Quantifying Uncertainty: Evaluating Trading Algorithms using Probabilistic Programming ODSC Boston 2015
ConferencesModelingPredictive AnalyticsODSC East 2015|Speaker Slidesposted by Open Data Science December 14, 2014 Open Data Science
There exist a large number of metrics to evaluate the performance and risk of a trading strategy. Although those metrics have proven to be useful tools in practice, most of them require a large amount of data and yield unstable results on shorter timescales. Quantopian allows users to develop and launch trading algorithms that invest in the stock market. As we have launched live trading less than a year ago, estimating performance with few data points becomes critical. Bayesian modeling is a flexible statistical framework well suited for this problem as uncertainty can be directly quantified in terms of the posterior distribution.
Thomas Wiecki received his PhD from Brown University where he developed Bayesian models to help understand brain disorders. He currently works as a quantitative researcher at Quantopian Inc. where he helps to build the worlds’ first browser based algorithmic trading platform. Among other projects, he is involved in the development of PyMC — a probabilistic programming framework written in Python.