{
  "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'>"
    },
    {
      "name": "cross_validation PurgedKFold split",
      "status": "pass",
      "detail": [
        [
          157,
          80
        ],
        [
          156,
          80
        ],
        [
          159,
          80
        ]
      ]
    },
    {
      "name": "cross_validation PurgedSplit split",
      "status": "pass",
      "detail": [
        240,
        59
      ]
    },
    {
      "name": "cross_validation PurgedWalkForwardCV split",
      "status": "pass",
      "detail": [
        [
          78,
          80
        ],
        [
          160,
          80
        ]
      ]
    },
    {
      "name": "cross_validation CombinatorialPurgedCV split",
      "status": "pass",
      "detail": [
        [
          158,
          80
        ],
        [
          154,
          80
        ],
        [
          154,
          80
        ]
      ]
    },
    {
      "name": "cross_validation probability_weighted_accuracy",
      "status": "pass",
      "detail": 1.0
    },
    {
      "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",
      "detail": [
        100,
        2
      ]
    },
    {
      "name": "datasets load_dollar_bar_sample",
      "status": "pass",
      "detail": [
        1000,
        7
      ]
    },
    {
      "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
      ]
    },
    {
      "name": "features period_autocorr",
      "status": "pass",
      "detail": [
        219
      ]
    },
    {
      "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
      ]
    },
    {
      "name": "filters cusum_filter",
      "status": "pass",
      "detail": 150
    },
    {
      "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
      }
    },
    {
      "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
      ]
    }
  ]
}