This basic Python trading system simulates buying and selling stocks based on predefined price thresholds, making it a great starting point for anyone exploring algorithmic trading. The program initializes with a balance, representing available funds for trading, and a portfolio to store acquired stocks. Users can set parameters such as the stock ticker, buy and sell target prices, and quantity to purchase per transaction, tailoring the system to meet specific strategies.
At its core, the system uses methods for buying and selling stocks. The buy method checks if the available balance is sufficient to cover the purchase and then deducts the amount from the balance while adding the purchased quantity to the portfolio. The sell method, on the other hand, verifies if there are enough shares in the portfolio to sell, removes them, and adds the proceeds back to the balance. Every transaction is logged with details like action type (buy or sell), stock ticker, price, and quantity, providing a record of the trading history.
The check_price function is designed to simulate price retrieval and in a real-world application can be replaced by API calls to financial data providers for live prices. In the provided example, it randomly generates a price between a given range, introducing an element of variability to simulate real-world trading conditions. The core logic in the run_strategy method decides whether to buy, sell, or hold a stock on each iteration, based on the current price in relation to the specified buy and sell thresholds.
Additional features like display_portfolio and display_transaction_history provide insights into trading performance, showing current holdings, cash balance, and a history of all executed trades. The code simulates trading over a series of days to observe how the strategy unfolds, making it a valuable tool for backtesting.
For practical application, this system can be expanded by adding live data integration, portfolio analytics, risk management tools, or order scheduling for executing trades at specific times. Its flexible structure allows for easy customization, making it an effective introductory project for aspiring algorithmic traders and developers interested in finance technology.