This is a summary of links featured on Quantocracy on Tuesday, 03/19/2019. To see our most recent links, visit the Quant Mashup. Read on readers!
Fractional Differencing Implementation (FD Part 3) [Kid Quant]Well…That took a lot longer than I expected it too. 6 weeks later and I finally have the last installation in these series of posts. It's also the longest one so you could say it was worth the wait. I recently found out that Python 2.7 (the python I've used for EVERY project) will soon be deprecated. In other words, any support or bug-fixes will cease to exist. In an effort not to
Using Dynamic Mode Decomposition (DMD) to Rotate Long-Short Exposure Between Market Sectors [Quantoisseur]Part 1 Theoretical Background The Dynamic Mode Decomposition (DMD) was originally developed for its application in fluid dynamics where it could decompose complex flows into simpler low-rank spatio-temporal features. The power of this method lies in the fact that it does not depend on any principle equations of the dynamic system it is analyzing and is thus equation-free . Also, unlike
Monte Carlo Simulation of strategy returns [Philipp Kahler]Monte Carlo Simulation uses the historic returns of your trading strategy to generate scenarios for future strategy returns. It provides a visual approach to volatility and can overcome limitations of other statistical methods. Monte Carlo Simulation Monte Carlo is the synonymous for a random process like the numbers picked by a roulette wheel. The Monte Carlo Simulation does the same to your