{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.utils import to_categorical, plot_model\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, Dense, GRU, Embedding, Bidirectional, TimeDistributed\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from tqdm import tqdm\n",
    "import tensorflow.keras.backend as K\n",
    "import os\n",
    "import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import psutil\n",
    "# Ignore harmless warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using CPU\n"
     ]
    }
   ],
   "source": [
    "gpu_devices = tf.config.list_physical_devices('GPU')\n",
    "\n",
    "if gpu_devices:\n",
    "    print('Using GPU')\n",
    "    for gpu in gpu_devices:\n",
    "        tf.config.experimental.set_memory_growth(gpu, True)\n",
    "else:\n",
    "    print('Using CPU')\n",
    "    tf.config.optimizer.set_jit(True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "used: 91.7% free: 0.62GB\n"
     ]
    }
   ],
   "source": [
    "import psutil\n",
    "print('used: {}% free: {:.2f}GB'.format(psutil.virtual_memory().percent, float(psutil.virtual_memory().free)/1024**3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "DATA_STORE = Path('model_data.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get News headlines\n",
    "def get_news():\n",
    "    import h5py\n",
    "    import s3fs\n",
    "    data = pd.DataFrame()\n",
    "    s3 = s3fs.S3FileSystem(anon=False, key='AKIAVKQQJHFPQ35IN6F6', \n",
    "                           secret='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',\n",
    "                           client_kwargs={'region_name':'us-west-2'})\n",
    "    with h5py.File(s3.open(\"charlanguagemodeldata/news_db.h5\", 'rb'), 'r', lib_version='latest') as f:\n",
    "        tickers = list(f.keys())\n",
    "        for i in tqdm(tickers):\n",
    "            temp_df = pd.DataFrame(f[i+'/table'].value)# took one day to realize\n",
    "            temp_df = temp_df[['versionCreated', 'text', 'ticker']]\n",
    "            temp_df = temp_df.rename(columns={'versionCreated':'time', 'text': 'headline'})\n",
    "            temp_df[temp_df.columns[temp_df.dtypes == object]] = temp_df.select_dtypes([object]).stack().str.decode('utf-8').unstack()\n",
    "            temp_df.time = pd.to_datetime(temp_df.time, unit='ns')\n",
    "            temp_df = temp_df.set_index('time')\n",
    "            print()\n",
    "            data = data.append(temp_df)\n",
    "        data.index = data.index.strftime('%Y-%m-%d %H:%M:%S.%fZ') #get full time to drop duplicates\n",
    "        data.index = pd.to_datetime(data.index)\n",
    "        data = data.sort_index()\n",
    "        data = data.reset_index().set_index(['ticker', 'time']) #drop identical(ticker-datetime)\n",
    "        data = data[~data.headline.duplicated()].reset_index().set_index('time')\n",
    "        data.index = data.index.strftime('%Y-%m-%d %H:%M:%S')\n",
    "        data.index = pd.to_datetime(data.index)\n",
    "    f.close()\n",
    "    return data \n",
    "\n",
    "def get_prices(interval):\n",
    "    import h5py\n",
    "    import s3fs\n",
    "    idx = pd.IndexSlice\n",
    "    data = pd.DataFrame()\n",
    "    s3 = s3fs.S3FileSystem(anon=False, key='AKIAVKQQJHFPQ35IN6F6', \n",
    "                           secret='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',\n",
    "                           client_kwargs={'region_name':'us-west-2'})\n",
    "    with h5py.File(s3.open(\"charlanguagemodeldata/universe.h5\", 'rb'), 'r', lib_version='latest') as f:\n",
    "        print(list(f.keys()))\n",
    "        print(list(f.get('prices').keys())) \n",
    "        get_columns =['time', 'ticker', 'Open', 'Close']\n",
    "        data = pd.DataFrame(f['prices/'+interval+'/table'].value,columns=get_columns)\n",
    "        data[data.columns[data.dtypes == object]] = data.select_dtypes([object]).stack().str.decode('utf-8').unstack()\n",
    "        data.time = pd.to_datetime(data.time, unit='ns')\n",
    "        data = data.set_index(['ticker', 'time'])\n",
    "        data = (data.sort_index(level = 0,sort_remaining=0)\n",
    "                                    .loc[idx[:, '2019':], :]\n",
    "                                    .sort_index())\n",
    "        data = data[~data.index.duplicated()] #while scraping data,if duplicates are stored\n",
    "    f.close()\n",
    "    return data"
   ]
  },
  {
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    },
    {
     "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>ticker</th>\n",
       "      <th>headline</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-07-08 07:20:18</th>\n",
       "      <td>CSCO</td>\n",
       "      <td>Dow Jones Selected Stocks 1710 - July 08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-08 19:50:01</th>\n",
       "      <td>CNP</td>\n",
       "      <td>NYSE ORDER IMBALANCE &lt;CNP.N&gt; 156400.0 SHARES O...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-08 23:16:50</th>\n",
       "      <td>CSCO</td>\n",
       "      <td>Dow Jones Selected Stocks - July 09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-09 06:01:05</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>RCS - IMImobile PLC - Vauxhall launch Apple Bu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-09 06:12:22</th>\n",
       "      <td>BLK</td>\n",
       "      <td>REG - iShares PLC - Net Asset Value(s)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-15 15:49:06</th>\n",
       "      <td>AAL</td>\n",
       "      <td>BUZZ-U.S. STOCKS ON THE MOVE-Charles Schwab, W...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-15 16:00:00</th>\n",
       "      <td>AMZN</td>\n",
       "      <td>Merkle Enters into Strategic Collaboration Agr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-15 16:02:57</th>\n",
       "      <td>AEP</td>\n",
       "      <td>CLASS ACTION UPDATE for AEP, FENC and BMRN: Le...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-15 16:04:09</th>\n",
       "      <td>ADBE</td>\n",
       "      <td>Adobe (ADBE) Up 6.4% Since Last Earnings Repor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-15 16:06:53</th>\n",
       "      <td>AAL</td>\n",
       "      <td>American Airlines Group Inc. - American Airlin...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    ticker                                           headline\n",
       "time                                                                         \n",
       "2019-07-08 07:20:18   CSCO           Dow Jones Selected Stocks 1710 - July 08\n",
       "2019-07-08 19:50:01    CNP  NYSE ORDER IMBALANCE <CNP.N> 156400.0 SHARES O...\n",
       "2019-07-08 23:16:50   CSCO                Dow Jones Selected Stocks - July 09\n",
       "2019-07-09 06:01:05   AAPL  RCS - IMImobile PLC - Vauxhall launch Apple Bu...\n",
       "2019-07-09 06:12:22    BLK             REG - iShares PLC - Net Asset Value(s)\n",
       "2020-10-15 15:49:06    AAL  BUZZ-U.S. STOCKS ON THE MOVE-Charles Schwab, W...\n",
       "2020-10-15 16:00:00   AMZN  Merkle Enters into Strategic Collaboration Agr...\n",
       "2020-10-15 16:02:57    AEP  CLASS ACTION UPDATE for AEP, FENC and BMRN: Le...\n",
       "2020-10-15 16:04:09   ADBE  Adobe (ADBE) Up 6.4% Since Last Earnings Repor...\n",
       "2020-10-15 16:06:53    AAL  American Airlines Group Inc. - American Airlin..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "news = get_news()\n",
    "news.head().append(news.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 568560 entries, 2019-07-08 07:20:18 to 2020-10-15 16:06:53\n",
      "Data columns (total 2 columns):\n",
      " #   Column    Non-Null Count   Dtype \n",
      "---  ------    --------------   ----- \n",
      " 0   ticker    568560 non-null  object\n",
      " 1   headline  568560 non-null  object\n",
      "dtypes: object(2)\n",
      "memory usage: 13.0+ MB\n"
     ]
    }
   ],
   "source": [
    "news.info(verbose = 1, memory_usage = 1, null_counts= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resample News be Frequency\n",
    "daily_news = news.copy().groupby('ticker').resample('D').agg({'headline': ' '.join})\n",
    "daily_news['headline'] = daily_news['headline'].replace('', np.nan)\n",
    "daily_news = daily_news.dropna()\n",
    "\n",
    "hourly_news = news.copy().groupby('ticker').resample('H').agg({'headline': ' '.join})\n",
    "hourly_news['headline'] = hourly_news['headline'].replace('', np.nan)\n",
    "hourly_news = hourly_news.dropna()\n",
    "\n",
    "min_15_news = news.copy().groupby('ticker').resample('15min').agg({'headline': ' '.join})\n",
    "min_15_news['headline'] = min_15_news['headline'].replace('', np.nan)\n",
    "min_15_news = min_15_news.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Daily Data Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['model_data', 'prices']\n",
      "['15_min', 'daily', 'hourly']\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 231075 entries, ('A', Timestamp('2019-01-02 00:00:00')) to ('ZTS', Timestamp('2020-10-14 00:00:00'))\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   Open    231075 non-null  float64\n",
      " 1   Close   231075 non-null  float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 4.4+ MB\n"
     ]
    }
   ],
   "source": [
    "daily = get_prices('daily')\n",
    "daily.info(verbose = 1, memory_usage = 1, null_counts= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 111261 entries, ('A', Timestamp('2019-07-11 00:00:00')) to ('ZTS', Timestamp('2020-10-14 00:00:00'))\n",
      "Data columns (total 5 columns):\n",
      " #   Column    Non-Null Count   Dtype  \n",
      "---  ------    --------------   -----  \n",
      " 0   headline  111261 non-null  object \n",
      " 1   Open      111261 non-null  float64\n",
      " 2   Close     111261 non-null  float64\n",
      " 3   returns   110905 non-null  float64\n",
      " 4   label     111261 non-null  float64\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 9.7+ MB\n"
     ]
    }
   ],
   "source": [
    "daily['returns'] = daily.Close.groupby('ticker').pct_change().shift(-1)\n",
    "daily['label'] = daily.returns.where(daily.returns > 0, -1).where(daily.returns < 0, 1)\n",
    "\n",
    "intersect = daily.index.intersection(daily_news.index)\n",
    "\n",
    "daily_news = daily_news.loc[intersect, :].sort_index()\n",
    "daily = daily.loc[intersect, :].sort_index()\n",
    "\n",
    "daily = daily_news.join(daily[['Open', 'Close', 'returns', 'label']])\n",
    "\n",
    "\n",
    "daily.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily = daily.dropna() #drop based on returns NaN\n",
    "# daily['headline'] = daily.headline.apply(lambda x: '<s>' + x + '<\\s') #parse start/end tokens\n",
    "with pd.HDFStore(DATA_STORE) as store:\n",
    "    store.put('model_data/daily', daily)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hourly Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['model_data', 'prices']\n",
      "['15_min', 'daily', 'hourly']\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 1888760 entries, ('A', Timestamp('2019-01-02 14:00:00')) to ('ZTS', Timestamp('2020-10-14 20:00:00'))\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count    Dtype  \n",
      "---  ------  --------------    -----  \n",
      " 0   Open    1888760 non-null  float64\n",
      " 1   Close   1888760 non-null  float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 36.3+ MB\n"
     ]
    }
   ],
   "source": [
    "hourly = get_prices('hourly')\n",
    "hourly.info(verbose = 1, memory_usage = 1, null_counts= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 180259 entries, ('A', Timestamp('2019-07-11 13:00:00')) to ('ZTS', Timestamp('2020-10-14 16:00:00'))\n",
      "Data columns (total 5 columns):\n",
      " #   Column    Non-Null Count   Dtype  \n",
      "---  ------    --------------   -----  \n",
      " 0   headline  180259 non-null  object \n",
      " 1   Open      180259 non-null  float64\n",
      " 2   Close     180259 non-null  float64\n",
      " 3   returns   180215 non-null  float64\n",
      " 4   label     180259 non-null  float64\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 12.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# # last price after 1 hr compared to first price when news was released.\n",
    "def o_c_pct_change(df):\n",
    "    return (df.Close.shift(-1) - df.Open)/ df.Open\n",
    "\n",
    "hourly['returns'] = hourly.groupby('ticker', group_keys=False).apply(o_c_pct_change)\n",
    "hourly['label'] = hourly.returns.where(hourly.returns > 0, -1).where(hourly.returns < 0, 1)\n",
    "\n",
    "hourly_intersect = hourly.index.intersection(hourly_news.index)\n",
    "\n",
    "hourly_news = hourly_news.loc[hourly_intersect, :].sort_index()\n",
    "hourly = hourly.loc[hourly_intersect, :].sort_index()\n",
    "\n",
    "hourly = hourly_news.join(hourly[['Open', 'Close', 'returns', 'label']])\n",
    "\n",
    "hourly['headline'] = hourly['headline'].replace('', np.nan)\n",
    "\n",
    "hourly.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "hourly = hourly.dropna()\n",
    "hourly = hourly[['headline', 'label']]\n",
    "# hourly['headline'] = hourly.headline.apply(lambda x: '<s>' + x + '<\\s') #parse start/end tokens\n",
    "with pd.HDFStore(DATA_STORE) as store:\n",
    "    store.put('model_data/hourly', hourly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 15 Min Data Process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['model_data', 'prices']\n",
      "['15_min', 'daily', 'hourly']\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 6362236 entries, ('A', Timestamp('2019-01-02 14:30:00')) to ('ZTS', Timestamp('2020-10-14 20:00:00'))\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count    Dtype  \n",
      "---  ------  --------------    -----  \n",
      " 0   Open    6362236 non-null  float64\n",
      " 1   Close   6362236 non-null  float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 134.3+ MB\n"
     ]
    }
   ],
   "source": [
    "min_15 = get_prices('15_min')\n",
    "min_15.info(verbose = 1, memory_usage = 1, null_counts= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 184156 entries, ('A', Timestamp('2019-07-11 16:30:00')) to ('ZTS', Timestamp('2020-10-14 16:45:00'))\n",
      "Data columns (total 5 columns):\n",
      " #   Column    Non-Null Count   Dtype  \n",
      "---  ------    --------------   -----  \n",
      " 0   headline  184156 non-null  object \n",
      " 1   Open      184156 non-null  float64\n",
      " 2   Close     184156 non-null  float64\n",
      " 3   returns   184146 non-null  float64\n",
      " 4   label     184156 non-null  float64\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 12.8+ MB\n"
     ]
    }
   ],
   "source": [
    "min_15['returns'] = min_15.groupby('ticker', group_keys=False).apply(o_c_pct_change)\n",
    "min_15['label'] = min_15.returns.where(min_15.returns > 0, -1).where(min_15.returns < 0, 1)\n",
    "\n",
    "min15_intersect = min_15.index.intersection(min_15_news.index)\n",
    "\n",
    "min_15_news = min_15_news.loc[min15_intersect, :].sort_index()\n",
    "min_15 = min_15.loc[min15_intersect, :].sort_index()\n",
    "\n",
    "min_15 = min_15_news.join(min_15[['Open', 'Close','returns', 'label']])\n",
    "\n",
    "min_15['headline'] = min_15['headline'].replace('', np.nan)\n",
    "min_15.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_15 = min_15.dropna()\n",
    "min_15 = min_15[['headline', 'label']]\n",
    "# min_15['headline'] = min_15.headline.apply(lambda x: '<s>' + x + '<\\s') #parse start/end tokens\n",
    "with pd.HDFStore(DATA_STORE) as store:\n",
    "    store.put('model_data/15_min', min_15)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/model_data/15_min', '/model_data/daily', '/model_data/hourly']\n"
     ]
    }
   ],
   "source": [
    "with pd.HDFStore(DATA_STORE) as store:\n",
    "    print(store.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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