ml-finance-python
python scripts for finance machine learning
git clone https://9o.is/git/ml-finance-python.git
factor_library.ipynb
(727660B)
1 {
2 "cells": [
3 {
4 "cell_type": "markdown",
5 "metadata": {},
6 "source": [
7 "# Alpha Factor Library"
8 ]
9 },
10 {
11 "cell_type": "markdown",
12 "metadata": {},
13 "source": [
14 "This notebook contains a mumber of alpha factor candidates that we can use as features in ML models on the Quantopian platform."
15 ]
16 },
17 {
18 "cell_type": "code",
19 "execution_count": 277,
20 "metadata": {},
21 "outputs": [],
22 "source": [
23 "import pandas as pd\n",
24 "import numpy as np\n",
25 "from time import time\n",
26 "import talib\n",
27 "import re\n",
28 "from statsmodels.api import OLS\n",
29 "from sklearn.metrics import mean_squared_error\n",
30 "from scipy.stats import spearmanr\n",
31 "from sklearn.linear_model import LinearRegression, Ridge, RidgeCV, Lasso, LassoCV, LogisticRegression\n",
32 "from sklearn.preprocessing import StandardScaler\n",
33 "\n",
34 "from quantopian.research import run_pipeline\n",
35 "from quantopian.pipeline import Pipeline, factors, filters, classifiers\n",
36 "from quantopian.pipeline.data.builtin import USEquityPricing\n",
37 "from quantopian.pipeline.factors import (Latest, \n",
38 " Returns, \n",
39 " AverageDollarVolume, \n",
40 " SimpleMovingAverage,\n",
41 " EWMA,\n",
42 " BollingerBands,\n",
43 " CustomFactor,\n",
44 " MarketCap,\n",
45 " SimpleBeta)\n",
46 "from quantopian.pipeline.filters import QTradableStocksUS, StaticAssets\n",
47 "from quantopian.pipeline.data.quandl import fred_usdontd156n as libor\n",
48 "from empyrical import max_drawdown, sortino_ratio\n",
49 "\n",
50 "import seaborn as sns\n",
51 "import matplotlib.pyplot as plt"
52 ]
53 },
54 {
55 "cell_type": "markdown",
56 "metadata": {},
57 "source": [
58 "## Data Sources"
59 ]
60 },
61 {
62 "cell_type": "code",
63 "execution_count": 3,
64 "metadata": {},
65 "outputs": [],
66 "source": [
67 "################\n",
68 "# Fundamentals #\n",
69 "################\n",
70 "\n",
71 "# Morningstar fundamentals (2002 - Ongoing)\n",
72 "# https://www.quantopian.com/help/fundamentals\n",
73 "from quantopian.pipeline.data import Fundamentals\n",
74 "\n",
75 "#####################\n",
76 "# Analyst Estimates #\n",
77 "#####################\n",
78 "\n",
79 "# Earnings Surprises - Zacks (27 May 2006 - Ongoing)\n",
80 "# https://www.quantopian.com/data/zacks/earnings_surprises\n",
81 "from quantopian.pipeline.data.zacks import EarningsSurprises\n",
82 "from quantopian.pipeline.factors.zacks import BusinessDaysSinceEarningsSurprisesAnnouncement\n",
83 "\n",
84 "##########\n",
85 "# Events #\n",
86 "##########\n",
87 "\n",
88 "# Buyback Announcements - EventVestor (01 Jun 2007 - Ongoing)\n",
89 "# https://www.quantopian.com/data/eventvestor/buyback_auth\n",
90 "from quantopian.pipeline.data.eventvestor import BuybackAuthorizations\n",
91 "from quantopian.pipeline.factors.eventvestor import BusinessDaysSinceBuybackAuth\n",
92 "\n",
93 "# CEO Changes - EventVestor (01 Jan 2007 - Ongoing)\n",
94 "# https://www.quantopian.com/data/eventvestor/ceo_change\n",
95 "from quantopian.pipeline.data.eventvestor import CEOChangeAnnouncements\n",
96 "\n",
97 "# Dividends - EventVestor (01 Jan 2007 - Ongoing)\n",
98 "# https://www.quantopian.com/data/eventvestor/dividends\n",
99 "from quantopian.pipeline.data.eventvestor import (\n",
100 " DividendsByExDate,\n",
101 " DividendsByPayDate,\n",
102 " DividendsByAnnouncementDate,\n",
103 ")\n",
104 "from quantopian.pipeline.factors.eventvestor import (\n",
105 " BusinessDaysSincePreviousExDate,\n",
106 " BusinessDaysUntilNextExDate,\n",
107 " BusinessDaysSinceDividendAnnouncement,\n",
108 ")\n",
109 "\n",
110 "# Earnings Calendar - EventVestor (01 Jan 2007 - Ongoing)\n",
111 "# https://www.quantopian.com/data/eventvestor/earnings_calendar\n",
112 "from quantopian.pipeline.data.eventvestor import EarningsCalendar\n",
113 "from quantopian.pipeline.factors.eventvestor import (\n",
114 " BusinessDaysUntilNextEarnings,\n",
115 " BusinessDaysSincePreviousEarnings\n",
116 ")\n",
117 "\n",
118 "# 13D Filings - EventVestor (01 Jan 2007 - Ongoing)\n",
119 "# https://www.quantopian.com/data/eventvestor/_13d_filings\n",
120 "from quantopian.pipeline.data.eventvestor import _13DFilings\n",
121 "from quantopian.pipeline.factors.eventvestor import BusinessDaysSince13DFilingsDate\n",
122 "\n",
123 "#############\n",
124 "# Sentiment #\n",
125 "#############\n",
126 "\n",
127 "# News Sentiment - Sentdex Sentiment Analysis (15 Oct 2012 - Ongoing)\n",
128 "# https://www.quantopian.com/data/sentdex/sentiment\n",
129 "from quantopian.pipeline.data.sentdex import sentiment"
130 ]
131 },
132 {
133 "cell_type": "markdown",
134 "metadata": {},
135 "source": [
136 "### Setup"
137 ]
138 },
139 {
140 "cell_type": "code",
141 "execution_count": 4,
142 "metadata": {
143 "collapsed": true
144 },
145 "outputs": [],
146 "source": [
147 "# trading days per period\n",
148 "MONTH = 21\n",
149 "YEAR = 12 * MONTH"
150 ]
151 },
152 {
153 "cell_type": "code",
154 "execution_count": 5,
155 "metadata": {
156 "collapsed": true
157 },
158 "outputs": [],
159 "source": [
160 "START = '2014-01-01'\n",
161 "END = '2015-12-31'"
162 ]
163 },
164 {
165 "cell_type": "markdown",
166 "metadata": {},
167 "source": [
168 "#### Universe"
169 ]
170 },
171 {
172 "cell_type": "code",
173 "execution_count": 6,
174 "metadata": {
175 "collapsed": true
176 },
177 "outputs": [],
178 "source": [
179 "def Q100US():\n",
180 " return filters.make_us_equity_universe(\n",
181 " target_size=100,\n",
182 " rankby=factors.AverageDollarVolume(window_length=200),\n",
183 " mask=filters.default_us_equity_universe_mask(),\n",
184 " groupby=classifiers.fundamentals.Sector(),\n",
185 " max_group_weight=0.3,\n",
186 " smoothing_func=lambda f: f.downsample('month_start'),\n",
187 " )"
188 ]
189 },
190 {
191 "cell_type": "code",
192 "execution_count": 7,
193 "metadata": {},
194 "outputs": [],
195 "source": [
196 "# UNIVERSE = StaticAssets(symbols(['MSFT', 'AAPL']))\n",
197 "UNIVERSE = Q100US()"
198 ]
199 },
200 {
201 "cell_type": "code",
202 "execution_count": 8,
203 "metadata": {},
204 "outputs": [],
205 "source": [
206 "class AnnualizedData(CustomFactor):\n",
207 " # Get the sum of the last 4 reported values\n",
208 " window_length = 260\n",
209 "\n",
210 " def compute(self, today, assets, out, asof_date, values):\n",
211 " for asset in range(len(assets)):\n",
212 " # unique asof dates indicate availability of new figures\n",
213 " _, filing_dates = np.unique(asof_date[:, asset], return_index=True)\n",
214 " quarterly_values = values[filing_dates[-4:], asset]\n",
215 " # ignore annual windows with <4 quarterly data points\n",
216 " if len(~np.isnan(quarterly_values)) != 4: \n",
217 " out[asset] = np.nan\n",
218 " else:\n",
219 " out[asset] = np.sum(quarterly_values)"
220 ]
221 },
222 {
223 "cell_type": "code",
224 "execution_count": 9,
225 "metadata": {
226 "collapsed": true
227 },
228 "outputs": [],
229 "source": [
230 "class AnnualAvg(CustomFactor):\n",
231 " window_length = 252\n",
232 " \n",
233 " def compute(self, today, assets, out, values):\n",
234 " out[:] = (values[0] + values[-1])/2"
235 ]
236 },
237 {
238 "cell_type": "code",
239 "execution_count": 10,
240 "metadata": {
241 "collapsed": true
242 },
243 "outputs": [],
244 "source": [
245 "def factor_pipeline(factors):\n",
246 " start = time()\n",
247 " pipe = Pipeline({k: v(mask=UNIVERSE).rank() for k, v in factors.items()},\n",
248 " screen=UNIVERSE)\n",
249 " result = run_pipeline(pipe, start_date=START, end_date=END)\n",
250 " return result, time() - start"
251 ]
252 },
253 {
254 "cell_type": "markdown",
255 "metadata": {},
256 "source": [
257 "### Value Factors"
258 ]
259 },
260 {
261 "cell_type": "code",
262 "execution_count": 11,
263 "metadata": {},
264 "outputs": [],
265 "source": [
266 "class ValueFactors:\n",
267 " \"\"\"Definitions of factors for cross-sectional trading algorithms\"\"\"\n",
268 " \n",
269 " @staticmethod\n",
270 " def PriceToSalesTTM(**kwargs):\n",
271 " \"\"\"Last closing price divided by sales per share\"\"\" \n",
272 " return Fundamentals.ps_ratio.latest\n",
273 "\n",
274 " @staticmethod\n",
275 " def PriceToEarningsTTM(**kwargs):\n",
276 " \"\"\"Closing price divided by earnings per share (EPS)\"\"\"\n",
277 " return Fundamentals.pe_ratio.latest\n",
278 " \n",
279 " @staticmethod\n",
280 " def PriceToDilutedEarningsTTM(mask):\n",
281 " \"\"\"Closing price divided by diluted EPS\"\"\"\n",
282 " last_close = USEquityPricing.close.latest\n",
283 " diluted_eps = AnnualizedData(inputs = [Fundamentals.diluted_eps_earnings_reports_asof_date,\n",
284 " Fundamentals.diluted_eps_earnings_reports],\n",
285 " mask=mask)\n",
286 " return last_close / diluted_eps\n",
287 "\n",
288 " @staticmethod\n",
289 " def PriceToForwardEarnings(**kwargs):\n",
290 " \"\"\"Price to Forward Earnings\"\"\"\n",
291 " return Fundamentals.forward_pe_ratio.latest\n",
292 " \n",
293 " @staticmethod\n",
294 " def DividendYield(**kwargs):\n",
295 " \"\"\"Dividends per share divided by closing price\"\"\"\n",
296 " return Fundamentals.trailing_dividend_yield.latest\n",
297 "\n",
298 " @staticmethod\n",
299 " def PriceToFCF(mask):\n",
300 " \"\"\"Price to Free Cash Flow\"\"\"\n",
301 " last_close = USEquityPricing.close.latest\n",
302 " fcf_share = AnnualizedData(inputs = [Fundamentals.fcf_per_share_asof_date,\n",
303 " Fundamentals.fcf_per_share],\n",
304 " mask=mask)\n",
305 " return last_close / fcf_share\n",
306 "\n",
307 " @staticmethod\n",
308 " def PriceToOperatingCashflow(mask):\n",
309 " \"\"\"Last Close divided by Operating Cash Flows\"\"\"\n",
310 " last_close = USEquityPricing.close.latest\n",
311 " cfo_per_share = AnnualizedData(inputs = [Fundamentals.cfo_per_share_asof_date,\n",
312 " Fundamentals.cfo_per_share],\n",
313 " mask=mask) \n",
314 " return last_close / cfo_per_share\n",
315 "\n",
316 " @staticmethod\n",
317 " def PriceToBook(mask):\n",
318 " \"\"\"Closing price divided by book value\"\"\"\n",
319 " last_close = USEquityPricing.close.latest\n",
320 " book_value_per_share = AnnualizedData(inputs = [Fundamentals.book_value_per_share_asof_date,\n",
321 " Fundamentals.book_value_per_share],\n",
322 " mask=mask) \n",
323 " return last_close / book_value_per_share\n",
324 "\n",
325 "\n",
326 " @staticmethod\n",
327 " def EVToFCF(mask):\n",
328 " \"\"\"Enterprise Value divided by Free Cash Flows\"\"\"\n",
329 " fcf = AnnualizedData(inputs = [Fundamentals.free_cash_flow_asof_date,\n",
330 " Fundamentals.free_cash_flow],\n",
331 " mask=mask)\n",
332 " return Fundamentals.enterprise_value.latest / fcf\n",
333 "\n",
334 " @staticmethod\n",
335 " def EVToEBITDA(mask):\n",
336 " \"\"\"Enterprise Value to Earnings Before Interest, Taxes, Deprecation and Amortization (EBITDA)\"\"\"\n",
337 " ebitda = AnnualizedData(inputs = [Fundamentals.ebitda_asof_date,\n",
338 " Fundamentals.ebitda],\n",
339 " mask=mask)\n",
340 "\n",
341 " return Fundamentals.enterprise_value.latest / ebitda\n",
342 "\n",
343 " @staticmethod\n",
344 " def EBITDAYield(mask):\n",
345 " \"\"\"EBITDA divided by latest close\"\"\"\n",
346 " ebitda = AnnualizedData(inputs = [Fundamentals.ebitda_asof_date,\n",
347 " Fundamentals.ebitda],\n",
348 " mask=mask)\n",
349 " return USEquityPricing.close.latest / ebitda"
350 ]
351 },
352 {
353 "cell_type": "code",
354 "execution_count": 12,
355 "metadata": {},
356 "outputs": [],
357 "source": [
358 "VALUE_FACTORS = {\n",
359 " 'DividendYield' : ValueFactors.DividendYield,\n",
360 " 'EBITDAYield' : ValueFactors.EBITDAYield,\n",
361 " 'EVToEBITDA' : ValueFactors.EVToEBITDA,\n",
362 " 'EVToFCF' : ValueFactors.EVToFCF,\n",
363 " 'PriceToBook' : ValueFactors.PriceToBook,\n",
364 " 'PriceToDilutedEarningsTTM': ValueFactors.PriceToDilutedEarningsTTM,\n",
365 " 'PriceToEarningsTTM' : ValueFactors.PriceToEarningsTTM,\n",
366 " 'PriceToFCF' : ValueFactors.PriceToFCF,\n",
367 " 'PriceToForwardEarnings' : ValueFactors.PriceToForwardEarnings,\n",
368 " 'PriceToOperatingCashflow' : ValueFactors.PriceToOperatingCashflow,\n",
369 " 'PriceToSalesTTM' : ValueFactors.PriceToSalesTTM,\n",
370 "}"
371 ]
372 },
373 {
374 "cell_type": "code",
375 "execution_count": 13,
376 "metadata": {},
377 "outputs": [
378 {
379 "name": "stderr",
380 "output_type": "stream",
381 "text": [
382 "/usr/local/lib/python2.7/dist-packages/numpy/lib/arraysetops.py:200: FutureWarning: In the future, NAT != NAT will be True rather than False.\n",
383 " flag = np.concatenate(([True], aux[1:] != aux[:-1]))\n"
384 ]
385 },
386 {
387 "name": "stdout",
388 "output_type": "stream",
389 "text": [
390 "Pipeline run time 91.12 secs\n",
391 "<class 'pandas.core.frame.DataFrame'>\n",
392 "MultiIndex: 50362 entries, (2014-01-02 00:00:00+00:00, Equity(24 [AAPL])) to (2015-12-31 00:00:00+00:00, Equity(47208 [GPRO]))\n",
393 "Data columns (total 11 columns):\n",
394 "DividendYield 40772 non-null float64\n",
395 "EBITDAYield 49823 non-null float64\n",
396 "EVToEBITDA 49823 non-null float64\n",
397 "EVToFCF 46400 non-null float64\n",
398 "PriceToBook 50343 non-null float64\n",
399 "PriceToDilutedEarningsTTM 50215 non-null float64\n",
400 "PriceToEarningsTTM 48956 non-null float64\n",
401 "PriceToFCF 49133 non-null float64\n",
402 "PriceToForwardEarnings 39607 non-null float64\n",
403 "PriceToOperatingCashflow 50343 non-null float64\n",
404 "PriceToSalesTTM 50362 non-null float64\n",
405 "dtypes: float64(11)\n",
406 "memory usage: 4.6+ MB\n"
407 ]
408 }
409 ],
410 "source": [
411 "value_result, t = factor_pipeline(VALUE_FACTORS)\n",
412 "print('Pipeline run time {:.2f} secs'.format(t))\n",
413 "value_result.info()"
414 ]
415 },
416 {
417 "cell_type": "markdown",
418 "metadata": {},
419 "source": [
420 "### Momentum"
421 ]
422 },
423 {
424 "cell_type": "code",
425 "execution_count": 14,
426 "metadata": {},
427 "outputs": [],
428 "source": [
429 "class MomentumFactors:\n",
430 " \"\"\"Custom Momentum Factors\"\"\"\n",
431 " class PercentAboveLow(CustomFactor):\n",
432 " \"\"\"Percentage of current close above low \n",
433 " in lookback window of window_length days\n",
434 " \"\"\"\n",
435 " inputs = [USEquityPricing.close]\n",
436 " window_length = 252\n",
437 "\n",
438 " def compute(self, today, assets, out, close):\n",
439 " out[:] = close[-1] / np.min(close, axis=0) - 1\n",
440 "\n",
441 " class PercentBelowHigh(CustomFactor):\n",
442 " \"\"\"Percentage of current close below high \n",
443 " in lookback window of window_length days\n",
444 " \"\"\"\n",
445 " \n",
446 " inputs = [USEquityPricing.close]\n",
447 " window_length = 252\n",
448 " \n",
449 " def compute(self, today, assets, out, close):\n",
450 " out[:] = close[-1] / np.max(close, axis=0) - 1\n",
451 "\n",
452 " @staticmethod\n",
453 " def make_dx(timeperiod=14):\n",
454 " class DX(CustomFactor):\n",
455 " \"\"\"Directional Movement Index\"\"\"\n",
456 " inputs = [USEquityPricing.high, \n",
457 " USEquityPricing.low, \n",
458 " USEquityPricing.close]\n",
459 " window_length = timeperiod + 1\n",
460 " \n",
461 " def compute(self, today, assets, out, high, low, close):\n",
462 " out[:] = [talib.DX(high[:, i], \n",
463 " low[:, i], \n",
464 " close[:, i], \n",
465 " timeperiod=timeperiod)[-1] \n",
466 " for i in range(len(assets))]\n",
467 " return DX \n",
468 "\n",
469 " @staticmethod\n",
470 " def make_mfi(timeperiod=14):\n",
471 " class MFI(CustomFactor):\n",
472 " \"\"\"Money Flow Index\"\"\"\n",
473 " inputs = [USEquityPricing.high, \n",
474 " USEquityPricing.low, \n",
475 " USEquityPricing.close,\n",
476 " USEquityPricing.volume]\n",
477 " window_length = timeperiod + 1\n",
478 " \n",
479 " def compute(self, today, assets, out, high, low, close, vol):\n",
480 " out[:] = [talib.MFI(high[:, i], \n",
481 " low[:, i], \n",
482 " close[:, i],\n",
483 " vol[:, i],\n",
484 " timeperiod=timeperiod)[-1] \n",
485 " for i in range(len(assets))]\n",
486 " return MFI \n",
487 "\n",
488 " @staticmethod\n",
489 " def make_oscillator(fastperiod=12, slowperiod=26, matype=0):\n",
490 " class PPO(CustomFactor):\n",
491 " \"\"\"12/26-Day Percent Price Oscillator\"\"\"\n",
492 " inputs = [USEquityPricing.close]\n",
493 " window_length = slowperiod\n",
494 "\n",
495 " def compute(self, today, assets, out, close_prices):\n",
496 " out[:] = [talib.PPO(close,\n",
497 " fastperiod=fastperiod,\n",
498 " slowperiod=slowperiod, \n",
499 " matype=matype)[-1]\n",
500 " for close in close_prices.T]\n",
501 " return PPO\n",
502 "\n",
503 " @staticmethod\n",
504 " def make_stochastic_oscillator(fastk_period=5, slowk_period=3, slowd_period=3, \n",
505 " slowk_matype=0, slowd_matype=0): \n",
506 " class StochasticOscillator(CustomFactor):\n",
507 " \"\"\"20-day Stochastic Oscillator \"\"\"\n",
508 " inputs = [USEquityPricing.high, \n",
509 " USEquityPricing.low, \n",
510 " USEquityPricing.close]\n",
511 " outputs = ['slowk', 'slowd']\n",
512 " window_length = fastk_period * 2\n",
513 " \n",
514 " def compute(self, today, assets, out, high, low, close):\n",
515 " slowk, slowd = [talib.STOCH(high[:, i],\n",
516 " low[:, i],\n",
517 " close[:, i], \n",
518 " fastk_period=fastk_period,\n",
519 " slowk_period=slowk_period, \n",
520 " slowk_matype=slowk_matype, \n",
521 " slowd_period=slowd_period, \n",
522 " slowd_matype=slowd_matype)[-1] \n",
523 " for i in range(len(assets))]\n",
524 "\n",
525 " out.slowk[:], out.slowd[:] = slowk[-1], slowd[-1]\n",
526 " return StochasticOscillator\n",
527 " \n",
528 " @staticmethod\n",
529 " def make_trendline(timeperiod=252): \n",
530 " class Trendline(CustomFactor):\n",
531 " inputs = [USEquityPricing.close]\n",
532 " \"\"\"52-Week Trendline\"\"\"\n",
533 " window_length = timeperiod\n",
534 "\n",
535 " def compute(self, today, assets, out, close_prices):\n",
536 " out[:] = [talib.LINEARREG_SLOPE(close, \n",
537 " timeperiod=timeperiod)[-1] \n",
538 " for close in close_prices.T]\n",
539 " return Trendline"
540 ]
541 },
542 {
543 "cell_type": "code",
544 "execution_count": 15,
545 "metadata": {
546 "collapsed": true
547 },
548 "outputs": [],
549 "source": [
550 "MOMENTUM_FACTORS = {\n",
551 " 'Percent Above Low' : MomentumFactors.PercentAboveLow,\n",
552 " 'Percent Below High' : MomentumFactors.PercentBelowHigh,\n",
553 " 'Price Oscillator' : MomentumFactors.make_oscillator(),\n",
554 " 'Money Flow Index' : MomentumFactors.make_mfi(),\n",
555 " 'Directional Movement Index' : MomentumFactors.make_dx(),\n",
556 " 'Trendline' : MomentumFactors.make_trendline()\n",
557 "}"
558 ]
559 },
560 {
561 "cell_type": "code",
562 "execution_count": 16,
563 "metadata": {},
564 "outputs": [
565 {
566 "name": "stdout",
567 "output_type": "stream",
568 "text": [
569 "Pipeline run time 20.53 secs\n",
570 "<class 'pandas.core.frame.DataFrame'>\n",
571 "MultiIndex: 50362 entries, (2014-01-02 00:00:00+00:00, Equity(24 [AAPL])) to (2015-12-31 00:00:00+00:00, Equity(47208 [GPRO]))\n",
572 "Data columns (total 6 columns):\n",
573 "Directional Movement Index 50362 non-null float64\n",
574 "Money Flow Index 50362 non-null float64\n",
575 "Percent Above Low 49536 non-null float64\n",
576 "Percent Below High 49536 non-null float64\n",
577 "Price Oscillator 50355 non-null float64\n",
578 "Trendline 49536 non-null float64\n",
579 "dtypes: float64(6)\n",
580 "memory usage: 2.7+ MB\n"
581 ]
582 }
583 ],
584 "source": [
585 "momentum_result, t = factor_pipeline(MOMENTUM_FACTORS)\n",
586 "print('Pipeline run time {:.2f} secs'.format(t))\n",
587 "momentum_result.info()"
588 ]
589 },
590 {
591 "cell_type": "markdown",
592 "metadata": {},
593 "source": [
594 "### Efficiency"
595 ]
596 },
597 {
598 "cell_type": "code",
599 "execution_count": 17,
600 "metadata": {
601 "collapsed": true
602 },
603 "outputs": [],
604 "source": [
605 "class EfficiencyFactors:\n",
606 "\n",
607 " @staticmethod\n",
608 " def CapexToAssets(mask):\n",
609 " \"\"\"Capital Expenditure divided by Total Assets\"\"\"\n",
610 " capex = AnnualizedData(inputs = [Fundamentals.capital_expenditure_asof_date,\n",
611 " Fundamentals.capital_expenditure],\n",
612 " mask=mask) \n",
613 " assets = Fundamentals.total_assets.latest\n",
614 " return - capex / assets\n",
615 "\n",
616 " @staticmethod\n",
617 " def CapexToSales(mask):\n",
618 " \"\"\"Capital Expenditure divided by Total Revenue\"\"\"\n",
619 " capex = AnnualizedData(inputs = [Fundamentals.capital_expenditure_asof_date,\n",
620 " Fundamentals.capital_expenditure],\n",
621 " mask=mask) \n",
622 " revenue = AnnualizedData(inputs = [Fundamentals.total_revenue_asof_date,\n",
623 " Fundamentals.total_revenue],\n",
624 " mask=mask) \n",
625 " return - capex / revenue\n",
626 " \n",
627 " @staticmethod\n",
628 " def CapexToFCF(mask):\n",
629 " \"\"\"Capital Expenditure divided by Free Cash Flows\"\"\"\n",
630 " capex = AnnualizedData(inputs = [Fundamentals.capital_expenditure_asof_date,\n",
631 " Fundamentals.capital_expenditure],\n",
632 " mask=mask) \n",
633 " free_cash_flow = AnnualizedData(inputs = [Fundamentals.free_cash_flow_asof_date,\n",
634 " Fundamentals.free_cash_flow],\n",
635 " mask=mask) \n",
636 " return - capex / free_cash_flow\n",
637 "\n",
638 " @staticmethod\n",
639 " def EBITToAssets(mask):\n",
640 " \"\"\"Earnings Before Interest and Taxes (EBIT) divided by Total Assets\"\"\"\n",
641 " ebit = AnnualizedData(inputs = [Fundamentals.ebit_asof_date,\n",
642 " Fundamentals.ebit],\n",
643 " mask=mask) \n",
644 " assets = Fundamentals.total_assets.latest\n",
645 " return ebit / assets\n",
646 " \n",
647 " @staticmethod\n",
648 " def CFOToAssets(mask):\n",
649 " \"\"\"Operating Cash Flows divided by Total Assets\"\"\"\n",
650 " cfo = AnnualizedData(inputs = [Fundamentals.operating_cash_flow_asof_date,\n",
651 " Fundamentals.operating_cash_flow],\n",
652 " mask=mask) \n",
653 " assets = Fundamentals.total_assets.latest\n",
654 " return cfo / assets \n",
655 " \n",
656 " @staticmethod\n",
657 " def RetainedEarningsToAssets(mask):\n",
658 " \"\"\"Retained Earnings divided by Total Assets\"\"\"\n",
659 " retained_earnings = AnnualizedData(inputs = [Fundamentals.retained_earnings_asof_date,\n",
660 " Fundamentals.retained_earnings],\n",
661 " mask=mask) \n",
662 " assets = Fundamentals.total_assets.latest\n",
663 " return retained_earnings / assets"
664 ]
665 },
666 {
667 "cell_type": "code",
668 "execution_count": 18,
669 "metadata": {},
670 "outputs": [],
671 "source": [
672 "EFFICIENCY_FACTORS = {\n",
673 " 'CFO To Assets' :EfficiencyFactors.CFOToAssets,\n",
674 " 'Capex To Assets' :EfficiencyFactors.CapexToAssets,\n",
675 " 'Capex To FCF' :EfficiencyFactors.CapexToFCF,\n",
676 " 'Capex To Sales' :EfficiencyFactors.CapexToSales,\n",
677 " 'EBIT To Assets' :EfficiencyFactors.EBITToAssets,\n",
678 " 'Retained Earnings To Assets' :EfficiencyFactors.RetainedEarningsToAssets\n",
679 " }"
680 ]
681 },
682 {
683 "cell_type": "code",
684 "execution_count": 19,
685 "metadata": {},
686 "outputs": [
687 {
688 "name": "stdout",
689 "output_type": "stream",
690 "text": [
691 "Pipeline run time 35.88 secs\n",
692 "<class 'pandas.core.frame.DataFrame'>\n",
693 "MultiIndex: 50362 entries, (2014-01-02 00:00:00+00:00, Equity(24 [AAPL])) to (2015-12-31 00:00:00+00:00, Equity(47208 [GPRO]))\n",
694 "Data columns (total 6 columns):\n",
695 "CFO To Assets 50351 non-null float64\n",
696 "Capex To Assets 46997 non-null float64\n",
697 "Capex To FCF 45799 non-null float64\n",
698 "Capex To Sales 46997 non-null float64\n",
699 "EBIT To Assets 46635 non-null float64\n",
700 "Retained Earnings To Assets 50349 non-null float64\n",
701 "dtypes: float64(6)\n",
702 "memory usage: 2.7+ MB\n"
703 ]
704 }
705 ],
706 "source": [
707 "efficiency_result, t = factor_pipeline(EFFICIENCY_FACTORS)\n",
708 "print('Pipeline run time {:.2f} secs'.format(t))\n",
709 "efficiency_result.info()"
710 ]
711 },
712 {
713 "cell_type": "markdown",
714 "metadata": {},
715 "source": [
716 "### Risk"
717 ]
718 },
719 {
720 "cell_type": "code",
721 "execution_count": 20,
722 "metadata": {},
723 "outputs": [],
724 "source": [
725 "class RiskFactors:\n",
726 "\n",
727 " @staticmethod\n",
728 " def LogMarketCap(mask):\n",
729 " \"\"\"Log of Market Capitalization log(Close Price * Shares Outstanding)\"\"\"\n",
730 " return np.log(MarketCap(mask=mask))\n",
731 " \n",
732 " class DownsideRisk(CustomFactor):\n",
733 " \"\"\"Mean returns divided by std of 1yr daily losses (Sortino Ratio)\"\"\"\n",
734 " inputs = [USEquityPricing.close]\n",
735 " window_length = 252\n",
736 "\n",
737 " def compute(self, today, assets, out, close):\n",
738 " ret = pd.DataFrame(close).pct_change()\n",
739 " out[:] = ret.mean().div(ret.where(ret<0).std())\n",
740 "\n",
741 " @staticmethod\n",
742 " def MarketBeta(**kwargs):\n",
743 " \"\"\"Slope of 1-yr regression of price returns against index returns\"\"\"\n",
744 " return SimpleBeta(target=symbols('SPY'), regression_length=252) \n",
745 "\n",
746 " class DownsideBeta(CustomFactor):\n",
747 " \"\"\"Slope of 1yr regression of returns on negative index returns\"\"\"\n",
748 " inputs = [USEquityPricing.close]\n",
749 " window_length = 252\n",
750 "\n",
751 " def compute(self, today, assets, out, close):\n",
752 " t = len(close)\n",
753 " assets = pd.DataFrame(close).pct_change()\n",
754 " \n",
755 " start_date = (today - pd.DateOffset(years=1)).strftime('%Y-%m-%d')\n",
756 " spy = get_pricing('SPY', \n",
757 " start_date=start_date, \n",
758 " end_date=today.strftime('%Y-%m-%d')).reset_index(drop=True)\n",
759 " spy_neg_ret = (spy\n",
760 " .close_price\n",
761 " .iloc[-t:]\n",
762 " .pct_change()\n",
763 " .pipe(lambda x: x.where(x<0)))\n",
764 " \n",
765 " out[:] = assets.apply(lambda x: x.cov(spy_neg_ret)).div(spy_neg_ret.var()) \n",
766 "\n",
767 " class Vol3M(CustomFactor):\n",
768 " \"\"\"3-month Volatility: Standard deviation of returns over 3 months\"\"\"\n",
769 "\n",
770 " inputs = [USEquityPricing.close]\n",
771 " window_length = 63\n",
772 "\n",
773 " def compute(self, today, assets, out, close):\n",
774 " out[:] = np.log1p(pd.DataFrame(close).pct_change()).std()"
775 ]
776 },
777 {
778 "cell_type": "code",
779 "execution_count": 21,
780 "metadata": {},
781 "outputs": [],
782 "source": [
783 "RISK_FACTORS = {\n",
784 " 'Log Market Cap' : RiskFactors.LogMarketCap,\n",
785 " 'Downside Risk' : RiskFactors.DownsideRisk,\n",
786 " 'Index Beta' : RiskFactors.MarketBeta,\n",
787 "# 'Downside Beta' : RiskFactors.DownsideBeta, \n",
788 " 'Volatility 3M' : RiskFactors.Vol3M, \n",
789 "}"
790 ]
791 },
792 {
793 "cell_type": "code",
794 "execution_count": 22,
795 "metadata": {},
796 "outputs": [
797 {
798 "name": "stdout",
799 "output_type": "stream",
800 "text": [
801 "Pipeline run time 46.10 secs\n",
802 "<class 'pandas.core.frame.DataFrame'>\n",
803 "MultiIndex: 50362 entries, (2014-01-02 00:00:00+00:00, Equity(24 [AAPL])) to (2015-12-31 00:00:00+00:00, Equity(47208 [GPRO]))\n",
804 "Data columns (total 4 columns):\n",
805 "Downside Risk 50362 non-null float64\n",
806 "Index Beta 50079 non-null float64\n",
807 "Log Market Cap 50362 non-null float64\n",
808 "Volatility 3M 50362 non-null float64\n",
809 "dtypes: float64(4)\n",
810 "memory usage: 1.9+ MB\n"
811 ]
812 }
813 ],
814 "source": [
815 "risk_result, t = factor_pipeline(RISK_FACTORS)\n",
816 "print('Pipeline run time {:.2f} secs'.format(t))\n",
817 "risk_result.info()"
818 ]
819 },
820 {
821 "cell_type": "markdown",
822 "metadata": {},
823 "source": [
824 "### Growth"
825 ]
826 },
827 {
828 "cell_type": "code",
829 "execution_count": 23,
830 "metadata": {},
831 "outputs": [],
832 "source": [
833 "def growth_pipeline():\n",
834 " revenue = AnnualizedData(inputs = [Fundamentals.total_revenue_asof_date,\n",
835 " Fundamentals.total_revenue],\n",
836 " mask=UNIVERSE)\n",
837 " eps = AnnualizedData(inputs = [Fundamentals.diluted_eps_earnings_reports_asof_date,\n",
838 " Fundamentals.diluted_eps_earnings_reports],\n",
839 " mask=UNIVERSE) \n",
840 "\n",
841 " return Pipeline({'Sales': revenue,\n",
842 " 'EPS': eps,\n",
843 " 'Total Assets': Fundamentals.total_assets.latest,\n",
844 " 'Net Debt': Fundamentals.net_debt.latest},\n",
845 " screen=UNIVERSE)"
846 ]
847 },
848 {
849 "cell_type": "code",
850 "execution_count": 24,
851 "metadata": {},
852 "outputs": [
853 {
854 "name": "stdout",
855 "output_type": "stream",
856 "text": [
857 "Pipeline run time 22.21 secs\n",
858 "<class 'pandas.core.frame.DataFrame'>\n",
859 "MultiIndex: 50362 entries, (2014-01-02 00:00:00+00:00, Equity(24 [AAPL])) to (2015-12-31 00:00:00+00:00, Equity(47208 [GPRO]))\n",
860 "Data columns (total 12 columns):\n",
861 "EPS 50215 non-null float64\n",
862 "Net Debt 47413 non-null float64\n",
863 "Sales 50351 non-null float64\n",
864 "Total Assets 50362 non-null float64\n",
865 "EPS Growth 3M 50152 non-null float64\n",
866 "EPS Growth 12M 49963 non-null float64\n",
867 "Net Debt Growth 3M 47350 non-null float64\n",
868 "Net Debt Growth 12M 47171 non-null float64\n",
869 "Sales Growth 3M 50288 non-null float64\n",
870 "Sales Growth 12M 50099 non-null float64\n",
871 "Total Assets Growth 3M 50299 non-null float64\n",
872 "Total Assets Growth 12M 50110 non-null float64\n",
873 "dtypes: float64(12)\n",
874 "memory usage: 5.0+ MB\n"
875 ]
876 }
877 ],
878 "source": [
879 "start_timer = time()\n",
880 "growth_result = run_pipeline(growth_pipeline(), start_date=START, end_date=END)\n",
881 "\n",
882 "for col in growth_result.columns:\n",
883 " for month in [3, 12]:\n",
884 " new_col = col + ' Growth {}M'.format(month)\n",
885 " kwargs = {new_col: growth_result[col].pct_change(month*MONTH).groupby(level=1).rank()} \n",
886 " growth_result = growth_result.assign(**kwargs)\n",
887 "print('Pipeline run time {:.2f} secs'.format(time() - start_timer))\n",
888 "growth_result.info()"
889 ]
890 },
891 {
892 "cell_type": "markdown",
893 "metadata": {},
894 "source": [
895 "### Quality"
896 ]
897 },
898 {
899 "cell_type": "code",
900 "execution_count": 25,
901 "metadata": {},
902 "outputs": [],
903 "source": [
904 "class QualityFactors:\n",
905 " \n",
906 " @staticmethod\n",
907 " def AssetTurnover(mask):\n",
908 " \"\"\"Sales divided by average of year beginning and year end assets\"\"\"\n",
909 "\n",
910 " assets = AnnualAvg(inputs=[Fundamentals.total_assets],\n",
911 " mask=mask)\n",
912 " sales = AnnualizedData([Fundamentals.total_revenue_asof_date,\n",
913 " Fundamentals.total_revenue], mask=mask)\n",
914 " return sales / assets\n",
915 " \n",
916 " @staticmethod\n",
917 " def CurrentRatio(mask):\n",
918 " \"\"\"Total current assets divided by total current liabilities\"\"\"\n",
919 "\n",
920 " assets = Fundamentals.current_assets.latest\n",
921 " liabilities = Fundamentals.current_liabilities.latest\n",
922 " return assets / liabilities\n",
923 " \n",
924 " @staticmethod\n",
925 " def AssetToEquityRatio(mask):\n",
926 " \"\"\"Total current assets divided by common equity\"\"\"\n",
927 "\n",
928 " assets = Fundamentals.current_assets.latest\n",
929 " equity = Fundamentals.common_stock.latest\n",
930 " return assets / equity \n",
931 "\n",
932 " \n",
933 " @staticmethod\n",
934 " def InterestCoverage(mask):\n",
935 " \"\"\"EBIT divided by interest expense\"\"\"\n",
936 "\n",
937 " ebit = AnnualizedData(inputs = [Fundamentals.ebit_asof_date,\n",
938 " Fundamentals.ebit], mask=mask) \n",
939 " \n",
940 " interest_expense = AnnualizedData(inputs = [Fundamentals.interest_expense_asof_date,\n",
941 " Fundamentals.interest_expense], mask=mask)\n",
942 " return ebit / interest_expense\n",
943 "\n",
944 " @staticmethod\n",
945 " def DebtToAssetRatio(mask):\n",
946 " \"\"\"Total Debts divided by Total Assets\"\"\"\n",
947 "\n",
948 " debt = Fundamentals.total_debt.latest\n",
949 " assets = Fundamentals.total_assets.latest\n",
950 " return debt / assets\n",
951 " \n",
952 " @staticmethod\n",
953 " def DebtToEquityRatio(mask):\n",
954 " \"\"\"Total Debts divided by Common Stock Equity\"\"\"\n",
955 "\n",
956 " debt = Fundamentals.total_debt.latest\n",
957 " equity = Fundamentals.common_stock.latest\n",
958 " return debt / equity \n",
959 "\n",
960 " @staticmethod\n",
961 " def WorkingCapitalToAssets(mask):\n",
962 " \"\"\"Current Assets less Current liabilities (Working Capital) divided by Assets\"\"\"\n",
963 "\n",
964 " working_capital = Fundamentals.working_capital.latest\n",
965 " assets = Fundamentals.total_assets.latest\n",
966 " return working_capital / assets\n",
967 " \n",
968 " @staticmethod\n",
969 " def WorkingCapitalToSales(mask):\n",
970 " \"\"\"Current Assets less Current liabilities (Working Capital), divided by Sales\"\"\"\n",
971 "\n",
972 " working_capital = Fundamentals.working_capital.latest\n",
973 " sales = AnnualizedData([Fundamentals.total_revenue_asof_date,\n",
974 " Fundamentals.total_revenue], mask=mask) \n",
975 " return working_capital / sales \n",
976 " \n",
977 " \n",
978 " class MertonsDD(CustomFactor):\n",
979 " \"\"\"Merton's Distance to Default \"\"\"\n",
980 " \n",
981 " inputs = [Fundamentals.total_assets,\n",
982 " Fundamentals.total_liabilities, \n",
983 " libor.value, \n",
984 " USEquityPricing.close]\n",
985 " window_length = 252\n",
986 "\n",
987 " def compute(self, today, assets, out, tot_assets, tot_liabilities, r, close):\n",
988 " mertons = []\n",
989 "\n",
990 " for col_assets, col_liabilities, col_r, col_close in zip(tot_assets.T, tot_liabilities.T,\n",
991 " r.T, close.T):\n",
992 " vol_1y = np.nanstd(col_close)\n",
993 " numerator = np.log(\n",
994 " col_assets[-1] / col_liabilities[-1]) + ((252 * col_r[-1]) - ((vol_1y ** 2) / 2))\n",
995 " mertons.append(numerator / vol_1y)\n",
996 "\n",
997 " out[:] = mertons "
998 ]
999 },
1000 {
1001 "cell_type": "code",
1002 "execution_count": 26,
1003 "metadata": {
1004 "scrolled": true
1005 },
1006 "outputs": [],
1007 "source": [
1008 "QUALITY_FACTORS = {\n",
1009 " 'AssetToEquityRatio' : QualityFactors.AssetToEquityRatio,\n",
1010 " 'AssetTurnover' : QualityFactors.AssetTurnover,\n",
1011 " 'CurrentRatio' : QualityFactors.CurrentRatio,\n",
1012 " 'DebtToAssetRatio' : QualityFactors.DebtToAssetRatio,\n",
1013 " 'DebtToEquityRatio' : QualityFactors.DebtToEquityRatio,\n",
1014 " 'InterestCoverage' : QualityFactors.InterestCoverage,\n",
1015 " 'MertonsDD' : QualityFactors.MertonsDD,\n",
1016 " 'WorkingCapitalToAssets': QualityFactors.WorkingCapitalToAssets,\n",
1017 " 'WorkingCapitalToSales' : QualityFactors.WorkingCapitalToSales,\n",
1018 "}\n",
1019 " "
1020 ]
1021 },
1022 {
1023 "cell_type": "code",
1024 "execution_count": 27,
1025 "metadata": {},
1026 "outputs": [
1027 {
1028 "name": "stdout",
1029 "output_type": "stream",
1030 "text": [
1031 "Pipeline run time 34.41 secs\n",
1032 "<class 'pandas.core.frame.DataFrame'>\n",
1033 "MultiIndex: 50362 entries, (2014-01-02 00:00:00+00:00, Equity(24 [AAPL])) to (2015-12-31 00:00:00+00:00, Equity(47208 [GPRO]))\n",
1034 "Data columns (total 9 columns):\n",
1035 "AssetToEquityRatio 45176 non-null float64\n",
1036 "AssetTurnover 50314 non-null float64\n",
1037 "CurrentRatio 45680 non-null float64\n",
1038 "DebtToAssetRatio 50080 non-null float64\n",
1039 "DebtToEquityRatio 48492 non-null float64\n",
1040 "InterestCoverage 35250 non-null float64\n",
1041 "MertonsDD 50362 non-null float64\n",
1042 "WorkingCapitalToAssets 45680 non-null float64\n",
1043 "WorkingCapitalToSales 45669 non-null float64\n",
1044 "dtypes: float64(9)\n",
1045 "memory usage: 3.8+ MB\n"
1046 ]
1047 }
1048 ],
1049 "source": [
1050 "quality_result, t = factor_pipeline(QUALITY_FACTORS)\n",
1051 "print('Pipeline run time {:.2f} secs'.format(t))\n",
1052 "quality_result.info()"
1053 ]
1054 },
1055 {
1056 "cell_type": "markdown",
1057 "metadata": {},
1058 "source": [
1059 "### Payout"
1060 ]
1061 },
1062 {
1063 "cell_type": "code",
1064 "execution_count": 28,
1065 "metadata": {},
1066 "outputs": [],
1067 "source": [
1068 "class PayoutFactors:\n",
1069 "\n",
1070 " @staticmethod\n",
1071 " def DividendPayoutRatio(mask):\n",
1072 " \"\"\"Dividends Per Share divided by Earnings Per Share\"\"\"\n",
1073 "\n",
1074 " dps = AnnualizedData(inputs = [Fundamentals.dividend_per_share_earnings_reports_asof_date,\n",
1075 " Fundamentals.dividend_per_share_earnings_reports], mask=mask) \n",
1076 " \n",
1077 " eps = AnnualizedData(inputs = [Fundamentals.basic_eps_earnings_reports_asof_date,\n",
1078 " Fundamentals.basic_eps_earnings_reports], mask=mask)\n",
1079 " return dps / eps\n",
1080 " \n",
1081 " @staticmethod\n",
1082 " def DividendGrowth(**kwargs):\n",
1083 " \"\"\"Annualized percentage DPS change\"\"\" \n",
1084 " return Fundamentals.dps_growth.latest "
1085 ]
1086 },
1087 {
1088 "cell_type": "code",
1089 "execution_count": 29,
1090 "metadata": {},
1091 "outputs": [],
1092 "source": [
1093 "PAYOUT_FACTORS = {\n",
1094 " 'Dividend Payout Ratio': PayoutFactors.DividendPayoutRatio,\n",
1095 " 'Dividend Growth': PayoutFactors.DividendGrowth\n",
1096 "}"
1097 ]
1098 },
1099 {
1100 "cell_type": "code",
1101 "execution_count": 30,
1102 "metadata": {},
1103 "outputs": [
1104 {
1105 "name": "stdout",
1106 "output_type": "stream",
1107 "text": [
1108 "Pipeline run time 21.85 secs\n",
1109 "<class 'pandas.core.frame.DataFrame'>\n",
1110 "MultiIndex: 50362 entries, (2014-01-02 00:00:00+00:00, Equity(24 [AAPL])) to (2015-12-31 00:00:00+00:00, Equity(47208 [GPRO]))\n",
1111 "Data columns (total 2 columns):\n",
1112 "Dividend Growth 40517 non-null float64\n",
1113 "Dividend Payout Ratio 39947 non-null float64\n",
1114 "dtypes: float64(2)\n",
1115 "memory usage: 1.2+ MB\n"
1116 ]
1117 }
1118 ],
1119 "source": [
1120 "payout_result, t = factor_pipeline(PAYOUT_FACTORS)\n",
1121 "print('Pipeline run time {:.2f} secs'.format(t))\n",
1122 "payout_result.info()"
1123 ]
1124 },
1125 {
1126 "cell_type": "markdown",
1127 "metadata": {},
1128 "source": [
1129 "### Profitability"
1130 ]
1131 },
1132 {
1133 "cell_type": "code",
1134 "execution_count": 31,
1135 "metadata": {},
1136 "outputs": [],
1137 "source": [
1138 "class ProfitabilityFactors:\n",
1139 " \n",
1140 " @staticmethod\n",
1141 " def GrossProfitMargin(mask):\n",
1142 " \"\"\"Gross Profit divided by Net Sales\"\"\"\n",
1143 "\n",
1144 " gross_profit = AnnualizedData([Fundamentals.gross_profit_asof_date,\n",
1145 " Fundamentals.gross_profit], mask=mask) \n",
1146 " sales = AnnualizedData([Fundamentals.total_revenue_asof_date,\n",
1147 " Fundamentals.total_revenue], mask=mask)\n",
1148 " return gross_profit / sales \n",
1149 " \n",
1150 " @staticmethod\n",
1151 " def NetIncomeMargin(mask):\n",
1152 " \"\"\"Net income divided by Net Sales\"\"\"\n",
1153 "\n",
1154 " net_income = AnnualizedData([Fundamentals.net_income_income_statement_asof_date,\n",
1155 " Fundamentals.net_income_income_statement], mask=mask) \n",
1156 " sales = AnnualizedData([Fundamentals.total_revenue_asof_date,\n",
1157 " Fundamentals.total_revenue], mask=mask)\n",
1158 " return net_income / sales "
1159 ]
1160 },
1161 {
1162 "cell_type": "code",
1163 "execution_count": 32,
1164 "metadata": {
1165 "collapsed": true
1166 },
1167 "outputs": [],
1168 "source": [
1169 "PROFITABIILTY_FACTORS = {\n",
1170 " 'Gross Profit Margin': ProfitabilityFactors.GrossProfitMargin,\n",
1171 " 'Net Income Margin': ProfitabilityFactors.NetIncomeMargin,\n",
1172 " 'Return on Equity': Fundamentals.roe.latest,\n",
1173 " 'Return on Assets': Fundamentals.roa.latest,\n",
1174 " 'Return on Invested Capital': Fundamentals.roic.latest\n",
1175 "}"
1176 ]
1177 },
1178 {
1179 "cell_type": "code",
1180 "execution_count": 33,
1181 "metadata": {},
1182 "outputs": [
1183 {
1184 "name": "stdout",
1185 "output_type": "stream",
1186 "text": [
1187 "Pipeline run time 21.65 secs\n",
1188 "<class 'pandas.core.frame.DataFrame'>\n",
1189 "MultiIndex: 50362 entries, (2014-01-02 00:00:00+00:00, Equity(24 [AAPL])) to (2015-12-31 00:00:00+00:00, Equity(47208 [GPRO]))\n",
1190 "Data columns (total 2 columns):\n",
1191 "Dividend Growth 40517 non-null float64\n",
1192 "Dividend Payout Ratio 39947 non-null float64\n",
1193 "dtypes: float64(2)\n",
1194 "memory usage: 1.2+ MB\n"
1195 ]
1196 }
1197 ],
1198 "source": [
1199 "profitability_result, t = factor_pipeline(PAYOUT_FACTORS)\n",
1200 "print('Pipeline run time {:.2f} secs'.format(t))\n",
1201 "payout_result.info()"
1202 ]
1203 },
1204 {
1205 "cell_type": "code",
1206 "execution_count": 34,
1207 "metadata": {},
1208 "outputs": [],
1209 "source": [
1210 "# profitability_pipeline().show_graph(format='png')"
1211 ]
1212 },
1213 {
1214 "cell_type": "markdown",
1215 "metadata": {},
1216 "source": [
1217 "### Build Dataset"
1218 ]
1219 },
1220 {
1221 "cell_type": "markdown",
1222 "metadata": {},
1223 "source": [
1224 "#### Get Returns"
1225 ]
1226 },
1227 {
1228 "cell_type": "code",
1229 "execution_count": 165,
1230 "metadata": {},
1231 "outputs": [
1232 {
1233 "name": "stdout",
1234 "output_type": "stream",
1235 "text": [
1236 "<class 'pandas.core.frame.DataFrame'>\n",
1237 "MultiIndex: 50362 entries, (2014-01-02 00:00:00+00:00, Equity(24 [AAPL])) to (2015-12-31 00:00:00+00:00, Equity(47208 [GPRO]))\n",
1238 "Data columns (total 4 columns):\n",
1239 "Returns10D 50362 non-null float64\n",
1240 "Returns1D 50362 non-null float64\n",
1241 "Returns20D 50360 non-null float64\n",
1242 "Returns5D 50362 non-null float64\n",
1243 "dtypes: float64(4)\n",
1244 "memory usage: 1.9+ MB\n"
1245 ]
1246 }
1247 ],
1248 "source": [
1249 "lookahead = [1, 5, 10, 20]\n",
1250 "returns = run_pipeline(Pipeline({'Returns{}D'.format(i): Returns(inputs=[USEquityPricing.close], \n",
1251 " window_length=i+1, mask=UNIVERSE) for i in lookahead},\n",
1252 " screen=UNIVERSE),\n",
1253 " start_date=START, \n",
1254 " end_date=END)\n",
1255 "return_cols = ['Returns{}D'.format(i) for i in lookahead]\n",
1256 "returns.info()"
1257 ]
1258 },
1259 {
1260 "cell_type": "code",
1261 "execution_count": 166,
1262 "metadata": {},
1263 "outputs": [],
1264 "source": [
1265 "data = pd.concat([returns,\n",
1266 " value_result,\n",
1267 " momentum_result,\n",
1268 " quality_result,\n",
1269 " payout_result,\n",
1270 " growth_result,\n",
1271 " efficiency_result,\n",
1272 " risk_result], axis=1).sortlevel()\n",
1273 "data.index.names = ['date', 'asset']"
1274 ]
1275 },
1276 {
1277 "cell_type": "code",
1278 "execution_count": 167,
1279 "metadata": {},
1280 "outputs": [],
1281 "source": [
1282 "data['stock'] = data.index.get_level_values('asset').map(lambda x: x.asset_name)"
1283 ]
1284 },
1285 {
1286 "cell_type": "markdown",
1287 "metadata": {},
1288 "source": [
1289 "#### Remove columns and rows with less than 80% of data availability"
1290 ]
1291 },
1292 {
1293 "cell_type": "code",
1294 "execution_count": 168,
1295 "metadata": {},
1296 "outputs": [
1297 {
1298 "name": "stdout",
1299 "output_type": "stream",
1300 "text": [
1301 "2,985 rows and 3 columns dropped\n"
1302 ]
1303 }
1304 ],
1305 "source": [
1306 "rows_before, cols_before = data.shape\n",
1307 "data = (data\n",
1308 " .dropna(axis=1, thresh=int(len(data)*.8))\n",
1309 " .dropna(thresh=int(len(data.columns) * .8)))\n",
1310 "data = data.fillna(data.median())\n",
1311 "rows_after, cols_after = data.shape\n",
1312 "print('{:,d} rows and {:,d} columns dropped'.format(rows_before-rows_after, cols_before-cols_after))"
1313 ]
1314 },
1315 {
1316 "cell_type": "code",
1317 "execution_count": 169,
1318 "metadata": {},
1319 "outputs": [
1320 {
1321 "name": "stdout",
1322 "output_type": "stream",
1323 "text": [
1324 "<class 'pandas.core.frame.DataFrame'>\n",
1325 "MultiIndex: 47377 entries, (2014-01-02 00:00:00+00:00, Equity(24 [AAPL])) to (2015-12-31 00:00:00+00:00, Equity(47208 [GPRO]))\n",
1326 "Data columns (total 52 columns):\n",
1327 "AssetToEquityRatio 47377 non-null float64\n",
1328 "AssetTurnover 47377 non-null float64\n",
1329 "CFO To Assets 47377 non-null float64\n",
1330 "Capex To Assets 47377 non-null float64\n",
1331 "Capex To FCF 47377 non-null float64\n",
1332 "Capex To Sales 47377 non-null float64\n",
1333 "CurrentRatio 47377 non-null float64\n",
1334 "DebtToAssetRatio 47377 non-null float64\n",
1335 "DebtToEquityRatio 47377 non-null float64\n",
1336 "Directional Movement Index 47377 non-null float64\n",
1337 "Dividend Growth 47377 non-null float64\n",
1338 "DividendYield 47377 non-null float64\n",
1339 "Downside Risk 47377 non-null float64\n",
1340 "EBIT To Assets 47377 non-null float64\n",
1341 "EBITDAYield 47377 non-null float64\n",
1342 "EPS 47377 non-null float64\n",
1343 "EPS Growth 12M 47377 non-null float64\n",
1344 "EPS Growth 3M 47377 non-null float64\n",
1345 "EVToEBITDA 47377 non-null float64\n",
1346 "EVToFCF 47377 non-null float64\n",
1347 "Index Beta 47377 non-null float64\n",
1348 "Log Market Cap 47377 non-null float64\n",
1349 "MertonsDD 47377 non-null float64\n",
1350 "Money Flow Index 47377 non-null float64\n",
1351 "Net Debt 47377 non-null float64\n",
1352 "Net Debt Growth 12M 47377 non-null float64\n",
1353 "Net Debt Growth 3M 47377 non-null float64\n",
1354 "Percent Above Low 47377 non-null float64\n",
1355 "Percent Below High 47377 non-null float64\n",
1356 "Price Oscillator 47377 non-null float64\n",
1357 "PriceToBook 47377 non-null float64\n",
1358 "PriceToDilutedEarningsTTM 47377 non-null float64\n",
1359 "PriceToEarningsTTM 47377 non-null float64\n",
1360 "PriceToFCF 47377 non-null float64\n",
1361 "PriceToOperatingCashflow 47377 non-null float64\n",
1362 "PriceToSalesTTM 47377 non-null float64\n",
1363 "Retained Earnings To Assets 47377 non-null float64\n",
1364 "Returns10D 47377 non-null float64\n",
1365 "Returns1D 47377 non-null float64\n",
1366 "Returns20D 47377 non-null float64\n",
1367 "Returns5D 47377 non-null float64\n",
1368 "Sales 47377 non-null float64\n",
1369 "Sales Growth 12M 47377 non-null float64\n",
1370 "Sales Growth 3M 47377 non-null float64\n",
1371 "Total Assets 47377 non-null float64\n",
1372 "Total Assets Growth 12M 47377 non-null float64\n",
1373 "Total Assets Growth 3M 47377 non-null float64\n",
1374 "Trendline 47377 non-null float64\n",
1375 "Volatility 3M 47377 non-null float64\n",
1376 "WorkingCapitalToAssets 47377 non-null float64\n",
1377 "WorkingCapitalToSales 47377 non-null float64\n",
1378 "stock 47377 non-null object\n",
1379 "dtypes: float64(51), object(1)\n",
1380 "memory usage: 19.2+ MB\n"
1381 ]
1382 }
1383 ],
1384 "source": [
1385 "data.sort_index(1).info()"
1386 ]
1387 },
1388 {
1389 "cell_type": "code",
1390 "execution_count": 170,
1391 "metadata": {},
1392 "outputs": [
1393 {
1394 "data": {
1395 "image/png": 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AnoGV+gCg47WdsKTzN2E5nAkTJmjBggWaMGGCXnnllX9IRIYNG6ZHH31UkpROp7V27VpV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\nWFgYVq1ahbJly8LX1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UxMTIyYPXu2WLNmjcjJyRGzZ88WnTp1EuPGjXul3hfduHFDjBo1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Rkpubiw0bNuD333+Hi4sLZs6cKab6IQk9PT2sX78ed+7cwYYNG3Dx4kWpgVF1dbXEik2vXr3w\n8eNHicfQdaNmSomLjgpXc6QxymL1b/g//PCDQrNdP/30E37//XcsWbIEQF0v8sKFC1UWsOTn50Nf\nXx95eXnIy8sTkwhVtPWMzWZDX1+f+ictwytky5YtyM/Px/Dhw8Hj8bBz504MHjxYKXUgVSOsCDBB\nY4afjYyMEBYWRhm+pqWlyWxxefbsGSIjIyEQCKj/A3W/3+fPn0s9jsvlgsvloqioiFIbEiIrYMnL\ny6Oq3jExMXjx4oXc64Wy3i1xcXF49eoVUlJSsHr1aggEAjg6OsLR0VHseiiJI0eOICUlBfPnz8eh\nQ4dw5swZhWS41c2+ffuQkZGBpKQkHD9+HCYmJhg8eLBUqW+6MzwAqAqxMihjIiocxN+8eTNyc3PF\nWqNldTMIP5tGRkZUAGBoaIjU1FRER0fL9DejO+CvLKampjA1NcWqVauQnZ2Nffv2Yf369bh165bE\n19NpP6uvRCf6WJYn1KNHj6juBIFAQD0WCATUzA1TkICF0CQQKkAJZYOFCOU1JV1A6Kh9AfQ2gVwu\nF8XFxYiPj8fu3btRU1MjV2r2xx9/xLVr17Bs2TLo6OhAT0+vQWuBEGnZUj6fL3UjR9eNmiklLjoq\nXEKOHj3aoB0kNjZWZpD46tUrlJSUwMzMDCdPnkR2dja+++47uf3Pubm5KC0txejRo7Fv3z7cu3cP\ns2fPltjfL/QBEVYDr169Cg6Hg27dumHWrFkSAz46ymKixmGiCDNv8loCdHV10b59e2pTq6i0paI0\nZuMD1LVh3rhxA6amppg0aRLmzZsn9zORlZUl1sIkEAjg6emp0YBFdDMvCVXOLMgym5Sn7hcaGor4\n+HhcvXqVMi2V1XY5c+ZMqppTfyZF2ua+MTM2tbW1qKqqgkAgQFlZGYyMjOTOH9DxbunatStmzZqF\nWbNm4eXLlzh16hRWrFgBXV1dMdO8+hgYGKBly5bg8XgQCASYNGkSvL29pZr/nT9/XqxjAAA4HA6c\nnJxkvidlGTJkCIYMGYInT57g7t27OHnyJFJSUtTixs6qpwAAIABJREFUTWRvbw8bGxtMnTpV4d8r\nHRPRjRs3IisrC+bm5uDz+fjll18wdOhQqV5eGRkZDSrY+vr6WLt2LTw8PCQGLKID/nl5edTzwgF/\nVVZZhGRlZSEpKQnnz59H3759ERERodLvL3q/fPnyJfLy8sBms9GvXz+Zc131q9qi30dWS6Y6IAEL\noUlQfwPG4/EQEhIiU+EjPDwcRUVFuHnzJiwtLdG5c2e5wQpAb8Dfw8MD8+bNg5OTE7p06YKoqCjY\n29vLPI9wVkX0gnjnzh2YmJjA3t5erDIxduxYbNiwAX5+ftTGraysDFu2bJF6k6PrRu3k5IRly5ZJ\nVOKSFhAsXLgQM2fOlKrEJYkVK1bghx9+kKrCJYkrV67g8uXLSElJwZMnT6jna2pqkJycLDNgWb16\nNQICAnDnzh0cO3YMS5cuRXh4uFzDtuDgYGzduhXXrl1DZmYm/P39ERAQIHHWKCgoCHp6ehg/fjye\nPXuG5cuXY926dSgqKkJISIjEQGLlypVYuHChVGUxSQg/W+fOnQObzRYb5lZE1tjY2Bg//vgjysrK\nkJSUhNTUVIWcupnC1tYWwcHBCr0XITU1NaiurqYCvurqaqq9U1O0bNmSsZ8rHbPJjRs3IjAwkJKx\nVdTw79y5c2JVsqCgIKp9yNvbW2K7VmNmbGbMmAEOhwMPDw9MnToV+vr6sLS0lHmMMGtPh5qaGjx4\n8AC5ubkoKyuT28o3YMAAHDlyBKNHj8bMmTPx+eefS0zuZGZmIisrCwcPHsTLly/Fzrd//36VByzz\n5s1DcXEx+vTpgxEjRiAwMFCiv5UqSExMxLVr13Ds2DFERkZi+PDhcHJykln9oGMimpmZKRZ88/l8\nmTOD0hJmQoEJSah6wF8aOTk5SEpKwpkzZ9CtWzc4OTnB19dXrZLssbGxOHHiBIYMGQIul4sdO3Zg\nxowZcHNzk/h6UcEL0ZbbQYMGwczMTG3rlAQJWAhNgr///pvaYOnr64PP58t1cqej9gXQG/B3dnbG\n5MmTYWBggIqKCjg4OKB///4yz1NaWoqcnBzY2NiAxWLhypUr6NWrF169eoUzZ85g586d1GuXLl2K\nmJgYfPXVVzAwMACfzwePx4OHh4fUXm66btRMKXHRUeEyNzeHrq4uLl26hD59+oj5dMjL9ujo6KB/\n//6UxPHQoUMV2tAKfUFiY2MxY8YMGBkZSZ01evjwITWzk5CQAAcHBzg7OwOA1GwrHWUx4Wf/999/\nR2xsLPW8o6Mjvv/+e7nvKSwsDPHx8Rg6dCgyMjIwYcIEjTrCCxFufPfs2YOff/6Zel6RKoGXlxem\nTp2KXr16URUGWbK3TPDZZ5/hm2++YeRcdKqpokPVylC/VVE0eSCtjbExMzaixpK2trZ49+6d1M2t\nsPp69OhRiVVDaZ+J2tpaXL16FUlJSUhPT8eIESPw9ddfY/PmzXJbnQICAvDx40e0aNEC165dQ1lZ\nmcSMv6GhIVq1agUej4eysjLqeRaLJVGYpLFs2LABLBYLubm5lLCJItCpAOnr68PGxgZWVla4evUq\nfvrpJyQmJsLY2Bjr1q2TGLjTMRH94osvUFxcTN1XSktLZcqxt2/fnkpaivLvv/9KrQY2dsBfUTZu\n3IipU6fizz//bLR/nKKcOnUK//zzDxXI8Xg8eHt7Sw1YhERERODRo0diLbcWFhZUWzETkICF0CSI\ni4tDamoq5s6di0OHDuHs2bNyB8XoSr7SGfAXDt2PHTsWPj4+GDx4sNyh+6dPn+LPP/+kbqrz5s3D\nokWLsHfv3gZOzGw2G/Pnz8f8+fNRWVkJAHJbZBrjRs2UEpeyKlytW7fGiBEjcOLECVy+fJmqgvXs\n2VOuh0htbS1+/vlnnDt3DsuWLUNmZiaqqqrknlNPTw9BQUG4efMm/P39cfnyZamZOWF2H6hrB5M3\nGCz6vpRRFhNSXl6O8+fPw8LCQswcTx4uLi5wcHDArFmz0L17d6XPqy4WL16MBw8eoF27dsjPzweb\nzYapqSl8fHyktmUK/YacnJwwbtw4PH78mPpMaNo8sr7Xh7ZRXFws0ytK2t8mHeUgOjM2omZ3wP+C\nkY4dO0r1gOjUqRMASByal3XOoUOHYuzYsXByckJISIhYdU/U1E+UQ4cOUfeXFi1aICsri8pIh4WF\nNRjq7tq1K7755hvY2NhAX18f7969U6s07KlTp5CcnExl03ft2oVvv/1W6u+1MRWg69evIykpCbdv\n38aYMWMQHBwMU1NTPHnyBCtXrhQzBhZCx0T06dOnmDhxIr744gvw+Xw8e/YMPXr0oFTA6ic1/P39\nsXjxYvTq1Qv9+/dHbW0t7t69i1evXkmtrtMd8FcWWa1v6kQ0ANfR0VHobzEzM1Niyy2TkICF0CQw\nMDCAgYEBeDwe+Hw+bG1t4eXlRQ2ES4Ku5CudAX/Rofvp06crNHRfUlKCvLw89OvXD0Bdv/vz58/x\n8uXLBhtp0Ztz69atsWzZMrEKjCQa40at7SxfvhxsNpvaRBw9ehT//POPzJ/J1q1bcerUKURHR8PA\nwACFhYViKjjS2LlzJy5fvoyFCxdSbXrSZo1atmyJU6dO4e3bt3j69CnGjBkDAGobToyIiMCePXso\ntZeePXsq9HuPjo7G2bNnERQUhHfv3sHW1hb29vZypbiVxcbGBiUlJdRNsba2Fu3atUPbtm3h7+/f\nYMj/4cOHCAsLw4IFC2BqaoqqqipkZ2dj/fr1CAoKkriZEfUbat26NeNtCrLw8/PT9BJkUj/TTxdF\nNjx0ZmzqD/AnJCRQlVRpG33h9TkvL69BwLBy5UqprbI9evQQk1sWbXPbunWrxODozJkzYpVT0dc9\nePBA4nkAICoqChcuXKCCK0UqiHQ4e/asmHpjTU0NPD09pQYsjakA/fXXX3B2dkZQUJBYG1aPHj3g\n6uoq9lppJqIZGRkAZM92STKirKyslJrA6969O06cOIErV65QyQxPT0+MGTNG6ueW7oB/U8DOzg7T\np0+nfKBu376NadOmyT1OG1puScBCaBIMGjQIhw8fhpWVFXx8fNClSxep6lhC6Kh9AfQG/OkM3a9b\ntw7+/v5UJsvQ0BDLly+nMlKi1L85v3nzRu77oONG3VQoKSlpkJ2Sl+3p2rUrzMzM8ODBA/Tu3RuW\nlpbUhkEWGzZsEAuErKyspLo+h4aGYufOnXj37h327NkDAwMDVFdXY8GCBXIleZWBy+VSrWqbN28W\na41ThM8//xxeXl7w8vJCUVERoqKi8PXXXyM7O1tlawSAyZMnY+TIkdTfzOXLl3H79m24u7tj8eLF\nDQKWX375BXv37qVmp4C6KsXo0aOxatUqWgaCBOkYGRnJVEmShqzg4+nTpxKPoTNjQ6eSc+rUKRw8\neBB5eXlin2cejydTaa6+wMXjx48lnlfaemS9rj737t3DhQsXGJGIFc2ms9lsmedsTAUoMjISKSkp\nOHDgAObMmYMHDx5Q5pP1q8eNMRFt06YNEhISqICKx+PhxIkTMtuf2Ww2rK2tFfJhE0XZAX9tpqam\nBrq6upg9ezYmTpyIe/fugcViwcfHR+x6Kw1taLklAQuhSbB27VpqkzZixAiUlZVh9OjRMo+hK/lK\nZ8C//tD9ypUrYWdnJ/M8o0ePllgmlwSddgo6btSiMKXEpYwKlxAzMzNkZmZS2fScnByJLRuiRERE\n4NWrV3j27BkcHR3x119/oaKiQqoW/6lTp/DLL78gLy9PbGNdW1srtf2sc+fODT4/BgYGOHXqlNTf\nGR1lsXXr1mH79u1wdHQU+77CTK08nf6ioiKcO3cO586dQ0lJCWxsbCQGYI3lzp07Yoo61tbW2Lt3\nL5YuXSrx51FTUyPx5mliYiJ1joCu3xCBvus7neCDzoxNfRS57tnb22PcuHEIDw/H3LlzxY6l2yKo\nyjY3AOjXrx/KysrUPrcwefJkTJs2DRYWFuDz+bh7926Daock6FSAAgMD0aFDB6Snp2POnDlIT0/H\n3r17JUrrN8ZEdOnSpRg8eDASExPh5uaGCxcuYMOGDUp9D0VRdsBfm5k9ezZV/TMxMVHYZ0ibWm5J\nwELQaoSmT4sWLaJ6itu1a4dr167JDQjoqH0B9Ab8nZ2dMWrUKMr8Sd4gIFDXliOpf/zatWsNnhM6\nDwuzXfUft2zZssExdNyoAeaVuJRR4Ro5ciRYLBYEAgEOHjwIAwMDsNlsfPjwAZ07d5bZgpOdnS3W\nc7548WLMmDFD6uvt7e1hb2+PX375RWwWRVaWMj8/H2FhYSgoKICZmRkCAgJgaGgocyNDR1lMWK1Z\nv349rK2tlXarX7hwISZNmoR169aptcWha9euWLRoEYYMGULN2HzyySc4ffq0RBNXWT8naYphdP2G\nCMC2bduo/xcVFaGwsBCWlpZUckgaqgg+FIGu2Z2BgQECAwNx7do1yguHx+Ph559/xpkzZxQ6tyLB\nR3l5OS5fvkw9rqiowOXLlyEQCFBRUSH1uOfPn2PixIno3r07dHR01BZc+/j4wNbWllJ2mj9/vkK/\nOzoVoFevXmHz5s3U9dXT01PuHCUdE1E+n48lS5bgxo0bmD17Njw9PbFs2TJKfl+VKDvg3xzRppZb\nErAQtBpRQzIh3bt3R2VlpVzTJzpqX4ByA/7l5eU4deoUOBwOCgoKYGdnh7dv3yI1NVXuezt9+jTO\nnj0rVRFLlJcvX8LR0VHs5zBlyhQAkJpRp+tGzbQSlzIqXNevX5f5vWRRU1MDHo9HvffS0lKFlIpm\nzJiB2NhYvHnzBn5+fkhPT8eAAQMk9kxv3LgRvr6+MDc3x7lz57Blyxa5rWB0lMWEnDlzBlu2bIGZ\nmRkcHBwwduxYhaSA4+LiwOFwEBcXBzabjYEDB8LR0VFp4zd5bN26FZcuXcLjx49RW1sLBwcHjBs3\nDh8+fMCECRMavF5WtURaqxFdvyHC/5CkqGhoaEj18msKumZ3QJ0amJ6eHm7dugUbGxukp6c3+H6i\n0Glz69u3L06ePEk97tOnD/VYVruTOhTB6vPgwQMcOXKEEq8YMGCAwiIQdCpAPB4Pb9++pa6v+fn5\n4HK5co8pKSlpcP+SFbDweDzk5uaiRYsWuHLlCrp164Znz54pvE5lUHbAX5t5+PAhli5dKvXrkmaD\ntA0SsBC0GjqmT0LoqH0Byg34W1lZwcTEBH5+frC2tgabzaY2m/Lo2bOnVBf4+pw7d06h14lC142a\naSUuZVS4hBQVFWH37t2oqKig5DMtLCxkblxnzZoFNzc3vHz5EnPnzsXjx4+lGjCK4u/vj2HDhlED\nocXFxdi/f79Ep20+n0+5NDs4OMhUYBJCV1kMqGtf5PP5uH37Ns6ePYt9+/bBxMREbpAUEBCAtm3b\nUhKV6enpSEtLk9n2SIf3798jOzsbOTk5YLPZqKmpwZgxYyh52/rQaTUSbjKFPdqilJeXU8axBOlI\nU1TUdMAyduxY2seWlZVRFdWQkBBUVFRg48aNUmWm6Xz26otvVFVVgc1mS6x4i9K2bVscPnwYb968\nQUBAAK5fv44BAwYofX5pXLt2jRKvmDlzJiVeMXPmTAQFBcmdBaNTAVq+fDl8fHzw9OlTODg4gMVi\nybyecLlc+Pr6omvXrkolSgIDA1FaWopVq1YhPDwc5eXl8Pb2Vvh4ZVB2wF+b6dy5s1KKnEK0qeWW\nBCwErYaO6ZMQOmpfgHID/lu2bAGHw0FAQADGjx9PVT0Ugc/nw8HBAQMGDBB7n5IukjweD7t378ai\nRYuo0vnDhw+RnJwsVQddNJtSP9unyLAjU0pcyqhwCQkICIC3tzdlMNmhQwesXbtWpsO6nZ0drKys\n8OjRI+jr6+OLL75QyJfg3bt38PLywunTpwHUtdcJKyL1odPT3lhlMTabDX19feqfrMFiIUVFRWI/\nY0dHR7Xc9P38/DB8+HD4+vpSgdG6deuk9q3TqZQ4Ojri/fv3mD9/PmJiYqiKYE1NDby8vGhtRP9r\n0FVUVDfff/89WCwWampq8OTJE6r6+urVK/Tt27dBMksUHo+HoqIisNlsPHv2DF26dBEbpK9PY6p0\n169fR0hICBWU6+vrIzQ0VOoc3tq1azF69Gj8+++/AOqqvStXrpRqmKssjRWvoFMBsrS0xPHjx/Hm\nzRvo6+s3EDEQJTU1FZs2bUKnTp1QVlaGrVu3KtRq9O7dO0pVEwB+++03vH37Vm2zQHQG/LWVNm3a\nYPjw4Uofp00ttyRgIWg1dEyfhNBR+wKUG/B3cnKCk5MTKioqkJKSgj179uDx48eIiIiAi4uLzIqE\nMhrmERERAJRrjbO1tcWnn34q8ftlZWXJPSdTSlzKqHAJ4fP5sLGxoZyyR40aJXUuZ8mSJTIDB3ml\n8NraWspAE6irgkhrcxOtZEl6LEmuszHKYv7+/rhx4wZMTU0xadIkzJs3T6HsH4/HE+vNLioqUssG\ntaqqSmzmycLCQiGlPmW4ePEiYmNjkZmZKZYwYLPZtG7Q/0XoKiqqG2H77po1a7B3714qqHj+/Dn2\n7Nkj89jFixcjIyNDrMqgroHpnTt3IjY2Fl26dAEAFBYWws/PT2qFtaqqCjNmzEBycjKAuvZeVYpe\n0BGvEIVOBejYsWM4fPhwA2UxSe3KMTExOH78ONq2bYvCwkIEBwdT13JppKenw8/PDwkJCdQ1Lj8/\nH8uXL8fOnTtpKY7Jg8kBf3Ujbw5XGtrUcksCFoJWQ8f0SYiyal+NGfBv27Yt3Nzc4ObmhuLiYnA4\nHKxZs0aiClhqaiomTpyIhw8fSvxekjZZdFrjfH19xbwDFPEVEEXdSlx0VLiE6Orq4tq1a+Dz+Xj9\n+jXOnDkj1loliqwg6/Xr1zLPA9QFVGvXrkVWVhasrKzw5ZdfSq0a1e8RltUzLISOspiQiRMnIjg4\nWKG5FVGWL1+OmTNngs1mg8/ng81myzQ5pQufzxcz3bt7967U+SS6TJgwARMmTMDJkyflumQTJFNf\nUXHBggXU5lsbEFZXhHTr1k1MEEQSJSUlVHsunZZaZdDT0xP7eRkbG0vtDgBAzUMI/74vXryo0r8L\nOuIVotCpAO3fvx/R0dEKfW709PSotlBjY2OFZgmFQaFoQqZv376Ijo6WK+xCFyYH/NWNsNV4yZIl\nDSrcrq6uUrsGtKnllgQsBK2GjumTEGXVvhoz4C9K586dMWfOHKmzCO/evQMApQzb6LTG1dfPV8RX\nAGBOiYuOCpeQ8PBw6nc7Z84cmJubSzVMFAaANTU1uHz5sphq0L59++S28fXu3Rv79++nfAlevnwp\nVWmOTlWLrrJYSEgIdu/eLZZpVrSveMSIEUhOTkZFRQVYLJbUNTeWwMBAhIeHU+1tffr0QVBQkFrO\n1aFDB0ybNg3FxcVgsVj4/PPPsXLlSowYMUIt52tOpKenIyEhAaGhoQDqkh0+Pj7UPJamGThwINzc\n3GBubg42m43s7Gy5SY1///0XgwcPRvfu3dW+PiMjI4SFhWHEiBEQCARIS0uTmZUODAxEYGAgsrOz\nYWVlhb59+6o0YUBHvEIUOhWgL774QqZ8vSh0WmdZLJZE77CePXvKHe6nC5MD/upGNEE4atQo6l7B\n5/PRv39/qcdpU8stCVgIWg9d0ydl1L6Axg34K4Nw6HPRokXIy8tDZWWlXHMuOq1xsm4Csr7GtBKX\nMipcQlgsFpycnODs7IzevXsrlOVZtmwZPvnkE6Snp2PChAlIS0tT6He6adMm9O3bF+PHj4ePjw8G\nDhwIAwMDBAcHN3gtnaoWHWWxxYsXA4DSHgbyRAakBX106dOnTwO/H0XaEekQGRmJHTt2UMFkbm4u\nVq9eTWZYFGDHjh1irYvBwcHw9fVt0BKqKYKCgpCXl4f8/HwIBAJ89dVXcluUcnNzMXnyZLRp00ZM\nMldUhlhVhIaGIj4+HlevXgWLxcKgQYMwdepUqa/v1asXNm3aREl75+fnq1RevLGfeToVoA4dOsDN\nzQ0WFhZiCTZJbbCyFNmkJVw+fPggMcv/4cMHmRLSjYHJAX91I0wQ7t+/XylhF21quSUBC6HZooza\nF9C4AX86fP/993j79q3YjAeLxZKY1WxMa5zo91YGppS4lFHh+vjxI/z9/ZGbm4v+/fujqqoKjx49\nwrhx47B69WqpbWFAnUdCdHQ0vLy8sGHDBrx9+xZBQUFyVd2ys7Ph7++PgwcPYtq0aZg1a5ZULxo6\nVS06ymKfffYZHjx4gMOHD+Px48eUbOnMmTNltmQ8ePAA7969g5WVFWxsbOSqGakDRdoR6dCpUyex\nyle/fv1gbGys8vM0R2pra8WM5NRtaKgslZWVuHjxIt68eYO1a9ciPT1drlpTYmKi2te1ceNGBAYG\nQk9PDy4uLnBxcVHouMjISJSWllLD7b/99hvatm0rcXNPh8bOHNCpAA0dOhRDhw5V6PvTCagcHR2x\nZMkSrFq1iqrk5OTkICIiQi1BBNMD/kzh6uqKffv24c2bN/D396fmk6RV2bWp5ZYELIRmizJqX0Dj\nBvzp8PbtW4UzmHRa4+j4CojClBKXMipc27dvR69evbB9+3bqfdfU1GDXrl0IDw+XeVPl8Xh48eIF\ndHR08OTJE3Tt2lVuH7zwuNevXyM+Ph67du1CbW0t3r59K/G1dKpadNojRGVLZ82apbBs6bFjx/Ds\n2TMkJiZi165d6NKlC+zt7TF+/HjGpDrlVROVRRjgCX1Dhg8fDhaLhVu3bqnl77Y5YmdnB1dXV5iZ\nmYHP5yMjI0PjmxNRhEmNO3fuAJCd1BDy+vVr/Pzzz6ioqEBUVBSSk5NhYWGBrl27qmxdjx49onXc\nnTt38Mcff1CPw8PDaUnOqgs6FSBHR0dwOBzk5ORAR0eH8naSBJ2Aas6cOejUqRPWrVuHFy9eQCAQ\noFu3bvDx8cHkyZOV/n6y0MSAP1OsW7cOo0ePpsSIFFWo04aWWxKwEJotyqh9AaqpYijDkCFD8PDh\nQ6nzEPVRtjVOFW0BTChxKaPCde/ePbEbPVA3gL98+XK5G6ylS5ciOzsbCxcuxLx581BZWanQJsHd\n3R0+Pj5wcnJC165dERUVpfDQpSLBBx1lscbIlpqYmGDBggVYsGABHj58iMTERERGRsLU1BR79+5V\n6H01BmUrffIQzoIZGxvD2NiYSkqo0teiuTNv3jzY2dkhJycHurq6mDNnjtYoAwHKJTWEBAQEYMaM\nGdS1u23btvDz81Npda+4uFhmRVTa9YXP54td+zMzM1UeyDcGOhUgJrydpk6dKrPVTlVoYsCfKegq\n1GlDyy0JWAjNDrpqX40Z8KdDamoqYmNj0aZNGzFzrmvXrqnk+zd2w8GUEpcyKlyyjDalmREKEd3E\nHzx4EB07dpTZQibExcUFU6dOhb6+PiorKzFlyhT07dtX4mvpVLXoKIs1VrZUIBDg+vXr4HA4SEtL\ng5WVFRwcHOQepyhCJ2hJ51WkuqcMonNIRUVFKCwshKWlJZWsIEgnLi4O7u7uiIiIEPt9CdszVdWi\n1FiUSWqIHjNhwgTExsYCAEaPHi1XCllZeDyeUuIpQoKCghAcHIynT5+CxWLhyy+/lDgTpynoVICY\n8naytbVt8JyOjg66deuGFStWwNTUtNHn0MSAP1PQVajThpZbErAQmh2NUfuiO+BPh6ioKJVcXNUF\nU0pcyqhwlZWVSTTtEggE1Dnrc+3aNezZsweHDh1CbW0tZs+ejaKiIggEAqxfv16um7bo0L23tzcG\nDRokdeieTraJjrIYXdnSzMxMcDgcXL16FWZmZnBwcEBwcLDYULIqUFYMQBUcOHAAKSkp+PDhA06e\nPImtW7eiU6dOmDdvHuNraSoIkxra3uIiKakhb6ZCR0cHN27cgEAgQFlZGVJTU1UewBoZGdESY8nJ\nyVFoVk1T0KkAMeXt5OrqijZt2lCBy8WLF1FaWooRI0YgLCxMJX42mhjwZwpl55O0qeWWBCyEZgdT\nal+NJSIiAr/99pvMqoEmYUqJSxkVroEDB1LGn/WRFvxFRUVh27ZtAIDTp0+jsrISycnJePv2LXx9\nfeUGLKJD9y4uLjKH7ulUtegoi9GVLXV1dYWJiQnMzMwgEAiQnJxMtQYAqlMJ00Q7UWpqKuLi4ihJ\nbX9/f7i7u5OARQbCxMz58+c1EmQqysuXLxvMziUlJcmUNg4PD0dUVBRKSkrg7e0NMzMzlavgCTfn\nynLlyhVYWFioVBlMldCpADHl7XTx4kWxYO/bb7+Ft7c3vv/+e5Wdg+kBfybp1asX9u3bBwMDA5SX\nl+Ply5cyP4fa1HKrnTslAqERMK32RZdWrVrBzs4O/fr1E8twy3NeVzdMK3Epo8JFZ8NhYGBAKSBd\nvHgRX3/9NdhsNtq1ayfT3E2IMkP3dKCjLEa3b1iS63RzQdgiJKw+VVdXqyXD2xxp164dduzYATMz\nM7FrkY2NjQZXVXdtyM7OxoEDB1BUVEQ9X1NTI7dy27lzZ8ydOxdPnjyhNt10AwxpCBMhgHLtiNnZ\n2Zg6dSpatmwJfX19lbcDNxY6FSCmvJ0MDAywadMmDBkyBGw2G1lZWeDxeLhy5QpatWqlknMwOeDP\nNKGhoRg4cCBsbGzg4+MDCwsLsFgsqcGlNrXckoCF0OxgWu2LLrNnz27wnCLO6+qGaSUudQcEXC4X\nfD4f1dXVuHDhgljG/f3793KPlzR0P2nSJJWtj46yGN0KhjYNUqsaJycn+Pj44NmzZwgKCsL169cx\nc+ZMTS9L6+FyueByuSgqKkJJSYnY1zQdsLRv3x46Ojrgcrl49eoV9TybzUZ4eLjMY8PCwnD79m1K\n+Wz37t0YOXKkTONbugjbEd+/f4/4+Hhs3bqVaqGRhFA8QFtRpgLEtLfTTz/9hBMnTiAtLQ0CgQDd\nu3fHnj178OHDB+zcuVNl52FqwJ9pcnNzsWHDBvz+++9wcXHBzJkzpSYIRdGGllsSsBCaHUyrfdFl\nyJAhtJzX1Q3TSlyNUeFShK+++grTpk0Dl8u8//3zAAAaCUlEQVSFtbU1NTi5YcOGBkGtJOr7K7i5\nuSEpKUll66uPOgQemjsPHjxAXl4eCgoK0KpVK1y5cgWHDx+W6UlDqGuj27RpEwwNDVFeXo7IyEiY\nm5trelkURkZG+Pbbb+W2bUrizp07OHbsGPX3xOfz4ebmpuolApDejigtYKHjccUkylSAmPZ2YrPZ\n6Nq1q5iC14ULF+T6adGBiQF/puFyuSguLkZ8fDx2796NmpoahRKE2tBySwIWQrODabUvutB1Xlc3\nTCtxKaPCJeTo0aP49ttvxZ6LjY2VmCny8PDAuHHjxIzA9PX1YWlpqbDR25s3b5CcnIzExESUlJSo\n9ObYWL+c/zqSPGmysrLketIQgJiYGBw/fhxt27ZFYWEhgoODKRlzbeL777+nrt3CKm6/fv1kDlh3\n794dr1+/hqGhIQCgvLxc5sxLY1C2HZGOxxWTKFMBYtrbadasWTA2Nm5guKwOmBjwZxoPDw/MmzcP\nTk5O6NKlC6KiomBvby/3OG1ouSUBC6FZwqTaF13oOq+rG6aVuJRR4bpy5QouX76MlJQUsVazmpoa\nJCcnKzUMXz/gqc+7d+9w6tQpcDgcPHr0CJMmTUJpaSlSU1NlHqcsTOrY15evrY+2yNgqgzRPmjFj\nxsj1pPmvo6enRyUhjI2NUV1dreEVSebEiRNij4uLi6V6QgkpLCyEra0tevXqhdraWjx79gw9evSA\nm5sbWCyWwqa9iuDk5ARvb28UFBQo1I6ojMeVJlC2AsSkt5Oenh62b9+u0u8pDSYG/JnG2dkZkydP\nhoGBASoqKuDg4ID+/fvLPU4bWm5JwEIgaAi6zuvqhmklLmVUuMzNzaGrq4tLly6JSR+zWCy5AYiy\njBo1CiYmJli9ejXGjh0LHR0dtQSTTLaByJKvbaoD6o31pPkvUz941aYKtCw6d+6MnJwcma8R9QRR\nNx4eHrCxsUFmZib09fWxYMECme2IynhcaQI6FSB1ezsJGT9+PC5cuIChQ4eKiaaooxWNiQF/phEO\n3Y8dOxY+Pj4YPHiwzKF7QHtabknAQiBoCLrO6+qGaSUuZYbuW7dujREjRoDD4ahdsSQsLAyJiYnY\nsGEDJk6cCEdHR5V+f03wzTffUP9/+PAhVTHjcrnYsmWLyoM+JqDrSUOQ3Y7IYrHw999/a3iFdQir\nIkDdOktKSmTOnz1//hyHDx/G06dPwWaz8eWXX8LDwwNdu3ZVy/rS09ORkJCA0NBQAHXKSj4+Phg2\nbJjE14t6XM2dO1ctksuNQZkKEFPeTkL++uuvBskVFoulFgVEpgb8mUR06H769Olyh+61qeWWBCwE\ngoYQ/UMXthmJSnc2JRqjxEVHhUtZVR46ODs7w9nZGaWlpUhOTsaOHTvw+PFjbN++HdOmTUOPHj1U\ndi6mCQwMxOPHj/H48WOYmZkhOzsbc+fO1fSyaEHXk4bAbDtiYxCtlrBYLHzyySfo0KGDxNfeunUL\ngYGBmDNnDpydnSEQCHD//n3Mnz8f/v7+atlg7dixgzIsBoDg4GD4+vpKbDvjcrng8XgIDQ3V2gqg\nMhUgprydhDCpsMbkgD9TKDt0r00ttyRgIRAYZs6cOWJqZdHR0dSw/Zo1ayQaBWo7jVHioqPCpawq\nT2Po0KEDPDw84OHhgRcvXiAxMRHLli3DyZMnVX4upnj06BH++OMPeHl5Ye/evXj16hX27Nmj6WXR\noqlsurURbVGlkoaotLokVqxY0eC5rVu34rfffhPzXDE1NYW1tTWWLVumlg1WbW0tVWEGIDWYqq/K\ntnXrVpiZmal8PY1FmQoQU95OQkNdFxcXiZ8JdVQDmRzwZ4r6Q/crV66EnZ2d1NdrU8stCVgIBIbh\ncrlij9PT06n/SzMK1ARMKnEpq8KlKcUSIyMjzJ8/Xy2BEZPU1taisrISAFBaWoquXbsiNzdXw6ui\nh7Zvugn0EQ0ClEGSQWTnzp3Vdn21s7ODq6sr5fmSkZEhUQK+KaiyKVsBYurvb/HixQCAnTt3NliX\nIn5adGBywJ8pnJ2dMWrUKCQnJ8PV1RWlpaUy1fO0qeWWBCwEAsPUvwCI3kS1IXvDlBJXY1S46qvy\npKWlwcfHR4F3RwAAT09PJCcnw9PTE1OnToWuri5Gjx6t6WURCGL069cPgwYNwuXLlxU+prq6Gjwe\nr8EMRXV1NT58+KDqJQIA5s2bBzs7O+Tk5EBXVxdz5syReD3UdlU2ba4ACU2fw8LCsGPHDnzyyScA\n6u5XmzdvBofDUfk5mRzwVzfl5eXU/bagoAB2dnZ4+/at3PutNrXckoCFQNAw2hCkiMKUEldjVLjq\nq/L88MMPahuobY6IOjhPmDABVVVVaNeunQZXRCA05OrVqxg0aBBOnjwJFovVILljZWXV4BgnJycs\nXboUa9eupSo0jx49wpYtW+Dp6anS9cXFxcHd3b2BXHhGRgaAhjLh2q7K1hQqQDNmzMDcuXMRGBiI\nI0eO4Pnz5/j555/Vci4mB/zVjZWVFUxMTODn5wdra2uw2WyF7rfa1HLLEmhTDwqB8B9g1KhRGD58\nOPU4PT0dw4cPh0AgwM2bN3H16lUNrk4cdSpxnThxAomJibh//z6lwhUeHt7Ac6H+MbJQ5TAknf75\npsKxY8dw6NAhVFZWim0Cm+KNmND8KS0tRX5+PnR0dNC7d2+0adNG5utPnjyJgwcPUl5QRkZGlLCH\nKrl06RKsra1x/PhxiV8XVeUDgCFDhqBnz54A/qfK1rNnT61RZfPy8hKTLq7/WFt49uwZFi1aBEtL\nSwQFBWl6OU0CDocDDoeD7OxsjB8/HlOmTEFERITce6o2QQIWAoFhRGdWJCEazGiS+kpc4eHhKlfi\nAkCpcHE4HNy7dw8+Pj5SVbgkbQxqamoQFxeH4uJipVpH5HH06FGZX2+KEsBCpkyZgujo6AY6+k3V\nW4DQPOFyuQgICEBmZib69++PyspKPH78GJMmTcLKlSsVSqBUVlZCR0dHrW08S5YswU8//ST3dS9e\nvJD5dU3PY3l7e4uJvtR/rEnqD9u/f/8eRUVFVACoymBPEwP+TFFRUYGUlBRwOBzcvXsXHh4ecHFx\nkTnHoi2QgIVAIEjE09MThw8fprJsAoEA7u7u+Ouvv9R2TqEKV2JiokIqXElJSfjll18wceJEzJ49\nW20b7sePH4t5loSHh2tVqVxZFi5c2GRVwQj/HTZv3oxPPvkEixcvpjaOXC4XP/30EyorKxEcHCz1\n2OvXryMkJARsNhs1NTXQ19fHxo0bMXjwYJWvMzAwEO3atYOZmZnY7IyNjY3Kz6VOtLkCJAz2iouL\nJYoqqDLYe/36NT777DM8f/5c4oC/aKt0U6a4uBgcDgeJiYn4559/NL0cuZAZFgKBIBFNKHEpqsJ1\n/fp17Ny5E6ampti/fz86duyotjWFhITg/v37KCgogKmpKe7fv485c+ao7XxM0KFDB7i5ucHCwkJs\nmLR+zz2BoEmysrLwxx9/iD2nr6+PlStXym3/3LlzJ2JjY6kqYmFhIfz8/HDkyBGVrpHL5YLL5aKo\nqAglJSViX2tqAYs2J2GEAYmfnx8OHz6s1nNpYsBfE3Tu3Blz5sxpMvczErAQCASJaKMS14MHD7B9\n+3a0atUKkZGRtGVPlSEvL4/yfImJicGLFy+wb98+tZ9XnQwdOhRDhw7V9DIIBJno6kreorBYLEpt\nSxp6enpiLY/GxsZiwbkqqK+qFRkZCXNzc5Weg0k03ZKmCIaGhnB3d8egQYPEqlnqSLYwOeBPkA8J\nWAgEhomOjpb5daGJpKbRRiUuZ2dn9OrVCwMHDpR441C1qzJQV2mqqqqCQCBAWVkZjIyMmqxniRBH\nR0dwOBzk5ORAR0cHAwcOhKOjo6aXRSCIUV5eLnEuTSAQoKKiQuaxRkZGCAsLw4gRIyAQCJCWlqby\nDXlTUNVqbowdO5axc9nY2KBHjx7UgP/vv//O2LkJDSEBC4HAMO3btwcAZGZmoqysDMOGDaNuqJ9/\n/rmGVydZiYvL5SItLQ2A6pS46KhwnTlzRiXnVoYZM2aAw+HAw8MDU6dOhb6+PoYNG8b4OlRJQEAA\n2rZti+HDh4PH4yE9PR1paWkICwvT9NIIBIq+fftKnWXr06ePzGNDQ0MRHx+Pq1evgsViYdCgQWJy\n3qpA231VmiNMJFvqD9vX1tbi5MmTyMrKAtC0h+6bMiRgIRAYxsPDAwBw7tw57N+/n3p+3rx5WLBg\ngaaWRSFJh0NUiUtVAQuddi5NtCz07t0bAwYMAADY2tri3bt3ePz4MePrUCVFRUXYunUr9djR0RHe\n3t4aXBGB0BDRzygAVFVVgc1my1T82rhxIwIDA6GnpwcXFxe4uLiobX3a7qvSHGEi2SJUfJM24E/Q\nDCRgIRA0xP/93//hwYMHVKawoKBAruwlE9T3DkhKSsLvv/9OKXGpClFZYEkqXJqWDX7+/DmePn2K\nbdu2YfXq1dTztbW1CAkJwblz5zS4usbB4/HEbsZFRUVqF1QgEOgiSfErNDQUFhYWDV776NEjxtYl\n6gIuVNWaPn26VqhqNVeYSLYwOeBPUBwSsBAIGsLf3x8BAQF48eIF2Gw2OnfurFUqTUwpcWmrCldV\nVRVu3bqF169fi7WlsNlsraiENYbly5dj5syZYLPZ4PP5YLPZ2Lhxo6aXRSBIRBnFr+LiYplKYMIK\ntyrQZlWt5gqTyRYmB/wJ8iEBC4GgIUaNGoWjR4+Cx+OJXQw1DdNKXNqqwtWvXz/069cPDg4O+OKL\nL1BQUAAdHR2YmJgoZFinzYwYMQLJycmoqKgAi8XCp59+quklEQhSUUbxi8fjoaysjJF1NQVVrebC\nlStXMHToUKxYsYKxZAuTA/4E+ZCAhUDQEGlpaQgPDweXy0VKSgqioqJgaWkJa2trja6LaSUubVfh\nevToEXx9fdGzZ09wuVy8fPkSfn5+sLW11fTSlKY5OzgTmi/KKH4ZGRlpjdIiQXX8+eefWLt2Lbp1\n6wZ7e3sMGDAAQ4YMoTxT1AFRU9QuiNM9gaAhPDw8EB0djSVLluDQoUN48+YNFi5cqFYneUWQN0ej\n6qziyZMn8fHjR3z66acIDw+nVLgiIiJUeh66uLm5ITY2Fq1atQIAVFZWYu7cuYiLi9PwypRH6OD8\n9OnTBlW9srIyDBw4UEMrIxCkw+PxEB8fj+zsbDHFL0k+LatWrcK2bds0sEoCE+Tn5+PmzZu4efMm\nsrKyYGhoiJEjR2LRokUqP9fq1asbDPjX1tYSNUUNQSosBIKG0NXVRfv27alMd8eOHbVCZYbpNgdt\nV+HS0dGhghUAaN26tVRDO22nXbt2eP/+PdavX4+YmBhKEa62thY//PAD6cknaBV0FL9Eg5WioiIU\nFhbC0tISXC63ybdyEoBevXqhW7du+OKLL9CrVy/8+++/4HA4aglYiJqidtE077oEQjPA2NgYP/74\nI8rKypCUlITU1FR8+eWXml4WYzQVFS5zc3MsXLgQw4cPp9pRBg8erOll0eLixYuIjY1FZmYmpkyZ\nQj3PZrMxfPhwDa6MQGhIYxS/Dhw4gJSUFLx//x7x8fHYunUrDA0NMX/+fBWukMAUFy9exM2bN5GR\nkQE+nw8zMzMMGTIErq6u6NChg1rOSdQUtQvSEkYgaAg+n4+EhARkZGRAX18f5ubmmDx5MthstqaX\nxgi5ublISUnB0aNHMXr0aOp5NpsNS0tLjcsai3L9+nWxdpSmvrk/efIkvv76a00vg0CQib29vcyM\ntizFL09PTxw+fBheXl44dOgQBAIB3N3dNd5yS6CHo6MjPnz4gK+++gpjxoyBubm52ipmwgH/zMxM\nBAUFNRjwHzp0qFrOS5ANqbAQCBri48ePaN26NeUlIOzTVpUxo7aj7SpcS5YsoQzERo4ciZEjR2p4\nRapDT08PixYtwu7duwEAs2fPhqurKxwcHDS8MgLhfzRG8au2thbA/8wcq6urSXa8CZOYmIiysjLc\nunUL586dQ1RUFNhsNiwsLGBpaYlx48ap7FyaGPAnyIdUWAgEDeHm5gZjY2N06tSJeo7FYv3nNN45\nHA527typdSpc3t7eOHjwoEbXoC7c3NwQExODNm3aAKjbzPn4+DRJIQFC80VYHaHDkSNHcOrUKRQU\nFGDcuHG4fv06Zs6cie+++07FqyRoguLiYly+fBl//fUXcnJykJ2drfJzMDngT5APqbAQCBpCT08P\n27dv1/QyNM6hQ4cQHx/fQIVL0wHLs2fPEBkZKfXrTTmwrK2thYGBAfWYz+eD5K4I2oZwdoAOHh4e\nsLGxQWZmJvT19bFgwQIxLxdC0+L58+e4efMmbty4gVu3buGTTz7BiBEjsGDBAgwbNkwt52RywJ8g\nHxKwEAgaYvz48bhw4QKGDh0qZoLWsmVLDa6KebRVhatly5bo3bu3ppehFjw9PTF16lT07NkTfD4f\nT58+xZIlSzS9LAJBjMYofqWnpyMhIQGhoaEAAF9fX/j4+Khtc0tQLwsXLsTIkSMxYcIE+Pn5oW3b\ntmo7lyYG/AnyIS1hBIKGsLOza9BTzWKxcPbsWQ2tSDNERESgoKBATIWrd+/eWLlypUbX1Zh2lKZA\nVVUV8vPzoaOjg549e/7nAmVC06G+4ld4eLhcxS93d3dERkbCxMQEQJ0Hka+vL2l7JMiFyQF/guJo\nPo1JIPxHOX36tKaXoBX4+fmJqXDNnj1bK1S4mrOJYlFREXbv3o2Kigr89NNPSExMhIWFBeMePASC\nIqSmpiIuLg5eXl4AAH9/f7i7u8sMWGpra6lgBQDJjBMUhskBf4LikICFQGCYoKAghISEwMXFRaJR\n5N9//62BVTGPtqtw+fn5aXoJaiMgIADe3t749ddfAdRt5tauXdusK0qEpgsdxS87Ozu4urrCzMwM\nfD4fGRkZRMqboDDt27fHxIkTMXHiRLEB/wMHDqhlwJ8gHxKwEAgMs3jxYgCgNuuiVFZWMr0cjVFe\nXq7pJfxn4fP5sLGxQUxMDABg1KhRlMQxgaBtODk5wdvbGwUFBQgKCqIUv2Qxb9482NnZIScnB7q6\nupgzZw6pIBIUQhMD/gT5kICFQGAYoZZ7mzZtkJCQQPkM8Hg8nDhxAhcuXNDk8hijOatwaTu6urq4\ndu0a+Hw+Xr9+jTNnzoiphhEI2oQyil9xcXFwd3dHRESEWAU7IyMDALmuEOTD5IA/QXFIwEIgaIil\nS5di8ODBSExMhJubGy5cuIANGzZoelmM0ZxVuLSd8PBw/PjjjygrK8PcuXNhZmaGzZs3a3pZBIJE\nlFH8ElZR+vTpw+gaCc2HhIQETS+BIAESsBAIGoLP52PJkiW4ceMGZs+eDU9PTyxbtgwTJ07U9NIY\n4bPPPsM333yj6WX8Jzl+/DjCw8M1vQwCQSF27NghVo0NDg6WqvhlbW0NADh//rzEtlsCgdA0IQEL\ngaAheDwecnNz0aJFC1y5cgXdunXDs2fPNL0sxmjOKlzazps3b3DlyhUMGjQIenp61PNE2pigjdBR\n/GrXrh127NgBMzMzsc+4jY2NWtZIIBDUC/FhIRA0RG5uLkpLS9GxY0eEh4ejvLwcXl5e+PbbbzW9\nNEIzx97eHjweT+y5/6IHEKFp8Ouvv+LMmTMNFL+kDd5zuVwEBgaCz+eLmfICIK2PBEIThQQsBIKG\nOHr0aIPgJDY2FrNmzdLQiggEAkE7KSgooBS/BgwYIFXxKzU1FZs2bYKhoSHKy8sRGRkJc3NzhldL\nIBBUDQlYCASGuXLlCi5fvoyUlBRMnjyZer62thZJSUm4dOmSBldHaM4QDyBCU0Ka4pcQSYpf7u7u\n2LdvH9q2bYvCwkIEBwdT8t0EAqHpQmZYCASGMTc3h66uLi5duiSmksVisTB9+nQNrozQ3JHlAUQg\naBt0FL/09PQoGVpjY2NUV1erZW0EAoFZSMBCIDBM69atMWLECHA4HBQVFaGwsBCWlpbgcrnQ19fX\n9PIIzZjPPvsMDx48wJEjR5Cfnw82m40BAwZg5syZUn0tCARNQUfxq34lRlJlhkAgND1ISxiBoCEO\nHDiAlJQUvH//HvHx8QgPD4ehoSHmz5+v6aURminXrl1DWFgYFixYAFNTU1RVVSE7OxsHDhxAUFAQ\nRo0apeklEggNCAwMRLt27RRS/BoyZAh69uwJABAIBHjy5Al69uwJgUAAFotF2h4JhCYKqbAQCBoi\nNTUVcXFx8PLyAgD4+/vD3d2dBCwEtfHLL79g79696NatG/XcwIEDMXr0aKxatYoELAStg8vlgsvl\noqioCCUlJWJfkxSwENM/AqF5QgIWAkFD1NbWAvhfy0J1dTVqamo0uSRCM6empkYsWBFiYmICNput\ngRURCNKho/glTT2MQCA0bUjAQiBoCCcnJ3h7e6OgoABBQUFIS0uDj4+PppdFaMbI6ucn81MEbSMm\nJgbHjx8nil8EAoEELASCpvDw8ICNjQ0yMzOhr6+PH374AV27dtX0sgjNmOzsbIlKdAKBAE+fPmV+\nQQSCDIjiF4FAEEICFgKBYQQCARISElBQUIABAwZgypQpAOpawqKiorB8+XINr5DQXCH9/YSmBFH8\nIhAIQohKGIHAMIGBgeDxeDAzM8PZs2cxatQodO/eHdu2bYO9vT0JWAgEAgFE8YtAIPwPErAQCAzj\n7u6OuLg4AACPx4OVlRVGjhyJ1atXw9jYWMOrIxAIBO3gxYsXMr9OBuwJhP8OpCWMQGAYUR8BPT09\n9OnTBz/++KMGV0QgEAjaBwlICASCEKJjSSAwDOnLJhAIBAKBQFAc0hJGIDAM6csmEAgEAoFAUBwS\nsBAIDEP6sgkEAoFAIBAUhwQsBAKBQCAQCAQCQWshMywEAoFAIBAIBAJBayEBC4FAIBAIBAKBQNBa\nSMBCIBAIBAKBQCAQtBYSsBAIBAKBQCAQCASt5f8BlooJuml+qTYAAAAASUVORK5CYII=\n",
1396 "text/plain": [
1397 "<matplotlib.figure.Figure at 0x7f241dc5ad10>"
1398 ]
1399 },
1400 "metadata": {},
1401 "output_type": "display_data"
1402 }
1403 ],
1404 "source": [
1405 "g = sns.clustermap(data.drop(['stock'] + return_cols, axis=1).corr())\n",
1406 "plt.gcf().set_size_inches((14,14));"
1407 ]
1408 },
1409 {
1410 "cell_type": "markdown",
1411 "metadata": {},
1412 "source": [
1413 "#### Prepare Features"
1414 ]
1415 },
1416 {
1417 "cell_type": "code",
1418 "execution_count": 171,
1419 "metadata": {},
1420 "outputs": [
1421 {
1422 "name": "stdout",
1423 "output_type": "stream",
1424 "text": [
1425 "<class 'pandas.core.frame.DataFrame'>\n",
1426 "MultiIndex: 47377 entries, (2014-01-02 00:00:00+00:00, Equity(24 [AAPL])) to (2015-12-31 00:00:00+00:00, Equity(47208 [GPRO]))\n",
1427 "Columns: 182 entries, DividendYield to stock_YELP INC\n",
1428 "dtypes: float64(182)\n",
1429 "memory usage: 66.1+ MB\n"
1430 ]
1431 }
1432 ],
1433 "source": [
1434 "X = pd.get_dummies(data.drop(return_cols, axis=1), drop_first=True)\n",
1435 "X.info()"
1436 ]
1437 },
1438 {
1439 "cell_type": "markdown",
1440 "metadata": {},
1441 "source": [
1442 "#### Shifted Returns"
1443 ]
1444 },
1445 {
1446 "cell_type": "code",
1447 "execution_count": 172,
1448 "metadata": {
1449 "scrolled": true
1450 },
1451 "outputs": [
1452 {
1453 "name": "stdout",
1454 "output_type": "stream",
1455 "text": [
1456 "<class 'pandas.core.frame.DataFrame'>\n",
1457 "MultiIndex: 47377 entries, (2014-01-02 00:00:00+00:00, Equity(24 [AAPL])) to (2015-12-31 00:00:00+00:00, Equity(47208 [GPRO]))\n",
1458 "Data columns (total 4 columns):\n",
1459 "Returns1D 47242 non-null float64\n",
1460 "Returns5D 46706 non-null float64\n",
1461 "Returns10D 46036 non-null float64\n",
1462 "Returns20D 44696 non-null float64\n",
1463 "dtypes: float64(4)\n",
1464 "memory usage: 1.8+ MB\n"
1465 ]
1466 }
1467 ],
1468 "source": [
1469 "y = data.loc[:, return_cols]\n",
1470 "shifted_y = []\n",
1471 "for col in y.columns:\n",
1472 " t = int(re.search(r'\\d+', col).group(0))\n",
1473 " shifted_y.append(y.groupby(level='asset')['Returns{}D'.format(t)].shift(-t).to_frame(col))\n",
1474 "y = pd.concat(shifted_y, axis=1)\n",
1475 "y.info()"
1476 ]
1477 },
1478 {
1479 "cell_type": "code",
1480 "execution_count": 175,
1481 "metadata": {},
1482 "outputs": [
1483 {
1484 "data": {
1485 "image/png": 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U83g88nq98nq9HBMWRrABAACG+IQaqBZ8HHBMWBfBBgAA1MMn1ADshmADAADq4RNq4Jwb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\nwQYAAACA7f0/9mHOqH2g4isAAAAASUVORK5CYII=\n",
1486 "text/plain": [
1487 "<matplotlib.figure.Figure at 0x7f241d338590>"
1488 ]
1489 },
1490 "metadata": {},
1491 "output_type": "display_data"
1492 }
1493 ],
1494 "source": [
1495 "ax = sns.boxplot(y[return_cols])\n",
1496 "ax.set_title('Return Distriubtions');"
1497 ]
1498 },
1499 {
1500 "cell_type": "markdown",
1501 "metadata": {},
1502 "source": [
1503 "### Linear Regression"
1504 ]
1505 },
1506 {
1507 "cell_type": "markdown",
1508 "metadata": {},
1509 "source": [
1510 "#### Statsmodels"
1511 ]
1512 },
1513 {
1514 "cell_type": "code",
1515 "execution_count": 180,
1516 "metadata": {},
1517 "outputs": [
1518 {
1519 "data": {
1520 "text/html": [
1521 "<table class=\"simpletable\">\n",
1522 "<caption>OLS Regression Results</caption>\n",
1523 "<tr>\n",
1524 " <th>Dep. Variable:</th> <td>Returns1D</td> <th> R-squared: </th> <td> 0.004</td> \n",
1525 "</tr>\n",
1526 "<tr>\n",
1527 " <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.000</td> \n",
1528 "</tr>\n",
1529 "<tr>\n",
1530 " <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 1.084</td> \n",
1531 "</tr>\n",
1532 "<tr>\n",
1533 " <th>Date:</th> <td>Fri, 03 Aug 2018</td> <th> Prob (F-statistic):</th> <td> 0.214</td> \n",
1534 "</tr>\n",
1535 "<tr>\n",
1536 " <th>Time:</th> <td>21:40:26</td> <th> Log-Likelihood: </th> <td>1.3306e+05</td>\n",
1537 "</tr>\n",
1538 "<tr>\n",
1539 " <th>No. Observations:</th> <td> 44878</td> <th> AIC: </th> <td>-2.658e+05</td>\n",
1540 "</tr>\n",
1541 "<tr>\n",
1542 " <th>Df Residuals:</th> <td> 44703</td> <th> BIC: </th> <td>-2.642e+05</td>\n",
1543 "</tr>\n",
1544 "<tr>\n",
1545 " <th>Df Model:</th> <td> 174</td> <th> </th> <td> </td> \n",
1546 "</tr>\n",
1547 "<tr>\n",
1548 " <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
1549 "</tr>\n",
1550 "</table>\n",
1551 "<table class=\"simpletable\">\n",
1552 "<tr>\n",
1553 " <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
1554 "</tr>\n",
1555 "<tr>\n",
1556 " <th>DividendYield</th> <td> 1.249e-06</td> <td> 4.7e-07</td> <td> 2.659</td> <td> 0.008</td> <td> 3.28e-07 2.17e-06</td>\n",
1557 "</tr>\n",
1558 "<tr>\n",
1559 " <th>EBITDAYield</th> <td>-5.537e-06</td> <td> 9.48e-06</td> <td> -0.584</td> <td> 0.559</td> <td>-2.41e-05 1.3e-05</td>\n",
1560 "</tr>\n",
1561 "<tr>\n",
1562 " <th>EVToEBITDA</th> <td> 4.87e-06</td> <td> 8.7e-06</td> <td> 0.560</td> <td> 0.575</td> <td>-1.22e-05 2.19e-05</td>\n",
1563 "</tr>\n",
1564 "<tr>\n",
1565 " <th>EVToFCF</th> <td> 3.621e-06</td> <td> 1.08e-05</td> <td> 0.334</td> <td> 0.738</td> <td>-1.76e-05 2.48e-05</td>\n",
1566 "</tr>\n",
1567 "<tr>\n",
1568 " <th>PriceToBook</th> <td>-5.411e-07</td> <td> 1.03e-05</td> <td> -0.052</td> <td> 0.958</td> <td>-2.08e-05 1.97e-05</td>\n",
1569 "</tr>\n",
1570 "<tr>\n",
1571 " <th>PriceToDilutedEarningsTTM</th> <td> 4.561e-06</td> <td> 4.64e-06</td> <td> 0.983</td> <td> 0.326</td> <td>-4.54e-06 1.37e-05</td>\n",
1572 "</tr>\n",
1573 "<tr>\n",
1574 " <th>PriceToEarningsTTM</th> <td> 6e-08</td> <td> 2.05e-07</td> <td> 0.293</td> <td> 0.770</td> <td>-3.41e-07 4.61e-07</td>\n",
1575 "</tr>\n",
1576 "<tr>\n",
1577 " <th>PriceToFCF</th> <td>-9.612e-07</td> <td> 9.07e-06</td> <td> -0.106</td> <td> 0.916</td> <td>-1.87e-05 1.68e-05</td>\n",
1578 "</tr>\n",
1579 "<tr>\n",
1580 " <th>PriceToOperatingCashflow</th> <td>-6.258e-07</td> <td> 6.16e-06</td> <td> -0.102</td> <td> 0.919</td> <td>-1.27e-05 1.14e-05</td>\n",
1581 "</tr>\n",
1582 "<tr>\n",
1583 " <th>PriceToSalesTTM</th> <td>-2.088e-06</td> <td> 5.16e-07</td> <td> -4.048</td> <td> 0.000</td> <td> -3.1e-06 -1.08e-06</td>\n",
1584 "</tr>\n",
1585 "<tr>\n",
1586 " <th>Directional Movement Index</th> <td>-3.169e-07</td> <td> 2.09e-06</td> <td> -0.152</td> <td> 0.879</td> <td> -4.4e-06 3.77e-06</td>\n",
1587 "</tr>\n",
1588 "<tr>\n",
1589 " <th>Money Flow Index</th> <td>-2.768e-06</td> <td> 2.7e-06</td> <td> -1.025</td> <td> 0.305</td> <td>-8.06e-06 2.52e-06</td>\n",
1590 "</tr>\n",
1591 "<tr>\n",
1592 " <th>Percent Above Low</th> <td> 2.875e-06</td> <td> 5.17e-06</td> <td> 0.556</td> <td> 0.578</td> <td>-7.26e-06 1.3e-05</td>\n",
1593 "</tr>\n",
1594 "<tr>\n",
1595 " <th>Percent Below High</th> <td>-1.786e-06</td> <td> 4.14e-06</td> <td> -0.431</td> <td> 0.666</td> <td> -9.9e-06 6.33e-06</td>\n",
1596 "</tr>\n",
1597 "<tr>\n",
1598 " <th>Price Oscillator</th> <td> 9.18e-07</td> <td> 2.92e-06</td> <td> 0.314</td> <td> 0.753</td> <td>-4.81e-06 6.64e-06</td>\n",
1599 "</tr>\n",
1600 "<tr>\n",
1601 " <th>Trendline</th> <td> 2.007e-06</td> <td> 4.41e-06</td> <td> 0.455</td> <td> 0.649</td> <td>-6.64e-06 1.07e-05</td>\n",
1602 "</tr>\n",
1603 "<tr>\n",
1604 " <th>AssetToEquityRatio</th> <td> 5.633e-07</td> <td> 3.98e-07</td> <td> 1.414</td> <td> 0.157</td> <td>-2.17e-07 1.34e-06</td>\n",
1605 "</tr>\n",
1606 "<tr>\n",
1607 " <th>AssetTurnover</th> <td> -1.27e-05</td> <td> 1.74e-05</td> <td> -0.731</td> <td> 0.465</td> <td>-4.68e-05 2.14e-05</td>\n",
1608 "</tr>\n",
1609 "<tr>\n",
1610 " <th>CurrentRatio</th> <td> 3.246e-07</td> <td> 5.2e-07</td> <td> 0.625</td> <td> 0.532</td> <td>-6.94e-07 1.34e-06</td>\n",
1611 "</tr>\n",
1612 "<tr>\n",
1613 " <th>DebtToAssetRatio</th> <td> 5.697e-07</td> <td> 3.18e-07</td> <td> 1.792</td> <td> 0.073</td> <td>-5.35e-08 1.19e-06</td>\n",
1614 "</tr>\n",
1615 "<tr>\n",
1616 " <th>DebtToEquityRatio</th> <td>-5.451e-07</td> <td> 3.69e-07</td> <td> -1.476</td> <td> 0.140</td> <td>-1.27e-06 1.79e-07</td>\n",
1617 "</tr>\n",
1618 "<tr>\n",
1619 " <th>MertonsDD</th> <td> -0.0001</td> <td> 4.64e-05</td> <td> -2.661</td> <td> 0.008</td> <td> -0.000 -3.25e-05</td>\n",
1620 "</tr>\n",
1621 "<tr>\n",
1622 " <th>WorkingCapitalToAssets</th> <td> 1.452e-07</td> <td> 6.31e-07</td> <td> 0.230</td> <td> 0.818</td> <td>-1.09e-06 1.38e-06</td>\n",
1623 "</tr>\n",
1624 "<tr>\n",
1625 " <th>WorkingCapitalToSales</th> <td> -2.24e-05</td> <td> 2.03e-05</td> <td> -1.106</td> <td> 0.269</td> <td>-6.21e-05 1.73e-05</td>\n",
1626 "</tr>\n",
1627 "<tr>\n",
1628 " <th>Dividend Growth</th> <td>-1.505e-08</td> <td> 1.96e-07</td> <td> -0.077</td> <td> 0.939</td> <td> -4e-07 3.69e-07</td>\n",
1629 "</tr>\n",
1630 "<tr>\n",
1631 " <th>EPS</th> <td>-1.219e-07</td> <td> 3.1e-07</td> <td> -0.393</td> <td> 0.694</td> <td> -7.3e-07 4.86e-07</td>\n",
1632 "</tr>\n",
1633 "<tr>\n",
1634 " <th>Net Debt</th> <td>-1.968e-14</td> <td> 1.12e-14</td> <td> -1.760</td> <td> 0.078</td> <td>-4.16e-14 2.23e-15</td>\n",
1635 "</tr>\n",
1636 "<tr>\n",
1637 " <th>Sales</th> <td> 5.85e-15</td> <td> 1.04e-14</td> <td> 0.563</td> <td> 0.574</td> <td>-1.45e-14 2.62e-14</td>\n",
1638 "</tr>\n",
1639 "<tr>\n",
1640 " <th>Total Assets</th> <td>-1.665e-14</td> <td> 7.87e-15</td> <td> -2.116</td> <td> 0.034</td> <td>-3.21e-14 -1.23e-15</td>\n",
1641 "</tr>\n",
1642 "<tr>\n",
1643 " <th>EPS Growth 3M</th> <td>-5.625e-08</td> <td> 4.75e-07</td> <td> -0.118</td> <td> 0.906</td> <td>-9.87e-07 8.75e-07</td>\n",
1644 "</tr>\n",
1645 "<tr>\n",
1646 " <th>EPS Growth 12M</th> <td> 6.686e-07</td> <td> 4.76e-07</td> <td> 1.405</td> <td> 0.160</td> <td>-2.64e-07 1.6e-06</td>\n",
1647 "</tr>\n",
1648 "<tr>\n",
1649 " <th>Net Debt Growth 3M</th> <td> 8.433e-08</td> <td> 6.03e-07</td> <td> 0.140</td> <td> 0.889</td> <td> -1.1e-06 1.27e-06</td>\n",
1650 "</tr>\n",
1651 "<tr>\n",
1652 " <th>Net Debt Growth 12M</th> <td> 4.558e-07</td> <td> 6.14e-07</td> <td> 0.743</td> <td> 0.458</td> <td>-7.47e-07 1.66e-06</td>\n",
1653 "</tr>\n",
1654 "<tr>\n",
1655 " <th>Sales Growth 3M</th> <td> 2.469e-07</td> <td> 6.4e-07</td> <td> 0.386</td> <td> 0.699</td> <td>-1.01e-06 1.5e-06</td>\n",
1656 "</tr>\n",
1657 "<tr>\n",
1658 " <th>Sales Growth 12M</th> <td>-4.861e-07</td> <td> 6.53e-07</td> <td> -0.745</td> <td> 0.456</td> <td>-1.77e-06 7.93e-07</td>\n",
1659 "</tr>\n",
1660 "<tr>\n",
1661 " <th>Total Assets Growth 3M</th> <td> 4.535e-07</td> <td> 7.16e-07</td> <td> 0.633</td> <td> 0.527</td> <td> -9.5e-07 1.86e-06</td>\n",
1662 "</tr>\n",
1663 "<tr>\n",
1664 " <th>Total Assets Growth 12M</th> <td>-3.477e-07</td> <td> 7.64e-07</td> <td> -0.455</td> <td> 0.649</td> <td>-1.84e-06 1.15e-06</td>\n",
1665 "</tr>\n",
1666 "<tr>\n",
1667 " <th>CFO To Assets</th> <td> 1.731e-05</td> <td> 8.66e-06</td> <td> 1.999</td> <td> 0.046</td> <td> 3.36e-07 3.43e-05</td>\n",
1668 "</tr>\n",
1669 "<tr>\n",
1670 " <th>Capex To Assets</th> <td>-2.097e-05</td> <td> 1.87e-05</td> <td> -1.124</td> <td> 0.261</td> <td>-5.75e-05 1.56e-05</td>\n",
1671 "</tr>\n",
1672 "<tr>\n",
1673 " <th>Capex To FCF</th> <td> 1.29e-06</td> <td> 1.05e-05</td> <td> 0.123</td> <td> 0.902</td> <td>-1.92e-05 2.18e-05</td>\n",
1674 "</tr>\n",
1675 "<tr>\n",
1676 " <th>Capex To Sales</th> <td> 1.637e-05</td> <td> 2e-05</td> <td> 0.820</td> <td> 0.412</td> <td>-2.28e-05 5.55e-05</td>\n",
1677 "</tr>\n",
1678 "<tr>\n",
1679 " <th>EBIT To Assets</th> <td> 7.612e-06</td> <td> 8.75e-06</td> <td> 0.870</td> <td> 0.385</td> <td>-9.55e-06 2.48e-05</td>\n",
1680 "</tr>\n",
1681 "<tr>\n",
1682 " <th>Retained Earnings To Assets</th> <td>-3.547e-05</td> <td> 1.84e-05</td> <td> -1.923</td> <td> 0.054</td> <td>-7.16e-05 6.76e-07</td>\n",
1683 "</tr>\n",
1684 "<tr>\n",
1685 " <th>Downside Risk</th> <td> 4.943e-07</td> <td> 5.32e-06</td> <td> 0.093</td> <td> 0.926</td> <td>-9.93e-06 1.09e-05</td>\n",
1686 "</tr>\n",
1687 "<tr>\n",
1688 " <th>Index Beta</th> <td>-8.585e-08</td> <td> 1.09e-07</td> <td> -0.786</td> <td> 0.432</td> <td> -3e-07 1.28e-07</td>\n",
1689 "</tr>\n",
1690 "<tr>\n",
1691 " <th>Log Market Cap</th> <td> 1.847e-05</td> <td> 1.86e-05</td> <td> 0.995</td> <td> 0.320</td> <td>-1.79e-05 5.49e-05</td>\n",
1692 "</tr>\n",
1693 "<tr>\n",
1694 " <th>Volatility 3M</th> <td>-8.138e-06</td> <td> 4.53e-06</td> <td> -1.798</td> <td> 0.072</td> <td> -1.7e-05 7.33e-07</td>\n",
1695 "</tr>\n",
1696 "<tr>\n",
1697 " <th>stock_3D SYSTEMS CORP</th> <td> 0.0157</td> <td> 0.004</td> <td> 3.600</td> <td> 0.000</td> <td> 0.007 0.024</td>\n",
1698 "</tr>\n",
1699 "<tr>\n",
1700 " <th>stock_3M COMPANY</th> <td> 0.0108</td> <td> 0.004</td> <td> 2.823</td> <td> 0.005</td> <td> 0.003 0.018</td>\n",
1701 "</tr>\n",
1702 "<tr>\n",
1703 " <th>stock_ABBOTT LABORATORIES</th> <td> 0.0069</td> <td> 0.003</td> <td> 2.315</td> <td> 0.021</td> <td> 0.001 0.013</td>\n",
1704 "</tr>\n",
1705 "<tr>\n",
1706 " <th>stock_ABBVIE INC</th> <td> 0.0163</td> <td> 0.006</td> <td> 2.916</td> <td> 0.004</td> <td> 0.005 0.027</td>\n",
1707 "</tr>\n",
1708 "<tr>\n",
1709 " <th>stock_ALLERGAN INC</th> <td> 0.0103</td> <td> 0.003</td> <td> 3.563</td> <td> 0.000</td> <td> 0.005 0.016</td>\n",
1710 "</tr>\n",
1711 "<tr>\n",
1712 " <th>stock_ALLERGAN PLC</th> <td> 0.0135</td> <td> 0.004</td> <td> 3.310</td> <td> 0.001</td> <td> 0.006 0.022</td>\n",
1713 "</tr>\n",
1714 "<tr>\n",
1715 " <th>stock_ALTABA INC</th> <td> 0.0161</td> <td> 0.005</td> <td> 3.560</td> <td> 0.000</td> <td> 0.007 0.025</td>\n",
1716 "</tr>\n",
1717 "<tr>\n",
1718 " <th>stock_ALTRIA GROUP INC.</th> <td> 0.0111</td> <td> 0.004</td> <td> 2.847</td> <td> 0.004</td> <td> 0.003 0.019</td>\n",
1719 "</tr>\n",
1720 "<tr>\n",
1721 " <th>stock_AMAZON.COM INC</th> <td> 0.0123</td> <td> 0.005</td> <td> 2.683</td> <td> 0.007</td> <td> 0.003 0.021</td>\n",
1722 "</tr>\n",
1723 "<tr>\n",
1724 " <th>stock_AMERICAN AIRLINES GROUP INC</th> <td> 0.0143</td> <td> 0.005</td> <td> 2.720</td> <td> 0.007</td> <td> 0.004 0.025</td>\n",
1725 "</tr>\n",
1726 "<tr>\n",
1727 " <th>stock_AMERICAN EXPRESS COMPANY</th> <td> 0.0065</td> <td> 0.002</td> <td> 2.656</td> <td> 0.008</td> <td> 0.002 0.011</td>\n",
1728 "</tr>\n",
1729 "<tr>\n",
1730 " <th>stock_AMERICAN INTL GROUP INC</th> <td> 0.0118</td> <td> 0.004</td> <td> 2.850</td> <td> 0.004</td> <td> 0.004 0.020</td>\n",
1731 "</tr>\n",
1732 "<tr>\n",
1733 " <th>stock_AMGEN INC</th> <td> 0.0064</td> <td> 0.003</td> <td> 2.421</td> <td> 0.015</td> <td> 0.001 0.012</td>\n",
1734 "</tr>\n",
1735 "<tr>\n",
1736 " <th>stock_ANADARKO PETROLEUM CORP</th> <td> 0.0077</td> <td> 0.002</td> <td> 3.231</td> <td> 0.001</td> <td> 0.003 0.012</td>\n",
1737 "</tr>\n",
1738 "<tr>\n",
1739 " <th>stock_APACHE CORP</th> <td> 0.0045</td> <td> 0.004</td> <td> 1.262</td> <td> 0.207</td> <td> -0.003 0.012</td>\n",
1740 "</tr>\n",
1741 "<tr>\n",
1742 " <th>stock_APPLE INC</th> <td> 0.0086</td> <td> 0.004</td> <td> 2.278</td> <td> 0.023</td> <td> 0.001 0.016</td>\n",
1743 "</tr>\n",
1744 "<tr>\n",
1745 " <th>stock_APPLIED MATERIALS INC</th> <td> 0.0075</td> <td> 0.003</td> <td> 2.572</td> <td> 0.010</td> <td> 0.002 0.013</td>\n",
1746 "</tr>\n",
1747 "<tr>\n",
1748 " <th>stock_ARCONIC INC</th> <td> 0.0026</td> <td> 0.002</td> <td> 1.210</td> <td> 0.226</td> <td> -0.002 0.007</td>\n",
1749 "</tr>\n",
1750 "<tr>\n",
1751 " <th>stock_AT&T INC. COM</th> <td> 0.0103</td> <td> 0.004</td> <td> 2.420</td> <td> 0.016</td> <td> 0.002 0.019</td>\n",
1752 "</tr>\n",
1753 "<tr>\n",
1754 " <th>stock_Alphabet Inc. Cl A</th> <td> 0.0192</td> <td> 0.005</td> <td> 3.528</td> <td> 0.000</td> <td> 0.009 0.030</td>\n",
1755 "</tr>\n",
1756 "<tr>\n",
1757 " <th>stock_BAKER HUGHES INC</th> <td> 0.0078</td> <td> 0.003</td> <td> 2.946</td> <td> 0.003</td> <td> 0.003 0.013</td>\n",
1758 "</tr>\n",
1759 "<tr>\n",
1760 " <th>stock_BANK OF AMERICA CORP</th> <td> 0.0404</td> <td> 0.016</td> <td> 2.532</td> <td> 0.011</td> <td> 0.009 0.072</td>\n",
1761 "</tr>\n",
1762 "<tr>\n",
1763 " <th>stock_BERKSHIRE HATHAWAY INC CL-B</th> <td> 0.0171</td> <td> 0.006</td> <td> 3.069</td> <td> 0.002</td> <td> 0.006 0.028</td>\n",
1764 "</tr>\n",
1765 "<tr>\n",
1766 " <th>stock_BIOGEN INC</th> <td> 0.0120</td> <td> 0.004</td> <td> 3.275</td> <td> 0.001</td> <td> 0.005 0.019</td>\n",
1767 "</tr>\n",
1768 "<tr>\n",
1769 " <th>stock_BOEING CO</th> <td> 0.0053</td> <td> 0.003</td> <td> 1.864</td> <td> 0.062</td> <td> -0.000 0.011</td>\n",
1770 "</tr>\n",
1771 "<tr>\n",
1772 " <th>stock_BOOKING HOLDINGS INC</th> <td> 0.0153</td> <td> 0.005</td> <td> 3.232</td> <td> 0.001</td> <td> 0.006 0.025</td>\n",
1773 "</tr>\n",
1774 "<tr>\n",
1775 " <th>stock_BRISTOL MYERS SQUIBB COMPANY</th> <td> 0.0097</td> <td> 0.003</td> <td> 3.001</td> <td> 0.003</td> <td> 0.003 0.016</td>\n",
1776 "</tr>\n",
1777 "<tr>\n",
1778 " <th>stock_BROADCOM CORP</th> <td> 0.0134</td> <td> 0.004</td> <td> 3.087</td> <td> 0.002</td> <td> 0.005 0.022</td>\n",
1779 "</tr>\n",
1780 "<tr>\n",
1781 " <th>stock_BROADCOM INC</th> <td> 0.0173</td> <td> 0.006</td> <td> 3.112</td> <td> 0.002</td> <td> 0.006 0.028</td>\n",
1782 "</tr>\n",
1783 "<tr>\n",
1784 " <th>stock_CATERPILLAR INC</th> <td> 0.0042</td> <td> 0.003</td> <td> 1.502</td> <td> 0.133</td> <td> -0.001 0.010</td>\n",
1785 "</tr>\n",
1786 "<tr>\n",
1787 " <th>stock_CELGENE CORP</th> <td> 0.0091</td> <td> 0.003</td> <td> 2.905</td> <td> 0.004</td> <td> 0.003 0.015</td>\n",
1788 "</tr>\n",
1789 "<tr>\n",
1790 " <th>stock_CHESAPEAKE ENERGY CORP</th> <td> 0.0041</td> <td> 0.005</td> <td> 0.874</td> <td> 0.382</td> <td> -0.005 0.013</td>\n",
1791 "</tr>\n",
1792 "<tr>\n",
1793 " <th>stock_CHEVRON CORPORATION</th> <td> 0.0137</td> <td> 0.006</td> <td> 2.386</td> <td> 0.017</td> <td> 0.002 0.025</td>\n",
1794 "</tr>\n",
1795 "<tr>\n",
1796 " <th>stock_CISCO SYSTEMS INC</th> <td> 0.0066</td> <td> 0.003</td> <td> 2.363</td> <td> 0.018</td> <td> 0.001 0.012</td>\n",
1797 "</tr>\n",
1798 "<tr>\n",
1799 " <th>stock_CITIGROUP</th> <td> 0.0371</td> <td> 0.014</td> <td> 2.713</td> <td> 0.007</td> <td> 0.010 0.064</td>\n",
1800 "</tr>\n",
1801 "<tr>\n",
1802 " <th>stock_COCA-COLA CO</th> <td> 0.0101</td> <td> 0.004</td> <td> 2.629</td> <td> 0.009</td> <td> 0.003 0.018</td>\n",
1803 "</tr>\n",
1804 "<tr>\n",
1805 " <th>stock_COMCAST CORP</th> <td> 0.0081</td> <td> 0.003</td> <td> 2.917</td> <td> 0.004</td> <td> 0.003 0.013</td>\n",
1806 "</tr>\n",
1807 "<tr>\n",
1808 " <th>stock_CONOCOPHILLIPS</th> <td> 0.0125</td> <td> 0.005</td> <td> 2.566</td> <td> 0.010</td> <td> 0.003 0.022</td>\n",
1809 "</tr>\n",
1810 "<tr>\n",
1811 " <th>stock_COVIDIEN PLC</th> <td> 0.0196</td> <td> 0.005</td> <td> 3.839</td> <td> 0.000</td> <td> 0.010 0.030</td>\n",
1812 "</tr>\n",
1813 "<tr>\n",
1814 " <th>stock_CVS HEALTH CORP</th> <td> 0.0077</td> <td> 0.004</td> <td> 2.122</td> <td> 0.034</td> <td> 0.001 0.015</td>\n",
1815 "</tr>\n",
1816 "<tr>\n",
1817 " <th>stock_DEERE & CO</th> <td> 0.0050</td> <td> 0.003</td> <td> 1.671</td> <td> 0.095</td> <td> -0.001 0.011</td>\n",
1818 "</tr>\n",
1819 "<tr>\n",
1820 " <th>stock_DELTA AIR LINES INC</th> <td> 0.0141</td> <td> 0.005</td> <td> 2.882</td> <td> 0.004</td> <td> 0.005 0.024</td>\n",
1821 "</tr>\n",
1822 "<tr>\n",
1823 " <th>stock_DIRECTV</th> <td> 0.0114</td> <td> 0.005</td> <td> 2.405</td> <td> 0.016</td> <td> 0.002 0.021</td>\n",
1824 "</tr>\n",
1825 "<tr>\n",
1826 " <th>stock_DOLLAR GENERAL CORP</th> <td> 0.0143</td> <td> 0.005</td> <td> 2.899</td> <td> 0.004</td> <td> 0.005 0.024</td>\n",
1827 "</tr>\n",
1828 "<tr>\n",
1829 " <th>stock_DOW CHEMICAL CO</th> <td> 0.0055</td> <td> 0.003</td> <td> 1.960</td> <td> 0.050</td> <td>-5.93e-07 0.011</td>\n",
1830 "</tr>\n",
1831 "<tr>\n",
1832 " <th>stock_E.I. Du Pont De Nemours A</th> <td> 0.0060</td> <td> 0.003</td> <td> 2.109</td> <td> 0.035</td> <td> 0.000 0.012</td>\n",
1833 "</tr>\n",
1834 "<tr>\n",
1835 " <th>stock_EBAY INC</th> <td> 0.0166</td> <td> 0.005</td> <td> 3.281</td> <td> 0.001</td> <td> 0.007 0.027</td>\n",
1836 "</tr>\n",
1837 "<tr>\n",
1838 " <th>stock_EMC CORPORATION</th> <td> 0.0082</td> <td> 0.003</td> <td> 2.886</td> <td> 0.004</td> <td> 0.003 0.014</td>\n",
1839 "</tr>\n",
1840 "<tr>\n",
1841 " <th>stock_EOG RESOURCES INC</th> <td> 0.0099</td> <td> 0.003</td> <td> 3.577</td> <td> 0.000</td> <td> 0.004 0.015</td>\n",
1842 "</tr>\n",
1843 "<tr>\n",
1844 " <th>stock_EXPRESS SCRIPTS HOLDING CO</th> <td> 0.0034</td> <td> 0.003</td> <td> 1.166</td> <td> 0.244</td> <td> -0.002 0.009</td>\n",
1845 "</tr>\n",
1846 "<tr>\n",
1847 " <th>stock_EXXON MOBIL CORPORATION</th> <td> 0.0128</td> <td> 0.007</td> <td> 1.936</td> <td> 0.053</td> <td> -0.000 0.026</td>\n",
1848 "</tr>\n",
1849 "<tr>\n",
1850 " <th>stock_FACEBOOK INC</th> <td> 0.0199</td> <td> 0.006</td> <td> 3.452</td> <td> 0.001</td> <td> 0.009 0.031</td>\n",
1851 "</tr>\n",
1852 "<tr>\n",
1853 " <th>stock_FEDEX CORPORATION</th> <td> 0.0079</td> <td> 0.003</td> <td> 2.618</td> <td> 0.009</td> <td> 0.002 0.014</td>\n",
1854 "</tr>\n",
1855 "<tr>\n",
1856 " <th>stock_FIRST SOLAR INC</th> <td> 0.0180</td> <td> 0.005</td> <td> 3.411</td> <td> 0.001</td> <td> 0.008 0.028</td>\n",
1857 "</tr>\n",
1858 "<tr>\n",
1859 " <th>stock_FORD MOTOR CO(NEW)</th> <td> 0.0057</td> <td> 0.004</td> <td> 1.626</td> <td> 0.104</td> <td> -0.001 0.013</td>\n",
1860 "</tr>\n",
1861 "<tr>\n",
1862 " <th>stock_FREEPORT-MCMORAN INC</th> <td> 0.0090</td> <td> 0.004</td> <td> 2.333</td> <td> 0.020</td> <td> 0.001 0.017</td>\n",
1863 "</tr>\n",
1864 "<tr>\n",
1865 " <th>stock_GENERAL ELECTRIC CO</th> <td> 0.0165</td> <td> 0.005</td> <td> 3.025</td> <td> 0.002</td> <td> 0.006 0.027</td>\n",
1866 "</tr>\n",
1867 "<tr>\n",
1868 " <th>stock_GENERAL MOTORS CO</th> <td> 0.0131</td> <td> 0.006</td> <td> 2.358</td> <td> 0.018</td> <td> 0.002 0.024</td>\n",
1869 "</tr>\n",
1870 "<tr>\n",
1871 " <th>stock_GILEAD SCIENCES INC</th> <td> 0.0108</td> <td> 0.003</td> <td> 3.141</td> <td> 0.002</td> <td> 0.004 0.017</td>\n",
1872 "</tr>\n",
1873 "<tr>\n",
1874 " <th>stock_GOLDMAN SACHS GROUP INC</th> <td> 0.0317</td> <td> 0.007</td> <td> 4.257</td> <td> 0.000</td> <td> 0.017 0.046</td>\n",
1875 "</tr>\n",
1876 "<tr>\n",
1877 " <th>stock_GOPRO INC</th> <td> 0.0199</td> <td> 0.006</td> <td> 3.550</td> <td> 0.000</td> <td> 0.009 0.031</td>\n",
1878 "</tr>\n",
1879 "<tr>\n",
1880 " <th>stock_HALLIBURTON CO (HOLDING CO)</th> <td> 0.0084</td> <td> 0.003</td> <td> 2.752</td> <td> 0.006</td> <td> 0.002 0.014</td>\n",
1881 "</tr>\n",
1882 "<tr>\n",
1883 " <th>stock_HOME DEPOT INC</th> <td> 0.0070</td> <td> 0.003</td> <td> 2.052</td> <td> 0.040</td> <td> 0.000 0.014</td>\n",
1884 "</tr>\n",
1885 "<tr>\n",
1886 " <th>stock_HP INC</th> <td> 0.0051</td> <td> 0.003</td> <td> 1.624</td> <td> 0.104</td> <td> -0.001 0.011</td>\n",
1887 "</tr>\n",
1888 "<tr>\n",
1889 " <th>stock_INTEL CORP</th> <td> 0.0087</td> <td> 0.003</td> <td> 2.535</td> <td> 0.011</td> <td> 0.002 0.015</td>\n",
1890 "</tr>\n",
1891 "<tr>\n",
1892 " <th>stock_INTL BUSINESS MACHINES CORP</th> <td> 0.0087</td> <td> 0.004</td> <td> 2.280</td> <td> 0.023</td> <td> 0.001 0.016</td>\n",
1893 "</tr>\n",
1894 "<tr>\n",
1895 " <th>stock_JOHNSON AND JOHNSON</th> <td> 0.0111</td> <td> 0.004</td> <td> 2.834</td> <td> 0.005</td> <td> 0.003 0.019</td>\n",
1896 "</tr>\n",
1897 "<tr>\n",
1898 " <th>stock_JPMORGAN CHASE & CO COM STK</th> <td> 0.0545</td> <td> 0.019</td> <td> 2.818</td> <td> 0.005</td> <td> 0.017 0.092</td>\n",
1899 "</tr>\n",
1900 "<tr>\n",
1901 " <th>stock_KEURIG GREEN MOUNTAIN INC</th> <td> 0.0146</td> <td> 0.004</td> <td> 3.591</td> <td> 0.000</td> <td> 0.007 0.023</td>\n",
1902 "</tr>\n",
1903 "<tr>\n",
1904 " <th>stock_KINDER MORGAN INC</th> <td> 0.0132</td> <td> 0.005</td> <td> 2.470</td> <td> 0.014</td> <td> 0.003 0.024</td>\n",
1905 "</tr>\n",
1906 "<tr>\n",
1907 " <th>stock_LAS VEGAS SANDS CORP</th> <td> 0.0127</td> <td> 0.005</td> <td> 2.488</td> <td> 0.013</td> <td> 0.003 0.023</td>\n",
1908 "</tr>\n",
1909 "<tr>\n",
1910 " <th>stock_LILLY ELI & CO</th> <td> 0.0102</td> <td> 0.004</td> <td> 2.885</td> <td> 0.004</td> <td> 0.003 0.017</td>\n",
1911 "</tr>\n",
1912 "<tr>\n",
1913 " <th>stock_LINKEDIN CORP</th> <td> 0.0197</td> <td> 0.006</td> <td> 3.505</td> <td> 0.000</td> <td> 0.009 0.031</td>\n",
1914 "</tr>\n",
1915 "<tr>\n",
1916 " <th>stock_LOWES COMPANIES INC</th> <td> 0.0075</td> <td> 0.003</td> <td> 2.483</td> <td> 0.013</td> <td> 0.002 0.013</td>\n",
1917 "</tr>\n",
1918 "<tr>\n",
1919 " <th>stock_LYONDELLBASELL INDUSTRIES NV</th> <td> 0.0135</td> <td> 0.005</td> <td> 2.593</td> <td> 0.010</td> <td> 0.003 0.024</td>\n",
1920 "</tr>\n",
1921 "<tr>\n",
1922 " <th>stock_MARATHON PETROLEUM CORP</th> <td> 0.0128</td> <td> 0.006</td> <td> 2.268</td> <td> 0.023</td> <td> 0.002 0.024</td>\n",
1923 "</tr>\n",
1924 "<tr>\n",
1925 " <th>stock_MASTERCARD INCORPORATED</th> <td> 0.0206</td> <td> 0.006</td> <td> 3.731</td> <td> 0.000</td> <td> 0.010 0.031</td>\n",
1926 "</tr>\n",
1927 "<tr>\n",
1928 " <th>stock_MCDONALDS CORP</th> <td> 0.0099</td> <td> 0.004</td> <td> 2.513</td> <td> 0.012</td> <td> 0.002 0.018</td>\n",
1929 "</tr>\n",
1930 "<tr>\n",
1931 " <th>stock_MEDTRONIC PLC</th> <td> 0.0119</td> <td> 0.004</td> <td> 3.390</td> <td> 0.001</td> <td> 0.005 0.019</td>\n",
1932 "</tr>\n",
1933 "<tr>\n",
1934 " <th>stock_MERCK & CO INC</th> <td> 0.0110</td> <td> 0.004</td> <td> 2.966</td> <td> 0.003</td> <td> 0.004 0.018</td>\n",
1935 "</tr>\n",
1936 "<tr>\n",
1937 " <th>stock_METLIFE INC</th> <td> 0.0260</td> <td> 0.008</td> <td> 3.353</td> <td> 0.001</td> <td> 0.011 0.041</td>\n",
1938 "</tr>\n",
1939 "<tr>\n",
1940 " <th>stock_MICHAEL KORS HOLDINGS LTD</th> <td> 0.0214</td> <td> 0.006</td> <td> 3.646</td> <td> 0.000</td> <td> 0.010 0.033</td>\n",
1941 "</tr>\n",
1942 "<tr>\n",
1943 " <th>stock_MICRON TECHNOLOGY INC</th> <td> 0.0102</td> <td> 0.003</td> <td> 3.287</td> <td> 0.001</td> <td> 0.004 0.016</td>\n",
1944 "</tr>\n",
1945 "<tr>\n",
1946 " <th>stock_MICROSOFT CORP</th> <td> 0.0115</td> <td> 0.004</td> <td> 3.028</td> <td> 0.002</td> <td> 0.004 0.019</td>\n",
1947 "</tr>\n",
1948 "<tr>\n",
1949 " <th>stock_MONDELEZ INTERNATIONAL INC</th> <td> 0.0133</td> <td> 0.005</td> <td> 2.875</td> <td> 0.004</td> <td> 0.004 0.022</td>\n",
1950 "</tr>\n",
1951 "<tr>\n",
1952 " <th>stock_MONSANTO COMPANY</th> <td> 0.0153</td> <td> 0.005</td> <td> 3.201</td> <td> 0.001</td> <td> 0.006 0.025</td>\n",
1953 "</tr>\n",
1954 "<tr>\n",
1955 " <th>stock_MORGAN STANLEY</th> <td> 0.0273</td> <td> 0.007</td> <td> 3.884</td> <td> 0.000</td> <td> 0.014 0.041</td>\n",
1956 "</tr>\n",
1957 "<tr>\n",
1958 " <th>stock_MYLAN NV</th> <td> 0.0111</td> <td> 0.003</td> <td> 3.260</td> <td> 0.001</td> <td> 0.004 0.018</td>\n",
1959 "</tr>\n",
1960 "<tr>\n",
1961 " <th>stock_NATIONAL OILWELL VARCO INC.</th> <td> 0.0131</td> <td> 0.005</td> <td> 2.642</td> <td> 0.008</td> <td> 0.003 0.023</td>\n",
1962 "</tr>\n",
1963 "<tr>\n",
1964 " <th>stock_NETFLIX INC</th> <td> 0.0179</td> <td> 0.005</td> <td> 3.555</td> <td> 0.000</td> <td> 0.008 0.028</td>\n",
1965 "</tr>\n",
1966 "<tr>\n",
1967 " <th>stock_NEWMONT MINING CORP (HOLDING COMPANY)</th> <td> 0.0098</td> <td> 0.004</td> <td> 2.773</td> <td> 0.006</td> <td> 0.003 0.017</td>\n",
1968 "</tr>\n",
1969 "<tr>\n",
1970 " <th>stock_NEWS CP - CL A</th> <td> 0.0129</td> <td> 0.004</td> <td> 3.348</td> <td> 0.001</td> <td> 0.005 0.020</td>\n",
1971 "</tr>\n",
1972 "<tr>\n",
1973 " <th>stock_NIKE INC CL-B</th> <td> 0.0104</td> <td> 0.004</td> <td> 2.884</td> <td> 0.004</td> <td> 0.003 0.018</td>\n",
1974 "</tr>\n",
1975 "<tr>\n",
1976 " <th>stock_OCCIDENTAL PETROLEUM CORP</th> <td> 0.0118</td> <td> 0.004</td> <td> 3.153</td> <td> 0.002</td> <td> 0.004 0.019</td>\n",
1977 "</tr>\n",
1978 "<tr>\n",
1979 " <th>stock_ORACLE CORP</th> <td> 0.0118</td> <td> 0.004</td> <td> 3.297</td> <td> 0.001</td> <td> 0.005 0.019</td>\n",
1980 "</tr>\n",
1981 "<tr>\n",
1982 " <th>stock_PANDORA MEDIA INC</th> <td> 0.0235</td> <td> 0.006</td> <td> 3.942</td> <td> 0.000</td> <td> 0.012 0.035</td>\n",
1983 "</tr>\n",
1984 "<tr>\n",
1985 " <th>stock_PENNEY J.C. CO INC (HOLDING COMPANY)</th> <td> 0.0027</td> <td> 0.003</td> <td> 0.905</td> <td> 0.365</td> <td> -0.003 0.009</td>\n",
1986 "</tr>\n",
1987 "<tr>\n",
1988 " <th>stock_PEPSICO INC</th> <td> 0.0104</td> <td> 0.004</td> <td> 2.533</td> <td> 0.011</td> <td> 0.002 0.018</td>\n",
1989 "</tr>\n",
1990 "<tr>\n",
1991 " <th>stock_PFIZER INC</th> <td> 0.0134</td> <td> 0.004</td> <td> 3.287</td> <td> 0.001</td> <td> 0.005 0.021</td>\n",
1992 "</tr>\n",
1993 "<tr>\n",
1994 " <th>stock_PHILIP MORRIS INTERNATIONAL INC</th> <td> 0.0154</td> <td> 0.006</td> <td> 2.576</td> <td> 0.010</td> <td> 0.004 0.027</td>\n",
1995 "</tr>\n",
1996 "<tr>\n",
1997 " <th>stock_PIONEER NAT RES CO</th> <td> 0.0177</td> <td> 0.004</td> <td> 4.022</td> <td> 0.000</td> <td> 0.009 0.026</td>\n",
1998 "</tr>\n",
1999 "<tr>\n",
2000 " <th>stock_PRECISION CASTPARTS CORP</th> <td> 0.0145</td> <td> 0.004</td> <td> 3.574</td> <td> 0.000</td> <td> 0.007 0.022</td>\n",
2001 "</tr>\n",
2002 "<tr>\n",
2003 " <th>stock_PROCTER & GAMBLE CO</th> <td> 0.0115</td> <td> 0.004</td> <td> 2.722</td> <td> 0.006</td> <td> 0.003 0.020</td>\n",
2004 "</tr>\n",
2005 "<tr>\n",
2006 " <th>stock_QUALCOMM INC</th> <td> 0.0131</td> <td> 0.004</td> <td> 3.223</td> <td> 0.001</td> <td> 0.005 0.021</td>\n",
2007 "</tr>\n",
2008 "<tr>\n",
2009 " <th>stock_REGENERON PHARMACEUTICALS INC</th> <td> 0.0117</td> <td> 0.005</td> <td> 2.380</td> <td> 0.017</td> <td> 0.002 0.021</td>\n",
2010 "</tr>\n",
2011 "<tr>\n",
2012 " <th>stock_SALESFORCE.COM INC</th> <td> 0.0173</td> <td> 0.005</td> <td> 3.443</td> <td> 0.001</td> <td> 0.007 0.027</td>\n",
2013 "</tr>\n",
2014 "<tr>\n",
2015 " <th>stock_SALIX PHARMACEUTICALS LTD</th> <td>-2.111e-18</td> <td> 7.34e-17</td> <td> -0.029</td> <td> 0.977</td> <td>-1.46e-16 1.42e-16</td>\n",
2016 "</tr>\n",
2017 "<tr>\n",
2018 " <th>stock_SANDISK CORP</th> <td> 0.0154</td> <td> 0.004</td> <td> 3.629</td> <td> 0.000</td> <td> 0.007 0.024</td>\n",
2019 "</tr>\n",
2020 "<tr>\n",
2021 " <th>stock_SCHLUMBERGER LTD.</th> <td> 0.0120</td> <td> 0.004</td> <td> 3.003</td> <td> 0.003</td> <td> 0.004 0.020</td>\n",
2022 "</tr>\n",
2023 "<tr>\n",
2024 " <th>stock_SKYWORKS SOLUTIONS INC</th> <td> 0.0191</td> <td> 0.005</td> <td> 3.796</td> <td> 0.000</td> <td> 0.009 0.029</td>\n",
2025 "</tr>\n",
2026 "<tr>\n",
2027 " <th>stock_SOLARCITY CORP</th> <td> 0.0208</td> <td> 0.006</td> <td> 3.549</td> <td> 0.000</td> <td> 0.009 0.032</td>\n",
2028 "</tr>\n",
2029 "<tr>\n",
2030 " <th>stock_SOUTHWEST AIRLINES CO</th> <td> 0.0092</td> <td> 0.003</td> <td> 2.885</td> <td> 0.004</td> <td> 0.003 0.015</td>\n",
2031 "</tr>\n",
2032 "<tr>\n",
2033 " <th>stock_STARBUCKS CORPORATION</th> <td> 0.0147</td> <td> 0.004</td> <td> 3.658</td> <td> 0.000</td> <td> 0.007 0.023</td>\n",
2034 "</tr>\n",
2035 "<tr>\n",
2036 " <th>stock_SUNEDISON INC</th> <td> 0.0183</td> <td> 0.006</td> <td> 3.237</td> <td> 0.001</td> <td> 0.007 0.029</td>\n",
2037 "</tr>\n",
2038 "<tr>\n",
2039 " <th>stock_TARGET CORPORATION</th> <td> 0.0104</td> <td> 0.004</td> <td> 2.349</td> <td> 0.019</td> <td> 0.002 0.019</td>\n",
2040 "</tr>\n",
2041 "<tr>\n",
2042 " <th>stock_TESLA INC</th> <td> 0.0196</td> <td> 0.006</td> <td> 3.520</td> <td> 0.000</td> <td> 0.009 0.031</td>\n",
2043 "</tr>\n",
2044 "<tr>\n",
2045 " <th>stock_TEXAS INSTRUMENTS INC</th> <td> 0.0141</td> <td> 0.004</td> <td> 3.229</td> <td> 0.001</td> <td> 0.006 0.023</td>\n",
2046 "</tr>\n",
2047 "<tr>\n",
2048 " <th>stock_TIME WARNER CABLE INC</th> <td> 0.0140</td> <td> 0.005</td> <td> 2.883</td> <td> 0.004</td> <td> 0.004 0.024</td>\n",
2049 "</tr>\n",
2050 "<tr>\n",
2051 " <th>stock_TIME WARNER INC.</th> <td> 0.0049</td> <td> 0.002</td> <td> 2.324</td> <td> 0.020</td> <td> 0.001 0.009</td>\n",
2052 "</tr>\n",
2053 "<tr>\n",
2054 " <th>stock_TWITTER INC</th> <td> 0.0185</td> <td> 0.006</td> <td> 3.201</td> <td> 0.001</td> <td> 0.007 0.030</td>\n",
2055 "</tr>\n",
2056 "<tr>\n",
2057 " <th>stock_UNION PACIFIC CORPORATION</th> <td> 0.0130</td> <td> 0.004</td> <td> 3.275</td> <td> 0.001</td> <td> 0.005 0.021</td>\n",
2058 "</tr>\n",
2059 "<tr>\n",
2060 " <th>stock_UNITED CONTINENTAL HOLDINGS IN</th> <td> 0.0097</td> <td> 0.005</td> <td> 2.019</td> <td> 0.044</td> <td> 0.000 0.019</td>\n",
2061 "</tr>\n",
2062 "<tr>\n",
2063 " <th>stock_UNITED PARCEL SERVICE INC.CL B</th> <td> 0.0101</td> <td> 0.005</td> <td> 2.184</td> <td> 0.029</td> <td> 0.001 0.019</td>\n",
2064 "</tr>\n",
2065 "<tr>\n",
2066 " <th>stock_UNITED TECHNOLOGIES CORP</th> <td> 0.0113</td> <td> 0.004</td> <td> 2.830</td> <td> 0.005</td> <td> 0.003 0.019</td>\n",
2067 "</tr>\n",
2068 "<tr>\n",
2069 " <th>stock_UNITEDHEALTH GROUP INC</th> <td> 0.0100</td> <td> 0.004</td> <td> 2.453</td> <td> 0.014</td> <td> 0.002 0.018</td>\n",
2070 "</tr>\n",
2071 "<tr>\n",
2072 " <th>stock_VALERO ENERGY CORP (NEW)</th> <td> 0.0099</td> <td> 0.004</td> <td> 2.405</td> <td> 0.016</td> <td> 0.002 0.018</td>\n",
2073 "</tr>\n",
2074 "<tr>\n",
2075 " <th>stock_VERIZON COMMUNICATIONS</th> <td> 0.0116</td> <td> 0.005</td> <td> 2.369</td> <td> 0.018</td> <td> 0.002 0.021</td>\n",
2076 "</tr>\n",
2077 "<tr>\n",
2078 " <th>stock_VISA INC</th> <td> 0.0189</td> <td> 0.005</td> <td> 3.588</td> <td> 0.000</td> <td> 0.009 0.029</td>\n",
2079 "</tr>\n",
2080 "<tr>\n",
2081 " <th>stock_WALGREENS BOOTS ALLIANCE INC</th> <td> 0.0109</td> <td> 0.004</td> <td> 2.732</td> <td> 0.006</td> <td> 0.003 0.019</td>\n",
2082 "</tr>\n",
2083 "<tr>\n",
2084 " <th>stock_WALMART INC</th> <td> 0.0078</td> <td> 0.006</td> <td> 1.208</td> <td> 0.227</td> <td> -0.005 0.020</td>\n",
2085 "</tr>\n",
2086 "<tr>\n",
2087 " <th>stock_WALT DISNEY CO</th> <td> 0.0102</td> <td> 0.003</td> <td> 3.309</td> <td> 0.001</td> <td> 0.004 0.016</td>\n",
2088 "</tr>\n",
2089 "<tr>\n",
2090 " <th>stock_WELLS FARGO & CO(NEW)</th> <td> 0.0434</td> <td> 0.013</td> <td> 3.348</td> <td> 0.001</td> <td> 0.018 0.069</td>\n",
2091 "</tr>\n",
2092 "<tr>\n",
2093 " <th>stock_WILLIAMS COMPANIES</th> <td> 0.0109</td> <td> 0.004</td> <td> 2.720</td> <td> 0.007</td> <td> 0.003 0.019</td>\n",
2094 "</tr>\n",
2095 "<tr>\n",
2096 " <th>stock_WYNN RESORTS LTD</th> <td> 0.0109</td> <td> 0.005</td> <td> 2.254</td> <td> 0.024</td> <td> 0.001 0.020</td>\n",
2097 "</tr>\n",
2098 "<tr>\n",
2099 " <th>stock_YELP INC</th> <td> 0.0231</td> <td> 0.006</td> <td> 3.978</td> <td> 0.000</td> <td> 0.012 0.034</td>\n",
2100 "</tr>\n",
2101 "</table>\n",
2102 "<table class=\"simpletable\">\n",
2103 "<tr>\n",
2104 " <th>Omnibus:</th> <td>41.371</td> <th> Durbin-Watson: </th> <td> 1.329</td>\n",
2105 "</tr>\n",
2106 "<tr>\n",
2107 " <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td> 45.849</td>\n",
2108 "</tr>\n",
2109 "<tr>\n",
2110 " <th>Skew:</th> <td>-0.034</td> <th> Prob(JB): </th> <td>1.11e-10</td>\n",
2111 "</tr>\n",
2112 "<tr>\n",
2113 " <th>Kurtosis:</th> <td> 3.141</td> <th> Cond. No. </th> <td>5.40e+21</td>\n",
2114 "</tr>\n",
2115 "</table>"
2116 ],
2117 "text/plain": [
2118 "<class 'statsmodels.iolib.summary.Summary'>\n",
2119 "\"\"\"\n",
2120 " OLS Regression Results \n",
2121 "==============================================================================\n",
2122 "Dep. Variable: Returns1D R-squared: 0.004\n",
2123 "Model: OLS Adj. R-squared: 0.000\n",
2124 "Method: Least Squares F-statistic: 1.084\n",
2125 "Date: Fri, 03 Aug 2018 Prob (F-statistic): 0.214\n",
2126 "Time: 21:40:26 Log-Likelihood: 1.3306e+05\n",
2127 "No. Observations: 44878 AIC: -2.658e+05\n",
2128 "Df Residuals: 44703 BIC: -2.642e+05\n",
2129 "Df Model: 174 \n",
2130 "Covariance Type: nonrobust \n",
2131 "===============================================================================================================\n",
2132 " coef std err t P>|t| [95.0% Conf. Int.]\n",
2133 "---------------------------------------------------------------------------------------------------------------\n",
2134 "DividendYield 1.249e-06 4.7e-07 2.659 0.008 3.28e-07 2.17e-06\n",
2135 "EBITDAYield -5.537e-06 9.48e-06 -0.584 0.559 -2.41e-05 1.3e-05\n",
2136 "EVToEBITDA 4.87e-06 8.7e-06 0.560 0.575 -1.22e-05 2.19e-05\n",
2137 "EVToFCF 3.621e-06 1.08e-05 0.334 0.738 -1.76e-05 2.48e-05\n",
2138 "PriceToBook -5.411e-07 1.03e-05 -0.052 0.958 -2.08e-05 1.97e-05\n",
2139 "PriceToDilutedEarningsTTM 4.561e-06 4.64e-06 0.983 0.326 -4.54e-06 1.37e-05\n",
2140 "PriceToEarningsTTM 6e-08 2.05e-07 0.293 0.770 -3.41e-07 4.61e-07\n",
2141 "PriceToFCF -9.612e-07 9.07e-06 -0.106 0.916 -1.87e-05 1.68e-05\n",
2142 "PriceToOperatingCashflow -6.258e-07 6.16e-06 -0.102 0.919 -1.27e-05 1.14e-05\n",
2143 "PriceToSalesTTM -2.088e-06 5.16e-07 -4.048 0.000 -3.1e-06 -1.08e-06\n",
2144 "Directional Movement Index -3.169e-07 2.09e-06 -0.152 0.879 -4.4e-06 3.77e-06\n",
2145 "Money Flow Index -2.768e-06 2.7e-06 -1.025 0.305 -8.06e-06 2.52e-06\n",
2146 "Percent Above Low 2.875e-06 5.17e-06 0.556 0.578 -7.26e-06 1.3e-05\n",
2147 "Percent Below High -1.786e-06 4.14e-06 -0.431 0.666 -9.9e-06 6.33e-06\n",
2148 "Price Oscillator 9.18e-07 2.92e-06 0.314 0.753 -4.81e-06 6.64e-06\n",
2149 "Trendline 2.007e-06 4.41e-06 0.455 0.649 -6.64e-06 1.07e-05\n",
2150 "AssetToEquityRatio 5.633e-07 3.98e-07 1.414 0.157 -2.17e-07 1.34e-06\n",
2151 "AssetTurnover -1.27e-05 1.74e-05 -0.731 0.465 -4.68e-05 2.14e-05\n",
2152 "CurrentRatio 3.246e-07 5.2e-07 0.625 0.532 -6.94e-07 1.34e-06\n",
2153 "DebtToAssetRatio 5.697e-07 3.18e-07 1.792 0.073 -5.35e-08 1.19e-06\n",
2154 "DebtToEquityRatio -5.451e-07 3.69e-07 -1.476 0.140 -1.27e-06 1.79e-07\n",
2155 "MertonsDD -0.0001 4.64e-05 -2.661 0.008 -0.000 -3.25e-05\n",
2156 "WorkingCapitalToAssets 1.452e-07 6.31e-07 0.230 0.818 -1.09e-06 1.38e-06\n",
2157 "WorkingCapitalToSales -2.24e-05 2.03e-05 -1.106 0.269 -6.21e-05 1.73e-05\n",
2158 "Dividend Growth -1.505e-08 1.96e-07 -0.077 0.939 -4e-07 3.69e-07\n",
2159 "EPS -1.219e-07 3.1e-07 -0.393 0.694 -7.3e-07 4.86e-07\n",
2160 "Net Debt -1.968e-14 1.12e-14 -1.760 0.078 -4.16e-14 2.23e-15\n",
2161 "Sales 5.85e-15 1.04e-14 0.563 0.574 -1.45e-14 2.62e-14\n",
2162 "Total Assets -1.665e-14 7.87e-15 -2.116 0.034 -3.21e-14 -1.23e-15\n",
2163 "EPS Growth 3M -5.625e-08 4.75e-07 -0.118 0.906 -9.87e-07 8.75e-07\n",
2164 "EPS Growth 12M 6.686e-07 4.76e-07 1.405 0.160 -2.64e-07 1.6e-06\n",
2165 "Net Debt Growth 3M 8.433e-08 6.03e-07 0.140 0.889 -1.1e-06 1.27e-06\n",
2166 "Net Debt Growth 12M 4.558e-07 6.14e-07 0.743 0.458 -7.47e-07 1.66e-06\n",
2167 "Sales Growth 3M 2.469e-07 6.4e-07 0.386 0.699 -1.01e-06 1.5e-06\n",
2168 "Sales Growth 12M -4.861e-07 6.53e-07 -0.745 0.456 -1.77e-06 7.93e-07\n",
2169 "Total Assets Growth 3M 4.535e-07 7.16e-07 0.633 0.527 -9.5e-07 1.86e-06\n",
2170 "Total Assets Growth 12M -3.477e-07 7.64e-07 -0.455 0.649 -1.84e-06 1.15e-06\n",
2171 "CFO To Assets 1.731e-05 8.66e-06 1.999 0.046 3.36e-07 3.43e-05\n",
2172 "Capex To Assets -2.097e-05 1.87e-05 -1.124 0.261 -5.75e-05 1.56e-05\n",
2173 "Capex To FCF 1.29e-06 1.05e-05 0.123 0.902 -1.92e-05 2.18e-05\n",
2174 "Capex To Sales 1.637e-05 2e-05 0.820 0.412 -2.28e-05 5.55e-05\n",
2175 "EBIT To Assets 7.612e-06 8.75e-06 0.870 0.385 -9.55e-06 2.48e-05\n",
2176 "Retained Earnings To Assets -3.547e-05 1.84e-05 -1.923 0.054 -7.16e-05 6.76e-07\n",
2177 "Downside Risk 4.943e-07 5.32e-06 0.093 0.926 -9.93e-06 1.09e-05\n",
2178 "Index Beta -8.585e-08 1.09e-07 -0.786 0.432 -3e-07 1.28e-07\n",
2179 "Log Market Cap 1.847e-05 1.86e-05 0.995 0.320 -1.79e-05 5.49e-05\n",
2180 "Volatility 3M -8.138e-06 4.53e-06 -1.798 0.072 -1.7e-05 7.33e-07\n",
2181 "stock_3D SYSTEMS CORP 0.0157 0.004 3.600 0.000 0.007 0.024\n",
2182 "stock_3M COMPANY 0.0108 0.004 2.823 0.005 0.003 0.018\n",
2183 "stock_ABBOTT LABORATORIES 0.0069 0.003 2.315 0.021 0.001 0.013\n",
2184 "stock_ABBVIE INC 0.0163 0.006 2.916 0.004 0.005 0.027\n",
2185 "stock_ALLERGAN INC 0.0103 0.003 3.563 0.000 0.005 0.016\n",
2186 "stock_ALLERGAN PLC 0.0135 0.004 3.310 0.001 0.006 0.022\n",
2187 "stock_ALTABA INC 0.0161 0.005 3.560 0.000 0.007 0.025\n",
2188 "stock_ALTRIA GROUP INC. 0.0111 0.004 2.847 0.004 0.003 0.019\n",
2189 "stock_AMAZON.COM INC 0.0123 0.005 2.683 0.007 0.003 0.021\n",
2190 "stock_AMERICAN AIRLINES GROUP INC 0.0143 0.005 2.720 0.007 0.004 0.025\n",
2191 "stock_AMERICAN EXPRESS COMPANY 0.0065 0.002 2.656 0.008 0.002 0.011\n",
2192 "stock_AMERICAN INTL GROUP INC 0.0118 0.004 2.850 0.004 0.004 0.020\n",
2193 "stock_AMGEN INC 0.0064 0.003 2.421 0.015 0.001 0.012\n",
2194 "stock_ANADARKO PETROLEUM CORP 0.0077 0.002 3.231 0.001 0.003 0.012\n",
2195 "stock_APACHE CORP 0.0045 0.004 1.262 0.207 -0.003 0.012\n",
2196 "stock_APPLE INC 0.0086 0.004 2.278 0.023 0.001 0.016\n",
2197 "stock_APPLIED MATERIALS INC 0.0075 0.003 2.572 0.010 0.002 0.013\n",
2198 "stock_ARCONIC INC 0.0026 0.002 1.210 0.226 -0.002 0.007\n",
2199 "stock_AT&T INC. COM 0.0103 0.004 2.420 0.016 0.002 0.019\n",
2200 "stock_Alphabet Inc. Cl A 0.0192 0.005 3.528 0.000 0.009 0.030\n",
2201 "stock_BAKER HUGHES INC 0.0078 0.003 2.946 0.003 0.003 0.013\n",
2202 "stock_BANK OF AMERICA CORP 0.0404 0.016 2.532 0.011 0.009 0.072\n",
2203 "stock_BERKSHIRE HATHAWAY INC CL-B 0.0171 0.006 3.069 0.002 0.006 0.028\n",
2204 "stock_BIOGEN INC 0.0120 0.004 3.275 0.001 0.005 0.019\n",
2205 "stock_BOEING CO 0.0053 0.003 1.864 0.062 -0.000 0.011\n",
2206 "stock_BOOKING HOLDINGS INC 0.0153 0.005 3.232 0.001 0.006 0.025\n",
2207 "stock_BRISTOL MYERS SQUIBB COMPANY 0.0097 0.003 3.001 0.003 0.003 0.016\n",
2208 "stock_BROADCOM CORP 0.0134 0.004 3.087 0.002 0.005 0.022\n",
2209 "stock_BROADCOM INC 0.0173 0.006 3.112 0.002 0.006 0.028\n",
2210 "stock_CATERPILLAR INC 0.0042 0.003 1.502 0.133 -0.001 0.010\n",
2211 "stock_CELGENE CORP 0.0091 0.003 2.905 0.004 0.003 0.015\n",
2212 "stock_CHESAPEAKE ENERGY CORP 0.0041 0.005 0.874 0.382 -0.005 0.013\n",
2213 "stock_CHEVRON CORPORATION 0.0137 0.006 2.386 0.017 0.002 0.025\n",
2214 "stock_CISCO SYSTEMS INC 0.0066 0.003 2.363 0.018 0.001 0.012\n",
2215 "stock_CITIGROUP 0.0371 0.014 2.713 0.007 0.010 0.064\n",
2216 "stock_COCA-COLA CO 0.0101 0.004 2.629 0.009 0.003 0.018\n",
2217 "stock_COMCAST CORP 0.0081 0.003 2.917 0.004 0.003 0.013\n",
2218 "stock_CONOCOPHILLIPS 0.0125 0.005 2.566 0.010 0.003 0.022\n",
2219 "stock_COVIDIEN PLC 0.0196 0.005 3.839 0.000 0.010 0.030\n",
2220 "stock_CVS HEALTH CORP 0.0077 0.004 2.122 0.034 0.001 0.015\n",
2221 "stock_DEERE & CO 0.0050 0.003 1.671 0.095 -0.001 0.011\n",
2222 "stock_DELTA AIR LINES INC 0.0141 0.005 2.882 0.004 0.005 0.024\n",
2223 "stock_DIRECTV 0.0114 0.005 2.405 0.016 0.002 0.021\n",
2224 "stock_DOLLAR GENERAL CORP 0.0143 0.005 2.899 0.004 0.005 0.024\n",
2225 "stock_DOW CHEMICAL CO 0.0055 0.003 1.960 0.050 -5.93e-07 0.011\n",
2226 "stock_E.I. Du Pont De Nemours A 0.0060 0.003 2.109 0.035 0.000 0.012\n",
2227 "stock_EBAY INC 0.0166 0.005 3.281 0.001 0.007 0.027\n",
2228 "stock_EMC CORPORATION 0.0082 0.003 2.886 0.004 0.003 0.014\n",
2229 "stock_EOG RESOURCES INC 0.0099 0.003 3.577 0.000 0.004 0.015\n",
2230 "stock_EXPRESS SCRIPTS HOLDING CO 0.0034 0.003 1.166 0.244 -0.002 0.009\n",
2231 "stock_EXXON MOBIL CORPORATION 0.0128 0.007 1.936 0.053 -0.000 0.026\n",
2232 "stock_FACEBOOK INC 0.0199 0.006 3.452 0.001 0.009 0.031\n",
2233 "stock_FEDEX CORPORATION 0.0079 0.003 2.618 0.009 0.002 0.014\n",
2234 "stock_FIRST SOLAR INC 0.0180 0.005 3.411 0.001 0.008 0.028\n",
2235 "stock_FORD MOTOR CO(NEW) 0.0057 0.004 1.626 0.104 -0.001 0.013\n",
2236 "stock_FREEPORT-MCMORAN INC 0.0090 0.004 2.333 0.020 0.001 0.017\n",
2237 "stock_GENERAL ELECTRIC CO 0.0165 0.005 3.025 0.002 0.006 0.027\n",
2238 "stock_GENERAL MOTORS CO 0.0131 0.006 2.358 0.018 0.002 0.024\n",
2239 "stock_GILEAD SCIENCES INC 0.0108 0.003 3.141 0.002 0.004 0.017\n",
2240 "stock_GOLDMAN SACHS GROUP INC 0.0317 0.007 4.257 0.000 0.017 0.046\n",
2241 "stock_GOPRO INC 0.0199 0.006 3.550 0.000 0.009 0.031\n",
2242 "stock_HALLIBURTON CO (HOLDING CO) 0.0084 0.003 2.752 0.006 0.002 0.014\n",
2243 "stock_HOME DEPOT INC 0.0070 0.003 2.052 0.040 0.000 0.014\n",
2244 "stock_HP INC 0.0051 0.003 1.624 0.104 -0.001 0.011\n",
2245 "stock_INTEL CORP 0.0087 0.003 2.535 0.011 0.002 0.015\n",
2246 "stock_INTL BUSINESS MACHINES CORP 0.0087 0.004 2.280 0.023 0.001 0.016\n",
2247 "stock_JOHNSON AND JOHNSON 0.0111 0.004 2.834 0.005 0.003 0.019\n",
2248 "stock_JPMORGAN CHASE & CO COM STK 0.0545 0.019 2.818 0.005 0.017 0.092\n",
2249 "stock_KEURIG GREEN MOUNTAIN INC 0.0146 0.004 3.591 0.000 0.007 0.023\n",
2250 "stock_KINDER MORGAN INC 0.0132 0.005 2.470 0.014 0.003 0.024\n",
2251 "stock_LAS VEGAS SANDS CORP 0.0127 0.005 2.488 0.013 0.003 0.023\n",
2252 "stock_LILLY ELI & CO 0.0102 0.004 2.885 0.004 0.003 0.017\n",
2253 "stock_LINKEDIN CORP 0.0197 0.006 3.505 0.000 0.009 0.031\n",
2254 "stock_LOWES COMPANIES INC 0.0075 0.003 2.483 0.013 0.002 0.013\n",
2255 "stock_LYONDELLBASELL INDUSTRIES NV 0.0135 0.005 2.593 0.010 0.003 0.024\n",
2256 "stock_MARATHON PETROLEUM CORP 0.0128 0.006 2.268 0.023 0.002 0.024\n",
2257 "stock_MASTERCARD INCORPORATED 0.0206 0.006 3.731 0.000 0.010 0.031\n",
2258 "stock_MCDONALDS CORP 0.0099 0.004 2.513 0.012 0.002 0.018\n",
2259 "stock_MEDTRONIC PLC 0.0119 0.004 3.390 0.001 0.005 0.019\n",
2260 "stock_MERCK & CO INC 0.0110 0.004 2.966 0.003 0.004 0.018\n",
2261 "stock_METLIFE INC 0.0260 0.008 3.353 0.001 0.011 0.041\n",
2262 "stock_MICHAEL KORS HOLDINGS LTD 0.0214 0.006 3.646 0.000 0.010 0.033\n",
2263 "stock_MICRON TECHNOLOGY INC 0.0102 0.003 3.287 0.001 0.004 0.016\n",
2264 "stock_MICROSOFT CORP 0.0115 0.004 3.028 0.002 0.004 0.019\n",
2265 "stock_MONDELEZ INTERNATIONAL INC 0.0133 0.005 2.875 0.004 0.004 0.022\n",
2266 "stock_MONSANTO COMPANY 0.0153 0.005 3.201 0.001 0.006 0.025\n",
2267 "stock_MORGAN STANLEY 0.0273 0.007 3.884 0.000 0.014 0.041\n",
2268 "stock_MYLAN NV 0.0111 0.003 3.260 0.001 0.004 0.018\n",
2269 "stock_NATIONAL OILWELL VARCO INC. 0.0131 0.005 2.642 0.008 0.003 0.023\n",
2270 "stock_NETFLIX INC 0.0179 0.005 3.555 0.000 0.008 0.028\n",
2271 "stock_NEWMONT MINING CORP (HOLDING COMPANY) 0.0098 0.004 2.773 0.006 0.003 0.017\n",
2272 "stock_NEWS CP - CL A 0.0129 0.004 3.348 0.001 0.005 0.020\n",
2273 "stock_NIKE INC CL-B 0.0104 0.004 2.884 0.004 0.003 0.018\n",
2274 "stock_OCCIDENTAL PETROLEUM CORP 0.0118 0.004 3.153 0.002 0.004 0.019\n",
2275 "stock_ORACLE CORP 0.0118 0.004 3.297 0.001 0.005 0.019\n",
2276 "stock_PANDORA MEDIA INC 0.0235 0.006 3.942 0.000 0.012 0.035\n",
2277 "stock_PENNEY J.C. CO INC (HOLDING COMPANY) 0.0027 0.003 0.905 0.365 -0.003 0.009\n",
2278 "stock_PEPSICO INC 0.0104 0.004 2.533 0.011 0.002 0.018\n",
2279 "stock_PFIZER INC 0.0134 0.004 3.287 0.001 0.005 0.021\n",
2280 "stock_PHILIP MORRIS INTERNATIONAL INC 0.0154 0.006 2.576 0.010 0.004 0.027\n",
2281 "stock_PIONEER NAT RES CO 0.0177 0.004 4.022 0.000 0.009 0.026\n",
2282 "stock_PRECISION CASTPARTS CORP 0.0145 0.004 3.574 0.000 0.007 0.022\n",
2283 "stock_PROCTER & GAMBLE CO 0.0115 0.004 2.722 0.006 0.003 0.020\n",
2284 "stock_QUALCOMM INC 0.0131 0.004 3.223 0.001 0.005 0.021\n",
2285 "stock_REGENERON PHARMACEUTICALS INC 0.0117 0.005 2.380 0.017 0.002 0.021\n",
2286 "stock_SALESFORCE.COM INC 0.0173 0.005 3.443 0.001 0.007 0.027\n",
2287 "stock_SALIX PHARMACEUTICALS LTD -2.111e-18 7.34e-17 -0.029 0.977 -1.46e-16 1.42e-16\n",
2288 "stock_SANDISK CORP 0.0154 0.004 3.629 0.000 0.007 0.024\n",
2289 "stock_SCHLUMBERGER LTD. 0.0120 0.004 3.003 0.003 0.004 0.020\n",
2290 "stock_SKYWORKS SOLUTIONS INC 0.0191 0.005 3.796 0.000 0.009 0.029\n",
2291 "stock_SOLARCITY CORP 0.0208 0.006 3.549 0.000 0.009 0.032\n",
2292 "stock_SOUTHWEST AIRLINES CO 0.0092 0.003 2.885 0.004 0.003 0.015\n",
2293 "stock_STARBUCKS CORPORATION 0.0147 0.004 3.658 0.000 0.007 0.023\n",
2294 "stock_SUNEDISON INC 0.0183 0.006 3.237 0.001 0.007 0.029\n",
2295 "stock_TARGET CORPORATION 0.0104 0.004 2.349 0.019 0.002 0.019\n",
2296 "stock_TESLA INC 0.0196 0.006 3.520 0.000 0.009 0.031\n",
2297 "stock_TEXAS INSTRUMENTS INC 0.0141 0.004 3.229 0.001 0.006 0.023\n",
2298 "stock_TIME WARNER CABLE INC 0.0140 0.005 2.883 0.004 0.004 0.024\n",
2299 "stock_TIME WARNER INC. 0.0049 0.002 2.324 0.020 0.001 0.009\n",
2300 "stock_TWITTER INC 0.0185 0.006 3.201 0.001 0.007 0.030\n",
2301 "stock_UNION PACIFIC CORPORATION 0.0130 0.004 3.275 0.001 0.005 0.021\n",
2302 "stock_UNITED CONTINENTAL HOLDINGS IN 0.0097 0.005 2.019 0.044 0.000 0.019\n",
2303 "stock_UNITED PARCEL SERVICE INC.CL B 0.0101 0.005 2.184 0.029 0.001 0.019\n",
2304 "stock_UNITED TECHNOLOGIES CORP 0.0113 0.004 2.830 0.005 0.003 0.019\n",
2305 "stock_UNITEDHEALTH GROUP INC 0.0100 0.004 2.453 0.014 0.002 0.018\n",
2306 "stock_VALERO ENERGY CORP (NEW) 0.0099 0.004 2.405 0.016 0.002 0.018\n",
2307 "stock_VERIZON COMMUNICATIONS 0.0116 0.005 2.369 0.018 0.002 0.021\n",
2308 "stock_VISA INC 0.0189 0.005 3.588 0.000 0.009 0.029\n",
2309 "stock_WALGREENS BOOTS ALLIANCE INC 0.0109 0.004 2.732 0.006 0.003 0.019\n",
2310 "stock_WALMART INC 0.0078 0.006 1.208 0.227 -0.005 0.020\n",
2311 "stock_WALT DISNEY CO 0.0102 0.003 3.309 0.001 0.004 0.016\n",
2312 "stock_WELLS FARGO & CO(NEW) 0.0434 0.013 3.348 0.001 0.018 0.069\n",
2313 "stock_WILLIAMS COMPANIES 0.0109 0.004 2.720 0.007 0.003 0.019\n",
2314 "stock_WYNN RESORTS LTD 0.0109 0.005 2.254 0.024 0.001 0.020\n",
2315 "stock_YELP INC 0.0231 0.006 3.978 0.000 0.012 0.034\n",
2316 "==============================================================================\n",
2317 "Omnibus: 41.371 Durbin-Watson: 1.329\n",
2318 "Prob(Omnibus): 0.000 Jarque-Bera (JB): 45.849\n",
2319 "Skew: -0.034 Prob(JB): 1.11e-10\n",
2320 "Kurtosis: 3.141 Cond. No. 5.40e+21\n",
2321 "==============================================================================\n",
2322 "\n",
2323 "Warnings:\n",
2324 "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
2325 "[2] The smallest eigenvalue is 1.4e-16. This might indicate that there are\n",
2326 "strong multicollinearity problems or that the design matrix is singular.\n",
2327 "\"\"\""
2328 ]
2329 },
2330 "execution_count": 180,
2331 "metadata": {},
2332 "output_type": "execute_result"
2333 }
2334 ],
2335 "source": [
2336 "target = 'Returns1D'\n",
2337 "model_data = pd.concat([y[[target]], X], axis=1).dropna()\n",
2338 "model_data = model_data[model_data[target].between(model_data[target].quantile(.025), \n",
2339 " model_data[target].quantile(.975))]\n",
2340 "\n",
2341 "model = OLS(endog=model_data[target], exog=model_data.drop(target, axis=1))\n",
2342 "trained_model = model.fit()\n",
2343 "trained_model.summary()"
2344 ]
2345 },
2346 {
2347 "cell_type": "code",
2348 "execution_count": 184,
2349 "metadata": {},
2350 "outputs": [
2351 {
2352 "data": {
2353 "text/html": [
2354 "<table class=\"simpletable\">\n",
2355 "<caption>OLS Regression Results</caption>\n",
2356 "<tr>\n",
2357 " <th>Dep. Variable:</th> <td>Returns5D</td> <th> R-squared: </th> <td> 0.016</td> \n",
2358 "</tr>\n",
2359 "<tr>\n",
2360 " <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.012</td> \n",
2361 "</tr>\n",
2362 "<tr>\n",
2363 " <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 4.124</td> \n",
2364 "</tr>\n",
2365 "<tr>\n",
2366 " <th>Date:</th> <td>Fri, 03 Aug 2018</td> <th> Prob (F-statistic):</th> <td>2.28e-66</td> \n",
2367 "</tr>\n",
2368 "<tr>\n",
2369 " <th>Time:</th> <td>21:41:14</td> <th> Log-Likelihood: </th> <td> 95279.</td> \n",
2370 "</tr>\n",
2371 "<tr>\n",
2372 " <th>No. Observations:</th> <td> 44370</td> <th> AIC: </th> <td>-1.902e+05</td>\n",
2373 "</tr>\n",
2374 "<tr>\n",
2375 " <th>Df Residuals:</th> <td> 44195</td> <th> BIC: </th> <td>-1.887e+05</td>\n",
2376 "</tr>\n",
2377 "<tr>\n",
2378 " <th>Df Model:</th> <td> 174</td> <th> </th> <td> </td> \n",
2379 "</tr>\n",
2380 "<tr>\n",
2381 " <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
2382 "</tr>\n",
2383 "</table>\n",
2384 "<table class=\"simpletable\">\n",
2385 "<tr>\n",
2386 " <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
2387 "</tr>\n",
2388 "<tr>\n",
2389 " <th>DividendYield</th> <td> 4.504e-06</td> <td> 1.08e-06</td> <td> 4.185</td> <td> 0.000</td> <td> 2.39e-06 6.61e-06</td>\n",
2390 "</tr>\n",
2391 "<tr>\n",
2392 " <th>EBITDAYield</th> <td> 9.323e-06</td> <td> 2.17e-05</td> <td> 0.430</td> <td> 0.667</td> <td>-3.32e-05 5.19e-05</td>\n",
2393 "</tr>\n",
2394 "<tr>\n",
2395 " <th>EVToEBITDA</th> <td> 1.425e-05</td> <td> 1.98e-05</td> <td> 0.719</td> <td> 0.472</td> <td>-2.46e-05 5.31e-05</td>\n",
2396 "</tr>\n",
2397 "<tr>\n",
2398 " <th>EVToFCF</th> <td> 2.654e-05</td> <td> 2.47e-05</td> <td> 1.075</td> <td> 0.283</td> <td>-2.19e-05 7.49e-05</td>\n",
2399 "</tr>\n",
2400 "<tr>\n",
2401 " <th>PriceToBook</th> <td> 1.582e-05</td> <td> 2.36e-05</td> <td> 0.669</td> <td> 0.503</td> <td>-3.05e-05 6.21e-05</td>\n",
2402 "</tr>\n",
2403 "<tr>\n",
2404 " <th>PriceToDilutedEarningsTTM</th> <td> 5.462e-07</td> <td> 1.06e-05</td> <td> 0.052</td> <td> 0.959</td> <td>-2.01e-05 2.12e-05</td>\n",
2405 "</tr>\n",
2406 "<tr>\n",
2407 " <th>PriceToEarningsTTM</th> <td> 1.449e-07</td> <td> 4.67e-07</td> <td> 0.310</td> <td> 0.757</td> <td>-7.71e-07 1.06e-06</td>\n",
2408 "</tr>\n",
2409 "<tr>\n",
2410 " <th>PriceToFCF</th> <td> -1.94e-05</td> <td> 2.07e-05</td> <td> -0.939</td> <td> 0.348</td> <td>-5.99e-05 2.11e-05</td>\n",
2411 "</tr>\n",
2412 "<tr>\n",
2413 " <th>PriceToOperatingCashflow</th> <td>-4.155e-05</td> <td> 1.42e-05</td> <td> -2.934</td> <td> 0.003</td> <td>-6.93e-05 -1.38e-05</td>\n",
2414 "</tr>\n",
2415 "<tr>\n",
2416 " <th>PriceToSalesTTM</th> <td>-8.975e-06</td> <td> 1.19e-06</td> <td> -7.530</td> <td> 0.000</td> <td>-1.13e-05 -6.64e-06</td>\n",
2417 "</tr>\n",
2418 "<tr>\n",
2419 " <th>Directional Movement Index</th> <td>-4.457e-06</td> <td> 4.75e-06</td> <td> -0.938</td> <td> 0.348</td> <td>-1.38e-05 4.86e-06</td>\n",
2420 "</tr>\n",
2421 "<tr>\n",
2422 " <th>Money Flow Index</th> <td>-5.894e-06</td> <td> 6.16e-06</td> <td> -0.956</td> <td> 0.339</td> <td> -1.8e-05 6.19e-06</td>\n",
2423 "</tr>\n",
2424 "<tr>\n",
2425 " <th>Percent Above Low</th> <td>-1.985e-05</td> <td> 1.18e-05</td> <td> -1.675</td> <td> 0.094</td> <td>-4.31e-05 3.38e-06</td>\n",
2426 "</tr>\n",
2427 "<tr>\n",
2428 " <th>Percent Below High</th> <td> 7.651e-06</td> <td> 9.42e-06</td> <td> 0.812</td> <td> 0.417</td> <td>-1.08e-05 2.61e-05</td>\n",
2429 "</tr>\n",
2430 "<tr>\n",
2431 " <th>Price Oscillator</th> <td> 3.706e-06</td> <td> 6.64e-06</td> <td> 0.558</td> <td> 0.577</td> <td>-9.31e-06 1.67e-05</td>\n",
2432 "</tr>\n",
2433 "<tr>\n",
2434 " <th>Trendline</th> <td> 1.414e-05</td> <td> 1.01e-05</td> <td> 1.402</td> <td> 0.161</td> <td>-5.64e-06 3.39e-05</td>\n",
2435 "</tr>\n",
2436 "<tr>\n",
2437 " <th>AssetToEquityRatio</th> <td>-1.226e-07</td> <td> 8.91e-07</td> <td> -0.138</td> <td> 0.891</td> <td>-1.87e-06 1.62e-06</td>\n",
2438 "</tr>\n",
2439 "<tr>\n",
2440 " <th>AssetTurnover</th> <td> -7.3e-06</td> <td> 3.95e-05</td> <td> -0.185</td> <td> 0.854</td> <td>-8.48e-05 7.02e-05</td>\n",
2441 "</tr>\n",
2442 "<tr>\n",
2443 " <th>CurrentRatio</th> <td> 4.11e-07</td> <td> 1.18e-06</td> <td> 0.347</td> <td> 0.728</td> <td>-1.91e-06 2.73e-06</td>\n",
2444 "</tr>\n",
2445 "<tr>\n",
2446 " <th>DebtToAssetRatio</th> <td> 2.484e-06</td> <td> 7.25e-07</td> <td> 3.427</td> <td> 0.001</td> <td> 1.06e-06 3.9e-06</td>\n",
2447 "</tr>\n",
2448 "<tr>\n",
2449 " <th>DebtToEquityRatio</th> <td>-5.365e-07</td> <td> 8.37e-07</td> <td> -0.641</td> <td> 0.521</td> <td>-2.18e-06 1.1e-06</td>\n",
2450 "</tr>\n",
2451 "<tr>\n",
2452 " <th>MertonsDD</th> <td> -0.0004</td> <td> 0.000</td> <td> -3.508</td> <td> 0.000</td> <td> -0.001 -0.000</td>\n",
2453 "</tr>\n",
2454 "<tr>\n",
2455 " <th>WorkingCapitalToAssets</th> <td> 1.341e-06</td> <td> 1.44e-06</td> <td> 0.932</td> <td> 0.351</td> <td>-1.48e-06 4.16e-06</td>\n",
2456 "</tr>\n",
2457 "<tr>\n",
2458 " <th>WorkingCapitalToSales</th> <td>-3.118e-05</td> <td> 4.62e-05</td> <td> -0.674</td> <td> 0.500</td> <td> -0.000 5.95e-05</td>\n",
2459 "</tr>\n",
2460 "<tr>\n",
2461 " <th>Dividend Growth</th> <td>-2.629e-07</td> <td> 4.5e-07</td> <td> -0.585</td> <td> 0.559</td> <td>-1.14e-06 6.18e-07</td>\n",
2462 "</tr>\n",
2463 "<tr>\n",
2464 " <th>EPS</th> <td> 2.901e-07</td> <td> 7.04e-07</td> <td> 0.412</td> <td> 0.680</td> <td>-1.09e-06 1.67e-06</td>\n",
2465 "</tr>\n",
2466 "<tr>\n",
2467 " <th>Net Debt</th> <td>-8.116e-14</td> <td> 2.56e-14</td> <td> -3.172</td> <td> 0.002</td> <td>-1.31e-13 -3.1e-14</td>\n",
2468 "</tr>\n",
2469 "<tr>\n",
2470 " <th>Sales</th> <td>-5.373e-14</td> <td> 2.4e-14</td> <td> -2.239</td> <td> 0.025</td> <td>-1.01e-13 -6.69e-15</td>\n",
2471 "</tr>\n",
2472 "<tr>\n",
2473 " <th>Total Assets</th> <td>-4.104e-14</td> <td> 1.8e-14</td> <td> -2.276</td> <td> 0.023</td> <td>-7.64e-14 -5.7e-15</td>\n",
2474 "</tr>\n",
2475 "<tr>\n",
2476 " <th>EPS Growth 3M</th> <td>-2.787e-06</td> <td> 1.08e-06</td> <td> -2.582</td> <td> 0.010</td> <td> -4.9e-06 -6.72e-07</td>\n",
2477 "</tr>\n",
2478 "<tr>\n",
2479 " <th>EPS Growth 12M</th> <td> 1.257e-06</td> <td> 1.08e-06</td> <td> 1.161</td> <td> 0.246</td> <td>-8.64e-07 3.38e-06</td>\n",
2480 "</tr>\n",
2481 "<tr>\n",
2482 " <th>Net Debt Growth 3M</th> <td> 2.628e-07</td> <td> 1.37e-06</td> <td> 0.191</td> <td> 0.848</td> <td>-2.43e-06 2.95e-06</td>\n",
2483 "</tr>\n",
2484 "<tr>\n",
2485 " <th>Net Debt Growth 12M</th> <td> 2.592e-06</td> <td> 1.4e-06</td> <td> 1.858</td> <td> 0.063</td> <td>-1.43e-07 5.33e-06</td>\n",
2486 "</tr>\n",
2487 "<tr>\n",
2488 " <th>Sales Growth 3M</th> <td> 3.824e-06</td> <td> 1.45e-06</td> <td> 2.634</td> <td> 0.008</td> <td> 9.79e-07 6.67e-06</td>\n",
2489 "</tr>\n",
2490 "<tr>\n",
2491 " <th>Sales Growth 12M</th> <td> -2.38e-06</td> <td> 1.48e-06</td> <td> -1.604</td> <td> 0.109</td> <td>-5.29e-06 5.28e-07</td>\n",
2492 "</tr>\n",
2493 "<tr>\n",
2494 " <th>Total Assets Growth 3M</th> <td>-6.292e-07</td> <td> 1.63e-06</td> <td> -0.386</td> <td> 0.699</td> <td>-3.82e-06 2.56e-06</td>\n",
2495 "</tr>\n",
2496 "<tr>\n",
2497 " <th>Total Assets Growth 12M</th> <td> -1.46e-06</td> <td> 1.73e-06</td> <td> -0.842</td> <td> 0.400</td> <td>-4.86e-06 1.94e-06</td>\n",
2498 "</tr>\n",
2499 "<tr>\n",
2500 " <th>CFO To Assets</th> <td> 4.634e-05</td> <td> 1.98e-05</td> <td> 2.342</td> <td> 0.019</td> <td> 7.56e-06 8.51e-05</td>\n",
2501 "</tr>\n",
2502 "<tr>\n",
2503 " <th>Capex To Assets</th> <td> -0.0001</td> <td> 4.26e-05</td> <td> -3.232</td> <td> 0.001</td> <td> -0.000 -5.42e-05</td>\n",
2504 "</tr>\n",
2505 "<tr>\n",
2506 " <th>Capex To FCF</th> <td> 3.673e-05</td> <td> 2.39e-05</td> <td> 1.534</td> <td> 0.125</td> <td>-1.02e-05 8.37e-05</td>\n",
2507 "</tr>\n",
2508 "<tr>\n",
2509 " <th>Capex To Sales</th> <td> 0.0001</td> <td> 4.56e-05</td> <td> 2.247</td> <td> 0.025</td> <td> 1.31e-05 0.000</td>\n",
2510 "</tr>\n",
2511 "<tr>\n",
2512 " <th>EBIT To Assets</th> <td> 8.146e-06</td> <td> 2e-05</td> <td> 0.408</td> <td> 0.683</td> <td> -3.1e-05 4.73e-05</td>\n",
2513 "</tr>\n",
2514 "<tr>\n",
2515 " <th>Retained Earnings To Assets</th> <td> -0.0001</td> <td> 4.2e-05</td> <td> -3.036</td> <td> 0.002</td> <td> -0.000 -4.51e-05</td>\n",
2516 "</tr>\n",
2517 "<tr>\n",
2518 " <th>Downside Risk</th> <td> 1.166e-05</td> <td> 1.22e-05</td> <td> 0.960</td> <td> 0.337</td> <td>-1.22e-05 3.55e-05</td>\n",
2519 "</tr>\n",
2520 "<tr>\n",
2521 " <th>Index Beta</th> <td>-7.363e-07</td> <td> 2.49e-07</td> <td> -2.958</td> <td> 0.003</td> <td>-1.22e-06 -2.48e-07</td>\n",
2522 "</tr>\n",
2523 "<tr>\n",
2524 " <th>Log Market Cap</th> <td> 9.103e-05</td> <td> 4.24e-05</td> <td> 2.148</td> <td> 0.032</td> <td> 7.98e-06 0.000</td>\n",
2525 "</tr>\n",
2526 "<tr>\n",
2527 " <th>Volatility 3M</th> <td>-5.492e-05</td> <td> 1.04e-05</td> <td> -5.306</td> <td> 0.000</td> <td>-7.52e-05 -3.46e-05</td>\n",
2528 "</tr>\n",
2529 "<tr>\n",
2530 " <th>stock_3D SYSTEMS CORP</th> <td> 0.0568</td> <td> 0.010</td> <td> 5.697</td> <td> 0.000</td> <td> 0.037 0.076</td>\n",
2531 "</tr>\n",
2532 "<tr>\n",
2533 " <th>stock_3M COMPANY</th> <td> 0.0426</td> <td> 0.009</td> <td> 4.865</td> <td> 0.000</td> <td> 0.025 0.060</td>\n",
2534 "</tr>\n",
2535 "<tr>\n",
2536 " <th>stock_ABBOTT LABORATORIES</th> <td> 0.0264</td> <td> 0.007</td> <td> 3.854</td> <td> 0.000</td> <td> 0.013 0.040</td>\n",
2537 "</tr>\n",
2538 "<tr>\n",
2539 " <th>stock_ABBVIE INC</th> <td> 0.0572</td> <td> 0.013</td> <td> 4.482</td> <td> 0.000</td> <td> 0.032 0.082</td>\n",
2540 "</tr>\n",
2541 "<tr>\n",
2542 " <th>stock_ALLERGAN INC</th> <td> 0.0487</td> <td> 0.007</td> <td> 7.349</td> <td> 0.000</td> <td> 0.036 0.062</td>\n",
2543 "</tr>\n",
2544 "<tr>\n",
2545 " <th>stock_ALLERGAN PLC</th> <td> 0.0543</td> <td> 0.009</td> <td> 5.812</td> <td> 0.000</td> <td> 0.036 0.073</td>\n",
2546 "</tr>\n",
2547 "<tr>\n",
2548 " <th>stock_ALTABA INC</th> <td> 0.0605</td> <td> 0.010</td> <td> 5.857</td> <td> 0.000</td> <td> 0.040 0.081</td>\n",
2549 "</tr>\n",
2550 "<tr>\n",
2551 " <th>stock_ALTRIA GROUP INC.</th> <td> 0.0453</td> <td> 0.009</td> <td> 5.074</td> <td> 0.000</td> <td> 0.028 0.063</td>\n",
2552 "</tr>\n",
2553 "<tr>\n",
2554 " <th>stock_AMAZON.COM INC</th> <td> 0.0513</td> <td> 0.010</td> <td> 4.905</td> <td> 0.000</td> <td> 0.031 0.072</td>\n",
2555 "</tr>\n",
2556 "<tr>\n",
2557 " <th>stock_AMERICAN AIRLINES GROUP INC</th> <td> 0.0490</td> <td> 0.012</td> <td> 4.079</td> <td> 0.000</td> <td> 0.025 0.073</td>\n",
2558 "</tr>\n",
2559 "<tr>\n",
2560 " <th>stock_AMERICAN EXPRESS COMPANY</th> <td> 0.0232</td> <td> 0.006</td> <td> 4.177</td> <td> 0.000</td> <td> 0.012 0.034</td>\n",
2561 "</tr>\n",
2562 "<tr>\n",
2563 " <th>stock_AMERICAN INTL GROUP INC</th> <td> 0.0452</td> <td> 0.009</td> <td> 4.770</td> <td> 0.000</td> <td> 0.027 0.064</td>\n",
2564 "</tr>\n",
2565 "<tr>\n",
2566 " <th>stock_AMGEN INC</th> <td> 0.0266</td> <td> 0.006</td> <td> 4.400</td> <td> 0.000</td> <td> 0.015 0.038</td>\n",
2567 "</tr>\n",
2568 "<tr>\n",
2569 " <th>stock_ANADARKO PETROLEUM CORP</th> <td> 0.0347</td> <td> 0.005</td> <td> 6.387</td> <td> 0.000</td> <td> 0.024 0.045</td>\n",
2570 "</tr>\n",
2571 "<tr>\n",
2572 " <th>stock_APACHE CORP</th> <td> 0.0130</td> <td> 0.009</td> <td> 1.487</td> <td> 0.137</td> <td> -0.004 0.030</td>\n",
2573 "</tr>\n",
2574 "<tr>\n",
2575 " <th>stock_APPLE INC</th> <td> 0.0471</td> <td> 0.009</td> <td> 5.478</td> <td> 0.000</td> <td> 0.030 0.064</td>\n",
2576 "</tr>\n",
2577 "<tr>\n",
2578 " <th>stock_APPLIED MATERIALS INC</th> <td> 0.0333</td> <td> 0.007</td> <td> 4.965</td> <td> 0.000</td> <td> 0.020 0.046</td>\n",
2579 "</tr>\n",
2580 "<tr>\n",
2581 " <th>stock_ARCONIC INC</th> <td> 0.0090</td> <td> 0.005</td> <td> 1.822</td> <td> 0.068</td> <td> -0.001 0.019</td>\n",
2582 "</tr>\n",
2583 "<tr>\n",
2584 " <th>stock_AT&T INC. COM</th> <td> 0.0372</td> <td> 0.010</td> <td> 3.817</td> <td> 0.000</td> <td> 0.018 0.056</td>\n",
2585 "</tr>\n",
2586 "<tr>\n",
2587 " <th>stock_Alphabet Inc. Cl A</th> <td> 0.0713</td> <td> 0.012</td> <td> 5.733</td> <td> 0.000</td> <td> 0.047 0.096</td>\n",
2588 "</tr>\n",
2589 "<tr>\n",
2590 " <th>stock_BAKER HUGHES INC</th> <td> 0.0296</td> <td> 0.006</td> <td> 4.920</td> <td> 0.000</td> <td> 0.018 0.041</td>\n",
2591 "</tr>\n",
2592 "<tr>\n",
2593 " <th>stock_BANK OF AMERICA CORP</th> <td> 0.1209</td> <td> 0.037</td> <td> 3.306</td> <td> 0.001</td> <td> 0.049 0.193</td>\n",
2594 "</tr>\n",
2595 "<tr>\n",
2596 " <th>stock_BERKSHIRE HATHAWAY INC CL-B</th> <td> 0.0629</td> <td> 0.013</td> <td> 4.939</td> <td> 0.000</td> <td> 0.038 0.088</td>\n",
2597 "</tr>\n",
2598 "<tr>\n",
2599 " <th>stock_BIOGEN INC</th> <td> 0.0551</td> <td> 0.008</td> <td> 6.605</td> <td> 0.000</td> <td> 0.039 0.071</td>\n",
2600 "</tr>\n",
2601 "<tr>\n",
2602 " <th>stock_BOEING CO</th> <td> 0.0230</td> <td> 0.007</td> <td> 3.524</td> <td> 0.000</td> <td> 0.010 0.036</td>\n",
2603 "</tr>\n",
2604 "<tr>\n",
2605 " <th>stock_BOOKING HOLDINGS INC</th> <td> 0.0600</td> <td> 0.011</td> <td> 5.539</td> <td> 0.000</td> <td> 0.039 0.081</td>\n",
2606 "</tr>\n",
2607 "<tr>\n",
2608 " <th>stock_BRISTOL MYERS SQUIBB COMPANY</th> <td> 0.0405</td> <td> 0.007</td> <td> 5.456</td> <td> 0.000</td> <td> 0.026 0.055</td>\n",
2609 "</tr>\n",
2610 "<tr>\n",
2611 " <th>stock_BROADCOM CORP</th> <td> 0.0564</td> <td> 0.010</td> <td> 5.697</td> <td> 0.000</td> <td> 0.037 0.076</td>\n",
2612 "</tr>\n",
2613 "<tr>\n",
2614 " <th>stock_BROADCOM INC</th> <td> 0.0733</td> <td> 0.013</td> <td> 5.818</td> <td> 0.000</td> <td> 0.049 0.098</td>\n",
2615 "</tr>\n",
2616 "<tr>\n",
2617 " <th>stock_CATERPILLAR INC</th> <td> 0.0139</td> <td> 0.006</td> <td> 2.172</td> <td> 0.030</td> <td> 0.001 0.027</td>\n",
2618 "</tr>\n",
2619 "<tr>\n",
2620 " <th>stock_CELGENE CORP</th> <td> 0.0385</td> <td> 0.007</td> <td> 5.398</td> <td> 0.000</td> <td> 0.025 0.053</td>\n",
2621 "</tr>\n",
2622 "<tr>\n",
2623 " <th>stock_CHESAPEAKE ENERGY CORP</th> <td> 0.0269</td> <td> 0.011</td> <td> 2.555</td> <td> 0.011</td> <td> 0.006 0.048</td>\n",
2624 "</tr>\n",
2625 "<tr>\n",
2626 " <th>stock_CHEVRON CORPORATION</th> <td> 0.0562</td> <td> 0.013</td> <td> 4.283</td> <td> 0.000</td> <td> 0.030 0.082</td>\n",
2627 "</tr>\n",
2628 "<tr>\n",
2629 " <th>stock_CISCO SYSTEMS INC</th> <td> 0.0285</td> <td> 0.006</td> <td> 4.464</td> <td> 0.000</td> <td> 0.016 0.041</td>\n",
2630 "</tr>\n",
2631 "<tr>\n",
2632 " <th>stock_CITIGROUP</th> <td> 0.1090</td> <td> 0.031</td> <td> 3.474</td> <td> 0.001</td> <td> 0.048 0.171</td>\n",
2633 "</tr>\n",
2634 "<tr>\n",
2635 " <th>stock_COCA-COLA CO</th> <td> 0.0384</td> <td> 0.009</td> <td> 4.352</td> <td> 0.000</td> <td> 0.021 0.056</td>\n",
2636 "</tr>\n",
2637 "<tr>\n",
2638 " <th>stock_COMCAST CORP</th> <td> 0.0325</td> <td> 0.006</td> <td> 5.155</td> <td> 0.000</td> <td> 0.020 0.045</td>\n",
2639 "</tr>\n",
2640 "<tr>\n",
2641 " <th>stock_CONOCOPHILLIPS</th> <td> 0.0456</td> <td> 0.011</td> <td> 4.089</td> <td> 0.000</td> <td> 0.024 0.067</td>\n",
2642 "</tr>\n",
2643 "<tr>\n",
2644 " <th>stock_COVIDIEN PLC</th> <td> 0.0743</td> <td> 0.012</td> <td> 6.354</td> <td> 0.000</td> <td> 0.051 0.097</td>\n",
2645 "</tr>\n",
2646 "<tr>\n",
2647 " <th>stock_CVS HEALTH CORP</th> <td> 0.0358</td> <td> 0.008</td> <td> 4.345</td> <td> 0.000</td> <td> 0.020 0.052</td>\n",
2648 "</tr>\n",
2649 "<tr>\n",
2650 " <th>stock_DEERE & CO</th> <td> 0.0115</td> <td> 0.007</td> <td> 1.683</td> <td> 0.092</td> <td> -0.002 0.025</td>\n",
2651 "</tr>\n",
2652 "<tr>\n",
2653 " <th>stock_DELTA AIR LINES INC</th> <td> 0.0545</td> <td> 0.011</td> <td> 4.896</td> <td> 0.000</td> <td> 0.033 0.076</td>\n",
2654 "</tr>\n",
2655 "<tr>\n",
2656 " <th>stock_DIRECTV</th> <td> 0.0383</td> <td> 0.011</td> <td> 3.558</td> <td> 0.000</td> <td> 0.017 0.059</td>\n",
2657 "</tr>\n",
2658 "<tr>\n",
2659 " <th>stock_DOLLAR GENERAL CORP</th> <td> 0.0508</td> <td> 0.011</td> <td> 4.509</td> <td> 0.000</td> <td> 0.029 0.073</td>\n",
2660 "</tr>\n",
2661 "<tr>\n",
2662 " <th>stock_DOW CHEMICAL CO</th> <td> 0.0234</td> <td> 0.006</td> <td> 3.686</td> <td> 0.000</td> <td> 0.011 0.036</td>\n",
2663 "</tr>\n",
2664 "<tr>\n",
2665 " <th>stock_E.I. Du Pont De Nemours A</th> <td> 0.0204</td> <td> 0.007</td> <td> 3.137</td> <td> 0.002</td> <td> 0.008 0.033</td>\n",
2666 "</tr>\n",
2667 "<tr>\n",
2668 " <th>stock_EBAY INC</th> <td> 0.0611</td> <td> 0.012</td> <td> 5.279</td> <td> 0.000</td> <td> 0.038 0.084</td>\n",
2669 "</tr>\n",
2670 "<tr>\n",
2671 " <th>stock_EMC CORPORATION</th> <td> 0.0341</td> <td> 0.007</td> <td> 5.245</td> <td> 0.000</td> <td> 0.021 0.047</td>\n",
2672 "</tr>\n",
2673 "<tr>\n",
2674 " <th>stock_EOG RESOURCES INC</th> <td> 0.0413</td> <td> 0.006</td> <td> 6.573</td> <td> 0.000</td> <td> 0.029 0.054</td>\n",
2675 "</tr>\n",
2676 "<tr>\n",
2677 " <th>stock_EXPRESS SCRIPTS HOLDING CO</th> <td> 0.0177</td> <td> 0.007</td> <td> 2.628</td> <td> 0.009</td> <td> 0.005 0.031</td>\n",
2678 "</tr>\n",
2679 "<tr>\n",
2680 " <th>stock_EXXON MOBIL CORPORATION</th> <td> 0.0677</td> <td> 0.015</td> <td> 4.484</td> <td> 0.000</td> <td> 0.038 0.097</td>\n",
2681 "</tr>\n",
2682 "<tr>\n",
2683 " <th>stock_FACEBOOK INC</th> <td> 0.0762</td> <td> 0.013</td> <td> 5.785</td> <td> 0.000</td> <td> 0.050 0.102</td>\n",
2684 "</tr>\n",
2685 "<tr>\n",
2686 " <th>stock_FEDEX CORPORATION</th> <td> 0.0321</td> <td> 0.007</td> <td> 4.657</td> <td> 0.000</td> <td> 0.019 0.046</td>\n",
2687 "</tr>\n",
2688 "<tr>\n",
2689 " <th>stock_FIRST SOLAR INC</th> <td> 0.0484</td> <td> 0.012</td> <td> 4.004</td> <td> 0.000</td> <td> 0.025 0.072</td>\n",
2690 "</tr>\n",
2691 "<tr>\n",
2692 " <th>stock_FORD MOTOR CO(NEW)</th> <td> 0.0213</td> <td> 0.008</td> <td> 2.647</td> <td> 0.008</td> <td> 0.006 0.037</td>\n",
2693 "</tr>\n",
2694 "<tr>\n",
2695 " <th>stock_FREEPORT-MCMORAN INC</th> <td> 0.0269</td> <td> 0.009</td> <td> 3.063</td> <td> 0.002</td> <td> 0.010 0.044</td>\n",
2696 "</tr>\n",
2697 "<tr>\n",
2698 " <th>stock_GENERAL ELECTRIC CO</th> <td> 0.0556</td> <td> 0.013</td> <td> 4.444</td> <td> 0.000</td> <td> 0.031 0.080</td>\n",
2699 "</tr>\n",
2700 "<tr>\n",
2701 " <th>stock_GENERAL MOTORS CO</th> <td> 0.0485</td> <td> 0.013</td> <td> 3.829</td> <td> 0.000</td> <td> 0.024 0.073</td>\n",
2702 "</tr>\n",
2703 "<tr>\n",
2704 " <th>stock_GILEAD SCIENCES INC</th> <td> 0.0459</td> <td> 0.008</td> <td> 5.868</td> <td> 0.000</td> <td> 0.031 0.061</td>\n",
2705 "</tr>\n",
2706 "<tr>\n",
2707 " <th>stock_GOLDMAN SACHS GROUP INC</th> <td> 0.0965</td> <td> 0.017</td> <td> 5.644</td> <td> 0.000</td> <td> 0.063 0.130</td>\n",
2708 "</tr>\n",
2709 "<tr>\n",
2710 " <th>stock_GOPRO INC</th> <td> 0.0687</td> <td> 0.013</td> <td> 5.377</td> <td> 0.000</td> <td> 0.044 0.094</td>\n",
2711 "</tr>\n",
2712 "<tr>\n",
2713 " <th>stock_HALLIBURTON CO (HOLDING CO)</th> <td> 0.0322</td> <td> 0.007</td> <td> 4.601</td> <td> 0.000</td> <td> 0.018 0.046</td>\n",
2714 "</tr>\n",
2715 "<tr>\n",
2716 " <th>stock_HOME DEPOT INC</th> <td> 0.0336</td> <td> 0.008</td> <td> 4.320</td> <td> 0.000</td> <td> 0.018 0.049</td>\n",
2717 "</tr>\n",
2718 "<tr>\n",
2719 " <th>stock_HP INC</th> <td> 0.0254</td> <td> 0.007</td> <td> 3.507</td> <td> 0.000</td> <td> 0.011 0.040</td>\n",
2720 "</tr>\n",
2721 "<tr>\n",
2722 " <th>stock_INTEL CORP</th> <td> 0.0383</td> <td> 0.008</td> <td> 4.891</td> <td> 0.000</td> <td> 0.023 0.054</td>\n",
2723 "</tr>\n",
2724 "<tr>\n",
2725 " <th>stock_INTL BUSINESS MACHINES CORP</th> <td> 0.0329</td> <td> 0.009</td> <td> 3.784</td> <td> 0.000</td> <td> 0.016 0.050</td>\n",
2726 "</tr>\n",
2727 "<tr>\n",
2728 " <th>stock_JOHNSON AND JOHNSON</th> <td> 0.0431</td> <td> 0.009</td> <td> 4.833</td> <td> 0.000</td> <td> 0.026 0.061</td>\n",
2729 "</tr>\n",
2730 "<tr>\n",
2731 " <th>stock_JPMORGAN CHASE & CO COM STK</th> <td> 0.1531</td> <td> 0.044</td> <td> 3.455</td> <td> 0.001</td> <td> 0.066 0.240</td>\n",
2732 "</tr>\n",
2733 "<tr>\n",
2734 " <th>stock_KEURIG GREEN MOUNTAIN INC</th> <td> 0.0569</td> <td> 0.009</td> <td> 6.135</td> <td> 0.000</td> <td> 0.039 0.075</td>\n",
2735 "</tr>\n",
2736 "<tr>\n",
2737 " <th>stock_KINDER MORGAN INC</th> <td> 0.0427</td> <td> 0.012</td> <td> 3.497</td> <td> 0.000</td> <td> 0.019 0.067</td>\n",
2738 "</tr>\n",
2739 "<tr>\n",
2740 " <th>stock_LAS VEGAS SANDS CORP</th> <td> 0.0494</td> <td> 0.012</td> <td> 4.235</td> <td> 0.000</td> <td> 0.027 0.072</td>\n",
2741 "</tr>\n",
2742 "<tr>\n",
2743 " <th>stock_LILLY ELI & CO</th> <td> 0.0411</td> <td> 0.008</td> <td> 5.097</td> <td> 0.000</td> <td> 0.025 0.057</td>\n",
2744 "</tr>\n",
2745 "<tr>\n",
2746 " <th>stock_LINKEDIN CORP</th> <td> 0.0700</td> <td> 0.013</td> <td> 5.460</td> <td> 0.000</td> <td> 0.045 0.095</td>\n",
2747 "</tr>\n",
2748 "<tr>\n",
2749 " <th>stock_LOWES COMPANIES INC</th> <td> 0.0307</td> <td> 0.007</td> <td> 4.474</td> <td> 0.000</td> <td> 0.017 0.044</td>\n",
2750 "</tr>\n",
2751 "<tr>\n",
2752 " <th>stock_LYONDELLBASELL INDUSTRIES NV</th> <td> 0.0478</td> <td> 0.012</td> <td> 4.043</td> <td> 0.000</td> <td> 0.025 0.071</td>\n",
2753 "</tr>\n",
2754 "<tr>\n",
2755 " <th>stock_MARATHON PETROLEUM CORP</th> <td> 0.0508</td> <td> 0.013</td> <td> 3.939</td> <td> 0.000</td> <td> 0.026 0.076</td>\n",
2756 "</tr>\n",
2757 "<tr>\n",
2758 " <th>stock_MASTERCARD INCORPORATED</th> <td> 0.0778</td> <td> 0.013</td> <td> 6.157</td> <td> 0.000</td> <td> 0.053 0.103</td>\n",
2759 "</tr>\n",
2760 "<tr>\n",
2761 " <th>stock_MCDONALDS CORP</th> <td> 0.0380</td> <td> 0.009</td> <td> 4.236</td> <td> 0.000</td> <td> 0.020 0.056</td>\n",
2762 "</tr>\n",
2763 "<tr>\n",
2764 " <th>stock_MEDTRONIC PLC</th> <td> 0.0482</td> <td> 0.008</td> <td> 6.017</td> <td> 0.000</td> <td> 0.033 0.064</td>\n",
2765 "</tr>\n",
2766 "<tr>\n",
2767 " <th>stock_MERCK & CO INC</th> <td> 0.0425</td> <td> 0.008</td> <td> 5.002</td> <td> 0.000</td> <td> 0.026 0.059</td>\n",
2768 "</tr>\n",
2769 "<tr>\n",
2770 " <th>stock_METLIFE INC</th> <td> 0.0738</td> <td> 0.018</td> <td> 4.162</td> <td> 0.000</td> <td> 0.039 0.109</td>\n",
2771 "</tr>\n",
2772 "<tr>\n",
2773 " <th>stock_MICHAEL KORS HOLDINGS LTD</th> <td> 0.0770</td> <td> 0.013</td> <td> 5.750</td> <td> 0.000</td> <td> 0.051 0.103</td>\n",
2774 "</tr>\n",
2775 "<tr>\n",
2776 " <th>stock_MICRON TECHNOLOGY INC</th> <td> 0.0363</td> <td> 0.007</td> <td> 5.112</td> <td> 0.000</td> <td> 0.022 0.050</td>\n",
2777 "</tr>\n",
2778 "<tr>\n",
2779 " <th>stock_MICROSOFT CORP</th> <td> 0.0468</td> <td> 0.009</td> <td> 5.410</td> <td> 0.000</td> <td> 0.030 0.064</td>\n",
2780 "</tr>\n",
2781 "<tr>\n",
2782 " <th>stock_MONDELEZ INTERNATIONAL INC</th> <td> 0.0519</td> <td> 0.011</td> <td> 4.934</td> <td> 0.000</td> <td> 0.031 0.073</td>\n",
2783 "</tr>\n",
2784 "<tr>\n",
2785 " <th>stock_MONSANTO COMPANY</th> <td> 0.0556</td> <td> 0.011</td> <td> 5.111</td> <td> 0.000</td> <td> 0.034 0.077</td>\n",
2786 "</tr>\n",
2787 "<tr>\n",
2788 " <th>stock_MORGAN STANLEY</th> <td> 0.0829</td> <td> 0.016</td> <td> 5.139</td> <td> 0.000</td> <td> 0.051 0.115</td>\n",
2789 "</tr>\n",
2790 "<tr>\n",
2791 " <th>stock_MYLAN NV</th> <td> 0.0430</td> <td> 0.008</td> <td> 5.515</td> <td> 0.000</td> <td> 0.028 0.058</td>\n",
2792 "</tr>\n",
2793 "<tr>\n",
2794 " <th>stock_NATIONAL OILWELL VARCO INC.</th> <td> 0.0397</td> <td> 0.011</td> <td> 3.525</td> <td> 0.000</td> <td> 0.018 0.062</td>\n",
2795 "</tr>\n",
2796 "<tr>\n",
2797 " <th>stock_NETFLIX INC</th> <td> 0.0690</td> <td> 0.011</td> <td> 6.016</td> <td> 0.000</td> <td> 0.047 0.092</td>\n",
2798 "</tr>\n",
2799 "<tr>\n",
2800 " <th>stock_NEWMONT MINING CORP (HOLDING COMPANY)</th> <td> 0.0312</td> <td> 0.008</td> <td> 3.782</td> <td> 0.000</td> <td> 0.015 0.047</td>\n",
2801 "</tr>\n",
2802 "<tr>\n",
2803 " <th>stock_NEWS CP - CL A</th> <td> 0.0462</td> <td> 0.009</td> <td> 5.253</td> <td> 0.000</td> <td> 0.029 0.063</td>\n",
2804 "</tr>\n",
2805 "<tr>\n",
2806 " <th>stock_NIKE INC CL-B</th> <td> 0.0466</td> <td> 0.008</td> <td> 5.636</td> <td> 0.000</td> <td> 0.030 0.063</td>\n",
2807 "</tr>\n",
2808 "<tr>\n",
2809 " <th>stock_OCCIDENTAL PETROLEUM CORP</th> <td> 0.0420</td> <td> 0.009</td> <td> 4.899</td> <td> 0.000</td> <td> 0.025 0.059</td>\n",
2810 "</tr>\n",
2811 "<tr>\n",
2812 " <th>stock_ORACLE CORP</th> <td> 0.0434</td> <td> 0.008</td> <td> 5.331</td> <td> 0.000</td> <td> 0.027 0.059</td>\n",
2813 "</tr>\n",
2814 "<tr>\n",
2815 " <th>stock_PANDORA MEDIA INC</th> <td> 0.0953</td> <td> 0.014</td> <td> 6.986</td> <td> 0.000</td> <td> 0.069 0.122</td>\n",
2816 "</tr>\n",
2817 "<tr>\n",
2818 " <th>stock_PENNEY J.C. CO INC (HOLDING COMPANY)</th> <td> 0.0163</td> <td> 0.007</td> <td> 2.353</td> <td> 0.019</td> <td> 0.003 0.030</td>\n",
2819 "</tr>\n",
2820 "<tr>\n",
2821 " <th>stock_PEPSICO INC</th> <td> 0.0387</td> <td> 0.009</td> <td> 4.139</td> <td> 0.000</td> <td> 0.020 0.057</td>\n",
2822 "</tr>\n",
2823 "<tr>\n",
2824 " <th>stock_PFIZER INC</th> <td> 0.0498</td> <td> 0.009</td> <td> 5.350</td> <td> 0.000</td> <td> 0.032 0.068</td>\n",
2825 "</tr>\n",
2826 "<tr>\n",
2827 " <th>stock_PHILIP MORRIS INTERNATIONAL INC</th> <td> 0.0577</td> <td> 0.014</td> <td> 4.236</td> <td> 0.000</td> <td> 0.031 0.084</td>\n",
2828 "</tr>\n",
2829 "<tr>\n",
2830 " <th>stock_PIONEER NAT RES CO</th> <td> 0.0716</td> <td> 0.010</td> <td> 7.136</td> <td> 0.000</td> <td> 0.052 0.091</td>\n",
2831 "</tr>\n",
2832 "<tr>\n",
2833 " <th>stock_PRECISION CASTPARTS CORP</th> <td> 0.0510</td> <td> 0.009</td> <td> 5.448</td> <td> 0.000</td> <td> 0.033 0.069</td>\n",
2834 "</tr>\n",
2835 "<tr>\n",
2836 " <th>stock_PROCTER & GAMBLE CO</th> <td> 0.0442</td> <td> 0.010</td> <td> 4.581</td> <td> 0.000</td> <td> 0.025 0.063</td>\n",
2837 "</tr>\n",
2838 "<tr>\n",
2839 " <th>stock_QUALCOMM INC</th> <td> 0.0532</td> <td> 0.009</td> <td> 5.739</td> <td> 0.000</td> <td> 0.035 0.071</td>\n",
2840 "</tr>\n",
2841 "<tr>\n",
2842 " <th>stock_REGENERON PHARMACEUTICALS INC</th> <td> 0.0537</td> <td> 0.011</td> <td> 4.675</td> <td> 0.000</td> <td> 0.031 0.076</td>\n",
2843 "</tr>\n",
2844 "<tr>\n",
2845 " <th>stock_SALESFORCE.COM INC</th> <td> 0.0610</td> <td> 0.011</td> <td> 5.303</td> <td> 0.000</td> <td> 0.038 0.083</td>\n",
2846 "</tr>\n",
2847 "<tr>\n",
2848 " <th>stock_SALIX PHARMACEUTICALS LTD</th> <td> -4.65e-16</td> <td> 2.62e-15</td> <td> -0.178</td> <td> 0.859</td> <td> -5.6e-15 4.67e-15</td>\n",
2849 "</tr>\n",
2850 "<tr>\n",
2851 " <th>stock_SANDISK CORP</th> <td> 0.0624</td> <td> 0.010</td> <td> 6.426</td> <td> 0.000</td> <td> 0.043 0.081</td>\n",
2852 "</tr>\n",
2853 "<tr>\n",
2854 " <th>stock_SCHLUMBERGER LTD.</th> <td> 0.0441</td> <td> 0.009</td> <td> 4.843</td> <td> 0.000</td> <td> 0.026 0.062</td>\n",
2855 "</tr>\n",
2856 "<tr>\n",
2857 " <th>stock_SKYWORKS SOLUTIONS INC</th> <td> 0.0772</td> <td> 0.011</td> <td> 6.749</td> <td> 0.000</td> <td> 0.055 0.100</td>\n",
2858 "</tr>\n",
2859 "<tr>\n",
2860 " <th>stock_SOLARCITY CORP</th> <td> 0.0820</td> <td> 0.013</td> <td> 6.129</td> <td> 0.000</td> <td> 0.056 0.108</td>\n",
2861 "</tr>\n",
2862 "<tr>\n",
2863 " <th>stock_SOUTHWEST AIRLINES CO</th> <td> 0.0430</td> <td> 0.007</td> <td> 5.909</td> <td> 0.000</td> <td> 0.029 0.057</td>\n",
2864 "</tr>\n",
2865 "<tr>\n",
2866 " <th>stock_STARBUCKS CORPORATION</th> <td> 0.0606</td> <td> 0.009</td> <td> 6.590</td> <td> 0.000</td> <td> 0.043 0.079</td>\n",
2867 "</tr>\n",
2868 "<tr>\n",
2869 " <th>stock_SUNEDISON INC</th> <td> 0.0234</td> <td> 0.017</td> <td> 1.401</td> <td> 0.161</td> <td> -0.009 0.056</td>\n",
2870 "</tr>\n",
2871 "<tr>\n",
2872 " <th>stock_TARGET CORPORATION</th> <td> 0.0356</td> <td> 0.010</td> <td> 3.541</td> <td> 0.000</td> <td> 0.016 0.055</td>\n",
2873 "</tr>\n",
2874 "<tr>\n",
2875 " <th>stock_TESLA INC</th> <td> 0.0720</td> <td> 0.013</td> <td> 5.659</td> <td> 0.000</td> <td> 0.047 0.097</td>\n",
2876 "</tr>\n",
2877 "<tr>\n",
2878 " <th>stock_TEXAS INSTRUMENTS INC</th> <td> 0.0522</td> <td> 0.010</td> <td> 5.206</td> <td> 0.000</td> <td> 0.033 0.072</td>\n",
2879 "</tr>\n",
2880 "<tr>\n",
2881 " <th>stock_TIME WARNER CABLE INC</th> <td> 0.0468</td> <td> 0.011</td> <td> 4.218</td> <td> 0.000</td> <td> 0.025 0.069</td>\n",
2882 "</tr>\n",
2883 "<tr>\n",
2884 " <th>stock_TIME WARNER INC.</th> <td> 0.0227</td> <td> 0.005</td> <td> 4.748</td> <td> 0.000</td> <td> 0.013 0.032</td>\n",
2885 "</tr>\n",
2886 "<tr>\n",
2887 " <th>stock_TWITTER INC</th> <td> 0.0686</td> <td> 0.013</td> <td> 5.194</td> <td> 0.000</td> <td> 0.043 0.094</td>\n",
2888 "</tr>\n",
2889 "<tr>\n",
2890 " <th>stock_UNION PACIFIC CORPORATION</th> <td> 0.0493</td> <td> 0.009</td> <td> 5.446</td> <td> 0.000</td> <td> 0.032 0.067</td>\n",
2891 "</tr>\n",
2892 "<tr>\n",
2893 " <th>stock_UNITED CONTINENTAL HOLDINGS IN</th> <td> 0.0379</td> <td> 0.011</td> <td> 3.469</td> <td> 0.001</td> <td> 0.016 0.059</td>\n",
2894 "</tr>\n",
2895 "<tr>\n",
2896 " <th>stock_UNITED PARCEL SERVICE INC.CL B</th> <td> 0.0366</td> <td> 0.011</td> <td> 3.462</td> <td> 0.001</td> <td> 0.016 0.057</td>\n",
2897 "</tr>\n",
2898 "<tr>\n",
2899 " <th>stock_UNITED TECHNOLOGIES CORP</th> <td> 0.0390</td> <td> 0.009</td> <td> 4.277</td> <td> 0.000</td> <td> 0.021 0.057</td>\n",
2900 "</tr>\n",
2901 "<tr>\n",
2902 " <th>stock_UNITEDHEALTH GROUP INC</th> <td> 0.0455</td> <td> 0.009</td> <td> 4.866</td> <td> 0.000</td> <td> 0.027 0.064</td>\n",
2903 "</tr>\n",
2904 "<tr>\n",
2905 " <th>stock_VALERO ENERGY CORP (NEW)</th> <td> 0.0432</td> <td> 0.009</td> <td> 4.596</td> <td> 0.000</td> <td> 0.025 0.062</td>\n",
2906 "</tr>\n",
2907 "<tr>\n",
2908 " <th>stock_VERIZON COMMUNICATIONS</th> <td> 0.0392</td> <td> 0.011</td> <td> 3.502</td> <td> 0.000</td> <td> 0.017 0.061</td>\n",
2909 "</tr>\n",
2910 "<tr>\n",
2911 " <th>stock_VISA INC</th> <td> 0.0769</td> <td> 0.012</td> <td> 6.389</td> <td> 0.000</td> <td> 0.053 0.100</td>\n",
2912 "</tr>\n",
2913 "<tr>\n",
2914 " <th>stock_WALGREENS BOOTS ALLIANCE INC</th> <td> 0.0444</td> <td> 0.009</td> <td> 4.859</td> <td> 0.000</td> <td> 0.026 0.062</td>\n",
2915 "</tr>\n",
2916 "<tr>\n",
2917 " <th>stock_WALMART INC</th> <td> 0.0554</td> <td> 0.015</td> <td> 3.742</td> <td> 0.000</td> <td> 0.026 0.084</td>\n",
2918 "</tr>\n",
2919 "<tr>\n",
2920 " <th>stock_WALT DISNEY CO</th> <td> 0.0432</td> <td> 0.007</td> <td> 6.119</td> <td> 0.000</td> <td> 0.029 0.057</td>\n",
2921 "</tr>\n",
2922 "<tr>\n",
2923 " <th>stock_WELLS FARGO & CO(NEW)</th> <td> 0.1324</td> <td> 0.030</td> <td> 4.449</td> <td> 0.000</td> <td> 0.074 0.191</td>\n",
2924 "</tr>\n",
2925 "<tr>\n",
2926 " <th>stock_WILLIAMS COMPANIES</th> <td> 0.0472</td> <td> 0.009</td> <td> 5.143</td> <td> 0.000</td> <td> 0.029 0.065</td>\n",
2927 "</tr>\n",
2928 "<tr>\n",
2929 " <th>stock_WYNN RESORTS LTD</th> <td> 0.0410</td> <td> 0.011</td> <td> 3.710</td> <td> 0.000</td> <td> 0.019 0.063</td>\n",
2930 "</tr>\n",
2931 "<tr>\n",
2932 " <th>stock_YELP INC</th> <td> 0.0728</td> <td> 0.013</td> <td> 5.489</td> <td> 0.000</td> <td> 0.047 0.099</td>\n",
2933 "</tr>\n",
2934 "</table>\n",
2935 "<table class=\"simpletable\">\n",
2936 "<tr>\n",
2937 " <th>Omnibus:</th> <td>71.266</td> <th> Durbin-Watson: </th> <td> 1.433</td>\n",
2938 "</tr>\n",
2939 "<tr>\n",
2940 " <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td> 77.090</td>\n",
2941 "</tr>\n",
2942 "<tr>\n",
2943 " <th>Skew:</th> <td>-0.067</td> <th> Prob(JB): </th> <td>1.82e-17</td>\n",
2944 "</tr>\n",
2945 "<tr>\n",
2946 " <th>Kurtosis:</th> <td> 3.154</td> <th> Cond. No. </th> <td>3.42e+20</td>\n",
2947 "</tr>\n",
2948 "</table>"
2949 ],
2950 "text/plain": [
2951 "<class 'statsmodels.iolib.summary.Summary'>\n",
2952 "\"\"\"\n",
2953 " OLS Regression Results \n",
2954 "==============================================================================\n",
2955 "Dep. Variable: Returns5D R-squared: 0.016\n",
2956 "Model: OLS Adj. R-squared: 0.012\n",
2957 "Method: Least Squares F-statistic: 4.124\n",
2958 "Date: Fri, 03 Aug 2018 Prob (F-statistic): 2.28e-66\n",
2959 "Time: 21:41:14 Log-Likelihood: 95279.\n",
2960 "No. Observations: 44370 AIC: -1.902e+05\n",
2961 "Df Residuals: 44195 BIC: -1.887e+05\n",
2962 "Df Model: 174 \n",
2963 "Covariance Type: nonrobust \n",
2964 "===============================================================================================================\n",
2965 " coef std err t P>|t| [95.0% Conf. Int.]\n",
2966 "---------------------------------------------------------------------------------------------------------------\n",
2967 "DividendYield 4.504e-06 1.08e-06 4.185 0.000 2.39e-06 6.61e-06\n",
2968 "EBITDAYield 9.323e-06 2.17e-05 0.430 0.667 -3.32e-05 5.19e-05\n",
2969 "EVToEBITDA 1.425e-05 1.98e-05 0.719 0.472 -2.46e-05 5.31e-05\n",
2970 "EVToFCF 2.654e-05 2.47e-05 1.075 0.283 -2.19e-05 7.49e-05\n",
2971 "PriceToBook 1.582e-05 2.36e-05 0.669 0.503 -3.05e-05 6.21e-05\n",
2972 "PriceToDilutedEarningsTTM 5.462e-07 1.06e-05 0.052 0.959 -2.01e-05 2.12e-05\n",
2973 "PriceToEarningsTTM 1.449e-07 4.67e-07 0.310 0.757 -7.71e-07 1.06e-06\n",
2974 "PriceToFCF -1.94e-05 2.07e-05 -0.939 0.348 -5.99e-05 2.11e-05\n",
2975 "PriceToOperatingCashflow -4.155e-05 1.42e-05 -2.934 0.003 -6.93e-05 -1.38e-05\n",
2976 "PriceToSalesTTM -8.975e-06 1.19e-06 -7.530 0.000 -1.13e-05 -6.64e-06\n",
2977 "Directional Movement Index -4.457e-06 4.75e-06 -0.938 0.348 -1.38e-05 4.86e-06\n",
2978 "Money Flow Index -5.894e-06 6.16e-06 -0.956 0.339 -1.8e-05 6.19e-06\n",
2979 "Percent Above Low -1.985e-05 1.18e-05 -1.675 0.094 -4.31e-05 3.38e-06\n",
2980 "Percent Below High 7.651e-06 9.42e-06 0.812 0.417 -1.08e-05 2.61e-05\n",
2981 "Price Oscillator 3.706e-06 6.64e-06 0.558 0.577 -9.31e-06 1.67e-05\n",
2982 "Trendline 1.414e-05 1.01e-05 1.402 0.161 -5.64e-06 3.39e-05\n",
2983 "AssetToEquityRatio -1.226e-07 8.91e-07 -0.138 0.891 -1.87e-06 1.62e-06\n",
2984 "AssetTurnover -7.3e-06 3.95e-05 -0.185 0.854 -8.48e-05 7.02e-05\n",
2985 "CurrentRatio 4.11e-07 1.18e-06 0.347 0.728 -1.91e-06 2.73e-06\n",
2986 "DebtToAssetRatio 2.484e-06 7.25e-07 3.427 0.001 1.06e-06 3.9e-06\n",
2987 "DebtToEquityRatio -5.365e-07 8.37e-07 -0.641 0.521 -2.18e-06 1.1e-06\n",
2988 "MertonsDD -0.0004 0.000 -3.508 0.000 -0.001 -0.000\n",
2989 "WorkingCapitalToAssets 1.341e-06 1.44e-06 0.932 0.351 -1.48e-06 4.16e-06\n",
2990 "WorkingCapitalToSales -3.118e-05 4.62e-05 -0.674 0.500 -0.000 5.95e-05\n",
2991 "Dividend Growth -2.629e-07 4.5e-07 -0.585 0.559 -1.14e-06 6.18e-07\n",
2992 "EPS 2.901e-07 7.04e-07 0.412 0.680 -1.09e-06 1.67e-06\n",
2993 "Net Debt -8.116e-14 2.56e-14 -3.172 0.002 -1.31e-13 -3.1e-14\n",
2994 "Sales -5.373e-14 2.4e-14 -2.239 0.025 -1.01e-13 -6.69e-15\n",
2995 "Total Assets -4.104e-14 1.8e-14 -2.276 0.023 -7.64e-14 -5.7e-15\n",
2996 "EPS Growth 3M -2.787e-06 1.08e-06 -2.582 0.010 -4.9e-06 -6.72e-07\n",
2997 "EPS Growth 12M 1.257e-06 1.08e-06 1.161 0.246 -8.64e-07 3.38e-06\n",
2998 "Net Debt Growth 3M 2.628e-07 1.37e-06 0.191 0.848 -2.43e-06 2.95e-06\n",
2999 "Net Debt Growth 12M 2.592e-06 1.4e-06 1.858 0.063 -1.43e-07 5.33e-06\n",
3000 "Sales Growth 3M 3.824e-06 1.45e-06 2.634 0.008 9.79e-07 6.67e-06\n",
3001 "Sales Growth 12M -2.38e-06 1.48e-06 -1.604 0.109 -5.29e-06 5.28e-07\n",
3002 "Total Assets Growth 3M -6.292e-07 1.63e-06 -0.386 0.699 -3.82e-06 2.56e-06\n",
3003 "Total Assets Growth 12M -1.46e-06 1.73e-06 -0.842 0.400 -4.86e-06 1.94e-06\n",
3004 "CFO To Assets 4.634e-05 1.98e-05 2.342 0.019 7.56e-06 8.51e-05\n",
3005 "Capex To Assets -0.0001 4.26e-05 -3.232 0.001 -0.000 -5.42e-05\n",
3006 "Capex To FCF 3.673e-05 2.39e-05 1.534 0.125 -1.02e-05 8.37e-05\n",
3007 "Capex To Sales 0.0001 4.56e-05 2.247 0.025 1.31e-05 0.000\n",
3008 "EBIT To Assets 8.146e-06 2e-05 0.408 0.683 -3.1e-05 4.73e-05\n",
3009 "Retained Earnings To Assets -0.0001 4.2e-05 -3.036 0.002 -0.000 -4.51e-05\n",
3010 "Downside Risk 1.166e-05 1.22e-05 0.960 0.337 -1.22e-05 3.55e-05\n",
3011 "Index Beta -7.363e-07 2.49e-07 -2.958 0.003 -1.22e-06 -2.48e-07\n",
3012 "Log Market Cap 9.103e-05 4.24e-05 2.148 0.032 7.98e-06 0.000\n",
3013 "Volatility 3M -5.492e-05 1.04e-05 -5.306 0.000 -7.52e-05 -3.46e-05\n",
3014 "stock_3D SYSTEMS CORP 0.0568 0.010 5.697 0.000 0.037 0.076\n",
3015 "stock_3M COMPANY 0.0426 0.009 4.865 0.000 0.025 0.060\n",
3016 "stock_ABBOTT LABORATORIES 0.0264 0.007 3.854 0.000 0.013 0.040\n",
3017 "stock_ABBVIE INC 0.0572 0.013 4.482 0.000 0.032 0.082\n",
3018 "stock_ALLERGAN INC 0.0487 0.007 7.349 0.000 0.036 0.062\n",
3019 "stock_ALLERGAN PLC 0.0543 0.009 5.812 0.000 0.036 0.073\n",
3020 "stock_ALTABA INC 0.0605 0.010 5.857 0.000 0.040 0.081\n",
3021 "stock_ALTRIA GROUP INC. 0.0453 0.009 5.074 0.000 0.028 0.063\n",
3022 "stock_AMAZON.COM INC 0.0513 0.010 4.905 0.000 0.031 0.072\n",
3023 "stock_AMERICAN AIRLINES GROUP INC 0.0490 0.012 4.079 0.000 0.025 0.073\n",
3024 "stock_AMERICAN EXPRESS COMPANY 0.0232 0.006 4.177 0.000 0.012 0.034\n",
3025 "stock_AMERICAN INTL GROUP INC 0.0452 0.009 4.770 0.000 0.027 0.064\n",
3026 "stock_AMGEN INC 0.0266 0.006 4.400 0.000 0.015 0.038\n",
3027 "stock_ANADARKO PETROLEUM CORP 0.0347 0.005 6.387 0.000 0.024 0.045\n",
3028 "stock_APACHE CORP 0.0130 0.009 1.487 0.137 -0.004 0.030\n",
3029 "stock_APPLE INC 0.0471 0.009 5.478 0.000 0.030 0.064\n",
3030 "stock_APPLIED MATERIALS INC 0.0333 0.007 4.965 0.000 0.020 0.046\n",
3031 "stock_ARCONIC INC 0.0090 0.005 1.822 0.068 -0.001 0.019\n",
3032 "stock_AT&T INC. COM 0.0372 0.010 3.817 0.000 0.018 0.056\n",
3033 "stock_Alphabet Inc. Cl A 0.0713 0.012 5.733 0.000 0.047 0.096\n",
3034 "stock_BAKER HUGHES INC 0.0296 0.006 4.920 0.000 0.018 0.041\n",
3035 "stock_BANK OF AMERICA CORP 0.1209 0.037 3.306 0.001 0.049 0.193\n",
3036 "stock_BERKSHIRE HATHAWAY INC CL-B 0.0629 0.013 4.939 0.000 0.038 0.088\n",
3037 "stock_BIOGEN INC 0.0551 0.008 6.605 0.000 0.039 0.071\n",
3038 "stock_BOEING CO 0.0230 0.007 3.524 0.000 0.010 0.036\n",
3039 "stock_BOOKING HOLDINGS INC 0.0600 0.011 5.539 0.000 0.039 0.081\n",
3040 "stock_BRISTOL MYERS SQUIBB COMPANY 0.0405 0.007 5.456 0.000 0.026 0.055\n",
3041 "stock_BROADCOM CORP 0.0564 0.010 5.697 0.000 0.037 0.076\n",
3042 "stock_BROADCOM INC 0.0733 0.013 5.818 0.000 0.049 0.098\n",
3043 "stock_CATERPILLAR INC 0.0139 0.006 2.172 0.030 0.001 0.027\n",
3044 "stock_CELGENE CORP 0.0385 0.007 5.398 0.000 0.025 0.053\n",
3045 "stock_CHESAPEAKE ENERGY CORP 0.0269 0.011 2.555 0.011 0.006 0.048\n",
3046 "stock_CHEVRON CORPORATION 0.0562 0.013 4.283 0.000 0.030 0.082\n",
3047 "stock_CISCO SYSTEMS INC 0.0285 0.006 4.464 0.000 0.016 0.041\n",
3048 "stock_CITIGROUP 0.1090 0.031 3.474 0.001 0.048 0.171\n",
3049 "stock_COCA-COLA CO 0.0384 0.009 4.352 0.000 0.021 0.056\n",
3050 "stock_COMCAST CORP 0.0325 0.006 5.155 0.000 0.020 0.045\n",
3051 "stock_CONOCOPHILLIPS 0.0456 0.011 4.089 0.000 0.024 0.067\n",
3052 "stock_COVIDIEN PLC 0.0743 0.012 6.354 0.000 0.051 0.097\n",
3053 "stock_CVS HEALTH CORP 0.0358 0.008 4.345 0.000 0.020 0.052\n",
3054 "stock_DEERE & CO 0.0115 0.007 1.683 0.092 -0.002 0.025\n",
3055 "stock_DELTA AIR LINES INC 0.0545 0.011 4.896 0.000 0.033 0.076\n",
3056 "stock_DIRECTV 0.0383 0.011 3.558 0.000 0.017 0.059\n",
3057 "stock_DOLLAR GENERAL CORP 0.0508 0.011 4.509 0.000 0.029 0.073\n",
3058 "stock_DOW CHEMICAL CO 0.0234 0.006 3.686 0.000 0.011 0.036\n",
3059 "stock_E.I. Du Pont De Nemours A 0.0204 0.007 3.137 0.002 0.008 0.033\n",
3060 "stock_EBAY INC 0.0611 0.012 5.279 0.000 0.038 0.084\n",
3061 "stock_EMC CORPORATION 0.0341 0.007 5.245 0.000 0.021 0.047\n",
3062 "stock_EOG RESOURCES INC 0.0413 0.006 6.573 0.000 0.029 0.054\n",
3063 "stock_EXPRESS SCRIPTS HOLDING CO 0.0177 0.007 2.628 0.009 0.005 0.031\n",
3064 "stock_EXXON MOBIL CORPORATION 0.0677 0.015 4.484 0.000 0.038 0.097\n",
3065 "stock_FACEBOOK INC 0.0762 0.013 5.785 0.000 0.050 0.102\n",
3066 "stock_FEDEX CORPORATION 0.0321 0.007 4.657 0.000 0.019 0.046\n",
3067 "stock_FIRST SOLAR INC 0.0484 0.012 4.004 0.000 0.025 0.072\n",
3068 "stock_FORD MOTOR CO(NEW) 0.0213 0.008 2.647 0.008 0.006 0.037\n",
3069 "stock_FREEPORT-MCMORAN INC 0.0269 0.009 3.063 0.002 0.010 0.044\n",
3070 "stock_GENERAL ELECTRIC CO 0.0556 0.013 4.444 0.000 0.031 0.080\n",
3071 "stock_GENERAL MOTORS CO 0.0485 0.013 3.829 0.000 0.024 0.073\n",
3072 "stock_GILEAD SCIENCES INC 0.0459 0.008 5.868 0.000 0.031 0.061\n",
3073 "stock_GOLDMAN SACHS GROUP INC 0.0965 0.017 5.644 0.000 0.063 0.130\n",
3074 "stock_GOPRO INC 0.0687 0.013 5.377 0.000 0.044 0.094\n",
3075 "stock_HALLIBURTON CO (HOLDING CO) 0.0322 0.007 4.601 0.000 0.018 0.046\n",
3076 "stock_HOME DEPOT INC 0.0336 0.008 4.320 0.000 0.018 0.049\n",
3077 "stock_HP INC 0.0254 0.007 3.507 0.000 0.011 0.040\n",
3078 "stock_INTEL CORP 0.0383 0.008 4.891 0.000 0.023 0.054\n",
3079 "stock_INTL BUSINESS MACHINES CORP 0.0329 0.009 3.784 0.000 0.016 0.050\n",
3080 "stock_JOHNSON AND JOHNSON 0.0431 0.009 4.833 0.000 0.026 0.061\n",
3081 "stock_JPMORGAN CHASE & CO COM STK 0.1531 0.044 3.455 0.001 0.066 0.240\n",
3082 "stock_KEURIG GREEN MOUNTAIN INC 0.0569 0.009 6.135 0.000 0.039 0.075\n",
3083 "stock_KINDER MORGAN INC 0.0427 0.012 3.497 0.000 0.019 0.067\n",
3084 "stock_LAS VEGAS SANDS CORP 0.0494 0.012 4.235 0.000 0.027 0.072\n",
3085 "stock_LILLY ELI & CO 0.0411 0.008 5.097 0.000 0.025 0.057\n",
3086 "stock_LINKEDIN CORP 0.0700 0.013 5.460 0.000 0.045 0.095\n",
3087 "stock_LOWES COMPANIES INC 0.0307 0.007 4.474 0.000 0.017 0.044\n",
3088 "stock_LYONDELLBASELL INDUSTRIES NV 0.0478 0.012 4.043 0.000 0.025 0.071\n",
3089 "stock_MARATHON PETROLEUM CORP 0.0508 0.013 3.939 0.000 0.026 0.076\n",
3090 "stock_MASTERCARD INCORPORATED 0.0778 0.013 6.157 0.000 0.053 0.103\n",
3091 "stock_MCDONALDS CORP 0.0380 0.009 4.236 0.000 0.020 0.056\n",
3092 "stock_MEDTRONIC PLC 0.0482 0.008 6.017 0.000 0.033 0.064\n",
3093 "stock_MERCK & CO INC 0.0425 0.008 5.002 0.000 0.026 0.059\n",
3094 "stock_METLIFE INC 0.0738 0.018 4.162 0.000 0.039 0.109\n",
3095 "stock_MICHAEL KORS HOLDINGS LTD 0.0770 0.013 5.750 0.000 0.051 0.103\n",
3096 "stock_MICRON TECHNOLOGY INC 0.0363 0.007 5.112 0.000 0.022 0.050\n",
3097 "stock_MICROSOFT CORP 0.0468 0.009 5.410 0.000 0.030 0.064\n",
3098 "stock_MONDELEZ INTERNATIONAL INC 0.0519 0.011 4.934 0.000 0.031 0.073\n",
3099 "stock_MONSANTO COMPANY 0.0556 0.011 5.111 0.000 0.034 0.077\n",
3100 "stock_MORGAN STANLEY 0.0829 0.016 5.139 0.000 0.051 0.115\n",
3101 "stock_MYLAN NV 0.0430 0.008 5.515 0.000 0.028 0.058\n",
3102 "stock_NATIONAL OILWELL VARCO INC. 0.0397 0.011 3.525 0.000 0.018 0.062\n",
3103 "stock_NETFLIX INC 0.0690 0.011 6.016 0.000 0.047 0.092\n",
3104 "stock_NEWMONT MINING CORP (HOLDING COMPANY) 0.0312 0.008 3.782 0.000 0.015 0.047\n",
3105 "stock_NEWS CP - CL A 0.0462 0.009 5.253 0.000 0.029 0.063\n",
3106 "stock_NIKE INC CL-B 0.0466 0.008 5.636 0.000 0.030 0.063\n",
3107 "stock_OCCIDENTAL PETROLEUM CORP 0.0420 0.009 4.899 0.000 0.025 0.059\n",
3108 "stock_ORACLE CORP 0.0434 0.008 5.331 0.000 0.027 0.059\n",
3109 "stock_PANDORA MEDIA INC 0.0953 0.014 6.986 0.000 0.069 0.122\n",
3110 "stock_PENNEY J.C. CO INC (HOLDING COMPANY) 0.0163 0.007 2.353 0.019 0.003 0.030\n",
3111 "stock_PEPSICO INC 0.0387 0.009 4.139 0.000 0.020 0.057\n",
3112 "stock_PFIZER INC 0.0498 0.009 5.350 0.000 0.032 0.068\n",
3113 "stock_PHILIP MORRIS INTERNATIONAL INC 0.0577 0.014 4.236 0.000 0.031 0.084\n",
3114 "stock_PIONEER NAT RES CO 0.0716 0.010 7.136 0.000 0.052 0.091\n",
3115 "stock_PRECISION CASTPARTS CORP 0.0510 0.009 5.448 0.000 0.033 0.069\n",
3116 "stock_PROCTER & GAMBLE CO 0.0442 0.010 4.581 0.000 0.025 0.063\n",
3117 "stock_QUALCOMM INC 0.0532 0.009 5.739 0.000 0.035 0.071\n",
3118 "stock_REGENERON PHARMACEUTICALS INC 0.0537 0.011 4.675 0.000 0.031 0.076\n",
3119 "stock_SALESFORCE.COM INC 0.0610 0.011 5.303 0.000 0.038 0.083\n",
3120 "stock_SALIX PHARMACEUTICALS LTD -4.65e-16 2.62e-15 -0.178 0.859 -5.6e-15 4.67e-15\n",
3121 "stock_SANDISK CORP 0.0624 0.010 6.426 0.000 0.043 0.081\n",
3122 "stock_SCHLUMBERGER LTD. 0.0441 0.009 4.843 0.000 0.026 0.062\n",
3123 "stock_SKYWORKS SOLUTIONS INC 0.0772 0.011 6.749 0.000 0.055 0.100\n",
3124 "stock_SOLARCITY CORP 0.0820 0.013 6.129 0.000 0.056 0.108\n",
3125 "stock_SOUTHWEST AIRLINES CO 0.0430 0.007 5.909 0.000 0.029 0.057\n",
3126 "stock_STARBUCKS CORPORATION 0.0606 0.009 6.590 0.000 0.043 0.079\n",
3127 "stock_SUNEDISON INC 0.0234 0.017 1.401 0.161 -0.009 0.056\n",
3128 "stock_TARGET CORPORATION 0.0356 0.010 3.541 0.000 0.016 0.055\n",
3129 "stock_TESLA INC 0.0720 0.013 5.659 0.000 0.047 0.097\n",
3130 "stock_TEXAS INSTRUMENTS INC 0.0522 0.010 5.206 0.000 0.033 0.072\n",
3131 "stock_TIME WARNER CABLE INC 0.0468 0.011 4.218 0.000 0.025 0.069\n",
3132 "stock_TIME WARNER INC. 0.0227 0.005 4.748 0.000 0.013 0.032\n",
3133 "stock_TWITTER INC 0.0686 0.013 5.194 0.000 0.043 0.094\n",
3134 "stock_UNION PACIFIC CORPORATION 0.0493 0.009 5.446 0.000 0.032 0.067\n",
3135 "stock_UNITED CONTINENTAL HOLDINGS IN 0.0379 0.011 3.469 0.001 0.016 0.059\n",
3136 "stock_UNITED PARCEL SERVICE INC.CL B 0.0366 0.011 3.462 0.001 0.016 0.057\n",
3137 "stock_UNITED TECHNOLOGIES CORP 0.0390 0.009 4.277 0.000 0.021 0.057\n",
3138 "stock_UNITEDHEALTH GROUP INC 0.0455 0.009 4.866 0.000 0.027 0.064\n",
3139 "stock_VALERO ENERGY CORP (NEW) 0.0432 0.009 4.596 0.000 0.025 0.062\n",
3140 "stock_VERIZON COMMUNICATIONS 0.0392 0.011 3.502 0.000 0.017 0.061\n",
3141 "stock_VISA INC 0.0769 0.012 6.389 0.000 0.053 0.100\n",
3142 "stock_WALGREENS BOOTS ALLIANCE INC 0.0444 0.009 4.859 0.000 0.026 0.062\n",
3143 "stock_WALMART INC 0.0554 0.015 3.742 0.000 0.026 0.084\n",
3144 "stock_WALT DISNEY CO 0.0432 0.007 6.119 0.000 0.029 0.057\n",
3145 "stock_WELLS FARGO & CO(NEW) 0.1324 0.030 4.449 0.000 0.074 0.191\n",
3146 "stock_WILLIAMS COMPANIES 0.0472 0.009 5.143 0.000 0.029 0.065\n",
3147 "stock_WYNN RESORTS LTD 0.0410 0.011 3.710 0.000 0.019 0.063\n",
3148 "stock_YELP INC 0.0728 0.013 5.489 0.000 0.047 0.099\n",
3149 "==============================================================================\n",
3150 "Omnibus: 71.266 Durbin-Watson: 1.433\n",
3151 "Prob(Omnibus): 0.000 Jarque-Bera (JB): 77.090\n",
3152 "Skew: -0.067 Prob(JB): 1.82e-17\n",
3153 "Kurtosis: 3.154 Cond. No. 3.42e+20\n",
3154 "==============================================================================\n",
3155 "\n",
3156 "Warnings:\n",
3157 "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
3158 "[2] The smallest eigenvalue is 3.44e-14. This might indicate that there are\n",
3159 "strong multicollinearity problems or that the design matrix is singular.\n",
3160 "\"\"\""
3161 ]
3162 },
3163 "execution_count": 184,
3164 "metadata": {},
3165 "output_type": "execute_result"
3166 }
3167 ],
3168 "source": [
3169 "target = 'Returns5D'\n",
3170 "model_data = pd.concat([y[[target]], X], axis=1).dropna()\n",
3171 "model_data = model_data[model_data[target].between(model_data[target].quantile(.025), \n",
3172 " model_data[target].quantile(.975))]\n",
3173 "\n",
3174 "model = OLS(endog=model_data[target], exog=model_data.drop(target, axis=1))\n",
3175 "trained_model = model.fit()\n",
3176 "trained_model.summary()"
3177 ]
3178 },
3179 {
3180 "cell_type": "code",
3181 "execution_count": 185,
3182 "metadata": {
3183 "scrolled": false
3184 },
3185 "outputs": [
3186 {
3187 "data": {
3188 "text/html": [
3189 "<table class=\"simpletable\">\n",
3190 "<caption>OLS Regression Results</caption>\n",
3191 "<tr>\n",
3192 " <th>Dep. Variable:</th> <td>Returns10D</td> <th> R-squared: </th> <td> 0.035</td> \n",
3193 "</tr>\n",
3194 "<tr>\n",
3195 " <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.031</td> \n",
3196 "</tr>\n",
3197 "<tr>\n",
3198 " <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 9.045</td> \n",
3199 "</tr>\n",
3200 "<tr>\n",
3201 " <th>Date:</th> <td>Fri, 03 Aug 2018</td> <th> Prob (F-statistic):</th> <td>5.42e-219</td>\n",
3202 "</tr>\n",
3203 "<tr>\n",
3204 " <th>Time:</th> <td>21:41:36</td> <th> Log-Likelihood: </th> <td> 78861.</td> \n",
3205 "</tr>\n",
3206 "<tr>\n",
3207 " <th>No. Observations:</th> <td> 43734</td> <th> AIC: </th> <td>-1.574e+05</td>\n",
3208 "</tr>\n",
3209 "<tr>\n",
3210 " <th>Df Residuals:</th> <td> 43559</td> <th> BIC: </th> <td>-1.559e+05</td>\n",
3211 "</tr>\n",
3212 "<tr>\n",
3213 " <th>Df Model:</th> <td> 174</td> <th> </th> <td> </td> \n",
3214 "</tr>\n",
3215 "<tr>\n",
3216 " <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
3217 "</tr>\n",
3218 "</table>\n",
3219 "<table class=\"simpletable\">\n",
3220 "<tr>\n",
3221 " <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
3222 "</tr>\n",
3223 "<tr>\n",
3224 " <th>DividendYield</th> <td> 6.757e-06</td> <td> 1.55e-06</td> <td> 4.370</td> <td> 0.000</td> <td> 3.73e-06 9.79e-06</td>\n",
3225 "</tr>\n",
3226 "<tr>\n",
3227 " <th>EBITDAYield</th> <td>-1.021e-05</td> <td> 3.11e-05</td> <td> -0.329</td> <td> 0.743</td> <td>-7.11e-05 5.07e-05</td>\n",
3228 "</tr>\n",
3229 "<tr>\n",
3230 " <th>EVToEBITDA</th> <td> 9.163e-05</td> <td> 2.83e-05</td> <td> 3.233</td> <td> 0.001</td> <td> 3.61e-05 0.000</td>\n",
3231 "</tr>\n",
3232 "<tr>\n",
3233 " <th>EVToFCF</th> <td> 2.372e-05</td> <td> 3.51e-05</td> <td> 0.676</td> <td> 0.499</td> <td> -4.5e-05 9.25e-05</td>\n",
3234 "</tr>\n",
3235 "<tr>\n",
3236 " <th>PriceToBook</th> <td> 8.448e-05</td> <td> 3.34e-05</td> <td> 2.533</td> <td> 0.011</td> <td> 1.91e-05 0.000</td>\n",
3237 "</tr>\n",
3238 "<tr>\n",
3239 " <th>PriceToDilutedEarningsTTM</th> <td>-1.332e-05</td> <td> 1.5e-05</td> <td> -0.889</td> <td> 0.374</td> <td>-4.27e-05 1.61e-05</td>\n",
3240 "</tr>\n",
3241 "<tr>\n",
3242 " <th>PriceToEarningsTTM</th> <td>-6.983e-07</td> <td> 6.67e-07</td> <td> -1.047</td> <td> 0.295</td> <td> -2e-06 6.08e-07</td>\n",
3243 "</tr>\n",
3244 "<tr>\n",
3245 " <th>PriceToFCF</th> <td>-4.525e-06</td> <td> 2.91e-05</td> <td> -0.155</td> <td> 0.877</td> <td>-6.16e-05 5.26e-05</td>\n",
3246 "</tr>\n",
3247 "<tr>\n",
3248 " <th>PriceToOperatingCashflow</th> <td>-8.243e-05</td> <td> 2.02e-05</td> <td> -4.071</td> <td> 0.000</td> <td> -0.000 -4.27e-05</td>\n",
3249 "</tr>\n",
3250 "<tr>\n",
3251 " <th>PriceToSalesTTM</th> <td>-2.324e-05</td> <td> 1.72e-06</td> <td> -13.493</td> <td> 0.000</td> <td>-2.66e-05 -1.99e-05</td>\n",
3252 "</tr>\n",
3253 "<tr>\n",
3254 " <th>Directional Movement Index</th> <td>-2.235e-05</td> <td> 6.75e-06</td> <td> -3.308</td> <td> 0.001</td> <td>-3.56e-05 -9.11e-06</td>\n",
3255 "</tr>\n",
3256 "<tr>\n",
3257 " <th>Money Flow Index</th> <td> 4.299e-06</td> <td> 8.77e-06</td> <td> 0.490</td> <td> 0.624</td> <td>-1.29e-05 2.15e-05</td>\n",
3258 "</tr>\n",
3259 "<tr>\n",
3260 " <th>Percent Above Low</th> <td>-4.475e-05</td> <td> 1.71e-05</td> <td> -2.623</td> <td> 0.009</td> <td>-7.82e-05 -1.13e-05</td>\n",
3261 "</tr>\n",
3262 "<tr>\n",
3263 " <th>Percent Below High</th> <td> 1.295e-05</td> <td> 1.34e-05</td> <td> 0.965</td> <td> 0.335</td> <td>-1.34e-05 3.93e-05</td>\n",
3264 "</tr>\n",
3265 "<tr>\n",
3266 " <th>Price Oscillator</th> <td> 6.06e-06</td> <td> 9.48e-06</td> <td> 0.640</td> <td> 0.522</td> <td>-1.25e-05 2.46e-05</td>\n",
3267 "</tr>\n",
3268 "<tr>\n",
3269 " <th>Trendline</th> <td> 2.48e-05</td> <td> 1.44e-05</td> <td> 1.722</td> <td> 0.085</td> <td>-3.42e-06 5.3e-05</td>\n",
3270 "</tr>\n",
3271 "<tr>\n",
3272 " <th>AssetToEquityRatio</th> <td>-2.548e-06</td> <td> 1.3e-06</td> <td> -1.964</td> <td> 0.049</td> <td>-5.09e-06 -5.59e-09</td>\n",
3273 "</tr>\n",
3274 "<tr>\n",
3275 " <th>AssetTurnover</th> <td> -0.0001</td> <td> 5.63e-05</td> <td> -2.054</td> <td> 0.040</td> <td> -0.000 -5.31e-06</td>\n",
3276 "</tr>\n",
3277 "<tr>\n",
3278 " <th>CurrentRatio</th> <td> 3.392e-06</td> <td> 1.69e-06</td> <td> 2.007</td> <td> 0.045</td> <td> 7.96e-08 6.7e-06</td>\n",
3279 "</tr>\n",
3280 "<tr>\n",
3281 " <th>DebtToAssetRatio</th> <td> 2.605e-06</td> <td> 1.04e-06</td> <td> 2.500</td> <td> 0.012</td> <td> 5.63e-07 4.65e-06</td>\n",
3282 "</tr>\n",
3283 "<tr>\n",
3284 " <th>DebtToEquityRatio</th> <td>-2.576e-08</td> <td> 1.21e-06</td> <td> -0.021</td> <td> 0.983</td> <td> -2.4e-06 2.35e-06</td>\n",
3285 "</tr>\n",
3286 "<tr>\n",
3287 " <th>MertonsDD</th> <td> -0.0010</td> <td> 0.000</td> <td> -6.752</td> <td> 0.000</td> <td> -0.001 -0.001</td>\n",
3288 "</tr>\n",
3289 "<tr>\n",
3290 " <th>WorkingCapitalToAssets</th> <td> 3.97e-06</td> <td> 2.04e-06</td> <td> 1.943</td> <td> 0.052</td> <td>-3.38e-08 7.97e-06</td>\n",
3291 "</tr>\n",
3292 "<tr>\n",
3293 " <th>WorkingCapitalToSales</th> <td> -0.0002</td> <td> 6.58e-05</td> <td> -2.654</td> <td> 0.008</td> <td> -0.000 -4.56e-05</td>\n",
3294 "</tr>\n",
3295 "<tr>\n",
3296 " <th>Dividend Growth</th> <td>-9.485e-07</td> <td> 6.45e-07</td> <td> -1.471</td> <td> 0.141</td> <td>-2.21e-06 3.16e-07</td>\n",
3297 "</tr>\n",
3298 "<tr>\n",
3299 " <th>EPS</th> <td> 5.254e-07</td> <td> 1e-06</td> <td> 0.525</td> <td> 0.599</td> <td>-1.43e-06 2.48e-06</td>\n",
3300 "</tr>\n",
3301 "<tr>\n",
3302 " <th>Net Debt</th> <td> -1.53e-13</td> <td> 3.65e-14</td> <td> -4.193</td> <td> 0.000</td> <td>-2.25e-13 -8.15e-14</td>\n",
3303 "</tr>\n",
3304 "<tr>\n",
3305 " <th>Sales</th> <td>-1.061e-13</td> <td> 3.43e-14</td> <td> -3.094</td> <td> 0.002</td> <td>-1.73e-13 -3.89e-14</td>\n",
3306 "</tr>\n",
3307 "<tr>\n",
3308 " <th>Total Assets</th> <td>-7.923e-14</td> <td> 2.58e-14</td> <td> -3.068</td> <td> 0.002</td> <td> -1.3e-13 -2.86e-14</td>\n",
3309 "</tr>\n",
3310 "<tr>\n",
3311 " <th>EPS Growth 3M</th> <td>-6.126e-06</td> <td> 1.53e-06</td> <td> -4.002</td> <td> 0.000</td> <td>-9.13e-06 -3.13e-06</td>\n",
3312 "</tr>\n",
3313 "<tr>\n",
3314 " <th>EPS Growth 12M</th> <td> 2.182e-06</td> <td> 1.53e-06</td> <td> 1.423</td> <td> 0.155</td> <td>-8.24e-07 5.19e-06</td>\n",
3315 "</tr>\n",
3316 "<tr>\n",
3317 " <th>Net Debt Growth 3M</th> <td>-1.748e-06</td> <td> 1.95e-06</td> <td> -0.897</td> <td> 0.370</td> <td>-5.57e-06 2.07e-06</td>\n",
3318 "</tr>\n",
3319 "<tr>\n",
3320 " <th>Net Debt Growth 12M</th> <td> 5.675e-06</td> <td> 1.98e-06</td> <td> 2.872</td> <td> 0.004</td> <td> 1.8e-06 9.55e-06</td>\n",
3321 "</tr>\n",
3322 "<tr>\n",
3323 " <th>Sales Growth 3M</th> <td> 4.22e-06</td> <td> 2.06e-06</td> <td> 2.051</td> <td> 0.040</td> <td> 1.87e-07 8.25e-06</td>\n",
3324 "</tr>\n",
3325 "<tr>\n",
3326 " <th>Sales Growth 12M</th> <td> -6.66e-06</td> <td> 2.1e-06</td> <td> -3.169</td> <td> 0.002</td> <td>-1.08e-05 -2.54e-06</td>\n",
3327 "</tr>\n",
3328 "<tr>\n",
3329 " <th>Total Assets Growth 3M</th> <td>-2.435e-07</td> <td> 2.31e-06</td> <td> -0.105</td> <td> 0.916</td> <td>-4.77e-06 4.28e-06</td>\n",
3330 "</tr>\n",
3331 "<tr>\n",
3332 " <th>Total Assets Growth 12M</th> <td>-2.601e-06</td> <td> 2.46e-06</td> <td> -1.059</td> <td> 0.290</td> <td>-7.42e-06 2.21e-06</td>\n",
3333 "</tr>\n",
3334 "<tr>\n",
3335 " <th>CFO To Assets</th> <td> 4.355e-05</td> <td> 2.81e-05</td> <td> 1.551</td> <td> 0.121</td> <td>-1.15e-05 9.86e-05</td>\n",
3336 "</tr>\n",
3337 "<tr>\n",
3338 " <th>Capex To Assets</th> <td> -0.0001</td> <td> 6.1e-05</td> <td> -2.246</td> <td> 0.025</td> <td> -0.000 -1.74e-05</td>\n",
3339 "</tr>\n",
3340 "<tr>\n",
3341 " <th>Capex To FCF</th> <td> 6.95e-05</td> <td> 3.42e-05</td> <td> 2.030</td> <td> 0.042</td> <td> 2.4e-06 0.000</td>\n",
3342 "</tr>\n",
3343 "<tr>\n",
3344 " <th>Capex To Sales</th> <td> 0.0002</td> <td> 6.51e-05</td> <td> 3.016</td> <td> 0.003</td> <td> 6.87e-05 0.000</td>\n",
3345 "</tr>\n",
3346 "<tr>\n",
3347 " <th>EBIT To Assets</th> <td> 1.444e-05</td> <td> 2.83e-05</td> <td> 0.511</td> <td> 0.610</td> <td> -4.1e-05 6.99e-05</td>\n",
3348 "</tr>\n",
3349 "<tr>\n",
3350 " <th>Retained Earnings To Assets</th> <td> -0.0004</td> <td> 6.01e-05</td> <td> -6.676</td> <td> 0.000</td> <td> -0.001 -0.000</td>\n",
3351 "</tr>\n",
3352 "<tr>\n",
3353 " <th>Downside Risk</th> <td> 3.345e-05</td> <td> 1.73e-05</td> <td> 1.932</td> <td> 0.053</td> <td>-4.83e-07 6.74e-05</td>\n",
3354 "</tr>\n",
3355 "<tr>\n",
3356 " <th>Index Beta</th> <td> -2.09e-06</td> <td> 3.55e-07</td> <td> -5.886</td> <td> 0.000</td> <td>-2.79e-06 -1.39e-06</td>\n",
3357 "</tr>\n",
3358 "<tr>\n",
3359 " <th>Log Market Cap</th> <td> 0.0002</td> <td> 6.04e-05</td> <td> 3.147</td> <td> 0.002</td> <td> 7.17e-05 0.000</td>\n",
3360 "</tr>\n",
3361 "<tr>\n",
3362 " <th>Volatility 3M</th> <td>-8.754e-05</td> <td> 1.47e-05</td> <td> -5.939</td> <td> 0.000</td> <td> -0.000 -5.86e-05</td>\n",
3363 "</tr>\n",
3364 "<tr>\n",
3365 " <th>stock_3D SYSTEMS CORP</th> <td> 0.1609</td> <td> 0.014</td> <td> 11.264</td> <td> 0.000</td> <td> 0.133 0.189</td>\n",
3366 "</tr>\n",
3367 "<tr>\n",
3368 " <th>stock_3M COMPANY</th> <td> 0.1423</td> <td> 0.012</td> <td> 11.416</td> <td> 0.000</td> <td> 0.118 0.167</td>\n",
3369 "</tr>\n",
3370 "<tr>\n",
3371 " <th>stock_ABBOTT LABORATORIES</th> <td> 0.0903</td> <td> 0.010</td> <td> 9.116</td> <td> 0.000</td> <td> 0.071 0.110</td>\n",
3372 "</tr>\n",
3373 "<tr>\n",
3374 " <th>stock_ABBVIE INC</th> <td> 0.1833</td> <td> 0.018</td> <td> 10.070</td> <td> 0.000</td> <td> 0.148 0.219</td>\n",
3375 "</tr>\n",
3376 "<tr>\n",
3377 " <th>stock_ALLERGAN INC</th> <td> 0.1434</td> <td> 0.009</td> <td> 15.113</td> <td> 0.000</td> <td> 0.125 0.162</td>\n",
3378 "</tr>\n",
3379 "<tr>\n",
3380 " <th>stock_ALLERGAN PLC</th> <td> 0.1683</td> <td> 0.013</td> <td> 12.648</td> <td> 0.000</td> <td> 0.142 0.194</td>\n",
3381 "</tr>\n",
3382 "<tr>\n",
3383 " <th>stock_ALTABA INC</th> <td> 0.1825</td> <td> 0.015</td> <td> 12.388</td> <td> 0.000</td> <td> 0.154 0.211</td>\n",
3384 "</tr>\n",
3385 "<tr>\n",
3386 " <th>stock_ALTRIA GROUP INC.</th> <td> 0.1533</td> <td> 0.013</td> <td> 12.015</td> <td> 0.000</td> <td> 0.128 0.178</td>\n",
3387 "</tr>\n",
3388 "<tr>\n",
3389 " <th>stock_AMAZON.COM INC</th> <td> 0.1494</td> <td> 0.015</td> <td> 10.035</td> <td> 0.000</td> <td> 0.120 0.179</td>\n",
3390 "</tr>\n",
3391 "<tr>\n",
3392 " <th>stock_AMERICAN AIRLINES GROUP INC</th> <td> 0.1433</td> <td> 0.017</td> <td> 8.373</td> <td> 0.000</td> <td> 0.110 0.177</td>\n",
3393 "</tr>\n",
3394 "<tr>\n",
3395 " <th>stock_AMERICAN EXPRESS COMPANY</th> <td> 0.0785</td> <td> 0.008</td> <td> 9.844</td> <td> 0.000</td> <td> 0.063 0.094</td>\n",
3396 "</tr>\n",
3397 "<tr>\n",
3398 " <th>stock_AMERICAN INTL GROUP INC</th> <td> 0.1021</td> <td> 0.014</td> <td> 7.505</td> <td> 0.000</td> <td> 0.075 0.129</td>\n",
3399 "</tr>\n",
3400 "<tr>\n",
3401 " <th>stock_AMGEN INC</th> <td> 0.0915</td> <td> 0.009</td> <td> 10.550</td> <td> 0.000</td> <td> 0.074 0.108</td>\n",
3402 "</tr>\n",
3403 "<tr>\n",
3404 " <th>stock_ANADARKO PETROLEUM CORP</th> <td> 0.0978</td> <td> 0.008</td> <td> 12.627</td> <td> 0.000</td> <td> 0.083 0.113</td>\n",
3405 "</tr>\n",
3406 "<tr>\n",
3407 " <th>stock_APACHE CORP</th> <td> 0.0568</td> <td> 0.014</td> <td> 4.038</td> <td> 0.000</td> <td> 0.029 0.084</td>\n",
3408 "</tr>\n",
3409 "<tr>\n",
3410 " <th>stock_APPLE INC</th> <td> 0.1309</td> <td> 0.012</td> <td> 10.689</td> <td> 0.000</td> <td> 0.107 0.155</td>\n",
3411 "</tr>\n",
3412 "<tr>\n",
3413 " <th>stock_APPLIED MATERIALS INC</th> <td> 0.1080</td> <td> 0.010</td> <td> 11.289</td> <td> 0.000</td> <td> 0.089 0.127</td>\n",
3414 "</tr>\n",
3415 "<tr>\n",
3416 " <th>stock_ARCONIC INC</th> <td> 0.0422</td> <td> 0.007</td> <td> 5.984</td> <td> 0.000</td> <td> 0.028 0.056</td>\n",
3417 "</tr>\n",
3418 "<tr>\n",
3419 " <th>stock_AT&T INC. COM</th> <td> 0.1122</td> <td> 0.014</td> <td> 8.068</td> <td> 0.000</td> <td> 0.085 0.139</td>\n",
3420 "</tr>\n",
3421 "<tr>\n",
3422 " <th>stock_Alphabet Inc. Cl A</th> <td> 0.2024</td> <td> 0.018</td> <td> 11.450</td> <td> 0.000</td> <td> 0.168 0.237</td>\n",
3423 "</tr>\n",
3424 "<tr>\n",
3425 " <th>stock_BAKER HUGHES INC</th> <td> 0.0945</td> <td> 0.009</td> <td> 11.076</td> <td> 0.000</td> <td> 0.078 0.111</td>\n",
3426 "</tr>\n",
3427 "<tr>\n",
3428 " <th>stock_BANK OF AMERICA CORP</th> <td> 0.2669</td> <td> 0.052</td> <td> 5.090</td> <td> 0.000</td> <td> 0.164 0.370</td>\n",
3429 "</tr>\n",
3430 "<tr>\n",
3431 " <th>stock_BERKSHIRE HATHAWAY INC CL-B</th> <td> 0.1630</td> <td> 0.018</td> <td> 8.968</td> <td> 0.000</td> <td> 0.127 0.199</td>\n",
3432 "</tr>\n",
3433 "<tr>\n",
3434 " <th>stock_BIOGEN INC</th> <td> 0.1688</td> <td> 0.012</td> <td> 14.185</td> <td> 0.000</td> <td> 0.145 0.192</td>\n",
3435 "</tr>\n",
3436 "<tr>\n",
3437 " <th>stock_BOEING CO</th> <td> 0.0769</td> <td> 0.009</td> <td> 8.299</td> <td> 0.000</td> <td> 0.059 0.095</td>\n",
3438 "</tr>\n",
3439 "<tr>\n",
3440 " <th>stock_BOOKING HOLDINGS INC</th> <td> 0.1898</td> <td> 0.015</td> <td> 12.281</td> <td> 0.000</td> <td> 0.159 0.220</td>\n",
3441 "</tr>\n",
3442 "<tr>\n",
3443 " <th>stock_BRISTOL MYERS SQUIBB COMPANY</th> <td> 0.1327</td> <td> 0.011</td> <td> 12.504</td> <td> 0.000</td> <td> 0.112 0.153</td>\n",
3444 "</tr>\n",
3445 "<tr>\n",
3446 " <th>stock_BROADCOM CORP</th> <td> 0.1655</td> <td> 0.014</td> <td> 11.708</td> <td> 0.000</td> <td> 0.138 0.193</td>\n",
3447 "</tr>\n",
3448 "<tr>\n",
3449 " <th>stock_BROADCOM INC</th> <td> 0.2090</td> <td> 0.018</td> <td> 11.611</td> <td> 0.000</td> <td> 0.174 0.244</td>\n",
3450 "</tr>\n",
3451 "<tr>\n",
3452 " <th>stock_CATERPILLAR INC</th> <td> 0.0647</td> <td> 0.009</td> <td> 7.093</td> <td> 0.000</td> <td> 0.047 0.083</td>\n",
3453 "</tr>\n",
3454 "<tr>\n",
3455 " <th>stock_CELGENE CORP</th> <td> 0.1326</td> <td> 0.010</td> <td> 12.974</td> <td> 0.000</td> <td> 0.113 0.153</td>\n",
3456 "</tr>\n",
3457 "<tr>\n",
3458 " <th>stock_CHESAPEAKE ENERGY CORP</th> <td> 0.0682</td> <td> 0.016</td> <td> 4.174</td> <td> 0.000</td> <td> 0.036 0.100</td>\n",
3459 "</tr>\n",
3460 "<tr>\n",
3461 " <th>stock_CHEVRON CORPORATION</th> <td> 0.1637</td> <td> 0.019</td> <td> 8.770</td> <td> 0.000</td> <td> 0.127 0.200</td>\n",
3462 "</tr>\n",
3463 "<tr>\n",
3464 " <th>stock_CISCO SYSTEMS INC</th> <td> 0.0975</td> <td> 0.009</td> <td> 10.689</td> <td> 0.000</td> <td> 0.080 0.115</td>\n",
3465 "</tr>\n",
3466 "<tr>\n",
3467 " <th>stock_CITIGROUP</th> <td> 0.2358</td> <td> 0.045</td> <td> 5.236</td> <td> 0.000</td> <td> 0.148 0.324</td>\n",
3468 "</tr>\n",
3469 "<tr>\n",
3470 " <th>stock_COCA-COLA CO</th> <td> 0.1313</td> <td> 0.013</td> <td> 10.439</td> <td> 0.000</td> <td> 0.107 0.156</td>\n",
3471 "</tr>\n",
3472 "<tr>\n",
3473 " <th>stock_COMCAST CORP</th> <td> 0.0961</td> <td> 0.009</td> <td> 10.691</td> <td> 0.000</td> <td> 0.078 0.114</td>\n",
3474 "</tr>\n",
3475 "<tr>\n",
3476 " <th>stock_CONOCOPHILLIPS</th> <td> 0.1432</td> <td> 0.016</td> <td> 9.039</td> <td> 0.000</td> <td> 0.112 0.174</td>\n",
3477 "</tr>\n",
3478 "<tr>\n",
3479 " <th>stock_COVIDIEN PLC</th> <td> 0.2103</td> <td> 0.017</td> <td> 12.561</td> <td> 0.000</td> <td> 0.177 0.243</td>\n",
3480 "</tr>\n",
3481 "<tr>\n",
3482 " <th>stock_CVS HEALTH CORP</th> <td> 0.1147</td> <td> 0.012</td> <td> 9.823</td> <td> 0.000</td> <td> 0.092 0.138</td>\n",
3483 "</tr>\n",
3484 "<tr>\n",
3485 " <th>stock_DEERE & CO</th> <td> 0.0556</td> <td> 0.010</td> <td> 5.698</td> <td> 0.000</td> <td> 0.036 0.075</td>\n",
3486 "</tr>\n",
3487 "<tr>\n",
3488 " <th>stock_DELTA AIR LINES INC</th> <td> 0.1560</td> <td> 0.016</td> <td> 9.833</td> <td> 0.000</td> <td> 0.125 0.187</td>\n",
3489 "</tr>\n",
3490 "<tr>\n",
3491 " <th>stock_DIRECTV</th> <td> 0.1262</td> <td> 0.015</td> <td> 8.223</td> <td> 0.000</td> <td> 0.096 0.156</td>\n",
3492 "</tr>\n",
3493 "<tr>\n",
3494 " <th>stock_DOLLAR GENERAL CORP</th> <td> 0.1543</td> <td> 0.016</td> <td> 9.600</td> <td> 0.000</td> <td> 0.123 0.186</td>\n",
3495 "</tr>\n",
3496 "<tr>\n",
3497 " <th>stock_DOW CHEMICAL CO</th> <td> 0.0810</td> <td> 0.009</td> <td> 8.981</td> <td> 0.000</td> <td> 0.063 0.099</td>\n",
3498 "</tr>\n",
3499 "<tr>\n",
3500 " <th>stock_E.I. Du Pont De Nemours A</th> <td> 0.0722</td> <td> 0.009</td> <td> 7.789</td> <td> 0.000</td> <td> 0.054 0.090</td>\n",
3501 "</tr>\n",
3502 "<tr>\n",
3503 " <th>stock_EBAY INC</th> <td> 0.1879</td> <td> 0.016</td> <td> 11.397</td> <td> 0.000</td> <td> 0.156 0.220</td>\n",
3504 "</tr>\n",
3505 "<tr>\n",
3506 " <th>stock_EMC CORPORATION</th> <td> 0.1141</td> <td> 0.009</td> <td> 12.320</td> <td> 0.000</td> <td> 0.096 0.132</td>\n",
3507 "</tr>\n",
3508 "<tr>\n",
3509 " <th>stock_EOG RESOURCES INC</th> <td> 0.1212</td> <td> 0.009</td> <td> 13.575</td> <td> 0.000</td> <td> 0.104 0.139</td>\n",
3510 "</tr>\n",
3511 "<tr>\n",
3512 " <th>stock_EXPRESS SCRIPTS HOLDING CO</th> <td> 0.0745</td> <td> 0.010</td> <td> 7.775</td> <td> 0.000</td> <td> 0.056 0.093</td>\n",
3513 "</tr>\n",
3514 "<tr>\n",
3515 " <th>stock_EXXON MOBIL CORPORATION</th> <td> 0.1865</td> <td> 0.021</td> <td> 8.679</td> <td> 0.000</td> <td> 0.144 0.229</td>\n",
3516 "</tr>\n",
3517 "<tr>\n",
3518 " <th>stock_FACEBOOK INC</th> <td> 0.2171</td> <td> 0.019</td> <td> 11.586</td> <td> 0.000</td> <td> 0.180 0.254</td>\n",
3519 "</tr>\n",
3520 "<tr>\n",
3521 " <th>stock_FEDEX CORPORATION</th> <td> 0.1002</td> <td> 0.010</td> <td> 10.231</td> <td> 0.000</td> <td> 0.081 0.119</td>\n",
3522 "</tr>\n",
3523 "<tr>\n",
3524 " <th>stock_FIRST SOLAR INC</th> <td> 0.1560</td> <td> 0.017</td> <td> 9.046</td> <td> 0.000</td> <td> 0.122 0.190</td>\n",
3525 "</tr>\n",
3526 "<tr>\n",
3527 " <th>stock_FORD MOTOR CO(NEW)</th> <td> 0.0733</td> <td> 0.011</td> <td> 6.384</td> <td> 0.000</td> <td> 0.051 0.096</td>\n",
3528 "</tr>\n",
3529 "<tr>\n",
3530 " <th>stock_FREEPORT-MCMORAN INC</th> <td> 0.0974</td> <td> 0.013</td> <td> 7.766</td> <td> 0.000</td> <td> 0.073 0.122</td>\n",
3531 "</tr>\n",
3532 "<tr>\n",
3533 " <th>stock_GENERAL ELECTRIC CO</th> <td> 0.1500</td> <td> 0.018</td> <td> 8.332</td> <td> 0.000</td> <td> 0.115 0.185</td>\n",
3534 "</tr>\n",
3535 "<tr>\n",
3536 " <th>stock_GENERAL MOTORS CO</th> <td> 0.1448</td> <td> 0.018</td> <td> 8.032</td> <td> 0.000</td> <td> 0.109 0.180</td>\n",
3537 "</tr>\n",
3538 "<tr>\n",
3539 " <th>stock_GILEAD SCIENCES INC</th> <td> 0.1445</td> <td> 0.011</td> <td> 12.934</td> <td> 0.000</td> <td> 0.123 0.166</td>\n",
3540 "</tr>\n",
3541 "<tr>\n",
3542 " <th>stock_GOLDMAN SACHS GROUP INC</th> <td> 0.2410</td> <td> 0.025</td> <td> 9.822</td> <td> 0.000</td> <td> 0.193 0.289</td>\n",
3543 "</tr>\n",
3544 "<tr>\n",
3545 " <th>stock_GOPRO INC</th> <td> 0.1946</td> <td> 0.018</td> <td> 10.636</td> <td> 0.000</td> <td> 0.159 0.230</td>\n",
3546 "</tr>\n",
3547 "<tr>\n",
3548 " <th>stock_HALLIBURTON CO (HOLDING CO)</th> <td> 0.1050</td> <td> 0.010</td> <td> 10.582</td> <td> 0.000</td> <td> 0.086 0.124</td>\n",
3549 "</tr>\n",
3550 "<tr>\n",
3551 " <th>stock_HOME DEPOT INC</th> <td> 0.1160</td> <td> 0.011</td> <td> 10.522</td> <td> 0.000</td> <td> 0.094 0.138</td>\n",
3552 "</tr>\n",
3553 "<tr>\n",
3554 " <th>stock_HP INC</th> <td> 0.0879</td> <td> 0.010</td> <td> 8.554</td> <td> 0.000</td> <td> 0.068 0.108</td>\n",
3555 "</tr>\n",
3556 "<tr>\n",
3557 " <th>stock_INTEL CORP</th> <td> 0.1170</td> <td> 0.011</td> <td> 10.511</td> <td> 0.000</td> <td> 0.095 0.139</td>\n",
3558 "</tr>\n",
3559 "<tr>\n",
3560 " <th>stock_INTL BUSINESS MACHINES CORP</th> <td> 0.1135</td> <td> 0.012</td> <td> 9.176</td> <td> 0.000</td> <td> 0.089 0.138</td>\n",
3561 "</tr>\n",
3562 "<tr>\n",
3563 " <th>stock_JOHNSON AND JOHNSON</th> <td> 0.1375</td> <td> 0.013</td> <td> 10.821</td> <td> 0.000</td> <td> 0.113 0.162</td>\n",
3564 "</tr>\n",
3565 "<tr>\n",
3566 " <th>stock_JPMORGAN CHASE & CO COM STK</th> <td> 0.3443</td> <td> 0.064</td> <td> 5.416</td> <td> 0.000</td> <td> 0.220 0.469</td>\n",
3567 "</tr>\n",
3568 "<tr>\n",
3569 " <th>stock_KEURIG GREEN MOUNTAIN INC</th> <td> 0.1655</td> <td> 0.013</td> <td> 12.506</td> <td> 0.000</td> <td> 0.140 0.191</td>\n",
3570 "</tr>\n",
3571 "<tr>\n",
3572 " <th>stock_KINDER MORGAN INC</th> <td> 0.1372</td> <td> 0.017</td> <td> 7.888</td> <td> 0.000</td> <td> 0.103 0.171</td>\n",
3573 "</tr>\n",
3574 "<tr>\n",
3575 " <th>stock_LAS VEGAS SANDS CORP</th> <td> 0.1657</td> <td> 0.017</td> <td> 9.963</td> <td> 0.000</td> <td> 0.133 0.198</td>\n",
3576 "</tr>\n",
3577 "<tr>\n",
3578 " <th>stock_LILLY ELI & CO</th> <td> 0.1296</td> <td> 0.011</td> <td> 11.275</td> <td> 0.000</td> <td> 0.107 0.152</td>\n",
3579 "</tr>\n",
3580 "<tr>\n",
3581 " <th>stock_LINKEDIN CORP</th> <td> 0.1981</td> <td> 0.018</td> <td> 10.815</td> <td> 0.000</td> <td> 0.162 0.234</td>\n",
3582 "</tr>\n",
3583 "<tr>\n",
3584 " <th>stock_LOWES COMPANIES INC</th> <td> 0.1054</td> <td> 0.010</td> <td> 10.827</td> <td> 0.000</td> <td> 0.086 0.125</td>\n",
3585 "</tr>\n",
3586 "<tr>\n",
3587 " <th>stock_LYONDELLBASELL INDUSTRIES NV</th> <td> 0.1509</td> <td> 0.017</td> <td> 8.959</td> <td> 0.000</td> <td> 0.118 0.184</td>\n",
3588 "</tr>\n",
3589 "<tr>\n",
3590 " <th>stock_MARATHON PETROLEUM CORP</th> <td> 0.1477</td> <td> 0.018</td> <td> 8.006</td> <td> 0.000</td> <td> 0.112 0.184</td>\n",
3591 "</tr>\n",
3592 "<tr>\n",
3593 " <th>stock_MASTERCARD INCORPORATED</th> <td> 0.2324</td> <td> 0.018</td> <td> 12.916</td> <td> 0.000</td> <td> 0.197 0.268</td>\n",
3594 "</tr>\n",
3595 "<tr>\n",
3596 " <th>stock_MCDONALDS CORP</th> <td> 0.1322</td> <td> 0.013</td> <td> 10.363</td> <td> 0.000</td> <td> 0.107 0.157</td>\n",
3597 "</tr>\n",
3598 "<tr>\n",
3599 " <th>stock_MEDTRONIC PLC</th> <td> 0.1496</td> <td> 0.011</td> <td> 13.103</td> <td> 0.000</td> <td> 0.127 0.172</td>\n",
3600 "</tr>\n",
3601 "<tr>\n",
3602 " <th>stock_MERCK & CO INC</th> <td> 0.1372</td> <td> 0.012</td> <td> 11.332</td> <td> 0.000</td> <td> 0.113 0.161</td>\n",
3603 "</tr>\n",
3604 "<tr>\n",
3605 " <th>stock_METLIFE INC</th> <td> 0.1720</td> <td> 0.025</td> <td> 6.765</td> <td> 0.000</td> <td> 0.122 0.222</td>\n",
3606 "</tr>\n",
3607 "<tr>\n",
3608 " <th>stock_MICHAEL KORS HOLDINGS LTD</th> <td> 0.2136</td> <td> 0.019</td> <td> 11.178</td> <td> 0.000</td> <td> 0.176 0.251</td>\n",
3609 "</tr>\n",
3610 "<tr>\n",
3611 " <th>stock_MICRON TECHNOLOGY INC</th> <td> 0.1122</td> <td> 0.010</td> <td> 11.094</td> <td> 0.000</td> <td> 0.092 0.132</td>\n",
3612 "</tr>\n",
3613 "<tr>\n",
3614 " <th>stock_MICROSOFT CORP</th> <td> 0.1369</td> <td> 0.012</td> <td> 11.104</td> <td> 0.000</td> <td> 0.113 0.161</td>\n",
3615 "</tr>\n",
3616 "<tr>\n",
3617 " <th>stock_MONDELEZ INTERNATIONAL INC</th> <td> 0.1580</td> <td> 0.015</td> <td> 10.549</td> <td> 0.000</td> <td> 0.129 0.187</td>\n",
3618 "</tr>\n",
3619 "<tr>\n",
3620 " <th>stock_MONSANTO COMPANY</th> <td> 0.1725</td> <td> 0.015</td> <td> 11.144</td> <td> 0.000</td> <td> 0.142 0.203</td>\n",
3621 "</tr>\n",
3622 "<tr>\n",
3623 " <th>stock_MORGAN STANLEY</th> <td> 0.2094</td> <td> 0.023</td> <td> 9.058</td> <td> 0.000</td> <td> 0.164 0.255</td>\n",
3624 "</tr>\n",
3625 "<tr>\n",
3626 " <th>stock_MYLAN NV</th> <td> 0.1362</td> <td> 0.011</td> <td> 12.231</td> <td> 0.000</td> <td> 0.114 0.158</td>\n",
3627 "</tr>\n",
3628 "<tr>\n",
3629 " <th>stock_NATIONAL OILWELL VARCO INC.</th> <td> 0.1420</td> <td> 0.016</td> <td> 8.817</td> <td> 0.000</td> <td> 0.110 0.174</td>\n",
3630 "</tr>\n",
3631 "<tr>\n",
3632 " <th>stock_NETFLIX INC</th> <td> 0.2052</td> <td> 0.016</td> <td> 12.512</td> <td> 0.000</td> <td> 0.173 0.237</td>\n",
3633 "</tr>\n",
3634 "<tr>\n",
3635 " <th>stock_NEWMONT MINING CORP (HOLDING COMPANY)</th> <td> 0.0949</td> <td> 0.012</td> <td> 7.982</td> <td> 0.000</td> <td> 0.072 0.118</td>\n",
3636 "</tr>\n",
3637 "<tr>\n",
3638 " <th>stock_NEWS CP - CL A</th> <td> 0.1394</td> <td> 0.013</td> <td> 11.131</td> <td> 0.000</td> <td> 0.115 0.164</td>\n",
3639 "</tr>\n",
3640 "<tr>\n",
3641 " <th>stock_NIKE INC CL-B</th> <td> 0.1440</td> <td> 0.012</td> <td> 12.236</td> <td> 0.000</td> <td> 0.121 0.167</td>\n",
3642 "</tr>\n",
3643 "<tr>\n",
3644 " <th>stock_OCCIDENTAL PETROLEUM CORP</th> <td> 0.1303</td> <td> 0.012</td> <td> 10.690</td> <td> 0.000</td> <td> 0.106 0.154</td>\n",
3645 "</tr>\n",
3646 "<tr>\n",
3647 " <th>stock_ORACLE CORP</th> <td> 0.1384</td> <td> 0.012</td> <td> 11.935</td> <td> 0.000</td> <td> 0.116 0.161</td>\n",
3648 "</tr>\n",
3649 "<tr>\n",
3650 " <th>stock_PANDORA MEDIA INC</th> <td> 0.2348</td> <td> 0.020</td> <td> 11.961</td> <td> 0.000</td> <td> 0.196 0.273</td>\n",
3651 "</tr>\n",
3652 "<tr>\n",
3653 " <th>stock_PENNEY J.C. CO INC (HOLDING COMPANY)</th> <td> 0.0553</td> <td> 0.010</td> <td> 5.558</td> <td> 0.000</td> <td> 0.036 0.075</td>\n",
3654 "</tr>\n",
3655 "<tr>\n",
3656 " <th>stock_PEPSICO INC</th> <td> 0.1325</td> <td> 0.013</td> <td> 9.962</td> <td> 0.000</td> <td> 0.106 0.159</td>\n",
3657 "</tr>\n",
3658 "<tr>\n",
3659 " <th>stock_PFIZER INC</th> <td> 0.1554</td> <td> 0.013</td> <td> 11.701</td> <td> 0.000</td> <td> 0.129 0.181</td>\n",
3660 "</tr>\n",
3661 "<tr>\n",
3662 " <th>stock_PHILIP MORRIS INTERNATIONAL INC</th> <td> 0.2017</td> <td> 0.019</td> <td> 10.405</td> <td> 0.000</td> <td> 0.164 0.240</td>\n",
3663 "</tr>\n",
3664 "<tr>\n",
3665 " <th>stock_PIONEER NAT RES CO</th> <td> 0.1980</td> <td> 0.014</td> <td> 13.850</td> <td> 0.000</td> <td> 0.170 0.226</td>\n",
3666 "</tr>\n",
3667 "<tr>\n",
3668 " <th>stock_PRECISION CASTPARTS CORP</th> <td> 0.1519</td> <td> 0.014</td> <td> 11.190</td> <td> 0.000</td> <td> 0.125 0.179</td>\n",
3669 "</tr>\n",
3670 "<tr>\n",
3671 " <th>stock_PROCTER & GAMBLE CO</th> <td> 0.1412</td> <td> 0.014</td> <td> 10.286</td> <td> 0.000</td> <td> 0.114 0.168</td>\n",
3672 "</tr>\n",
3673 "<tr>\n",
3674 " <th>stock_QUALCOMM INC</th> <td> 0.1585</td> <td> 0.013</td> <td> 12.011</td> <td> 0.000</td> <td> 0.133 0.184</td>\n",
3675 "</tr>\n",
3676 "<tr>\n",
3677 " <th>stock_REGENERON PHARMACEUTICALS INC</th> <td> 0.1549</td> <td> 0.018</td> <td> 8.846</td> <td> 0.000</td> <td> 0.121 0.189</td>\n",
3678 "</tr>\n",
3679 "<tr>\n",
3680 " <th>stock_SALESFORCE.COM INC</th> <td> 0.1797</td> <td> 0.016</td> <td> 10.945</td> <td> 0.000</td> <td> 0.148 0.212</td>\n",
3681 "</tr>\n",
3682 "<tr>\n",
3683 " <th>stock_SALIX PHARMACEUTICALS LTD</th> <td> 1.482e-11</td> <td> 1.92e-11</td> <td> 0.771</td> <td> 0.440</td> <td>-2.28e-11 5.25e-11</td>\n",
3684 "</tr>\n",
3685 "<tr>\n",
3686 " <th>stock_SANDISK CORP</th> <td> 0.1842</td> <td> 0.014</td> <td> 13.280</td> <td> 0.000</td> <td> 0.157 0.211</td>\n",
3687 "</tr>\n",
3688 "<tr>\n",
3689 " <th>stock_SCHLUMBERGER LTD.</th> <td> 0.1437</td> <td> 0.013</td> <td> 11.126</td> <td> 0.000</td> <td> 0.118 0.169</td>\n",
3690 "</tr>\n",
3691 "<tr>\n",
3692 " <th>stock_SKYWORKS SOLUTIONS INC</th> <td> 0.1829</td> <td> 0.016</td> <td> 11.217</td> <td> 0.000</td> <td> 0.151 0.215</td>\n",
3693 "</tr>\n",
3694 "<tr>\n",
3695 " <th>stock_SOLARCITY CORP</th> <td> 0.2218</td> <td> 0.019</td> <td> 11.593</td> <td> 0.000</td> <td> 0.184 0.259</td>\n",
3696 "</tr>\n",
3697 "<tr>\n",
3698 " <th>stock_SOUTHWEST AIRLINES CO</th> <td> 0.1205</td> <td> 0.010</td> <td> 11.615</td> <td> 0.000</td> <td> 0.100 0.141</td>\n",
3699 "</tr>\n",
3700 "<tr>\n",
3701 " <th>stock_STARBUCKS CORPORATION</th> <td> 0.1770</td> <td> 0.013</td> <td> 13.501</td> <td> 0.000</td> <td> 0.151 0.203</td>\n",
3702 "</tr>\n",
3703 "<tr>\n",
3704 " <th>stock_SUNEDISON INC</th> <td> 0.1297</td> <td> 0.042</td> <td> 3.095</td> <td> 0.002</td> <td> 0.048 0.212</td>\n",
3705 "</tr>\n",
3706 "<tr>\n",
3707 " <th>stock_TARGET CORPORATION</th> <td> 0.1226</td> <td> 0.014</td> <td> 8.556</td> <td> 0.000</td> <td> 0.094 0.151</td>\n",
3708 "</tr>\n",
3709 "<tr>\n",
3710 " <th>stock_TESLA INC</th> <td> 0.2039</td> <td> 0.018</td> <td> 11.215</td> <td> 0.000</td> <td> 0.168 0.240</td>\n",
3711 "</tr>\n",
3712 "<tr>\n",
3713 " <th>stock_TEXAS INSTRUMENTS INC</th> <td> 0.1730</td> <td> 0.014</td> <td> 12.080</td> <td> 0.000</td> <td> 0.145 0.201</td>\n",
3714 "</tr>\n",
3715 "<tr>\n",
3716 " <th>stock_TIME WARNER CABLE INC</th> <td> 0.1446</td> <td> 0.016</td> <td> 9.138</td> <td> 0.000</td> <td> 0.114 0.176</td>\n",
3717 "</tr>\n",
3718 "<tr>\n",
3719 " <th>stock_TIME WARNER INC.</th> <td> 0.0757</td> <td> 0.007</td> <td> 11.026</td> <td> 0.000</td> <td> 0.062 0.089</td>\n",
3720 "</tr>\n",
3721 "<tr>\n",
3722 " <th>stock_TWITTER INC</th> <td> 0.1978</td> <td> 0.019</td> <td> 10.476</td> <td> 0.000</td> <td> 0.161 0.235</td>\n",
3723 "</tr>\n",
3724 "<tr>\n",
3725 " <th>stock_UNION PACIFIC CORPORATION</th> <td> 0.1502</td> <td> 0.013</td> <td> 11.655</td> <td> 0.000</td> <td> 0.125 0.175</td>\n",
3726 "</tr>\n",
3727 "<tr>\n",
3728 " <th>stock_UNITED CONTINENTAL HOLDINGS IN</th> <td> 0.1245</td> <td> 0.016</td> <td> 7.991</td> <td> 0.000</td> <td> 0.094 0.155</td>\n",
3729 "</tr>\n",
3730 "<tr>\n",
3731 " <th>stock_UNITED PARCEL SERVICE INC.CL B</th> <td> 0.1248</td> <td> 0.015</td> <td> 8.288</td> <td> 0.000</td> <td> 0.095 0.154</td>\n",
3732 "</tr>\n",
3733 "<tr>\n",
3734 " <th>stock_UNITED TECHNOLOGIES CORP</th> <td> 0.1288</td> <td> 0.013</td> <td> 9.937</td> <td> 0.000</td> <td> 0.103 0.154</td>\n",
3735 "</tr>\n",
3736 "<tr>\n",
3737 " <th>stock_UNITEDHEALTH GROUP INC</th> <td> 0.1402</td> <td> 0.013</td> <td> 10.559</td> <td> 0.000</td> <td> 0.114 0.166</td>\n",
3738 "</tr>\n",
3739 "<tr>\n",
3740 " <th>stock_VALERO ENERGY CORP (NEW)</th> <td> 0.1296</td> <td> 0.013</td> <td> 9.686</td> <td> 0.000</td> <td> 0.103 0.156</td>\n",
3741 "</tr>\n",
3742 "<tr>\n",
3743 " <th>stock_VERIZON COMMUNICATIONS</th> <td> 0.1198</td> <td> 0.016</td> <td> 7.496</td> <td> 0.000</td> <td> 0.088 0.151</td>\n",
3744 "</tr>\n",
3745 "<tr>\n",
3746 " <th>stock_VISA INC</th> <td> 0.2206</td> <td> 0.017</td> <td> 12.867</td> <td> 0.000</td> <td> 0.187 0.254</td>\n",
3747 "</tr>\n",
3748 "<tr>\n",
3749 " <th>stock_WALGREENS BOOTS ALLIANCE INC</th> <td> 0.1369</td> <td> 0.013</td> <td> 10.559</td> <td> 0.000</td> <td> 0.111 0.162</td>\n",
3750 "</tr>\n",
3751 "<tr>\n",
3752 " <th>stock_WALMART INC</th> <td> 0.1522</td> <td> 0.021</td> <td> 7.225</td> <td> 0.000</td> <td> 0.111 0.194</td>\n",
3753 "</tr>\n",
3754 "<tr>\n",
3755 " <th>stock_WALT DISNEY CO</th> <td> 0.1309</td> <td> 0.010</td> <td> 13.023</td> <td> 0.000</td> <td> 0.111 0.151</td>\n",
3756 "</tr>\n",
3757 "<tr>\n",
3758 " <th>stock_WELLS FARGO & CO(NEW)</th> <td> 0.2969</td> <td> 0.043</td> <td> 6.946</td> <td> 0.000</td> <td> 0.213 0.381</td>\n",
3759 "</tr>\n",
3760 "<tr>\n",
3761 " <th>stock_WILLIAMS COMPANIES</th> <td> 0.1325</td> <td> 0.013</td> <td> 10.104</td> <td> 0.000</td> <td> 0.107 0.158</td>\n",
3762 "</tr>\n",
3763 "<tr>\n",
3764 " <th>stock_WYNN RESORTS LTD</th> <td> 0.1418</td> <td> 0.016</td> <td> 8.999</td> <td> 0.000</td> <td> 0.111 0.173</td>\n",
3765 "</tr>\n",
3766 "<tr>\n",
3767 " <th>stock_YELP INC</th> <td> 0.1947</td> <td> 0.019</td> <td> 10.257</td> <td> 0.000</td> <td> 0.157 0.232</td>\n",
3768 "</tr>\n",
3769 "</table>\n",
3770 "<table class=\"simpletable\">\n",
3771 "<tr>\n",
3772 " <th>Omnibus:</th> <td> 0.654</td> <th> Durbin-Watson: </th> <td> 1.503</td>\n",
3773 "</tr>\n",
3774 "<tr>\n",
3775 " <th>Prob(Omnibus):</th> <td> 0.721</td> <th> Jarque-Bera (JB): </th> <td> 0.669</td>\n",
3776 "</tr>\n",
3777 "<tr>\n",
3778 " <th>Skew:</th> <td>-0.007</td> <th> Prob(JB): </th> <td> 0.716</td>\n",
3779 "</tr>\n",
3780 "<tr>\n",
3781 " <th>Kurtosis:</th> <td> 2.986</td> <th> Cond. No. </th> <td>6.39e+16</td>\n",
3782 "</tr>\n",
3783 "</table>"
3784 ],
3785 "text/plain": [
3786 "<class 'statsmodels.iolib.summary.Summary'>\n",
3787 "\"\"\"\n",
3788 " OLS Regression Results \n",
3789 "==============================================================================\n",
3790 "Dep. Variable: Returns10D R-squared: 0.035\n",
3791 "Model: OLS Adj. R-squared: 0.031\n",
3792 "Method: Least Squares F-statistic: 9.045\n",
3793 "Date: Fri, 03 Aug 2018 Prob (F-statistic): 5.42e-219\n",
3794 "Time: 21:41:36 Log-Likelihood: 78861.\n",
3795 "No. Observations: 43734 AIC: -1.574e+05\n",
3796 "Df Residuals: 43559 BIC: -1.559e+05\n",
3797 "Df Model: 174 \n",
3798 "Covariance Type: nonrobust \n",
3799 "===============================================================================================================\n",
3800 " coef std err t P>|t| [95.0% Conf. Int.]\n",
3801 "---------------------------------------------------------------------------------------------------------------\n",
3802 "DividendYield 6.757e-06 1.55e-06 4.370 0.000 3.73e-06 9.79e-06\n",
3803 "EBITDAYield -1.021e-05 3.11e-05 -0.329 0.743 -7.11e-05 5.07e-05\n",
3804 "EVToEBITDA 9.163e-05 2.83e-05 3.233 0.001 3.61e-05 0.000\n",
3805 "EVToFCF 2.372e-05 3.51e-05 0.676 0.499 -4.5e-05 9.25e-05\n",
3806 "PriceToBook 8.448e-05 3.34e-05 2.533 0.011 1.91e-05 0.000\n",
3807 "PriceToDilutedEarningsTTM -1.332e-05 1.5e-05 -0.889 0.374 -4.27e-05 1.61e-05\n",
3808 "PriceToEarningsTTM -6.983e-07 6.67e-07 -1.047 0.295 -2e-06 6.08e-07\n",
3809 "PriceToFCF -4.525e-06 2.91e-05 -0.155 0.877 -6.16e-05 5.26e-05\n",
3810 "PriceToOperatingCashflow -8.243e-05 2.02e-05 -4.071 0.000 -0.000 -4.27e-05\n",
3811 "PriceToSalesTTM -2.324e-05 1.72e-06 -13.493 0.000 -2.66e-05 -1.99e-05\n",
3812 "Directional Movement Index -2.235e-05 6.75e-06 -3.308 0.001 -3.56e-05 -9.11e-06\n",
3813 "Money Flow Index 4.299e-06 8.77e-06 0.490 0.624 -1.29e-05 2.15e-05\n",
3814 "Percent Above Low -4.475e-05 1.71e-05 -2.623 0.009 -7.82e-05 -1.13e-05\n",
3815 "Percent Below High 1.295e-05 1.34e-05 0.965 0.335 -1.34e-05 3.93e-05\n",
3816 "Price Oscillator 6.06e-06 9.48e-06 0.640 0.522 -1.25e-05 2.46e-05\n",
3817 "Trendline 2.48e-05 1.44e-05 1.722 0.085 -3.42e-06 5.3e-05\n",
3818 "AssetToEquityRatio -2.548e-06 1.3e-06 -1.964 0.049 -5.09e-06 -5.59e-09\n",
3819 "AssetTurnover -0.0001 5.63e-05 -2.054 0.040 -0.000 -5.31e-06\n",
3820 "CurrentRatio 3.392e-06 1.69e-06 2.007 0.045 7.96e-08 6.7e-06\n",
3821 "DebtToAssetRatio 2.605e-06 1.04e-06 2.500 0.012 5.63e-07 4.65e-06\n",
3822 "DebtToEquityRatio -2.576e-08 1.21e-06 -0.021 0.983 -2.4e-06 2.35e-06\n",
3823 "MertonsDD -0.0010 0.000 -6.752 0.000 -0.001 -0.001\n",
3824 "WorkingCapitalToAssets 3.97e-06 2.04e-06 1.943 0.052 -3.38e-08 7.97e-06\n",
3825 "WorkingCapitalToSales -0.0002 6.58e-05 -2.654 0.008 -0.000 -4.56e-05\n",
3826 "Dividend Growth -9.485e-07 6.45e-07 -1.471 0.141 -2.21e-06 3.16e-07\n",
3827 "EPS 5.254e-07 1e-06 0.525 0.599 -1.43e-06 2.48e-06\n",
3828 "Net Debt -1.53e-13 3.65e-14 -4.193 0.000 -2.25e-13 -8.15e-14\n",
3829 "Sales -1.061e-13 3.43e-14 -3.094 0.002 -1.73e-13 -3.89e-14\n",
3830 "Total Assets -7.923e-14 2.58e-14 -3.068 0.002 -1.3e-13 -2.86e-14\n",
3831 "EPS Growth 3M -6.126e-06 1.53e-06 -4.002 0.000 -9.13e-06 -3.13e-06\n",
3832 "EPS Growth 12M 2.182e-06 1.53e-06 1.423 0.155 -8.24e-07 5.19e-06\n",
3833 "Net Debt Growth 3M -1.748e-06 1.95e-06 -0.897 0.370 -5.57e-06 2.07e-06\n",
3834 "Net Debt Growth 12M 5.675e-06 1.98e-06 2.872 0.004 1.8e-06 9.55e-06\n",
3835 "Sales Growth 3M 4.22e-06 2.06e-06 2.051 0.040 1.87e-07 8.25e-06\n",
3836 "Sales Growth 12M -6.66e-06 2.1e-06 -3.169 0.002 -1.08e-05 -2.54e-06\n",
3837 "Total Assets Growth 3M -2.435e-07 2.31e-06 -0.105 0.916 -4.77e-06 4.28e-06\n",
3838 "Total Assets Growth 12M -2.601e-06 2.46e-06 -1.059 0.290 -7.42e-06 2.21e-06\n",
3839 "CFO To Assets 4.355e-05 2.81e-05 1.551 0.121 -1.15e-05 9.86e-05\n",
3840 "Capex To Assets -0.0001 6.1e-05 -2.246 0.025 -0.000 -1.74e-05\n",
3841 "Capex To FCF 6.95e-05 3.42e-05 2.030 0.042 2.4e-06 0.000\n",
3842 "Capex To Sales 0.0002 6.51e-05 3.016 0.003 6.87e-05 0.000\n",
3843 "EBIT To Assets 1.444e-05 2.83e-05 0.511 0.610 -4.1e-05 6.99e-05\n",
3844 "Retained Earnings To Assets -0.0004 6.01e-05 -6.676 0.000 -0.001 -0.000\n",
3845 "Downside Risk 3.345e-05 1.73e-05 1.932 0.053 -4.83e-07 6.74e-05\n",
3846 "Index Beta -2.09e-06 3.55e-07 -5.886 0.000 -2.79e-06 -1.39e-06\n",
3847 "Log Market Cap 0.0002 6.04e-05 3.147 0.002 7.17e-05 0.000\n",
3848 "Volatility 3M -8.754e-05 1.47e-05 -5.939 0.000 -0.000 -5.86e-05\n",
3849 "stock_3D SYSTEMS CORP 0.1609 0.014 11.264 0.000 0.133 0.189\n",
3850 "stock_3M COMPANY 0.1423 0.012 11.416 0.000 0.118 0.167\n",
3851 "stock_ABBOTT LABORATORIES 0.0903 0.010 9.116 0.000 0.071 0.110\n",
3852 "stock_ABBVIE INC 0.1833 0.018 10.070 0.000 0.148 0.219\n",
3853 "stock_ALLERGAN INC 0.1434 0.009 15.113 0.000 0.125 0.162\n",
3854 "stock_ALLERGAN PLC 0.1683 0.013 12.648 0.000 0.142 0.194\n",
3855 "stock_ALTABA INC 0.1825 0.015 12.388 0.000 0.154 0.211\n",
3856 "stock_ALTRIA GROUP INC. 0.1533 0.013 12.015 0.000 0.128 0.178\n",
3857 "stock_AMAZON.COM INC 0.1494 0.015 10.035 0.000 0.120 0.179\n",
3858 "stock_AMERICAN AIRLINES GROUP INC 0.1433 0.017 8.373 0.000 0.110 0.177\n",
3859 "stock_AMERICAN EXPRESS COMPANY 0.0785 0.008 9.844 0.000 0.063 0.094\n",
3860 "stock_AMERICAN INTL GROUP INC 0.1021 0.014 7.505 0.000 0.075 0.129\n",
3861 "stock_AMGEN INC 0.0915 0.009 10.550 0.000 0.074 0.108\n",
3862 "stock_ANADARKO PETROLEUM CORP 0.0978 0.008 12.627 0.000 0.083 0.113\n",
3863 "stock_APACHE CORP 0.0568 0.014 4.038 0.000 0.029 0.084\n",
3864 "stock_APPLE INC 0.1309 0.012 10.689 0.000 0.107 0.155\n",
3865 "stock_APPLIED MATERIALS INC 0.1080 0.010 11.289 0.000 0.089 0.127\n",
3866 "stock_ARCONIC INC 0.0422 0.007 5.984 0.000 0.028 0.056\n",
3867 "stock_AT&T INC. COM 0.1122 0.014 8.068 0.000 0.085 0.139\n",
3868 "stock_Alphabet Inc. Cl A 0.2024 0.018 11.450 0.000 0.168 0.237\n",
3869 "stock_BAKER HUGHES INC 0.0945 0.009 11.076 0.000 0.078 0.111\n",
3870 "stock_BANK OF AMERICA CORP 0.2669 0.052 5.090 0.000 0.164 0.370\n",
3871 "stock_BERKSHIRE HATHAWAY INC CL-B 0.1630 0.018 8.968 0.000 0.127 0.199\n",
3872 "stock_BIOGEN INC 0.1688 0.012 14.185 0.000 0.145 0.192\n",
3873 "stock_BOEING CO 0.0769 0.009 8.299 0.000 0.059 0.095\n",
3874 "stock_BOOKING HOLDINGS INC 0.1898 0.015 12.281 0.000 0.159 0.220\n",
3875 "stock_BRISTOL MYERS SQUIBB COMPANY 0.1327 0.011 12.504 0.000 0.112 0.153\n",
3876 "stock_BROADCOM CORP 0.1655 0.014 11.708 0.000 0.138 0.193\n",
3877 "stock_BROADCOM INC 0.2090 0.018 11.611 0.000 0.174 0.244\n",
3878 "stock_CATERPILLAR INC 0.0647 0.009 7.093 0.000 0.047 0.083\n",
3879 "stock_CELGENE CORP 0.1326 0.010 12.974 0.000 0.113 0.153\n",
3880 "stock_CHESAPEAKE ENERGY CORP 0.0682 0.016 4.174 0.000 0.036 0.100\n",
3881 "stock_CHEVRON CORPORATION 0.1637 0.019 8.770 0.000 0.127 0.200\n",
3882 "stock_CISCO SYSTEMS INC 0.0975 0.009 10.689 0.000 0.080 0.115\n",
3883 "stock_CITIGROUP 0.2358 0.045 5.236 0.000 0.148 0.324\n",
3884 "stock_COCA-COLA CO 0.1313 0.013 10.439 0.000 0.107 0.156\n",
3885 "stock_COMCAST CORP 0.0961 0.009 10.691 0.000 0.078 0.114\n",
3886 "stock_CONOCOPHILLIPS 0.1432 0.016 9.039 0.000 0.112 0.174\n",
3887 "stock_COVIDIEN PLC 0.2103 0.017 12.561 0.000 0.177 0.243\n",
3888 "stock_CVS HEALTH CORP 0.1147 0.012 9.823 0.000 0.092 0.138\n",
3889 "stock_DEERE & CO 0.0556 0.010 5.698 0.000 0.036 0.075\n",
3890 "stock_DELTA AIR LINES INC 0.1560 0.016 9.833 0.000 0.125 0.187\n",
3891 "stock_DIRECTV 0.1262 0.015 8.223 0.000 0.096 0.156\n",
3892 "stock_DOLLAR GENERAL CORP 0.1543 0.016 9.600 0.000 0.123 0.186\n",
3893 "stock_DOW CHEMICAL CO 0.0810 0.009 8.981 0.000 0.063 0.099\n",
3894 "stock_E.I. Du Pont De Nemours A 0.0722 0.009 7.789 0.000 0.054 0.090\n",
3895 "stock_EBAY INC 0.1879 0.016 11.397 0.000 0.156 0.220\n",
3896 "stock_EMC CORPORATION 0.1141 0.009 12.320 0.000 0.096 0.132\n",
3897 "stock_EOG RESOURCES INC 0.1212 0.009 13.575 0.000 0.104 0.139\n",
3898 "stock_EXPRESS SCRIPTS HOLDING CO 0.0745 0.010 7.775 0.000 0.056 0.093\n",
3899 "stock_EXXON MOBIL CORPORATION 0.1865 0.021 8.679 0.000 0.144 0.229\n",
3900 "stock_FACEBOOK INC 0.2171 0.019 11.586 0.000 0.180 0.254\n",
3901 "stock_FEDEX CORPORATION 0.1002 0.010 10.231 0.000 0.081 0.119\n",
3902 "stock_FIRST SOLAR INC 0.1560 0.017 9.046 0.000 0.122 0.190\n",
3903 "stock_FORD MOTOR CO(NEW) 0.0733 0.011 6.384 0.000 0.051 0.096\n",
3904 "stock_FREEPORT-MCMORAN INC 0.0974 0.013 7.766 0.000 0.073 0.122\n",
3905 "stock_GENERAL ELECTRIC CO 0.1500 0.018 8.332 0.000 0.115 0.185\n",
3906 "stock_GENERAL MOTORS CO 0.1448 0.018 8.032 0.000 0.109 0.180\n",
3907 "stock_GILEAD SCIENCES INC 0.1445 0.011 12.934 0.000 0.123 0.166\n",
3908 "stock_GOLDMAN SACHS GROUP INC 0.2410 0.025 9.822 0.000 0.193 0.289\n",
3909 "stock_GOPRO INC 0.1946 0.018 10.636 0.000 0.159 0.230\n",
3910 "stock_HALLIBURTON CO (HOLDING CO) 0.1050 0.010 10.582 0.000 0.086 0.124\n",
3911 "stock_HOME DEPOT INC 0.1160 0.011 10.522 0.000 0.094 0.138\n",
3912 "stock_HP INC 0.0879 0.010 8.554 0.000 0.068 0.108\n",
3913 "stock_INTEL CORP 0.1170 0.011 10.511 0.000 0.095 0.139\n",
3914 "stock_INTL BUSINESS MACHINES CORP 0.1135 0.012 9.176 0.000 0.089 0.138\n",
3915 "stock_JOHNSON AND JOHNSON 0.1375 0.013 10.821 0.000 0.113 0.162\n",
3916 "stock_JPMORGAN CHASE & CO COM STK 0.3443 0.064 5.416 0.000 0.220 0.469\n",
3917 "stock_KEURIG GREEN MOUNTAIN INC 0.1655 0.013 12.506 0.000 0.140 0.191\n",
3918 "stock_KINDER MORGAN INC 0.1372 0.017 7.888 0.000 0.103 0.171\n",
3919 "stock_LAS VEGAS SANDS CORP 0.1657 0.017 9.963 0.000 0.133 0.198\n",
3920 "stock_LILLY ELI & CO 0.1296 0.011 11.275 0.000 0.107 0.152\n",
3921 "stock_LINKEDIN CORP 0.1981 0.018 10.815 0.000 0.162 0.234\n",
3922 "stock_LOWES COMPANIES INC 0.1054 0.010 10.827 0.000 0.086 0.125\n",
3923 "stock_LYONDELLBASELL INDUSTRIES NV 0.1509 0.017 8.959 0.000 0.118 0.184\n",
3924 "stock_MARATHON PETROLEUM CORP 0.1477 0.018 8.006 0.000 0.112 0.184\n",
3925 "stock_MASTERCARD INCORPORATED 0.2324 0.018 12.916 0.000 0.197 0.268\n",
3926 "stock_MCDONALDS CORP 0.1322 0.013 10.363 0.000 0.107 0.157\n",
3927 "stock_MEDTRONIC PLC 0.1496 0.011 13.103 0.000 0.127 0.172\n",
3928 "stock_MERCK & CO INC 0.1372 0.012 11.332 0.000 0.113 0.161\n",
3929 "stock_METLIFE INC 0.1720 0.025 6.765 0.000 0.122 0.222\n",
3930 "stock_MICHAEL KORS HOLDINGS LTD 0.2136 0.019 11.178 0.000 0.176 0.251\n",
3931 "stock_MICRON TECHNOLOGY INC 0.1122 0.010 11.094 0.000 0.092 0.132\n",
3932 "stock_MICROSOFT CORP 0.1369 0.012 11.104 0.000 0.113 0.161\n",
3933 "stock_MONDELEZ INTERNATIONAL INC 0.1580 0.015 10.549 0.000 0.129 0.187\n",
3934 "stock_MONSANTO COMPANY 0.1725 0.015 11.144 0.000 0.142 0.203\n",
3935 "stock_MORGAN STANLEY 0.2094 0.023 9.058 0.000 0.164 0.255\n",
3936 "stock_MYLAN NV 0.1362 0.011 12.231 0.000 0.114 0.158\n",
3937 "stock_NATIONAL OILWELL VARCO INC. 0.1420 0.016 8.817 0.000 0.110 0.174\n",
3938 "stock_NETFLIX INC 0.2052 0.016 12.512 0.000 0.173 0.237\n",
3939 "stock_NEWMONT MINING CORP (HOLDING COMPANY) 0.0949 0.012 7.982 0.000 0.072 0.118\n",
3940 "stock_NEWS CP - CL A 0.1394 0.013 11.131 0.000 0.115 0.164\n",
3941 "stock_NIKE INC CL-B 0.1440 0.012 12.236 0.000 0.121 0.167\n",
3942 "stock_OCCIDENTAL PETROLEUM CORP 0.1303 0.012 10.690 0.000 0.106 0.154\n",
3943 "stock_ORACLE CORP 0.1384 0.012 11.935 0.000 0.116 0.161\n",
3944 "stock_PANDORA MEDIA INC 0.2348 0.020 11.961 0.000 0.196 0.273\n",
3945 "stock_PENNEY J.C. CO INC (HOLDING COMPANY) 0.0553 0.010 5.558 0.000 0.036 0.075\n",
3946 "stock_PEPSICO INC 0.1325 0.013 9.962 0.000 0.106 0.159\n",
3947 "stock_PFIZER INC 0.1554 0.013 11.701 0.000 0.129 0.181\n",
3948 "stock_PHILIP MORRIS INTERNATIONAL INC 0.2017 0.019 10.405 0.000 0.164 0.240\n",
3949 "stock_PIONEER NAT RES CO 0.1980 0.014 13.850 0.000 0.170 0.226\n",
3950 "stock_PRECISION CASTPARTS CORP 0.1519 0.014 11.190 0.000 0.125 0.179\n",
3951 "stock_PROCTER & GAMBLE CO 0.1412 0.014 10.286 0.000 0.114 0.168\n",
3952 "stock_QUALCOMM INC 0.1585 0.013 12.011 0.000 0.133 0.184\n",
3953 "stock_REGENERON PHARMACEUTICALS INC 0.1549 0.018 8.846 0.000 0.121 0.189\n",
3954 "stock_SALESFORCE.COM INC 0.1797 0.016 10.945 0.000 0.148 0.212\n",
3955 "stock_SALIX PHARMACEUTICALS LTD 1.482e-11 1.92e-11 0.771 0.440 -2.28e-11 5.25e-11\n",
3956 "stock_SANDISK CORP 0.1842 0.014 13.280 0.000 0.157 0.211\n",
3957 "stock_SCHLUMBERGER LTD. 0.1437 0.013 11.126 0.000 0.118 0.169\n",
3958 "stock_SKYWORKS SOLUTIONS INC 0.1829 0.016 11.217 0.000 0.151 0.215\n",
3959 "stock_SOLARCITY CORP 0.2218 0.019 11.593 0.000 0.184 0.259\n",
3960 "stock_SOUTHWEST AIRLINES CO 0.1205 0.010 11.615 0.000 0.100 0.141\n",
3961 "stock_STARBUCKS CORPORATION 0.1770 0.013 13.501 0.000 0.151 0.203\n",
3962 "stock_SUNEDISON INC 0.1297 0.042 3.095 0.002 0.048 0.212\n",
3963 "stock_TARGET CORPORATION 0.1226 0.014 8.556 0.000 0.094 0.151\n",
3964 "stock_TESLA INC 0.2039 0.018 11.215 0.000 0.168 0.240\n",
3965 "stock_TEXAS INSTRUMENTS INC 0.1730 0.014 12.080 0.000 0.145 0.201\n",
3966 "stock_TIME WARNER CABLE INC 0.1446 0.016 9.138 0.000 0.114 0.176\n",
3967 "stock_TIME WARNER INC. 0.0757 0.007 11.026 0.000 0.062 0.089\n",
3968 "stock_TWITTER INC 0.1978 0.019 10.476 0.000 0.161 0.235\n",
3969 "stock_UNION PACIFIC CORPORATION 0.1502 0.013 11.655 0.000 0.125 0.175\n",
3970 "stock_UNITED CONTINENTAL HOLDINGS IN 0.1245 0.016 7.991 0.000 0.094 0.155\n",
3971 "stock_UNITED PARCEL SERVICE INC.CL B 0.1248 0.015 8.288 0.000 0.095 0.154\n",
3972 "stock_UNITED TECHNOLOGIES CORP 0.1288 0.013 9.937 0.000 0.103 0.154\n",
3973 "stock_UNITEDHEALTH GROUP INC 0.1402 0.013 10.559 0.000 0.114 0.166\n",
3974 "stock_VALERO ENERGY CORP (NEW) 0.1296 0.013 9.686 0.000 0.103 0.156\n",
3975 "stock_VERIZON COMMUNICATIONS 0.1198 0.016 7.496 0.000 0.088 0.151\n",
3976 "stock_VISA INC 0.2206 0.017 12.867 0.000 0.187 0.254\n",
3977 "stock_WALGREENS BOOTS ALLIANCE INC 0.1369 0.013 10.559 0.000 0.111 0.162\n",
3978 "stock_WALMART INC 0.1522 0.021 7.225 0.000 0.111 0.194\n",
3979 "stock_WALT DISNEY CO 0.1309 0.010 13.023 0.000 0.111 0.151\n",
3980 "stock_WELLS FARGO & CO(NEW) 0.2969 0.043 6.946 0.000 0.213 0.381\n",
3981 "stock_WILLIAMS COMPANIES 0.1325 0.013 10.104 0.000 0.107 0.158\n",
3982 "stock_WYNN RESORTS LTD 0.1418 0.016 8.999 0.000 0.111 0.173\n",
3983 "stock_YELP INC 0.1947 0.019 10.257 0.000 0.157 0.232\n",
3984 "==============================================================================\n",
3985 "Omnibus: 0.654 Durbin-Watson: 1.503\n",
3986 "Prob(Omnibus): 0.721 Jarque-Bera (JB): 0.669\n",
3987 "Skew: -0.007 Prob(JB): 0.716\n",
3988 "Kurtosis: 2.986 Cond. No. 6.39e+16\n",
3989 "==============================================================================\n",
3990 "\n",
3991 "Warnings:\n",
3992 "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
3993 "[2] The condition number is large, 6.39e+16. This might indicate that there are\n",
3994 "strong multicollinearity or other numerical problems.\n",
3995 "\"\"\""
3996 ]
3997 },
3998 "execution_count": 185,
3999 "metadata": {},
4000 "output_type": "execute_result"
4001 }
4002 ],
4003 "source": [
4004 "target = 'Returns10D'\n",
4005 "model_data = pd.concat([y[[target]], X], axis=1).dropna()\n",
4006 "model_data = model_data[model_data[target].between(model_data[target].quantile(.025), \n",
4007 " model_data[target].quantile(.975))]\n",
4008 "\n",
4009 "model = OLS(endog=model_data[target], exog=model_data.drop(target, axis=1))\n",
4010 "trained_model = model.fit()\n",
4011 "trained_model.summary()"
4012 ]
4013 },
4014 {
4015 "cell_type": "code",
4016 "execution_count": 186,
4017 "metadata": {},
4018 "outputs": [
4019 {
4020 "data": {
4021 "text/html": [
4022 "<table class=\"simpletable\">\n",
4023 "<caption>OLS Regression Results</caption>\n",
4024 "<tr>\n",
4025 " <th>Dep. Variable:</th> <td>Returns20D</td> <th> R-squared: </th> <td> 0.072</td> \n",
4026 "</tr>\n",
4027 "<tr>\n",
4028 " <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.068</td> \n",
4029 "</tr>\n",
4030 "<tr>\n",
4031 " <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 19.10</td> \n",
4032 "</tr>\n",
4033 "<tr>\n",
4034 " <th>Date:</th> <td>Fri, 03 Aug 2018</td> <th> Prob (F-statistic):</th> <td> 0.00</td> \n",
4035 "</tr>\n",
4036 "<tr>\n",
4037 " <th>Time:</th> <td>21:42:21</td> <th> Log-Likelihood: </th> <td> 62705.</td> \n",
4038 "</tr>\n",
4039 "<tr>\n",
4040 " <th>No. Observations:</th> <td> 42460</td> <th> AIC: </th> <td>-1.251e+05</td>\n",
4041 "</tr>\n",
4042 "<tr>\n",
4043 " <th>Df Residuals:</th> <td> 42288</td> <th> BIC: </th> <td>-1.236e+05</td>\n",
4044 "</tr>\n",
4045 "<tr>\n",
4046 " <th>Df Model:</th> <td> 171</td> <th> </th> <td> </td> \n",
4047 "</tr>\n",
4048 "<tr>\n",
4049 " <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
4050 "</tr>\n",
4051 "</table>\n",
4052 "<table class=\"simpletable\">\n",
4053 "<tr>\n",
4054 " <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
4055 "</tr>\n",
4056 "<tr>\n",
4057 " <th>DividendYield</th> <td> 1.193e-05</td> <td> 2.23e-06</td> <td> 5.351</td> <td> 0.000</td> <td> 7.56e-06 1.63e-05</td>\n",
4058 "</tr>\n",
4059 "<tr>\n",
4060 " <th>EBITDAYield</th> <td> -0.0001</td> <td> 4.51e-05</td> <td> -3.287</td> <td> 0.001</td> <td> -0.000 -5.98e-05</td>\n",
4061 "</tr>\n",
4062 "<tr>\n",
4063 " <th>EVToEBITDA</th> <td> 0.0003</td> <td> 4.07e-05</td> <td> 7.808</td> <td> 0.000</td> <td> 0.000 0.000</td>\n",
4064 "</tr>\n",
4065 "<tr>\n",
4066 " <th>EVToFCF</th> <td> -3.67e-06</td> <td> 4.99e-05</td> <td> -0.074</td> <td> 0.941</td> <td> -0.000 9.42e-05</td>\n",
4067 "</tr>\n",
4068 "<tr>\n",
4069 " <th>PriceToBook</th> <td> 1.86e-05</td> <td> 4.73e-05</td> <td> 0.393</td> <td> 0.694</td> <td>-7.41e-05 0.000</td>\n",
4070 "</tr>\n",
4071 "<tr>\n",
4072 " <th>PriceToDilutedEarningsTTM</th> <td>-5.406e-05</td> <td> 2.12e-05</td> <td> -2.549</td> <td> 0.011</td> <td>-9.56e-05 -1.25e-05</td>\n",
4073 "</tr>\n",
4074 "<tr>\n",
4075 " <th>PriceToEarningsTTM</th> <td> -8.22e-07</td> <td> 9.41e-07</td> <td> -0.873</td> <td> 0.382</td> <td>-2.67e-06 1.02e-06</td>\n",
4076 "</tr>\n",
4077 "<tr>\n",
4078 " <th>PriceToFCF</th> <td> 2.678e-06</td> <td> 4.12e-05</td> <td> 0.065</td> <td> 0.948</td> <td> -7.8e-05 8.33e-05</td>\n",
4079 "</tr>\n",
4080 "<tr>\n",
4081 " <th>PriceToOperatingCashflow</th> <td> -0.0001</td> <td> 2.91e-05</td> <td> -4.551</td> <td> 0.000</td> <td> -0.000 -7.54e-05</td>\n",
4082 "</tr>\n",
4083 "<tr>\n",
4084 " <th>PriceToSalesTTM</th> <td>-4.302e-05</td> <td> 2.5e-06</td> <td> -17.222</td> <td> 0.000</td> <td>-4.79e-05 -3.81e-05</td>\n",
4085 "</tr>\n",
4086 "<tr>\n",
4087 " <th>Directional Movement Index</th> <td>-3.389e-05</td> <td> 9.51e-06</td> <td> -3.566</td> <td> 0.000</td> <td>-5.25e-05 -1.53e-05</td>\n",
4088 "</tr>\n",
4089 "<tr>\n",
4090 " <th>Money Flow Index</th> <td> 2.884e-05</td> <td> 1.23e-05</td> <td> 2.336</td> <td> 0.019</td> <td> 4.64e-06 5.3e-05</td>\n",
4091 "</tr>\n",
4092 "<tr>\n",
4093 " <th>Percent Above Low</th> <td> -7.04e-05</td> <td> 2.47e-05</td> <td> -2.855</td> <td> 0.004</td> <td> -0.000 -2.21e-05</td>\n",
4094 "</tr>\n",
4095 "<tr>\n",
4096 " <th>Percent Below High</th> <td> 4.825e-05</td> <td> 1.89e-05</td> <td> 2.559</td> <td> 0.010</td> <td> 1.13e-05 8.52e-05</td>\n",
4097 "</tr>\n",
4098 "<tr>\n",
4099 " <th>Price Oscillator</th> <td>-5.428e-06</td> <td> 1.33e-05</td> <td> -0.407</td> <td> 0.684</td> <td>-3.15e-05 2.07e-05</td>\n",
4100 "</tr>\n",
4101 "<tr>\n",
4102 " <th>Trendline</th> <td> 4.502e-05</td> <td> 2.04e-05</td> <td> 2.204</td> <td> 0.028</td> <td> 4.98e-06 8.51e-05</td>\n",
4103 "</tr>\n",
4104 "<tr>\n",
4105 " <th>AssetToEquityRatio</th> <td>-8.615e-06</td> <td> 1.86e-06</td> <td> -4.637</td> <td> 0.000</td> <td>-1.23e-05 -4.97e-06</td>\n",
4106 "</tr>\n",
4107 "<tr>\n",
4108 " <th>AssetTurnover</th> <td> -0.0002</td> <td> 8.11e-05</td> <td> -2.720</td> <td> 0.007</td> <td> -0.000 -6.16e-05</td>\n",
4109 "</tr>\n",
4110 "<tr>\n",
4111 " <th>CurrentRatio</th> <td> 1.14e-05</td> <td> 2.4e-06</td> <td> 4.756</td> <td> 0.000</td> <td> 6.7e-06 1.61e-05</td>\n",
4112 "</tr>\n",
4113 "<tr>\n",
4114 " <th>DebtToAssetRatio</th> <td> 2.131e-06</td> <td> 1.5e-06</td> <td> 1.421</td> <td> 0.155</td> <td>-8.08e-07 5.07e-06</td>\n",
4115 "</tr>\n",
4116 "<tr>\n",
4117 " <th>DebtToEquityRatio</th> <td> 4.261e-06</td> <td> 1.74e-06</td> <td> 2.445</td> <td> 0.014</td> <td> 8.45e-07 7.68e-06</td>\n",
4118 "</tr>\n",
4119 "<tr>\n",
4120 " <th>MertonsDD</th> <td> -0.0013</td> <td> 0.000</td> <td> -6.145</td> <td> 0.000</td> <td> -0.002 -0.001</td>\n",
4121 "</tr>\n",
4122 "<tr>\n",
4123 " <th>WorkingCapitalToAssets</th> <td> 5.08e-06</td> <td> 2.89e-06</td> <td> 1.759</td> <td> 0.079</td> <td> -5.8e-07 1.07e-05</td>\n",
4124 "</tr>\n",
4125 "<tr>\n",
4126 " <th>WorkingCapitalToSales</th> <td> -0.0004</td> <td> 9.31e-05</td> <td> -4.593</td> <td> 0.000</td> <td> -0.001 -0.000</td>\n",
4127 "</tr>\n",
4128 "<tr>\n",
4129 " <th>Dividend Growth</th> <td>-3.037e-06</td> <td> 9.15e-07</td> <td> -3.319</td> <td> 0.001</td> <td>-4.83e-06 -1.24e-06</td>\n",
4130 "</tr>\n",
4131 "<tr>\n",
4132 " <th>EPS</th> <td> 2.228e-06</td> <td> 1.41e-06</td> <td> 1.583</td> <td> 0.113</td> <td> -5.3e-07 4.99e-06</td>\n",
4133 "</tr>\n",
4134 "<tr>\n",
4135 " <th>Net Debt</th> <td>-2.368e-13</td> <td> 5.14e-14</td> <td> -4.609</td> <td> 0.000</td> <td>-3.37e-13 -1.36e-13</td>\n",
4136 "</tr>\n",
4137 "<tr>\n",
4138 " <th>Sales</th> <td>-2.182e-13</td> <td> 5.03e-14</td> <td> -4.341</td> <td> 0.000</td> <td>-3.17e-13 -1.2e-13</td>\n",
4139 "</tr>\n",
4140 "<tr>\n",
4141 " <th>Total Assets</th> <td>-2.513e-13</td> <td> 3.73e-14</td> <td> -6.741</td> <td> 0.000</td> <td>-3.24e-13 -1.78e-13</td>\n",
4142 "</tr>\n",
4143 "<tr>\n",
4144 " <th>EPS Growth 3M</th> <td>-1.786e-05</td> <td> 2.15e-06</td> <td> -8.320</td> <td> 0.000</td> <td>-2.21e-05 -1.37e-05</td>\n",
4145 "</tr>\n",
4146 "<tr>\n",
4147 " <th>EPS Growth 12M</th> <td> 2.263e-06</td> <td> 2.15e-06</td> <td> 1.053</td> <td> 0.292</td> <td>-1.95e-06 6.47e-06</td>\n",
4148 "</tr>\n",
4149 "<tr>\n",
4150 " <th>Net Debt Growth 3M</th> <td> -4.19e-06</td> <td> 2.73e-06</td> <td> -1.533</td> <td> 0.125</td> <td>-9.55e-06 1.17e-06</td>\n",
4151 "</tr>\n",
4152 "<tr>\n",
4153 " <th>Net Debt Growth 12M</th> <td> 1.181e-06</td> <td> 2.76e-06</td> <td> 0.428</td> <td> 0.669</td> <td>-4.23e-06 6.59e-06</td>\n",
4154 "</tr>\n",
4155 "<tr>\n",
4156 " <th>Sales Growth 3M</th> <td>-7.312e-07</td> <td> 2.88e-06</td> <td> -0.254</td> <td> 0.800</td> <td>-6.38e-06 4.92e-06</td>\n",
4157 "</tr>\n",
4158 "<tr>\n",
4159 " <th>Sales Growth 12M</th> <td>-1.036e-05</td> <td> 2.94e-06</td> <td> -3.525</td> <td> 0.000</td> <td>-1.61e-05 -4.6e-06</td>\n",
4160 "</tr>\n",
4161 "<tr>\n",
4162 " <th>Total Assets Growth 3M</th> <td> 7.478e-06</td> <td> 3.24e-06</td> <td> 2.310</td> <td> 0.021</td> <td> 1.13e-06 1.38e-05</td>\n",
4163 "</tr>\n",
4164 "<tr>\n",
4165 " <th>Total Assets Growth 12M</th> <td>-3.848e-06</td> <td> 3.44e-06</td> <td> -1.120</td> <td> 0.263</td> <td>-1.06e-05 2.89e-06</td>\n",
4166 "</tr>\n",
4167 "<tr>\n",
4168 " <th>CFO To Assets</th> <td> 9.296e-05</td> <td> 3.95e-05</td> <td> 2.352</td> <td> 0.019</td> <td> 1.55e-05 0.000</td>\n",
4169 "</tr>\n",
4170 "<tr>\n",
4171 " <th>Capex To Assets</th> <td> -0.0002</td> <td> 8.77e-05</td> <td> -1.768</td> <td> 0.077</td> <td> -0.000 1.68e-05</td>\n",
4172 "</tr>\n",
4173 "<tr>\n",
4174 " <th>Capex To FCF</th> <td> 0.0002</td> <td> 4.86e-05</td> <td> 3.199</td> <td> 0.001</td> <td> 6.03e-05 0.000</td>\n",
4175 "</tr>\n",
4176 "<tr>\n",
4177 " <th>Capex To Sales</th> <td> 0.0003</td> <td> 9.32e-05</td> <td> 2.992</td> <td> 0.003</td> <td> 9.62e-05 0.000</td>\n",
4178 "</tr>\n",
4179 "<tr>\n",
4180 " <th>EBIT To Assets</th> <td> 4.836e-06</td> <td> 4e-05</td> <td> 0.121</td> <td> 0.904</td> <td>-7.35e-05 8.32e-05</td>\n",
4181 "</tr>\n",
4182 "<tr>\n",
4183 " <th>Retained Earnings To Assets</th> <td> -0.0010</td> <td> 8.46e-05</td> <td> -12.222</td> <td> 0.000</td> <td> -0.001 -0.001</td>\n",
4184 "</tr>\n",
4185 "<tr>\n",
4186 " <th>Downside Risk</th> <td>-2.381e-05</td> <td> 2.45e-05</td> <td> -0.972</td> <td> 0.331</td> <td>-7.18e-05 2.42e-05</td>\n",
4187 "</tr>\n",
4188 "<tr>\n",
4189 " <th>Index Beta</th> <td>-4.429e-06</td> <td> 5.04e-07</td> <td> -8.793</td> <td> 0.000</td> <td>-5.42e-06 -3.44e-06</td>\n",
4190 "</tr>\n",
4191 "<tr>\n",
4192 " <th>Log Market Cap</th> <td> 0.0001</td> <td> 8.62e-05</td> <td> 1.329</td> <td> 0.184</td> <td>-5.44e-05 0.000</td>\n",
4193 "</tr>\n",
4194 "<tr>\n",
4195 " <th>Volatility 3M</th> <td> -0.0002</td> <td> 2.08e-05</td> <td> -7.260</td> <td> 0.000</td> <td> -0.000 -0.000</td>\n",
4196 "</tr>\n",
4197 "<tr>\n",
4198 " <th>stock_3D SYSTEMS CORP</th> <td> 0.3173</td> <td> 0.020</td> <td> 15.578</td> <td> 0.000</td> <td> 0.277 0.357</td>\n",
4199 "</tr>\n",
4200 "<tr>\n",
4201 " <th>stock_3M COMPANY</th> <td> 0.3182</td> <td> 0.018</td> <td> 17.973</td> <td> 0.000</td> <td> 0.284 0.353</td>\n",
4202 "</tr>\n",
4203 "<tr>\n",
4204 " <th>stock_ABBOTT LABORATORIES</th> <td> 0.2168</td> <td> 0.014</td> <td> 14.970</td> <td> 0.000</td> <td> 0.188 0.245</td>\n",
4205 "</tr>\n",
4206 "<tr>\n",
4207 " <th>stock_ABBVIE INC</th> <td> 0.3427</td> <td> 0.026</td> <td> 13.306</td> <td> 0.000</td> <td> 0.292 0.393</td>\n",
4208 "</tr>\n",
4209 "<tr>\n",
4210 " <th>stock_ALLERGAN INC</th> <td> 0.3183</td> <td> 0.014</td> <td> 23.540</td> <td> 0.000</td> <td> 0.292 0.345</td>\n",
4211 "</tr>\n",
4212 "<tr>\n",
4213 " <th>stock_ALLERGAN PLC</th> <td> 0.3267</td> <td> 0.019</td> <td> 17.361</td> <td> 0.000</td> <td> 0.290 0.364</td>\n",
4214 "</tr>\n",
4215 "<tr>\n",
4216 " <th>stock_ALTABA INC</th> <td> 0.3407</td> <td> 0.021</td> <td> 16.280</td> <td> 0.000</td> <td> 0.300 0.382</td>\n",
4217 "</tr>\n",
4218 "<tr>\n",
4219 " <th>stock_ALTRIA GROUP INC.</th> <td> 0.3366</td> <td> 0.018</td> <td> 18.421</td> <td> 0.000</td> <td> 0.301 0.372</td>\n",
4220 "</tr>\n",
4221 "<tr>\n",
4222 " <th>stock_AMAZON.COM INC</th> <td> 0.2840</td> <td> 0.021</td> <td> 13.457</td> <td> 0.000</td> <td> 0.243 0.325</td>\n",
4223 "</tr>\n",
4224 "<tr>\n",
4225 " <th>stock_AMERICAN AIRLINES GROUP INC</th> <td> 0.2416</td> <td> 0.024</td> <td> 10.047</td> <td> 0.000</td> <td> 0.194 0.289</td>\n",
4226 "</tr>\n",
4227 "<tr>\n",
4228 " <th>stock_AMERICAN EXPRESS COMPANY</th> <td> 0.1996</td> <td> 0.011</td> <td> 17.471</td> <td> 0.000</td> <td> 0.177 0.222</td>\n",
4229 "</tr>\n",
4230 "<tr>\n",
4231 " <th>stock_AMERICAN INTL GROUP INC</th> <td> 0.2697</td> <td> 0.020</td> <td> 13.737</td> <td> 0.000</td> <td> 0.231 0.308</td>\n",
4232 "</tr>\n",
4233 "<tr>\n",
4234 " <th>stock_AMGEN INC</th> <td> 0.2179</td> <td> 0.012</td> <td> 17.559</td> <td> 0.000</td> <td> 0.194 0.242</td>\n",
4235 "</tr>\n",
4236 "<tr>\n",
4237 " <th>stock_ANADARKO PETROLEUM CORP</th> <td> 0.2158</td> <td> 0.011</td> <td> 19.604</td> <td> 0.000</td> <td> 0.194 0.237</td>\n",
4238 "</tr>\n",
4239 "<tr>\n",
4240 " <th>stock_APACHE CORP</th> <td> 0.1494</td> <td> 0.056</td> <td> 2.651</td> <td> 0.008</td> <td> 0.039 0.260</td>\n",
4241 "</tr>\n",
4242 "<tr>\n",
4243 " <th>stock_APPLE INC</th> <td> 0.3383</td> <td> 0.018</td> <td> 19.158</td> <td> 0.000</td> <td> 0.304 0.373</td>\n",
4244 "</tr>\n",
4245 "<tr>\n",
4246 " <th>stock_APPLIED MATERIALS INC</th> <td> 0.2390</td> <td> 0.014</td> <td> 17.579</td> <td> 0.000</td> <td> 0.212 0.266</td>\n",
4247 "</tr>\n",
4248 "<tr>\n",
4249 " <th>stock_ARCONIC INC</th> <td> 0.1068</td> <td> 0.010</td> <td> 10.685</td> <td> 0.000</td> <td> 0.087 0.126</td>\n",
4250 "</tr>\n",
4251 "<tr>\n",
4252 " <th>stock_AT&T INC. COM</th> <td> 0.2514</td> <td> 0.020</td> <td> 12.594</td> <td> 0.000</td> <td> 0.212 0.291</td>\n",
4253 "</tr>\n",
4254 "<tr>\n",
4255 " <th>stock_Alphabet Inc. Cl A</th> <td> 0.4051</td> <td> 0.025</td> <td> 16.157</td> <td> 0.000</td> <td> 0.356 0.454</td>\n",
4256 "</tr>\n",
4257 "<tr>\n",
4258 " <th>stock_BAKER HUGHES INC</th> <td> 0.2139</td> <td> 0.012</td> <td> 17.586</td> <td> 0.000</td> <td> 0.190 0.238</td>\n",
4259 "</tr>\n",
4260 "<tr>\n",
4261 " <th>stock_BANK OF AMERICA CORP</th> <td> 0.7417</td> <td> 0.076</td> <td> 9.797</td> <td> 0.000</td> <td> 0.593 0.890</td>\n",
4262 "</tr>\n",
4263 "<tr>\n",
4264 " <th>stock_BERKSHIRE HATHAWAY INC CL-B</th> <td> 0.3567</td> <td> 0.026</td> <td> 13.681</td> <td> 0.000</td> <td> 0.306 0.408</td>\n",
4265 "</tr>\n",
4266 "<tr>\n",
4267 " <th>stock_BIOGEN INC</th> <td> 0.3593</td> <td> 0.017</td> <td> 21.207</td> <td> 0.000</td> <td> 0.326 0.392</td>\n",
4268 "</tr>\n",
4269 "<tr>\n",
4270 " <th>stock_BOEING CO</th> <td> 0.2107</td> <td> 0.013</td> <td> 15.879</td> <td> 0.000</td> <td> 0.185 0.237</td>\n",
4271 "</tr>\n",
4272 "<tr>\n",
4273 " <th>stock_BOOKING HOLDINGS INC</th> <td> 0.3602</td> <td> 0.022</td> <td> 16.449</td> <td> 0.000</td> <td> 0.317 0.403</td>\n",
4274 "</tr>\n",
4275 "<tr>\n",
4276 " <th>stock_BRISTOL MYERS SQUIBB COMPANY</th> <td> 0.3115</td> <td> 0.015</td> <td> 20.543</td> <td> 0.000</td> <td> 0.282 0.341</td>\n",
4277 "</tr>\n",
4278 "<tr>\n",
4279 " <th>stock_BROADCOM CORP</th> <td> 0.2813</td> <td> 0.020</td> <td> 14.099</td> <td> 0.000</td> <td> 0.242 0.320</td>\n",
4280 "</tr>\n",
4281 "<tr>\n",
4282 " <th>stock_BROADCOM INC</th> <td> 0.3808</td> <td> 0.026</td> <td> 14.895</td> <td> 0.000</td> <td> 0.331 0.431</td>\n",
4283 "</tr>\n",
4284 "<tr>\n",
4285 " <th>stock_CATERPILLAR INC</th> <td> 0.1730</td> <td> 0.013</td> <td> 13.255</td> <td> 0.000</td> <td> 0.147 0.199</td>\n",
4286 "</tr>\n",
4287 "<tr>\n",
4288 " <th>stock_CELGENE CORP</th> <td> 0.2960</td> <td> 0.015</td> <td> 20.285</td> <td> 0.000</td> <td> 0.267 0.325</td>\n",
4289 "</tr>\n",
4290 "<tr>\n",
4291 " <th>stock_CHESAPEAKE ENERGY CORP</th> <td>-4.999e-14</td> <td> 8.77e-14</td> <td> -0.570</td> <td> 0.568</td> <td>-2.22e-13 1.22e-13</td>\n",
4292 "</tr>\n",
4293 "<tr>\n",
4294 " <th>stock_CHEVRON CORPORATION</th> <td> 0.3485</td> <td> 0.027</td> <td> 13.048</td> <td> 0.000</td> <td> 0.296 0.401</td>\n",
4295 "</tr>\n",
4296 "<tr>\n",
4297 " <th>stock_CISCO SYSTEMS INC</th> <td> 0.2298</td> <td> 0.013</td> <td> 17.569</td> <td> 0.000</td> <td> 0.204 0.255</td>\n",
4298 "</tr>\n",
4299 "<tr>\n",
4300 " <th>stock_CITIGROUP</th> <td> 0.6481</td> <td> 0.065</td> <td> 9.957</td> <td> 0.000</td> <td> 0.521 0.776</td>\n",
4301 "</tr>\n",
4302 "<tr>\n",
4303 " <th>stock_COCA-COLA CO</th> <td> 0.2999</td> <td> 0.018</td> <td> 16.645</td> <td> 0.000</td> <td> 0.265 0.335</td>\n",
4304 "</tr>\n",
4305 "<tr>\n",
4306 " <th>stock_COMCAST CORP</th> <td> 0.2309</td> <td> 0.013</td> <td> 17.938</td> <td> 0.000</td> <td> 0.206 0.256</td>\n",
4307 "</tr>\n",
4308 "<tr>\n",
4309 " <th>stock_CONOCOPHILLIPS</th> <td> 0.2784</td> <td> 0.022</td> <td> 12.429</td> <td> 0.000</td> <td> 0.235 0.322</td>\n",
4310 "</tr>\n",
4311 "<tr>\n",
4312 " <th>stock_COVIDIEN PLC</th> <td> 0.3792</td> <td> 0.024</td> <td> 15.807</td> <td> 0.000</td> <td> 0.332 0.426</td>\n",
4313 "</tr>\n",
4314 "<tr>\n",
4315 " <th>stock_CVS HEALTH CORP</th> <td> 0.2568</td> <td> 0.017</td> <td> 15.384</td> <td> 0.000</td> <td> 0.224 0.289</td>\n",
4316 "</tr>\n",
4317 "<tr>\n",
4318 " <th>stock_DEERE & CO</th> <td> 0.1513</td> <td> 0.014</td> <td> 10.763</td> <td> 0.000</td> <td> 0.124 0.179</td>\n",
4319 "</tr>\n",
4320 "<tr>\n",
4321 " <th>stock_DELTA AIR LINES INC</th> <td> 0.2806</td> <td> 0.022</td> <td> 12.572</td> <td> 0.000</td> <td> 0.237 0.324</td>\n",
4322 "</tr>\n",
4323 "<tr>\n",
4324 " <th>stock_DIRECTV</th> <td> 0.2139</td> <td> 0.022</td> <td> 9.864</td> <td> 0.000</td> <td> 0.171 0.256</td>\n",
4325 "</tr>\n",
4326 "<tr>\n",
4327 " <th>stock_DOLLAR GENERAL CORP</th> <td> 0.2719</td> <td> 0.023</td> <td> 11.971</td> <td> 0.000</td> <td> 0.227 0.316</td>\n",
4328 "</tr>\n",
4329 "<tr>\n",
4330 " <th>stock_DOW CHEMICAL CO</th> <td> 0.1912</td> <td> 0.013</td> <td> 14.832</td> <td> 0.000</td> <td> 0.166 0.216</td>\n",
4331 "</tr>\n",
4332 "<tr>\n",
4333 " <th>stock_E.I. Du Pont De Nemours A</th> <td> 0.1633</td> <td> 0.013</td> <td> 12.300</td> <td> 0.000</td> <td> 0.137 0.189</td>\n",
4334 "</tr>\n",
4335 "<tr>\n",
4336 " <th>stock_EBAY INC</th> <td> 0.3558</td> <td> 0.023</td> <td> 15.256</td> <td> 0.000</td> <td> 0.310 0.402</td>\n",
4337 "</tr>\n",
4338 "<tr>\n",
4339 " <th>stock_EMC CORPORATION</th> <td> 0.2526</td> <td> 0.013</td> <td> 19.131</td> <td> 0.000</td> <td> 0.227 0.278</td>\n",
4340 "</tr>\n",
4341 "<tr>\n",
4342 " <th>stock_EOG RESOURCES INC</th> <td> 0.2644</td> <td> 0.013</td> <td> 20.854</td> <td> 0.000</td> <td> 0.240 0.289</td>\n",
4343 "</tr>\n",
4344 "<tr>\n",
4345 " <th>stock_EXPRESS SCRIPTS HOLDING CO</th> <td> 0.1640</td> <td> 0.014</td> <td> 11.874</td> <td> 0.000</td> <td> 0.137 0.191</td>\n",
4346 "</tr>\n",
4347 "<tr>\n",
4348 " <th>stock_EXXON MOBIL CORPORATION</th> <td> 0.4155</td> <td> 0.031</td> <td> 13.352</td> <td> 0.000</td> <td> 0.355 0.477</td>\n",
4349 "</tr>\n",
4350 "<tr>\n",
4351 " <th>stock_FACEBOOK INC</th> <td> 0.3934</td> <td> 0.026</td> <td> 14.853</td> <td> 0.000</td> <td> 0.341 0.445</td>\n",
4352 "</tr>\n",
4353 "<tr>\n",
4354 " <th>stock_FEDEX CORPORATION</th> <td> 0.2296</td> <td> 0.014</td> <td> 16.546</td> <td> 0.000</td> <td> 0.202 0.257</td>\n",
4355 "</tr>\n",
4356 "<tr>\n",
4357 " <th>stock_FIRST SOLAR INC</th> <td> 0.3130</td> <td> 0.025</td> <td> 12.617</td> <td> 0.000</td> <td> 0.264 0.362</td>\n",
4358 "</tr>\n",
4359 "<tr>\n",
4360 " <th>stock_FORD MOTOR CO(NEW)</th> <td> 0.1766</td> <td> 0.017</td> <td> 10.680</td> <td> 0.000</td> <td> 0.144 0.209</td>\n",
4361 "</tr>\n",
4362 "<tr>\n",
4363 " <th>stock_FREEPORT-MCMORAN INC</th> <td> 0.1663</td> <td> 0.018</td> <td> 9.357</td> <td> 0.000</td> <td> 0.131 0.201</td>\n",
4364 "</tr>\n",
4365 "<tr>\n",
4366 " <th>stock_GENERAL ELECTRIC CO</th> <td> 0.3810</td> <td> 0.026</td> <td> 14.621</td> <td> 0.000</td> <td> 0.330 0.432</td>\n",
4367 "</tr>\n",
4368 "<tr>\n",
4369 " <th>stock_GENERAL MOTORS CO</th> <td> 0.2661</td> <td> 0.026</td> <td> 10.406</td> <td> 0.000</td> <td> 0.216 0.316</td>\n",
4370 "</tr>\n",
4371 "<tr>\n",
4372 " <th>stock_GILEAD SCIENCES INC</th> <td> 0.3193</td> <td> 0.016</td> <td> 20.030</td> <td> 0.000</td> <td> 0.288 0.351</td>\n",
4373 "</tr>\n",
4374 "<tr>\n",
4375 " <th>stock_GOLDMAN SACHS GROUP INC</th> <td> 0.5289</td> <td> 0.035</td> <td> 14.945</td> <td> 0.000</td> <td> 0.460 0.598</td>\n",
4376 "</tr>\n",
4377 "<tr>\n",
4378 " <th>stock_GOPRO INC</th> <td> 0.3186</td> <td> 0.026</td> <td> 12.297</td> <td> 0.000</td> <td> 0.268 0.369</td>\n",
4379 "</tr>\n",
4380 "<tr>\n",
4381 " <th>stock_HALLIBURTON CO (HOLDING CO)</th> <td> 0.2384</td> <td> 0.014</td> <td> 16.933</td> <td> 0.000</td> <td> 0.211 0.266</td>\n",
4382 "</tr>\n",
4383 "<tr>\n",
4384 " <th>stock_HOME DEPOT INC</th> <td> 0.2761</td> <td> 0.016</td> <td> 17.535</td> <td> 0.000</td> <td> 0.245 0.307</td>\n",
4385 "</tr>\n",
4386 "<tr>\n",
4387 " <th>stock_HP INC</th> <td> 0.2021</td> <td> 0.015</td> <td> 13.749</td> <td> 0.000</td> <td> 0.173 0.231</td>\n",
4388 "</tr>\n",
4389 "<tr>\n",
4390 " <th>stock_INTEL CORP</th> <td> 0.2633</td> <td> 0.016</td> <td> 16.526</td> <td> 0.000</td> <td> 0.232 0.294</td>\n",
4391 "</tr>\n",
4392 "<tr>\n",
4393 " <th>stock_INTL BUSINESS MACHINES CORP</th> <td> 0.2830</td> <td> 0.018</td> <td> 15.965</td> <td> 0.000</td> <td> 0.248 0.318</td>\n",
4394 "</tr>\n",
4395 "<tr>\n",
4396 " <th>stock_JOHNSON AND JOHNSON</th> <td> 0.3200</td> <td> 0.018</td> <td> 17.592</td> <td> 0.000</td> <td> 0.284 0.356</td>\n",
4397 "</tr>\n",
4398 "<tr>\n",
4399 " <th>stock_JPMORGAN CHASE & CO COM STK</th> <td> 0.8913</td> <td> 0.092</td> <td> 9.698</td> <td> 0.000</td> <td> 0.711 1.071</td>\n",
4400 "</tr>\n",
4401 "<tr>\n",
4402 " <th>stock_KEURIG GREEN MOUNTAIN INC</th> <td> 0.3387</td> <td> 0.019</td> <td> 18.109</td> <td> 0.000</td> <td> 0.302 0.375</td>\n",
4403 "</tr>\n",
4404 "<tr>\n",
4405 " <th>stock_KINDER MORGAN INC</th> <td> 0.2413</td> <td> 0.025</td> <td> 9.811</td> <td> 0.000</td> <td> 0.193 0.289</td>\n",
4406 "</tr>\n",
4407 "<tr>\n",
4408 " <th>stock_LAS VEGAS SANDS CORP</th> <td> 0.2950</td> <td> 0.024</td> <td> 12.543</td> <td> 0.000</td> <td> 0.249 0.341</td>\n",
4409 "</tr>\n",
4410 "<tr>\n",
4411 " <th>stock_LILLY ELI & CO</th> <td> 0.2792</td> <td> 0.016</td> <td> 16.965</td> <td> 0.000</td> <td> 0.247 0.311</td>\n",
4412 "</tr>\n",
4413 "<tr>\n",
4414 " <th>stock_LINKEDIN CORP</th> <td> 0.3443</td> <td> 0.026</td> <td> 13.316</td> <td> 0.000</td> <td> 0.294 0.395</td>\n",
4415 "</tr>\n",
4416 "<tr>\n",
4417 " <th>stock_LOWES COMPANIES INC</th> <td> 0.2297</td> <td> 0.014</td> <td> 16.545</td> <td> 0.000</td> <td> 0.202 0.257</td>\n",
4418 "</tr>\n",
4419 "<tr>\n",
4420 " <th>stock_LYONDELLBASELL INDUSTRIES NV</th> <td> 0.2773</td> <td> 0.024</td> <td> 11.680</td> <td> 0.000</td> <td> 0.231 0.324</td>\n",
4421 "</tr>\n",
4422 "<tr>\n",
4423 " <th>stock_MARATHON PETROLEUM CORP</th> <td> 0.2467</td> <td> 0.026</td> <td> 9.411</td> <td> 0.000</td> <td> 0.195 0.298</td>\n",
4424 "</tr>\n",
4425 "<tr>\n",
4426 " <th>stock_MASTERCARD INCORPORATED</th> <td> 0.4555</td> <td> 0.025</td> <td> 17.920</td> <td> 0.000</td> <td> 0.406 0.505</td>\n",
4427 "</tr>\n",
4428 "<tr>\n",
4429 " <th>stock_MCDONALDS CORP</th> <td> 0.2920</td> <td> 0.018</td> <td> 16.033</td> <td> 0.000</td> <td> 0.256 0.328</td>\n",
4430 "</tr>\n",
4431 "<tr>\n",
4432 " <th>stock_MEDTRONIC PLC</th> <td> 0.3192</td> <td> 0.016</td> <td> 19.660</td> <td> 0.000</td> <td> 0.287 0.351</td>\n",
4433 "</tr>\n",
4434 "<tr>\n",
4435 " <th>stock_MERCK & CO INC</th> <td> 0.3003</td> <td> 0.017</td> <td> 17.338</td> <td> 0.000</td> <td> 0.266 0.334</td>\n",
4436 "</tr>\n",
4437 "<tr>\n",
4438 " <th>stock_METLIFE INC</th> <td> 0.3781</td> <td> 0.037</td> <td> 10.338</td> <td> 0.000</td> <td> 0.306 0.450</td>\n",
4439 "</tr>\n",
4440 "<tr>\n",
4441 " <th>stock_MICHAEL KORS HOLDINGS LTD</th> <td> 0.3756</td> <td> 0.027</td> <td> 13.904</td> <td> 0.000</td> <td> 0.323 0.429</td>\n",
4442 "</tr>\n",
4443 "<tr>\n",
4444 " <th>stock_MICRON TECHNOLOGY INC</th> <td> 0.2171</td> <td> 0.014</td> <td> 15.211</td> <td> 0.000</td> <td> 0.189 0.245</td>\n",
4445 "</tr>\n",
4446 "<tr>\n",
4447 " <th>stock_MICROSOFT CORP</th> <td> 0.3047</td> <td> 0.018</td> <td> 17.242</td> <td> 0.000</td> <td> 0.270 0.339</td>\n",
4448 "</tr>\n",
4449 "<tr>\n",
4450 " <th>stock_MONDELEZ INTERNATIONAL INC</th> <td> 0.2891</td> <td> 0.021</td> <td> 13.662</td> <td> 0.000</td> <td> 0.248 0.331</td>\n",
4451 "</tr>\n",
4452 "<tr>\n",
4453 " <th>stock_MONSANTO COMPANY</th> <td> 0.3312</td> <td> 0.022</td> <td> 15.138</td> <td> 0.000</td> <td> 0.288 0.374</td>\n",
4454 "</tr>\n",
4455 "<tr>\n",
4456 " <th>stock_MORGAN STANLEY</th> <td> 0.4563</td> <td> 0.033</td> <td> 13.722</td> <td> 0.000</td> <td> 0.391 0.521</td>\n",
4457 "</tr>\n",
4458 "<tr>\n",
4459 " <th>stock_MYLAN NV</th> <td> 0.2558</td> <td> 0.016</td> <td> 16.113</td> <td> 0.000</td> <td> 0.225 0.287</td>\n",
4460 "</tr>\n",
4461 "<tr>\n",
4462 " <th>stock_NATIONAL OILWELL VARCO INC.</th> <td> 0.2658</td> <td> 0.023</td> <td> 11.681</td> <td> 0.000</td> <td> 0.221 0.310</td>\n",
4463 "</tr>\n",
4464 "<tr>\n",
4465 " <th>stock_NETFLIX INC</th> <td> 0.3875</td> <td> 0.023</td> <td> 16.682</td> <td> 0.000</td> <td> 0.342 0.433</td>\n",
4466 "</tr>\n",
4467 "<tr>\n",
4468 " <th>stock_NEWMONT MINING CORP (HOLDING COMPANY)</th> <td> 0.1446</td> <td> 0.017</td> <td> 8.514</td> <td> 0.000</td> <td> 0.111 0.178</td>\n",
4469 "</tr>\n",
4470 "<tr>\n",
4471 " <th>stock_NEWS CP - CL A</th> <td> 0.2669</td> <td> 0.018</td> <td> 15.067</td> <td> 0.000</td> <td> 0.232 0.302</td>\n",
4472 "</tr>\n",
4473 "<tr>\n",
4474 " <th>stock_NIKE INC CL-B</th> <td> 0.3016</td> <td> 0.017</td> <td> 18.078</td> <td> 0.000</td> <td> 0.269 0.334</td>\n",
4475 "</tr>\n",
4476 "<tr>\n",
4477 " <th>stock_OCCIDENTAL PETROLEUM CORP</th> <td> 0.2756</td> <td> 0.017</td> <td> 15.892</td> <td> 0.000</td> <td> 0.242 0.310</td>\n",
4478 "</tr>\n",
4479 "<tr>\n",
4480 " <th>stock_ORACLE CORP</th> <td> 0.2984</td> <td> 0.017</td> <td> 18.058</td> <td> 0.000</td> <td> 0.266 0.331</td>\n",
4481 "</tr>\n",
4482 "<tr>\n",
4483 " <th>stock_PANDORA MEDIA INC</th> <td> 0.3925</td> <td> 0.028</td> <td> 14.067</td> <td> 0.000</td> <td> 0.338 0.447</td>\n",
4484 "</tr>\n",
4485 "<tr>\n",
4486 " <th>stock_PENNEY J.C. CO INC (HOLDING COMPANY)</th> <td> 0.0737</td> <td> 0.014</td> <td> 5.167</td> <td> 0.000</td> <td> 0.046 0.102</td>\n",
4487 "</tr>\n",
4488 "<tr>\n",
4489 " <th>stock_PEPSICO INC</th> <td> 0.2931</td> <td> 0.019</td> <td> 15.449</td> <td> 0.000</td> <td> 0.256 0.330</td>\n",
4490 "</tr>\n",
4491 "<tr>\n",
4492 " <th>stock_PFIZER INC</th> <td> 0.3336</td> <td> 0.019</td> <td> 17.563</td> <td> 0.000</td> <td> 0.296 0.371</td>\n",
4493 "</tr>\n",
4494 "<tr>\n",
4495 " <th>stock_PHILIP MORRIS INTERNATIONAL INC</th> <td> 0.3844</td> <td> 0.028</td> <td> 13.959</td> <td> 0.000</td> <td> 0.330 0.438</td>\n",
4496 "</tr>\n",
4497 "<tr>\n",
4498 " <th>stock_PIONEER NAT RES CO</th> <td> 0.3579</td> <td> 0.020</td> <td> 17.747</td> <td> 0.000</td> <td> 0.318 0.397</td>\n",
4499 "</tr>\n",
4500 "<tr>\n",
4501 " <th>stock_PRECISION CASTPARTS CORP</th> <td> 0.3008</td> <td> 0.020</td> <td> 14.774</td> <td> 0.000</td> <td> 0.261 0.341</td>\n",
4502 "</tr>\n",
4503 "<tr>\n",
4504 " <th>stock_PROCTER & GAMBLE CO</th> <td> 0.3094</td> <td> 0.020</td> <td> 15.745</td> <td> 0.000</td> <td> 0.271 0.348</td>\n",
4505 "</tr>\n",
4506 "<tr>\n",
4507 " <th>stock_QUALCOMM INC</th> <td> 0.3232</td> <td> 0.019</td> <td> 17.225</td> <td> 0.000</td> <td> 0.286 0.360</td>\n",
4508 "</tr>\n",
4509 "<tr>\n",
4510 " <th>stock_REGENERON PHARMACEUTICALS INC</th> <td> 0.3153</td> <td> 0.043</td> <td> 7.267</td> <td> 0.000</td> <td> 0.230 0.400</td>\n",
4511 "</tr>\n",
4512 "<tr>\n",
4513 " <th>stock_SALESFORCE.COM INC</th> <td> 0.3156</td> <td> 0.023</td> <td> 13.571</td> <td> 0.000</td> <td> 0.270 0.361</td>\n",
4514 "</tr>\n",
4515 "<tr>\n",
4516 " <th>stock_SALIX PHARMACEUTICALS LTD</th> <td>-5.165e-15</td> <td> 7.41e-15</td> <td> -0.697</td> <td> 0.486</td> <td>-1.97e-14 9.37e-15</td>\n",
4517 "</tr>\n",
4518 "<tr>\n",
4519 " <th>stock_SANDISK CORP</th> <td> 0.3350</td> <td> 0.020</td> <td> 17.064</td> <td> 0.000</td> <td> 0.296 0.373</td>\n",
4520 "</tr>\n",
4521 "<tr>\n",
4522 " <th>stock_SCHLUMBERGER LTD.</th> <td> 0.3011</td> <td> 0.018</td> <td> 16.396</td> <td> 0.000</td> <td> 0.265 0.337</td>\n",
4523 "</tr>\n",
4524 "<tr>\n",
4525 " <th>stock_SKYWORKS SOLUTIONS INC</th> <td> 0.3422</td> <td> 0.023</td> <td> 14.843</td> <td> 0.000</td> <td> 0.297 0.387</td>\n",
4526 "</tr>\n",
4527 "<tr>\n",
4528 " <th>stock_SOLARCITY CORP</th> <td> 0.3803</td> <td> 0.027</td> <td> 14.070</td> <td> 0.000</td> <td> 0.327 0.433</td>\n",
4529 "</tr>\n",
4530 "<tr>\n",
4531 " <th>stock_SOUTHWEST AIRLINES CO</th> <td> 0.2513</td> <td> 0.015</td> <td> 16.796</td> <td> 0.000</td> <td> 0.222 0.281</td>\n",
4532 "</tr>\n",
4533 "<tr>\n",
4534 " <th>stock_STARBUCKS CORPORATION</th> <td> 0.3558</td> <td> 0.019</td> <td> 19.139</td> <td> 0.000</td> <td> 0.319 0.392</td>\n",
4535 "</tr>\n",
4536 "<tr>\n",
4537 " <th>stock_SUNEDISON INC</th> <td> 1.713e-16</td> <td> 2.85e-16</td> <td> 0.601</td> <td> 0.548</td> <td>-3.87e-16 7.3e-16</td>\n",
4538 "</tr>\n",
4539 "<tr>\n",
4540 " <th>stock_TARGET CORPORATION</th> <td> 0.2298</td> <td> 0.020</td> <td> 11.314</td> <td> 0.000</td> <td> 0.190 0.270</td>\n",
4541 "</tr>\n",
4542 "<tr>\n",
4543 " <th>stock_TESLA INC</th> <td> 0.3540</td> <td> 0.026</td> <td> 13.789</td> <td> 0.000</td> <td> 0.304 0.404</td>\n",
4544 "</tr>\n",
4545 "<tr>\n",
4546 " <th>stock_TEXAS INSTRUMENTS INC</th> <td> 0.3573</td> <td> 0.021</td> <td> 17.423</td> <td> 0.000</td> <td> 0.317 0.397</td>\n",
4547 "</tr>\n",
4548 "<tr>\n",
4549 " <th>stock_TIME WARNER CABLE INC</th> <td> 0.2637</td> <td> 0.022</td> <td> 11.821</td> <td> 0.000</td> <td> 0.220 0.307</td>\n",
4550 "</tr>\n",
4551 "<tr>\n",
4552 " <th>stock_TIME WARNER INC.</th> <td> 0.1773</td> <td> 0.010</td> <td> 18.052</td> <td> 0.000</td> <td> 0.158 0.197</td>\n",
4553 "</tr>\n",
4554 "<tr>\n",
4555 " <th>stock_TWITTER INC</th> <td> 0.3333</td> <td> 0.027</td> <td> 12.463</td> <td> 0.000</td> <td> 0.281 0.386</td>\n",
4556 "</tr>\n",
4557 "<tr>\n",
4558 " <th>stock_UNION PACIFIC CORPORATION</th> <td> 0.3164</td> <td> 0.018</td> <td> 17.283</td> <td> 0.000</td> <td> 0.281 0.352</td>\n",
4559 "</tr>\n",
4560 "<tr>\n",
4561 " <th>stock_UNITED CONTINENTAL HOLDINGS IN</th> <td> 0.1975</td> <td> 0.022</td> <td> 8.977</td> <td> 0.000</td> <td> 0.154 0.241</td>\n",
4562 "</tr>\n",
4563 "<tr>\n",
4564 " <th>stock_UNITED PARCEL SERVICE INC.CL B</th> <td> 0.2391</td> <td> 0.021</td> <td> 11.199</td> <td> 0.000</td> <td> 0.197 0.281</td>\n",
4565 "</tr>\n",
4566 "<tr>\n",
4567 " <th>stock_UNITED TECHNOLOGIES CORP</th> <td> 0.2750</td> <td> 0.018</td> <td> 14.928</td> <td> 0.000</td> <td> 0.239 0.311</td>\n",
4568 "</tr>\n",
4569 "<tr>\n",
4570 " <th>stock_UNITEDHEALTH GROUP INC</th> <td> 0.2970</td> <td> 0.019</td> <td> 15.679</td> <td> 0.000</td> <td> 0.260 0.334</td>\n",
4571 "</tr>\n",
4572 "<tr>\n",
4573 " <th>stock_VALERO ENERGY CORP (NEW)</th> <td> 0.2539</td> <td> 0.019</td> <td> 13.356</td> <td> 0.000</td> <td> 0.217 0.291</td>\n",
4574 "</tr>\n",
4575 "<tr>\n",
4576 " <th>stock_VERIZON COMMUNICATIONS</th> <td> 0.2472</td> <td> 0.023</td> <td> 10.825</td> <td> 0.000</td> <td> 0.202 0.292</td>\n",
4577 "</tr>\n",
4578 "<tr>\n",
4579 " <th>stock_VISA INC</th> <td> 0.4192</td> <td> 0.024</td> <td> 17.309</td> <td> 0.000</td> <td> 0.372 0.467</td>\n",
4580 "</tr>\n",
4581 "<tr>\n",
4582 " <th>stock_WALGREENS BOOTS ALLIANCE INC</th> <td> 0.2793</td> <td> 0.018</td> <td> 15.194</td> <td> 0.000</td> <td> 0.243 0.315</td>\n",
4583 "</tr>\n",
4584 "<tr>\n",
4585 " <th>stock_WALMART INC</th> <td> 0.3251</td> <td> 0.031</td> <td> 10.594</td> <td> 0.000</td> <td> 0.265 0.385</td>\n",
4586 "</tr>\n",
4587 "<tr>\n",
4588 " <th>stock_WALT DISNEY CO</th> <td> 0.3131</td> <td> 0.014</td> <td> 21.827</td> <td> 0.000</td> <td> 0.285 0.341</td>\n",
4589 "</tr>\n",
4590 "<tr>\n",
4591 " <th>stock_WELLS FARGO & CO(NEW)</th> <td> 0.7175</td> <td> 0.062</td> <td> 11.586</td> <td> 0.000</td> <td> 0.596 0.839</td>\n",
4592 "</tr>\n",
4593 "<tr>\n",
4594 " <th>stock_WILLIAMS COMPANIES</th> <td> 0.2522</td> <td> 0.019</td> <td> 13.501</td> <td> 0.000</td> <td> 0.216 0.289</td>\n",
4595 "</tr>\n",
4596 "<tr>\n",
4597 " <th>stock_WYNN RESORTS LTD</th> <td> 0.2461</td> <td> 0.022</td> <td> 11.047</td> <td> 0.000</td> <td> 0.202 0.290</td>\n",
4598 "</tr>\n",
4599 "<tr>\n",
4600 " <th>stock_YELP INC</th> <td> 0.3195</td> <td> 0.027</td> <td> 11.882</td> <td> 0.000</td> <td> 0.267 0.372</td>\n",
4601 "</tr>\n",
4602 "</table>\n",
4603 "<table class=\"simpletable\">\n",
4604 "<tr>\n",
4605 " <th>Omnibus:</th> <td>26.157</td> <th> Durbin-Watson: </th> <td> 1.605</td>\n",
4606 "</tr>\n",
4607 "<tr>\n",
4608 " <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td> 26.187</td>\n",
4609 "</tr>\n",
4610 "<tr>\n",
4611 " <th>Skew:</th> <td> 0.059</td> <th> Prob(JB): </th> <td>2.06e-06</td>\n",
4612 "</tr>\n",
4613 "<tr>\n",
4614 " <th>Kurtosis:</th> <td> 2.970</td> <th> Cond. No. </th> <td>1.95e+19</td>\n",
4615 "</tr>\n",
4616 "</table>"
4617 ],
4618 "text/plain": [
4619 "<class 'statsmodels.iolib.summary.Summary'>\n",
4620 "\"\"\"\n",
4621 " OLS Regression Results \n",
4622 "==============================================================================\n",
4623 "Dep. Variable: Returns20D R-squared: 0.072\n",
4624 "Model: OLS Adj. R-squared: 0.068\n",
4625 "Method: Least Squares F-statistic: 19.10\n",
4626 "Date: Fri, 03 Aug 2018 Prob (F-statistic): 0.00\n",
4627 "Time: 21:42:21 Log-Likelihood: 62705.\n",
4628 "No. Observations: 42460 AIC: -1.251e+05\n",
4629 "Df Residuals: 42288 BIC: -1.236e+05\n",
4630 "Df Model: 171 \n",
4631 "Covariance Type: nonrobust \n",
4632 "===============================================================================================================\n",
4633 " coef std err t P>|t| [95.0% Conf. Int.]\n",
4634 "---------------------------------------------------------------------------------------------------------------\n",
4635 "DividendYield 1.193e-05 2.23e-06 5.351 0.000 7.56e-06 1.63e-05\n",
4636 "EBITDAYield -0.0001 4.51e-05 -3.287 0.001 -0.000 -5.98e-05\n",
4637 "EVToEBITDA 0.0003 4.07e-05 7.808 0.000 0.000 0.000\n",
4638 "EVToFCF -3.67e-06 4.99e-05 -0.074 0.941 -0.000 9.42e-05\n",
4639 "PriceToBook 1.86e-05 4.73e-05 0.393 0.694 -7.41e-05 0.000\n",
4640 "PriceToDilutedEarningsTTM -5.406e-05 2.12e-05 -2.549 0.011 -9.56e-05 -1.25e-05\n",
4641 "PriceToEarningsTTM -8.22e-07 9.41e-07 -0.873 0.382 -2.67e-06 1.02e-06\n",
4642 "PriceToFCF 2.678e-06 4.12e-05 0.065 0.948 -7.8e-05 8.33e-05\n",
4643 "PriceToOperatingCashflow -0.0001 2.91e-05 -4.551 0.000 -0.000 -7.54e-05\n",
4644 "PriceToSalesTTM -4.302e-05 2.5e-06 -17.222 0.000 -4.79e-05 -3.81e-05\n",
4645 "Directional Movement Index -3.389e-05 9.51e-06 -3.566 0.000 -5.25e-05 -1.53e-05\n",
4646 "Money Flow Index 2.884e-05 1.23e-05 2.336 0.019 4.64e-06 5.3e-05\n",
4647 "Percent Above Low -7.04e-05 2.47e-05 -2.855 0.004 -0.000 -2.21e-05\n",
4648 "Percent Below High 4.825e-05 1.89e-05 2.559 0.010 1.13e-05 8.52e-05\n",
4649 "Price Oscillator -5.428e-06 1.33e-05 -0.407 0.684 -3.15e-05 2.07e-05\n",
4650 "Trendline 4.502e-05 2.04e-05 2.204 0.028 4.98e-06 8.51e-05\n",
4651 "AssetToEquityRatio -8.615e-06 1.86e-06 -4.637 0.000 -1.23e-05 -4.97e-06\n",
4652 "AssetTurnover -0.0002 8.11e-05 -2.720 0.007 -0.000 -6.16e-05\n",
4653 "CurrentRatio 1.14e-05 2.4e-06 4.756 0.000 6.7e-06 1.61e-05\n",
4654 "DebtToAssetRatio 2.131e-06 1.5e-06 1.421 0.155 -8.08e-07 5.07e-06\n",
4655 "DebtToEquityRatio 4.261e-06 1.74e-06 2.445 0.014 8.45e-07 7.68e-06\n",
4656 "MertonsDD -0.0013 0.000 -6.145 0.000 -0.002 -0.001\n",
4657 "WorkingCapitalToAssets 5.08e-06 2.89e-06 1.759 0.079 -5.8e-07 1.07e-05\n",
4658 "WorkingCapitalToSales -0.0004 9.31e-05 -4.593 0.000 -0.001 -0.000\n",
4659 "Dividend Growth -3.037e-06 9.15e-07 -3.319 0.001 -4.83e-06 -1.24e-06\n",
4660 "EPS 2.228e-06 1.41e-06 1.583 0.113 -5.3e-07 4.99e-06\n",
4661 "Net Debt -2.368e-13 5.14e-14 -4.609 0.000 -3.37e-13 -1.36e-13\n",
4662 "Sales -2.182e-13 5.03e-14 -4.341 0.000 -3.17e-13 -1.2e-13\n",
4663 "Total Assets -2.513e-13 3.73e-14 -6.741 0.000 -3.24e-13 -1.78e-13\n",
4664 "EPS Growth 3M -1.786e-05 2.15e-06 -8.320 0.000 -2.21e-05 -1.37e-05\n",
4665 "EPS Growth 12M 2.263e-06 2.15e-06 1.053 0.292 -1.95e-06 6.47e-06\n",
4666 "Net Debt Growth 3M -4.19e-06 2.73e-06 -1.533 0.125 -9.55e-06 1.17e-06\n",
4667 "Net Debt Growth 12M 1.181e-06 2.76e-06 0.428 0.669 -4.23e-06 6.59e-06\n",
4668 "Sales Growth 3M -7.312e-07 2.88e-06 -0.254 0.800 -6.38e-06 4.92e-06\n",
4669 "Sales Growth 12M -1.036e-05 2.94e-06 -3.525 0.000 -1.61e-05 -4.6e-06\n",
4670 "Total Assets Growth 3M 7.478e-06 3.24e-06 2.310 0.021 1.13e-06 1.38e-05\n",
4671 "Total Assets Growth 12M -3.848e-06 3.44e-06 -1.120 0.263 -1.06e-05 2.89e-06\n",
4672 "CFO To Assets 9.296e-05 3.95e-05 2.352 0.019 1.55e-05 0.000\n",
4673 "Capex To Assets -0.0002 8.77e-05 -1.768 0.077 -0.000 1.68e-05\n",
4674 "Capex To FCF 0.0002 4.86e-05 3.199 0.001 6.03e-05 0.000\n",
4675 "Capex To Sales 0.0003 9.32e-05 2.992 0.003 9.62e-05 0.000\n",
4676 "EBIT To Assets 4.836e-06 4e-05 0.121 0.904 -7.35e-05 8.32e-05\n",
4677 "Retained Earnings To Assets -0.0010 8.46e-05 -12.222 0.000 -0.001 -0.001\n",
4678 "Downside Risk -2.381e-05 2.45e-05 -0.972 0.331 -7.18e-05 2.42e-05\n",
4679 "Index Beta -4.429e-06 5.04e-07 -8.793 0.000 -5.42e-06 -3.44e-06\n",
4680 "Log Market Cap 0.0001 8.62e-05 1.329 0.184 -5.44e-05 0.000\n",
4681 "Volatility 3M -0.0002 2.08e-05 -7.260 0.000 -0.000 -0.000\n",
4682 "stock_3D SYSTEMS CORP 0.3173 0.020 15.578 0.000 0.277 0.357\n",
4683 "stock_3M COMPANY 0.3182 0.018 17.973 0.000 0.284 0.353\n",
4684 "stock_ABBOTT LABORATORIES 0.2168 0.014 14.970 0.000 0.188 0.245\n",
4685 "stock_ABBVIE INC 0.3427 0.026 13.306 0.000 0.292 0.393\n",
4686 "stock_ALLERGAN INC 0.3183 0.014 23.540 0.000 0.292 0.345\n",
4687 "stock_ALLERGAN PLC 0.3267 0.019 17.361 0.000 0.290 0.364\n",
4688 "stock_ALTABA INC 0.3407 0.021 16.280 0.000 0.300 0.382\n",
4689 "stock_ALTRIA GROUP INC. 0.3366 0.018 18.421 0.000 0.301 0.372\n",
4690 "stock_AMAZON.COM INC 0.2840 0.021 13.457 0.000 0.243 0.325\n",
4691 "stock_AMERICAN AIRLINES GROUP INC 0.2416 0.024 10.047 0.000 0.194 0.289\n",
4692 "stock_AMERICAN EXPRESS COMPANY 0.1996 0.011 17.471 0.000 0.177 0.222\n",
4693 "stock_AMERICAN INTL GROUP INC 0.2697 0.020 13.737 0.000 0.231 0.308\n",
4694 "stock_AMGEN INC 0.2179 0.012 17.559 0.000 0.194 0.242\n",
4695 "stock_ANADARKO PETROLEUM CORP 0.2158 0.011 19.604 0.000 0.194 0.237\n",
4696 "stock_APACHE CORP 0.1494 0.056 2.651 0.008 0.039 0.260\n",
4697 "stock_APPLE INC 0.3383 0.018 19.158 0.000 0.304 0.373\n",
4698 "stock_APPLIED MATERIALS INC 0.2390 0.014 17.579 0.000 0.212 0.266\n",
4699 "stock_ARCONIC INC 0.1068 0.010 10.685 0.000 0.087 0.126\n",
4700 "stock_AT&T INC. COM 0.2514 0.020 12.594 0.000 0.212 0.291\n",
4701 "stock_Alphabet Inc. Cl A 0.4051 0.025 16.157 0.000 0.356 0.454\n",
4702 "stock_BAKER HUGHES INC 0.2139 0.012 17.586 0.000 0.190 0.238\n",
4703 "stock_BANK OF AMERICA CORP 0.7417 0.076 9.797 0.000 0.593 0.890\n",
4704 "stock_BERKSHIRE HATHAWAY INC CL-B 0.3567 0.026 13.681 0.000 0.306 0.408\n",
4705 "stock_BIOGEN INC 0.3593 0.017 21.207 0.000 0.326 0.392\n",
4706 "stock_BOEING CO 0.2107 0.013 15.879 0.000 0.185 0.237\n",
4707 "stock_BOOKING HOLDINGS INC 0.3602 0.022 16.449 0.000 0.317 0.403\n",
4708 "stock_BRISTOL MYERS SQUIBB COMPANY 0.3115 0.015 20.543 0.000 0.282 0.341\n",
4709 "stock_BROADCOM CORP 0.2813 0.020 14.099 0.000 0.242 0.320\n",
4710 "stock_BROADCOM INC 0.3808 0.026 14.895 0.000 0.331 0.431\n",
4711 "stock_CATERPILLAR INC 0.1730 0.013 13.255 0.000 0.147 0.199\n",
4712 "stock_CELGENE CORP 0.2960 0.015 20.285 0.000 0.267 0.325\n",
4713 "stock_CHESAPEAKE ENERGY CORP -4.999e-14 8.77e-14 -0.570 0.568 -2.22e-13 1.22e-13\n",
4714 "stock_CHEVRON CORPORATION 0.3485 0.027 13.048 0.000 0.296 0.401\n",
4715 "stock_CISCO SYSTEMS INC 0.2298 0.013 17.569 0.000 0.204 0.255\n",
4716 "stock_CITIGROUP 0.6481 0.065 9.957 0.000 0.521 0.776\n",
4717 "stock_COCA-COLA CO 0.2999 0.018 16.645 0.000 0.265 0.335\n",
4718 "stock_COMCAST CORP 0.2309 0.013 17.938 0.000 0.206 0.256\n",
4719 "stock_CONOCOPHILLIPS 0.2784 0.022 12.429 0.000 0.235 0.322\n",
4720 "stock_COVIDIEN PLC 0.3792 0.024 15.807 0.000 0.332 0.426\n",
4721 "stock_CVS HEALTH CORP 0.2568 0.017 15.384 0.000 0.224 0.289\n",
4722 "stock_DEERE & CO 0.1513 0.014 10.763 0.000 0.124 0.179\n",
4723 "stock_DELTA AIR LINES INC 0.2806 0.022 12.572 0.000 0.237 0.324\n",
4724 "stock_DIRECTV 0.2139 0.022 9.864 0.000 0.171 0.256\n",
4725 "stock_DOLLAR GENERAL CORP 0.2719 0.023 11.971 0.000 0.227 0.316\n",
4726 "stock_DOW CHEMICAL CO 0.1912 0.013 14.832 0.000 0.166 0.216\n",
4727 "stock_E.I. Du Pont De Nemours A 0.1633 0.013 12.300 0.000 0.137 0.189\n",
4728 "stock_EBAY INC 0.3558 0.023 15.256 0.000 0.310 0.402\n",
4729 "stock_EMC CORPORATION 0.2526 0.013 19.131 0.000 0.227 0.278\n",
4730 "stock_EOG RESOURCES INC 0.2644 0.013 20.854 0.000 0.240 0.289\n",
4731 "stock_EXPRESS SCRIPTS HOLDING CO 0.1640 0.014 11.874 0.000 0.137 0.191\n",
4732 "stock_EXXON MOBIL CORPORATION 0.4155 0.031 13.352 0.000 0.355 0.477\n",
4733 "stock_FACEBOOK INC 0.3934 0.026 14.853 0.000 0.341 0.445\n",
4734 "stock_FEDEX CORPORATION 0.2296 0.014 16.546 0.000 0.202 0.257\n",
4735 "stock_FIRST SOLAR INC 0.3130 0.025 12.617 0.000 0.264 0.362\n",
4736 "stock_FORD MOTOR CO(NEW) 0.1766 0.017 10.680 0.000 0.144 0.209\n",
4737 "stock_FREEPORT-MCMORAN INC 0.1663 0.018 9.357 0.000 0.131 0.201\n",
4738 "stock_GENERAL ELECTRIC CO 0.3810 0.026 14.621 0.000 0.330 0.432\n",
4739 "stock_GENERAL MOTORS CO 0.2661 0.026 10.406 0.000 0.216 0.316\n",
4740 "stock_GILEAD SCIENCES INC 0.3193 0.016 20.030 0.000 0.288 0.351\n",
4741 "stock_GOLDMAN SACHS GROUP INC 0.5289 0.035 14.945 0.000 0.460 0.598\n",
4742 "stock_GOPRO INC 0.3186 0.026 12.297 0.000 0.268 0.369\n",
4743 "stock_HALLIBURTON CO (HOLDING CO) 0.2384 0.014 16.933 0.000 0.211 0.266\n",
4744 "stock_HOME DEPOT INC 0.2761 0.016 17.535 0.000 0.245 0.307\n",
4745 "stock_HP INC 0.2021 0.015 13.749 0.000 0.173 0.231\n",
4746 "stock_INTEL CORP 0.2633 0.016 16.526 0.000 0.232 0.294\n",
4747 "stock_INTL BUSINESS MACHINES CORP 0.2830 0.018 15.965 0.000 0.248 0.318\n",
4748 "stock_JOHNSON AND JOHNSON 0.3200 0.018 17.592 0.000 0.284 0.356\n",
4749 "stock_JPMORGAN CHASE & CO COM STK 0.8913 0.092 9.698 0.000 0.711 1.071\n",
4750 "stock_KEURIG GREEN MOUNTAIN INC 0.3387 0.019 18.109 0.000 0.302 0.375\n",
4751 "stock_KINDER MORGAN INC 0.2413 0.025 9.811 0.000 0.193 0.289\n",
4752 "stock_LAS VEGAS SANDS CORP 0.2950 0.024 12.543 0.000 0.249 0.341\n",
4753 "stock_LILLY ELI & CO 0.2792 0.016 16.965 0.000 0.247 0.311\n",
4754 "stock_LINKEDIN CORP 0.3443 0.026 13.316 0.000 0.294 0.395\n",
4755 "stock_LOWES COMPANIES INC 0.2297 0.014 16.545 0.000 0.202 0.257\n",
4756 "stock_LYONDELLBASELL INDUSTRIES NV 0.2773 0.024 11.680 0.000 0.231 0.324\n",
4757 "stock_MARATHON PETROLEUM CORP 0.2467 0.026 9.411 0.000 0.195 0.298\n",
4758 "stock_MASTERCARD INCORPORATED 0.4555 0.025 17.920 0.000 0.406 0.505\n",
4759 "stock_MCDONALDS CORP 0.2920 0.018 16.033 0.000 0.256 0.328\n",
4760 "stock_MEDTRONIC PLC 0.3192 0.016 19.660 0.000 0.287 0.351\n",
4761 "stock_MERCK & CO INC 0.3003 0.017 17.338 0.000 0.266 0.334\n",
4762 "stock_METLIFE INC 0.3781 0.037 10.338 0.000 0.306 0.450\n",
4763 "stock_MICHAEL KORS HOLDINGS LTD 0.3756 0.027 13.904 0.000 0.323 0.429\n",
4764 "stock_MICRON TECHNOLOGY INC 0.2171 0.014 15.211 0.000 0.189 0.245\n",
4765 "stock_MICROSOFT CORP 0.3047 0.018 17.242 0.000 0.270 0.339\n",
4766 "stock_MONDELEZ INTERNATIONAL INC 0.2891 0.021 13.662 0.000 0.248 0.331\n",
4767 "stock_MONSANTO COMPANY 0.3312 0.022 15.138 0.000 0.288 0.374\n",
4768 "stock_MORGAN STANLEY 0.4563 0.033 13.722 0.000 0.391 0.521\n",
4769 "stock_MYLAN NV 0.2558 0.016 16.113 0.000 0.225 0.287\n",
4770 "stock_NATIONAL OILWELL VARCO INC. 0.2658 0.023 11.681 0.000 0.221 0.310\n",
4771 "stock_NETFLIX INC 0.3875 0.023 16.682 0.000 0.342 0.433\n",
4772 "stock_NEWMONT MINING CORP (HOLDING COMPANY) 0.1446 0.017 8.514 0.000 0.111 0.178\n",
4773 "stock_NEWS CP - CL A 0.2669 0.018 15.067 0.000 0.232 0.302\n",
4774 "stock_NIKE INC CL-B 0.3016 0.017 18.078 0.000 0.269 0.334\n",
4775 "stock_OCCIDENTAL PETROLEUM CORP 0.2756 0.017 15.892 0.000 0.242 0.310\n",
4776 "stock_ORACLE CORP 0.2984 0.017 18.058 0.000 0.266 0.331\n",
4777 "stock_PANDORA MEDIA INC 0.3925 0.028 14.067 0.000 0.338 0.447\n",
4778 "stock_PENNEY J.C. CO INC (HOLDING COMPANY) 0.0737 0.014 5.167 0.000 0.046 0.102\n",
4779 "stock_PEPSICO INC 0.2931 0.019 15.449 0.000 0.256 0.330\n",
4780 "stock_PFIZER INC 0.3336 0.019 17.563 0.000 0.296 0.371\n",
4781 "stock_PHILIP MORRIS INTERNATIONAL INC 0.3844 0.028 13.959 0.000 0.330 0.438\n",
4782 "stock_PIONEER NAT RES CO 0.3579 0.020 17.747 0.000 0.318 0.397\n",
4783 "stock_PRECISION CASTPARTS CORP 0.3008 0.020 14.774 0.000 0.261 0.341\n",
4784 "stock_PROCTER & GAMBLE CO 0.3094 0.020 15.745 0.000 0.271 0.348\n",
4785 "stock_QUALCOMM INC 0.3232 0.019 17.225 0.000 0.286 0.360\n",
4786 "stock_REGENERON PHARMACEUTICALS INC 0.3153 0.043 7.267 0.000 0.230 0.400\n",
4787 "stock_SALESFORCE.COM INC 0.3156 0.023 13.571 0.000 0.270 0.361\n",
4788 "stock_SALIX PHARMACEUTICALS LTD -5.165e-15 7.41e-15 -0.697 0.486 -1.97e-14 9.37e-15\n",
4789 "stock_SANDISK CORP 0.3350 0.020 17.064 0.000 0.296 0.373\n",
4790 "stock_SCHLUMBERGER LTD. 0.3011 0.018 16.396 0.000 0.265 0.337\n",
4791 "stock_SKYWORKS SOLUTIONS INC 0.3422 0.023 14.843 0.000 0.297 0.387\n",
4792 "stock_SOLARCITY CORP 0.3803 0.027 14.070 0.000 0.327 0.433\n",
4793 "stock_SOUTHWEST AIRLINES CO 0.2513 0.015 16.796 0.000 0.222 0.281\n",
4794 "stock_STARBUCKS CORPORATION 0.3558 0.019 19.139 0.000 0.319 0.392\n",
4795 "stock_SUNEDISON INC 1.713e-16 2.85e-16 0.601 0.548 -3.87e-16 7.3e-16\n",
4796 "stock_TARGET CORPORATION 0.2298 0.020 11.314 0.000 0.190 0.270\n",
4797 "stock_TESLA INC 0.3540 0.026 13.789 0.000 0.304 0.404\n",
4798 "stock_TEXAS INSTRUMENTS INC 0.3573 0.021 17.423 0.000 0.317 0.397\n",
4799 "stock_TIME WARNER CABLE INC 0.2637 0.022 11.821 0.000 0.220 0.307\n",
4800 "stock_TIME WARNER INC. 0.1773 0.010 18.052 0.000 0.158 0.197\n",
4801 "stock_TWITTER INC 0.3333 0.027 12.463 0.000 0.281 0.386\n",
4802 "stock_UNION PACIFIC CORPORATION 0.3164 0.018 17.283 0.000 0.281 0.352\n",
4803 "stock_UNITED CONTINENTAL HOLDINGS IN 0.1975 0.022 8.977 0.000 0.154 0.241\n",
4804 "stock_UNITED PARCEL SERVICE INC.CL B 0.2391 0.021 11.199 0.000 0.197 0.281\n",
4805 "stock_UNITED TECHNOLOGIES CORP 0.2750 0.018 14.928 0.000 0.239 0.311\n",
4806 "stock_UNITEDHEALTH GROUP INC 0.2970 0.019 15.679 0.000 0.260 0.334\n",
4807 "stock_VALERO ENERGY CORP (NEW) 0.2539 0.019 13.356 0.000 0.217 0.291\n",
4808 "stock_VERIZON COMMUNICATIONS 0.2472 0.023 10.825 0.000 0.202 0.292\n",
4809 "stock_VISA INC 0.4192 0.024 17.309 0.000 0.372 0.467\n",
4810 "stock_WALGREENS BOOTS ALLIANCE INC 0.2793 0.018 15.194 0.000 0.243 0.315\n",
4811 "stock_WALMART INC 0.3251 0.031 10.594 0.000 0.265 0.385\n",
4812 "stock_WALT DISNEY CO 0.3131 0.014 21.827 0.000 0.285 0.341\n",
4813 "stock_WELLS FARGO & CO(NEW) 0.7175 0.062 11.586 0.000 0.596 0.839\n",
4814 "stock_WILLIAMS COMPANIES 0.2522 0.019 13.501 0.000 0.216 0.289\n",
4815 "stock_WYNN RESORTS LTD 0.2461 0.022 11.047 0.000 0.202 0.290\n",
4816 "stock_YELP INC 0.3195 0.027 11.882 0.000 0.267 0.372\n",
4817 "==============================================================================\n",
4818 "Omnibus: 26.157 Durbin-Watson: 1.605\n",
4819 "Prob(Omnibus): 0.000 Jarque-Bera (JB): 26.187\n",
4820 "Skew: 0.059 Prob(JB): 2.06e-06\n",
4821 "Kurtosis: 2.970 Cond. No. 1.95e+19\n",
4822 "==============================================================================\n",
4823 "\n",
4824 "Warnings:\n",
4825 "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
4826 "[2] The smallest eigenvalue is 9.91e-12. This might indicate that there are\n",
4827 "strong multicollinearity problems or that the design matrix is singular.\n",
4828 "\"\"\""
4829 ]
4830 },
4831 "execution_count": 186,
4832 "metadata": {},
4833 "output_type": "execute_result"
4834 }
4835 ],
4836 "source": [
4837 "target = 'Returns20D'\n",
4838 "model_data = pd.concat([y[[target]], X], axis=1).dropna()\n",
4839 "model_data = model_data[model_data[target].between(model_data[target].quantile(.025), \n",
4840 " model_data[target].quantile(.975))]\n",
4841 "\n",
4842 "model = OLS(endog=model_data[target], exog=model_data.drop(target, axis=1))\n",
4843 "trained_model = model.fit()\n",
4844 "trained_model.summary()"
4845 ]
4846 },
4847 {
4848 "cell_type": "markdown",
4849 "metadata": {},
4850 "source": [
4851 "#### sklearn"
4852 ]
4853 },
4854 {
4855 "cell_type": "code",
4856 "execution_count": 142,
4857 "metadata": {
4858 "collapsed": true
4859 },
4860 "outputs": [],
4861 "source": [
4862 "def time_series_split(d, nfolds=5, min_train=21):\n",
4863 " \"\"\"Generate train/test dates for nfolds \n",
4864 " with at least min_train train obs\n",
4865 " \"\"\"\n",
4866 " train_dates = d[:min_train].tolist()\n",
4867 " n = int(len(dates)/(nfolds + 1)) + 1\n",
4868 " test_folds = [d[i:i + n] for i in range(min_train, len(d), n)]\n",
4869 " for test_dates in test_folds:\n",
4870 " if len(train_dates) > min_train:\n",
4871 " yield train_dates, test_dates\n",
4872 " train_dates.extend(test_dates)"
4873 ]
4874 },
4875 {
4876 "cell_type": "code",
4877 "execution_count": 264,
4878 "metadata": {},
4879 "outputs": [
4880 {
4881 "name": "stdout",
4882 "output_type": "stream",
4883 "text": [
4884 "<class 'pandas.core.frame.DataFrame'>\n",
4885 "DatetimeIndex: 45114 entries, 2014-01-02 to 2015-12-16\n",
4886 "Columns: 183 entries, Returns10D to stock_YELP INC\n",
4887 "dtypes: float64(183)\n",
4888 "memory usage: 63.3 MB\n",
4889 "None\n"
4890 ]
4891 }
4892 ],
4893 "source": [
4894 "target = 'Returns10D'\n",
4895 "outliers = .01\n",
4896 "model_data = pd.concat([y[[target]], X], axis=1).dropna().reset_index('asset', drop=True)\n",
4897 "model_data = model_data[model_data[target].between(*model_data[target].quantile([outliers, 1-outliers]).values)] \n",
4898 "\n",
4899 "model_data[target] = np.log1p(model_data[target])\n",
4900 "features = model_data.drop(target, axis=1).columns\n",
4901 "dates = model_data.index.unique()\n",
4902 "\n",
4903 "print(model_data.info())"
4904 ]
4905 },
4906 {
4907 "cell_type": "code",
4908 "execution_count": 265,
4909 "metadata": {},
4910 "outputs": [
4911 {
4912 "data": {
4913 "text/plain": [
4914 "count 45114.000000\n",
4915 "mean 0.001159\n",
4916 "std 0.045740\n",
4917 "min -0.157448\n",
4918 "25% -0.025013\n",
4919 "50% 0.002817\n",
4920 "75% 0.028880\n",
4921 "max 0.146139\n",
4922 "Name: Returns10D, dtype: float64"
4923 ]
4924 },
4925 "execution_count": 265,
4926 "metadata": {},
4927 "output_type": "execute_result"
4928 }
4929 ],
4930 "source": [
4931 "model_data[target].describe()"
4932 ]
4933 },
4934 {
4935 "cell_type": "code",
4936 "execution_count": 266,
4937 "metadata": {
4938 "collapsed": true
4939 },
4940 "outputs": [],
4941 "source": [
4942 "idx = pd.IndexSlice"
4943 ]
4944 },
4945 {
4946 "cell_type": "code",
4947 "execution_count": 267,
4948 "metadata": {
4949 "scrolled": false
4950 },
4951 "outputs": [],
4952 "source": [
4953 "nfolds = 250\n",
4954 "lr = LinearRegression()\n",
4955 "\n",
4956 "test_results, result_idx, preds = [], [], pd.DataFrame()\n",
4957 "for train_dates, test_dates in time_series_split(dates, nfolds=nfolds):\n",
4958 " \n",
4959 " X_train = model_data.loc[idx[train_dates], features]\n",
4960 " y_train = model_data.loc[idx[train_dates], target]\n",
4961 " lr.fit(X=X_train, y=y_train)\n",
4962 " \n",
4963 " X_test = model_data.loc[idx[test_dates], features]\n",
4964 " y_test = model_data.loc[idx[test_dates], target]\n",
4965 " y_pred = lr.predict(X_test)\n",
4966 " \n",
4967 " rmse = np.sqrt(mean_squared_error(y_pred=y_pred, y_true=y_test))\n",
4968 " ic, pval = spearmanr(y_pred, y_test)\n",
4969 " \n",
4970 " test_results.append([rmse, ic, pval])\n",
4971 " preds = preds.append(y_test.to_frame('actuals').assign(predicted=y_pred))\n",
4972 " result_idx.append(train_dates[-1])"
4973 ]
4974 },
4975 {
4976 "cell_type": "code",
4977 "execution_count": 268,
4978 "metadata": {},
4979 "outputs": [],
4980 "source": [
4981 "test_result = pd.DataFrame(test_results, columns=['rmse', 'ic', 'pval'], index=result_idx)"
4982 ]
4983 },
4984 {
4985 "cell_type": "code",
4986 "execution_count": 274,
4987 "metadata": {},
4988 "outputs": [
4989 {
4990 "data": {
4991 "image/png": 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oS89rK+a2OGp2l54nJykhFxEZYNLLz07IAZwcnLhp3JWYMLHh+Nc97qeotpQ/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RAAAg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\n2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XLEuu17Yp32PbFO+45Yl31PrNMrQ6zXK0Os1ytDrNef9WlB7uzsbLgiDlBTU4OTk5Ph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\nBEEQBEEQBCP4P5WwsHZojBouAAAAAElFTkSuQmCC\n",
4992 "text/plain": [
4993 "<matplotlib.figure.Figure at 0x7f23e387a1d0>"
4994 ]
4995 },
4996 "metadata": {},
4997 "output_type": "display_data"
4998 }
4999 ],
5000 "source": [
5001 "fig, axes = plt.subplots(nrows=2)\n",
5002 "rolling_result = test_result.rolling(21).mean()\n",
5003 "rolling_result[['ic', 'pval']].plot(ax=axes[0], title='Information Coefficient')\n",
5004 "axes[0].axhline(test_result.ic.mean(), lw=1, ls='--', color='k')\n",
5005 "rolling_result[['rmse']].plot(ax=axes[1], title='Root Mean Squared Error')\n",
5006 "axes[1].axhline(test_result.rmse.mean(), lw=1, ls='--', color='k')\n",
5007 "plt.tight_layout();"
5008 ]
5009 },
5010 {
5011 "cell_type": "code",
5012 "execution_count": 273,
5013 "metadata": {},
5014 "outputs": [
5015 {
5016 "data": {
5017 "image/png": 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KAMCHDW2ij2clX5bIsmxzevjAFK4GocvjRzga38ZEOJZECcccifLPtBomrjEl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\nT9ROaH1dq5N+zphsqyglXc355LwyaW+q81cb0CzThoTLEnxo+i185qE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6Xe1yOp14/vnn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\niRdBEAQRcZB4EQRBEBHH/wfKaRcPhOky/gAAAABJRU5ErkJggg==\n",
5018 "text/plain": [
5019 "<matplotlib.figure.Figure at 0x7f23f40e51d0>"
5020 ]
5021 },
5022 "metadata": {},
5023 "output_type": "display_data"
5024 }
5025 ],
5026 "source": [
5027 "preds_cleaned = preds[(preds.predicted.between(*preds.predicted.quantile([.001, .999]).values))]\n",
5028 "sns.jointplot(x='actuals', y='predicted', data=preds_cleaned, stat_func=spearmanr);"
5029 ]
5030 },
5031 {
5032 "cell_type": "markdown",
5033 "metadata": {
5034 "collapsed": true
5035 },
5036 "source": [
5037 "### Ridge Regression: Regularization"
5038 ]
5039 },
5040 {
5041 "cell_type": "code",
5042 "execution_count": 228,
5043 "metadata": {},
5044 "outputs": [],
5045 "source": [
5046 "nfolds = 250\n",
5047 "alphas = np.logspace(-10, 10, 21)\n",
5048 "scaler = StandardScaler()\n",
5049 "\n",
5050 "result = pd.DataFrame()\n",
5051 "for alpha in alphas:\n",
5052 " test_results = []\n",
5053 " lr_ridge = Ridge(alpha=alpha)\n",
5054 " for train_dates, test_dates in time_series_split(dates, nfolds=nfolds):\n",
5055 "\n",
5056 " X_train = model_data.loc[idx[train_dates], features]\n",
5057 " y_train = model_data.loc[idx[train_dates], target]\n",
5058 " lr_ridge.fit(X=scaler.fit_transform(X_train), y=y_train)\n",
5059 "\n",
5060 " X_test = model_data.loc[idx[test_dates], features]\n",
5061 " y_test = model_data.loc[idx[test_dates], target]\n",
5062 " y_pred = lr_ridge.predict(scaler.transform(X_test))\n",
5063 "\n",
5064 " rmse = np.sqrt(mean_squared_error(y_pred=y_pred, y_true=y_test))\n",
5065 " ic, pval = spearmanr(y_pred, y_test)\n",
5066 " \n",
5067 " test_results.append([train_dates[-1], rmse, ic, pval, alpha])\n",
5068 " result = result.append(pd.DataFrame(test_results, columns=['date', 'rmse', 'ic', 'pval', 'alpha']))"
5069 ]
5070 },
5071 {
5072 "cell_type": "code",
5073 "execution_count": 218,
5074 "metadata": {},
5075 "outputs": [
5076 {
5077 "data": {
5078 "text/html": [
5079 "<div>\n",
5080 "<table border=\"1\" class=\"dataframe\">\n",
5081 " <thead>\n",
5082 " <tr style=\"text-align: right;\">\n",
5083 " <th></th>\n",
5084 " <th>rmse</th>\n",
5085 " <th>ic</th>\n",
5086 " <th>pval</th>\n",
5087 " <th>alpha</th>\n",
5088 " </tr>\n",
5089 " </thead>\n",
5090 " <tbody>\n",
5091 " <tr>\n",
5092 " <th>count</th>\n",
5093 " <td>2596.000000</td>\n",
5094 " <td>2596.000000</td>\n",
5095 " <td>2.596000e+03</td>\n",
5096 " <td>2596.000000</td>\n",
5097 " </tr>\n",
5098 " <tr>\n",
5099 " <th>mean</th>\n",
5100 " <td>0.041313</td>\n",
5101 " <td>0.103199</td>\n",
5102 " <td>2.613618e-01</td>\n",
5103 " <td>10101.010101</td>\n",
5104 " </tr>\n",
5105 " <tr>\n",
5106 " <th>std</th>\n",
5107 " <td>0.010541</td>\n",
5108 " <td>0.135611</td>\n",
5109 " <td>3.065827e-01</td>\n",
5110 " <td>28575.475238</td>\n",
5111 " </tr>\n",
5112 " <tr>\n",
5113 " <th>min</th>\n",
5114 " <td>0.026114</td>\n",
5115 " <td>-0.336232</td>\n",
5116 " <td>2.597238e-14</td>\n",
5117 " <td>0.000010</td>\n",
5118 " </tr>\n",
5119 " <tr>\n",
5120 " <th>25%</th>\n",
5121 " <td>0.035069</td>\n",
5122 " <td>0.006434</td>\n",
5123 " <td>7.973453e-03</td>\n",
5124 " <td>0.001000</td>\n",
5125 " </tr>\n",
5126 " <tr>\n",
5127 " <th>50%</th>\n",
5128 " <td>0.038960</td>\n",
5129 " <td>0.104842</td>\n",
5130 " <td>1.109634e-01</td>\n",
5131 " <td>1.000000</td>\n",
5132 " </tr>\n",
5133 " <tr>\n",
5134 " <th>75%</th>\n",
5135 " <td>0.044604</td>\n",
5136 " <td>0.195338</td>\n",
5137 " <td>4.629843e-01</td>\n",
5138 " <td>1000.000000</td>\n",
5139 " </tr>\n",
5140 " <tr>\n",
5141 " <th>max</th>\n",
5142 " <td>0.186661</td>\n",
5143 " <td>0.539700</td>\n",
5144 " <td>9.990920e-01</td>\n",
5145 " <td>100000.000000</td>\n",
5146 " </tr>\n",
5147 " </tbody>\n",
5148 "</table>\n",
5149 "</div>"
5150 ],
5151 "text/plain": [
5152 " rmse ic pval alpha\n",
5153 "count 2596.000000 2596.000000 2.596000e+03 2596.000000\n",
5154 "mean 0.041313 0.103199 2.613618e-01 10101.010101\n",
5155 "std 0.010541 0.135611 3.065827e-01 28575.475238\n",
5156 "min 0.026114 -0.336232 2.597238e-14 0.000010\n",
5157 "25% 0.035069 0.006434 7.973453e-03 0.001000\n",
5158 "50% 0.038960 0.104842 1.109634e-01 1.000000\n",
5159 "75% 0.044604 0.195338 4.629843e-01 1000.000000\n",
5160 "max 0.186661 0.539700 9.990920e-01 100000.000000"
5161 ]
5162 },
5163 "execution_count": 218,
5164 "metadata": {},
5165 "output_type": "execute_result"
5166 }
5167 ],
5168 "source": [
5169 "result.describe()"
5170 ]
5171 },
5172 {
5173 "cell_type": "code",
5174 "execution_count": 232,
5175 "metadata": {},
5176 "outputs": [
5177 {
5178 "data": {
5179 "text/html": [
5180 "<div>\n",
5181 "<table border=\"1\" class=\"dataframe\">\n",
5182 " <thead>\n",
5183 " <tr style=\"text-align: right;\">\n",
5184 " <th></th>\n",
5185 " <th>rmse</th>\n",
5186 " <th>ic</th>\n",
5187 " <th>pval</th>\n",
5188 " </tr>\n",
5189 " <tr>\n",
5190 " <th>alpha</th>\n",
5191 " <th></th>\n",
5192 " <th></th>\n",
5193 " <th></th>\n",
5194 " </tr>\n",
5195 " </thead>\n",
5196 " <tbody>\n",
5197 " <tr>\n",
5198 " <th>1.000000e-10</th>\n",
5199 " <td>0.042368</td>\n",
5200 " <td>0.109963</td>\n",
5201 " <td>0.260145</td>\n",
5202 " </tr>\n",
5203 " <tr>\n",
5204 " <th>1.000000e-09</th>\n",
5205 " <td>0.042360</td>\n",
5206 " <td>0.109947</td>\n",
5207 " <td>0.260122</td>\n",
5208 " </tr>\n",
5209 " <tr>\n",
5210 " <th>1.000000e-08</th>\n",
5211 " <td>0.042359</td>\n",
5212 " <td>0.109942</td>\n",
5213 " <td>0.260093</td>\n",
5214 " </tr>\n",
5215 " <tr>\n",
5216 " <th>1.000000e-07</th>\n",
5217 " <td>0.042359</td>\n",
5218 " <td>0.109942</td>\n",
5219 " <td>0.260093</td>\n",
5220 " </tr>\n",
5221 " <tr>\n",
5222 " <th>1.000000e-06</th>\n",
5223 " <td>0.042358</td>\n",
5224 " <td>0.109942</td>\n",
5225 " <td>0.260093</td>\n",
5226 " </tr>\n",
5227 " <tr>\n",
5228 " <th>1.000000e-05</th>\n",
5229 " <td>0.042352</td>\n",
5230 " <td>0.109942</td>\n",
5231 " <td>0.260122</td>\n",
5232 " </tr>\n",
5233 " <tr>\n",
5234 " <th>1.000000e-04</th>\n",
5235 " <td>0.042315</td>\n",
5236 " <td>0.109966</td>\n",
5237 " <td>0.260172</td>\n",
5238 " </tr>\n",
5239 " <tr>\n",
5240 " <th>1.000000e-03</th>\n",
5241 " <td>0.042227</td>\n",
5242 " <td>0.110292</td>\n",
5243 " <td>0.260159</td>\n",
5244 " </tr>\n",
5245 " <tr>\n",
5246 " <th>1.000000e-02</th>\n",
5247 " <td>0.042099</td>\n",
5248 " <td>0.111161</td>\n",
5249 " <td>0.259692</td>\n",
5250 " </tr>\n",
5251 " <tr>\n",
5252 " <th>1.000000e-01</th>\n",
5253 " <td>0.041870</td>\n",
5254 " <td>0.111786</td>\n",
5255 " <td>0.259461</td>\n",
5256 " </tr>\n",
5257 " <tr>\n",
5258 " <th>1.000000e+00</th>\n",
5259 " <td>0.041424</td>\n",
5260 " <td>0.111308</td>\n",
5261 " <td>0.257907</td>\n",
5262 " </tr>\n",
5263 " <tr>\n",
5264 " <th>1.000000e+01</th>\n",
5265 " <td>0.040923</td>\n",
5266 " <td>0.109983</td>\n",
5267 " <td>0.262471</td>\n",
5268 " </tr>\n",
5269 " <tr>\n",
5270 " <th>1.000000e+02</th>\n",
5271 " <td>0.040834</td>\n",
5272 " <td>0.102059</td>\n",
5273 " <td>0.262682</td>\n",
5274 " </tr>\n",
5275 " <tr>\n",
5276 " <th>1.000000e+03</th>\n",
5277 " <td>0.040544</td>\n",
5278 " <td>0.091980</td>\n",
5279 " <td>0.283376</td>\n",
5280 " </tr>\n",
5281 " <tr>\n",
5282 " <th>1.000000e+04</th>\n",
5283 " <td>0.039948</td>\n",
5284 " <td>0.089116</td>\n",
5285 " <td>0.283867</td>\n",
5286 " </tr>\n",
5287 " <tr>\n",
5288 " <th>1.000000e+05</th>\n",
5289 " <td>0.039902</td>\n",
5290 " <td>0.077597</td>\n",
5291 " <td>0.225071</td>\n",
5292 " </tr>\n",
5293 " <tr>\n",
5294 " <th>1.000000e+06</th>\n",
5295 " <td>0.039993</td>\n",
5296 " <td>0.065869</td>\n",
5297 " <td>0.220311</td>\n",
5298 " </tr>\n",
5299 " <tr>\n",
5300 " <th>1.000000e+07</th>\n",
5301 " <td>0.040015</td>\n",
5302 " <td>0.063724</td>\n",
5303 " <td>0.218205</td>\n",
5304 " </tr>\n",
5305 " <tr>\n",
5306 " <th>1.000000e+08</th>\n",
5307 " <td>0.040018</td>\n",
5308 " <td>0.063412</td>\n",
5309 " <td>0.217982</td>\n",
5310 " </tr>\n",
5311 " <tr>\n",
5312 " <th>1.000000e+09</th>\n",
5313 " <td>0.040018</td>\n",
5314 " <td>0.063412</td>\n",
5315 " <td>0.217846</td>\n",
5316 " </tr>\n",
5317 " <tr>\n",
5318 " <th>1.000000e+10</th>\n",
5319 " <td>0.040018</td>\n",
5320 " <td>0.063411</td>\n",
5321 " <td>0.217820</td>\n",
5322 " </tr>\n",
5323 " </tbody>\n",
5324 "</table>\n",
5325 "</div>"
5326 ],
5327 "text/plain": [
5328 " rmse ic pval\n",
5329 "alpha \n",
5330 "1.000000e-10 0.042368 0.109963 0.260145\n",
5331 "1.000000e-09 0.042360 0.109947 0.260122\n",
5332 "1.000000e-08 0.042359 0.109942 0.260093\n",
5333 "1.000000e-07 0.042359 0.109942 0.260093\n",
5334 "1.000000e-06 0.042358 0.109942 0.260093\n",
5335 "1.000000e-05 0.042352 0.109942 0.260122\n",
5336 "1.000000e-04 0.042315 0.109966 0.260172\n",
5337 "1.000000e-03 0.042227 0.110292 0.260159\n",
5338 "1.000000e-02 0.042099 0.111161 0.259692\n",
5339 "1.000000e-01 0.041870 0.111786 0.259461\n",
5340 "1.000000e+00 0.041424 0.111308 0.257907\n",
5341 "1.000000e+01 0.040923 0.109983 0.262471\n",
5342 "1.000000e+02 0.040834 0.102059 0.262682\n",
5343 "1.000000e+03 0.040544 0.091980 0.283376\n",
5344 "1.000000e+04 0.039948 0.089116 0.283867\n",
5345 "1.000000e+05 0.039902 0.077597 0.225071\n",
5346 "1.000000e+06 0.039993 0.065869 0.220311\n",
5347 "1.000000e+07 0.040015 0.063724 0.218205\n",
5348 "1.000000e+08 0.040018 0.063412 0.217982\n",
5349 "1.000000e+09 0.040018 0.063412 0.217846\n",
5350 "1.000000e+10 0.040018 0.063411 0.217820"
5351 ]
5352 },
5353 "execution_count": 232,
5354 "metadata": {},
5355 "output_type": "execute_result"
5356 }
5357 ],
5358 "source": [
5359 "result.groupby('alpha').mean()"
5360 ]
5361 },
5362 {
5363 "cell_type": "code",
5364 "execution_count": 230,
5365 "metadata": {},
5366 "outputs": [
5367 {
5368 "data": {
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uv/zyqPMkItPFF19sffzxx5ZlWdacOXMsv9+f9EynnHJK1BmC3nnn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\n",
5370 "text/plain": [
5371 "<matplotlib.figure.Figure at 0x7f23e26c8c10>"
5372 ]
5373 },
5374 "metadata": {},
5375 "output_type": "display_data"
5376 }
5377 ],
5378 "source": [
5379 "sns.boxplot(y='ic', x='alpha', data=result);"
5380 ]
5381 },
5382 {
5383 "cell_type": "code",
5384 "execution_count": 250,
5385 "metadata": {},
5386 "outputs": [
5387 {
5388 "data": {
5389 "image/png": 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8R4+9sk4ffL7X6pEAAPUIIAAAusDQAX304E/PV2K8W797ea0+XEUEAUBPQAABANBFhmX3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\nb/wAnTf+xOly67bXvdX2hi+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OJa0G0NSpU7Vw4ULNmjVLmzdvVlZWlnw+nyQpOztbFRUV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\nSUVORK5CYII=\n",
5390 "text/plain": [
5391 "<matplotlib.figure.Figure at 0x7f23e3a755d0>"
5392 ]
5393 },
5394 "metadata": {},
5395 "output_type": "display_data"
5396 }
5397 ],
5398 "source": [
5399 "ax = result.groupby('alpha')['ic', 'rmse'].mean().plot(logx=True)\n",
5400 "ax.axhline(test_result.ic.mean())\n",
5401 "ax.axhline(test_result.rmse.mean());"
5402 ]
5403 },
5404 {
5405 "cell_type": "code",
5406 "execution_count": null,
5407 "metadata": {},
5408 "outputs": [],
5409 "source": [
5410 "nfolds = 250\n",
5411 "alphas = np.logspace(-5, 5, 11)\n",
5412 "scaler = StandardScaler()\n",
5413 "\n",
5414 "result2 = pd.DataFrame()\n",
5415 "for alpha in alphas:\n",
5416 " test_results = []\n",
5417 " print(alpha)\n",
5418 " lr_lasso = Lasso(alpha=alpha)\n",
5419 " for i, (train_dates, test_dates) in enumerate(time_series_split(dates, nfolds=nfolds)):\n",
5420 " if i % 50 == 0:\n",
5421 " print('\\t{}'.format(i))\n",
5422 "\n",
5423 " X_train = model_data.loc[idx[train_dates], features]\n",
5424 " y_train = model_data.loc[idx[train_dates], target]\n",
5425 " lr_lasso.fit(X=scaler.fit_transform(X_train), y=y_train)\n",
5426 "\n",
5427 " X_test = model_data.loc[idx[test_dates], features]\n",
5428 " y_test = model_data.loc[idx[test_dates], target]\n",
5429 " y_pred = lr_lasso.predict(scaler.transform(X_test))\n",
5430 "\n",
5431 "# mse = mean_squared_error(y_pred=y_pred, y_true=y_test)\n",
5432 " rmse = np.sqrt(np.sum((y_test-y_pred)**2))\n",
5433 " ic, pval = spearmanr(y_pred, y_test)\n",
5434 " \n",
5435 " test_results.append([train_dates[-1], rmse, ic, pval, alpha])\n",
5436 " result2 = result2.append(pd.DataFrame(test_results, columns=['date', 'rmse', 'ic', 'pval', 'alpha']))"
5437 ]
5438 },
5439 {
5440 "cell_type": "code",
5441 "execution_count": 224,
5442 "metadata": {},
5443 "outputs": [
5444 {
5445 "data": {
5446 "text/html": [
5447 "<div>\n",
5448 "<table border=\"1\" class=\"dataframe\">\n",
5449 " <thead>\n",
5450 " <tr style=\"text-align: right;\">\n",
5451 " <th></th>\n",
5452 " <th>rmse</th>\n",
5453 " <th>ic</th>\n",
5454 " <th>pval</th>\n",
5455 " </tr>\n",
5456 " <tr>\n",
5457 " <th>alpha</th>\n",
5458 " <th></th>\n",
5459 " <th></th>\n",
5460 " <th></th>\n",
5461 " </tr>\n",
5462 " </thead>\n",
5463 " <tbody>\n",
5464 " <tr>\n",
5465 " <th>0.00001</th>\n",
5466 " <td>0.543842</td>\n",
5467 " <td>0.110453</td>\n",
5468 " <td>0.246888</td>\n",
5469 " </tr>\n",
5470 " <tr>\n",
5471 " <th>0.00010</th>\n",
5472 " <td>0.534970</td>\n",
5473 " <td>0.102119</td>\n",
5474 " <td>0.267977</td>\n",
5475 " </tr>\n",
5476 " <tr>\n",
5477 " <th>0.00100</th>\n",
5478 " <td>0.529708</td>\n",
5479 " <td>0.056725</td>\n",
5480 " <td>0.283635</td>\n",
5481 " </tr>\n",
5482 " <tr>\n",
5483 " <th>0.01000</th>\n",
5484 " <td>0.530549</td>\n",
5485 " <td>NaN</td>\n",
5486 " <td>NaN</td>\n",
5487 " </tr>\n",
5488 " <tr>\n",
5489 " <th>0.10000</th>\n",
5490 " <td>0.530549</td>\n",
5491 " <td>NaN</td>\n",
5492 " <td>NaN</td>\n",
5493 " </tr>\n",
5494 " <tr>\n",
5495 " <th>1.00000</th>\n",
5496 " <td>0.530549</td>\n",
5497 " <td>NaN</td>\n",
5498 " <td>NaN</td>\n",
5499 " </tr>\n",
5500 " <tr>\n",
5501 " <th>10.00000</th>\n",
5502 " <td>0.530549</td>\n",
5503 " <td>NaN</td>\n",
5504 " <td>NaN</td>\n",
5505 " </tr>\n",
5506 " <tr>\n",
5507 " <th>100.00000</th>\n",
5508 " <td>0.530549</td>\n",
5509 " <td>NaN</td>\n",
5510 " <td>NaN</td>\n",
5511 " </tr>\n",
5512 " <tr>\n",
5513 " <th>1000.00000</th>\n",
5514 " <td>0.530549</td>\n",
5515 " <td>NaN</td>\n",
5516 " <td>NaN</td>\n",
5517 " </tr>\n",
5518 " <tr>\n",
5519 " <th>10000.00000</th>\n",
5520 " <td>0.530549</td>\n",
5521 " <td>NaN</td>\n",
5522 " <td>NaN</td>\n",
5523 " </tr>\n",
5524 " <tr>\n",
5525 " <th>100000.00000</th>\n",
5526 " <td>0.530549</td>\n",
5527 " <td>NaN</td>\n",
5528 " <td>NaN</td>\n",
5529 " </tr>\n",
5530 " </tbody>\n",
5531 "</table>\n",
5532 "</div>"
5533 ],
5534 "text/plain": [
5535 " rmse ic pval\n",
5536 "alpha \n",
5537 "0.00001 0.543842 0.110453 0.246888\n",
5538 "0.00010 0.534970 0.102119 0.267977\n",
5539 "0.00100 0.529708 0.056725 0.283635\n",
5540 "0.01000 0.530549 NaN NaN\n",
5541 "0.10000 0.530549 NaN NaN\n",
5542 "1.00000 0.530549 NaN NaN\n",
5543 "10.00000 0.530549 NaN NaN\n",
5544 "100.00000 0.530549 NaN NaN\n",
5545 "1000.00000 0.530549 NaN NaN\n",
5546 "10000.00000 0.530549 NaN NaN\n",
5547 "100000.00000 0.530549 NaN NaN"
5548 ]
5549 },
5550 "execution_count": 224,
5551 "metadata": {},
5552 "output_type": "execute_result"
5553 }
5554 ],
5555 "source": [
5556 "result2.groupby('alpha').mean()"
5557 ]
5558 },
5559 {
5560 "cell_type": "markdown",
5561 "metadata": {},
5562 "source": [
5563 "### Logistic Regression: Classification"
5564 ]
5565 },
5566 {
5567 "cell_type": "code",
5568 "execution_count": 275,
5569 "metadata": {},
5570 "outputs": [
5571 {
5572 "name": "stdout",
5573 "output_type": "stream",
5574 "text": [
5575 "<class 'pandas.core.frame.DataFrame'>\n",
5576 "DatetimeIndex: 47377 entries, 2014-01-02 to 2015-12-31\n",
5577 "Columns: 183 entries, Returns10D to stock_YELP INC\n",
5578 "dtypes: float64(182), int64(1)\n",
5579 "memory usage: 66.5 MB\n",
5580 "None\n"
5581 ]
5582 }
5583 ],
5584 "source": [
5585 "target = 'Returns10D'\n",
5586 "label = (y[target] > 0).astype(int).to_frame(target)\n",
5587 "model_data = pd.concat([label, X], axis=1).dropna().reset_index('asset', drop=True)\n",
5588 "\n",
5589 "features = model_data.drop(target, axis=1).columns\n",
5590 "dates = model_data.index.unique()\n",
5591 "\n",
5592 "print(model_data.info())"
5593 ]
5594 },
5595 {
5596 "cell_type": "code",
5597 "execution_count": 285,
5598 "metadata": {},
5599 "outputs": [
5600 {
5601 "name": "stdout",
5602 "output_type": "stream",
5603 "text": [
5604 "1e-05\n",
5605 "0.0001\n",
5606 "0.001\n",
5607 "0.01\n",
5608 "0.1\n",
5609 "1.0\n",
5610 "10.0\n",
5611 "100.0\n",
5612 "1000.0\n",
5613 "10000.0\n",
5614 "100000.0\n"
5615 ]
5616 }
5617 ],
5618 "source": [
5619 "nfolds = 250\n",
5620 "Cs = np.logspace(-5, 5, 11)\n",
5621 "scaler = StandardScaler()\n",
5622 "\n",
5623 "# logistic_result = pd.DataFrame(columns=['date', 'rmse', 'ic', 'pval', 'C'])\n",
5624 "logistic_preds = pd.DataFrame(columns=['actuals', 'predicted', 'C'])\n",
5625 "for C in Cs:\n",
5626 " result = []\n",
5627 " print(C)\n",
5628 " log_reg = LogisticRegression(C=C)\n",
5629 " for i, (train_dates, test_dates) in enumerate(time_series_split(dates, nfolds=nfolds)):\n",
5630 "\n",
5631 " X_train = model_data.loc[idx[train_dates], features]\n",
5632 " y_train = model_data.loc[idx[train_dates], target]\n",
5633 " log_reg.fit(X=scaler.fit_transform(X_train), y=y_train)\n",
5634 "\n",
5635 " X_test = model_data.loc[idx[test_dates], features]\n",
5636 " y_test = model_data.loc[idx[test_dates], target]\n",
5637 " y_pred = log_reg.predict_proba(scaler.transform(X_test))[:, 1]\n",
5638 " \n",
5639 "# rmse = np.sqrt(mean_squared_error(y_pred=y_pred, y_true=y_test))\n",
5640 "# ic, pval = spearmanr(y_pred, y_test)\n",
5641 " logistic_preds = (logistic_preds\n",
5642 " .append(y_test\n",
5643 " .to_frame('actuals')\n",
5644 " .assign(predicted=y_pred, C=C)))\n",
5645 " \n",
5646 "# result.append([train_dates[-1], rmse, ic, pval, alpha])\n",
5647 "# logistic_result = logistic_result.append(pd.DataFrame(result))"
5648 ]
5649 },
5650 {
5651 "cell_type": "code",
5652 "execution_count": 286,
5653 "metadata": {
5654 "scrolled": true
5655 },
5656 "outputs": [
5657 {
5658 "name": "stdout",
5659 "output_type": "stream",
5660 "text": [
5661 "<class 'pandas.core.frame.DataFrame'>\n",
5662 "DatetimeIndex: 497002 entries, 2014-02-06 to 2015-12-31\n",
5663 "Data columns (total 3 columns):\n",
5664 "C 497002 non-null float64\n",
5665 "actuals 497002 non-null float64\n",
5666 "predicted 497002 non-null float64\n",
5667 "dtypes: float64(3)\n",
5668 "memory usage: 15.2 MB\n"
5669 ]
5670 }
5671 ],
5672 "source": [
5673 "logistic_preds.info()"
5674 ]
5675 },
5676 {
5677 "cell_type": "code",
5678 "execution_count": 287,
5679 "metadata": {
5680 "collapsed": true
5681 },
5682 "outputs": [],
5683 "source": [
5684 "from sklearn.metrics import roc_auc_score"
5685 ]
5686 },
5687 {
5688 "cell_type": "code",
5689 "execution_count": 292,
5690 "metadata": {},
5691 "outputs": [
5692 {
5693 "data": {
5694 "text/plain": [
5695 "C\n",
5696 "0.00001 0.532385\n",
5697 "0.00010 0.539814\n",
5698 "0.00100 0.549100\n",
5699 "0.01000 0.554121\n",
5700 "0.10000 0.561894\n",
5701 "1.00000 0.569875\n",
5702 "10.00000 0.574998\n",
5703 "100.00000 0.575875\n",
5704 "1000.00000 0.575214\n",
5705 "10000.00000 0.574488\n",
5706 "100000.00000 0.574317\n",
5707 "dtype: float64"
5708 ]
5709 },
5710 "execution_count": 292,
5711 "metadata": {},
5712 "output_type": "execute_result"
5713 }
5714 ],
5715 "source": [
5716 "auc = logistic_preds.groupby('C').apply(lambda x: roc_auc_score(y_true=x.actuals.astype(int), \n",
5717 " y_score=x.predicted))\n",
5718 "auc"
5719 ]
5720 },
5721 {
5722 "cell_type": "code",
5723 "execution_count": 296,
5724 "metadata": {},
5725 "outputs": [
5726 {
5727 "data": {
5728 "image/png": 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vO014vLPqRwRowcq9GjBhvr743zYVFBabPQ8AgMtC/AAA/iCyQTW9+WRXDbyjpTysFr3/\n7QYNfm2BVm46bPY0AADKjfgBAFyQ1WLohk71NX1Ed90UU18H07I1duZyjXtvuQ6kZZk9DwCAS+Zh\n9gAAgHPz9/HUgNtb6i8d6+rdr9YrZeNhrdpyVLd1aaj4uMby9uIfJQAA18CZHwBAmdSPCNRLj3XS\nsH7tFOTvpX8t+FWPTpivn1L3yuHgrbEBAM6P+AEAlJlhGOrcqpbeGdZNd/+libJy8vX6rF80LGmJ\ntu07afY8AAAuivgBAFwyu6eH+vZsqqnDuqtTy5ratOu4hry1UEmfr1Z6Vp7Z8wAAuCDiBwBQbuEh\nPhpxXwe9OKCTaof56/vluzVgwnzNWbxdhUW8NTYAwLkQPwCAy9aqcXVNeqqrHrmtueRw6N2v1ivx\njZ+0ZutRs6cBAFCC+AEAVAgPq0W3xDbU9BFx6tmxrvYeztSz05dp/IcrdPh4jtnzAAAgfgAAFSvQ\nz0uD7mqtNxK7qGndYC1be1ADJ87XJ//drNz8QrPnAQDcGPEDAKgUjeoE6ZXBsXqqb1v5+dg0+4ct\nemziAi1Zs5+3xgYAmKJMd6YbP3681qxZI8MwNHLkSLVo0aLkuW7duikiIkKGYcgwDL322mtatGiR\nvv76axmGIYfDoQ0bNuiXX36ptB8CAOCcDMNQ16g66hBZQ5/P/1VfLdyuiR+tVIuGoer/txaqVzPA\n7IkAADdSavykpKRo9+7dmj17trZv365Ro0Zp9uzZJc8bhqGZM2fKbreXfO7OO+/UnXfeWfL9//3v\nfythOgDAVfjYbbrvxmbq0eEqzZyzXikbDyvx9f/pr53qq2+vpvL38TR7IgDADZT6srfk5GTFxcVJ\nkho2bKiMjAxlZ2eXPO9wOC768oUpU6Zo4MCBFTAVAODqIqr7afRDHTXm4Y6qGeqrb5fu1IDx8zV3\n2U4VFfNSOABA5So1ftLS0hQSElLyODg4WGlpaed9zZgxY9S3b1+98cYb531+3bp1qlmzpqpVq1ZB\ncwEAVUG7a8I1+elueuCmSBUWFWnqv9dqyJsLtWHHMbOnAQCqsEt+w4Pfn+VJTEzU8OHD9fHHH2vr\n1q2aN29eyXOff/65br/99stfCQCocmweFt1+fSNNGx6nbu3qaMeBdA2fskSvfrxSaSdPmT0PAFAF\nGY5S3nInKSlJYWFhio+PlyTFxcVpzpw58vHx+cPXzpo1S8ePH9egQYMkSb169dK3334rD4/S31ch\nNTW1PPsBAFXE3rQ8zV15UgeOF8hmNRTb3F8x1/jLajHMngYAcCJRUVHl/t5SqyQmJkZJSUmKj4/X\nhg0bFB4eXhI+WVlZSkxM1LRp02Sz2ZSSkqJevXpJko4cOSJfX98yhc9Zl/ODuKvU1FSOWzlw3MqH\n41Y+HLeyiZJ0Sw+H5qfs0Uf/2aQFazKUVeCjZxKiZPcs+z9LwP/myovjVj4ct/LhuJXP5Z4wKfWf\nJm3atFFkZKT69Okjq9Wq0aNH68svv5S/v7/i4uLUtWtX9e7dW3a7Xc2aNVPPnj0lSUePHuVaHwDA\nJbFYDPW4tq6iW0ZoVNJ8rdh4SKOnJ2v0Q9fKj3eEAwBcpjL9p7QhQ4ac97hJkyYlHyckJCghIeEP\n3xMZGakZM2Zc5jwAgDvy87apb5dQLdwiLV69X8OnLNHY/tGqFuht9jQAgAu75Dc8AADgSvCwGnr6\nnijdFFNfuw9laujkxdp/NMvsWQAAF0b8AACclsViqP/fWuieXk115MQpDZ28WL/uPWH2LACAiyJ+\nAABOzTAM9enRRAPvbKWsnHyNemepVm89YvYsAIALIn4AAC7hhuh6GtqvvQoKHRo7c7kWr95v9iQA\ngIshfgAALiOmZYTG9u8om4dVr368Ut8t3Wn2JACACyF+AAAupWWj6ho/MEaBvl6a9sVazfp+s0q5\nXzcAAJKIHwCAC2pYO0gTB3dWeIiPPp23Re/8e62KigkgAMDFET8AAJcUEeqnVwbHqn5EgOYm79Kr\n/1ipgsIis2cBAJwY8QMAcFkhAXaNH9hZkQ2qaenaA3r+3eXKyS0wexYAwEkRPwAAl+brbdO4/tHq\n2LyG1m5L08h3lupEZq7ZswAAToj4AQC4PE+bVcP7tVePDldp+750DUtaokPHss2eBQBwMsQPAKBK\nsFotGhzfWnd1v1oH07I1dPJi7TyQbvYsAIATIX4AAFWGYRjq99dmeuTW5jqRmacRU5Zo/fY0s2cB\nAJwE8QMAqHJuua6hnronSrn5RRo9I1nL1x80exIAwAkQPwCAKqlr29p67qFrZbEYGv/BCv3w826z\nJwEATEb8AACqrKim4Xrp0U7y9bZp0mer9a8Fv8rh4GaoAOCuiB8AQJXWpG6IJg6KVWiQtz78bqPe\nm7NBxcUEEAC4I+IHAFDl1Qn31yuDYlUn3E9fL9quN2f/osKiYrNnAQCuMOIHAOAWqgd7a8LjsWpS\nN1g/pe7Ti3//Wbl5hWbPAgBcQcQPAMBtBPh66sUBnRTVNEypm4/o2enLlJGdb/YsAMAVQvwAANyK\n3ctDzz54rbpG1daW3Sc0fMpiHT1xyuxZAIArgPgBALgdD6tFT/Zpq1uva6i9h7M0dPIi7T2cafYs\nAEAlI34AAG7JYjH00C2Ruu/GZkpLz9WwpMXasvu42bMAAJWI+AEAuC3DMHRnt6v1RHxrZZ8q0Khp\ny5S6+bDZswAAlYT4AQC4vR7X1tXI+zvIUezQC+/9rJ9+2Wf2JABAJSB+AACQdG3zmho3oJPsnla9\n/kmq5izabvYkAEAFI34AADgjskE1jX+8s0ICvPTu1+v10X82yuFwmD0LAFBBiB8AAM5RPyJQEwfF\nqmaorz6f/6smf7ZaRUXFZs8CAFQA4gcAgN+pUc1XrwyKVcPagfphxR5N+ChFeQVFZs8CAFwm4gcA\ngAsI8vfSy4/FqGWjUC1ff0hjZiQr61SB2bMAAJeB+AEA4E/42G16/pGOimkZoQ07jmnElCU6npFr\n9iwAQDkRPwAAXITNw6pnEtrphk71tOtghoZOXqwDaVlmzwIAlAPxAwBAKawWQ4/d3lJ9/9JEh4/n\naNjkJdq+76TZswAAl4j4AQCgDAzD0N09m+rR21sqPTtPI6Yu1dptR82eBQC4BMQPAACX4MaY+nrm\n3nYqKCzSmBnLtXTtAbMnAQDKiPgBAOASxbaupecfjpbNw9DEj1I0N3mX2ZMAAGVA/AAAUA6tGlfX\ny491VoCvp6b+a41m/7BFDofD7FkAgIsgfgAAKKdGdYI0cVCswoK99cl/N2v6l+tUXEwAAYCzIn4A\nALgMtar76ZXBsapXM0DfLd2p1z5JVUFhsdmzAAAXQPwAAHCZqgV6a/zjndWsfogWr96vce8tV05u\ngdmzAAC/Q/wAAFAB/LxtGjegkzo0q6HVW49q1LRlSs/KM3sWAOAcxA8AABXEy2bVyPvbK679Vdq2\n96SGJS3WkeM5Zs8CAJxB/AAAUIGsVoue6N1ad1zfSPuPZuuZyYu1+2CG2bMAACJ+AACocIZh6P6b\nIvXQLZE6npGrYVOWaOPOY2bPAgC3R/wAAFBJbuvSSE/e3Va5eYV6btoyrdh4yOxJAODWiB8AACpR\nt3Z19OyD10qGoZfeX6H5KXvMngQAbov4AQCgkrW7JlwvPdpJPl4eemv2Kn3xv21mTwIAt0T8AABw\nBTStF6IJgzqrWqBd73+7Qe9/s0EOh8PsWQDgVogfAACukLo1AvTK4FjVqu6nL37aprdmr1JhUbHZ\nswDAbRA/AABcQWHBPpo4qLMaXxWkBSv36uUPVig3v9DsWQDgFogfAACusEA/L734aIzaNglTysbD\nGj09WVk5+WbPAoAqj/gBAMAE3l4eevbBa3Vdm1ratOu4hk1ZomPpp8yeBQBVGvEDAIBJbB4WPdU3\nSjfHNtCeQ5l6ZvJi7TuSafYsAKiyiB8AAExksRh65NbmSrjhGh09cUrDkpZo654TZs8CgCqJ+AEA\nwGSGYSg+rrEG3dVaWTn5GvXOUq3acsTsWQBQ5RA/AAA4iZ4d62r4fe1VVOzQuPeWa/Gq/WZPAoAq\nhfgBAMCJRLeI0Nj+0fK0WfXqJyv17ZIdZk8CgCqD+AEAwMm0aBiq8QM7K9DPS9O/XKeP/7tJDofD\n7FkA4PKIHwAAnFCDWoF6ZVCsalbz1T9/2Kqp/16romICCAAuB/EDAICTqhnqq4mDO6tBRKD+m7xL\nr/wjRfkFRWbPAgCXRfwAAODEgv3tenlgjFo0DNWytQc1duZy5eQWmD0LAFwS8QMAgJPz9bbp+Uc6\nKrpFTa3dlqYRU5fqRGau2bMAwOUQPwAAuABPm1XD+rVXz451tWN/uoZNXqJDx7LNngUALoX4AQDA\nRVgthh6/s5V692isg8eyNXTyYu08kG72LABwGcQPAAAuxDAM3dvrGvW/rYVOZuVp+JQlWr89zexZ\nAOASiB8AAFzQzbEN9PQ9UcovKNLoGclKXnfQ7EkA4PSIHwAAXNR1bWrruYc6ymoxNOHDFZr3826z\nJwGAUyN+AABwYW2bhOmlx2Lk6+2pyZ+t1ufzt8rh4GaoAHAhxA8AAC6u8VXBemVwZ1UP9tZH/9mk\nuanpKiomgADg94gfAACqgNph/np1cKzq1vDXiq1ZeuUfKcorKDJ7FgA4FeIHAIAqolqgtyYMilW9\nMC8tW3tQo6cvU2ZOvtmzAMBpED8AAFQhft423Xt9qGJb19LGncc1LGmxjhzPMXsWADgF4gcAgCrG\nw2ro6XuidFuXhtp7OEvPTF7EzVABQMQPAABVksVi6KFbmuvhW5vrRGaehiUt0ZqtR82eBQCmIn4A\nAKjCbr2uoYYmtFNBYbGen5msn1L3mj0JAExD/AAAUMV1blVL4wZEy8vTQ6/P+kX/WvAr9wIC4JaI\nHwAA3ECLhqGaOKizQgPt+vC7jZrx5TruBQTA7RA/AAC4ibo1AvTqE9epXs0Afbt0pyZ+xL2AALgX\n4gcAADcSGuStCY93VstGoUped1DPTeNeQADcB/EDAICb8fW26flHOuq61rW0addxDZ28WIe5FxAA\nN0D8AADghmweVj11T5T+1rWR9h3J0jOTFmnHfu4FBKBqK1P8jB8/Xn369NHdd9+tdevWnfdct27d\ndO+99yohIUH9+vXTkSNHJElz5szRrbfeqjvuuEMLFy6s+OUAAOCyWCyGHrw5Uo/c2lwns/I0fMoS\nrdpyxOxZAFBpPEr7gpSUFO3evVuzZ8/W9u3bNWrUKM2ePbvkecMwNHPmTNnt9pLPnTx5UlOmTNFX\nX32l7OxsTZo0SV26dKmcnwAAAFyWW65rqGqB3np9VqrGzlyuJ3q3Ubd2dcyeBQAVrtQzP8nJyYqL\ni5MkNWzYUBkZGcrOzi553uFw/OFeAcuWLVNMTIy8vb0VGhqqcePGVfBsAABQkWJaRWhc/2jZvTz0\n5qe/6PP5W7kXEIAqp9T4SUtLU0hISMnj4OBgpaWlnfc1Y8aMUd++ffXGG29Ikvbv369Tp07pscce\n07333qvk5OQKng0AACpa87P3Agry1kf/2aRpX6zlXkAAqpRSX/b2e7//r0CJiYmKjY1VUFCQBg4c\nqO+//14Oh0MnT57U1KlTtW/fPvXr10//+9//Kmw0AACoHHVrBOi1J2L1/LvL9Z9lu3Q8I1dP39tO\nXjar2dMA4LIZjlLOaSclJSksLEzx8fGSpLi4OM2ZM0c+Pj5/+NpZs2bp+PHjqlWrlo4ePar+/ftL\nkm666SZ99NFH551B+r3U1NTL+TkAAEAFys0v1j8XH9POw3mqE+qpu7tUk48XAQTAfFFRUeX+3lLP\n/MTExCgpKUnx8fHasGGDwsPDS8InKytLiYmJmjZtmmw2m1JSUtSrVy+1bt1aI0aM0COPPKKTJ08q\nJyfnouFTET+Iu0pNTeW4lQPHrXw4buXDcSsfjlv5VdSx69C+WG/PXqWFq/bpk0WZGts/WuEhf/yP\nn1UF/5srH45b+XDcyudyT5iUGj9t2rRRZGSk+vTpI6vVqtGjR+vLL7+Uv7+/4uLi1LVrV/Xu3Vt2\nu13NmjVTz549JUm9evVSfHy8DMPQ6NGjL2skAAC48mweFg3p21bVAu364qdtembSIo15uKMa1g4y\nexoAlEvoTzVwAAAgAElEQVSZrvkZMmTIeY+bNGlS8nFCQoISEhL+8D3x8fElL5UDAACuyWIx9MDN\nkaoWZNfMr9drxNQlGnFfB7VpEmb2NAC4ZGW6ySkAAHBvt8Q21LB+7VVY5NDYmcu1YOUesycBwCUj\nfgAAQJnEtIzQCwM6ydvLQ29+ukqf/ci9gAC4FuIHAACUWWSDanplcKyqB3vrH3M36R3uBQTAhRA/\nAADgktQJ99erg2NVr2aA5i7bpQkfrlBeQZHZswCgVMQPAAC4ZNUCvTXh8c5qdXWolq8/pGffWaqM\n7HyzZwHARRE/AACgXHy9bRrzcLS6tq2tzbtPaOjkRTp0LNvsWQDwp4gfAABQbjYPi568u63uuL6R\n9h/N1jOTF2vbvpNmzwKACyJ+AADAZbFYDN1/U6QG/K2F0rPyNHLqEv2y+YjZswDgD4gfAABQIW7q\n3EDDz9wLaNx7yzU/hXsBAXAuxA8AAKgwnc65F9Bbs7kXEADnQvwAAIAK9Yd7Af17rYqKis2eBQDE\nDwAAqHhn7wVUPyJAc5N3afyHKcrNLzR7FgA3R/wAAIBKcfZeQK2vrq6fNxzSs9OWKT0rz+xZANwY\n8QMAACqNj92m0Q93VNeo2tqy+4SGTl7MvYAAmIb4AQAAlcrmYdGQu9vqzm5X60Batp6ZtFjb9nIv\nIABXHvEDAAAqnWEYuu/GZnr0by2Unp2nEVOXKHXzYbNnAXAzxA8AALhibuzcQCPua6/iYofGvfez\nflzBvYAAXDnEDwAAuKKiW0TohUc7ycfLQ2//c5X++cMW7gUE4IogfgAAwBXXrP7pewGFBXvr4/9u\n1pR/reFeQAAqHfEDAABMUSfcX68+cZ0aRATq++W79fIH3AsIQOUifgAAgGlCAuwa/3iMWjeurhUb\nD+nZd7gXEIDKQ/wAAABT+dhtGv1QR10fVVtb9pzQM5MX62Aa9wICUPGIHwAAYDqbh0VP3t1Wd3W/\nWgfTsjV08mL9uveE2bMAVDHEDwAAcAqGYajfX5vp0dtbKj07TyOnLtXKTdwLCEDFIX4AAIBTuTGm\nvkbc10HFxQ698Pef9eOK3WZPAlBFED8AAMDpRLeoqRcfjZGv3UNv/3O1ZnMvIAAVgPgBAABO6Zr6\nIafvBRTio0+4FxCACkD8AAAAp1U7zF+vDY5Vg1qn7wX00gcrlJvHvYAAlA/xAwAAnFpwgF3jB8ao\nTePqStl4WKOmLeVeQADKhfgBAABOz8du0+iHO6pbuzrauuck9wICUC7EDwAAcAkeVov+r08bxcc1\n1sG0bD0zeZG27uFeQADKjvgBAAAuwzAMJdxwjQbe0VKZ2fka+Q73AgJQdsQPAABwOTd0qq8R93eQ\n48y9gOb9zL2AAJSO+AEAAC6pY/OaeumxGPnabZr82Wp9+v1m7gUE4KKIHwAA4LKa1gvRK4M7KyzE\nR7PmbVHS59wLCMCfI34AAIBLO3svoIa1AzXv59168X3uBQTgwogfAADg8oID7Hr5sRi1bRKmlZsO\na+Q7S3Uyk3sBATgf8QMAAKoEH7tNzz10rbq1q6Nf957U0MmLdSAty+xZAJwI8QMAAKqMs/cC6h3X\nWAePZWvo5MXcCwhACeIHAABUKYZh6N4brtHAO1uV3AsoZeMhs2cBcALEDwAAqJJuiK6nkfd3kMMh\nvfj+Cn2/nHsBAe6O+AEAAFXWtc1r6qXHOsnXblPS56s1i3sBAW6N+AEAAFVa07ohevWJWIWH+OjT\neVs0+bPVKuReQIBbIn4AAECVV6u6n159IlaNagfqhxV79OLff9Yp7gUEuB3iBwAAuIVgf7teHthZ\nbZuGKXXzEe4FBLgh4gcAALgNby8PPffgterevo62cS8gwO0QPwAAwK14WC1K7N1GvXucvhfQM5MW\na8vu42bPAnAFED8AAMDtGIahe3tdo8fvbKWsnHyNfGeZUrdlq6iYd4IDqjLiBwAAuK1e0fU06oFr\nZTGkb1ac0JC3FmrDjmNmzwJQSYgfAADg1jpE1tA7w7qrZT0f7difruFTlmjCRyk6fDzH7GkAKhjx\nAwAA3F5okLdu73T6fkBNrgrW0jUH9NjE+frH3E28JTZQhRA/AAAAZzStG6JXBsfqqb5tFeDrqc9+\n3KpHJ/yo+Sl7VMz1QIDLI34AAADOYbEY6hpVR9OGdVefHk2UlVOgt2av0tOTFmnTTt4VDnBlxA8A\nAMAF2L08dE+vpnpneHdd17qWft17UkOTFuvVj1fq6IlTZs8DUA7EDwAAwEWEBfvomYR2mjiosxrV\nDtSiVfv16MT5mvX9ZuXmcz0Q4EqIHwAAgDJoVr+aXk/sosTebeRr99Cn87bosQnz9dMv++RwcD0Q\n4AqIHwAAgDKyWAzFdbhK04Z3113dr1Z6dr5e/yRVQycv1tY9J8yeB6AUxA8AAMAl8rHb1O+vzTR1\naDd1allTm3ef0FNvL9Kbn/6iY+lcDwQ4Kw+zBwAAALiqGtV8NeK+Dlq3PU3vfrVOC1bu1bK1B3Rn\n96t1W5dG8rJZzZ4I4Byc+QEAALhMLRqG6s0nu2rQXa1l9/TQx3M3a+DE+Vq8ej/XAwFOhPgBAACo\nAFaLoZ4d62ra8O66vWsjHc/I1Sv/WKkRU5dq276TZs8DIOIHAACgQvl62/TAzZGaMrSbro2soQ07\njmnIWws16Z+rdCIj1+x5gFsjfgAAACpBRKifnn3wWr04oJOuCvfXDyv2aMCE+frXgl9VUFhk9jzA\nLRE/AAAAlahV4+p6e0hXDbyjpTysFn343UYNfGWBktcd4Hog4AojfgAAACqZ1WrRDZ3qa8bION16\nXUMdPXFKL3+QomenLdPOA+lmzwPcBvEDAABwhfh52/Twrc2V9Mz1andNuNZuS9P/vfGTkj5frfSs\nPLPnAVUe8QMAAHCF1Q7z15iHO2rsI9GqFean75fvVv/xP+rLn7apoLDY7HlAlUX8AAAAmKRt0zBN\neup69b+thSyGob9/s0GDXl2gFRsOcT0QUAmIHwAAABN5WC26ObaBpo+I000x9XXoeI5e+PvPGj0j\nWbsPZZg9D6hSiB8AAAAnEODrqQG3t9Skp7qqTePqWr31qJ54/SdN+2KtMrLzzZ4HVAnEDwAAgBOp\nWyNAY/tHa/RD16pmNR99t3Sn+o//UXMWbVdhEdcDAZeD+AEAAHAyhmGofbMamvx0Nz10S3PJ4dC7\nX6/X4Nf+p5WbDps9D3BZxA8AAICTsnlYdFuXhpo+Ik43RNfTgaNZGjtzuZ5/N1l7D2eaPQ9wOR5m\nDwAAAMDFBfp5aeCdrXRDp3qa+fV6pW4+otVb/6cbY+rr7r80kZ+Pp9kTAZfAmR8AAAAXUT8iUC8+\n2kkj7++gsGAfzVm8Q/3Hz9d3S3eqiOuBgFIRPwAAAC7EMAxFt6ipKUOv1/03NlNhUbGmfbFWT7zx\nk1ZtOWL2PMCpET8AAAAuyOZh1R3drtb0Ed3Vo8NV2ns4U6NnJOuF937WgaNZZs8DnBLX/AAAALiw\nYH+7nujdRjfG1Ne7X6/Xio2H9MuWw7qpcwP16dFEvt42sycCToMzPwAAAFVAw9pBGj8wRsP7tVdI\noLe+WrhdAyb8qP8m71JRscPseYBTIH4AAACqCMMwFNMqQu8M7aaEG65RXn6RpvxrjZ588yet25Zm\n9jzAdGV62dv48eO1Zs0aGYahkSNHqkWLFiXPdevWTRERETIMQ4Zh6LXXXtOuXbuUmJioq6++Wg6H\nQ02aNNGzzz5baT8EAAAAfuNpsyo+rrG6t6+jj/6zSQtW7tXId5YqukVNPXhzpGpU8zV7ImCKUuMn\nJSVFu3fv1uzZs7V9+3aNGjVKs2fPLnneMAzNnDlTdru95HO7du1Shw4d9Pbbb1fOagAAAJSqWqC3\nnry7rW7qXF/vfrVeyesOKmXjYd3WpaHu6n61fOxcDwT3UurL3pKTkxUXFydJatiwoTIyMpSdnV3y\nvMPhkMPxx9eRXuhzAAAAuPKurhOsiYM665l7oxTk76V/LfhVAybM1w8/71Yx1wPBjZQaP2lpaQoJ\nCSl5HBwcrLS0818zOmbMGPXt21dvvPFGyee2b9+ugQMH6p577tGyZcsqcDIAAAAulWEYuq5Nbb0z\nrJv69myqnNxCTfpstYa8vVAbdhwzex5wRVzyW13//oxOYmKiYmNjFRQUpIEDB2revHlq3bq1Bg0a\npBtuuEF79+5Vv3799MMPP8jDg3fWBgAAMJPd00N3/6WJenS4Sh9+t1E//bJPw6csUedWEXrgpkiF\nhfiYPRGoNIajlNenJSUlKSwsTPHx8ZKkuLg4zZkzRz4+f/w/xqxZs3T8+HENGjTovM/fddddeuut\nt1SrVq0//eukpqaWZz8AAAAuw960PP039aT2HyuQh1Xq1NRfMc385WXjTYHhnKKiosr9vaWeiomJ\niVFSUpLi4+O1YcMGhYeHl4RPVlaWEhMTNW3aNNlsNqWkpKhXr1765ptvdPToUT344IM6evSojh07\npvDw8Er9QdxVamoqx60cOG7lw3ErH45b+XDcyo9jVz7uetyiJN3Sw6GfftmnD7/bqEUbMrV+b4Hu\nu7GZuratLYvFuOj3u+txu1wct/K53BMmpcZPmzZtFBkZqT59+shqtWr06NH68ssv5e/vr7i4OHXt\n2lW9e/eW3W5Xs2bN1LNnT2VnZ+upp57S/PnzVVhYqLFjx/KSNwAAACdlsRjq1q6OolvU1L8X/Kov\nf9qmNz/9Rd8t3aFHbmuhpnVDSv9DABdQpiIZMmTIeY+bNGlS8nFCQoISEhLOe97X11fTpk2rgHkA\nAAC4Ury9PHTvDdfoL9fW1fvfbtCSNQf0zKTF6tKmtu6/qZlCg7zNnghcFk7HAAAA4DxhIT4a1q+9\nbtpxTO9+vU4LV+1T8vqDuvP6Rvrb9Y1k9+RfIeGauJINAAAAFxTZoJreSOyixN6t5Wv30Kx5W/TY\nxAVa+Ms+7ukIl0T8AAAA4E9ZLIbiOtTVtOHddWe3q3UyM0+vfZKqYUlLtHXPCbPnAZeE+AEAAECp\nfOw23XdjM70zrJuiW9TUpl3H9dTbizR/TTpngeAyiB8AAACUWY1qvhp5fwe9/FiMaob6avGGTH02\nf6vZs4AyIX4AAABwyVo0CtXLj8Uo0Neqj+du1jeLd5g9CSgV8QMAAIByCQ3yVr9u1RXs76UZX63T\njyv2mD0JuCjiBwAAAOVWzd9D4wZ0kp+3TZM/W6Wlaw+YPQn4U8QPAAAALku9mgEa2z9aXp5Wvfbx\nSv2y+YjZk4ALIn4AAABw2RpfFaznHuwoi2HopQ9WaMOOY2ZPAv6A+AEAAECFaNEoVMPva6+iomKN\ne2+5tu07afYk4DzEDwAAACpM+2Y19FTfKJ3KK9SYGcnaezjT7ElACeIHAAAAFSq2TS09fmcrZWTn\n69lpy3ToWLbZkwBJxA8AAAAqQc+O9fTQLZE6npGr56Yv07H0U2ZPAogfAAAAVI7bujRSnx5NdOhY\njp6bnqz0rDyzJ8HNET8AAACoNH17NtEtsQ2093Cmnn83WTm5BWZPghsjfgAAAFBpDMPQQ7c0V1z7\nq7RtX7rGvfezcvMLzZ4FN0X8AAAAoFJZLIYGxbdWTKsIbdhxTBM+TFFBYbHZs+CGiB8AAABUOqvF\n0FN9oxTVNEypm4/o9VmpKip2mD0Lbob4AQAAwBVh87Bo+H3tFdmgmpauOaApn69WMQGEK4j4AQAA\nwBVj9/TQ6IeuVaPagfphxR699816ORwEEK4M4gcAAABXlI/dpucfiVadcH/NWbRDn87bYvYkuAni\nBwAAAFdcoJ+XXhgQrRrVfPTpvC36auF2syfBDRA/AAAAMEW1QG+9MKCTQgLsem/Oen2/fLfZk1DF\nET8AAAAwTY1qvnphQLT8fTw15V+rtXjVfrMnoQojfgAAAGCqq2oEaFz/aHl7eej1WalK2XjI7Emo\noogfAAAAmK5RnSCNfqijrFaLJnyYonXb08yehCqI+AEAAIBTiGxQTSPvb69ih0MvvLdcW/ecMHsS\nqhjiBwAAAE4jqmm4nr63nfLyi/T8u8nafTDD7EmoQogfAAAAOJWYlhEaHN9GmTkFem76Mh1IyzJ7\nEqoI4gcAAABOJ67DVXrktuY6kZmn56YtU9rJU2ZPQhVA/AAAAMAp3RLbUPf2aqojJ07puenLlJ6V\nZ/YkuDjiBwAAAE4rPq6x/ta1kfYdydLoGcnKOlVg9iS4MOIHAAAATsswDD1wUzP17FhXO/ana9zM\n5crNKzR7FlwU8QMAAACnZhiGHrujla5rXUubdh3XSx+sUEFhkdmz4IKIHwAAADg9q8XQk33bqn2z\ncK3eelSvfpyqoqJis2fBxRA/AAAAcAkeVouG9Wuvlo1ClbzuoCZ9tlrFxQ6zZ8GFED8AAABwGV42\nq0Y90EFNrgrWgpV79e5X6+RwEEAoG+IHAAAALsXHbtOYRzqqXs0Afbt0p/4xd5PZk+AiiB8AAAC4\nHH8fT43rH62aob76fP6v+veCX82eBBdA/AAAAMAlBQfY9eKATgoNtOuD7zZq7rKdZk+CkyN+AAAA\n4LLCQnz0wqOdFOjnqXe+WKufUveaPQlOjPgBAACAS6sd5q9x/TvJx8tDb85epeXrD5o9CU6K+AEA\nAIDLa1ArUGMejpbNw6KJH63Umq1HzZ4EJ0T8AAAAoEq4pn6Inn2ggyTpxfd/1uZdx01eBGdD/AAA\nAKDKaN04TEMT2im/sFjPz1yunQfSzZ4EJ0L8AAAAoEqJblFT/9enjbJPFWj09GTtP5pl9iQ4CeIH\nAAAAVc71UXX06O0tdTIrT89OW6YjJ3LMngQnQPwAAACgSroxpr76/fUapZ08peemLdOJzFyzJ8Fk\nxA8AAACqrLu6N9Zd3a/WgbRsjZ6erKycfLMnwUTEDwAAAKq0hBuu0Y0x9bXrYIaef3e5cnILzJ4E\nkxA/AAAAqNIMw1D/21ro+qja2rLnhF56f4XyC4rMngUTED8AAACo8iwWQ4m926hj8xpauy1NEz9a\nqcKiYrNn4QojfgAAAOAWrFaLhia0U+urq2vFxkN669NVKi52mD0LVxDxAwAAALdh87Bq1AMddE29\nEC1ctU/Tvlgrh4MAchfEDwAAANyK3ctDox/uqAYRgZqbvEsffLuRAHITxA8AAADcjp+3TWP7R6tW\ndT998dM2fTZ/q9mTcAUQPwAAAHBLQf5eemFAJ1UP9tbHczfrm8U7zJ6ESkb8AAAAwG1VD/bWi492\nUrC/l2Z8tU7zU/aYPQmViPgBAACAW4sI9dO4AZ3k523TpH+u0rK1B8yehEpC/AAAAMDt1asZoLH9\no+XladWrH6/UL5uPmD0JlYD4AQAAACQ1vipYzz3YUYZh6KUPVmjDjmNmT0IFI34AAACAM1o0CtXw\n+9qrqKhY495brm37Tpo9CRWI+AEAAADO0aFZDQ3p21an8go1Zkay9h7ONHsSKgjxAwAAAPzOdW1q\n6/E7WykjO1/PTV+mQ8eyzZ6ECkD8AAAAABfQs2M9PXhzpI6l5+q56ct0LP2U2ZNwmYgfAAAA4E/8\nrWsj9e7RWIeO5Wj0jGRlZOebPQmXgfgBAAAALuKenk11c2wD7TmUqTHvJisnt8DsSSgn4gcAAAC4\nCMMw9PAtzRXX/ipt23tS4977WXkFRWbPQjkQPwAAAEApLBZDg+JbK6ZlhDbsOKYJH6aooLDY7Fm4\nRMQPAAAAUAZWi6Gn7olS26ZhWrnpsF6flaqiYofZs3AJiB8AAACgjGweFo24r70iG1TT0jUHNOXz\n1XI4CCBXQfwAAAAAl8Du6aHnHrxWjWoH6ocVe/TenA0EkIsgfgAAAIBL5Ott0/OPRKtOuL++XrRd\ns+dtMXsSyoD4AQAAAMoh0M9LLwyIVniIj2bN26KvFm43exJKQfwAAAAA5VQt0FsvPtpJIQF2vTdn\nveb9vNvsSbgI4gcAAAC4DDWq+eqFAdHy9/FU0uertXj1frMn4U8QPwAAAMBluqpGgMb1j5bd00Ov\nf5KqlZsOmz0JF0D8AAAAABWgUZ0gjXm4o6xWi8Z/sELrtqeZPQm/Q/wAAAAAFSSyQTWNvL+9ih0O\nvfDecm3dc8LsSThHmeJn/Pjx6tOnj+6++26tW7fuvOe6deume++9VwkJCerXr5+OHDlS8lxeXp56\n9Oihr776qmJXAwAAAE4qqmm4nr6nnfLyi/T8u8nafTDD7Ek4w6O0L0hJSdHu3bs1e/Zsbd++XaNG\njdLs2bNLnjcMQzNnzpTdbv/D906dOlVBQUEVuxgAAABwcjGtIjQ4r7Xe/udqPTd9mSYOilXNUF+z\nZ7m9Us/8JCcnKy4uTpLUsGFDZWRkKDs7u+R5h8NxwTva7tixQzt27FCXLl0qcC4AAADgGuI61NUj\ntzXXicw8PTt9mdJOnjJ7ktsrNX7S0tIUEhJS8jg4OFhpaedfvDVmzBj17dtXr7/+esnnJk6cqOHD\nh1fgVAAAAMC13BLbUPf0aqojx3P03PRlSs/KM3uSWyv1ZW+/9/uzPImJiYqNjVVQUJAGDhyo77//\nXqdOnVKbNm1Uq1atC34PAAAA4C56xzVW9qkCfbVwu0bPSNZLj8WYPcltGY5SyiQpKUlhYWGKj4+X\nJMXFxWnOnDny8fH5w9fOmjVLx44d086dO7V3715ZLBYdOnRIXl5eGjt2rKKjo//0r5OamnqZPwoA\nAADgnBwOh75ZcVK/bM9WneqeSrg+VJ4evPFyeURFRZX7e0s98xMTE6OkpCTFx8drw4YNCg8PLwmf\nrKwsJSYmatq0abLZbEpJSVGvXr00ePDgku9PSkpS7dq1Lxo+FfGDuKvU1FSOWzlw3MqH41Y+HLfy\n4biVH8eufDhu5cNxK7s2bR1645NULVq9X5/8lKYJg7vLz8fT7Fku5XJPmJQaP23atFFkZKT69Okj\nq9Wq0aNH68svv5S/v7/i4uLUtWtX9e7dW3a7Xc2aNVPPnj0vaxAAAABQFVkthp7s21aFxcVatvag\nnp60SKMf7qiIUD+zp7mNMl3zM2TIkPMeN2nSpOTjhIQEJSQk/On3Dho0qJzTAAAAgKrFw2rRsIT2\neuXvC7R0U5aefnuRRt7fQc0bhpo9zS3wQkMAAADgCrJYDPVoE6RBd7VWTm6hnpu+TAtW7jF7llsg\nfgAAAAAT9OxYV2P7R8vL00NvfrpK/5i7ScXFvEtyZSJ+AAAAAJO0urq6XnsiVjWr+eqzH7fqlY9X\nKq+gyOxZVRbxAwAAAJiodpi/Xn0iVpENqmnpmgMaOXWJTmTkmj2rSiJ+AAAAAJMF+nnphQHR6tau\njrbuOamnJi3SroMZZs+qcogfAAAAwAnYPKz6vz5tdO8NTXX0xCkNnbxYKzcdNntWlUL8AAAAAE7C\nMAz1jmuioQntVFRUrBfeW65vFu8we1aVQfwAAAAATia2dS29PDBGAb5emvHVOk3/Yq2KiorNnuXy\niB8AAADACTWpG6LXE69T3Rr++nbpTr3w95+Vk1tg9iyXRvwAAAAATiosxEevDI5V26ZhSt18REMn\nL9aR4zlmz3JZxA8AAADgxHzsNv1/e/ceF2WBqHH8mRkQHIXiruSlsjDxUm64xiJIfigrO22dTqin\ndqPs5OoxqdSWgyXsSlJeckNk3XRPtnmUrdO2h/2Ua1t7WsioUEP6UAaZh1QiAV0RRJDL+WMXPusV\nZsB5X3h/3790RmceHl9m5pHhZdnDk3VnzFWqqDqhRVn5+rLiqNGx+iTGDwAAAGByDoddc/95gh69\ne7zq6puUmrNTBcWHjY7V5zB+AAAAgD7in2Kv1jNzbpLDYdPKV3fpt+9+qfb2dqNj9RmMHwAAAKAP\niRoTppWPxSkkYKC2bN+nX+R+qtMtrUbH6hMYPwAAAEAfc+VQf61ZGKeIEZfrz7sO6plfFep4fZPR\nsUyP8QMAAAD0QQH+vloxf4pirg9X6de1WpJVoENHThgdy9QYPwAAAEAf5ePt0FMPRCkxIULf1jZo\ncVaB9pZXGx3LtBg/AAAAQB9mt9v0o9vH6InZE9XU3KK0lwr1zscVRscyJcYPAAAA0A9Mixqh5XN/\nIKevl9a9VqyX/1CqtjbOBPePGD8AAABAPzFuVLBWJ8fpipBB+t37X+m53xTpVFOL0bFMg/EDAAAA\n9CPhwYO1amGcJlwTrMLPvlVKzgeqPd5odCxTYPwAAAAA/Yyfc4DS/y1at3x/hPYfOq5FL+Zr/6G/\nGh3LcIwfAAAAoB/y9rLrscQblDQjUkfrTill/Qf6pLTK6FiGYvwAAAAA/ZTNZtO9067Vfzw4SW3t\nUsbLH+v3f/lK7e3WPBEC4wcAAADo56LHh+u5f49RgJ+Pfp1Xqpw3StTS2mZ0LI9j/AAAAAAWcO3w\nAK1Jnqqrwv31x8L/0882faT6xtNGx/Ioxg8AAABgEcGXD9TzC2I1KTJMxWXVempdvqpqG4yO5TGM\nHwAAAMBCBvp4aelDk/XDuFE6+F29Fr2Yr88P1BodyyMYPwAAAIDFOOw2PfLDcZr/L9ervvG0lv7y\nQ72/+6DRsS45xg8AAABgUbdHX6n0R26Sj7dda7bu0dYd+/r1meAYPwAAAICFTRwdqpWPxSos0Klt\n73yp1f+1W82nW42OdUkwfgAAAACLGzHEX2uS4zTmykDlf3pYS3+5U3890WR0rF7H+AEAAACgywb7\nKOMnP9DUicO0r+KYFmXl65uqOqNj9SrGDwAAAABJ0gBvhxbd/z39662jdeToSS1ZV6A9Xx4xOlav\nYfwAAAAA6GSz2TR7+nVadP+Naj7dpp9t+kjbPzxgdKxewfgBAAAAcI747w3Tinkx8nN6K+eNEm38\nn8/U2ta3zwTH+AEAAABwXmOuCtTqhXEaHjZYeflf69mXP9bJU6eNjuU2xg8AAACACxoSNEgrH4vT\nDREhKvr8O6Ws/0DVxxqNjuUWxg8AAACAixo80Ftpj9yk26Kv1IHKOi3O+ovKDx4zOpbLGD8AAAAA\numBnJpMAAAvqSURBVOTlsGv+vRM0565xOnaiSSnrd+rDkkqjY7mE8QMAAACgW2w2m+6eOkpPPzRZ\ndpuU+UqR/vvP5Wpv7xsnQmD8AAAAAHDJ98cO0fMLYhV0ma9eeetzrXutWKdb2oyO1SXGDwAAAACX\nXX3FZVqTHKdrhl2mP33yjdJeKtSJk81Gx7ooxg8AAAAAtwRdNlCZ86coevxQfba/Rkuy8lVZXW90\nrAti/AAAAABwm6+Pl1J+PEn33nyNDlc3aHFWvj7bX2N0rPNi/AAAAADoEbvdpqQ7x2ph4g06eapF\ny371od4r+sboWOdg/AAAAADoFbdMHqmfz42W7wAv/SL3U/3m7c/V1maeM8ExfgAAAAD0mgnXhGjV\nwlgNDRqk198r18pXd+lUc4vRsSQxfgAAAAD0smGhflqdHKexVwdpZ0mlUnN26ljdKaNjMX4AAAAA\n9D7/QQO0fG60pkUNV/nBv2pRVr4OVB43NBPjBwAAAMAl4e3l0OOzJupHt49R9bFG/TS7QLu++M6w\nPIwfAAAAAJeMzWZTYkKEfvrjKLW2tmv5rz/SHwq+NiQL4wcAAADAJTfl+iu0Yn6M/Af76KXff6YN\nvytRa2ubRzMwfgAAAAB4xOiRgVqzME5XDvXXWzsP6Of/+bFOnjrtsftn/AAAAADwmNBAp55fMEU3\nXheqPfuOaMm6An139KRH7pvxAwAAAMCjnL7eeubhybpzylX6puqEFr+Yr30VRy/5/TJ+AAAAAHic\nw2HX3HsmaO4941XX0KTUnJ0q+PTwJb1Pxg8AAAAAw9w55Wo9M+cmeTnsWrlll377py/V3t5+Se6L\n8QMAAADAUFFjwrTqsViFBAzUlj/u09pte3S6pbXX74fxAwAAAMBwI4f6a83COI0eEaD/3X1IT2/4\nUMfrm3r1Phg/AAAAAEwhwN9Xz86P0ZTrw/X5gaNaklWgg9+d6LXbZ/wAAAAAMA0fb4eWPBClmQkR\n+ra2QUvWFWhvWXWv3DbjBwAAAICp2O02PXD7GD0xe6KamluUtrFQOz6q6Pnt9kI2AAAAAOh106JG\nKOMnMXL6eiv79eIe3x7jBwAAAIBpjb06SKuTYzViiF+Pb4vxAwAAAMDUwoMHK3vxzT2+HcYPAAAA\nANOz2Ww9vg3GDwAAAABLYPwAAAAAsATGDwAAAABLYPwAAAAAsATGDwAAAABLYPwAAAAAsATGDwAA\nAABLYPwAAAAAsATGDwAAAABLYPwAAAAAsATGDwAAAABLYPwAAAAAsATGDwAAAABLYPwAAAAAsASv\n7vyhzMxM7d27VzabTampqRo/fnznddOmTVN4eLhsNptsNptWr14tf39/paSkqLa2Vs3NzZo3b57i\n4+Mv1ccAAAAAAF3qcvwUFRWpoqJCubm52r9/v5YuXarc3NzO6202mzZt2iRfX9/Oy95++22NHz9e\nc+bMUWVlpR566CHGDwAAAABDdTl+CgsLlZCQIEkaNWqU6urq1NDQoEGDBkmS2tvb1d7efsbfueOO\nOzp/XVlZqaFDh/ZmZgAAAABwWZfjp6amRuPGjev8fUBAgGpqajrHjySlpaXp0KFDioqK0pNPPtl5\n+axZs3TkyBFt2LChl2MDAAAAgGtcPuHB2V/lSU5OVkpKirZs2aKysjK98847ndfl5uYqJydHixcv\n7nlSAAAAAOgBW/vZa+Ys2dnZCg0NVWJioiQpISFBeXl5cjqd5/zZrVu36ujRo7r55psVFBSkIUOG\nSJJmzJihV199VYGBgRe8n927d/fk4wAAAABgATfeeKPbf7fLt73FxMQoOztbiYmJKi0tVVhYWOfw\nqa+vV3JysjZs2CBvb28VFRXptttuU1FRkSorK5Wamqqamho1NjZedPj09IMAAAAAgK50OX4mTpyo\nsWPHatasWXI4HFq2bJnefPNN+fn5KSEhQfHx8Zo5c6Z8fX0VGRmp6dOnq6mpSampqbr//vvV1NSk\ntLQ0T3wsAAAAAHBBXb7tDQAAAAD6A5dPeAAAAAAAfRHjBwAAAIAlMH4AAAAAWALjBwAAAIAlONLT\n09M9dWdlZWWdZ42bMGGCJCkzM1Pr16/XG2+8oeuuu06hoaHKzs5WXl6eioqKFBwcrODgYE9FNKWu\nehs9erTCwsIkSdXV1Zo+fbqSkpJks9mMjG0K3T3m9uzZo7Vr12r79u0aNmyYQkNDDU5urO72Vlxc\nrHXr1undd99VeHi4QkJCDE5urO5+rlZXV2vp0qWqr69XZGSkwamN193jraSkRFlZWXrvvfcUGRkp\nPz8/g5Mbx5XnBUkcc3/nam88xv2Nq73xnPo3rvYm8TpOcr03V3eDx77y09jYqIyMDEVHR3deVlRU\npIqKCuXm5iojI0MZGRmd1/n6+qq1tdWynzAdutPbs88+23nd5s2bNXnyZCOimo4rx5yfn58yMjKU\nlJSkTz75xKjIpuBKb06nU2lpaXrwwQe1a9cuoyKbgiufq3a7XTNnzjQqqqm4crzl5uYqPT1d8+bN\n02uvvWZUZMO5+rwgccxJ7vXGY5x7vfGc6l5vEq/j3O3Nld3gsfHj4+OjTZs2nRGqsLBQCQkJkqRR\no0aprq5ODQ0Nmjlzpp566iklJSXplVde8VREU3Klt7y8PN16660aMGCAUXFNxZXurr32WhUWFuqF\nF17ovN6qXOktIiJCzc3N2rp1q+6++26jIpuCK70FBQXJ4XAYFdVUXOmtpaVF3t7eCg0NVW1trVGR\nDedKZx045tzrjcc493rjOdW93ngd515vru4Gj40fu91+zj9mTU2NAgMDO38fGBiompoaffXVV/Ly\n8pKfn5+am5s9FdGUXOmtpKREBQUF+uKLL/TWW295OqrpuNrd1KlTtXbtWm3evNnDSc2lO70FBASo\npqZG9fX1WrVqlRYtWiR/f39PRzUVV3rrwI9Zc+3zdODAgWpublZVVZXCw8M9HdU0XOns9ddfP+Nd\nFVY+5tzpjcc493rjOdX13pYvX67i4mLLv45z53jbv3+/S7vBq9dT90BbW5skqampSSkpKfL29taj\njz5qcCrz6+jt6aefliQdPnxYM2bMMDJSn9HR3fHjx7Vs2TI1NjbqrrvuMjiV+XW8gNq4caMaGhqU\nk5OjqKgo3XLLLQYnM7eO3goLC7Vt2zY1NDQoICDAsv8z2l0dn6ezZs1Senq62tra9MQTTxicytw6\nOrvvvvskccx119m9rV27lse4bji7t4KCAp5Tu+Hs3jrwOu7izu7t/fffd2k3GDp+QkNDz/hf0CNH\njigkJEQjR45UfHy8ccFM7kK9dcjMzDQiVp9wsWMuNjbWwGTmdqHeeAF6cRc73v7x/cw404V6czqd\nWrFihYHJzKur54Xo6GiOufPoqjce486vq95iY2N5Tj2PrnrrwOu4M3XVW3x8vEu7wdBTXcfExGjH\njh2SpNLSUoWFhcnpdBoZqU+gN/fRnXvozT305h56cx2duYfe3ENv7qE39/R2bx77yk9paamee+45\nVVZWysvLSzt27FB2drYiIyM7T2e3bNkyT8XpM+jNfXTnHnpzD725h95cR2fuoTf30Jt76M09nujN\n1m7l734EAAAAYBmGvu0NAAAAADyF8QMAAADAEhg/AAAAACyB8QMAAADAEhg/AAAAACyB8QMAAADA\nEhg/AAAAACzBYz/kFACA7qiurtbKlStVXl4up9Mpm82mBQsWKDo62uhoAIA+jh9yCgAwlcTERN1z\nzz2aPXu2JKmsrEwPP/ywtm3bpuHDhxucDgDQl/GVHwCAaRQWFsput3cOH0mKiIjQ9u3b5efnZ2Ay\nAEB/wPf8AABMo7y8XOPGjTvncoYPAKA3MH4AAKbhcDjU1tZmdAwAQD/F+AEAmEZERIT27NlzzuVl\nZWU6deqUAYkAAP0J4wcAYBqTJk3S4MGDtXHjxs7LysvLNX/+fFVVVRmYDADQH3C2NwCAqTQ0NCgz\nM1N79+7V5ZdfLh8fHz3++OPn/V4gAABcwfgBAAAAYAm87Q0AAACAJTB+AAAAAFgC4wcAAACAJTB+\nAAAAAFgC4wcAAACAJTB+AAAAAFgC4wcAAACAJTB+AAAAAFjC/wOfafOm7lU9WwAAAABJRU5ErkJg\ngg==\n",
5729 "text/plain": [
5730 "<matplotlib.figure.Figure at 0x7f241de44690>"
5731 ]
5732 },
5733 "metadata": {},
5734 "output_type": "display_data"
5735 }
5736 ],
5737 "source": [
5738 "auc.sort_index(ascending=False).plot(logx=True);"
5739 ]
5740 },
5741 {
5742 "cell_type": "code",
5743 "execution_count": null,
5744 "metadata": {
5745 "collapsed": true
5746 },
5747 "outputs": [],
5748 "source": [
5749 "logistic_preds.info()"
5750 ]
5751 },
5752 {
5753 "cell_type": "markdown",
5754 "metadata": {},
5755 "source": [
5756 "### Ordinal Logit"
5757 ]
5758 },
5759 {
5760 "cell_type": "code",
5761 "execution_count": null,
5762 "metadata": {
5763 "collapsed": true
5764 },
5765 "outputs": [],
5766 "source": [
5767 "target = 'Returns10D'\n",
5768 "label = (y[target] > 0).astype(int).to_frame(target)\n",
5769 "model_data = pd.concat([label, X], axis=1).dropna().reset_index('asset', drop=True)\n",
5770 "\n",
5771 "features = model_data.drop(target, axis=1).columns\n",
5772 "dates = model_data.index.unique()\n",
5773 "\n",
5774 "print(model_data.info())"
5775 ]
5776 },
5777 {
5778 "cell_type": "code",
5779 "execution_count": null,
5780 "metadata": {
5781 "collapsed": true
5782 },
5783 "outputs": [],
5784 "source": []
5785 },
5786 {
5787 "cell_type": "code",
5788 "execution_count": null,
5789 "metadata": {
5790 "collapsed": true
5791 },
5792 "outputs": [],
5793 "source": []
5794 },
5795 {
5796 "cell_type": "markdown",
5797 "metadata": {},
5798 "source": [
5799 "## TA-Lib"
5800 ]
5801 },
5802 {
5803 "cell_type": "code",
5804 "execution_count": null,
5805 "metadata": {},
5806 "outputs": [],
5807 "source": [
5808 "class Technical:\n",
5809 " @staticmethod\n",
5810 " def make_bbands(timeperiod=5, nbdevup=2, nbdevdn=2, matype=0):\n",
5811 " class BBANDS(CustomFactor):\n",
5812 " \"\"\"Lower, middle, and upper Bollinger Bands\"\"\"\n",
5813 "\n",
5814 " inputs = [USEquityPricing.close]\n",
5815 " outputs = ['upper', 'middle', 'lower']\n",
5816 " window_length = timeperiod\n",
5817 "\n",
5818 " def compute(self, today, assets, out, close_prices):\n",
5819 " bb = []\n",
5820 " for close in close_prices.T:\n",
5821 " u, m, l = talib.BBANDS(close, timeperiod=timeperiod, \n",
5822 " nbdevup=nbdevup, nbdevdn=nbdevdn, \n",
5823 " matype=matype)\n",
5824 " bb.append((u[-1], m[-1], l[-1]))\n",
5825 " out.upper[:], out.middle[:], out.lower[:] = list(zip(*bb))\n",
5826 " return BBANDS\n",
5827 " \n",
5828 " @staticmethod\n",
5829 " def make_ema(timeperiod=30):\n",
5830 " class EMA(CustomFactor):\n",
5831 " \"\"\"Double Exponential Moving Average\"\"\"\n",
5832 " inputs = [USEquityPricing.close]\n",
5833 " window_length = timeperiod\n",
5834 " \n",
5835 " def compute(self, today, assets, out, close_prices):\n",
5836 " out[:] = [talib.EMA(p, timeperiod=timeperiod)[-1] for p in close_prices.T]\n",
5837 " return EMA \n",
5838 " \n",
5839 " @staticmethod\n",
5840 " def make_dx(timeperiod=14):\n",
5841 " class DX(CustomFactor):\n",
5842 " \"\"\"Directional Movement Index\"\"\"\n",
5843 " inputs = [USEquityPricing.high, \n",
5844 " USEquityPricing.low, \n",
5845 " USEquityPricing.close]\n",
5846 " window_length = timeperiod + 1\n",
5847 " \n",
5848 " def compute(self, today, assets, out, high, low, close):\n",
5849 " out[:] = [talib.DX(high[:, i], \n",
5850 " low[:, i], \n",
5851 " close[:, i], \n",
5852 " timeperiod=timeperiod)[-1] \n",
5853 " for i in range(len(assets))]\n",
5854 " return DX \n",
5855 " \n",
5856 " \n",
5857 " @staticmethod\n",
5858 " def make_mfi(timeperiod=14):\n",
5859 " class MFI(CustomFactor):\n",
5860 " \"\"\"Money Flow Index\"\"\"\n",
5861 " inputs = [USEquityPricing.high, \n",
5862 " USEquityPricing.low, \n",
5863 " USEquityPricing.close,\n",
5864 " USEquityPricing.volume]\n",
5865 " window_length = timeperiod + 1\n",
5866 " \n",
5867 " def compute(self, today, assets, out, high, low, close, vol):\n",
5868 " out[:] = [talib.MFI(high[:, i], \n",
5869 " low[:, i], \n",
5870 " close[:, i],\n",
5871 " vol[:, i],\n",
5872 " timeperiod=timeperiod)[-1] \n",
5873 " for i in range(len(assets))]\n",
5874 " return MFI "
5875 ]
5876 },
5877 {
5878 "cell_type": "code",
5879 "execution_count": 1,
5880 "metadata": {
5881 "ExecuteTime": {
5882 "end_time": "2018-09-10T03:19:05.197299Z",
5883 "start_time": "2018-09-10T03:19:05.193996Z"
5884 }
5885 },
5886 "outputs": [],
5887 "source": [
5888 "def test_pipeline():\n",
5889 " stocks = StaticAssets(symbols(['MSFT', 'AAPL']))\n",
5890 "# DX = Technical.make_dx()\n",
5891 " MFI = Technical.make_mfi()\n",
5892 " \n",
5893 " ewma = EWMA(inputs=[USEquityPricing.high],\n",
5894 " window_length=30, \n",
5895 " decay_rate=.2,\n",
5896 " mask=stocks)\n",
5897 " bb = BollingerBands(window_length=30, k=2, mask=stocks)\n",
5898 " return Pipeline({'adx': Technical.make_dx()(mask=stocks),\n",
5899 " 'mfi': MFI(mask=stocks),\n",
5900 " 'ewma': ewma,\n",
5901 " 'lower': bb.lower,\n",
5902 " 'mid': bb.middle,\n",
5903 " 'up': bb.upper},\n",
5904 " screen=stocks)"
5905 ]
5906 },
5907 {
5908 "cell_type": "code",
5909 "execution_count": null,
5910 "metadata": {},
5911 "outputs": [],
5912 "source": [
5913 "start_timer = time()\n",
5914 "result = run_pipeline(test_pipeline(), \n",
5915 " start_date='2018-05-01',\n",
5916 " end_date='2018-07-31')\n",
5917 "print('Pipeline run time {:.2f} secs'.format(time() - start_timer))\n",
5918 "result.tail(10)"
5919 ]
5920 },
5921 {
5922 "cell_type": "code",
5923 "execution_count": 31,
5924 "metadata": {
5925 "collapsed": true
5926 },
5927 "outputs": [],
5928 "source": [
5929 "def momentum_pipeline_alt():\n",
5930 " ewma = EWMA(inputs=[USEquityPricing.high],\n",
5931 " window_length=30, \n",
5932 " decay_rate=.2,\n",
5933 " mask=UNIVERSE)\n",
5934 " bb = BollingerBands(window_length=30, k=2, mask=UNIVERSE)\n",
5935 " \n",
5936 " STOCH = MomentumFactors.make_stochastic_oscillator()\n",
5937 " spo = STOCH(mask=UNIVERSE)\n",
5938 "\n",
5939 " columns = {'ewma': ewma,\n",
5940 " 'so_slowk': spo.slowk,\n",
5941 " 'so_slowd': spo.slowd,\n",
5942 " 'bb_lower': bb.lower,\n",
5943 " 'bb_mid': bb.middle,\n",
5944 " 'bb_up': bb.upper}\n",
5945 " columns.update({k: v(mask=UNIVERSE) for k, v in MOMENTUM_FACTORS.items()})\n",
5946 " \n",
5947 " return Pipeline(columns,\n",
5948 " screen=UNIVERSE)"
5949 ]
5950 },
5951 {
5952 "cell_type": "code",
5953 "execution_count": null,
5954 "metadata": {
5955 "collapsed": true
5956 },
5957 "outputs": [],
5958 "source": []
5959 }
5960 ],
5961 "metadata": {
5962 "kernelspec": {
5963 "display_name": "Python 3",
5964 "language": "python",
5965 "name": "python3"
5966 },
5967 "language_info": {
5968 "codemirror_mode": {
5969 "name": "ipython",
5970 "version": 3
5971 },
5972 "file_extension": ".py",
5973 "mimetype": "text/x-python",
5974 "name": "python",
5975 "nbconvert_exporter": "python",
5976 "pygments_lexer": "ipython3",
5977 "version": "3.7.0"
5978 },
5979 "toc": {
5980 "base_numbering": 1,
5981 "nav_menu": {},
5982 "number_sections": true,
5983 "sideBar": true,
5984 "skip_h1_title": false,
5985 "title_cell": "Table of Contents",
5986 "title_sidebar": "Contents",
5987 "toc_cell": false,
5988 "toc_position": {},
5989 "toc_section_display": true,
5990 "toc_window_display": true
5991 }
5992 },
5993 "nbformat": 4,
5994 "nbformat_minor": 2
5995 }