Complete trading model created by Justin Gianfelice. Output and indicators received are based on stock ticker input and choice of trading strategy. Two versions are attached, one being the original Python code and the second being an optimized version in Pine script, ready to be implemented into a trading facilitator such as TradingView. The Python version emphasizes serves as a professional-grade research and analysis tool, whereas the Pine script simplifies these strategies into one clean, implementable strategy.
Core Capabilities
- Market Regime Detection
- Multi-Factor Models
- Machine Learning
- Risk Management
- Performance Analytics
- Walk-Forward Optimization
- Backtesting Engine
Strategy logic
- Modern factor-based approach
- Multi-strategy signals (MA, Mean Reversion, RSI, BB, Momentum)
- Regime-aware allocation with volatility targeting
- Portfolio construction and optimization
- Signal generation flow
Performance Analytics
- 50+ metrics
- 9 risk-adjusted ratios
- 7 drawdown metrics
- 4 distribution metrics
- Trading metrics
- Benchmarks for success
Assumptions & Constraints
- Data Assumptions
- Regime Stabiliy
- Correlation Assumptions
- Rebalancing Rationale
- Market Assumptions
Costs
- 4 cost components explained
- Total cost: ~36 bps per round-trip
- Annual impact calculations
Benchmarks
- SPY buy-and-hold
- Equal-weight portfolio
- Equal-weight factors
- Typical results vs benchmarks
- Multiple comparison dimensions
Risk controls
- 4-layer risk framework
- Volatility targeting
- Diversification rules
- Dynamic risk adjustment by regime
- Specific thresholds and actions
What I Learned:
- Rebalancing timing sensitivity (±2-3% annual)
- Sample size loss from multi-timeframe (30%+ loss)
- Regime persistence (25 days average)
- Factor crowding effects
- Transaction costs matter (2-5% drag)
- ML accuracy ceiling (55-60% is good)
- Drawdown duration hurts psychology