Chapter 16 Strategy Optimisation In prior chapters we have considered how to create both an underlying predictive model (such as with the Suppor Vector Machine and Random Forest Classifier) as well as a trading strategy - Random search - Document - PDFSEARCH.IO

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	Chapter 16 Strategy Optimisation In prior chapters we have considered how to create both an underlying predictive model (such as with the Suppor Vector Machine and Random Forest Classifier) as well as a trading strategy Add to Reading List Document Date: 2015-05-18 04:58:44 Open Document File Size: 389,26 KB Share Result on Facebook Currency pence / / IndustryTerm sklearn.grid_search / parameter search space / grid search / actual grid search / computational systems / statistical learning algorithm / grid search cross validation procedure / exhaustive parameter search / cartesian product / grid_search.py / grid_search.py import datetime import sklearn / cross-validated hyperparameter grid search / train_test_split sklearn.grid_search import / / MarketIndex case 1250 / ix[X. / ix[y. / / NaturalFeature Random Forest / / Organization US Federal Reserve / / Position model for classification within the strategy / / ProgrammingLanguage C / Python / / Technology underlying statistical learning algorithm / machine learning / simulation / / SocialTag Overfitting Grid search Support vector machine Test set Model selection Training set Parameter Economic model Statistics Machine learning

Chapter 16 Strategy Optimisation In prior chapters we have considered how to create both an underlying predictive model (such as with the Suppor Vector Machine and Random Forest Classifier) as well as a trading strategyAdd to Reading List