Python Automated Trading Development, Testing Moving Average Strategy Using Zipline

An automated trading system refers to a program that automatically trades stocks or other financial assets. These systems can help people make trading decisions and are designed to maximize profits using specific algorithms or strategies. In this course, we will test a simple moving average strategy using the Zipline library.

Introduction to Zipline

Zipline is a Python-based backtesting library developed by Quantopian, which provides the ability to create and test stock and options strategies. Using Zipline, you can perform the following tasks:

  • Easy and fast data retrieval
  • Test strategies in a way similar to real trading through algorithms
  • Collect and analyze various performance metrics

Overview of Moving Average Strategy

Moving averages are indicators that show the trend of a stock’s price over a specific period. Here, we will use two moving averages (short-term and long-term) to generate buy and sell signals.

– A buy signal occurs when the short moving average crosses above the long moving average.

– A sell signal occurs when the short moving average crosses below the long moving average.

Environment Setup

To use Zipline, you must first install the necessary packages. You can use the following code to install the required libraries. 

pip install zipline

Preparing Data

Zipline primarily uses data in OHLC (Opening, High, Low, Closing) format. We will use data provided by Yahoo Finance. To do this, we can use the pandas_datareader library to fetch the data. 

pip install pandas_datareader

Implementing the Moving Average Strategy

First, we set up the basic code structure to implement the moving average strategy. 

import zipline
from zipline.api import order, record, symbol, set_benchmark
from zipline import run_algorithm
import pandas as pd
import datetime

def initialize(context):
    context.asset = symbol('AAPL')
    context.short_window = 40
    context.long_window = 100
    context.order_amount = 10

def handle_data(context, data):
    short_mavg = data.history(context.asset, 'close', context.short_window, '1d').mean()
    long_mavg = data.history(context.asset, 'close', context.long_window, '1d').mean()

    if short_mavg > long_mavg:
        order(context.asset, context.order_amount)
    elif short_mavg < long_mavg:
        order(context.asset, -context.order_amount)

    record(AAPL=data.current(context.asset, 'close'))

start = datetime.datetime(2016, 1, 1)
end = datetime.datetime(2021, 1, 1)

run_algorithm(start=start, end=end, initialize=initialize, capital_base=10000, handle_data=handle_data)

Code Explanation

- initialize: This function is executed once when the algorithm starts. It sets the assets for trading and the lengths of the moving averages.

- handle_data: This function is executed every hour, calculating current prices and moving averages, and then executing buy or sell orders.

- run_algorithm: This function sets the start and end points of the algorithm and provides the initial capital.

Results Analysis

The results of the strategy can be verified through various metrics provided by Zipline. These metrics include the stock's return, maximum drawdown, and Sharpe ratio, among others. You can visualize the results with the code below. 

import matplotlib.pyplot as plt

# Visualizing results
results = run_algorithm(start=start, end=end, initialize=initialize, capital_base=10000, handle_data=handle_data)

plt.figure(figsize=(12, 8))
plt.plot(results.index, results.portfolio_value, label='Total Portfolio Value', color='blue')
plt.plot(results.index, results.AAPL, label='AAPL Closing Price', color='orange')
plt.title('Backtest Results')
plt.legend()
plt.show()

Moving to Advanced Strategies

After dealing with the basic moving average strategy above, you can include more advanced strategies and technical indicators. Here are some ideas:

  • RSI (Relative Strength Index): An indicator to determine if a stock is overbought or oversold.
  • Bollinger Bands: A method to measure the stock's volatility and determine the price range.
  • Investment Ratio Adjustment: Adjusting the proportions of each asset in the portfolio for more precise risk management.

Conclusion

In this course, we explored how to implement a simple moving average-based automated trading strategy using Zipline and conduct backtesting. Utilizing Zipline allows for easier testing of more complex and diverse strategies, providing useful insights that can be applied to real-world investments.
We encourage you to try various automated trading strategies based on different ideas in the future.

Appendix: Frequently Asked Questions

What if I am having trouble installing Zipline?

Zipline may only work properly in certain environments. It is recommended to install it using a virtual environment like Anaconda. Additionally, it may often require specific versions of numpy and pandas, so check the documentation for details.

What data can I use?

Zipline is compatible with several data providers. You can fetch data from sources like Yahoo Finance and Quandl, and to add custom data, you can use the zipline.data module.

Are there other libraries besides Zipline?

In addition to Zipline, various Python libraries like Backtrader, Alpaca, and PyAlgoTrade can be used to build automated trading systems. Each library has its own advantages and disadvantages, so it is important to compare them thoroughly and choose the one that fits your requirements.