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| { | |
| "summary": { | |
| "pass": 82 | |
| }, | |
| "results": [ | |
| { | |
| "name": "import package afml.cross_validation", | |
| "status": "pass", | |
| "detail": "<module 'afml.cross_validation' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\cross_validation\\\\__init__.py'>" | |
| }, | |
| { | |
| "name": "import package afml.data_structures", | |
| "status": "pass", | |
| "detail": "<module 'afml.data_structures' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\data_structures\\\\__init__.py'>" | |
| }, | |
| { | |
| "name": "import package afml.datasets", | |
| "status": "pass", | |
| "detail": "<module 'afml.datasets' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\datasets\\\\__init__.py'>" | |
| }, | |
| { | |
| "name": "import package afml.features", | |
| "status": "pass", | |
| "detail": "<module 'afml.features' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\features\\\\__init__.py'>" | |
| }, | |
| { | |
| "name": "import package afml.filters", | |
| "status": "pass", | |
| "detail": "<module 'afml.filters' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\filters\\\\__init__.py'>" | |
| }, | |
| { | |
| "name": "import package afml.labeling", | |
| "status": "pass", | |
| "detail": "<module 'afml.labeling' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\labeling\\\\__init__.py'>" | |
| }, | |
| { | |
| "name": "import package afml.sample_weights", | |
| "status": "pass", | |
| "detail": "<module 'afml.sample_weights' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\sample_weights\\\\__init__.py'>" | |
| }, | |
| { | |
| "name": "import package afml.strategies", | |
| "status": "pass", | |
| "detail": "<module 'afml.strategies' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\strategies\\\\__init__.py'>" | |
| }, | |
| { | |
| "name": "import module afml.cross_validation.anchored_walkforward", | |
| "status": "pass", | |
| "detail": "<module 'afml.cross_validation.anchored_walkforward' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\cross_validation\\\\anchored_walkforward.py'>" | |
| }, | |
| { | |
| "name": "import module afml.cross_validation.combinatorial", | |
| "status": "pass", | |
| "detail": "<module 'afml.cross_validation.combinatorial' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\cross_validation\\\\combinatorial.py'>" | |
| }, | |
| { | |
| "name": "import module afml.cross_validation.cpcv_usage", | |
| "status": "pass", | |
| "detail": "<module 'afml.cross_validation.cpcv_usage' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\cross_validation\\\\cpcv_usage.py'>" | |
| }, | |
| { | |
| "name": "import module afml.cross_validation.cross_validation", | |
| "status": "pass", | |
| "detail": "<module 'afml.cross_validation.cross_validation' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\cross_validation\\\\cross_validation.py'>" | |
| }, | |
| { | |
| "name": "import module afml.cross_validation.hyper_fit", | |
| "status": "pass", | |
| "detail": "<module 'afml.cross_validation.hyper_fit' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\cross_validation\\\\hyper_fit.py'>" | |
| }, | |
| { | |
| "name": "import module afml.cross_validation.hyper_fit_analysis", | |
| "status": "pass", | |
| "detail": "<module 'afml.cross_validation.hyper_fit_analysis' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\cross_validation\\\\hyper_fit_analysis.py'>" | |
| }, | |
| { | |
| "name": "import module afml.cross_validation.optuna_hyper_fit", | |
| "status": "pass", | |
| "detail": "<module 'afml.cross_validation.optuna_hyper_fit' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\cross_validation\\\\optuna_hyper_fit.py'>" | |
| }, | |
| { | |
| "name": "import module afml.cross_validation.pbo", | |
| "status": "pass", | |
| "detail": "<module 'afml.cross_validation.pbo' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\cross_validation\\\\pbo.py'>" | |
| }, | |
| { | |
| "name": "import module afml.cross_validation.scoring", | |
| "status": "pass", | |
| "detail": "<module 'afml.cross_validation.scoring' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\cross_validation\\\\scoring.py'>" | |
| }, | |
| { | |
| "name": "import module afml.cross_validation.trial_tracker", | |
| "status": "pass", | |
| "detail": "<module 'afml.cross_validation.trial_tracker' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\cross_validation\\\\trial_tracker.py'>" | |
| }, | |
| { | |
| "name": "import module afml.data_structures.bars", | |
| "status": "pass", | |
| "detail": "<module 'afml.data_structures.bars' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\data_structures\\\\bars.py'>" | |
| }, | |
| { | |
| "name": "import module afml.datasets.load_datasets", | |
| "status": "pass", | |
| "detail": "<module 'afml.datasets.load_datasets' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\datasets\\\\load_datasets.py'>" | |
| }, | |
| { | |
| "name": "import module afml.features.fracdiff", | |
| "status": "pass", | |
| "detail": "<module 'afml.features.fracdiff' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\features\\\\fracdiff.py'>" | |
| }, | |
| { | |
| "name": "import module afml.features.fractals", | |
| "status": "pass", | |
| "detail": "<module 'afml.features.fractals' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\features\\\\fractals.py'>" | |
| }, | |
| { | |
| "name": "import module afml.features.meta_labeling_features", | |
| "status": "pass", | |
| "detail": "<module 'afml.features.meta_labeling_features' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\features\\\\meta_labeling_features.py'>" | |
| }, | |
| { | |
| "name": "import module afml.features.moving_averages", | |
| "status": "pass", | |
| "detail": "<module 'afml.features.moving_averages' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\features\\\\moving_averages.py'>" | |
| }, | |
| { | |
| "name": "import module afml.features.returns", | |
| "status": "pass", | |
| "detail": "<module 'afml.features.returns' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\features\\\\returns.py'>" | |
| }, | |
| { | |
| "name": "import module afml.features.stationary", | |
| "status": "pass", | |
| "detail": "<module 'afml.features.stationary' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\features\\\\stationary.py'>" | |
| }, | |
| { | |
| "name": "import module afml.features.trading_session", | |
| "status": "pass", | |
| "detail": "<module 'afml.features.trading_session' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\features\\\\trading_session.py'>" | |
| }, | |
| { | |
| "name": "import module afml.features.volatility_regime", | |
| "status": "pass", | |
| "detail": "<module 'afml.features.volatility_regime' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\features\\\\volatility_regime.py'>" | |
| }, | |
| { | |
| "name": "import module afml.filters.filters", | |
| "status": "pass", | |
| "detail": "<module 'afml.filters.filters' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\filters\\\\filters.py'>" | |
| }, | |
| { | |
| "name": "import module afml.labeling.fixed_time_horizon", | |
| "status": "pass", | |
| "detail": "<module 'afml.labeling.fixed_time_horizon' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\labeling\\\\fixed_time_horizon.py'>" | |
| }, | |
| { | |
| "name": "import module afml.labeling.trend_scanning", | |
| "status": "pass", | |
| "detail": "<module 'afml.labeling.trend_scanning' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\labeling\\\\trend_scanning.py'>" | |
| }, | |
| { | |
| "name": "import module afml.labeling.triple_barrier", | |
| "status": "pass", | |
| "detail": "<module 'afml.labeling.triple_barrier' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\labeling\\\\triple_barrier.py'>" | |
| }, | |
| { | |
| "name": "import module afml.sample_weights.attribution", | |
| "status": "pass", | |
| "detail": "<module 'afml.sample_weights.attribution' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\sample_weights\\\\attribution.py'>" | |
| }, | |
| { | |
| "name": "import module afml.sample_weights.optimized_attribution", | |
| "status": "pass", | |
| "detail": "<module 'afml.sample_weights.optimized_attribution' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\sample_weights\\\\optimized_attribution.py'>" | |
| }, | |
| { | |
| "name": "import module afml.strategies.bollinger_features", | |
| "status": "pass", | |
| "detail": "<module 'afml.strategies.bollinger_features' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\strategies\\\\bollinger_features.py'>" | |
| }, | |
| { | |
| "name": "import module afml.strategies.genetic_optimizer", | |
| "status": "pass", | |
| "detail": "<module 'afml.strategies.genetic_optimizer' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\strategies\\\\genetic_optimizer.py'>" | |
| }, | |
| { | |
| "name": "import module afml.strategies.ma_crossover_feature_engine", | |
| "status": "pass", | |
| "detail": "<module 'afml.strategies.ma_crossover_feature_engine' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\strategies\\\\ma_crossover_feature_engine.py'>" | |
| }, | |
| { | |
| "name": "import module afml.strategies.ma_whipsaw_ratio", | |
| "status": "pass", | |
| "detail": "<module 'afml.strategies.ma_whipsaw_ratio' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\strategies\\\\ma_whipsaw_ratio.py'>" | |
| }, | |
| { | |
| "name": "import module afml.strategies.signal_processing", | |
| "status": "pass", | |
| "detail": "<module 'afml.strategies.signal_processing' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\strategies\\\\signal_processing.py'>" | |
| }, | |
| { | |
| "name": "import module afml.strategies.strategy_optimizer", | |
| "status": "pass", | |
| "detail": "<module 'afml.strategies.strategy_optimizer' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\strategies\\\\strategy_optimizer.py'>" | |
| }, | |
| { | |
| "name": "import module afml.strategies.trading_strategies", | |
| "status": "pass", | |
| "detail": "<module 'afml.strategies.trading_strategies' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\strategies\\\\trading_strategies.py'>" | |
| }, | |
| { | |
| "name": "import module afml.strategies.trend_scanning_optimizer", | |
| "status": "pass", | |
| "detail": "<module 'afml.strategies.trend_scanning_optimizer' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\strategies\\\\trend_scanning_optimizer.py'>" | |
| }, | |
| { | |
| "name": "import module afml.strategies.trend_scanning_optimizer_1", | |
| "status": "pass", | |
| "detail": "<module 'afml.strategies.trend_scanning_optimizer_1' from 'C:\\\\Users\\\\aksha\\\\Downloads\\\\afml\\\\afml\\\\strategies\\\\trend_scanning_optimizer_1.py'>" | |
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| { | |
| "name": "cross_validation PurgedKFold split", | |
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| 80 | |
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| { | |
| "name": "cross_validation PurgedSplit split", | |
| "status": "pass", | |
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| 240, | |
| 59 | |
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| }, | |
| { | |
| "name": "cross_validation PurgedWalkForwardCV split", | |
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| 80 | |
| ], | |
| [ | |
| 160, | |
| 80 | |
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| { | |
| "name": "cross_validation CombinatorialPurgedCV split", | |
| "status": "pass", | |
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| [ | |
| 158, | |
| 80 | |
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| [ | |
| 154, | |
| 80 | |
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| [ | |
| 154, | |
| 80 | |
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| { | |
| "name": "cross_validation probability_weighted_accuracy", | |
| "status": "pass", | |
| "detail": 1.0 | |
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| { | |
| "name": "cross_validation ml_cross_val_score", | |
| "status": "pass", | |
| "detail": [ | |
| 0.925, | |
| 0.9375, | |
| 0.9125 | |
| ] | |
| }, | |
| { | |
| "name": "data_structures calculate_ticks_per_period", | |
| "status": "pass", | |
| "detail": 60 | |
| }, | |
| { | |
| "name": "data_structures tick bars", | |
| "status": "pass", | |
| "detail": [ | |
| 10, | |
| 9 | |
| ] | |
| }, | |
| { | |
| "name": "data_structures volume bars", | |
| "status": "pass", | |
| "detail": [ | |
| 16, | |
| 9 | |
| ] | |
| }, | |
| { | |
| "name": "data_structures dollar bars", | |
| "status": "pass", | |
| "detail": [ | |
| 150, | |
| 9 | |
| ] | |
| }, | |
| { | |
| "name": "datasets load_stock_prices", | |
| "status": "pass", | |
| "detail": [ | |
| 2141, | |
| 23 | |
| ] | |
| }, | |
| { | |
| "name": "datasets load_tick_sample", | |
| "status": "pass", | |
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| 100, | |
| 2 | |
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| }, | |
| { | |
| "name": "datasets load_dollar_bar_sample", | |
| "status": "pass", | |
| "detail": [ | |
| 1000, | |
| 7 | |
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| }, | |
| { | |
| "name": "features frac_diff", | |
| "status": "pass", | |
| "detail": [ | |
| 240, | |
| 1 | |
| ] | |
| }, | |
| { | |
| "name": "features frac_diff_ffd", | |
| "status": "pass", | |
| "detail": [] | |
| }, | |
| { | |
| "name": "features moving_average_differences", | |
| "status": "pass", | |
| "detail": [ | |
| 240, | |
| 3 | |
| ] | |
| }, | |
| { | |
| "name": "features moving_average_crossovers", | |
| "status": "pass", | |
| "detail": [ | |
| 240, | |
| 3 | |
| ] | |
| }, | |
| { | |
| "name": "features lagged_returns", | |
| "status": "pass", | |
| "detail": [ | |
| 240, | |
| 9 | |
| ] | |
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| { | |
| "name": "features period_autocorr", | |
| "status": "pass", | |
| "detail": [ | |
| 219 | |
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| }, | |
| { | |
| "name": "features return_distribution", | |
| "status": "pass", | |
| "detail": [ | |
| 236, | |
| 3 | |
| ] | |
| }, | |
| { | |
| "name": "features time_features", | |
| "status": "pass", | |
| "detail": [ | |
| 240, | |
| 18 | |
| ] | |
| }, | |
| { | |
| "name": "features stationary", | |
| "status": "pass", | |
| "detail": [] | |
| }, | |
| { | |
| "name": "features fractals", | |
| "status": "pass", | |
| "detail": [ | |
| 240, | |
| 18 | |
| ] | |
| }, | |
| { | |
| "name": "features market_regime", | |
| "status": "pass", | |
| "detail": [ | |
| 240, | |
| 7 | |
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| }, | |
| { | |
| "name": "filters cusum_filter", | |
| "status": "pass", | |
| "detail": 150 | |
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| { | |
| "name": "filters z_score_filter", | |
| "status": "pass", | |
| "detail": 65 | |
| }, | |
| { | |
| "name": "labeling fixed_time_horizon", | |
| "status": "pass", | |
| "detail": { | |
| "1.0": 105, | |
| "0.0": 69, | |
| "-1.0": 65 | |
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| { | |
| "name": "labeling trend_scanning", | |
| "status": "pass", | |
| "detail": [ | |
| 221, | |
| 7 | |
| ] | |
| }, | |
| { | |
| "name": "labeling triple_barrier_events", | |
| "status": "pass", | |
| "detail": [ | |
| 20, | |
| 4 | |
| ] | |
| }, | |
| { | |
| "name": "labeling get_bins", | |
| "status": "pass", | |
| "detail": [ | |
| 20, | |
| 4 | |
| ] | |
| }, | |
| { | |
| "name": "sample_weights return", | |
| "status": "pass", | |
| "detail": [ | |
| 20 | |
| ] | |
| }, | |
| { | |
| "name": "sample_weights return_optimized", | |
| "status": "pass", | |
| "detail": [ | |
| 20 | |
| ] | |
| }, | |
| { | |
| "name": "sample_weights time_decay", | |
| "status": "pass", | |
| "detail": [ | |
| 20 | |
| ] | |
| }, | |
| { | |
| "name": "strategies BollingerStrategy generate_signals", | |
| "status": "pass", | |
| "detail": { | |
| "0": 191, | |
| "-1": 35, | |
| "1": 14 | |
| } | |
| }, | |
| { | |
| "name": "strategies MACrossoverStrategy generate_signals", | |
| "status": "pass", | |
| "detail": { | |
| "1": 129, | |
| "-1": 82, | |
| "0": 29 | |
| } | |
| }, | |
| { | |
| "name": "strategies signal_processing get_entries", | |
| "status": "pass", | |
| "detail": [ | |
| [ | |
| 240 | |
| ], | |
| 12 | |
| ] | |
| }, | |
| { | |
| "name": "strategies bollinger_features", | |
| "status": "pass", | |
| "detail": [ | |
| 196, | |
| 46 | |
| ] | |
| }, | |
| { | |
| "name": "strategies ma_whipsaw_ratio", | |
| "status": "pass", | |
| "detail": 0.5454545454545454 | |
| }, | |
| { | |
| "name": "strategies ForexFeatureEngine", | |
| "status": "pass", | |
| "detail": [ | |
| 240, | |
| 86 | |
| ] | |
| } | |
| ] | |
| } |