Machine Learning and Deep Learning Algorithm Trading, Backtesting Strategies Using Zipline

1. Introduction

Algorithmic trading is a method to automate trading in financial markets, which has seen a surge in interest in recent years. The advancements in machine learning and deep learning technologies are setting new standards for such automation. In this course, we will explore in detail how to backtest machine learning and deep learning-based trading strategies using Zipline.

2. Basics of Algorithmic Trading

Algorithmic trading is a system that automatically executes trades based on specific conditions. This system analyzes price data, generates trading signals, and helps make trading decisions faster and more efficiently than human traders. The advantages of algorithmic trading include speed of execution, removal of emotions, and handling of large amounts of data.

3. Introduction to Machine Learning and Deep Learning

Machine learning is a collection of algorithms that learn patterns from data and make predictions. Deep learning is a branch of machine learning that uses artificial neural networks to learn more complex patterns. In the stock market, machine learning and deep learning are widely used for price prediction, generating trading signals, and more.

4. Introduction to Zipline

Zipline is a Python-based algorithmic trading library that provides tools for implementing backtesting and real-time trading systems. Zipline offers a complete trading pipeline that includes data collection, signal generation, and trade execution stages. It also comes with various analytical functions for financial data, making it ideal for quantitative trading.

Installation can be done using the following command:

pip install zipline

5. Performing Backtesting with Zipline

5.1. Data Preparation

The first step is to prepare the data needed for trading. Zipline can fetch data from external data sources like Yahoo Finance and Quandl. Once the necessary data is ready, it needs to be converted into Zipline’s format.

5.2. Defining the Strategy

The next step is to define the trading strategy. For example, you can use a Moving Average Crossover strategy. This strategy involves buying when a short-term moving average crosses above a long-term moving average and selling when it crosses below. Implemented in code, it looks like this:

from zipline import algo

def initialize(context):
    context.asset = symbol('AAPL')
    context.short_window = 20
    context.long_window = 50

def handle_data(context, data):
    short_mavg = data.history(context.asset, 'price', context.short_window, '1d').mean()
    long_mavg = data.history(context.asset, 'price', context.long_window, '1d').mean()
    
    if short_mavg > long_mavg:
        order(context.asset, 10)  # Buy 10 shares
    elif short_mavg < long_mavg:
        order(context.asset, -10)  # Sell 10 shares
                

5.3. Running the Backtest

Now we will execute the strategy and perform the backtest. Zipline provides a simple method to run backtests. You can run the backtest using the following code:

from zipline import run_algorithm
from datetime import datetime

run_algorithm(start=datetime(2015, 1, 1), 
               end=datetime(2016, 1, 1), 
               initialize=initialize, 
               capital_base=100000, 
               handle_data=handle_data)
                

6. Strategy Evaluation and Performance Analysis

Evaluating the results of the backtest is crucial. There are several metrics to judge the performance of a trading strategy. Key metrics include total return, Sharpe ratio, maximum drawdown, and win rate. These metrics can help identify ways to improve strategy performance.

7. Improving Strategies with Machine Learning

You can improve trading strategies using machine learning techniques. For example, using various technical indicators as features, you can build a price prediction model through regression analysis. Here's a simple example.

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

# Prepare features and label
X = ...  # Create features
y = ...  # Closing price data

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Train the model
model = LinearRegression()
model.fit(X_train, y_train)

# Prediction
predictions = model.predict(X_test)
            

8. Conclusion

In this course, we explored the basics of algorithmic trading and backtesting using Zipline. Future lessons will cover advanced machine learning techniques and various trading strategies. Continuous learning and experimentation are essential for success in the world of algorithmic trading.