The project starts by selecting a straightforward trading strategy, such as a moving average crossover strategy. For example, when the short-term moving average crosses above the long-term moving average, it generates a buy signal, and when the short-term moving average crosses below the long-term moving average, it generates a sell signal.
Using historical price data obtained through the Alpaca API, the model calculates the moving averages and executes buy/sell orders based on the defined strategy. The paper trading platform simulates the trading process and tracks the virtual portfolio's performance.
The Alpaca API provides access to real-time market data and allows users to place orders and manage their virtual portfolios. It also offers features like account balance tracking and performance metrics, enabling users to assess the effectiveness of their trading strategy.
The project may involve backtesting the trading strategy on historical data to evaluate its performance over different market conditions and time periods.
While this is a simple model, it serves as a starting point for traders and developers interested in algorithmic trading and financial analysis. As users gain experience and expertise, they can enhance the model with more sophisticated strategies and risk management techniques. Paper trading with the Alpaca API offers a risk-free and educational environment to refine and optimize trading algorithms before deploying them in real markets.
Here I want to make a simple recommender system to gauge the similarity between shows, users and to help me predict whether a user will enjoy a particular movie.