Use the subject references for methodology, defaults, units, state, outputs and refusals. This index closes names and source ownership. A signature or definition identifies the current package contract; it does not assert that every configuration was executed by the introductory experiment. Module re-exports and version metadata are distinguished from callable contracts.
Declared entries
| Entry | Contract kind | Authority |
|---|---|---|
limen.Cohort |
signature |
limen/cohort/cohort.py |
limen.Cohort.__call__ |
signature |
limen/cohort/cohort.py |
limen.Cohort.__init__ |
signature |
limen/cohort/cohort.py |
limen.Cohort.from_python |
signature |
limen/cohort/cohort.py |
limen.Cohort.predict |
signature |
limen/cohort/cohort.py |
limen.Cohort.predict_all |
signature |
limen/cohort/cohort.py |
limen.Cohort.set_members |
signature |
limen/cohort/cohort.py |
limen.HistoricalData |
signature |
limen/data/historical_data.py |
limen.HistoricalData.__init__ |
signature |
limen/data/historical_data.py |
limen.HistoricalData.get_any_file |
signature |
limen/data/historical_data.py |
limen.HistoricalData.get_arrow_file |
signature |
limen/data/historical_data.py |
limen.HistoricalData.get_binance_file |
signature |
limen/data/historical_data.py |
limen.HistoricalData.get_spot_dollar_klines |
signature |
limen/data/historical_data.py |
limen.HistoricalData.get_spot_klines |
signature |
limen/data/historical_data.py |
limen.Log |
signature |
limen/log/log.py |
limen.Log.__init__ |
signature |
limen/log/log.py |
limen.Log.experiment_backtest_results |
signature |
limen/log/_experiment_backtest_results.py |
limen.Log.experiment_confusion_metrics |
signature |
limen/log/_experiment_confusion_metrics.py |
limen.Log.experiment_parameter_correlation |
signature |
limen/log/_experiment_parameter_correlation.py |
limen.Log.experiment_parameter_importance |
signature |
limen/log/_experiment_parameter_importance.py |
limen.Log.permutation_confusion_metrics |
signature |
limen/log/_permutation_confusion_metrics.py |
limen.Log.permutation_prediction_performance |
signature |
limen/log/log.py |
limen.Log.read_from_file |
signature |
limen/log/_read_from_file.py |
limen.MLManifest |
signature |
limen/experiment/manifest_core.py |
limen.MLManifest.add_to_data_dict |
signature |
limen/experiment/manifest_core.py |
limen.MLManifest.prepare_data |
signature |
limen/experiment/manifest_core.py |
limen.MLManifest.run_model |
signature |
limen/experiment/manifest_core.py |
limen.MLManifest.sensor_input_prep |
signature |
limen/experiment/manifest_core.py |
limen.MLManifest.set_feature_ablation |
signature |
limen/experiment/manifest_core.py |
limen.MLManifest.set_feature_shims |
signature |
limen/experiment/_column_shims.py |
limen.MLManifest.set_objective |
signature |
limen/experiment/manifest_core.py |
limen.MLManifest.set_pca_compression |
signature |
limen/experiment/manifest_core.py |
limen.MLManifest.set_scaler |
signature |
limen/experiment/manifest_core.py |
limen.MLManifest.set_scaler_from_params |
signature |
limen/experiment/manifest_core.py |
limen.MLManifest.set_strict_mode |
signature |
limen/experiment/manifest_core.py |
limen.MLManifest.with_calibration |
signature |
limen/experiment/manifest_core.py |
limen.Manifest |
signature |
limen/experiment/manifest_core.py |
limen.Manifest.add_feature |
signature |
limen/experiment/manifest_core.py |
limen.Manifest.add_indicator |
signature |
limen/experiment/manifest_core.py |
limen.Manifest.compute_test_bars |
signature |
limen/experiment/manifest_core.py |
limen.Manifest.fetch_data |
signature |
limen/experiment/manifest_core.py |
limen.Manifest.fetch_test_data |
signature |
limen/experiment/manifest_core.py |
limen.Manifest.prepare_data |
signature |
limen/experiment/manifest_core.py |
limen.Manifest.resolve_backtest_config |
signature |
limen/experiment/manifest_core.py |
limen.Manifest.resolve_model_kwargs |
signature |
limen/experiment/manifest_core.py |
limen.Manifest.resolve_trade_policy |
signature |
limen/experiment/manifest_core.py |
limen.Manifest.run_model |
signature |
limen/experiment/manifest_core.py |
limen.Manifest.set_acceptance |
signature |
limen/experiment/manifest_core.py |
limen.Manifest.set_backtest_config |
signature |
limen/experiment/manifest_core.py |
limen.Manifest.set_bar_formation |
signature |
limen/experiment/manifest_core.py |
limen.Manifest.set_data_source |
signature |
limen/experiment/manifest_core.py |
limen.Manifest.set_pre_split_data_selector |
signature |
limen/experiment/manifest_core.py |
limen.Manifest.set_required_bar_columns |
signature |
limen/experiment/manifest_core.py |
limen.Manifest.set_split_config |
signature |
limen/experiment/manifest_core.py |
limen.Manifest.set_split_dates |
signature |
limen/experiment/manifest_core.py |
limen.Manifest.set_split_walk_forward |
signature |
limen/experiment/manifest_core.py |
limen.Manifest.set_test_data_source |
signature |
limen/experiment/manifest_core.py |
limen.Manifest.with_allocation |
signature |
limen/experiment/manifest_core.py |
limen.Manifest.with_params_override |
signature |
limen/experiment/manifest_core.py |
limen.Manifest.with_reference_architecture |
signature |
limen/experiment/manifest_core.py |
limen.Manifest.with_target_label |
signature |
limen/experiment/manifest_core.py |
limen.ReconstructionError |
signature |
limen/inference/errors.py |
limen.RuleBasedManifest |
signature |
limen/experiment/manifest_core.py |
limen.RuleBasedManifest.prepare_data |
signature |
limen/experiment/manifest_core.py |
limen.RuleBasedManifest.sensor_input_prep |
signature |
limen/experiment/manifest_core.py |
limen.RuleBasedManifest.set_objective |
signature |
limen/experiment/manifest_core.py |
limen.RuleBasedManifest.with_strategy |
signature |
limen/experiment/manifest_core.py |
limen.Sensor |
signature |
limen/inference/sensor.py |
limen.Sensor.__call__ |
signature |
limen/inference/sensor.py |
limen.Sensor.__init__ |
signature |
limen/inference/sensor.py |
limen.Sensor.from_model |
signature |
limen/inference/sensor.py |
limen.Sensor.from_python |
signature |
limen/inference/sensor.py |
limen.Sensor.predict |
signature |
limen/inference/sensor.py |
limen.Sensor.predict_all |
signature |
limen/inference/sensor.py |
limen.Sensor.prediction_mode |
signature |
limen/inference/sensor.py |
limen.Sensor.round_params |
signature |
limen/inference/sensor.py |
limen.Sensor.trade_contract |
signature |
limen/inference/sensor.py |
limen.Trainer |
signature |
limen/inference/trainer.py |
limen.Trainer.__init__ |
signature |
limen/inference/trainer.py |
limen.Trainer.train |
signature |
limen/inference/trainer.py |
limen.UniversalExperimentLoop |
signature |
limen/experiment/experiment_core.py |
limen.UniversalExperimentLoop.__init__ |
signature |
limen/experiment/experiment_core.py |
limen.UniversalExperimentLoop.fold_results |
signature |
limen/experiment/experiment_core.py |
limen.UniversalExperimentLoop.interrupted |
signature |
limen/experiment/experiment_core.py |
limen.UniversalExperimentLoop.run |
signature |
limen/experiment/experiment_core.py |
limen.__version__ |
metadata |
limen/__init__.py |
limen.backtest.ExecutionResult |
signature |
limen/backtest/long_flat_strategy.py |
limen.backtest.ProductSpec |
signature |
limen/backtest/trade_contract.py |
limen.backtest.TradeInputs |
signature |
limen/backtest/trade_contract.py |
limen.backtest.TradePolicy |
signature |
limen/backtest/trade_contract.py |
limen.backtest.backtest_snapshot |
signature |
limen/backtest/backtest_snapshot.py |
limen.backtest.backtest_snapshot.BACKTEST_SNAPSHOT_COLUMNS |
definition |
limen/backtest/backtest_snapshot.py |
limen.backtest.backtest_snapshot.backtest_snapshot |
signature |
limen/backtest/backtest_snapshot.py |
limen.backtest.execution_events.OBSERVATION_COLUMNS |
definition |
limen/backtest/execution_events.py |
limen.backtest.execution_events.SIGNAL_COLUMNS |
definition |
limen/backtest/execution_events.py |
limen.backtest.execution_events.execution_events |
signature |
limen/backtest/execution_events.py |
limen.backtest.execution_events.validate_observations |
signature |
limen/backtest/execution_events.py |
limen.backtest.execution_events.with_predictions |
signature |
limen/backtest/execution_events.py |
limen.backtest.funding_adapter.ADAPTER_VERSION |
definition |
limen/backtest/funding_adapter.py |
limen.backtest.funding_adapter.FUNDING_KEYS |
definition |
limen/backtest/funding_adapter.py |
limen.backtest.funding_adapter.FundingAdapter |
signature |
limen/backtest/funding_adapter.py |
limen.backtest.funding_adapter.FundingAdapter.__call__ |
signature |
limen/backtest/funding_adapter.py |
limen.backtest.funding_adapter.FundingCashflow |
signature |
limen/backtest/funding_adapter.py |
limen.backtest.funding_adapter.FundingContext |
signature |
limen/backtest/funding_adapter.py |
limen.backtest.funding_adapter.funding_adapter |
signature |
limen/backtest/funding_adapter.py |
limen.backtest.funding_adapter.prepare_funding |
signature |
limen/backtest/funding_adapter.py |
limen.backtest.funding_adapter.resolve_adapter |
signature |
limen/backtest/funding_adapter.py |
limen.backtest.funding_adapter.scheduled_times |
signature |
limen/backtest/funding_adapter.py |
limen.backtest.funding_presets.BASELINE_RATE_8H |
definition |
limen/backtest/funding_presets.py |
limen.backtest.funding_presets.FundingPreset |
signature |
limen/backtest/funding_presets.py |
limen.backtest.funding_presets.PRESETS |
definition |
limen/backtest/funding_presets.py |
limen.backtest.funding_presets.PRESET_VERSION |
definition |
limen/backtest/funding_presets.py |
limen.backtest.long_flat_strategy |
signature |
limen/backtest/long_flat_strategy.py |
limen.backtest.long_flat_strategy.ExecutionResult |
signature |
limen/backtest/long_flat_strategy.py |
limen.backtest.long_flat_strategy.long_flat_strategy |
signature |
limen/backtest/long_flat_strategy.py |
limen.backtest.observed_costs.CalibrationReport |
signature |
limen/backtest/observed_costs.py |
limen.backtest.observed_costs.ComparisonReport |
signature |
limen/backtest/observed_costs.py |
limen.backtest.observed_costs.CostCalibration |
signature |
limen/backtest/observed_costs.py |
limen.backtest.observed_costs.CostCalibration.compare |
signature |
limen/backtest/observed_costs.py |
limen.backtest.observed_costs.CostCalibration.digest |
signature |
limen/backtest/observed_costs.py |
limen.backtest.observed_costs.CostCalibration.from_history |
signature |
limen/backtest/observed_costs.py |
limen.backtest.observed_costs.CostCalibration.read |
signature |
limen/backtest/observed_costs.py |
limen.backtest.observed_costs.CostCalibration.report |
signature |
limen/backtest/observed_costs.py |
limen.backtest.observed_costs.CostCalibration.write |
signature |
limen/backtest/observed_costs.py |
limen.backtest.observed_costs.CostCandidate |
signature |
limen/backtest/observed_costs.py |
limen.backtest.observed_costs.CostClock |
definition |
limen/backtest/observed_costs.py |
limen.backtest.observed_costs.CostComparison |
signature |
limen/backtest/observed_costs.py |
limen.backtest.observed_costs.CostComparison.digest |
signature |
limen/backtest/observed_costs.py |
limen.backtest.observed_costs.CostComparison.ledger |
signature |
limen/backtest/observed_costs.py |
limen.backtest.observed_costs.CostComparison.report |
signature |
limen/backtest/observed_costs.py |
limen.backtest.observed_costs.CostComparison.write |
signature |
limen/backtest/observed_costs.py |
limen.backtest.observed_costs.CostConditions |
signature |
limen/backtest/observed_costs.py |
limen.backtest.observed_costs.CostDisposition |
definition |
limen/backtest/observed_costs.py |
limen.backtest.trade_contract.BPS |
definition |
limen/backtest/trade_contract.py |
limen.backtest.trade_contract.ExecutionEvent |
signature |
limen/backtest/trade_contract.py |
limen.backtest.trade_contract.FundingPolicy |
signature |
limen/backtest/trade_contract.py |
limen.backtest.trade_contract.JsonScalar |
definition |
limen/backtest/trade_contract.py |
limen.backtest.trade_contract.JsonValue |
definition |
limen/backtest/trade_contract.py |
limen.backtest.trade_contract.NANOSECONDS |
definition |
limen/backtest/trade_contract.py |
limen.backtest.trade_contract.PredictionMode |
definition |
limen/backtest/trade_contract.py |
limen.backtest.trade_contract.ProductSpec |
signature |
limen/backtest/trade_contract.py |
limen.backtest.trade_contract.RULE_VERSION |
definition |
limen/backtest/trade_contract.py |
limen.backtest.trade_contract.SourceBinding |
signature |
limen/backtest/trade_contract.py |
limen.backtest.trade_contract.TradeInputs |
signature |
limen/backtest/trade_contract.py |
limen.backtest.trade_contract.TradeLedger |
signature |
limen/backtest/trade_contract.py |
limen.backtest.trade_contract.TradePolicy |
signature |
limen/backtest/trade_contract.py |
limen.backtest.trade_contract.contract_digest |
signature |
limen/backtest/trade_contract.py |
limen.backtest.trade_contract.export_trade_contract |
signature |
limen/backtest/trade_contract.py |
limen.backtest.trade_contract.finite_number |
signature |
limen/backtest/trade_contract.py |
limen.backtest.trade_contract.json_value |
signature |
limen/backtest/trade_contract.py |
limen.backtest.trade_contract.source_binding |
signature |
limen/backtest/trade_contract.py |
limen.backtest.trade_execution |
signature |
limen/backtest/trade_execution.py |
limen.backtest.trade_execution.trade_execution |
signature |
limen/backtest/trade_execution.py |
limen.calibration.CalibratorProtocol |
signature |
limen/calibration/pipeline.py |
limen.calibration.CalibratorProtocol.__call__ |
signature |
limen/calibration/pipeline.py |
limen.calibration.ThresholdOptimizerProtocol |
signature |
limen/calibration/pipeline.py |
limen.calibration.ThresholdOptimizerProtocol.__call__ |
signature |
limen/calibration/pipeline.py |
limen.calibration.apply_calibrated_predict |
signature |
limen/calibration/pipeline.py |
limen.calibration.fit_calibrator |
signature |
limen/calibration/pipeline.py |
limen.calibration.grid_threshold_optimizer |
signature |
limen/calibration/threshold.py |
limen.calibration.sklearn_probability_calibrator |
signature |
limen/calibration/probability.py |
limen.cli.cli |
signature |
limen/cli/main.py |
limen.cohort.BUILTIN_SELECTORS |
definition |
limen/cohort/sfc/__init__.py |
limen.cohort.Cohort |
signature |
limen/cohort/cohort.py |
limen.cohort.Cohort.__call__ |
signature |
limen/cohort/cohort.py |
limen.cohort.Cohort.__init__ |
signature |
limen/cohort/cohort.py |
limen.cohort.Cohort.from_python |
signature |
limen/cohort/cohort.py |
limen.cohort.Cohort.predict |
signature |
limen/cohort/cohort.py |
limen.cohort.Cohort.predict_all |
signature |
limen/cohort/cohort.py |
limen.cohort.Cohort.set_members |
signature |
limen/cohort/cohort.py |
limen.cohort.sfc.BUILTIN_SELECTORS |
definition |
limen/cohort/sfc/__init__.py |
limen.cohort.sfc.Selector |
definition |
limen/cohort/sfc/__init__.py |
limen.cohort.sfc.SelectorContext |
definition |
limen/cohort/sfc/__init__.py |
limen.cohort.sfc.select_all |
signature |
limen/cohort/sfc/all.py |
limen.cohort.sfc.select_backtest_pareto |
signature |
limen/cohort/sfc/backtest_pareto.py |
limen.cohort.sfc.select_diverse_metrics |
signature |
limen/cohort/sfc/diverse_metrics.py |
limen.cohort.sfc.select_top_n |
signature |
limen/cohort/sfc/top_n.py |
limen.data.HistoricalData |
signature |
limen/data/historical_data.py |
limen.data.HistoricalData.__init__ |
signature |
limen/data/historical_data.py |
limen.data.HistoricalData.get_any_file |
signature |
limen/data/historical_data.py |
limen.data.HistoricalData.get_arrow_file |
signature |
limen/data/historical_data.py |
limen.data.HistoricalData.get_binance_file |
signature |
limen/data/historical_data.py |
limen.data.HistoricalData.get_spot_dollar_klines |
signature |
limen/data/historical_data.py |
limen.data.HistoricalData.get_spot_klines |
signature |
limen/data/historical_data.py |
limen.data.bars.liquidity_bars |
signature |
limen/data/bars/standard_bars.py |
limen.data.bars.trade_bars |
signature |
limen/data/bars/standard_bars.py |
limen.data.bars.volume_bars |
signature |
limen/data/bars/standard_bars.py |
limen.data.compute_data_bars |
signature |
limen/data/utils/compute_data_bars.py |
limen.data.utils.compute_data_bars |
signature |
limen/data/utils/compute_data_bars.py |
limen.data.utils.random_slice |
signature |
limen/data/utils/random_slice.py |
limen.data.utils.split_by_dates |
signature |
limen/data/utils/splits.py |
limen.data.utils.split_data_to_prep_output |
signature |
limen/data/utils/splits.py |
limen.data.utils.split_data_to_rule_based_prep_output |
signature |
limen/data/utils/splits.py |
limen.data.utils.split_random |
signature |
limen/data/utils/splits.py |
limen.data.utils.split_sequential |
signature |
limen/data/utils/splits.py |
limen.data.utils.split_walk_forward |
signature |
limen/data/utils/split_walk_forward.py |
limen.data.utils.split_walk_forward.split_walk_forward |
signature |
limen/data/utils/split_walk_forward.py |
limen.experiment.CalibrationConfig |
signature |
limen/experiment/manifest_core.py |
limen.experiment.CalibrationConfig.resolve |
signature |
limen/experiment/manifest_core.py |
limen.experiment.GridStrategy |
signature |
limen/experiment/param_search/grid_strategy.py |
limen.experiment.GridStrategy.__init__ |
signature |
limen/experiment/param_search/grid_strategy.py |
limen.experiment.GridStrategy.get_state |
signature |
limen/experiment/param_search/grid_strategy.py |
limen.experiment.GridStrategy.is_finite |
signature |
limen/experiment/param_search/grid_strategy.py |
limen.experiment.GridStrategy.on_domain_changed |
signature |
limen/experiment/param_search/grid_strategy.py |
limen.experiment.GridStrategy.set_state |
signature |
limen/experiment/param_search/grid_strategy.py |
limen.experiment.MLManifest |
signature |
limen/experiment/manifest_core.py |
limen.experiment.MLManifest.add_to_data_dict |
signature |
limen/experiment/manifest_core.py |
limen.experiment.MLManifest.prepare_data |
signature |
limen/experiment/manifest_core.py |
limen.experiment.MLManifest.run_model |
signature |
limen/experiment/manifest_core.py |
limen.experiment.MLManifest.sensor_input_prep |
signature |
limen/experiment/manifest_core.py |
limen.experiment.MLManifest.set_feature_ablation |
signature |
limen/experiment/manifest_core.py |
limen.experiment.MLManifest.set_feature_shims |
signature |
limen/experiment/_column_shims.py |
limen.experiment.MLManifest.set_objective |
signature |
limen/experiment/manifest_core.py |
limen.experiment.MLManifest.set_pca_compression |
signature |
limen/experiment/manifest_core.py |
limen.experiment.MLManifest.set_scaler |
signature |
limen/experiment/manifest_core.py |
limen.experiment.MLManifest.set_scaler_from_params |
signature |
limen/experiment/manifest_core.py |
limen.experiment.MLManifest.set_strict_mode |
signature |
limen/experiment/manifest_core.py |
limen.experiment.MLManifest.with_calibration |
signature |
limen/experiment/manifest_core.py |
limen.experiment.Manifest |
signature |
limen/experiment/manifest_core.py |
limen.experiment.Manifest.add_feature |
signature |
limen/experiment/manifest_core.py |
limen.experiment.Manifest.add_indicator |
signature |
limen/experiment/manifest_core.py |
limen.experiment.Manifest.compute_test_bars |
signature |
limen/experiment/manifest_core.py |
limen.experiment.Manifest.fetch_data |
signature |
limen/experiment/manifest_core.py |
limen.experiment.Manifest.fetch_test_data |
signature |
limen/experiment/manifest_core.py |
limen.experiment.Manifest.prepare_data |
signature |
limen/experiment/manifest_core.py |
limen.experiment.Manifest.resolve_backtest_config |
signature |
limen/experiment/manifest_core.py |
limen.experiment.Manifest.resolve_model_kwargs |
signature |
limen/experiment/manifest_core.py |
limen.experiment.Manifest.resolve_trade_policy |
signature |
limen/experiment/manifest_core.py |
limen.experiment.Manifest.run_model |
signature |
limen/experiment/manifest_core.py |
limen.experiment.Manifest.set_acceptance |
signature |
limen/experiment/manifest_core.py |
limen.experiment.Manifest.set_backtest_config |
signature |
limen/experiment/manifest_core.py |
limen.experiment.Manifest.set_bar_formation |
signature |
limen/experiment/manifest_core.py |
limen.experiment.Manifest.set_data_source |
signature |
limen/experiment/manifest_core.py |
limen.experiment.Manifest.set_pre_split_data_selector |
signature |
limen/experiment/manifest_core.py |
limen.experiment.Manifest.set_required_bar_columns |
signature |
limen/experiment/manifest_core.py |
limen.experiment.Manifest.set_split_config |
signature |
limen/experiment/manifest_core.py |
limen.experiment.Manifest.set_split_dates |
signature |
limen/experiment/manifest_core.py |
limen.experiment.Manifest.set_split_walk_forward |
signature |
limen/experiment/manifest_core.py |
limen.experiment.Manifest.set_test_data_source |
signature |
limen/experiment/manifest_core.py |
limen.experiment.Manifest.with_allocation |
signature |
limen/experiment/manifest_core.py |
limen.experiment.Manifest.with_params_override |
signature |
limen/experiment/manifest_core.py |
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limen/experiment/manifest_core.py |
limen.experiment.Manifest.with_target_label |
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limen/experiment/manifest_core.py |
limen.experiment.RandomStrategy |
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limen/experiment/param_search/random_strategy.py |
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limen/experiment/_factorized_contract.py |
limen.experiment.RuleBasedManifest |
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limen/experiment/manifest_core.py |
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limen/experiment/manifest_core.py |
limen.experiment.RuleBasedManifest.sensor_input_prep |
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limen/experiment/manifest_core.py |
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limen/experiment/experiment_core.py |
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limen/experiment/convergence_report.py |
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limen/experiment/convergence_report.py |
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limen/experiment/family_acceptance_report.py |
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limen/experiment/_holdout_freeze.py |
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limen/experiment/_holdout_freeze.py |
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limen/experiment/_holdout_freeze.py |
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limen/experiment/_holdout_report.py |
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limen/experiment/_holdout_history.py |
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limen/experiment/_holdout_history.py |
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limen/experiment/param_search/search_strategy.py |
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limen/experiment/param_search/search_strategy.py |
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limen/experiment/param_search/search_strategy.py |
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limen/experiment/param_search/sobol_strategy.py |
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limen/experiment/param_search/sobol_strategy.py |
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limen/experiment/param_search/sobol_strategy.py |
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limen/experiment/param_search/sobol_strategy.py |
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limen/experiment/param_search/grid_strategy.py |
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limen/experiment/param_search/grid_strategy.py |
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limen/experiment/param_search/sobol_strategy.py |
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limen/experiment/param_search/sobol_strategy.py |
limen.experiment.reducer.ACTION_SUGGEST |
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limen/experiment/reducer/pruning_strategy.py |
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limen/experiment/reducer/budget_reducer.py |
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limen/experiment/reducer/budget_reducer.py |
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limen/experiment/reducer/budget_reducer.py |
limen.experiment.reducer.CorrelationReducer |
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limen/experiment/reducer/correlation_reducer.py |
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limen/experiment/reducer/correlation_reducer.py |
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limen/experiment/reducer/correlation_reducer.py |
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limen/experiment/reducer/correlation_reducer.py |
limen.experiment.reducer.FILTER_EXCLUDE_VALUE |
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limen/experiment/reducer/filter_types.py |
limen.experiment.reducer.FILTER_KEEP_BETWEEN |
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limen/experiment/reducer/filter_types.py |
limen.experiment.reducer.FILTER_KEEP_VALUES |
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limen/experiment/reducer/filter_types.py |
limen.experiment.reducer.FILTER_SAMPLE |
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limen/experiment/reducer/filter_types.py |
limen.experiment.reducer.FocusReducer |
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limen/experiment/reducer/focus_reducer.py |
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limen/experiment/reducer/focus_reducer.py |
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limen/experiment/reducer/focus_reducer.py |
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limen/experiment/reducer/focus_reducer.py |
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limen/experiment/reducer/focus_reducer.py |
limen.experiment.reducer.PruningStrategy |
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limen/experiment/reducer/pruning_strategy.py |
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limen/experiment/reducer/pruning_strategy.py |
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limen/experiment/reducer/pruning_strategy.py |
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limen.experiment.reducer.PruningStrategy.set_state |
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limen/experiment/reducer/pruning_strategy.py |
limen.experiment.reducer.REDUCER_REGISTRY |
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limen/experiment/reducer/registry.py |
limen.experiment.reducer.SanityReducer |
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limen/experiment/reducer/sanity_reducer.py |
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limen/experiment/reducer/sanity_reducer.py |
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limen/experiment/reducer/sanity_reducer.py |
limen.experiment.reducer.SaturationReducer |
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limen/experiment/reducer/saturation_reducer.py |
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limen/experiment/reducer/saturation_reducer.py |
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limen/experiment/reducer/saturation_reducer.py |
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limen/experiment/reducer/saturation_reducer.py |
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limen/experiment/reducer/saturation_reducer.py |
limen.experiment.reducer.TRIM_RANDOM |
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limen/experiment/reducer/budget_reducer.py |
limen.experiment.reducer.TRIM_WORST_FIRST |
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limen/experiment/reducer/budget_reducer.py |
limen.features |
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limen/features/__init__.py |
limen.features.absorption_intensity |
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limen/features/absorption_intensity.py |
limen.features.active_lines |
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limen/features/active_lines.py |
limen.features.active_quantile_count |
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limen/features/active_quantile_count.py |
limen.features.amihud_illiquidity |
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limen/features/amihud_illiquidity.py |
limen.features.atr_percent_sma |
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limen/features/atr_percent_sma.py |
limen.features.atr_sma |
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limen/features/atr_sma.py |
limen.features.body_to_range |
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limen/features/body_to_range.py |
limen.features.breakout_features |
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limen/features/breakout_features.py |
limen.features.breakout_percentile_regime |
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limen/features/breakout_percentile_regime.py |
limen.features.bulk_volume_classification |
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limen/features/bulk_volume_classification.py |
limen.features.calendar_time_features |
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limen/features/calendar_time_features.py |
limen.features.close_ma_distance_atr |
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limen/features/close_ma_distance_atr.py |
limen.features.close_position |
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limen/features/close_position.py |
limen.features.close_position_rolling |
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limen/features/close_position_rolling.py |
limen.features.close_to_extremes |
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limen/features/close_to_extremes.py |
limen.features.common_strategy_positions.POSITION_COLUMN |
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limen/features/common_strategy_positions.py |
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limen/features/common_strategy_positions.py |
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limen.features.cusum_filter |
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limen/features/cusum_filter.py |
limen.features.cyclical_time_features |
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limen/features/cyclical_time_features.py |
limen.features.distance_from_high |
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limen/features/distance_from_high.py |
limen.features.distance_from_low |
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limen/features/distance_from_low.py |
limen.features.distance_from_ma |
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limen/features/distance_from_ma.py |
limen.features.dollar_bar_crash_reversal |
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limen/features/dollar_bar_crash_reversal.py |
limen.features.dollar_volume |
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limen/features/dollar_volume.py |
limen.features.downside_volatility_ratio |
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limen/features/downside_volatility_ratio.py |
limen.features.dynamic_stop_loss |
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limen/features/dynamic_stop_loss.py |
limen.features.dynamic_target |
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limen/features/dynamic_target.py |
limen.features.ema_alignment |
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limen/features/ema_alignment.py |
limen.features.entry_score_microstructure |
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limen/features/entry_score_microstructure.py |
limen.features.feature_aliases |
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limen/features/feature_aliases.py |
limen.features.find_min_d |
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limen/features/fractional_diff.py |
limen.features.fractional_diff |
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limen/features/fractional_diff.py |
limen.features.gap_high |
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limen/features/gap_high.py |
limen.features.garman_klass_volatility |
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limen/features/garman_klass_volatility.py |
limen.features.hh_hl_structure_regime |
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limen/features/hh_hl_structure_regime.py |
limen.features.hours_since_big_move |
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limen/features/hours_since_big_move.py |
limen.features.hours_since_quantile_line |
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limen/features/hours_since_quantile_line.py |
limen.features.ichimoku_cloud |
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limen/features/ichimoku_cloud.py |
limen.features.illiquidity_shock |
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limen/features/illiquidity_shock.py |
limen.features.is_funding_hour |
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limen/features/is_funding_hour.py |
limen.features.is_us_open_hour |
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limen/features/is_us_open_hour.py |
limen.features.jump_variation_proxy |
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limen/features/jump_variation_proxy.py |
limen.features.kaufman_efficiency_ratio |
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limen/features/kaufman_efficiency_ratio.py |
limen.features.kline_imbalance |
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limen/features/kline_imbalance.py |
limen.features.lag_column |
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limen/features/lagged_features.py |
limen.features.lag_columns |
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limen/features/lagged_features.py |
limen.features.lag_range |
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limen/features/lagged_features.py |
limen.features.lag_range_cols |
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limen/features/lagged_features.py |
limen.features.liquidity_drop |
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limen/features/liquidity_drop.py |
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limen/features/liquidity_range.py |
limen.features.log_returns |
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limen/features/log_returns.py |
limen.features.ma_slope_regime |
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limen/features/ma_slope_regime.py |
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limen/features/maker_liquidity_share.py |
limen.features.maker_volume_ratio |
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limen/features/maker_volume_ratio.py |
limen.features.maker_volume_share |
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limen/features/maker_volume_share.py |
limen.features.market_regime |
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limen/features/market_regime.py |
limen.features.micro_momentum |
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limen/features/micro_momentum.py |
limen.features.momentum_confirmation |
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limen/features/momentum_confirmation.py |
limen.features.momentum_periods |
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limen/features/momentum_periods.py |
limen.features.momentum_weight |
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limen/features/momentum_weight.py |
limen.features.narrow_range |
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limen/features/narrow_range.py |
limen.features.order_flow_imbalance |
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limen/features/order_flow_imbalance.py |
limen.features.parkinson_vol_of_vol |
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limen/features/parkinson_vol_of_vol.py |
limen.features.parkinson_volatility |
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limen/features/parkinson_volatility.py |
limen.features.position_in_candle |
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limen/features/position_in_candle.py |
limen.features.position_in_range |
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limen/features/position_in_range.py |
limen.features.price_lines |
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limen/features/price_lines.py |
limen.features.price_lines.line_active_count |
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limen/features/price_lines.py |
limen.features.price_lines.line_recency |
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limen.features.price_lines.price_lines |
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limen/features/price_lines.py |
limen.features.price_range_position |
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limen/features/price_range_position.py |
limen.features.price_vs_band_regime |
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limen/features/price_vs_band_regime.py |
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limen/features/quantile_line_density.py |
limen.features.quantile_price_lines |
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limen/features/quantile_price_lines.py |
limen.features.range_overlap |
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limen/features/range_overlap.py |
limen.features.range_pct |
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limen/features/range_pct.py |
limen.features.range_per_dollar_volume |
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limen/features/range_per_dollar_volume.py |
limen.features.realized_kurtosis |
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limen/features/realized_kurtosis.py |
limen.features.realized_semivariance |
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limen/features/realized_semivariance.py |
limen.features.realized_skewness |
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limen/features/realized_skewness.py |
limen.features.regime_multiplier |
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limen/features/regime_multiplier.py |
limen.features.rejection_intensity |
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limen/features/rejection_intensity.py |
limen.features.relative_range_seasonality |
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limen/features/relative_range_seasonality.py |
limen.features.relative_volatility_seasonality |
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limen/features/relative_volatility_seasonality.py |
limen.features.relative_volume_seasonality |
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limen/features/relative_volume_seasonality.py |
limen.features.return_autocorrelation |
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limen/features/return_autocorrelation.py |
limen.features.return_per_dollar_volume |
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limen/features/return_per_dollar_volume.py |
limen.features.return_volatility_correlation |
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limen/features/return_volatility_correlation.py |
limen.features.returns_lags |
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limen/features/returns_lags.py |
limen.features.rogers_satchell_volatility |
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limen/features/rogers_satchell_volatility.py |
limen.features.rolling_zscore |
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limen/features/rolling_zscore.py |
limen.features.sma_crossover |
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limen/features/sma_crossover.py |
limen.features.sma_ratios |
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limen/features/sma_ratios.py |
limen.features.spread |
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limen/features/spread.py |
limen.features.spread_percent |
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limen/features/spread_percent.py |
limen.features.stochastic_k_abs |
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limen/features/stochastic_k_abs.py |
limen.features.tail_event_intensity |
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limen/features/tail_event_intensity.py |
limen.features.taker_imbalance_ratio |
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limen/features/taker_imbalance_ratio.py |
limen.features.time_to_funding |
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limen/features/time_to_funding.py |
limen.features.trade_density |
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limen/features/trade_density.py |
limen.features.trade_imbalance |
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limen/features/trade_imbalance.py |
limen.features.trade_size_ratio |
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limen/features/trade_size_ratio.py |
limen.features.trend_coherence |
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limen/features/trend_coherence.py |
limen.features.trend_strength |
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limen/features/trend_strength.py |
limen.features.volatility_1h |
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limen/sfd/reference_architecture/gaussian_hmm_binary.py |
limen.sfd.reference_architecture.GaussianHMMBinary.predict |
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limen/sfd/reference_architecture/gaussian_hmm_binary.py |
limen.sfd.reference_architecture.GaussianHMMBinary.evaluate |
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limen/sfd/reference_architecture/gaussian_hmm_binary.py |
limen.sfd.reference_architecture.gaussian_hmm_binary |
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limen/sfd/reference_architecture/gaussian_hmm_binary.py |
limen.sfd.reference_architecture.gaussian_hmm_binary.GaussianHMMBinary |
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limen/sfd/reference_architecture/gaussian_hmm_binary.py |
limen.sfd.reference_architecture.gaussian_hmm_binary.GaussianHMMBinary.__init__ |
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limen/sfd/reference_architecture/gaussian_hmm_binary.py |
limen.sfd.reference_architecture.gaussian_hmm_binary.GaussianHMMBinary.train |
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limen/sfd/reference_architecture/gaussian_hmm_binary.py |
limen.sfd.reference_architecture.gaussian_hmm_binary.GaussianHMMBinary.predict |
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limen/sfd/reference_architecture/gaussian_hmm_binary.py |
limen.sfd.reference_architecture.gaussian_hmm_binary.GaussianHMMBinary.evaluate |
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limen/sfd/reference_architecture/gaussian_hmm_binary.py |
limen.sfd.reference_architecture.gaussian_hmm_binary.gaussian_hmm_binary |
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limen/sfd/reference_architecture/gaussian_hmm_binary.py |
limen.sfd.foundational_sfd.lstm_binary |
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limen/sfd/foundational_sfd/lstm_binary.py |
limen.sfd.foundational_sfd.lstm_binary.manifest |
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limen/sfd/foundational_sfd/lstm_binary.py |
limen.sfd.foundational_sfd.lstm_binary.params |
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limen/sfd/foundational_sfd/lstm_binary.py |
limen.sfd.reference_architecture.LSTMBinary |
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limen/sfd/reference_architecture/lstm_binary.py |
limen.sfd.reference_architecture.LSTMBinary.__init__ |
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limen/sfd/reference_architecture/lstm_binary.py |
limen.sfd.reference_architecture.LSTMBinary.train |
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limen/sfd/reference_architecture/lstm_binary.py |
limen.sfd.reference_architecture.LSTMBinary.predict |
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limen/sfd/reference_architecture/lstm_binary.py |
limen.sfd.reference_architecture.LSTMBinary.evaluate |
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limen/sfd/reference_architecture/lstm_binary.py |
limen.sfd.reference_architecture.lstm_binary |
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limen/sfd/reference_architecture/lstm_binary.py |
limen.sfd.reference_architecture.lstm_binary.LSTMBinary |
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limen/sfd/reference_architecture/lstm_binary.py |
limen.sfd.reference_architecture.lstm_binary.LSTMBinary.__init__ |
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limen/sfd/reference_architecture/lstm_binary.py |
limen.sfd.reference_architecture.lstm_binary.LSTMBinary.train |
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limen/sfd/reference_architecture/lstm_binary.py |
limen.sfd.reference_architecture.lstm_binary.LSTMBinary.predict |
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limen/sfd/reference_architecture/lstm_binary.py |
limen.sfd.reference_architecture.lstm_binary.LSTMBinary.evaluate |
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limen/sfd/reference_architecture/lstm_binary.py |
limen.sfd.reference_architecture.lstm_binary.lstm_binary |
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limen/sfd/reference_architecture/lstm_binary.py |
limen.sfd.foundational_sfd.residual_regressor |
module |
limen/sfd/foundational_sfd/residual_regressor.py |
limen.sfd.foundational_sfd.residual_regressor.manifest |
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limen/sfd/foundational_sfd/residual_regressor.py |
limen.sfd.foundational_sfd.residual_regressor.params |
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limen/sfd/foundational_sfd/residual_regressor.py |
limen.sfd.reference_architecture.ResidualRegressor |
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limen/sfd/reference_architecture/residual_regressor.py |
limen.sfd.reference_architecture.ResidualRegressor.__init__ |
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limen/sfd/reference_architecture/residual_regressor.py |
limen.sfd.reference_architecture.ResidualRegressor.train |
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limen/sfd/reference_architecture/residual_regressor.py |
limen.sfd.reference_architecture.ResidualRegressor.predict |
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limen/sfd/reference_architecture/residual_regressor.py |
limen.sfd.reference_architecture.ResidualRegressor.evaluate |
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limen/sfd/reference_architecture/residual_regressor.py |
limen.sfd.reference_architecture.residual_regressor |
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limen/sfd/reference_architecture/residual_regressor.py |
limen.sfd.reference_architecture.residual_regressor.ResidualRegressor |
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limen/sfd/reference_architecture/residual_regressor.py |
limen.sfd.reference_architecture.residual_regressor.ResidualRegressor.__init__ |
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limen/sfd/reference_architecture/residual_regressor.py |
limen.sfd.reference_architecture.residual_regressor.ResidualRegressor.train |
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limen/sfd/reference_architecture/residual_regressor.py |
limen.sfd.reference_architecture.residual_regressor.ResidualRegressor.predict |
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limen/sfd/reference_architecture/residual_regressor.py |
limen.sfd.reference_architecture.residual_regressor.ResidualRegressor.evaluate |
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limen/sfd/reference_architecture/residual_regressor.py |
limen.sfd.reference_architecture.residual_regressor.residual_regressor |
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limen/sfd/reference_architecture/residual_regressor.py |
limen.sfd.foundational_sfd.cnn1d_binary |
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limen/sfd/foundational_sfd/cnn1d_binary.py |
limen.sfd.foundational_sfd.cnn1d_binary.manifest |
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limen/sfd/foundational_sfd/cnn1d_binary.py |
limen.sfd.foundational_sfd.cnn1d_binary.params |
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limen/sfd/foundational_sfd/cnn1d_binary.py |
limen.sfd.reference_architecture.CNN1DBinary |
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limen/sfd/reference_architecture/cnn1d_binary.py |
limen.sfd.reference_architecture.CNN1DBinary.__init__ |
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limen/sfd/reference_architecture/cnn1d_binary.py |
limen.sfd.reference_architecture.CNN1DBinary.train |
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limen/sfd/reference_architecture/cnn1d_binary.py |
limen.sfd.reference_architecture.CNN1DBinary.predict |
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limen/sfd/reference_architecture/cnn1d_binary.py |
limen.sfd.reference_architecture.CNN1DBinary.evaluate |
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limen/sfd/reference_architecture/cnn1d_binary.py |
limen.sfd.reference_architecture.cnn1d_binary |
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limen/sfd/reference_architecture/cnn1d_binary.py |
limen.sfd.reference_architecture.cnn1d_binary.CNN1DBinary |
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limen/sfd/reference_architecture/cnn1d_binary.py |
limen.sfd.reference_architecture.cnn1d_binary.CNN1DBinary.__init__ |
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limen/sfd/reference_architecture/cnn1d_binary.py |
limen.sfd.reference_architecture.cnn1d_binary.CNN1DBinary.train |
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limen/sfd/reference_architecture/cnn1d_binary.py |
limen.sfd.reference_architecture.cnn1d_binary.CNN1DBinary.predict |
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limen/sfd/reference_architecture/cnn1d_binary.py |
limen.sfd.reference_architecture.cnn1d_binary.CNN1DBinary.evaluate |
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limen/sfd/reference_architecture/cnn1d_binary.py |
limen.sfd.reference_architecture.cnn1d_binary.cnn1d_binary |
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limen/sfd/reference_architecture/cnn1d_binary.py |
limen.sfd.foundational_sfd.entropy_var_regressor |
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limen/sfd/foundational_sfd/entropy_var_regressor.py |
limen.sfd.foundational_sfd.entropy_var_regressor.manifest |
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limen/sfd/foundational_sfd/entropy_var_regressor.py |
limen.sfd.foundational_sfd.entropy_var_regressor.params |
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limen/sfd/foundational_sfd/entropy_var_regressor.py |
limen.sfd.reference_architecture.EntropyVaRRegressor |
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limen/sfd/reference_architecture/entropy_var_regressor.py |
limen.sfd.reference_architecture.EntropyVaRRegressor.__init__ |
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limen/sfd/reference_architecture/entropy_var_regressor.py |
limen.sfd.reference_architecture.EntropyVaRRegressor.train |
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limen/sfd/reference_architecture/entropy_var_regressor.py |
limen.sfd.reference_architecture.EntropyVaRRegressor.predict |
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limen/sfd/reference_architecture/entropy_var_regressor.py |
limen.sfd.reference_architecture.EntropyVaRRegressor.evaluate |
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limen/sfd/reference_architecture/entropy_var_regressor.py |
limen.sfd.reference_architecture.entropy_var_regressor |
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limen/sfd/reference_architecture/entropy_var_regressor.py |
limen.sfd.reference_architecture.entropy_var_regressor.EntropyVaRRegressor |
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limen/sfd/reference_architecture/entropy_var_regressor.py |
limen.sfd.reference_architecture.entropy_var_regressor.EntropyVaRRegressor.__init__ |
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limen/sfd/reference_architecture/entropy_var_regressor.py |
limen.sfd.reference_architecture.entropy_var_regressor.EntropyVaRRegressor.train |
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limen/sfd/reference_architecture/entropy_var_regressor.py |
limen.sfd.reference_architecture.entropy_var_regressor.EntropyVaRRegressor.predict |
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limen/sfd/reference_architecture/entropy_var_regressor.py |
limen.sfd.reference_architecture.entropy_var_regressor.EntropyVaRRegressor.evaluate |
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limen/sfd/reference_architecture/entropy_var_regressor.py |
limen.sfd.reference_architecture.entropy_var_regressor.entropy_var_regressor |
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limen/sfd/reference_architecture/entropy_var_regressor.py |
limen.sfd.foundational_sfd.feature_link_binary |
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limen/sfd/foundational_sfd/feature_link_binary.py |
limen.sfd.foundational_sfd.feature_link_binary.manifest |
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limen/sfd/foundational_sfd/feature_link_binary.py |
limen.sfd.foundational_sfd.feature_link_binary.params |
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limen/sfd/foundational_sfd/feature_link_binary.py |
limen.sfd.reference_architecture.FeatureLinkedBinary |
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limen/sfd/reference_architecture/feature_link_binary.py |
limen.sfd.reference_architecture.FeatureLinkedBinary.__init__ |
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limen/sfd/reference_architecture/feature_link_binary.py |
limen.sfd.reference_architecture.FeatureLinkedBinary.train |
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limen/sfd/reference_architecture/feature_link_binary.py |
limen.sfd.reference_architecture.FeatureLinkedBinary.predict |
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limen/sfd/reference_architecture/feature_link_binary.py |
limen.sfd.reference_architecture.FeatureLinkedBinary.evaluate |
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limen/sfd/reference_architecture/feature_link_binary.py |
limen.sfd.reference_architecture.feature_link_binary |
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limen/sfd/reference_architecture/feature_link_binary.py |
limen.sfd.reference_architecture.feature_link_binary.FeatureLinkedBinary |
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limen/sfd/reference_architecture/feature_link_binary.py |
limen.sfd.reference_architecture.feature_link_binary.FeatureLinkedBinary.__init__ |
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limen/sfd/reference_architecture/feature_link_binary.py |
limen.sfd.reference_architecture.feature_link_binary.FeatureLinkedBinary.train |
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limen/sfd/reference_architecture/feature_link_binary.py |
limen.sfd.reference_architecture.feature_link_binary.FeatureLinkedBinary.predict |
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limen/sfd/reference_architecture/feature_link_binary.py |
limen.sfd.reference_architecture.feature_link_binary.FeatureLinkedBinary.evaluate |
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limen/sfd/reference_architecture/feature_link_binary.py |
limen.sfd.reference_architecture.feature_link_binary.feature_link_binary |
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limen/sfd/reference_architecture/feature_link_binary.py |
limen.sfd.reference_architecture.feature_link_binary.recorded_rows |
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limen/sfd/reference_architecture/feature_link_binary.py |
limen.sfd.foundational_sfd.pseudo_label_binary |
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limen/sfd/foundational_sfd/pseudo_label_binary.py |
limen.sfd.foundational_sfd.pseudo_label_binary.manifest |
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limen/sfd/foundational_sfd/pseudo_label_binary.py |
limen.sfd.foundational_sfd.pseudo_label_binary.params |
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limen/sfd/foundational_sfd/pseudo_label_binary.py |
limen.sfd.reference_architecture.PseudoLabelBinary |
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limen/sfd/reference_architecture/pseudo_label_binary.py |
limen.sfd.reference_architecture.PseudoLabelBinary.__init__ |
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limen/sfd/reference_architecture/pseudo_label_binary.py |
limen.sfd.reference_architecture.PseudoLabelBinary.train |
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limen/sfd/reference_architecture/pseudo_label_binary.py |
limen.sfd.reference_architecture.PseudoLabelBinary.predict |
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limen/sfd/reference_architecture/pseudo_label_binary.py |
limen.sfd.reference_architecture.PseudoLabelBinary.evaluate |
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limen/sfd/reference_architecture/pseudo_label_binary.py |
limen.sfd.reference_architecture.pseudo_label_binary |
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limen/sfd/reference_architecture/pseudo_label_binary.py |
limen.sfd.reference_architecture.pseudo_label_binary.PseudoLabelBinary |
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limen/sfd/reference_architecture/pseudo_label_binary.py |
limen.sfd.reference_architecture.pseudo_label_binary.PseudoLabelBinary.__init__ |
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limen/sfd/reference_architecture/pseudo_label_binary.py |
limen.sfd.reference_architecture.pseudo_label_binary.PseudoLabelBinary.train |
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limen/sfd/reference_architecture/pseudo_label_binary.py |
limen.sfd.reference_architecture.pseudo_label_binary.PseudoLabelBinary.predict |
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limen/sfd/reference_architecture/pseudo_label_binary.py |
limen.sfd.reference_architecture.pseudo_label_binary.PseudoLabelBinary.evaluate |
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limen/sfd/reference_architecture/pseudo_label_binary.py |
limen.sfd.reference_architecture.pseudo_label_binary.pseudo_label_binary |
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limen/sfd/reference_architecture/pseudo_label_binary.py |
limen.sfd.reference_architecture.pseudo_label_binary.recorded_rows |
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limen/sfd/reference_architecture/pseudo_label_binary.py |
limen.sfd.foundational_sfd.weight_link_binary |
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limen/sfd/foundational_sfd/weight_link_binary.py |
limen.sfd.foundational_sfd.weight_link_binary.manifest |
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limen/sfd/foundational_sfd/weight_link_binary.py |
limen.sfd.foundational_sfd.weight_link_binary.params |
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limen/sfd/foundational_sfd/weight_link_binary.py |
limen.sfd.reference_architecture.WeightLinkedBinary |
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limen/sfd/reference_architecture/weight_link_binary.py |
limen.sfd.reference_architecture.WeightLinkedBinary.__init__ |
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limen/sfd/reference_architecture/weight_link_binary.py |
limen.sfd.reference_architecture.WeightLinkedBinary.train |
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limen/sfd/reference_architecture/weight_link_binary.py |
limen.sfd.reference_architecture.WeightLinkedBinary.predict |
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limen/sfd/reference_architecture/weight_link_binary.py |
limen.sfd.reference_architecture.WeightLinkedBinary.evaluate |
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limen/sfd/reference_architecture/weight_link_binary.py |
limen.sfd.reference_architecture.weight_link_binary |
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limen/sfd/reference_architecture/weight_link_binary.py |
limen.sfd.reference_architecture.weight_link_binary.WeightLinkedBinary |
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limen/sfd/reference_architecture/weight_link_binary.py |
limen.sfd.reference_architecture.weight_link_binary.WeightLinkedBinary.__init__ |
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limen/sfd/reference_architecture/weight_link_binary.py |
limen.sfd.reference_architecture.weight_link_binary.WeightLinkedBinary.train |
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limen/sfd/reference_architecture/weight_link_binary.py |
limen.sfd.reference_architecture.weight_link_binary.WeightLinkedBinary.predict |
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limen/sfd/reference_architecture/weight_link_binary.py |
limen.sfd.reference_architecture.weight_link_binary.WeightLinkedBinary.evaluate |
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limen/sfd/reference_architecture/weight_link_binary.py |
limen.sfd.reference_architecture.weight_link_binary.recorded_rows |
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limen/sfd/reference_architecture/weight_link_binary.py |
limen.sfd.reference_architecture.weight_link_binary.weight_link_binary |
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limen/sfd/reference_architecture/weight_link_binary.py |
Additional component contracts
These source-bound entries supplement the topic references. They serve direct Python use and optional native extension or maintenance. Compiled Research continues through YAML and CLI.
limen.Cohort
class Cohort: _REASON_PRIORITY: ClassVar[dict[PredictionReason, int]] = {'inside-training-window': 2, 'null-features': 1, 'warm-up': 0} member_ids: list[tuple[str | None, str] | tuple[str, str, int, str]] architecture_id: str supports_probabilities: bool | None aggregation_mode: str | NoneMerged source PR · f5957b6aPackage signature · Limen 6.0.1 · current package validation · package source
Combine explicitly selected, reconstructed sensors for inference.
limen.Cohort.__call__
def __call__(self, raw_klines: pl.DataFrame) -> list[BarPrediction]: ...Merged source PR · f5957b6aPackage signature · Limen 6.0.1 · current package validation · package source
limen.HistoricalData
class HistoricalData: DEFAULT_SPOT_KLINES_DATASET_REPO: Final[str] = _DEFAULT_SPOT_DATASET_REPO DEFAULT_SPOT_DOLLAR_KLINES_DATASET_REPO: Final[str] = _DEFAULT_SPOT_DOLLAR_DATASET_REPOMerged source PR · 0eb799c4Package signature · Limen 6.0.1 · current package validation · package source
Stateful file-backed data access surface for Limen.
limen.HistoricalData.__init__
def __init__(self) -> None: ...Merged source PR · 0eb799c4Package signature · Limen 6.0.1 · current package validation · package source
limen.Log
class Log: ...Merged source PR · 2cdb94eePackage signature · Limen 6.0.1 · current package validation · package source
Log object for storing and analyzing experiment results.
limen.MLManifest
@dataclassclass MLManifest(Manifest): scaler: FittedTransformEntry | None = None ablation_config: AblationConfig | None = None pca_compression_config: PCACompressionConfig | None = None data_dict_extension: Callable[..., dict[str, Any]] | None = None prediction_calibration_config: CalibrationConfig | None = None decoder_lookback: int = 1 strict_mode: bool = False objective: ObjectiveConfig | None = None feature_shims: list[dict[str, object]] = field(default_factory=list[dict[str, object]])Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
Manifest for ML pipelines with scaler, ablation, and calibration support.
limen.MLManifest.prepare_data
@overridedef prepare_data(self, raw_data: pl.DataFrame, round_params: dict[str, Any]) -> dict[str, Any]: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
limen.MLManifest.run_model
@overridedef run_model(self, data: dict[str, Any], round_params: dict[str, Any]) -> dict[str, Any]: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
limen.MLManifest.sensor_input_prep
def sensor_input_prep(self, raw_klines: pl.DataFrame, fitted_params: dict[str, Any], round_params: dict[str, Any]) -> tuple[pl.DataFrame, int]: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
Prepare live features using fitted target exclusions, scaler and PCA.
Preserve null rows and return the leading indicator warm-up count.
Fitted target metadata takes precedence over static class declarations.
limen.MLManifest.set_objective
def set_objective(self, metric: str='backtest_total_return', direction: str='maximize') -> 'MLManifest': ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
limen.Manifest
@dataclassclass Manifest: data_source_config: DataSourceConfig | None = None test_data_source_config: DataSourceConfig | None = None pre_split_data_selector: PipelineStep | None = None split_config: tuple[int, int, int] = (8, 1, 2) split_dates: tuple[date | Any, date | Any, date | Any, date | Any, date | Any, date | Any] | None = None split_walk_forward: WalkForwardConfig | None = None acceptance: dict[str, float] | None = None _walk_forward_fold: int | None = field(default=None, repr=False) val_predict_guard: bool = True test_predict_guard: bool = True bar_formation: PipelineStep | None = None required_bar_columns: list[str] = field(default_factory=list[str]) feature_transforms: list[TransformEntry] = field(default_factory=list[TransformEntry]) target_column: str | None = None target_class_config: TargetClassConfig | None = None architecture_function: Callable[..., dict[str, Any]] | None = None architecture_params: dict[str, ParamValue] = field(default_factory=dict[str, ParamValue]) metrics_params: dict[str, ParamValue] = field(default_factory=dict[str, ParamValue]) backtest_config: BacktestConfig | None = None allocation_config: _allocation_stage.AllocationConfig | None = None exit_rules_config: RuleBasedConfig | None = field(default=None, init=False, repr=False)Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
Base manifest with shared data pipeline configuration for Loop experiments.
limen.Manifest.compute_test_bars
@_holdout.preparationdef compute_test_bars(self, raw_data: pl.DataFrame, round_params: dict[str, Any]) -> pl.DataFrame: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
Compute test split bar data from raw data using manifest bar formation configuration.
NOTE: Used by Log system to reconstruct the same test bar data that was used in training.
Args:
raw_data (pl.DataFrame): Raw input dataset
round_params (Dict[str, Any]): Parameter values for current round
Returns:
pl.DataFrame: Bar-formed test split data
limen.Manifest.fetch_data
def fetch_data(self) -> pl.DataFrame: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
Fetch data using configured data source.
limen.Manifest.fetch_test_data
def fetch_test_data(self) -> pl.DataFrame: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
Fetch data using configured test data source.
limen.Manifest.prepare_data
def prepare_data(self, raw_data: pl.DataFrame, round_params: dict[str, Any]) -> dict[str, Any]: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
Interface method — implemented by MLManifest and RuleBasedManifest.
Raises:
NotImplementedError: Always. Construct MLManifest or RuleBasedManifest instead of Manifest directly.
limen.Manifest.resolve_backtest_config
def resolve_backtest_config(self, round_params: Mapping[str, object]) -> dict[str, float | None]: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
Resolve all backtest fields; an absent configuration returns an empty mapping.
limen.Manifest.resolve_model_kwargs
def resolve_model_kwargs(self, round_params: dict[str, Any]) -> dict[str, Any]: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
Resolve model function kwargs from round_params using signature inspection.
Maps round_params keys to model function parameters, falling back
to defaults for unspecified parameters.
Args:
round_params (dict[str, Any]): Parameter values for current round
Returns:
dict[str, Any]: Keyword arguments for the model function
Raises:
ValueError: If model function is not configured or required parameters
are missing from round_params
limen.Manifest.resolve_trade_policy
def resolve_trade_policy(self, round_params: Mapping[str, object]) -> TradePolicy | None: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
limen.Manifest.run_model
def run_model(self, data: dict[str, Any], round_params: dict[str, Any]) -> dict[str, Any]: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
Execute model training and evaluation using configured functions.
Args:
data (dict): Prepared data dictionary
round_params (dict[str, Any]): Parameter values for current round
Returns:
dict: Results including predictions, metrics, and optional extras
Raises:
ValueError: If required model function parameters are missing from round_params
NOTE: Auto-maps parameters from round_params to model function signature.
Parameters in round_params override model function defaults.
Parameters not in round_params use model function defaults.
Required parameters (no defaults) must be in round_params.
limen.Manifest.with_allocation
def with_allocation(self: _TManifest, method: Callable[..., dict[str, object]], params: Mapping[str, object] | None=None) -> _TManifest: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
limen.ReconstructionError
class ReconstructionError(Exception): ...Merged source PR · a77d5205Package signature · Limen 6.0.1 · current package validation · package source
Raised when metric validation detects deviation beyond tolerance.
limen.RuleBasedManifest
@dataclassclass RuleBasedManifest(Manifest): strategy: 'RuleBasedConfig | None' = field(default=None, init=False, repr=False) objective: ObjectiveConfig | None = NoneMerged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
Manifest for rule-based pipelines with predicate conditions and entry signals.
limen.RuleBasedManifest.prepare_data
@override@_holdout.preparationdef prepare_data(self, raw_data: pl.DataFrame, round_params: dict[str, Any]) -> dict[str, Any]: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
Compute final data dictionary from raw data using the rule-based pipeline.
Args:
raw_data (pl.DataFrame): Raw input dataset
round_params (Dict[str, Any]): Parameter values for current round
Returns:
dict: Final data dictionary ready for model training
limen.RuleBasedManifest.set_objective
def set_objective(self, metric: str='backtest_total_return', direction: str='maximize') -> 'RuleBasedManifest': ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
limen.RuleBasedManifest.sensor_input_prep
def sensor_input_prep(self, raw_klines: pl.DataFrame, fitted_params: dict[str, object], round_params: dict[str, object]) -> tuple[pl.DataFrame, int]: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
Prepare rule features without fitting targets or recomputing evaluation labels.
limen.Sensor
class Sensor: _trade_contract: Mapping[str, JsonValue] | None = None _trade_digest: str | None = NoneMerged source PR · eea9a3ecPackage signature · Limen 6.0.1 · current package validation · package source
Inference wrapper around a trained YAML model for live bar-by-bar prediction.
limen.Sensor.__init__
def __init__(self, yaml_reference: dict[str, Any], model: ReferenceModel, fitted_params: dict[str, Any], round_params: dict[str, Any], permutation_id: str | None=None, manifest_id: str | None=None, *, trade_contract: Mapping[str, JsonValue] | None=None) -> None: ...Merged source PR · eea9a3ecPackage signature · Limen 6.0.1 · current package validation · package source
limen.Sensor.predict_all
def predict_all(self, raw_klines: pl.DataFrame) -> list[BarPrediction]: ...Merged source PR · eea9a3ecPackage signature · Limen 6.0.1 · current package validation · package source
Prepare raw klines and return one prediction per input bar.
Warm-up bars and bars inside the training window have prediction=None
with reason set. Invalid numeric outputs have reason='sensor-error'.
Valid bars have predictions populated. Output length
equals the post-bar-formation row count, not necessarily len(raw_klines).
Args:
raw_klines (pl.DataFrame): Raw klines from live feed, same schema as
the manifest data source
Returns:
list[BarPrediction]: One entry per bar in the post-bar-formation data.
Length equals len(raw_klines) when bar_type is 'base' (no bar
aggregation). When bar formation is active, length equals the
aggregated bar count, which is smaller than len(raw_klines).
Returns reason='sensor-error' on unexpected exceptions rather
than raising.
limen.Sensor.prediction_mode
@propertydef prediction_mode(self) -> Literal['binary', 'target_exposure', 'python']: ...Merged source PR · eea9a3ecPackage signature · Limen 6.0.1 · current package validation · package source
limen.Sensor.round_params
@propertydef round_params(self) -> dict[str, Any]: ...Merged source PR · eea9a3ecPackage signature · Limen 6.0.1 · current package validation · package source
limen.Sensor.trade_contract
@propertydef trade_contract(self) -> Mapping[str, JsonValue] | None: ...Merged source PR · eea9a3ecPackage signature · Limen 6.0.1 · current package validation · package source
limen.Trainer
class Trainer: ...Merged source PR · 0eb799c4Package signature · Limen 6.0.1 · current package validation · package source
Retrain selected permutations from a completed YAML experiment.
Validates permutations by re-running manifest.prepare_data() and
manifest.run_model(), comparing metrics against the original experiment
log. The validated model is used directly as the Sensor model.
limen.UniversalExperimentLoop
class UniversalExperimentLoop: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
limen.UniversalExperimentLoop.fold_results
@propertydef fold_results(self) -> pl.DataFrame: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
limen.UniversalExperimentLoop.interrupted
@propertydef interrupted(self) -> bool: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.ExecutionResult
class ExecutionResult(NamedTuple): pos: npt.NDArray[np.float64] gross: npt.NDArray[np.float64] net: npt.NDArray[np.float64]Merged source PR · 724a4ebfPackage signature · Limen 6.0.1 · current package validation · package source
Per-bar execution arrays consumed by the snapshot ledger.
Attributes:
pos (npt.NDArray[np.float64]): Position held per bar (0 or 1 under the
all-in model; a strategy may return a deployed fraction).
gross (npt.NDArray[np.float64]): Per-bar gross return before costs.
net (npt.NDArray[np.float64]): Per-bar net return after entry and exit costs.
limen.backtest.backtest_snapshot.BACKTEST_SNAPSHOT_COLUMNS
BACKTEST_SNAPSHOT_COLUMNS = ['edge_bps_p5', 'edge_bps_p50', 'edge_bps_p95', 'pnl_bps_p5', 'pnl_bps_p50', 'pnl_bps_p95', 'cost_bps_p5', 'cost_bps_p50', 'cost_bps_p95', 'drawdown_bps_p5', 'drawdown_bps_p50', 'drawdown_bps_p95', 'wins_per_bar', 'pnl_per_bar_bps', 'avg_win_bps', 'avg_loss_bps', 'cvar_95_pnl_bps', 'trades_per_bar', 'inventory_per_bar', 'cost_per_bar_bps']Merged source PR · 62d79072Package definition · Limen 6.0.1 · current package validation · package source
limen.backtest.execution_events.OBSERVATION_COLUMNS
OBSERVATION_COLUMNS: tuple[str, ...] = ('row_id', 'start_ns', 'end_ns', 'open_available_at_ns', 'available_at_ns', 'open', 'high', 'low', 'close')Merged source PR · 497b5f83Package definition · Limen 6.0.1 · current package validation · package source
limen.backtest.execution_events.SIGNAL_COLUMNS
SIGNAL_COLUMNS: tuple[str, ...] = ('row_id', 'available_at_ns', 'target')Merged source PR · 497b5f83Package definition · Limen 6.0.1 · current package validation · package source
limen.backtest.execution_events.execution_events
def execution_events(inputs: TradeInputs, policy: TradePolicy) -> tuple[ExecutionEvent, ...]: ...Merged source PR · 497b5f83Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.execution_events.validate_observations
def validate_observations(inputs: TradeInputs, policy: TradePolicy) -> None: ...Merged source PR · 497b5f83Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.execution_events.with_predictions
def with_predictions(inputs: TradeInputs, predictions: object) -> TradeInputs: ...Merged source PR · 497b5f83Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.funding_adapter.ADAPTER_VERSION
ADAPTER_VERSION = 'limen-funding-v1'Merged source PR · 691521b6Package definition · Limen 6.0.1 · current package validation · package source
limen.backtest.funding_adapter.FUNDING_KEYS
FUNDING_KEYS = frozenset(('rate', 'rate_unit', 'rate_basis_seconds', 'mechanism', 'settlement_interval_seconds', 'settlement_phase_utc_seconds', 'cash_settlement_interval_seconds', 'cash_settlement_phase_utc_seconds', 'valuation', 'currency', 'history_interpretation', 'approximation'))Merged source PR · 691521b6Package definition · Limen 6.0.1 · current package validation · package source
limen.backtest.funding_adapter.FundingAdapter
class FundingAdapter(Protocol): ...Merged source PR · 691521b6Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.funding_adapter.FundingAdapter.__call__
def __call__(self, context: FundingContext, *, params: Mapping[str, JsonValue]) -> FundingCashflow: ...Merged source PR · 691521b6Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.funding_adapter.FundingCashflow
@dataclass(frozen=True)class FundingCashflow: event_id: str time_ns: int recognized_delta: float cash_delta: float currency: strMerged source PR · 691521b6Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.funding_adapter.FundingContext
@dataclass(frozen=True)class FundingContext: event_id: str start_ns: int end_ns: int kind: Literal['payment', 'accrual', 'cash_settlement'] accrued_funding: float quantity: float valuation_price: float rate_decimal: float rate_basis_seconds: float mechanism: Literal['discrete', 'continuous'] currency: strMerged source PR · 691521b6Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.funding_adapter.funding_adapter
def funding_adapter(context: FundingContext, *, params: Mapping[str, JsonValue]) -> FundingCashflow: ...Merged source PR · 691521b6Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.funding_adapter.prepare_funding
def prepare_funding(policy: FundingPolicy, history: pl.DataFrame | None, start: int, end: int) -> pl.DataFrame: ...Merged source PR · 691521b6Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.funding_adapter.resolve_adapter
def resolve_adapter(policy: FundingPolicy) -> FundingAdapter: ...Merged source PR · 691521b6Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.funding_adapter.scheduled_times
def scheduled_times(start: int, end: int, interval_seconds: object, phase_seconds: object) -> range: ...Merged source PR · 691521b6Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.funding_presets.BASELINE_RATE_8H
BASELINE_RATE_8H = 2.8e-05Merged source PR · 691521b6Package definition · Limen 6.0.1 · current package validation · package source
limen.backtest.funding_presets.FundingPreset
@dataclass(frozen=True)class FundingPreset: instrument: str params: Mapping[str, JsonValue] calibration: Mapping[str, JsonValue]Merged source PR · 691521b6Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.funding_presets.PRESETS
PRESETS: Mapping[str, FundingPreset] = {'binance_btcusdt': FundingPreset('BTCUSDT', {'rate': BASELINE_RATE_8H, 'mechanism': 'discrete', 'rate_unit': 'decimal', 'rate_basis_seconds': 28800.0, 'settlement_interval_seconds': 28800.0, 'settlement_phase_utc_seconds': 0.0, 'valuation': 'execution_proxy', 'currency': 'USDT', 'approximation': 'scenario'}, _calibration()), 'hyperliquid_btc': FundingPreset('BTC', {'rate': BASELINE_RATE_8H / 8, 'mechanism': 'discrete', 'rate_unit': 'decimal', 'rate_basis_seconds': 3600.0, 'settlement_interval_seconds': 3600.0, 'settlement_phase_utc_seconds': 0.0, 'valuation': 'execution_proxy', 'currency': 'USDC', 'approximation': 'scenario'}, _calibration()), 'deribit_btc_usdc': FundingPreset('BTC_USDC-PERPETUAL', {'rate': BASELINE_RATE_8H, 'mechanism': 'continuous', 'rate_unit': 'decimal', 'rate_basis_seconds': 28800.0, 'cash_settlement_interval_seconds': 86400.0, 'cash_settlement_phase_utc_seconds': 28800.0, 'valuation': 'execution_proxy', 'currency': 'USDC', 'approximation': 'scenario'}, _calibration())}Merged source PR · 691521b6Package definition · Limen 6.0.1 · current package validation · package source
limen.backtest.funding_presets.PRESET_VERSION
PRESET_VERSION = '2026-10-08'Merged source PR · 691521b6Package definition · Limen 6.0.1 · current package validation · package source
limen.backtest.observed_costs.CalibrationReport
class CalibrationReport(TypedDict): digest: str method: Literal['minute_midpoint_1s_v1'] source: JsonValue clocks: JsonValue estimates: JsonValue support: JsonValue sensitivity: JsonValue exclusions: JsonValue limitations: JsonValueMerged source PR · f0c121a4Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.observed_costs.ComparisonReport
class ComparisonReport(TypedDict): digest: str calibration_digest: str candidates: JsonValue ranks: JsonValue failures: JsonValue unavailable: JsonValue limitations: JsonValueMerged source PR · f0c121a4Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.observed_costs.CostCalibration
@dataclass(frozen=True, slots=True, eq=False)class CostCalibration: _payload: dict[str, JsonValue] _digest: str = field(init=False)Merged source PR · f0c121a4Package signature · Limen 6.0.1 · current package validation · package source
Immutable recorded-price stress with separately reported account fees.
Pin digest in the caller's research specification before later read.
The method preserves original candidate fees; observed cross-account fees
never establish the fees a different account will pay.
limen.backtest.observed_costs.CostCalibration.compare
def compare(self, candidates: Mapping[str, CostCandidate]) -> CostComparison: ...Merged source PR · f0c121a4Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.observed_costs.CostCalibration.digest
@propertydef digest(self) -> str: ...Merged source PR · f0c121a4Package signature · Limen 6.0.1 · current package validation · package source
Return the versioned canonical content identity.
limen.backtest.observed_costs.CostCalibration.read
@classmethoddef read(cls, path: str | Path, *, expected_digest: str) -> CostCalibration: ...Merged source PR · f0c121a4Package signature · Limen 6.0.1 · current package validation · package source
Read externally pinned content without re-estimating its rates.
limen.backtest.observed_costs.CostCalibration.report
def report(self) -> CalibrationReport: ...Merged source PR · f0c121a4Package signature · Limen 6.0.1 · current package validation · package source
Return copied estimates, source claims and explicit transfer limits.
limen.backtest.observed_costs.CostCalibration.write
def write(self, path: str | Path) -> str: ...Merged source PR · f0c121a4Package signature · Limen 6.0.1 · current package validation · package source
Publish once; the same canonical bytes may be written idempotently.
limen.backtest.observed_costs.CostCandidate
@dataclass(frozen=True, eq=False)class CostCandidate: inputs: InitVar[TradeInputs] policy: InitVar[TradePolicy] research_id: InitVar[str] conditions: InitVar[CostConditions] benchmark: InitVar[pl.DataFrame] _snapshot: dict[str, object] = field(init=False) _seal: str = field(init=False)Merged source PR · f0c121a4Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.observed_costs.CostClock
CostClock = Literal['assumed_exchange_label']Merged source PR · f0c121a4Package definition · Limen 6.0.1 · current package validation · package source
limen.backtest.observed_costs.CostComparison
@dataclass(frozen=True, slots=True, eq=False)class CostComparison: _payload: dict[str, JsonValue] _ledgers: dict[tuple[str, str], TradeLedger] _digest: str = field(init=False)Merged source PR · f0c121a4Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.observed_costs.CostComparison.digest
@propertydef digest(self) -> str: ...Merged source PR · f0c121a4Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.observed_costs.CostComparison.ledger
def ledger(self, candidate: str, scenario: Literal['original', 'calibrated']) -> TradeLedger: ...Merged source PR · f0c121a4Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.observed_costs.CostComparison.report
def report(self) -> ComparisonReport: ...Merged source PR · f0c121a4Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.observed_costs.CostComparison.write
def write(self, path: str | Path) -> str: ...Merged source PR · f0c121a4Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.observed_costs.CostConditions
class CostConditions(TypedDict): venue: str environment: Literal['mainnet'] product_kind: Literal['linear_perpetual'] instrument: str base_currency: str quote_currency: str reference_id: str reference_convention: Literal['minute_midpoint_label'] clock: CostClockMerged source PR · f0c121a4Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.observed_costs.CostDisposition
CostDisposition = Literal['original', 'retrospective_cost_scenario', 'retrospective_low_effective_support', 'unavailable', 'economic_failure']Merged source PR · f0c121a4Package definition · Limen 6.0.1 · current package validation · package source
limen.backtest.trade_contract.BPS
BPS = 10000.0Merged source PR · 62d79072Package definition · Limen 6.0.1 · current package validation · package source
limen.backtest.trade_contract.ExecutionEvent
@dataclass(frozen=True)class ExecutionEvent: event_id: str time_ns: int kind: Literal['observation', 'funding', 'timer', 'signal'] source_row_id: str | None available_at_ns: int observation_phase: Literal['open', 'close'] | None = NoneMerged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.trade_contract.JsonScalar
JsonScalar: TypeAlias = str | int | float | bool | NoneMerged source PR · 62d79072Package definition · Limen 6.0.1 · current package validation · package source
limen.backtest.trade_contract.JsonValue
JsonValue: TypeAlias = JsonScalar | list['JsonValue'] | dict[str, 'JsonValue']Merged source PR · 62d79072Package definition · Limen 6.0.1 · current package validation · package source
limen.backtest.trade_contract.NANOSECONDS
NANOSECONDS = 1000000000Merged source PR · 62d79072Package definition · Limen 6.0.1 · current package validation · package source
limen.backtest.trade_contract.PredictionMode
PredictionMode: TypeAlias = Literal['binary', 'target_exposure']Merged source PR · 62d79072Package definition · Limen 6.0.1 · current package validation · package source
limen.backtest.trade_contract.RULE_VERSION
RULE_VERSION = 'limen-trade-v1'Merged source PR · 62d79072Package definition · Limen 6.0.1 · current package validation · package source
limen.backtest.trade_contract.SourceBinding
@dataclass(frozen=True)class SourceBinding: identity: str checksum: str start_ns: int end_ns: int precision_ns: int interpretation: strMerged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.trade_contract.TradeLedger
@dataclass(frozen=True)class TradeLedger: states: pl.DataFrame intents: pl.DataFrame fills: pl.DataFrame episodes: pl.DataFrame funding: pl.DataFrame metrics: Mapping[str, float] contract_digest: strMerged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.trade_contract.contract_digest
def contract_digest(contract: Mapping[str, JsonValue]) -> str: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.trade_contract.export_trade_contract
def export_trade_contract(policy: TradePolicy, inputs: TradeInputs) -> dict[str, JsonValue]: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.trade_contract.finite_number
def finite_number(value: object, name: str) -> float: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.trade_contract.json_value
def json_value(value: object) -> JsonValue: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.trade_contract.source_binding
def source_binding(frame: pl.DataFrame, identity: str, start_ns: int, end_ns: int, precision_ns: int, interpretation: str) -> SourceBinding: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
limen.backtest.trade_execution
def trade_execution(inputs: TradeInputs, policy: TradePolicy) -> TradeLedger: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
limen.calibration.CalibratorProtocol
class CalibratorProtocol(Protocol): ...Merged source PR · f323297dPackage signature · Limen 6.0.1 · current package validation · package source
Protocol for probability calibration functions.
limen.calibration.ThresholdOptimizerProtocol
class ThresholdOptimizerProtocol(Protocol): ...Merged source PR · f323297dPackage signature · Limen 6.0.1 · current package validation · package source
Protocol for threshold optimisation functions.
limen.calibration.apply_calibrated_predict
def apply_calibrated_predict(model: Any, config: CalibrationConfigProtocol, data: dict[str, Any]) -> dict[str, Any]: ...Merged source PR · f323297dPackage signature · Limen 6.0.1 · current package validation · package source
Apply calibration and threshold optimisation to a fitted model's predictions.
Args:
model: Fitted classifier with predict_proba method
config (CalibrationConfigProtocol): Resolved calibration configuration
data (dict): Data dictionary with x_val, y_val, x_test keys
Returns:
dict: Results with '_preds', '_probs', 'optimal_threshold' and 'val_score'
(val_score is None when no threshold_func is configured)
limen.calibration.fit_calibrator
def fit_calibrator(model: Any, config: CalibrationConfigProtocol, x_val: npt.NDArray[Any] | pl.DataFrame, y_val: npt.NDArray[Any] | pl.Series) -> tuple[Any, float, float | None]: ...Merged source PR · f323297dPackage signature · Limen 6.0.1 · current package validation · package source
Fit calibrator on validation data.
Args:
model (Any): Fitted classifier with predict_proba method
config (CalibrationConfigProtocol): Resolved calibration configuration
x_val (np.ndarray or pl.DataFrame): Validation features
y_val (np.ndarray or pl.Series): Validation labels
Returns:
tuple: (fitted_calibrator, optimal_threshold, val_score)
val_score is None when no threshold_func is configured
limen.cli.cli
@click.group()@click.version_option(version=__version__, prog_name='limen')def cli() -> None: ...Merged source PR · 235b4d7dPackage signature · Limen 6.0.1 · current package validation · package source
Limen — declarative ML experiment runner.
Define experiments in YAML; validate, compile, search and save with the CLI.
Quick start:
limen validate experiment.yaml Check YAML; profile it with limen profile
limen run experiment.yaml Run the experiment
limen run --dry-run experiment.yaml Validate + compile only, no execution
YAML structure:
schema_version: "1.0"
metadata:
name: my_experiment
mode: development # development | production
sfd:
manifest:
type: ml # ml | rule_based
...
params:
lookback: [12, 24, 48]
...
uel:
n_permutations: 100
search_strategy:
type: random # random | grid
output_format: csv # csv | parquet
Template discovery: limen list-templates
limen.cohort.sfc.Selector
Selector = Callable[..., list[int | str]]Merged source PR · f07daaeePackage definition · Limen 6.0.1 · current package validation · package source
limen.cohort.sfc.SelectorContext
SelectorContext = dict[str, Any]Merged source PR · f07daaeePackage definition · Limen 6.0.1 · current package validation · package source
limen.data.bars.liquidity_bars
def liquidity_bars(data: pl.DataFrame, liquidity_threshold: float) -> pl.DataFrame: ...Merged source PR · 0eb799c4Package signature · Limen 6.0.1 · current package validation · package source
Compute liquidity bars with fixed liquidity sampling.
Args:
data (pl.DataFrame): Klines data
liquidity_threshold (float): Liquidity threshold per bar
Returns:
pl.DataFrame: Standard liquidity data bars
limen.data.bars.trade_bars
def trade_bars(data: pl.DataFrame, trade_threshold: int) -> pl.DataFrame: ...Merged source PR · 0eb799c4Package signature · Limen 6.0.1 · current package validation · package source
Compute trade bars with fixed trade count sampling.
Args:
data (pl.DataFrame): Klines data
trade_threshold (int): Number of trades per bar
Returns:
pl.DataFrame: Standard trade count data bars
limen.data.bars.volume_bars
def volume_bars(data: pl.DataFrame, volume_threshold: float) -> pl.DataFrame: ...Merged source PR · 0eb799c4Package signature · Limen 6.0.1 · current package validation · package source
Compute volume bars with fixed volume size sampling.
Args:
data (pl.DataFrame): Klines data
volume_threshold (float): Volume threshold per bar
Returns:
pl.DataFrame: Standard volume data bars
limen.data.compute_data_bars
def compute_data_bars(data: pl.DataFrame, **params: Any) -> pl.DataFrame: ...Merged source PR · 0eb799c4Package signature · Limen 6.0.1 · current package validation · package source
Compute adaptive bar formation based on bar_type parameter.
Args:
data (pl.DataFrame): Klines dataset with 'datetime', 'open', 'high', 'low', 'close', 'volume', 'no_of_trades', 'liquidity_sum', 'maker_ratio', 'maker_volume', 'maker_liquidity' columns
**params: Bar formation parameters including bar_type and threshold parameters
Returns:
pl.DataFrame: Data bars with columns 'datetime', 'open', 'high', 'low', 'close', 'volume', 'no_of_trades', 'liquidity_sum', 'maker_ratio', 'maker_volume', 'maker_liquidity', 'mean', 'bar_count', 'base_interval'
limen.data.utils.random_slice
def random_slice(df: pl.DataFrame, rows: int, *, safe_range_low: float=0.25, safe_range_high: float=0.75, seed: int | None=None) -> pl.DataFrame: ...Merged source PR · 8b752e26Package signature · Limen 6.0.1 · current package validation · package source
Compute contiguous slice from DataFrame within specified safe range.
Args:
df (pl.DataFrame): Input DataFrame to slice from
rows (int): Positive, non-boolean integer number of rows to include
safe_range_low (float): Lower bound of safe range as fraction of total rows
safe_range_high (float): Upper bound of safe range as fraction of total rows
seed (int | None): Random seed for reproducible results
Returns:
pl.DataFrame: Contiguous slice of the original DataFrame maintaining row order
limen.data.utils.split_data_to_prep_output
def split_data_to_prep_output(split_data: list[pl.DataFrame], cols: list[str], all_datetimes: list[datetime] | list[int]) -> dict[str, Any]: ...Merged source PR · 65b613d4Package signature · Limen 6.0.1 · current package validation · package source
Compute data preparation output dictionary from split data and column names.
Args:
split_data (list): List of three DataFrames representing train, validation, and test splits
cols (list): Column names where the last column is the target variable
all_datetimes (list): List of all datetimes
Returns:
dict: Dictionary with train, validation, and test features and targets
limen.data.utils.split_data_to_rule_based_prep_output
def split_data_to_rule_based_prep_output(split_data: list[pl.DataFrame], all_datetimes: list[datetime] | list[int]) -> dict[str, Any]: ...Merged source PR · 65b613d4Package signature · Limen 6.0.1 · current package validation · package source
Compute data preparation output dictionary for rule-based strategies.
Args:
split_data (list): List of three DataFrames representing train, validation, and test splits
all_datetimes (list): List of all datetimes
Returns:
dict: Dictionary with train, val, test full DataFrames and _alignment metadata
limen.data.utils.split_random
def split_random(data: pl.DataFrame, ratios: Sequence[int], seed: int | None=None) -> list[pl.DataFrame]: ...Merged source PR · 65b613d4Package signature · Limen 6.0.1 · current package validation · package source
Compute random data splits with proportional lengths based on ratios.
Args:
data (pl.DataFrame): Polars DataFrame to split randomly
ratios (Sequence[int]): Sequence of positive integers defining split proportions
seed (int | None): Random seed; None draws a fresh, disjoint partition each call
Returns:
List[pl.DataFrame]: Disjoint, randomly shuffled partitions containing every input row
limen.data.utils.split_sequential
def split_sequential(data: pl.DataFrame, ratios: Sequence[int]) -> list[pl.DataFrame]: ...Merged source PR · 65b613d4Package signature · Limen 6.0.1 · current package validation · package source
Compute sequential data splits with proportional lengths based on ratios.
Args:
data (pl.DataFrame): Polars DataFrame to split sequentially
ratios (Sequence[int]): Sequence of positive integers defining split proportions
Returns:
List[pl.DataFrame]: List of DataFrames partitioned sequentially without losing or duplicating rows
limen.data.utils.split_walk_forward
def split_walk_forward(data: pl.DataFrame, *, n_folds: int, test_bars: int, purge_bars: int, embargo_bars: int, anchored: bool) -> list[tuple[pl.DataFrame, pl.DataFrame]]: ...Merged source PR · 6289c725Package signature · Limen 6.0.1 · current package validation · package source
Split an already time-ordered frame by row position.
The final n_folds * test_bars rows form contiguous test windows.
Each candidate train ends purge_bars before its test start and
excludes embargo_bars rows following every earlier test window.
Anchored trains start at row zero; rolling candidate trains retain
the first fold's width before embargo removal.
limen.experiment.CalibrationConfig
@dataclassclass CalibrationConfig: calibration_func: CalibratorProtocol | None = None calibration_params: dict[str, Any] = field(default_factory=dict[str, Any]) threshold_func: ThresholdOptimizerProtocol | None = None threshold_params: dict[str, Any] = field(default_factory=dict[str, Any])Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
Stores probability calibration and threshold function references with their params.
limen.experiment.CalibrationConfig.resolve
def resolve(self, round_params: dict[str, Any]) -> 'CalibrationConfig': ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
Return a new config with string params resolved from round_params.
limen.experiment.GridStrategy
class GridStrategy(SearchStrategy): ...Merged source PR · c08feea8Package signature · Limen 6.0.1 · current package validation · package source
Finite mixed-radix traversal; seeded shuffle uses a versioned permutation.
limen.experiment.GridStrategy.__init__
def __init__(self, domain: ParamDomain, *, seed: int | None=None, shuffle: bool=False) -> None: ...Merged source PR · c08feea8Package signature · Limen 6.0.1 · current package validation · package source
limen.experiment.GridStrategy.get_state
@overridedef get_state(self) -> dict[str, Any]: ...Merged source PR · c08feea8Package signature · Limen 6.0.1 · current package validation · package source
limen.experiment.GridStrategy.is_finite
@property@overridedef is_finite(self) -> bool: ...Merged source PR · c08feea8Package signature · Limen 6.0.1 · current package validation · package source
limen.experiment.GridStrategy.on_domain_changed
@overridedef on_domain_changed(self, _domain: ParamDomain, _changed_params: list[str]) -> None: ...Merged source PR · c08feea8Package signature · Limen 6.0.1 · current package validation · package source
limen.experiment.GridStrategy.set_state
@overridedef set_state(self, state: dict[str, Any]) -> None: ...Merged source PR · c08feea8Package signature · Limen 6.0.1 · current package validation · package source
limen.experiment.RandomStrategy
class RandomStrategy(SearchStrategy): ...Merged source PR · c40ba91ePackage signature · Limen 6.0.1 · current package validation · package source
Lazy random sampling from the parameter domain.
Samples each parameter independently via uniform random choice.
Infinite — never raises StopIteration. Uses _is_unseen for
dedup to avoid repeating previously seen combinations.
limen.experiment.RandomStrategy.__init__
def __init__(self, domain: ParamDomain, *, seed: int | None=None) -> None: ...Merged source PR · c40ba91ePackage signature · Limen 6.0.1 · current package validation · package source
limen.experiment.RandomStrategy.get_state
@overridedef get_state(self) -> dict[str, Any]: ...Merged source PR · c40ba91ePackage signature · Limen 6.0.1 · current package validation · package source
limen.experiment.RandomStrategy.on_domain_changed
@overridedef on_domain_changed(self, _domain: ParamDomain, _changed_params: list[str]) -> None: ...Merged source PR · c40ba91ePackage signature · Limen 6.0.1 · current package validation · package source
limen.experiment.RandomStrategy.set_state
@overridedef set_state(self, state: dict[str, Any]) -> None: ...Merged source PR · c40ba91ePackage signature · Limen 6.0.1 · current package validation · package source
limen.experiment.STRATEGY_REGISTRY
STRATEGY_REGISTRY: dict[str, type[SearchStrategy]] = {'random': RandomStrategy, 'grid': GridStrategy, 'sobol': SobolStrategy}Merged source PR · de9d6abcPackage definition · Limen 6.0.1 · current package validation · package source
limen.experiment.acceptance_report.acceptance_report
def acceptance_report(directory: Path, *, acceptance: Mapping[str, float] | None=None) -> dict[str, object] | None: ...Merged source PR · d07e6197Package signature · Limen 6.0.1 · current package validation · package source
limen.experiment.acceptance_report.read_acceptance
def read_acceptance(value: object) -> dict[str, float]: ...Merged source PR · d07e6197Package signature · Limen 6.0.1 · current package validation · package source
limen.experiment.convergence_report.convergence_header
def convergence_header(path: Path, header: list[str]) -> list[str]: ...Merged source PR · 7b10743aPackage signature · Limen 6.0.1 · current package validation · package source
Extend older result headers without inferring past warning evidence.
limen.experiment.convergence_report.convergence_report
def convergence_report(results: pl.DataFrame, *, parameter_columns: Sequence[str]) -> dict[str, object]: ...Merged source PR · 7b10743aPackage signature · Limen 6.0.1 · current package validation · package source
Describe recorded warning frequency, without inferring missing evidence.
limen.experiment.convergence_report.convergence_warning
def convergence_warning(caught: Sequence[warnings.WarningMessage], succeeded: bool) -> bool | None: ...Merged source PR · 7b10743aPackage signature · Limen 6.0.1 · current package validation · package source
Record category evidence only for completed rounds.
limen.experiment.convergence_report.convergence_warnings
@contextmanagerdef convergence_warnings() -> Iterator[list[warnings.WarningMessage]]: ...Merged source PR · 7b10743aPackage signature · Limen 6.0.1 · current package validation · package source
Observe filtered warnings while preserving errors and external display.
limen.experiment.holdout.FrozenHoldoutSelection
class FrozenHoldoutSelection: ...Merged source PR · b1933c15Package signature · Limen 6.0.1 · current package validation · package source
limen.experiment.holdout.FrozenHoldoutSelection.__init__
def __init__(self, token: object, /) -> None: ...Merged source PR · b1933c15Package signature · Limen 6.0.1 · current package validation · package source
limen.experiment.holdout.final_holdout_report
def final_holdout_report(frozen: FrozenHoldoutSelection, *, project_root: str | Path, reservation_id: str, start: str | None=None, end: str | None=None) -> dict[str, object]: ...Merged source PR · 9c6c5ad1Package signature · Limen 6.0.1 · current package validation · package source
Allocate durable exposure, then assess a causal tape from an owned fitted copy.
limen.experiment.holdout.freeze_holdout_selection
def freeze_holdout_selection(selection: Sensor | Cohort, *, context_start: str, scenarios: list[Mapping[str, object]], evaluation_target: NextBarUpTarget | NextBarDownTarget | ThresholdBinaryTarget | None=None, round_locator: Mapping[str, object] | None=None) -> FrozenHoldoutSelection: ...Merged source PR · b1933c15Package signature · Limen 6.0.1 · current package validation · package source
Own the fitted selection and preregister its final-assessment specification.
limen.experiment.holdout.read_holdout_report
def read_holdout_report(project_root: str | Path, attempt_id: str) -> dict[str, object]: ...Merged source PR · 9c6c5ad1Package signature · Limen 6.0.1 · current package validation · package source
Read completed evidence, verifying hashes and schemas without evaluating.
limen.experiment.holdout.release_holdout
def release_holdout(project_root: str | Path, reservation_id: str) -> None: ...Merged source PR · 075d5c0dPackage signature · Limen 6.0.1 · current package validation · package source
Lift research exclusion while preserving the post-release exposure record.
limen.experiment.holdout.reserve_holdout
def reserve_holdout(project_root: str | Path, start: str, end: str, *, interval_seconds: int) -> str: ...Merged source PR · 075d5c0dPackage signature · Limen 6.0.1 · current package validation · package source
Exclude the continuous suffix from the earliest active reservation onward.
limen.experiment.holdout.withdraw_holdout
def withdraw_holdout(project_root: str | Path, reservation_id: str) -> None: ...Merged source PR · 075d5c0dPackage signature · Limen 6.0.1 · current package validation · package source
Retire an unused reservation without deleting its identity or history.
limen.experiment.param_search.SearchStrategy
class SearchStrategy(ABC): ...Merged source PR · ac822ae8Package signature · Limen 6.0.1 · current package validation · package source
Abstract base for all search strategies.
Strategies are iterators that yield dict[str, Any] combination of parameters.
They hold a reference to a ParamDomain and register as observers
so they can react to domain mutations.
limen.experiment.param_search.SearchStrategy.__init__
def __init__(self, domain: ParamDomain, *, seed: int | None=None) -> None: ...Merged source PR · ac822ae8Package signature · Limen 6.0.1 · current package validation · package source
Initialize the SearchStrategy.
Args:
domain (ParamDomain): ParamDomain to generate combinations from
seed (int | None): Optional random seed for reproducibility
limen.experiment.param_search.SearchStrategy.compute_param_hash
def compute_param_hash(self, combo: dict[str, Any]) -> str: ...Merged source PR · ac822ae8Package signature · Limen 6.0.1 · current package validation · package source
Compute deterministic hash of a parameter combination.
Returns the full 64 hex characters (256 bits) of the SHA-256
digest.
NOTE:
Strips _-prefixed keys because domain params never
start with underscore, but log rows include MSQ metadata
columns (_id, _round_index, etc.) that must not
affect the hash.
Args:
combo (dict[str, Any]): Parameter combination or log row
Returns:
str: 64-character hex digest
limen.experiment.param_search.SearchStrategy.domain
@propertydef domain(self) -> ParamDomain: ...Merged source PR · ac822ae8Package signature · Limen 6.0.1 · current package validation · package source
limen.experiment.param_search.SearchStrategy.generated_count
@propertydef generated_count(self) -> int: ...Merged source PR · ac822ae8Package signature · Limen 6.0.1 · current package validation · package source
limen.experiment.param_search.SearchStrategy.get_state
@abstractmethoddef get_state(self) -> dict[str, Any]: ...Merged source PR · ac822ae8Package signature · Limen 6.0.1 · current package validation · package source
Export state for checkpointing.
limen.experiment.param_search.SearchStrategy.is_finite
@propertydef is_finite(self) -> bool: ...Merged source PR · ac822ae8Package signature · Limen 6.0.1 · current package validation · package source
Whether this strategy has a finite number of combinations.
Override to return True for exhaustive strategies (e.g. Grid).
limen.experiment.param_search.SearchStrategy.mark_seen
def mark_seen(self, combo: dict[str, Any]) -> str: ...Merged source PR · ac822ae8Package signature · Limen 6.0.1 · current package validation · package source
Register a combination as seen and return its hash.
Called by MSQ after a combo passes filters and is yielded.
Reuses _id if already attached by _is_unseen.
Args:
combo (dict[str, Any]): Parameter combination
Returns:
str: The param hash
limen.experiment.param_search.SearchStrategy.on_domain_changed
def on_domain_changed(self, _domain: ParamDomain, _changed_params: list[str]) -> None: ...Merged source PR · ac822ae8Package signature · Limen 6.0.1 · current package validation · package source
Called when ParamDomain is mutated. Override if strategy
maintains state dependent on the domain.
limen.experiment.param_search.SearchStrategy.rebuild_seen_from_log
def rebuild_seen_from_log(self, hashes: Iterable[str]) -> None: ...Merged source PR · ac822ae8Package signature · Limen 6.0.1 · current package validation · package source
Populate _seen from an iterable of hashes. Used on resume
to reconstruct dedup state from the experiment log.
Args:
hashes (Iterable[str]): Hash strings from previous runs
limen.experiment.param_search.SearchStrategy.set_state
@abstractmethoddef set_state(self, state: dict[str, Any]) -> None: ...Merged source PR · ac822ae8Package signature · Limen 6.0.1 · current package validation · package source
Restore state from checkpoint.
limen.experiment.param_search.SearchStrategy.update_from_feedback
def update_from_feedback(self, _log: Any, _interventions: list[dict[str, Any]]) -> None: ...Merged source PR · ac822ae8Package signature · Limen 6.0.1 · current package validation · package source
Hook for strategies that adapt based on experiment feedback.
Default is no-op. Stateful strategies (e.g. TPE) override this.
limen.experiment.param_search.SobolStrategy
class SobolStrategy(SearchStrategy): ...Merged source PR · de9d6abcPackage signature · Limen 6.0.1 · current package validation · package source
Scrambled Sobol points projected onto the current discrete domain.
limen.experiment.param_search.SobolStrategy.__init__
def __init__(self, domain: ParamDomain, *, seed: int) -> None: ...Merged source PR · de9d6abcPackage signature · Limen 6.0.1 · current package validation · package source
limen.experiment.param_search.SobolStrategy.get_state
@overridedef get_state(self) -> dict[str, object]: ...Merged source PR · de9d6abcPackage signature · Limen 6.0.1 · current package validation · package source
limen.experiment.param_search.SobolStrategy.on_domain_changed
@overridedef on_domain_changed(self, _domain: ParamDomain, _changed_params: list[str]) -> None: ...Merged source PR · de9d6abcPackage signature · Limen 6.0.1 · current package validation · package source
limen.experiment.param_search.SobolStrategy.set_state
@overridedef set_state(self, state: dict[str, object]) -> None: ...Merged source PR · de9d6abcPackage signature · Limen 6.0.1 · current package validation · package source
limen.experiment.reducer.ACTION_SUGGEST
ACTION_SUGGEST = 'suggest'Merged source PR · c3239401Package definition · Limen 6.0.1 · current package validation · package source
limen.experiment.reducer.BudgetReducer
class BudgetReducer(PruningStrategy): ...Merged source PR · f38e0549Package signature · Limen 6.0.1 · current package validation · package source
Trim the search queue when projected completion exceeds resource
budgets (walltime or permutation count).
Purely resource-driven — no statistical analysis of results.
Supports two trim strategies:
-
random: uniformly downsample permutations to the target count
-
worst_first: rank parameter values by mean metric performance
and remove combinations containing the worst-performing values
A random trim is always emitted as a fallback to ensure the queue
is reduced to the target count if value-based pruning alone is
insufficient.
NOTE: Fires at most once per experiment — once trimming occurs,
further calls to analyze_and_intervene return empty. The
check_after_pct gate prevents premature trimming when throughput
estimates are unreliable early in the experiment.
limen.experiment.reducer.BudgetReducer.analyze_and_intervene
@overridedef analyze_and_intervene(self, log: pl.DataFrame, msq: Any) -> list[dict[str, Any]]: ...Merged source PR · f38e0549Package signature · Limen 6.0.1 · current package validation · package source
Check resource budgets and trim the queue if over budget.
Computes throughput from elapsed time and completed rounds,
then projects whether the remaining queue can finish within
the configured budget.
limen.experiment.reducer.BudgetReducer.get_state
@overridedef get_state(self) -> dict[str, Any]: ...Merged source PR · f38e0549Package signature · Limen 6.0.1 · current package validation · package source
Export state for checkpointing.
limen.experiment.reducer.BudgetReducer.set_state
@overridedef set_state(self, state: dict[str, Any]) -> None: ...Merged source PR · f38e0549Package signature · Limen 6.0.1 · current package validation · package source
Restore state from checkpoint.
limen.experiment.reducer.CorrelationReducer
class CorrelationReducer(PruningStrategy): ...Merged source PR · ada04783Package signature · Limen 6.0.1 · current package validation · package source
Prune parameter values based on bootstrap correlation analysis.
Two pruning modes:
- Wrong-direction: parameters with strong negative correlation
(for maximization) get their worst-performing value removed
- Low-impact: parameters with negligible |correlation| and high
sign stability get a keep_is suggestion for the best value
NOTE: Only acts on numeric parameters. Categorical/string parameters
are coerced to numeric internally and silently skipped when coercion
produces all-NaN or constant columns.
limen.experiment.reducer.CorrelationReducer.analyze_and_intervene
@overridedef analyze_and_intervene(self, log: pl.DataFrame, msq: Any) -> list[dict[str, Any]]: ...Merged source PR · ada04783Package signature · Limen 6.0.1 · current package validation · package source
Analyze parameter correlations and return interventions.
Computes bootstrap correlations between each parameter and the
target metric. Emits remove_is for wrong-direction parameters
and keep_is suggestions for low-impact parameters.
limen.experiment.reducer.CorrelationReducer.get_state
@overridedef get_state(self) -> dict[str, Any]: ...Merged source PR · ada04783Package signature · Limen 6.0.1 · current package validation · package source
Export state for checkpointing.
limen.experiment.reducer.CorrelationReducer.set_state
@overridedef set_state(self, state: dict[str, Any]) -> None: ...Merged source PR · ada04783Package signature · Limen 6.0.1 · current package validation · package source
Restore state from checkpoint.
limen.experiment.reducer.FILTER_EXCLUDE_VALUE
FILTER_EXCLUDE_VALUE = 'exclude_value'Merged source PR · 15bf9527Package definition · Limen 6.0.1 · current package validation · package source
limen.experiment.reducer.FILTER_KEEP_BETWEEN
FILTER_KEEP_BETWEEN = 'keep_between'Merged source PR · 15bf9527Package definition · Limen 6.0.1 · current package validation · package source
limen.experiment.reducer.FILTER_KEEP_VALUES
FILTER_KEEP_VALUES = 'keep_values'Merged source PR · 15bf9527Package definition · Limen 6.0.1 · current package validation · package source
limen.experiment.reducer.FILTER_SAMPLE
FILTER_SAMPLE = 'sample'Merged source PR · 15bf9527Package definition · Limen 6.0.1 · current package validation · package source
limen.experiment.reducer.FocusReducer
class FocusReducer(PruningStrategy): ...Merged source PR · dde4ba0ePackage signature · Limen 6.0.1 · current package validation · package source
Detect breakthrough results and narrow the parameter space for
exploitation using reversible named filters.
Monitors a target metric for values crossing a configurable
threshold. On breakthrough, narrows each parameter around the
winning combination and injects fine-grained linspace variations
for numeric parameters. Snaps back to full exploration after
focus_timeout rounds without improvement.
NOTE: Configuring multiple FocusReducer instances that operate on
overlapping parameters may cause conflicting filters. Use one
FocusReducer per experiment, or ensure parameter spaces are disjoint.
limen.experiment.reducer.FocusReducer.analyze_and_intervene
@overridedef analyze_and_intervene(self, log: pl.DataFrame, msq: Any) -> list[dict[str, Any]]: ...Merged source PR · dde4ba0ePackage signature · Limen 6.0.1 · current package validation · package source
Analyze log for breakthrough results and return interventions.
When OFF, scans for metric crossing the threshold. When ON,
checks for improvement or timeout snap-back.
limen.experiment.reducer.FocusReducer.get_state
@overridedef get_state(self) -> dict[str, Any]: ...Merged source PR · dde4ba0ePackage signature · Limen 6.0.1 · current package validation · package source
Export state for checkpointing.
limen.experiment.reducer.FocusReducer.set_state
@overridedef set_state(self, state: dict[str, Any]) -> None: ...Merged source PR · dde4ba0ePackage signature · Limen 6.0.1 · current package validation · package source
Restore state from checkpoint.
limen.experiment.reducer.PruningStrategy
class PruningStrategy(ABC): ...Merged source PR · c3239401Package signature · Limen 6.0.1 · current package validation · package source
Abstract base for all pruning strategies (reducers).
Reducers are pure analysis components. They receive an experiment log
and an MSQ reference (for observability only), analyze the data, and
return a list of interventions communicated to MSQ for modifying the
param space.
limen.experiment.reducer.PruningStrategy.__init__
def __init__(self, *, active: bool=True) -> None: ...Merged source PR · c3239401Package signature · Limen 6.0.1 · current package validation · package source
Initialize the PruningStrategy.
Args:
active (bool): Whether this reducer is enabled. Inactive reducers
return empty intervention lists from analyze_and_intervene
limen.experiment.reducer.PruningStrategy.active
@propertydef active(self) -> bool: ... @active.setterdef active(self, value: bool) -> None: ...Merged source PR · c3239401Package signature · Limen 6.0.1 · current package validation · package source
limen.experiment.reducer.REDUCER_REGISTRY
REDUCER_REGISTRY: dict[str, type[PruningStrategy]] = {'budget': BudgetReducer, 'correlation': CorrelationReducer, 'focus': FocusReducer, 'sanity': SanityReducer, 'saturation': SaturationReducer}Merged source PR · f1804ec1Package definition · Limen 6.0.1 · current package validation · package source
limen.experiment.reducer.SanityReducer
class SanityReducer(PruningStrategy): ...Merged source PR · f38e0549Package signature · Limen 6.0.1 · current package validation · package source
Remove parameter values whose trials produce null or NaN in the
target metric above a configurable threshold.
Counts both null and NaN (for float columns) in the metric column.
Values are pruned when missing_count / total > nan_threshold and
total >= min_observations.
Optionally emits suggestion interventions (logged but not dispatched)
for zero-metric, execution timeout, and warning rate signals.
limen.experiment.reducer.SanityReducer.analyze_and_intervene
@overridedef analyze_and_intervene(self, log: pl.DataFrame, msq: Any) -> list[dict[str, Any]]: ...Merged source PR · f38e0549Package signature · Limen 6.0.1 · current package validation · package source
Analyze experiment log and return interventions.
Returns remove_is interventions for NaN rate exceeding the
threshold, plus suggestion interventions for zero-metric,
execution timeout, and warning rate signals.
limen.experiment.reducer.SanityReducer.get_state
@overridedef get_state(self) -> dict[str, Any]: ...Merged source PR · f38e0549Package signature · Limen 6.0.1 · current package validation · package source
Export state for checkpointing.
limen.experiment.reducer.SanityReducer.set_state
@overridedef set_state(self, state: dict[str, Any]) -> None: ...Merged source PR · f38e0549Package signature · Limen 6.0.1 · current package validation · package source
Restore state from checkpoint.
limen.experiment.reducer.SaturationReducer
class SaturationReducer(PruningStrategy): ...Merged source PR · dde4ba0ePackage signature · Limen 6.0.1 · current package validation · package source
Detect parameter values whose metric variance has collapsed and
apply partial pruning via named filters.
Uses Coefficient of Variation (CV = std / |mean|) over a rolling
window of recent observations. Saturated values get a sample filter
that retains retain_fraction of combinations for continued monitoring.
limen.experiment.reducer.SaturationReducer.analyze_and_intervene
@overridedef analyze_and_intervene(self, log: pl.DataFrame, msq: Any) -> list[dict[str, Any]]: ...Merged source PR · dde4ba0ePackage signature · Limen 6.0.1 · current package validation · package source
Analyze metric variance per parameter value and return interventions.
Computes CV for each parameter value over a rolling window.
Emits set_filter for saturated values and clear_filter for
values that recover from saturation.
limen.experiment.reducer.SaturationReducer.get_state
@overridedef get_state(self) -> dict[str, Any]: ...Merged source PR · dde4ba0ePackage signature · Limen 6.0.1 · current package validation · package source
Export state for checkpointing.
limen.experiment.reducer.SaturationReducer.set_state
@overridedef set_state(self, state: dict[str, Any]) -> None: ...Merged source PR · dde4ba0ePackage signature · Limen 6.0.1 · current package validation · package source
Restore state from checkpoint.
limen.experiment.reducer.TRIM_RANDOM
TRIM_RANDOM = 'random'Merged source PR · f38e0549Package definition · Limen 6.0.1 · current package validation · package source
limen.experiment.reducer.TRIM_WORST_FIRST
TRIM_WORST_FIRST = 'worst_first'Merged source PR · f38e0549Package definition · Limen 6.0.1 · current package validation · package source
limen.features.common_strategy_positions.POSITION_COLUMN
POSITION_COLUMN = 'common_strategy_position'Merged source PR · d6e324c0Package definition · Limen 6.0.1 · current package validation · package source
limen.features.common_strategy_positions.common_strategy_positions
def common_strategy_positions(data: pl.DataFrame | pl.LazyFrame, strategy: str) -> pl.DataFrame | pl.LazyFrame: ...Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source
Append one common-strategy position column without dropping warm-up rows.
Lazy execution evaluates the complete input frame once, so optimizations
cannot move a filter or slice ahead of the strategy's indicator history.
limen.features.price_lines.line_active_count
def line_active_count(long: LineArrays, short: LineArrays, n_rows: int) -> npt.NDArray[np.intp]: ...Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source
limen.features.price_lines.line_recency
def line_recency(ends: npt.NDArray[np.intp], n_rows: int, lookback: int) -> npt.NDArray[np.float64]: ...Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source
limen.features.volatility_barriers.volatility_barriers
def volatility_barriers(data: pl.DataFrame, *, span: int=24, regime_span: int=168, min_periods: int=24, take_profit_multiple: float=2.0, stop_loss_multiple: float=1.0, minimum_bps: float=10.0, maximum_bps: float=2000.0, low_regime_ratio: float=0.75, high_regime_ratio: float=1.25, low_multiplier: float=0.8, normal_multiplier: float=1.0, high_multiplier: float=1.2) -> pl.DataFrame: ...Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source
Produce entry distances from strictly prior simple-return EWMA volatility.
limen.indicators.cdladvancedblock.candle_totals
def candle_totals(values: npt.NDArray[np.float64], period: int, start: int, count: int, initial: float=0.0) -> npt.NDArray[np.float64]: ...Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source
limen.indicators.linearreg.linearreg_components
def linearreg_components(values: npt.NDArray[np.float64], period: int) -> tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]]: ...Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source
limen.indicators.midprice.window_extrema
def window_extrema(high: npt.NDArray[np.float64], low: npt.NDArray[np.float64], period: int) -> tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]]: ...Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source
limen.inference.sensor.PredictionReason
PredictionReason = Literal['warm-up', 'inside-training-window', 'null-features', 'sensor-error']Merged source PR · ad5b70f2Package definition · Limen 6.0.1 · current package validation · package source
limen.log.ConfusionNotApplicableError
class ConfusionNotApplicableError(ValueError): ...Merged source PR · 2cdb94eePackage signature · Limen 6.0.1 · current package validation · package source
Recorded outputs carry no binary classification interpretation.
limen.log.permutation_prediction_performance
def permutation_prediction_performance(self: Any, round_id: int) -> pl.DataFrame: ...Merged source PR · 2cdb94eePackage signature · Limen 6.0.1 · current package validation · package source
Create prediction performance table based on round id.
Preparation failures propagate; retained round parameters are never replaced with defaults.
Args:
round_id (int): Round ID (i.e. nth permutation in an experiment)
Returns:
pl.DataFrame: Table with predictions, actuals, hit/miss, datetime, open, close, and price_change
limen.sfd.foundational_sfd.ridge_regressor.manifest
def manifest() -> MLManifest: ...Merged source PR · 44eff3b2Package signature · Limen 6.0.1 · current package validation · package source
Build a next-return SFD with lagged returns and train-fitted scaling.
limen.sfd.foundational_sfd.ridge_regressor.params
def params() -> dict[str, list[object]]: ...Merged source PR · 44eff3b2Package signature · Limen 6.0.1 · current package validation · package source
Return a small search space over native sklearn Ridge parameters.
limen.sfd.foundational_sfd.xgboost_regressor.manifest
def manifest() -> MLManifest: ...Merged source PR · 12e7ef4ePackage signature · Limen 6.0.1 · current package validation · package source
Build independently selectable research groups with per-round preparation.
limen.sfd.foundational_sfd.xgboost_regressor.params
def params() -> dict[str, list[object]]: ...Merged source PR · 12e7ef4ePackage signature · Limen 6.0.1 · current package validation · package source
Expose model defaults and a discrete research space over existing features.
limen.sfd.reference_architecture.DLinearRegressor
class DLinearRegressor(ReferenceModel): ...Merged source PR · 872d1c8aPackage signature · Limen 6.0.1 · current package validation · package source
DLinear regression model with train/evaluate interface.
Implements canonical DLinear semantics (Zeng et al. 2023): the lookback
window is decomposed by a centered moving average with replicate edge
padding into trend and remainder components, each component gets its own
linear head, and the head outputs are summed. The fit is the exact
minimizer of the ridge-regularized DLinear MSE objective, computed in
closed form by SVD, so training is deterministic and needs no seed.
The lookback window is read from the feature columns named
'ret_1_lag_{i}', ordered by lag descending so each row is the
window in time order (oldest bar first). All other feature columns are
ignored.
limen.sfd.reference_architecture.DLinearRegressor.evaluate
@overridedef evaluate(self, data: dict[str, Any], inline_metrics: bool=True) -> dict[str, Any]: ...Merged source PR · 872d1c8aPackage signature · Limen 6.0.1 · current package validation · package source
Evaluate trained model on test data.
Args:
data (dict): Data dictionary with x_test, y_test, and optionally price_data_for_backtest
inline_metrics (bool): Whether to include confusion_* and backtest_* keys
Returns:
dict: Metrics dict, optionally with flattened confusion_* and backtest_* keys
limen.sfd.reference_architecture.DLinearRegressor.predict
@overridedef predict(self, data: dict[str, Any]) -> dict[str, Any]: ...Merged source PR · 872d1c8aPackage signature · Limen 6.0.1 · current package validation · package source
Compute continuous predictions from feature data.
Args:
data (dict): Data dictionary with x_test holding the window columns
Returns:
dict: Prediction results with '_preds' key
limen.sfd.reference_architecture.DLinearRegressor.train
@overridedef train(self, data: dict[str, Any], **params: Any) -> 'DLinearRegressor': ...Merged source PR · 872d1c8aPackage signature · Limen 6.0.1 · current package validation · package source
Fit the DLinear heads in closed form on provided data.
Args:
data (dict): Data dictionary with x_train (polars DataFrame holding
the lookback window columns) and y_train
**params: kernel_size (odd int) and alpha (non-negative float)
Returns:
DLinearRegressor: Self with fitted weights stored
limen.sfd.reference_architecture.DirectionSizingModel
class DirectionSizingModel(ReferenceModel): prediction_mode: ClassVar[Literal['binary', 'target_exposure']] = 'target_exposure' direction_model: ComponentEstimator sizing_model: ComponentEstimatorMerged source PR · 98767d50Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.DirectionSizingModel.evaluate
@overridedef evaluate(self, data: dict[str, object], inline_metrics: bool=True) -> dict[str, object]: ...Merged source PR · 98767d50Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.DirectionSizingModel.predict
@overridedef predict(self, data: dict[str, object]) -> dict[str, object]: ...Merged source PR · 98767d50Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.DirectionSizingModel.train
@overridedef train(self, data: dict[str, object], **params: object) -> DirectionSizingModel: ...Merged source PR · 98767d50Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.LightGBMBinary
class LightGBMBinary(ReferenceModel): ...Merged source PR · 872d1c8aPackage signature · Limen 6.0.1 · current package validation · package source
LightGBM binary classifier with train/evaluate interface.
limen.sfd.reference_architecture.LightGBMBinary.__init__
def __init__(self, prediction_calibration_config: 'CalibrationConfig | None'=None) -> None: ...Merged source PR · 872d1c8aPackage signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.LightGBMBinary.evaluate
@overridedef evaluate(self, data: dict[str, Any], inline_metrics: bool=True) -> dict[str, Any]: ...Merged source PR · 872d1c8aPackage signature · Limen 6.0.1 · current package validation · package source
Evaluate test predictions and preserve configured model output records.
limen.sfd.reference_architecture.LightGBMBinary.predict
@overridedef predict(self, data: dict[str, Any]) -> dict[str, Any]: ...Merged source PR · 872d1c8aPackage signature · Limen 6.0.1 · current package validation · package source
Compute binary predictions from feature data.
On the first call with calibration configured, fits the calibrator on
validation data and stores it for reuse. Subsequent calls (including
inference without val data) use the stored calibrator directly.
Args:
data (dict): Data dictionary. Training call requires x_val, y_val, x_test;
inference call requires only x_test
Returns:
dict: Prediction results with '_preds' and '_probs' keys; calibrated path
also includes 'optimal_threshold' and 'val_score'
limen.sfd.reference_architecture.LightGBMBinary.train
@overridedef train(self, data: dict[str, Any], **params: Any) -> 'LightGBMBinary': ...Merged source PR · 872d1c8aPackage signature · Limen 6.0.1 · current package validation · package source
Train LightGBM binary classifier on provided data.
Fits with early stopping against the validation split when one is
present and early_stopping_rounds is set; otherwise fits plainly on
the training split.
Args:
data (dict): Data dictionary with x_train, y_train, and optionally x_val, y_val
**params: LGBMClassifier hyperparameters plus early_stopping_rounds
Returns:
LightGBMBinary: Self with fitted model stored
limen.sfd.reference_architecture.LogRegBinary
class LogRegBinary(ReferenceModel): ...Merged source PR · 25e47866Package signature · Limen 6.0.1 · current package validation · package source
Logistic regression binary classifier with train/evaluate interface.
Declared non-deterministic because sklearn solver refits are not
bit-reproducible across BLAS builds and thread counts even with a fixed
random_state, so reconstruction validation must use relative tolerance.
limen.sfd.reference_architecture.LogRegBinary.__init__
def __init__(self, prediction_calibration_config: 'CalibrationConfig | None'=None) -> None: ...Merged source PR · 25e47866Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.LogRegBinary.evaluate
@overridedef evaluate(self, data: dict[str, Any], inline_metrics: bool=True) -> dict[str, Any]: ...Merged source PR · 25e47866Package signature · Limen 6.0.1 · current package validation · package source
Evaluate trained model on test data.
Args:
data (dict): Data dictionary with x_test, y_test, and optionally price_data_for_backtest
inline_metrics (bool): Whether to include confusion_* and backtest_* keys
Returns:
dict: Metrics dict, optionally with flattened confusion_* and backtest_* keys
limen.sfd.reference_architecture.LogRegBinary.predict
@overridedef predict(self, data: dict[str, Any]) -> dict[str, Any]: ...Merged source PR · 25e47866Package signature · Limen 6.0.1 · current package validation · package source
Compute binary predictions from feature data.
On the first call with calibration configured, fits the calibrator on
validation data and stores it for reuse. Subsequent calls (including
inference without val data) use the stored calibrator directly.
Args:
data (dict): Data dictionary. Training call requires x_val, y_val, x_test;
inference call requires only x_test
Returns:
dict: Prediction results with '_preds' and '_probs' keys; calibrated path
also includes 'optimal_threshold' and 'val_score'
limen.sfd.reference_architecture.LogRegBinary.train
@overridedef train(self, data: dict[str, Any], **params: Any) -> 'LogRegBinary': ...Merged source PR · 25e47866Package signature · Limen 6.0.1 · current package validation · package source
Train logistic regression classifier on provided data.
Args:
data (dict): Data dictionary with x_train, y_train
**params: LogisticRegression hyperparameters
limen.sfd.reference_architecture.RandomBinary
class RandomBinary(ReferenceModel): ...Merged source PR · 872d1c8aPackage signature · Limen 6.0.1 · current package validation · package source
Random binary classifier with train/evaluate interface.
limen.sfd.reference_architecture.RandomBinary.__init__
def __init__(self) -> None: ...Merged source PR · 872d1c8aPackage signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.RandomBinary.evaluate
@overridedef evaluate(self, data: dict[str, Any], inline_metrics: bool=True) -> dict[str, Any]: ...Merged source PR · 872d1c8aPackage signature · Limen 6.0.1 · current package validation · package source
Evaluate random classifier on test data.
Args:
data (dict): Data dictionary with x_test, y_test, and optionally price_data_for_backtest
inline_metrics (bool): Whether to include confusion_* and backtest_* keys
Returns:
dict: Metrics dict, optionally with flattened confusion_* and backtest_* keys
limen.sfd.reference_architecture.RandomBinary.predict
@overridedef predict(self, data: dict[str, Any]) -> dict[str, Any]: ...Merged source PR · 872d1c8aPackage signature · Limen 6.0.1 · current package validation · package source
Compute random binary predictions.
Args:
data (dict): Data dictionary with x_test
Returns:
dict: Prediction results with '_preds' and '_probs' keys
limen.sfd.reference_architecture.RandomBinary.train
@overridedef train(self, data: dict[str, Any], **params: Any) -> 'RandomBinary': ...Merged source PR · 872d1c8aPackage signature · Limen 6.0.1 · current package validation · package source
Store random weights configuration. No actual training.
Args:
data (dict): Data dictionary (unused for random classifier)
**params: Must include 'random_weights' if overriding default
Returns:
RandomBinary: Self with configuration stored
limen.sfd.reference_architecture.ReferenceModel
class ReferenceModel(ABC): deterministic: bool = False prediction_mode: ClassVar[Literal['binary', 'target_exposure']] = 'binary'Merged source PR · 15bf9527Package signature · Limen 6.0.1 · current package validation · package source
Base class for class-based reference architecture models.
limen.sfd.reference_architecture.ReferenceModel.__init__
def __init__(self) -> None: ...Merged source PR · 15bf9527Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.RidgeRegressor
class RidgeRegressor(ReferenceModel): ...Merged source PR · 872d1c8aPackage signature · Limen 6.0.1 · current package validation · package source
Sklearn ridge regression with Limen's train/predict/evaluate contract.
Each train call fits a fresh estimator on the training split only.
Solver behavior, validation, and warnings belong to sklearn. The
deterministic flag is conservative because stochastic solvers are allowed.
limen.sfd.reference_architecture.RidgeRegressor.__init__
def __init__(self) -> None: ...Merged source PR · 872d1c8aPackage signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.RidgeRegressor.evaluate
@overridedef evaluate(self, data: dict[str, object], inline_metrics: bool=True) -> dict[str, object]: ...Merged source PR · 872d1c8aPackage signature · Limen 6.0.1 · current package validation · package source
Score continuous predictions, with optional standard directional metrics.
limen.sfd.reference_architecture.RidgeRegressor.predict
@overridedef predict(self, data: dict[str, object]) -> dict[str, _Predictions]: ...Merged source PR · 872d1c8aPackage signature · Limen 6.0.1 · current package validation · package source
Return continuous predictions; only x_test is required.
limen.sfd.reference_architecture.RidgeRegressor.train
@overridedef train(self, data: dict[str, object], **params: object) -> 'RidgeRegressor': ...Merged source PR · 872d1c8aPackage signature · Limen 6.0.1 · current package validation · package source
Fit sklearn Ridge using native constructor parameters.
limen.sfd.reference_architecture.RuleBasedStrategy
class RuleBasedStrategy(ReferenceModel): ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
Rule-based strategy that applies boolean predicate logic per bar to produce long/flat positions.
limen.sfd.reference_architecture.RuleBasedStrategy.__init__
def __init__(self, sharpe_std_threshold: float=0.5, sharpe_degradation_threshold: float=0.3) -> None: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.RuleBasedStrategy.evaluate
@overridedef evaluate(self, data: dict[str, Any], inline_metrics: bool=True) -> dict[str, Any]: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
Evaluate strategy across all splits and return rule-based metrics.
Args:
data (dict): Data dict with 'train', 'val', 'test' DataFrames and 'strategy' config
inline_metrics (bool): Unused — included for interface compatibility
Returns:
dict: Tier 1 position stats, Tier 2 backtest metrics per split,
Tier 3 stability metrics, and '_preds' key
limen.sfd.reference_architecture.RuleBasedStrategy.predict
@overridedef predict(self, data: dict[str, Any]) -> dict[str, Any]: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
Apply boolean logic tree to test split and return per-bar position signals.
Args:
data (dict): Data dict with 'test' DataFrame and 'strategy' config
Returns:
dict: {'_preds': np.ndarray} of 0/1 integer positions
limen.sfd.reference_architecture.RuleBasedStrategy.train
@overridedef train(self, data: dict[str, Any], **params: Any) -> 'RuleBasedStrategy': ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
Own the selected rule configuration without learned fitting.
limen.sfd.reference_architecture.TabPFNBinary
class TabPFNBinary(ReferenceModel): ...Merged source PR · 86326751Package signature · Limen 6.0.1 · current package validation · package source
TabPFN binary classifier with train/evaluate interface.
limen.sfd.reference_architecture.TabPFNBinary.__init__
def __init__(self, prediction_calibration_config: 'CalibrationConfig | None'=None) -> None: ...Merged source PR · 86326751Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.TabPFNBinary.evaluate
@overridedef evaluate(self, data: dict[str, Any], inline_metrics: bool=True) -> dict[str, Any]: ...Merged source PR · 86326751Package signature · Limen 6.0.1 · current package validation · package source
Evaluate trained model on test data.
Args:
data (dict): Data dictionary with x_val, y_val, x_test, y_test,
and optionally price_data_for_backtest
inline_metrics (bool): Whether to include confusion_* and backtest_* keys
Returns:
dict: Metrics dict, optionally with flattened confusion_* and backtest_* keys
limen.sfd.reference_architecture.TabPFNBinary.predict
@overridedef predict(self, data: dict[str, Any]) -> dict[str, Any]: ...Merged source PR · 86326751Package signature · Limen 6.0.1 · current package validation · package source
Compute binary predictions with optional calibration and threshold tuning.
On the first call with calibration configured, fits the calibrator on
validation data and stores it for reuse. Subsequent calls (including
inference without val data) use the stored calibrator directly.
Args:
data (dict): Data dictionary. Training call requires x_val, y_val, x_test;
inference call requires only x_test
Returns:
dict: Prediction results with '_preds' and '_probs' keys; calibrated path
also includes 'optimal_threshold' and 'val_score'
limen.sfd.reference_architecture.TabPFNBinary.train
@overridedef train(self, data: dict[str, Any], **params: Any) -> 'TabPFNBinary': ...Merged source PR · 86326751Package signature · Limen 6.0.1 · current package validation · package source
Train TabPFN classifier on provided data.
Args:
data (dict): Data dictionary with x_train, y_train
**params: TabPFN hyperparameters including n_ensemble_configurations, device
Returns:
TabPFNBinary: Self with fitted model stored
limen.sfd.reference_architecture.XGBoostRegressor
class XGBoostRegressor(ReferenceModel): ...Merged source PR · 872d1c8aPackage signature · Limen 6.0.1 · current package validation · package source
XGBoost regression model with train/evaluate interface.
limen.sfd.reference_architecture.XGBoostRegressor.evaluate
@overridedef evaluate(self, data: dict[str, Any], inline_metrics: bool=True) -> dict[str, Any]: ...Merged source PR · 872d1c8aPackage signature · Limen 6.0.1 · current package validation · package source
Evaluate test predictions and preserve the configured boosting count.
limen.sfd.reference_architecture.XGBoostRegressor.predict
@overridedef predict(self, data: dict[str, Any]) -> dict[str, Any]: ...Merged source PR · 872d1c8aPackage signature · Limen 6.0.1 · current package validation · package source
Predict continuous returns for the test features.
limen.sfd.reference_architecture.XGBoostRegressor.train
@overridedef train(self, data: dict[str, Any], **params: Any) -> 'XGBoostRegressor': ...Merged source PR · 872d1c8aPackage signature · Limen 6.0.1 · current package validation · package source
Fit the training split; early stopping uses only nonempty validation data.
limen.sfd.reference_architecture.direction_sizing
def direction_sizing(data: dict[str, object], *, direction_factory: ComponentFactory=_DIRECTION_FACTORY, sizing_factory: ComponentFactory=_SIZING_FACTORY, direction_params: Mapping[str, JsonValue] | None=None, sizing_params: Mapping[str, JsonValue] | None=None, direction_features: Sequence[str] | None=None, sizing_features: Sequence[str] | None=None, conditional_size: bool=False, folds: int=5, min_train_samples: int=20, return_scale: float=0.01, max_size: float=1.0, seed: int=42, inline_metrics: bool=True) -> dict[str, object]: ...Merged source PR · 98767d50Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.direction_sizing.ComponentEstimator
class ComponentEstimator(Protocol): ...Merged source PR · 98767d50Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.direction_sizing.ComponentEstimator.fit
def fit(self, x: Array, y: Array) -> object: ...Merged source PR · 98767d50Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.direction_sizing.ComponentEstimator.predict
def predict(self, x: Array) -> Array: ...Merged source PR · 98767d50Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.direction_sizing.ComponentFactory
class ComponentFactory(Protocol): deterministic: boolMerged source PR · 98767d50Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.direction_sizing.ComponentFactory.__call__
def __call__(self, *, seed: int, **params: JsonValue) -> ComponentEstimator: ...Merged source PR · 98767d50Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.direction_sizing.FoldFeatures
@dataclass(frozen=True)class FoldFeatures: train: pl.DataFrame predict: pl.DataFrame train_rows: tuple[str, ...] predict_rows: tuple[str, ...]Merged source PR · 98767d50Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.direction_sizing.FoldPreparation
@runtime_checkableclass FoldPreparation(Protocol): raw_features: pl.DataFrame row_ids: tuple[str, ...] identities: pl.DataFrame deterministic: boolMerged source PR · 98767d50Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.direction_sizing.FoldPreparation.fit_transform
def fit_transform(self, train_rows: Sequence[str], predict_rows: Sequence[str]) -> FoldFeatures: ...Merged source PR · 98767d50Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.dlinear_regressor
def dlinear_regressor(data: dict[str, Any], kernel_size: int=DEFAULT_KERNEL_SIZE, alpha: float=DEFAULT_ALPHA) -> dict[str, Any]: ...Merged source PR · 872d1c8aPackage signature · Limen 6.0.1 · current package validation · package source
Compute DLinear regression predictions and evaluation metrics.
Args:
data (dict): Data dictionary with x_train, y_train, x_test, y_test
kernel_size (int): Odd moving-average kernel size for the decomposition
alpha (float): Ridge regularization strength on the component heads
Returns:
dict: Results with continuous metrics, predictions, inline confusion metrics,
and backtest metrics when price_data_for_backtest is in data
limen.sfd.reference_architecture.lightgbm_binary
def lightgbm_binary(data: dict[str, Any], objective: str | None=None, boosting_type: str='gbdt', num_leaves: int=31, max_depth: int=-1, learning_rate: float=0.1, n_estimators: int=100, subsample_for_bin: int=200000, min_split_gain: float=0.0, min_child_weight: float=0.001, min_child_samples: int=20, subsample: float=1.0, subsample_freq: int=0, colsample_bytree: float=1.0, reg_alpha: float=0.0, reg_lambda: float=0.0, class_weight: float | str | dict[int, float] | None=None, random_state: int=42, n_jobs: int | None=None, importance_type: str='split', early_stopping_rounds: int | None=DEFAULT_EARLY_STOPPING_ROUNDS, deterministic: bool=True, force_row_wise: bool=True, verbosity: int=-1, prediction_calibration_config: 'CalibrationConfig | None'=None) -> dict[str, Any]: ...Merged source PR · 872d1c8aPackage signature · Limen 6.0.1 · current package validation · package source
Compute LightGBM binary predictions and evaluation metrics.
Args:
data (dict): Data dictionary with x_train, y_train, x_val, y_val, x_test, y_test
objective (str | None): LightGBM objective; None lets LightGBM select the
binary default. Must be a binary objective ('binary', 'cross_entropy')
boosting_type (str): Boosting algorithm ('gbdt', 'dart', 'rf')
num_leaves (int): Maximum tree leaves per booster
max_depth (int): Maximum tree depth, -1 for no limit
learning_rate (float): Boosting learning rate
n_estimators (int): Number of boosting rounds (upper bound under early stopping)
subsample_for_bin (int): Number of samples for histogram bin construction
min_split_gain (float): Minimum loss reduction to split a leaf
min_child_weight (float): Minimum sum of instance Hessian in a leaf
min_child_samples (int): Minimum samples in a leaf
subsample (float): Row subsampling ratio per boosting round
subsample_freq (int): Subsampling frequency, 0 disables
colsample_bytree (float): Feature subsampling ratio per tree
reg_alpha (float): L1 regularization weight
reg_lambda (float): L2 regularization weight
class_weight (float, str, or dict): Class weights. Numeric values keep the
legacy {0: value, 1: 1} shorthand; 'balanced'/dicts pass through to LightGBM
random_state (int): Random seed
n_jobs (int | None): Number of parallel threads, None for LightGBM default
importance_type (str): Feature importance type ('split' or 'gain')
early_stopping_rounds (int | None): Early-stopping patience against the
validation split; None or 0 disables early stopping
deterministic (bool): Enforce reproducible LightGBM training; pair with
force_row_wise and a fixed random_state
force_row_wise (bool): Force row-wise histogram building, required for
deterministic training
verbosity (int): LightGBM verbosity, -1 silences
prediction_calibration_config (CalibrationConfig | None): Optional calibration config
Returns:
dict: Results with binary metrics, predictions, inline confusion metrics,
and backtest metrics when price_data_for_backtest is in data
limen.sfd.reference_architecture.meta_allocation.MetaAllocationModel
class MetaAllocationModel(ReferenceModel): prediction_mode: ClassVar[Literal['binary', 'target_exposure']] = 'target_exposure'Merged source PR · 94445fdcPackage signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.meta_allocation.MetaAllocationModel.evaluate
@overridedef evaluate(self, data: dict[str, object], inline_metrics: bool=True) -> dict[str, object]: ...Merged source PR · 94445fdcPackage signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.meta_allocation.MetaAllocationModel.predict
@overridedef predict(self, data: dict[str, object]) -> dict[str, object]: ...Merged source PR · 94445fdcPackage signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.meta_allocation.MetaAllocationModel.train
@overridedef train(self, data: dict[str, object], **params: object) -> MetaAllocationModel: ...Merged source PR · 94445fdcPackage signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.meta_allocation.meta_allocation
def meta_allocation(data: dict[str, object], *, context: AllocationContext, folds: int=5, internal_validation_fraction: float=0.2, min_train_samples: int=20, features: Sequence[str] | None=None, include_primary_proba: bool=False, probability_factory: ComponentFactory=DIRECTION_FACTORY, sizing_factory: ComponentFactory=SIZING_FACTORY, probability_params: Mapping[str, JsonValue] | None=None, sizing_params: Mapping[str, JsonValue] | None=None, probability_threshold: float=0.5, return_scale: float=0.01, max_size: float=1.0, risk_budget_fraction: float | None=None, seed: int=42) -> dict[str, object]: ...Merged source PR · 94445fdcPackage signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.random_binary
def random_binary(data: dict[str, Any], random_weights: float=0.5) -> dict[str, Any]: ...Merged source PR · 872d1c8aPackage signature · Limen 6.0.1 · current package validation · package source
Random binary classifier for testing and demonstration purposes.
Args:
data (dict): Data dictionary with x_test, y_test
random_weights (float): Probability weight for class 1 (0.0 to 1.0)
Returns:
dict: Results with binary metrics, predictions, inline confusion metrics,
and backtest metrics when price_data_for_backtest is in data
limen.sfd.reference_architecture.rule_based
def rule_based(data: dict[str, Any], sharpe_std_threshold: float=0.5, sharpe_degradation_threshold: float=0.3) -> dict[str, Any]: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
Apply a rule-based strategy to the given data and return evaluation metrics.
Args:
data (dict): Data dict with 'train', 'val', 'test' DataFrames and 'strategy' config
sharpe_std_threshold (float): Max sharpe_std for is_stable to be True
sharpe_degradation_threshold (float): Max sharpe_degradation for is_stable to be True
Returns:
dict: Rule-based metrics with Tier 1, Tier 2, Tier 3 keys and '_preds'
limen.sfd.reference_architecture.tabpfn_binary
def tabpfn_binary(data: dict[str, Any], n_ensemble_configurations: int=4, device: object='cpu', prediction_calibration_config: 'CalibrationConfig | None'=None, *, categorical_features_indices: Sequence[int] | None=None, softmax_temperature: float=0.9, balance_probabilities: bool=False, average_before_softmax: bool=False, model_path: object=TABPFN_MODEL_PATH, ignore_pretraining_limits: bool=True, inference_precision: object='auto', fit_mode: Literal['low_memory', 'fit_preprocessors', 'fit_with_cache', 'batched']='fit_preprocessors', memory_saving_mode: str | bool | float='auto', random_state: int | np.random.RandomState | np.random.Generator | None=0, n_preprocessing_jobs: int=1, inference_config: object=None, differentiable_input: bool=False, eval_metric: object=None, tuning_config: object=None) -> dict[str, Any]: ...Merged source PR · 86326751Package signature · Limen 6.0.1 · current package validation · package source
Fit TabPFN with the explicit classifier controls and optional Limen calibration. Return binary metrics, predictions, the fitted model and applicable inline confusion/backtest metrics. Calibrated results also contain optimal_threshold and val_score.
limen.sfd.reference_architecture.xgboost_regressor
def xgboost_regressor(data: dict[str, Any], learning_rate: float=0.01, max_depth: int=3, n_estimators: int=500, min_child_weight: int=10, subsample: float=0.6, colsample_bytree: float=0.6, gamma: float=0.5, reg_alpha: float=0.5, reg_lambda: float=5.0, objective: str='reg:squarederror', booster: str='gbtree', early_stopping_rounds: int | None=50, random_state: int=42, *, eval_metric: str | list[str] | None=None, max_leaves: int | None=None, max_bin: int | None=None, grow_policy: str | None=None, sampling_method: str | None=None, colsample_bylevel: float | None=None, colsample_bynode: float | None=None, max_delta_step: float | None=None, tree_method: str | None=None, num_parallel_tree: int | None=None, base_score: float | None=None, scale_pos_weight: float | None=None, n_jobs: int | None=None, verbosity: int | None=None, device: str | None=None, enable_categorical: bool | None=None, max_cat_to_onehot: int | None=None, max_cat_threshold: int | None=None, importance_type: str | None=None, interaction_constraints: str | list[list[int | str]] | None=None, monotone_constraints: str | tuple[int, ...] | dict[str, int] | None=None, multi_strategy: str | None=None, validate_parameters: bool | None=None) -> dict[str, Any]: ...Merged source PR · 872d1c8aPackage signature · Limen 6.0.1 · current package validation · package source
Train and evaluate a regressor, forwarding optional XGBoost controls.
limen.sfd.rule_based.RuleBasedConfig
@dataclassclass RuleBasedConfig: conditions: list[dict[str, Any]] entry: strMerged source PR · fe791657Package signature · Limen 6.0.1 · current package validation · package source
Rule-based strategy configuration: boolean predicate conditions and entry signal id.
limen.sfd.rule_based.build_predicate
def build_predicate(condition: dict[str, Any], round_params: dict[str, Any]) -> pl.Expr: ...Merged source PR · fe791657Package signature · Limen 6.0.1 · current package validation · package source
Route a condition config dict to the appropriate predicate function.
Args:
condition (dict): Condition config with 'type' key and type-specific fields.
Optional 'persistence_n' or 'recency_n' fields wrap the result with
the corresponding temporal modifier.
round_params (dict): Parameter values for template substitution
Returns:
pl.Expr: Boolean predicate expression
limen.sfd.rule_based.common_strategies.STRATEGY_NAMES
STRATEGY_NAMES = tuple(STRATEGY_PARAMETERS)Merged source PR · ec254c73Package definition · Limen 6.0.1 · current package validation · package source
limen.sfd.rule_based.common_strategies.STRATEGY_PARAMETERS
STRATEGY_PARAMETERS: Mapping[str, Mapping[str, int | float]] = MappingProxyType({'buy_and_hold': MappingProxyType({}), 'sma_cross': MappingProxyType({'fast_period': 50, 'slow_period': 200}), 'ema_cross': MappingProxyType({'fast_period': 12, 'slow_period': 26}), 'macd': MappingProxyType({'fast_period': 12, 'slow_period': 26, 'signal_period': 9}), 'rsi_mean_reversion': MappingProxyType({'period': 14, 'entry_threshold': 30.0, 'exit_threshold': 70.0}), 'bollinger_mean_reversion': MappingProxyType({'period': 20, 'standard_deviations': 2.0, 'ddof': 0}), 'donchian_breakout': MappingProxyType({'entry_period': 20, 'exit_period': 10}), 'time_series_momentum': MappingProxyType({'period': 20})})Merged source PR · ec254c73Package definition · Limen 6.0.1 · current package validation · package source
limen.sfd.rule_based.common_strategies.strategy_signals
def strategy_signals(data: pl.DataFrame, strategy: str) -> npt.NDArray[np.int8]: ...Merged source PR · ec254c73Package signature · Limen 6.0.1 · current package validation · package source
Compute one fixed common-strategy baseline as completed-bar long/flat intent.
Rows retain input order. Prices must be finite and positive; when supplied,
datetime must be strictly increasing. Donchian also requires high and low.
Each call starts flat with no indicator history. Warm-up rows remain flat,
and stateful strategies hold until their own exit rule occurs. The returned
array has one 0/1 value per input row, including warm-up and the final row.
Execution lag, fills, fees and final liquidation belong to the backtester.
See docs/Common-Strategies.md for the fixed parameters and exact rules.
limen.utils.find_price_lines.LineArrays
LineArrays = tuple[npt.NDArray[np.intp], npt.NDArray[np.intp], npt.NDArray[np.float64]]Merged source PR · b8a8c559Package definition · Limen 6.0.1 · current package validation · package source
limen.utils.find_price_lines.price_line_arrays
def price_line_arrays(close: npt.ArrayLike, max_duration_hours: int, min_height_pct: float) -> tuple[LineArrays, LineArrays]: ...Merged source PR · b8a8c559Package signature · Limen 6.0.1 · current package validation · package source
limen.yaml.CompiledSFD
class CompiledSFD: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
SFD-compatible object built from a validated YAML experiment dict.
limen.yaml.CompiledSFD.__init__
def __init__(self, yaml_dict: dict[str, Any], *, project_root: str | Path | None=None) -> None: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
limen.yaml.CompiledSFD.manifest
def manifest(self) -> Manifest: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
Return the compiled Manifest, built once and cached.
limen.yaml.CompiledSFD.params
def params(self) -> dict[str, list[Any]]: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
Return the parameter search space.
limen.yaml.GitError
class GitError(Exception): ...Merged source PR · c3239401Package signature · Limen 6.0.1 · current package validation · package source
Raised when Git operations fail or the YAML file is not in a repository.
limen.yaml.GitError.__init__
def __init__(self, path: str, message: str) -> None: ...Merged source PR · c3239401Package signature · Limen 6.0.1 · current package validation · package source
limen.yaml.ProfileResult
@dataclassclass ProfileResult: total_permutations: int param_cardinalities: dict[str, int] complexity_rating: str sample_permutations_attempted: int = 0 sample_permutations_completed: int = 0 sample_time_seconds_per_permutation: float | None = None warnings: list[str] = field(default_factory=list[str]) errors: list[str] = field(default_factory=list[str]) data_quality_warnings: list[str] = field(default_factory=list[str])Merged source PR · 235b4d7dPackage signature · Limen 6.0.1 · current package validation · package source
Summary of a YAML experiment profile run.
limen.yaml.ResolutionError
class ResolutionError(Exception): ...Merged source PR · c3239401Package signature · Limen 6.0.1 · current package validation · package source
Raised when a namespaced path cannot be resolved to a Python object.
limen.yaml.ResolutionError.__init__
def __init__(self, path: str, allowed_namespaces: list[str]) -> None: ...Merged source PR · c3239401Package signature · Limen 6.0.1 · current package validation · package source
limen.yaml.VERSION
VERSION = '1.0'Merged source PR · 62d79072Package definition · Limen 6.0.1 · current package validation · package source
limen.yaml.ValidationError
class ValidationError(Exception): ...Merged source PR · c3239401Package signature · Limen 6.0.1 · current package validation · package source
Raised when YAML validation fails; carries all collected errors.
limen.yaml.ValidationError.__init__
def __init__(self, errors: list['YAMLError']) -> None: ...Merged source PR · c3239401Package signature · Limen 6.0.1 · current package validation · package source
limen.yaml.ValidationResult
@dataclassclass ValidationResult: valid: bool errors: list[YAMLError] = field(default_factory=list[YAMLError]) warnings: list[YAMLError] = field(default_factory=list[YAMLError]) mode: str = 'development'Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
Result of a YAML validation pass.
limen.yaml.YAMLError
@dataclassclass YAMLError: message: str path: str = '' line: int | None = None column: int | None = None suggestion: str | None = NoneMerged source PR · c3239401Package signature · Limen 6.0.1 · current package validation · package source
A single validation error or warning with location and suggestion.
limen.yaml.build_manifest
def build_manifest(yaml_dict: dict[str, Any]) -> Manifest: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
Build the declared ML or rule-based manifest.
limen.yaml.build_search_strategy
def build_search_strategy(yaml_dict: dict[str, Any]) -> RandomStrategy | GridStrategy | SobolStrategy: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
Build the declared search, requiring a literal seed for shuffled YAML grids.
limen.yaml.find_project_root
def find_project_root(start: Path) -> Path | None: ...Merged source PR · c40ba91ePackage signature · Limen 6.0.1 · current package validation · package source
Walk up the directory tree looking for limen.toml.
Args:
start (Path): Directory to start searching from
Returns:
Path | None: The directory containing limen.toml, or None if not found
limen.yaml.get_store_path
def get_store_path(start: Path) -> Path: ...Merged source PR · c40ba91ePackage signature · Limen 6.0.1 · current package validation · package source
Locate the manifest store by finding the project root.
Args:
start (Path): Directory to start searching from
Returns:
Path: Path to manifests/committed/
Raises:
FileNotFoundError: If no limen.toml is found in the directory tree
limen.yaml.make_covering_array
def make_covering_array(params: dict[str, list[Any]], seed: int=42) -> list[dict[str, Any]]: ...Merged source PR · 235b4d7dPackage signature · Limen 6.0.1 · current package validation · package source
Build a randomised strength-1 covering array for the given parameter space.
Every value of every parameter appears in at least one permutation.
N = max cardinality across all params. Each parameter's column is filled
with round-robin values then shuffled independently, removing systematic
correlation between parameter assignments.
Args:
params (dict): Parameter name to list of values
seed (int): Random seed for reproducibility
Returns:
list[dict]: N permutations, each a dict of param name to sampled value
limen.yaml.parse
def parse(source: str | Path) -> tuple[dict[str, Any], list[YAMLError]]: ...Merged source PR · 22c25affPackage signature · Limen 6.0.1 · current package validation · package source
Parse a YAML experiment file or string into a dict.
Mapping-valued keys produce a located parse error before construction.
Args:
source (str | Path): Path instance to read from disk, or raw YAML string
Returns:
tuple[dict, list[YAMLError]]: Parsed dict and list of parse errors (empty on success)
limen.yaml.profile
def profile(compiled_sfd: 'CompiledSFD') -> ProfileResult: ...Merged source PR · 235b4d7dPackage signature · Limen 6.0.1 · current package validation · package source
Profile a compiled YAML experiment.
Computes static fields (permutation count, complexity) always. Fetches
data via the experiment's own data_source and runs a randomised strength-1
covering array of permutations to record timing and surface data quality
warnings.
Args:
compiled_sfd (CompiledSFD): Compiled SFD built from a validated yaml_dict
Returns:
ProfileResult: Static fields always populated; runtime fields populated
when data loads successfully and at least one permutation completes
limen.yaml.read_limen_toml
def read_limen_toml(project_root: Path) -> dict[str, Any]: ...Merged source PR · c40ba91ePackage signature · Limen 6.0.1 · current package validation · package source
Read limen.toml from the project root.
Args:
project_root (Path): Directory containing limen.toml
Returns:
dict: Parsed contents of limen.toml
limen.yaml.resolve
def resolve(path: str) -> Any: ...Merged source PR · a650de0dPackage signature · Limen 6.0.1 · current package validation · package source
Resolve a dotted path without leaving the allowed namespaces.
limen.yaml.resolve_manifest_uri
def resolve_manifest_uri(uri: str, start: Path) -> tuple[Path, Path]: ...Merged source PR · 4fd7958bPackage signature · Limen 6.0.1 · current package validation · package source
Return (manifest_path, project_root) for a full or unique short URI.
Search from start and verify stored lineage and canonical content against
the complete filename ID. Malformed input, missing project/files and
ambiguity raise ValueError. Integrity failures use "Integrity check failed",
chaining stored canonicalization TypeError/ValueError.
Verification covers this read, not later changes to the returned path.
limen.yaml.validate
def validate(yaml_dict: dict[str, Any]) -> ValidationResult: ...Merged source PR · 62d79072Package signature · Limen 6.0.1 · current package validation · package source
Collect YAML errors and warnings; validity requires no errors.
limen.sfd.foundational_sfd.rbf_svm_binary.manifest
def manifest() -> Manifest: ...Merged source PR · cee01889Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.foundational_sfd.rbf_svm_binary.params
def params() -> dict[str, list[object]]: ...Merged source PR · cee01889Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.RBFSVMBinary
class RBFSVMBinary(ReferenceModel): ...Merged source PR · cee01889Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.RBFSVMBinary.__init__
def __init__(self, prediction_calibration_config: 'CalibrationConfig | None'=None) -> None: ...Merged source PR · cee01889Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.RBFSVMBinary.train
@overridedef train(self, data: dict[str, object], **params: object) -> 'RBFSVMBinary': ...Merged source PR · cee01889Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.RBFSVMBinary.predict
@overridedef predict(self, data: dict[str, object]) -> dict[str, object]: ...Merged source PR · cee01889Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.RBFSVMBinary.evaluate
@overridedef evaluate(self, data: dict[str, object], inline_metrics: bool=True) -> dict[str, object]: ...Merged source PR · cee01889Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.rbf_svm_binary
def rbf_svm_binary(data: dict[str, object], C: float=1.0, gamma: str | float='scale', shrinking: bool=True, tol: float=0.001, cache_size: float=200, class_weight: str | dict[int, float] | None=None, verbose: bool=False, max_iter: int=-1, random_state: int | None=42, prediction_calibration_config: 'CalibrationConfig | None'=None) -> dict[str, object]: ...Merged source PR · cee01889Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.foundational_sfd.gaussian_hmm_binary.manifest
def manifest() -> Manifest: ...Merged source PR · 61cf2d56Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.foundational_sfd.gaussian_hmm_binary.params
def params() -> dict[str, list[object]]: ...Merged source PR · 61cf2d56Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.GaussianHMMBinary
class GaussianHMMBinary(ReferenceModel): ...Merged source PR · 61cf2d56Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.GaussianHMMBinary.__init__
def __init__(self) -> None: ...Merged source PR · 61cf2d56Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.GaussianHMMBinary.train
@overridedef train(self, data: dict[str, object], **params: object) -> 'GaussianHMMBinary': ...Merged source PR · 61cf2d56Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.GaussianHMMBinary.predict
@overridedef predict(self, data: dict[str, object]) -> dict[str, object]: ...Merged source PR · 61cf2d56Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.GaussianHMMBinary.evaluate
@overridedef evaluate(self, data: dict[str, object], inline_metrics: bool=True) -> dict[str, object]: ...Merged source PR · 61cf2d56Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.gaussian_hmm_binary
def gaussian_hmm_binary(data: dict[str, object], n_components: int=2, covariance_type: str='diag', min_covar: float=0.001, startprob_prior: float=1.0, transmat_prior: float=1.0, means_prior: float=0.0, means_weight: float=0.0, covars_prior: float=0.01, covars_weight: float=1.0, random_state: int | None=42, n_iter: int=30, tol: float=0.01, verbose: bool=False, implementation: str='scaling') -> dict[str, object]: ...Merged source PR · 61cf2d56Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.foundational_sfd.lstm_binary.manifest
def manifest() -> Manifest: ...Merged source PR · 6723f554Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.foundational_sfd.lstm_binary.params
def params() -> dict[str, list[object]]: ...Merged source PR · 6723f554Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.LSTMBinary
class LSTMBinary(WindowBinary): ...Merged source PR · 59ab3593Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.LSTMBinary.__init__
def __init__(self) -> None: ...Merged source PR · 59ab3593Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.LSTMBinary.train
@overridedef train(self, data: dict[str, object], *, epochs: int=10, batch_size: int=64, learning_rate: float=0.01, weight_decay: float=0.0, betas: tuple[float, float]=(0.9, 0.999), eps: float=1e-08, random_state: int=42, **params: object) -> 'LSTMBinary': ...Merged source PR · 59ab3593Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.LSTMBinary.predict
@overridedef predict(self, data: dict[str, object]) -> dict[str, object]: ...Merged source PR · 59ab3593Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.LSTMBinary.evaluate
@overridedef evaluate(self, data: dict[str, object], inline_metrics: bool=True) -> dict[str, object]: ...Merged source PR · 59ab3593Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.lstm_binary
def lstm_binary(data: dict[str, object], hidden_size: int=16, num_layers: int=1, bias: bool=True, dropout: float=0.0, bidirectional: bool=False, proj_size: int=0, epochs: int=10, batch_size: int=64, learning_rate: float=0.01, weight_decay: float=0.0, betas: tuple[float, float]=(0.9, 0.999), eps: float=1e-08, random_state: int=42) -> dict[str, object]: ...Merged source PR · 59ab3593Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.foundational_sfd.residual_regressor.manifest
def manifest() -> MLManifest: ...Merged source PR · 866af615Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.foundational_sfd.residual_regressor.params
def params() -> dict[str, list[object]]: ...Merged source PR · 866af615Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.ResidualRegressor
class ResidualRegressor(ReferenceModel): ...Merged source PR · 866af615Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.ResidualRegressor.__init__
def __init__(self) -> None: ...Merged source PR · 866af615Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.ResidualRegressor.train
@overridedef train(self, data: dict[str, object], *, ridge_params: dict[str, object] | None=None, xgboost_params: dict[str, object] | None=None, correction_scale: float=1.0, **params: object) -> 'ResidualRegressor': ...Merged source PR · 866af615Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.ResidualRegressor.predict
@overridedef predict(self, data: dict[str, object]) -> dict[str, _FloatArray]: ...Merged source PR · 866af615Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.ResidualRegressor.evaluate
@overridedef evaluate(self, data: dict[str, object], inline_metrics: bool=True) -> dict[str, object]: ...Merged source PR · 866af615Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.residual_regressor
def residual_regressor(data: dict[str, object], ridge_params: dict[str, object] | None=None, xgboost_params: dict[str, object] | None=None, correction_scale: float=1.0) -> dict[str, object]: ...Merged source PR · 866af615Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.foundational_sfd.cnn1d_binary.manifest
def manifest() -> Manifest: ...Merged source PR · 59ab3593Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.foundational_sfd.cnn1d_binary.params
def params() -> dict[str, list[object]]: ...Merged source PR · 59ab3593Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.CNN1DBinary
class CNN1DBinary(WindowBinary): ...Merged source PR · 59ab3593Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.CNN1DBinary.__init__
def __init__(self) -> None: ...Merged source PR · 59ab3593Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.CNN1DBinary.train
@overridedef train(self, data: dict[str, object], *, epochs: int=10, batch_size: int=64, learning_rate: float=0.01, weight_decay: float=0.0, betas: tuple[float, float]=(0.9, 0.999), eps: float=1e-08, random_state: int=42, **params: object) -> 'CNN1DBinary': ...Merged source PR · 59ab3593Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.CNN1DBinary.predict
@overridedef predict(self, data: dict[str, object]) -> dict[str, object]: ...Merged source PR · 59ab3593Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.CNN1DBinary.evaluate
@overridedef evaluate(self, data: dict[str, object], inline_metrics: bool=True) -> dict[str, object]: ...Merged source PR · 59ab3593Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.cnn1d_binary
def cnn1d_binary(data: dict[str, object], out_channels: int=8, conv_layers: int=1, kernel_size: int=3, stride: int=1, dilation: int=1, bias: bool=True, activation: str='relu', pooling: str='mean', dropout: float=0.0, epochs: int=10, batch_size: int=64, learning_rate: float=0.01, weight_decay: float=0.0, betas: tuple[float, float]=(0.9, 0.999), eps: float=1e-08, random_state: int=42) -> dict[str, object]: ...Merged source PR · 59ab3593Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.foundational_sfd.entropy_var_regressor.manifest
def manifest() -> MLManifest: ...Merged source PR · 18ae5825Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.foundational_sfd.entropy_var_regressor.params
def params() -> dict[str, list[object]]: ...Merged source PR · 18ae5825Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.EntropyVaRRegressor
class EntropyVaRRegressor(ReferenceModel): ...Merged source PR · 18ae5825Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.EntropyVaRRegressor.__init__
def __init__(self) -> None: ...Merged source PR · 18ae5825Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.EntropyVaRRegressor.train
@overridedef train(self, data: dict[str, object], quantile: float=0.05, **params: object) -> 'EntropyVaRRegressor': ...Merged source PR · 18ae5825Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.EntropyVaRRegressor.predict
@overridedef predict(self, data: dict[str, object]) -> dict[str, _FloatArray]: ...Merged source PR · 18ae5825Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.EntropyVaRRegressor.evaluate
@overridedef evaluate(self, data: dict[str, object], inline_metrics: bool=True) -> dict[str, object]: ...Merged source PR · 18ae5825Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.entropy_var_regressor
def entropy_var_regressor(data: dict[str, object], quantile: float=0.05, alpha: float=0.0, fit_intercept: bool=True, solver: str='highs', solver_options: dict[str, object] | None=None) -> dict[str, object]: ...Merged source PR · 18ae5825Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.foundational_sfd.feature_link_binary.manifest
def manifest() -> MLManifest: ...Merged source PR · e4379056Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.foundational_sfd.feature_link_binary.params
def params() -> dict[str, list[object]]: ...Merged source PR · e4379056Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.FeatureLinkedBinary
class FeatureLinkedBinary(ReferenceModel): ...Merged source PR · 25e47866Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.FeatureLinkedBinary.__init__
def __init__(self) -> None: ...Merged source PR · 25e47866Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.FeatureLinkedBinary.train
@overridedef train(self, data: dict[str, object], *, upstream_params: dict[str, object] | None=None, downstream_params: dict[str, object] | None=None, n_splits: int=3, observation_interval_seconds: int=3600, **params: object) -> 'FeatureLinkedBinary': ...Merged source PR · 25e47866Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.FeatureLinkedBinary.predict
@overridedef predict(self, data: dict[str, object]) -> dict[str, object]: ...Merged source PR · 25e47866Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.FeatureLinkedBinary.evaluate
@overridedef evaluate(self, data: dict[str, object], inline_metrics: bool=True) -> dict[str, object]: ...Merged source PR · 25e47866Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.feature_link_binary
def feature_link_binary(data: dict[str, object], upstream_params: dict[str, object] | None=None, downstream_params: dict[str, object] | None=None, n_splits: int=3, observation_interval_seconds: int=3600) -> dict[str, object]: ...Merged source PR · 25e47866Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.feature_link_binary.recorded_rows
def recorded_rows(data_dict: dict[str, object], split_data: list[pl.DataFrame], round_params: dict[str, object], fitted_params: dict[str, object]) -> dict[str, object]: ...Merged source PR · 25e47866Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.foundational_sfd.pseudo_label_binary.manifest
def manifest() -> MLManifest: ...Merged source PR · bfbadb06Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.foundational_sfd.pseudo_label_binary.params
def params() -> dict[str, list[object]]: ...Merged source PR · bfbadb06Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.PseudoLabelBinary
class PseudoLabelBinary(ReferenceModel): ...Merged source PR · bfbadb06Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.PseudoLabelBinary.__init__
def __init__(self) -> None: ...Merged source PR · bfbadb06Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.PseudoLabelBinary.train
@overridedef train(self, data: dict[str, object], *, pool_path: str, pool_sha256: str, pool_start: str, pool_end: str, training_cutoff: str, observation_interval_seconds: int, confidence_threshold: float=0.8, upstream_params: dict[str, object] | None=None, downstream_params: dict[str, object] | None=None, **params: object) -> 'PseudoLabelBinary': ...Merged source PR · bfbadb06Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.PseudoLabelBinary.predict
@overridedef predict(self, data: dict[str, object]) -> dict[str, object]: ...Merged source PR · bfbadb06Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.PseudoLabelBinary.evaluate
@overridedef evaluate(self, data: dict[str, object], inline_metrics: bool=True) -> dict[str, object]: ...Merged source PR · bfbadb06Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.pseudo_label_binary
def pseudo_label_binary(data: dict[str, object], pool_path: str, pool_sha256: str, pool_start: str, pool_end: str, training_cutoff: str, observation_interval_seconds: int, confidence_threshold: float=0.8, upstream_params: dict[str, object] | None=None, downstream_params: dict[str, object] | None=None) -> dict[str, object]: ...Merged source PR · bfbadb06Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.pseudo_label_binary.recorded_rows
def recorded_rows(data_dict: dict[str, object], split_data: list[pl.DataFrame], round_params: dict[str, object], fitted_params: dict[str, object]) -> dict[str, object]: ...Merged source PR · bfbadb06Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.foundational_sfd.weight_link_binary.manifest
def manifest() -> MLManifest: ...Merged source PR · 25e47866Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.foundational_sfd.weight_link_binary.params
def params() -> dict[str, list[object]]: ...Merged source PR · 25e47866Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.WeightLinkedBinary
class WeightLinkedBinary(ReferenceModel): ...Merged source PR · 25e47866Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.WeightLinkedBinary.__init__
def __init__(self) -> None: ...Merged source PR · 25e47866Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.WeightLinkedBinary.train
@overridedef train(self, data: dict[str, object], *, upstream_params: dict[str, object] | None=None, downstream_params: dict[str, object] | None=None, n_splits: int=3, weight_floor: float=0.1, weight_power: float=1.0, observation_interval_seconds: int=3600, **params: object) -> 'WeightLinkedBinary': ...Merged source PR · 25e47866Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.WeightLinkedBinary.predict
@overridedef predict(self, data: dict[str, object]) -> dict[str, object]: ...Merged source PR · 25e47866Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.WeightLinkedBinary.evaluate
@overridedef evaluate(self, data: dict[str, object], inline_metrics: bool=True) -> dict[str, object]: ...Merged source PR · 25e47866Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.weight_link_binary
def weight_link_binary(data: dict[str, object], upstream_params: dict[str, object] | None=None, downstream_params: dict[str, object] | None=None, n_splits: int=3, weight_floor: float=0.1, weight_power: float=1.0, observation_interval_seconds: int=3600) -> dict[str, object]: ...Merged source PR · 25e47866Package signature · Limen 6.0.1 · current package validation · package source
limen.sfd.reference_architecture.weight_link_binary.recorded_rows
def recorded_rows(data_dict: dict[str, object], split_data: list[pl.DataFrame], round_params: dict[str, object], fitted_params: dict[str, object]) -> dict[str, object]: ...Merged source PR · 25e47866Package signature · Limen 6.0.1 · current package validation · package source