EURUSD-bot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports and Data Download"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<pandas.core.indexing._iLocIndexer at 0x22747142b60>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import yfinance as yf\n",
    "import pandas as pd\n",
    "\n",
    "# Yahoo Finance APi typically only provides 15-minute interval data for the last 60 days\n",
    "dataF = yf.download(\"EURUSD=X\", start=\"2023-12-07\", end=\"2024-02-03\", interval='15m')\n",
    "dataF.iloc[-1:,:]\n",
    "dataF.Open.iloc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining Signal Function\n",
    "\n",
    "> Do the last few candles have engulfing patterns?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def signal_generator(df):\n",
    "    open = df.Open.iloc[-1]\n",
    "    close = df.Close.iloc[-1]\n",
    "    previous_open = df.Open.iloc[-2]\n",
    "    previous_close = df.Close.iloc[-2]\n",
    "\n",
    "    # Bearish Pattern\n",
    "    if (open>close and previous_open<previous_close and close<previous_open and open>=previous_close):\n",
    "        return 1    # Selling signal\n",
    "    \n",
    "    # Bullish Pattern\n",
    "    elif (open<close and previous_open>previous_close and close>previous_open and open<=previous_close):\n",
    "        return 2    # Buying signal\n",
    "    \n",
    "    # No Clear Pattern\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "signal = []\n",
    "signal.append(0)\n",
    "for i in range(1, len(dataF)):\n",
    "    df = dataF[i-1:i+1]\n",
    "    signal.append(signal_generator(df))\n",
    "\n",
    "#signal_generator(data)\n",
    "dataF[\"signal\"] = signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "signal\n",
       "0    3549\n",
       "2     158\n",
       "1     148\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Market Print Statements\n",
    "\n",
    "dataF.signal.value_counts()\n",
    "#dataF.iloc[:, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Market Connection and Trade Execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from apscheduler.schedulers.blocking import BlockingScheduler\n",
    "from oandapyV20 import API\n",
    "import oandapyV20.endpoints.orders as orders\n",
    "from oandapyV20.contrib.requests import MarketOrderRequest\n",
    "from oanda_candles import Pair, Gran, CandleCollector, CandleClient\n",
    "from oandapyV20.contrib.requests import TakeProfitDetails, StopLossDetails"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'access_token' from 'config' (c:\\Users\\Arjun\\miniconda3\\lib\\site-packages\\config\\__init__.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[8], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mconfig\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m access_token, accountID\n\u001b[0;32m      2\u001b[0m \u001b[38;5;66;03m#Get the latest candlestick data for EUR/USD pair\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_candles\u001b[39m(n):\n",
      "\u001b[1;31mImportError\u001b[0m: cannot import name 'access_token' from 'config' (c:\\Users\\Arjun\\miniconda3\\lib\\site-packages\\config\\__init__.py)"
     ]
    }
   ],
   "source": [
    "from config import access_token, accountID\n",
    "#Get the latest candlestick data for EUR/USD pair\n",
    "def get_candles(n):\n",
    "    client = CandleClient(access_token, real=False)\n",
    "    collector = client.get_collector(Pair.EUR_USD, Gran.M15)\n",
    "    candles = collector.grab(n)\n",
    "    return candles\n",
    "\n",
    "candles = get_candles(3)    # Fetch the latest 3 candles\n",
    "for candle in candles:\n",
    "    print(float(str(candle.bid.o))>1)   # Print whether the opening price of the candle is greater than 1\n",
    "\n",
    "def trading_job():\n",
    "    # Fetching latest price data\n",
    "    candles = get_candles(3)\n",
    "    dfstream = pd.DataFrame(columns=['Open', 'Close', 'High', \"Low\"])\n",
    "\n",
    "    # Parsing candle data for trading signals\n",
    "    i=0\n",
    "    for candle in candles:\n",
    "        dfstream.loc[i, ['Open']] = float(str(candle.bid.o))\n",
    "        dfstream.loc[i, ['Close']] = float(str(candle.bid.c))\n",
    "        dfstream.loc[i, ['High']] = float(str(candle.bid.h))\n",
    "        dfstream.loc[i, ['Low']] = float(str(candle.bid.l))\n",
    "        i=i+1\n",
    "\n",
    "    dfstream['Open'] = dfstream['Open'].astype(float)\n",
    "    dfstream['Close'] = dfstream['Close'].astype(float)\n",
    "    dfstream['High'] = dfstream['High'].astype(float)\n",
    "    dfstream['Low'] = dfstream['Low'].astype(float)\n",
    "\n",
    "    # Generating a trading signal\n",
    "    signal = signal_generator(dfstream.iloc[:-1, :])\n",
    "\n",
    "    # EXECUTING ORDERS\n",
    "    client = API(access_token)\n",
    "\n",
    "    SLTPRatio = 2.\n",
    "    previous_candleR = abs(dfstream['Open'].iloc[-2]-dfstream['Close'].iloc[-2])\n",
    "\n",
    "    SLBuy = float(str(candle.bid.o))-previous_candleR\n",
    "    SLSell = float(str(candle.bid.o))+previous_candleR\n",
    "\n",
    "    TPBuy = float(str(candle.bid.o))+previous_candleR*SLTPRatio\n",
    "    TPSell = float(str(candle.bid.o))-previous_candleR*SLTPRatio\n",
    "\n",
    "    print(dfstream.iloc[:-1, :])\n",
    "    print(TPBuy, \" \", SLBuy, \" \", TPSell, \" \", SLSell)\n",
    "\n",
    "    # Sell\n",
    "    if signal == 1:\n",
    "        mo = MarketOrderRequest(instrument=\"EUR_USD\", units=-10000, takeProfitOnFill=TakeProfitDetails(price=TPSell).data, stopLossOnFill=StopLossDetails(price=SLSell).data)\n",
    "        r = orders.OrderCreate(accountID, data=mo.data)\n",
    "        rv = client.request(r)\n",
    "        print(rv)\n",
    "\n",
    "    # Buy\n",
    "    elif signal == 2:\n",
    "        mo = MarketOrderRequest(instrument=\"EUR_USD\", units=10000, takeProfitOnFill=TakeProfitDetails(price=TPBuy).data, stopLossOnFill=StopLossDetails(price=SLBuy).data)\n",
    "        r = orders.OrderCreate(accountID, data=mo.data)\n",
    "        rv = client.request(r)\n",
    "        print(rv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Executing Trades Automatically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MANUAL\n",
    "#trading_job()\n",
    "\n",
    "# SCHEDULED\n",
    "scheduler = BlockingScheduler()\n",
    "scheduler.add_job(trading_job, 'cron', day_of_week='mon-sun', hour='00-23', minute='0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59', second='0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59', start_date='2023-09-26 01:00:00')\n",
    "scheduler.start()"
   ]
  }
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