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BestTradingBot - Quick Start Guide

This guide provides a step-by-step walkthrough to get you up and running with BestTradingBot quickly.

1. Installation

# Clone the repository (if you haven't already)
git clone https://github.com/gianlucamazza/btb-besttradingbot.git
cd btb-besttradingbot

# Create and activate a virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Install the package in development mode
pip install -e .

2. Data Preparation

Before developing trading strategies or running backtests, you need data:

# Create necessary directories
mkdir -p data/processed

# Run data preparation command
btb prepare-data --config config/backtest_config.yaml --output-dir data/processed

# This will create processed data files in data/processed/ directory
# You should see files like BTCUSDT_1h_processed.csv

3. Train a Model

# Train an LSTM model
btb train --data data/processed/BTCUSDT_1h_processed.csv --model lstm --config config/model_config.yaml


# This will save a model to the models/ directory

4. Run a Backtest

# Run a backtest using the configuration file
btb backtest --config config/backtest_config.yaml

# Generate a backtest report
btb backtest --config config/backtest_config.yaml --report results/backtest_report.html

5. Analyze Results in Jupyter Notebooks

# Start Jupyter
jupyter notebook

# Open the following notebooks:
# 5_demo_run.ipynb - For a quick demonstration
# 3_strategy_testing.ipynb - For detailed strategy analysis

6. Live Trading Setup

For live trading with Bybit, you need to:

  1. Create a .env file with your API credentials:
cp .env.example .env
# Edit .env and add your Bybit API key and secret

  1. Configure trading parameters in config/trading_config.yaml

  2. Start live trading:

btb run --config config/trading_config.yaml

7. Model and Strategy Development

To develop your own strategies:

  1. Explore existing strategies in btb/strategies/
  2. Create a new strategy file, e.g., btb/strategies/my_strategy.py
  3. Implement the BaseStrategy interface with your logic
  4. Register your strategy in btb/strategies/factory.py
  5. Test your strategy through backtest

To develop your own models:

  1. Explore existing models in btb/models/
  2. Create a new model file, e.g., btb/models/my_model.py
  3. Implement the BaseModel interface with your architecture
  4. Train your model using the training utilities

8. Understanding Results

When running a backtest, pay attention to:

  • Total Return: Overall percentage gain/loss
  • Sharpe Ratio: Risk-adjusted return (higher is better, >1 is good)
  • Maximum Drawdown: Largest percentage drop from peak to trough
  • Win Rate: Percentage of profitable trades

The HTML report will provide visualizations of: - Equity curve - Drawdown periods - Trade distributions - Monthly returns

9. Command Line Interface (CLI) Reference

Backtest: 

btb backtest --config CONFIG_FILE [--output OUTPUT_FILE] [--report REPORT_FILE] [--plot] [--verbose]

Train: 

btb train --data DATA_FILE --model MODEL_TYPE [--config CONFIG_FILE] [--output OUTPUT_DIR] [--epochs EPOCHS]

Train All Models: 

btb train-all --data-dir DATA_DIR --model MODEL_TYPE [--config CONFIG_FILE] [--output OUTPUT_DIR] [--symbols SYMBOLS] [--timeframes TIMEFRAMES] [--parallel]

Data Preparation: 

btb prepare-data --config CONFIG_FILE [--output-dir OUTPUT_DIR] [--verbose]

Live Trading: 

btb run --config CONFIG_FILE [--verbose]

Cleanup: 

btb cleanup [--logs] [--cache] [--pycache] [--verbose]

10. Common Issues and Solutions

  • Exchange API issues: Ensure your API keys have the correct permissions
  • Model training errors: Check data dimensions and ensure sequence lengths match
  • Backtesting performance: Optimize strategy parameters in config files
  • Memory usage concerns: Reduce sequence lengths or batch sizes for larger datasets

Next Steps

  • Explore walk-forward analysis for robust strategy validation
  • Try different combinations of technical indicators
  • Experiment with ensemble strategies combining multiple models
  • Check documentation for advanced usage scenarios

For full documentation, please refer to the main Home page and other documentation sections.