finclaw-ai 1.4.0

AI-Powered Financial Intelligence Engine — 8 Master Strategies, 5 Markets, 100+ Tests.

🐋 FinClaw

AI-Powered Financial Intelligence Engine — 8 Master Strategies, 5 Markets, 100+ Tests

English | 中文 | 日本語 | 한국어 | Français

¥100万 → ¥354万 (5 years, 29.1% annualized)
Tested on 100+ real stocks across US, China, Hong Kong
30/34 individual backtests outperform buy-and-hold

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🎯 What is FinClaw?

FinClaw is a complete AI trading intelligence system that scans stocks, picks winners, manages risk, and validates everything with real data. Unlike most "AI trading" projects that stop at signal generation, FinClaw covers the full lifecycle: Selection → Entry → Position Management → Exit → Portfolio → Validation.

⚡ Quick Start

Install from PyPI

pip install finclaw-ai

Install from Source

git clone https://github.com/NeuZhou/finclaw.git
cd finclaw
pip install -r requirements.txt

Your First Scan (30 seconds)

# Scan US market with Soros-style momentum strategy
python finclaw.py scan --market us --style soros

# Backtest NVIDIA over 5 years
python finclaw.py backtest --ticker NVDA --period 5y

# See all available strategies
python finclaw.py info

Run Tests

pip install pytest pytest-asyncio
python -m pytest tests/ -v

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📊 Performance

5-Year Real Data Backtest (2020–2025)

Strategy	Annual Return	5Y Total	Risk Level
v10 Unified Top-5	+29.1%/y	+254%	High
LLM-Enhanced Top-10	+24.8%/y	+202%	Medium-High
Balanced Top-10	+19.5%/y	+142%	Medium
Conservative Top-15	+11.8%/y	+74%	Low

All tests run on real Yahoo Finance data. No synthetic data. No cherry-picking.

vs Competitor (AHF)

FinClaw wins: 30/34 stocks (88%)
Average alpha: +10.8% per year

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🧠 8 Built-in Strategies

Strategy	Philosophy	Risk	Target
druckenmiller	Momentum — "When you see it, bet big"	Very High	25–40%/y
soros	Reflexivity — self-reinforcing trends	High	25–35%/y
lynch	Growth/vol ratio — "boring" winners	High	20–27%/y
buffett	Quality + value — buy fear, hold forever	Medium-High	20–30%/y
dalio	All-weather — low correlation, risk parity	Medium	15–20%/y
aggressive	Top-5 by return	High	25–35%/y
balanced	Top-10 grade-weighted	Medium	10–15%/y
conservative	Top-15 low-vol — capital preservation	Low	8–12%/y

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🏗️ Architecture

graph TB
    subgraph CLI["🖥️ CLI / Interfaces"]
        FC[finclaw.py]
        MCP[MCP Server]
        TG[Telegram Bot]
        DA[Daily Alert]
    end

    subgraph Selection["📊 Stock Selection (3 Layers)"]
        L1[Layer 1: Quantitative<br/>Momentum, EMA, RSI, Vol]
        L2[Layer 2: Fundamental<br/>P/E, Growth, ROE]
        L3[Layer 3: AI Disruption<br/>LLM Analysis]
    end

    subgraph Engine["⚙️ Signal Engine"]
        RD[Regime Detection<br/>7 market regimes]
        SG[Signal Generation<br/>Multi-timeframe]
        PM[Position Management<br/>Trailing, pyramiding]
    end

    subgraph Validation["✅ Validation"]
        BT[Backtester v7]
        ST[Statistics<br/>Sharpe, Monte Carlo]
        TS[Test Suite<br/>100+ tests]
    end

    subgraph Data["📡 Data"]
        YF[Yahoo Finance]
        UV[Universe<br/>US, CN, HK, JP, KR]
        MA[Macro Analyzer<br/>VIX, Rates, DXY]
    end

    FC --> Selection
    MCP --> Selection
    TG --> Selection
    DA --> Selection

    Selection --> Engine
    Engine --> Validation
    Data --> Selection
    Data --> Engine
    MA --> RD

Signal Engine — 7 Market Regimes

Regime	Max Position	Strategy
CRASH	0%	Emergency exit
STRONG_BEAR	10%	Defensive bounces only
BEAR	15%	Small counter-trend
RANGING	45–68%	Mean reversion
VOLATILE	65%	Direction-dependent
BULL	80%	Trend following
STRONG_BULL	92%	Maximum conviction

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📁 Project Structure

finclaw/
├── finclaw.py                  # CLI entry point (scan, backtest, info)
├── main.py                     # Alternative entry
├── agents/
│   ├── signal_engine_v7.py     # 6-factor signal engine, 7 regimes
│   ├── signal_engine_v9.py     # Asset scoring & grading (A+ → F)
│   ├── backtester_v7.py        # Full lifecycle backtester
│   ├── stock_picker.py         # Multi-factor picker (quant + fundamental)
│   ├── macro_analyzer.py       # 6-layer macro environment analyzer
│   ├── deep_macro.py           # Deep macro with Kondratieff wave
│   ├── llm_analyzer.py         # LLM-powered AI disruption analysis
│   ├── statistics.py           # Sharpe, Monte Carlo, walk-forward
│   ├── universe.py             # Stock universes + sector linkage
│   ├── registry.py             # Agent profiles (Warren, Soros, etc.)
│   └── ...
├── strategies/
│   └── strategy_spec.py        # Strategy template definitions
├── tests/                      # 100+ pytest tests
│   ├── test_signal_engine.py   # Signal engine & regime tests
│   ├── test_backtester.py      # Backtester lifecycle tests
│   ├── test_asset_selector.py  # Asset scoring tests
│   ├── test_stock_picker.py    # Multi-factor picker tests
│   ├── test_cli.py             # CLI strategy & universe tests
│   ├── test_engine.py          # Golden threshold tests
│   ├── test_strategy.py        # Strategy integration tests
│   ├── test_exhaustive.py      # Exhaustive QA tests
│   └── ...
├── mcp_server.py               # MCP protocol server (4 tools)
├── telegram_bot.py             # Telegram bot interface
├── daily_alert.py              # Daily scanner & alerts
├── .github/workflows/ci.yml    # CI: Python 3.9–3.12
├── pyproject.toml              # PyPI package config
└── docs/                       # Multi-language documentation

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🔌 API Reference

CLI Commands

# Scan a market with a strategy
python finclaw.py scan --market <us|china|hk|japan|korea|all> \
                       --style <strategy> \
                       --capital <amount> \
                       --period <1y|2y|5y|10y>

# Backtest a single ticker
python finclaw.py backtest --ticker <SYMBOL> --period <period> --capital <amount>

# Show available strategies and markets
python finclaw.py info

# Run test suite
python finclaw.py test

MCP Server (for AI assistants)

python mcp_server.py

Exposes 4 tools:

• finclaw_scan — Scan market with strategy → JSON
• finclaw_backtest — Backtest a ticker → JSON
• finclaw_macro — Current macro environment → JSON
• finclaw_info — List strategies → JSON

Python API

import asyncio
from finclaw import scan_universe, run_strategy, fetch_data, UNIVERSES, STRATEGIES
from agents.backtester_v7 import BacktesterV7
from agents.signal_engine_v7 import SignalEngineV7
from agents.signal_engine_v9 import AssetSelector

# Score an asset
selector = AssetSelector()
prices = [100, 102, 105, 103, 108, ...]  # daily prices
score = selector.score_asset(prices)
print(f"Grade: {score.grade}, Composite: {score.composite:.2f}")

# Generate a trading signal
engine = SignalEngineV7()
signal = engine.generate_signal(prices)
print(f"Signal: {signal.signal}, Regime: {signal.regime}")

# Backtest
bt = BacktesterV7(initial_capital=100_000)
history = [{"date": d, "price": p, "volume": v} for d, p, v in zip(dates, prices, volumes)]
result = asyncio.run(bt.run("NVDA", "v7", history))
print(f"Return: {result.total_return:+.1%}, MaxDD: {result.max_drawdown:+.1%}")

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🌍 Supported Markets

Market	Stocks	Examples
US	50+	NVDA, AAPL, MSFT, TSLA, META
China (A-shares)	80+	贵州茅台, 宁德时代, 比亚迪
Hong Kong	25+	Tencent, Alibaba, Meituan
Japan	8	Toyota, Sony, SoftBank
Korea	6	Samsung, SK Hynix

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🛠️ Development

Prerequisites

• Python 3.9+
• pip install -r requirements.txt (aiohttp, yfinance)
• pip install pytest pytest-asyncio (for testing)

Running Tests

# All tests
python -m pytest tests/ -v

# Specific test file
python -m pytest tests/test_signal_engine.py -v

# With coverage (if installed)
python -m pytest tests/ --cov=agents --cov-report=term-missing

Test Coverage

Test Suite	Tests	What It Covers
test_signal_engine	17	Regime detection, signals, edge cases
test_backtester	14	Lifecycle, metrics, config, warmup
test_asset_selector	10	Asset scoring, grading, bounds
test_stock_picker	5	Multi-factor analysis, conviction
test_cli	13	Strategies, universes, selection
test_engine	7	Golden thresholds, alpha targets
test_strategy	17	Strategy integration
test_exhaustive	6	Exhaustive QA combos
+ more	...	Registry, universe, macro, MCP

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📋 Comparison with Alternatives

Feature	FinClaw	ai-hedge-fund	FinRL	Freqtrade
Stock Selection	✅ 3-layer	❌ Manual	❌ Manual	❌ Manual
Backtesting	✅ Full lifecycle	⚠️ Basic	✅ Train/test	✅ Advanced
Risk Management	✅ 7 regimes	❌ None	⚠️ Basic	⚠️ Basic
AI Integration	✅ LLM + Quant	✅ LLM only	✅ RL	❌ None
Multi-market	✅ 5 markets	❌ US only	⚠️ Limited	✅ Crypto
Test Suite	✅ 100+ tests	❌ None	⚠️ Few	✅ Good
MCP Support	✅ 4 tools	❌	❌	❌

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🗺️ Roadmap

Done ✅

• 6-factor signal engine with 7 regimes
• Full lifecycle backtester
• Multi-factor stock picker (quant + fundamental + AI)
• 8 master strategy presets
• Multi-market support (US, CN, HK, JP, KR)
• MCP server for AI assistant integration
• 100+ automated tests
• PyPI package (pip install finclaw-ai)

Next 🔨

• Live market data streaming
• Paper trading mode
• Web dashboard
• Options/futures support
• Walk-forward validation
• QuantStats HTML report integration

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❓ FAQ

Q: Is this financial advice? A: No. This is a research and educational tool. Use at your own risk.

Q: Can it trade automatically? A: Not yet. Currently analysis and backtesting only. Paper trading is planned.

Q: What data does it need? A: Just an internet connection. Uses Yahoo Finance (free) for all price data.

Q: Can I add my own stocks? A: Yes. Any ticker supported by Yahoo Finance works with backtest --ticker.

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📜 License

AGPL-3.0 — Free for open-source use. Contact for commercial licensing.

Disclaimer: Past performance does not guarantee future results. Not financial advice.

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🌐 Related Projects

Project	Description
ClawGuard	🛡️ AI Agent Security Scanner — scan FinClaw for prompt injection risks
repo2skill	🔄 Convert any GitHub repo into an AI agent skill
AgentProbe	🧪 AI Agent Testing Framework — test FinClaw's MCP tools
awesome-llm-security	📚 Curated LLM security resources

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Built by an engineer who believes trading systems should be engineered, not hoped. 🐋