strategy_return_hist

GPU Accelerated Backtesting and ML for Quant Trading Strategies

In algorithmic trading large amounts of time series data are analyzed to derive buy and sell orders so that the strategy is profitable but also risk measures are at an acceptable level.
Bootstrapping walk forward optimization is becoming increasingly popular to avoid curve fitting and data snooping. It is computationally extremely expensive but can be very well distributed to a GPU cluster.

We present a framework for bootstrapping walk forward optimization of trading strategies on GPU clusters, which allows us to analyze strategies in minutes instead of days. Moreover, we show how signal generation can be combined with Machine Learning to make the strategies more adaptive to further improve the robustness and profitability.

Presentation slides