Building Quantitative Trading Models

When To Use

• Developing a new systematic trading strategy from hypothesis through backtest validation
• Formalizing a discretionary trading idea into a rules-based, testable signal framework
• Evaluating or stress-testing an existing quant model against new market regimes
• Building alpha signal pipelines for equities, futures, options, or structured products
• Documenting model methodology for internal risk review, compliance, or investor due diligence

Inputs To Gather

• Strategy hypothesis: The economic rationale or market inefficiency the model aims to exploit (mean-reversion, momentum, carry, volatility premium, structural flow, etc.)
• Universe definition: Asset class, ticker universe, and any liquidity/market-cap filters
• Data sources: Price data vendor, frequency (tick, minute, daily), fundamental data feeds, alternative data if applicable; confirm start/end dates and survivorship-bias treatment [VERIFY]
• Benchmark and risk-free rate: Index for relative performance; risk-free proxy (e.g., 3-month T-bill, OIS) [VERIFY]
• Execution assumptions: Estimated slippage, commission schedule, borrow costs (for short strategies), and market-impact model
• Constraints: Max position size, sector/factor exposure limits, gross/net leverage caps, turnover limits, regulatory constraints (e.g., Volcker, UCITS) [VERIFY]
• Backtest parameters: In-sample / out-of-sample split dates, walk-forward window length, rebalance frequency