Futures Learning

Algorithmic Trading of Futures via Machine Learning

A lgorithmic trading of securities has become a staple of modern approaches to financial investment. In this project, I attempt to obtain an effective strategy for trading a collection of 27 financial futures based solely on their past trading data. All of the strategies that I consider are based on predictions of the future price and volatility of the various securities under consideration, an...

Reductionism or layered complexity? The futures of futures studies

Futures studies is likely to evolve through changes in five areas. They are: (1) forecasting to anticipatory action learning; (2) reductionist to complex; (3) horizontal to vertical; (4) from short-term empiricist research to the return of long-term history, including grand narratives; and (5) scenario development to moral futures.  2002 Elsevier Science Ltd. All rights

Knowledge management for new times with new technologies to promote new learning: A futures approach to self- managed learning

Model Calibration and Automated Trading Agent for Euro Futures

We explored the application of a machine learning method, Logitboost, to automatically calibrate a trading model using different versions of the same technical analysis indicators. This approach takes advantage of boosting’s feature selection capability to select an optimal combination of technical indicators and design a new set of trading rules. We tested this approach with high frequency dat...

Reinforcement Learning on a Futures Market Simulator

In recent years, market forecasting by machine learning methods has been flourishing. Most existing works use a past market data set, because they assume that each trader’s individual decisions do not affect market prices at all. Meanwhile, there have been attempts to analyze economic phenomena by constructing virtual market simulators, in which human and artificial traders really make trades. ...