Stock Strategy Backtester Clean

Backtest stock trading strategies on historical OHLCV data and report win rate, return, CAGR, drawdown, Sharpe ratio, and trade logs. Use when evaluating or...

Version Notice

• Use this clean slug if your environment pins to stock-strategy-backtester-clean.

Overview

Run repeatable, long-only stock strategy backtests from daily OHLCV CSV files. Use bundled scripts to generate consistent metrics and trade-level output, then summarize with investor-friendly conclusions.

Quick Start

1. Prepare a CSV with at least Date and Close columns.
2. Run a baseline backtest:

python scripts/backtest_strategy.py \
  --csv /path/to/prices.csv \
  --strategy sma-crossover \
  --fast-window 20 \
  --slow-window 60

3. Export artifacts for review:

python scripts/backtest_strategy.py \
  --csv /path/to/prices.csv \
  --strategy rsi-reversion \
  --rsi-period 14 \
  --rsi-entry 30 \
  --rsi-exit 55 \
  --commission-bps 5 \
  --slippage-bps 2

Workflow

1. Validate data

• Ensure Date is parseable and sorted ascending.
• Ensure Open/High/Low/Close are numeric; missing Open/High/Low falls back to Close.

2. Pick strategy logic

• sma-crossover: trend-following with fast/slow moving averages.
• rsi-reversion: buy oversold and exit on momentum recovery.
• breakout: enter on highs breakout and exit on lows breakdown.

3. Set realistic assumptions

• Always set --commission-bps and --slippage-bps.
• Avoid reporting cost-free backtests as production-ready.

4. Compare variants

• Change one parameter block at a time.
• Compare on the same date range and same cost model.

5. Produce final summary

• Report: total_return_pct, cagr_pct, win_rate_pct, max_drawdown_pct, sharpe_ratio, profit_factor, and trade count.
• Use trade CSV to explain where alpha is coming from.

Supported Commands

• Baseline SMA strategy:

python scripts/backtest_strategy.py \
  --csv /path/to/prices.csv \
  --strategy sma-crossover \
  --fast-window 10 \
  --slow-window 50

• Breakout strategy:

python scripts/backtest_strategy.py \
  --csv /path/to/prices.csv \
  --strategy breakout \
  --lookback 20

• JSON-only output (for automation pipelines):

python scripts/backtest_strategy.py \
  --csv /path/to/prices.csv \
  --strategy rsi-reversion \
  --quiet

Output Contract

• Script prints a JSON object to stdout with:
• strategy
• period
• metrics
• config
• trades

Analysis Guardrails

1. Use out-of-sample logic

• Prefer walk-forward validation over one-shot tuning.

2. Avoid leakage

• Compute signals from bar t, execute at bar t+1 open.

3. Report downside with upside

• Never present return without drawdown and trade count.

4. Treat results as research

• Backtests are not guarantees and should not be framed as financial advice.

References

• Metrics details: references/backtest-metrics.md