AI-native quantitative finance in your terminal
Quick Start · Comparison · Architecture · Roadmap · Contributing
Most quant tools make you configure databases, install heavy dependencies, and write boilerplate before you see your first result. FinClaw gets you from zero to insight in one command.
- 🚀 Zero API keys needed — uses Yahoo Finance by default
- ⚡ Pure NumPy core — installs in seconds, not minutes
- 🤖 AI-powered — strategy generation, copilot, MCP server, and A2A protocol built in
- 🌍 12+ exchanges — stocks, crypto, and Chinese A-shares in one tool
pip install finclaw-ai
finclaw demo # See all features — no API key needed
finclaw quote AAPL # Real-time stock quote
finclaw copilot # AI financial assistant$ finclaw quote BTC-USDT
📊 BTC-USDT $67,342.50 +1,285.30 +1.95% 🟢
┌──────────────────────────────────────────────┐
│ Bid: 67,340.20 Ask: 67,344.80 │
│ 24h Vol: 28,451 BTC ($1.92B) │
│ 24h High: 68,100.00 Low: 65,820.40 │
│ Funding: +0.0103% Open Interest: $18.2B │
└──────────────────────────────────────────────┘
$ finclaw backtest momentum --symbol NVDA --start 2023-01-01
📈 Backtest Results: NVDA | momentum
════════════════════════════════════════════════
Period 2023-01-01 → 2024-12-31 (504 days)
Total Return +142.3% (+55.2%/yr)
Alpha +18.7% vs SPY
Max Drawdown -12.1%
Sharpe Ratio 1.85
Win Rate 63.8% (30/47 trades)
Profit Factor 2.41
How does FinClaw stack up? Compared against the most popular open-source quant tools.
| Feature | FinClaw | Freqtrade | Jesse | Backtrader |
|---|---|---|---|---|
| Setup & UX | ||||
Zero-config install (pip install) |
✅ | ✅ | ||
| Interactive CLI with Rich TUI | ✅ | ✅ Basic | ❌ | ❌ Library only |
| Terminal candlestick charts | ✅ | ❌ | ❌ | ❌ |
| AI & Agents | ||||
| AI strategy generation (NL → code) | ✅ | ❌ | ❌ | ❌ |
| Natural language copilot | ✅ | ❌ | ❌ | ❌ |
| MCP server (Claude / Cursor / VS Code) | ✅ | ❌ | ❌ | ❌ |
| A2A protocol (agent-to-agent) | ✅ | ❌ | ❌ | ❌ |
| Trading | ||||
| Backtesting engine | ✅ | ✅ | ✅ | ✅ |
| Paper trading | ✅ | ✅ Dry-run | ✅ | ❌ |
| Live trading | 🔜 | ✅ | ✅ | ✅ via broker |
| Multi-exchange (12+) | ✅ | ✅ ccxt | ✅ 5 exchanges | ❌ |
| Strategy | ||||
| Built-in strategies (20+) | ✅ | ✅ Sample | ✅ Sample | ❌ |
| Plugin system (pip-installable) | ✅ | ✅ | ❌ | ❌ |
| YAML strategy DSL | ✅ | ❌ | ❌ | ❌ |
| Backtrader compatibility | ✅ | ❌ | ❌ | ✅ Native |
| Data & Crypto | ||||
| Stocks + Crypto + CN Stocks | ✅ All | ❌ Crypto only | ❌ Crypto only | ✅ Via feeds |
| BTC on-chain metrics | ✅ | ❌ | ❌ | ❌ |
| DeFi TVL / protocol analytics | ✅ | ❌ | ❌ | ❌ |
| Social sentiment analysis | ✅ | ❌ | ❌ | ❌ |
| Fear & Greed Index | ✅ | ❌ | ❌ | ❌ |
| Dependencies | ||||
| Pure NumPy core (no heavy deps) | ✅ | ❌ TA-Lib, ccxt | ❌ TA-Lib | ❌ matplotlib |
🔑 Key differentiators explained
- AI Strategy Generation: Describe a strategy in plain English or Chinese → FinClaw generates production-ready Python code using any LLM (OpenAI, DeepSeek, Ollama local, etc.)
- MCP Integration: First quant tool to support the Model Context Protocol — let AI agents like Claude or Cursor directly call financial tools
- A2A Protocol: Agent-to-agent communication means FinClaw can collaborate with other AI agents autonomously
- Social Sentiment: Real-time sentiment scoring from news and social feeds, integrated into signal generation
- DeFi Analytics: DeFi Llama integration for TVL, protocol comparison, and yield data — none of the competitors offer this
finclaw quote AAPL,MSFT,NVDA # Multi-ticker quotes
finclaw analyze TSLA --indicators rsi,macd,bollinger,sma50
finclaw chart AAPL --type candle # Terminal candlestick chart
finclaw news AAPL # Financial news
finclaw sentiment TSLA # Sentiment analysisfinclaw backtest -t AAPL,MSFT --strategy momentum --start 2023-01-01
finclaw backtest -t NVDA --benchmark SPY # Compare to benchmark
finclaw strategy list # 20+ built-in strategies
finclaw strategy backtest trend-following --symbol AAPLfinclaw paper start --balance 100000
finclaw paper buy AAPL 50
finclaw paper sell MSFT 20
finclaw paper dashboard
finclaw paper run-strategy golden-cross --symbols AAPL,MSFT# Generate strategies from plain English
finclaw generate-strategy "buy when RSI < 30 and MACD golden cross"
finclaw generate-strategy --market crypto --risk high "momentum on volume spike"
# Interactive AI assistant
finclaw copilot
> 分析一下特斯拉最近的走势
> 帮我写一个基于布林带的策略
# AI-optimize existing strategies
finclaw optimize-strategy my_strategy.py --data AAPL --period 1ySupports: OpenAI, Anthropic, DeepSeek, Gemini, Ollama (local), Groq, Mistral, Moonshot.
finclaw btc-metrics # On-chain dashboard (hashrate, MVRV, miner outflow)
finclaw funding-rates # Multi-exchange funding rate comparison + arbitrage
finclaw fear-greed --history 7 # Fear & Greed Index with history
finclaw defi-tvl --top 10 # DeFi Total Value LockedExpose FinClaw as tools for Claude, Cursor, VS Code, or OpenClaw:
{
"mcpServers": {
"finclaw": {
"command": "finclaw",
"args": ["mcp", "serve"]
}
}
}10 MCP tools available: get_quote, get_history, list_exchanges, run_backtest, analyze_portfolio, get_indicators, screen_stocks, get_sentiment, compare_strategies, get_funding_rates.
# Create a plugin in 5 minutes
finclaw init-strategy my_strategy
cd finclaw-strategy-my_strategy
pip install -e .
finclaw backtest --strategy plugin:my_strategy -t AAPL
# Or use YAML DSL
finclaw strategy create # Interactive builder
finclaw strategy dsl-backtest my_strategy.yaml --symbol AAPL
finclaw strategy optimize my_strategy.yaml --param rsi_period:10:30:5Compatible with Backtrader strategies, TA-Lib indicators, and basic Pine Script.
Crypto: Binance, Bybit, OKX, Coinbase, Kraken (with WebSocket for Binance/Bybit/OKX) US Stocks: Yahoo Finance, Alpaca, Polygon, Alpha Vantage CN Stocks: AkShare, BaoStock, Tushare
finclaw exchanges list # See all adapters
finclaw exchanges compare yahoo binance alpaca
finclaw quote BTCUSDT --exchange binancefinclaw a2a serve --port 8081 # Start A2A server
finclaw a2a card # Print agent cardfrom finclaw import FinClaw
fc = FinClaw()
# Quote
quote = fc.quote("AAPL")
print(f"AAPL: ${quote['price']:.2f} ({quote['change_pct']:+.1f}%)")
# Backtest
result = fc.backtest(strategy="momentum", ticker="NVDA", start="2023-01-01")
print(f"Return: {result.total_return:.1%} | Sharpe: {result.sharpe_ratio:.2f}")Full API documentation: docs/API.md
graph TB
subgraph UI["🖥️ User Interfaces"]
CLI["CLI"]
MCP["MCP Server"]
A2A["A2A Protocol"]
TG["Telegram Bot"]
API["REST API"]
COP["Copilot Chat"]
end
subgraph AI["🤖 AI Strategy Engine"]
GEN["Strategy Generator<br/><i>natural language → code</i>"]
OPT["Strategy Optimizer"]
PINE["Pine Script / YAML DSL"]
LLM["LLM Hub<br/><i>OpenAI · Anthropic · DeepSeek<br/>Gemini · Ollama · Groq</i>"]
end
subgraph STRAT["📐 Strategy Layer"]
BUILT["20+ Built-in Strategies"]
PLUG["Plugin System<br/><i>pip-installable</i>"]
BT_COMPAT["Backtrader Compatible"]
end
subgraph ENGINE["⚙️ Execution Engine"]
BACK["Backtester"]
PAPER["Paper Trading"]
RISK["Risk Engine"]
SCREEN["Stock Screener"]
end
subgraph DATA["📡 Data Layer — 12+ Exchange Adapters"]
direction LR
subgraph STOCKS["Stocks"]
YAHOO["Yahoo Finance"]
ALPACA["Alpaca"]
POLY["Polygon"]
AV["Alpha Vantage"]
end
subgraph CN["CN Stocks"]
AK["AkShare"]
BAO["BaoStock"]
TU["Tushare"]
end
subgraph CRYPTO["Crypto"]
BIN["Binance <i>WS</i>"]
BYBIT["Bybit <i>WS</i>"]
OKX["OKX <i>WS</i>"]
CB["Coinbase"]
KRA["Kraken"]
end
end
subgraph ONCHAIN["₿🔗 On-Chain & DeFi"]
BTC_M["BTC Metrics<br/><i>hashrate · MVRV · miner flow</i>"]
FUND["Funding Rates"]
LN["Lightning Network"]
DEFI["DeFi Llama TVL"]
FG["Fear & Greed Index"]
SENT["Social Sentiment"]
end
UI --> AI
UI --> STRAT
AI --> STRAT
STRAT --> ENGINE
ENGINE --> DATA
ENGINE --> ONCHAIN
DATA --> ENGINE
ONCHAIN --> ENGINE
style UI fill:#1a1a2e,stroke:#e94560,color:#fff
style AI fill:#16213e,stroke:#0f3460,color:#fff
style STRAT fill:#0f3460,stroke:#533483,color:#fff
style ENGINE fill:#533483,stroke:#e94560,color:#fff
style DATA fill:#1a1a2e,stroke:#0f3460,color:#fff
style ONCHAIN fill:#16213e,stroke:#e94560,color:#fff
flowchart LR
subgraph Sources["Data Sources"]
EX["12+ Exchanges"]
CHAIN["On-Chain APIs"]
SOCIAL["Social Feeds"]
DEFI["DeFi Llama"]
end
CACHE["Smart Cache<br/>SQLite + Memory"]
subgraph Processing["Processing"]
NORM["Normalize<br/>OHLCV"]
IND["Indicators<br/>RSI · MACD · BB"]
SIGNAL["Signal<br/>Generator"]
end
subgraph Decision["Decision"]
STRAT["Strategy<br/>Engine"]
RISK["Risk<br/>Manager"]
AI["AI<br/>Copilot"]
end
subgraph Output["Actions"]
ALERT["🔔 Alert"]
TRADE["💹 Trade"]
REPORT["📊 Report"]
AGENT["🤖 MCP/A2A"]
end
Sources --> CACHE --> NORM --> IND --> SIGNAL --> STRAT --> RISK --> Output
AI -.->|"optimize"| STRAT
SOCIAL --> SIGNAL
DEFI --> SIGNAL
style Sources fill:#0d1117,stroke:#58a6ff,color:#c9d1d9
style Processing fill:#0d1117,stroke:#3fb950,color:#c9d1d9
style Decision fill:#0d1117,stroke:#d29922,color:#c9d1d9
style Output fill:#0d1117,stroke:#f85149,color:#c9d1d9
FinClaw includes an EvoSkill-inspired strategy evolution engine that automatically improves trading strategies through iterative backtest-driven mutation.
Seed Strategy → Evaluate → Analyze Failures → Propose Mutations → Mutate → Evaluate Child → Update Frontier → Repeat
# Evolve the golden-cross strategy on AAPL over 20 generations
finclaw evolve --symbol AAPL --generations 20
# Use a specific seed strategy
finclaw evolve --symbol NVDA --strategy rsi-mean-reversion --generations 15
# Save the best evolved strategy
finclaw evolve --symbol TSLA --generations 10 --output best_strategy.yaml --verbose| Mutation Type | Description | Example |
|---|---|---|
| Parameter Tune | Adjust indicator periods | sma(20) → sma(30) |
| Indicator Swap | Replace one indicator with another | sma → ema |
| Add Filter | Add confirmation conditions | Add volume > sma_volume(20) * 1.5 |
| Remove Filter | Remove overly restrictive conditions | Remove ADX filter |
| Adjust Risk | Modify stop-loss/take-profit | stop_loss: 5% → 3% |
| Combine Strategy | Merge two strategy configs | Golden Cross + RSI Reversion |
See examples/ for runnable strategies:
- simple_momentum.py — SMA + RSI momentum strategy
- crypto_rsi.py — Crypto RSI oversold/overbought
- ai_generated.py — BB squeeze mean reversion (AI-generated)
python examples/simple_momentum.py AAPL
python examples/crypto_rsi.py BTC-USD
python examples/ai_generated.py TSLA- Rich CLI with terminal charts
- 20+ built-in strategies with backtesting
- MCP server for AI agents
- A2A protocol support
- AI strategy generation (multi-LLM)
- DeFi/on-chain analytics
- Strategy evolution engine
- Live trading (Binance, Bybit, OKX)
- Web dashboard
- Mobile companion app
- Strategy marketplace
- Real-time portfolio tracking
- Options analytics
- Advanced risk modeling (Monte Carlo, VaR)
See GitHub Issues for the full list.
git clone https://github.com/NeuZhou/finclaw.git
cd finclaw && pip install -e ".[dev]"
pytestSee CONTRIBUTING.md for guidelines.
FinClaw is part of the NeuZhou open source toolkit for AI agents:
| Project | What it does | Link |
|---|---|---|
| repo2skill | Convert any repo into an AI agent skill | GitHub |
| ClawGuard | Security scanner for AI agents (285+ patterns) | GitHub |
| AgentProbe | Behavioral testing framework for agents | GitHub |
| FinClaw | AI-powered financial intelligence engine | You are here |
The workflow: Generate skills with repo2skill → Scan for vulnerabilities with ClawGuard → Test behavior with AgentProbe → See it in action with FinClaw.