VIA and SYS Historical Data from Binance API .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from binance.client import Client\n",
    "import time\n",
    "import pandas as pd\n",
    "import datetime\n",
    "\n",
    "#instantiate with API Keys\n",
    "client = Client(\"\", \"\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Get historical candlesticks data\n",
    "def get_historical_klines(symbol, interval, startTime):\n",
    "    klines = client.get_historical_klines(symbol, Client.KLINE_INTERVAL_1DAY, startTime)\n",
    "    candlesticks = []\n",
    "    for tick in klines:\n",
    "        dict = {}\n",
    "        dict['Open Time'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(tick[0]/1000))\n",
    "        dict['Open'] = tick[1]\n",
    "        dict['High'] = tick[2]\n",
    "        dict['Low'] = tick[3]\n",
    "        dict['Close'] = tick[4]\n",
    "        dict['Volume'] = tick[5]\n",
    "        dict['Close time'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(tick[6]/1000))\n",
    "        dict['Quote asset volume'] = tick[7]\n",
    "        dict['Number of trades'] = tick[8]\n",
    "        dict['Taker buy base asset volume'] = tick[9]\n",
    "        dict['Taker buy quote asset volume'] = tick[10]\n",
    "        dict['Ignore'] = tick[11]\n",
    "        candlesticks.append(dict)\n",
    "    klines_df = pd.DataFrame(candlesticks)\n",
    "    klines_df['Open Time'] = pd.to_datetime(klines_df['Open Time'])\n",
    "    klines_df['Close time'] = pd.to_datetime(klines_df['Close time'])\n",
    "    klines_df.index = klines_df['Open Time']\n",
    "    klines_df = klines_df[['Close', 'Open', 'Low', 'High', 'Volume', 'Number of trades']]\n",
    "    klines_df['Close'] = pd.to_numeric(klines_df['Close'])\n",
    "    klines_df['Open'] = pd.to_numeric(klines_df['Open'])\n",
    "    klines_df['High'] = pd.to_numeric(klines_df['High'])\n",
    "    klines_df['Volume'] = pd.to_numeric(klines_df['Volume'])\n",
    "    klines_df['Number of trades'] = pd.to_numeric(klines_df['Number of trades'])\n",
    "    return klines_df\n",
    "\n",
    "\n",
    "sys_klines = get_historical_klines(\"SYSBTC\", Client.KLINE_INTERVAL_1DAY, '60 days ago UTC')\n",
    "via_klines = get_historical_klines(\"VIABTC\", Client.KLINE_INTERVAL_1DAY, '180 days ago UTC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_historical_aggregate_trades(startTime, symbol):\n",
    "    trades = []\n",
    "    for num in range(1,50):\n",
    "        agg_trades = client.get_aggregate_trades(symbol=symbol, startTime=str(startTime), endTime=str(startTime+3600000))\n",
    "        for trade in agg_trades:\n",
    "            trades.append(trade)\n",
    "        startTime = int(agg_trades[-1]['T'])\n",
    "    agg_trades_df = pd.DataFrame(trades)\n",
    "    agg_trades_df['p'] = pd.to_numeric(agg_trades_df['p'])\n",
    "    agg_trades_df['q'] = pd.to_numeric(agg_trades_df['q'])\n",
    "    agg_trades_df['T'] = pd.to_datetime(agg_trades_df['T'],  unit='ms')\n",
    "    pd.options.display.float_format = '{:.4f}'.format\n",
    "    agg_trades_df['volume'] = agg_trades_df['p'] * agg_trades_df['q'] * 6500 \n",
    "    agg_trades_df.columns = [['Was the trade the best price match?', 'Timestamp', 'Aggregate TradeID', 'First TradeID', 'Last Trade ID', 'Was the buyer the maker?', 'Price', 'Quantity', 'Volume']]\n",
    "    return agg_trades_df\n",
    "    \n",
    "\n",
    "\n",
    "via_historical_agg_trades = get_historical_aggregate_trades(1520294400000, 'VIABTC')\n",
    "sys_historical_agg_trades = get_historical_aggregate_trades(1530650644504, 'SYSBTC')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_historical_individual_trades(startingId, symbol):\n",
    "    all_trades = []\n",
    "    for num in range(1,20):\n",
    "        new_trades = client.get_historical_trades(symbol=symbol, fromId=startingId)\n",
    "        for trade in new_trades:\n",
    "            all_trades.append(trade)\n",
    "        startingId = new_trades[-1]['id']\n",
    "    historical_trades_df = pd.DataFrame(all_trades)\n",
    "    historical_trades_df['time'] = pd.to_datetime(historical_trades_df['time'], unit='ms')\n",
    "    return historical_trades_df\n",
    "\n",
    "\n",
    "historical_individual_trades_for_sys = get_historical_individual_trades('901620', 'SYSBTC')\n",
    "historical_individual_trades_for_via = get_historical_individual_trades('165944', 'VIABTC')\n",
    "\n",
    "historical_individual_trades_for_via.to_csv('/users/anthony/desktop/via-btc/individual-trades.csv')"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}