Update README.md

README.md

@@ -47,41 +47,24 @@ Mean-Variance Portfolio Optimization is a widely used method in finance for cons
 Methodology
 1. Basic Concepts
 
-    Expected Return (μpμp​): The anticipated gain or loss from an investment, based on historical data or other factors.
-    Risk (Variance (σp2σp2​)): A measure of the dispersion of returns. In portfolio optimization, we seek to minimize the variance of the portfolio returns.
-
-2. Mathematical Formulation
-
-The basic idea is to find the optimal weights wiwi​ for each asset in the portfolio to maximize the expected portfolio return μpμp​ while minimizing the portfolio variance σp2σp2​. This can be expressed mathematically as:
-   
-   ```bash
-   Maximize:   μ_p = ∑(w_i * μ_i)
-   Subject to: ∑w_i = 1
-   Minimize:   σ_p^2 = ∑(∑(w_i * w_j * σ_ij))
-
-
-where:
-    NN is the number of assets in the portfolio.
-    μiμi​ is the expected return of asset ii.
-    σijσij​ is the covariance between the returns of assets ii and jj.
+    Expected Return: The anticipated gain or loss from an investment, based on historical data or other factors.
+    Risk (Variance): A measure of the dispersion of returns. In portfolio optimization, we seek to minimize the variance of the portfolio returns.
 
 3. Optimization Algorithm
 
 Our implementation utilizes the following steps:
-
-    Input Data: Historical returns for each asset in the portfolio.
-    Objective Function: Construct an objective function that combines the expected return and variance.
-    Optimization Algorithm: We employ a mean-variance optimization algorithm that iteratively adjusts the weights to find the optimal combination.
-    Convergence Criteria: The algorithm iterates over a specified number of iterations (e.g., 5000) or until convergence is achieved.
+Input Data: Historical returns for each asset in the portfolio.
+Objective Function: Construct an objective function that combines the expected return and variance.
+Optimization Algorithm: We employ a mean-variance optimization algorithm that iteratively adjusts the weights to find the optimal combination.
+Convergence Criteria: The algorithm iterates over a specified number of iterations (e.g., 5000) or until convergence is achieved.
 
 4. Implementation
 
 In our project, we have implemented the Mean-Variance Portfolio Optimization method with 5000 iterations. The process involves:
-
-    Input: Historical return data for each equity in the Indian market.
-    Objective: Maximize expected return while minimizing portfolio variance.
-    Optimization: Utilize an iterative approach, adjusting weights to find the optimal allocation.
-    Output: The final set of weights that represent the optimal portfolio allocation.
+Input: Historical return data for each equity in the Indian market.
+Objective: Maximize expected return while minimizing portfolio variance.
+Optimization: Utilize an iterative approach, adjusting weights to find the optimal allocation.
+Output: The final set of weights that represent the optimal portfolio allocation.
 
     
 #### Contributing