Strategy Backtester

Validates historical behavior of stock ranking, factor, and portfolio-selection strategies using reproducible backtests, benchmark comparison, turnover, draw...

Purpose

Use this skill to test whether a ranking, factor mix, or portfolio-selection rule had useful historical behavior before treating it as an investment signal.

Scope

• Equity ranking and selection strategies.
• Periodic rebalance backtests from local CSV inputs.
• Benchmark comparison when benchmark data is available.
• Bias and robustness review.

Non-goals

• Do not claim that historical performance predicts future returns.
• Do not optimize parameters until a preferred result appears.
• Do not issue absolute buy/sell instructions.
• Do not fetch live market data.

Input contract

Required inputs:

• SIGNAL_CSV: rows with date, ticker, and score.
• PRICE_CSV: rows with date, ticker, and close.
• REBALANCE_FREQUENCY: monthly, quarterly, or yearly.
• TOP_N: number of selected names per rebalance.

Optional inputs:

• BENCHMARK_CSV: rows with date and close or return.
• FEE_BPS: round-trip fee assumption in basis points.
• SLIPPAGE_BPS: slippage assumption in basis points.
• UNIVERSE_HISTORY: point-in-time membership if available.

Execution workflow

1. Validate input files and required columns.
2. Estimate whether the test window and symbol coverage are sufficient.
3. Run scripts/backtest_strategy.py with explicit rebalance, fee, slippage, and top-N assumptions.
4. Review performance metrics and benchmark comparison.
5. Identify bias risks and robustness gaps.
6. Return the required output sections.

Required output format

1. Backtest Setup

• Strategy name, test window, rebalance frequency, top-N, fees, slippage, benchmark.

2. Performance Summary

• Total return, CAGR, volatility, max drawdown, Sharpe, Sortino, turnover, hit rate when available.

3. Benchmark Comparison

• Relative return, relative drawdown, and tracking observations when benchmark data exists.

4. Robustness and Bias Warnings

• Survivorship bias, lookahead bias, data-snooping risk, liquidity assumptions, fee/slippage sensitivity.

5. Confidence and Data Gaps

• Confidence level and missing inputs that could change the conclusion.

6. Handoff Bundle

• Include strategy_name, test_window, rebalance_frequency, fee_assumption, slippage_assumption, benchmark, metrics, bias_warnings, confidence, and data_gaps.

Shared confidence rubric

• High: point-in-time signals, adequate price coverage, benchmark available, fees/slippage included, and test window covers multiple market regimes.
• Medium: usable history and price coverage, but one major robustness input is missing.
• Low: short history, missing benchmark, sparse price coverage, likely survivorship/lookahead risk, or no fee/slippage assumptions.

Guardrails

• Separate observed backtest results from assumptions and inference.
• Always state that backtests are historical simulations, not forecasts.
• Downgrade confidence if the test appears overfit or data is not point-in-time.
• Treat backtest output as one input to stock-picker-orchestrator, not as a trading command.

Trigger examples

• "Backtest this VN30 value-quality ranking."
• "Check whether this stock ranking strategy beat VNINDEX historically."
• "Validate this screening rule before using it for shortlist selection."