Automated Trading Development in Python, Backtesting of KOSDAQ Market using Zipline

In recent years, robo-advisors and algorithmic trading have increased interest in financial markets. Against this background, Python has become popular among many traders due to its easy syntax and powerful libraries. In particular, Zipline is a representative algorithmic trading backtesting library written in Python, which can be easily applied in both securities and KOSDAQ markets.

Introduction to Zipline

Zipline is an open-source algorithmic trading library written in Python, designed to perform backtests on financial assets like stocks. Zipline offers users a more flexible environment by complementing the advantages and disadvantages of other libraries such as Backtrader. Additionally, Zipline allows for the evaluation of an algorithm’s performance based on historical data, enabling the verification of a strategy’s validity before applying it in actual trading.

Installing Zipline

To use Zipline, you first need to install it. You can install Zipline using the following command:

pip install zipline

However, since Zipline only operates in Python 3.5 or lower, it is advisable to set up a virtual environment that matches the version.

Setting Up the Environment for Backtesting

Before starting backtesting with Zipline, basic environment setup is necessary. At this stage, you prepare data for the example and define your strategy.

Preparing the Data

In the ever-changing financial market, historical data plays an important role. Zipline processes stock data through built-in data providers. However, the data can also be downloaded directly. You need to download data from services like Yahoo Finance and convert it into a format that Zipline can use.

Downloading Example Data

import pandas as pd

# Download specific stock data from Yahoo Finance and save it as a CSV file
data = pd.read_csv('https://query1.finance.yahoo.com/v7/finance/download/AAPL?period1=1501545600&period2=1596556800&interval=1d&events=history')
data.to_csv('AAPL.csv', index=False)

Configuring Zipline Parameters

To run a backtest with Zipline, you need to set several basic parameters. Once the data is prepared, configure the data loader to be used by Zipline.

from zipline import run_algorithm
from datetime import datetime

def initialize(context):
    context.asset = symbol('AAPL')

def handle_data(context, data):
    order(context.asset, 10)

start_date = datetime(2015, 8, 1)
end_date = datetime(2020, 8, 1)

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

Implementing the Algorithm

To implement the algorithm, you need to define the ‘initialize’ and ‘handle_data’ functions. The ‘initialize’ function initializes the types of assets to include in the portfolio. The ‘handle_data’ function defines the trading logic. Below is an example of a simple buying algorithm.

def initialize(context):
    context.asset = symbol('AAPL')
    context.buy_threshold = 100

def handle_data(context, data):
    current_price = data.current(context.asset, 'price')
    if current_price < context.buy_threshold:
        order(context.asset, 10)

Analyzing Results

Zipline generates an analysis report that includes trade records and performance metrics. This allows you to check metrics such as returns, volatility, and the Sharpe ratio.

Visualizing Reports

You can use visualization libraries like Matplotlib to visualize the results. Below is a basic method for visualizing returns.

import matplotlib.pyplot as plt

# Load returns data
returns = ...

# Visualize returns
plt.plot(returns.index, returns.values)
plt.title('Trading Strategy Returns')
plt.xlabel('Date')
plt.ylabel('Returns')
plt.show()

Conclusion

Developing algorithmic trading using Zipline is relatively accessible and useful for testing and analyzing investment strategies. I encourage you to implement your investment strategies through the basic examples presented in this article. It is important to conduct sufficient backtesting before applying it in a real trading environment and to proceed with trading based on a thorough understanding of each algorithm.