predict? Before we begin, a sample ML problem setup looks like below. Overfitting is the most dangerous pitfall of a trading strategy A complex algorithm may perform wonderfully on a backtest but fails miserably on new unseen data this algorithm has not really uncovered any trend in data and no real predictive power. If your model needs re-training after every datapoint, its probably not a very good model. It is also useful for algorithms that weight inputs like regression and neural networks and algorithms that use distance measures like K-Nearest Neighbors. Lets create/modify some features again and try to improve our model.
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Eventually our model may perform well for this set of training and test data, but there is no guarantee that it will predict well on new data. Important Note on Transaction Costs : Why are the next steps important? This provides you with realistic expectation of how your model is expected to perform on new and unseen data when you start trading live. Fair_value_params import FairValueTradingParams class Problem1Solver def getTrainingDataSet(self return "trainingData1" def getSymbolsToTrade(self return 'MQK' def getCustomFeatures(self return 'my_custom_feature MyCustomFeature def getFeatureConfigDicts(self expma5dic 'featureKey 'emabasis5 'featureId 'exponential_moving_average 'params 'period 5, 'featureName 'basis' expma10dic 'featureKey 'emabasis10 'featureId 'exponential_moving_average 'params 'period 10, 'featureName 'basis' expma2dic 'featureKey 'emabasis3 'featureId 'exponential_moving_average. We make a prediction Y(Predicted, t) using our model and compare it with actual value only at time. Webinar Video : If you prefer listening to reading and would like to see a video version of this post, you can watch this webinar link instead. Generally, I would recommend creating many different views and transforms of your data, then exercise a handful of algorithms on each view of your dataset. It was good learning for both us and them (hopefully!). We create a new data dataframe for the stock with all the features. Hence, it is necessary to ensure you have a clean dataset that you havent used to train or validate your model.
This means you cannot use Y as a feature in your predictive model. Why Take This Course, by the end of this course, you should be able to: Understand data structures used for algorithmic trading. Creating a Trade Strategy.