AI Optimized Stock Portfolio Recommender -- 2

Diversified and optimized portfolio recommender An AI model that recommends an optimal stock portfolio across multiple sectors, based on user-defined preferences. Objectives: The model will take user inputs such as expected returns, risk tolerance, investment time frame, and sector preferences and give optimized portfolio recommendation as output(predicted weights of stocks, expected return, sharp ratio and risk(standard deviation) of portfolio). It will focus on stocks of various sectors from the Bombay Stock Exchange (BSE). It must dynamically adjust to new market data and optimize based on selected constraints. Data collection: APIs: yahoo finance and Alpha Vantage for BSE stocks data Metrics calculation: historical stock returns standard deviation of returns for volatility correlation between stocks risk free rate(The theoretical return of an investment with zero risk), sharp ratio of individual stocks MA & EMA Beta for individual stocks(Measures a stock’s sensitivity to market movements) Data pre-processing Handling missing values outliers normalization, if needed sector wise grouping User inputs: Risk Tolerance: High-risk (focus on maximizing returns) or low-risk (focus on minimizing volatility). Investment Time Frame: Short-term or long-term investments. Sector Preferences: Choose 2-3 sectors (e.g., technology, energy, finance). Using these inputs to filter stocks and guide the optimization process. Implementing the core genetic algorithm for portfolio optimization with local search: Population Initialization: Generate a population of random portfolios, each containing a mix of stocks with random weights that sum to 1. Fitness Function: Define a fitness function that evaluates portfolios based on: Maximizing Returns: Based on the weighted sum of stock returns. Minimizing Risk: Using portfolio variance (volatility). Sharpe Ratio: To balance risk and return. Selection: Choose portfolios with better fitness scores for reproduction. Crossover: Combine two parent portfolios to generate ospring portfolios. Mutation: Introduce random changes to portfolio weights to maintain diversity. Replacement: Replace weaker portfolios with better-performing ones in each generation. Iteration: Repeat until a stopping condition (e.g., a fixed number of generations) is met. Output: Optimized portfolio weights for selected stocks. Using classical portfolio optimization techniques to complement the genetic algorithm. MPT: Model the trade-o between risk and return using the ecient frontier. MVO: Minimize portfolio variance while maximizing expected returns using the covariance matrix of stock returns. Compare the results of MPT/MVO with those from the genetic algorithm. Visualizing stock correlations to enhance portfolio diversification. Compute the correlation matrix of stock returns. Build a correlation network where nodes represent stocks, and edges represent correlations between them. Use this network to select uncorrelated stocks, helping to minimize portfolio risk. Ensuring the model evolves dynamically by fetching updated stock data periodically. Schedule regular data fetching using cron jobs or scheduling libraries like schedule in Python. Performance evaluation metrics: Portfolio return Portfolio variance tracking error Most Important you need improvise stuff on your own and add few more functionalities This is my college project, i dont have time to complete it and i still wanaa ace this project