Here I focused upon Portfolio Optimisation and forecasting using sub modules offered by qiskit. I also compared how the classical and quantum approaches compare to each other, thus providing a brief insight upon how quantum computing techniques may be used to solve NP hard problems (mean-variance optimization problem in this case) Taking inspiration from my previous project upon Monte carlo simulation of time series, I have extended the discussion into quantum techniques as I was curious to see how they perform in comparision to classical techniques.
I used a Qiskit simulator (qasm_simulator) to simulate the circuits of qubits, applied hadamard gate superposition to the qubits to generate the portfolios. I also compared the quantum approach with classical optimisation techniques & discussed the differences. Monte Carlo Simulation of the assets was also done to forecast their future variations.
Important
Do refer the following links for the complete overview and documentation of the project Medium Documentation - Link , Alternate approach via Classical methods - Medium Link , Github Repository Link,
Important
Below are the references I used to learn and refer