Automatic Trading Development with Python, Setting Initial Investment Amount in Zipline

The automated trading system is a method that is gaining increasing popularity for trading stocks and financial assets. This article will discuss how to develop an automated trading system using Python. In particular, we will focus on setting the initial investment amount through the Zipline library.

Introduction to Zipline

Zipline is a Python library developed by Quantopian that provides tools for financial data analysis and algorithmic trading. Using Zipline, you can perform backtesting and validate investment strategies. In this course, we will explain how to set the initial investment amount through Zipline.

Installing Zipline

To use Zipline, it must be installed in your Python environment. The current version has been confirmed to work on Python 3.6 and above. You can install it using the following command:

pip install zipline

Setting the Initial Investment Amount

When building an automated trading system, the initial investment amount is an important factor. This amount determines how much capital will be used in each trade and has a significant impact on the performance of the overall investment strategy.

Zipline provides a basic method for setting the initial investment amount, which is typically used as an input variable for the algorithm. If the initial amount is too small, the number of trades affecting the portfolio may be limited; if it is too large, risk management and diversification may become difficult.

How to Set the Initial Investment Amount in Zipline

In Zipline, the initial investment amount is set through the run_algorithm function’s capital_base parameter. Below is an example code for setting the initial investment amount.


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

# Define trading algorithm
def initialize(context):
    context.asset = symbol('AAPL')
    context.capital_base = 10000  # Set initial investment amount

def handle_data(context, data):
    # Buy and sell logic
    if data.current(context.asset, 'price') < 150:
        order(context.asset, 10)  # Buy 10 shares
    elif data.current(context.asset, 'price') > 200:
        order(context.asset, -10)  # Sell 10 shares

    # Record portfolio status
    record(AAPL=data.current(context.asset, 'price'))

# Perform backtest
start = pd.Timestamp('2020-01-01', tz='utc')
end = pd.Timestamp('2021-01-01', tz='utc')

result = run_algorithm(start=start, end=end, 
                       initialize=initialize, 
                       capital_base=10000,  # Initial investment amount
                       handle_data=handle_data,
                       data_frequency='daily',
                       bundle='quantopian-quandl')

Explanation of Example Code

In the above code, a simple algorithm is defined. The initialize function selects the asset to buy and sets the initial investment amount. The handle_data function executes buy or sell orders based on the current asset price. Below, each element will be explained in more detail.

  • initialize(context): This function is used by Zipline to initialize the algorithm settings. It primarily sets the assets and variables to be used in the algorithm.
  • handle_data(context, data): This function runs the algorithm based on the given data every day. It makes trading decisions based on stock prices.
  • record(): This function records specific data for later analysis or visualization.

Investment Strategy and Risk Management

When devising an investment strategy, risk management is very important in addition to the initial investment amount. There needs to be a plan regarding how exposed the investment portfolio is to risk and how to manage those risks.

Risk Management Techniques

  • Diversification: Diversifying assets to reduce the impact of losses from specific assets on the overall portfolio.
  • Setting Stop-Loss: A method to automatically sell when a predefined loss percentage is reached, minimizing losses.
  • Using Leverage: Proper use of leverage can maximize asset investment returns; however, this can increase risk.

Performing Backtests Using Zipline

Zipline offers a backtesting feature that evaluates the performance of algorithms based on historical data. This allows for pre-validation of the effectiveness of investment strategies. After setting the initial investment amount, backtests can be conducted to analyze how much return can be obtained.


# Visualization of results
import matplotlib.pyplot as plt

plt.figure(figsize=(12, 8))
plt.plot(result.index, result.portfolio_value)
plt.title('Portfolio Value Over Time')
plt.xlabel('Date')
plt.ylabel('Portfolio Value')
plt.grid()
plt.show()

Conclusion

In this course, we learned about the importance and methods of setting the initial investment amount in automated trading development using Python and Zipline. The initial investment amount significantly affects investment performance, making it essential to establish an investment strategy based on this. Moving forward, we can implement more complex algorithms and various investment strategies, with Zipline serving as a powerful tool for these tasks.

Additionally, it is important to remember that risk management techniques can lead to safer investments, and validating performance through backtesting is also crucial. I hope this article helps you in the development of your automated trading system.

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