StratEvo

io.github.NeuZhou由 GitHub 账号 NeuZhou 发布v6.0.3更新于 Nov 3, 2026

Quantitative trading: quotes, backtesting, screening, and genetic algorithm strategy evolution.

已验证STDIO仅桌面金融数据与分析

概览

AI 生成的概览

StratEvo 是一个本地量化交易引擎，让助手获取行情、回测、筛选并用遗传算法演化交易策略。

可以做什么
  • 执行量化交易流程：获取行情报价、回测与市场筛选
  • 用遗传算法在 484 个以上市场因子上演化策略，调整因子权重与风险参数
  • 通过前向滚动检验、蒙特卡洛、CPCV 与偏差检测验证候选策略
  • 查看项目公布的实时信号与模拟盘（paper trading）表现
适用场景
适合希望让助手探索或回测系统化交易思路、筛选市场，或运行遗传算法策略演化与稳健性检验，而不是手写规则的场景。
运行要求
以本地 stdio 进程运行，从 PyPI 包 stratevo 安装，需要 Python 环境。未声明任何认证、环境变量或请求头。README 说明完整的演化引擎、模拟盘、信号生成与实盘交易所连接器属于付费 Pro 版本，需联系作者获取。
安装前请注意
涉及真实金融市场：README 中的回测数据明确不是实盘交易，早期版本还曾因前视偏差缺陷出现虚高收益。实盘交易所连接器意味着交易操作与可能的资金支出。Pro 引擎需商业联系而非直接安装，README 还列出了邮箱与 Discord 邀请链接。

安装

在 SourceWeft 中

  1. 打开 控制台中的 StratEvo，将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only，通过 STDIO。 STDIO 服务会启动本地进程，因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

README


🦀 StratEvo

Stop writing trading strategies. Evolve them.

A genetic algorithm engine that breeds and walk-forward validates trading strategies across 484+ market factors.

[484+ Evolvable Factors] [Markets] [Validation] [Discord]

Live Signals · Paper Trading · How It Works · Results · Robustness · Get Access

📡 Live Signals

Real-time buy/sell signals from evolved strategies. Updated daily. All signals are committed to git history — you can verify every one.

Latest Signals

DateMarketActionAssetEntry PriceDNAStatus
Signals will be posted here as Paper Trading goes live

📁 Full signal history: signals/

📊 Paper Trading Performance

Forward-testing evolved strategies on real market data with simulated execution. No hindsight, no cherry-picking.

Paper Trading active — Crypto V13 live since 2026-04-18.

Current Paper Portfolio

StrategyMarketStart DateDaysReturnSharpeMaxDDTradesStatus
Crypto V13Crypto2026-04-180————🟢 Live

📁 Daily P&L reports: paper-trading/
📈 Equity curves: paper-trading/charts/

Equity Curve (demo — real data accumulating)

[Equity Curve]

Drawdown

[Drawdown]

How It Works

Most quant tools make you write the strategy. StratEvo evolves them instead.

You write the rules        →  StratEvo discovers the rulesYou tune parameters        →  GA tunes parameters  You test on one period     →  Walk-forward tests on multiple windowsYou hope it generalizes    →  Monte Carlo measures if it does
  Random DNA population (484 factor weights + risk parameters)       │       ▼  ┌──────────────────────┐  │  Walk-Forward Test   │  Multi-window out-of-sample validation  │  each DNA candidate  │  Real fees, slippage, position caps  └──────────┬───────────┘             │             ▼  Keep the survivors (fitness = Sharpe × Return / MaxDD)             │             ▼  Mutate + Crossover → next generation             │             ▼  Repeat for N generations

Each DNA is a weight vector across 484+ factors plus risk/position parameters — all evolvable:

ParameterRangeWhat it controls
Factor weights (×484)0.0–1.0Which factors matter and how much
hold_days2–60Day trades through swing trades
trailing_stop%Trail below peak to lock in profits
market_regimesensitivityReduce exposure automatically in bear markets
kelly_fraction0–1Position sizing from recent win rate

Evolution Results

Numbers from our running evolution engines. Updated as generations progress.

🇺🇸 US Stocks V8 (100 S&P 500 stocks — Gen 136)

MetricBest DNA
Annual Return33.5%
Sharpe Ratio1.47
Max Drawdown17.0%
Win Rate55.5%
Profit Factor1.75
Total Trades179

₿ Crypto V13 (17 assets — Gen 53)

MetricBest DNA
Annual Return69.0%
Sharpe Ratio2.27
Max Drawdown13.0%
Win Rate50.0%
Profit Factor1.58
Total Trades174

These are backtests with walk-forward validation, not live trades. That's the whole point of paper trading — proving it works forward, not just backward.

Anti-Overfitting

We learned this the hard way. An early version showed 25,000% returns. Turned out to be a bug — look-ahead bias.

DefenseWhat it does
Walk-ForwardMulti-window OOS validation. Must profit on data it never trained on.
Monte Carlo1,000 shuffled iterations. p-value < 0.05 or it's luck.
CPCVCombinatorial Purged Cross-Validation. Industry standard for a reason.
Arena ModeMultiple strategies compete head-to-head. Crowded signals get penalized.
Bias DetectionLook-ahead, snooping, survivorship — flagged automatically.
Turnover PenaltyExcessive trading is punished. Real transaction costs baked in.

An honest 33% beats a fake 25,000%.

484+ Factors

CategoryCountExamples
Crypto-Native200Funding rate, whale detection, liquidation cascade
Momentum14ROC, acceleration, trend strength
Volume & Flow13OBV, smart money, Wyckoff VSA
Volatility13ATR, Bollinger squeeze, vol-of-vol
Mean Reversion12Z-score, Keltner channel position
Trend Following14ADX, EMA golden cross, MA fan
Qlib Alpha15811Microsoft Qlib compatible factors
+ 5 more categories37Risk, quality, price structure, sentiment, DRL

All factor weights are discovered by evolution. Zero manual tuning.

Strategy Styles

The algorithm converges on recognizable trading styles on its own:

StyleWhat the DNA learned
Value SeekerBuys cheap, holds patient
Momentum RiderChases runners, dumps laggards
Mean ReverterBets on bounce-backs
Flow ReaderFollows the money — volume leads price
Volatility HunterProfits from vol expansion
Crypto Native200 factors built for 24/7 markets

Get Access

StratEvo Pro includes the evolution engine, paper trading, signal generation, and live exchange connectors.

📧 Contact: [email protected]
💬 Discord: discord.gg/kAQD7Cj8

Technical Papers

Check back daily for updated signals and paper trading results.

来源：README.md，提交 345d7c6

工具

0
SourceWeft 尚未在沙箱中启动过该软件包，因此其工具未知。

版本历史

1
  1. v6.0.3最新Sep 16, 2026

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