Backtesting modern portfolio optimization and risk management strategies in Python.
Updated: Nov 2, 2019
In our blog we publish testing results and news on the development of solutions. You are welcome to submit your ideas for backtesting. We can implement them for you using the available open-source data and report the results here in the blog. Modern portfolio optimization in Python. Monte Carlo simulation.
A request template example is provided below:
Decision point of time: 04/20/2013
Strategy: Long/Short, Long only
Market cap: from USD 100mn to USD 5bn
Region: Nordic countries
Industries: Software & IT Services, Utilities, Banking&Investment, Real Estate
Macroeconomic scenario: Early growth, high volatility
Min number of stocks in a portfolio: 30
Max number of stocks in a portfolio: 50
Risk predisposition: Very low/Low/Balanced/…
Expert knowledge*: In the coming 6 months IT&Software stocks are expected to grow, Utility stocks to fall, and small-cap Real Estate stocks to outperform the market index SP&500.
*It represents your specialized knowledge/assumptions about the selected short-list. You can list stocks which in your opinion are certain to fall, industries that are on average to outperform the market, and etc.