AI-Trader

中文版說明 (Chinese Subpage)

A professional, config-driven backtesting framework for algorithmic trading, built on Backtrader. Seamlessly test, optimize, and integrate trading strategies with Large Language Models (LLMs) across stocks, crypto, and forex markets.

Key Features

• Config-Driven Workflows: Define and manage backtests with version-controllable YAML files for reproducible results.
• Seamless LLM Integration: Built-in MCP (Model Context Protocol) server allows AI assistants like Claude to run backtests, fetch data, and analyze strategies.
• Multi-Market Support: Test strategies on US stocks, Taiwan stocks, cryptocurrencies, and forex.
• Extensive Strategy Library: Comes with over 20 built-in strategies, from classic indicators to advanced adaptive models.
• Powerful CLI: A rich command-line interface to run backtests, fetch market data, and list strategies.
• Developer Friendly: Easily create and test custom strategies with simple helpers and a clear structure.

Quick Start

1. Installation

Option A: Install from PyPI (Recommended for using the CLI)

pip install ai-trader

Use this if you want to:

• Use the CLI commands: ai-trader run, ai-trader fetch, ai-trader quick
• Run backtests on your own data files
• Use as a library in your Python projects

Option B: Install from Source (Recommended for examples and config templates)

git clone https://github.com/whchien/ai-trader.git
cd ai-trader

# Install dependencies (choose one method)
uv sync        # Recommended (fastest, modern tool)
# poetry install   # Or use Poetry
# pip install -e .  # Or traditional pip with editable install

Use this if you want to:

• Run the config-based examples in config/backtest/
• Use the example data files in data/
• Run the example scripts in scripts/examples/
• Contribute or customize strategies

2. Run a Backtest via CLI

If you cloned from source, run a predefined backtest using a configuration file:

# Run a backtest from a config file (requires source installation)
ai-trader run config/backtest/classic/sma_example.yaml

Or, run a quick backtest on any data file (works with both pip and source installation):

# Quick backtest on your own data file
ai-trader quick CrossSMAStrategy your_data.csv --cash 100000

3. Fetch Market Data

Download historical data for any supported market:

# US Stock (default: saves to CSV)
ai-trader fetch TSM --market us_stock --start-date 2020-01-01

# Taiwan Stock (台灣股票)
ai-trader fetch 2330 --market tw_stock --start-date 2020-01-01

# Cryptocurrency
ai-trader fetch BTC-USD --market crypto --start-date 2020-01-01

# With SQLite persistent caching (NEW!)
ai-trader fetch AAPL --market us_stock --start-date 2024-01-01 --storage sqlite

# Save to both CSV and SQLite
ai-trader fetch AAPL --market us_stock --start-date 2024-01-01 --storage both

Persistent Data Storage with SQLite

By default, ai-trader fetch saves data to CSV. For faster repeated backtests, use SQLite:

# First fetch: Downloads from API and caches in SQLite (~2-3 seconds)
ai-trader fetch AAPL --market us_stock --start-date 2024-01-01 --storage sqlite

# Repeated fetch: Loads from cache (~50ms, no API call)
ai-trader fetch AAPL --market us_stock --start-date 2024-01-01 --storage sqlite

# Check cached data
ai-trader data list
ai-trader data info

# Clean old data
ai-trader data clean --market us_stock --before 2020-01-01

Learn more about SQLite Storage →

Core Workflows

1. Configuration-Based Backtesting

The most robust way to run backtests is with a YAML config file.

my_backtest.yaml:

broker:
  cash: 1000000
  commission: 0.001425

data:
  file: "data/us_stock/TSM.csv"
  start_date: "2020-01-01"
  end_date: "2023-12-31"

strategy:
  class: "CrossSMAStrategy"
  params:
    fast: 10
    slow: 30

sizer:
  type: "percent"
  params:
    percents: 95

Run it:

ai-trader run my_backtest.yaml

See config/backtest/ for more examples.

2. Python-Based Backtesting

For more granular control or integration into other Python scripts.

Simple approach:

from ai_trader import run_backtest
from ai_trader.backtesting.strategies.classic.sma import CrossSMAStrategy

# Run backtest with example data
results = run_backtest(
    strategy=CrossSMAStrategy,
    data_source=None,  # Uses built-in example data
    cash=1000000,
    strategy_params={"fast": 10, "slow": 30}
)

Step-by-step control: See scripts/examples/02_step_by_step.py for a detailed example.

3. LLM Integration (MCP Server)

Run ai-trader as a server to let AI assistants interact with your backtesting engine.

Start the Server (for testing):

python -m ai_trader.mcp

Configure with Claude Desktop (Recommended):

1. Locate your Claude Desktop configuration file:

   • macOS/Linux: ~/.config/Claude/claude_desktop_config.json
   • Windows: %APPDATA%\Claude\claude_desktop_config.json

2. Add the ai-trader MCP server to the mcpServers section:

{
  "mcpServers": {
    "ai-trader": {
      "command": "python3",
      "args": ["-m", "ai_trader.mcp"],
      "cwd": "/path/to/ai-trader"
    }
  }
}

Configuration Notes:

• Replace /path/to/ai-trader with your actual ai-trader project directory
• If using a virtual environment, use the full path to the Python executable: /path/to/.venv/bin/python3
• Restart Claude Desktop after updating the config file

Once configured, you can use Claude to interact with your backtesting engine with natural language commands like:

• "Run a backtest of the CrossSMAStrategy on TSM data from 2020-2022."
• "List all available trading strategies."
• "Fetch Apple stock data from 2021 to 2024."

Creating Custom Strategies

Option 1: Using Claude Code Skill