preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "convinced-kelly",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "enabling-level",
   "metadata": {},
   "outputs": [],
   "source": [
    "observation_date = dt.datetime(year=2021, month=2, day=27)\n",
    "\n",
    "# We need data from both the observation date and the previous day due to bleed over in the orderflow streaming pipeline\n",
    "FILE_NAMES = [\n",
    "\"data/btc-usd-coinbase-trades-{td:%Y}-{td:%m}-{td:%d}-consolidated.csv\".format(td=observation_date - dt.timedelta(days=1))\n",
    ",\"data/btc-usd-coinbase-trades-{td:%Y}-{td:%m}-{td:%d}-consolidated.csv\".format(td=observation_date)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "wanted-joseph",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = None\n",
    "for file in FILE_NAMES:\n",
    "    data = pd.read_csv(file)\n",
    "    if df is None:\n",
    "        df = data\n",
    "    else:\n",
    "        df = df.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "different-notification",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['timestamp'] >= dt.datetime(year=2021,month=2,day=27, tzinfo=dt.timezone.utc).timestamp()]\n",
    "df = df[df['timestamp'] < dt.datetime(year=2021,month=2,day=28, tzinfo=dt.timezone.utc).timestamp()]\n",
    "df['datetime'] = pd.to_datetime(df['timestamp'], unit='s', utc=True)\n",
    "df = df.sort_values(by=['externalTradeId']).set_index('datetime')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "guilty-peter",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Calculate Open, High, Low, Close\n",
    "resampler = df.resample('1t', closed='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "graduate-lighting",
   "metadata": {},
   "outputs": [],
   "source": [
    "ohlc = pd.DataFrame(list(\n",
    "    map(\n",
    "        lambda x: {'open': (g:=resampler.get_group(x).sort_values(by='externalTradeId')['price']).iloc[0]\n",
    "                   , 'high': g.max()\n",
    "                   , 'low': g.min()\n",
    "                   , 'close': g.iloc[-1]\n",
    "                   , 'datetime': x\n",
    "                  }\n",
    "        , resampler.groups\n",
    "    )\n",
    ")).set_index('datetime')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "uniform-charm",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plit\n",
    "import mplfinance as mpf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "executive-rotation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x575 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpf.plot(ohlc[:60], type='candle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "urban-industry",
   "metadata": {},
   "outputs": [],
   "source": [
    "ohlc['timestamp'] = (f := lambda series: [int(pydt.timestamp()) for pydt in series])(ohlc.index.to_pydatetime())\n",
    "ohlc = ohlc.set_index('timestamp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "automatic-collectible",
   "metadata": {},
   "outputs": [],
   "source": [
    "out = ohlc.reset_index().to_records(index=False).tobytes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "prime-crossing",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"data/{td:%y}-{td:%m}-{td:%d}-ohlc\".format(td=observation_date), 'wb') as out_file:\n",
    "    out_file.write(out)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}