Python Automated Trading Development, PyQt QTableWidget

Developing an automated trading system using Python is an excellent way to maximize investment efficiency through the automation of financial trading. Here, we will explore how to use PyQt’s QTableWidget for data visualization and user interface (UI) configuration. Additionally, we will design a method to monitor real-time stock data using QTableWidget and integrate it with an automated trading system.

1. Overview of Python Automated Trading System

An automated trading algorithm refers to a trading program making decisions on behalf of the user to execute their strategy. Python has gained popularity in data analysis and machine learning fields due to its easy syntax and powerful libraries.

1.1 Necessity of Automated Trading Systems

Automated trading systems offer several key advantages:

  • 24/7 Monitoring: While humans can rest or become fatigued, programs can continuously observe the market.
  • Accuracy: Programs execute trades according to specified algorithms, eliminating emotional influences.
  • Speed: Automated trading can execute orders within milliseconds.

2. Introduction to PyQt and QTableWidget

PyQt is a library for GUI applications written in Python, based on the Qt framework. PyQt provides various widgets, among which QTableWidget is very useful for displaying data in a tabular format.

2.1 Key Features of QTableWidget

  • Supports cell-based data entry and display
  • Allows storage of various data types in each cell
  • Provides sorting and searching features
  • Supports custom styling

2.2 Utilizing QTableWidget

Let’s dive into how to use QTableWidget with example code.

2.3 Basic QTableWidget Example


import sys
from PyQt5.QtWidgets import QApplication, QTableWidget, QTableWidgetItem, QVBoxLayout, QWidget

class TableWidgetExample(QWidget):
    def __init__(self):
        super().__init__()
        self.setWindowTitle("QTableWidget Example")
        self.resize(600, 400)

        self.tableWidget = QTableWidget(self)
        self.tableWidget.setRowCount(5)
        self.tableWidget.setColumnCount(3)

        self.tableWidget.setHorizontalHeaderLabels(["Code", "Stock Name", "Current Price"])
        
        # Insert data
        self.tableWidget.setItem(0, 0, QTableWidgetItem("005930"))
        self.tableWidget.setItem(0, 1, QTableWidgetItem("Samsung Electronics"))
        self.tableWidget.setItem(0, 2, QTableWidgetItem("90000"))

        layout = QVBoxLayout()
        layout.addWidget(self.tableWidget)
        self.setLayout(layout)

if __name__ == "__main__":
    app = QApplication(sys.argv)
    mainWin = TableWidgetExample()
    mainWin.show()
    sys.exit(app.exec_())
    

The code above is an example of creating a basic QTableWidget. It sets up 5 rows and 3 columns and inserts Samsung Electronics data in the first row. Now, we will extend this widget to integrate it with an automated trading system.

3. Basic Structure of the Automated Trading System

The basic elements constituting an automated trading system are data feed, strategy logic, order execution, and reporting. I will explain how to visualize these elements in real time using PyQt and QTableWidget.

3.1 Real-Time Data Feed

You can retrieve real-time data using stock data APIs. For example, I will show you how to use the yfinance library to fetch data for Apple stocks.


import yfinance as yf

def get_real_time_data():
    ticker = 'AAPL'
    data = yf.download(ticker, period='1d', interval='1m')
    return data
    

3.2 Automated Trading Strategy Logic

A simple trading strategy is the moving average crossover strategy. This involves buying when the short-term moving average of stock prices exceeds the long-term moving average, and selling in the opposite case. Let’s implement this in code.


def moving_average_strategy(data):
    short_window = 5
    long_window = 20
    
    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
    

3.3 Integrating with QTableWidget

Now, we will update the previously created QTableWidget with real-time data and calculated signals. I will explain how to set up a timer to automatically update the data and display the signals.


from PyQt5.QtCore import QTimer
import pandas as pd
import numpy as np

class TradingApp(QWidget):
    def __init__(self):
        super().__init__()
        self.setWindowTitle("Automated Trading System")
        self.resize(800, 600)
        
        # Initialize QTableWidget and data
        self.tableWidget = QTableWidget(self)
        self.tableWidget.setRowCount(10)
        self.tableWidget.setColumnCount(4)
        self.tableWidget.setHorizontalHeaderLabels(["Code", "Current Price", "Signal", "Position"])
        
        self.layout = QVBoxLayout()
        self.layout.addWidget(self.tableWidget)
        self.setLayout(self.layout)
        
        # Data update timer
        self.timer = QTimer(self)
        self.timer.timeout.connect(self.update_table)
        self.timer.start(10000)  # Update every 10 seconds

    def update_table(self):
        data = get_real_time_data()  # Fetch data
        signals = moving_average_strategy(data)

        for i in range(len(signals)):
            self.tableWidget.setItem(i, 0, QTableWidgetItem("AAPL"))  # Code
            self.tableWidget.setItem(i, 1, QTableWidgetItem(str(signals['price'].iloc[i])))  # Current Price
            self.tableWidget.setItem(i, 2, QTableWidgetItem(str(signals['signal'].iloc[i])))  # Signal
            self.tableWidget.setItem(i, 3, QTableWidgetItem(str(signals['positions'].iloc[i])))  # Position

if __name__ == "__main__":
    app = QApplication(sys.argv)
    mainWin = TradingApp()
    mainWin.show()
    sys.exit(app.exec_())
    

4. Conclusion

Today, we explored the process of developing a simple automated trading system using PyQt’s QTableWidget. We learned how to build a UI that provides real-time data to users and displays it based on automated trading strategies. Furthermore, you can develop complex systems utilizing advanced strategies, reporting, and database integration.

I hope you continue to explore more topics and develop better trading systems.

Automated Trading Development, PyQt QSpinBox 481

Python has established itself as a powerful tool for data analysis and automation. Especially in developing automated trading systems, the utility of Python is highly regarded by many investors. In this article, we will explore how to implement real-time data adjustment functionality using PyQt’s QSpinBox, and how to develop a more intuitive automated trading system through it.

1. Overview of Automated Trading Systems

An automated trading system is a system that automatically trades stocks, cryptocurrencies, and more according to pre-set algorithms. Such systems minimize emotional involvement and enable more accurate trading. Python is a powerful tool that allows for easy development of automated trading systems using various financial data APIs and libraries.

2. What is PyQt?

PyQt is a library that allows the use of the Qt framework in Python. Qt is a powerful GUI development toolkit that supports application development across various platforms. Using PyQt, you can create trading tools that interact with users through an intuitive user interface.

3. What is QSpinBox?

QSpinBox is a GUI component for receiving integer values from users. Users can increase or decrease the integer value by clicking the arrow buttons, which makes it very useful for setting specific values. In automated trading systems, it allows adjustments to frequently used parameters, such as purchase quantity and stop-loss ratio.

4. Setting Up the Development Environment

You need to set up the following environment:

  • Install Python 3.x
  • Install PyQt5: pip install PyQt5
  • Install the automated trading library (e.g., pip install requests)

5. Example Code for QSpinBox

Below is a simple example code using PyQt5’s QSpinBox. This example shows a GUI program where the user inputs the purchase quantity and clicks a button to display that quantity.

import sys
from PyQt5.QtWidgets import QApplication, QWidget, QVBoxLayout, QLabel, QSpinBox, QPushButton


class AutoTradeApp(QWidget):
    def __init__(self):
        super().__init__()
        
        self.initUI()
        
    def initUI(self):
        self.setWindowTitle('Automated Trading Quantity Adjustment')
        self.setGeometry(100, 100, 300, 200)

        layout = QVBoxLayout()

        self.label = QLabel('Please set the purchase quantity:')
        layout.addWidget(self.label)

        self.spinBox = QSpinBox(self)
        self.spinBox.setMinimum(1)  # Minimum quantity 1
        self.spinBox.setMaximum(100)  # Maximum quantity 100
        layout.addWidget(self.spinBox)

        self.button = QPushButton('Confirm Quantity', self)
        self.button.clicked.connect(self.show_value)
        layout.addWidget(self.button)

        self.setLayout(layout)

    def show_value(self):
        quantity = self.spinBox.value()
        self.label.setText(f'Selected purchase quantity: {quantity}')


if __name__ == '__main__':
    app = QApplication(sys.argv)
    ex = AutoTradeApp()
    ex.show()
    sys.exit(app.exec_())

5.1 Explanation of the Code

Let’s take a look at some important parts of the code above.

  • QSpinBox Settings: The setMinimum and setMaximum methods are used to specify the range of integers that can be set.
  • Button Click Event: The clicked.connect(self.show_value) line calls the show_value method when the button is clicked.
  • Real-time Update: When the user adjusts the quantity and clicks the button, the selected quantity is displayed on the label.

6. Integrating into Automated Trading System

I will explain how to integrate the QSpinBox implemented above as part of an automated trading system. Building an automated trading system requires communication with financial APIs. Here, we will implement a simple method to send a trading request as example code.

import requests

class AutoTradeApp(QWidget):
    # ... (remaining code is the same as before)

    def place_order(self, quantity):
        # Here, we set the URL and payload as an example before sending the actual API request.
        api_url = 'https://api.yourbroker.com/place_order'
        payload = {
            'symbol': 'AAPL',  # Example for Apple stock
            'quantity': quantity,
            'type': 'buy'
        }

        response = requests.post(api_url, json=payload)
        
        if response.status_code == 200:
            self.label.setText(f'Order completed: bought {quantity} shares')
        else:
            self.label.setText('Order failed!')

    def show_value(self):
        quantity = self.spinBox.value()
        self.place_order(quantity)

In the above code, the place_order method is the process of submitting an order to the stock trading API. Here, a request is sent to buy Apple (AAPL) shares as an example.

6.1 API Connection and Authentication

API connections for automated trading systems typically require an authentication token. It is advisable to securely store the API key in environment variables or configuration files and add it to the headers during requests. For example:

headers = {
    'Authorization': 'Bearer YOUR_API_TOKEN'
}
response = requests.post(api_url, json=payload, headers=headers)

7. Error Handling and Log Management

Error handling is crucial for automated trading systems that operate in real time. You should verify success using the API request response code and provide appropriate messages for failures. Additionally, keeping a trade log can be useful for future analysis and debugging.

import logging

logging.basicConfig(filename='trade.log', level=logging.INFO)

def place_order(self, quantity):
    # ...
    
    if response.status_code == 200:
        logging.info(f'Order completed: bought {quantity} shares')
    else:
        logging.error(f'Order failed: {response.content}')

8. Conclusion

In this article, we introduced a way to intuitively adjust the purchase quantity of an automated trading system using PyQt’s QSpinBox. We also covered the implementation of order placement and error handling through APIs to build a more stable automated trading environment. These features will further enhance the usefulness of automated trading systems. Based on this, we encourage you to develop your own automated trading system.

Lastly, automated trading systems are investment tools that carry risks. Therefore, please proceed cautiously with thorough testing and analysis.

Automatic Trading Development with Python, PyQt QRadioButton and QGroupBox

An automated trading system is effective for trading various financial products such as stocks, foreign exchange, and cryptocurrencies.
In this article, we will explain how to build an automated trading UI using Python’s PyQt library.
In particular, we will implement a feature that allows users to easily select options using QRadioButton and QGroupBox.

What is PyQt?

PyQt is a Qt GUI framework for Python that helps you easily build GUIs for complex applications.
By leveraging Qt’s powerful features, you can create desktop applications and it provides various widgets to facilitate UI development.

Introduction to QRadioButton and QGroupBox

QRadioButton: QRadioButton is a radio button that allows users to select one option among multiple choices.
It is typically provided by grouping related options together and is designed to allow only one selection at a time.

QGroupBox: QGroupBox helps to visually present groups of widgets to the user more clearly.
Widgets within the group can be controlled as a single unit, which clarifies the UI structure and provides direction to the user.

Basic Structure of the Automated Trading System

An automated trading system typically includes the following components:

  • Data collection: Collect market data from stocks, foreign exchange, cryptocurrencies, etc.
  • Strategy implementation: Define and implement trading strategies
  • Order execution: Process orders in real-time
  • UI composition: Interface for user interaction

Environment Setup

To use PyQt, you first need to install the required libraries. You can install them by entering the command as follows.

pip install PyQt5

Example of Using QRadioButton and QGroupBox

Below is an example code of a basic PyQt application utilizing QRadioButton and QGroupBox.
This application is designed to allow users to select one of the trading options (buy or sell).

Example Code


import sys
from PyQt5.QtWidgets import QApplication, QWidget, QVBoxLayout, QGroupBox, QRadioButton, QPushButton, QLabel

class TradingApp(QWidget):
    def __init__(self):
        super().__init__()
        self.setWindowTitle('Automated Trading System')
        self.setGeometry(100, 100, 300, 250)
        self.initUI()
    
    def initUI(self):
        layout = QVBoxLayout()

        # Create QGroupBox
        self.groupbox = QGroupBox('Trading Options')
        self.radio_buy = QRadioButton('Buy')
        self.radio_sell = QRadioButton('Sell')

        # Add QRadioButton to QGroupBox
        self.groupbox_layout = QVBoxLayout()
        self.groupbox_layout.addWidget(self.radio_buy)
        self.groupbox_layout.addWidget(self.radio_sell)
        self.groupbox.setLayout(self.groupbox_layout)

        # Button to display selected option
        self.button = QPushButton('Confirm Selection')
        self.button.clicked.connect(self.show_selection)

        # Add widgets to layout
        layout.addWidget(self.groupbox)
        layout.addWidget(self.button)

        # Set layout
        self.setLayout(layout)
        self.label = QLabel('Selected Option: None')
        layout.addWidget(self.label)
    
    def show_selection(self):
        if self.radio_buy.isChecked():
            self.label.setText('Selected Option: Buy')
        elif self.radio_sell.isChecked():
            self.label.setText('Selected Option: Sell')
        else:
            self.label.setText('Selected Option: None')

if __name__ == '__main__':
    app = QApplication(sys.argv)
    window = TradingApp()
    window.show()
    sys.exit(app.exec_())
    

The above code creates a basic PyQt application that provides a UI for selecting trading options. The user can select either the ‘Buy’ or ‘Sell’ option and, upon clicking the ‘Confirm Selection’ button, the selected option will be displayed on the screen.

Code Explanation

TradingApp Class: Inherits from QWidget to define the basic application.
The __init__ method sets the window title and size, and calls the initUI method.

initUI Method: Initializes and arranges the UI elements. It adds a QGroupBox and two QRadioButtons.

show_selection Method: Changes the text of the QLabel based on the user’s selected option.

Execution Result

Running the code will create a basic UI as shown below. Users can choose between buy or sell, and the chosen option will be displayed in the QLabel upon clicking the button.

Automated Trading UI Example

Moving Forward

Based on the above example, you can add more features to the automated trading system.
For example, you can automatically execute algorithms based on the selected trading option or carry out trades based on user-defined conditions.
Additionally, you can implement features to add real-time data feeds or perform trades on specific stocks.

The next step is to add logic for trading based on user input. For instance, after allowing the user to input buy and sell prices for a specific stock,
try writing algorithms that automatically execute trades based on those conditions. In this process, you can use APIs to connect to the real market.

Conclusion

In this article, we learned how to construct the UI for an automated trading program using PyQt,
and we explored the basic usage of QRadioButton and QGroupBox.
By implementing separate data collection and algorithm processing parts, you can create a practical automated trading system.
In the future, try to challenge yourself with the use of various libraries and APIs for a more advanced automated trading system.

© 2023 Automated Trading Development Blog.

Automated Trading Development in Python, PyQt QPushButton

The development of an automated trading system using Python has gained significant interest among investors in recent years. Moreover, configuring a GUI (Graphical User Interface) to provide a user-friendly interface is essential. This article will detail how to structure the user interface of a Python-based automated trading system using the PyQt library. In particular, it will provide practical examples by focusing on QPushButton for handling button click events and implementing automated trading logic.

1. What is PyQt?

PyQt is a library that allows you to develop Qt GUI applications in Python. Qt is a C++-based framework that provides advanced user interfaces that can run on various platforms. PyQt makes it easy to use many of its features with Python syntax. Many investors choose this library because of its convenience.

2. Installing PyQt

PyQt5 is one of the latest versions of PyQt, and it can be easily installed via pip. Enter the following command in the terminal or command prompt:

pip install PyQt5

Once the installation is complete, you can import and use PyQt.

3. Basic Structure of a PyQt Application

A PyQt application mainly consists of the following structure. Here is a basic code snippet.


import sys
from PyQt5.QtWidgets import QApplication, QMainWindow

class MyWindow(QMainWindow):
    def __init__(self):
        super().__init__()
        self.setWindowTitle("Automated Trading System")
        self.setGeometry(100, 100, 800, 600)

if __name__ == "__main__":
    app = QApplication(sys.argv)
    window = MyWindow()
    window.show()
    sys.exit(app.exec_())
    

4. Understanding QPushButton

QPushButton is a button class provided by PyQt. You can set it up to trigger specific events when the user clicks the button. The code to add a basic QPushButton is as follows.


from PyQt5.QtWidgets import QPushButton, QVBoxLayout

class MyWindow(QMainWindow):
    def __init__(self):
        super().__init__()
        self.setWindowTitle("Automated Trading System")
        self.setGeometry(100, 100, 800, 600)
        
        self.button = QPushButton("Start Trading")
        self.button.clicked.connect(self.start_trading)

        layout = QVBoxLayout()
        layout.addWidget(self.button)

        central_widget = QWidget()
        central_widget.setLayout(layout)
        self.setCentralWidget(central_widget)

    def start_trading(self):
        print("Start Trading button clicked")
    

5. Button Click Events and Automated Trading Logic

To check that the button click event works correctly, let’s customize the automated trading logic. We will add code to simulate trading virtually.


import random

class MyWindow(QMainWindow):
    def __init__(self):
        super().__init__()
        self.setWindowTitle("Automated Trading System")
        self.setGeometry(100, 100, 800, 600)
        
        self.button = QPushButton("Start Trading")
        self.button.clicked.connect(self.start_trading)

        layout = QVBoxLayout()
        layout.addWidget(self.button)

        central_widget = QWidget()
        central_widget.setLayout(layout)
        self.setCentralWidget(central_widget)

    def start_trading(self):
        market_price = random.uniform(100, 200)  # Random market price
        print(f"Current market price: {market_price:.2f} currency")
        # Order logic can be implemented (buy/sell)
    

6. Enhancing GUI Design and Usability

Designing a user-friendly interface is extremely important. Here, we will add a QLabel to provide information to the user.


from PyQt5.QtWidgets import QLabel

class MyWindow(QMainWindow):
    def __init__(self):
        super().__init__()
        self.setWindowTitle("Automated Trading System")
        self.setGeometry(100, 100, 800, 600)
        
        self.button = QPushButton("Start Trading")
        self.button.clicked.connect(self.start_trading)

        self.label = QLabel("Please click the start button.")
        
        layout = QVBoxLayout()
        layout.addWidget(self.label)
        layout.addWidget(self.button)

        central_widget = QWidget()
        central_widget.setLayout(layout)
        self.setCentralWidget(central_widget)

    def start_trading(self):
        market_price = random.uniform(100, 200)  # Random market price
        self.label.setText(f"Current market price: {market_price:.2f} currency")
    

7. Implementing More Advanced Automated Trading Logic

Now you can build a more advanced automated trading system using actual financial APIs. For example, you can connect to real-time data using APIs like Alpaca, Binance, Upbit, and allow users to execute buy and sell orders based on button clicks.


import requests

def start_trading(self):
    # Use API keys and secret keys for authentication here.
    market_price = random.uniform(100, 200)  # Random market price
    self.label.setText(f"Current market price: {market_price:.2f} currency")
    
    # Example buy order
    if market_price < 150:
        print("Executing buy order")
        # Example API call
        # response = requests.post("API_ENDPOINT", ...)

    # Example sell order
    elif market_price > 170:
        print("Executing sell order")
        # Example API call
        # response = requests.post("API_ENDPOINT", ...)
    

8. Error Handling and Exception Management

When developing an automated trading system, error handling logic that considers exceptional situations is essential. Please add try-except statements to handle network errors, API response errors, etc.


def start_trading(self):
    try:
        market_price = random.uniform(100, 200)  # Random market price
        self.label.setText(f"Current market price: {market_price:.2f} currency")

        if market_price < 150:
            print("Executing buy order")
            # Example API call
            # response = requests.post("API_ENDPOINT", ...)
            # if response.status_code != 200:
            #     raise Exception("Buy order failed")

    except Exception as e:
        print(f"Error occurred: {e}")
        self.label.setText("An error has occurred.")
    

9. Conclusion

In this tutorial, we focused on building a Python-based automated trading system using PyQt. Starting from event handling with QPushButton, we covered basic trading logic implementation, enhanced user interface design, connecting with APIs, and basic concepts of error handling and exception management. Through this, you have laid a foundation for creating more advanced automated trading systems.

I hope you continue to improve your skills through practice, and if you have any additional questions or need assistance, please feel free to reach out.

Automated Trading Development, PyQt QLineEdit and QStatusBar

Python is widely used in data analysis and finance, and it has become a powerful tool for developing automated trading systems. In this article, we will take a closer look at how to create a user interface using PyQt, and develop an automated trading program utilizing QLineEdit and QStatusBar.

Introduction to Python and PyQt

Python is used in various fields due to its simple and intuitive syntax, and it is particularly effective for processing and analyzing financial data. PyQt is a binding of the Qt framework developed in Python and C++, which allows for easy construction of advanced GUI applications. Users can easily place graphical elements and minimize code writing to reduce development time.

Setting Up the Environment

To install PyQt, you can use pip to install the necessary packages. Use the command below.

pip install PyQt5

Utilizing the QLineEdit Widget

QLineEdit is a widget for text input that helps users to enter information such as trading items or prices. Let’s look at how to create and manipulate a basic QLineEdit widget.

Basic Usage of QLineEdit

To use the QLineEdit widget, you need to structure a PyQt5 GUI application. Below is an example code featuring a basic QLineEdit.


import sys
from PyQt5.QtWidgets import QApplication, QWidget, QVBoxLayout, QLineEdit, QLabel

class MyApp(QWidget):
    def __init__(self):
        super().__init__()

        self.initUI()

    def initUI(self):
        layout = QVBoxLayout()

        self.label = QLabel('Enter Stock Code:')
        self.line_edit = QLineEdit(self)
        self.line_edit.setPlaceholderText('Please enter the stock code.')

        layout.addWidget(self.label)
        layout.addWidget(self.line_edit)

        self.setLayout(layout)
        self.setWindowTitle('Python Automated Trading')
        self.show()

if __name__ == '__main__':
    app = QApplication(sys.argv)
    ex = MyApp()
    sys.exit(app.exec_())
        

Signals with QLineEdit

QLineEdit emits signals based on user input. For example, you can use the returnPressed() signal when the user presses the Enter key after completing their input. You can connect this signal to a slot to process the input value.


        self.line_edit.returnPressed.connect(self.processInput)

    def processInput(self):
        input_text = self.line_edit.text()
        print(f'Entered Stock Code: {input_text}')
        # You can add the automated trading logic using the stock code here.
        

Displaying Status Messages with QStatusBar

QStatusBar is a widget that provides status messages to the user about the application. It is useful for delivering important information or notifications to the user during trading.

Basic Usage of QStatusBar

Add a QStatusBar to help users understand the operational status of the program. Below is an example code using QStatusBar.


from PyQt5.QtWidgets import QMainWindow, QStatusBar

class MyApp(QMainWindow):
    def __init__(self):
        super().__init__()
        self.setWindowTitle('Python Automated Trading')

        self.status_bar = QStatusBar()
        self.setStatusBar(self.status_bar)

        self.showStatus('Program Started')

    def showStatus(self, message):
        self.status_bar.showMessage(message)

if __name__ == '__main__':
    app = QApplication(sys.argv)
    mainWin = MyApp()
    mainWin.show()
    sys.exit(app.exec_())
        

Updating Status Messages

Update the status messages during trading to help users understand the process. For instance, display messages when trading starts or finishes.


def startTrading(self):
    self.showStatus('Trading Started')
    # Execute trading logic
    self.showStatus('Trading Completed: Profit 10%')
        

Integrating Automated Trading Logic

Let’s integrate the automated trading logic for the stock code received through QLineEdit and QStatusBar. Below is a simple automated trading strategy example.

Example of a Simple Trading Strategy

Automated trading systems typically operate based on various external data. Here, we will set criteria for buying and selling based on the stock code and inform the progress through QStatusBar during trading.


def processInput(self):
    input_text = self.line_edit.text()
    self.showStatus(f'Starting trading with stock code {input_text}')

    # Example of simple trading logic
    if input_text:  # If the entered stock code exists
        self.showStatus(f'Buying {input_text}...')
        # Buy logic (e.g., API call, orders, etc.)
        self.showStatus(f'{input_text} purchase completed.')

        # Sell logic
        self.showStatus(f'Selling {input_text}...')
        # Sell logic
        self.showStatus(f'{input_text} sale completed.')
    else:
        self.showStatus('Please enter a stock code and try again.')
        

Running the Program and Checking Results

When you run the code above, a simple GUI will be created, allowing users to enter stock codes in QLineEdit and press Enter to execute trades. QStatusBar will display the trading status in real-time.

Example Screen

The image below shows an example screen of the program running. Users can enter stock codes in the input field and check the current proceedings in real-time on the status bar below.

Example of Automated Trading Program Execution

Conclusion

In this article, we covered the basics of developing automated trading using Python and explained how to utilize QLineEdit and QStatusBar in PyQt. We laid the foundation for understanding the basic structure of an automated trading program, which can be further developed into more advanced algorithms. In the future, we can implement more complex strategies and various user interface elements to build an advanced trading system.

We hope you show continuous interest in financial technology development through Python. Thank you.

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