ml-finance-python

python scripts for finance machine learning

git clone https://9o.is/git/ml-finance-python.git

notebook.ipynb

(259177B)


      1 {
      2  "cells": [
      3   {
      4    "cell_type": "markdown",
      5    "metadata": {},
      6    "source": [
      7     "# Arbitrage Pricing Theory\n",
      8     "\n",
      9     "By Evgenia \"Jenny\" Nitishinskaya, Delaney Granizo-Mackenzie, and Maxwell Margenot.\n",
     10     "\n",
     11     "Part of the Quantopian Lecture Series:\n",
     12     "\n",
     13     "* [www.quantopian.com/lectures](https://www.quantopian.com/lectures)\n",
     14     "* [github.com/quantopian/research_public](https://github.com/quantopian/research_public)\n",
     15     "\n",
     16     "\n",
     17     "---\n",
     18     "\n",
     19     "Arbitrage pricing theory is a major asset pricing theory that relies on expressing the returns using a linear factor model:\n",
     20     "\n",
     21     "$$R_i = a_i + b_{i1} F_1 + b_{i2} F_2 + \\ldots + b_{iK} F_K + \\epsilon_i$$\n",
     22     "\n",
     23     "This theory states that if we have modelled our rate of return as above, then the expected returns obey\n",
     24     "\n",
     25     "$$ E(R_i) = R_F + b_{i1} \\lambda_1 + b_{i2} \\lambda_2 + \\ldots + b_{iK} \\lambda_K $$\n",
     26     "\n",
     27     "where $R_F$ is the risk-free rate, and $\\lambda_j$ is the risk premium - the return in excess of the risk-free rate - for factor $j$. This premium arises because investors require higher returns to compensate them for incurring risk. This generalizes the capital asset pricing model (CAPM), which uses the return on the market as its only factor.\n",
     28     "\n",
     29     "We can compute $\\lambda_j$ by constructing a portfolio that has a sensitivity of 1 to factor $j$ and 0 to all others (called a <i>pure factor portfolio</i> for factor $j$), and measure its return in excess of the risk-free rate. Alternatively, we could compute the factor sensitivities for $K$ well-diversified (no asset-specific risk, i.e. $\\epsilon_p = 0$) portfolios, and then solve the resulting system of linear equations."
     30    ]
     31   },
     32   {
     33    "cell_type": "markdown",
     34    "metadata": {},
     35    "source": [
     36     "## Arbitrage\n",
     37     "\n",
     38     "There are generally many, many securities in our universe. If we use different ones to compute the $\\lambda$s, will our results be consistent? If our results are inconsistent, there is an <i>arbitrage opportunity</i> (in expectation). Arbitrage is an operation that earns a profit without incurring risk and with no net investment of money, and an arbitrage opportunity is an opportunity to conduct such an operation. In this case, we mean that there is a risk-free operation with <i>expected</i> positive return that requires no net investment. It occurs when expectations of returns are inconsistent, i.e. risk is not priced consistently across securities.\n",
     39     "\n",
     40     "For instance, there is an arbitrage opportunity in the following case: say there is an asset with expected rate of return 0.2 for the next year and a $\\beta$ of 1.2 with the market, while the market is expected to have a rate of return of 0.1, and the risk-free rate on 1-year bonds is 0.05. Then the APT model tells us that the expected rate of return on the asset should be\n",
     41     "\n",
     42     "$$ R_F + \\beta \\lambda = 0.05 + 1.2 (0.1 - 0.05) = 0.11$$\n",
     43     "\n",
     44     "This does not agree with the prediction that the asset will have a rate of return of 0.2. So, if we buy \\$100 of our asset, short \\$120 of the market, and buy \\$20 of bonds, we will have invested no net money and are not exposed to any systematic risk (we are market-neutral), but we expect to earn $0.2 \\cdot 100 - 0.1 \\cdot 120 + 20 \\cdot 0.05 = 9$ dollars at the end of the year.\n",
     45     "\n",
     46     "The APT assumes that these opportunities will be taken advantage of until prices shift and the arbitrage opportunities disappear. That is, it assumes that there are arbitrageurs who have sufficient amounts of patience and capital. This provides a justification for the use of empirical factor models in pricing securities: if the model were inconsistent, there would be an arbitrage opportunity, and so the prices would adjust."
     47    ]
     48   },
     49   {
     50    "cell_type": "markdown",
     51    "metadata": {},
     52    "source": [
     53     "##Goes Both Ways\n",
     54     "\n",
     55     "Often knowing $E(R_i)$ is incredibly difficult, but notice that this model tells us what the expected returns should be if the market is fully arbitraged. This lays the groundwork for long-short equity strategies based on factor model ranking systems. If you know what the expected return of an asset is given that the market is arbitraged, and you hypothesize that the market will be mostly arbitraged over the timeframe on which you are trading, then you can construct a ranking.\n",
     56     "\n",
     57     "##Long-Short Equity\n",
     58     "\n",
     59     "To do this, estimate the expected return for each asset on the market, then rank them. Long the top percentile and short the bottom percentile, and you will make money on the difference in returns. Said another way, if the assets at the top of the ranking on average tend to make $5\\%$ more per year than the market, and assets at the bottom tend to make $5\\%$ less, then you will make $(M + 0.05) - (M - 0.05) = 0.10$ or $10\\%$ percent per year, where $M$ is the market return that gets canceled out.\n",
     60     "\n",
     61     "Long-short equity accepts that any individual asset is very difficult to model, relies on broad trends holding true. We can't accurately predict expected returns for an asset, but we can predict the expected returns for a group of 1000 assets as the errors average out.\n",
     62     "\n",
     63     "We will have a full lecture on long-short models later.\n"
     64    ]
     65   },
     66   {
     67    "cell_type": "markdown",
     68    "metadata": {},
     69    "source": [
     70     "##How many factors do you want?\n",
     71     "\n",
     72     "As discussed in other lectures, noteably Overfitting, having more factors will explain more and more of your returns, but at the cost of being more and more fit to noise in your data. Do discover true signals and make good predictions going forward, you want to select as few parameters as possible that still explain a large amount of the variance in returns."
     73    ]
     74   },
     75   {
     76    "cell_type": "markdown",
     77    "metadata": {},
     78    "source": [
     79     "##Example: Computing Expected Returns for Two Assets"
     80    ]
     81   },
     82   {
     83    "cell_type": "code",
     84    "execution_count": 1,
     85    "metadata": {
     86     "collapsed": true
     87    },
     88    "outputs": [],
     89    "source": [
     90     "import numpy as np\n",
     91     "import pandas as pd\n",
     92     "from statsmodels import regression\n",
     93     "import matplotlib.pyplot as plt"
     94    ]
     95   },
     96   {
     97    "cell_type": "markdown",
     98    "metadata": {},
     99    "source": [
    100     "Let's get some data."
    101    ]
    102   },
    103   {
    104    "cell_type": "code",
    105    "execution_count": 2,
    106    "metadata": {
    107     "collapsed": false
    108    },
    109    "outputs": [],
    110    "source": [
    111     "start_date = '2014-06-30'\n",
    112     "end_date = '2015-06-30'\n",
    113     "\n",
    114     "# We will look at the returns of an asset one-month into the future to model future returns.\n",
    115     "offset_start_date = '2014-07-31'\n",
    116     "offset_end_date = '2015-07-31'\n",
    117     "\n",
    118     "# Get returns data for our assets\n",
    119     "asset1 = get_pricing('HSC', fields='price', start_date=offset_start_date, end_date=offset_end_date).pct_change()[1:]\n",
    120     "asset2 = get_pricing('MSFT', fields='price', start_date=offset_start_date, end_date=offset_end_date).pct_change()[1:]\n",
    121     "# Get returns for the market\n",
    122     "bench = get_pricing('SPY', fields='price', start_date=start_date, end_date=end_date).pct_change()[1:]\n",
    123     "# Use an ETF that tracks 3-month T-bills as our risk-free rate of return\n",
    124     "treasury_ret = get_pricing('BIL', fields='price', start_date=start_date, end_date=end_date).pct_change()[1:]"
    125    ]
    126   },
    127   {
    128    "cell_type": "code",
    129    "execution_count": 3,
    130    "metadata": {
    131     "collapsed": false
    132    },
    133    "outputs": [],
    134    "source": [
    135     "# Define a constant to compute intercept\n",
    136     "constant = pd.TimeSeries(np.ones(len(asset1.index)), index=asset1.index)\n",
    137     "\n",
    138     "df = pd.DataFrame({'R1': asset1,\n",
    139     "              'R2': asset2,\n",
    140     "              'SPY': bench,\n",
    141     "              'RF': treasury_ret,\n",
    142     "              'Constant': constant})\n",
    143     "df = df.dropna()"
    144    ]
    145   },
    146   {
    147    "cell_type": "markdown",
    148    "metadata": {},
    149    "source": [
    150     "We'll start by computing static regressions over the whole time period."
    151    ]
    152   },
    153   {
    154    "cell_type": "code",
    155    "execution_count": 4,
    156    "metadata": {
    157     "collapsed": false
    158    },
    159    "outputs": [
    160     {
    161      "name": "stdout",
    162      "output_type": "stream",
    163      "text": [
    164       "p-value 6.68669273225e-26\n",
    165       "SPY         1.768275\n",
    166       "RF         -8.594705\n",
    167       "Constant   -0.002203\n",
    168       "dtype: float64\n",
    169       "p-value 6.48439859144e-23\n",
    170       "SPY         1.208441\n",
    171       "RF          5.352250\n",
    172       "Constant   -0.000133\n",
    173       "dtype: float64\n"
    174      ]
    175     }
    176    ],
    177    "source": [
    178     "OLS_model = regression.linear_model.OLS(df['R1'], df[['SPY', 'RF', 'Constant']])\n",
    179     "fitted_model = OLS_model.fit()\n",
    180     "print 'p-value', fitted_model.f_pvalue\n",
    181     "print fitted_model.params\n",
    182     "R1_params = fitted_model.params\n",
    183     "\n",
    184     "OLS_model = regression.linear_model.OLS(df['R2'], df[['SPY', 'RF', 'Constant']])\n",
    185     "fitted_model = OLS_model.fit()\n",
    186     "print 'p-value', fitted_model.f_pvalue\n",
    187     "print fitted_model.params\n",
    188     "R2_params = fitted_model.params"
    189    ]
    190   },
    191   {
    192    "cell_type": "markdown",
    193    "metadata": {},
    194    "source": [
    195     "As we've said before in other lectures, these numbers don't tell us too much by themselves. We need to look at the distribution of estimated coefficients and whether it's stable. Let's look at the rolling 100-day regression to see how it looks."
    196    ]
    197   },
    198   {
    199    "cell_type": "code",
    200    "execution_count": 5,
    201    "metadata": {
    202     "collapsed": false
    203    },
    204    "outputs": [
    205     {
    206      "data": {
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lrbfeok2bNgwZMsS93Gq1kpiYyNatWwkKCuKpp55yL9Pr9e7n586d48EHHyQlJYXZs2dj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\nsIBmzZoxYcIExo4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tFgsajaZKfbt3787+/fsBZ+vM5UHWjdZkW2Q2f5/Kx//dzuIRifgb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\nSCITlIxDdmRGc6b5PO3WTq7N3kiINmRc6xaY8qnpGnjXYMHgDs1M0O9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Nv5a+/j6+8+5P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\n",
    208       "text/plain": [
    209        "<matplotlib.figure.Figure at 0x7f99db72b8d0>"
    210       ]
    211      },
    212      "metadata": {},
    213      "output_type": "display_data"
    214     }
    215    ],
    216    "source": [
    217     "model = pd.stats.ols.MovingOLS(y = df['R1'], x=df[['SPY', 'RF']], \n",
    218     "                             window_type='rolling', \n",
    219     "                             window=100)\n",
    220     "rolling_parameter_estimates = model.beta\n",
    221     "rolling_parameter_estimates.plot();\n",
    222     "\n",
    223     "plt.hlines(R1_params['SPY'], df.index[0], df.index[-1], linestyles='dashed', colors='blue')\n",
    224     "plt.hlines(R1_params['RF'], df.index[0], df.index[-1], linestyles='dashed', colors='green')\n",
    225     "plt.hlines(R1_params['Constant'], df.index[0], df.index[-1], linestyles='dashed', colors='red')\n",
    226     "\n",
    227     "plt.title('Asset1 Computed Betas');\n",
    228     "plt.legend(['Market Beta', 'Risk Free Beta', 'Intercept', 'Market Beta Static', 'Risk Free Beta Static', 'Intercept Static']);"
    229    ]
    230   },
    231   {
    232    "cell_type": "code",
    233    "execution_count": 6,
    234    "metadata": {
    235     "collapsed": false
    236    },
    237    "outputs": [
    238     {
    239      "data": {
    240       "image/png": 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OS2Wy43QMBoOBzmVMDSvg7eql6WEiIiIiIlVclUlazmUkcTTpOK3q3IZPOU+4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\nM1qDFgAAAABJRU5ErkJggg==\n",
    241       "text/plain": [
    242        "<matplotlib.figure.Figure at 0x7f99db72b710>"
    243       ]
    244      },
    245      "metadata": {},
    246      "output_type": "display_data"
    247     }
    248    ],
    249    "source": [
    250     "model = pd.stats.ols.MovingOLS(y = df['R2'], x=df[['SPY', 'RF']], \n",
    251     "                             window_type='rolling', \n",
    252     "                             window=100)\n",
    253     "rolling_parameter_estimates = model.beta\n",
    254     "rolling_parameter_estimates.plot();\n",
    255     "\n",
    256     "plt.hlines(R2_params['SPY'], df.index[0], df.index[-1], linestyles='dashed', colors='blue')\n",
    257     "plt.hlines(R2_params['RF'], df.index[0], df.index[-1], linestyles='dashed', colors='green')\n",
    258     "plt.hlines(R2_params['Constant'], df.index[0], df.index[-1], linestyles='dashed', colors='red')\n",
    259     "\n",
    260     "plt.title('Asset2 Computed Betas');\n",
    261     "plt.legend(['Market Beta', 'Risk Free Beta', 'Intercept', 'Market Beta Static', 'Risk Free Beta Static', 'Intercept Static']);"
    262    ]
    263   },
    264   {
    265    "cell_type": "markdown",
    266    "metadata": {},
    267    "source": [
    268     "It might seem like the market betas are stable here, but let's zoom in to check."
    269    ]
    270   },
    271   {
    272    "cell_type": "code",
    273    "execution_count": 7,
    274    "metadata": {
    275     "collapsed": false
    276    },
    277    "outputs": [
    278     {
    279      "data": {
    280       "image/png": 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ezWLfsRz2H8/mwMlcHA4nQ3q34WfXd6Z7+0hOZxfz3MLvMBrg0SmDiQoP8GoM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\n",
    281       "text/plain": [
    282        "<matplotlib.figure.Figure at 0x7f99db60a950>"
    283       ]
    284      },
    285      "metadata": {},
    286      "output_type": "display_data"
    287     }
    288    ],
    289    "source": [
    290     "model = pd.stats.ols.MovingOLS(y = df['R2'], x=df[['SPY', 'RF']], \n",
    291     "                             window_type='rolling', \n",
    292     "                             window=100)\n",
    293     "rolling_parameter_estimates = model.beta\n",
    294     "rolling_parameter_estimates['SPY'].plot();\n",
    295     "\n",
    296     "plt.hlines(R2_params['SPY'], df.index[0], df.index[-1], linestyles='dashed', colors='blue')\n",
    297     "\n",
    298     "plt.title('Asset2 Computed Betas');\n",
    299     "plt.legend(['Market Beta', 'Market Beta Static']);"
    300    ]
    301   },
    302   {
    303    "cell_type": "markdown",
    304    "metadata": {},
    305    "source": [
    306     "As you can see, the plot scale massively affects how we perceive estimate quality."
    307    ]
    308   },
    309   {
    310    "cell_type": "markdown",
    311    "metadata": {},
    312    "source": [
    313     "##Predicting the Future\n",
    314     "\n",
    315     "Let's use this model to predict future prices for these assets."
    316    ]
    317   },
    318   {
    319    "cell_type": "code",
    320    "execution_count": 8,
    321    "metadata": {
    322     "collapsed": true
    323    },
    324    "outputs": [],
    325    "source": [
    326     "start_date = '2014-07-25'\n",
    327     "end_date = '2015-07-25'\n",
    328     "\n",
    329     "# We will look at the returns of an asset one-month into the future to model future returns.\n",
    330     "offset_start_date = '2014-08-25'\n",
    331     "offset_end_date = '2015-08-25'\n",
    332     "\n",
    333     "# Get returns data for our assets\n",
    334     "asset1 = get_pricing('HSC', fields='price', start_date=offset_start_date, end_date=offset_end_date).pct_change()[1:]\n",
    335     "# Get returns for the market\n",
    336     "bench = get_pricing('SPY', fields='price', start_date=start_date, end_date=end_date).pct_change()[1:]\n",
    337     "# Use an ETF that tracks 3-month T-bills as our risk-free rate of return\n",
    338     "treasury_ret = get_pricing('BIL', fields='price', start_date=start_date, end_date=end_date).pct_change()[1:]\n",
    339     "\n",
    340     "\n",
    341     "# Define a constant to compute intercept\n",
    342     "constant = pd.TimeSeries(np.ones(len(asset1.index)), index=asset1.index)\n",
    343     "\n",
    344     "df = pd.DataFrame({'R1': asset1,\n",
    345     "              'SPY': bench,\n",
    346     "              'RF': treasury_ret,\n",
    347     "              'Constant': constant})\n",
    348     "df = df.dropna()"
    349    ]
    350   },
    351   {
    352    "cell_type": "markdown",
    353    "metadata": {},
    354    "source": [
    355     "We'll perform a historical regression to get our model parameter estimates."
    356    ]
    357   },
    358   {
    359    "cell_type": "code",
    360    "execution_count": 9,
    361    "metadata": {
    362     "collapsed": false
    363    },
    364    "outputs": [
    365     {
    366      "name": "stdout",
    367      "output_type": "stream",
    368      "text": [
    369       "p-value 3.74649506793e-24\n",
    370       "SPY         1.738003\n",
    371       "RF         -7.382430\n",
    372       "Constant   -0.002555\n",
    373       "dtype: float64\n"
    374      ]
    375     }
    376    ],
    377    "source": [
    378     "OLS_model = regression.linear_model.OLS(df['R1'], df[['SPY', 'RF', 'Constant']])\n",
    379     "fitted_model = OLS_model.fit()\n",
    380     "print 'p-value', fitted_model.f_pvalue\n",
    381     "print fitted_model.params\n",
    382     "\n",
    383     "b_SPY = fitted_model.params['SPY']\n",
    384     "b_RF = fitted_model.params['RF']\n",
    385     "a = fitted_model.params['Constant']"
    386    ]
    387   },
    388   {
    389    "cell_type": "markdown",
    390    "metadata": {},
    391    "source": [
    392     "Get the factor data for the last month so we can predict the next month."
    393    ]
    394   },
    395   {
    396    "cell_type": "code",
    397    "execution_count": 10,
    398    "metadata": {
    399     "collapsed": true
    400    },
    401    "outputs": [],
    402    "source": [
    403     "start_date = '2015-07-25'\n",
    404     "end_date = '2015-08-25'\n",
    405     "\n",
    406     "# Get returns for the market\n",
    407     "last_month_bench = get_pricing('SPY', fields='price', start_date=start_date, end_date=end_date).pct_change()[1:]\n",
    408     "# Use an ETF that tracks 3-month T-bills as our risk-free rate of return\n",
    409     "last_month_treasury_ret = get_pricing('BIL', fields='price', start_date=start_date, end_date=end_date).pct_change()[1:]"
    410    ]
    411   },
    412   {
    413    "cell_type": "markdown",
    414    "metadata": {},
    415    "source": [
    416     "Make our predictions."
    417    ]
    418   },
    419   {
    420    "cell_type": "code",
    421    "execution_count": 11,
    422    "metadata": {
    423     "collapsed": false
    424    },
    425    "outputs": [],
    426    "source": [
    427     "predictions = b_SPY * last_month_bench + b_RF * last_month_treasury_ret + a\n",
    428     "predictions.index = predictions.index + pd.DateOffset(months=1)"
    429    ]
    430   },
    431   {
    432    "cell_type": "code",
    433    "execution_count": 12,
    434    "metadata": {
    435     "collapsed": false
    436    },
    437    "outputs": [
    438     {
    439      "data": {
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Us2fPxsqVKxEVFYWEhAQ8++yzmDVrFgBg5MiR6NChg95DJCKq5mx9EsD24O5auFCO2dmA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\ncFxysnTyyeYmxrFjbo8GQLg64wxz4+a669weSfARKIU5b6D0n/9Ib71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GybJMoMT6JAAA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    441       "text/plain": [
    442        "<matplotlib.figure.Figure at 0x7f99d9d99510>"
    443       ]
    444      },
    445      "metadata": {},
    446      "output_type": "display_data"
    447     }
    448    ],
    449    "source": [
    450     "plt.plot(asset1.index[-30:], asset1.values[-30:], 'b-')\n",
    451     "plt.plot(predictions.index, predictions, 'b--')\n",
    452     "plt.ylabel('Returns')\n",
    453     "plt.legend(['Actual', 'Predicted']);"
    454    ]
    455   },
    456   {
    457    "cell_type": "markdown",
    458    "metadata": {},
    459    "source": [
    460     "Of course, this analysis hasn't yet told us anything about the quality of our predictions. To check the quality of our predictions we need to use techniques such as out of sample testing or cross-validation. For the purposes of long-short equity ranking systems, the Spearman Correlation lecture details a way to check the quality of a ranking system.\n",
    461     "\n",
    462     "##Important Note!\n",
    463     "\n",
    464     "Again, any of these individual predictions will probably be inaccurate. Industry-quality modeling makes predictions for thousands of assets and relies on broad tends holding. If I told you that I have a predictive model with a 51% success rate, you would not make one prediction and bet all your money on it. You would make thousands of predictions and divide your money between them."
    465    ]
    466   },
    467   {
    468    "cell_type": "markdown",
    469    "metadata": {},
    470    "source": [
    471     "*This presentation is for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation for any security; nor does it constitute an offer to provide investment advisory or other services by Quantopian, Inc. (\"Quantopian\"). Nothing contained herein constitutes investment advice or offers any opinion with respect to the suitability of any security, and any views expressed herein should not be taken as advice to buy, sell, or hold any security or as an endorsement of any security or company.  In preparing the information contained herein, Quantopian, Inc. has not taken into account the investment needs, objectives, and financial circumstances of any particular investor. Any views expressed and data illustrated herein were prepared based upon information, believed to be reliable, available to Quantopian, Inc. at the time of publication. Quantopian makes no guarantees as to their accuracy or completeness. All information is subject to change and may quickly become unreliable for various reasons, including changes in market conditions or economic circumstances.*"
    472    ]
    473   }
    474  ],
    475  "metadata": {
    476   "kernelspec": {
    477    "name": "python3",
    478    "language": "python",
    479    "display_name": "Python 3"
    480   },
    481   "language_info": {
    482    "codemirror_mode": {
    483     "name": "ipython",
    484     "version": 2
    485    },
    486    "file_extension": ".py",
    487    "mimetype": "text/x-python",
    488    "name": "python",
    489    "nbconvert_exporter": "python",
    490    "pygments_lexer": "ipython2",
    491    "version": "2.7.11"
    492   }
    493  },
    494  "nbformat": 4,
    495  "nbformat_minor": 0
    496 }