📈 Algorithmic Trading Framework

Professional backtesting and strategy development framework implementing the Research-Backtest-Implement methodology

🎯 Overview

A production-grade framework for developing, testing, and deploying quantitative trading strategies. Built on the RBI (Research-Backtest-Implement) methodology, this system ensures rigorous validation before any strategy goes live.

Key Capabilities:

Historical backtesting with realistic market conditions
Multi-strategy portfolio optimization
Comprehensive risk management
Real-time performance monitoring

🏗️ The RBI Methodology

📚 Research Phase

Evidence-Based Development

Academic Sources → Market Analysis → Strategy Hypothesis

Review academic papers (Google Scholar, SSRN)
Analyze market microstructure
Identify statistical edges
Document trading rules clearly

📊 Backtest Phase

Rigorous Historical Validation

Clean Data → Strategy Testing → Walk-Forward Optimization → Out-of-Sample Validation

Multi-timeframe testing (1min to 1day)
Transaction cost modeling (commissions, slippage)
Realistic order execution simulation
Statistical significance testing

💻 Implement Phase

Controlled Deployment

Paper Trading → Small Live Position → Gradual Scaling → Full Deployment

Start with $10 positions
Monitor for 2 weeks minimum
Scale only after consistent performance
Continuous risk monitoring

✨ Key Features

Backtesting Engine

High-fidelity simulation: Models real market conditions including spreads, slippage, and partial fills
Multi-asset support: Stocks, crypto, futures
Multiple timeframes: From 1-minute to daily data
Performance metrics: Sharpe ratio, max drawdown, win rate, profit factor

Risk Management

Position sizing: Kelly criterion, fixed fractional, volatility-based
Stop losses: Time-based, price-based, volatility-adjusted
Portfolio limits: Maximum exposure per asset, sector, total
Drawdown protection: Automatic position reduction on losses

Strategy Library

Example strategies included (for educational purposes):

Mean Reversion: RSI-based oversold/overbought
Trend Following: Moving average crossovers
Momentum: Breakout with volume confirmation
Statistical Arbitrage: Pairs trading framework

Data Pipeline

Multi-source support: Yahoo Finance, Alpha Vantage, custom APIs
Data validation: Automatic cleaning and outlier detection
Caching system: Fast backtests with local data storage
Real-time feeds: WebSocket integration for live trading

📁 Project Structure

Algorithmic-Trading-Framework/
│
├── strategies/
│   ├── base_strategy.py        # Abstract strategy class
│   ├── mean_reversion.py       # RSI-based mean reversion
│   ├── trend_following.py      # MA crossover strategy
│   └── momentum_breakout.py    # Volume-confirmed breakouts
│
├── backtesting/
│   ├── engine.py               # Core backtesting engine
│   ├── broker.py               # Order execution simulation
│   ├── metrics.py              # Performance calculations
│   └── optimizer.py            # Parameter optimization
│
├── risk/
│   ├── position_sizer.py       # Position sizing algorithms
│   ├── risk_manager.py         # Real-time risk monitoring
│   └── portfolio_manager.py    # Multi-strategy allocation
│
├── data/
│   ├── data_fetcher.py         # Market data retrieval
│   ├── data_cleaner.py         # Data preprocessing
│   └── data_cache.py           # Local data storage
│
├── utils/
│   ├── indicators.py           # Technical indicators
│   ├── visualizer.py           # Performance charts
│   └── logger.py               # Trade logging
│
├── examples/
│   ├── simple_backtest.py      # Basic strategy example
│   ├── portfolio_backtest.py   # Multi-strategy example
│   └── optimization.py         # Parameter tuning example
│
├── requirements.txt
├── .env.example
└── README.md

🚀 Quick Start

Installation

# Clone the repository
git clone https://github.com/callowo-stack/Algorithmic-Trading-Framework.git
cd Algorithmic-Trading-Framework

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

# Install dependencies
pip install -r requirements.txt

Simple Backtest Example

from strategies import MeanReversionStrategy
from backtesting import BacktestEngine
from data import DataFetcher

# Fetch historical data
data = DataFetcher.get_data(
    ticker="SPY",
    start_date="2020-01-01",
    end_date="2023-12-31",
    timeframe="1d"
)

# Initialize strategy
strategy = MeanReversionStrategy(
    rsi_period=14,
    rsi_oversold=30,
    rsi_overbought=70
)

# Run backtest
engine = BacktestEngine(
    initial_capital=100000,
    commission=0.001,  # 0.1% per trade
    slippage=0.0005    # 0.05% slippage
)

results = engine.run(strategy, data)

# Display results
print(f"Total Return: {results['total_return']:.2%}")
print(f"Sharpe Ratio: {results['sharpe_ratio']:.2f}")
print(f"Max Drawdown: {results['max_drawdown']:.2%}")
print(f"Win Rate: {results['win_rate']:.2%}")

# Visualize performance
results.plot()

📊 Strategy Development Workflow

1. Research & Hypothesis

"""
Strategy Hypothesis: RSI Mean Reversion
- Entry: RSI < 30 (oversold)
- Exit: RSI > 70 (overbought)
- Expected Edge: Mean reversion in ranging markets
- Risk: Poor performance in strong trends
"""

2. Implement Strategy

class MyStrategy(BaseStrategy):
    def on_data(self, data):
        rsi = self.calculate_rsi(data, period=14)
        
        if rsi < 30 and not self.position:
            self.buy(size=self.calculate_position_size())
        
        elif rsi > 70 and self.position:
            self.sell()

3. Backtest

# Test on multiple timeframes and assets
results = []
for ticker in ["SPY", "QQQ", "IWM"]:
    result = engine.run(strategy, ticker)
    results.append(result)

# Walk-forward optimization
optimized = engine.optimize(
    strategy,
    param_ranges={'rsi_period': range(10, 20)},
    walk_forward_windows=12
)

4. Analyze Results

# Check statistical significance
if results['trades'] > 100 and results['sharpe'] > 1.5:
    print("Strategy shows promise - proceed to paper trading")
else:
    print("Strategy needs refinement")

🎯 Performance Metrics

The framework calculates comprehensive metrics:

Returns

Total Return: Cumulative strategy performance
Annualized Return: CAGR over backtest period
Monthly Returns: Period-by-period breakdown

Risk

Sharpe Ratio: Risk-adjusted returns
Sortino Ratio: Downside risk-adjusted returns
Maximum Drawdown: Largest peak-to-trough decline
Volatility: Standard deviation of returns

Trade Statistics

Win Rate: Percentage of profitable trades
Profit Factor: Gross profit / Gross loss
Average Win/Loss: Mean P&L per trade type
Expectancy: Expected value per trade

🔑 Key Technologies

Python 3.11+: Core language
Pandas: Data manipulation
NumPy: Numerical computations
TA-Lib: Technical indicators (optional)
Matplotlib/Plotly: Visualization
Backtrader: Backtesting framework (alternative engine)

📈 Example Results

Strategy: RSI Mean Reversion (SPY, 2020-2023)
==================================================
Initial Capital:     $100,000
Final Portfolio:     $145,230
Total Return:        45.23%
Annualized Return:   12.67%
Sharpe Ratio:        1.84
Max Drawdown:        -12.4%
Win Rate:            62.3%
Total Trades:        247
Profit Factor:       1.92

⚠️ Risk Disclaimer

For Educational Purposes Only

Past performance does not guarantee future results
All strategies carry risk of substantial losses
This software is provided for learning and research
Not financial advice - consult licensed professionals
The author assumes no liability for trading losses

🎓 Educational Value

This project demonstrates:

Quantitative Finance: Strategy development, backtesting, risk metrics
Software Engineering: Clean architecture, design patterns, testing
Data Engineering: Pipeline design, caching, validation
Statistical Analysis: Hypothesis testing, optimization
Production Systems: Logging, monitoring, error handling

🛠️ Advanced Features

Walk-Forward Optimization

# Optimize on rolling windows
results = engine.walk_forward_optimize(
    strategy=MyStrategy,
    data=data,
    train_period=252,  # 1 year training
    test_period=63,    # 3 months testing
    step=21            # Roll forward monthly
)

Monte Carlo Simulation

# Assess strategy robustness
mc_results = engine.monte_carlo(
    strategy=MyStrategy,
    simulations=1000,
    randomize='returns'  # or 'trades', 'sequence'
)
print(f"95% Confidence Interval: {mc_results['ci_95']}")

Multi-Strategy Portfolio

# Combine multiple strategies
portfolio = Portfolio([
    (MeanReversionStrategy(), 0.4),
    (TrendFollowingStrategy(), 0.3),
    (MomentumStrategy(), 0.3)
])

results = engine.run(portfolio, data)

🤝 Contributing

Improvements welcome:

Additional strategy examples
More data source integrations
Enhanced visualization tools
Machine learning strategy templates
Real-time trading connectors

📄 License

MIT License - See LICENSE file

📬 Contact

Calvin McCormick
GitHub: @callowo-stack Linkedin: [https://www.linkedin.com/in/calvinm9/] Portfolio: [www.calvinmccormick.com]

Built for systematic strategy development and rigorous backtesting

Name		Name	Last commit message	Last commit date
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examples		examples
strategies		strategies
README.md		README.md
requirements.txt		requirements.txt

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