python automated trading development, chart drawing

Python Automated Trading Development: Drawing Charts

In the process of developing an automated trading system, it is very important to effectively visualize market data. This course will detail how to draw charts using Python and how to implement automated trading strategies through them. The topics covered will include:

  • The Importance of Charts
  • Installation of Essential Libraries for Drawing Charts
  • Visualization of Price Data Over Time
  • Customizing Charts for Applying Automated Trading Strategies
  • How to Update Charts in Real Time

1. The Importance of Charts

When building an automated trading system, charts play an important role. Charts visually represent price change patterns, helping traders easily understand the market’s state and identify trends. They are also essential for generating trading signals through various technical analysis indicators.

2. Installation of Essential Libraries for Drawing Charts

In Python, various libraries can be used to draw charts, with the most commonly used libraries being Matplotlib and Pandas. You can install the libraries as follows:

pip install matplotlib pandas

3. Visualization of Price Data Over Time

Now let’s visualize price data over time through actual example code. The code below is an example of loading stock price data and drawing a chart.

import pandas as pd
import matplotlib.pyplot as plt

# Load data (assumed CSV file)
data = pd.read_csv('stock_data.csv')
data['Date'] = pd.to_datetime(data['Date'])  # Convert date format
data.set_index('Date', inplace=True)  # Set date as index

# Draw chart
plt.figure(figsize=(12, 6))
plt.plot(data['Close'], label='Close', color='blue')
plt.title('Stock Close Price Changes')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend()
plt.grid()
plt.show()

The ‘stock_data.csv’ file contains the stock price data. The ‘Close’ column represents the closing price, which is visualized over time.

4. Customizing Charts for Applying Automated Trading Strategies

By adding technical analysis indicators and trading signals to the chart, you can better understand the market’s state than simply viewing the price data. For example, you can determine trading points by adding a moving average (MA). Let’s add a moving average to the code as follows.

# Add moving average line
data['MA20'] = data['Close'].rolling(window=20).mean()  # 20-day moving average

# Draw chart (including moving average line)
plt.figure(figsize=(12, 6))
plt.plot(data['Close'], label='Close', color='blue')
plt.plot(data['MA20'], label='20-day Moving Average', color='orange', linestyle='--')
plt.title('Stock Close Price and Moving Average')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend()
plt.grid()
plt.show()

In the above code, a 20-day moving average line was added to change the appearance of the chart. This allows you to implement buy signals when the price is above the moving average line and sell signals when it is below.

5. How to Update Charts in Real Time

Since the automated trading system must receive market data in real time, it is important to update the charts in real time. To do this, you can utilize the animation feature of Matplotlib. Below is an example of updating real-time price data:

import numpy as np
from matplotlib.animation import FuncAnimation

# Initial data setup
x_data = []
y_data = []

fig, ax = plt.subplots()
line, = ax.plot([], [], label='Real-time Price', color='blue')
ax.set_xlim(0, 100)  # Set x-axis range
ax.set_ylim(0, 100)  # Set y-axis range
ax.legend()
ax.grid()

def init():
    line.set_data([], [])
    return line,

def update(frame):
    x_data.append(frame)
    y_data.append(np.random.randint(0, 100))  # Assuming price with random data
    line.set_data(x_data, y_data)
    return line,

ani = FuncAnimation(fig, update, frames=np.arange(0, 100), init_func=init, blit=True)
plt.show()

The above code is a simple example of updating a real-time chart using randomly generated price data. In an actual automated trading system, you can use real-time price data received through an API to update the chart.

Conclusion

Drawing charts using Python is a very important aspect of visual verification and strategy development in automated trading system development. The topics covered in this course include drawing stock data charts, adding moving averages, and methods for real-time data updates. With these fundamental charting skills, you can further develop your automated trading system.

Now you are equipped with the basic knowledge needed to draw fundamental charts using Python and to build automated trading strategies. It would also be a good experience to implement complex trading strategies along with various technical indicators. I hope you can create a more advanced automated trading system through your future work.