Systematic Alpha Research Framework

A rigorous pipeline for the engineering, calibration, and stress-testing of systematic trading strategies.

Github Repository

Overview

This section documents the quantitative methodologies used to develop tradeable systematic strategies: constructing robust execution systems that can capture theoretical edges while withstanding market friction and regime shifts.

The research framework prioritizes generalization over raw performance. It employs a Global Optimization protocol designed to identify a single, stable parameter configuration that functions effectively across diverse historical periods, rather than overfitting parameters to specific market cycles.

Infrastructure & Methodology

The reliability of these strategies relies on the integrity of the underlying validation engine.

1. Walk-Forward Methodology

A detailed breakdown of the validation protocol.

Global Robustness: Searching for parameter sets that survive diverse market conditions.
Degradation Analysis: A quantitative check to reject strategies where \(\text{Sharpe}_{IS} \gg \text{Sharpe}_{OOS}\).

2. Parameter Stability Analysis

Ensuring the strategy is not poised on a “knife-edge” of optimization.

Sensitivity Mapping: Verifying that small changes in inputs do not cause catastrophic failures in outputs.
Convexity Check: Prioritizing broad, flat regions of the solution space over narrow, high peaks.

3. Backtest Engine Architecture

Technical documentation of the Python-based simulation engine.

Hybrid Architecture: Vectorized signal calculation for throughput (\(O(1)\)) + Event-driven execution for path-dependent accuracy.
Friction Modeling: Implementation of slippage, commission, and stale-data pruning.

Strategy Universe

The current portfolio of strategies, categorized by logic class and their validation status in the Walk-Forward framework.

Adaptive & Momentum

Strategy ID	Logic Class	Validation Status	Risk Profile	Script
SMI Momentum	Mean Reversion / Oscillator	Very Robust (-19.4% Degradation)	Moderate (High Win Rate)	View Script
AMA-KAMA	Dual Adaptive Trend / Mean Reversion	Robust (35.66% Degradation)	Conservative	View Script

Volatility Breakout

Strategy ID	Logic Class	Validation Status	Risk Profile	Script
MABW	Volatility Expansion / Breakout	Rejected (100% Degradation)	Aggressive / Unstable	View Script

Strategy Research Pipeline

The development lifecycle adheres to an institutional-grade workflow, strictly separating the Calibration Phase (Signal Design & Optimization) from the Validation Phase (Walk-Forward & Stress Testing).

graph TD
    %% Color Definitions
    classDef ideation fill:#e3f2fd,stroke:#1565c0,stroke-width:2px
    classDef calibration fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px
    classDef validation fill:#e8f5e8,stroke:#2e7d32,stroke-width:2px
    classDef decision fill:#fff3e0,stroke:#ef6c00,stroke-width:2px
    classDef outcome fill:#ffebee,stroke:#c62828,stroke-width:2px
    classDef success fill:#e8f5e8,stroke:#2e7d32,stroke-width:3px
    
    %% Process Flow
    subgraph "Phase I: Ideation & Design"
        A[Hypothesis Formulation] --> B[Signal Specification<br/>• Define metrics<br/>• Set thresholds]
        B --> C[Prototype Implementation<br/>• Vectorized backtest<br/>• Initial validation]
    end
    
    subgraph "Phase II: Calibration (In-Sample)"
        C --> D{Global Parameter Search<br/>Optuna TPE Sampler}
        D --> E[Cross-Window Optimization<br/>• Rolling windows<br/>• Stability scoring]
        E --> F[Candidate Parameter Set]
        F --> G[Regime Stability Constraint<br/>Maximize consistency across regimes]
    end
    
    subgraph "Phase III: Validation (Out-of-Sample)"
        G --> H[Walk-Forward Analysis<br/>• Expanding windows<br/>• Out-of-sample testing]
        H --> I{Model Degradation Assessment}
        
        I -->|Significant Decay| J[⛔ REJECT<br/>Overfitting Detected]
        I -->|Stable Performance| K[Parameter Sensitivity Analysis<br/>• Local robustness<br/>• Gradient checking]
        
        K --> L[Final Holdout Validation<br/>• Unseen data<br/>• Final performance metrics]
        L --> M[✅ ACCEPT<br/>Model Validated]
    end
    
    %% Styling
    class A,B,C ideation
    class D,E,F,G calibration
    class H,K,L validation
    class D,I decision
    class J outcome
    class M success
    
    %% Additional Relationships
    J -.->|Return to Phase I| A
    K -->|If unstable| J

Phase Details

Hypothesis & Design: Strategies begin with a structural market premise (e.g., volatility clustering, mean reversion constraints). Signals are implemented using pure functions to ensure stateless reproducibility.
Global Parameter Search: Instead of optimizing each window individually (which leads to curve-fitting), we search for a single parameter set that maximizes the robust objective function across all training windows simultaneously.
Walk-Forward Validation: The candidate parameters are applied to unseen data. We measure Performance Degradation—the gap between In-Sample training results and Out-of-Sample test results—to quantify the strategy’s “optimism bias.”

Tech Stack

Language: Python 3.10+
Core Libraries: Pandas, NumPy (Vectorization)
Optimization: Optuna (Tree-structured Parzen Estimator)
Validation: Custom Walk-Forward Engine
Visualization: Matplotlib, Seaborn