TradeClaw

Open-source AI market intelligence for traders who prefer evidence over noise.

Self-hosted by default

Product

  • Dashboard
  • Screener
  • Backtest
  • Track record
  • Live demo

Transparency

  • What we tested and killed
  • Methodology
  • Why long-term
  • Open data
  • Calibration

Resources

  • Blog
  • Docs
  • API reference
  • How it works
  • FAQ
  • Glossary

Community

  • Discord
  • Weekly digest
  • Contribute
  • Contributors
  • Sponsors

Open source

  • GitHub repo
  • Star history
  • Self-host guide
  • Security
  • Data freshness
  • Roadmap

© 2026 TradeClaw. MIT licensed.

Terms|Privacy|Trading involves risk. Signals are informational only and are not financial advice.
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TradeClaw
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Live signalsStar
Open Source · Self-Hosted · Free Forever

Why developers choose TradeClaw

TradeClaw is an MIT-licensed, self-hostable signal platform with a web dashboard, REST endpoints, Docker configuration, and optional Telegram delivery. This page compares repository-visible capabilities; it does not claim market-wide uniqueness or performance parity.

Star on GitHubTry Live Demo

Feature Comparison

Repository-oriented capability snapshot; verify all third-party entries with their current documentation

FeatureTradeClawTradingViewTA-Libpandas-ta3Commas
Terms / pricing

Third-party terms can change; verify them with each vendor or project.

MIT-licensed codeSee vendorSee project termsSee project termsSee vendor
Open Source
Self-Hostable
Web Dashboard UI
REST API included
Live signals (5-min cadence)
Multi-asset (Forex + Crypto + Commodities)
Backtesting built-in
Telegram bot alerts
Webhook integrations
Plugin / custom indicators
Paper trading simulator
One-click Docker deploy
No vendor lock-in
SupportedPartial / paid tierNot available

Measurement unavailable

No published latency benchmark

This repository does not currently include a reproducible cross-library benchmark harness or a captured result artifact. TradeClaw therefore publishes no latency numbers and claims no performance parity with TA-Lib or pandas-ta. A future comparison must include the benchmark code, environment, inputs, and raw output.

Code Comparison

Same task: calculate RSI and generate a BUY/SELL signal

TradeClaw — RSI Signal
# 1. Start TradeClaw (one command)
docker compose up -d

# 2. Get RSI signal via REST API
curl https://localhost:3000/api/signals?symbol=EURUSD&indicator=RSI

# Response
{
  "symbol": "EURUSD",
  "indicator": "RSI",
  "value": 42.7,
  "signal": "BUY",
  "strength": "medium",
  "timestamp": "2026-03-28T05:00:00Z"
}

TradeClaw: 2 steps — start Docker, call API. Others: fetch data, install libraries, write signal logic yourself.

Why not just use X?

Honest answers about when to use TradeClaw vs alternatives

Review the TradeClaw self-hosting path

MIT-licensed source with documented Docker Compose setup and required environment configuration.

Terminal
git clone https://github.com/naimkatiman/tradeclaw
cd tradeclaw
docker compose up -d
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