7/7.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "55a2a234",
   "metadata": {},
   "source": [
    "# Algorithmic Trading with Deep Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bd1bbded",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import cvxpy as cvx\n",
    "import yfinance as yf\n",
    "import pandas_datareader as pdr\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "91526efd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ticker</th>\n",
       "      <th>Security</th>\n",
       "      <th>Sector</th>\n",
       "      <th>GICS Sub-Industry</th>\n",
       "      <th>Headquarters Location</th>\n",
       "      <th>Date added</th>\n",
       "      <th>CIK</th>\n",
       "      <th>Founded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MMM</td>\n",
       "      <td>3M</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Industrial Conglomerates</td>\n",
       "      <td>Saint Paul, Minnesota</td>\n",
       "      <td>1957-03-04</td>\n",
       "      <td>66740</td>\n",
       "      <td>1902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AOS</td>\n",
       "      <td>A. O. Smith</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Building Products</td>\n",
       "      <td>Milwaukee, Wisconsin</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>91142</td>\n",
       "      <td>1916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABT</td>\n",
       "      <td>Abbott</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Health Care Equipment</td>\n",
       "      <td>North Chicago, Illinois</td>\n",
       "      <td>1957-03-04</td>\n",
       "      <td>1800</td>\n",
       "      <td>1888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ABBV</td>\n",
       "      <td>AbbVie</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Pharmaceuticals</td>\n",
       "      <td>North Chicago, Illinois</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1551152</td>\n",
       "      <td>2013 (1888)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACN</td>\n",
       "      <td>Accenture</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>IT Consulting &amp; Other Services</td>\n",
       "      <td>Dublin, Ireland</td>\n",
       "      <td>2011-07-06</td>\n",
       "      <td>1467373</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Ticker     Security                  Sector               GICS Sub-Industry  \\\n",
       "0    MMM           3M             Industrials        Industrial Conglomerates   \n",
       "1    AOS  A. O. Smith             Industrials               Building Products   \n",
       "2    ABT       Abbott             Health Care           Health Care Equipment   \n",
       "3   ABBV       AbbVie             Health Care                 Pharmaceuticals   \n",
       "4    ACN    Accenture  Information Technology  IT Consulting & Other Services   \n",
       "\n",
       "     Headquarters Location  Date added      CIK      Founded  \n",
       "0    Saint Paul, Minnesota  1957-03-04    66740         1902  \n",
       "1     Milwaukee, Wisconsin  2017-07-26    91142         1916  \n",
       "2  North Chicago, Illinois  1957-03-04     1800         1888  \n",
       "3  North Chicago, Illinois  2012-12-31  1551152  2013 (1888)  \n",
       "4          Dublin, Ireland  2011-07-06  1467373         1989  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tickers_data = pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')[0]\n",
    "tickers_data = tickers_data.rename(columns={'Symbol':'Ticker',\n",
    "                                            'GICS Sector': 'Sector'})\n",
    "tickers_data.loc[:,'Ticker'] = tickers_data.loc[:,'Ticker'].apply(lambda x:str(x).replace('.', '-' ))\n",
    "tickers_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "149fa772",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = '2000-01-01'\n",
    "end = '2020-01-01'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8306e52a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  503 of 503 completed"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "7 Failed downloads:\n",
      "['OTIS', 'ABNB', 'CEG', 'CARR', 'GEHC', 'KVUE', 'VLTO']: Exception(\"%ticker%: Data doesn't exist for startDate = 946702800, endDate = 1577854800\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ticker</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>Dividends</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Stock Splits</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>43.757744</td>\n",
       "      <td>51.502148</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.464592</td>\n",
       "      <td>48.193848</td>\n",
       "      <td>56.330471</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4674353.0</td>\n",
       "      <td>2000-01-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>0.847207</td>\n",
       "      <td>0.999442</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.004464</td>\n",
       "      <td>0.907924</td>\n",
       "      <td>0.936384</td>\n",
       "      <td>0.0</td>\n",
       "      <td>535796800.0</td>\n",
       "      <td>2000-01-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABT</td>\n",
       "      <td>9.081107</td>\n",
       "      <td>15.711531</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.160433</td>\n",
       "      <td>15.599306</td>\n",
       "      <td>15.823756</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10635087.0</td>\n",
       "      <td>2000-01-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACGL</td>\n",
       "      <td>1.277778</td>\n",
       "      <td>1.277778</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.416667</td>\n",
       "      <td>1.263889</td>\n",
       "      <td>1.409722</td>\n",
       "      <td>0.0</td>\n",
       "      <td>433800.0</td>\n",
       "      <td>2000-01-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ADBE</td>\n",
       "      <td>16.274673</td>\n",
       "      <td>16.390625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.875000</td>\n",
       "      <td>16.062500</td>\n",
       "      <td>16.812500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7384400.0</td>\n",
       "      <td>2000-01-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Ticker  Adj Close      Close  Dividends       High        Low       Open  \\\n",
       "0      A  43.757744  51.502148        0.0  56.464592  48.193848  56.330471   \n",
       "1   AAPL   0.847207   0.999442        0.0   1.004464   0.907924   0.936384   \n",
       "2    ABT   9.081107  15.711531        0.0  16.160433  15.599306  15.823756   \n",
       "3   ACGL   1.277778   1.277778        0.0   1.416667   1.263889   1.409722   \n",
       "4   ADBE  16.274673  16.390625        0.0  16.875000  16.062500  16.812500   \n",
       "\n",
       "   Stock Splits       Volume       Date  \n",
       "0           0.0    4674353.0 2000-01-03  \n",
       "1           0.0  535796800.0 2000-01-03  \n",
       "2           0.0   10635087.0 2000-01-03  \n",
       "3           0.0     433800.0 2000-01-03  \n",
       "4           0.0    7384400.0 2000-01-03  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tickers = yf.Tickers(list(tickers_data.loc[:,'Ticker']))\n",
    "\n",
    "data = tickers.history(start=start, end=end, auto_adjust=False, interval='1d')\n",
    "data = data.stack(level=1).rename_axis(['Date', 'Ticker']).reset_index(level=1)\n",
    "data['Date'] = data.index\n",
    "data = data.reset_index(drop=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "53ed115a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Ticker</th>\n",
       "      <th>A</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ACGL</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADI</th>\n",
       "      <th>ADM</th>\n",
       "      <th>ADP</th>\n",
       "      <th>ADSK</th>\n",
       "      <th>AEE</th>\n",
       "      <th>...</th>\n",
       "      <th>WMT</th>\n",
       "      <th>WRB</th>\n",
       "      <th>WST</th>\n",
       "      <th>WY</th>\n",
       "      <th>XEL</th>\n",
       "      <th>XOM</th>\n",
       "      <th>XRAY</th>\n",
       "      <th>YUM</th>\n",
       "      <th>ZBRA</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>56.330471</td>\n",
       "      <td>0.936384</td>\n",
       "      <td>15.823756</td>\n",
       "      <td>1.409722</td>\n",
       "      <td>16.812500</td>\n",
       "      <td>46.7500</td>\n",
       "      <td>10.884354</td>\n",
       "      <td>42.428928</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>32.5625</td>\n",
       "      <td>...</td>\n",
       "      <td>68.375</td>\n",
       "      <td>1.887517</td>\n",
       "      <td>7.71875</td>\n",
       "      <td>71.0625</td>\n",
       "      <td>19.6250</td>\n",
       "      <td>39.75000</td>\n",
       "      <td>7.958333</td>\n",
       "      <td>6.930715</td>\n",
       "      <td>25.930555</td>\n",
       "      <td>59.03125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>48.730328</td>\n",
       "      <td>0.966518</td>\n",
       "      <td>15.459024</td>\n",
       "      <td>1.263889</td>\n",
       "      <td>15.750000</td>\n",
       "      <td>44.7500</td>\n",
       "      <td>10.714286</td>\n",
       "      <td>40.941933</td>\n",
       "      <td>8.046875</td>\n",
       "      <td>32.3125</td>\n",
       "      <td>...</td>\n",
       "      <td>65.500</td>\n",
       "      <td>1.854595</td>\n",
       "      <td>7.68750</td>\n",
       "      <td>69.7500</td>\n",
       "      <td>19.0625</td>\n",
       "      <td>38.68750</td>\n",
       "      <td>7.833333</td>\n",
       "      <td>6.649892</td>\n",
       "      <td>25.027779</td>\n",
       "      <td>54.62500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>47.389126</td>\n",
       "      <td>0.926339</td>\n",
       "      <td>15.066236</td>\n",
       "      <td>1.284722</td>\n",
       "      <td>14.562500</td>\n",
       "      <td>42.8125</td>\n",
       "      <td>10.770975</td>\n",
       "      <td>41.189766</td>\n",
       "      <td>7.750000</td>\n",
       "      <td>32.3125</td>\n",
       "      <td>...</td>\n",
       "      <td>63.875</td>\n",
       "      <td>1.711934</td>\n",
       "      <td>7.65625</td>\n",
       "      <td>67.2500</td>\n",
       "      <td>19.4375</td>\n",
       "      <td>39.00000</td>\n",
       "      <td>7.833333</td>\n",
       "      <td>6.604960</td>\n",
       "      <td>23.569445</td>\n",
       "      <td>52.75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>44.080830</td>\n",
       "      <td>0.947545</td>\n",
       "      <td>15.262630</td>\n",
       "      <td>1.388889</td>\n",
       "      <td>15.359375</td>\n",
       "      <td>43.4375</td>\n",
       "      <td>10.544218</td>\n",
       "      <td>41.140202</td>\n",
       "      <td>7.375000</td>\n",
       "      <td>33.5625</td>\n",
       "      <td>...</td>\n",
       "      <td>63.000</td>\n",
       "      <td>1.733882</td>\n",
       "      <td>7.62500</td>\n",
       "      <td>71.1875</td>\n",
       "      <td>20.1250</td>\n",
       "      <td>40.31250</td>\n",
       "      <td>7.875000</td>\n",
       "      <td>6.560029</td>\n",
       "      <td>24.819445</td>\n",
       "      <td>52.75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>42.247852</td>\n",
       "      <td>0.861607</td>\n",
       "      <td>15.487080</td>\n",
       "      <td>1.375000</td>\n",
       "      <td>15.390625</td>\n",
       "      <td>42.0000</td>\n",
       "      <td>10.770975</td>\n",
       "      <td>42.329796</td>\n",
       "      <td>7.015625</td>\n",
       "      <td>33.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>64.500</td>\n",
       "      <td>1.733882</td>\n",
       "      <td>7.53125</td>\n",
       "      <td>72.0625</td>\n",
       "      <td>20.0000</td>\n",
       "      <td>42.96875</td>\n",
       "      <td>7.958333</td>\n",
       "      <td>6.548796</td>\n",
       "      <td>23.666668</td>\n",
       "      <td>53.75000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 362 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Ticker              A      AAPL        ABT      ACGL       ADBE      ADI  \\\n",
       "Date                                                                       \n",
       "2000-01-03  56.330471  0.936384  15.823756  1.409722  16.812500  46.7500   \n",
       "2000-01-04  48.730328  0.966518  15.459024  1.263889  15.750000  44.7500   \n",
       "2000-01-05  47.389126  0.926339  15.066236  1.284722  14.562500  42.8125   \n",
       "2000-01-06  44.080830  0.947545  15.262630  1.388889  15.359375  43.4375   \n",
       "2000-01-07  42.247852  0.861607  15.487080  1.375000  15.390625  42.0000   \n",
       "\n",
       "Ticker            ADM        ADP      ADSK      AEE  ...     WMT       WRB  \\\n",
       "Date                                                 ...                     \n",
       "2000-01-03  10.884354  42.428928  8.500000  32.5625  ...  68.375  1.887517   \n",
       "2000-01-04  10.714286  40.941933  8.046875  32.3125  ...  65.500  1.854595   \n",
       "2000-01-05  10.770975  41.189766  7.750000  32.3125  ...  63.875  1.711934   \n",
       "2000-01-06  10.544218  41.140202  7.375000  33.5625  ...  63.000  1.733882   \n",
       "2000-01-07  10.770975  42.329796  7.015625  33.5000  ...  64.500  1.733882   \n",
       "\n",
       "Ticker          WST       WY      XEL       XOM      XRAY       YUM  \\\n",
       "Date                                                                  \n",
       "2000-01-03  7.71875  71.0625  19.6250  39.75000  7.958333  6.930715   \n",
       "2000-01-04  7.68750  69.7500  19.0625  38.68750  7.833333  6.649892   \n",
       "2000-01-05  7.65625  67.2500  19.4375  39.00000  7.833333  6.604960   \n",
       "2000-01-06  7.62500  71.1875  20.1250  40.31250  7.875000  6.560029   \n",
       "2000-01-07  7.53125  72.0625  20.0000  42.96875  7.958333  6.548796   \n",
       "\n",
       "Ticker           ZBRA      ZION  \n",
       "Date                             \n",
       "2000-01-03  25.930555  59.03125  \n",
       "2000-01-04  25.027779  54.62500  \n",
       "2000-01-05  23.569445  52.75000  \n",
       "2000-01-06  24.819445  52.75000  \n",
       "2000-01-07  23.666668  53.75000  \n",
       "\n",
       "[5 rows x 362 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_open = data.pivot(index='Date', columns='Ticker', values='Open')\n",
    "_open = _open.dropna(axis=1)\n",
    "_open.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fc544702",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>Ticker</th>\n",
       "      <th>A</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ACGL</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADI</th>\n",
       "      <th>ADM</th>\n",
       "      <th>ADP</th>\n",
       "      <th>ADSK</th>\n",
       "      <th>AEE</th>\n",
       "      <th>...</th>\n",
       "      <th>WMT</th>\n",
       "      <th>WRB</th>\n",
       "      <th>WST</th>\n",
       "      <th>WY</th>\n",
       "      <th>XEL</th>\n",
       "      <th>XOM</th>\n",
       "      <th>XRAY</th>\n",
       "      <th>YUM</th>\n",
       "      <th>ZBRA</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>43.757744</td>\n",
       "      <td>0.847207</td>\n",
       "      <td>9.081107</td>\n",
       "      <td>1.277778</td>\n",
       "      <td>16.274673</td>\n",
       "      <td>28.706917</td>\n",
       "      <td>6.473896</td>\n",
       "      <td>25.075771</td>\n",
       "      <td>8.052907</td>\n",
       "      <td>10.858145</td>\n",
       "      <td>...</td>\n",
       "      <td>43.717709</td>\n",
       "      <td>1.238296</td>\n",
       "      <td>5.431209</td>\n",
       "      <td>11.691638</td>\n",
       "      <td>7.112459</td>\n",
       "      <td>18.499044</td>\n",
       "      <td>6.695477</td>\n",
       "      <td>4.725972</td>\n",
       "      <td>25.027779</td>\n",
       "      <td>36.146160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>40.415142</td>\n",
       "      <td>0.775779</td>\n",
       "      <td>8.821647</td>\n",
       "      <td>1.270833</td>\n",
       "      <td>14.909400</td>\n",
       "      <td>27.254671</td>\n",
       "      <td>6.406457</td>\n",
       "      <td>25.075771</td>\n",
       "      <td>7.660815</td>\n",
       "      <td>10.858145</td>\n",
       "      <td>...</td>\n",
       "      <td>42.081860</td>\n",
       "      <td>1.200194</td>\n",
       "      <td>5.487316</td>\n",
       "      <td>11.252419</td>\n",
       "      <td>7.276233</td>\n",
       "      <td>18.144707</td>\n",
       "      <td>6.695477</td>\n",
       "      <td>4.630974</td>\n",
       "      <td>24.666668</td>\n",
       "      <td>34.395832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>37.908188</td>\n",
       "      <td>0.787131</td>\n",
       "      <td>8.805431</td>\n",
       "      <td>1.388889</td>\n",
       "      <td>15.204173</td>\n",
       "      <td>27.652542</td>\n",
       "      <td>6.305304</td>\n",
       "      <td>24.834644</td>\n",
       "      <td>7.178246</td>\n",
       "      <td>11.278186</td>\n",
       "      <td>...</td>\n",
       "      <td>41.223064</td>\n",
       "      <td>1.181144</td>\n",
       "      <td>5.464875</td>\n",
       "      <td>11.848501</td>\n",
       "      <td>7.556990</td>\n",
       "      <td>19.133886</td>\n",
       "      <td>6.811223</td>\n",
       "      <td>4.654724</td>\n",
       "      <td>25.138889</td>\n",
       "      <td>34.355133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>36.464775</td>\n",
       "      <td>0.719014</td>\n",
       "      <td>9.113544</td>\n",
       "      <td>1.375000</td>\n",
       "      <td>15.328292</td>\n",
       "      <td>26.896578</td>\n",
       "      <td>6.339021</td>\n",
       "      <td>25.166183</td>\n",
       "      <td>6.740915</td>\n",
       "      <td>11.236184</td>\n",
       "      <td>...</td>\n",
       "      <td>41.672909</td>\n",
       "      <td>1.249727</td>\n",
       "      <td>5.408764</td>\n",
       "      <td>12.402756</td>\n",
       "      <td>7.486798</td>\n",
       "      <td>20.123062</td>\n",
       "      <td>6.820127</td>\n",
       "      <td>4.615142</td>\n",
       "      <td>23.777779</td>\n",
       "      <td>34.843597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>39.503513</td>\n",
       "      <td>0.753073</td>\n",
       "      <td>9.210839</td>\n",
       "      <td>1.451389</td>\n",
       "      <td>16.072985</td>\n",
       "      <td>27.652542</td>\n",
       "      <td>6.440176</td>\n",
       "      <td>25.738827</td>\n",
       "      <td>7.540174</td>\n",
       "      <td>11.404200</td>\n",
       "      <td>...</td>\n",
       "      <td>44.821903</td>\n",
       "      <td>1.230675</td>\n",
       "      <td>5.464875</td>\n",
       "      <td>11.994913</td>\n",
       "      <td>7.486798</td>\n",
       "      <td>20.063992</td>\n",
       "      <td>6.802316</td>\n",
       "      <td>4.512234</td>\n",
       "      <td>23.513889</td>\n",
       "      <td>34.924999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 362 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Ticker              A      AAPL       ABT      ACGL       ADBE        ADI  \\\n",
       "Date                                                                        \n",
       "2000-01-03  43.757744  0.847207  9.081107  1.277778  16.274673  28.706917   \n",
       "2000-01-04  40.415142  0.775779  8.821647  1.270833  14.909400  27.254671   \n",
       "2000-01-05  37.908188  0.787131  8.805431  1.388889  15.204173  27.652542   \n",
       "2000-01-06  36.464775  0.719014  9.113544  1.375000  15.328292  26.896578   \n",
       "2000-01-07  39.503513  0.753073  9.210839  1.451389  16.072985  27.652542   \n",
       "\n",
       "Ticker           ADM        ADP      ADSK        AEE  ...        WMT  \\\n",
       "Date                                                  ...              \n",
       "2000-01-03  6.473896  25.075771  8.052907  10.858145  ...  43.717709   \n",
       "2000-01-04  6.406457  25.075771  7.660815  10.858145  ...  42.081860   \n",
       "2000-01-05  6.305304  24.834644  7.178246  11.278186  ...  41.223064   \n",
       "2000-01-06  6.339021  25.166183  6.740915  11.236184  ...  41.672909   \n",
       "2000-01-07  6.440176  25.738827  7.540174  11.404200  ...  44.821903   \n",
       "\n",
       "Ticker           WRB       WST         WY       XEL        XOM      XRAY  \\\n",
       "Date                                                                       \n",
       "2000-01-03  1.238296  5.431209  11.691638  7.112459  18.499044  6.695477   \n",
       "2000-01-04  1.200194  5.487316  11.252419  7.276233  18.144707  6.695477   \n",
       "2000-01-05  1.181144  5.464875  11.848501  7.556990  19.133886  6.811223   \n",
       "2000-01-06  1.249727  5.408764  12.402756  7.486798  20.123062  6.820127   \n",
       "2000-01-07  1.230675  5.464875  11.994913  7.486798  20.063992  6.802316   \n",
       "\n",
       "Ticker           YUM       ZBRA       ZION  \n",
       "Date                                        \n",
       "2000-01-03  4.725972  25.027779  36.146160  \n",
       "2000-01-04  4.630974  24.666668  34.395832  \n",
       "2000-01-05  4.654724  25.138889  34.355133  \n",
       "2000-01-06  4.615142  23.777779  34.843597  \n",
       "2000-01-07  4.512234  23.513889  34.924999  \n",
       "\n",
       "[5 rows x 362 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_adj_close = data.pivot(index='Date', columns='Ticker', values='Adj Close')\n",
    "_adj_close = _adj_close.dropna(axis=1)\n",
    "_adj_close.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4aed1d61",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c9010d89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'address1': 'One Apple Park Way',\n",
       " 'city': 'Cupertino',\n",
       " 'state': 'CA',\n",
       " 'zip': '95014',\n",
       " 'country': 'United States',\n",
       " 'phone': '408 996 1010',\n",
       " 'website': 'https://www.apple.com',\n",
       " 'industry': 'Consumer Electronics',\n",
       " 'industryKey': 'consumer-electronics',\n",
       " 'industryDisp': 'Consumer Electronics',\n",
       " 'sector': 'Technology',\n",
       " 'sectorKey': 'technology',\n",
       " 'sectorDisp': 'Technology',\n",
       " 'longBusinessSummary': 'Apple Inc. designs, manufactures, and markets smartphones, personal computers, tablets, wearables, and accessories worldwide. The company offers iPhone, a line of smartphones; Mac, a line of personal computers; iPad, a line of multi-purpose tablets; and wearables, home, and accessories comprising AirPods, Apple TV, Apple Watch, Beats products, and HomePod. It also provides AppleCare support and cloud services; and operates various platforms, including the App Store that allow customers to discover and download applications and digital content, such as books, music, video, games, and podcasts. In addition, the company offers various services, such as Apple Arcade, a game subscription service; Apple Fitness+, a personalized fitness service; Apple Music, which offers users a curated listening experience with on-demand radio stations; Apple News+, a subscription news and magazine service; Apple TV+, which offers exclusive original content; Apple Card, a co-branded credit card; and Apple Pay, a cashless payment service, as well as licenses its intellectual property. The company serves consumers, and small and mid-sized businesses; and the education, enterprise, and government markets. It distributes third-party applications for its products through the App Store. The company also sells its products through its retail and online stores, and direct sales force; and third-party cellular network carriers, wholesalers, retailers, and resellers. Apple Inc. was incorporated in 1977 and is headquartered in Cupertino, California.',\n",
       " 'fullTimeEmployees': 164000,\n",
       " 'companyOfficers': [{'maxAge': 1,\n",
       "   'name': 'Mr. Timothy D. Cook',\n",
       "   'age': 61,\n",
       "   'title': 'CEO & Director',\n",
       "   'yearBorn': 1961,\n",
       "   'fiscalYear': 2022,\n",
       "   'totalPay': 16425933,\n",
       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0},\n",
       "  {'maxAge': 1,\n",
       "   'name': 'Mr. Luca  Maestri',\n",
       "   'age': 59,\n",
       "   'title': 'CFO & Senior VP',\n",
       "   'yearBorn': 1963,\n",
       "   'fiscalYear': 2022,\n",
       "   'totalPay': 5019783,\n",
       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0},\n",
       "  {'maxAge': 1,\n",
       "   'name': 'Mr. Jeffrey E. Williams',\n",
       "   'age': 58,\n",
       "   'title': 'Chief Operating Officer',\n",
       "   'yearBorn': 1964,\n",
       "   'fiscalYear': 2022,\n",
       "   'totalPay': 5018337,\n",
       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0},\n",
       "  {'maxAge': 1,\n",
       "   'name': 'Ms. Katherine L. Adams',\n",
       "   'age': 58,\n",
       "   'title': 'Senior VP, General Counsel & Secretary',\n",
       "   'yearBorn': 1964,\n",
       "   'fiscalYear': 2022,\n",
       "   'totalPay': 5015208,\n",
       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0},\n",
       "  {'maxAge': 1,\n",
       "   'name': \"Ms. Deirdre  O'Brien\",\n",
       "   'age': 55,\n",
       "   'title': 'Senior Vice President of Retail',\n",
       "   'yearBorn': 1967,\n",
       "   'fiscalYear': 2022,\n",
       "   'totalPay': 5019783,\n",
       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0},\n",
       "  {'maxAge': 1,\n",
       "   'name': 'Mr. Chris  Kondo',\n",
       "   'title': 'Senior Director of Corporate Accounting',\n",
       "   'fiscalYear': 2022,\n",
       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0},\n",
       "  {'maxAge': 1,\n",
       "   'name': 'Mr. James  Wilson',\n",
       "   'title': 'Chief Technology Officer',\n",
       "   'fiscalYear': 2022,\n",
       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0},\n",
       "  {'maxAge': 1,\n",
       "   'name': 'Ms. Mary  Demby',\n",
       "   'title': 'Chief Information Officer',\n",
       "   'fiscalYear': 2022,\n",
       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0},\n",
       "  {'maxAge': 1,\n",
       "   'name': 'Ms. Nancy  Paxton',\n",
       "   'title': 'Senior Director of Investor Relations & Treasury',\n",
       "   'fiscalYear': 2022,\n",
       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0},\n",
       "  {'maxAge': 1,\n",
       "   'name': 'Mr. Greg  Joswiak',\n",
       "   'title': 'Senior Vice President of Worldwide Marketing',\n",
       "   'fiscalYear': 2022,\n",
       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0}],\n",
       " 'auditRisk': 4,\n",
       " 'boardRisk': 1,\n",
       " 'compensationRisk': 5,\n",
       " 'shareHolderRightsRisk': 1,\n",
       " 'overallRisk': 1,\n",
       " 'governanceEpochDate': 1698451200,\n",
       " 'compensationAsOfEpochDate': 1672444800,\n",
       " 'maxAge': 86400,\n",
       " 'priceHint': 2,\n",
       " 'previousClose': 166.89,\n",
       " 'open': 166.91,\n",
       " 'dayLow': 166.84,\n",
       " 'dayHigh': 168.96,\n",
       " 'regularMarketPreviousClose': 166.89,\n",
       " 'regularMarketOpen': 166.91,\n",
       " 'regularMarketDayLow': 166.84,\n",
       " 'regularMarketDayHigh': 168.96,\n",
       " 'dividendRate': 0.96,\n",
       " 'dividendYield': 0.0057,\n",
       " 'exDividendDate': 1691712000,\n",
       " 'payoutRatio': 0.156,\n",
       " 'fiveYearAvgDividendYield': 0.85,\n",
       " 'beta': 1.308,\n",
       " 'trailingPE': 28.224833,\n",
       " 'forwardPE': 27.17609,\n",
       " 'volume': 58499129,\n",
       " 'regularMarketVolume': 58499129,\n",
       " 'averageVolume': 58993159,\n",
       " 'averageVolume10days': 57437060,\n",
       " 'averageDailyVolume10Day': 57437060,\n",
       " 'bid': 168.02,\n",
       " 'ask': 168.03,\n",
       " 'bidSize': 1000,\n",
       " 'askSize': 800,\n",
       " 'marketCap': 2629985107968,\n",
       " 'fiftyTwoWeekLow': 124.17,\n",
       " 'fiftyTwoWeekHigh': 198.23,\n",
       " 'priceToSalesTrailing12Months': 6.850115,\n",
       " 'fiftyDayAverage': 176.904,\n",
       " 'twoHundredDayAverage': 170.53055,\n",
       " 'trailingAnnualDividendRate': 0.93,\n",
       " 'trailingAnnualDividendYield': 0.005572533,\n",
       " 'currency': 'USD',\n",
       " 'enterpriseValue': 2676788559872,\n",
       " 'profitMargins': 0.24681,\n",
       " 'floatShares': 15616878002,\n",
       " 'sharesOutstanding': 15634199552,\n",
       " 'sharesShort': 93026130,\n",
       " 'sharesShortPriorMonth': 107325921,\n",
       " 'sharesShortPreviousMonthDate': 1694736000,\n",
       " 'dateShortInterest': 1697155200,\n",
       " 'sharesPercentSharesOut': 0.006,\n",
       " 'heldPercentInsiders': 0.00071999995,\n",
       " 'heldPercentInstitutions': 0.61317,\n",
       " 'shortRatio': 1.6,\n",
       " 'shortPercentOfFloat': 0.006,\n",
       " 'impliedSharesOutstanding': 15634199552,\n",
       " 'bookValue': 3.852,\n",
       " 'priceToBook': 43.670822,\n",
       " 'lastFiscalYearEnd': 1663977600,\n",
       " 'nextFiscalYearEnd': 1695513600,\n",
       " 'mostRecentQuarter': 1688169600,\n",
       " 'earningsQuarterlyGrowth': 0.023,\n",
       " 'netIncomeToCommon': 94760001536,\n",
       " 'trailingEps': 5.96,\n",
       " 'forwardEps': 6.19,\n",
       " 'pegRatio': 4.33,\n",
       " 'lastSplitFactor': '4:1',\n",
       " 'lastSplitDate': 1598832000,\n",
       " 'enterpriseToRevenue': 6.972,\n",
       " 'enterpriseToEbitda': 21.594,\n",
       " '52WeekChange': 0.09703922,\n",
       " 'SandP52WeekChange': 0.06337583,\n",
       " 'lastDividendValue': 0.24,\n",
       " 'lastDividendDate': 1691712000,\n",
       " 'exchange': 'NMS',\n",
       " 'quoteType': 'EQUITY',\n",
       " 'symbol': 'AAPL',\n",
       " 'underlyingSymbol': 'AAPL',\n",
       " 'shortName': 'Apple Inc.',\n",
       " 'longName': 'Apple Inc.',\n",
       " 'firstTradeDateEpochUtc': 345479400,\n",
       " 'timeZoneFullName': 'America/New_York',\n",
       " 'timeZoneShortName': 'EDT',\n",
       " 'uuid': '8b10e4ae-9eeb-3684-921a-9ab27e4d87aa',\n",
       " 'messageBoardId': 'finmb_24937',\n",
       " 'gmtOffSetMilliseconds': -14400000,\n",
       " 'currentPrice': 168.22,\n",
       " 'targetHighPrice': 226.34,\n",
       " 'targetLowPrice': 140.52,\n",
       " 'targetMeanPrice': 187.73,\n",
       " 'targetMedianPrice': 188.62,\n",
       " 'recommendationMean': 2.1,\n",
       " 'recommendationKey': 'buy',\n",
       " 'numberOfAnalystOpinions': 37,\n",
       " 'totalCash': 62482001920,\n",
       " 'totalCashPerShare': 3.996,\n",
       " 'ebitda': 123957002240,\n",
       " 'totalDebt': 109280002048,\n",
       " 'quickRatio': 0.814,\n",
       " 'currentRatio': 0.982,\n",
       " 'totalRevenue': 383932989440,\n",
       " 'debtToEquity': 181.305,\n",
       " 'revenuePerShare': 24.22,\n",
       " 'returnOnAssets': 0.20896,\n",
       " 'returnOnEquity': 1.60093,\n",
       " 'grossProfits': 170782000000,\n",
       " 'freeCashflow': 90680500224,\n",
       " 'operatingCashflow': 113071996928,\n",
       " 'earningsGrowth': 0.05,\n",
       " 'revenueGrowth': -0.014,\n",
       " 'grossMargins': 0.43449003,\n",
       " 'ebitdaMargins': 0.32286,\n",
       " 'operatingMargins': 0.28116,\n",
       " 'financialCurrency': 'USD',\n",
       " 'trailingPegRatio': 2.1778}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tickers.tickers['AAPL'].info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bdabe6df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1369a4d60>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 10))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.grid(visible=True, linestyle='--')\n",
    "ax.set_title('AAPL')\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Price')\n",
    "ax.plot(_adj_close.index, _adj_close.loc[:,['AAPL']].values, marker='.', color='#0F00FF', markersize=1, linestyle='-')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67e94619",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a080b05e",
   "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>Ticker</th>\n",
       "      <th>A</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ACGL</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADI</th>\n",
       "      <th>ADM</th>\n",
       "      <th>ADP</th>\n",
       "      <th>ADSK</th>\n",
       "      <th>AEE</th>\n",
       "      <th>...</th>\n",
       "      <th>WMT</th>\n",
       "      <th>WRB</th>\n",
       "      <th>WST</th>\n",
       "      <th>WY</th>\n",
       "      <th>XEL</th>\n",
       "      <th>XOM</th>\n",
       "      <th>XRAY</th>\n",
       "      <th>YUM</th>\n",
       "      <th>ZBRA</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>-0.076389</td>\n",
       "      <td>-0.084310</td>\n",
       "      <td>-0.028571</td>\n",
       "      <td>-0.005435</td>\n",
       "      <td>-0.083889</td>\n",
       "      <td>-0.050589</td>\n",
       "      <td>-0.010417</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.048689</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.037418</td>\n",
       "      <td>-0.030770</td>\n",
       "      <td>0.010330</td>\n",
       "      <td>-0.037567</td>\n",
       "      <td>0.023026</td>\n",
       "      <td>-0.019154</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.020101</td>\n",
       "      <td>-0.014428</td>\n",
       "      <td>-0.048424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-0.062030</td>\n",
       "      <td>0.014633</td>\n",
       "      <td>-0.001838</td>\n",
       "      <td>0.092897</td>\n",
       "      <td>0.019771</td>\n",
       "      <td>0.014598</td>\n",
       "      <td>-0.015789</td>\n",
       "      <td>-0.009616</td>\n",
       "      <td>-0.062992</td>\n",
       "      <td>0.038684</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.020408</td>\n",
       "      <td>-0.015873</td>\n",
       "      <td>-0.004090</td>\n",
       "      <td>0.052974</td>\n",
       "      <td>0.038585</td>\n",
       "      <td>0.054516</td>\n",
       "      <td>0.017287</td>\n",
       "      <td>0.005129</td>\n",
       "      <td>0.019144</td>\n",
       "      <td>-0.001183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>-0.038077</td>\n",
       "      <td>-0.086538</td>\n",
       "      <td>0.034991</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>0.008163</td>\n",
       "      <td>-0.027338</td>\n",
       "      <td>0.005347</td>\n",
       "      <td>0.013350</td>\n",
       "      <td>-0.060925</td>\n",
       "      <td>-0.003724</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010912</td>\n",
       "      <td>0.058065</td>\n",
       "      <td>-0.010267</td>\n",
       "      <td>0.046778</td>\n",
       "      <td>-0.009288</td>\n",
       "      <td>0.051698</td>\n",
       "      <td>0.001307</td>\n",
       "      <td>-0.008504</td>\n",
       "      <td>-0.054144</td>\n",
       "      <td>0.014218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>0.083334</td>\n",
       "      <td>0.047369</td>\n",
       "      <td>0.010676</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.048583</td>\n",
       "      <td>0.028106</td>\n",
       "      <td>0.015957</td>\n",
       "      <td>0.022754</td>\n",
       "      <td>0.118568</td>\n",
       "      <td>0.014953</td>\n",
       "      <td>...</td>\n",
       "      <td>0.075565</td>\n",
       "      <td>-0.015244</td>\n",
       "      <td>0.010374</td>\n",
       "      <td>-0.032883</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.002935</td>\n",
       "      <td>-0.002612</td>\n",
       "      <td>-0.022298</td>\n",
       "      <td>-0.011098</td>\n",
       "      <td>0.002336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td>0.060577</td>\n",
       "      <td>-0.017588</td>\n",
       "      <td>-0.007042</td>\n",
       "      <td>0.033493</td>\n",
       "      <td>0.038610</td>\n",
       "      <td>0.087050</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.024590</td>\n",
       "      <td>-0.010099</td>\n",
       "      <td>-0.007366</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018248</td>\n",
       "      <td>-0.009288</td>\n",
       "      <td>0.020533</td>\n",
       "      <td>-0.004359</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.013980</td>\n",
       "      <td>0.023561</td>\n",
       "      <td>0.040350</td>\n",
       "      <td>0.033668</td>\n",
       "      <td>-0.018065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 362 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Ticker             A      AAPL       ABT      ACGL      ADBE       ADI  \\\n",
       "Date                                                                     \n",
       "2000-01-04 -0.076389 -0.084310 -0.028571 -0.005435 -0.083889 -0.050589   \n",
       "2000-01-05 -0.062030  0.014633 -0.001838  0.092897  0.019771  0.014598   \n",
       "2000-01-06 -0.038077 -0.086538  0.034991 -0.010000  0.008163 -0.027338   \n",
       "2000-01-07  0.083334  0.047369  0.010676  0.055556  0.048583  0.028106   \n",
       "2000-01-10  0.060577 -0.017588 -0.007042  0.033493  0.038610  0.087050   \n",
       "\n",
       "Ticker           ADM       ADP      ADSK       AEE  ...       WMT       WRB  \\\n",
       "Date                                                ...                       \n",
       "2000-01-04 -0.010417  0.000000 -0.048689  0.000000  ... -0.037418 -0.030770   \n",
       "2000-01-05 -0.015789 -0.009616 -0.062992  0.038684  ... -0.020408 -0.015873   \n",
       "2000-01-06  0.005347  0.013350 -0.060925 -0.003724  ...  0.010912  0.058065   \n",
       "2000-01-07  0.015957  0.022754  0.118568  0.014953  ...  0.075565 -0.015244   \n",
       "2000-01-10  0.000000  0.024590 -0.010099 -0.007366  ... -0.018248 -0.009288   \n",
       "\n",
       "Ticker           WST        WY       XEL       XOM      XRAY       YUM  \\\n",
       "Date                                                                     \n",
       "2000-01-04  0.010330 -0.037567  0.023026 -0.019154  0.000000 -0.020101   \n",
       "2000-01-05 -0.004090  0.052974  0.038585  0.054516  0.017287  0.005129   \n",
       "2000-01-06 -0.010267  0.046778 -0.009288  0.051698  0.001307 -0.008504   \n",
       "2000-01-07  0.010374 -0.032883  0.000000 -0.002935 -0.002612 -0.022298   \n",
       "2000-01-10  0.020533 -0.004359  0.000000 -0.013980  0.023561  0.040350   \n",
       "\n",
       "Ticker          ZBRA      ZION  \n",
       "Date                            \n",
       "2000-01-04 -0.014428 -0.048424  \n",
       "2000-01-05  0.019144 -0.001183  \n",
       "2000-01-06 -0.054144  0.014218  \n",
       "2000-01-07 -0.011098  0.002336  \n",
       "2000-01-10  0.033668 -0.018065  \n",
       "\n",
       "[5 rows x 362 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns = ((_adj_close - _adj_close.shift(1))/_adj_close.shift(1)).iloc[1:,:]\n",
    "returns.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9adc86c2",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3f6ce7b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "split_date = '2016-01-01'\n",
    "\n",
    "x_train = returns.loc[:split_date,:].values\n",
    "x_test = returns.loc[split_date:,:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "98bba8dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "window_size = 20\n",
    "\n",
    "X_train = []\n",
    "Y_train = []\n",
    "for i in range(x_train.shape[0]-window_size):\n",
    "    X_train.append(x_train[i:i+window_size,:])\n",
    "    Y_train.append(x_train[i+window_size,:])\n",
    "X_train = np.array(X_train, dtype='float32')\n",
    "Y_train = np.array(Y_train, dtype='float32')\n",
    "\n",
    "X_test = []\n",
    "Y_test = []\n",
    "for i in range(x_test.shape[0]-window_size):\n",
    "    X_test.append(x_test[i:i+window_size,:])\n",
    "    Y_test.append(x_test[i+window_size,:])\n",
    "X_test = np.array(X_test, dtype='float32')\n",
    "Y_test = np.array(Y_test, dtype='float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "522ae598",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4004, 20, 362), (4004, 362), (986, 20, 362), (986, 362))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, Y_train.shape, X_test.shape, Y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "86a95243",
   "metadata": {},
   "outputs": [],
   "source": [
    "class PortfolioLayer(tf.keras.layers.Layer):\n",
    "        \n",
    "        def __init__(self, top, bottom):\n",
    "            super().__init__()\n",
    "            self.top = top\n",
    "            self.bottom = bottom\n",
    "                \n",
    "        def call(self, inputs): \n",
    "            top_largest, _ = tf.math.top_k(inputs, k=self.top)\n",
    "            sup = tf.reduce_min(top_largest, axis=1, keepdims=True)\n",
    "            long = tf.math.sign(1. + tf.math.sign(inputs-sup))\n",
    "            top_mask = long*inputs\n",
    "            \n",
    "            bottom_smallest, _ = tf.math.top_k(-inputs, k=self.bottom)\n",
    "            bottom_smallest = -bottom_smallest\n",
    "            inf = tf.reduce_max(bottom_smallest, axis=1, keepdims=True)\n",
    "            short = tf.math.sign(1. + tf.math.sign(inf - inputs))\n",
    "            bottom_mask = short*inputs\n",
    "        \n",
    "            portfolio = (top_mask+bottom_mask)/tf.reduce_sum(top_mask+bottom_mask, axis=1, keepdims=True)\n",
    "            return portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "72c3bc5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TradingLostFunction(tf.keras.losses.Loss):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        \n",
    "    def call(self, y_true, y_pred):    \n",
    "        return -tf.reduce_sum(tf.reduce_sum(y_true*y_pred, axis=1), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d997937b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Model():\n",
    "    f1 = tf.keras.layers.LSTM(units=64,\n",
    "                              activation='tanh',\n",
    "                              recurrent_activation='sigmoid',\n",
    "                              kernel_initializer='glorot_uniform',\n",
    "                              bias_initializer='zeros',\n",
    "                              recurrent_initializer='zeros',\n",
    "                              return_sequences=False,\n",
    "                              return_state = False)\n",
    "    f2 = tf.keras.layers.Dropout(rate=0.5)\n",
    "    f3 = tf.keras.layers.Dense(units=64,\n",
    "                              activation='tanh',\n",
    "                              kernel_initializer='glorot_uniform',\n",
    "                              bias_initializer='zeros')\n",
    "    f4 = tf.keras.layers.Dropout(rate=0.5)\n",
    "    f5 = tf.keras.layers.Dense(units=Y_train.shape[1],\n",
    "                              activation='linear',\n",
    "                              kernel_initializer='glorot_uniform',\n",
    "                              bias_initializer='zeros')\n",
    "    f6 = PortfolioLayer(top=10,\n",
    "                        bottom=10)  \n",
    "    x = tf.keras.Input(shape=X_train.shape[1:])\n",
    "    a1 = f1(x)\n",
    "    a2 = f2(a1)\n",
    "    a3 = f3(a2)\n",
    "    a4 = f4(a3)\n",
    "    a5 = f5(a4)\n",
    "    y = f6(a5)\n",
    "    model = tf.keras.Model(x, y) \n",
    "    loss = TradingLostFunction() \n",
    "    optimizer = tf.keras.optimizers.Adam(learning_rate=0.1, \n",
    "                                         beta_1=0.9, \n",
    "                                         beta_2=0.999, \n",
    "                                         epsilon=1e-07)\n",
    "    model.compile(loss=loss, \n",
    "                  optimizer=optimizer)\n",
    "    model.summary()\n",
    "    callback1 = tf.keras.callbacks.ReduceLROnPlateau(monitor='loss', \n",
    "                                                     patience=100,\n",
    "                                                     min_delta=0.001,\n",
    "                                                     factor=0.1, \n",
    "                                                     min_lr=0.0001)\n",
    "    callback2 = tf.keras.callbacks.EarlyStopping(monitor='loss',\n",
    "                                                 patience=200,\n",
    "                                                 min_delta=0.001)\n",
    "    model.fit(X_train, \n",
    "              Y_train, \n",
    "              epochs=1, \n",
    "              batch_size=64, \n",
    "              callbacks=[callback1, callback2], \n",
    "              verbose=True)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ae17b18b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 20, 362)]         0         \n",
      "                                                                 \n",
      " lstm (LSTM)                 (None, 64)                109312    \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 64)                0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 64)                4160      \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 362)               23530     \n",
      "                                                                 \n",
      " portfolio_layer (PortfolioL  (None, 362)              0         \n",
      " ayer)                                                           \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 137,002\n",
      "Trainable params: 137,002\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-28 00:30:31.573385: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63/63 [==============================] - 2s 14ms/step - loss: 33.5819 - lr: 0.1000\n"
     ]
    }
   ],
   "source": [
    "model = Model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "12afeccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x148181eb0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.save_weights('dl_algorithmic_trading')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b45961a8",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "2679304d",
   "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>Ticker</th>\n",
       "      <th>A</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ACGL</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADI</th>\n",
       "      <th>ADM</th>\n",
       "      <th>ADP</th>\n",
       "      <th>ADSK</th>\n",
       "      <th>AEE</th>\n",
       "      <th>...</th>\n",
       "      <th>WMT</th>\n",
       "      <th>WRB</th>\n",
       "      <th>WST</th>\n",
       "      <th>WY</th>\n",
       "      <th>XEL</th>\n",
       "      <th>XOM</th>\n",
       "      <th>XRAY</th>\n",
       "      <th>YUM</th>\n",
       "      <th>ZBRA</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-02-02</th>\n",
       "      <td>0.134824</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.127968</td>\n",
       "      <td>...</td>\n",
       "      <td>0.186752</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-03</th>\n",
       "      <td>0.134824</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.127968</td>\n",
       "      <td>...</td>\n",
       "      <td>0.186752</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-04</th>\n",
       "      <td>0.134824</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.127968</td>\n",
       "      <td>...</td>\n",
       "      <td>0.186752</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-05</th>\n",
       "      <td>0.134824</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.127968</td>\n",
       "      <td>...</td>\n",
       "      <td>0.186752</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-08</th>\n",
       "      <td>0.134824</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.127968</td>\n",
       "      <td>...</td>\n",
       "      <td>0.186752</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 362 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Ticker             A  AAPL  ABT  ACGL  ADBE  ADI  ADM  ADP  ADSK       AEE  \\\n",
       "Date                                                                         \n",
       "2016-02-02  0.134824   0.0 -0.0   0.0  -0.0  0.0  0.0 -0.0   0.0 -0.127968   \n",
       "2016-02-03  0.134824   0.0 -0.0   0.0  -0.0  0.0  0.0 -0.0   0.0 -0.127968   \n",
       "2016-02-04  0.134824   0.0 -0.0   0.0  -0.0  0.0  0.0 -0.0   0.0 -0.127968   \n",
       "2016-02-05  0.134824   0.0 -0.0   0.0  -0.0  0.0  0.0 -0.0   0.0 -0.127968   \n",
       "2016-02-08  0.134824   0.0 -0.0   0.0  -0.0  0.0  0.0 -0.0   0.0 -0.127968   \n",
       "\n",
       "Ticker      ...       WMT  WRB  WST   WY  XEL  XOM  XRAY  YUM  ZBRA  ZION  \n",
       "Date        ...                                                            \n",
       "2016-02-02  ...  0.186752 -0.0  0.0  0.0 -0.0 -0.0  -0.0 -0.0   0.0   0.0  \n",
       "2016-02-03  ...  0.186752 -0.0  0.0  0.0 -0.0 -0.0  -0.0 -0.0   0.0   0.0  \n",
       "2016-02-04  ...  0.186752 -0.0  0.0  0.0 -0.0 -0.0  -0.0 -0.0   0.0   0.0  \n",
       "2016-02-05  ...  0.186752 -0.0  0.0  0.0 -0.0 -0.0  -0.0 -0.0   0.0   0.0  \n",
       "2016-02-08  ...  0.186752 -0.0  0.0  0.0 -0.0 -0.0  -0.0 -0.0   0.0   0.0  \n",
       "\n",
       "[5 rows x 362 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio_positions = pd.DataFrame(model.predict(X_test),\n",
    "                                   columns=returns.loc[split_date:,:].iloc[window_size:,:].columns,\n",
    "                                   index=returns.loc[split_date:,:].iloc[window_size:,:].index)\n",
    "\n",
    "portfolio_positions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "b2f19108",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(986, 986.00024)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio_positions.shape[0], np.sum(portfolio_positions.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f781a696",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "60a7af77",
   "metadata": {},
   "outputs": [],
   "source": [
    "mae = np.mean(np.linalg.norm(model.predict(X_train[1:,:])-model.predict(X_train[:-1,:]), ord=1, axis=1, keepdims=True))\n",
    "std = np.std(np.linalg.norm(model.predict(X_train[1:,:])-model.predict(X_train[:-1,:]), ord=1, axis=1, keepdims=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "61e58d5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\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>signals</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-02-03</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-04</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-05</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-08</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-09</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            signals\n",
       "Date               \n",
       "2016-02-03      0.0\n",
       "2016-02-04      0.0\n",
       "2016-02-05      0.0\n",
       "2016-02-08      0.0\n",
       "2016-02-09      0.0"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signals = pd.DataFrame(np.linalg.norm(model.predict(X_test[1:,:])-model.predict(X_test[:-1,:]), ord=1, axis=1, keepdims=True),\n",
    "                       columns=['signals'],\n",
    "                       index=returns.loc[split_date:,:].iloc[window_size+1:,:].index)\n",
    "\n",
    "signals.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "9384ee4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ACGL</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADI</th>\n",
       "      <th>ADM</th>\n",
       "      <th>ADP</th>\n",
       "      <th>ADSK</th>\n",
       "      <th>AEE</th>\n",
       "      <th>...</th>\n",
       "      <th>WMT</th>\n",
       "      <th>WRB</th>\n",
       "      <th>WST</th>\n",
       "      <th>WY</th>\n",
       "      <th>XEL</th>\n",
       "      <th>XOM</th>\n",
       "      <th>XRAY</th>\n",
       "      <th>YUM</th>\n",
       "      <th>ZBRA</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-03-02</th>\n",
       "      <td>0.134824</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.127968</td>\n",
       "      <td>...</td>\n",
       "      <td>0.186752</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-03</th>\n",
       "      <td>0.134824</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.127968</td>\n",
       "      <td>...</td>\n",
       "      <td>0.186752</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-27</th>\n",
       "      <td>0.134824</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.127968</td>\n",
       "      <td>...</td>\n",
       "      <td>0.186752</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-28</th>\n",
       "      <td>0.134824</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.127968</td>\n",
       "      <td>...</td>\n",
       "      <td>0.186752</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>0.134824</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.127968</td>\n",
       "      <td>...</td>\n",
       "      <td>0.186752</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 362 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A  AAPL  ABT  ACGL  ADBE  ADI  ADM  ADP  ADSK       AEE  \\\n",
       "Date                                                                         \n",
       "2016-03-02  0.134824   0.0 -0.0   0.0  -0.0  0.0  0.0 -0.0   0.0 -0.127968   \n",
       "2016-03-03  0.134824   0.0 -0.0   0.0  -0.0  0.0  0.0 -0.0   0.0 -0.127968   \n",
       "2018-12-27  0.134824   0.0 -0.0   0.0  -0.0  0.0  0.0 -0.0   0.0 -0.127968   \n",
       "2018-12-28  0.134824   0.0 -0.0   0.0  -0.0  0.0  0.0 -0.0   0.0 -0.127968   \n",
       "2019-01-07  0.134824   0.0 -0.0   0.0  -0.0  0.0  0.0 -0.0   0.0 -0.127968   \n",
       "\n",
       "            ...       WMT  WRB  WST   WY  XEL  XOM  XRAY  YUM  ZBRA  ZION  \n",
       "Date        ...                                                            \n",
       "2016-03-02  ...  0.186752 -0.0  0.0  0.0 -0.0 -0.0  -0.0 -0.0   0.0   0.0  \n",
       "2016-03-03  ...  0.186752 -0.0  0.0  0.0 -0.0 -0.0  -0.0 -0.0   0.0   0.0  \n",
       "2018-12-27  ...  0.186752 -0.0  0.0  0.0 -0.0 -0.0  -0.0 -0.0   0.0   0.0  \n",
       "2018-12-28  ...  0.186752 -0.0  0.0  0.0 -0.0 -0.0  -0.0 -0.0   0.0   0.0  \n",
       "2019-01-07  ...  0.186752 -0.0  0.0  0.0 -0.0 -0.0  -0.0 -0.0   0.0   0.0  \n",
       "\n",
       "[5 rows x 362 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value = mae + 2*std\n",
    "\n",
    "condition = 'signals > {}'.format(value)\n",
    "portfolio_positions = pd.concat([portfolio_positions, signals], sort=False, axis=1).query(condition)\n",
    "portfolio_positions = portfolio_positions.iloc[:,:-1]\n",
    "portfolio_positions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "fd812c1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 6.0)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio_positions.shape[0], np.sum(portfolio_positions.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "9642693f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:xlabel='Date'>, <AxesSubplot:xlabel='Date'>,\n",
       "       <AxesSubplot:xlabel='Date'>, <AxesSubplot:xlabel='Date'>,\n",
       "       <AxesSubplot:xlabel='Date'>, <AxesSubplot:xlabel='Date'>,\n",
       "       <AxesSubplot:xlabel='Date'>, <AxesSubplot:xlabel='Date'>,\n",
       "       <AxesSubplot:xlabel='Date'>, <AxesSubplot:xlabel='Date'>,\n",
       "       <AxesSubplot:xlabel='Date'>, <AxesSubplot:xlabel='Date'>,\n",
       "       <AxesSubplot:xlabel='Date'>, <AxesSubplot:xlabel='Date'>,\n",
       "       <AxesSubplot:xlabel='Date'>, <AxesSubplot:xlabel='Date'>,\n",
       "       <AxesSubplot:xlabel='Date'>, <AxesSubplot:xlabel='Date'>,\n",
       "       <AxesSubplot:xlabel='Date'>, <AxesSubplot:xlabel='Date'>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "portfolio_positions.loc[:,list(portfolio_positions.describe().T.query('mean != 0.').T.columns)].plot(subplots=True, figsize=(20,20))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edfcf56b",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "f74dffd8",
   "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>Ticker</th>\n",
       "      <th>A</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ACGL</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADI</th>\n",
       "      <th>ADM</th>\n",
       "      <th>ADP</th>\n",
       "      <th>ADSK</th>\n",
       "      <th>AEE</th>\n",
       "      <th>...</th>\n",
       "      <th>WMT</th>\n",
       "      <th>WRB</th>\n",
       "      <th>WST</th>\n",
       "      <th>WY</th>\n",
       "      <th>XEL</th>\n",
       "      <th>XOM</th>\n",
       "      <th>XRAY</th>\n",
       "      <th>YUM</th>\n",
       "      <th>ZBRA</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-03-02</th>\n",
       "      <td>38.919998</td>\n",
       "      <td>25.145000</td>\n",
       "      <td>38.560001</td>\n",
       "      <td>23.026667</td>\n",
       "      <td>88.500000</td>\n",
       "      <td>55.610001</td>\n",
       "      <td>36.169998</td>\n",
       "      <td>84.849998</td>\n",
       "      <td>54.090000</td>\n",
       "      <td>46.980000</td>\n",
       "      <td>...</td>\n",
       "      <td>65.949997</td>\n",
       "      <td>23.577778</td>\n",
       "      <td>63.680000</td>\n",
       "      <td>27.309999</td>\n",
       "      <td>39.529999</td>\n",
       "      <td>82.660004</td>\n",
       "      <td>60.119999</td>\n",
       "      <td>54.299065</td>\n",
       "      <td>65.470001</td>\n",
       "      <td>22.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-03</th>\n",
       "      <td>39.139999</td>\n",
       "      <td>25.592501</td>\n",
       "      <td>38.849998</td>\n",
       "      <td>23.070000</td>\n",
       "      <td>87.930000</td>\n",
       "      <td>55.669998</td>\n",
       "      <td>36.900002</td>\n",
       "      <td>85.910004</td>\n",
       "      <td>55.250000</td>\n",
       "      <td>46.779999</td>\n",
       "      <td>...</td>\n",
       "      <td>66.139999</td>\n",
       "      <td>23.617779</td>\n",
       "      <td>64.129997</td>\n",
       "      <td>26.920000</td>\n",
       "      <td>39.349998</td>\n",
       "      <td>82.660004</td>\n",
       "      <td>60.220001</td>\n",
       "      <td>54.917328</td>\n",
       "      <td>66.529999</td>\n",
       "      <td>23.780001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-27</th>\n",
       "      <td>66.980003</td>\n",
       "      <td>39.375000</td>\n",
       "      <td>71.050003</td>\n",
       "      <td>26.549999</td>\n",
       "      <td>227.580002</td>\n",
       "      <td>85.489998</td>\n",
       "      <td>41.130001</td>\n",
       "      <td>130.089996</td>\n",
       "      <td>128.710007</td>\n",
       "      <td>65.430000</td>\n",
       "      <td>...</td>\n",
       "      <td>92.070000</td>\n",
       "      <td>32.488888</td>\n",
       "      <td>96.529999</td>\n",
       "      <td>21.639999</td>\n",
       "      <td>49.590000</td>\n",
       "      <td>69.180000</td>\n",
       "      <td>37.330002</td>\n",
       "      <td>91.580002</td>\n",
       "      <td>156.979996</td>\n",
       "      <td>40.669998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-28</th>\n",
       "      <td>66.339996</td>\n",
       "      <td>39.632500</td>\n",
       "      <td>71.570000</td>\n",
       "      <td>26.389999</td>\n",
       "      <td>225.559998</td>\n",
       "      <td>86.089996</td>\n",
       "      <td>40.840000</td>\n",
       "      <td>130.080002</td>\n",
       "      <td>129.190002</td>\n",
       "      <td>65.059998</td>\n",
       "      <td>...</td>\n",
       "      <td>92.669998</td>\n",
       "      <td>32.515556</td>\n",
       "      <td>96.980003</td>\n",
       "      <td>21.900000</td>\n",
       "      <td>49.349998</td>\n",
       "      <td>68.320000</td>\n",
       "      <td>37.279999</td>\n",
       "      <td>91.790001</td>\n",
       "      <td>157.070007</td>\n",
       "      <td>40.490002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>67.589996</td>\n",
       "      <td>37.389999</td>\n",
       "      <td>69.550003</td>\n",
       "      <td>26.420000</td>\n",
       "      <td>232.649994</td>\n",
       "      <td>84.269997</td>\n",
       "      <td>42.230000</td>\n",
       "      <td>131.479996</td>\n",
       "      <td>134.259995</td>\n",
       "      <td>64.019997</td>\n",
       "      <td>...</td>\n",
       "      <td>95.059998</td>\n",
       "      <td>32.288891</td>\n",
       "      <td>99.730003</td>\n",
       "      <td>23.580000</td>\n",
       "      <td>48.259998</td>\n",
       "      <td>72.540001</td>\n",
       "      <td>40.250000</td>\n",
       "      <td>91.669998</td>\n",
       "      <td>157.350006</td>\n",
       "      <td>43.310001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 362 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Ticker              A       AAPL        ABT       ACGL        ADBE        ADI  \\\n",
       "Date                                                                            \n",
       "2016-03-02  38.919998  25.145000  38.560001  23.026667   88.500000  55.610001   \n",
       "2016-03-03  39.139999  25.592501  38.849998  23.070000   87.930000  55.669998   \n",
       "2018-12-27  66.980003  39.375000  71.050003  26.549999  227.580002  85.489998   \n",
       "2018-12-28  66.339996  39.632500  71.570000  26.389999  225.559998  86.089996   \n",
       "2019-01-07  67.589996  37.389999  69.550003  26.420000  232.649994  84.269997   \n",
       "\n",
       "Ticker            ADM         ADP        ADSK        AEE  ...        WMT  \\\n",
       "Date                                                      ...              \n",
       "2016-03-02  36.169998   84.849998   54.090000  46.980000  ...  65.949997   \n",
       "2016-03-03  36.900002   85.910004   55.250000  46.779999  ...  66.139999   \n",
       "2018-12-27  41.130001  130.089996  128.710007  65.430000  ...  92.070000   \n",
       "2018-12-28  40.840000  130.080002  129.190002  65.059998  ...  92.669998   \n",
       "2019-01-07  42.230000  131.479996  134.259995  64.019997  ...  95.059998   \n",
       "\n",
       "Ticker            WRB        WST         WY        XEL        XOM       XRAY  \\\n",
       "Date                                                                           \n",
       "2016-03-02  23.577778  63.680000  27.309999  39.529999  82.660004  60.119999   \n",
       "2016-03-03  23.617779  64.129997  26.920000  39.349998  82.660004  60.220001   \n",
       "2018-12-27  32.488888  96.529999  21.639999  49.590000  69.180000  37.330002   \n",
       "2018-12-28  32.515556  96.980003  21.900000  49.349998  68.320000  37.279999   \n",
       "2019-01-07  32.288891  99.730003  23.580000  48.259998  72.540001  40.250000   \n",
       "\n",
       "Ticker            YUM        ZBRA       ZION  \n",
       "Date                                          \n",
       "2016-03-02  54.299065   65.470001  22.799999  \n",
       "2016-03-03  54.917328   66.529999  23.780001  \n",
       "2018-12-27  91.580002  156.979996  40.669998  \n",
       "2018-12-28  91.790001  157.070007  40.490002  \n",
       "2019-01-07  91.669998  157.350006  43.310001  \n",
       "\n",
       "[5 rows x 362 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trading_prices = _open.shift(-1).loc[portfolio_positions.index,:]\n",
    "trading_prices.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "bff300eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Ticker</th>\n",
       "      <th>A</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ACGL</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADI</th>\n",
       "      <th>ADM</th>\n",
       "      <th>ADP</th>\n",
       "      <th>ADSK</th>\n",
       "      <th>AEE</th>\n",
       "      <th>...</th>\n",
       "      <th>WMT</th>\n",
       "      <th>WRB</th>\n",
       "      <th>WST</th>\n",
       "      <th>WY</th>\n",
       "      <th>XEL</th>\n",
       "      <th>XOM</th>\n",
       "      <th>XRAY</th>\n",
       "      <th>YUM</th>\n",
       "      <th>ZBRA</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <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>2016-03-03</th>\n",
       "      <td>0.005653</td>\n",
       "      <td>0.017797</td>\n",
       "      <td>0.007521</td>\n",
       "      <td>0.001882</td>\n",
       "      <td>-0.006441</td>\n",
       "      <td>0.001079</td>\n",
       "      <td>0.020183</td>\n",
       "      <td>0.012493</td>\n",
       "      <td>0.021446</td>\n",
       "      <td>-0.004257</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002881</td>\n",
       "      <td>0.001697</td>\n",
       "      <td>0.007067</td>\n",
       "      <td>-0.014280</td>\n",
       "      <td>-0.004554</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001663</td>\n",
       "      <td>0.011386</td>\n",
       "      <td>0.016191</td>\n",
       "      <td>0.042983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-27</th>\n",
       "      <td>0.711293</td>\n",
       "      <td>0.538537</td>\n",
       "      <td>0.828829</td>\n",
       "      <td>0.150845</td>\n",
       "      <td>1.588195</td>\n",
       "      <td>0.535657</td>\n",
       "      <td>0.114634</td>\n",
       "      <td>0.514259</td>\n",
       "      <td>1.329593</td>\n",
       "      <td>0.398675</td>\n",
       "      <td>...</td>\n",
       "      <td>0.392047</td>\n",
       "      <td>0.375611</td>\n",
       "      <td>0.505224</td>\n",
       "      <td>-0.196137</td>\n",
       "      <td>0.260229</td>\n",
       "      <td>-0.163078</td>\n",
       "      <td>-0.380106</td>\n",
       "      <td>0.667598</td>\n",
       "      <td>1.359537</td>\n",
       "      <td>0.710261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-28</th>\n",
       "      <td>-0.009555</td>\n",
       "      <td>0.006540</td>\n",
       "      <td>0.007319</td>\n",
       "      <td>-0.006026</td>\n",
       "      <td>-0.008876</td>\n",
       "      <td>0.007018</td>\n",
       "      <td>-0.007051</td>\n",
       "      <td>-0.000077</td>\n",
       "      <td>0.003729</td>\n",
       "      <td>-0.005655</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006517</td>\n",
       "      <td>0.000821</td>\n",
       "      <td>0.004662</td>\n",
       "      <td>0.012015</td>\n",
       "      <td>-0.004840</td>\n",
       "      <td>-0.012431</td>\n",
       "      <td>-0.001339</td>\n",
       "      <td>0.002293</td>\n",
       "      <td>0.000573</td>\n",
       "      <td>-0.004426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>0.018842</td>\n",
       "      <td>-0.056582</td>\n",
       "      <td>-0.028224</td>\n",
       "      <td>0.001137</td>\n",
       "      <td>0.031433</td>\n",
       "      <td>-0.021141</td>\n",
       "      <td>0.034035</td>\n",
       "      <td>0.010763</td>\n",
       "      <td>0.039244</td>\n",
       "      <td>-0.015985</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025790</td>\n",
       "      <td>-0.006971</td>\n",
       "      <td>0.028356</td>\n",
       "      <td>0.076712</td>\n",
       "      <td>-0.022087</td>\n",
       "      <td>0.061768</td>\n",
       "      <td>0.079667</td>\n",
       "      <td>-0.001307</td>\n",
       "      <td>0.001783</td>\n",
       "      <td>0.069647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-08</th>\n",
       "      <td>0.009025</td>\n",
       "      <td>0.011567</td>\n",
       "      <td>-0.008914</td>\n",
       "      <td>0.039743</td>\n",
       "      <td>0.005846</td>\n",
       "      <td>0.018393</td>\n",
       "      <td>0.011130</td>\n",
       "      <td>0.002890</td>\n",
       "      <td>0.003426</td>\n",
       "      <td>0.009685</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006838</td>\n",
       "      <td>-0.001790</td>\n",
       "      <td>0.029881</td>\n",
       "      <td>0.000848</td>\n",
       "      <td>0.012640</td>\n",
       "      <td>0.000827</td>\n",
       "      <td>-0.018634</td>\n",
       "      <td>-0.000654</td>\n",
       "      <td>0.002796</td>\n",
       "      <td>0.000462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 362 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Ticker             A      AAPL       ABT      ACGL      ADBE       ADI  \\\n",
       "Date                                                                     \n",
       "2016-03-03  0.005653  0.017797  0.007521  0.001882 -0.006441  0.001079   \n",
       "2018-12-27  0.711293  0.538537  0.828829  0.150845  1.588195  0.535657   \n",
       "2018-12-28 -0.009555  0.006540  0.007319 -0.006026 -0.008876  0.007018   \n",
       "2019-01-07  0.018842 -0.056582 -0.028224  0.001137  0.031433 -0.021141   \n",
       "2019-01-08  0.009025  0.011567 -0.008914  0.039743  0.005846  0.018393   \n",
       "\n",
       "Ticker           ADM       ADP      ADSK       AEE  ...       WMT       WRB  \\\n",
       "Date                                                ...                       \n",
       "2016-03-03  0.020183  0.012493  0.021446 -0.004257  ...  0.002881  0.001697   \n",
       "2018-12-27  0.114634  0.514259  1.329593  0.398675  ...  0.392047  0.375611   \n",
       "2018-12-28 -0.007051 -0.000077  0.003729 -0.005655  ...  0.006517  0.000821   \n",
       "2019-01-07  0.034035  0.010763  0.039244 -0.015985  ...  0.025790 -0.006971   \n",
       "2019-01-08  0.011130  0.002890  0.003426  0.009685  ...  0.006838 -0.001790   \n",
       "\n",
       "Ticker           WST        WY       XEL       XOM      XRAY       YUM  \\\n",
       "Date                                                                     \n",
       "2016-03-03  0.007067 -0.014280 -0.004554  0.000000  0.001663  0.011386   \n",
       "2018-12-27  0.505224 -0.196137  0.260229 -0.163078 -0.380106  0.667598   \n",
       "2018-12-28  0.004662  0.012015 -0.004840 -0.012431 -0.001339  0.002293   \n",
       "2019-01-07  0.028356  0.076712 -0.022087  0.061768  0.079667 -0.001307   \n",
       "2019-01-08  0.029881  0.000848  0.012640  0.000827 -0.018634 -0.000654   \n",
       "\n",
       "Ticker          ZBRA      ZION  \n",
       "Date                            \n",
       "2016-03-03  0.016191  0.042983  \n",
       "2018-12-27  1.359537  0.710261  \n",
       "2018-12-28  0.000573 -0.004426  \n",
       "2019-01-07  0.001783  0.069647  \n",
       "2019-01-08  0.002796  0.000462  \n",
       "\n",
       "[5 rows x 362 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trading_returns = ((trading_prices - trading_prices.shift(1))/trading_prices.shift(1)).iloc[1:,:]\n",
    "trading_returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "9fdec64b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2016-03-03   -0.014807\n",
       "2018-12-27    2.666103\n",
       "2018-12-28    0.024350\n",
       "2019-01-07    0.113210\n",
       "2019-01-08   -0.029481\n",
       "dtype: float64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio_returns = np.sum(trading_returns*portfolio_positions.shift(1).iloc[1:,:], axis=1)\n",
    "portfolio_returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "07564688",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Portfolio Returns'}, ylabel='Density'>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.set_title('Portfolio Returns')\n",
    "sns.kdeplot(data=portfolio_returns, ax=ax, color='#0F00FF')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "183b9194",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "ef1b3be3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2016-03-03    0.985193\n",
       "2018-12-27    3.611818\n",
       "2018-12-28    3.699767\n",
       "2019-01-07    4.118619\n",
       "2019-01-08    3.997196\n",
       "dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "discount_factor = np.cumprod(portfolio_returns+1)\n",
    "discount_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "8f803b6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = portfolio_positions.index[0]\n",
    "end = portfolio_positions.index[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "b9690fa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "market_portfolio_positions = _adj_close.loc[start:end,:].div(_adj_close.loc[start:end,:].sum(axis=1), axis=0)\n",
    "market_trading_prices = _open.shift(-1).loc[market_portfolio_positions.index,:]\n",
    "market_trading_returns = ((market_trading_prices - market_trading_prices.shift(1))/market_trading_prices.shift(1)).iloc[1:,:]\n",
    "market_portfolio_returns = np.sum(market_trading_returns*market_portfolio_positions.shift(1).iloc[1:,:], axis=1)\n",
    "market_discount_factor = np.cumprod(market_portfolio_returns+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "dad64f3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x29aa4edf0>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 10))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.grid(visible=True, linestyle='--')\n",
    "ax.set_title('u$s1 Wealth Evolution')\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Wealth')\n",
    "ax.plot(discount_factor.index, discount_factor.values, marker='.', color='#0F00FF', markersize=10, linestyle='--', label='Wealth')\n",
    "ax.plot(market_discount_factor.index, market_discount_factor.values, marker='.', color='#C7C3FF', markersize=1, linestyle='--', label='Default')\n",
    "ax.plot(discount_factor.index, np.ones(discount_factor.shape), marker='.', color='#FF00AE', markersize=1, linestyle='-', label='u$s 1 Initial Wealth')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "57e30b5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio_positions.to_csv('deeplearning_trading.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab42e968",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "c446eae2",
   "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>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-03-02 00:00:00-05:00</th>\n",
       "      <td>1986.449951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-03 00:00:00-05:00</th>\n",
       "      <td>1993.400024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-04 00:00:00-05:00</th>\n",
       "      <td>1999.989990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-07 00:00:00-05:00</th>\n",
       "      <td>2001.760010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-08 00:00:00-05:00</th>\n",
       "      <td>1979.260010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Adj Close\n",
       "Date                                  \n",
       "2016-03-02 00:00:00-05:00  1986.449951\n",
       "2016-03-03 00:00:00-05:00  1993.400024\n",
       "2016-03-04 00:00:00-05:00  1999.989990\n",
       "2016-03-07 00:00:00-05:00  2001.760010\n",
       "2016-03-08 00:00:00-05:00  1979.260010"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tickers = yf.Ticker(\"^GSPC\")\n",
    "\n",
    "sp500 = tickers.history(start=start, end=end, auto_adjust=False, interval='1d')\n",
    "sp500 = sp500.loc[:,['Adj Close']]\n",
    "sp500.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "20b2f125",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1362db640>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 10))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.grid(visible=True, linestyle='--')\n",
    "ax.set_title('Index Evolution')\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Index')\n",
    "ax.plot(sp500.index, sp500.loc[:,'Adj Close'].values, marker='.', color='#32A899', markersize=1, linestyle='--', label='S&P500')\n",
    "ax.legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8 [ds]",
   "language": "python",
   "name": "ds"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}