Stock Backtest Analyst 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...

Stock Strategy Backtester Clean

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:

bash

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

3. Export artifacts for review:

bash

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:

bash

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

• Breakout strategy:

bash

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

• JSON-only output (for automation pipelines):

bash

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