- Excel Historical Data Downloader for Interactive Brokers (IB) Trader Workstation (TWS)
~~$300.00~~$150.00URL to demonstration video: (to be added later) This solution provides a historical time-series downloader in Excel for Interactive Brokers (IB) Trader Workstation (TWS). It provides the user with one-click solution to download historical data from IB. - Portfolio Optimization for 10 Securities Using Lagrange Multipliers, No Short-Selling, Weights Sum to 1
~~$5,000.00~~$3,888.00Portfolio Optimization for 10 Securities Using Lagrange Multipliers, No Short-Selling, Weights Sum to 1 Problem: Construct the Optimal Portfolio that: delivers the target return (mu_Target) with minimum risk Minimize the risk of the portfolio (in this case, measured as half the variance) While maintaining an expected return target of (mu_Target) By adjusting the investment weights on each asset Subject to the budget constraint that the weights sum to 1 Method: Since constraints are equalities => We can use Method Lagrange Supports up to 5 securities. Able to do more if requested. Please contact us. Constraints: No short-selling (ie. No negative weights) Solution 00: Basic MPT with only budget constraint that weights sum to 1 Solution 01: Tweaked solution where no negative weights are allowed, but budget contraint fails, as sum of weights exceed 1. Solution 02: Maintain that no negative weights are allowed, but normalize weights such that they sum to 1. This yields a practical solution, but usually unable to meet target return. - Portfolio Optimization for 20 Securities Using Lagrange Multipliers, No Short-Selling, Weights Sum to 1
~~$7,000.00~~$4,888.00Portfolio Optimization for 20 Securities Using Lagrange Multipliers, No Short-Selling, Weights Sum to 1 Problem: Construct the Optimal Portfolio that: delivers the target return (mu_Target) with minimum risk Minimize the risk of the portfolio (in this case, measured as half the variance) While maintaining an expected return target of (mu_Target) By adjusting the investment weights on each asset Subject to the budget constraint that the weights sum to 1 Method: Since constraints are equalities => We can use Method Lagrange Supports up to 5 securities. Able to do more if requested. Please contact us. Constraints: No short-selling (ie. No negative weights) Solution 00: Basic MPT with only budget constraint that weights sum to 1 Solution 01: Tweaked solution where no negative weights are allowed, but budget contraint fails, as sum of weights exceed 1. Solution 02: Maintain that no negative weights are allowed, but normalize weights such that they sum to 1. This yields a practical solution, but usually unable to meet target return. - Portfolio Optimization for 4 Securities Using Lagrange Multipliers
~~$2,000.00~~$100.00Portfolio Optimization for 4 Securities Using Lagrange Multipliers Problem: Construct the Optimal Portfolio that: delivers the target return (mu_Target) with minimum risk Minimize the risk of the portfolio (in this case, measured as half the variance) While maintaining an expected return target of (mu_Target) By adjusting the investment weights on each asset Subject to the budget constraint that the weights sum to 1 Method: Since constraints are equalities => We can use Method Lagrange - Portfolio Optimization for 5 Securities Using Lagrange Multipliers, No Short-Selling, Weights Sum to 1
~~$4,000.00~~$150.00Portfolio Optimization for 5 Securities Using Lagrange Multipliers, No Short-Selling, Weights Sum to 1 Problem: Construct the Optimal Portfolio that: delivers the target return (mu_Target) with minimum risk Minimize the risk of the portfolio (in this case, measured as half the variance) While maintaining an expected return target of (mu_Target) By adjusting the investment weights on each asset Subject to the budget constraint that the weights sum to 1 Method: Since constraints are equalities => We can use Method Lagrange Supports up to 5 securities. Able to do more if requested. Please contact us. Constraints: No short-selling (ie. No negative weights) Solution 00: Basic MPT with only budget constraint that weights sum to 1 Solution 01: Tweaked solution where no negative weights are allowed, but budget contraint fails, as sum of weights exceed 1. Solution 02: Maintain that no negative weights are allowed, but normalize weights such that they sum to 1. This yields a practical solution, but usually unable to meet target return. - Trading Strategy BackTesting (in Excel or Python)$888.00Backtest any strategy for any asset class.
- in Excel (.xlsm)
- in Python (.ipynb on Google Cloud Platform).