Documentation

Code Reference

Everything you need to write, test, and deploy your trading strategies.

Writing Your Strategy

Code structure, syntax, and best practices

Strategy Structure

Every strategy is a Python-style class with two required methods:__init__ for setup andon_data for trading logic.

class Strategy:
    """Your strategy docstring - describe what it does"""
    
    def __init__(self):
        # Define which assets to trade - ANY ticker works
        self.assets = ['AAPL', 'MSFT', 'BTC-USD', 'SPY']
        
        # Your parameters
        self.lookback = 20
        self.threshold = 0.05
        
    def on_data(self, data):
        """Called on each data update with price data for all assets"""
        for asset in self.assets:
            if asset not in data:
                continue
                
            # Your trading logic here
            if self.should_buy(asset, data):
                self.buy(asset, size=0.25)  # 25% of portfolio
            elif self.should_sell(asset, data):
                self.close(asset)
    
    def should_buy(self, asset, data):
        # Custom helper method
        return data[asset].rsi_14 < 30
    
    def should_sell(self, asset, data):
        return data[asset].rsi_14 > 70

Defining Your Universe

Your code defines which assets to trade. There are no restrictions — use any ticker symbol.

# Single asset

self.asset = 'SPY'

# Multiple assets

self.assets = ['AAPL', 'MSFT', 'GOOGL']

# Crypto

self.assets = ['BTC-USD', 'ETH-USD']

# Mixed portfolio

self.assets = ['SPY', 'GLD', 'BTC-USD', 'AAPL']

Example Templates

The editor includes several example strategies. Load them to learn the patterns, then customize to make them your own.

Multi-Asset Momentum

Rank assets by momentum, hold top performers

Pairs Mean Reversion

Trade spread between correlated pairs

Trend Following

MA crossover across asset classes

RSI Multi-Asset

Buy oversold, sell overbought across universe

Crypto Momentum

Momentum with volatility filter for crypto

Open Editor