🦀 StratEvo

Stop writing trading strategies. Evolve them.

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

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

How It Works

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

Code

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

Code

  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

GT-Score: A Generalizable Fitness Function for Walk-Forward Strategy Evolution

Check back daily for updated signals and paper trading results.