This connects Claude to a genetic algorithm engine that evolves quantitative trading strategies instead of requiring you to write them. It exposes operations for backtesting across 484+ market factors (momentum, volume, volatility, crypto-native metrics), screening assets, and running walk-forward validation with Monte Carlo robustness checks. The engine breeds strategy DNA through multi-generation evolution, testing fitness with real transaction costs and anti-overfitting safeguards like combinatorial purged cross-validation. Reach for this when you want to discover trading rules programmatically rather than tune them manually, or when you need to validate strategies across US stocks and crypto with proper out-of-sample testing. It includes paper trading tracking and signal generation with git-committed history.
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
Real-time buy/sell signals from evolved strategies. Updated daily. All signals are committed to git history — you can verify every one.
| Date | Market | Action | Asset | Entry Price | DNA | Status |
|---|---|---|---|---|---|---|
| Signals will be posted here as Paper Trading goes live |
📁 Full signal history: signals/
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.
| 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/


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)
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▼
┌──────────────────────┐
│ 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 |
Numbers from our running evolution engines. Updated as generations progress.
| 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 |
| 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.
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%.
| 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.
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 |
StratEvo Pro includes the evolution engine, paper trading, signal generation, and live exchange connectors.
📧 Contact: neuzhou@outlook.com
💬 Discord: discord.gg/kAQD7Cj8
Check back daily for updated signals and paper trading results.
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