AI-Trader
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):
-
Locate your Claude Desktop configuration file:
- macOS/Linux:
~/.config/Claude/claude_desktop_config.json - Windows:
%APPDATA%\Claude\claude_desktop_config.json
- macOS/Linux:
-
Add the
ai-traderMCP server to themcpServerssection:
{
"mcpServers": {
"ai-trader": {
"command": "python3",
"args": ["-m", "ai_trader.mcp"],
"cwd": "/path/to/ai-trader"
}
}
}
Configuration Notes:
- Replace
/path/to/ai-traderwith 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 Skills (Recommended)
The fastest way to create a new strategy is with the /add-strategy skill in Claude Code. The skill guides you through the process interactively:
/add-strategy classic
This will prompt you for:
- Strategy name (e.g., "MACDBBands")
- Description
- Parameters with defaults
- Entry and exit conditions
- Any custom indicators
The skill automatically handles:
- File creation with proper naming conventions
- Comprehensive docstrings
- Automatic registration in
__init__.py - Syntax validation
Learn more about Claude Code skills: https://code.claude.com/docs/en/skills
Option 2: Manual Creation
Create a new file in ai_trader/backtesting/strategies/classic/ and inherit from BaseStrategy.
# ai_trader/backtesting/strategies/classic/my_strategy.py
import backtrader as bt
from ai_trader.backtesting.strategies.base import BaseStrategy
class MyCustomStrategy(BaseStrategy):
params = dict(period=20)
def __init__(self):
self.sma = bt.indicators.SMA(self.data.close, period=self.p.period)
def next(self):
if not self.position and self.data.close[0] > self.sma[0]:
self.buy()
elif self.position and self.data.close[0] < self.sma[0]:
self.close()
The new strategy is automatically available to the CLI and run_backtest function.
Documentation & Resources
- Strategy Examples: Details on built-in strategies.
- Example Scripts: 5 complete working examples for different use cases.
- Config Templates: YAML configuration templates.
- Migration Guide: For upgrading from v0.1.x.
Contributing
Contributions are welcome! Feel free to report bugs, suggest features, or submit pull requests.
Show Your Support
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License
This project is licensed under the GNU General Public License v3 (GPL-3.0). See the LICENSE file for details.
