Using machine learning techniques for quantitative investment strategies: A commodity case study

Using machine learning techniques for quantitative investment strategies: A commodity case study
♦ Combining quantitative and fundamental signals as inputs to machine learning algorithms
♦ Getting signals for trading and security selection: Discovering the relationship between data and assets
♦ Working with high volumes and more types of data: Does it help to better investment decisions?
♦ Use various machine learning techniques for different components of your strategy
♦ Setting up price predictions techniques and evaluating them using python (regression, recurrent neural networks, reinforcement learning)
♦ Avoid over-fitting and use of appropriate time horizon windows
♦ Optimisation, asset picking and constructing portfolios
♦ Pricing signals versus reality: How to integrate prediction techniques to form a systematic trading strategy taking into account practicalities (transaction costs etc.)?

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