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🦀 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

Date Market Action Asset Entry Price DNA Status
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

Strategy Market Start Date Days Return Sharpe MaxDD Trades Status
Crypto V13 Crypto 2026-04-18 0 🟢 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 rules
You tune parameters        →  GA tunes parameters  
You test on one period     →  Walk-forward tests on multiple windows
You 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:

Parameter Range What it controls
Factor weights (×484) 0.0–1.0 Which factors matter and how much
hold_days 2–60 Day trades through swing trades
trailing_stop % Trail below peak to lock in profits
market_regime sensitivity Reduce exposure automatically in bear markets
kelly_fraction 0–1 Position 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)

Metric Best DNA
Annual Return 33.5%
Sharpe Ratio 1.47
Max Drawdown 17.0%
Win Rate 55.5%
Profit Factor 1.75
Total Trades 179

₿ Crypto V13 (17 assets — Gen 53)

Metric Best DNA
Annual Return 69.0%
Sharpe Ratio 2.27
Max Drawdown 13.0%
Win Rate 50.0%
Profit Factor 1.58
Total Trades 174

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.

Defense What it does
Walk-Forward Multi-window OOS validation. Must profit on data it never trained on.
Monte Carlo 1,000 shuffled iterations. p-value < 0.05 or it's luck.
CPCV Combinatorial Purged Cross-Validation. Industry standard for a reason.
Arena Mode Multiple strategies compete head-to-head. Crowded signals get penalized.
Bias Detection Look-ahead, snooping, survivorship — flagged automatically.
Turnover Penalty Excessive trading is punished. Real transaction costs baked in.

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


484+ Factors

Category Count Examples
Crypto-Native 200 Funding rate, whale detection, liquidation cascade
Momentum 14 ROC, acceleration, trend strength
Volume & Flow 13 OBV, smart money, Wyckoff VSA
Volatility 13 ATR, Bollinger squeeze, vol-of-vol
Mean Reversion 12 Z-score, Keltner channel position
Trend Following 14 ADX, EMA golden cross, MA fan
Qlib Alpha158 11 Microsoft Qlib compatible factors
+ 5 more categories 37 Risk, 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:

Style What the DNA learned
Value Seeker Buys cheap, holds patient
Momentum Rider Chases runners, dumps laggards
Mean Reverter Bets on bounce-backs
Flow Reader Follows the money — volume leads price
Volatility Hunter Profits from vol expansion
Crypto Native 200 factors built for 24/7 markets

Get Access

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

📧 Contact: neuzhou@outlook.com
💬 Discord: discord.gg/kAQD7Cj8


Technical Papers


Check back daily for updated signals and paper trading results.

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AI-Powered Quantitative Trading Engine — Evolve strategies with genetic algorithms

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