StratEvo

io.github.NeuZhouPublished by GitHub account NeuZhouv6.0.3Updated Nov 3, 2026

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

VerifiedSTDIODesktop onlyFinanceData & Analytics

Overview

AI-generated overview

StratEvo is a local quantitative trading engine that lets an assistant fetch quotes, backtest, screen, and evolve trading strategies with genetic algorithms.

What you can do
  • Run quantitative trading workflows: quotes, backtesting and screening of markets and assets
  • Evolve trading strategies with a genetic algorithm over 484+ market factors, tuning factor weights and risk parameters
  • Validate candidates with walk-forward, Monte Carlo, CPCV and bias-detection checks
  • Review live signals and paper-trading performance reported by the project
When to use it
Worth adding when you want an assistant to explore or backtest systematic trading ideas, screen markets, or run genetic-algorithm strategy evolution and robustness checks rather than hand-writing rules.
Requirements
Runs as a local stdio process installed from the PyPI package stratevo, so a Python environment is needed. No authentication, environment variables or headers are declared. The README states that the full evolution engine, paper trading, signal generation and live exchange connectors are part of a paid Pro offering obtained by contacting the author.
Before you install
It concerns real financial markets: backtest figures in the README are explicitly not live trades, and an earlier version reportedly showed inflated returns from a look-ahead bug. Live exchange connectors imply trading actions and possible spending. The Pro engine is gated behind a commercial contact rather than open installation, and the README lists an email address and Discord invite.

Installation

In SourceWeft

  1. Open StratEvo in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

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.

Source: README.md at commit 345d7c6

Tools

0
SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

1
  1. v6.0.3LatestSep 16, 2026

Score

40% of the applicable checks met

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  • Reached in the last 30 days25 pts

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  • Auth declared10 pts

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  • Tools listed15 pts

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  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts

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StratEvo MCP Server | SourceWeft