Backtesting

Backtesting

Backtesting is a process commonly used in financial industries to test a model's effectiveness by comparing estimated results with realized data from existing historic data. It is a form of simulation that allows researchers to evaluate how a model works in practice, particularly in forecasting. When backtesting a strategy, researchers must be aware of any assumptions made and take measures to weaken their impacts on profits in real trading.From: Safety, Reliability and Risk Analysis: Beyond the Horizon [2013], Forecasting Ranking in Harness Racing Using Probabilities Induced by Expected Positions [2019], Online Portfolio Selection [2019]

Evolutionary Computing and Swarm Intelligence for Hyper Parameters Optimization Problem in Convolutional Neural Networks

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Published in Ali Ahmadian, Soheil Salahshour, Soft Computing Approach for Mathematical Modeling of Engineering Problems, 2021

Senthil kumar Mohan, A John, Ananth kumar Tamilarasan

Overfitting is the issue in which, during training and optimization, The model performs exceptionally well and out of sample very poorly. Using strategies such as cross-validation, backtesting or regularisation can prevent overfitting. The evaluation of the hyperparameter optimization model is the average of k model evaluations from each k-fold of your data using a technique such as k-fold cross validation. Techniques like this can help ensure that the data is generalised by the calculation that you optimise, even uncertain, for correlations.

Forecasting Ranking in Harness Racing Using Probabilities Induced by Expected Positions

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Published in Applied Artificial Intelligence, 2019

Fredrik Armerin, Jonas Hallgren, Timo Koski

Three experiments were conducted and are presented in Table 1. The first experiment is a crossvalidation where 50% of the data are used to calibrate the model and the other half to validate. The second experiment is done in the same way but only 10% of the data are used for calibration. The third experiment differs from the other two. Initially the model is calibrated against 10% of the data but then, as new data become available, the model is recalibrated. In forecasting this is referred to as backtesting and simulates how the model works in practice.

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Evolutionary Computing and Swarm Intelligence for Hyper Parameters Optimization Problem in Convolutional Neural Networks

Forecasting Ranking in Harness Racing Using Probabilities Induced by Expected Positions