Trading Simulator and Strategy Backtesting Framework

This project is a comprehensive tool for traders and developers to backtest trading strategies, visualize their historical performance, and practice live trading simulation. With features for plotting buy/sell points on candlestick charts and tracking real-time positions and PnL, it bridges the gap between theoretical strategy testing and practical trading practice.

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Features

1. Backtesting Framework

• Evaluate the historical performance of multiple trading strategies.
• Support for popular technical indicators such as RSI, SMA, Bollinger Bands, MACD, etc.
• Includes pre-built strategies such as:
  • Bollinger Bands (BB) Strategy
  • Intraday Gap Strategy
  • MACD Crossover Strategy
  • Mean Reversion Strategy
  • RSI Strategy
  • SMA Strategy

2. Interactive Candlestick Visualization

• Visualize candlestick charts with buy/sell points marked based on strategy logic.
• Retain zoom levels, custom drawings, and annotations when navigating charts.
• Switch timeframes interactively while retaining chart states.

3. Live Trading Simulation Tool

• Simulates a real-time trading environment by advancing candles one at a time upon clicking the "Next" button.
• Allows users to create buy or sell positions based on current chart data.
• Tracks PnL throughout the simulation, helping users refine their trading decisions.

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Project Structure

📦trading-simulator
 ┣ 📂Charts              # Chart visualization module
 ┃ ┗ 📜candle_chart.py   # Logic for plotting candlestick charts
 ┣ 📂data                # Folder for storing historical trading data
 ┃ ┗ 📜NIFTY50-Minute_data.csv
 ┣ 📂Evaluation          # Strategy performance evaluation module
 ┃ ┗ 📜strategy_performance.py
 ┣ 📂Practice            # Live trading simulation module
 ┃ ┣ 📜performance.py    # PnL tracking logic
 ┃ ┣ 📜random.py         # Randomized data handling
 ┃ ┗ 📜practice_plotter.py # Live trading simulation UI
 ┣ 📂preprocessing       # Data preprocessing module
 ┃ ┣ 📜cleaning.py       # Data cleaning logic
 ┃ ┣ 📜data_ingest.py    # Data ingestion logic
 ┃ ┣ 📜indicator.py      # Indicator calculation logic
 ┃ ┗ 📜timeframe.py      # Timeframe conversion logic
 ┣ 📂Strategy            # Trading strategies implementation
 ┃ ┣ 📜BB_strategy.py    # Bollinger Bands strategy
 ┃ ┣ 📜Intraday_gap_strategy.py # Intraday gap strategy
 ┃ ┣ 📜MACD_crossover_strategy.py # MACD crossover strategy
 ┃ ┣ 📜Mean_reversion_strategy.py # Mean reversion strategy
 ┃ ┣ 📜multi_indicator_strategy.py # Multi-indicator strategy
 ┃ ┣ 📜RSI_strategy.py   # RSI strategy
 ┃ ┗ 📜SMA_strategy.py   # Simple Moving Average strategy
 ┣ 📜.gitignore          # Git ignore file
 ┣ 📜backtest.py         # Main backtesting script
 ┣ 📜README.md           # Project documentation
 ┗ 📜requirements.txt    # Python dependencies

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Installation

1. Clone the repository:

   git clone https://github.com/SandyDRawat/Algo_trader
   cd Algo_trader

2. Create and activate a virtual environment:

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install required packages:

   pip install -r requirements.txt

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Usage

Backtesting

To backtest a trading strategy:

python backtest.py

This script:

• Loads historical data.
• Cleans and preprocesses it.
• Applies selected indicators and strategies.
• Evaluates the strategy’s performance.

Interactive Practice Tool

To run the live trading simulation:

python practice_plotter.py

This script:

• Displays an interactive candlestick chart.
• Allows users to simulate trades based on candle-by-candle progression.
• Tracks real-time positions and PnL.

Other Functionalities

• Data Cleaning: Use cleaning.py to preprocess and clean raw data.
• Indicator Calculation: Add technical indicators using indicator.py.
• Timeframe Conversion: Use timeframe.py to resample data to desired timeframes.

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Requirements

• Python 3.8 or higher
• Dash
• Plotly
• Pandas
• Numpy

Install all dependencies with:

pip install -r requirements.txt

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Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.

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Happy trading and learning!