Harmony

Problem

Financial markets are noisy, adversarial environments where predictive accuracy alone is insufficient. Sustainable trading systems require disciplined evaluation, strict risk controls, and infrastructure that behaves predictably under stress.

Impact

A modular trading system with independent forecasting, risk, and execution components. Each module can be evaluated, replaced, or improved independently.

Project Goals

• Design a forecasting and execution pipeline that survives regime shifts.
• Emphasize risk management over raw returns.
• Build components that can be evaluated independently and replaced safely.
• Treat trading as a systems engineering problem, not a single model.

Architecture Decisions

• Market data ingestion feeding normalized feature pipelines.
• Forecast models generating short- and medium-horizon signals.
• Risk engine enforcing position sizing, drawdown limits, and exposure controls.
• Execution layer with paper trading simulation and metrics monitoring.

Evaluation Methodology

• Risk-adjusted return metrics (Sharpe, Sortino) over raw P&L.
• Maximum drawdown tracking and regime-stability analysis.
• Backtest vs. forward-test divergence monitoring.
• Leakage-aware evaluation to prevent look-ahead bias.

Delivery and Operations

• Modular component architecture allowing independent evaluation and replacement.
• Incremental development with infrastructure-first approach.
• Design decisions documented for future team onboarding.

Current Scope

• Market data ingestion and normalization pipeline.
• Forecasting models for short- and medium-horizon signals.
• Risk engine enforcing position sizing and drawdown limits.
• Backtesting framework with leakage-aware evaluation.
• Execution simulation via paper trading.

Design Principles

• No model is trusted without adversarial backtesting.
• Risk constraints override model confidence.
• Components must fail safely.
• Performance claims require statistically sound evaluation.