ai-trader 0.3.0

Professional algorithmic trading backtesting framework using Backtrader

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

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

(Poetry users can run poetry install)

2. Run a Backtest via CLI (Recommended)

Run a predefined backtest using a configuration file:

# Run a backtest from a config file
ai-trader run config/backtest/classic/sma_example.yaml

Or, run a quick backtest on a specific data file:

# Quick backtest without a config file
ai-trader quick CrossSMAStrategy data/us_stock/tsm.csv --cash 100000

3. Fetch Market Data

Download historical data for any supported market:

# US Stock
ai-trader fetch AAPL --market us_stock --start-date 2020-01-01

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

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:

python -m ai_trader.mcp

Once running, you can configure an agent like Claude Desktop to connect to it. This enables 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

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

If you find this project helpful, please give it a star !

License

This project is licensed under the MIT License. See the LICENSE file for details.