The automated trading system involves programming trading strategies that allow the computer to buy and
sell stocks based on signals. These systems provide tailored strategies suited to the investor’s desires,
enabling consistent trading by eliminating emotional factors. This article will detail how to backtest a
moving average strategy using Python’s Zipline library.
1. Introduction to Zipline
Zipline is an open-source backtesting library written in Python, specifically designed to perform historical
simulations on financial data developed by Quantopian. Zipline provides users with the ability to easily define
their strategies and evaluate performance based on past data.
2. Overview of the Moving Average Strategy
The Moving Average (MA) strategy is a useful method for smoothing stock price movements to identify trends.
Generally, it generates buy and sell signals by comparing short-term and long-term moving averages. The basic
principles of the moving average strategy are as follows:
- Buy Signal: When the short-term moving average crosses above the long-term moving average
- Sell Signal: When the short-term moving average crosses below the long-term moving average
3. Setting Up the Environment
It is recommended to use Anaconda for installing Zipline. You can install Zipline and the required libraries
using the command below.
conda install -c conda-forge zipline
4. Downloading Data
Historical price data is needed for backtesting. Since Zipline does not provide direct access to data from
Yahoo Finance, I will explain how to download data using the `pandas-datareader` library. You can retrieve
the data using the code below.
import pandas as pd
from pandas_datareader import data
import datetime
# Setting the date range for data download
start = datetime.datetime(2015, 1, 1)
end = datetime.datetime(2021, 1, 1)
# Downloading Apple stock data
stock_data = data.DataReader('AAPL', 'yahoo', start, end)
stock_data.to_csv('aapl.csv') # Saving data as CSV
5. Setting Up the Zipline Environment
To define the algorithm to be used in Zipline, a few essential configurations are needed. The process of
importing the required libraries and data is as follows.
from zipline import run_algorithm
from zipline.api import order, record, symbol
import pytz
from datetime import datetime
# Settings required for using Zipline
def initialize(context):
context.asset = symbol('AAPL') # Trading Apple stock
def handle_data(context, data):
# Calculating moving averages
short_mavg = data.history(context.asset, 'price', 20, '1d').mean()
long_mavg = data.history(context.asset, 'price', 50, '1d').mean()
# Generating buy and sell signals
if short_mavg > long_mavg:
order(context.asset, 10) # Buying 10 shares
elif short_mavg < long_mavg:
order(context.asset, -10) # Selling 10 shares
# Recording performance
record(AAPL=data.current(context.asset, 'price'))
6. Running the Backtest
The next step is to backtest the algorithm. You can run the backtest using Zipline's `run_algorithm`
function. The code below sets up a backtest running from January 1, 2015, to January 1, 2021.
if __name__ == '__main__':
start_date = datetime(2015, 1, 1, tzinfo=pytz.UTC)
end_date = datetime(2021, 1, 1, tzinfo=pytz.UTC)
run_algorithm(start=start_date,
end=end_date,
initialize=initialize,
capital_base=10000,
handle_data=handle_data,
data_frequency='daily')
7. Analyzing Results
Once the backtest is complete, Zipline records the trading history and performance data. You can visualize
the results graphically or evaluate performance metrics (e.g., Sharpe ratio, maximum drawdown, etc.). The
following is a basic method for analyzing results.
import matplotlib.pyplot as plt
from zipline import run_algorithm
# Visualizing the results
result = run_algorithm(start=start_date,
end=end_date,
initialize=initialize,
capital_base=10000,
handle_data=handle_data,
data_frequency='daily')
plt.figure(figsize=(12, 6))
plt.plot(result.index, result['AAPL'], label='AAPL Price')
plt.title('AAPL Holding Performance')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend()
plt.show()
8. Conclusion
This article describes how to backtest a moving average strategy using Zipline. The moving average strategy
is simple and easy to understand, making it a popular choice among many traders. By implementing and
backtesting this strategy using Zipline, you can gain insights that assist with real investment decisions.
As the next step, it is advisable to research more advanced strategies and combine various indicators to
build a more sophisticated automated trading system. I hope you can utilize the in-depth features of
Zipline and venture into the world of algorithmic trading.