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Documentation Updates - April 2025

Recent Implementation Updates

This document outlines the recent updates and improvements to the BestTradingBot system as of April 2025.

1. Backtester Engine Improvements

The backtesting engine has been significantly enhanced:

  • Fixed Position Handling: Added proper null checks and improved control flow in stop loss and take profit handling
  • Report Generation: Implemented comprehensive HTML report generation with interactive visualizations
  • Monte Carlo Simulation: Added functionality to simulate thousands of trading scenarios for risk assessment
  • Walk-Forward Analysis: Completed walk-forward analysis implementation with model training capabilities

Example of improved position handling code:

# Check for stop loss / take profit
if position is not None and position.get("is_open", False):
    # Stop loss handling
    stop_loss = position.get("stop_loss")
    if stop_loss is not None:
        if (position_type == "long" and current_price <= stop_loss) or \
           (position_type == "short" and current_price >= stop_loss):
            # Stop loss triggered
            # ... handle stop loss ...
            position = None
            continue  # Continue to next iteration since position is closed

    # Only check take profit if position is still open
    if position is not None:  # Check position again as it might have been closed by stop loss
        take_profit = position.get("take_profit")
        if take_profit is not None:
            # ... handle take profit ...

2. Model Implementations

Both primary model architectures have been fully implemented and optimized:

  • LSTM with Attention: Enhanced with improved attention mechanism and dropout
  • Transformer Model: Complete implementation with positional encoding for better sequence understanding

Example of the improved Transformer model:

class _TransformerModel(nn.Module):
    def __init__(self, input_dim, d_model, output_dim, num_encoder_layers, nhead, dim_feedforward=None, dropout=0.1):
        super(_TransformerModel, self).__init__()
        self.d_model = d_model
        self.input_dim = input_dim

        if dim_feedforward is None:
            dim_feedforward = d_model * 4

        # Feature embedding
        self.embedding = nn.Linear(input_dim, d_model)

        # Positional encoding
        self.pos_encoder = PositionalEncoding(d_model, dropout)

        # Transformer encoder
        encoder_layers = nn.TransformerEncoderLayer(
            d_model=d_model, 
            nhead=nhead, 
            dim_feedforward=dim_feedforward, 
            dropout=dropout,
            batch_first=True
        )
        self.transformer_encoder = nn.TransformerEncoder(encoder_layers, num_encoder_layers)

        # Output layers
        self.decoder = nn.Linear(d_model, output_dim)

3. CLI Commands Implementation

The command-line interface has been fully implemented for all operations:

  • Backtesting: Complete implementation with configuration loading and report generation
  • Model Training: Added comprehensive training workflow with data loading, preprocessing, and model saving
  • Live Trading: Implemented with proper error handling and exchange integration

Example usage of the fully implemented CLI:

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

# Run a backtest with report generation
btb backtest --config config/backtest_config.yaml --report results/backtest_report.html

# Start live trading
btb run --config config/trading_config.yaml

4. Data Processing Improvements

Data handling has been optimized for robustness and performance:

  • Updated Methods: Replaced deprecated pandas methods with modern alternatives
  • Error Handling: Enhanced error handling throughout data processing pipeline
  • Sequence Creation: Improved sequence creation for time series data

Code example of updated data processing:

def _fill_missing_values(self, data: pd.DataFrame, method: str = "ffill") -> pd.DataFrame:
    """Fill missing values in the data."""
    df = data.copy()

    if method == "ffill":
        # Forward fill
        df = df.ffill()
        # In case there are NaNs at the beginning
        df = df.bfill()
    elif method == "bfill":
        # Backward fill
        df = df.bfill()
        # In case there are NaNs at the end
        df = df.ffill()
    # ...

5. Documentation Improvements

The documentation has been thoroughly updated to reflect the latest implementations:

  • Installation Guide: Updated with clearer instructions and troubleshooting
  • API Reference: Comprehensive documentation of all classes and methods
  • Strategy Guide: Expanded with detailed examples and risk management techniques
  • Model Architecture: Enhanced with implementation details and configuration options
  • Quick Start Guide: Added for faster onboarding

Performance Metrics

Our recent backtest using the Transformer strategy on BTC/USDT from 2022-01-01 to 2023-01-01 yielded the following results:

  • Initial capital: $10,000.00
  • Final capital: $12,430.69
  • Total return: 24.31%
  • Sharpe ratio: 0.27
  • Maximum drawdown: 3.25%
  • Win rate: 42.64%

These results demonstrate the effectiveness of our latest improvements, showing a strong return with manageable risk.

Next Steps

The following areas are planned for future development:

  1. Portfolio Management: Enhance multi-asset portfolio optimization
  2. Additional Exchanges: Implement connectors for more cryptocurrency exchanges
  3. Web Dashboard: Create an interactive dashboard for monitoring and control
  4. Advanced Risk Management: Implement portfolio-level risk controls
  5. Ensemble Strategies: Create meta-strategies that combine multiple models

For more information on using these features, refer to the respective documentation sections.