In the tutorial How To Perturb Your Simulation With The Random Function, I described a method of extracting statistics from a simulation that I believe are more realistic than the typical backtest consisting of one run, and results based on either conscious or subconscious optimization. My method involves adding randomness into the buying decision, causing lower ranked stocks to sometimes be bought instead of the highest ranked stocks. Then multiple simulation runs are performed using the Optimizer, and the resulting statistics are averaged. I will now refer to these results as "sober statistics". (Folks, remember that you heard this term here first!) In this post, I will expand on this technique and explain why it is important during the system development process, and end-product evaluation.Read More
Advanced concepts in stock investment portfolio design. Fundamentals, technical analysis and many other related topics are discussed.
In Part 2 of Adaptive Ranking Systems, tests were performed using the Short Interest Ratio (SiRatio) company factor as the singular ranking factor. The objective of the testing was to estimate the historical Annualized Return and Maximum Drawdown for a series of simulations. Each simulation adjusted the ranking threshold, above which stocks were rejected for purchase. From the test results, it was concluded that a rank threshold of approximately 70 appeared to minimize drawdown.Read More