Here we publish testing results and news on the development of solutions. You are welcome to submit your ideas for backtesting. We implement them for you using the available open-source data and report results here below. Modern portfolio optimization in Python. Monte Carlo simulation.
A request template 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 100-150 days IT&Software stocks are expected to grow, Utility stocks to fall, and small-cap Real Estate stocks to outperform the market index e.g. 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.
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