Stock Trading Engine 📈

A professional-grade stock trading bot featuring a modern web dashboard for backtesting trading strategies with interactive visualizations and comprehensive performance analytics.

🎯 Two Interfaces Available

1. Modern Web Dashboard (Recommended) ⭐

Professional Plotly Dash interface with:

Interactive candlestick charts with zoom/pan
Real-time indicator overlays
Dynamic strategy parameter tuning
Comprehensive performance metrics
Trade log and portfolio visualization

2. Legacy Desktop GUI

Traditional Tkinter interface for basic backtesting

✨ Key Features

📊 Advanced Visualizations

Interactive Candlestick Charts: Fully interactive charts with zoom, pan, and hover capabilities
Technical Indicators: Real-time overlay of moving averages, Bollinger Bands, RSI, MACD, and more
Buy/Sell Markers: Clear visual indicators for all trade signals
Portfolio Tracking: Real-time portfolio value chart with performance tracking

🎛️ 9 Professional Trading Strategies

Moving Average Crossover - Classic trend-following strategy
RSI Strategy - Momentum-based using Relative Strength Index
MACD Strategy - Trend and momentum combined
Bollinger Bands - Volatility-based mean reversion
Stochastic Oscillator - Overbought/oversold momentum indicator
Breakout Strategy - Captures price breakouts with ATR filtering
Mean Reversion - Z-score based statistical arbitrage
EMA Crossover - Exponential moving average trend following
Volume Weighted - Volume-based strategy using On-Balance Volume

🎚️ Hyperparameter Tuning

Dynamic Parameter Controls: Each strategy displays its own customizable parameters
Real-time Adjustment: Change parameters and instantly re-run backtests
Optimization-Ready: Easy experimentation to find optimal settings

📈 Comprehensive Performance Metrics

Returns: Total return, final portfolio value
Risk Metrics: Sharpe ratio, maximum drawdown, volatility
Trade Statistics: Win rate, profit factor, average win/loss
Trade Log: Detailed record of all executed trades

💼 Backtesting Engine

Realistic Simulation: Includes commission costs and slippage
Position Tracking: Accurate portfolio and cash management
Multiple Timeframes: From intraday (5-min) to multi-year daily data

🛠️ Technologies Used

Core

Python 3.8+: Modern Python with type hints and async support
Pandas & NumPy: High-performance data manipulation and numerical computing

Web Dashboard

Plotly Dash: Interactive web framework for data applications
Dash Bootstrap Components: Professional UI components
Plotly: Industry-standard interactive graphing library

Data & Analysis

yfinance: Real-time and historical stock data from Yahoo Finance
TA-Lib (ta): Technical analysis library with 150+ indicators
mplfinance: Financial charting for legacy GUI

Legacy GUI

Tkinter: Cross-platform GUI framework
Matplotlib: Static chart generation

🚀 Quick Start

Installation

Clone the repository

git clone <your-repo-url>
cd trading_bot

Install dependencies

pip install -r requirements.txt

Running the Application

Option 1: Web Dashboard (Recommended)

python run_app.py

The dashboard will automatically open at http://127.0.0.1:8050/

Option 2: Legacy Desktop GUI

python main.py

📖 Usage Guide

Web Dashboard Quick Start

Enter Stock Symbol (e.g., AAPL, TSLA, GOOGL)
Select Timeframe (1d to max)
Choose Trading Strategy from dropdown
Adjust Strategy Parameters in the dynamic controls
Set Initial Capital and commission rate
Click "Run Backtest"
Analyze Results in the charts and metrics

Understanding Results

Performance Metrics

Returns

Total Return (%): Overall profit/loss
Final Portfolio Value: Ending value after all trades

Risk Metrics

Sharpe Ratio: Risk-adjusted returns (higher is better)
- < 1: Poor risk-adjusted performance
- 1-2: Good performance
- > 2: Excellent performance
Max Drawdown: Largest peak-to-trough decline (lower is better)
Volatility: Annual price volatility

Trade Statistics

Win Rate: Percentage of profitable trades
Profit Factor: Gross profit / gross loss (> 1 is profitable)
Average Win/Loss: Mean profit per winning/losing trade

Strategy Selection Tips

Trending Markets:

Moving Average Crossover
EMA Crossover
MACD Strategy

Range-Bound Markets:

Bollinger Bands
Mean Reversion
RSI Strategy

Volatile Markets:

Breakout Strategy
Bollinger Bands
Stochastic Oscillator

🎯 Strategy Optimization

Hyperparameter Tuning Workflow

Start with defaults - Run initial backtest
Analyze performance - Check metrics and trade quality
Adjust parameters - Modify one parameter at a time
Compare results - Did performance improve?
Iterate - Repeat until satisfied

Key Parameters by Strategy

Moving Average Crossover

Short Window (10-50): Fast MA period
Long Window (50-200): Slow MA period

RSI Strategy

Window (7-21): Calculation period
Oversold (20-35): Buy threshold
Overbought (65-80): Sell threshold

Bollinger Bands

Window (10-30): MA period
Std Dev (1.5-3): Band width multiplier

MACD

Fast (8-16): Fast EMA
Slow (20-30): Slow EMA
Signal (6-12): Signal line

See USAGE.md for detailed parameter guides.

📁 Project Structure

trading_bot/
├── app.py                  # Main web dashboard application
├── run_app.py             # Launch script for web dashboard
├── strategies.py          # All trading strategy implementations
├── backtester.py          # Backtesting engine with metrics
├── data_fetcher.py        # Yahoo Finance data fetching
├── gui.py                 # Legacy Tkinter GUI
├── main.py                # Legacy GUI launcher
├── trading_logic.py       # Legacy strategy implementations
├── requirements.txt       # Python dependencies
├── README.md              # This file
└── USAGE.md              # Detailed usage guide

📊 Strategy Details

Moving Average Crossover

Classic trend-following strategy that buys when short MA crosses above long MA, sells when it crosses below.

Best for: Trending markets, medium to long-term trades

RSI Strategy

Uses Relative Strength Index to identify overbought (>70) and oversold (<30) conditions.

Best for: Range-bound markets, mean reversion

MACD Strategy

Combines trend and momentum using MACD line crossing signal line.

Best for: Identifying trend changes, momentum shifts

Bollinger Bands

Buys when price touches lower band (oversold), sells at upper band (overbought).

Best for: Mean reversion, volatility trading

Stochastic Oscillator

Momentum indicator comparing closing price to price range over period.

Best for: Short-term reversals, overbought/oversold

Breakout Strategy

Captures price movements beyond recent highs/lows with ATR filtering.

Best for: Volatile markets, trend initiation

Mean Reversion

Statistical strategy using z-scores to identify price extremes.

Best for: Stable stocks, range-bound markets

EMA Crossover

Similar to MA crossover but more responsive to recent prices.

Best for: Faster trend identification, active trading

Volume Weighted

Uses On-Balance Volume to confirm price movements with volume.

Best for: High-volume stocks, confirmation signals

⚠️ Disclaimer

This software is for educational and research purposes only.

Past performance does not guarantee future results
Backtesting has limitations (survivorship bias, overfitting, etc.)
Not financial advice - consult professionals before trading
Trading involves risk of loss - never invest more than you can afford to lose
Authors are not responsible for financial losses from using this software

🔧 Troubleshooting

Common Issues

"Could not fetch data" error

Check internet connection
Verify stock symbol is valid
Try different timeframe

No trades in backtest

Parameters may be too conservative
Increase timeframe or adjust thresholds
Check if sufficient data exists for strategy window

Application won't start

# Reinstall dependencies
pip install --upgrade -r requirements.txt

# Check Python version
python --version  # Should be 3.8+

Port already in use

Change port in run_app.py: app.run_server(port=8051)
Or kill process using port 8050

🚀 Future Enhancements

Potential improvements:

📚 Additional Resources

Documentation: See USAGE.md for comprehensive guide
Strategy Theory: Research technical analysis and quantitative trading
Python Libraries:

📝 License

See LICENSE file for details.

🤝 Contributing

Contributions welcome! Areas for improvement:

Additional trading strategies
Enhanced performance metrics
UI/UX improvements
Documentation and examples
Bug fixes and optimizations

Built with ❤️ for traders and quants

Happy Trading! 📈🚀

Name		Name	Last commit message	Last commit date
Latest commit History 19 Commits
trading_bot_env		trading_bot_env
.gitattributes		.gitattributes
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
INSTALL.md		INSTALL.md
LICENSE		LICENSE
PROJECT_STRUCTURE.txt		PROJECT_STRUCTURE.txt
QUICKSTART.md		QUICKSTART.md
README.md		README.md
USAGE.md		USAGE.md
VISUAL_GUIDE.md		VISUAL_GUIDE.md
app.py		app.py
app_enhanced.py		app_enhanced.py
backtester.py		backtester.py
config_template.py		config_template.py
data_fetcher.py		data_fetcher.py
enhanced_charts.py		enhanced_charts.py
examples.py		examples.py
gui.py		gui.py
main.py		main.py
notifications.py		notifications.py
realtime_engine.py		realtime_engine.py
requirements.txt		requirements.txt
run_app.py		run_app.py
run_enhanced.py		run_enhanced.py
setup.sh		setup.sh
strategies.py		strategies.py
trading_logic.py		trading_logic.py
verify_installation.py		verify_installation.py

License

matthewd-so/trading_bot

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