As interest in stock investment has recently increased, many investors are looking to build automated trading systems. In particular, Python is favored by many developers due to its powerful libraries and ease of use. This article will provide a detailed explanation of the development process for automated trading using Python, as well as how to set up a development environment using the Kiwoom Securities API. After reading this article, you will gain a foundational understanding to create your own automated trading program, along with practical development examples.
1. What is an Automated Trading System?
An automated trading system is designed for a computer program to analyze market data and execute trades automatically without user instructions. This helps traders make consistent investment decisions without being swayed by emotions in the market. The automated trading system generates trading signals based on specific algorithms and uses these signals to make buy or sell decisions. This system requires a combination of programming knowledge and trading strategies to operate successfully.
2. What is the Kiwoom Securities API?
The Kiwoom Securities API is a tool that enables trading various financial products such as stocks, futures, and options through a program provided by Kiwoom Securities. This API offers users a range of functions, including real-time data, trade execution, balance checking, and yield analysis. Using the Kiwoom Securities API, you can easily develop and operate your own trading system.
3. Setting Up the Development Environment
3.1. Preparation Steps
To develop an automated trading system, the following preparations are necessary.
- Install Python: Download and install Python version 3.6 or higher.
- Open a Kiwoom Securities Account: Open an account with Kiwoom Securities and apply for API usage.
- Install Python Libraries: The following libraries will be used for development.
- pykiwoom: A library that helps use the Kiwoom Securities API in Python.
- pandas: A library for data analysis and manipulation.
- numpy: A library for numerical calculations.
- matplotlib: A library for data visualization.
3.2. Installing Python
To install Python, download the installation file suitable for your operating system from [python.org](https://www.python.org/downloads/) and install it. After installation, open the command prompt or terminal and enter the command `python –version` to verify if it was installed correctly.
3.3. Installing Libraries
Use pip to install the previously mentioned libraries. Enter the following commands in the command prompt one by one.
pip install pykiwoom
pip install pandas
pip install numpy
pip install matplotlib
3.4. Configuring the Kiwoom Securities API
To use the Kiwoom Securities API, you need to install and log in to the Kiwoom Securities HTS ‘Hero Moon’. After logging in, you can apply for API usage through the ‘Option’ – ‘API Settings’ menu. Once the application is completed, you can receive an API key.
4. Creating a Basic Trading System
Now that the development environment is ready, let’s implement a basic trading system. The basic structure of an automated trading system is as follows.
- Collect market data
- Generate trading signals according to the algorithm
- Execute trades
- Record and analyze results
4.1. Data Collection
First, let’s collect stock data using the Kiwoom Securities API. Below is an example of requesting data for a specific stock using pykiwoom.
from pykiwoom.kiwoom import Kiwoom
import pandas as pd
kiwoom = Kiwoom()
kiwoom.CommConnect()
# Set stock code
code = "005930" # Samsung Electronics
# Request data
df = kiwoom.get_master_last_price(code) # Request current price
print(df)
4.2. Generating Trading Signals
Now let’s set a basic trading algorithm. For example, we can generate trading signals based on the Simple Moving Average (SMA).
def generate_signal(data):
data['SMA_5'] = data['close'].rolling(window=5).mean()
data['SMA_20'] = data['close'].rolling(window=20).mean()
data['signal'] = 0
data['signal'][5:] = np.where(data['SMA_5'][5:] > data['SMA_20'][5:], 1, 0) # Buy signal
return data
4.3. Executing Trades
You can write code to execute trades based on signals as follows.
def execute_trade(signal):
if signal == 1:
# Execute buy
kiwoom.SendOrder("buysignal", "0101", code, 1, 0, 0, "03", "")
elif signal == 0:
# Execute sell
kiwoom.SendOrder("sellsignal", "0101", code, 1, 0, 0, "03", "")
5. Integrating the Entire System
Finally, let’s integrate all the above elements into a single program. We will periodically collect market data, generate signals, and execute trades.
import time
while True:
# Collect data
data = kiwoom.get_market_data(code)
# Generate trading signal
signal_data = generate_signal(data)
latest_signal = signal_data['signal'].iloc[-1]
# Execute trade
execute_trade(latest_signal)
# Wait for 30 seconds
time.sleep(30)
6. Conclusion
In this article, we explored how to build a basic automated trading system using Python and the Kiwoom Securities API. We examined the flow from data collection to generating trading signals and executing trades. I hope this script helps you in developing your own more complex and sophisticated trading systems. Based on a deep understanding of programming and investing, I encourage you to try various strategies.
In the future, I also plan to prepare articles on how to enhance automated trading systems using machine learning and data analysis techniques. Thank you for your interest!