ML-Based Backtest Engine

Building a Python-based backtesting engine that automates pattern detection and validates strategies using historical statistical data instead of subjective observation.
(In development)

Intuition is inconsistent.
Data creates clarity.

Manual pattern recognition in trading is often biased and lacks statistical validation. Decisions based on subjective observation limit long-term consistency.

I built a Python-based backtesting engine that automates pattern detection and evaluates strategies using historical data and machine learning models.

The result is a measurable, data-driven framework where strategies are validated statistically — not emotionally.
In development.

Objectivity. Replaces subjective pattern observation with statistical analysis.
Validation. Strategies are tested against historical performance data.
Efficiency. Automates large-scale pattern detection.
Clarity. Provides measurable performance insights.
Strategic Confidence. Decisions are based on probability, not intuition.

ML-Based Backtest Engine

Building a Python-based backtesting engine that automates pattern detection and validates strategies using historical statistical data instead of subjective observation.
(In development)

Intuition is inconsistent.
Data creates clarity.

Impact

Data-driven strategic validation

Explore My Other Works

ML-Based Backtest Engine

Building a Python-based backtesting engine that automates pattern detection and validates strategies using historical statistical data instead of subjective observation. (In development)

Intuition is inconsistent.Data creates clarity.

Impact

Data-driven strategic validation

Explore My Other Works

Building a Python-based backtesting engine that automates pattern detection and validates strategies using historical statistical data instead of subjective observation.
(In development)

Intuition is inconsistent.
Data creates clarity.