Python Automated Trading Development, Data Visualization using matplotlib

In the process of developing an automated trading system, data visualization is an important step that helps to grasp a lot of information at a glance.
This article will explore how to visualize stock data or the prices of volatile assets using the matplotlib library and how to utilize the results in an automated trading system.

1. Importance of Data Visualization

Data visualization is extremely important for understanding and predicting complex data patterns in the stock market.

1.1. Trend Analysis in the Market

Visualized data makes it easy to identify market trends, volatility, and patterns.
For example, price charts are a quick way to grasp stock price rises and falls over a specific period.

1.2. Decision Support

When making investment decisions, visualized information allows for better judgments. It enables the quick identification of buy and sell points, thus creating opportunities to minimize losses and maximize profits.

2. Introduction to matplotlib

matplotlib is the most widely used data visualization library in Python.
It allows for the easy creation of both complex visualizations and simple graphs. You can create various types of plots using this library.

2.1. Installation Method

matplotlib can be easily installed via pip with the following command:

pip install matplotlib

3. Preparing the Data

It is necessary to prepare the data that will be used in the automated trading system.
For example, you can retrieve large amounts of stock price data using the pandas library.
Below is an example code:

import pandas as pd

# Example of retrieving stock price data from Yahoo Finance
data = pd.read_csv('https://query1.finance.yahoo.com/v7/finance/download/AAPL?period1=0&period2=9999999999&interval=1d&events=history')
data['Date'] = pd.to_datetime(data['Date'])
data.set_index('Date', inplace=True)

4. Visualizing Stock Prices

The most basic way to visualize stock price data is to create a time series chart.
The code below shows how to visualize stock data using matplotlib.

import matplotlib.pyplot as plt

# Visualizing stock prices
plt.figure(figsize=(12, 6))
plt.plot(data['Close'], label='Close Price', color='blue')
plt.title('AAPL Closing Prices')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend()
plt.grid()
plt.show()

4.1. Adding Indicators to the Graph

You can perform a more in-depth analysis by adding indicators such as moving averages to the stock price graph.

# Adding moving average
data['SMA_20'] = data['Close'].rolling(window=20).mean()

plt.figure(figsize=(12, 6))
plt.plot(data['Close'], label='Close Price', color='blue')
plt.plot(data['SMA_20'], label='20-Day SMA', color='orange')
plt.title('AAPL Closing Prices with 20-Day SMA')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend()
plt.grid()
plt.show()

5. Various Visualization Techniques

Here are various visualization techniques using matplotlib.

5.1. Histogram

A histogram is useful for visualizing the distribution of stock prices. The code below generates a histogram displaying the distribution of stock returns.

returns = data['Close'].pct_change()
plt.figure(figsize=(12, 6))
plt.hist(returns.dropna(), bins=50, alpha=0.7, color='blue')
plt.title('Histogram of Returns')
plt.xlabel('Returns')
plt.ylabel('Frequency')
plt.grid()
plt.show()

5.2. Scatter Plot

A scatter plot is useful for visualizing the relationship between two variables. For example, you can visualize the relationship between stock prices and trading volume.

plt.figure(figsize=(12, 6))
plt.scatter(data['Volume'], data['Close'], alpha=0.5, color='purple')
plt.title('Volume vs Close Price')
plt.xlabel('Volume')
plt.ylabel('Close Price')
plt.grid()
plt.show()

6. Utilizing the Results

The visualization results generated above serve as essential reference materials for automated trading strategies.
Based on this data, trading signals that meet specific conditions can be established.

def trading_signal(data):
    signals = []
    for i in range(len(data)):
        if data['Close'][i] > data['SMA_20'][i]:
            signals.append('Buy')
        else:
            signals.append('Sell')
    data['Signal'] = signals

trading_signal(data)

# Visualizing the signals
plt.figure(figsize=(12, 6))
plt.plot(data['Close'], label='Close Price', color='blue')
plt.plot(data['SMA_20'], label='20-Day SMA', color='orange')
plt.scatter(data.index, data[data['Signal'] == 'Buy']['Close'], marker='^', color='green', label='Buy Signal', alpha=1)
plt.scatter(data.index, data[data['Signal'] == 'Sell']['Close'], marker='v', color='red', label='Sell Signal', alpha=1)
plt.title('Trading Signals')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend()
plt.grid()
plt.show()

Conclusion

In this course, we learned how to visualize stock data using the matplotlib library in Python.
Data visualization plays a crucial role in automated trading systems, helping to make investment decisions.
Practice with real data and develop your own automated trading strategy.

Thank you!