Python Automatic Trading Development, COM and Python

1. Introduction

Recently, the digitalization of financial markets has increased interest in automatic trading algorithms. Python is very popular for developing automated trading systems due to its flexibility and powerful libraries. In this course, we will explain in detail how to build an automated trading system with Python using COM (Component Object Model). COM is a technology developed by Microsoft that supports interoperability between various programming languages. This allows us to integrate Python with financial trading platforms.

2. Understanding COM

COM defines how software components call and communicate with each other. Using COM, you can combine components written in various programming languages. COM is primarily used in Windows environments and is widely used for integration with Microsoft Office applications, browsers, and financial data providers.

Using Python and COM, you can collect and process data from applications like Excel automatically. Additionally, you can develop automated trading systems utilizing the APIs of specific brokers.

3. Integrating Python with COM

To use COM in Python, you need to use the pywin32 library. This library allows you to create and manipulate COM objects in Python. First, you need to install this library. You can install it using the following command.

pip install pywin32

Once the installation is complete, you can create COM objects to interact with applications like Excel.

3.1 Connecting to Excel

Let’s look at example code to connect to Excel through COM:

import win32com.client

excel = win32com.client.Dispatch('Excel.Application')  # Run the Excel application
excel.Visible = True  # Set Excel window to be visible

wb = excel.Workbooks.Add()  # Add a new workbook
ws = wb.Worksheets.Add()  # Add a new worksheet
ws.Cells(1, 1).Value = "Hello, Automated Trading!"  # Enter value in cell

# Exit after completing the work in Excel
excel.Quit()

The code above is a simple example that runs the Excel application, adds a new workbook and worksheet, and enters a value into a cell. Based on this, more complex automated trading systems can be created.

4. Integrating with Financial APIs

As mentioned earlier, using COM allows you to implement automation trading by integrating with various types of data sources. Now, let’s examine financial APIs and how to request and process data using Python.

Well-known financial APIs include Alpha Vantage, Tradier, and OANDA. Through these APIs, you can query real-time price information and make trading requests. I will introduce how to integrate with a real API using a simple Python example.

4.1 Using the Alpha Vantage API

You need to obtain an API key from Alpha Vantage. With the API key, you can request real-time stock prices in the following way:

import requests

def get_stock_price(symbol):
    API_KEY = 'YOUR_API_KEY'  # Enter your API key here
    url = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol={symbol}&interval=1min&apikey={API_KEY}'
    response = requests.get(url)
    data = response.json()

    # Extract the most recent price information for the stock
    last_refreshed = data['Meta Data']['3. Last Refreshed']
    latest_price = data['Time Series (1min)'][last_refreshed]['1. open']
    
    return float(latest_price)

symbol = 'AAPL'  # Stock code (e.g., Apple)
price = get_stock_price(symbol)
print(f"The latest price of {symbol}: {price}")

This code is an example of fetching the real-time price of a specific stock through the Alpha Vantage API. You can create more sophisticated automated trading systems through analysis and statistical processing of the financial market.

5. Designing Strategies

An automated trading system requires an appropriate trading strategy. You can establish trading strategies by utilizing various technical analysis tools and indicators.

5.1 Basic Trading Strategy

One of the simplest strategies is the moving average crossover strategy. This strategy generates a buy signal when the short-term moving average crosses above the long-term moving average, and a sell signal when it crosses below.

You can implement the moving average crossover strategy through the following simple code:

import pandas as pd
import numpy as np

def moving_average_strategy(data, short_window=20, long_window=50):
    signals = pd.DataFrame(index=data.index)
    signals['price'] = data['close']
    signals['short_mavg'] = data['close'].rolling(window=short_window, min_periods=1).mean()
    signals['long_mavg'] = data['close'].rolling(window=long_window, min_periods=1).mean()
    signals['signal'] = 0.0
    signals['signal'][short_window:] = np.where(signals['short_mavg'][short_window:] > signals['long_mavg'][short_window:], 1.0, 0.0)
    signals['positions'] = signals['signal'].diff()

    return signals

# Load example data
data = pd.read_csv('stock_data.csv')  # Load data file
signals = moving_average_strategy(data)

print(signals)

The code above calculates the short-term and long-term moving averages from stock price data and generates buy and sell signals based on them.

6. Executing Orders

After a trading signal is generated, you need to execute real orders. To do this, you must send order requests through each broker’s API. For example, here’s how to use the Tradier API:

def place_order(symbol, quantity, order_type='market'):
    API_URL = 'https://api.tradier.com/v1/accounts/{account_id}/orders'
    headers = {
        'Authorization': 'Bearer {YOUR_ACCESS_TOKEN}',  # User access token
        'Content-Type': 'application/json'
    }
    order_data = {
        'order': {
            'symbol': symbol,
            'quantity': quantity,
            'side': 'buy',  # Buy
            'type': order_type,
            'duration': 'GTC'  # Good 'Til Canceled
        }
    }
    
    response = requests.post(API_URL, headers=headers, json=order_data)
    return response.json()

order_response = place_order('AAPL', 10)  # Buy 10 shares of Apple stock
print(order_response)

The code above demonstrates how to execute real orders through the Tradier API. By adjusting the parameters of the order, you can use various order types and strategies.

7. Analyzing and Improving Results

After developing an automated trading system, the process of analyzing and improving results is important. You can improve the system by evaluating metrics such as return rate, win rate, and maximum drawdown.

7.1 Performance Metric Analysis

You should analyze metrics such as return and risk through backtesting. For example, the following code can be used to analyze performance.

def evaluate_performance(signals):
    returns = signals['price'].pct_change()
    strategy_returns = returns * signals['signal'].shift(1)  # Strategy returns
    cumulative_returns = (1 + strategy_returns).cumprod()  # Cumulative returns
    return cumulative_returns

performance = evaluate_performance(signals)
print(performance)

This code is an example that calculates cumulative returns based on returns identified according to the signals. It helps to judge the effectiveness of the strategy.

8. Conclusion

In this course, we covered how to design an automated trading system using Python and COM. By integrating with Excel and financial APIs through COM, we can collect and process data, allowing the implementation of various automated trading strategies. We also presented methods for performance analysis and improvement.

An automated trading system demands speed and accuracy in execution. It also requires flexibility to adapt to changes in the market. Therefore, continuous monitoring and improvement are crucial. We hope that interest in investment and trading using Python will continue to grow.