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shyam gupta
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README.md
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@@ -51,24 +51,22 @@ Methodology
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Risk (Variance): A measure of the dispersion of returns. In portfolio optimization, we seek to minimize the variance of the portfolio returns.
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2. Optimization Algorithm
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Our implementation utilizes the following steps:
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Input Data: Historical returns for each asset in the portfolio.
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Objective Function: Construct an objective function that combines the expected return and variance.
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Optimization Algorithm: We employ a mean-variance optimization algorithm that iteratively adjusts the weights to find the optimal combination.
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Convergence Criteria: The algorithm iterates over a specified number of iterations (e.g., 5000) or until convergence is achieved.
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3. Implementation
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In our project, we have implemented the Mean-Variance Portfolio Optimization method with 5000 iterations. The process involves:
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Input: Historical return data for each equity in the Indian market.
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Objective: Maximize expected return while minimizing portfolio variance.
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Optimization: Utilize an iterative approach, adjusting weights to find the optimal allocation.
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Output: The final set of weights that represent the optimal portfolio allocation.
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#### Contributing
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We welcome contributions! If you have any ideas for improvements, open an issue or submit a pull request.
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License
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51 |
Risk (Variance): A measure of the dispersion of returns. In portfolio optimization, we seek to minimize the variance of the portfolio returns.
|
52 |
|
53 |
2. Optimization Algorithm
|
54 |
+
|
55 |
+
Our implementation utilizes the following steps:
|
56 |
+
Input Data: Historical returns for each asset in the portfolio.
|
57 |
+
Objective Function: Construct an objective function that combines the expected return and variance.
|
58 |
+
Optimization Algorithm: We employ a mean-variance optimization algorithm that iteratively adjusts the weights to find the optimal combination.
|
59 |
+
Convergence Criteria: The algorithm iterates over a specified number of iterations (e.g., 5000) or until convergence is achieved.
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60 |
|
61 |
3. Implementation
|
62 |
|
63 |
+
In our project, we have implemented the Mean-Variance Portfolio Optimization method with 5000 iterations. The process involves:
|
64 |
+
Input: Historical return data for each equity in the Indian market.
|
65 |
+
Objective: Maximize expected return while minimizing portfolio variance.
|
66 |
+
Optimization: Utilize an iterative approach, adjusting weights to find the optimal allocation.
|
67 |
+
Output: The final set of weights that represent the optimal portfolio allocation.
|
68 |
|
|
|
69 |
#### Contributing
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We welcome contributions! If you have any ideas for improvements, open an issue or submit a pull request.
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License
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