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Quick Start Guide

This guide will help you write and run your first trading strategy with RustyBT.

Installation

If you haven't installed RustyBT yet:

pip install rustybt

For more installation options, see the Installation Guide.

Your First Strategy

Create a file called my_strategy.py:

from rustybt.api import order_target, record, symbol

def initialize(context):
    """Initialize strategy - called once at start."""
    context.i = 0
    context.asset = symbol('AAPL')

def handle_data(context, data):
    """Handle each bar of data - called on every trading day."""
    # Skip first 300 days to get full windows
    context.i += 1
    if context.i < 300:
        return

    # Compute moving averages
    short_mavg = data.history(
        context.asset,
        'price',
        bar_count=100,
        frequency="1d"
    ).mean()

    long_mavg = data.history(
        context.asset,
        'price',
        bar_count=300,
        frequency="1d"
    ).mean()

    # Trading logic: Buy when short MA > long MA
    if short_mavg > long_mavg:
        order_target(context.asset, 100)
    elif short_mavg < long_mavg:
        order_target(context.asset, 0)

    # Record values for analysis
    record(
        AAPL=data.current(context.asset, 'price'),
        short_mavg=short_mavg,
        long_mavg=long_mavg
    )

Ingest Sample Data

Before running your first backtest, you need to ingest some market data:

rustybt ingest -b yfinance-profiling

This downloads and caches free sample data from Yahoo Finance (20 top US stocks, 2 years of history). No API key required! You only need to do this once.

Data Bundles

RustyBT supports multiple data sources: - yfinance-profiling: Free Yahoo Finance data (recommended for quick start) - csvdir: Your own CSV files - see CSV Data Import - Custom adapters: Live data sources - see Creating Data Adapters

Run the Backtest

rustybt run -f my_strategy.py -b yfinance-profiling --start 2024-01-01 --end 2025-09-30

Note the -b yfinance-profiling flag to specify which data bundle to use.

Bundle Date Range

The yfinance-profiling bundle fetches the last 2 years of data from today. The dates shown above (2024-01-01 to 2025-09-30) are examples that work with data ingested in October 2025.

If you ingested data at a different time, adjust your dates accordingly. Use dates within the last year of your ingested data to ensure the 300-day moving average has enough historical data.

Alternative: Python API Execution

You can also run strategies directly from Python without the CLI. This provides a more Pythonic workflow with better IDE integration and debugging support.

Update your my_strategy.py to include execution code:

from rustybt.api import order_target, record, symbol
from rustybt.utils.run_algo import run_algorithm
import pandas as pd

def initialize(context):
    """Initialize strategy - called once at start."""
    context.i = 0
    context.asset = symbol('AAPL')

def handle_data(context, data):
    """Handle each bar of data - called on every trading day."""
    context.i += 1
    if context.i < 300:
        return

    # Compute moving averages
    short_mavg = data.history(context.asset, 'price', bar_count=100, frequency="1d").mean()
    long_mavg = data.history(context.asset, 'price', bar_count=300, frequency="1d").mean()

    # Trading logic
    if short_mavg > long_mavg:
        order_target(context.asset, 100)
    elif short_mavg < long_mavg:
        order_target(context.asset, 0)

    # Record values for analysis
    record(
        AAPL=data.current(context.asset, 'price'),
        short_mavg=short_mavg,
        long_mavg=long_mavg
    )

if __name__ == "__main__":
    result = run_algorithm(
        initialize=initialize,
        handle_data=handle_data,
        bundle='yfinance-profiling',
        start=pd.Timestamp('2024-01-01'),
        end=pd.Timestamp('2025-09-30'),
        capital_base=10000,
        data_frequency='daily'
    )

    print(f"\n{'='*50}")
    print("Backtest Results")
    print(f"{'='*50}")
    print(f"Total return: {result['returns'].iloc[-1]:.2%}")
    print(f"Sharpe ratio: {result['sharpe']:.2f}")
    print(f"Max drawdown: {result['max_drawdown']:.2%}")
    print(f"{'='*50}\n")

Then run with:

python my_strategy.py

Benefits of Python API:

  • ✅ Standard Python development workflow
  • ✅ Easy debugging with IDE breakpoints
  • ✅ Better integration with notebooks and scripts
  • ✅ Direct access to results DataFrame for analysis
  • ✅ More Pythonic and familiar to Python developers

Understanding the Output

RustyBT will display: - Trade execution logs - Performance metrics - Final portfolio statistics

Troubleshooting

Common Issues

"no data for bundle" 

# Solution: Ingest data first
rustybt ingest -b yfinance-profiling

# Then run with the bundle flag
rustybt run -f my_strategy.py -b yfinance-profiling --start 2024-01-01 --end 2025-09-30

"Error: No bundle registered with the name 'yfinance-profiling'" 

# Solution: Upgrade to latest rustybt version
pip install --upgrade rustybt

"fatal: bad revision 'HEAD'" or Segmentation Fault 

# Solution: Reinstall rustybt
pip install --upgrade --force-reinstall rustybt

This usually happens when installing from a non-git directory or with a corrupted installation.

"ModuleNotFoundError" or Import Errors 

# Solution: Check Python version and reinstall
python --version  # Should be 3.12 or higher
pip install --upgrade rustybt

"Quandl API key required"

If you see errors about Quandl requiring an API key, you're using the old default bundle. Switch to the free Yahoo Finance bundle:

rustybt ingest -b yfinance-profiling
rustybt run -f my_strategy.py -b yfinance-profiling --start 2020-01-01 --end 2023-12-31

Data Issues - For custom data: See CSV Data Import Guide - For live data: See Creating Data Adapters Guide - For debugging: See Troubleshooting Guide

Next Steps

Learn More Features

Try Advanced Examples

  • Multi-Strategy Portfolio: See examples/allocation_algorithms_tutorial.py
  • Strategy Optimization: See examples/optimization/
  • Live Trading: See Testnet Setup Guide

Explore the API