Discovering profitable stocks for intraday trading

The primary goal of this research is to unearth effective strategies for selecting stocks that are most likely profitable for intraday trading.

• Predicting Price Changes: At its heart, the system uses past stock price movements to forecast future activity. Imagine being able to predict a stock’s price rise before it happens—that’s what this system aims to do.
  
• Finding Patterns: Beyond predictions, the BeSt system looks for patterns in how stocks have performed over time, identifying which stocks are likely to do well together or in sequence. This helps in anticipating market movements.
  
• Prioritizing Profitable Stocks: Not all stocks are treated equally; the system prioritizes those that have historically provided better returns following certain patterns.
  

█ Conclusion: For intraday traders, the BeSt system offers a promising tool that enhances profitability and provides a deeper understanding of market dynamics. Turning complex data into actionable trading insights represents a significant step forward in the quest for optimal trading strategies. As technology and data science continue to advance, the BeSt system is well-positioned to become an indispensable part of every trader's toolkit.

These algorithms predict the value of continuous variables based on the analysis of historical data.The goal is to predict the daily percentage variation in the price of a stock on the next trading day by analyzing the historical prices of market stocks on the preceding days. Stocks with the maximal predicted variation are recommended as the most tradeable on the subsequent trading day.

• Data Preparation: The historical price data of various stocks are collected, focusing primarily on daily percentage variations in stock prices.
  
• Model Training: Regression algorithms are used to create predictive models. These models analyze the historical prices and try to forecast the price movements of the stocks for the next trading day.
  
• Stock Selection: Stocks predicted to have the highest percentage variation in their prices the next day are flagged as potential candidates for trading. This prediction is based on the regression model’s output, which calculates the expected price change from one day to the next.
  

This involves the use of unsupervised data mining techniques to discover recurrent sequences of items in large datasets. In this context, items are stocks, and the time stamps correspond to the closures of consecutive trading days. A sequence is an ordered list of itemsets, where an itemset is a set of items occurring at a given time stamp. Given the best-performing stocks on past and current trading days, a sequence indicates that if an arbitrary set of stocks is in the top list on preceding days, a given stock is likely to occur in the top list on the next day. Weighted sequences, rather than traditional ones, are used to weigh differently the occurrences of different stocks on the same trading day according to their daily profits.

• Data Handling: The process starts with collecting historical stock data, particularly focusing on the closing prices across consecutive trading days. This data is then prepared into a sequence format where each sequence represents the ordered list of stock performances over multiple days.
  
• Mining Process: Using sequence mining algorithms, the system searches for common patterns or sequences in the stock data. These patterns reveal which stocks frequently perform well in sequence—meaning if certain stocks are performing well today, which stocks are likely to perform well tomorrow based on historical patterns.
  
• Weighted Sequences: To refine the selection, the concept of weighted sequences is applied. This approach gives different weights to the occurrences of stocks based on their profit performances on particular days. For example, if a stock consistently shows higher gains than others on specific days following certain trends, it will be weighted more heavily in the predictive model.
  
• Stock Recommendations: The system identifies sequences with the highest recurrence and profitability. Stocks appearing in these sequences are recommended for trading. These stocks are expected to perform well in the short term, aligning with intraday trading goals.
  

• High Profitability: The BeSt system outperforms traditional stock selection methods like Support Vector Machines, Linear Regression, and random selection strategies. The sequence-based strategies used by BeSt, in particular, have proven to yield higher profits, demonstrating the system's ability to effectively identify the most promising stocks for intraday trading.
  
• Effective Trend Capture: The system is highly adept at identifying underlying trends in stock price movements. This capability allows traders to make informed decisions based on a solid analysis of historical data, ensuring that trades align with the most likely future movements of the market.
  [*]Scalability: The BeSt system can handle large datasets efficiently, making it suitable for analyzing the numerous stocks listed on major stock exchanges. This scalability is crucial for intraday traders who need to quickly sift through vast amounts of data to identify trading opportunities.
  
• Interpretability of Results: Unlike many other data-driven trading systems, the BeSt system provides interpretable results. This feature is particularly beneficial for traders who prefer to understand the logic behind the recommended trades. The system's transparency helps build trust and allows users to learn from the system's insights.