Search
× Search
Wednesday, June 19, 2019

Archived Discussions

Recent member discussions

The Algorithmic Traders' Association prides itself on providing a forum for the publication and dissemination of its members' white papers, research, reflections, works in progress, and other contributions. Please Note that archive searches and some of our members' publications are reserved for members only, so please log in or sign up to gain the most from our members' contributions.

Forecasting Markets with XGBoost & Ensemble Methods

photo

 private private,

 Thursday, June 21, 2018

This tutorial uses R (unfortunately; no offense to the R-fans, I used to be one of you, but I've been forced to switch) with scikit-learn (sklearn) to make a demonstrable improvement in forecast accuracy in equity markets using advanced ML techniques. I've created a similar model in Python, and for the $BTC (bitcoin) fans out there, I made one for $BTC price forecasts. While it may appear to work given the advancements in CV, though, if you haven't read The Black Swan (Taleb) or Fooled by Randomness, I suggest you do before relying on any model that uses empirical observations based on historical data to attempt to project the trajectory of the future. Taleb uses a great analogy in The Black Swan to convey the risk associated with the use empirical observation to predict events of the future (a rather sad one, to be honest) >> It is that of the loving Turkey who, gets fed, day-in and day-out, at the same time, by the same friendly human beings that take care of him. This occurs every day for 1,000 days and with each passing day, the Turkey becomes more confident that he can expect the same to occur again on the following day, until day 1,001, which happens to be the Wednesday before Thanksgiving. The poor Turkey, relying on observations about history, became very confident that his caring, loving human friends would feed him again, according to the norm, on day 1,001...but on that day everything he thought he knew was shattered. As it turns out, all of his prior observations were in a sense "anti-knowledge," at least insofar as their usefulness: they blinded the Turkey to his eventual slaughter, giving him a false confidence that grew stronger with sample size. Those prior observations gave the poor Turkey a misleading sense of confidence....when dealing in financial markets, let's not be turkeys, yeah? Because the resulting financial calamity can be enormous.


Print

5 comments on article "Forecasting Markets with XGBoost & Ensemble Methods "

photo

 Olubunmi Omidire, VP Financial Accounting at Credit Suisse

 Saturday, June 23, 2018



Interesting.......the love we have for a chicken is only skin deep


photo

 Jean-Marc Bloch-Lambert, CIO at The Pentagon Group

 Saturday, June 23, 2018



Great reading back in 2007 for the Taleb's Fooled by Randomness


photo

 Mohamed Abass, Machine Learning For Time Series Prediction

 Saturday, June 23, 2018



Good job


photo

 Keith Campbell Golding, Financial Practitioner

 Sunday, June 24, 2018



Absolutely endorse your comments on Taleb.


photo

 Andreas Junge, CEO, Methodica Ventures

 Tuesday, June 26, 2018



The blow up of short vol funds in February is a great example of your turkey fable and of Talebs tenet that short gamma is long term suicide. The whole idea that you can forecast anything in financial markets is a product of wishful hubris economics and engineering. That's not to say you can't make money in financial markets - even systematically. To do that however you have to identify and rely on statistical regularities and appreciate that even those regularities may only be regular for a while.

Please login or register to post comments.

TRADING FUTURES AND OPTIONS INVOLVES SUBSTANTIAL RISK OF LOSS AND IS NOT SUITABLE FOR ALL INVESTORS
Terms Of UsePrivacy StatementCopyright 2018 Algorithmic Traders Association