Quantifying Uncertainty: Evaluating Trading Algorithms using Probabilistic Programming ODSC Boston 2015

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.

Slides are available here

Speaker Bio
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.