Stock Strategy Backtester Clean

这是一款仅做多的股票策略回测工具，支持SMA交叉、RSI均值回归、突破三种经典策略。可基于含Date/Close列的OHLCV CSV数据，设置手续费、滑点等真实交易成本，输出总收益、CAGR、胜率、最大回撤、夏普比率等核心指标及交易明细，适用于策略评估对比、参数调优、量化交易成本影响等场景，支持JSON输出适配自动化流程，结果仅供研究参考。

taylen

热度 83.46

最近更新 2026-05-11 17:27:16

分类：数据依赖：代码执行 / 读写文件应用场景：数据处理 / 报告撰写 / 财务

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add stock-strategy-backtester-clean

Metadata

name: stock-strategy-backtester-clean
description: Backtest stock trading strategies on historical OHLCV data and report win rate, return, CAGR, drawdown, Sharpe ratio, and trade logs. Use when evaluating or comparing strategy rules (SMA crossover, RSI mean reversion, breakout), quantifying transaction-cost impact, tuning parameters, or generating performance summaries from CSV data. Trigger for requests like "回测股票策略胜率", "测收益率", "compare two strategy backtests", and "build a strategy report from historical prices".

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

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

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

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

Validate data

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

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.

Set realistic assumptions

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

Compare variants

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

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

Use out-of-sample logic

Prefer walk-forward validation over one-shot tuning.

Avoid leakage

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

Report downside with upside

Never present return without drawdown and trade count.

Treat results as research

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

References

Metrics details: references/backtest-metrics.md