Fin_ML_work_Roll_EMH_Stock_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Financial Machine Learning - EGES 3\n",
    "### Assignment 2 Practical Part - Skander Chouchene\n",
    "    Parts:\n",
    "    I/Style factor attribution of portfolio returns\n",
    "    II/The Roll’s measure (spread)\n",
    "    III/Testing the EMH\n",
    "    IV/High frequency stock direction prediction\n",
    "    V/Execution strategies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# I) Style factor attribution of portfolio returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-2-42662e525862>:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support sep=None with delim_whitespace=False; you can avoid this warning by specifying engine='python'.\n",
      "  factors=pd.read_csv(\"factors.csv\",sep=None,index_col=0)\n",
      "<ipython-input-2-42662e525862>:2: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support sep=None with delim_whitespace=False; you can avoid this warning by specifying engine='python'.\n",
      "  momentum=pd.read_csv(\"momentum.csv\",sep=None,index_col=0).iloc[:-2]\n"
     ]
    }
   ],
   "source": [
    "factors=pd.read_csv(\"factors.csv\",sep=None,index_col=0)\n",
    "momentum=pd.read_csv(\"momentum.csv\",sep=None,index_col=0).iloc[:-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio_ret=pd.read_csv(\"daily_portef_ret.csv\",index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "factors[\"Mkt\"]=factors[\"Mkt-RF\"]+factors[\"RF\"]\n",
    "factors.drop([\"Mkt-RF\",\"RF\"],inplace=True,axis=1)\n",
    "\n",
    "factors=factors.loc[20100104:20200924]\n",
    "momentum=momentum.loc['20100104':'20200924']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def strtodateformat(ch):\n",
    "    return ch[:4]+'-'+ch[4:6]+'-'+ch[6:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "l=factors.index.copy()\n",
    "l=l.astype(str)\n",
    "l=[strtodateformat(k) for k in l]\n",
    "factors[\"l\"]=l\n",
    "factors.set_index(\"l\",inplace=True)\n",
    "factors.index=pd.to_datetime(factors.index, format='%Y-%m-%d', errors='ignore')\n",
    "\n",
    "\n",
    "j=momentum.index.copy()\n",
    "j=[strtodateformat(k) for k in j]\n",
    "momentum[\"j\"]=j\n",
    "momentum.set_index(\"j\",inplace=True)\n",
    "momentum.index=pd.to_datetime(momentum.index, format='%Y-%m-%d', errors='ignore')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio_ret=portfolio_ret.join(factors)\n",
    "portfolio_ret=portfolio_ret.join(momentum)\n",
    "portfolio_ret.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Portfolio</th>\n",
       "      <th>SMB</th>\n",
       "      <th>HML</th>\n",
       "      <th>RMW</th>\n",
       "      <th>CMA</th>\n",
       "      <th>Mkt</th>\n",
       "      <th>Mom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-09-24</th>\n",
       "      <td>4201.281095</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.25</td>\n",
       "      <td>-0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-23</th>\n",
       "      <td>4483.961919</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.26</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-2.48</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-22</th>\n",
       "      <td>4984.438765</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>-1.65</td>\n",
       "      <td>0.60</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-21</th>\n",
       "      <td>1907.606756</td>\n",
       "      <td>-2.58</td>\n",
       "      <td>-1.91</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-1.08</td>\n",
       "      <td>2.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-18</th>\n",
       "      <td>9913.841793</td>\n",
       "      <td>0.74</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-1.11</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.88</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Portfolio   SMB   HML   RMW   CMA   Mkt  Mom   \n",
       "2020-09-24  4201.281095 -0.20  0.32  0.49  0.11  0.25   -0.06\n",
       "2020-09-23  4483.961919 -0.63  0.19  0.26 -0.12 -2.48    0.29\n",
       "2020-09-22  4984.438765 -0.45 -1.65  0.60 -0.55  1.02    1.90\n",
       "2020-09-21  1907.606756 -2.58 -1.91 -0.46 -0.32 -1.08    2.90\n",
       "2020-09-18  9913.841793  0.74 -0.17 -1.11 -0.46 -0.88    0.94"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio_ret.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We should scale down the returns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(portfolio_ret[\"Portfolio\"].to_numpy().reshape(-1, 1))\n",
    "portfolio_ret[\"Portfolio\"]=scaler.transform(portfolio_ret[\"Portfolio\"].to_numpy().reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.003763634616077871"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "X_train, X_test, y_train, y_test=train_test_split(portfolio_ret.drop(\"Portfolio\",axis=1),portfolio_ret[\"Portfolio\"],test_size=0.2)\n",
    "reg=LinearRegression()\n",
    "reg.fit(X_train,y_train)\n",
    "pred=reg.predict(X_test)\n",
    "mean_squared_error(pred,y_test)\n",
    "reg.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary for model\n",
      "                                 OLS Regression Results                                \n",
      "=======================================================================================\n",
      "Dep. Variable:                      y   R-squared (uncentered):                   0.002\n",
      "Model:                            OLS   Adj. R-squared (uncentered):              0.000\n",
      "Method:                 Least Squares   F-statistic:                              1.082\n",
      "Date:                Fri, 06 Nov 2020   Prob (F-statistic):                       0.370\n",
      "Time:                        20:24:36   Log-Likelihood:                         -3791.0\n",
      "No. Observations:                2674   AIC:                                      7594.\n",
      "Df Residuals:                    2668   BIC:                                      7629.\n",
      "Df Model:                           6                                                  \n",
      "Covariance Type:            nonrobust                                                  \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "x1             0.0307      0.037      0.820      0.412      -0.043       0.104\n",
      "x2            -0.0476      0.043     -1.120      0.263      -0.131       0.036\n",
      "x3             0.0477      0.057      0.843      0.399      -0.063       0.159\n",
      "x4             0.0229      0.074      0.308      0.758      -0.123       0.169\n",
      "x5            -0.0246      0.019     -1.285      0.199      -0.062       0.013\n",
      "x6             0.0104      0.028      0.372      0.710      -0.045       0.065\n",
      "==============================================================================\n",
      "Omnibus:                     3777.551   Durbin-Watson:                   0.269\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           846476.431\n",
      "Skew:                           8.352   Prob(JB):                         0.00\n",
      "Kurtosis:                      88.548   Cond. No.                         4.79\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "\n",
      " \n",
      "\n"
     ]
    }
   ],
   "source": [
    "portfolio_ret.dropna(inplace=True)\n",
    "X=portfolio_ret.iloc[:,1:].values\n",
    "y=portfolio_ret[\"Portfolio\"].values\n",
    "size = int(len(X) * 0.8)\n",
    "model = sm.OLS(y,X)\n",
    "results = model.fit()\n",
    "print(\"Summary for model\")\n",
    "print(results.summary())\n",
    "print(\"\\n \\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# II) The Roll’s measure (spread)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) Importing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "directory=os.getcwd()+\"\\\\DOW\\\\\"\n",
    "files = glob.glob(os.getcwd()+\"\\\\DOW\\\\*.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "DOW=[]\n",
    "for i in range(len(files)):\n",
    "    df=pd.read_csv(files[i])\n",
    "    df.Date=pd.to_datetime(df.Date, format='%Y-%m-%d', errors='ignore')\n",
    "    DOW.append(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "DJI=pd.read_csv(\"DJI.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#DOW[0].reindex(index=DOW[0].index[::-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_names=[\"AAPL\",\"AMGN\",\"AXP\",\"BA\",\"CAT\",\"CRM\",\"CSCO\",\"CVX\",\"DIS\",\"DOW\",\"GS\",\"HD\",\"HON\",\"IBM\",\"INTC\",\"JNJ\",\"JPM\",\"KO\",\"MCD\",\"MMM\",\"MRK\",\"MSFT\",\"NKE\",\"PG\",\"TRV\",\"UNH\",\"V\",\"VZ\",\"WBA\",\"WMT\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) 3) 4) 5) Computing Features/Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining Returns and SMA functions\n",
    "def returns(df,n):\n",
    "    ch=\"Returns_\"+str(n)\n",
    "    d=10000*np.log(df[\"Close\"]/df.shift(n)[\"Close\"])\n",
    "    return d,ch\n",
    "\n",
    "def SMA(df,t,n):\n",
    "    return (df[\"Close\"].iloc[t-n:t].sum())/n\n",
    "\n",
    "\n",
    "def sma_change(df,n):\n",
    "    \n",
    "    ch=str(n)\n",
    "    ch=\"sma_change_\"+ch\n",
    "    d=[np.nan for i in range(df.shape[0])]\n",
    "    \n",
    "    for t in range(n,df.shape[0]):\n",
    "        d[t]=-1+df[\"Close\"].iloc[t]/SMA(df,t,n)\n",
    "    return d,ch\n",
    "\n",
    "def roll():\n",
    "    return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "l=[]\n",
    "for i in range(len(DOW)):\n",
    "    k=DOW[i]\n",
    "    for n in [1,2,5,10,20,60]:\n",
    "        d,ch=returns(DOW[i],n)\n",
    "        k[ch]=d                \n",
    "        d2,ch2=sma_change(DOW[i],n)\n",
    "        k[ch2]=d2\n",
    "    l.append(k)\n",
    "        #DOW[i].dropna(axis=0,inplace=True)\n",
    "DOW=l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(DOW)):\n",
    "    stock,ch=returns(DOW[i],-1)\n",
    "    DOW[i][\"next_return\"]=stock\n",
    "    DOW[i].dropna(axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "DOW_df=pd.concat(DOW)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Returns_1</th>\n",
       "      <th>sma_change_1</th>\n",
       "      <th>Returns_2</th>\n",
       "      <th>sma_change_2</th>\n",
       "      <th>Returns_5</th>\n",
       "      <th>sma_change_5</th>\n",
       "      <th>Returns_10</th>\n",
       "      <th>sma_change_10</th>\n",
       "      <th>Returns_20</th>\n",
       "      <th>sma_change_20</th>\n",
       "      <th>Returns_60</th>\n",
       "      <th>sma_change_60</th>\n",
       "      <th>next_return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2000-03-29</td>\n",
       "      <td>1.244420</td>\n",
       "      <td>1.244978</td>\n",
       "      <td>1.194894</td>\n",
       "      <td>1.213728</td>\n",
       "      <td>1.048830</td>\n",
       "      <td>239836800</td>\n",
       "      <td>-231.777260</td>\n",
       "      <td>-0.022911</td>\n",
       "      <td>-263.172441</td>\n",
       "      <td>-0.024445</td>\n",
       "      <td>-589.187447</td>\n",
       "      <td>-3.298931e-02</td>\n",
       "      <td>1564.528545</td>\n",
       "      <td>0.027010</td>\n",
       "      <td>422.604732</td>\n",
       "      <td>0.062816</td>\n",
       "      <td>1942.547706</td>\n",
       "      <td>0.189302</td>\n",
       "      <td>778.995499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2000-03-30</td>\n",
       "      <td>1.192522</td>\n",
       "      <td>1.229353</td>\n",
       "      <td>1.119978</td>\n",
       "      <td>1.122768</td>\n",
       "      <td>0.970228</td>\n",
       "      <td>414400000</td>\n",
       "      <td>-778.995499</td>\n",
       "      <td>-0.074943</td>\n",
       "      <td>-1010.772759</td>\n",
       "      <td>-0.085663</td>\n",
       "      <td>-1166.780120</td>\n",
       "      <td>-9.483538e-02</td>\n",
       "      <td>338.678302</td>\n",
       "      <td>-0.063881</td>\n",
       "      <td>302.746292</td>\n",
       "      <td>-0.018991</td>\n",
       "      <td>2044.326693</td>\n",
       "      <td>0.096336</td>\n",
       "      <td>-769.796458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>2000-03-31</td>\n",
       "      <td>1.137835</td>\n",
       "      <td>1.225446</td>\n",
       "      <td>1.125000</td>\n",
       "      <td>1.212612</td>\n",
       "      <td>1.047865</td>\n",
       "      <td>404633600</td>\n",
       "      <td>769.796458</td>\n",
       "      <td>0.080020</td>\n",
       "      <td>-9.199041</td>\n",
       "      <td>0.037975</td>\n",
       "      <td>-209.474168</td>\n",
       "      <td>1.649332e-07</td>\n",
       "      <td>829.622287</td>\n",
       "      <td>0.007885</td>\n",
       "      <td>592.454431</td>\n",
       "      <td>0.057962</td>\n",
       "      <td>2668.851444</td>\n",
       "      <td>0.180078</td>\n",
       "      <td>185.797358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2000-04-03</td>\n",
       "      <td>1.209821</td>\n",
       "      <td>1.245536</td>\n",
       "      <td>1.155692</td>\n",
       "      <td>1.190290</td>\n",
       "      <td>1.028576</td>\n",
       "      <td>328563200</td>\n",
       "      <td>-185.797358</td>\n",
       "      <td>-0.018408</td>\n",
       "      <td>583.999100</td>\n",
       "      <td>0.019354</td>\n",
       "      <td>-458.168840</td>\n",
       "      <td>-1.423463e-02</td>\n",
       "      <td>805.117510</td>\n",
       "      <td>-0.018543</td>\n",
       "      <td>588.970196</td>\n",
       "      <td>0.035336</td>\n",
       "      <td>3388.192914</td>\n",
       "      <td>0.153043</td>\n",
       "      <td>460.509323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2000-04-04</td>\n",
       "      <td>1.184152</td>\n",
       "      <td>1.187500</td>\n",
       "      <td>1.042411</td>\n",
       "      <td>1.136719</td>\n",
       "      <td>0.982283</td>\n",
       "      <td>660329600</td>\n",
       "      <td>-460.509323</td>\n",
       "      <td>-0.045007</td>\n",
       "      <td>-646.306681</td>\n",
       "      <td>-0.053878</td>\n",
       "      <td>-887.282982</td>\n",
       "      <td>-4.981806e-02</td>\n",
       "      <td>-581.667054</td>\n",
       "      <td>-0.069778</td>\n",
       "      <td>354.775308</td>\n",
       "      <td>-0.014180</td>\n",
       "      <td>2464.871089</td>\n",
       "      <td>0.095100</td>\n",
       "      <td>-237.704289</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Open      High       Low     Close  Adj Close     Volume  \\\n",
       "60 2000-03-29  1.244420  1.244978  1.194894  1.213728   1.048830  239836800   \n",
       "61 2000-03-30  1.192522  1.229353  1.119978  1.122768   0.970228  414400000   \n",
       "62 2000-03-31  1.137835  1.225446  1.125000  1.212612   1.047865  404633600   \n",
       "63 2000-04-03  1.209821  1.245536  1.155692  1.190290   1.028576  328563200   \n",
       "64 2000-04-04  1.184152  1.187500  1.042411  1.136719   0.982283  660329600   \n",
       "\n",
       "     Returns_1  sma_change_1    Returns_2  sma_change_2    Returns_5  \\\n",
       "60 -231.777260     -0.022911  -263.172441     -0.024445  -589.187447   \n",
       "61 -778.995499     -0.074943 -1010.772759     -0.085663 -1166.780120   \n",
       "62  769.796458      0.080020    -9.199041      0.037975  -209.474168   \n",
       "63 -185.797358     -0.018408   583.999100      0.019354  -458.168840   \n",
       "64 -460.509323     -0.045007  -646.306681     -0.053878  -887.282982   \n",
       "\n",
       "    sma_change_5   Returns_10  sma_change_10  Returns_20  sma_change_20  \\\n",
       "60 -3.298931e-02  1564.528545       0.027010  422.604732       0.062816   \n",
       "61 -9.483538e-02   338.678302      -0.063881  302.746292      -0.018991   \n",
       "62  1.649332e-07   829.622287       0.007885  592.454431       0.057962   \n",
       "63 -1.423463e-02   805.117510      -0.018543  588.970196       0.035336   \n",
       "64 -4.981806e-02  -581.667054      -0.069778  354.775308      -0.014180   \n",
       "\n",
       "     Returns_60  sma_change_60  next_return  \n",
       "60  1942.547706       0.189302   778.995499  \n",
       "61  2044.326693       0.096336  -769.796458  \n",
       "62  2668.851444       0.180078   185.797358  \n",
       "63  3388.192914       0.153043   460.509323  \n",
       "64  2464.871089       0.095100  -237.704289  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DOW_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5) Correlation between predictors and Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import pearsonr\n",
    "def corr_and_pval(data):\n",
    "    coeffmat = np.zeros((data.shape[1], data.shape[1]))\n",
    "    pvalmat = np.zeros((data.shape[1], data.shape[1]))\n",
    "\n",
    "    for i in range(data.shape[1]):    \n",
    "        for j in range(data.shape[1]):\n",
    "            corrtest = pearsonr(data[data.columns[i]], data[data.columns[j]])\n",
    "\n",
    "            coeffmat[i,j] = corrtest[0]\n",
    "            pvalmat[i,j] = corrtest[1]\n",
    "    return coeffmat, pvalmat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1,ax2) =plt.subplots(2,1,figsize=(30,5))\n",
    "data=DOW_df.iloc[:,1:]\n",
    "corr_mat=data.corr(method=\"pearson\").loc[['next_return'],:]\n",
    "#plt.figure(figsize=(20,60))\n",
    "    \n",
    "title=\"Correlation between variables and next_return\"\n",
    "title2=\"Statistical significance (p-value) between variables and next_return\"\n",
    "    \n",
    "sns.heatmap(corr_mat,vmax=1,square=True,annot=True,cmap='RdYlBu_r',cbar=False,ax=ax1)\n",
    "ax1.set_title(title,fontdict={'fontsize': 18})\n",
    "    \n",
    "sns.heatmap(np.transpose(corr_and_pval(data)[1][0].reshape(corr_and_pval(data)[1][:,0].shape[0],1)),xticklabels=data.columns,vmax=1,square=True,annot=True,cmap=\"autumn\",cbar=False,ax=ax2)\n",
    "ax2.set_title(title2,fontdict={'fontsize': 18})\n",
    "\n",
    "plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9, hspace=2, wspace=0.5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6) Scatter plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bscatter(X,Y,k):\n",
    "    Xnom, thresh=pd.qcut(X, k, retbins=True)\n",
    "    tp = pd.DataFrame({'Xnom': Xnom, 'X': X, 'Y':Y})\n",
    "    GMn  = tp.groupby('Xnom').mean()\n",
    "    SeMn = tp.groupby('Xnom').std()/ np.sqrt(tp.groupby('Xnom').count())\n",
    "    Xq = GMn.X\n",
    "    Yq = GMn.Y\n",
    "    XSe = SeMn.X\n",
    "    YSe = SeMn.Y\n",
    "    return(Xq,XSe,Yq,YSe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,10),nrows=6,ncols=3)\n",
    "title='20-Binned scatter plots for the predictors and Y'\n",
    "fig.suptitle(title, fontsize=16)\n",
    "columns=DOW_df.columns[1:-1]    \n",
    "for j in range(len(columns)):\n",
    "    col=columns[j]\n",
    "    Xq,XSe,Yq,YSe=bscatter(DOW_df[col].values,DOW_df['next_return'].values, 20)\n",
    "\n",
    "    ax[j//3,j%3].scatter(x=Xq,y=Yq)\n",
    "    ax[j//3,j%3].set_xlabel(col)\n",
    "    ax[j//3,j%3].set_ylabel(\"next_return\")\n",
    "\n",
    "plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9, hspace=0.5, wspace=0.5)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7) Binning Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sign(x):\n",
    "    if x==0:\n",
    "        return 0\n",
    "    else:\n",
    "        return int(x/abs(x))\n",
    "    \n",
    "for stock in DOW:\n",
    "    Y=stock[\"next_return\"]\n",
    "    mean=stock[\"next_return\"].mean()\n",
    "    Y=Y-mean\n",
    "    Y=Y.apply(sign)\n",
    "    stock[\"Y\"]=Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8) Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_val,y_train,y_val=train_test_split(stock.iloc[:,6:-2],stock[\"Y\"],test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score,classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.49613899613899615"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=LogisticRegression()\n",
    "model.fit(X_train,y_train)\n",
    "pred=model.predict(X_val)\n",
    "accuracy_score(pred,y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       1.00      0.50      0.66      1036\n",
      "           1       0.00      0.00      0.00         0\n",
      "\n",
      "    accuracy                           0.50      1036\n",
      "   macro avg       0.50      0.25      0.33      1036\n",
      "weighted avg       1.00      0.50      0.66      1036\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ALEX\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1221: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(pred,y_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5173745173745173"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda=LinearDiscriminantAnalysis()\n",
    "lda.fit(X_train,y_train)\n",
    "pred=lda.predict(X_val)\n",
    "accuracy_score(pred,y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.67      0.51      0.58       676\n",
      "           1       0.37      0.53      0.43       360\n",
      "\n",
      "    accuracy                           0.52      1036\n",
      "   macro avg       0.52      0.52      0.51      1036\n",
      "weighted avg       0.57      0.52      0.53      1036\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(pred,y_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  III) Testing the EMH"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normality (using Jarque-Bera)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.stattools import jarque_bera\n",
    "\n",
    "l=[jarque_bera(stock[\"next_return\"]) for stock in DOW]\n",
    "normality=pd.DataFrame(l,index=stock_names,columns=[\"JB\",\"p_value\",\"skewness\",\"kurtosis\"]).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMGN</th>\n",
       "      <th>AXP</th>\n",
       "      <th>BA</th>\n",
       "      <th>CAT</th>\n",
       "      <th>CRM</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>CVX</th>\n",
       "      <th>DIS</th>\n",
       "      <th>DOW</th>\n",
       "      <th>...</th>\n",
       "      <th>MRK</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>NKE</th>\n",
       "      <th>PG</th>\n",
       "      <th>TRV</th>\n",
       "      <th>UNH</th>\n",
       "      <th>V</th>\n",
       "      <th>VZ</th>\n",
       "      <th>WBA</th>\n",
       "      <th>WMT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>JB</th>\n",
       "      <td>3.055620e+06</td>\n",
       "      <td>7420.590930</td>\n",
       "      <td>28938.275269</td>\n",
       "      <td>71100.056936</td>\n",
       "      <td>5077.269883</td>\n",
       "      <td>9397.591494</td>\n",
       "      <td>19052.752577</td>\n",
       "      <td>117215.321569</td>\n",
       "      <td>21377.727154</td>\n",
       "      <td>1794.974330</td>\n",
       "      <td>...</td>\n",
       "      <td>184036.650616</td>\n",
       "      <td>22720.834594</td>\n",
       "      <td>23114.629699</td>\n",
       "      <td>21134.605358</td>\n",
       "      <td>91681.072407</td>\n",
       "      <td>89682.806282</td>\n",
       "      <td>10816.022199</td>\n",
       "      <td>9067.667271</td>\n",
       "      <td>14486.398620</td>\n",
       "      <td>12693.334543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p_value</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skewness</th>\n",
       "      <td>4.327240e+00</td>\n",
       "      <td>-0.249259</td>\n",
       "      <td>-0.024566</td>\n",
       "      <td>0.560530</td>\n",
       "      <td>0.199767</td>\n",
       "      <td>-0.343909</td>\n",
       "      <td>-0.078187</td>\n",
       "      <td>0.589185</td>\n",
       "      <td>0.197047</td>\n",
       "      <td>1.140002</td>\n",
       "      <td>...</td>\n",
       "      <td>1.457098</td>\n",
       "      <td>0.170416</td>\n",
       "      <td>0.183550</td>\n",
       "      <td>0.128678</td>\n",
       "      <td>0.316614</td>\n",
       "      <td>-0.100524</td>\n",
       "      <td>0.122142</td>\n",
       "      <td>-0.051283</td>\n",
       "      <td>0.354413</td>\n",
       "      <td>-0.236382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurtosis</th>\n",
       "      <td>1.216693e+02</td>\n",
       "      <td>8.842311</td>\n",
       "      <td>14.579056</td>\n",
       "      <td>21.115307</td>\n",
       "      <td>7.833674</td>\n",
       "      <td>10.423379</td>\n",
       "      <td>12.394192</td>\n",
       "      <td>26.274321</td>\n",
       "      <td>12.944449</td>\n",
       "      <td>13.890038</td>\n",
       "      <td>...</td>\n",
       "      <td>32.054895</td>\n",
       "      <td>13.254468</td>\n",
       "      <td>13.342149</td>\n",
       "      <td>12.892154</td>\n",
       "      <td>23.600393</td>\n",
       "      <td>23.383287</td>\n",
       "      <td>12.122531</td>\n",
       "      <td>9.480883</td>\n",
       "      <td>11.161863</td>\n",
       "      <td>10.654235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  AAPL         AMGN           AXP            BA          CAT  \\\n",
       "JB        3.055620e+06  7420.590930  28938.275269  71100.056936  5077.269883   \n",
       "p_value   0.000000e+00     0.000000      0.000000      0.000000     0.000000   \n",
       "skewness  4.327240e+00    -0.249259     -0.024566      0.560530     0.199767   \n",
       "kurtosis  1.216693e+02     8.842311     14.579056     21.115307     7.833674   \n",
       "\n",
       "                  CRM          CSCO            CVX           DIS          DOW  \\\n",
       "JB        9397.591494  19052.752577  117215.321569  21377.727154  1794.974330   \n",
       "p_value      0.000000      0.000000       0.000000      0.000000     0.000000   \n",
       "skewness    -0.343909     -0.078187       0.589185      0.197047     1.140002   \n",
       "kurtosis    10.423379     12.394192      26.274321     12.944449    13.890038   \n",
       "\n",
       "          ...            MRK          MSFT           NKE            PG  \\\n",
       "JB        ...  184036.650616  22720.834594  23114.629699  21134.605358   \n",
       "p_value   ...       0.000000      0.000000      0.000000      0.000000   \n",
       "skewness  ...       1.457098      0.170416      0.183550      0.128678   \n",
       "kurtosis  ...      32.054895     13.254468     13.342149     12.892154   \n",
       "\n",
       "                   TRV           UNH             V           VZ           WBA  \\\n",
       "JB        91681.072407  89682.806282  10816.022199  9067.667271  14486.398620   \n",
       "p_value       0.000000      0.000000      0.000000     0.000000      0.000000   \n",
       "skewness      0.316614     -0.100524      0.122142    -0.051283      0.354413   \n",
       "kurtosis     23.600393     23.383287     12.122531     9.480883     11.161863   \n",
       "\n",
       "                   WMT  \n",
       "JB        12693.334543  \n",
       "p_value       0.000000  \n",
       "skewness     -0.236382  \n",
       "kurtosis     10.654235  \n",
       "\n",
       "[4 rows x 30 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The returns do not follow a normal distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Autocorrelation test (Ljung-Box Test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ALEX\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:568: FutureWarning: fft=True will become the default in a future version of statsmodels. To suppress this warning, explicitly set fft=False.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([3.37981423e-03, 1.35177876e-02, 2.93843712e-02, 9.82097747e-04,\n",
       "       7.64214722e-04, 8.98316440e-04, 2.93165901e-04, 2.51584293e-04,\n",
       "       4.91921529e-04, 1.67068283e-04, 3.12497615e-04, 3.60036590e-05,\n",
       "       1.80034044e-05, 7.04329107e-06, 5.00745109e-06, 7.53583197e-06,\n",
       "       2.07610376e-07, 4.16462167e-07, 7.69836596e-07, 1.46141411e-06,\n",
       "       9.00839059e-07, 8.46224930e-07, 4.55126854e-07, 5.48277381e-07,\n",
       "       9.77220807e-07, 1.26077542e-06, 2.11000767e-06, 2.78901488e-06,\n",
       "       4.71616483e-06, 4.68081789e-06, 7.46189425e-06, 1.17757024e-05,\n",
       "       1.46629637e-05, 1.85165863e-05, 2.90206587e-05, 4.13559744e-05,\n",
       "       4.70248315e-05, 7.11609800e-05, 4.74280721e-05, 6.15232838e-05])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import acf\n",
    "l=[acf(stock[\"next_return\"],qstat=True)[2] for stock in DOW]\n",
    "l[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For all the DOW components, the p_values for the Ljung-Box statistical test are significantly small. Therefore we reject the test's null hypothesis (which states that the data is independently distributed).\n",
    "#### => The data is autocorrelated"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DOW index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([39770.12823739]),\n",
       " array([0.]),\n",
       " array([0.38316438]),\n",
       " array([16.81190102]))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret_dji=returns(DJI,-1)[0]\n",
    "ret_dji=pd.DataFrame(ret_dji).dropna()\n",
    "jarque_bera(ret_dji)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The DOW does not follow a normal distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ALEX\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:568: FutureWarning: fft=True will become the default in a future version of statsmodels. To suppress this warning, explicitly set fft=False.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1.67432748e-17, 5.73034520e-17, 9.32405183e-17, 6.40863749e-17,\n",
       "       1.75575948e-16, 8.07740698e-18, 6.93690703e-20, 6.54773001e-20,\n",
       "       2.71756486e-21, 9.06422490e-21, 2.12656786e-20, 1.09673168e-21,\n",
       "       3.95219979e-22, 9.21974956e-22, 4.15099102e-26, 7.22370315e-32,\n",
       "       1.24161310e-31, 2.34444444e-33, 8.02105313e-33, 2.63271720e-32,\n",
       "       5.04757292e-32, 1.24496379e-31, 3.40290976e-31, 9.72719367e-31,\n",
       "       2.15959791e-30, 3.59839879e-30, 1.54537338e-30, 3.48658100e-30,\n",
       "       5.95253649e-30, 1.26890163e-29, 1.76059809e-29, 4.16480330e-29,\n",
       "       9.48142683e-29, 3.16601622e-31, 7.81968799e-31, 6.01201253e-31,\n",
       "       1.55133744e-30, 3.66334266e-30, 3.50756609e-30, 8.44597711e-30])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acf(ret_dji,qstat=True)[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For the DOW index, the p_values for the Ljung-Box statistical test are significantly small. Therefore we reject the test's null hypothesis.\n",
    "#### => The DOW is autocorrelated"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion (Market Efficiency):\n",
    "#### The DOW components as well as all the DOW itself are autocorrelated. This means that the market is not efficient."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IV) High frequency stock direction prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "tick_data = pd.read_csv('IVE_tickbidask.txt', sep=\",\", header=None)\n",
    "tick_data.columns = [\"Date\",\"Time\",\"Price\",\"Bid\",\"Ask\",\"Size\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "tick_data[\"Datetime\"]=tick_data[\"Date\"]+\" \"+tick_data[\"Time\"]\n",
    "tick_data.drop([\"Date\",\"Time\"],axis=1,inplace=True)\n",
    "tick_data.Datetime=pd.to_datetime(tick_data.Datetime,  format='%m/%d/%Y %H:%M:%S', errors='ignore')\n",
    "#tick_data.Datetime=pd.to_datetime(tick_data.Datetime).dt.strftime('%d/%m/%Y %H:%M:%S')\n",
    "tick_data.set_index(\"Datetime\",inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) Tick rule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tick_direction(df):\n",
    "    tick_dir=[]\n",
    "    for i in range(1,df.shape[0]):\n",
    "        pi=df[\"Price\"].iloc[i]\n",
    "        j=i-1\n",
    "        pj=df[\"Price\"].iloc[j]\n",
    "        \n",
    "        while pi==pj:\n",
    "            j-=1\n",
    "            pj=df[\"Price\"].iloc[j]\n",
    "        \n",
    "        if pi>pj:\n",
    "            tick=\"Buy\"\n",
    "        if pi<pj:\n",
    "            tick=\"Sell\"\n",
    "        \n",
    "        tick_dir.append(tick)\n",
    "    \n",
    "    return tick_dir\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "tick_dir=tick_direction(tick_data)\n",
    "tick_dir.insert(0,np.nan)\n",
    "\n",
    "tick_data[\"TickDirection\"]= tick_dir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) Lee Ready algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sign_to_trade(x):\n",
    "    if x[0]==1.0:\n",
    "        return \"Buy\"\n",
    "    elif x[0]==-1.0:\n",
    "        return \"Sell\"\n",
    "\n",
    "\n",
    "mid=0.5*tick_data[\"Bid\"]+0.5*tick_data[\"Ask\"]\n",
    "\n",
    "diff=(tick_data[\"Price\"]-mid)\n",
    "diff=pd.DataFrame(diff/abs(diff),index=diff.index,columns=[\"sign\"])\n",
    "tick_data[\"LRDirection\"]=diff.apply(lambda x: sign_to_trade(x),axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "tick_data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) One-minute data summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Bid</th>\n",
       "      <th>Ask</th>\n",
       "      <th>Size</th>\n",
       "      <th>TickDirection</th>\n",
       "      <th>LRDirection</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:30:00</th>\n",
       "      <td>50.71</td>\n",
       "      <td>50.70</td>\n",
       "      <td>50.79</td>\n",
       "      <td>638.0</td>\n",
       "      <td>Sell</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:31:00</th>\n",
       "      <td>50.75</td>\n",
       "      <td>50.75</td>\n",
       "      <td>50.76</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Buy</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:32:00</th>\n",
       "      <td>50.78</td>\n",
       "      <td>50.76</td>\n",
       "      <td>50.78</td>\n",
       "      <td>300.0</td>\n",
       "      <td>Buy</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:33:00</th>\n",
       "      <td>50.81</td>\n",
       "      <td>50.80</td>\n",
       "      <td>50.81</td>\n",
       "      <td>500.0</td>\n",
       "      <td>Sell</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:34:00</th>\n",
       "      <td>50.82</td>\n",
       "      <td>50.80</td>\n",
       "      <td>50.82</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Buy</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Price    Bid    Ask   Size TickDirection LRDirection\n",
       "Datetime                                                                 \n",
       "2009-09-28 09:30:00  50.71  50.70  50.79  638.0          Sell        Sell\n",
       "2009-09-28 09:31:00  50.75  50.75  50.76  100.0           Buy        Sell\n",
       "2009-09-28 09:32:00  50.78  50.76  50.78  300.0           Buy         Buy\n",
       "2009-09-28 09:33:00  50.81  50.80  50.81  500.0          Sell         Buy\n",
       "2009-09-28 09:34:00  50.82  50.80  50.82  100.0           Buy         Buy"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_summary=tick_data.resample(\"T\").first()\n",
    "\n",
    "data_summary.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_summary.drop(data_summary.index[:5],inplace=True)\n",
    "data_summary.drop(data_summary.index[data_summary.shape[0]-5:],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### VWAP and quantities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "qty=tick_data.resample(\"T\").sum()[\"Size\"]\n",
    "qty_x_price=tick_data[\"Price\"]*tick_data[\"Size\"]\n",
    "qty_x_price=qty_x_price.resample(\"T\").sum()\n",
    "\n",
    "VWAP=qty_x_price/qty\n",
    "\n",
    "data_summary[\"VWAP\"]=VWAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Quantity over 1 min\n",
    "quantity=tick_data.resample(\"T\").sum()\n",
    "\n",
    "#Buy share TickDirection over 1 min\n",
    "buffer=pd.DataFrame(tick_data[\"TickDirection\"])\n",
    "buffer=buffer[buffer['TickDirection']==\"Buy\"]\n",
    "total_buy_Tick=buffer.resample(\"T\").count()\n",
    "data_summary[\"total_buy_TD\"]=total_buy_Tick\n",
    "\n",
    "#Sell share TickDirection over 1 min\n",
    "buffer=pd.DataFrame(tick_data[\"TickDirection\"])\n",
    "buffer=buffer[buffer['TickDirection']==\"Sell\"]\n",
    "total_sell_Tick=buffer.resample(\"T\").count()\n",
    "data_summary[\"total_sell_TD\"]=total_sell_Tick\n",
    "\n",
    "#Buy share LDR over 1 min\n",
    "buffer=pd.DataFrame(tick_data[\"LRDirection\"])\n",
    "buffer=buffer[buffer['LRDirection']==\"Buy\"]\n",
    "total_buy_LRD=buffer.resample(\"T\").count()\n",
    "data_summary[\"total_buy_LRD\"]=total_buy_LRD\n",
    "\n",
    "#Sell share LDR over 1 min\n",
    "buffer=pd.DataFrame(tick_data[\"LRDirection\"])\n",
    "buffer=buffer[buffer['LRDirection']==\"Sell\"]\n",
    "total_sell_LRD=buffer.resample(\"T\").count()\n",
    "data_summary[\"total_sell_LRD\"]=total_sell_LRD\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5) Computing features and applying models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_summary.dropna(inplace=True)\n",
    "data_summary[\"5min_ret\"]=10000*np.log(data_summary[\"Price\"]/data_summary.shift(-5)[\"Price\"])\n",
    "#data_summary.dropna(inplace=True)\n",
    "data_summary[\"5min_ret_sign\"]=(data_summary[\"5min_ret\"]/abs(data_summary[\"5min_ret\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def returns2(df,n):\n",
    "    ch=\"Returns_\"+str(n)\n",
    "    d=10000*np.log(df[\"Price\"]/df.shift(n)[\"Price\"])\n",
    "    return d,ch\n",
    "for n in [5,15,20,30]:\n",
    "    d,ch=returns2(data_summary,n)\n",
    "    data_summary[ch]=d\n",
    "#data_summary.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "d,ch=returns2(data_summary,1)\n",
    "data_summary[ch]=d\n",
    "\n",
    "for n in [5,15,20,30]:\n",
    "    ch=\"Returns_\"+str(n)\n",
    "    chstd=\"Std_ret_\"+str(n)\n",
    "    freq=str(n)+\"T\"\n",
    "    m=data_summary[\"Returns_1\"].resample(freq).mean().tolist()\n",
    "    m=[q for q in m for i in range(n)]\n",
    "    m=m[:len(data_summary)]\n",
    "    std=(data_summary[\"Returns_1\"]-m)**2\n",
    "    std=std.resample(freq).mean()\n",
    "    #data_summary[chstd]=np.sqrt(std)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_summary[\"TickImbalance\"]=(data_summary[\"total_buy_TD\"]-data_summary[\"total_sell_TD\"])/(data_summary[\"total_buy_TD\"]+data_summary[\"total_sell_TD\"])\n",
    "\n",
    "data_summary[\"LRImbalance\"]=(data_summary[\"total_buy_LRD\"]-data_summary[\"total_sell_LRD\"])/(data_summary[\"total_buy_LRD\"]+data_summary[\"total_sell_LRD\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "under_sample=data_summary.resample(\"5t\").first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "under_sample.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Bid</th>\n",
       "      <th>Ask</th>\n",
       "      <th>Size</th>\n",
       "      <th>TickDirection</th>\n",
       "      <th>LRDirection</th>\n",
       "      <th>VWAP</th>\n",
       "      <th>total_buy_TD</th>\n",
       "      <th>total_sell_TD</th>\n",
       "      <th>total_buy_LRD</th>\n",
       "      <th>total_sell_LRD</th>\n",
       "      <th>5min_ret</th>\n",
       "      <th>5min_ret_sign</th>\n",
       "      <th>Returns_5</th>\n",
       "      <th>Returns_15</th>\n",
       "      <th>Returns_20</th>\n",
       "      <th>Returns_30</th>\n",
       "      <th>Returns_1</th>\n",
       "      <th>TickImbalance</th>\n",
       "      <th>LRImbalance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-09-28 10:05:00</th>\n",
       "      <td>51.210</td>\n",
       "      <td>51.21</td>\n",
       "      <td>51.23</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Sell</td>\n",
       "      <td>Sell</td>\n",
       "      <td>51.210000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-13.659871</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>11.723331</td>\n",
       "      <td>21.738119</td>\n",
       "      <td>41.091928</td>\n",
       "      <td>79.550550</td>\n",
       "      <td>-9.758955</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 10:10:00</th>\n",
       "      <td>51.280</td>\n",
       "      <td>51.27</td>\n",
       "      <td>51.28</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Buy</td>\n",
       "      <td>Buy</td>\n",
       "      <td>51.289841</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.851946</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.659871</td>\n",
       "      <td>27.338427</td>\n",
       "      <td>35.397990</td>\n",
       "      <td>92.076270</td>\n",
       "      <td>1.950268</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 10:15:00</th>\n",
       "      <td>51.250</td>\n",
       "      <td>51.24</td>\n",
       "      <td>51.25</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Buy</td>\n",
       "      <td>Buy</td>\n",
       "      <td>51.250000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.198482</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-5.851946</td>\n",
       "      <td>19.531256</td>\n",
       "      <td>21.486482</td>\n",
       "      <td>48.899853</td>\n",
       "      <td>-1.951029</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 10:20:00</th>\n",
       "      <td>51.208</td>\n",
       "      <td>51.20</td>\n",
       "      <td>51.21</td>\n",
       "      <td>200.0</td>\n",
       "      <td>Sell</td>\n",
       "      <td>Buy</td>\n",
       "      <td>51.224667</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-8.198482</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-8.198482</td>\n",
       "      <td>-0.390556</td>\n",
       "      <td>11.332774</td>\n",
       "      <td>21.347563</td>\n",
       "      <td>-2.343109</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.619048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 10:25:00</th>\n",
       "      <td>51.250</td>\n",
       "      <td>51.23</td>\n",
       "      <td>51.25</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Buy</td>\n",
       "      <td>Buy</td>\n",
       "      <td>51.250000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.951029</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>8.198482</td>\n",
       "      <td>-5.851946</td>\n",
       "      <td>7.807925</td>\n",
       "      <td>21.486482</td>\n",
       "      <td>-1.951029</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Price    Bid    Ask   Size TickDirection LRDirection  \\\n",
       "Datetime                                                                     \n",
       "2009-09-28 10:05:00  51.210  51.21  51.23  100.0          Sell        Sell   \n",
       "2009-09-28 10:10:00  51.280  51.27  51.28  100.0           Buy         Buy   \n",
       "2009-09-28 10:15:00  51.250  51.24  51.25  100.0           Buy         Buy   \n",
       "2009-09-28 10:20:00  51.208  51.20  51.21  200.0          Sell         Buy   \n",
       "2009-09-28 10:25:00  51.250  51.23  51.25  100.0           Buy         Buy   \n",
       "\n",
       "                          VWAP  total_buy_TD  total_sell_TD  total_buy_LRD  \\\n",
       "Datetime                                                                     \n",
       "2009-09-28 10:05:00  51.210000           0.0              4              1   \n",
       "2009-09-28 10:10:00  51.289841           9.0              0              9   \n",
       "2009-09-28 10:15:00  51.250000           2.0              0              2   \n",
       "2009-09-28 10:20:00  51.224667          14.0              7             17   \n",
       "2009-09-28 10:25:00  51.250000           4.0              0              4   \n",
       "\n",
       "                     total_sell_LRD   5min_ret  5min_ret_sign  Returns_5  \\\n",
       "Datetime                                                                   \n",
       "2009-09-28 10:05:00             3.0 -13.659871           -1.0  11.723331   \n",
       "2009-09-28 10:10:00             0.0   5.851946            1.0  13.659871   \n",
       "2009-09-28 10:15:00             0.0   8.198482            1.0  -5.851946   \n",
       "2009-09-28 10:20:00             4.0  -8.198482           -1.0  -8.198482   \n",
       "2009-09-28 10:25:00             0.0  -1.951029           -1.0   8.198482   \n",
       "\n",
       "                     Returns_15  Returns_20  Returns_30  Returns_1  \\\n",
       "Datetime                                                             \n",
       "2009-09-28 10:05:00   21.738119   41.091928   79.550550  -9.758955   \n",
       "2009-09-28 10:10:00   27.338427   35.397990   92.076270   1.950268   \n",
       "2009-09-28 10:15:00   19.531256   21.486482   48.899853  -1.951029   \n",
       "2009-09-28 10:20:00   -0.390556   11.332774   21.347563  -2.343109   \n",
       "2009-09-28 10:25:00   -5.851946    7.807925   21.486482  -1.951029   \n",
       "\n",
       "                     TickImbalance  LRImbalance  \n",
       "Datetime                                         \n",
       "2009-09-28 10:05:00      -1.000000    -0.500000  \n",
       "2009-09-28 10:10:00       1.000000     1.000000  \n",
       "2009-09-28 10:15:00       1.000000     1.000000  \n",
       "2009-09-28 10:20:00       0.333333     0.619048  \n",
       "2009-09-28 10:25:00       1.000000     1.000000  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "under_sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_drop=[\"Price\",\"Bid\",\"Ask\",\"Size\",\"5min_ret\",\"5min_ret_sign\"]\n",
    "y=under_sample[\"5min_ret_sign\"]\n",
    "X=under_sample.drop(to_drop,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "le.fit(X[\"TickDirection\"].tolist())\n",
    "X[\"TickDirection\"]=le.transform(X[\"TickDirection\"])\n",
    "\n",
    "le.fit(X[\"LRDirection\"])\n",
    "X[\"LRDirection\"]=le.transform(X[\"LRDirection\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_val,y_train,y_val=train_test_split(X,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The validation accuracy is: 0.5584239130434783\n"
     ]
    }
   ],
   "source": [
    "model=LogisticRegression()\n",
    "model.fit(X_train,y_train)\n",
    "pred=model.predict(X_val)\n",
    "print(\"The validation accuracy is:\", accuracy_score(pred,y_val))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gaussian Naive-Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The validation accuracy is: 0.4917346014492754\n"
     ]
    }
   ],
   "source": [
    "gnb = GaussianNB()\n",
    "gnb.fit(X_train,y_train)\n",
    "pred=gnb.predict(X_val)\n",
    "print(\"The validation accuracy is:\", accuracy_score(pred,y_val))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5) Execution Strategies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Trade Size (|qt|)</th>\n",
       "      <th>Price (pt)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09:30:02</td>\n",
       "      <td>500</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>09:30:02</td>\n",
       "      <td>500</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>09:32:28</td>\n",
       "      <td>2400</td>\n",
       "      <td>3.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>09:33:35</td>\n",
       "      <td>100</td>\n",
       "      <td>3.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>09:33:38</td>\n",
       "      <td>100</td>\n",
       "      <td>3.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Time  Trade Size (|qt|)  Price (pt)\n",
       "0  09:30:02                500        2.99\n",
       "1  09:30:02                500        2.99\n",
       "2  09:32:28               2400        3.03\n",
       "3  09:33:35                100        3.03\n",
       "4  09:33:38                100        3.02"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xyz=pd.read_excel(\"xyz_transactions.xlsx\")\n",
    "xyz.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.05"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#10'o clock is at index 88\n",
    "decision_price=xyz.iloc[88]\n",
    "decision_price=decision_price.iloc[2]\n",
    "decision_price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 20% POV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0600000000000005"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq=int(xyz.shape[0]/20)\n",
    "prices=[xyz[\"Price (pt)\"].iloc[freq*i-1] for i in range(1,21)]\n",
    "pov20_avg_price=sum(prices)/len(prices)\n",
    "pov20_avg_price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10% POV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0550000000000006"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq=int(xyz.shape[0]/10)\n",
    "prices=[xyz[\"Price (pt)\"].iloc[freq*i-1] for i in range(1,11)]\n",
    "pov10_avg_price=sum(prices)/len(prices)\n",
    "pov10_avg_price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5% POV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.054"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq=int(xyz.shape[0]/5)\n",
    "prices=[xyz[\"Price (pt)\"].iloc[freq*i-1] for i in range(1,6)]\n",
    "pov5_avg_price=sum(prices)/len(prices)\n",
    "pov5_avg_price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VWAP to the close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "VWAP=sum(xyz[\"Trade Size (|qt|)\"].iloc[88:]*xyz[\"Price (pt)\"].iloc[88:])/xyz[\"Trade Size (|qt|)\"].iloc[88:].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.054371583874377"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VWAP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TWAP (every 5 min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "xyz.Time=pd.to_datetime(xyz.Time, format='%H:%M:%S', errors='ignore')\n",
    "xyz=xyz.set_index(\"Time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trade Size (|qt|)</th>\n",
       "      <th>Price (pt)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1900-01-01 09:30:00</th>\n",
       "      <td>500</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-01-01 09:35:00</th>\n",
       "      <td>400</td>\n",
       "      <td>3.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-01-01 09:40:00</th>\n",
       "      <td>100</td>\n",
       "      <td>3.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-01-01 09:45:00</th>\n",
       "      <td>100</td>\n",
       "      <td>3.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-01-01 09:50:00</th>\n",
       "      <td>100</td>\n",
       "      <td>3.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Trade Size (|qt|)  Price (pt)\n",
       "Time                                              \n",
       "1900-01-01 09:30:00                500        2.99\n",
       "1900-01-01 09:35:00                400        3.02\n",
       "1900-01-01 09:40:00                100        3.01\n",
       "1900-01-01 09:45:00                100        3.04\n",
       "1900-01-01 09:50:00                100        3.04"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "every_5_min=xyz.resample(\"5T\").first()\n",
    "every_5_min.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "TWAP=every_5_min.mean()[\"Price (pt)\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0623128205128225"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TWAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Decision Price</th>\n",
       "      <th>20% POV</th>\n",
       "      <th>10% POV</th>\n",
       "      <th>5% POV</th>\n",
       "      <th>VWAP</th>\n",
       "      <th>TWAP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>3.05</td>\n",
       "      <td>3.06</td>\n",
       "      <td>3.055</td>\n",
       "      <td>3.054</td>\n",
       "      <td>3.054372</td>\n",
       "      <td>3.062313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Decision Price  20% POV  10% POV  5% POV      VWAP      TWAP\n",
       "Price            3.05     3.06    3.055   3.054  3.054372  3.062313"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_comparison=pd.DataFrame([[decision_price,pov20_avg_price,pov10_avg_price,pov5_avg_price,VWAP,TWAP]],columns=[\"Decision Price\",\"20% POV\",\"10% POV\",\"5% POV\",\"VWAP\",\"TWAP\"],index=[\"Price\"])\n",
    "price_comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The strategy to go with is the 5% POV, it's the closest to the decision price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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