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    },
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    },
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        "train_bidask_longshort_models"
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      "classes": [
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      ]
    },
    "afml.production.model_development": {
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        "_default_target_config",
        "best_weighting_scheme",
        "calculate_rolling_metrics",
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        "create_feature_engineering_pipeline",
        "develop_production_model",
        "develop_production_model_with_enhanced_logging",
        "generate_events_triple_barrier",
        "get_bar_size",
        "get_model_type",
        "get_optimal_sample_weight",
        "is_tree",
        "load_and_prepare_training_data",
        "load_ticks",
        "train_model_with_cv",
        "weighted_estimator"
      ],
      "classes": [
        "LogAnalyzer",
        "LoggingModelDevelopmentPipeline",
        "ModelDevelopmentLogger",
        "ModelDevelopmentPipeline",
        "PipelineMonitor",
        "ProductionReadinessChecker",
        "_WeightedEstimator"
      ]
    },
    "afml.production.model_development_optuna": {
      "functions": [
        "get_model_type"
      ],
      "classes": [
        "ModelDevelopmentPipelineOptuna"
      ]
    },
    "afml.production.model_export": {
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        "complete_export_workflow",
        "export_model_to_onnx",
        "extract_onnx_metadata",
        "validate_onnx_predictions"
      ],
      "classes": []
    },
    "afml.production.optimization_framework": {
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        "print_optimization_insights"
      ],
      "classes": [
        "ParameterOptimizer",
        "StrategyConfig"
      ]
    },
    "afml.production.parameter_grid_search": {
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      "classes": [
        "MultiConfigPipeline",
        "ParameterGridSearch"
      ]
    },
    "afml.production.utils": {
      "functions": [],
      "classes": [
        "ConfigPathGenerator",
        "ModelFileManager"
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    },
    "afml.sample_weights": {
      "functions": [],
      "classes": []
    },
    "afml.sample_weights.attribution": {
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        "_apply_weight_by_return",
        "get_weights_by_return",
        "get_weights_by_time_decay"
      ],
      "classes": []
    },
    "afml.sample_weights.optimized_attribution": {
      "functions": [
        "_apply_weight_by_return_optimized",
        "get_weights_by_return_optimized",
        "get_weights_by_time_decay_optimized"
      ],
      "classes": []
    },
    "afml.sampling": {
      "functions": [],
      "classes": []
    },
    "afml.sampling.bootstrapping": {
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        "get_active_indices",
        "get_ind_mat_average_uniqueness",
        "get_ind_matrix",
        "pack_active_indices",
        "seq_bootstrap"
      ],
      "classes": []
    },
    "afml.sampling.concurrent": {
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        "_get_average_uniqueness",
        "get_av_uniqueness_from_triple_barrier",
        "get_num_conc_events",
        "num_concurrent_events"
      ],
      "classes": []
    },
    "afml.sampling.optimized_concurrent": {
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        "_get_average_uniqueness_optimized",
        "get_av_uniqueness_from_triple_barrier_optimized",
        "get_num_conc_events_optimized"
      ],
      "classes": []
    },
    "afml.strategies": {
      "functions": [],
      "classes": []
    },
    "afml.strategies.bollinger_features": {
      "functions": [
        "create_bollinger_features",
        "plot_bbands",
        "plot_bbands_dual_bbp_bw"
      ],
      "classes": []
    },
    "afml.strategies.genetic_optimizer": {
      "functions": [
        "calculate_performance_metrics",
        "get_dynamic_seed",
        "get_optimal_triple_barrier_labels",
        "get_positions_from_events",
        "save_optimization_results",
        "select_knee_point"
      ],
      "classes": [
        "GAOptimal",
        "OptimizationConfig",
        "ParetoOptimizer",
        "SingleObjectiveOptimizer",
        "TripleBarrierEvaluator"
      ]
    },
    "afml.strategies.ma_crossover_feature_engine": {
      "functions": [],
      "classes": [
        "ForexFeatureEngine"
      ]
    },
    "afml.strategies.ma_whipsaw_ratio": {
      "functions": [
        "calculate_enhanced_whipsaw_metrics",
        "calculate_ma_whipsaw_ratio"
      ],
      "classes": []
    },
    "afml.strategies.signal_processing": {
      "functions": [
        "get_entries"
      ],
      "classes": []
    },
    "afml.strategies.strategy_optimizer": {
      "functions": [
        "create_example_test_suite",
        "optimize_strategy_barriers"
      ],
      "classes": [
        "BarrierOptimizationResult",
        "GeometricBrownianMotion",
        "IntegratedTestResult",
        "OrnsteinUhlenbeck",
        "ProcessSelector",
        "StrategyTestFramework"
      ]
    },
    "afml.strategies.trading_strategies": {
      "functions": [],
      "classes": [
        "BaseStrategy",
        "BollingerStrategy",
        "MACrossoverStrategy"
      ]
    },
    "afml.strategies.trend_scanning_optimizer": {
      "functions": [],
      "classes": [
        "TrendScanningEvaluator"
      ]
    },
    "afml.strategies.trend_scanning_optimizer_1": {
      "functions": [],
      "classes": [
        "TrendScanningEvaluator"
      ]
    },
    "afml.util": {
      "functions": [],
      "classes": []
    },
    "afml.util.constants": {
      "functions": [],
      "classes": []
    },
    "afml.util.fix_mlfinlab_imports": {
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        "replace_imports_in_folder"
      ],
      "classes": []
    },
    "afml.util.misc": {
      "functions": [
        "count_max_decimals",
        "crop_data_frame_in_batches",
        "date_conversion",
        "dict_to_key",
        "expand_params",
        "flatten_column_names",
        "get_folder_size",
        "indices_to_mask",
        "is_first_weekday",
        "is_last_weekday",
        "is_trading_day",
        "log_column_changes",
        "log_df_info",
        "log_performance",
        "markdown_to_notebook",
        "optimize_dtypes",
        "set_resampling_freq",
        "smart_subscript",
        "to_subscript",
        "value_counts_data"
      ],
      "classes": [
        "DataFrameFormatter"
      ]
    },
    "afml.util.multiprocess": {
      "functions": [
        "expand_call",
        "lin_parts",
        "mp_pandas_obj",
        "nested_parts",
        "process_jobs",
        "process_jobs_",
        "report_progress"
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      "classes": []
    },
    "afml.util.pipelines": {
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        "make_custom_pipeline",
        "set_pipeline_params"
      ],
      "classes": [
        "MyPipeline"
      ]
    },
    "afml.util.test": {
      "functions": [],
      "classes": []
    },
    "afml.util.volatility": {
      "functions": [
        "get_daily_vol",
        "get_garman_klass_vol",
        "get_parkinson_vol",
        "get_period_vol",
        "get_yang_zhang_vol",
        "two_time_scale_realized_vol"
      ],
      "classes": []
    }
  },
  "smoke_results": [
    {
      "name": "datasets.load_samples",
      "status": "pass",
      "output": {
        "tick": [
          100,
          2
        ],
        "stock": [
          2141,
          23
        ],
        "dollar": [
          1000,
          7
        ]
      }
    },
    {
      "name": "data_structures.make_bars",
      "status": "pass",
      "output": {
        "tick_bars": [
          5,
          9
        ],
        "time_bars": [
          3,
          9
        ]
      }
    },
    {
      "name": "features.core",
      "status": "pass",
      "output": {
        "lagged": [
          400,
          9
        ],
        "dist": [
          400,
          3
        ],
        "time_features": [
          400,
          20
        ],
        "frac_diff": [
          357
        ],
        "is_stationary": []
      }
    },
    {
      "name": "filters.core",
      "status": "pass",
      "output": {
        "cusum_events": 154,
        "z_score_events": 0
      }
    },
    {
      "name": "labeling.core",
      "status": "pass",
      "output": {
        "triple_events": [
          30,
          4
        ],
        "fixed": [
          500
        ],
        "trend": [
          481,
          7
        ]
      }
    },
    {
      "name": "sampling.sample_weights",
      "status": "pass",
      "output": {
        "concurrent": [
          15
        ],
        "weights": [
          10
        ]
      }
    },
    {
      "name": "cross_validation.core",
      "status": "pass",
      "output": {
        "splits": 3,
        "train_times": [
          53
        ],
        "pbo": 1.0
      }
    },
    {
      "name": "strategies.basic",
      "status": "pass",
      "output": {
        "bollinger_signals": {
          "0": 262,
          "1": 23,
          "-1": 15
        },
        "ma_signals": {
          "-1": 148,
          "1": 123,
          "0": 29
        }
      }
    },
    {
      "name": "mt5.live_xau_btc_m5",
      "status": "pass",
      "output": {
        "XAUUSD": 390,
        "BTCUSD": 864
      }
    }
  ]
}