casemark/building-quantitative-trading-models

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

Workflow

1. Signal Construction

• Translate the strategy hypothesis into one or more quantitative signals (e.g., z-score of rolling mean reversion, cross-sectional momentum rank, implied-vs-realized vol spread)
• Normalize signals to a common scale (z-score, percentile rank, or min-max) for comparability
• Define signal decay profile — how quickly the signal loses predictive power after generation
• For multi-signal models, specify combination method: linear weighting, ensemble ranking, or machine-learning stacker

2. Portfolio Construction Rules

• Map signals to position sizes: linear scaling, bucket allocation, or Kelly-criterion-derived sizing
• Apply constraints: sector neutrality, beta neutrality, max single-name weight, gross leverage cap
• Define rebalance trigger: calendar-based (daily/weekly/monthly) or signal-threshold-based
• Specify handling of corporate actions, delistings, and index reconstitutions

3. Backtesting Framework

• Split data into in-sample (model fitting), validation (parameter selection), and out-of-sample (final evaluation) periods — minimum 3:1 ratio of in-sample to out-of-sample
• Use walk-forward or expanding-window methodology; avoid single fixed-window backtests
• Apply realistic transaction costs at each rebalance: commissions + half-spread slippage + market impact estimate
• Account for look-ahead bias: ensure no data leakage from future observations into signal generation
• Handle survivorship bias: include delisted securities with full return histories

4. Performance Evaluation

Calculate and report the following metrics for both in-sample and out-of-sample periods:

• Returns: CAGR, cumulative return, monthly return distribution
• Risk: Annualized volatility, max drawdown (depth, duration, recovery), CVaR (95th/99th)
• Risk-adjusted: Sharpe ratio, Sortino ratio, Calmar ratio, information ratio vs. benchmark
• Turnover and costs: Annual turnover rate, net-of-cost Sharpe, break-even cost analysis
• Stability: Rolling 12-month Sharpe, hit rate by month/quarter, profit factor
• Regime analysis: Performance during identified market regimes (rising rates, vol spikes, credit stress, low-liquidity)

5. Robustness and Validation

• Parameter sensitivity: Vary key parameters (lookback window, z-score threshold, rebalance frequency) +/- 20% and confirm Sharpe degradation < 0.3
• Universe perturbation: Randomly drop 10-20% of instruments and re-run; results should remain directionally consistent
• Deflated Sharpe ratio: Adjust for number of strategy variations tested to guard against multiple-testing bias [VERIFY methodology per Harvey, Liu & Zhu (2016)]
• Correlation to known factors: Regress strategy returns against Fama-French factors, momentum, and volatility factors; isolate residual alpha
• Out-of-sample decay: Compare in-sample vs. out-of-sample Sharpe — a decline > 50% signals likely overfitting

6. Risk and Compliance Overlay

• Define stop-loss rules: strategy-level drawdown limit triggering position reduction or halt
• Specify VaR/CVaR limits at portfolio and single-name level
• Flag any regulatory constraints applicable to the strategy's asset class and domicile [VERIFY]
• Document model risk classification per SR 11-7 / internal model governance standards if applicable [VERIFY]

Output

Deliver a structured model document containing:

• Executive summary: Strategy thesis, asset class, expected Sharpe range, capital requirements
• Signal specification: Mathematical definition, data dependencies, update frequency
• Backtest report: Full performance table (in-sample and out-of-sample), equity curve, drawdown chart, monthly heatmap
• Robustness appendix: Parameter sensitivity grids, factor regression output, deflated Sharpe calculation
• Implementation notes: Data pipeline requirements, execution venue preferences, latency tolerance, monitoring/alerting thresholds
• Risk limits: Position limits, drawdown triggers, leverage caps, kill-switch conditions
• Limitations and assumptions: Explicit list of all assumptions (e.g., continuous liquidity, stable borrow rates, no regime breaks) with materiality assessment

Quality Checks

• No look-ahead bias: every signal value at time t uses only data available at or before t
• Survivorship bias addressed: delisted and merged securities included with proper return series
• Transaction costs are realistic — not zero, not understated; compare net Sharpe to gross Sharpe
• Out-of-sample results exist and are reported separately from in-sample; no cherry-picked evaluation window
• Deflated Sharpe or equivalent multiple-testing correction applied if more than five strategy variants were tested
• Signal rationale has an economic explanation — purely data-mined patterns without intuition are flagged
• All data sources, vendor names, and time ranges are specified so the backtest is fully reproducible
• Limitations section is honest about regimes, liquidity assumptions, and capacity constraints