ticker_picker.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import ta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Daily Timeframe Data Grabber\n",
    "def daily_df(ticker):\n",
    "\n",
    "    API_URL = \"https://www.alphavantage.co/query\"\n",
    "\n",
    "    data = {\n",
    "        \"function\": \"TIME_SERIES_DAILY\",\n",
    "        \"symbol\": ticker,\n",
    "        \"outputsize\": \"compact\",\n",
    "        \"datatype\": \"json\",\n",
    "        \"apikey\": \"PVGTC9P6IUS7XLQ9\",\n",
    "        }\n",
    "\n",
    "    response = requests.get(API_URL, params=data)\n",
    "    \n",
    "    if response.status_code == 200:\n",
    "        stock_data = response.json()\n",
    "        meta_data = stock_data['Meta Data']\n",
    "        daily_data = stock_data['Time Series (Daily)']\n",
    "        daily_dataframe_rev = pd.DataFrame.from_dict(daily_data)\n",
    "        daily_dataframe = daily_dataframe_rev.transpose()     \n",
    "        daily_dataframe.columns = ['open', 'high', 'low', 'close', 'volume']\n",
    "        df_entries2float(daily_dataframe)\n",
    "        return daily_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting dataframe entries to floats\n",
    "def df_entries2float(df):\n",
    "    for i in df.columns:\n",
    "        column2convert = df[i]\n",
    "        column_size = column2convert.size\n",
    "        for k in range(0,column_size):\n",
    "            column2convert[k] = float(column2convert[k])\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reversing dataframe\n",
    "def rev(df):\n",
    "    return df.sort_index(ascending = True, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Running TA with proper formatting\n",
    "def tech_indicator(indicator, daily_df):\n",
    "    indicator_df = pd.DataFrame(indicator)\n",
    "    indicator_df = indicator_df.sort_index(ascending=False, axis=0)\n",
    "    resulting_df = daily_df.join(indicator_df)\n",
    "    return resulting_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#All technical indicators in use\n",
    "def run_indicators(df):\n",
    "    high = rev(df['high'])\n",
    "    low = rev(df['low'])    \n",
    "    close = rev(df['close'])\n",
    "    volume = rev(df['volume'])\n",
    "    \n",
    "    df = tech_indicator(ta.momentum.wr(high,low,close),df)\n",
    "    df = tech_indicator(ta.momentum.money_flow_index(high,low,close,volume),df)\n",
    "    df = tech_indicator(ta.momentum.stoch_signal(high,low,close),df)\n",
    "    df = tech_indicator(ta.momentum.tsi(close),df)\n",
    "    df = tech_indicator(ta.trend.macd(close),df)\n",
    "    df = tech_indicator(ta.trend.trix(close),df)\n",
    "    df = tech_indicator(ta.trend.aroon_up(close),df)\n",
    "    df = tech_indicator(ta.trend.aroon_down(close),df)\n",
    "    df = tech_indicator(ta.volatility.keltner_channel_hband_indicator(high,low,close),df)\n",
    "    df = tech_indicator(ta.volatility.keltner_channel_lband_indicator(high,low,close),df)\n",
    "    df = tech_indicator((ta.volatility.bollinger_hband_indicator(close)),df)\n",
    "    df = tech_indicator((ta.volatility.bollinger_lband_indicator(close)),df)  \n",
    "    df = tech_indicator(ta.volume.chaikin_money_flow(high,low,close,volume),df)\n",
    "    \n",
    "    df['bbi'] = df['bbihband'].subtract(df['bbilband'])\n",
    "    df['kci'] = df['kci_hband'].subtract(df['kci_lband'])\n",
    "    del df['bbihband']\n",
    "    del df['bbilband']\n",
    "    del df['kci_hband']\n",
    "    del df['kci_lband']\n",
    "    df = tech_indicator(ta.momentum.rsi(close),df)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Mapping values\n",
    "def map_values(df):\n",
    "    length = len(df)\n",
    "    \n",
    "    df = df.copy()\n",
    "\n",
    "    #############\n",
    "    ####TREND####\n",
    "    #############\n",
    "    \n",
    "    #Mapping MACD values\n",
    "    macd = df.columns.get_loc('MACD_12_26')\n",
    "    for i in range(length):\n",
    "        if 0 < df.iloc[i,macd]:\n",
    "            df.iloc[i,macd] = 1\n",
    "        elif df.iloc[i,macd] < 0:\n",
    "            df.iloc[i,macd] = -1\n",
    "        else:\n",
    "            df.iloc[i,macd] = 0\n",
    "            \n",
    "    #Mapping TRIX values\n",
    "    trix = df.columns.get_loc('trix_15')\n",
    "    for i in range(length):\n",
    "        if 0 < df.iloc[i,trix]:\n",
    "            df.iloc[i,trix] = 1\n",
    "        elif df.iloc[i,trix] < 0:\n",
    "            df.iloc[i,trix] = -1\n",
    "        else:\n",
    "            df.iloc[i,trix] = 0\n",
    "            \n",
    "    #Mapping Aroon values\n",
    "    values = []\n",
    "    aroon_up = df.columns.get_loc('aroon_up25')\n",
    "    aroon_down = df.columns.get_loc('aroon_down25')\n",
    "    for i in range(length):\n",
    "        if df.iloc[i,aroon_up] >= 70 and df.iloc[i,aroon_down] <= 30:\n",
    "            values.append(2.0)\n",
    "        elif df.iloc[i,aroon_down] >= 70 and df.iloc[i,aroon_up] <= 30:\n",
    "            values.append(-2.0)\n",
    "        elif df.iloc[i,aroon_up] > df.iloc[i,aroon_down]:\n",
    "            values.append(1.0)\n",
    "        elif df.iloc[i,aroon_down] > df.iloc[i,aroon_up]:\n",
    "            values.append(-1.0)\n",
    "        else:\n",
    "            values.append(0.0)\n",
    "    df['aroon'] = values\n",
    "    \n",
    "    #Mapping Chaikin values\n",
    "    cmf = df.columns.get_loc('cmf')\n",
    "    for i in range(length):\n",
    "        if 0.05 <= df.iloc[i,cmf] < 0.1:\n",
    "            df.iloc[i,cmf] = 1\n",
    "        elif -0.1 < df.iloc[i,cmf] <= -0.05:\n",
    "            df.iloc[i,cmf] = -1\n",
    "        elif df.iloc[i,cmf] >= 0.1:\n",
    "            df.iloc[i,cmf] = 2\n",
    "        elif df.iloc[i,cmf] <= -0.1:\n",
    "            df.iloc[i,cmf] = -2\n",
    "        else:\n",
    "            df.iloc[i,cmf] = 0\n",
    "            \n",
    "    #############\n",
    "    #OSCILLATORS#\n",
    "    #############\n",
    "    \n",
    "    #Mapping MFI values\n",
    "    mfi = df.columns.get_loc('mfi_14')\n",
    "    for i in range(length):\n",
    "        if 0 <= df.iloc[i,mfi] <= 20:\n",
    "            df.iloc[i,mfi] = 2\n",
    "        elif 80 <= df.iloc[i,mfi] <= 100:\n",
    "            df.iloc[i,mfi] = -2\n",
    "        elif 20 <= df.iloc[i,mfi] < 30:\n",
    "            df.iloc[i,mfi] = 1\n",
    "        elif 70 < df.iloc[i,mfi] <= 80:\n",
    "            df.iloc[i,mfi] = -1\n",
    "        else:\n",
    "            df.iloc[i,mfi] = 0\n",
    "            \n",
    "    #Mapping rsi values\n",
    "    rsi = df.columns.get_loc('rsi')\n",
    "    for i in range(length):\n",
    "        if 70 <= df.iloc[i,rsi] < 100:\n",
    "            df.iloc[i,rsi] = -2\n",
    "        elif 0 <= df.iloc[i,rsi] <= 30:\n",
    "            df.iloc[i,rsi] = 2\n",
    "        elif 30 < df.iloc[i,rsi] <= 40:\n",
    "            df.iloc[i,rsi] = 1\n",
    "        elif 60 <= df.iloc[i,rsi] < 70:\n",
    "            df.iloc[i,rsi] = -1\n",
    "        else:\n",
    "            df.iloc[i,rsi] = 0\n",
    "\n",
    "    #Mapping Stoch_d values\n",
    "    stoch = df.columns.get_loc('stoch_d')\n",
    "    for i in range(length):\n",
    "        if 80 <= df.iloc[i,stoch] <= 100:\n",
    "            df.iloc[i,stoch] = -2\n",
    "        elif 0 <= df.iloc[i,stoch] <= 20:\n",
    "            df.iloc[i,stoch] = 2\n",
    "        elif 20 < df.iloc[i,stoch] <= 30:\n",
    "            df.iloc[i,stoch] = 1\n",
    "        elif 70 <= df.iloc[i,stoch] < 80:\n",
    "            df.iloc[i,stoch] = -1\n",
    "        else:\n",
    "            df.iloc[i,stoch] = 0\n",
    "            \n",
    "    #Mapping TSI values\n",
    "    tsi = df.columns.get_loc('tsi')\n",
    "    for i in range(length):\n",
    "        if df.iloc[i,tsi] < -25:\n",
    "            df.iloc[i,tsi] = -2\n",
    "        elif df.iloc[i,tsi] <= -10:\n",
    "            df.iloc[i,tsi] = -1\n",
    "        elif df.iloc[i,tsi] <= 10:\n",
    "            df.iloc[i,tsi] = 0\n",
    "        elif df.iloc[i,tsi] < 25:\n",
    "            df.iloc[i,tsi] = 1\n",
    "        else:\n",
    "            df.iloc[i,tsi] = 2\n",
    "            \n",
    "    #Mapping wr values\n",
    "    wr = df.columns.get_loc('wr')\n",
    "    for i in range(length):\n",
    "        if -100 <= df.iloc[i,wr] <= -80:\n",
    "            df.iloc[i,wr] = 2\n",
    "        elif -20<= df.iloc[i,wr] <= 0:\n",
    "            df.iloc[i,wr] = -2\n",
    "        elif -30 <= df.iloc[i,wr] < -20:\n",
    "            df.iloc[i,wr] = -1\n",
    "        elif -80 < df.iloc[i,wr] <= -70:\n",
    "            df.iloc[i,wr] = 1\n",
    "        else:\n",
    "            df.iloc[i,wr] = 0\n",
    "            \n",
    "    del df['aroon_up25']\n",
    "    del df['aroon_down25']\n",
    "    \n",
    "    df['Overall Rating'] = (df['wr'] + df['mfi_14'] + df['stoch_d'] + \n",
    "                          df['tsi'] + df['MACD_12_26'] + df['trix_15'] + \n",
    "                          df['cmf'] + df['bbi'] + df['kci'] + df['rsi'] +\n",
    "                          df['aroon'])\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicting stock based on the ticker\n",
    "def stock_predict(ticker):\n",
    "    df = daily_df(ticker)\n",
    "    df = run_indicators(df)\n",
    "    df = map_values(df)\n",
    "    df = df[:-45]\n",
    "    return df #Returns indicator signals and consensus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Returning mapped indicator data less OHLCV\n",
    "def indicator_data(ticker):\n",
    "    df = daily_df(ticker)\n",
    "    df = run_indicators(df)\n",
    "    df = map_values(df)\n",
    "    df = df[:-45]\n",
    "    df = df.rename(columns = {'wr':'William\\'s PR','mfi_14':'Money Flow Index','stoch_d':'Stochastic Oscilattor',\n",
    "                              'tsi':'True Strength Index','MACD_12_26':'MACD','trix_15':'Trix','cmf':'Chaikin Money Flow',\n",
    "                              'bbi':'Bollinger Bands','kci':'Keltner Channel','rsi':'Relative Strength Index',\n",
    "                              'aroon':'Aroon'})\n",
    "    del df['open']\n",
    "    del df['close']\n",
    "    del df['volume']\n",
    "    del df['low']\n",
    "    del df['high']\n",
    "    df = df.astype(float)\n",
    "    df = df.astype(str)\n",
    "    for i in range(len(test)):\n",
    "        for k in range(0, 11):\n",
    "            if df.iloc[i][k] == str(-2.0):\n",
    "                df.iloc[i][k] = \"Strong Sell\"\n",
    "            elif df.iloc[i][k] == str(-1.0):\n",
    "                df.iloc[i][k] = \"Sell\"\n",
    "            elif df.iloc[i][k] == str(0.0):\n",
    "                df.iloc[i][k] = \"Neutral\"\n",
    "            elif df.iloc[i][k] == str(1.0):\n",
    "                df.iloc[i][k] = \"Buy\"\n",
    "            elif df.iloc[i][k] == str(2.0):\n",
    "                df.iloc[i][k] = \"Strong Buy\"  \n",
    "    return df #Returns indicator signals and consensus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicting stock based on the DataFrame input\n",
    "def stock_predict_HC(df):\n",
    "    df = run_indicators(df)\n",
    "    df = map_values(df)\n",
    "    df = df[:-45]\n",
    "    return df #Returns indicator signals and consensus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating dataframe of signals for all constituents\n",
    "constituents = pd.read_csv('constituents_csv.csv')\n",
    "ticker = constituents['Symbol'][0]\n",
    "raw_data = pd.read_csv(\"Stock Data/\" + ticker + \".csv\", index_col=0) \n",
    "predicted_data_raw = stock_predict_HC(raw_data)\n",
    "predicted_data_preprocessed = pd.DataFrame(predicted_data_raw['Overall Rating'])\n",
    "predicted_data_abs_final = predicted_data_preprocessed.rename(columns = {'Overall Rating':ticker})\n",
    "\n",
    "for i in range(1, len(constituents)):\n",
    "    try:\n",
    "        ticker = constituents['Symbol'][i]\n",
    "        raw_data = pd.read_csv(\"Stock Data/\" + ticker + \".csv\", index_col=0) \n",
    "        predicted_data_raw = stock_predict_HC(raw_data)\n",
    "        predicted_data_preprocessed = pd.DataFrame(predicted_data_raw['Overall Rating'])\n",
    "        predicted_data_final = predicted_data_preprocessed.rename(columns = {'Overall Rating':ticker})\n",
    "        predicted_data_abs_final = predicted_data_abs_final.join(predicted_data_final)      \n",
    "    except:\n",
    "        continue\n",
    "\n",
    "predicted_data_abs_final = predicted_data_abs_final.sort_values(by=[predicted_data_abs_final.index[0]], axis = 1, ascending = False)\n",
    "predicted_data_abs_final = predicted_data_abs_final.dropna(axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data_averaged = predicted_data_abs_final.copy() \n",
    "data_averaged.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "ao10 = [] \n",
    "\n",
    "for i in range(len(data_averaged.columns)):\n",
    "    \n",
    "    sum = 0\n",
    "    \n",
    "    for j in range(10):\n",
    "        sum += data_averaged.iloc[j][i]\n",
    "        \n",
    "    average = sum/10\n",
    "    ao10.append(average)\n",
    "    \n",
    "data_averaged = pd.DataFrame(np.array([ao10]), columns=data_averaged.columns).append(data_averaged, ignore_index=False)\n",
    "data_averaged = data_averaged.rename(index={0: 'average of 10'})\n",
    "data_averaged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data_averaged = data_averaged.sort_values(by=[data_averaged.index[0]], axis = 1, ascending = False)\n",
    "data_averaged.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "def top20(df, position='buy'):\n",
    "    if position == 'buy':\n",
    "        top20_buy = pd.DataFrame(df.iloc[0,:20])\n",
    "        top20_buy = top20_buy.reset_index()\n",
    "        top20_buy = top20_buy.rename(columns={'index':'Ticker','average of 10':'Score'})\n",
    "        top20_buy.index += 1\n",
    "        return top20_buy\n",
    "    if position == 'sell':\n",
    "        top20_sell = pd.DataFrame(df.iloc[0,-20:])\n",
    "        top20_sell = top20_sell.rename(columns={'index':'Ticker','average of 10':'Score'})\n",
    "        top20_sell = top20_sell.sort_values(by=['Score'],axis=0,ascending=True)\n",
    "        top20_sell = top20_sell.reset_index()\n",
    "        top20_sell.index += 1\n",
    "        top20_sell = top20_sell.rename(columns={'index':'Ticker'})\n",
    "        return top20_sell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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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>Ticker</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HCA</td>\n",
       "      <td>-8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FCX</td>\n",
       "      <td>-8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CCL</td>\n",
       "      <td>-7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DVA</td>\n",
       "      <td>-7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MS</td>\n",
       "      <td>-7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>FDX</td>\n",
       "      <td>-7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AXP</td>\n",
       "      <td>-7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SCHW</td>\n",
       "      <td>-7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SLB</td>\n",
       "      <td>-7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MMM</td>\n",
       "      <td>-7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CVX</td>\n",
       "      <td>-7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>PFE</td>\n",
       "      <td>-7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>MTD</td>\n",
       "      <td>-7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MGM</td>\n",
       "      <td>-7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>BEN</td>\n",
       "      <td>-7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>COF</td>\n",
       "      <td>-7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>APTV</td>\n",
       "      <td>-6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>MET</td>\n",
       "      <td>-6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>AZO</td>\n",
       "      <td>-6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>PKI</td>\n",
       "      <td>-6.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ticker  Score\n",
       "1     HCA   -8.7\n",
       "2     FCX   -8.0\n",
       "3     CCL   -7.9\n",
       "4     DVA   -7.6\n",
       "5      MS   -7.5\n",
       "6     FDX   -7.5\n",
       "7     AXP   -7.5\n",
       "8    SCHW   -7.4\n",
       "9     SLB   -7.4\n",
       "10    MMM   -7.4\n",
       "11    CVX   -7.3\n",
       "12    PFE   -7.2\n",
       "13    MTD   -7.1\n",
       "14    MGM   -7.1\n",
       "15    BEN   -7.1\n",
       "16    COF   -7.0\n",
       "17   APTV   -6.9\n",
       "18    MET   -6.9\n",
       "19    AZO   -6.9\n",
       "20    PKI   -6.9"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top20(data_averaged, 'sell')"
   ]
  }
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