Market_Neutral_Strategy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pandas_datareader\n",
    "from pandas_datareader import get_data_yahoo as yh\n",
    "import itertools\n",
    "import time\n",
    "import requests\n",
    "import datetime\n",
    "\n",
    "asset_count=5\n",
    "ma_short=15\n",
    "ma_long=50\n",
    "pv_period=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "myassetlist = [\n",
    "    'TSLA',\n",
    "    'GOOG',\n",
    "    'AAPL',\n",
    "    'SPY',\n",
    "    'XLI',\n",
    "    'GME',\n",
    "    'AMC',\n",
    "    'BB',\n",
    "    'NOK',\n",
    "    'NAKD',\n",
    "    'PSA',\n",
    "    'KWEB'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = yh(myassetlist)['Close'] # Get close prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('data.csv') # run this if you wanna export to csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rank the assets. Higher rank number means more bullish"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>20_day_returns</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Symbols</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GME</th>\n",
       "      <td>0.118048</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TSLA</th>\n",
       "      <td>0.058759</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAKD</th>\n",
       "      <td>0.032877</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMC</th>\n",
       "      <td>0.030142</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>0.028590</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BB</th>\n",
       "      <td>0.021061</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>0.019097</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KWEB</th>\n",
       "      <td>0.016131</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPY</th>\n",
       "      <td>0.011531</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLI</th>\n",
       "      <td>0.010850</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSA</th>\n",
       "      <td>0.000228</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOK</th>\n",
       "      <td>-0.000204</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         20_day_returns  rank\n",
       "Symbols                      \n",
       "GME            0.118048  12.0\n",
       "TSLA           0.058759  11.0\n",
       "NAKD           0.032877  10.0\n",
       "AMC            0.030142   9.0\n",
       "AAPL           0.028590   8.0\n",
       "BB             0.021061   7.0\n",
       "GOOG           0.019097   6.0\n",
       "KWEB           0.016131   5.0\n",
       "SPY            0.011531   4.0\n",
       "XLI            0.010850   3.0\n",
       "PSA            0.000228   2.0\n",
       "NOK           -0.000204   1.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Existing Strategies\n",
    "\n",
    "# Get 20 day returns and rank\n",
    "ranked_momentum = pd.DataFrame(df.pct_change(20).mean().sort_values(ascending = False)) # Ascending to descending returns\n",
    "ranked_momentum.columns = ['20_day_returns']\n",
    "ranked_momentum['rank'] = ranked_momentum['20_day_returns'].rank()\n",
    "ranked_momentum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>MA_crossover</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Symbols</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GME</th>\n",
       "      <td>0.312841</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMC</th>\n",
       "      <td>0.262873</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLI</th>\n",
       "      <td>0.051888</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSA</th>\n",
       "      <td>0.030551</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPY</th>\n",
       "      <td>0.016131</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>0.013156</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOK</th>\n",
       "      <td>-0.036290</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>-0.042371</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KWEB</th>\n",
       "      <td>-0.099433</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TSLA</th>\n",
       "      <td>-0.101160</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BB</th>\n",
       "      <td>-0.142505</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAKD</th>\n",
       "      <td>-0.202257</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         MA_crossover  rank\n",
       "Symbols                    \n",
       "GME          0.312841  12.0\n",
       "AMC          0.262873  11.0\n",
       "XLI          0.051888  10.0\n",
       "PSA          0.030551   9.0\n",
       "SPY          0.016131   8.0\n",
       "GOOG         0.013156   7.0\n",
       "NOK         -0.036290   6.0\n",
       "AAPL        -0.042371   5.0\n",
       "KWEB        -0.099433   4.0\n",
       "TSLA        -0.101160   3.0\n",
       "BB          -0.142505   2.0\n",
       "NAKD        -0.202257   1.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#moving average short/long term\n",
    "# (short ma - long ma) / long ma\n",
    "ranked_ma_assets = pd.DataFrame(((df.tail(ma_short).mean()-df.tail(ma_long).mean())/df.tail(ma_long).mean()).sort_values(ascending = False))\n",
    "ranked_ma_assets.columns = ['MA_crossover']\n",
    "ranked_ma_assets['rank'] = ranked_ma_assets.rank()\n",
    "ranked_ma_assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>VPT_Indicator</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Symbols</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>0.033338</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TSLA</th>\n",
       "      <td>0.014387</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMC</th>\n",
       "      <td>0.001565</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GME</th>\n",
       "      <td>0.001167</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAKD</th>\n",
       "      <td>-0.000370</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOK</th>\n",
       "      <td>-0.000609</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BB</th>\n",
       "      <td>-0.005537</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPY</th>\n",
       "      <td>-0.026348</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLI</th>\n",
       "      <td>-0.026683</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSA</th>\n",
       "      <td>-0.040595</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KWEB</th>\n",
       "      <td>-0.066687</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>-1.673679</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         VPT_Indicator  rank\n",
       "Symbols                     \n",
       "AAPL          0.033338  12.0\n",
       "TSLA          0.014387  11.0\n",
       "AMC           0.001565  10.0\n",
       "GME           0.001167   9.0\n",
       "NAKD         -0.000370   8.0\n",
       "NOK          -0.000609   7.0\n",
       "BB           -0.005537   6.0\n",
       "SPY          -0.026348   5.0\n",
       "XLI          -0.026683   4.0\n",
       "PSA          -0.040595   3.0\n",
       "KWEB         -0.066687   2.0\n",
       "GOOG         -1.673679   1.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#VPT indicator\n",
    "# Reference: https://www.investopedia.com/terms/v/vptindicator.asp\n",
    "\n",
    "vf=yh(myassetlist)['Volume']\n",
    "ranked_pv_assets = pd.DataFrame(((vf*df.pct_change()).cumsum()).pct_change(pv_period).tail(1).sum().sort_values(ascending = False))\n",
    "ranked_pv_assets\n",
    "\n",
    "ranked_pv_assets.columns = ['VPT_Indicator']\n",
    "ranked_pv_assets['rank'] = ranked_pv_assets.rank()\n",
    "ranked_pv_assets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combine strategies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Clara\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "combined = pd.DataFrame(pd.concat([ranked_momentum,ranked_ma_assets,ranked_pv_assets])['rank'])\n",
    "combined = combined.reset_index()\n",
    "combined = combined.groupby('Symbols').sum().sort_values('rank',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Symbols</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMC</th>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BB</th>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GME</th>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KWEB</th>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAKD</th>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOK</th>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSA</th>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPY</th>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TSLA</th>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLI</th>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         rank\n",
       "Symbols      \n",
       "AAPL     25.0\n",
       "AMC      30.0\n",
       "BB       15.0\n",
       "GME      33.0\n",
       "GOOG     14.0\n",
       "KWEB     11.0\n",
       "NAKD     19.0\n",
       "NOK      14.0\n",
       "PSA      14.0\n",
       "SPY      17.0\n",
       "TSLA     25.0\n",
       "XLI      17.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x176dfe932e8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.sort_values(by='rank').plot.bar(figsize=(20,15), fontsize=20, rot=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Telegram Notification\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from telegram import *\n",
    "from telegram.ext import *\n",
    "import schedule\n",
    "import pandas as pd\n",
    "\n",
    "tf=pd.read_csv('telegram.csv')\n",
    "key=tf.iloc[0,0]\n",
    "chat_id= tf.iloc[0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "bot=Bot(key)\n",
    "updater=Updater(key, use_context=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_stock(summary, key, chat_id):\n",
    "    summary=str(summary)\n",
    "    key=str(key)\n",
    "    chat_id=str(chat_id)\n",
    "    send_table='https://api.telegram.org/bot' + key + '/sendMessage?chat_id=' + chat_id + '&parse_mode=Markdown&text=' + summary\n",
    "    response=requests.get(send_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_stock(combined, key, chat_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#while True:\n",
    " #   getStock(combined)\n",
    "   # time.sleep(216000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Backtesting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import date, timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=0\n",
    "backtest_period=5\n",
    "backtest_date=\"2019-04-30\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "historical_data_close=yh(myassetlist,end=backtest_date)['Close']\n",
    "historical_data_vol=yh(myassetlist,end=backtest_date)['Volume']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Clara\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:19: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for x in range(0,backtest_period):\n",
    "    # Get 20 day returns and rank\n",
    "    ranked_momentum = pd.DataFrame(historical_data_close.pct_change(20).mean().sort_values(ascending = False)) # Ascending to descending returns\n",
    "    ranked_momentum.columns = ['20_day_returns']\n",
    "    ranked_momentum[backtest_date] = ranked_momentum['20_day_returns'].rank()\n",
    "    \n",
    "    #moving average short/long term\n",
    "    ranked_ma_assets = pd.DataFrame(((historical_data_close.tail(ma_short).mean()-historical_data_close.tail(ma_long).mean())/historical_data_close.tail(ma_long).mean()).sort_values(ascending = False))\n",
    "    ranked_ma_assets.columns = ['MA_crossover']\n",
    "    ranked_ma_assets[backtest_date] = ranked_ma_assets.rank()\n",
    "    vf=yh(myassetlist)['Volume']\n",
    "    \n",
    "    #VPT indicator\n",
    "    ranked_pv_assets = pd.DataFrame(((historical_data_vol*historical_data_close.pct_change()).cumsum()).pct_change(pv_period).tail(1).sum().sort_values(ascending = False))\n",
    "    ranked_pv_assets.columns = ['VPT_Indicator']\n",
    "    ranked_pv_assets[backtest_date] = ranked_pv_assets.rank()\n",
    "    \n",
    "    #Combined ranking\n",
    "    combined = pd.DataFrame(pd.concat([ranked_momentum,ranked_ma_assets,ranked_pv_assets])[backtest_date])\n",
    "    combined = combined.reset_index()\n",
    "    combined = combined.groupby('Symbols').sum()\n",
    "    \n",
    "    if x==0:\n",
    "        historical_analysis = combined\n",
    "    else: \n",
    "        historical_analysis=pd.concat([historical_analysis,combined],axis=1,join=\"inner\")\n",
    "    x=x+1\n",
    "    historical_data_close=historical_data_close[:-1]\n",
    "    historical_data_vol=historical_data_vol[:-1]\n",
    "    backtest_date=historical_data_vol.tail(1).index.item()\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>2019-04-30</th>\n",
       "      <th>1556496000000000000</th>\n",
       "      <th>1556236800000000000</th>\n",
       "      <th>1556150400000000000</th>\n",
       "      <th>1556064000000000000</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Symbols</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>AAPL</th>\n",
       "      <td>24.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMC</th>\n",
       "      <td>20.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BB</th>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GME</th>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>31.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KWEB</th>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAKD</th>\n",
       "      <td>19.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOK</th>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSA</th>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPY</th>\n",
       "      <td>18.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TSLA</th>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLI</th>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         2019-04-30  1556496000000000000  1556236800000000000  \\\n",
       "Symbols                                                         \n",
       "AAPL           24.0                 33.0                 31.0   \n",
       "AMC            20.0                 19.0                 22.0   \n",
       "BB             20.0                 23.0                 22.0   \n",
       "GME            10.0                  7.0                  7.0   \n",
       "GOOG           31.0                 21.0                 22.0   \n",
       "KWEB           19.0                 19.0                 18.0   \n",
       "NAKD           19.0                 21.0                 20.0   \n",
       "NOK            18.0                 19.0                 19.0   \n",
       "PSA            12.0                 12.0                 12.0   \n",
       "SPY            18.0                 17.0                 17.0   \n",
       "TSLA           20.0                 20.0                 20.0   \n",
       "XLI            23.0                 23.0                 24.0   \n",
       "\n",
       "         1556150400000000000  1556064000000000000  \n",
       "Symbols                                            \n",
       "AAPL                    33.0                 34.0  \n",
       "AMC                     22.0                 23.0  \n",
       "BB                      19.0                 21.0  \n",
       "GME                      7.0                 11.0  \n",
       "GOOG                    22.0                 22.0  \n",
       "KWEB                    18.0                 25.0  \n",
       "NAKD                    20.0                 21.0  \n",
       "NOK                     19.0                 11.0  \n",
       "PSA                     15.0                 14.0  \n",
       "SPY                     17.0                 18.0  \n",
       "TSLA                    20.0                 20.0  \n",
       "XLI                     22.0                 14.0  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "historical_analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Production Script\n",
    "Do not touch this part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Assets here, separated by commas: SPY,GOOG,AAPL,QQQ\n",
      "Use Internal Data csv file? - reply y/n: n\n",
      "Results\n",
      "         rank\n",
      "Symbols      \n",
      "QQQ      10.0\n",
      "GOOG      8.0\n",
      "AAPL      6.0\n",
      "SPY       6.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Symbols'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %%writefile prod.py\n",
    "import pandas as pd\n",
    "import pandas_datareader\n",
    "from pandas_datareader import get_data_yahoo as yh\n",
    "import itertools\n",
    "\n",
    "# Define variables and inputs:\n",
    "asset_count=5\n",
    "ma_short=15\n",
    "ma_long=50\n",
    "pv_period=5\n",
    "\n",
    "\n",
    "myassetlist = input('Assets here, separated by commas: ')\n",
    "myassetlist = myassetlist.split(',')\n",
    "\n",
    "use_internal_data = input('Use Internal Data csv file? - reply y/n: ')\n",
    "\n",
    "\n",
    "if use_internal_data == 'y':\n",
    "    df = pd.read_csv('data.csv')[myassetlist]\n",
    "else:\n",
    "    df = yh(myassetlist)['Close'] # Get close prices\n",
    "    df.combine_first(pd.read_csv('data.csv', index_col = 'Date', parse_dates=True)).to_csv('data.csv') #update local data source\n",
    "    \n",
    "\n",
    "# Get 20 day returns and rank\n",
    "ranked_momentum = pd.DataFrame(df.pct_change(20).mean().sort_values(ascending = False)) # Ascending to descending returns\n",
    "ranked_momentum.columns = ['20_day_returns']\n",
    "ranked_momentum['rank'] = ranked_momentum['20_day_returns'].rank()\n",
    "ranked_momentum\n",
    "\n",
    "#moving average short/long term\n",
    "# (short ma - long ma) / long ma\n",
    "ranked_ma_assets = pd.DataFrame(((df.tail(ma_short).mean()-df.tail(ma_long).mean())/df.tail(ma_long).mean()).sort_values(ascending = False))\n",
    "ranked_ma_assets.columns = ['MA_crossover']\n",
    "ranked_ma_assets['rank'] = ranked_ma_assets.rank()\n",
    "ranked_ma_assets\n",
    "\n",
    "#VPT indicator\n",
    "# Reference: https://www.investopedia.com/terms/v/vptindicator.asp\n",
    "\n",
    "vf=yh(myassetlist)['Volume']\n",
    "ranked_pv_assets = pd.DataFrame(((vf*df.pct_change()).cumsum()).pct_change(pv_period).tail(1).sum().sort_values(ascending = False))\n",
    "ranked_pv_assets\n",
    "\n",
    "ranked_pv_assets.columns = ['VPT_Indicator']\n",
    "ranked_pv_assets['rank'] = ranked_pv_assets.rank()\n",
    "ranked_pv_assets\n",
    "\n",
    "combined = pd.DataFrame(pd.concat([ranked_momentum,ranked_ma_assets,ranked_pv_assets])['rank'])\n",
    "combined = combined.reset_index()\n",
    "combined.columns=['Symbols','rank']\n",
    "combined = combined.groupby('Symbols').sum().sort_values('rank',ascending=False)\n",
    "\n",
    "print('Results')\n",
    "print(combined)\n",
    "combined.sort_values(by='rank').plot.bar(figsize=(20,15), fontsize=20, rot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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