backtest_AUG_ETH.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 Month Test of Etherum Backtesting Using Augmento Sentiment Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Portfolio Value for ETH: $100000.00\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Final Portfolio Value for ETH: $109217.58\n",
      "ETH had a $9217.58 profit amount over its initial investment of $100000\n",
      "with a profit percentage of 0.09%.\n"
     ]
    }
   ],
   "source": [
    "import alpaca_trade_api as tradeapi\n",
    "from datetime import datetime, timedelta\n",
    "import time\n",
    "import backtrader as bt\n",
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "import warnings\n",
    "\n",
    "# Ignore all warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Initialize Alpaca API\n",
    "api = tradeapi.REST('PK5EPGQA17P4OY3NVWJ8', '5baoTG8Bey3Zdn66S3cEaiZ2TpdyUNLXV8r9wAaR', base_url='https://paper-api.alpaca.markets')\n",
    "\n",
    "# Define your prediction data or replace this with loading your actual prediction data\n",
    "predictions_df = pd.read_csv('Resources\\ETH_AUG_predictions.csv', parse_dates=True, infer_datetime_format=True)\n",
    "\n",
    "# Set the date range based on yf_data and predictions_df\n",
    "symbol = 'ETH-USD'\n",
    "start_date = '2023-11-22'\n",
    "end_date = '2023-12-22'\n",
    "\n",
    "# Filter predictions_df based on the date range\n",
    "predictions_df = predictions_df[(predictions_df['date'] >= start_date) & (predictions_df['date'] <= end_date)]\n",
    "\n",
    "# Convert Yahoo Finance data to Backtrader data feed\n",
    "class YahooFinanceData(bt.feeds.PandasData):\n",
    "    params = (\n",
    "        ('datetime', 'Date'),\n",
    "        ('open', 'Open'),\n",
    "        ('high', 'High'),\n",
    "        ('low', 'Low'),\n",
    "        ('close', 'Close'),\n",
    "        ('volume', 'Volume'),\n",
    "        ('openinterest', None),\n",
    "    )\n",
    "\n",
    "# Create a Backtrader Cerebro engine\n",
    "cerebro = bt.Cerebro()\n",
    "\n",
    "# Set our desired cash start to $100,000\n",
    "cerebro.broker.setcash(100000)\n",
    "print('Starting Portfolio Value for ETH: $%.2f' % cerebro.broker.getvalue())\n",
    "\n",
    "# Download Yahoo Finance data for ETH-USD with hourly frequency over the specified date range\n",
    "yf_data = yf.download(symbol, start=start_date, end=end_date, interval='1h')  # Adjust start and end dates\n",
    "yf_data['Date'] = yf_data.index  # Create a 'Date' column from the index\n",
    "yf_data.to_csv('Resources\\ETH_yf_data_1_month.csv', index='Date')\n",
    "data = YahooFinanceData(dataname=yf_data)\n",
    "\n",
    "# Add the data feed to the engine with a specific time range\n",
    "cerebro.adddata(data)\n",
    "\n",
    "# Define a Backtrader strategy\n",
    "class MyStrategy(bt.Strategy):\n",
    "    def __init__(self):\n",
    "        self.signal_index = 0  # Index to keep track of the current signal in predictions_df\n",
    "\n",
    "    def next(self):\n",
    "        # Get the current date and time from the data feed\n",
    "        current_datetime = self.datas[0].datetime.datetime()\n",
    "\n",
    "        # Check if there are more signals in predictions_df\n",
    "        if self.signal_index < len(predictions_df):\n",
    "            # Get the corresponding signal from predictions_df\n",
    "            signal_datetime = predictions_df.iloc[self.signal_index]['date']\n",
    "            signal = predictions_df.iloc[self.signal_index]['Predicted']\n",
    "\n",
    "            # Execute trades based on your signals\n",
    "            if signal == 1:\n",
    "                # Place a market order to buy\n",
    "                self.buy(data=self.datas[0], size=1)\n",
    "            elif signal == -1:\n",
    "                # Place a market order to sell\n",
    "                self.sell(data=self.datas[0], size=1)\n",
    "\n",
    "            # Move to the next signal in predictions_df\n",
    "            self.signal_index += 1\n",
    "\n",
    "# Add the strategy\n",
    "cerebro.addstrategy(MyStrategy)\n",
    "\n",
    "# Run the backtest\n",
    "cerebro.run()\n",
    "\n",
    "# Print the final results\n",
    "print('Final Portfolio Value for ETH: $%.2f' % cerebro.broker.getvalue())\n",
    "eth_sma_value = cerebro.broker.getvalue()\n",
    "initial_investment = 100000\n",
    "pnl_sma_eth = round(eth_sma_value - initial_investment, 2)\n",
    "print(f'ETH had a ${pnl_sma_eth} profit amount over its initial investment of ${initial_investment}')\n",
    "percentage = round(pnl_sma_eth/initial_investment,2)\n",
    "print(f'with a profit percentage of {percentage}%.')\n",
    "\n",
    "# Plot and save the figure\n",
    "eth_sma_plot = cerebro.plot(iplot=False, volume=True, savefig=True, figfilename='eth-backtrader-plot.png')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 Week of Etherum Backtesting Using Augmento Sentiment Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Portfolio Value for ETH: $100000.00\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Final Portfolio Value for ETH: $99616.15\n",
      "ETH had a $-383.85 profit amount over its initial investment of $100000\n",
      "with a profit percentage of -0.0%.\n"
     ]
    }
   ],
   "source": [
    "import alpaca_trade_api as tradeapi\n",
    "from datetime import datetime, timedelta\n",
    "import time\n",
    "import backtrader as bt\n",
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "import warnings\n",
    "\n",
    "# Ignore all warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Initialize Alpaca API\n",
    "api = tradeapi.REST('PKQD2ND8DVB2JMRYN7RU', 'P7G64gQ0PjnK4LZZ3V7R04qLdJj9WC9ajifMN52Z', base_url='https://paper-api.alpaca.markets')\n",
    "\n",
    "# Define your prediction data or replace this with loading your actual prediction data\n",
    "predictions_df = pd.read_csv('Resources\\ETH_AUG_predictions.csv', parse_dates=True, infer_datetime_format=True)\n",
    "\n",
    "# Set the date range based on yf_data and predictions_df\n",
    "symbol = 'ETH-USD'\n",
    "start_date = '2023-12-15'\n",
    "end_date = '2023-12-22'\n",
    "\n",
    "# Filter predictions_df based on the date range\n",
    "predictions_df = predictions_df[(predictions_df['date'] >= start_date) & (predictions_df['date'] <= end_date)]\n",
    "\n",
    "# Convert Yahoo Finance data to Backtrader data feed\n",
    "class YahooFinanceData(bt.feeds.PandasData):\n",
    "    params = (\n",
    "        ('datetime', 'Date'),\n",
    "        ('open', 'Open'),\n",
    "        ('high', 'High'),\n",
    "        ('low', 'Low'),\n",
    "        ('close', 'Close'),\n",
    "        ('volume', 'Volume'),\n",
    "        ('openinterest', None),\n",
    "    )\n",
    "\n",
    "# Create a Backtrader Cerebro engine\n",
    "cerebro = bt.Cerebro()\n",
    "\n",
    "# Set our desired cash start to $100,000\n",
    "cerebro.broker.setcash(100000)\n",
    "print('Starting Portfolio Value for ETH: $%.2f' % cerebro.broker.getvalue())\n",
    "\n",
    "# Download Yahoo Finance data for ETH-USD with hourly frequency over the specified date range\n",
    "yf_data = yf.download(symbol, start=start_date, end=end_date, interval='1h')  # Adjust start and end dates\n",
    "yf_data['Date'] = yf_data.index  # Create a 'Date' column from the index\n",
    "yf_data.to_csv('Resources\\ETH_yf_data_1_week.csv', index='Date')\n",
    "data = YahooFinanceData(dataname=yf_data)\n",
    "\n",
    "# Add the data feed to the engine with a specific time range\n",
    "cerebro.adddata(data)\n",
    "\n",
    "# Define a Backtrader strategy\n",
    "class MyStrategy(bt.Strategy):\n",
    "    def __init__(self):\n",
    "        self.signal_index = 0  # Index to keep track of the current signal in predictions_df\n",
    "\n",
    "    def next(self):\n",
    "        # Get the current date and time from the data feed\n",
    "        current_datetime = self.datas[0].datetime.datetime()\n",
    "\n",
    "        # Check if there are more signals in predictions_df\n",
    "        if self.signal_index < len(predictions_df):\n",
    "            # Get the corresponding signal from predictions_df\n",
    "            signal_datetime = predictions_df.iloc[self.signal_index]['date']\n",
    "            signal = predictions_df.iloc[self.signal_index]['Predicted']\n",
    "\n",
    "            # Execute trades based on your signals\n",
    "            if signal == 1:\n",
    "                # Place a market order to buy\n",
    "                self.buy(data=self.datas[0], size=1)\n",
    "            elif signal == -1:\n",
    "                # Place a market order to sell\n",
    "                self.sell(data=self.datas[0], size=1)\n",
    "\n",
    "            # Move to the next signal in predictions_df\n",
    "            self.signal_index += 1\n",
    "\n",
    "# Add the strategy\n",
    "cerebro.addstrategy(MyStrategy)\n",
    "\n",
    "# Run the backtest\n",
    "cerebro.run()\n",
    "\n",
    "# Print the final results\n",
    "print('Final Portfolio Value for ETH: $%.2f' % cerebro.broker.getvalue())\n",
    "eth_sma_value = cerebro.broker.getvalue()\n",
    "initial_investment = 100000\n",
    "pnl_sma_eth = round(eth_sma_value - initial_investment, 2)\n",
    "print(f'ETH had a ${pnl_sma_eth} profit amount over its initial investment of ${initial_investment}')\n",
    "percentage = round(pnl_sma_eth/initial_investment,2)\n",
    "print(f'with a profit percentage of {percentage}%.')\n",
    "\n",
    "# Plot and save the figure\n",
    "eth_sma_plot = cerebro.plot(iplot=False, volume=True, savefig=True, figfilename='eth-backtrader-plot.png')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 Days of Etherum Backtesting Using Augmento Sentiment Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Portfolio Value for ETH: $100000.00\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Final Portfolio Value for ETH: $100908.75\n",
      "ETH had a $908.75 profit amount over its initial investment of $100000\n",
      "with a profit percentage of 0.01%.\n"
     ]
    }
   ],
   "source": [
    "import alpaca_trade_api as tradeapi\n",
    "from datetime import datetime, timedelta\n",
    "import time\n",
    "import backtrader as bt\n",
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "import warnings\n",
    "\n",
    "# Ignore all warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Initialize Alpaca API\n",
    "api = tradeapi.REST('PKQD2ND8DVB2JMRYN7RU', 'P7G64gQ0PjnK4LZZ3V7R04qLdJj9WC9ajifMN52Z', base_url='https://paper-api.alpaca.markets')\n",
    "\n",
    "# Define your prediction data or replace this with loading your actual prediction data\n",
    "predictions_df = pd.read_csv('Resources\\ETH_AUG_predictions.csv', parse_dates=True, infer_datetime_format=True)\n",
    "\n",
    "# Set the date range based on yf_data and predictions_df\n",
    "symbol = 'ETH-USD'\n",
    "start_date = '2023-12-17'\n",
    "end_date = '2023-12-19'\n",
    "\n",
    "# Filter predictions_df based on the date range\n",
    "predictions_df = predictions_df[(predictions_df['date'] >= start_date) & (predictions_df['date'] <= end_date)]\n",
    "\n",
    "# Convert Yahoo Finance data to Backtrader data feed\n",
    "class YahooFinanceData(bt.feeds.PandasData):\n",
    "    params = (\n",
    "        ('datetime', 'Date'),\n",
    "        ('open', 'Open'),\n",
    "        ('high', 'High'),\n",
    "        ('low', 'Low'),\n",
    "        ('close', 'Close'),\n",
    "        ('volume', 'Volume'),\n",
    "        ('openinterest', None),\n",
    "    )\n",
    "\n",
    "# Create a Backtrader Cerebro engine\n",
    "cerebro = bt.Cerebro()\n",
    "\n",
    "# Set our desired cash start to $100,000\n",
    "cerebro.broker.setcash(100000)\n",
    "print('Starting Portfolio Value for ETH: $%.2f' % cerebro.broker.getvalue())\n",
    "\n",
    "# Download Yahoo Finance data for ETH-USD with hourly frequency over the specified date range\n",
    "yf_data = yf.download(symbol, start=start_date, end=end_date, interval='1h')  # Adjust start and end dates\n",
    "yf_data['Date'] = yf_data.index  # Create a 'Date' column from the index\n",
    "yf_data.to_csv('Resources\\ETH_yf_data_3_days.csv', index='Date')\n",
    "data = YahooFinanceData(dataname=yf_data)\n",
    "\n",
    "# Add the data feed to the engine with a specific time range\n",
    "cerebro.adddata(data)\n",
    "\n",
    "# Define a Backtrader strategy\n",
    "class MyStrategy(bt.Strategy):\n",
    "    def __init__(self):\n",
    "        self.signal_index = 0  # Index to keep track of the current signal in predictions_df\n",
    "\n",
    "    def next(self):\n",
    "        # Get the current date and time from the data feed\n",
    "        current_datetime = self.datas[0].datetime.datetime()\n",
    "\n",
    "        # Check if there are more signals in predictions_df\n",
    "        if self.signal_index < len(predictions_df):\n",
    "            # Get the corresponding signal from predictions_df\n",
    "            signal_datetime = predictions_df.iloc[self.signal_index]['date']\n",
    "            signal = predictions_df.iloc[self.signal_index]['Predicted']\n",
    "\n",
    "            # Execute trades based on your signals\n",
    "            if signal == 1:\n",
    "                # Place a market order to buy\n",
    "                self.buy(data=self.datas[0], size=1)\n",
    "            elif signal == -1:\n",
    "                # Place a market order to sell\n",
    "                self.sell(data=self.datas[0], size=1)\n",
    "\n",
    "            # Move to the next signal in predictions_df\n",
    "            self.signal_index += 1\n",
    "\n",
    "# Add the strategy\n",
    "cerebro.addstrategy(MyStrategy)\n",
    "\n",
    "# Run the backtest\n",
    "cerebro.run()\n",
    "\n",
    "# Print the final results\n",
    "print('Final Portfolio Value for ETH: $%.2f' % cerebro.broker.getvalue())\n",
    "eth_sma_value = cerebro.broker.getvalue()\n",
    "initial_investment = 100000\n",
    "pnl_sma_eth = round(eth_sma_value - initial_investment, 2)\n",
    "print(f'ETH had a ${pnl_sma_eth} profit amount over its initial investment of ${initial_investment}')\n",
    "percentage = round(pnl_sma_eth/initial_investment,2)\n",
    "print(f'with a profit percentage of {percentage}%.')\n",
    "\n",
    "# Plot and save the figure\n",
    "eth_sma_plot = cerebro.plot(iplot=False, volume=True, savefig=True, figfilename='eth-backtrader-plot.png')\n",
    "\n"
   ]
  }
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