Strategy Backtesting Bot

Overview

Strategy Backtesting Bot is a trading strategy testing framework that allows you to backtest various trading strategies using historical market data. It supports multiple asset types, configurable parameters, and Telegram bot integration for interactive backtesting.

Features

• Supports multiple strategies (SMA-RSI, MACD, Bollinger Bands, Stochastic, ATR Breakout)
• Multi-asset and single-asset trading
• Configurable portfolio settings (initial cash, fees, slippage, stop-loss, take-profit)
• Historical data fetching and storage in Parquet format
• Backtest result visualization and CSV export
• Telegram bot integration for remote testing and monitoring

Installation

1. Clone the repository:

   git clone https://github.com/velinamons/Strategy-back-testing.git
   cd Strategy-back-testing

2. Install the required dependencies:

   pip install -r requirements.txt

3. Create a .env file based on the .env.sample provided (get tg token using @BotFather):

   cp .env.sample .env

4. Modify config.json to specify symbols and timeframes to preload:

   {
       "symbols": {
           "BTCUSDT": {
               "30m": {"start_date": "2024-01-01", "end_date": "2025-01-01"},
               "1h": {"start_date": "2024-01-01", "end_date": "2025-01-01"}
           }
       },
       ...
   }

Running the Project

1. Start the backtesting system:

   python main.py

2. Wait for historical data to load.
3. Interact with the Telegram bot to trigger backtests and retrieve results using commands and by following instructions:

General Commands

/start

• Description: Starts the bot and shows the main menu.

/symbols

• Description: Lists available trading symbols.

/portfolio

• Description: Displays portfolio configuration.

/strategies

• Description: Shows available backtesting strategies.

Backtest Commands

/start_single_asset_backtest

• Description: Starts a single asset backtest by selecting a symbol.

/start_multi_asset_backtest

• Description: Starts a multi-asset backtest by selecting multiple symbols.

Strategies Implemented

1. SMA-RSI Strategy

• Combines Simple Moving Average (SMA) and Relative Strength Index (RSI) for trade signals.
• Parameters: short_window, long_window, rsi_window, rsi_buy, rsi_sell

2. MACD Strategy

• Uses Moving Average Convergence Divergence (MACD) crossovers to generate trade signals.
• Parameters: short_window, long_window, signal_window

3. Bollinger Bands Strategy

• Generates buy/sell signals based on price crossing Bollinger Bands.
• Parameters: window, num_std

4. Stochastic Strategy

• Uses the Stochastic Oscillator to identify overbought and oversold conditions.
• Parameters: k_window, d_window, stoch_buy, stoch_sell

5. ATR Breakout Strategy

• Identifies breakouts and trends using the Average True Range (ATR).
• Parameters: atr_window, atr_multiplier

Configuration

Modify config.json to:

• Define symbols and timeframes for data fetching.
• Adjust portfolio parameters (fees, slippage, stop-loss, take-profit).
• Enable or disable specific strategies.

Modify .env to:

• Set environment variables (log level, data storage paths, Telegram token, etc.).

Results

Backtest results are stored in the backtest_results/ directory:

• CSV files: Detailed trade history and performance metrics.
• PNG files: Visual representations of backtest performance.