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