├── README.md ├── chapter_10 ├── From_probabilities_to_bet_size.py └── dynamic_positive_size_and_limit_price.py ├── chapter_13 └── python_code_for _the determination_of_optimal_trading_rules.py ├── chapter_14 ├── algorithm_for_deriving_HHI_concentration.py ├── deriving_the_sequence_of_DD_and_TuW.py ├── deriving_the_timing_of_bets_from_a_series_of_rarget_positions.py └── implementation_of_a_holding_period_estimator.py ├── chapter_15 ├── calculating_the_strategy_risk_in_practice.py ├── computing_the_implie_predision.py ├── computing_the_implied_betting_frequency.py ├── targeting_a_sharpe_ratio_as_a_function_of_the_number_of_bets.py └── using_the_symPy_library_for_symbolic_operations.py ├── chapter_16 ├── CLA.pdf ├── CLA.py ├── CLA_main.py ├── HRP.py ├── HRP3_corr0.png ├── HRP3_corr1.png ├── Monte_carlo_experiment_onHRP_out_of_sample_performance.py ├── __pycache__ │ ├── CLA.cpython-37.pyc │ └── HRP.cpython-37.pyc ├── quasi_diagonalization.py ├── recursive_bisection.py ├── stats.csv └── tree_clustering_using_scipy_functionality.py ├── chapter_17 └── SADF_inner_loop.py ├── chapter_18 ├── A_library_built_using_the_lz_algorithm.py ├── Implementation_of_algorithms_discussed_in_gao.py ├── Plug_in_entropy_estimator.py └── function_that_computes_the_length_of_the_longest_match.py ├── chapter_19 └── Implementation_of_the_corwin_schultzz_algorithm.py ├── chapter_2 ├── Non_negative_rolled_price_series.py ├── Non_negative_rolled_price_series.txt ├── Symmetrix_cusum_filter.py ├── getRolledSeries.py ├── getRolledSeries.txt └── pcaWeights.py ├── chapter_20 ├── __pycache__ │ └── mpEngine.cpython-37.pyc ├── beginning_of_engine.py ├── enhancing_mpPandasObj.py ├── enhancing_processJobs.py ├── linParts.py ├── mpEngine.py ├── multiprocessing_implementation_of_a_one_touch_double_barrier.py ├── nestedParts.py ├── pool_test.py ├── principal_components_for_a_subset_of_the_columns.py ├── single_thread_implementation_of_one_touch_double_barrier.py └── un_vectorised_and_vectorized_cartesian_product.py ├── chapter_3 ├── Adding_a_vertical_barrier.py ├── Expanding_getEvents.py ├── Getting_the_time_of_first_touch.py ├── Labeling_for_side_and_size.py ├── daily_volatility_estimates.py ├── dropping_under_populated_labels.py ├── expanding_getBins.py └── triple_barrier_labeling.py ├── chapter_4 ├── Estimating_the_average_unqieness_of_a_label.py ├── Estimating_the_uniqueness_of_a_label.py ├── Implementation_of_time_decay_factors.py ├── build_an_indicator_matrix.py ├── compute_average_uniqueness.py ├── determination_of_sample_weight_by_absolute_return_attribution.py ├── exaple_of_sequntial_bootstrap.py ├── generating_a_random_t1_series.py ├── multi_threaded_monte_carlo.py ├── return_sample_from_sequential_bootstrap.py └── uniqueness_from_standard_and_sequential_bootstraps.py ├── chapter_5 ├── Finding_the_minimum_D_value_that_passes_the_adf_test.py ├── The_new_fixed_width_window_fracdiff_method.py ├── concat_example.py ├── standard_fracdiff.py └── weighting_function.py ├── chapter_6 ├── accuracy_of_the_bagging_classifier.py ├── cross_validation_class_when_observations_overlap.py ├── embargo_on_training_observations.py ├── purging_observation_in_the_training_set.py ├── threee_ways_of_setting_up_an_rf.py └── using_the_purgedKFold_class.py ├── chapter_8 ├── Implication_of_sfi.py ├── MDA_feature_importance.py ├── MDI_feature_importance.py ├── calling_all_components.py ├── calling_feature_importance_for_any_method.py ├── cearting_a_synthetic_dataset.py ├── computation_of_orthogonal_features.py ├── computation_of_weighted_kendall_tau.py └── feature_importance_plotting_function.py └── chapter_9 ├── an_enhanced_pupeline_class.py ├── grid_search_with_purged_KFold_cross_validation.py ├── randomized_search_with_purged_KFold_cv.py └── the_loguniform_gen_class.py /README.md: -------------------------------------------------------------------------------- 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