Intermediate
Practice Drills: Building a Basic Portfolio Optimization Model
A practice-drill course for quant aspirants, prop desk applicants and traders who want to systematise how they size positions. You know the Markowitz theory. Here you build the model yourself, with no black-box library doing the thinking for you. You start from five Nifty 50 stocks, build the return and covariance inputs, solve a two-stock minimum variance problem by formula, then solve the full five-stock minimum variance and max Sharpe problems in Google Sheets Solver. You rebuild the same model in Python with NumPy and scipy.optimize, trace and plot the efficient frontier, add weight caps, stress-test how fragile the weights are, and finish by converting optimal weights into a whole-share order list for a ₹5 lakh budget on Zerodha.
Mean-Variance OptimizationCovariance MatrixMinimum Variance PortfolioMax Sharpe PortfolioGoogle Sheets SolverNumPy and SciPyEfficient FrontierWeight ConstraintsDiscrete Allocation
MODULES
4
DURATION
~2.5 hrs
TRACK
Quantitative Finance
What You'll Master
Write any portfolio optimization problem as three pieces: inputs, an objective and constraints
Build annualised return and covariance inputs for five NSE stocks from daily prices
Solve two-stock minimum variance weights by formula and check them against a spreadsheet
Run minimum variance and max Sharpe optimizations in Google Sheets Solver using the Indian risk-free rate
Rebuild the same model in Python with NumPy portfolio math and scipy.optimize
Trace and plot the efficient frontier from your own code
Add position caps and measure how sensitive optimal weights are to small input changes
Convert optimal weights into a whole-share order list for a real INR budget
Access Level
LEARNER
Everything included
Full Text Playbooks
Actionable Exercises
Mobile Reading Mode
Lifetime Updates