Welcome to AlphaFlow

We're excited to introduce AlphaFlow, a powerful event-based backtesting library designed specifically for quantitative trading strategies. Built with Python, AlphaFlow provides a robust framework for testing your trading ideas with precision and accuracy.

What Makes AlphaFlow Different?

Event-Driven Architecture

Unlike traditional backtesting frameworks that process data bar-by-bar, AlphaFlow uses an event-driven architecture. This approach more accurately simulates real market conditions, where multiple events can occur simultaneously and must be handled in the correct order.

Avoiding Look-Ahead Bias

One of the most common pitfalls in backtesting is look-ahead bias - using information that wouldn't have been available at the time a trading decision was made. AlphaFlow's architecture makes it nearly impossible to introduce look-ahead bias into your strategies.

Flexibility and Extensibility

AlphaFlow is designed to be flexible and extensible. Whether you're testing simple moving average crossovers or complex machine learning models, AlphaFlow provides the tools you need.

Getting Started

Installing AlphaFlow is easy:

pip install alphaflow

Here's a simple example of how to use AlphaFlow:

import alphaflow as af
from alphaflow.strategies import BuyAndHoldStrategy
from alphaflow.brokers import SimpleBroker
from alphaflow.data_feeds import FMPDataFeed

# Create a flow (the central hub of your backtest)
flow = af.AlphaFlow()

# Configure the basics
flow.set_cash(100000)
flow.set_benchmark("SPY")
flow.add_equity("AAPL")

# Add a data source
flow.set_data_feed(FMPDataFeed())

# Add a strategy
flow.add_strategy(
    BuyAndHoldStrategy(symbol="AAPL", target_weight=1.0)
)

# Set a broker
flow.set_broker(SimpleBroker())

# Run the backtest
flow.run()

What's Next?

Check out our documentation for detailed guides, tutorials, and API references. Stay tuned for more blog posts covering advanced topics, strategy examples, and best practices.

Happy backtesting!