Developing Python Automated Trading, Setting Up Zipline Transaction Fees

It is very important to consider trading fees when developing an Algorithmic Trading system. Many traders often overlook fees, but they can have a significant impact on actual profit margins. Zipline is an open-source algorithmic trading library written in Python that provides functionality to easily set and test these trading fees.

Overview of Zipline

Zipline is a backtesting framework developed by Quantopian, primarily used for stocks and futures markets. With Zipline, you can implement various strategies and run simulations that include trading costs.

Installing Zipline

To use Zipline, you first need to install it as follows:

pip install zipline

The Importance of Trading Costs

Trading costs consist of several elements, notably including:

  • Spread: The difference between the buy and sell prices; the larger this difference, the higher the trading costs.
  • Commission: A fixed cost per trade, charged for each transaction.
  • Tax: Taxes incurred during trading, which vary according to each country’s tax laws.

These costs can negatively impact the performance of trading strategies, so they must be carefully considered when designing strategies.

Setting Trading Fees in Zipline

In Zipline, you can set trading fees using the set_commission() function. Here’s how to set fees in the Zipline initialization code.

from zipline.api import set_commission, commission

def initialize(context):
    # Set the commission.
    set_commission(commission.PerShare(cost=0.01, min_trade_cost=0.0))

In this code, the PerShare class sets a per-share commission for a specific stock. Here, cost=0.01 means that a fee of $0.01 per share will be charged for each transaction.

Types of Trading Commissions

Zipline supports various commission structures. The most common methods include:

  • PerShare: Set a fee per share. cost defines the per-share fee, and min_trade_cost defines the minimum fee per trade.
  • Fixed: Set a fixed fee per transaction. cost defines the fee charged per trade.

Example: Including Commission in a Zipline Strategy

The following example implements a simple buy and sell strategy using Zipline and runs a simulation that includes trading fees.

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

def initialize(context):
    set_commission(commission.PerShare(cost=0.01, min_trade_cost=0.0))

def handle_data(context, data):
    # Buy/sell under specific conditions
    if data.current(symbol('AAPL'), 'price') < 150:
        order(symbol('AAPL'), 10)  # Buy 10 shares of AAPL
    elif data.current(symbol('AAPL'), 'price') > 160:
        order(symbol('AAPL'), -10)  # Sell 10 shares of AAPL
    record(AAPL=data.current(symbol('AAPL'), 'price'))

start = datetime(2020, 1, 1)
end = datetime(2020, 12, 31)

# Download data and run the algorithm
run_algorithm(start=start, end=end, initialize=initialize, handle_data=handle_data, capital_base=10000)

Explanation of the Example

The above code monitors the price of AAPL stock from January 1, 2020, to December 31, 2020, implementing a simple strategy to buy 10 shares when the price is below $150 and to sell 10 shares when the price exceeds $160. The commission is set at $0.01 per share, so this fee applies for each buy and sell transaction.

Result Analysis

After executing the trading strategy, you can record price data using the record() function. It is important to review performance based on this data. Zipline’s simulator conducts performance analysis, including trading fees.

Check Simulation Results

import matplotlib.pyplot as plt

results = run_algorithm(...)

# Display performance graph
plt.plot(results.index, results.portfolio_value)
plt.title('Portfolio Value Over Time')
plt.xlabel('Date')
plt.ylabel('Portfolio Value')
plt.show()

Customizing Trading Fees

When analyzing the market, various strategies may be required. At that time, you may need to customize fees appropriately. Zipline supports customized commission structures, allowing you to set fees optimized for each strategy.

Example: Setting Trading Fees with External Parameters

def initialize(context):
    # Set the commission using external parameters
    cost_per_share = context.cost_per_share
    set_commission(commission.PerShare(cost=cost_per_share, min_trade_cost=0.0))

def run_my_algorithm(cost_per_share=0.01):
    run_algorithm(start=start, end=end, initialize=initialize, handle_data=handle_data, capital_base=10000,
                  cost_per_share=cost_per_share)

Conclusion

Using Zipline, you can efficiently build an automated trading system in Python, with easy and flexible settings for trading fees. Since trading fees significantly impact the performance of strategies, they must be designed and managed carefully. It is hoped that the examples provided in this material will help in developing more effective automated trading strategies.

Additionally, explore the various features of Zipline to conduct different experiments and develop your own trading strategies. Automated trading is more than just writing code; it requires a deep understanding of the market and a strategic approach.

References