOSFyecEZ7ZY8d6Bdrx3oB1m\nizXa3SFKaIl8PCby307xj/FLFB94rFK8YcxSuIkXG0zRuVdlrZ6XI/z/8Kle2O32iPSLiOSFNZ8T\nmNlixfObDqOpbRAAsOdEN9bfWsUFM4iiIJGPx0T+2yn+MX6J4gOPVYo3jFmKhDFR8jlVp3G7f35R\nFpKTVBifsKJvaALdg+OYmZ0SyS4SkQxw5nMC236kUziZAYCmtkFh1WYiiqxEPh4T+W+n+Mf4JYoP\nPFYp3jBmKRJGRWU3PM18VquUKC3IFG6fPj8UkX4Rkbww+UxERERERERElGDECw6mJHm+ML50tij5\n3MHkMxEFj8nnBFZXqUd5UZZwu7woC3WV+ij2iChxJfLxmMh/O8U/xi9RfOCxSvGGMUuR4K/sBgCU\nzs4Q/t9y3hD2PhGR/LDmcwLTqFVYf2uVcPlWXaWeNcSIoiSRj8dE/tsp/jF+ieIDj1WKN4xZigRx\n8jnZQ9kNACgRJZ/bukdgMluh1TAWiShwTD4nOI1ahVU1hdHuBhEhsY/HRP7bKf4xfoniA49VijeM\nWQq3cZP/shupOg30OSno7BuD1WbH2QvDKBPNyici8keS5HNHRwcUCoXX+2fPni3FboiIiIiIiIiI\nSAJGUc1nndb7bOZLZmegs28MwOSig0w+E1EwJEk+33fffTh+/DjmzZsHjUaD5uZm5OTkICUlBQDw\nj3/8w+tzDx8+jOeeew4bNmxAQ0MD7rzzTsyZMwcA8LnPfQ433HCDFF0kIiIiIiIiIqKLxieswv+T\nvcx8BiYXHdxx9AIA1n0mouBJknwuKCjAt771LSxduhQA0NTUhJ/+9Kf45S9/6fN5v/nNb/DWW28h\nNTUVAHD8+HF88YtfxBe/+EUpukVERERERERERB4YRWU3kv3MfHZo6TDAbrf7vPqdiEhMKUUjp0+f\nFhLPAFBeXo6Ojg6/z5szZw5eeOEF2O12AMCxY8fw/vvv4/Of/zy+/e1vY3R0VIruERERERERERGR\niHjms87HzOfCvDShLMfgiAndA+Nh7xsRyYckyWe9Xo/nn38ezc3NaGxsxJNPPomKigq/z7v++uuh\nUk39ulZVVYVvfvOb+P3vf4+ioiK88MILUnSPiIiIiIiIiIhExAsO+iq7oVQqUFGcLdxuONsf1n4R\nkbwo7I5pxyEYGBjAz372M+zbtw9JSUmoq6vDnXfeieTkZL/PbW9vx/33349NmzZheHgY6enpAIBT\np07hBz/4AV555ZVQu0dERERERERERCLrHtuMweEJAMCrj34c2Rk6r49964MW/OavxwAAKyr1+NYX\nLo9IH4ko/klS8zk7OxsPPvgg2traUFZWhvHx8YASz66+8pWv4Nvf/jYqKyuxa9cuLFq0KKDn9fQM\nB70vT/Ly0tkW23JrK5yk6mewpHyNuN/Y3LdcY9eXaL7PvrBfwQl37AKhx2+or50Urz3bkLYNqfoQ\nbrF6rsS2It9WPJ3vAuE/b4jUZ1ok9sN9BL+fcJL7d4ixcbPw/9ERI7IzdF73W5ybIvz/UFMPOi8Y\noFZJcjF9wn5PDCe5x26i7zea+55O7EoyUuzatQs333wz7r77bvT09ODaa6/Fhx9+GPDzHYXqH3/8\ncTz11FNYt24dDh06hLvuukuK7hERERERERER0UUWqw0miw0AoFAAWrXv9NDs3FRkpycBAMYmLDjV\nbgh7H4lIHiRJPv/4xz/Ga6+9hoyMDMycORO///3v8cwzzwT03MLCQmzcuBEAUFFRgddffx0bNmzA\nj3/8Y6SmpkrRPSIiIiIiIiIiushomlpsMFmrFiYFeqNQKFA9P1e4fehUb9j6RkTyIkny2WazIT8/\nX7g9f/58vwMXERERERERERFFnnFCvNigKqDnLJk3lXw+zOQzEQVIkprPs2bNwtatWwEAQ0NDeO21\n1zB79mwpmk44ZosV2490AgDqKvXQqAP7ECCKJ4xziiWMR6LYweMxvByvb1q6DtUl2Xx9icKMYxrF\nsnHRzGddUmCpofLibGjUSpgtNnQNjMMwMoHMtKRwdZGIZEKS5PMTTzyBJ598Ep2dnaivr8cVV1yB\nJ554QoqmE4rZYsXzmw6jqW0QALDnRDfW31oV5V4RSctbnPNknKKB8UgUO3g8hpfr61telMXXlyiM\nOKZRrBsXz3zWBpYa0qiVKJmVjuaL9Z5PthtwWUW+n2cRUaKTpOzGhg0b8JOf/AS7d+/Gnj178POf\n/9ypDAcFZvuRTuHkBACa2gaFX8qJ5IJxTrGE8UgUO3g8hhdfX6LI4jFHsc5omko+6wIsuwEA84uy\nhP+f5KKDRBQASZLPW7duhc1mk6IpIiIiIiIiIiIKo/EJ5wUHAzW/MFP4/6mOQR+PJCKaJEnZjays\nLHziE5/AwoULodPphO1PPfWUFM0njLpKPfac6Ha6HLKuUh/lXhFJi3FOsYTxSBQ7eDyGF19fosji\nMUexbtwU/IKDADB3Vobw/86+MdjtdigUCkn7RkTyElLy+dy5c5gzZw7+9V//FXa73ek+Dj7B06hV\nWH9rFRelIFljnFMsYTwSxQ4ej+Elfn254CBR+HFMo1hnFM181gUx8zk9RQOdVgWjyQqjyYrhMTMy\nUrXh6CIRyURIyedvfOMb+POf/4x33nkHv/zlL6XqU0LTqFVYVVMY7W4QhRXjnGIJ45EodvB4DC/H\n65uXl46enuFod4dI9jimUSwTLzio0wb+w4hCoUB+djJau0YAAN0D40w+E5FPISWfFQoF1q5di6am\nJqxbt87tvldffTWkzhERERERERERkbSMJlHN56TgUkP52SlC8rlrYAzzRHWgiYhchZR8fvXVV3Hi\nxAk88sgjuPfee51Kb7DsBhERERERERFR7HGu+RxcamhmdrLw/+6Bccn6RETyFFLyOS0tDbW1tdi4\ncSNycnI8PuaOO+7Ar371q1B2Iztmi1Wo/VVTlofXtjSjd2Acyxbk49rLiqZVC8zRptVmA+yA1WpD\ny4VhqACsW12BFJ1G4r+CYp04zqZbYy7UNsaMZmzY3AhgMg4dDCMT+Nkbh2Gz23H5gplITlKzDh5F\nja84d41h17FUiuNMTnoHx/H0awcAAA/fVoPcrGQ/z6BAiONwbX0ZDjT3CDV7AcgiBoXzGKsNJosN\n+xq7kZepwxduWMBzGAk5jlGVCnhwbfiOUbPFim0HO9DSYUBpYSZWLNJjd0MXgKk45fhJ4RJLsSV1\nXwJpL5DzGk2SGgXZydBqpT8Hd+yfteVjn3GaZTcAIF/0+dE9yOQzEfkWUvLZwVviGQC6urqk2IVs\nmC1WPL/psLDq8e//2QzHhPGzXSPYd7IXD31uSVAf0q5tujpyuh/P3r2CX94SiGtM7DnRjfW3VoUU\nV8G2MWY048GXdmL84uVcR07347ffvR6GkQmsf3GHEPet3aen3UeiUPmKc08xLB5LpTjO5KR3cBwP\nvbxLuP3Qy7vwzJ3LmYAOkWsc7mnqEcbP+YWZUABobjdM3henMWgyez6POds1gqNnduC5e67kOYwE\nInWMmi1WPLfxEE5ejMvdjT340wdnhMu795zoxr1rFuMXbx51Gz+JQhVLn81S9yWQ9oI5r3GQ8jVy\n3X95UVZcfi4livGQym5w5jMRBU4Z7Q4kmu1HOp2+XIkqlQAAWjqGhF+qp9umq3GTVZgxRYnBNSaa\n2gZDjqtg29iwudHphGbcZMUv3zyMn71x2C3up9tHolD5inNPMSweS6U4zuTEMePZ3zYKjmscisfP\nk+0GIfEMxG8Mbtnb6vU8xmi28RxGIpE6Rrcf6RQSzw7iuqJNbYPYsLmR4yeFRSx9Nkvdl0DaC+a8\nRqp+BdtHih3imc/J2uCSz9kZOuH/htEJyfpERPLE5DMRERERERERUQIR/xihSwpudnpmilb4/9Co\nyWn9LyIiV0w+R1hdpR7lRVnCbdd1GUsLMlBXqQ+pTVfJWpVTvV2SP9eYKC/KCjmugm1j3eoKJItq\nhyVrVbhrTRXu+2yVW9xPt49EofIV555iWDyWSnGcycnDt9UEtI2C4xqH4vFzfmEmykSry8drDNbX\nFns9j9FplDyHkUikjtG6Sj3mi+IScK4lWl6UhXWrKzh+UljE0mez1H0JpL1gzmuk6lewfaTYMR7C\nzOckrQpJF+PJYrVjTNQWEZErSWo+U+A0ahXW31ol6YKD4ja54CAB7nE2nYVEQm0jRafBs3evcFqs\nLS1Fi8y0JDx/z5VccJBigq849xTD4rFUiuNMTnKzkvHMncu54KDEXONQjgsOajWi8xguOBg24mM0\nnAsOatQqPLC22u+Cgxw/KRxiKbak7ksg7QV6XhOuBQfF++eCg7HPaBIln4Oc+QwAmaladJsm6z0P\njZqQys9rIvJC0uSzwWBAZqbzTIebb75Zyl3IgkatwqqaQuH23TcvlrxNIiliItQ2UnQa3OEhvjPT\nkvC9L14eSteIJOMrzr3FcCDPTUS5Wcl47p4ro90N2XGNw1U1hcjLS0dPz7BwO965Hks3LJ8bvc7I\nmOMYFcdPOGjUKtTXFqO+dmqba5xy/KRwiaXYkrovgbQXyHlNOMcAx/7DPc5QaGx2O4wTU2U3kjzM\nivcnI1UrLDZoGDFBn5MqWf+ISF4kKbtx4sQJrF69Gp/61KfQ2dmJ+vp6HDt2DABw++23S7ELIiIi\nIiIiIiIK0YTJCkeVZq1GCZUy+NSQU93nMZNEPSMiOZIk+fz9738fL7zwArKzs6HX6/HEE0/gscce\nk6JpIiIiIiIiIiKSiFG02GCw9Z4dMtKmks+GESaficg7ScpuGI1GzJs3T7i9YsUKPP3001I0HTVm\ni1XyWmFmixVvbT+Lf+w+B7sdyM3UosfgPkirlEBakgrzimbg89eXYe/JXowMG1FRnI1nXtsHw7gV\nuiQlPnF5MVYvmwOzxYZXNjeiu38ceRlJKJ+bjZXVBayvRTGld3Ac33x5BwaHJmC2ud+fogVuWH4J\n6z+TR77G5DGjGRs2NyJJp8Et15S61YdtOjeAH71+EADwzc8tQfmcbKf7dx/vxK/+dgIAcMcnF2DZ\npVML4zyzYQ8aO0YAABUFaXhonXO5mOOn+/Dj/zkMALj/lipcekmOW59Z85ASjafjVXwcXltTgFnZ\nybDagT0N53HmwhgAQKcCkpLUMIxN1aBUASiYmYbLy3KRnKwN6PMhHOdwcnHH01thvvh/DYAXHliJ\nbYc60NxugMJmx/yiLKxcwnNIik+O8wHAeZ0Gs8WKbYc60NJuwNxZ6YBSibPnDSgtyHSLd/H4sWzh\nTKda5YYRE7758g5YrMBVi/LR1jOOMxeGANgxJz8NarUaVpsN/YPjsCuA3EwdbDYF2rqHkZKsRk5a\nEnqHjOgfMUGrVmDWjFScajfAZgeSNErML8gAlAqc6RzCjNQk3HHzYry1/TSsAEpnpQv1mc0Wm9Pf\nCQD//fcT6DEYsaQsD+cuDKN/yIjLKvKhVSuhUimFsdDxWrT3jqEwJ8Xn3x/o+On6HAA8/4kD4sUG\ndUnTSwtx5jMRBUqS5HNWVhZOnDgh3H7rrbfcaj/HE7PFiuc3HUZT2yAAYM+Jbqy/tSqkD06zxYof\nbtiPc10jwjZPiWcAsNoAw7gV+5t7cOBkD+x298cYJ2z484dnsb+5F939YzBezOa1do9g/6k+7Gvs\nwf1rq/lhTzGhd3AcD728y+djxkzAH7edBiDNMUfy4WtMHjOa8eBLOzF+cfbG3oYuPHv3CuELpzjh\nBQA/ev2gUwJanHgGIPx/2aV6p8QzADR2jOCZDXuEBLQ48QwAP/6fw0IC2rXP5UVZjGlKCJ6O15uW\nz3E6VrYe6PD4XKMVMIoSzwBgBdDaNYLWi+dP/j4fwnEOJxfixDMAmAHc8dw2p8fsbe7F3qYePMBz\nSIozI2Mmp/OBI6f78ezdK6BRK/HjjYfQ3G4AAOxu7BGes7uxxyneXcePP77fIrT3wdHzONc5dU7w\n1q42p/0Pjgy49amtZ0z4f/+ICe2i2+MTgGHUINw2mm04enZw6u8ZH8O3/3O3cHvfxX7vPH4B53tG\nhX4dPt0Pm80Gk2XyC+NZ0XdN8f/3nOjGvWsW4+d/PCK8FgB8/v2BjJ+uz/mooQsKQNgHz39il/PM\n5+m9P5z5TESBkqTsxqOPPorHH38cp06dwtKlS/HKK6/g8ccfl6LpqNh+pFP4AAWAprZB4dfcUNoU\nJ54D5SnxLNbaNSIknsWa2w0h95lIKk+/diCox0txzJF8+BqTN2xuFL6AAcC4ySrMBgLglHj2tE2c\neHbdJk48O4i3iZNprtvC8TlCFA88xb6nY2W6/B1LPPa8M/t/CADgJM8hKQ798s3DHs8Hth/pdEq2\nuhLHu+v4IW5PnHiOppaOIad+GU1WIfHsS1PbIDZsbnR7LXz9/YGMn67POdlucNoHx+DYNW6a+rE3\nmTOfiSjMJJn5PGfOHLzwwgtITk6GzWZDX18f5s6dK0XTREREREREREQkEaO47MY0Zz6np4qSz6NM\nPhORd5LMfH711Vfxla98BampqTAYDLjzzjuxceNGKZqOirpKPcqLsoTb5UVZQv2qUNqcMzMt6Ocp\nFL7vL56ZBp3G/VVAHq8AACAASURBVG0sK8wMuc9EUnn4tpqgHi/FMUfy4WtMXre6wulSwWStSqh/\nCEzWeHYl3nbHJxe43e/YVlHgPmaLt91/S5Xb/Y5t4fgcIYoHnmLf07EyXf6OJR573mn8PwQAMJ/n\nkBSH7lpT5fF8oK5Sj7JC7+UgxfHuOn6I25ujD/57XDiUFmQ49UunVUGr9vOFEZNj4brVFW6vha+/\nP5Dx0/U58wsznfbBMTh2jU+Iym5Mc+ZzWvLUJ8uoMdDra4goEUky83nTpk144403AAD/n737Do+j\nOvcH/t2mXm0Vy5LcZGS5G2NjbBzAxBccQvKjY+A6JCGEBMLlXlNDbkJCHkpoCZcSiFPgKhBDAiGm\nXCcxpsQ2yA1LLiousi3JsnpbaVdbf3+sdzSzM9u0s/37eR49z+rszpmzs2dnZ945856ysjL89a9/\nxbXXXou1a9eqUX3EGfQ6rL9+oaqT1Rj0Ojy47pxxTTjY1D7ECQcprhXkpeOJ7y3Hkxv3csJBCpqv\nfXJGmgFP3r7C64SDs6bm4/4bzvY64aB7ckGlCQfvW3euzwkH586YiLuvW6g44aC4zZxwh5KJt++r\n+HsYzgkHw3EMlyhefuBiTjhICSsrI0U4HgCkEw7evXZRQBMOeu4/lCYcfHLj3riecNC9LZQmHBzP\n/lNpGYATDsYDkwojn8XBZ6PJ5uOVRJTsNE6nv6zC/l166aV47733YDC4dj42mw1XXnkl3n333ZAb\nGIiuriFV6ikszGZdrEtWVzip1c5gqbmNuN7YXHei9l1fovk5+8J2BSfcfRcIvf+Guu3U2PasQ906\n1GpDuMXqsRLrinxd8XS8C4T/uCFSv2mRWA/XEfx6wilRzyE2bWvGO9uaAQCXr5iKqy6oCHq9DocT\ntz7xEdwBpQ33XQSddnw31yfreWI4JWrf5Xqjv+7x9F1VRj6vXr0aN998My677DI4nU784x//wMUX\nX6xG1UREREREREREpJJh89hI5YzUQBMzSWm1GmSk6YW6hs025IgmISQiclMl+HzPPfdg8+bN2L17\nN/R6PW6++WasXr1ajaqJiIiIiIiIiEglI6NjOZoz0sYfFspMN4wFn01WBp+JSJEqwWeNRoOKigoU\nFBTAncVj165dWLp0qRrVxy2rzS7LmdXdb8Jjf9yDUasdl547BRcsnIyNW5pgcwA2iw0nu4eRma5H\nUX4GzirNgd3mwGf1nWjrHlFchwbAhCwdeox2SfmXF5fiuotnCjm23G0JV+4tpfdKsclqs+ODHc0w\nDpkj9llZbXZs/rwZ/9zTBrvdCZNFIekzgOkl2RgwWjClOAuzynKRkqIX8u3Z7Q5AA+i0WvaxOBWt\n/UR79zAeqd4NAPjRuiUoKciUPP/JFy149e+HAQA3X3oWLjy7XHjumdd240DLIABgXnkO1t+0RLLs\nG1sa8PfdpwAAly6ZjOtXj0122N1vwuOv7YVOB9y7djEK8tLVf3NEEaD03XWXWRwO/O3TY7BYHZg9\nLQ/zpk5ASooeiysLsXFLE8wWB050DsI8YoVZedcvSNEBWp0G2Rkp6B+yoDg/A3evXYTcrNQIvMvk\ncOeTWzEsOmScOy0PKXotOvtG0DNkgUYDzCzNhUGng8bphEavRWVZbsTmErFY7fhobyuAxDueDPex\nOAV/nGG12QPOee7t8xsxW/HK5gac7hnGoNE1r8nM0lxoHU4c7xyC0WyD/cy+76zSLIxYHOgZMMNu\ncyjOgZKqAxZVFWPtqpnY29QFwJV7+sPdJ7B5Vxs0AC5dUoo1y6f7bKd7GwAQ8l175rgeMVtlObI9\nl/fML23Qa/1uY/b1+DAiGfkcQvA5zQDABAAYZt5nIvJCleDzz372M3z00UcoLy+XlFdXV6tRfVyy\nWO145o1aNLb0AwB21nfiG5fOwo9+WyO85q//cuVZ8sy63We0oLVrBHubuv2uxwnIAs8A8OHeNjSe\n7MePv+kKlIjbMqs8D+uvX6jagYDVJn+vatZP6onGZ2W12fHYa3twvN3o97XN7a58Rb1Do9h3pAcA\n8JePj8JkkfZx9rH4E639RHv3sGS/+6Pf1uCR7ywTAtDiwDMA4fGFZ5dLAs8AcKBlEM+8tlsIQIsD\nzwCEx9evrkJ3vwn3vfSZ8Nx9L32GJ763nAFoijtK3907r56P597aL5S5HWzux8FmV9kf/9kkO77x\nx2IHYHfCbBkFALR2D+O/nt+OX/7gfAagVeAZeAaAg8f7Za/bf6xP8v/uhi7sbujC3WsXhf144aEN\nn+HAUdfvfyL91nt+j9Q+Fiflcy9f29hqs+PpjfvQ1DoglO1q6sauxi7c49HXvX1+VpsD9764Q3ac\nuv9Yr+I6D7f5PxYetQM1Bzuw81CHsA/d+GETrKJV/HX7Sew91osHbzrHZztrDnXA4XTiSJvrWKam\noUt4f8YRi6Ttdcd68eh3z8NLfzsoLL/j4Gmc6hqWvGZyYSaOnqlPaRuzr8cP8YSDoYx8lk46aPXx\nSiJKZuPLBu9h+/bt2Lx5M6qrqyV/yWzLrpOSk7LGln5h5J1Y6NM9etfaPYxtde3YVtcua4v7irUa\nwl0/qScan9W2uvaAAs/eeB7QA+xj8Sha+wml/a64TBx49iwTB57dxGXiwLNn2eOv7ZU9p1RGFOuU\nvrvVmxtkgWdPah7fPPvnWvUqS2KegedgNLUOROR4wR14BhLrt57HyuGndO7laxtvq2uXBJ7dDiv0\ndW+fX/XmBsXjVDWI96FWhVWcaDf6bWdT64AQeHZzv79fv1UrabvJYsezf66VLH+0bVD2mqOi+pS2\nMft6/JCMfA4p7cbYsgw+E5E3qgSfy8vL4XD4uZeSiIiIiIiIiIiiakQy8nl8Ew4CQJZo2WEzg89E\npEyV4HNOTg6++tWvYv369fjhD38o/CWz1UunYFZ5nvD/rPI8/GjdEtnrNJrwtaGsIBMrF5Rg5YIS\nWVvcOcDUEO76ST3R+KxWLijBtJKscS+fniK/TY99LP5Eaz+htN8Vl9186Vmy591l88pzZM+Jyy5d\nMln2vLvsgZsWy55TKiOKdUrf3XVrqiRlStQ8vrnr2oXqVZbEMkO4672yLDcixwvzKiYK/yfSbz2P\nlcNP6dzL1zZeuaAElWW5svKzFPq6t89v3ZoqxeNUNYj3oQaFVUwtyfLbzsqyXMwslR7LuN/f969e\nKGl7eooOd127ULJ8RWmO7DUVovqUtjH7evwYVinnM9NuEFEgNE5n6DdGvv322/KKNRpceeWVoVYd\nkK6uIVXqKSzMVrWuU+39sgkZEmnCQfH2CnUiMbW3vZp1hZNa7QyG1WbHvua+pJpwUM0+EaxorTsW\n+264Jxz0tq054aCyaH4vfAl33wVC3/eGuu3U2PaRrCMeJhyMlc8k3EJtYzgmHFRzX5Kbl4F3tjYB\nCP13ItaOLdU+Fo+n410g/Me83s69fEnmCQcLC7NxoqU3ISYcjMVjXjWE8zjN4XTi1l98BHcgaMN9\nF0Gn1Y5rvR/uacVr/3Ttty9aNBnfWFPlZwll0TxniuZnHE6J2He53thY93j6rirBZwDo6+uDyWSC\n0+mEw+FAa2srli9frkbVfsXSgSXrSry6wikZd1LJtN5orjtR+64vsRxMZbsCFw9BkFgJdLIO9epI\nluCzWywfd7GuyNbjrivcIhF8jsRvWiTWw3UEv55wSsRziBGzFT/41b8AAGkpOry4/sJxr/fzQ6fx\nm02HAABLqopw+xXzxtWmZD1PDKdE7Ltcb2ysezx9d/z3V4g8/fTTeP3112Gz2ZCXl4eOjg6cd955\nEQs+ExERERERERGRb2pNNghI024MM+0GEXmhSs7n999/Hx9//DG+8pWvoLq6Gq+88grKysoCXr62\nthbr1q0DAJw4cQI33HADbrrpJvz0pz+FSgOziYiIiIiIiIiSmmSywdTxTzYIAJlpDD4TkX+qjHwu\nLCxEdnY2KisrUV9fj0svvRS/+tWvAlp2w4YN2LRpEzIzXTk4H3vsMaxfvx5Lly7FQw89hA8//BCr\nV69Wo5mqsdrs+OSLNhxtG0CFnzx4nsttq2uH3e6A3QkcPzWAaZOyodNrAScwMGzF/9WcgEMUb9dp\nAI0WsNm91+umPTMxRXF+Gi5YMBlfWlSKmkMdAMKTXzUcwp0TltQX7GfW3j2Mh1/ZiVGb9wtLRXmp\nSNVpodPrcOvX5mLTtmOwORwwj9rQ1DqIzHQD7l17NhpO9gW8XkpcI2Yrqjc3IDXNgOsuqpDN2P3K\nBwfxaZ1rX3jBgmJ887K5kud/u2k/dhxy5VVcMacQ3/n6fOG53793ANsOdAIAVs4rwrcvl95KuHnH\nUbz56QkAwHUXTMWaFRXCc+5c01ot8MOb5LmmidQiPr4IJje+xWrHR3tbAfjej7q/YwCwdnUl9jZ1\n4XTfMP65q03x9VoNUDE5Cz2DVpQXZkKv1cBmc+BYxyCGTHbkpOswvSQHaSl6fH3lDDz6x92wWR24\n69qFMOi1shykpJ5vP75V8n96qhYpOh3KCjPQ1jMCODXIztBjxdxJOG9eCTZuceXx5GdB8cC9r7I7\ngYrJ2Ugx6IW8x76OVa02O7bubkFNfScK8tNx05n9nPj1VpsdW/e0oKahC6d7hpGWosPqc8px0dml\n+FddOz7Z14bTvaag2luUbcDgqB3QAClaDXKzUrFi/mRcd4krZ+6AcRTP/rkWDidwzqwCtHQOw+F0\nQqvRuHKxK+Sndm8Dh8OJGWWunO7i96yUj5nnX8knXCOfjWYGn4lImSrB56ysLLzzzjuYM2cO/vjH\nP6KoqAg9PT0BLTt16lQ8//zzuO+++wAAhw4dwtKlSwEAF1xwAbZv3x5TwWerzY6nNu7D4dYBAEBN\nQxd2N3Th7rWLfP5QW212PPNGLRpb+iXlNQ1dPtdndwIIIPAMQAhat/ea8cbHx/DO9uMYPTOTxc76\nTqy/PrZni/fcRu428wAodgX7mbV0DOBHv63xW29n/6jwWOn1/UaLpJx9JXmNmK2498UdMFlcO8pd\nhzrw5O0rhCCJOPAMQHjsDkCLA88Azjzej+98fb4k8AzgzOMDQgBaHHgGIDxes6IC7d3Dkj76o9/W\n4JHvLGMAmlTn7fjC337RarPjoQ2f4cDRHp+v9/yO7Wzsgr+b0hxO4HCbEQDQOzQqe37QZEftMdfF\nQ/Fx0PoXtiNFpxEuTtYd65V8nyk0noFnADCNOmCCAwMnBoSy/mEL3vj4GN78+JgwGRU/C4p1xhGL\nZF+1u9G1b/n8UAc0AJrOnLt57uusNjue+NMXONrmmlz4eIcRe0T7uZ31nbjz6vn41Z9rceTMawDA\nbHHgL58cw7s7TmDUGuDJmofOobFAnRnAoGkEb2w9grpjPfjWmirc//JnQjtOdholy+5u7MKuxi7c\nIzoHlR0TNXVL3jMAye/FrPI83Hn1fDz31n6efyWZYXHwOVXF4DNHPhORF6qk3Xj00UfR29uL8847\nD2VlZXjooYfwn//5nwEte8kll0CnG/txE6fZyMjIwNBQbE2GtK2uXQg8uzW1DghXi30t53liGG6j\noimUG1v6/bYx2jy3UTy0OdkF+5nd99y/wtIO9pXkVb25QTjJAgCTxS6MmgQgCTwrlYkDz55l4sCz\nm7hMHHj2LHukerfsOaUyolB5O77wt1/cVtcuBJ59vd7zOxbObGhOJyR3xXh+nymyxB81PwuKdb9+\nq1ayr3I73DogBJ4B+b5uW127EHh2E+/nGlv6Ub25QRJ4Fhtv4NmX+uN9ePy1vX73t4c9zkE999du\n7vesdNxevbmB519JaEQ0QjnUkc9pKTroztyCbbE6YA3klm0iSjqqjHwuLi7Gt7/9bQDAAw88EFJd\nWu1YPHx4eBg5OTl+l1FzllB/dWVlp3kt91xW/L+35SLJ3YZIbq9gKG0jpe0aiEjM2q2GaLZTjXWr\n+Zmp0RZf6433bR1rYuU9pSqMwktNM/htXyjPB7KsVuHSrlYbO9vNLdbaEylqvO9Q61CrDb6OL3zt\nFwPdfyt9xyIpkO+zWyx8JuEWzTb6+ixi9dgy0euKhz7rFkttFe/rAjlHi8Z+UBfgwGPxe/HVTm/v\nU2kZNY/lY+lzH6+EPIfQjR2oFk7I9BnHCER2Rgr6ja47nVIzUjExN31czYrWtk6EfqokIfsu1xtT\n6w5GSMHnqqoqr89pNBrU19cHXefs2bOxc+dOnHvuufj000+xfPlyv8t0dakzOrqwMNtvXYum5+Os\nslzJ6OfKslwsmp4vWdazrkXT8zGrPC+io59TDVph9POs8jwsmp4PILLbK5i6PLeRu83BrkPtdoWT\nWu0MllrbKNjP7Ik7v4Tbn/g45PV68rdeNftEsKK17kTtu56uu6gCuw51CCN90lN0uO6iCqF9Fywo\nlo1+vmBBsfD8ijmFstHPK+YUoqtrCCvnFclGP6+cVyQse90FU2Wjn6+7YCq6uobww5uWyFLG/PCm\nJTGz3YDofi98icQBVKjvO9Rtp8a2d9fh7fjC335x0fR8zKuYKIx+9vZ6z++YRhO+0c8aDSRpNzy/\nz77EymcSbpH8zmowNvrZ12eh9nEX64psPe66wi3cfff7Vy+U7KvczirLlaTd8NzXLZqej4rSHMno\nZ/F+blZ5Hq67qAKnuoyKo59TDTrVRz/PnpYvS7uh5CyPc1DP/bWb+DzQ87j9uosq0NEzEvL5l5JI\nHWck6jFvOLff6a6xNC56jdNnHCMQ6ak69J+p8mRrPxwWm+8FFETznCman3E4JWLf5XpjY93j6bsh\nBZ8bGtS7/U6jcd2q8cADD+DHP/4xrFYrKioqsGbNGtXWoQaDXod71i4KesJBg16H9dcv5ISDPoi3\nERAfbU52wX5m5cW5eOQ7yzjhIKkmI82AJ29f4XXCQXduZ28TDromF1SecNCV29n7hIPuyQWVJhws\nKcjEI99ZxgkHKew8jy8CnXDQoNfhZ7cuxztbXRPKeXu9+DsGhGfCwT9srueEgxHw+wcu5oSDlLCy\nMlKEfVUwEw4a9Drcd8PZficcvPeGsyM64eBA/wieueP8oCYcFO+vvU046P69EE84yPOv5DMkys0s\nztk8XpmiOoY56SARKdA4naGPX+nu7sa7776LkZEROJ1OOBwOtLa24oknnlCjjX7F2kgE1pVYdYVT\nMl4hS6b1RnPdidp3fYnlkbxsV+DiYQRerIyyZR3q1ZFsI59j+biLdUW2Hndd4Rbu35tIjrJNhPeS\nKOtwryecEvEc4n/+Uod9R1wTUt5x5XycM6swpPVK65uHc2YVBd2mZD1PDKdE7Ltcb2ysezx9V5UJ\nB3/wgx+goaEBmzZtgslkwtatWzFp0iQ1qiYiIiIiIiIiIhUYRSOfszPUGPk8dkP9sDn4lBtElPhU\nCT739fXhF7/4BVatWoV/+7d/Q3V1Nfbv369G1UREREREREREpIKhEYvwWI20G+I6xIFtIiK3kHI+\nu+Xl5QEApk+fjsbGRixatAh9fX1qVJ10RsxWvLK5Ad19JiypLIDF4cCW3a2w2JwozEmD02lDR79r\nh56drodGq0F2ugHFeemYNS0fFy4qBeA9rxlRtHT3m3D/S9thMttgGrXD7pHwZ9aUHNx51UJkpBlg\ntdnZh0k1A8ZRPPvnWgDAXdcuRG5WasDL1hxsx8vvuibPve1rs7Fsbonk+YPHevD0m666775uIebO\nmCg8N2K2es1FDYD9nOJKd78Jj7+2F1abDVa7ExqNBjOLs5GWlYLG5h6YLA7MmT4Bt14+Bwa9Vujb\n82dMxIt/3Q+H3YHMDAOOtLny9j/47+egIC89yu8q+bzywUEhB/6Mkkzcf9MSYd/DfRIlq0D7vtVm\nx7v/Oorahg5UlI7lXHb/3tsBlBekY8/hXmgcTiyunIiWHhNGLTYcaRmAzeHEV86bgkuWTsG/6tpR\nc/A0CnPTcPNlsyXHCBarHR/tbVVsj1JbxWXL5hQrzvnjfo17boCsrDQYh8zQ6fzPEUCJRxwgzlJh\n5DODz0TkjyrB5/POOw//8R//gfvvvx/f/va3cfDgQaSkpKhRdVIZMVtx74s7hBmKj3cYJc+390kn\nsRg0uW5pGRi2orV7BHuO9GBnfSe0Go0wo/PO+k6sv34hDygoqrr7Tbjvpc98vqbx5CD+87l/4cnv\nn4+X/nZQmHWbfZhCMWAcxfoXtguzxa9/YTueueP8gALQ4sAzAOGxOwAtDjwDwNNv1goBaM/9+a5D\nHXjy9hXCyaXVZsczb9Syn1Nc8LYP33+yX/J/7ZEe3P3CDpQVZeJo26DX+vqNFtz30md44nvLGYCO\nIHHgGQCOtQ/j7mc/wdN3XQgA3CdRUgr099hqs+PpjfuEc6yahi7sauzC7VfMw4O/+Vz4vd8tWuZE\n17BsfZu2n8DmmpOwnJl4+3iHEfubt+OpO84XBmA8tOEzHDjaI2uPUlvvvHo+nntrv1D2l4+PCm1x\nLwtIv9+e+H1PLnaHQ0iNoQGQpcJkspmiOoYZfCYiBaqk3fiv//ov3HPPPSgtLcXTTz+NGTNm4Lnn\nnlOj6qRSvblBOFgYryNtg8JBEQA0tvQLV8KJouXx1/YG9DqbHXj2z9KDY/ZhCsWzf66FeFpdpxPC\nKGh/xIFnpTJx4NmzzHN/brLYUb25Qfh/W107+znFjUD34QAwarX7DDyPt14KnTjw7Ga0uvZH3CdR\nsgq072+ra5ecYwHA4dYBPPvn2qDP39yBZzez1SEcI2yraxcCz57tUWpr9eYGSZm4Le5lPZfzxO97\nchk2jeVkzkw3QKvVhFwnRz4TkT+qjHwGgObmZrz++uvQ6XQ4//zzUVxcrFbVREREREREREQUgiFx\nyg0V8j0DriC2G0c+E5ESVUY+//KXv8TLL7+M0tJSFBUV4dlnn8XLL7+sRtVJZd2aKqSnhHa708zS\nHFSW5Qr/zyrPw8oFJT6WIAq/B25aHNDr9DpXTt5Z5XlCGfswheKuaxdCIxrQodG4ygJx29dm+yy7\n+zp5Pe4yz/15eooO69ZUCf+vXFDCfk5xI9B9OACkGnSoKM1RvV4K3QUL5ANDsgyu/RH3SZSsAu37\nKxeUSM6xAOCsslzcde3CoM/fUvTSkaZpBq1wjLByQQnmVYzNHyFuj1Jb162pkpSJ2+Je1nM5T/y+\nJxejeLJBFfI9A0Bm2tiYRndKDyIiMVVGPm/duhVvv/02DAbXzmvt2rW46qqrcNttt6lRfdLISDPg\nydtXcMJBSjgFeel44nvL8eTGvQFNOLj++oXsw6SK3KxUPHPH+eOacNCd29nbhINzZ0zE3dctVJxw\n0L0/9zbhoEGvYz+nuOHeh3PCwfj2zcvmAoDXCQe5T6JkFOjvsUGvw91rF2HPkR7ZhIPu33s1Jhw0\n6HX42a3L8c7WJll7vLVVXOZtwkH3azjhIA2NiEY+q5DvGWDaDSLyT5Xgc1ZWFkZGRpCb67oabLVa\nkZ2drUbVSScjzYDbr5gvKbv1qrPR1TUUVD2rFpep2SyikBXkpeP3P14TUF826HXsw6Sa3KxU/ORb\n545r2WVzSyQBZ09zZ0zE7x+4WPG5jDQDbrtiPgoLsxX7Pfs5xZOCvHQ8dcf5snJv/Vvct8f7/SP1\nffOyufjmZXMVPzfukyhZBdr3DXodvvalCpxXVSQpd//eu31tpf91XnruFFx67hTF51IM3tuj1FbP\nMqVlPV/jbd9NiW9geGzkc25Wiip1StJumK1wOl0XqYmI3EIKPv/85z8HAKSkpODKK6/EJZdcAq1W\ni48++gjTp09XpYFERERERERERBSageFR4XFupjrB51SDDga9FlabAza7E6NWO9JSVJtejIgSQEh7\nhLlz50Kj0WDuXNdtfO6rWzNnzuSVrnEYMVtRvbkBDocTM8rykKLXKubfstrsvC2S4o7VZscHO5ph\nHDJL+q273wOuPLkZaQb2cVJVOPuTr7rdfVsp7QZRvPLs8/4o7ePDjb8h3nX3m/D4a3uh0wH3rl3M\n1CdEKgpk3+PtNeLyKy6ujEhbKDn1G8dGPucFmIouEFnpBvQNuQLbRpOVwWcikghpj3DVVVcJj1ta\nWnDkyBGsXLkSp0+fRnl5eciNSyYjZivufXEHTBY7AGBXUzcAYGd9Jx69Y+zeLavNjmfeqEVjS7/w\n/PrrF/KAgmKat35rtTkk/b7uWC8e/e55eOlvB9nHSRXh3Gf6qlu2Tz/UgSdvX8EANMU1pT4vPkbx\n5Pk9qDvWG/bvAY+TvOvuN+G+lz4T/r/vpc/wxPeWMwBNpIJA9j3eXgNAUv7F0R7ceeW8ce+3uB8k\nX/qNopHPKqXdAFyTDrqDz8MmGwpy/SxARElFq0Yl77//Pm6//XY88sgj6O/vxw033IB33nlHjaqT\nRvXmBuHkTKyxpR9bdp0U/t9W1y4cSLifd1/VJopV3vqtZ783Wex49s+17OOkmnDuM33VrdS33aM/\nieKVUp8XH6N4isb3gMdJ3j3+2t6AyogoeIHse7y9xrP8wNGekPZb3A+SLwNhHPnsZjRz0kEiklIl\n+Lxhwwb86U9/QlZWFgoLC/H222/jN7/5jRpVExERERERERFRiAaM6ud8BjwmHTQx+ExEUqoEn7Va\nLbKysoT/i4qKoNPxtp5grFtThfQU+TabVZ6H1UvHZkJeuaAEs8rzJM8HknORKJq89VvPfp+eosNd\n1y5kHyfVhHOf6atupb69bk2VKuslihalPi8+RvEUje8Bj5O8e+CmxQGVEVHwAtn3eHuNZ/m8iokh\n7be4HyRv7A4HhkZcgWENgBw1g89pDD4TkXeqZIGvrKxEdXU1rFYr6uvr8frrr6OqiifZwchIM+DJ\n21coTjiYYhg7cTPodVh//UJOIEFxxd1v9zX3SSYcNOh1Qr8HxiajYh8ntYRzn+mrbvE+nRMOUqJQ\n6vPiYxRP4u8BEJkJB3mc5F1BXjqe+N5yTjhIFAaB7Ht8vUZcfsXFlRjoHwlrWyg5DQ5b4TzzOCvD\nAL1OlbGIxmGN1AAAIABJREFUrvrEaTcYfCYiD6oEn0dGRtDZ2YnU1FQ8+OCDOO+883D//ferUXVS\nyUgz4LYr5vt9nUGvw6rFZRFoEZF6DHodLlsxHV1dQ5JypX7PPk5qCmd/8lW3u28XFmbL+j1RvAr2\n+xTosY2a+BviXUFeOp6643zul4jCIJB9j7fXiMt9XdRTsy2UfCSTDWaql+8ZADLTx0JLw2abqnUT\nUfxTJfjc2tqKRx99FHfffbca1RERERERERERkUrEwee8LPVSbgBAVhpHPhORd6oEn7VaLVatWoXp\n06cjNdV1BU2j0eB///d/1aieiIiIiIiIiIjGqXdwLPg8IUfdkc9Mu0FEvqgSfL733ntlZRqNRo2q\nE4rVZmfurQCpta0SeZvH6nuLdLtidTtQ/IjFPhSLbQpEvLY7llhtdnyyrw1HWwdQUZqLC88uDXg7\ncvtTovDXlxOhryfCe6DQRLsPqLn+aL8XCkzvoFl4PCEnTdW6M0XB52Ezg89EJKVK8HnZsmVqVJPQ\nrDY7nnmjFo0t/QCAnfWdWH/9Qv4wK1BrWyXyNo/V9xbpdsXqdqD4EYt9KBbbFIh4bXcssdrseHrj\nPjS1DgAAahq6sKuxC/esXeR3O3L7U6Lw15cToa8nwnug0ES7D1is6q3f23uh2NM7NDbyeWIYg89G\nE3M+E5GUetObkk/b6tqFH2QAaGzpF64Ok5Ra2yqRt3msvrdItytWtwPFj1jsQ7HYpkDEa7tjyba6\ndiHw7Ha4dSCg7cjtT4nCX19OhL6eCO+BQhPtPrBl10nV1h/t90KB6xGPfM4OX9qNYabdICIPDD4T\nERERERERESWwPnHwOVflkc9pYzfVD5utcDidqtZPRPGNwecIWbmgBLPK84T/Z5XnYeWCkii2KHap\nta0SeZvH6nuLdLtidTtQ/IjFPhSLbQpEvLY7lqxcUILKslxJ2VlluQFtR25/ShT++nIi9PVEeA8U\nmmj3gdVLp6i2/mi/FwqM3eFA35BF+F/tkc96nRZpKa60LU4nYBpl6g0iGqNKzmfyz6DXYf31CzkR\nQwDU2laJvM1j9b1Ful2xuh0ofsRiH4rFNgUiXtsdSwx6He5eu2hcEw5y+1Oi8NeXE6GvJ8J7oNBE\nuw+kGNRbf7TfCwVmwGgRRiPnZBjC8hllpRtgttgBuFJvZKYZ/CxBRMmCwecIMuh1WLW4LNrNiAtq\nbatE3uax+t4i3a5Y3Q4UP2KxD8VimwIRr+2OJQa9DquXTMHqJeNbltufEoG/vpwIfT0R3gOFJtp9\nQM31R/u9kH+9g2OTDearPNmgW2aaAd0DrtQeRpMNRflhWQ0RxSGm3SAiIiIiIiIiSlBdAybhcaHK\n+Z7dstLHxjYaOekgEYkw+ExERERERERElKC6+seCzwV56WFZR2b6WJqNYTODz0Q0hsFnIiIiIiIi\nIqIEJQ4+F0Yg+MyRz0QkxuAzEREREREREVGC6u43C4/DlnZDNMHgMIPPRCTC4DMRERERERERUYIS\n53yORNqNIQafiUiEwWciIiIiIiIiogRkszvQNzgKANAAmJgTnpHPeVkpwuP+odGwrIOI4hODz0RE\nRERERERECahnwAznmcd52akw6MMTBpogCmr3DjL4TERjGHwmIiIiIiIiIkpAHX0jwuNwTTYISEdU\n9w6ZfbySiJINg89ERERERERERAnodO9YvudJEzLCtp7czBTotBoAwNCIFRarPWzrIqL4wuAzERER\nEREREVECOt07NvI5nMFnrVYjyfvcx7zPRHSGPtoNUHLllVciKysLAFBeXo5HH300yi0iIiIiIiIi\nIoovHeLg88TwBZ8BID8nDT1n8j33DppRHMZgNxHFj5gLPo+OunZU1dXVUW4JEREREREREVH8itTI\nZ8CV9/kIBgAAvRz5TERnxFzajYaGBphMJtxyyy24+eabUVtbG+0mERERERERERHFFbPFJqS/0Gk1\nKMhN87NEaCZkpwqPewY56SARucTcyOf09HTccsstuPbaa3H8+HHceuut+Pvf/w6tNubi5ERERERE\nREREMam9Z2zUc0FeOvS68MZVCvLShccdookOiSi5aZxOpzPajRCzWCxwOp1ITXVdMbv22mvx/PPP\no7i4OMotIyIiIiIiIiKKD/+oOYHn3twHADh/wWQ8cPPSsK5v/5FuPPjr7QCAmeV5+OV/XhjW9RFR\nfIi5kc9vv/02Ghsb8dBDD6GjowNGoxGFhYU+l+nqGlJl3YWF2ayLdcnqCie12hksNbcR1xub607U\nvutLND9nX9iu4IS77wKh999Qt50a2551qFuHWm0It1g9VmJdka8rno53gfAfN0TqNy0S6+E6gl9P\nOMX7OcSho93C46LcVL91hrredL1GeNzSMYTOzkFoNBofS6iz3vGK9nliOMV73+V6Y3fd4+m7MZfL\n4pprroHRaMRNN92E9evX47HHHmPKDSIiIiIiIiKiILR0GoXH5cXhv9CUk2FAZpprjOOoxS7kmyai\n5BZzI5/1ej2efPLJaDeDiIiIiIiIiCguOZ1OSfB5SlFW2Nep0WhQMjETR9oGAACneoYxISe8kxwS\nUezjkGIiIiIiIiIiogTS1W+CadQGAMhM0yM/OzUi6y2ZmCE8PtU94uOVRJQsGHwmIiIiIiIiIkog\njS39wuMZk3MDyr2shjLRCOvjpwcjsk4iim0MPhMRERERERERJZAmUfB51pS8iK23YnKu8PhI60DE\n1ktEsYvBZyIiIiIiIiKiBNJ4ciz4XFkeueDzlOIsGPSuUFP3gBkDRk46SJTsGHwmIiIiIiIiIkoQ\n7T3D6B4wAwBSDFpMm5QdsXXrddL1uScfJKLkxeAzEREREREREVGCqDnUITyeN30i9LrIhn5mlo2l\n3jjQ3BvRdRNR7GHwmYiIiIiIiIgoATicTnwuCj6fN6c44m1YWFEgPN53uBsOpzPibSCi2MHgMxER\nERERERFRAthZ34HOPhMAIC1FhwUVEyPehpmluchKNwAABoYtaD41GPE2EFHsYPCZiIiIiIiIiCjO\nDY5Y8JePjwr/r15ShhSDLuLt0Go1WDRzbPTztv3tEW8DEcUOBp+JiIiIiIiIiOKU0+lE48k+PPmn\nL9A7OAoAyEzTY825U6LWppULSoTHnx08jRGzNWptIaLo0ke7AURqsNrs2Fbnupp6xcWVUW4NJQtx\nv1u5oAQGfeRHFVD8Yv+hWMW+ScFin6F4wz5LiWLEbMOntafwSe0pdPSOCOUaALdcPgcZaYaote2s\nslyUFmairWsYFqsDm3eexFUXVEStPUQUPQw+U9yz2ux45o1aNLb0AwC+ONqDO6+cx4NICivPfrez\nvhPrr1/IfkcBYf+hWMW+ScGyWNlnKL5wP0eJwOl0YseB09j44WEMm22S5wx6Lb5x6SxJ2oto0Gg0\nWHPuFPzu/XoAwOaaFiybMwmlBZlRbRcRRR7TblDc21bXLhw8AsCBoz3CSAaicPHsd40t/ex3FDD2\nH4pV7JsUrC27TrLPUFzhfo7i3bDZipc3HcTv3q+XBJ7TUnS4aNFk/PRbS3H+/BIfNUTO8nmTMG1S\nNgDAZnfgub/UobvfFOVWEVGkceQzEREREREREZFKnE4n+oZG0dJphNXmQFqKDvnZqSiekAG9bvxj\nAPcf68EfPqhHv9EilBXkpuHyFdOwbHYxUlNiawS/VqPBty6bjUeqd8NidaCz34SfvbILX10+DSvm\nT0JORopsGdOoDSc7hnCiw4juARPMo3ZY7Q44nU4AgNMJOM88cLoXOlMmfo3w1JnXaTUapBi0SNHr\nkGrQISVFi/zcDBg0TkwvyUFpYSZ0Wo7PJAoHBp8p7q1cUIKd9Z3CCIZ5FRMlkxsQhYNnv5tVnsd+\nRwFj/6FYxb5JwVq9dAq27jzJPkNxg/s5Uovd4UBz+xBMozaMWuwYGLago3cEbd3DONkxJEuHAQA6\nrQaTJmSgtDATkwsyMXliJtJT9dBpNdDrtNBoAacD6ByyoKd3GA6HE8NmKzp6R7D/WC+OtA1I6rtg\nYQlu+HJlzAWdxcqLsvDdr83FS387AJvdiWGzDW9+dARvfXIU00qyMXliJjIzU9HdN4KWjiF09png\n9F+t6lINOkwtzsLUSTmYNCEduVmpSE/RIS1Vj6mTsqHVaKLQKqLEwOAzxT2DXof11y+UTDg40D/i\nZymi0Hj2O05WQ8Fg/6FYxb5JwUoxsM9QfOF+jtTgcDrx8Cu70dJpDGo5u8OJtu5htHUPh7T+nAwD\nvrGmCosrC0OqJ1IWVxbinrVn47fvHUL3gBmAa1scbRvE0bbBKLfOZdRqR1PrAJpaB2TPVU3Jw303\nLo5Cq4gSg8bpdEbjohIRERERERERERERJTAmtCEiIiIiIiIiIiIi1TH4TERERERERERERESqY/CZ\niIiIiIiIiIiIiFTH4DMRERERERERERERqY7BZyIiIiIiIiIiIiJSHYPPRERERERERERERKQ6Bp+J\niIiIiIiIiIiISHUMPhMRERERERERERGR6hh8JiIiIiIiIiIiIiLVMfhMRERERERERERERKpj8JmI\niIiIiIiIiIiIVMfgMxERERERERERERGpjsFnIiIiIiIiIiIiIlIdg89EREREREREREREpDoGn4mI\niIiIiIiIiIhIdVELPtfW1mLdunWy8ldeeQWXX3451q1bh3Xr1qG5uTkKrSMiIiIiIiIiIiKiUOij\nsdINGzZg06ZNyMzMlD138OBBPPHEE5gzZ04UWkZEREREREREREREaojKyOepU6fi+eefh9PplD13\n8OBBvPTSS7jxxhvxm9/8JgqtIyIiIiIiIiIiIqJQRSX4fMkll0Cn0yk+99WvfhUPP/wwXn31VezZ\nswcff/xxZBtHRERERERERERERCGLuQkHb775ZuTl5cFgMODCCy/EoUOHfL5eafQ0UTxg36V4xb5L\n8Yz9l+IV+y7FK/ZdilfsuxSv2Hcp1kQl57M3Q0ND+PrXv473338f6enp+Pzzz3HNNdf4XEaj0aCr\na0iV9RcWZrMu1iWrK1zU7LvBUnMbcb2xue5E7bu+RPNz9oXtCk44+y6gTv8Nddupse1Zh7p1qNWG\ncOIxL+sKRz3uusIpEscNkfpNi8R6uI7g1xMuPF/jesO97nBh30389UZz3ePpu1ENPms0GgDAe++9\nh5GREVx33XW4++678Y1vfAMpKSlYsWIFLrjggmg2kYiIiIiIiIiIiIjGIWrB57KyMmzcuBEAcPnl\nlwvll19+ueR/IiIiIiIiIiIiIoo/MZfzmYiIiIiIiIiIiIjiH4PPRERERERERERERKQ6Bp+JiIiI\niIiIiIiISHUMPhMRERERERERERGR6hh8JiIiIiIiIiIiIiLVMfhMRERERERERERERKpj8JmIiIiI\niIiIiIiIVMfgMxERERERERERERGpjsFnIiIiIiIiIiIiIlIdg89EREREREREREREpDoGn4mIiIiI\niIiIiIhIdfpoN4CIiIiIiIiIiIgoXtjsDryxpREf7WqBRqPBlxaW4MuLy6DVaqLdtJjD4DMRERER\nERERERFRABxOJza8ewi7GjqFsj9tOYzTvSNYd8msKLYsNjHtBhEREREREREREVEAtuxulQSe3T7a\n24ad9R1RaFFsY/CZiIiIiIiIiIiIyA+jyYpN25qF/5fNKcbc6ROE/9/+9BjsDkc0mhazGHwmIiIi\nIiIiIiIi8uMfu05iZNQGACgpyMQtX52N7/+/echMc2U27uwzYWe9fFR0MmPwmYiIiIiIiIiIiMgH\nq82BT/edEv5f95XZ0Ou0yEjTY/WScqH8oy/aotG8mMXgMxEREREREREREZEPe5o6MThiBQDkZ6di\nxfwS4blVi0uh1WgAAEdaB9DZNxKVNsYiBp+JiIiIiIiIiIiIfNhx4LTw+MKFk6HTjYVVczJSMG/G\nWO7nzw5y4kE3Bp+JiIiIiIiIiIiIvDCarKg/3if8v2L+JNlrVswbK9vT2BWRdsUDBp+JiIiIiIiI\niIiIvNjb1AW7wwkAqJicg4LcdNlrFlRMhF7nSr3R2mVE94Apom2MVQw+ExEREREREREREXmxt2ls\nJPPSqiLF16Sl6FE1NV/4v/ZIT9jbFQ8YfCYiIiIiIiIiIiJSYLU50HByLOXGorMKvL520cyx52qP\ndoe1XfGCwWciIiIiIiIiIiIiBUda+2GxOgAARfnpKMrP8Pra+TMmCo8PtwzAZneEvX2xTh+tFdfW\n1uKpp55CdXW1pHzr1q148cUXodfrcfXVV+Paa6+NUgsjo/FEH37xpy8AAP+2pAzXXFQBg14X5VYR\nhU979zB+/upOWGxOzCrPQ0aaAXotsG5NFTLSDNFuHiWgEbMV1ZsbACj3M1/Pt3cP45Hq3QCAH61b\ngpKCzAi1mih5HTzWg6ffrAUA3H3dQswVHcBTaLhtiQJntdmxra4dALByQQkA4IMdzRgYGAGcADQA\nnIBOp8XKBSVBn8Mp1b+trh1Z2WlYND1fqM/zdYGuR2k5d5nnOoiCxd8TSjb7m3uFx/OmT/D52sK8\ndBTkpqF7wIxRqx3H24cwsyw33E2MaVEJPm/YsAGbNm1CZqb0JN5qteLxxx/HW2+9hbS0NNxwww24\n+OKLMXFiYu7IxIFnAPjn7lbUHDqNR7+7nEE4SkgtHQP40W9rhP/rT/YLj2uP9uCpO85n3ydVjZit\nuOeF7TCfuUrt2c9GzFbc++IOmCx2AEDdsV48efsKZKQZ0N49LOmvP/ptDR75zjIGoInCSHwyCwBP\nv1nLk1qVcNsSBc5qs+OZN2rR2OI6Vv38UAecTieOtA0qvn5nfSfWX78w4GDuiNmKh1/Zjc5+k1C/\nBkBT6wAAoCgvHT/55hIY9FpJOwJdj2f7d9Z34s6r5+O5t/YLZUX56fjJzUt47E1B4+8JJaMDx8TB\nZ/99vWpqvnABsP5Eb9IHn6OSdmPq1Kl4/vnn4XQ6JeVHjx7FlClTkJ2dDYPBgHPOOQe7du2KRhMj\nQhx4dhscseGnv6+B1Wb3u3x3vwn3vLAd97ywHd39nEGTYt/6X33i9Tmz1YFXP6iPYGsoGbz6Qb0Q\neAbk/ax6c4MQeAYAk8UujIJ++NWdsvqUyoiSmXHEghf+Uot7XtiOF97ejxGzNaT6xCezvsooeNy2\nRIHbVtcuBGkB4HDrgNfAMwA0tvTjseo9eOGtWjz/dh1efmc/Boyj+GhvKz7a2yo5t7Pa7Hj41bHA\ns7t+d+AZADr7TXj41d345Is2STsaW/qFYEYw7W9s6Uf15gZJWWefCQ+/sjug805KfO7Ywrd/vtlv\nbIG/J5Rs+o2jaO0yAgB0Wg2qpub5XWa2aNLB+hN9Pl6ZHKIy8vmSSy5Ba2urrNxoNCI7O1v4PzMz\nE0NDQ37rKyzM9vuaQMVCXd2DFuxr7sNlK6Z7ret0jxH3vfSZ8P99L32GDQ9+GZMmZoWtXclaVzhF\ns53RWPeoxenz+c5Bc9jalWzbOtxi9T3J9pUDZtlrTg+M9TNDqvxn0JCqR2FhNixWeX+1WJ3jeu/x\nsr2ShRrvO9Q6YqENodZhHLHgWz//O8wW1wWe3qEuHDrRi9//9yXIykhRtR2BtDMWPpNwC0cbo92P\nWFf064mESLRVzXVkZacFvczxDiOOdxiF/3c1dsFx5lDii6M9+Nmty5Fi0OGDHc3o7PM/cKizz4TW\nnhHFtvl7r0rtT1UY4dzZb5Kdd6opnvqoN8lwDhFKbEEsXn+HE6GfKkmGvhup9dYdHwsez50xEeWl\n+ZLnldZ7/tl6bHj3EADg6KlB5OZlIMWgfqqjeOm/Ucv5rCQ7OxvDw8PC/8PDw8jN9T80vavLf4A6\nEIWF2RGta2ZZNo60Kr9moH9EWF6prvtf2C5b5v7ntuGpO84PuV2BSpa6wkmtdgZLzW0UKKvNjrRU\nHUyj3kdXdPaOhKVd0Xi/0V53ovZdX5S2dXuXUfa69i6j8LpJuamy5yflpqKrawglE9Nxqkd6clgy\nMV2yjkDyMEaz//kSy+0Kt1Dfd6jbTo1tHwt1vPzOfiHw7GYateNXr+3GbVfMH1c7zqRQldDA/2cW\nK59JuIXjOxvtfsS6oluPu65wC/fvTaDbI9D8yYum52NWeZ4wUrgwLw1d/fIL2r44RDuzA0d78M7W\nJqxaXAbjkHI9mak6DHscJ5dMyJC0Y1Z5HuaW5+LNv9f7fA+e7Z9VnofrLqpAw4k+WeDbOGRW9fOJ\ndF7pRD3mjeRxWrCxhfH+VvsSzXOmaH7G4ZQMfTdS6/287pTwuLIsV7IeX+udNCEDp3tHYLU58Hlt\nm2Q0tBriKdYQU8HnGTNm4MSJExgYGEB6ejp27dqFW265JdrNCgurzY6T7fKgiNvuxi5ceHYpJ4Gg\nhODOO+cr8AwAKfqoZAKiBKbXaWBzOGVlbs2n5D/W7rLvfm0efvqKNPXTd782T3hstdnx5J++EG7D\n/ezgadx7w9ncbxOFYEKWFj1Gh6yMiChUSnmQveVPNuh1WH/9QiFQvbiyEA/+5nMhVZdep8EV509F\nikGPHQdOS0Y8+7NsTjH+9OFh2OzS4xPF+wMdDkk7ls0pluRt9nwP4uD6nVfPR82hDgBjQeqf3LxE\nkmt6VnmeMNnheInX6dm+WeV5QeXCpvjA32pKJk6nU5KyaO4035MNis2emo/Tva47WOpP9KkefI4n\nUd1DaDSuAMB7772HN998EwaDAQ888ABuueUWrF27Ftdccw2Kioqi2cSw2VbXDovdewqCptYBn/m8\nHrhpcUBlRLHAM++cN1OKcyLQGko0VpsdH+1txQc7mmV5C88ql+fjEpd1DY7KnneXvfJ/8hzk4rKt\ne9sk+R+PtA1i69624N8AUZxat6YKaSnSQ8m0FB3Wrakad52eJ7PeyoiIgqWUB9nX+ZZBr8OqxWVY\ntbgMe5u6JHNE2OxOpKelYPXSKbjr2oWSC9tKivLThSBvzaEOWeAZAEYUBmkcPz0kaUfNoQ6v78Ed\nXK/+RxOq/9GE597aj5ULSrBqcZkQ/M1IM+Dn3zkX3796AdZdUhlyYNhznQ+/sntcOaopuoKNLfC3\nmpJJ94AZfUOu88O0FB3KiwJPR1MlCjY3JHne56iNfC4rK8PGjRsBAJdffrlQvmrVKqxatSpazYoY\nuz20nXNuVgq+el4Z/q+mFVqtBv+9bgkK8tJVah1RdDSfHoTVZufoCAqY5ygmzxE2qQb5z5y4rGhC\nOk52SkcrFU0IbF9ac/C0Ytml504JuP1E8SwjzYCXH1iNB1/4FO29ZuRkGvDgvy9BhkJeUSKiRGS1\n2fHS3w4KwWSDTgONRgOLTXqut3qx645Wq82OppOBByAqynIlI4vtDu/nkN6C66sWl0leZ9DrcNmK\n6arcqu25zk4/E9VRbCrIS8eP/v0cPPbaHmg0wAM3nsPYAtEZTaJ93MzSXGi1vi82is2aMjboqbl9\nEKNWO1LDkPc5HvDeiGjx01/FV8cBYMRsxcvv7BdmTn7iT1/g/c9b4XC6rrz/4vW9Ic8wTxQuKxeU\nYJbCCFRPRpONoyNIkXt0s+eM8f5GMU2bJM9HJS677qKZsufdZXdduxAa0b5ao3GVuRXmyifzUSoj\nSlRWmx2PvboLbT1mOJxAv9GKn/x+Z0jHI5mp8gMkpTIKXobCNQGlMqJE5Xk8GkzKCaVll80pxu/f\nOyQ5DrHanbLAc1FeOi48u1S4YF7T0KW4jrPKcjGzdOwuwMqyXKyYVyIZWbyroQuVZWNzIqmRNkNt\nRfljQctYbB/JDRhH8ehre+BwAnYH8OhrezBglN8d6Mbfakomh1vH9vFKd9X6kpORgskFmQAAu8OJ\nY20DqrYtnsRUzudkotN6j/tnpunww5sWCyP3jCMW3PviDuFWrz2Hu2W3apksdvzhgwbccVXgE/wQ\nRYpBr8OdV8/Hax8exmf75aNFxXyN6KDkFEyORk/HT8tH9YjL3vz4iOz5Nz8+gtuvmI/crFT84rbl\nePy1vQBctx/mZo1NUHjzZbOxv3k7zFZXn00zaHHzZbODe3NEcWxbXbvsFkJziMcjleUT8cWRblkZ\nhc7ulE8R5SojSg6eeZx9TTjobdl9zX0wDplluY19WXVm1PNHe1tlr19SVYjK0lzodFohSOtex8oF\nJbKL7IdbB3Djl2di2Zxi2XtYuaAEO+s7hdcX5aULrwsXz3XOKs8Tck1HasJBCt0zb3wBp+jnwel0\nlf3slvMUX8/fakomTS1jAWPxxb9AVZbn4VT3MADXQKnZQeSMTiQc+RwlKxeUeO24w2bX7Vvu0X2/\nfqtWlmNMyReHuzj6mWKS1WbHc2/tFwLPvu5UsdsYfCYpX6Ob/Y1islvl+RPFZd198ttD3WVWmx2/\ne78evUOj6B0axe/er5eMus5IM+CpO87HsqpCLKsqxFN3nM90A5RUvKUQaz41/lEdFaXy3P9KZRQ8\nu0N+/KhURpTIxPmTgw2KutNVKOVe9uVYa79sTgq32VPysXrpFKE94nUAUEzRodNpFd+De7CHe+Rx\nZ78Jz7213+u61eAOyq+7pFLIIZ2RZsCqxWW4bMV0Bp7jRPeAOaAyN/5WU7IYHLYIEwbqdRrMmBx8\nPxefqzaeDOx3IxEx+BwlBr0Od69dhHMqCxWfFwdXHE75iYFS8M7hBF75vwZV20mkBs/gocMJ6Lzs\nfZRGqhJ54z7RWlZViAsWTcadV8+XnOj0DFtky4jLFsyUX3l2lwUyMVFGmgG3XTEft10xn4FnSjqe\nt5a7ZaSNP9jQ0iH/DVAqo+AphZkZeiYKv11N3fjx73bCYrFJBh9VluXCbnfIUooB8Jqiw18ai5pD\nHegUXViPxIR/oQT0KTYoTZjpaxJN/lZTshCn3JhWkjOufVylKPh89NQgrEk62I7B5ygbHJEHRjyd\nNSVfVuZtoEo3J3mgGKSUSsPbnJsWmyOsIzQo/vga3eweVV/T0IVP952SjfDJz06V1Scuq23qkT3v\nLlP6xUgVAAAgAElEQVTut8l5sECk5LODygGN4gmBzwLuaVqJQp52hTIKnsJYBsUyIvIv0PlM3Dr7\nTHjj42NwArhx9UzcuHomnABe//AIqv/RhGfeqPU5pwUALKsqDDjtGFEwLDb5j4FSmRt/qylZSFNu\nBJfv2S0/O1W4I8Vmd6C5fVCVtsUbBp+jaFtdOw63Kt+aKg6u6L0NEVWwbE6RKm0jUlUQJ7dfHOmR\nHYBTclO6pdN94uVvdHJP/4isPnGZQ6FzCmUcJkjk0+le5Qves6bI04p5mzTUk9KcGL7myaDAKQ1c\ncDjB31tKCoHugwLlPjaZVhzcxbbDrQPQabXQabWS88BARihXTvGfP9kzKB6JvM8U/0at8sEVSmVu\n/K2mZHGkTTTZ4DjyPbtJU2/I0yklA+4hYpBep0FGqlYYju8tp6KSw22DzPtMMUcXxAUUIDK3CFJ8\nGe8tncNm+QmmuCw/K032vFIZEcl5uxFgaZU00GG12fGL1/ei+h9NqP5HE37x+l6vwR+l34tgf0Mo\nOD/+3U6MmK2qB+eIYoU7hYV7HzSeQQ7u78cHO5qFZQ16HYrPjGYLRsPxXsVczmLL5hQLI+WAsYFJ\ngXxPF82cgOx0PQB53mel9+EL9ws0YBzFw3/YiYf/sBMDxtFoN4coYqw2B052GIX/K0pDCD5PEQWf\nA5wvINHwaD6KvN2uZbM78cWRXtzz4g6MmK3Yf6RLYWlle5u6cdf//Is/DBRTgr01kShQ/iYcnFyY\nIVtGXNY1IB+56S5TymfrLcctUTIqmSD/fgHAb98/JPn/n7tacOzUWC7IY6eG8M9dLYrLegu4UOj0\nXo76O/tMeODlz/Dfv60JKThHFKsCmcPBF3Hw+tdv1Um+H+vWVPnMjatkV1M3ahq6kGYY+1KK93UW\nqyulmDt3c1FeOu68ej4A+Ayiu9v5xkfHMGSyyd6vr/fh731zv5DYDAq/DwatK/C8/oXtON5hxPEO\nI9a/sN0VZ1Dq8sF9DYhiXmuXUZiYuSgvHVnp45/fZ1b5WCrdI20DsAUxwDRRMPgcZfOn53mdeM1s\nsaN6cwMOHJXnJPXF7gB+/LsaHhxQzHDfmvjdK+YhM4CJqBhsoECJU3J8/+oFslyIbV3y4LK4rFMh\nT767rOZQp+w5pTKiZLVgpnxOCgA42Cwd0ffh3jbZa5TKrDY7nv1LnRBwyUrX43v/by7zm6rE17Uz\no8mGrn6z8D/vQKJ4NN5Ruv6W8xW8zkgz4GffOndc7TVbHVhaWSBLKbZl10nJ+jr7Tag51IFP9rX5\nDKIr5YkO9H24ibeFv/VR4lDKsGF1AL/8c61kbgCn01VGlAyOnRrLzRxqTvOJuWmYmOO6u9ZideDE\n6eSboFMf7QYkK6vNjqc27vOa89nN5nDAZAn+qojRZMMn+9qwesmU8TaRSFUGvQ5Wu0MxDYKYBuBk\nKhQUd0qOwsJsdHV5/JD7mWFL6aKzu2xw2Cx7zrPMarMLJ2IrF5Sw31JS+WSfchDC86K6U+F76HTK\nv3yffNEmOS4ymmx47I978PPvLON3i4h8slhdo3TdwdK/72zB6iWluHBRKVYuKMHO+k7hOc+Ji8XL\n7azvDPo4dNO2Y+Nut1arwarFZX5fZ3c4sGW3/KJdINzv11/g2HNbFOUFn1KEYl8wx65DI/J0nkMj\nVlhGbbJypTKieHZcNDHg9JKckOubNSUPOw6cBuC6mBdKGo94xJHPUeJrskE3DYATp40+XwMAhbnK\n+UmP+qmfKNI+2at8m7XY15aXM8hAqpmuMBGQuCwnQ97X3GWjCsMExWW8HZWSndWuPAPnAzeeI/l/\nepH8e2ixOmTfl6Nt8uOWzn4zR9pFAe9AonijNFr49S1H8MwbrlGa4524GAjvJH4VChNYrV46RZZS\nDE753VpF+emS76lSO29cPVN4v/5SlXlui85+E9MgJRhvx67esmhMK8yUlU8rzMTupm5ZuVIZUTxr\nFo1OViX4LJl0MPnyPjP4HMOcALoHfeduzkzT4cKFkzB/ujyfbrJdSaHYZrXZMWi0+H3dqV4zXn5n\nPyfOJFXMnjHBZ1nFZPl+0l1WkCMf8SMu83fCGuykPkTxQHxL9mwvufxPdkrvQJg9Xf49HBm1Y8O7\nhyTfDaUgDIWfOLiUna7HOTMnYtFZBUKOWKJ45v5tHu/ExYDrDqs7r54vfFc8J/Fbt6YK6SnBD5wo\nykvDinnyYG6KwZVS7MYvz8SyqkKcM6tAcQL61YtLJe9FnIps3SWV+Pl3zsXqJVOE17jfx7KqQlyw\naDLuvHq+322xenGpYtCe4pO3Y9fJBfJj3skF6Yq/37OnT8AEhcFvSmVE8co0akN79zAAQKMBphaH\nlnYDACpFkw4ebu1PurzPDD5HycoFJagoDf3qybDZjr98ehxNrYOS8lSDFivm88o0xQb3VfbuAf8T\nYe5u7EJNQxfuPTPhJlEoFs0s9FlWOUV+UO0uWzhzouw5cZndIT9gcJcFO6kPUTzwHDF1qndE8XWe\nQZIV80sUR1XtbuzCM2/UwmJ1fTcuXFSKaSXSUdLpKTrVRhgmu4k58olyJuYY8PNbzsWNq2eiKC8d\nQyYb9hzpwRtbj/CODoorFywq9RoAttsdXnM6+xsN7FZzqEPIRw9ILzgb9FpMKlCegFVMrwEWzpiI\nwtxUAK47O8RBbE97zkxM+PqWI9i044TkubPKcnHh2aWyZXwF2a0210SGNQ1d+HTfKdm6lbbFhWeX\njjtoT7HHYlVIl2G1eT1eXjq7GBrRD7hGAyydXYwZCqNAlcqI4tWJ00Nw399XWpCJ1HFcYPRUlJeO\niTmu/b/ZYpfklE4GDD5HiUGvw7LZRUEt42sC2VGPWQJGrQ7UHOoYR8uI1OdvAhQlpjMTbhKF4rm3\n5JOiiMuUJqh3l+04IN+HSsqUMg6cKQvkNl6ieOPZr3u83J3lmY2j5lCH4tcFcH03tuw6Kfw/bJKe\nGJssdh7PqKRnUH5Bt2fQCoNeB51WqzgBK/ddFC8+3dcGk0UexD2rLBe7G7u8psjyHC0czOjeQ8f7\nXBPzfdGG5lP+J4+yOYHaYz3oEg3G8PYd89zfer63pVWFQQeD/R2bhLItKD4cVeinR08NYfv+U7Ly\n7ftPYeOWJtmEgxu3NOF4h7wepTKieNV8WjzZoDoXVjQaDeaK7iY42NyrSr3xgsHnKNJpg9v83k7c\niIhImVJwzFvAzJPDIT+JlZR5S5BHlOSOnpLmbrZYApuEaFtdO7r65RN9Uvgp3dJPFO+WVRViaVUh\nmkTz4CgFe935kAF4TTezckEJKiZLAxB7mlxB7S17xzcRYCjE55HidEih3qkQSnoSin3eBl0YTfLf\naaUyN7vChR6lMqJ41dyubr5nt7nTx+6iPXicwWeKEM9bm9R0VlkuJ4SgmDGevq7XabBuTVWYWkTJ\nYlZ5vs8yb7cfAj4HNgMATCZ5DnN3WaC38RLFk2VzigPKa2o1S79Xh33cVlhZlgvbmVvilQKgnhNq\nkfqsNjt2N3YpPsd9F8ULz7Qb6Sk613Gkwo+5574m0AmEO3qHFdfd2WdCVrpeVl6Y5z8HrnvyQs/g\nsedxhPi9ib+Xnm1/euM+bNl1MqQUI5S41q6ulKXRWLu60uvrPfOZu79X3UPygRxKZUTx6nj72LGr\nmillZk/NF76Dze2DMJqSJ82o/FeSwspqswtX21cuKMH66xfiky/asGnHca9XF2eU5uBYm+98MDNL\nc3DOrCIcPzWAilJXDjBeraZY4b6N77Uth/HpPvltXUquWDkdGWny/JREwbjl8tm4+4XtQmqiVIMW\nt1w+W3h+d6PCbN2N3bhsxQxotfLhIeKyj+vkqQA+ruvA1740U+jz2+rakZWdhkXT87lPprhXc6hD\n8bZ2Tye7pbmgewfko5nzs1LwlfOmoKa+ExveOQAAmDE5G2kpOpjPrEOv0+CHNy3mdyfMfv/eIcnI\nUABYUlWI2VPysXJBCbc/xQXPtBtCyp4A7lLylo5i1eIyyWuMZu/7v68un4aP9rai88zdG5VlufiP\naxbgDx8cwp6mHq/LlRdm4OFXdwv5pHfWd+LRO1ZKjiMA18U/dwoi8ffSs+1NrQPC93lnfackdQaP\nTWhvU5csjcbepi44FC7SOJxj+czdaWUmFWTAoNeCt/9RIhscsaD7zLGrXqdFaWGmanVnpRswvSQH\nx04NwukEGk70YUlVcOl44xWDzxHkvjLtPkD4+64WrF7smijC120t/mbBLC/MwLmzi6DTavHty+fw\nIIISQnoqd08UOoNei7KiLBw9cwGvrCjrzEGzy8TcVBzvMEqWmXhmIqDMVAP6jdKr0ZmpYxdEnApp\nOZTKvPG8GMl9N8Uyq82OppN9Ab02O1N64XBJVZHse3bR2ZMBJ4TvJgAc88hFabM7sbepSxIAIvXV\nNMhHPc+eks/tTnFF6c4Ju8OhmObQsyzUtDNF+en40oISXLy4VPK7DgBDI77TDu05LA1Mu/PgLz2r\nQEiBAUAYxWy3O/DJvjbotFq/o5aVgujuOgsLs9HVxRy95GLQa2VzSBn0Wlk+8+ZTQ/jkizYU5Kag\nxeOnoyA3JRJNJQo78ajnKcVZ0OvUTRgxd9oEYbLBA829DD6T+jyvTHf2mfD6h0dQlJ/uc7mTp40+\nn+8dHMXrW44AAP6+8yR+8s2lHDFKMWXEbJWM6vAnPUWHZXOKw9wqShTuIK7SKJ5tde2S4NbRtkHJ\nidj0klzZiKTpJbln6pXfBvX/2bvz+Kjqe3/8r1kzk8lKNkIWlpAE2WWRighC0VqLFUstYotL/XrV\nVr791tZfvb11wf1RtY/2emt79V5LpfWCS+33W3svKkJBRMMSAkKAQEjIQkgm+57Zzu+PyZzMcmbN\nLGcmr+c/hJPMmc+c+ZzPfOZ9Puf9dt5mSNKge8D1S6UjOG22WPHyjipx9VFZYTp+cvtCsW3uFyPd\nVycRyYl7f/Xnx7ctcPm/1eYZ2PnocCPS9ZyryBFvxad4JFkbR7AHgQ+dbhPHL/eUFfuONWP30SaX\nh80sSPM4B9z3Y0hSQp+kQXvvCNq6hvDKe19iy4Z5Lo/ZV9XscVdBIE7VtrvMZ7yNwYdOt2HLhnku\n7XLHfO7kbFaxZzq6WcWZSFIrMOI27U1SK3D6omdO2tMXO2GxeJ5xUtuI4pFLv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q8X++uwRDA54XwInc+RovA44dKJWSd54qlQqo1BJ/L7GNKN6453uWcyqLxeW5+Hw09caR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\nEEIIIYQQQgjV/T+pZSnPiub++gAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<matplotlib.figure.Figure at 0x1145ef410>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "g = sns.PairGrid(sigopt_df, diag_sharey=False)\n",
     "g.map_lower(plt.scatter, cmap=\"Blues_d\")\n",
     "g.map_upper(sns.kdeplot, shade=True, bw='silverman', shade_lowest=False)\n",
     "g.map_diag(sns.kdeplot, lw=3)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 65,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
    "source": [
     "# Specify the values of the best parameter combo in the signals dict, and rebalance frequency\n",
     "# These values were pulled from the SigOpt export CSV\n",
     "# filename: SigOpt_Experiment_good_2.csv \n",
     "\n",
     "input_params = {}\n",
     "\n",
     "input_params['rebalance_freq'] = 12\n",
     "\n",
     "signals = OrderedDict([\n",
     "    ( 'rsi', {'func': talib.RSI, 'func_kwargs': {'timeperiod': 33 }, \n",
     "                   'lower': 44,  'width': 16} ),\n",
     "    ( 'roc', {'func': talib.ROCP, 'func_kwargs': {'timeperiod': 54 }, \n",
     "                   'lower': 0.0, 'width': 0.534} )  \n",
     "    ])\n",
     "\n",
     "input_params['signals_input'] = signals"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 66,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "rebal freq: 12\n",
       "OrderedDict([('rsi', {'width': 16, 'lower': 44, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 33}}), ('roc', {'width': 0.534, 'lower': 0.0, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 54}})])\n"
      ]
     }
    ],
    "source": [
     "algo_obj = zipline.TradingAlgorithm(\n",
     "    initialize=initialize_local_sigopt_rsi_roc,\n",
     "    handle_data=handle_data_local_sigopt_rsi_roc,\n",
     "    **input_params\n",
     ")\n",
     "\n",
     "# Run algorithm using the training data\n",
     "perf_manual = algo_obj.run(data)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 67,
    "metadata": {
     "collapsed": false,
     "scrolled": true
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "1.00996603411\n"
      ]
     },
     {
      "data": {
       "text/plain": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x10c0611d0>"
       ]
      },
      "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
       "image/png": 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BUFtby/z585k5cyZz586lrKwMgG3btnHnnXeSnZ1Nbm6u7+fk5uZyxx13MH36dAoKCjr6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\nQgS4/wDcrROLrTm40AAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<matplotlib.figure.Figure at 0x119379190>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "daily_rets = perf_manual.portfolio_value.pct_change().dropna()\n",
     "sharpe_ratio_rets = daily_rets.mean() / daily_rets.std() * np.sqrt(252)\n",
     "print sharpe_ratio_rets\n",
     "\n",
     "perf_manual.portfolio_value.plot()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 68,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "-1.10345060794\n"
      ]
     },
     {
      "data": {
       "text/plain": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x1162db810>"
       ]
      },
      "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
       "image/png": 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A9Ky0xnHw3KH2THb2WtoM2Pj5CVTUqi/7d4joyscMcDQgrC3nn45W4FRxA5Lj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\n",
       "text/plain": [
        "<matplotlib.figure.Figure at 0x1191afb50>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "# Run algorithm using the heldout, out-of-sample data\n",
     "perf_manual = algo_obj.run(data_heldout)\n",
     "\n",
     "daily_rets = perf_manual.portfolio_value.pct_change().dropna()\n",
     "sharpe_ratio_rets = daily_rets.mean() / daily_rets.std() * np.sqrt(252)\n",
     "print sharpe_ratio_rets\n",
     "\n",
     "perf_manual.portfolio_value.plot()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# (Above) Again, as before, seeing how poorly the strategy performs out-of-sample illustrates how important it is to perform additional analysis after using parameter optimization to discover a global maximum\n",
     "\n",
     "One way to combat this overfitting is to use performance on a cross-validated or holdout dataset as the metric being optimized. This forces the algorithm to be robust to never-before-seen data and allows the optimizer to find potentially better overall results. For an example of using SigOpt to tune on a cross-validated dataset, with results on a holdout dataset check out [this blog post](http://blog.sigopt.com/post/136340340198/sigopt-for-ml-using-model-tuning-to-beat-vegas) (with code)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# Let's take a deep dive into what we found"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### How fast did we find our best result?\n",
     "#### SigOpt typically discovers a higher global maximum nearly 10x faster than a tuned grid-search\n",
     "\n",
     "In practice SigOpt is able to find a good optima in a linear number of evaluations with respect to the number of parameters being tuned (usually between 10 and 20 times the dimensionality of the parameter space). Grid search, even an expertly tuned grid search, grows exponentially with the number of parameters. As the model being tuned becomes more complex it can quickly become completely intractable to tune a model using these traditional methods."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 105,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "image/png": 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q1KBBQ556agxvvz2jrPRiVE0P6ZQlwbUQQgghhKhScnIKr746raaHcVqQCxqF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G7qJDRLmTGhJfj9/mfQDS1LWwLAJcnohk6xpxhJEjHS4h2qobCrYycLqhdxsOcAPYlujIxSJRER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\nAAAATEK4BgAAAEzyv5G2J8h5hFP1AAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<matplotlib.figure.Figure at 0x10c0217d0>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6));\n",
     "plt.title(\"Grid Search vs. SigOpt: Values Returned for Objective Function by Iteration\");\n",
     "plt.plot(grid_df.value.values, alpha=0.5, color='forestgreen', label='grid search', marker='o', linestyle='')\n",
     "plt.plot(sigopt_df.value.values, alpha=0.7, color='steelblue', label='SigOpt', marker='o', linestyle='')\n",
     "plt.tight_layout()\n",
     "plt.legend()\n",
     "plt.xlabel(\"Iteration\")\n",
     "plt.ylabel(\"Objective Function Value\")\n",
     "plt.savefig('SigOpt_vs_GridSearch_time_comparison.png', dpi=300)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 106,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "image/png": 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hMS2EyOAitVwDyuU4Mh2AfHlu4Hwxv59CMdr460ZbH9GomZrDlmsig6uuixxc\ne3IcPS3WHomceIyQ0qA1I8xIF2wdrGYTH1QjStCKwmIsXFuEhWuLmGIVBTVTc9hyTaSSFYXFKC6r\nRkN9IzrkZODKvh0VX2aTW4KjoaW1MDtgpBBfE4d0k3X2M45CEp/A1v6sVJvQM9L57lfpKRa4Gt04\nM78l4BbtZoFID9R8OM9I1JqEiME1kQpWFBbjp6MVKN5XgcZGNw7gNL7afkT1ckSanVHk2c+MINgF\n02IywWI2Id1mFbZOPPtV7/Y5cLga0eSWAIh5s0CkB2y8iI9aqTkMrolUUFxWjcMlVWhsdGtajpys\n1LB/l/PEw6GnYhfsgtkkSchKETsI9d2vjlc6eANHRIbG4JqSworCYvy/E1VwuyWc0SYTYy/ooury\njx+rQb2jUdVlBsrPSUOfLrkJ/46ndfVoRR1MMKFbOzva29MwcUg3w6U0kPz4UBdR4th4oW8Mrsnw\nVhQWY+t3x3CqvPlBur04iY3/1W4oM7PZhE5d7MhIseLiXu2QGyFVQw5Wiwk9O+UgPTWxQ943sPaM\n/LCvtApVtQ148v2vYU+zeh+INEpKg5p4wSSiaMj9jAzJi8E1Gd5PRyq8gbUedOpiR+f2diFPhJ7A\nz3dItUa3G6WVDjS6JdQ2uPweVjNCSoOa1Lxgem6UUlOs3p4HIhIHn5HRLw7FR4bn0Dgdw1e7dpno\n3N7OEyGFpMZEE77pOxI4TCKRiDwpVpzpUX/Yck2Gl2byv4fs2DkLqTYrzu/aBvb02FIyvik+iVM1\nDQCAtlmpuLBbXtTfbWNPxXk982A2mWJapp540hbSUyw+Mzqa0c6eiur6RtjT/E8pyZTSINeEOWrk\nJHOkASIi5TC4JsOz21p285QUC7p2zokrgFhRWAykmdE2raWFYO/pGmHHFY0nGPSkLXTKzUBJefN3\ne3bIRorJhKfG9UnaHECOOUtERB5MCyHDO1pe6/1/Rkb8LanhWvtEk8jsiZ60hR4FdvRoZ0d2eop3\nm6qR0qBHou0bRpj5kYjkxRkf5cOWazK0BlcTyipaAsah57RnSyISSwsITFsoKLCjrKw66N9InzjS\nQPKQK12JjI29b/JiyzUZ2rGTtZB8XnfKz4z7t9jaR6GIuG94ehl8ex7IWBLpoaLkIlrvmxoSacln\ncE2GdqSs1u915wSC64lDuiEr1eZ97WntE/GuXsRgUM9E3Dc8vQzTxw/QdTkpfgyYiOKT6I0p00LI\n0HzzrU2AnB9RAAAgAElEQVQmoGNeRtTfDdadapRxRZkWID897RsipgIoUWYRt0M0AtfrwdH9NS4R\niU5PE1jp4bhNdEQly8yZM2cqUC5V1dU5tS4CJSAzM1WxOvzX9kM4frr5TrN92wyMHBzdiSLwrrXS\n4cT3h07j7E45uPycDhjUPR9ZabYwv6B/Xdpm4sCJGqRYLc0pAnGuj5L1J5KsNBsGdc/XfN8Ite92\naZsZtFx6qL9Yy6zVb+pBsPXaUXwKHbPTWq3XkdN1qHT4160nYIp1G6woLMa67w5j874TOHK6Dn07\n58a/EuRHD8dg3865+P7QaTib3ABaGlzUPlb0ctxu3nci6PspVgsGdc/3Hg9f7i7FiH6dW32OaSFk\naL5pIWfEkBKSDN2pnIBAO0o+lS/ivhuqzH9c/0Pc20nE7RCNYOtV5XAGXS+50pWYu50c9DDak16O\n23Cpk4GTcAXD4JqEFE1wUu9sxMmqeu/rRB5mJJILA5XoHK2oQ+3PaUsAt1O85AiY9BLwkLLY4NIi\n3I1psOMhEHOuSTgrCotRUl6NE6W1qKl2Yr90Cv/ecgg56TZYLS33i40/d295dC6IPrjWU/4ZGYvS\nsyMqve8qkQ8ZrMyuRjc6BFzgY9lORj2Gg61XdnoKRvXtGPTzHB6TRKKn4zaR52gYXJNwDp6sQXWV\nE+Un6vzer6tzhfhGs1hGClH6gT+lHtjQw4MgeipHMlJy31VqLNxgZT4zv3W3bKK/aYQgM9h6TR8/\nwDvWvBL0FPCQsWl13Aa7ZoW6MQ12PARiWggJydnQGNPnC3LT0L5t9COFAMrlnymVFqCXdAO9lEOv\n1BgGUal9V8n0gMAyy7Gd9JBDqgS110vEoSZJXGrv37FeswKPh2BMkiSFyscWhpJ37KQ83xn+orGi\nsBjbikr9Wq7b5KahY246Um2WVp+3Z9hwxYVnoGt7uyzlTdTCtUVB30/0Dl2p340ksP60Kkcwem1B\nl6tlRo71i+X4U7tujdjyrIRYz6HxOF7p8OsiZ2AtHzXqj0KL57zmOR7S0pp7jgIxLYSEM3FIN3xT\ndNz72mIxYeGvLtWwRKRHvoHn0Yo67D9RjS/3lOLCbnm4c9hZmpZNjjGxtZiuWO30AD2NHe5Lrzdt\nSmLuNlELz/FQUBC80Y5pISSkTrktKR72jBQNSxI7pdICQv1uqs2s2LBvsZRD7eDIN7B2OJsAAI1u\nCd8Un9Q8TUWOp/K1GMFB7fQAPY5ewLQnImNR4prF4JrE5JPNJNqEEEoFKMF+t21WKk7WNHjfUyMQ\n0Ft+piew9sVhxOJn1DzmaOlpWDrPkKTPL9+myo0zkREpcc1icE1Ccvg80Jie2jrPWu+UClACf1er\nQEAPAViw1gir2dRqeDdRadVDoMfW5GQUOJEFW9BJBEpOoJUIua9ZzLkmITkaWloj01PF242Vyl/U\nS16kHsrhGdIpPcUCh7MJVrPJO7ybEVpdjTrUnN7pZVg6pcdLJ5KbFs+JREvuaxZbrklIDqdvy7V4\nwbVa9JL/rJUJA7uid/scpNks3hZrrdNU5KSHHoJko7e0JyJR6CmlSmmMSkhIfmkhKeKlhagl2Vs3\nPa0RgcOIGYUeegiSkR5GMdFLCzoRtcbgmoQkelqImvQQCGiNQSjJSQ/7U7LfOJN4kumGkFEJCcfV\n6EZjk9v7Oo3BdVh6CASISH6eG+e0NBtG9e2odXEoCSQyxnsy3RAyKiHh+OZbA0wLIaLk5DuRBWf4\nI6XJ8UCiGj2pepjkicE1Cae+ISC4Zss1JTk9XEyIyNjkGKFG6Z5UvYxIwqiEhOObbw0A3x48hS2H\nTwLQV2DBgEcMoteTXi4mocok4jZNFqwn0hM59ke9DFHJofhIOI6AlutTderOQBgNTpEsBiPUk96G\ntzLCNk0GrCdjU2KyFiWHdjXa/sjgmoQTmHNtNpv8Xuth3Ey9BTwUHOtJftymYmA9GZdSgaqSY7zL\ntT/qZW4HBtcknMCWa7PFFOKTRMYX68VEr9MPkzo89b//RDWOnq7TujikACVvnPQ+cZVeJnlicE3C\nCcy5tpj9d2M9HPRq3z0zYIqPXlo5EhHLxSRYi9aCj3bK2vVqhG1qVL71n55igcPVhJLyGjS4ms+p\nrCeKxPNAotwBq5znDT3cAPCBRhJOYMt1dkYK6lzN72k1bmawBzHUGs9Tjw+0icIo465GO7xVsBat\nKodT1od9jLJNRRTpgTDf+u+Um4GS8ho0uiWUVjrQt3Mb1pNB6GmylmgfUpTzvKGHuR3Yck3C8c25\ntlpMuH7wmZrepYYKbi/r3U6VcjF3MjF6aOVIlFKtSfHSYpsme+9NPHm2HXLSYTWbYLWYhd33jS6e\n/VovqRGx7pNGOBd7sOWahFPvkxaSlmLV/C41VHD7n70nNL97psi03n/UFKxFKzs9RfbZ/dTepuy9\niW4IssD6T7VZ0Ldzm6TaTiJJZL9WY7KWSGIdFs9I52K2XJNwfNNC0lM5OyNzXClawVq0po8fIHxg\nxd6b6OilRZOik8h+rbfeLJHF03vA4JqE4x9ca9/5onVwywsmxcJIXa/UItrzkFHqPxnTgI5W1GH/\niWrsOnJaiHXW+tooh3iHNbTMnDlzpsJlU1xdnVPrIlACMjNTY6rDz789gpNVzRPHdGibgcvOk7dL\nO1Z9O+fi+0On4WxyA2gJbrPSbBG+KZ8ubTNx4EQNUqyW5ounisuOtf5IW1lpNgzqno9B3fORlWYz\nRP0dOV2HSof/Ongu4moeC1rJzExFtzYZUZ2HAusfaA4g1n13GJv3ncCR03Xo2zlX8TInsszAgKfS\n4cT3h06jS9tMIes71DHou18fraiDw9kEq9mEDjnpqG1o1P06q31tVGI/Xvfd4VbvOZvcOHCiBoO6\n5yMzMzXo99hyTcJxOFtyrvXQcg1o3xrELkBKZuy9aRbPeUiLmfESXWaypAH57teewPrM/Cyk2prT\nIUVYZ7WujXqb4VEfkQlRDPSYcx3vgxjRDlNElMyiOU708ACX1uI5D8X60JkctFimqDz7tafFWjRq\nPaSo1D4V77CGbLkm4fgG12k6abmOh97utIn0KNrjhL03ycMIubzR8uzXw/p08LZYexh1nfUk3l4x\nBtckFEmSUO+TFpIhcHCdLF2bkSTjg0kUPR4nytIiUE10mcmYBpSM6xwLJffjeFJbGFyTUJyNbjS5\nJe/rtBR9pIVQfNh6T6QtLYI2OZap9XMuWkjGdY6WkvtxPL1i4jb7UVKqD5j6XC8PNMZDT1PUqsk3\noD5W4UDHXP+TFXMvyVeyHidq0iJfPdFlGmnCEV/hni8w6jrLRU/PXYgbmVBSqlMhuFbrIcOJQ7ph\n8cY9qGlwAWi50zaywJbqOmcjSspr0CEnvVU+YSy/xYdBjSsZjxO1aRG06SVQ1NN5hDONJkYv+xTA\ntBASjG++NQCkp8gbXKudppBs3XyBLZDpKRY0uiWU+mzfaLYF00mSS7IdJ6SOeM8jSj0nwucLjIMt\n1yQUR6uWa3lzrtUeIkqpO209tcaE0yk3AyXlLds82lZJPQ/lpca2F6V+5aKnFinSp3iOiXjOI2xd\n1oZo5zwG1ySU1sE1d+FAej75B8uf7VFgB0xAus0qfKukGttez/VLFIkSQVKkY0LOZSp5Y2/k5wsS\nqQO1z3ly7C9MCyGhOBr800LkHufaCOOnRupa1HLou2BPdP9mVF/85uq+MT2Jrdd6UqNbl13HiePw\nj9pQKp0r3DERbpl6O48Ydbg9kWbklGsfZXBNQglsuZZ7nGujntw89JCrLEf+rNHriZSjh2PAtyzR\nBvlGuCHQ4sYw3DLjOY8oHZAb8fkCPTf4BJJrH2VwTUJxOP2DayXGuRb95Bbu5K+HVs9oxgyN5mSr\nx3pSoyVMb61totHDMQDEFuTr6YZAjxI5JmI9jyh9Y59sM41Gs2+LeM5jwiqpKtFcJt+W6xSrGVaL\n/PeHWj48JUeul+hDl0WbX6fHh9zi3fax1Lvo9as0UR58iiV3V88P8EZrRWExjlbUweFsQrrNgk5t\nMgDIEySFOyYi5THHcx7R03jKIghXB8u+2t/q84H7tprnPLny3hlck2pWFBajuKwax4/WoKbaib0o\nx8b/lsCengITpMg/AKC6zuX9v9z51lqT86GNUCd/ER6YET2QiPXCG0+98+IeXDTbUoRjwGg89eIZ\nHcjhakJJeQ16tLMnfEx7frvB1YTq+kZ0y89Cqs2MhWuLADTXd1aqTdbALFhALspNnRbkCI7VOufJ\nFchHFZ3U1tbi0KFD6N27N+rr65GRkRHzgogOnqzB8WM1OHXSvyuzoSG+rs10g019LmdQGao1hq2e\nocl1cYy1JSyeetdjq70eRLMt9XIMxBLki35D4Fv2DjnpLePaR9emEpLvMZtqsyDVZkFxeQ3saVbv\npFQHT9bAYjLBYjYpNiKRHA0jRg/OE23wCTznKbm95AjkI/apb968GRMmTMCDDz6IsrIyjBgxAl9+\n+WVcC6Pk45s7W3K0CqfK5csR7NYxW7bfSiZ6zFX2pUV+HXNak4sejoFYcneN9ABvqs2CM/OzcGZ+\nVsKTgAW7map0OP0mpQKAJklCus2q2DZLNI9/yaY9hj//hMolj2ffVvp8LUfee8Q9e+HChXj77bdx\n3333oX379li2bBkeffRRDBs2LK4FUvJYUViMPYdO4/ixGrhcbjgDZlfMzkmFzWpGr445MEutmzB+\nOFrR6j2Hswlut4RceyrseWmKlV0LarVO6b3VU4uWRS1TUURvldSTeFvBtBJLC5nIqUDcx8M7cLyq\n1XsipcIlKtS+Hap1WoTUwYjBtdvtRrt27byvzzrrLJhMJkULRcZQUl6NIwer0BAwNjUAtM1Px1k9\n2uL+K/qgoMCOsrLqVp/x5Mx5HK2og9lpRorZhPb5WThW5TDU5Bl66a7WA5EDiVix3uUjyraMp0tb\nLzcE8VCqXoIF7TnpKbCn+Yc2SgfyvHlITKQcdsA/1UYEEdNCOnbsiI0bNwIAqqqq8Je//AWdOnVS\nvGAkPmdDU9DA2mIzo2e3NhEPksD0AIezCVazCR18AulYh9DS03iaweihu1oP1B6OSuuhnljv0RF1\niEZfyZqCpES9BEspeOmmgcjLSvN7T8nziKc+S8prcPR0XVzL7NG+dYqjXvdftYRrndb6fB0NkyQF\n6Y/3UV5ejhdffBFfffUVJEnCxRdfjGeeecavNVtrwVo9SXvzV+3E7p9Oel9n2VOQlWHDPdecgx4d\nWk4moVquAfi1dpSU1+DM/OAHVazDnfl+1ygt31oJV38iEaHFUwmi1J9Rjt/AHjmPRPY5UepQCccr\nHX69XO1z0oO+pwTffbLB1YTSSgesFjOm/uIs9D2jTdS/U1Bgx+z3tsty/jHKg5GRjhO9nK8LCuxB\n34+YFpKfn48//OEPsheIjM/mbvm/xWLCuWfn41dXnh3Tb/imB1zYLQ8naxr8/h7L3aoIeVqUuHgv\nLsmUiiIiHr8UTLCUArVSaHz3Sc+DmgDwn70nYgquAXnOP3IO56q1SKk2ej9fRwyur7jiilbvmUwm\nfPbZZ4oUiIzB1ejGjwdPe1/n5qThukFnxvw7gSfJWO5WA4OsSIxyx5/MErm4iJzTKrJkO+6Yn0vB\nyHH+MdINaKQ8fb2fryMG1//4xz+8/29sbMSGDRvQ0NAQ5htEwE+HK+B0tTRdj7+4myx3ztHerQYL\nssqrG/zGPwVaLmpGuuNPZka6uCSDWI47owSlojx0SZEZZZ/UK723TocTMbg+44wz/F7fc889uP76\n6/E///M/ihXKKJKtRcZX0f875fe6b/e2svxutHerwYKsfHsqjpyuQ2efaXc9v8WgjEh9sRx3RgpK\nRQ4aRCb3NVlv+6TRgn2RZ8KMGFwXFhZ6h96TJAk//fQTW66jsKKwGLtLTuFUuQNNTW4U7z+N/24/\nggJ7KlKs8s8seKLKgXpX88gcaTYL2mVr29p68HjLwzVnFGSijT1Vw9K06No20ztxgagnHArNaBcX\n8meUoFTvXdqJ0mMApFTvpJ72Sb0F+3ITqYc5YnD96quvev9vMpnQpk0bvPzyy4oWygj+34kqHCqu\nRFOT/2AsVVXK35jUwoWTp+sVX060+nXPU32Z4YKsYAchg7L4Ldm0B7sPNfdUaH0hNfrFxWhiPe6M\nHpQagV4DIKV6J/W2T2oR7Hvq/GhFHUwwoWNuuiLXApF6mCMG10uXLlWjHIbTUN/UKrBOVgPPLlB9\nmbEGWQzK4rOisBil1S03cnq4kOqpJYnC43FnPCIFQEakdrDvG1g7fp6FuaS8Bg2uJs2vBVoKGVxP\nmTIl5JdMJpPfg47UWrbPwPYAkJZuhc1iRl5W85Tfcjry88D1gSwmEzrkardTWy1mXHRue/TslKPJ\n8mMNshiUxe7gyRqkpPifRrS+kOqtJYnC43GnDi1SNXwDrpz0FMWXFw57J+MTbr9ZUViMTbuPAQDq\nXU1I+3mwgEa3hNJKB1JtFlmvBSLVYcjg+te//nXILyUy/bnb7cbMmTOxd+9e2Gw2vPjii+jatWXD\nvPXWW1i5ciXatGkeI/KFF15A9+7d416eVnrlZ2EnTnhf9z2nAP8z8hxFlpXo5Ap6zI+TQ6xBFoOy\nFkbdJ0h/eNwpT81UDU8A5BtYW80m2NOsmrZkspckduH2my/2HPf7m9stweFsRKrVArM5/hgxHJHq\nMGQT6kUXXeT9l5WVBYvFArPZDLfbjYMHo59uOtCGDRvgcrmwfPlyTJ8+vVX+9q5duzBv3jwsXboU\nS5cuFTKwBoCTPrnVFosJN17UTbFlBZsCNtqpV5N1Kl4KLZZ9QoRpaImSXbhUDbl5rke+gfWZ+VlI\ntVkUW2a0lJiCXQ9WFBZj4doiLFxbhBWFxbL9brj9xvO39JTm1mqz2QRJAhoam2A1m9AhJ12R7SxK\nHUbMuX788cexY8cOVFRUoGfPnvjxxx8xYsQI3HjjjXEt8JtvvsGwYcMAAAMGDEBRkf8Ul7t27cLr\nr7+O8vJyDB8+HPfdd19cy9FauU8g0ikvU/E79Xi7VpkfR4FiHR5tyVcHUO5sBKDvlgSSF3s3KJQJ\nA7ti15HmScQ66CjfVqRekmiPr0i9EpHSOhI9hjvlZqCkvAZpNgsczkaYTM03U0pdC0Spw4jJv9u3\nb8cnn3yCUaNG4YUXXsD7778Pt9sd6Wsh1dTUICurpbXLYrH4/d7o0aPxwgsvYMmSJfj666/x+eef\nx70sLZ2sbHnIKz8nTfHleXa4aFusieRy67BeQrQkkHzY4yWWaHuY5GoBbZ+TjmF9OnhbrMMtk1qL\n5fgK1xgS7neiWUa4/cb3bx1y0ptTf9Jt6NMhm/WMKFqu27Vrh5SUFPTo0QN79uzBmDFjcPTo0bgX\nmJWVhdraWu9rt9sNs7klxr/jjju8wffll1+OH374AcOHDw/7mwUF9rjLowRJkvzSQrp0yJa1jEs2\n7cGB41UAgB7ts3HHiPjv4s7p0hb7f/4tj+z0FNw6rBcK2mYmVM5Y6K0Ok1k8+8QzNw9So2ikkFiP\nv+PV9a0eZHVKEtbtOobp4wfIWTRdkPOcq5Rwdfjg6P5Y8NFOVDmcAJqP58B6WrJpD0p96rW0uh5L\nvjqAW4f1QqcorwWB2ym/MSPsMqmFb/3FcnylplgRbFyytDRb2N+pdjgjLiPcftOvVzvv31JSrOic\nb2f9+ogquF68eDEuueQSzJ8/HwBQVVUV4VuhXXjhhdi0aROuueYa7NixA336tJykqqurMW7cOHz6\n6adIT0/Hli1boko/KSurjvgZNVXVOuH8eUIXAMhIschWxsC7zd2HTmH2e9vjfkjk2n6dsPhEtd8D\nAndc2gNocqu2XQsK7Lqrw2QW6z7B+hNbPPXX8HMaUKB6k8lw+4Lc51wlRFOHo/p29KYOjurbsdXn\nPWPV+yp3NuLv/9odVTd8sO1kMZkAE5BuswZdJjULrL9Yjq/29rSgAxqM6tsRy77aH/J3ol1GuP0m\n0j4VjlHSykLd1IYMrpcuXYpx48Zhzpw5+Pe//43zzjsPI0eOxKeffoqZM2fGXZBf/vKX+O9//4tJ\nkyYBAF566SV88sknqKurw0033YTf/va3uP3225GSkoJLL70Uv/jFL+JellbKK/0ncMnLli8tRIkc\naQ6FRYG4T1A4Ig2Jlahw59y2WamyBwhKBR1K56oG205NkoSsFD6HEatYjq9wI2iE+53A0T5CLSPc\nfhPvPqXXiYbkZJIkKehMJ08++SQ+//xzXHLJJbjhhhswdOhQtcsWNb3dDRfuPo7XP9rlff38nYNx\nZgd50h4Wri0K+r7ID5IF3rWLdEcrUlmVkswt10ao/3jrT5QhsRIV6pxbXt2AfHuq33uxDIEaTLzD\nqspxDCY6pKsRr01K82zz1BQr2tvT/M4fsRxfxysdfo0hvvUV7ne0OoaNtK+Earm2zAzRDH3VVVdh\n8uTJkCQJy5cvxyuvvILKykqcccYZyM7OVrKsMaurc2pdBD/f7T+JH4pPe1/fOLwnUqyWMN+I3pHT\ndah0+K+v5wSYlWYL8a34rSgsxrrvDmPzvhM4croOfTvnyv47mZmp3joMPMFXOpz4/tBpdGmbqcj6\nJULOssq1nbXgW3/JYkVhMf7+xV78dLwK9c4m2NNtut5Xw4m3/rq0zcSBEzVIsVoUO//oQahzbpMk\nwRownq+zyY0DJ2owqHt+XMta993hVu9F85tyHIN9O+fi+0On4WxqHmDAE+hEW69qX5tE53v9sFjM\nOFVT73f+iOX4ykqzYVD3fAzqnt/qc8F+x3O9qa53oaLOhXbZ6arW0+Z9J4K+n2K1xH3saCUzMzXo\n+yFbrgOVlZXhk08+wfr165GZmYn//d//lbWAidBbq9nSf+7Bpm+OAADSUy1Y9JtfJDTxTiC17jYT\nbcmI9nd8W10SuaNVuxVRrrtvubazEqLZpsnWcu3ZJvtPtKyzZ1zXVJtFuNaXcPVnhJZ5OQQ75yrR\n+hbvb8p1DIZrAY1GsvRmyMG3rlNSrHCqNJypHq43eiiDXEK1XEc9D3dDQwMaGhrgdDplDRSNyHcY\nvrzsdNm3l1qDqMs18YAaExiIPDRYqO3zx/U/KDIxQLRE26ZKTaQQKFh9eab7NRLR6l9Jwc65Skyg\npPWkTIkO6SrKBB/JTM0JhUJJZOI7UYQNrk+dOoVly5Zh0qRJmDp1KiRJwmuvvYa//e1vapVPSOUK\nj3Ft5DGt4724aHHCUPJCeLSiDrU/twAB2gQ2ejgJR0uLQNAzM5kvIwUVItW/0oKdc5UIEEQPOox8\nbZKb1jdSWjP6jVjI4Hrq1KkYOXIkdu3ahd/+9rdYt24dHnjgAXTo0EHN8glHkiS/2RnzVJhARily\nHfyx/I5IFxe5yhps+7ga3a1mNkvWwCYaagaCnvrqlJvhzbm1mk3o0c6u232VlKFEgGD0oENESvSK\naXWt00tQb/QbsZDB9ahRo/Dvf/8bL730EgYPHsxUkAChDrZqhwtOV8uMk2rMzqgUuQ7+WH8nnouL\nVicMOS6EwbZP4MxmWtDLSVhvfOurQ0460mwW9O3cBrdc3EPjksmL9R+ZEgGC0YMO0SjZK+a5fmSn\np6h2XInUgCWyqB9o1DO1H6ZaUViMPYdPo+JUPdxNzZvPZjGja34m3E0Svv2p3PvZ/7muHwb2aadq\n+eSU6AMu0fyOXA/jiPwwTeD2CTcGqZonwWi2qR4eaFT7ARm5jgs9CFd/Ih9TyUQPx6Boon1YV41h\n49SuPyOdv7QW6oFGBtdxePmjndi35xTc7sib7rk7B6FbB30NXag3ennSXW/0ENhEs031cmHXw/YS\nUbj6E+GYEmlEE6XKqpdjUBSx3IwbMbgm+TC4ltEjf/kvqisbIn4uPdWCBQ8ORXpqxFnmkxpPLMGJ\nENgA+qk/UbaX3uil/uIh0pBeSpZV5DrUQiwBsxr7GOtPGWrceMc8/bnH4cOH8fbbb6OiosLv/Zde\nekmekiVoyaY92H3oFAB1Wi2+/anML7A2m02wWEwwmUxIT7HA8vMDTlnpNoy+pBsDa4qb0tMVGw23\nV/IJ9yCr3vYFkcqajI5W1MHV6MbCtUV+sUS46cVJv7SeYj1i5Peb3/wGgwcPxuDBg73v6enhxn2l\nlTh8sApVFfXYJZ3A2o0HVF3+mT1z0a5NBg82IgpKpLQFomiJvF93zcvyC7w8gbVnhKbAQGzCwK5+\nvWKkf1rfzEYMrpuamvDEE08oXpB4VVU0oPJ0feQPKiAlywZ7lnpP+RKRWLRuPTG6wCAJ0O+IJiKV\nNRI592stgvTA1mhXoxtn5vuPjuMbiLFXjGIVcYbGgQMH4rPPPoPT6VSjPDFzOFyRP6QAm82MHt1y\nkG6z8iJJqlBrBkKSDydiUZZIw4qJVNZI5xq59mstZwH1HUa1Q26G4ssjdWk9lGjElut169Zh2bJl\nfu+ZTCbs3r1bsULFwtnQ5P2/2WxCxw5Z6NMxB1lp8uY6b953wmc5ZuTkpsJiiXr2eEpicrTMsAWU\nRKBFK6RIXfYilDXwXLNl/wl8uacUHXIzcFb7bFnrVMuue9/W6HAPLZKYtM6VjxiB/uc//1GjHHFz\nN7ZM2JKWZsWs2weH+XT8XKlmHnwUM7mCYq3zxyg+RkoFiESrG0CRuuxFKGtgLrLD2dyAVVpRh1Sr\nGYs37kFeVipO1viPmCXyfq11IEbK0PJmNmLTa11dHebNm4frr78e48aNw5w5c1BXV6dG2aKSYm5Z\nhe7tgw+JIkd3ukhdeqQfTAtIbsl03uC+bjyewNpXTYMLDS63LPu11l33vjjtvPFoOdtpxOB61qxZ\nqK+vx5w5czB37ly4XC48//zzapQtKqerWh5m7JjXOm9KzpwuHnykFT1dhCg2PG+QSIKda6xmk3ck\nDQ859ms93Xxy2nmSU8S0kKKiInz88cfe188//zyuueYaRQsVC6dPWkh+dlqrv8vZnS5Clx7pa4go\nuZOM7zcAACAASURBVNIC2G0prmQ5byRTCoyR+Z5r0lMsrUbSKK9uQEOjG8u+2i/L+VWEPHQj0NN1\nMRlE9UReZWWl3/+tVn1OjJKX0zq4puSi5dPnwcjZMuNpKSqvbsDJ2gaOGpIAjrwiPz21QlJiPOea\n3u1z0KNdS7pleXUD8u2pSLU2hw5ynF/ZYqw8vV0Xk0HE4PrOO+/ExIkT8fLLL+Oll17CjTfeiNtv\nv12NssUsWHDN7vTkEm3ep5rBlVxpAe1z0tE2K1X2i1uy4YVGOUyBMQbfgPeWi3t469Sebmv1WebV\n6x+fh1BfxCboG264Af369cP27dvhdruxaNEi9Omjzy7OtkHSQtidToHUHtVAzrSAZB81RI6uzWTf\nhkpKlhQYNeilG9+3TheuLdKkDESiCdlyvXHjRgDAhx9+iN27dyMjIwNZWVn44YcfsHr1atUKGK0U\nqznoXTXA1pRkEk1PBe/ixcQWZ1KDHlKG9LqvsydYTKw39YVsuS4qKsIVV1yBrVu3wmQytfr7hAkT\nFC1YrPJy0oKWE2BrihFE24pj9J6KZH5oTK4W52TehqLQqtU2kV4tOcus194Vo59fjYr1pr6QwfXD\nDz8MABgzZgwuu+wyv7+tX79e2VLFIS9ISgjp4yKV6HJjveBFevpc5OCKJ8nExbMN9dJFnwy0nI00\n3qA2mWZQ5egeYmK9qStkcP3pp5/C6XTi1Vdf9QbaAOByubB48WJcffXVqhQwWsHyrZOdVid8uZcb\n6wUvUk+F6AFqsp4k5bwpimUbJlPgpAd6bbUFQt9khStz26zUmG/M9NwAwJ5gMYWrNzYeyC9kznVN\nTQ22bt2K2tpabN261ftv586dePTRR9UsY1SMMgyfnPl+WuUWi5DTLHIefrIOXSXnUG+xbEMR9meS\nR7jc1HjyoIvLa+LKneawhqQWveb3iy5ky/XNN9+Mm2++GZs3b8ZZZ52F/Px8OBwOHD9+HN26dVOx\niNEJNoGMaNhCFpwSrTgitb6wVaGFGq32gdub1KVlq224Xq1wN1mhytzgM8lZ4HcinX+StYeK1KXn\nniKRRRzn+qeffsI999wDADh58iR+9atfYfny5YoXLFZGaLmWu4VMqyeE5V5uMrfisFXBn9Kt9sG2\nd3l1AxpcTX6fE7HHQxRaH+/x9GqFKrNnPPp4JGsPFZERRDzy33vvPbzzzjsAgDPOOAMffvghli1b\npnjBYrWu6EjSBhyhaHWRina5nhSY55dvi5gCI3IaRyKMnJKghyHPAgXb3vn2VJTXNHhfJ9PNnVa0\nPN5DBbWRGg2ClZlDoCUvPZ7fguE+qoyIwXVjYyNstpZAyWazhRzyTktOyS18wKHETq7VRSrScn1b\nCCVEbpFlK4725LxYiNYi37VtZlLe3GlFj8d7pEaDYGVWq4FjyaY9QgRyyUKk85vWPUVGZZk5c+bM\ncB8oLS3FX/7yFzgcDuzcuRO///3vcemll+KSSy5RqYjhvfvPPUhJsSCvIAMpVgsGdc/Xukhx69s5\nF98fOg1nU3Oenmcnz0oLPjlONLLSbBjUPR+Duucn9DtyL3fdd4e9/7dYzGhqcsPZ5MaBEzVC16Hc\njpyuQ6XD6feeJ8BTsz4DLxaVDie+P3QaXdpmol3bTNTVOcN8uzXf+vfQQ/2H2t4Th3TD5ed0UP04\nUkNmZmrM9WckKwqLse67w9i87wSOnK5D3865IT/bpW0mDpyoQYrVEvUxGM93YrGisBiHTtWi6efr\nhu+xabR9VRSxnt+0PgaV3keNLDMzNej7Eac/nz59OtatW4ft27fDarXijjvuwFVXXSV7ARORV5Bh\nmBYlPsRCvvQybGC49JR+vdpF/L4oDwnqZXuTOmJ9iDyeB6GVfnj64MkapKT4X8r5QBrFQqQH/EUR\nMbg2mUzo2bMn8vPzIUkSAGDbtm0YPHiw4oWLRr/z2yMzxWqYHSNZdnI9j+OqN6LfcIV6SNCeZkWq\nzeJ9Xy/1L/r2puhxpARSAq9vFDG4/t3vfodNmzahS5cufu8vXbpUsULFIi87HaP6dtS6GITYhoxj\nC2H09HDDlcjFItRDgkdO16Fzmwzvb2m9jh562N5E0eqal4XS6nq/9xjIaYvXNzJJnuboEEaOHIk1\na9YgLU2/Q92VlVUr+vsijTOsh+nOPTwn+FAPRhyvdGD11weRlmbDqL4dNXmAQqS61Vqoi0VBgT3s\nMbhwbVHQ9y0mE9J/7s5O9rHctRSp/owsnvOWHi356gDKK+sAMJDTC8/1DYh8fkvmY1B0BQX2oO9H\nDK6nTp2KV199FRkZGYoUTA5K7pQinXy1LGuoACqaE71WJxaR6lYPQl0sItUft7O+JfuF3QgtjC6L\nGX//124AvFEVUbIfgyILFVxHTAvJzs7G6NGjccEFFyA1teWpyJdeekm+0umYSDl5IpVVD7i9YhNv\nugS7SI3BCL08wdbBCDn2ndpm8pgi0pGIwfWwYcMwbNgwv/f0OM41aYsPcGhDlIDHCAFMMot1VA09\nCrcODEyJSE4Rg+uLLroIJpPJO1KI7/+TgUhBY6xllTMwE7F1UqS6DUakgIcPCYrNCL08RlgHIhJD\nxOB6ypQp3v83NjairKwM5557LlatWqVowfRCpKAxlrIqEZiJ1jopUt0Gw2CBiEg5ovQMkv5EnP58\n48aN3n9ffPEF3nvvPfTs2VONsumGVlOIxyPasoYLzOKlxymLIxGpbom0EmziH9GOGSOsA6lHpCnM\nSX8itlwHOu+887Br1y4lyqJbInVpi1RWPRB5e4me1kLi0GMvT6ytinpcB9Iv9gxSIiIG14sWLfL+\nX5Ik7Nu3D/n5+YoWipTHwEx8DBZITXpK+4o3rU1P60BExhUxuJYkyTs6iMlkwpAhQzB69GjFC0bK\nYmBmDAwW1JXMOZh66uWJt1VRT+tA+sYGKEpEyOD6u+++w3nnnYeHHnpIzfKQihiYiY/BgnpEGp2F\niBLDBihKRMjg+rnnnsPq1asBAC+//DJmzJihWqFIGcFa3XiyID3TU0sxczD1g62Kkenp2BEVG6Ao\nXlE90Lhlyxaly6ErRjwp+a7T0Yo67D9RjS/3lOLCbnm4c9hZGpeOqDW2FFMobFUMj8eOPNgzGB8j\nxlCxijgUX7Ix6vA7voG1w9kEAGh0S/im+GRU67eisBgL1xZh4doirCgsVrq4RIoMF5kIDuWmLxxG\nMzS9HTuUPIwaQ8WKwXUAtU9KagetnsDaV6T148FC1NxampVq8772tJayJVAbco+rzwYEosTxxq5Z\nyLSQH3/8EWeffbb3te//TSYTdu/erWzJBBVLd4iaXXfBchStZhM6RLEc5pqSFpTIq020u5I5mMZk\ntDQK5qQTaStscJ2MEjkpxXqCVjNo9eQopqdY4HA2wWo24cz85m5unnRJj+TOq5UjgGIOppgi3VQZ\nrQGBOemkFd7YNWNaSIBEun713h0yYWBX9G6fgzSbxdtiHc36MdeUtCJnXq3ej09SRrKmtTEnnbTA\n9LlmMU9/ngzU6vpV+w7P0+p2vNIR0/qFagXhE8GkNLYUU6KiaZU2Ymsbjx3SCtPnAJMkSZLWhUhU\nWVm11kUA0LqFBGg5QYe6axOl6y4wIP9iz/GY1zWUggK7buqQYidK/cVzfCYDUeovXgvXFgV9P/B8\nK8q5OBij16HRsf7EVVBgD/p+VGkha9aswR/+8AfU1tZ6J5ah1uLpDhGl6y7wyXx2sZNo2F2ZnKJN\naxPlXExE+hcxLWT+/PkoLS3FDz/8gLvvvhurVq3C7t278eSTT6pRPuHE2h3Crjsi9bC7MvlE+3Cf\nXs/FTL8jEk/EtJDx48fjww8/xPXXX4/Vq1ejsbERY8eOxdq1a9UqY0RG6E4R7QQqZxc7u8TExvoT\nWzLUX2Bamyi9FdGeZ5OhDtWixbWY9SeuuNNCLBaL32un09nqPUqMiE+zs4udiEQh94QzamH6nbpE\nvBaTPkUMrkeNGoVp06ahsrISb731FiZPnozRo0erUTbNqTVjl6gnUOYoEhGRUYh6LSb9iZhzfd99\n9+GLL75Ap06dcOzYMTz88MMYMWKEGmXTlNFm7FKCXnMUiYiMwIhDBBIlg4jB9QMPPIDx48dj2rRp\nSElJUaNMuqDmjF3xnkBFy9MmIqLocaZFdYlyM8Nrv/5FTAu56aab8K9//QtXXXUVnn76aWzdulWN\nciWVePKXmRtGRGR8TL9TjwjPEvHa70+t9N1YRQyuR4wYgYULF2L9+vUYNmwY5s6dmxRpIWpP+R3r\nCZS5YURExifqw5ii0vvNDK/9LfR8oxHV9Oc//fQTPv30U6xfvx4dO3bE7bffrnS5NKd2dxzzl4mI\niLTFa7E41EzfjVXE4Hrs2LEwm80YP348lixZgnbt2qlRLl3Q84QTWuaGMd+LiIg8eE1Qjyh54cku\n4iQye/bsQZ8++r6Lk2PwdRFPDlo86CLn5DEeHEBfbKw/sbH+xKdlHSpxTUg2sdYfH3Jtpod9L+ZJ\nZJ555hkAwOzZszFlyhS/f0ZLC9Fz3k44WuSGMd+LiIg8eE1Qn97zwtWi5wdQQ6aF3HzzzQCAhx56\nCIGN2yaTSdlSqUzPeTvhMDeMiIgoufDa30Kv6bshg+v+/fsDANavX49nn33W729PPPEEhgwZomzJ\nDEjE1JNAzPciIiIPXhNIS3q90QgZXD/99NM4ePAgioqKsHfvXu/7TU1NqK42Vn6eGicHo8z4yEkN\niIjIQ8/XBCM0aJGYQj7QeOjQIRw9ehSzZ8/Gs88+600NsVqt6NmzJ3Jzc1UtaDhyPMih9Mlh4dqi\noO/r6UQUreOVDr9umERvDvhAldhYf2Jj/YlP6zqU+5ogBz087BYtreuP4hfqgcaQLdddunRBly5d\n8O6772L16tW47bbbcPz4cbz77rs499xzFSuo2jwHYIOrCdX1jeiWn2WI7iwl79j12g1DRETq0+M1\nQdRnqcgYIs7QOH36dJSVlQEAMjMzIUkSHn/8ccULpgbfADTVZkG+PVWxZcU642MiU3qKOvoJERER\nkegiBtdHjhzBtGnTAABZWVmYNm0aSkpKFC+YGtQcQiiWIWMSDY45NBIRESWzWBu0iOQUMbg2m834\n8ccfva/3798Pm80W5hsUSrRjUzI4JiIiip+ex0Am44s4/fkTTzyBqVOnon379gCAU6dOYf78+YoX\nTA1qDyGkVl6aaEMjLdm0B7sPnQLAJ7qJiEgeeh0DmYwv4vTnAOB0OrF3715YrVb06NEDKSkpapQt\naok8ZavHIYTkeMpZj+sVzIrCYpRW18PpbPS+p9cnuik4PukuNtaf+FiHYmP9iSvm6c89Kioq8MIL\nL2Du3Llo164dnn/+eVRWVspeQK3ocRpRObqz9LhewTAFhowqkYeSiYhIXBHTQp599lkMHToUO3fu\nRGZmJtq1a4fHHnsMb7zxhhrlU5waqRrxDIuXaHdWvOvFQfeJEmeUSaOIiCh2EVuuDx8+jEmTJsFi\nsSA1NRXTpk3DsWPH1CibIcQ78ocnOFbzAQwthvDjE91kROyRISKRsectMRGDa6vV6jfdeXFxMSwW\ni6KFMhKRLrJalHXikG7ITm/J4ecT3URERNrhXBmJi5gW8tBDD2HKlCk4duwYHnjgAezYsQNz5sxR\no2wkiERTSW4d1gt//9duAHyim4xBtBF7iIg8OLtl4iIG17/4xS/Qr18/fPfdd2hqasKsWbOQn5+v\nRtkMQaSLbDxllSO3tFPbTB6wZCgTh3QTZsQeIiKSV8jgevny5Zg0aRIWLVrk9/7u3c0tjBkZGRgx\nYgS6d++ubAkFJ9JFNp6y8g6XKDiOsUtEIhKpUVCvIuZchxoGu7S0FFOnTpW9QEYkyrB4gFhlJdIz\nLR5KJiJKFGe3TFzIlutJkyYBaM65drlcOHDgAKxWK84880xYrc1fM5lM6pRScGrNzCiHWMvKO1wi\nIiJjYc9bYiLO0Lh9+3Y8/vjjyM3NhSRJqK2txYIFC3DeeeepVcaIOLORthJNe+HsVGJj/YmN9Sc+\n1qHYWH/iCjVDY8QHGufMmYPXXnsNZ599NgDg+++/x+9+9zusXLlS3hKSsHiHS0RERNQsYnANwBtY\nA0D//v3R2NioWIFIPCKlvRAREREpKWRwvWvXLkiShF69emH27NmYOHEiLBYLPv74YwwYMEDNMhIR\nERERCSFkcP3yyy97/3/s2DHMnj1blQIREREREYkqZHC9dOlSNctBRERERCS8sONcFxYW4s4778TA\ngQMxcOBA3HXXXdi2bZtaZSMiIiIiEkrIluvNmzfj8ccfxwMPPICnnnoKLpcLO3bswLRp07BgwQJc\nfPHFapaTSDO+U7x3zcvCxCHdtC0QERER6VbI4HrRokV44403cM4553jf69u3LwYMGIA5c+YwuKak\n4BtYA83TvS/euAcTBnblbFVERETUSsi0kJqaGr/A2qNfv36orKxUtFBEehE4+yQA1DS4vON6ExER\nEfkK2XLtcDjQ2Njonerco7GxEU1NTYoXjEhUTCMhIiJKXiFbrocOHYoFCxb4vdfY2Ig5c+Zg+PDh\nSpeLNLaisBgL1xZh4doirCgs1ro4mumal9XqvaxUW8iZKEOlkRyvdChWRiIiItIPkyRJUrA/1NbW\n4le/+hWOHTvmnZWxqKgIvXr1wqJFi5CamhrXAt1uN2bOnIm9e/fCZrPhxRdfRNeuLYHKxo0b8dpr\nr8FqteKGG27AxIkTI/5mWVl1XGWh4AIDRKAloFQiz7igwK7rOly8cQ9qGlwAmrdDuNkoF64tCvp+\npO+JTO/1R+Gx/sTHOhQb609cBQX2oO+HTAvJzMzEP/7xDxQWFuL777+H2WzGHXfcgUGDBiVUkA0b\nNsDlcmH58uXYuXMnXn75Zbz22msAAJfLhZdffhmrVq1CWloabrnlFlxxxRXIy8tLaJkUm3B5xkYN\nEMOZMLCrN8c6VIs1ERERERAmuAYAk8mEiy66CBdddJFsC/zmm28wbNgwAMCAAQNQVNTS0rd//350\n7doVdnvzncDAgQOxbds2jBo1SrblE8WqfU561DcVXfOyQrb6ExERkfGFnURGCTU1NcjKasljtVgs\ncLvd3r95AmugufW8uppdJWqLNc+YWkwc0g1ZqTbva086CIftIyIiSg5hW66VkJWVhdraWu9rt9sN\ns7k5xrfb7X5/q62tRU5OTsTfDJXzQvF5cHR/LPhoJ6ocTgBAdnoKpo8foOgyjVSHd//yHLzz5T4A\nwK3DeqGgbabGJVKekeovGbH+xMc6FBvrz1hUD64vvPBC/P/27j02ivLf4/in0u2mdKRII+hJWKr8\nSEEIRIqFHAKaEkzJr5rlUm5aGqlxaQheiITKxYLhlqh/taiYQLTIEahAo/xjEDA1QChCKiJQ8EKr\n0cCPEmp3LbSyc/7gsKelhdrysLNT3q+/dmZ2p9/tN0/62afPzuzfv1+TJk1SVVWV0tL+/9/tjz76\nqGpqalRfX6/ExEQdOXJE+fn5HZ6TLwKYlzX04cg646yhD9/V33F3+zKHR1Lefz96feNauFu9t/Z0\nt/7da+if+9FDd6N/7tXpLzTeLRMnTtSBAwc0c+ZMSdLatWu1e/du/fXXX5o+fboKCwuVn5+vcDis\nadOmqW/fvtEuEercOmMAAABcd8tL8bkJn/jcjU/t7kb/3I3+uR89dDf65163mrmO+hcaAQAAgO6K\ncA0AAAAYQrgGAAAADCFcAwAAAIYQrgEAAABDCNcAAACAIYRrAAAAwBDCNQAAAGAI4RoAAAAwhHAN\nAAAAGEK4BgAAAAwhXAMAAACGEK4BAAAAQwjXAAAAgCGEawAAAMAQwjUAAABgCOEaAAAAMIRwDQAA\nABhCuAYAAAAMIVwDAAAAhhCuAQAAAEPinS4AuKGs8pxq64KSJF+KpZyMVGcLAgAA6CRmrhETWgZr\nSaqtC2rDvmqdr290sCoAAIDOIVwjJrQM1jcErzar/GitA9UAAAB0DeEaAAAAMIRwjZjgS7Ha7LO8\nHvnTfQ5UAwAA0DWEa8SEnIxUWV5PZNvyehTITFO/5EQHqwIAAOgcwjVihj/dJ8vrYcYaAAC4Fpfi\nQ8zol5yoQGaa02UAAAB0GTPXAAAAgCGEawAAAMAQwjUAAABgCOEaAAAAMIRwDQAAABhCuAYAAAAM\nIVwDAAAAhhCuAQAAAEMI1wAAAIAhhGsAAADAEG5/7oCyynOqrQtKknwplnIyUp0tCAAAAEYQrqOs\nZbCWpNq6oDbsq5Y/3ad+yYn/+LWEcgAAgNjDspAoaxmsbwhebVb50drbvu5Wofx8faPxGgEAANA1\nhGuX6GooBwAAQPQQrqPMl2K12Wd5PfKn+xyoBgAAACYRrqMsJyNVltcT2ba8HgUy0zpcb00oBwAA\niH2Eawf4032yvJ5OheOuhnIAAABED1cLcUC/5EQFMtM6/Tp/ui+yxpoZawAAgNhDuHaRroZyAAAA\nRAfLQgAAAABDCNcAAACAIYRrAAAAwBDCNQAAAGAI4RoAAAAwhHANAAAAGEK4BgAAAAwhXAMAAACG\nEK4BAAAAQwjXAAAAgCGEawAAAMAQwjUAAABgSLzTBbhdWeU51dYFJUm+FEs5GanOFgQAAADHEK7v\nQMtgLUm1dUFt2Fctf7pP/ZITHawMAACYxGQa/imWhdyBlsH6huDVZpUfrXWgGgAAcDfcajLtfH2j\ng1UhVhGuAQAAboPJNHQG4foO+FKsNvssr0f+dJ8D1QAAAMBphOs7kJORKsvriWxbXo8CmWmstwYA\noBthMg2dQbi+Q/50nyyvh0EGAEA3xWQaOoOrhdyhfsmJCmSmOV0GAAC4i/zpvsgaaybTcDuEawAA\ngA4wmYZ/imUhAAAAgCGEawAAAMAQwjUAAABgCOEaAAAAMIRwDQAAABhCuAYAAAAMIVwDAAAAhhCu\nAQAAAEMI1wAAAIAhhGsAAADAEG5/DnRBWeU51dYFJUm+FEs5GanOFgQAAGICM9dAJ7UM1pJUWxfU\nhn3VOl/f6GBVAAAgFhCugU5qGaxvCF5tVvnRWgeqAQAAsYRwDQAAABhCuAY6yZditdlneT3yp/sc\nqAYAAMQSwjXQSTkZqbK8nsi25fUokJmmfsmJDlYFAABiAeEa6AJ/uk+W18OMNQAAaIVL8QFd0C85\nUYHMNKfLAAAAMYaZawAAAMAQwjUAAABgCOEaAAAAMIRwDQAAABhCuAYAAAAMIVwDAAAAhhCuAQAA\nAEO4zrUhZZXnVFsXlHT99tg5GanOFgQAAICoY+bagJbBWpJq64LasK9a5+sbHawKAAAA0Ua4NqBl\nsL4heLVZ5UdrHagGAAAATiFcAwAAAIZEdc31lStXtGjRIl26dElJSUlat26d+vTp0+o5q1at0rFj\nx5SUlKS4uDi99957siwrmmV2mi/FajN7bXk98qf7HKoIAAAATojqzPWnn36qtLQ0bdmyRX6/X++/\n/36b55w8eVKbNm3S5s2bVVpaGvPBWpJyMlJleT2RbcvrUSAzTf2SEx2sCgAAANEW1XB97NgxjR8/\nXpI0btw4HTp0qNXxcDismpoaLV++XLNmzdKOHTuiWd4d8af7ZHk9zFgDAADcw+7aspCysjKVlpa2\n2peSkqKkpCRJUlJSkhoaGlodb2xsVG5url544QX9/fffmjNnjoYNG6a0tLS7VaYx/ZITFciM/ToB\nAABw99y1cJ2Tk6OcnJxW+xYsWKBQKCRJCoVC6tWrV6vjiYmJys3Nldfrldfr1ZgxY3T69OkOw/WD\nD95vtnhEHT10N/rnbvTP/eihu9G/7iWqX2gcOXKkKioqNHz4cFVUVGjUqFGtjv/yyy9auHChdu3a\npWvXruno0aOaMmVKh+f9z38aOnwOYteDD95PD12M/rkb/XM/euhu9M+9bvWhKKrhetasWVq8eLFm\nz56thIQEvfvuu5Kkjz76SD6fT5mZmfL7/ZoxY4bi4+M1ZcoUDRw4MJolAgAAAF0WZ9u27XQRd4pP\nfO7Gp3Z3o3/uRv/cjx66G/1zr1vNXHMTGQAAAMAQwjUAAABgCOEaAAAAMIRwDQAAABhCuAYAAAAM\nIVwDAAAAhhCuAQAAAEMI1wAAAIAhhGsAAADAEMI1AAAAYAjhGgAAADAk3ukC0LGyynOqrQtKknwp\nlnIyUp0tCAAAAO1i5jrGtQzWklRbF9SGfdU6X9/oYFUAAABoD+E6xrUM1jcErzar/GitA9UAAADg\ndgjXAAAAgCGE6xjnS7Ha7LO8HvnTfQ5UAwAAgNshXMe4nIxUWV5PZNvyehTITFO/5EQHqwIAAEB7\n4mzbtp0uArf3+6WQ/uebHyVJs8f9S//VJ8nhigAAANAewjUAAABgCMtCAAAAAEMI1wAAAIAhhGsA\nAADAEMI1AAAAYAjhGgAAADAk3ukCuiocDmvFihU6c+aMPB6PVq9eLZ+PG6vEusmTJ8uyrt8Yp3//\n/goEAiosLNR9992nQYMGqaioSHFxcQ5XiZt99913euedd7R582bV1NS027Pt27dr27Ztio+PV0FB\ngZ566imny0YLLXt48uRJzZs3TwMGDJAkzZ49W5MmTaKHMaq5uVlLlizR77//rqamJhUUFGjgwIGM\nQ5dor38PPfSQAoGAUlNTJTEGux3bpb788ku7sLDQtm3brqqqsgsKChyuCB25cuWK7ff7W+0LBAJ2\nZWWlbdu2/eabb9p79uxxojTcxocffmhnZ2fbM2bMsG27/Z5duHDBzs7OtpuamuyGhgY7Ozvbvnr1\nqpNlo4Wbe7h9+3Z706ZNrZ5DD2PXjh077DVr1ti2bduXL1+2n3zySXvevHmMQ5dor3+Mwe7NtctC\njh07pnHjxkmSRowYoRMnTjhcETpy+vRpNTY2Kj8/X3l5eaqqqtLJkyf1xBNPSJLGjx+vgwcPOlwl\nbjZgwACVlJTI/r9L4rfXs++//14jR46Ux+ORZVkaMGCAqqurnSwbLdzcwxMnTujrr7/W888/r5My\nQgAABZBJREFUr6VLlyoUCun48eP0MEZlZWXp5ZdflnT9v7bx8fGMQxdpr38//PADY7Abc224DgaD\nkeUFktSjRw+Fw2EHK0JHEhMTlZ+fr40bN2rlypV6/fXXWx3v2bOnGhoaHKoOt/L000+rR48ekW27\nxX2nkpKS1NDQoGAwqPvvv7/V/mAwGNU6cWs393DEiBFavHixPvnkE/Xv318lJSUKhUL0MEb17Nkz\n0o9XXnlFr776aqu/d4zD2HZz/1577TUNHz6cMdiNuTZcW5alUCgU2Q6Hw7rvPte+nXtCamqqnn32\n2cjj3r17q66uLnI8FAqpV69eTpWHf6jlOAsGg+rVq1eb8UgvY9vEiRP12GOPRR6fOnWKHsa4P/74\nQ3l5efL7/crOzmYcukzL/v373/9mDHZzrk2jI0eOVEVFhSSpqqpKaWlpDleEjuzcuVPr1q2TJJ0/\nf16hUEhjx45VZWWlJKmiokKjRo1yskT8A0OGDGnTs+HDh+vbb79VU1OTGhoa9NNPP2nQoEEOV4pb\nefHFF3X8+HFJ0sGDBzVs2DB6GMMuXryouXPnatGiRZoyZYokxqGbtNc/xmD35tqrhUycOFEHDhzQ\nzJkzJUlr1651uCJ0ZNq0aXrjjTf03HPPSbres969e2v58uVqbm7WwIEDlZWV5XCVuJUbV3EpLCxs\n07O4uDjNmTNHs2fPVjgc1sKFC5WQkOBwxbjZjR6uXLlSK1euVHx8vPr27au33npLSUlJ9DBGffDB\nB2poaND69eu1fv16SdLSpUu1evVqxqELtNe/JUuWaO3atYzBbirObrmAEgAAAECXuXZZCAAAABBr\nCNcAAACAIYRrAAAAwBDCNQAAAGAI4RoAAAAwhHANAAAAGEK4BgAXGTx4sCSpoaFB8+fPN3be3Nzc\nyGO/32/svABwryFcA4AL1dfX69SpU8bOd+TIkcjj8vJyY+cFgHuNa+/QCAD3slWrVunChQtasGCB\niouLVV5ertLSUoXDYQ0dOlRFRUVKSEjQmDFjNGzYMNXV1amsrEwrVqzQjz/+qIsXL+qRRx5RSUmJ\n3n77bUnSjBkztG3bNg0ePFinT59WY2Ojli1bpjNnziguLk5z586V3+/Xzp079c033+jPP//Ur7/+\nqrFjx6qoqMjh3wgAxAZmrgHAhZYvX66+ffuquLhYZ8+eVVlZmbZu3ary8nL16dNHGzdulCRdvnxZ\ngUBAu3btUlVVlbxer7Zu3ao9e/boypUrqqio0LJlyyRJ27Zta/UziouL1adPH33xxRf6+OOPVVJS\nourqaklSVVWViouL9fnnn2v//v06e/ZsdH8BABCjmLkGABeybTvy+PDhw6qpqdH06dMlSc3NzRo6\ndGjk+IgRIyRJo0aNUu/evbVlyxb9/PPPqqmpUSgUuuXPOHz4sNasWSNJeuCBBzRhwgRVVlbKsiw9\n/vjj6tmzpySpf//+qq+vN/4eAcCNCNcA4HLhcFhZWVmRGehQKKRr165FjickJEiS9u7dq+LiYuXl\n5Wnq1Km6fPnybc9r23arEB8OhyPn9Xq9bZ4LAGBZCAC4Unx8fCToZmRk6KuvvtKlS5dk27ZWrFih\n0tLSNq85dOiQJk2apMmTJyslJUVHjhyJnKNHjx6tArkkjR49Wp999pkk6dKlS9q7d69Gjx5NkAaA\n2yBcA4CLxMXFSZJSUlL08MMPKy8vT4MHD9b8+fOVl5en7OxsSdJLL73U6vmSNH36dO3evVtTp05V\nUVGRJkyYoN9++02SNGHCBPn9fjU1NUVeM3/+fNXX1+uZZ55Rbm6uCgoKNGTIkFbnBAC0FmczBQEA\nAAAYwcw1AAAAYAjhGgAAADCEcA0AAAAYQrgGAAAADCFcAwAAAIYQrgEAAABDCNcAAACAIYRrAAAA\nwJD/BaNwi3Uhy7rdAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<matplotlib.figure.Figure at 0x1203de950>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6));\n",
     "plt.title(\"SigOpt: Each Trial Value and Max Value over time\");\n",
     "plt.plot(sigopt_df.value, alpha=0.7, color='steelblue', label='SigOpt', marker='o', linestyle='')\n",
     "np.maximum.accumulate(sigopt_df.value.fillna(0)).plot(lw=3.5)\n",
     "plt.tight_layout()\n",
     "plt.xlabel(\"Iteration\")\n",
     "plt.ylabel(\"Objective Function Value\")\n",
     "plt.savefig('SigOpt_max_over_time.png', dpi=300)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 103,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "image/png": 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u8yCGgwAAAORPhGsAAADARQjXFugz/yurSwAAAIAbEK4tkGqM1SUAAADADQjX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\nAAAAwBLKNQAAAGDJfwCH3ZrowyNeKAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<matplotlib.figure.Figure at 0x123788dd0>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6));\n",
     "plt.title(\"Grid Search VS SigOpt: Sorted Values Returned for Objective Function\");\n",
     "plt.plot(np.sort(grid_df.fillna(0.0).value), color='forestgreen', lw=3, marker='o', ls='')\n",
     "plt.plot(np.sort(sigopt_df.value), color='steelblue', lw=3, marker='o', ls='')\n",
     "plt.tight_layout()\n",
     "plt.legend()\n",
     "plt.ylabel(\"Objective Function Value\")\n",
     "plt.savefig('SigOpt_vs_GridSearch_sorted_value_comparison.png', dpi=300)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## What does the distribution of objective function values found look like?  \n",
     "##SigOpt vs. grid search"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 75,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "image/png": 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SMHaPJIpTE1XA+nTFK1a4IiIiipxAIACn0xGRffEaTeFi0kYUpyaqgHVlxSuvx80KV0RE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\n",
       "text/plain": [
        "<matplotlib.figure.Figure at 0x120373710>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(15,6));\n",
     "gs = plt.GridSpec(1,2);\n",
     "plt.subplot(gs[0]);\n",
     "sns.distplot(grid_df.value.dropna())\n",
     "plt.axvline(0.0,c='k',ls='--')\n",
     "plt.axvline(np.mean(grid_df.value),c='orange',ls='--', lw=3, label='mean')\n",
     "plt.legend()\n",
     "plt.title(\"Grid Search\");\n",
     "\n",
     "plt.subplot(gs[1]);\n",
     "sns.distplot(sigopt_df.value.dropna())\n",
     "plt.axvline(0.0,c='k',ls='--')\n",
     "plt.axvline(np.mean(sigopt_df.value),c='orange',ls='--', lw=3, label='mean')\n",
     "plt.legend()\n",
     "plt.title(\"SigOpt\");"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "collapsed": true
    },
    "source": [
     "## Make a Pyfolio Tearsheet if you have it installed"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Run the Zipline algo over the entire date range, then we'll highlight the Out-of-sample period within Pyfolio"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 76,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "rebal freq: 12\n",
       "OrderedDict([('rsi', {'width': 16, 'lower': 44, 'func': <built-in function RSI>, 'func_kwargs': {'timeperiod': 33}}), ('roc', {'width': 0.534, 'lower': 0.0, 'func': <built-in function ROCP>, 'func_kwargs': {'timeperiod': 54}})])\n"
      ]
     }
    ],
    "source": [
     "# Specify the values of the best parameter combo in the signals dict, and rebalance frequency\n",
     "# These values were pulled from the SigOpt export CSV\n",
     "# filename: SigOpt_Experiment_good_2.csv \n",
     "\n",
     "input_params = {}\n",
     "\n",
     "input_params['rebalance_freq'] = 12\n",
     "\n",
     "signals = OrderedDict([\n",
     "    ( 'rsi', {'func': talib.RSI, 'func_kwargs': {'timeperiod': 33 }, \n",
     "                   'lower': 44,  'width': 16} ),\n",
     "    ( 'roc', {'func': talib.ROCP, 'func_kwargs': {'timeperiod': 54 }, \n",
     "                   'lower': 0.0, 'width': 0.534} )  \n",
     "    ])\n",
     "\n",
     "input_params['signals_input'] = signals\n",
     "\n",
     "algo_obj = zipline.TradingAlgorithm(\n",
     "    initialize=initialize_local_sigopt_rsi_roc,\n",
     "    handle_data=handle_data_local_sigopt_rsi_roc,\n",
     "    **input_params\n",
     ")\n",
     "\n",
     "perf_manual = algo_obj.run(data_all)\n",
     "\n",
     "# First need to normalize, and localize all the timestamps\n",
     "perf_manual.index = perf_manual.index.normalize()\n",
     "if perf_manual.index.tzinfo is None:\n",
     "    perf_manual.index = perf_manual.index.tz_localize('UTC')\n",
     "returns = perf_manual.returns\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 77,
    "metadata": {
     "collapsed": false,
     "scrolled": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Entire data start date: 2004-01-02\n",
       "Entire data end date: 2016-01-07\n",
       "\n",
       "\n",
       "Out-of-Sample Months: 71\n",
       "Backtest Months: 72\n",
       "                         Backtest  Out of sample  All history\n",
       "annual_return                0.06          -0.03         0.01\n",
       "annual_volatility            0.06           0.04         0.05\n",
       "sharpe_ratio                 1.03          -0.89         0.25\n",
       "calmar_ratio                 0.94          -0.17         0.05\n",
       "stability_of_timeseries      0.94          -0.92         0.57\n",
       "max_drawdown                -0.06          -0.20        -0.20\n",
       "omega_ratio                  1.22           0.85         1.05\n",
       "sortino_ratio                1.49          -1.18         0.36\n",
       "skew                        -0.19          -0.33        -0.17\n",
       "kurtosis                     4.32           4.23         5.30\n",
       "information_ratio            0.00          -0.06        -0.02\n",
       "alpha                        0.06          -0.03         0.02\n",
       "beta                        -0.03          -0.05        -0.04\n",
       "\n",
       "Worst Drawdown Periods\n",
       "   net drawdown in %  peak date valley date recovery date duration\n",
       "0              20.49 2010-01-13  2015-11-06           NaT      NaN\n",
       "1               6.19 2006-07-21  2006-10-12    2007-02-27      158\n",
       "2               4.63 2005-04-25  2005-10-20    2006-04-18      257\n",
       "3               3.86 2008-06-30  2008-08-28    2008-09-29       66\n",
       "4               3.75 2008-01-09  2008-02-14    2008-04-11       68\n",
       "\n",
       "\n",
       "2-sigma returns daily    -0.006\n",
       "2-sigma returns weekly   -0.012\n",
       "dtype: float64\n"
      ]
     },
     {
      "data": {
       "image/png": 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l9eDDDz8A4JJLzks6x7ZjOI7T7m3ZHijEiYiIiIh0EnePoYR+fWNXN6PdWZbF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5mHetWJsEyDUKf9IGZO6uDScJfUoRaUgiAmRZyw0E8BFjmWZaEZ9ndIkWwjkNTfOWHMzEMEBFTZ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xa9cu/Pbbb9q0WbNmITExEWvXrs23LL458U2aNNgOCGA7IA22AwLYDkijPNrB/v17ERg4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N6XuAFv70s/o/d391DNLLA4IT+Z6jUD0oKxk4v6B0FT8yym9kk8Mi1/4GPHgQFJttfmsik6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\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IcRqqLdnAwM0fIOg4o/41q33XfCTVSNjUOeljbaaJyurzXIMUQBcH8Q+86PbJmcA1RIkl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WNazpmyQXnxbp7KO1Z6tziCmXmdFpen8gDF8+7kU0Dgk2fBwSta/5ZKJaSlybNUKDPnV5NTfrXc\nnPILCAh83hiOfhO66Nhbx73Dk/TnzyrsQFZeLDX548AmF/kFCo5CUeJiY2Px8fEhODiY1NRUatas\nycSJE6lYsSIAJ0+exMfHh/v371O2bFnGjh1L48aNNeXj4uLw9vbm1KlTGBoa0rlzZ0aPHo1YrDq9\nmZiYSN26dXXu6+PjQ4cOHT5MIwUEBIoMwpgk8KnTqJErEyb8zP79e7h58yYODo5MmjSNJ08i8PNb\nRWJiAg0bevLzzzMwNFSdrbl48Tx+fsu4ffsmVlbWtGjRmn79fkAqVZlt3b59i9Wrl3PlymVSU1Mo\nWdKBPn368913qpAbw4f/HzVq1CImJpqTJ/9FLJbQqtWXjBgxRvPbEBAQ+DwRxqS888GVOIVCoXG9\n7efnh4mJCcuWLaNv377s37+f2NhYhgwZwvDhw2ndujV79uxh2LBh7Nq1iwoVKgDw448/IhaLCQgI\nIDo6mkmTJiEWizUe5u7cuYNIJOLw4cMYGxtr7m1uLsSYERAQ0EYYk4oOUVFPcs6UDTY2tkgkn68B\nyurVK5k0aRqlSpVmzpwZjBs3ktq1a7FgwTIePrzPzJlTqF3bhU6dunL79k3GjPmRgQMHM3WqN0+f\nRrN4sQ9xcbFMnjyd5ORkfvppOJ6eTVi7dixKpZLffw9g/vw5tGvXGlBNuv74Ywt9+w5kwIDBXL58\nkV9+mUn16jVo3bpt4XaGgIBAoSOMSXnjg3/Nbty4wYULFzhw4IAmztL8+fNxc3Pj2LFjhIeH4+Li\nwqBBgwAYOXIkYWFhbNq0CW9vb86fP094eDhBQUE4OjpSuXJlxo8fz6xZsxg+fDiGhobcunWLkiVL\n4ujomC91DlgWSpXadtRrVFrr+rOoBNbOC2HykpY6ZeTpGcyesp/42ETKlrfip59ba9Lu3HzKMp8j\npKVl0NqrGl/3qFMgdciODUvOUt3FHtfG2dtHp8gVTPz3Ma/TFEjFIn5t7IiF0ZvViuORr1l9MRax\nSMSUhvZULmGsVT49PYOxkwKIjX2Ns7Md3lO7aaUfPnKZteuPoFAo+ba7B19/5aqVLpcrWDj9KPFx\nyZQuZ8nQCZ6atJDj9/lz/XmMjCX0GepK1Vr2OvVPl2cwbuZ+Yp8n4uxkxcxxrbXSr99+ypwlR0hL\nzaBjm2r07qL9LOTyDOZN+YfncYmULVeCkZNbaK5PGrYbAKVSyY2r0fx+cCDmFtrtf5t5/7uKa2Ur\nWrjo1jNNrqDnL8HM6VeLKqW1TRvk6RlMmfg/4mJfU668HZOnf61JO3HsOutWH0FsIGLi1E5UqqLr\nUj2/38VPjaI4Jn2urNt37b3L5jb49adMx45f4+6uGkNbt27L4sU+zJgxA6nUgnLlylOhQiUiIu4B\n8PvvATRs6EmPHr0BcHQsxdixkxg27AcGDx6OSGRAz5696dKlO0ZGKtvr777ry759u4mIiKBMmUoA\nVKxYmT59+gPg4ODIH39s4erVy0VywiQgIJC/CGNS3vjgSpyDgwOrV6+mXLlymmsikQiAV69eERYW\nRtu22h1Zv359Dhw4AEBoaCiOjo5akyFXV1cSExO5fv06NWvW5Pbt2zg7O+e5rhlyBX6zg7lzLY6q\nLnZaaVdCo/hz7UUy0jP0lv33yC3KV7Cmz68dWe5zhLAzD6jrVhaAVYuPM2VOe6xtzQjwDymwOmQl\nb5n3CW5fe0b1OrrKRGb23XtJHTsT+lS3YuetF2y/FU//Gtaa9NUXY1nfpiwvUjOYfTqa5S21lcxD\nhy9RqWJJlvh+z5xfd3E65BYNG1TSpC9fdYg/No9ALDbg628W0KljPa3yp45GUNa5BOPnuLBu0Wku\nnntMLVdHFAolm/1C8fHviCJDyYxRf+O7/iud+h86dotKztYsntWRX5Yc4XToAxrWK6tJ91lxHN9p\n7bGzMWPVRt1ncSLoDk4VrJgyzwu/Bcc5f/YhLvXLIJGI8VndBYD9Oy9Tt2HZLBU4eYaCCesucOne\nC+pXsdKbZ9numygUSkR60oICr1Choj3zFvRiwby9nA25Q/0Gqh2gtauCWLNhEC9fJDFv1i4Wreir\nUz6/3sVPlaI0Jn3uWFja5JzpA5P5bJqNjTnPnmUdz+1dz7Kp8+ckNzeUKvVmfJbJZBgYGODo6KiR\na2RkRFpaOgC3bt3k8eNIWrVq/JYEJSKRiPv3I6hTpx5ffdWZgwf3/pf3Ebdv3wJUu9tqMjtSMDU1\nIz09PU/tEBAQyJr0RS6a/2+19zYiESTLFaw8qD//u55lU+cXxqTC54MrcZaWljpBczdv3kxqaioe\nHh4sWbIEOzttZcXW1paoKJXDj6dPn+pNB4iOjtZMmFJTU+nTpw93796ldOnSDB06VOsMS26QyxU0\nbV+BkmWLoVRqp4klBkxc0JzZIwL1lr1+JYrGzVXKSl23slw+/4i6bmVJSU4nPT2DAP/TPIh4zncD\nGxZYHbKS16JjRRzLFgNlzvm9yhXD4D/NQq5QYmigrWZsaeeESCTi6fNULKS6rmwvXXlIqxYqJx3u\nDSoRGn5PS4lbv3oQRkaGyOUZKJVvJs9qbl99RsOmTgDUcnXk2oVoark68io+GVt7M0xMpZp2JSak\nYWqm7WL30vUoWjdW3a9hvbKEXnykUeKSU9JJk2ewatNp7t5/ztC+us/i5tWneDZXTb5d6pfm8vkn\nuNR/MwCkpKRzcPcVFq//Jss+TJcr6dqoDOXtzXSeIUDwlWeYGkuoWkb/4eKrVyJp3lI1kavfoALn\nwyI0StzG34chEom4/fQlZuYyveXz6138VClKY5JA9tx6laj170oWpoVUk48TcQ6Or5RvDVBSqSFt\n27anV6/vdfJZWVkTG/uMQYP6YWtrh4dHIzw9m2BlZc3Agd9p5TU01HV7rm8cFBAQKCBKJSAD6ph8\nAXEflwMSYUzKG4UeQCIoKIiFCxfSr18/nJ2dSUlJ0WyDqpFKpaSlqbwFJicnaw4wqjE0NEQkEpGa\nmgqozp+8fPmSwYMHs27dOurUqcOgQYMICXm3nQYjYwlf1NPvbbJqbTtMzHRfBDVJiWnI/gu8aCwz\nJDlZVf/Xr1K4cTWab3q7MtPnK1YuOFJgdchKXo16uTcnMjE0wFhiQMSLVLbdjOfripZa6SKRiJ23\nXjDs8EOaltE935OQkIKpiep5ymRSkpK0vT6WKG4GwJxfd9O1s5tO+aTENIz/60cjmYSUZJV3Novi\nMp7HJvHqZQoxUa+JinxJaopcp3xiYhom/5WXyQxJSn5z/5evUrh8LZp+PVxZOucr5i7TfRZJiWnI\nZKo+NpYZkpKsvVpz9O+bNG1dGYkk6wOxMiMxDatZ6017/jqVbf8+5P+8VEqZvnEkMSEFmYmqDjIT\nKclv9aFIJGL3jnOMHPIbTZpX03uP/HoXPxc+5jFJQKAgeXsRrVy58ty/H4GjYynNX3x8PMuXLyYp\nKZHAwH9ITk5m5cp19O7dF3d3T168iAfeTLwyL8oJCAgIvAvCmJQ9hXrCe+fOnUybNo127doxbtw4\nQL11qj3RT0tLQyZT7TIYGxvrpKenp6NUKjV5goKCADQTq6pVq3L79m02bNhAgwYNCrRNakxMpSQn\nqSb8SUlpmJiqJoFmFkbY2ZvjWEblBt/a1pwX8UlYFjf5IPXKLYtCY7gSm0wNaxltylkwLfgJ85s6\nYibVVVY6V7KkbXkLvtt/H09HM4wAn0V7uXTlIdWqlCIxSTWRTUxMxdRMezKsUCiYOWcnJjIpfb9r\noiPbxFRKyn/9mJyYjsxUpYwYGIjoN8KNuRMO41DaAqcKJbR24XxXHufS9SiqVrIl6a3nYGb65v4W\n5kaUtDOnbCnVs7CzNud5fBIl3noWJqZSjdKTnJiu2flTc+r4PX6a0kJvH/puu87liBfULG/JmK5V\n9eb591IMj+OS6Ocbwr3oBO48ec2Gcdo7YqZmxhrFTV8fduriShuvWvTrtRIPz8oahU+rDUX4XfyQ\nfKxjko1N/jtAKQiZBSkXQGqU/SfLIJOlwNv5pVIxVlZmOvX7nPrWwsJYI8Pc3FgzoVFfMzQUY2ws\nwcbGnOHDh9K5c2f8/VfQrVs3nj9/zrx5M7Gzs6NyZSfu3XMiKSmRsLBgatasyY0bN1i2bAEikYi0\ntDRsbMy15KnRd01AQEBAHyrlS6WA9er1Pf3792bZskV07Pg18fHPmTdvNra2tpQoYYWdnT2JiQkc\nPRpEtWrVuXPnFsuXL9GMSZnl6btHUaPQlDg/Pz+WLFlC7969mTJliuZ6yZIliYmJ0cobExODvb3q\n/Ja9vT3//vuvTjqgMWnKvCoOULFiRU6dOpWvbciOKtVLciEskhoupQg/+4CGjVQmeTKZFGOZIdFP\nXmJZ3IS42AQsiuk3gytMRtdTmYNFJaQzPCiSJc1LUcpcu1/lCiXDD0eyvGVpDA1EiA1EiP+bQ40b\nrXKbvu9AOGdD71KvTnlOn7lNsybau0U+i/ZhYWHMmJHt9dajYjUbroRHUa22PZdCn+Dq8cZ++t7N\nWOauak98XBK+U45gZPzmdR47VKUQ7gu8ztnzkdStVYrToQ9o6vHmXJKJTIqJzJBHUS+xKm7Cs7gE\nLDM9i8rV7LgY+pgvajty/lwkbp5OmjSlUklcTALFrfSbbI3tpl9xe5tOHqXp9F+bJq+/wPetymNi\nJOFto7BqX5Qi9Nxdatdx4lzIHTybqOTK5RmMGrqBxSv7IjEUI5aIMRDrrjIV9XfxQ/Exj0l5PXeQ\nmfw4y/Ah5apJS9XdbX8bhUL7Q/x2/rS0DOLiEpBK39SvIOr7Mfftq1cpGhmvX6dorquvpadnkJIi\n59mz1xQvXpL58xexdu0qtmzZipmZGR4ejRk2bCTPnr2mXj1PunXribf3LBITE/jii1pMnjyDX37x\n5vLly1SuXEtLnhp9196lDwQEBD5dMu+Uqf6tula+fAV8fBazdu0qdu3arjUmATRv3pLr16+yeLGP\nZkyaNs1ba0x6W56+exQ1CkWJW7t2LUuWLGHUqFEMHjxYK61u3bqcO3dO69qZM2eoV6+eJn3BggVE\nR0drJlFnzpzBzMyMqlWrEhcXR+vWrZk3bx6tWrXSyLhy5Yom5tP7IBLB08evObLnNj2HvPHgl9Vj\nb9qyMr9MO8DwflsoW86Kkg7FWL3kOINGNmHEhJbMnLgXpULJdz801Fk9zq865Cww5ywbrsSRLFcw\nPVh1/qd5GXMalzJj+60XjK5nS9tyFnx/4D5iAxF9q5fASKJtodumdS0m/LyVHn2W4lzODo+GlXnw\n8Bl/7gihf5+mbN56ApfaTnw3YAUAfksGaBn5ejQvx2Lv40z4YQ+lnCyxczBn4/KzfD+8PmKJAWP6\n7UYiMWDQWHe99W/TrDITZh+g55AtOJe1wsPViQeP4tm25xJjhzZhyuiW/DRtLwqlkiF9dZ9F45YV\nmT/9H0b1/5My5Upg71iMdUtPMnCEJy/ikzGzeLeI2Orx6WFMIn8ef5grRa9l6xpMn/wn/Xv7Ua68\nDY6lirN04UFG/NSWL71qMbDPKgwMDPiub2OMjAx1yhfEu/ipURTHJAGBd+HECe132MurA15e2jEK\nly1brfVvV9cGuLpmvVM8bNhIzQRKTUDAnxqFM7M8ffcQEBD4PBHGpLwjUio/7HG+Gzdu0LlzZzp3\n7syoUaO0Di2amZkRGRlJ586dGTRoEF5eXuzbt4/ffvuNnTt3atx/9+ihCto3bdo0nj17xqRJk/j2\n2281sZ4GDx7MnTt3mD1btc26fft2tmzZws6dO3P0EBcaMytP7XMw1X9+Lbc8SYzKU3lpDodEc6Ly\nmjN5Kg9g9NMPeSp/I+n93YgDVJYXz1N5gIfGaTlnyobSF+/mqXyim2fOmbLhdVpcnsoDOJgOzLOM\nosDHPibB570TZ2v7xuHPj3N2Zpv3UYb2+FdK/GYnTl+Igc9tJ+5DyS3Iugp8HBSld+ZTlxsZ+RAL\n3+6UNde1+Gi19zai0gkAHN2oupZfjk0+pj4oDJlquYXJB9+JO3jwIAqFgu3bt7N9+3atNPUq+IoV\nK/Dx8WHt2rU4Ozvj5+enmSwBLF++nBkzZtCrVy9MTU3p1q2bZrIE4Ovry6JFixg/fjwvXrygevXq\nrF+/XnDxLSAgoIMwJhUdBrbX77wnt9jY2OZTTQQEBASKCI/MSJYrCE86q+NeX6Bo88F34j52hJ04\nYScOhJ04+Hx24ooCRWlVsiB34mJi8tc1dlFb8S0qcj/VVe+iRmxsLD4+PgQHB5OamkrNmjWZOHGi\nxoy7a9euXLlyRatMt27dmDUr53lQUXpnPnW5Oe7EaeLE5a8S9zH1QWHIVMstTArVO6WAgICAgICA\ngED+olAoNNYAfn5+mJiYsGzZMvr27cuBAwewsLDg7t27LFiwQMtDrrGxcWFVWUBA4B0RlDgBAQEB\nAQEBgU+IGzducOHCBQ4cOKAx/Z4/fz5ubm4cO3YMFxcXkpOTqV27NlZWVoVcWwEBgfeh0IN9CwgI\nCAgICAgI5B8ODg6sXr2acuXKaa6p3be/evWKW7duYWxsjIODQ1YiBAQEPnKEnbhMvEhNylP5urZ5\nc1RQMlXXpvldiDLK21muR0Ob5qk8QPknV/NU/oYyb+cCqzjmzfkBQLDxT3kqXz6qZ57KJ0SfzVP5\naiUEhxkCAgICnyuWlpY0adJE69rmzZtJSUnBw8ODf/75B3Nzc8aOHcvZs2extLSkS5cufP/99zqx\nugQEBD5OCmUnLjY2lgkTJuDp6YmrqysDBgzg9u3bmvSTJ0/y1VdfUatWLTp27KgTSDcuLo6RI0fi\n6uqKu7s7vr6+ZGRkaOUJCgqiY8eO1KpVCy8vLw4ePPhB2iYgIFD0EMakT4MFB69o/QkICKgICgpi\n4cKF9OvXj/Lly3Pnzh1SUlJo1KgR69evp1evXixdupTly5cXdlUF8ptSCcickqhj8gU2cRY55xco\nMnzwnbjsDtvu37+f2NhYhgwZwvDhw2ndujV79uxh2LBh7Nq1iwoVKgDw448/IhaLCQgIIDo6mkmT\nJiEWixk9ejQAp0+fZsSIEYwaNYo2bdpw6NAhxo4di4ODA7Vq1frQTRYQEPiIEcakokNU1JNs01NS\nUrLMb2Nji0QiGJ8IfH7s3LmTadOm0a5dO8aPHw+owp4kJSVhamoKQMWKFXn9+jWrVq3ixx9/zFFm\nQXjlKyhPf5+63KQkM5CIMJTo7svo21TNz3p/LH1QWDILmw/+RcvpsG14eDguLi4MGjQIgJEjRxIW\nFsamTZvw9vbm/PnzhIeHExQUhKOjI5UrV2b8+PHMmjWL4cOHY2hoyIoVK+jQoQM//KBydT9gwABC\nQkI4e/bsO0+YdvhdokJNa2p5aNuNy9MV+I44Ru+xdSjlbJmjnPT0DMaN9Sc29hXOziWZ6d0r5zLy\nDMZN/4vY5wk4O1kzc4KXVvqS1cc4ceYeJjIplSvY8vPo1pnqmMHsKfuJj02kbHkrfvr5Tfqdm09Z\n5nOEtLQMWntV4+sedXTuL5dnMH9qEPFxSZQpV5wfJ70xzQg/E8mmVWcRiaD/8IbUqKPfrj5drmDc\nr8eIjU/CuUxxZo7w0Eo/fOo+K7deQGYkYUx/V+pUt9MrZ++aa5SvYUX1hm/Sn9x9xZ5V15CnZ1Cn\nuSPuHZ1075+ewdhJAcTGvsbZ2Q7vqd2073/kMmvXH0GhUPJtdw++/spV7/0BSnf5Eqdv23Oii+4H\nzsDQkNan/0dI/8m8uHRTb/mty8OpUtuWOp6lNNfCTjxi/9ZrKJXQolMFPNuU11sW3v9dzOt78KlT\n1Makz5l1+7IPP5I52Lc6v75A3wICnwN+fn4sWbKE3r17M2XKFM11kUikUeDUVKpUicTERBISEjAz\nM8tWblFyAf+py42LS8BCriRdrtBJUyohsx6XX/X+mPqgMGSq5RYmH9ycMqfDtmFhYdSvX1+rTP36\n9QkNDQUgNDQUR0dHHB0dNemurq4kJiZy/fp1kpKSCA8Pp23btloy1q5dq5lA5YaMDAXr55zlYvAT\nvSsZ+zZcQ6FQ5tp2/NChcCpVciRgy1iMjAw5ffpGzmWO3qCSsw0Bfn0wkko4fS5CK/3WvWf4L+7J\nphW9dRQ4gH+P3KJ8BWuW+PdEKpUQduaBJm3V4uNMmdOelRt7kZCQqvf+wUfu4VShBD5rOiE1EnPh\n7CNN2pa1ocxa0p4ZC7zY6Jd1bLlDJyOo5FScAN/2GEnFnD7/ZmVcoVCyaEMYm+Z7sXJGK+auDtEp\nn5GhYOu881w7/VTnOez3v06P8bUYtsid5ES5/vsfvkSliiXZsmG4qg9DbmmlL191iE3+Q/l904+s\n33iUrMImmpSyp8Kg7lm2s4b3CERiMegpniFXsHJmMOEnH+mMprs3XGbi4hZMWd6Sg/+7off+eX0X\n8/oefOoUlTFJACwsbbL9kxhKtf7U100tShR21QUEPjhr165lyZIljBo1SkuBA1WMuDlz5mhdu3z5\nMnZ2djkqcAICAh8HH1yJUx+2fXvCuXnzZlJTU/Hw8CA6Oho7O+3dGFtbW6KiVM4unj59qjcdIDo6\nmocPH6JQKFAqlQwePBh3d3e6du3KkSNH3qmeGekK3Ns64da6LJnn1ddDn2JsIqF0hWK5lnfp0n3c\n3CoD0NC9CqGht3MoAZeuPcGtrpOqjGs5Qi9GaqU/iHzOz3P20WdYAJev65oZXb8ShUs9VWDHum5l\nuXxepYSlJKeTnp5BgP9pRv/fH1SroX91+ubVGGrVVU1Ma7uW4sqFNw5HTEwMSUpIJTkpHZnMMOs2\n3HyGWy1VAPSGLg6EXonWpD1/mYKDrRlmJlKKmRshz1DwOlHbMUtGupL6X5amTstSWs8hLSWDjHQl\nR/93lzUTzlC2iv7d0EtXHuLmqjL6Hpl+AAAgAElEQVR5c29QidDwe1rp61cPwshIVX+lEv2KkEiE\ni894Lk5aqNc2wb6VB/LXiTw/r3+XQC5X0KS9Mx5tyukoeeMXNEdqJFbdH/33z+u7mNf34FOnqIxJ\nAgICnydXr14lKCiIhISEXJe5ceMGixYtomvXrnTt2pVnz55p/pKSkvDy8uKPP/5g9+7dPHz4kG3b\ntuHv758rU0oBAYGPg0IPMfD2YVtnZ2dSUlIwMjLSyiOVSklLU03uk5OTkUq1PTgaGhoiEolITU3V\nDHJTp06lWbNmrF+/nqZNmzJs2DBCQnR3erJCaiyhSh1bneuvX6QSfOA+rXuqFLKsdm4yk5iQgomp\nql0ymRFJSTnveiQmpmLyn4IkkxmSlKSt4HT48gsWzvqauVM6MP1XXScJSYlpyExU5Y1lhiQnq8q/\nfpXCjavRfNPblZk+X7Fygf7JZFJiGsZvlU9JTtekte1cnZ/672R0/x207lg16zYkpb9pg7GEpJQ3\nMkoUMyYmLon4Vyk8fvqaB49fkZyivaMmNRZTwcVat26v04i89YJGXcrRe0od9q65rvf+CQkpmJqo\n+12q04cliqtWHOf8upuund30yqg+eTD3NuwiNTZeJ83IujgVfviGq3NXqy7o0QGNjCVUr2uvV7a5\npapuW5aF09hLvyllXt/FvL4Hnxsf65gkICDw6RMTE0OfPn1YuXIlAAEBAXTp0oVhw4bRunVr7ty5\nkys5Bw8eRKFQsH37djw9PWnUqJHmb9OmTfTv358xY8bg5+dH+/btWb9+PZMnT6Zr164F2TwBAYF8\npFBPeb992HbcuHEAGBkZaSZHatLS0pDJZAAYGxvrpKenp6NUKpHJZBgaqiar3bt3p3t3lflblSpV\nuHLlChs3bqRBgwZ5qvPVs9HERSeydNwJnka+Jur+a0b6NsJIln1XmpoZk5SoUtySElMwM5Vlmdd3\nxREuXXtC1Up2JP2nOCUlpWFmqj2R7N3NFalUgmPJYhhKDJDLFfBWFhNTKclJb8qrlUgzCyPs7M1x\nLFMcAGtbc17EJ2FZ3ERLvomplJT/yicnpWFiqpqopqSk87t/KOt39yZDrmD84L9o0NgJQ6n4TRv8\nz3LpZixVnUu81YZ0zEzeTHYNDERM+KE+P3ofpqxDMSqXL4GFWe5CLJiYG2JpI8PaQWXTb2FtTMKL\nVMz+U4p8Fu3l0pWHVKtSisT/FObExFRMzbT7UKFQMHPOTkxkUvp+p+2Oufa8sVjVr4mhhSn2LRoi\nNpZiVqEsFYd8y22/rQA4eDXB1MmRFkEbsahSDsvqFTncrE+u2qC6v5JNi0Ixkklo802VXJeD3L+L\neX0PPic+1jGpKB3yLsgzAlKj7MfZL2z070hLpWKsrMz01k3o26LVB586Pj4+3Lt3j4EDB6JQKFi1\nahXu7u6MHz+e2bNn4+vry6pVq3KUM3r0aI1jpaz4/vvv+f777/Or6gIfK4/MSJYrCE86S+nSZQq7\nNgL5SKEpcVkdti1ZsiQxMTFaeWNiYrC3V+1k2Nvb67j3Vue3s7PDxsYGUB3QfZvy5ctz4sSJPNe7\nQeuyNGhdFoDNPqE071IxRwUOoEYNJ86evUXdehU4ffoGTZvVyDLv2GHNAdh36Cpnwx9Qt1ZpTofe\np6lHBU2el6+S6TMsgF0bBxL/IgmlEiSZPBNVqV6SC2GR1HApRfjZBzRspIodJpNJMZYZEv3kJZbF\nTYiLTcCimK5SWamaLZfCnlC9dkkunHtMfU9Vu5UKUGQoEUtESAwlGBhAhuL/2Tvz8Biv74F/JpNM\nkslCkYUgSIh9i4Q0IS2qqb0pLdWFoiiKb+1FLbXUHlto7NVNba2t1V/Q0tqSIJbEXoKEJJYsk2TW\n3x9Tk46ZLExIIvfzPPM8vOee8553cd3z3nvP0WJDbhA3ur9+D9GuA1c4HpuIb0N3jpy6zSstjTuQ\n81dS2TS/M8n3FPxv9gHsbK3N7it7HJmdNTI7KffuKHAsZ0v6vWzkzrkB4JhRXfTn3xPD8agrtGhe\niyPHLvFqsHENuXmLduHsbMdnIzqbnOPU+PlGf5dXr4Lv4omGAA7g2sYdXNu4A4CWa2dxYdEGNIqs\ngi/gXzavOoXcyYa3P25aaJ1HFPZdtPQ9KCuU5D6pNG3yfhZ2H6HMMb/3tUA9pYbU1AxkMmPfStsG\n+tJi90VNIvA8+Ouvvxg/fjxt2rQhKiqKlJQUvvzyS+rWrcuAAQP47LPPittFgUBQQiiW5ZT5bbb1\n9fXlxIkTRseOHTtGixYtDPKEhASSkpKM5A4ODtSrVw93d3c8PDyIjY01snHp0iU8PT2fyl+JBJJv\nZ7Aj4unrDoWENOfylUR695pLVpaSwMCCC1KHtK3H5Wsp9P54g17HvxbXE+4xf/l+yjnb0+vN5vT+\neD2fTtzK5/8zTWzySnsf/rmayrB+35KdpaJylXKsCvsDgE/HtWfa+J2MHPgD7w8MwMrKdB1g63Ze\n3Lh2j88GbCc7S4V7FWfWLj2CvdyGTj0aMHrADsYM3EHH0AbY2ZnfFxfSpiaXbzyg9/92kpWtJrC5\nB9dvpzF/jb6YtbXUih7Df+bTLyOZMMj8csZHSCSQejuTPWv0SWG6fdKA72afZNXYo7Tr7W32GkI6\nNOHKlSR6fbBEfw8DfLh+I5l5i3aSmprON98d4lTsdd7vv5z3+y8nIyPbxEbu+SU82pTm6FWdpnNG\n5+uveSNw51Y6P648Rdr9bH7fepEr51KZPSKS2SMiycpU5a/+FO+ipe9BWaC09UkCgeDFJDMzkypV\n9PuT//zzT2xsbAgICAD0y7QLu4VDIBC8+Eh0z7lHiI+PJzQ0lNDQUEaOHGnUITk6OpKQkEBoaCiD\nBg2iY8eO7Nq1i3Xr1rFt2zZD+u9evXoBMGXKFJKTk5kwYQLvvvuuodbTli1bmD59OlOmTMHf359f\nf/2VxYsXs2HDBvz88k4hD/B/CRMsur521UyDqSfiXkLBbfIh0VZZcKN8yFIXfuN0XtS6Z5kPP+sS\nC26UD9092lukD/Cd3f8s0q+V2Nsi/QyVZZki61fwskgfoIrDAIttlAZKep8EZXsmztU1tzjt8Jnb\nnspGXiUGStssVGmxK2binp6uXbsSEhLCgAED6NKlC1WrVmXNmjUAjB8/nkuXLrF169Zi9rJs90kl\nzW5Cwg2c57+Dp5PpdpTXdl5CIoEstZYVe4t2OWVJugfFYfOR3eLkuS+n/O9m2y1bthjJRo4cyeDB\ng1m+fDnz5s0jIiICLy8vwsPDDYMlgGXLljF16lT69OmDg4MDPXv2NAyWAMPG3IiICKZNm0atWrVY\nsmRJoQZLAoGgbCH6pNLDgM4Fr2DICxcX0+RAAkFJ4+OPP2bcuHGsWbOGrKwsJk+eDOj7kPPnz7No\n0aJi9lAgEJQUnnsQV5jNtsHBwQQHB+cpr1SpEsuWLcvXxqO0ugKBQJAfok8qPYhi3YIXnc6dO1O5\ncmVDfcqmTfX7pQMDAxk7dqxJzUqBQFB2KdbslAKBQCAQFBUL9hrvFf3sjYbF5IlA8PT4+vri6+tr\ndKygD00CQZ5UzcAeaC5vCKmQXDGtuD0SFBEiiBMIBAKBQCAoIRw6dIhjx46RlpZmNpHJjBkzisEr\ngUBQ0hBBnEAgEAgEAkEJYP369cyZMwcbGxsqVaqkz4osEAgEZhBB3GPE339gkX5VxyiL9H0qtLBI\nv0qOwiJ9JJZllgTQlbMsA5C70rJrUEZssEgfoGFKX4v0azo/WeHuxzmZHG2R/qnkCxbpA/xbR10g\nEAgEz4mNGzfyxhtvMHv2bOzs7IrbHYFAUIIpljpxKSkpjBs3jqCgIPz8/Ojfvz+XLl0yyA8fPky3\nbt1o0qQJXbt2NSmkm5qayogRI/Dz8+Pll19m/vz5aDQaAG7evEndunXN/tq3tzz1vEAgePEQfZJA\nICgJpKSk8M4774gATiAQFMhzn4nTarWG1Nvh4eHI5XKWLl1K37592b17NykpKQwZMoRhw4bRoUMH\nfvnlF4YOHcr27dvx9vYGYPjw4UilUjZt2kRSUhITJkxAKpUyatQoqlSpwl9//WV0zosXLzJw4EAG\nDx78vC9XIBCUcESfVHpITLydrzw7O/uJ2iuVjqSmWl4b82lturi4Ym0tFsQIcqlXrx5XrlyhVatW\nxe2KQCAo4Tz3/z3i4+M5deoUe/bsMdRZmjt3Li1btuTgwYPExMTQrFkzBg0aBMCIESOIjo5m48aN\nTJ8+nZMnTxITE0NkZCQeHh74+PgwduxYZsyYwbBhw7CxsaFixYqG86nVambNmsXrr79uUXpvdY6G\n38LOkaNQI7WxImRkA2wdbPJur9ay8IsD3E/NolrN8nwyLsggO/rHP2xeexJbO2s++MSPek3cCzy/\nSqVhzOg1pKSk4eVVmWnT+xRKZ/SETaSkpOPl5cb0yT2N5P+3/wwRa/ej1ep4951A3uxmWrNKpdIw\n+vMf9DZquTF90ptG8gN/xrEiYj9SKwlTJ3anro9xCnCVWsOYL34m5V4GXjUqMW1cRyN52KqDHDp2\nFbm9DB9vVz4fZb5Y+nfLYqjb1JXmQVUNx6IP3WT3d+fR6aBdd2+CQmqZ1c1Waxn323XSczTIpFbM\nDfHE2VZq1OZWmpIvIhNY/Wbhi2SvDztOg2bu+LXJv3imWqVh0vgfSE1Jp2YtNyZ+kXsPDx2MY/Uq\n/f0bP7k7dermnUL9x+Wn8GniQtMgD8Ox00dus3tjHFZSCX1GNqead/k89XesOotXo4o0ermy4die\nDfHER93F1k5K5VrOhA5pVJhLf6EorX1SWWT1rvNP2D7/5fEymRSlUmOJS09tM68C5IKyx507dwx/\n7tu3L1988QW2tra0aNECe3t7k/Zubm7P0z1BaeemI1lqLTGKoi32LSh+nnsQV6VKFVatWkXNmjUN\nxx5t3E1LSyM6Opo33njDSMff3589e/YAEBUVhYeHBx4euQNZPz8/MjMziYuLo3Hjxka6P/zwA4mJ\niaxbt84iv+MPJVGlXnmadanOucjbnP39Nr7dPfNs//eBa3h6VWDszGasXnSE0ydu0cTPA61Wxzfh\nUcxb0xWtRsfUkb8yf223As+/b18Mdep4sDjsY2bN3MyRI/EEBOS/72rf/8VSp3ZlwuZ/yMyvtnPk\n6EUCWtUxyJet3MeP33yKVGrFm28voHvXFjy+hXpf5FnqeLsTNrcPM+ft5MixywS09DbIl38dyabV\ng3jwUMHUWdtZGdbXWP9APHW8XFg8M5RZi/Zx5MQ1Avxyn/3Fq8msWdybcs6m/1EBaNRaVs08wtW4\nVOo2My7Wu2P9GSav6IBUKmHKgF8JfL2m2U3gu+Lv07yKIx82c2HbuVR+OptKf99cW0cT0ll6JAmV\n1jQLWF4+LZ1+iEvnk2nQvOAAPPL3s3jXdmfOgj4smLOT40cv499Kfw8jVkby9fpBPHygYM6M7Sxa\n3tf0fBota2Yd51rcPXyaGt+DXRvPM2bxq2Sk5fDtohiGzwoyq//t3JNcj7+Pd+NKRrLEa2kMntUK\nuZOsUNf+IlJa+6SyiHN5lyK1J7O1RpmjLvE2BS825mpQTpo0yWxbiURCXFzcs3ZJIBCUAp57EFe+\nfHmTDuubb74hJyeHwMBAwsLCTL4yubq6kpiYCOi/WJmTAyQlJRkNmJRKJeHh4fTt25dKlYwHr0+K\nT5A7j+IDrUaHlXX+GaMunUsm4JUaADTx8+D8qSSa+HmQdj8LV3dH5A76QbNarSUzQ4mDY/6D6NjY\nf+jwWjMAAl6uS1TUpQKDuNizN3itnX5m5eVWdYiKuWoUxK1dNQhbWxvUag06HWYDoNizCbzWTl9r\n6eWW3kSdvGYUxP30zVAkEglJFx/i5GQaiMWev02HV/R+BvjVJOp0glEQdz3hHp/P3EVaRg5jhrWl\nUT3jr9JqtZbgzl5U9nSGx2KssQvaIrOVolFr0WHef4COPuWx+lem1uqwsTJuZ20lYWW3Wny07bJZ\n/cdRq7W061obD89yJj6Z49zZBNq2199D/1benIy+ZgjiNnyvv3+X7jzE0cz9A1CrtLTuWBP36k7w\nWLrpiSvaIZFIuJ+chdzR/MywRqWlVUh1XKs5mqSrTr6VyQ8LT5GVoaLLwAZUr5P3TN6LSmntkwQC\nwYvBrFmzCt1WZKsUCASPKPbF+JGRkSxcuJB+/frh5eVFdnY2tra2Rm1kMhlKpT5rYlZWFjKZccBj\nY2ODRCIhJyfH6Pju3btRKBS8//77FvtpY6dffnfvViZn993irenN822vyFRiJ9cPqm3trcnO0n+Z\ndX7JnnspCtIeZpOtUJGY8JCcbHWBQVxmRjZyB/19sbe3RaHIybc9QEZGNg7yRzoyFArjzJMVXnIE\nYOZXO+gR2tK8jcxsHOSyPG1IJBJ+2n6CeYv3MH1SqKnfmTnI7W3+1bcx0e/yekP69W5Jcmomwyds\nYdv6/kZyWztrGvi6c/FMsoltp/L6a/t2aQxtOppfSgkgt9E/u6v3stl8NpX1b3kbyVt4OOapaw5b\nO2satahC/Om7hWqfmZGN/aN7KJeR9Z97IJFI2LH1BEsW7GHiVNP79+h89XzduHQ2xUQmkUg4tPsa\nW1ad5oPPzGc2ldlZU6eZC1fP3TORtWhXlVdCa5F2P4d100/w2XLTL8JljdLSJwkEgheD0NDcvv/W\nrVu4uLiY9Cmg3/MZHx//PF0TCAQlmGIN4rZt28aUKVPo1KkTY8aMAcDW1tYwOHqEUqk0rAu3s7Mz\nkatUKnQ6ncna8Z9//pmQkBDKlStXJP4mX0snMjyOkFENkMnzv3VyBxnZChUAWZkq7P/dP2dlJaHf\npy2ZPe7/qFLNmRreFQoM4AAcHO1QZOoHhIrMbBwdzM/aAMxbtJPYszeoX7cqmf8Ge5mZOTg4Gg9E\ntVot02ZuQ24vo+/7xoP3eWF7iT2bQH2fKmT+G3RkZubg4GBsA6Dnm350DmnCOx+uoE2QD/YSmL98\nP7Hnb1OvjhuKLP19UCiUOD6m/15PP2Qyazwql8PG2gq1Wlvgvcj1X8fGRVHY2lsT8rbprOTCv25z\n9o6CRm5y3qjzElMiE5gX4omjTGrG2rPDwdHOELiZew7d3/IjpGMT+vVZQWCQjyHgKyytO9XEv101\n5gzdT0N/d2ztC//PunW3mljLpFRwkyO1tkKj0SKVFkvS2hJBSe2TXFycnvRSisXms7QL+qWKL4pN\nmUxKxYqOT3S/StMze5bvwYtMu3bt2Lx5s8kybIDY2FgGDhzI6dOni8EzgUBQ0ii2IC48PJywsDDe\ne+89o7XflStX5u5d4xmOu3fv4u6u33vk7u5ukt77Ufv/LmlKS0sjKiqK8PDwIvE3PTmb35edp9PY\nxpRzyzuAekTt+i6cjUmkflN3YqNu4xdYzSC7eiGF2Ss7cz9VwfxJ+7G1K/gxNGpUg+PHL+Lbwpsj\nR+J55dW8E1CMGdUFgF17YjgedYUWzWtx5NglXg2ub9Ru3qJdODvb8dmIzqY2Ruj3AO3ae0pvo1kN\njhy/zKtt6hnaqNUaBn26nlVL+mJjI8XaWorUSgI6GD20rV5/3zmOx1zHt0k1jkT9wyuBubNgD9Oy\n+GDoJrZvGMD9Bwp0OrC2toJClqrbvOoUcicb3v64qVn5/wL1SzMT05UM3XmNJZ1qULWcaRD6rKnf\nsCpRJ67QtHkNThy9TFCw/h6q1RpGfrKexSv6Ym0jRWotxUpa+KUyGo2WJeMP8+mcIKxtrLCSSp5I\nX5GuZPmYv/lsRTCZaUp0Ol2ZDuBKcp+UnGxZ7cXHcXFxKnKbz9LuI0rD/rXC2lQqNaSmZiCTFe5+\nlaZn9ix9fRH56quvePAgNwnPihUreOmll0zanT9/HkfHJ1s5IhAIXlyKZcQWERFBWFgYI0eONNm8\n6+vry4kTJ4yOHTt2jBYtWhjkCQkJJCUlGckdHR2pVy83wDh16hQ6nQ5/f/8i8Tlm5w1UORoiw+PY\nNjWG03sS8m0f2LYmCf88YNzAX8jOUuFWxYkNy44DILW24rN+O5gz/v/oP7JwaYRDQppz+UoivXvN\nJStLSWBg/YJ1OjThypUken2wRK8T4MP1G8nMW7ST1NR0vvnuEKdir/N+/+W83385GRnZpjZea8SV\nq3fp1Tdcb6NVba7fSGFe2F6sraV0CmnCux+t5L0BX9P/gzbY2hrvywppW4/L11Lo/fEGvb5/La4n\n3GP+8v2Uc7an15vN6f3xej6duJXP/2c+M6UBCdy5lc6PK0+Rdj+b37de5Mq5VGaPiGT2iEiyMlVm\n1dbF3CVLpWVKZAIfbbvMt6eTSXiYw8K/jNOPP9Veg0KotO/QiGtX7vLRe/p76FH1JZYs1N+/1zs2\nYcAHK/m479e839f0/pmeT8LdWxlsWRWLVGqFf7vqfDX8APNGHOT1Xj7YFDDLKJFISLmdyc7V55E7\nyXi5kydLRh1m3fQThH5S9jJTPqI09kkCUy6mZRr9BILSgLe3N1FRUURFRQH6jLnR0dFGv5iYGGxt\nbfn888+L2VtBqaNqBvY1FDSXN8Ql1bm4vREUIRLd45kOnjHx8fGEhoYSGhrKyJEjjRItODo6kpCQ\nQGhoKIMGDaJjx47s2rWLdevWsW3bNkP67169egEwZcoUkpOTmTBhAu+++66h1hPAqlWr2LZtG7/9\n9tsT+bcsdohF19e+Wt57swqDz0vm9zUVFkmOwiJ9NIWcBssHXbZlX2CPKa9apN98y5OlITdHfO9m\nFunXdM4/6UxBnEyOtkg/Q1nwnsmC6FhjnsU2SgMlvU+Csj0T5+qaO+gYPnNbvm1vaoxXNVSV5j8j\nVppKDJSmZyZm4p6etm3bsnz5cqMPQCWR0vTOvOh2ExJu4Dz/HTzNZJl+beclJNX0dSsPbNAfS66Y\nZrGfULLuQXHYfGS3OHnuyyn37t2LVqtly5YtbNmyxUg2cuRIBg8ezPLly5k3bx4RERF4eXkRHh5u\nGCwBLFu2jKlTp9KnTx8cHBzo2bOn0WAJIDk52exyBIFAIPgvok8qPQzonP8KhLVHjWfXP2qVf4BU\nsWLRF/t+EpsuLq4FNxKUKfbv31/cLggEglLCcw/iRo0axahRo/JtExwcbLZuyiMqVarEsmXL8rWR\nV40VgUAg+C+iTyo9FDRrZWdnnIG1oPYuLk6F3pNWWJ6FTUHZQavVsm3bNg4ePEhWVpbRygCdTodE\nImHt2rXF6KFAICgpFHuJAYFAIBAIBAIBLFq0iIiICKpWrYqbmxtWVmU32ZRAIMgfEcQJBAKBQCAQ\nlAC2b99O3759GT9+vMW2UlJSmDdvHn/99Rc5OTk0btyY8ePHU7t2bQAOHz7MvHnz+Oeff/D09GT0\n6NG0adPG4vMKBILngwjiBAKBQPBC8NkbDYvbBYHAIjIyMmjbtq3FdrRarWFfbnh4OHK5nKVLl9K3\nb192795NSkoKQ4YMYdiwYXTo0IFffvmFoUOHsn37dry9vQuwLihV3HQkS60lRnGcatWqF7c3giJE\nBHGPMbSyr0X60apEi/SPJf1hkb6LfXmL9G9lplikD/Dl0X8s0l8dfs4ifZuNAy3SB2i3cY9F+ss7\n3y64UT70rB1qkb5AIBAISh9NmzYlJibG4lIk8fHxnDp1ij179hiSMM2dO5eWLVty8OBBYmJiaNas\nGYMGDQJgxIgRREdHs3HjRqZPn27xdQgEgmdPsSy2TklJYdy4cQQFBeHn50f//v25dOmSQX748GG6\ndetGkyZN6Nq1q0kh3dTUVEaMGIGfnx8vv/wy8+fPR6PJTemck5PDnDlzCA4Opnnz5vTp04eTJ08+\nt+sTCASlC9EnCQSCksCQIUPYtGkTy5Yt4++//yYmJsbkVxiqVKnCqlWrqFmzpuHYo1qoaWlpREdH\nmwSK/v7+hlp1AoGg5PPcZ+KKYop/+PDhSKVSNm3aRFJSEhMmTEAqlRoyzC1atIhdu3Yxd+5cqlat\nypo1axg4cCB79+7FxcXleV+yQCAowYg+qfSQmGjZDPfjKJVFX2LgWdh83nZdXFyxthYLdYqDDz/8\nECDPbLcSiYS4uLgC7ZQvX94ko+4333xDTk4OgYGBhIWF4ebmZiR3dXUlMdGy1UQCgeD58dx7aUun\n+E+ePElMTAyRkZF4eHjg4+PD2LFjmTFjBsOGDcPGxoajR4/SpUsXXn75ZQDGjx/Pjz/+yOnTp2nf\nvn2hfVWpNYyZtpuUe5l41ajItDEdjORxl+4wM2w/yhwNXUPq895bzc3a2bQ0irpN3WjRuprR8eTE\nDCLmHGViWP4+fbcshrpNXWkeVNVwLPrQTXZ/dx6dDtp19yYoxLTIuFqtYe7kSO6nKqhe8yWGT8jt\n0GOOJbBx5XEkEvhoWACNmuedivvH5afwaeJC0yAPw7HTR26ze2McVlIJfUY2p5p3wcs4baysmeA3\nlHK2zlx+8A/hsd8UqKPU6ZiVouShVoe3jRVDKxgXs/xLoebHNDU6oIujNR0cjV9plVrDmC9/0z9D\nzwpM+6ydkaz/6B2APnVzbFwSf24dQDknO0MbmdSaVW+MpZK8HLF3r/D5wa8BsLaS8lPoDAAkSGhe\nuQ6Nvv6AhzmZeV7L3ogL1Gj0EvVa6WtDadRaNkyO/vf8cPPiQ8ZuDMbe0Sbfe6JSaRgzeg0pKWl4\neVVm2vQ++bYvav0XjdLUJ5V1Vu86X6T2irPYd0m1+6RFyAVFy4YNG56J3cjISBYuXEi/fv3w8vIi\nOzsbW1tbozYymQylUvlMzi8oXnQU7iOYu3tlbGzyH4MISg7PPYgrzBT/G2+8YaTj7+/Pnj36PUpR\nUVF4eHjg4ZEbUPj5+ZGZmUlcXByNGzemadOmHDhwgD59+lC5cmV+/PFHZDIZPj4+T+TrvoMXqeNV\nicUzujIrbD9Hoq4T0MLTIJ+3/A/mT+mMm4sjKzccNdHXqLWEf/kXl8+nUq+Z8Revs1GJbI44jUaV\n93/KGrWWVTOPcDUulbrNjDINYVAAACAASURBVIvC7lh/hskrOiCVSpgy4FcCX69puI+P+Gv/VWp4\nV2Di7A6sWniYU8dv0tRfHwh+GxHFjLDOaDVaZoz9lfkRb5qeX6NlzazjXIu7h09T4/Pv2nieMYtf\nJSMth28XxTB8VlCe1/GINh4tib9/hc0XdzGq+QBql6/BpQf/5Kvzh0JDXVsrejnbsCA1h4tKLXVk\nuauAv3moYom7HVJgcFI2rzlIjfT3/XGZOrUqsnhqR2Yt/YMj0TcI8NVv7LWxlrJx8VsA/LjzDEH+\nnkYBHEC3Oq2JSbrA0qitLGo/nMauXsTevYJaq+HNLRMBeL/R6xy4HpNnAKfRaNm64Cw3LzykRuPc\nYs9Says+mu0HwIlfb+LdvGKBARzAvn0x1KnjweKwj5k1czNHjsQTEFC3QL2i0n/RKE19UlnHuXzR\nzlrKbK1R5qhLvM3SaFfwdLRs2bLIbW7bto0pU6bQqVMnxowZA4Ctra1JwKZUKrG3ty+UTRcXpyL3\n81nYLAt2FQpHsJZgY226Q+rRsFCl1eG4ajAVy8vztHM7U4Vq4W9UqeKZZxtLfS1Ou8/K1+LkuQdx\nlk7x37lzx6wcICkpicaNGzNx4kQGDx5M+/btkUqlSCQSwsLCqFbNeCasIGLjEunQpg4AAS08iTp9\n0xDEZWWrUKo1rNx4hCv/3OOTvgEm+mq1llc6e1PZsxz/qdcJ6Afw4xe05ctPf8/z/Gq1luDOXlT2\ndNZ/RvkPYxe0RWYrRaPWogOTAA7gwrm7BL6qn1lo6leVs6cSDUGcXG6DIiMHnQ7s7c0HDmqVltYd\na+Je3YnHL2DiinZIJBLuJ2chL0TgARCZ8BcSJFghobytM5mqrAJ1XnOwRqvTodHpeKAFh8cu8ytX\nO2QSCZp//Xv8PsTG36FDay8AAnyrERV72xDEPSIrW8VPu87y/fK3Tc7/U9wBvc8SKyrJy5OeozCS\n21vb8n7D1+n445g8r0Gj0uHbwYNKVR1MniOAMltD9G83GTivcBvZY2P/ocNrzfTX9HJdoqIuPVEQ\nZqn+i0Zp6pME+XMxzfhDSh1nh2LyRCB4eq5cucKSJUs4fvw46enpVKhQAV9fXz755BNDeYDCEh4e\nTlhYGO+99x6TJk0yHK9cuTJ37941anv37l3c3d0LZTc5uWgL2ru4OBW5zbJiNzU1A2e1DpVaayLT\n6UBSLYNyQPDcqwCoFjUza0el1pGamoFcXrjzl6R7UBw2H9ktToq9iuSTTvFnZWUhkxkvqbOxsUEi\nkZCTkwPA7NmzuXjxIosXL+ann37inXfeYdy4ccTHxz+Rb5mZSuRyfYBib2+DIiv3q9XDtGzOnE+i\nXy8/lszsxuyl+030be2sadiislnb9Zq6IXeUmZX9V7+Br/kO1am8/h59uzSGNh1Nl1ICKDKV2P3r\nv529DdlZKoPsjdAG/O+jbYz6aCsdutbL8/z1fN3MyiQSCYd2X2PJ+EM0DfQw28YcOnSEt5uFs8yR\ne9kPCqUjQT/LlqbRUUFqHKSV+/fvy++rCHEw/SaRmalEbq+/z/pnqDJpszvyAp3a+WBjLTWRPfL5\nwHtLqGDvxF3FfSNZqE8btl34E7U27xlVmZ0Ur6YV85Sf+TORRm3ckZr5imaOzIxs5A76529vb4tC\nkVMovaLSf9EpyX2SQCB4sblw4QI9e/YkKiqK9u3b079/f4KCgjh69Chvv/02Fy5cKLStiIgIwsLC\nGDlypFEAB+Dr68uJEyeMjh07dowWLVoUyXUIBIJnT7HuXH6aKX47OzsTuUqlQqfTYW9vz507d/jh\nhx9YsGABISEhANSvX5+LFy+yYsUKlixZUqBf81f8QWxcIvXquKJQ6Af9CoUSR4fcgZyzky2V3Zzw\nrKpfHudWyYl79xVgWYb/QqPV6ti4KApbe2tC3jY/iyJ3kJH9r/9ZCiVyB/1AMztbxfdroli74z00\nai1jB/9MqzY1sJGZD2LyonWnmvi3q8acoftp6O+OrX3hXqdBkRMIqfEKvXy6sP78FrNtIu4ruaDU\n4iOzYuBLMiIq27MnQ80PaSr6lc8dMGt1OpbeV2Engbecc2cE5686TGzcHerVdjEE3wqFCkcH08D5\n/w5f5csx7UyO/5fgb4bxXsMOfOrXg9l/bzIcf8M7gFG/F/xO5Ufckbt0/7RBods7ONqhyNQHB4rM\nbBwdCrf8paj0X2RKap9UmpaWPMsvkzLb/PsYKyvjjzwFtS9smyflWdh8XnZlMikVKzpa/ByL+wt1\naWX+/PnUqlWLjRs3IpfnLn1TKBT07duXRYsWsXLlygLtxMfHs2jRInr06EGPHj1ITk42yBwdHXnv\nvfcIDQ1l6dKldOzYkV27dnHmzBmmTZv2TK5LIBAUPcUWxD3tFL+7u7tJeu9H7d3c3Lhz5w46nY5G\njRoZtWnYsCF///13oXwb/Yl+adWu3+M4fjIB3yZVORJ1nVcCvQxt5PYy5PY23Ex8SMWX5CSnZlC+\nnD3wsHA3wEI2rzqF3MmGtz9ummebOvVdiY2+TYOmlTl14hb+QfqloDotaDU6pNYSrG2ssbICjVaL\nDYUL4jQaLUvGH+bTOUFY21hhJZVgJTVdzvk4nWq2RaHK4sDNI+Soc9DoTKf+HzHwJX2wtStdxf5M\nNW0drLGTYOLh6gcqHK2gf3nj4Gz0IP0evV2RFzh+6ha+jT04EpPAKwE1jNrpdDrupmRQqYL5ZVcf\nNAohQ5nFtgt/oFCZ+lzZoQLJisLNKJpDp9ORdi8Hx5dsC278L40a1eD48Yv4tvDmyJF4Xnm1UcFK\nRaj/olKS+6TStLTkWdh9REF7t7Ra4/XKBbUXe+JM7SqVGlJTM5DJnv45vqhLl54HUVFRzJ071yiA\nA5DL5QwYMICJEycWys7evXvRarVs2bKFLVuMP5aOHDmSwYMHs3z5cubNm0dERAReXl6Eh4cbkjsJ\nBIKST7Esp7Rkit/X15eEhASSkpKM5I6OjtSrV4/q1asjkUhMlildunSJGjVqPJGfIa/6cPmfVHoP\n+ZasbBWBfjW4fvM+81foC3JPGtWe/03ZyfvDfmBI3wCTr8D/RSKBO7fS+T7cuMZLwaFPbsM7t9L5\nceUp0u5n8/vWi1w5l8rsEZHMHhFJVqbpMsHW7by4ce0enw3YTnaWCvcqzqxdegR7uQ2dejRg9IAd\njBm4g46hDbCzK2Bfm0TC3VsZbFkVi1RqhX+76nw1/ADzRhzk9V4+hZrF++PmUdpXD2Ju0ATaVG3J\n1kt7C9QJdrDm/zLVjLmTzZ8KDW8523BbpSXivpL7Gh070tXE5WgZfSeb0XeyyXxsEBcSXJvL11Pp\nPWwzWVkqAlt4cv3mA+avOgzAvQdZODvlHUD9fPEQb9d7lW1vzaRrnSB+vXKMyUF9AahkX44H+WSj\nNIsE7iUq+G3dRQAyHyqxd3iyTFAhIc25fCWR3r3mkpWlJDCw/nPVfxEpLX2SQCB4sbG3tze7xx30\n2xj+W38yP0aNGkV8fLzZ3+DBgwEIDg5m165dxMbGsn37dgICTPf2CwSCkotEp3s85cazJT4+ntDQ\nUEJDQxk5ciT/Pb2joyMJCQmEhoYyaNAgwxT/unXr2LZtm+ELUa9evQCYMmUKycnJTJgwgXfffddQ\n62nKlCkcOnSI6dOnU716dXbu3El4eDg//PCDydfwx9Elr7bo+qJ1ltVYyW9vVWFwsbdsPeetzBSL\n9AG+PPqPRfqrw89ZpF9t40CL9AFcf9hjkf7yzjULbpQPPWuHWqRfFEh4tbhdeC6U9D4JyvZMnKur\ns+HPw2duy7ftTY3x4pKq0gJm4kSJgWdSYkDMxD09n3zyCampqWzYsAE7u9xsyVlZWfTt2xdHR0fW\nrFlTjB7qKct90vO0q1KpSErKf1yZmHibOptGUMPZzkT22s5LSKrp60Ae+Ld6RV6JTa6nK0kb/SPV\nqlU3Ky/I16JCJDYpPM99OWVRTPEvW7aMqVOn0qdPHxwcHOjZs6dhsAQwefJkwsPDmT59OqmpqdSp\nU4d169YVarAkEAjKFqJPKj0M6Fy0s8YVKxZ9Ae1nYfN523Vxcc2jteBZ89lnn9GjRw/at29P27Zt\nqVSpEsnJyRw4cICMjAy+/fbb4nZR8BxJSkokc3ooleV5r9i5kqJAXU4KmAZxANx05EGOhrjJHnhX\nePE/hJQlnvtMXElHzMSJmTgQM3FQdmbiSgOl6avks5yJu3s3rUhtl7YvvqXF7ov61ft5ceHCBZYv\nX05UVBRpaWk4Ozvj5+fH0KFDqVOnTnG7B5TtPul52k1IuIHz/HfwdMo7m/nfSRm4ynRmA7TXdl5C\nIoEHORq+ezX/IE7MxD2d3eKkWLNTCgQCgUAgEAhy8fHxKVTWWoFAULYRQZxAIBAIBAJBCeHs2bOc\nPn2a9HTzMwePEpMIBIKyjQjiBAKBQCAQCEoAGzZsYPbs2fm2EUGcQCAAEcSZ0HDPbxbpn/Eq3Fri\nvMhsGWSR/qwTBaftz4/6FZ4s3b1ZH4J8LNKv0v1Di/QfqO5ZpA+ws3cLi/Rb6ixLDHAjPc4i/evp\nty3SB2hTReyJEwgEgufJunXr6NChA9OnT6d8ecv2uAsEghebYqkTl5KSwrhx4wgKCsLPz4/+/ftz\n6dIlg/zw4cN069aNJk2a0LVrV5NCuqmpqYwYMQI/Pz9efvll5s+fb1Q7RalUsnDhQtq2bUuLFi0Y\nOnQoCQkJz+36BAJB6UL0SS8GC/aeNfoJBKWNhw8f0qdPHxHACYqOqhmU98qi3ozL2Iw6WdzeCIqQ\n5z4Tp9VqDam3w8PDkcvlLF26lL59+7J7925SUlIYMmQIw4YNo0OHDvzyyy8MHTqU7du34+3tDcDw\n4cORSqVs2rSJpKQkJkyYgFQqZdSoUQB8+eWX7Nu3j+nTp1O7dm3Wrl3Lu+++y65duyhXrtzzvmSB\nQFCCEX1S6SExMf8Z5uzs7Cdqr1QWfdr+Z2GztNktyKaLiyvW1mIhkDkCAwM5fvw4LVu2LG5XBAJB\nCee596Lx8fGcOnWKPXv2GOoszZ07l5YtW3Lw4EFiYmJo1qwZgwYNAmDEiBFER0ezceNGpk+fzsmT\nJ4mJiSEyMhIPDw98fHwYO3YsM2bMYNiwYSgUCn766SdmzpxJhw4dAJg2bRohISF8//33hV5LbmNl\nzbw2I6lg58z51GvMObHOIAuu2pzBjXug0WmZcTSCC/ev52trzg/n8POpSLtm7iYypVpL71l/MbNf\nE+pWczaSqVUaJo3/gdSUdGrWcmPiF28aZIcOxrF61X6kVhLGT+5OnbqmhVk1Sg3HV8WhUqiR2ljh\nN7g+MnnuI9eqtRz88iTNP/KhfHXHPP3/fc1Fqjcsj0/L3CWCV0+lcnDTFSQSaPthbTwbvpTvPdi0\nNIq6Td1o0bqa0fHkxAwi5hxlYlh7s3oqlYZxY9eTkpyGl7c7X0ztbSS/dSuVyZO+Ze26T83qq1Ua\nvpiwhdSUDGp6uTBuctfH7KsZ8F4Ek6a/SW0f0+fzX75bFkPdpq40D6pqOBZ96Ca7vzuPTgftunsT\nFFLLRE+l1jDmy99IuZeJl2cFpn3WzkjWf/QOAHQ6HbFxSfy5dQA45tZ7Uas1zJn0G/dSM/GsWYER\nE9sZjk8Ymqsbfy6J7/cOwMlMwU+AH5efwqeJC02DPAzHTh+5ze6NcVhJJfQZ2Zxq3mXv629p6ZME\nsHrX+Xzljxf7Lqi9KPb9/O9BURQTf5GZMmUKH374Ibdu3aJx48bI5XKTNt27dy8GzwQCQUnjuQdx\nVapUYdWqVdSsmVtHSyKRAJCWlkZ0dDRvvPGGkY6/vz979ujrdkVFReHh4YGHR+5A1M/Pj8zMTOLi\n9PuIdDodzZs3N8itrKyoU6cOJ06cKPSAKaTGy5xJucSasz8zLWAw9SvU4vy9qwAMadKTD3/9gvK2\njkxpNZCh+78ya0Ot0TJu9Slirz7Av25Fs22W7riAVqtDYkYW+ftZvGu7M2dBHxbM2cnxo5fxb6X/\n8h+xMpKv1w/i4QMFc2ZsZ9Hyvib6N47cpVKdctR+vRr//JnItQO38emUu2fv/PZ/0Gl1SMydHNBq\ntOxYdI7bF9NMgrQ/v79K76nN0Gp0/DT7NH3n+Jm1oVFrCf/yLy6fT6VeMzcj2dmoRDZHnEajynsA\n8fu+k9SuXYWFi/oze9YWjh65QKsA/Z67o0cuELZ4JyqVOk/9A/93Hq/absyc/w6LvtrDiaNX8Gvl\nZZB/vXw/Wq0Osw/gP9ewauYRrsalUreZ8V63HevPMHlFB6RSCVMG/Erg6zUN7/Mj9v1xmTq1KrJ4\nakdmLf2DI9E3CPDVPwcbaykbF78FwI87zxDk70k5Jzse/kf/UORlanhXZNKcjoQv+IOTx2/QzL86\n1tZS5q3S6+7edgbfAE+zAZxGo2XNrONci7uHT1Nj/3dtPM+Yxa+SkZbDt4tiGD7Lsj2ZpZHS0icJ\nwLm8S75y67RM4/bO+X9cktlao8zJu/94Gp6FzdJm91n5WhY4ePAgN27c4Nq1a+zYscNsGxHECQQC\nKIY9ceXLlyc4ONhooPvNN9+Qk5NDYGAgSUlJuLkZD/ZdXV1JTNQX0b5z545ZOWCk+6j9I27dusW9\ne4VPeLHz6p+sPfsLVhIrKtqXI0OlMMh67Z5AjkaJm7wiaUpFnjZUah09Wlen+8tVMVdS/a+zyTjY\nWVOvurOpEDh3NoEW/vqAw7+VNyejrxlkG74fip2dDXfvPMTRyd6sfrVWrtR6Vf+1U6vRYWWd+7jv\nnL2HtZ2Ucp55z8Bp1DqaveZB47aV0WF8ATJ7a3Iy1SizNcjs8v4WoFZreaWzN63fqGVyD6TWVoxf\n0Jb8qs2fOXMd/5b64qYBAT5ER182yKytpXy9emi++ufO3qS5n35w7tfKi1MxubOmx/6+jFxuS526\nlfOxoL+G4M5eBIbU5PGTjV3QFpmtFNCLHg/gAGLj79CyqX72LsC3GlGxpku8srJV/LTrLB+909xE\nduHcHZq20Os386/GmZPG+tnZKvbuOEvP9011AdQqLa071iTgdU8efwgTV7RDZivlfnIWckfLk9qU\nRkpLnyQQCF58li9fTlBQEFu3buXgwYNmfwKBQADFlNjkv0RGRrJw4UL69euHl5cX2dnZ2NraGrWR\nyWQolUoAsrKykMmMK9fb2NggkUjIycnBzc2NVq1a8dVXX3Hjxg2USiXr1q3j0qVLqFSqJ/JNh46t\nXeZR3taRlKwHRrJQ77asbD+RAwkn8tS3t5USUL+SWdm99Bx++vMGH3f0/vdcpmRmZGMv11+rvVxG\nlkJpkEkkEnZsPcGIIesIblvf7DmsbaVIZVLSExVcO3CbGm30ywVz0pT880eiYVbOXIAJYGMrpWaT\nCmZlviEerBt7gnVjjtP0tbyXxdjaWdOwhfkgqV5TN+SOMrOyR2RmZCOX698He7ktCkWOQdbCzxun\nPALYXP0c5I/uob2N4R7ev5fJz1uj+XBAa33DfCJBWztrGviaX2rpVF7v27dLY2jT0XQpJUBmphK5\nfa4PiizT93B35AU6tfPBxlpqIlNkKrH/V9/O3obsx/QP/HqBVzr4YG1G95H/9XzdzMokEgmHdl9j\nyfhDNA30MNumrFGS+ySBQPBik5aWRr9+/WjQoAHu7u5mfwKBQADFXGJg27ZtTJkyhU6dOjFmzBgA\nbG1tDYOjRyiVSuzt9YN1Ozs7E7lKpUKn0xnazJ07l3HjxvH6668jlUp59dVX6dq1K1evXn1iH9/8\n5TPeqt2WAY26s+TkD7m+X97PnmuH+a7jTA7dOkmWOje4mP9THGeuPaBxrfJ81qOeWbt/xt7lVqqC\nfvOPcjUpg8u301k/JsCojYOjnSHoyMzMwcHReCDZ/S0/Qjo2oV+fFQQG+RgCvv/y4EYG0Wsu4D+k\nPjb2+sedFHuPzJQcDs07TXpiFum3FLQe1wRrW/NBwOOocjQc+vEaw74ORKvRsXFiFHX8XbC2Kfpv\nAg6OdobALTMzGweH/IM2U31bFIZ7qDTcw78OXSTx9n2GDVzP9WspXLtyl+Wr+5m9h/mh1erYuCgK\nW3trQt6uaySbv+owsXF3qFfbBUWW3geFQoWjg+k5/u/wVb4c087kOIDcQUbWv/pZmSrkj+n//cdV\n/jfJvG5haN2pJv7tqjFn6H4a+rtja192Ew6U1D7JxcXJ0kt7LjafpV3QL9PLj4YuT54kpiCbT8Oz\nsFna7OZlUyaTUrGi4zN9T0ozfn5+nDx5klatWhW3K4IXhZuOPMjREDfZA+8K4t/di0SxjdbCw8MJ\nCwvjvffeY9KkSYbjlStX5u7du0Zt7969a/j65O7ubpLe+1H7R8uWXF1dWbduHRkZGeh0OpycnBg8\neDDVqxe+hlvPOu1RqLLZfe0w2WolGq0WAKnEivB2ExgSORuVVo1apzXIHjG6p/nA7b90D6xG90B9\nko+Ja0/x4Wu1kNta898dHfUbViXqxBWaNq/BiaOXCQrW21WrNYz8ZD2LV/TF2kaK1FqKldR0GZ8i\nNZuoiHgCPm2Ag0tu8OMZ5I5nkP5+Rq2Jp3aHqoUO4AB0Wh1arQ4rqQSptRUSKwk6bX6LGp+eho08\nOXH8Er6+Xhw7epHg4AZPpF+/gQcxUddo2tyTqGNXCQzWL83s3K0Znbs1A2DG5O30ej/giQM4gM2r\nTiF3suHtj5uayEYP0u8v2xV5geOnbuHb2IMjMQm8ElDDqJ1Op+NuSgaVKjiYPYdPfTdOR92iYVMP\nTp5IoGVQDSPd1LsZvFTRvG5+aDRalow/zKdzgrC2scJKKjH7HpUVSnKflJyc/tTXZQ4XF6cit/ks\n7T6iNOzdKk17156V3fxsKpUaUlMzkMme/D0pC4Ffz549mTRpEjdu3KBJkyY4OJj27V26dCkGzwRl\nHZVKRVJS7rYAhcJ8Flp398rY2JTN7RnPm0JPnUyfPp0zZ84UyUkjIiIICwtj5MiRRoMlAF9fX06c\nMF6ieOzYMVq0aGGQJyQkkJSUZCR3cHCgXr166HQ6Bg0axN9//42joyNOTk6kp6dz4sQJAgMDC+3j\nb/8coUutNqzt8AUdagRwICGK/zXvg0anZc+1v/jmjRlsCJnGurM/o9QWvCTq0XabG3czmf9T4Qo5\nt+/QiGtX7vLRe+FkZSnxqPoSSxbuxdpayusdmzDgg5V83Pdr3u/bBltb038wl369iSZHQ/TaC/z5\n1Sku/36Ts5uffDYSQIKEe4kKIjdcQmZvje8bVdkwPooNE6LwDamKTSGCQIkE7txK5/vwmMds583r\nrzfjypVE+ry7kCxFDh5VK7FgvvFm7/z023ZowD9Xkhn4QQRZWUqqeLzEskX7CvQ174vQX8OPK0+R\ndj+b37de5Mq5VGaPiGT2iEiyMk3fhZDg2ly+nkrvYZvJylIR2MKT6zcfMH/VYQDuPcjC2cnWRO8R\nbdrX5sa1VEZ+tJnsLBXuHuVYvUSv++B+Fo7Oeeua+i/h7q0MtqyKRSq1wr9ddb4afoB5Iw7yei8f\nbGSFD+aLm7LWJwkEghef4cOH8/DhQ7Zv387UqVMZM2aMyU8gKA6SkhLJnB6K8/x3cJ7/DkzubPjz\no1/m9FCjQE/wbJHodHntiDKmSZMmrFixwuJBR3x8PKGhoYSGhjJy5Ej+e3pHR0cSEhIIDQ1l0KBB\ndOzYkV27drFu3Tq2bdtmSP/dq1cvQJ+KNzk5mQkTJvDuu+8aaj2NHj2ay5cvM2vWLKRSKTNmzCAj\nI4Nt27ZhZZV/3NpgQ0+Lru+MV+Fn+8yR2dKy7ICzTuy1SL9+Bcu/ntSrYNma/aaVLFtGkq6yPFnE\nhfuFC7TzoqXOteBG+XAj73wzheJ6ev71sQpDmypfWmzjWVJW+iQo2zNxrq65iZ+Gz9xWpLZFiYHS\nVWKgLMzE3bx5s8A2VatWLbDNs6Ys90nP025Cwg2c57+Dp1PeK4X+TsrAVaYzu1TytZ2XkEjgQY6G\n717Nfznl9XQlaaN/pFo18+PYx32xsbZCpTZeiVaQjcLwLO7ts3xexUmhl1M2bty4SL4c7927F61W\ny5YtW9iyZYuRbOTIkQwePJjly5czb948IiIi8PLyIjw83DBYAli2bBlTp06lT58+ODg40LNnT8Ng\nCeCLL75g5syZ9OvXD4Dg4GDGjx9fqMGSQCAoHYg+qewxoLP5JE5PS8WKRV/o+lnYLG12C7Lp4mLZ\nR64XmZIQoAkEgtJBoYO4hg0bsnr1avbt20e9evXMFqCcMWNGgXZGjRrFqFGj8m0THBxMcHBwnvJK\nlSqxbNmyPOVOTk7MmTOnQF8EAkHpRfRJZY+iLhDt4uL0VHuznrfN0mb3WfkqEAgEglwKHcT99ttv\nuLq6kp2dzcmTJ5+lTwKBQFAgok8SPM6CvWeN/v7ZGw2LyROBQCAoIVTNoDxQb4a+zq5qUbPi9UdQ\nZBQ6iNu/f/+z9EMgEAieCNEnCQQCgUAgKKsUWYmB69ev4+npWVTmBAKBwCJEnyQQCEoDERERdO3a\n1VCSRCAoDlRaHYmJeSdFS0y8jZNOm6dc8PwpdBCXlpbGokWLOHHiBCqVPo26TqdDp9OhUCi4d+8e\ncXGWZfQrCRzu1cMifcldy1KrOl60LGX6qGaWZbd0ybJIXU+FakVg5Om5o7hhsY3Gi49ZpK+qalnG\nIs8hAy3Sr2BX2SL90kBZ6ZMEAsGLzbJly2jWrBlubm7UrVuXzZs307hx4+J2S1DGuJulwn7FxziX\nM1939kqKAnU5KWD3fB0T5EmhU6PNnj2bn376iapVq6LT6bC3t6devXpkZWWh1WpZunRpoU+akpLC\nuHHjCAoKws/Pj/79+3Pp0iWD/PDhw3Tr1o0mTZrQtWtXk0K6j1AqlXTt2pVffvnFRLZ+/XpeffVV\nmjZtykcffcT169cL7Z9AICj5iD5JIBC8CDg5ORnKlgAcPHiQHTt25Pl7GqZMmWJSA7NHjx7UrVvX\n6Dd58mSLr0dQeqksM6qt1QAAIABJREFUt8bTSWb252pfZIv3BEVEoZ/In3/+ybBhwxg8eDBr1qzh\nxIkThIWFkZmZSZ8+fXjw4EGh7Gi1WkPq7fDwcORyOUuXLqVv377s3r2blJQUhgwZwrBhw+jQoQO/\n/PILQ4cOZfv27Xh7exvsZGRkMGrUKC5evIhEYlzu+aeffmLp0qXMnj2bGjVqsGjRIgYMGMDu3buR\nyfKutSEQCEoPok/KRa1Wk5x8t9Dtlcpnk67+WdkVCF5kBg0axOzZs4mMjARgxYoV+bbv3r17oW3r\ndDqWLFnC5s2b6dmzp9HxK1eusGDBAlq1yq3NamcnZlkEgtJCoYO4hw8f0rx5cwC8vb1Zt24dAA4O\nDnz00UesXr2aHj0KXooYHx/PqVOn2LNnj6HO0ty5c2nZsiUHDx4kJiaGZs2aMWjQIABGjBhBdHQ0\nGzduZPr06QD8/fffTJkyBWdnZ7PnWL16Nf369aNDhw4ALFiwgKCgIPbt20fnzp0Ldb1qlYYvJmwh\nNSWDml4ujJvc1UiuUqkZ8F4Ek6a/SW0f88WtVWoNY2ZFknJfgZfnS0wbaZyi/K/oBMLWHUcCfDYw\nAP8meafPnr0uGv8GrrTzN12qePNuBpOWH2X9tPYm1zB94s/cS82gRq1KjJ7U0SBbvfwgx/66ir1c\nhldtV0aM65D3NXzxMyn3MvCqUYlp4zoaycNWHeTQsavI7WX4eLvy+SjzdgBUKg1jRq8hJSUNL6/K\nTJveJ8+2RWVDrday8IsD3E/NolrN8nwyLne56dE//mHz2pPY2lnzwSd+1Gti/jlma7RMPHGXdJUW\nmVTCnBauOMmkBvny8/f4+04WcmsJtcvJGNu4kqkNtZZxv10nPUeDTGrF3BBPnG2lRm1upSn5IjKB\n1W96mb3u0WPX/3vd7kyf2tukzc1bqXw+6Vs2rPvU+B6oNEwa/wOpKenUrOXGxC/eNMgOHYxj9ar9\nSK0kjJ/cnTp1izaF+7OkrPVJ+ZGcfJew7w7j4FyhUO1LU+HoJ0FkoxSURt5//33eeust0tPTCQ4O\nJjw8nHr16llsNyEhgYkTJ3L58mWqVKliIsvKyqJp06ZUrFjR4nMJSjA3HXmQoyFucv7FvgWlj0Iv\npyxfvjzp6fq6L56enqSkpBi+dLu7u3Pt2rVC2alSpQqrVq2iZs2ahmOPvlqnpaURHR2Nv7+/kY6/\nvz9RUVGGvx84cIA333yTH374wcR+amoq169fp2XLloZjcrmchg0bGtkoiAP/dx6v2m6sXN8fmcya\nE0evGMm/Xr4frVYHkjwMAPsOXaVOrQpsWtQdW5mUIzE3jeTLNkYRMbsz4V92ZPFa83uw1Bot/1t4\nmMjjN5GYOdmR2CQ+W3CYtAyliexgZDy1aruwbO0HyGTWRB3LfUZXLiWzcGVvlqx+L88ADmDfgXjq\neLmwKfwDbGXWHDlh/JwvXk1mzeLebFz+Xr4BHMC+fTHUqePBpm9HY2trw5Ej8fm2Lwobfx+4hqdX\nBWav7IxMJuX0iVsAaLU6vgmP4ssVnfh8XgfWhOW9B27PjQyaVbQjonUV2ldxYMs/xvWPLqcpCQ90\nJ6J1FbMBHMCu+Ps0r+LI2lBvOniX46ezqUbyownpjP31Omk55gfA+34/SZ06Vfj2m1H/XvcFI/mR\nIxf432frSEtTmOhG/n4W79ruRGwYjK2tNcePXjbIIlZGsmrdx8xe0IfwpfvyvAclkbLWJxWEg3MF\nnMu7FO73kmvh2z7J7xnYFQjKAnK5HDc3N2bNmkWTJk1wd3fP81dYTp48iYeHB7t27cLDw8NIdvHi\nRezs7EyCO4FAUHoodBAXEBDA119/ze3bt6levTrlypVj+/btAPzxxx+F/pJTvnx5goODjZYbffPN\nN+Tk5BAYGEhSUpJJhiZXV1cSE3MThnz++ecMHTrU7DKkpKQkALM2HskKw7mzN2nupx/U+bXy4lRM\n7v6VY39fRi63pU7d/JNHxMbfpWUTfccZ0LwqUWeMk5442NuQnpmDIkuF3N7GrA2VWkvP9t50f6Um\nOnQmcmuphNVT2pqRQNzZ2zT3qwGAb6uaxMYkGGQ3b9xj9he7+HTAJuLP5Z2NKPb8bVr66m0E+NUk\n6nSCkfx6wj0+n7mLD4Zu4kxc3nYAYmP/oWVLH72tl+sSFXUp3/ZFYePSuWQaNdc/pyZ+Hpw/pX8H\n0u5n4eruiNxBhqOzLWq1lkwzgTDAG9Uc6VlLP8Oi1oHNY/9qbmSomBqTzMBDtzl3P8esjY4+5Xmn\nkf7fiFqrw8bKOCC3tpKwslstdDpzTxJiz1ynpX8dAF4O8CEq+rKR3NpGypqIoZhTP3c2gRb++tk9\n/1benIzODW42fD8UOzsb7t55iKOTvdlzl1TKWp8kEAhefEJDQ9HpdMydO5cePXoQEhJC7969mT9/\nPikpKU9kq2vXrsyZM8dsX3jp0iWcnJwYPXo0rVu3pkuXLqxfvz7P/4MEAkHJo9BB3KeffkpSUhJj\nx47FysqKwYMH89VXXxEQEMCaNWt46623nsqByMhIFi5cSL9+/fDy8iI7OxtbW1ujNjKZDKXS/AD7\ncbKy9OkVH7dhY2NDTo75AbY5MjNykMv1AzJ7exuyFPrz37+Xyc9bo/lwQGt9w3z6u0yF0hCc2dvZ\noMhWGcl7dWlAr+HbeHv4Vnq8YX7phL2tNQGN8/7y5tfADScH83tqMjNzsH90fnsbsrJy72GHTg2Z\n+tWbTJzehXlf7s37GjJzcq/B3gaFwvg5dHm9IQtnvMnsSV344qu87QBkZmQjd7D915YtCkXhn8fT\n2lBkKrGT6/23tbcmO0sNgPNL9txLUZD2MJu7iekkJjwkJ1tt1oa9tRV2UiuupSvZci2N7p7GS+Y6\nVnNkjp8b03xdmHkq2awNuY0UO2srrt7LZvPZVEIbGC97a+HhiNNjyyv/S0ZGNg7yvK/br4U3TnkE\nYZkZ2dg/epflMsO7DPoZpx1bTzBiyDqC29bP8/wlkbLWJwkEghefW7du0b17dzZt2oSzszMNGzZE\nJpOxYcMGunXrZvTxyBIuX75MdnY2rVu3Zu3atfTp04clS5awbNmyIrEvEAiePYXeE1etWjV+/fVX\nrl69CkC/fv2oVKkS0dHRNGnShDfffLMAC6Zs27aNKVOm0KlTJ8aMGQPoBzqPD46USiX29oWbJXi0\nKdecDblcXmjfHBxtDQFLZqYSB0f9AOyvQxdJvH2fYQPXc/1aCtf+n70zj4uy6h74d5hhcNhcEREN\nFcQtU0RRwnLL5XXBNC01F8xyN8Ely63UyhI0yRSN3FDLt9fUzOWXuWfhAmq4K6iIirK4sAwwzPL7\nY2RwnAFGwQC9389nPsq995x7nvs8XJ4z995z4pJY+sNww0syQEh4JDEXkmjkUQ1llt5xUypV2D/S\nJis7l2XrotizfjBqtZYhk36lo28d8lqERJwkJjaVZvWrMnmIl8V2G12DnQ1Zef1nqgzOD8BbA1sh\nl8uoUbMiMpkVarUWmSzfpw9Zuo+Yc7do5OlsfA12xi+ig/vr9bi6VMQ6T09B9thXQJmZ89CebOzt\nLF/5CQneTEzMNRo1rv1EOmzt5GQr9fZnZeaisNM7dFZWEoZ/2Jr50/ZQs7YjdTyqYGdv7AyHnknl\nzL0cXq5sQ7da9nx2IpmvW1XH/rGluIHuFZFLJdS0tUYmkaDW6gxjsOivW5y5o6Spsy3/8azM7L0J\nBHdzw15esMP2KMELtxJzOp7GjWqR+dBxy8zMxs7e8rGzs69gcNwyM3MMz3Ieb77Vim7dmzH83WX4\ntW1g9CyXZV6kOcnJqfBzDCqVPXK5FLmN5dHDnqTtk/Cs9ELR4/A86yxvep+Vrc87wcHB2Nra8vPP\nP+Pikr/bJzExkYCAAEJCQli4cGGx+wkJCUGpVGJnpw8nX79+fdLT01m+fDkTJkwoUr48PTPlWa9S\naQ8yCdaygtdcZFIJUqmV2TZ5G0wkEpBKpU+tp6D6x9tayyRUrWpf7LEpT89XafJEf20VCgVNmjQx\n/NyrVy969er1VB2HhYURGhrK4MGDjcLeuri4kJRkHGUtKSnJ4n3geZNeUlIStWvnBwFJSkqifv36\nFtvXuIkrJ6Ku0ryFG1FHr+DXTr+VrWdvL3r21jtV82ZtYcAQX5OX3ikf+AKwfd9ljv1zC++mLkSe\nvEn7NvmJh7U60Gh1SKUSrGUyJFag0eYnUZwy9Okct0dp1KQmJ6PiecWrNtFHr/Hq6/pIeulpWUx4\nfz2rNr7Pg/tKdDqMHDiAKeM66q9h91mOnYjHu1ltIqOu0d4vPxrfg7Qsho5bz5a173OvAD2P0rRp\nHY4du4R3Sw8iIy/QvkNTi69lytS+enu2H38iHfUbO3HmRCKNm9cgJuoWrfzyn4krF1OYv7wn91KV\nhMzch00F41+HiS/rt6AkKtVMjLzNN22ccbUz3vaaptLw/p+JbOzoyn2VFh36rZF5TPLTnzdITFcx\n7rerfNujDrUqGjtRhTF1sj4K2fYdURw7fpmW3u5EHrlEh/ZNipDMp/HLtYg6HkfzFnU4fiSWtu30\nq75qtYbAsWtYvCwAmbUUqUyKlbSQQ55lkBdlTkpOTi+0PjU1A5VKgyrH/Gry48htZBa3fRKeld48\nihqHJ8XJyaFc6Cxvep+lrc87eUGSHnXgQD+PTJgwgXnz5pVIPxKJxODA5eHp6UlmZiYZGRnY29sX\nKl+enpnyrDc1NQNHtY5cdcFJttUaHRqN1mwbnU7vwOl0oNFonlqPuXprmZVJ21y1jrTUDGxtn35s\nxJxkORZvpwS4desWs2bNomPHjjRt2pQzZ86wYMGCJ85bEh4eTmhoKIGBgSZ5S7y9vTl+/LhR2dGj\nR2nZsqVFuqtWrYqbmxvHjh0zlGVmZnL27FmLdQB07NKEa3HJfDA0nKwsFTVdK/PdN08W+KFbO3di\n4+8ycOIWsrJz8fOuTfzNB4SER2KnsGagfxMGTdzKoMCtDOzVBEUF8+fi8sgLbHL9djohEScfqzOl\nfedGXItLYcywtWRlq3BxrUTY4n04OCro3a8FY4atYebkXwoNbNKtYyNir6YwcORasrJU+PnUIz7h\nLiFL91HRUcGAPi0YOHINH07/hRmTCg9s0q1bC2LjEhk4YIFel9+Tb997Uh1+HeuScO0+0z7YRnZW\nLs41HVj7nf7ZkMqsmDx8K199vIcRgW0K1LH28n2yNFrDubcf4x6QkJFL6JlUHOVS+tV1JODgLaYc\nvcNHr5g/h7X6RBJZuVpm703gvc2xbPgnmYQHOSz6y/gc4eOh6Q3X3dWLuLhEBry7iKysHPxebUR8\nfDLBC7c+Jm8q+0aXplyNS+K9wWFkZalwrVWZbxftQiaT0rV7M94fupyRAd8zJOB1bGwKfwbLGi/S\nnCQomoW7zhh9BILySEEOlJ2dHdnZ2SXSR79+/fjiiy+Myk6fPo2zs3ORDpygnFErg0ruWTSaF4t1\n0Mmi2wvKDRavxMXFxTFo0CBsbGx49dVXDS9JWVlZfPzxx8jlcrp3716EFn0472+++YZ+/frRr18/\nkpPzzxDZ29szePBg+vbty5IlS+jevTvbt2/n9OnTzJkzx+KLGj58OF9//TVubm54eHiwaNEiqlev\nbgjvbQkymZS5X/c3Khv/WPTFWfMK364lk1qxcEZnozI314qGlboBPZswoKdlKyrj33nF8P+XajiY\nrNRtXmg69jKZFZ9+ZZxPZkygfoWtz9ve9Hnbu8h+ZTIrFs411uFWu4phpW5gX28G9i1aj16XlIUL\nR1jUtqR0SGVWTJ7bwahs2Hh9pMHeA5vSe2DRq4EfNzMfcTJvpe7teo68Xc98aPk8prerBe1My/NW\n6vL4eYCnWXmZTMrC4OFGZW5uToaVujy2/PKxWdkvFhinJPhw0n8A6PVmS3q9WT4diRdtTiqKzLS7\nFrd9XlMMCATlnSZNmvDTTz/Rvn17o3KdTsfGjRtp3Pjpzy4/GrSke/fuLF68mCZNmtCiRQuOHj3K\nypUrmTFjxlPrFwgE/y4WO3Hz58+nXr16rFmzBqlUytatW5FIJHz66acolUpWrlxp0QvTrl270Gq1\nbNq0iU2bNhnVBQYGMnr0aJYuXUpwcDDh4eG4u7sTFhZmyN9kCQMGDCAtLY358+eTkZFBy5Yt+eGH\nH5DJRLZ5geB5QcxJ+Tg5VWfioLZFN3xI1arPJin3s9C7RLxTCl4gJk6cyKBBg+jduzfdu3enWrVq\npKSksGPHDmJjY1m5cuVT6350p8d7772HVColLCyMxMREXF1dmT59ukW5NQUCQdnA4jeI6OhogoOD\nsbGxQa02PvPQu3dvxo0bZ5GeoKAggoKCCm3Trl072rUzs2xhhgsXzOcJGzlyJCNHjrRIh0AgKH+I\nOSkfmUyGi4vl+Z6cnByQy5/N+YBnoVcgeFFo3rw54eHhLFq0iMWLF6PT6ZBIJDRp0oTw8HB8fX2f\nSu+6detMyoYNG8awYcOKa7JAICglLHbirK2tCwypnZaWhrV1+TpLIxAIyjdiThIIBM8jfn5++Pn5\noVQqSU9Px97e3iQIiUAgEFgc2OTVV1/lu+++486dO0ZL8kqlktWrV9OmTcGBIQQCgaCkEXOSQCB4\nnrG1tcXZ2Vk4cAKBwCyFrsQtX76cUaNGIZFImDp1KgMHDqRbt26GkN4hISHExcWRm5vLggUL/hWD\nnzWV0oq5FUhtWQLggkiuV6dY8tfT44slX2nDqWLJA8jcKxdLXuL/brHkG1xKLZY8wK8jGxZLvre1\nW9GNCkEnLd5ZKYXk+Ywu9iLOSQLLmfyfl0vbBIFAIChb3LDnfo6G87Nc8ajy/KfpeJEodCVu8eLF\nDBgwgKtXr+Lq6srWrVsZOnQoKpWKl156ibS0NHr06MHWrVtxcyveS6tAIBAUhZiTBAKBQCAQCIpw\n4pYuXcqtW7fo06cPERERVKlShaCgIH7++Wd2797NL7/8wrRp03B2di6WESkpKUybNo22bdvSqlUr\nRowYweXLlw31hw8fpnfv3jRr1gx/f38OHTpkVo9KpcLf359t27aZrZ89e7ZJDiiBQFB+EHOSQCAQ\nCAQCQRHbKTt16oSPjw8LFixg/vz57Nmzh6+++oqaNS2PglYUWq2W8ePHAxAWFoatrS1LliwhICCA\nHTt2kJKSwpgxYxg/fjxdunRh27ZtjBs3ji1btuDh4WHQk5GRQVBQEJcuXTJJmKzT6fj222/5+eef\n6d/fOPebQCAoP4g5qXio1WqSk5NQqZ5NioFnpVcgeFEYPXo0w4cPp3Xr1qVtikAgKOMUefDGwcGB\nefPm4e/vz5w5c/D392fatGm8/vrrJm2f5tvvCxcucOrUKXbu3GnIu7RgwQJat27NgQMHOHHiBF5e\nXowaNQrQ51CJjo4mIiKCuXPnAvD3338ze/ZsHB1NEy4nJCQwffp0YmNjn/hFL1etYeqcHaTczcS9\nTlXmTDVOzHv+8h2+CN2HKkeDf7fGDH6rxWPyWqZ+fYCUe0rcX6rMnA/9jOr3/H2NZT+eQmEjY/J7\nrWjRxHj81Lka5k7/lbupGdSpV40pM/NzXv2w9ABH/7qCwlaOe/3qTJxWeNLg9UuiaNjcmZav1TYq\nT07MIPyrI0wPfcOsXLZay7Q9CaSrtMilEha8URtHG6mhfu/VNFadSkang3eaVKF3A/Pn4b7adoVW\n9SrS6WGCbIBcjZb3w88AoNNBzPV0Ds7yoVIh15Gbq2HqlJWkpKTh7u7CnLmWnZ+bvyEGn4bV6ORt\n/AzsP5lI2K8XsbKS8FlAcxq+VLFAHb99f456TavSxDf/Pt2KS2Pb8nOoczW06OjKq/51TG1Wa5j6\n5V79c+BWmTmBxqHq/4pOIHT1MSTA5A988WlW+HP6NGOQm6th2kdrSElOw92jBp9+Zpz8++bNVGbN\n3MCq1R8Wqau0eZHnpOKSnJxE6I+HqVzNSST7FgjKIEeOHCEgIKC0zRAIBOUAi6NTtmrVii1bttCw\nYUNmzZplyJuU92nfvv1TGVCzZk1WrFhB3bp1DWV531qnpaURHR2Nj4+PkYyPjw9RUVGGn/fv30+f\nPn3YuHGjif6TJ0/i6urK9u3bcXV1fSLbdh+4hKd7NdYvHYiNXEZklHHQkOClBwmZ3ZP/fv8u6ek5\npvKHr+JZpzLrQ3piI5cSefKWoU6r1fHNmmgiFnRn2Wedmb/iiIn8gb0XqFffie9WDUUulxF19Kqh\nLu5yMouWD+TbHwYX6sBp1Fq+++xPov68wWOLAZyJSmTJZ4fJNGN7Htsv3aeFix2r/OvSpZ4j/zt3\n16h+eVQSq3rVJeLNeqz5JwWdTmdUr9bomLzhAnvPppr0by21Yu3oV1g7+hV6ejkxtvNLVLQtPCz8\n7t0n8PR0Zf2GKdjYWBMZaT4nV37/WiYtO87eE4kmqyEAy369SMT011g83ofFm86Z1aHRaPnxq5Oc\ni7xjcg07Vp5nwEfNGPfNq2Rlqs3K7/7zCp71qrD+mzf1z8GJG0b130VEET6/J2Gfd2fxqqOFXg88\n+RgA/LH7JPXr1yRifRByuTVHIi8a6o5EXmTKpNWkPVAWqacs8SLOSSWBnWMVHCtXx7GSU8l/noFe\ngeBFws/Pjx07dpjkvhQIBILHsTgEXkxMDPPmzeP06dP06NEDPz+/ooUsoFKlSiZJdNetW0dOTg5+\nfn6EhoaafJtevXp1EhMTDT/PmDGjQP3+/v74+/s/lW0x5xPp8ronAL4t3Yj65wa+LfXBErKyc1Gp\nNSyPiCTu2l3GBpgm4Iy5mEwXvzp6ea+aRJ25ja+X/pv3uw+yqVndHntbOaB3NtIzjSNbnj9zi3ad\n9FESvdvUJeZEAi1b618sb1y/y/xPt5ORnsPYoI40bGL+G321Wkv7nh64uFXkMf8KqcyKjxd25PMP\n/yhwDLrXr4TVQ8dFrQVrK2Mv5vuedbCRWaHW6kCHiaOUq9HSz8eZek4Kk/7zyFJp+N/R2/w4vlmB\nduQRE3ONLp29APB9tSFRUZfx9S04kmSuWkv/dm7Uc7E3cTABfv60HRKJhNt3s3AswIHU5Orw6Vob\np9r2RtegytagydWxf2McSQkZdBroYVY+5kISXdrqV3R8W9Qi6nQivi1qGertFNakZ+aADmwVRec2\ne9IxADh9Op43OjfXy/g2IDo6lja+DQCQyaR8/8M4AoaFFtl3WeJFnJMEhbNw1xmjn0W0SkF5w8HB\ngS1btrBr1y48PDywtbU11OUl/l61alUpWigod9TKoBLQaF4sALnfeJWuPYISo0gnTqlUsmjRIn78\n8UeqVq3Kd999xxtvmN96VxLs3buXRYsWMXz4cNzd3cnOzsbGxsaojVwuLzDJb0mSmanC9uGLvUJh\njTIrv88HadmcPnebLz7uhqNDBYZ9uJFta4cbyytzDS/ligoylNm5hroqFSuQlKrkXlo2yqxc4m+m\nkZWt5lEXKDMzB4Uiv/+sR/rv0uNl3hnSmrspmcyYvImVP40wew02FWS83NKFi6eTTeoaNS96q5mt\ntX6x9sq9HH4+d5c1vesa1VdW6B+hr/5KpE9D062UCrkU3/qVOXE1rcA+dpxKpkdzJ6ylRS8MZ2Zk\nY2unfx4UChuUyoJXEQEUNjJ8m1QnuoC0AxKJhE0HrxG88Qxzhpuf2OQVpHh4VePquXtG5cp0FQmX\n7tMvqCkKe2u+n3aUoLDXTG1Wqh55DqyNngOAAb2aMGDCZrQ6HbMnmG4JNNH3hGNgkLF9KGNrLNOy\nlXnns6zyIs9JAoHg+ebmzZt4eeX/LcrNzS2ktUAgeJEp1Ik7ePAgc+bMMUSD++STT8ye8SgpNm/e\nzOzZs+nRowdTp04FwMbGxuTlSKVSoVAonpkdIcsOEnM+kUae1VEq9ROoUqnC3i7/xc3RwQYXZwfc\naukdF+dqDty9p6SKNYSsPEbMxRQauVdBmZUnn2tYdQOwspIw7QMfJszdg1vNijSoVwVHezmPZqmz\ns7MhK08+U2V4cQd4a2Ar5HIZNWpWRCazQq3WIpNZvDu2SBYduc2ZpCyaVlfwH4+KzD5wk+DOtbGX\nS43aaXU6vvgzEYW1hKHNqhnKQ3Zc5XRCOq+85MDk7nUfV2/E3jOpzOtfv9A2IcGbiYm5RqPGtVFm\n6h0QZWY29nbmn4OQ/54h5so9mtWrzOR3Cv82vl+7OvRoU4sBcw/yejNnbG0sW6C2dbCmkpOCajX1\niVgdq1Ug434O9pX09ykkPJKYC0k08qj2yHOgMnoOsrJzWbYuij3rB6NWaxky6Vc6+tZBbtqdATv7\nChaNgYnMQ8ctMzMbOwtkyiIv6pzk5FQyuX1UKnvkD3+H5RY+50/Ks9ILRY+DXG7ctyXjVlJj+6x1\nlje9z8rW551169aVtgkCgaCcUOhf21GjRuHi4kJ4eDivvWa6wlCShIWFERoayuDBg41Cbru4uJCU\nlGTUNikpiRo1ajwzW6aM1W+l2v7HeY6dTMC7WS0io+Jp7+duaGOrkGOrsOZG4gOqVrYlOTWDShUV\noMxmygj9eZnt++M4FpOI98s1iDx1i/atXzLq51xcKutDepJ8V8mk+fupYCMzcuIaNanJyah4XvGq\nTfTRa7z6un7FJD0tiwnvr2fVxvd5cF+JTkeJOnAAk9roxzcxXcW4XfF8282NWo6mrsU3R+7gYGNF\nYGvj+zGlR+GOWx46nY47aTlUcyjMbYEpU/sCsH37cY4du4R3Sw8iIy/QvkNT8+2LcNxAv4V11MJI\nVkz2xVpmhVRqhdTMubmCkFeQIa8g5e4dJfYVbUi/m43tI2M05QP9Ftvt+y5z7J9beDd1IfLkTdq3\nyc9fptWBRqtDKpVgLZMhsQKNVltov02b1rFoDB7l5aZuHD92GW9vd44euUS7dk0svs6yxIs6JyUn\npxfdyAJSUzMMgUdUOSV/5kZuI3smevMoahxUKuO+i2rv5ORQYmP7LHWWN73P0tYXhZycHGJiYkhK\nSsLPz4/s7Oyo/ydRAAAgAElEQVRnOscIBILyR6Fv/m+//Tbbt29/5i9L4eHhhIaGEhgYaJIzydvb\nm+PHjxuVHT16lJYtWz5TmwC6dWhA7LVUBo7ZQFZ2Ln6t6hB/4x4hyw4CMDPoDSbN/o0h4zcyJsAX\nq8fOi3V7vS6x1+8zcNJvZGWr8WvhSvytNEJWHgNAJrWi34Rf+fDzvXwyyjSccPvOjbgWl8KYYWvJ\nylbh4lqJsMX7cHBU0LtfC8YMW8PMyb8UGZkyD4kE7txM56ewE8blhcis/ieFLLWW2Qdu8t62q2w4\nnUrCgxwWHblNapaaDadTibmTxXvbrvLetqtkFBKZLs8/up6SRcgOfZCWu5m5VFRY/s19t24tiI1L\nZOCABWRlqfDza2yxbN55vet3Mgj57xlkUit6+Nbi3c8PMeTLPxnRvT42j600mruG1FuZ7FypDybS\ne2wTfpx/khUfHaHTQA+TZwCgWzt3YuPvMnDiFv1z5F2b+JsPCAmPxE5hzUD/JgyauJVBgVsZ2KsJ\nigqFn4t7mjHo2tWLuLhE3h20iCxlDq61qrEwZKvxtRWppfR50eckgUDw/LN+/Xratm3LkCFDmDJl\nCjdu3OCzzz5j2LBhKJXlKwCVQCB4dkh05qI9/ItcuHCBvn370rdvXwIDA42CT9jb25OQkEDfvn0Z\nNWoU3bt3Z/v27axevZrNmzcbwn8/SsOGDQkODqZXr14mdUOGDMHNzY3PP/+8QHt0yT8U74Iy7hbd\nphCSi/lN2/X0+KIbFcIrG04VSx5A5m4+zYClSPwtSxtQIEd/L5488KtT0efMCqO3tVvRjQqjdtFB\nXgpDoy3+aojMqnOxdZRHytqcBCW3EpeYeKvcpRhYMqOv4f9JSQWfrYUnD2xS3lahyotesRL39Gza\ntIlZs2YxdOhQOnToQEBAAL/88gu3b99m6tSpvP3223z88celbWa5embKs96EhOs4hryDWyG7lf6+\nnUF1uQ6PKqa/H51/u4yktj535/61+rKCApsUpsdcvbXMily18c6h+HQVaVP+S+3aL5lTYRFiTrKc\nZ3d4wUJ27dqFVqtl06ZNbNq0yaguMDCQ0aNHs3TpUoKDgwkPD8fd3Z2wsDCzL0uWYC7MvEAgEOTx\nPM9JTk7VmTioLVWrPpuk3M9C75KCA32aIKJRCso7K1euJCAggGnTphmlGejUqRNBQUGsXr26TDhx\ngnLEDXvu52g4P8u1QAdNUD4pdScuKCiIoKCgQtvk5X2yhAsXCs6ZJQ4MCwSConie5ySZTIaLS02c\nnByQy5/Nt5LPQq9A8KJw48aNAreL169f3+Q8rkAgeHEp2WgYAoFAIBAIBIKnwtnZmX/++cds3fnz\n50VwE4FAYKDUV+IEAoFAIBAIBNCvXz+WLl2KQqGgffv2AGRlZbFnzx7CwsIYOnRo6RooEAjKDMKJ\ne5zsYp7nSCpeYBP7FfuLJd/8y0nFkj/Qr/iRr96o2LxY8vHp54sl79a8+JELuxX3N0NqWzz5uwnF\nEo+XFu85BHCv+GIGNhEIBILSYuTIkdy6dYuvv/6ar776CoDBgwcD0KNHD0aPHl2a5gkEgjKEcOIE\nAoFAIBAIygBWVlbMnTuX4cOHc+TIEe7fv4+DgwMtW7akYcOGpW2eQCAoQ5TambiUlBSmTZtG27Zt\nadWqFSNGjODy5cuG+sOHD9O7d2+aNWuGv78/hw4dMqtHpVLh7+/Ptm3bjMozMjL4/PPP6dChAy1a\ntGDQoEFER0c/02sSCATlFzEnlX8W7jpj9BEIyit16tShZcuWtGnThtdff104cIKnp1YGldyzaDQv\nFuugk6VtjaAEKZWVOK1Wy/jx4wEICwvD1taWJUuWEBAQwI4dO0hJSWHMmDGMHz+eLl26sG3bNsaN\nG8eWLVvw8PAw6MnIyCAoKIhLly6ZhOmeOXMmly5d4uuvv8bZ2Zn169czYsQItm7dSp06df7NyxUI\nBGUcMSc9GWq1muRk0yh5KtWzSV0gELxI/PDDD/zwww/cv3/fUFajRg0CAwN58803S9EygUBQligV\nJ+7ChQucOnWKnTt3GnIrLViwgNatW3PgwAFOnDiBl5cXo0aNAmDixIlER0cTERHB3LlzAfj77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iyR+4ua9Y8gBvVGxeLPl4SWqx5N3kbkU3KoLsYq5R20hti25UGHcTim5TCFekd4vXP+BeMbDo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VTEiYiIiIiI1CIq4kRERERERGoRFXEiIiIiIiK1iIo4ERERERGRWkRFnIiIiIiISC3iEEXc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\n4l3MnECoaQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<matplotlib.figure.Figure at 0x121df8690>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "# Render the tearsheet\n",
     "pf.create_returns_tear_sheet(returns, live_start_date='01-10-2010')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 2",
    "language": "python",
    "name": "python2"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
     "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
    "version": "2.7.10"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 0
 }