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FinClaw 🦀

Self-Evolving Trading Intelligence — genetic algorithms discover strategies you never would.

▶️ Watch: How FinClaw's Self-Evolving Engine Works (2 min)

FinClaw doesn't need you to write strategies — its genetic algorithm discovers and evolves them autonomously across 484 factor dimensions, then validates them with walk-forward testing and Monte Carlo simulation.

Disclaimer

This project is for educational and research purposes only. Not financial advice. Past performance does not guarantee future results. Always paper trade first.

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🚀 Quick Start

pip install finclaw-ai
finclaw demo          # See it in action
finclaw quote AAPL    # Real-time quotes
finclaw quote BTC/USDT # Crypto too

No API keys, no exchange accounts, no config files needed.

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?? See it in action (click to expand)

$ finclaw demo

??? ?? Real-Time Quotes ???

Symbol        Price     Change        %          Trend
--------------------------------------------------------
AAPL         189.84    +2.31  +1.23%  ???????_??█??
NVDA         875.28   +15.67  +1.82%  ?????_?????__
BTC/USDT  66,458.10    -1.24  -0.53%  ????__???_???

??? ?? Strategy Evolution Engine ???

FinClaw's core: genetic algorithms evolve strategies autonomously.
Population: 30  |  484 factor dimensions  |  Walk-forward validated

  Gen    Return    Fitness   Sharpe  Progress
  ---    ------    -------   ------  --------
    1     12.3%       45.2     1.2   ██████████
   10     34.5%      123.7     2.1   ██████████
   25     89.2%      456.3     3.4   ██████████
   50    234.7%     1205.8     4.8   ██████████
   75    567.3%     2890.4     5.6   ██████████
   89   2756.4%     4487.8     6.6   ██████████ ??

DNA evolved across 484 factors:
  Top weights: RSI ?0.34, Momentum ?0.25, MACD ?0.18, Volume ?0.12
  Walk-forward validated: ?  Monte Carlo robust: ?

??? ?? Backtest Results ???

Strategy:  +75.7%  (+32.5%/yr)    Buy&Hold:  +67.7%
Alpha:     +8.0%                  Sharpe:    1.85
MaxDD:     -8.3%                  Win Rate:  63%

??? ?? Paper Trading Portfolio ???

Symbol     Shares   Avg Cost      Price         P&L
----------------------------------------------------
AAPL           50     178.50     189.84  +$5,650.00
NVDA           20     810.00     875.28  +$1,305.60
BTC/USDT    0.015  66,458.00  66,458.00      $0.00
----------------------------------------------------
TOTAL                                   +$6,955.60

??? ?? AI Features ???

MCP Server  ? Expose FinClaw as tools for Claude, Cursor, VS Code
Copilot     ? Interactive AI financial assistant
Strategy AI ? Natural language ? trading code

Try it yourself:
  finclaw evolve --market crypto    # Run strategy evolution
  finclaw quote BTC/USDT            # Live crypto quote
  finclaw analyze TSLA              # Technical analysis
  finclaw copilot                   # AI financial chat

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Why FinClaw?

Most quant tools make you write the strategy. FinClaw evolves strategies for you.

	FinClaw	Freqtrade	Jesse	FinRL / Qlib
Strategy design	GA evolves 484-dim DNA	You write rules	You write rules	DRL trains agent
Continuous evolution	Strategy itself evolves	Bot runs, strategy fixed	Bot runs, strategy fixed	Training offline
Walk-forward validation	✅ Built-in (70/30 + Monte Carlo)	❌ Plugin needed	❌ Plugin needed	⚠️ Partial
Anti-overfitting	Arena + bias detection	Basic cross-validation	Basic	Varies
Zero API keys to start	✅ pip install && finclaw demo	❌ Needs exchange keys	❌ Needs keys	❌ Needs data setup
Market coverage	Crypto + A-shares + US	Crypto only	Crypto only	A-shares (Qlib)
MCP server (AI agents)	✅ Claude / Cursor / VS Code	❌	❌	❌
Factor library	484 factors, auto-weighted	~50 manual indicators	Manual indicators	Alpha158 (Qlib)

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📊 484 Factor Dimensions

284 general factors + 200 crypto-native factors, organized by category:

Category	Count	Examples
Crypto-Native	200	Funding rate proxy, session effects, whale detection, liquidation cascade
Momentum	14	ROC, acceleration, trend strength, quality momentum
Volume & Flow	13	OBV, smart money, volume-price divergence, Wyckoff VSA
Volatility	13	ATR, Bollinger squeeze, regime detection, vol-of-vol
Mean Reversion	12	Z-score, rubber band, Keltner channel position
Trend Following	14	ADX, EMA golden cross, higher-highs/higher-lows, MA fan
Qlib Alpha158	11	KMID, KSFT, CNTD, CORD, SUMP (Microsoft Qlib compatible)
Quality Filter	11	Earnings momentum proxy, relative strength, resilience
Risk Warning	11	Consecutive losses, death cross, gap-down, limit-down
Top Escape	10	Distribution detection, climax volume, smart money exit
Price Structure	10	Candlestick patterns, support/resistance, pivot points
Davis Double Play	8	Revenue acceleration, tech moat, supply exhaustion
Gap Analysis	8	Gap fill, gap momentum, gap reversal
Market Breadth	5	Advance/decline, sector rotation, new highs/lows
News Sentiment	2	EN/ZH keyword sentiment score + momentum
DRL Signal	2	Q-learning buy probability + state value estimate

Design principle: Technical, sentiment, DRL, fundamental — all signals are unified as factors returning [0, 1]. Weights are determined by the evolution engine, eliminating human bias from signal synthesis.

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🧬 Self-Evolution Engine

The genetic algorithm continuously discovers optimal strategies:

1. Seed — Initialize population with diverse factor weight configurations
2. Evaluate — Walk-forward backtest each DNA across all assets
3. Select — Keep top performers by fitness (Sharpe × Return / MaxDrawdown)
4. Mutate — Random weight perturbation, crossover, factor add/drop
5. Repeat — Runs 7×24 on your machine

finclaw evolve --market crypto --generations 50   # Crypto (main use case)
finclaw evolve --market cn --generations 50       # A-shares
finclaw evolve --market crypto --population 50 --mutation-rate 0.2 --elite 10

Evolution Results

Market	Generation	Annual Return	Sharpe	Max Drawdown
A-Shares	Gen 89	2,756%	6.56	26.5%
Crypto	Gen 19	16,066%	12.19	7.2%

⚠️ These are in-sample backtest results on historical data. Real performance will be significantly lower. Walk-forward out-of-sample validation is enabled by default — always check OOS metrics before trusting any evolved strategy. Run finclaw check-backtest to verify, and finclaw paper to paper trade before risking real capital.

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🏟️ Arena Mode (Anti-Overfitting)

Traditional backtests evaluate each strategy in isolation — overfitted strategies look great on history but fail live. FinClaw's Arena Mode fixes this:

• Multiple DNA strategies trade simultaneously in the same simulated market
• Crowding penalty: When >50% of DNAs buy on the same signal, price impact kicks in
• Overfitted strategies that only work in isolation get penalized in Arena rankings

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✅ Quality Assurance

• Walk-forward validation (70/30 train/test split)
• Monte Carlo simulation (1,000 iterations, p-value < 0.05)
• Bootstrap 95% confidence intervals
• Arena competition (multi-DNA market simulation)
• Bias detection (look-ahead, snooping, survivorship)
• Factor IC/IR analysis with decay curves
• Factor orthogonal matrix (auto-remove redundant factors)
• Turnover penalty in fitness function
• 4,900+ automated tests

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💻 CLI Reference

FinClaw ships with 170+ CLI commands. Here are the essentials:

Command	Description
finclaw demo	See all features in action
finclaw quote AAPL	Real-time US stock quote
finclaw quote BTC/USDT	Crypto quote via ccxt
finclaw evolve --market crypto	Run genetic algorithm evolution
finclaw backtest -t AAPL	Backtest a strategy on a stock
finclaw check-backtest	Verify backtest results
finclaw analyze TSLA	Technical analysis
finclaw screen	Stock screener
finclaw risk-report	Portfolio risk report
finclaw sentiment	Market sentiment
finclaw copilot	AI financial assistant
finclaw generate-strategy	Natural language → strategy code
finclaw mcp serve	Start MCP server for AI agents
finclaw paper	Paper trading mode
finclaw doctor	Environment check

Run finclaw --help for the full list.

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🤖 MCP Server (AI Agents)

Expose FinClaw as tools for Claude, Cursor, VS Code, or any MCP-compatible client:

{
  "mcpServers": {
    "finclaw": {
      "command": "finclaw",
      "args": ["mcp", "serve"]
    }
  }
}

Provides 10 tools: get_quote, get_history, list_exchanges, run_backtest, analyze_portfolio, get_indicators, screen_stocks, get_sentiment, compare_strategies, get_funding_rates.

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📡 Data Sources

Market	Source	API Key Required?
Crypto	ccxt (100+ exchanges)	No (public data)
US Stocks	Yahoo Finance	No
A-Shares	AKShare + BaoStock	No
News Sentiment	CryptoCompare + AKShare	No

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Architecture

┌──────────────────────────────────────────────────────┐
│             Evolution Engine (Core)                   │
│      Genetic Algorithm → Mutate → Backtest → Select   │
│                                                       │
│      Input: 484 factors × weights = DNA               │
│      Output: Walk-forward validated strategy           │
├──────────────────────────────────────────────────────┤
│   Technical(284) │ Sentiment │ DRL │ Davis │ Crypto(200)│
│       All → compute() → [0, 1]                        │
├──────────────────────────────────────────────────────┤
│   Arena Competition │ Bias Detection │ Monte Carlo     │
├──────────────────────────────────────────────────────┤
│   Paper Trading → Live Trading → 100+ Exchanges       │
└──────────────────────────────────────────────────────┘

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Roadmap

• 484-factor evolution engine
• Walk-forward validation + Monte Carlo
• Arena competition mode
• Bias detection suite
• News sentiment + DRL factors
• Davis Double Play factors
• Paper trading infrastructure
• MCP server for AI agents
• DEX execution (Uniswap V3 / Arbitrum)
• Multi-timeframe support (1h/4h/1d)
• Foundation model for price sequences

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🌐 Ecosystem

FinClaw is part of the NeuZhou AI agent toolkit:

Project	Description
FinClaw	AI-native quantitative finance engine
ClawGuard	AI Agent Immune System — 285+ threat patterns, zero dependencies
AgentProbe	Playwright for AI Agents — test, record, replay agent behaviors

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Contributing

git clone https://github.com/NeuZhou/finclaw.git
cd finclaw && pip install -e ".[dev]" && pytest

See CONTRIBUTING.md for guidelines. Report bugs · Request features

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License

MIT

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