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],
"filesAnalyzed": 5,
CUSTOM_THRESHOLD_METHODS,
EXTREMA_SELECTION_METHODS,
LABEL_WEIGHT_SUPPORT_POLICIES,
+ NORMALIZATION_TYPES,
PREDICTION_METHODS,
SKIMAGE_THRESHOLD_METHODS,
+ STANDARDIZATION_TYPES,
ExtremaSelectionMethod,
LabelTransformer,
LabelWeightSupportPolicy,
https://github.com/sponsors/robcaulk
"""
- version = "3.13.0-rc.13"
+ version = "3.13.0-rc.14"
_CALIBRATION_START_KEY: Final[str] = "quickadapter_calibration_start"
_DEPLOYMENT_COORDINATE_MARKER_KEY: Final[str] = "quickadapter_deployment_coordinates"
def _causal_mode(self) -> bool:
return get_causal_mode(self.ft_params, logger)
- def _label_horizon_candles(self, pair: str | None = None) -> int:
- if pair is None:
- return get_label_horizon_candles(self.ft_params, logger)
- label_params = self.get_optuna_params(pair, _OPTUNA_NAMESPACES.label)
- return get_label_horizon_candles({**self.ft_params, **label_params}, logger)
+ def _label_horizon_candles(
+ self, pair: str | None = None, *, warning_messages: set[str] | None = None
+ ) -> int:
+ config = self.ft_params
+ if pair is not None:
+ label_params = self.get_optuna_params(pair, _OPTUNA_NAMESPACES.label)
+ config = {**config, **label_params}
+ return get_label_horizon_candles(config, logger, warning_messages=warning_messages)
@property
def _optuna_label_candle_pool_full(self) -> list[int]:
self.regressor: Regressor = self.freqai_info.get("regressor", DEFAULT_REGRESSOR)
if self.regressor not in set(REGRESSORS):
+ logger.warning(
+ "%s, using default %r",
+ enum_error_message("freqai.regressor", self.regressor, REGRESSORS),
+ DEFAULT_REGRESSOR,
+ )
self.regressor = DEFAULT_REGRESSOR
self.freqai_info["regressor"] = self.regressor
self._log_model_configuration()
logger.info(f"Model Version: {self.version}")
logger.info(f"Regressor: {self.regressor}")
+ split_parameters = self.data_split_parameters
+ split_method = split_parameters.get(
+ "method", QuickAdapterRegressorV3.DATA_SPLIT_METHOD_DEFAULT
+ )
+ logger.info("Data Split:")
+ logger.info(f" method: {split_method}")
+ if split_method == QuickAdapterRegressorV3._DATA_SPLIT_TIMESERIES:
+ test_size = split_parameters.get("test_size")
+ logger.info(
+ f" test_size: {format_number(test_size) if test_size is not None else 'dynamic (dataset size and n_splits)'}"
+ )
+ logger.info(
+ f" n_splits: {split_parameters.get('n_splits', QuickAdapterRegressorV3.TIMESERIES_N_SPLITS_DEFAULT)}"
+ )
+ gap = split_parameters.get("gap")
+ if gap is None:
+ gap = QuickAdapterRegressorV3.TIMESERIES_GAP_DEFAULT
+ if gap == 0:
+ gap_source = (
+ "label_horizon_candles" if self._causal_mode else "label_period_candles"
+ )
+ logger.info(
+ f" gap: automatic from per-pair {gap_source} (deferred until training)"
+ )
+ else:
+ logger.info(f" gap: {gap}")
+ logger.info(
+ f" max_train_size: {split_parameters.get('max_train_size', QuickAdapterRegressorV3.TIMESERIES_MAX_TRAIN_SIZE_DEFAULT)}"
+ )
+ else:
+ logger.info(
+ f" test_size: {format_number(split_parameters.get('test_size', QuickAdapterRegressorV3._TEST_SIZE))}"
+ )
+ for key in ("train_size", "random_state", "stratify"):
+ if key in split_parameters:
+ logger.info(f" {key}: {split_parameters[key]}")
+ logger.info(f" validation_size: {format_number(self._get_validation_size())}")
+
+ logger.info("Causality and Ordering:")
+ logger.info(f" causal_mode: {self._causal_mode}")
+ logger.info(f" shuffle: {split_parameters.get('shuffle', False)}")
+ logger.info(f" shuffle_after_split: {self.ft_params.get('shuffle_after_split', False)}")
+ logger.info(
+ f" reverse_train_test_order: {self.ft_params.get('reverse_train_test_order', False)}"
+ )
+
+ logger.info("Training:")
+ logger.info(f" continual_learning: {self.continual_learning}")
+ logger.info("Configured Model Training Overrides:")
+ logger.info(" These overrides are not final fitted, HPO-selected or refit parameters.")
+ logger.info(
+ f" model_training_parameters: {format_dict(self.model_training_parameters, style=_FORMAT_STYLE_DICT)}"
+ )
+
logger.info("Optuna Hyperopt:")
optuna_config = self._optuna_config
logger.info(f" enabled: {optuna_config.get('enabled')}")
+ logger.info(f" active: {bool(self._optuna_hyperopt)}")
+ label_method = self.ft_params.get(
+ "label_method", QuickAdapterRegressorV3.LABEL_METHOD_DEFAULT
+ )
+ label_config = {}
+ formatted_label_weights = None
+ label_p_order = None
+ label_p_order_config = self.ft_params.get("label_p_order")
+ # Preserve the configured-enabled validation boundary even when HPO is inactive.
if optuna_config.get("enabled"):
+ label_config = self._resolve_label_method_config(label_method)
+ label_weights = self.ft_params.get("label_weights")
+ if label_weights is not None:
+ formatted_label_weights = [format_number(w) for w in label_weights]
+ if label_p_order_config is not None:
+ label_p_order = float(label_p_order_config)
+
+ if not self._optuna_hyperopt:
+ if not optuna_config.get("enabled"):
+ inactive_reason = "configuration disabled"
+ elif not self.freqai_info.get("enabled", False):
+ inactive_reason = "FreqAI disabled"
+ else:
+ inactive_reason = "zero validation/holdout size"
+ logger.info(f" inactive_reason: {inactive_reason}")
+ else:
logger.info(f" n_jobs: {optuna_config.get('n_jobs')}")
logger.info(f" sampler: {optuna_config.get('sampler')}")
logger.info(f" storage: {optuna_config.get('storage')}")
logger.info(f" label_sampler: {optuna_config.get('label_sampler')}")
logger.info(f" label_candles_step: {optuna_config.get('label_candles_step')}")
- label_method = self.ft_params.get(
- "label_method", QuickAdapterRegressorV3.LABEL_METHOD_DEFAULT
- )
+
logger.info(f" label_method: {label_method}")
- label_config = self._resolve_label_method_config(label_method)
QuickAdapterRegressorV3._log_label_method_config(label_config)
- label_weights = self.ft_params.get("label_weights")
- if label_weights is not None:
- formatted_label_weights = [format_number(w) for w in label_weights]
+ if formatted_label_weights is not None:
logger.info(f" label_weights: [{', '.join(formatted_label_weights)}]")
else:
logger.info(" label_weights: [1.0, ...] * n_objectives, l1 normalized (default)")
- label_p_order_config = self.ft_params.get("label_p_order")
if label_p_order_config is not None:
- logger.info(f" label_p_order: {format_number(float(label_p_order_config))}")
+ logger.info(f" label_p_order: {format_number(label_p_order)}")
else:
distance_metric = label_config["distance_metric"]
if distance_metric in {
)
logger.info(" Pipeline:")
logger.info(f" standardization: {col_pipeline['standardization']}")
- logger.info(
- f" robust_quantiles: ({format_number(col_pipeline['robust_quantiles'][0])}, {format_number(col_pipeline['robust_quantiles'][1])})"
- )
- logger.info(
- f" mmad_scaling_factor: {format_number(col_pipeline['mmad_scaling_factor'])}"
- )
+ if col_pipeline["standardization"] == STANDARDIZATION_TYPES[2]: # "robust"
+ logger.info(
+ f" robust_quantiles: ({format_number(col_pipeline['robust_quantiles'][0])}, {format_number(col_pipeline['robust_quantiles'][1])})"
+ )
+ elif col_pipeline["standardization"] == STANDARDIZATION_TYPES[3]: # "mmad"
+ logger.info(
+ f" mmad_scaling_factor: {format_number(col_pipeline['mmad_scaling_factor'])}"
+ )
logger.info(f" normalization: {col_pipeline['normalization']}")
- logger.info(
- f" minmax_range: ({format_number(col_pipeline['minmax_range'][0])}, {format_number(col_pipeline['minmax_range'][1])})"
- )
- logger.info(f" sigmoid_scale: {format_number(col_pipeline['sigmoid_scale'])}")
+ if col_pipeline["normalization"] == NORMALIZATION_TYPES[1]: # "minmax"
+ logger.info(
+ f" minmax_range: ({format_number(col_pipeline['minmax_range'][0])}, {format_number(col_pipeline['minmax_range'][1])})"
+ )
+ elif col_pipeline["normalization"] == NORMALIZATION_TYPES[2]: # "sigmoid"
+ logger.info(f" sigmoid_scale: {format_number(col_pipeline['sigmoid_scale'])}")
logger.info(f" gamma: {format_number(col_pipeline['gamma'])}")
col_prediction = get_label_column_config(
)
logger.info(" Prediction:")
logger.info(f" method: {col_prediction['method']}")
- logger.info(f" selection_method: {col_prediction['selection_method']}")
- logger.info(f" threshold_method: {col_prediction['threshold_method']}")
- logger.info(
- f" outlier_quantile: {format_number(col_prediction['outlier_quantile'])}"
- )
- logger.info(
- f" soft_extremum_alpha: {format_number(col_prediction['soft_extremum_alpha'])}"
- )
- logger.info(f" keep_fraction: {format_number(col_prediction['keep_fraction'])}")
+ if col_prediction["method"] == PREDICTION_METHODS[1]: # "thresholding"
+ logger.info(f" selection_method: {col_prediction['selection_method']}")
+ logger.info(f" threshold_method: {col_prediction['threshold_method']}")
+ logger.info(
+ f" outlier_quantile: {format_number(col_prediction['outlier_quantile'])}"
+ )
+ if (
+ col_prediction["threshold_method"] == CUSTOM_THRESHOLD_METHODS[1]
+ ): # "soft_extremum"
+ logger.info(
+ f" soft_extremum_alpha: {format_number(col_prediction['soft_extremum_alpha'])}"
+ )
+ if col_prediction["selection_method"] in EXTREMA_SELECTION_METHODS[:2]:
+ logger.info(
+ f" keep_fraction: {format_number(col_prediction['keep_fraction'])}"
+ )
if col_prediction["method"] == PREDICTION_METHODS[0]: # "none"
logger.warning(
f" Prediction method is 'none' for label [{label_col}]: "
f"entry signals based on them will never trigger."
)
- default_label_period_candles, default_label_natr_multiplier = self._label_defaults
- label_period_candles = self.ft_params.get(
- "label_period_candles", default_label_period_candles
- )
- label_natr_multiplier = float(
- self.ft_params.get("label_natr_multiplier", default_label_natr_multiplier)
- )
- logger.info("Label Hyperparameters:")
+ logger.info("Label Parameters:")
logger.info(f" fit_live_predictions_candles: {self._fit_live_predictions_candles}")
- if self._optuna_hyperopt:
- logger.info(f" label_period_candles: {label_period_candles} (initial value)")
+ horizon_warning_messages: set[str] = set()
+ for pair in self.pairs:
+ params = self.get_optuna_params(pair, _OPTUNA_NAMESPACES.label)
+ horizon = self._label_horizon_candles(pair, warning_messages=horizon_warning_messages)
logger.info(
- f" label_natr_multiplier: {format_number(label_natr_multiplier)} (initial value)"
+ f" {pair}: label_period_candles={params.get('label_period_candles')}, "
+ f"label_natr_multiplier={format_number(params.get('label_natr_multiplier'))}, "
+ f"label_horizon_candles={horizon} (resolved at startup)"
)
- logger.info(f" label_frequency_candles: {self._label_frequency_candles}")
- logger.info(f" min_label_period_candles: {self._min_label_period_candles}")
- logger.info(f" max_label_period_candles: {self._max_label_period_candles}")
- logger.info(
- f" min_label_natr_multiplier: {format_number(self._min_label_natr_multiplier)}"
- )
- logger.info(
- f" max_label_natr_multiplier: {format_number(self._max_label_natr_multiplier)}"
- )
+ label_frequency_candles = self._label_frequency_candles
if self._optuna_hyperopt:
- logger.info("Label Parameters:")
- for pair in self.pairs:
- params = self._optuna_label_params.get(pair, {})
- if params:
- logger.info(
- f" {pair}: label_period_candles={params.get('label_period_candles')}, "
- f"label_natr_multiplier={format_number(params.get('label_natr_multiplier'))}, "
- f"label_horizon_candles={self._label_horizon_candles(pair)}"
- )
- else:
- logger.info("Label Parameters:")
- logger.info(f" label_period_candles: {label_period_candles}")
- logger.info(f" label_natr_multiplier: {format_number(label_natr_multiplier)}")
- logger.info(f" label_horizon_candles: {self._label_horizon_candles()}")
+ logger.info("Label Hyperparameters:")
+ logger.info(f" label_frequency_candles: {label_frequency_candles}")
+ logger.info(f" min_label_period_candles: {self._min_label_period_candles}")
+ logger.info(f" max_label_period_candles: {self._max_label_period_candles}")
+ logger.info(
+ f" min_label_natr_multiplier: {format_number(self._min_label_natr_multiplier)}"
+ )
+ logger.info(
+ f" max_label_natr_multiplier: {format_number(self._max_label_natr_multiplier)}"
+ )
scaler = self.ft_params.get("scaler", QuickAdapterRegressorV3.SCALER_DEFAULT)
feature_range = self.ft_params.get("range", QuickAdapterRegressorV3.RANGE_DEFAULT)
- logger.info("Feature Parameters:")
- logger.info(f" scaler: {scaler}")
- logger.info(
+ # Preserve range resolution independently of whether the selected scaler uses it.
+ formatted_feature_range = (
f" range: ({format_number(feature_range[0])}, {format_number(feature_range[1])})"
)
+ logger.info("Feature Parameters:")
+ logger.info(f" scaler: {scaler}")
+ if scaler == QuickAdapterRegressorV3.SCALER_DEFAULT: # "minmax"
+ logger.info(formatted_feature_range)
logger.info("=" * 60)
from freqtrade.util import FtPrecise
from LabelTransformer import (
COMBINED_AGGREGATIONS,
+ FILL_BANDWIDTHS,
FILL_METHODS,
SMOOTHING_METHOD_MODES,
SMOOTHING_METHODS,
get_ma_fn,
get_reversal_confirmation_config,
get_smoothing_kernel_half_width,
+ get_take_profit_stage_natr_multiplier_fractions,
is_finite_number,
label_known_at_lookahead_column_name,
label_weight_column_name,
_ANNOTATION_LINE_OFFSET_CANDLES: Final[int] = 10
def version(self) -> str:
- return "3.13.0-rc.13"
+ return "3.13.0-rc.14"
timeframe = "5m"
timeframe_minutes = timeframe_to_minutes(timeframe)
position_adjustment_enable = True
- # {stage: (natr_multiplier_fraction, stake_percent, color)}
- partial_exit_stages: ClassVar[dict[int, tuple[float, float, str]]] = {
- 0: (0.4858, 0.4, "lime"),
- 1: (0.6180, 0.3, "yellow"),
- 2: (0.7640, 0.2, "coral"),
+ # {stage: (stake_percent, color)}. The reciprocal-extension fractions are
+ # not literals here: they resolve from the configured
+ # ``exit_pricing.take_profit_stage_natr_multiplier_fraction_series`` ladder,
+ # which is their single source of truth. The final rung stays the literal.
+ partial_exit_stages: ClassVar[dict[int, tuple[float, str]]] = {
+ 0: (0.4, "lime"),
+ 1: (0.3, "yellow"),
+ 2: (0.2, "coral"),
}
# (natr_multiplier_fraction, stake_percent, color)
# Final full-exit stage, derived from the configured partial exits.
_FINAL_EXIT_STAGE: Final[int] = max(partial_exit_stages.keys(), default=-1) + 1
+ # ``get_trade_exit_stage`` derives a stage from the number of filled take-profit
+ # exits, so every stage it can reach must exist here. A gap would price a
+ # missing stage at the final rung instead of arming it.
+ assert sorted(partial_exit_stages) == list(range(len(partial_exit_stages)))
+
_TAKE_PROFIT_ORDER_TAG_PREFIX: Final[str] = "take_profit_"
_FINAL_TAKE_PROFIT_STATE_KEY: Final[str] = "final_take_profit_state"
_FINAL_TAKE_PROFIT_STATE_VERSION: Final[int] = 3
def final_take_profit_retracement_fraction(self) -> float:
return float(self.exit_pricing["final_take_profit_retracement_fraction"])
+ @property
+ def take_profit_stage_natr_multiplier_fraction_series(self) -> str:
+ return str(self.exit_pricing["take_profit_stage_natr_multiplier_fraction_series"])
+
+ @cached_property
+ def partial_exit_stage_natr_multiplier_fractions(self) -> dict[int, float]:
+ """Reciprocal-extension fractions of the full take-profit distance, by stage."""
+ fractions = get_take_profit_stage_natr_multiplier_fractions(
+ self.take_profit_stage_natr_multiplier_fraction_series,
+ len(QuickAdapterV3.partial_exit_stages),
+ )
+ return dict(zip(sorted(QuickAdapterV3.partial_exit_stages), fractions, strict=True))
+
+ def get_exit_stage_natr_multiplier_fraction(self, exit_stage: int) -> float:
+ """Return a partial reciprocal-extension fraction or the final fraction ``1.0``."""
+ fractions = self.partial_exit_stage_natr_multiplier_fractions
+ if exit_stage in fractions:
+ return fractions[exit_stage]
+ return QuickAdapterV3._FINAL_EXIT_STAGE_PARAMS[0]
+
@cached_property
def reversal_confirmation(self) -> dict[str, int | float]:
return get_reversal_confirmation_config(self.config.get("reversal_confirmation"), logger)
self._log_strategy_configuration()
def _log_strategy_configuration(self) -> None:
+ label_weighting = self.label_weighting
+ label_smoothing = self.label_smoothing
+ reversal_confirmation = self.reversal_confirmation
+ trade_natr_method = self.trade_natr_method
+ final_take_profit_retracement_fraction = self.final_take_profit_retracement_fraction
+ fraction_series = self.take_profit_stage_natr_multiplier_fraction_series
+ partial_exit_stage_fractions = self.partial_exit_stage_natr_multiplier_fractions
+ protections = self.protections
+
logger.info("=" * 60)
logger.info("QuickAdapter Strategy Configuration")
logger.info("=" * 60)
- label_weighting = self.label_weighting
- label_smoothing = self.label_smoothing
for label_col in LABEL_COLUMNS:
logger.info(f"Label [{label_col}]:")
)
logger.info(" Weighting:")
logger.info(f" strategy: {col_weighting['strategy']}")
- logger.info(
- f" metric_coefficients: {format_dict(col_weighting['metric_coefficients'], style=_FORMAT_STYLE_DICT)}"
- )
- logger.info(f" aggregation: {col_weighting['aggregation']}")
- if col_weighting["aggregation"] == COMBINED_AGGREGATIONS[5]: # "softmax"
- logger.info(
- f" softmax_temperature: {format_number(col_weighting['softmax_temperature'])}"
- )
- fill_method = col_weighting["fill_method"]
- logger.info(f" fill_method: {fill_method}")
- if fill_method in (
- QuickAdapterV3._FILL_EPSILON,
- QuickAdapterV3._FILL_EPSILON_GAUSSIAN,
- ):
- logger.info(f" fill_epsilon: {format_number(col_weighting['fill_epsilon'])}")
- logger.info(f" fill_epsilon_baseline: {col_weighting['fill_epsilon_baseline']}")
- if fill_method in (
- QuickAdapterV3._FILL_GAUSSIAN,
- QuickAdapterV3._FILL_EPSILON_GAUSSIAN,
- ):
- logger.info(
- f" fill_sigma_candles: {format_number(col_weighting['fill_sigma_candles'])}"
- )
+ if col_weighting["strategy"] != QuickAdapterV3._WEIGHT_NONE:
+ if col_weighting["strategy"] == WEIGHT_STRATEGIES[8]: # "combined"
+ logger.info(
+ f" metric_coefficients: {format_dict(col_weighting['metric_coefficients'], style=_FORMAT_STYLE_DICT)}"
+ )
+ logger.info(f" aggregation: {col_weighting['aggregation']}")
+ if col_weighting["aggregation"] == COMBINED_AGGREGATIONS[5]: # "softmax"
+ logger.info(
+ f" softmax_temperature: {format_number(col_weighting['softmax_temperature'])}"
+ )
+ fill_method = col_weighting["fill_method"]
+ logger.info(f" fill_method: {fill_method}")
+ if fill_method in (
+ QuickAdapterV3._FILL_EPSILON,
+ QuickAdapterV3._FILL_EPSILON_GAUSSIAN,
+ ):
+ logger.info(f" fill_epsilon: {format_number(col_weighting['fill_epsilon'])}")
+ logger.info(
+ f" fill_epsilon_baseline: {col_weighting['fill_epsilon_baseline']}"
+ )
+ if fill_method in (
+ QuickAdapterV3._FILL_GAUSSIAN,
+ QuickAdapterV3._FILL_EPSILON_GAUSSIAN,
+ ):
+ logger.info(
+ f" fill_sigma_candles: {format_number(col_weighting['fill_sigma_candles'])}"
+ )
+ logger.info(f" fill_bandwidth: {col_weighting['fill_bandwidth']}")
+ if col_weighting["fill_bandwidth"] == FILL_BANDWIDTHS[1]: # "knn"
+ logger.info(
+ f" fill_sigma_min_candles: {format_number(col_weighting['fill_sigma_min_candles'])}"
+ )
+ logger.info(
+ f" fill_bandwidth_neighbors: {col_weighting['fill_bandwidth_neighbors']}"
+ )
+ logger.info(
+ f" fill_bandwidth_alpha: {format_number(col_weighting['fill_bandwidth_alpha'])}"
+ )
+ logger.info(f" support_policy: {col_weighting['support_policy']}")
logger.info(
- f" fill_sigma_min_candles: {format_number(col_weighting['fill_sigma_min_candles'])}"
+ f" min_pivot_equivalent_count: {col_weighting['min_pivot_equivalent_count']}"
)
- logger.info(f" fill_bandwidth: {col_weighting['fill_bandwidth']}")
logger.info(
- f" fill_bandwidth_neighbors: {col_weighting['fill_bandwidth_neighbors']}"
+ f" min_positive_label_weight_fraction: {format_number(col_weighting['min_positive_label_weight_fraction'])}"
)
logger.info(
- f" fill_bandwidth_alpha: {format_number(col_weighting['fill_bandwidth_alpha'])}"
+ f" min_effective_sample_size: {format_number(col_weighting['min_effective_sample_size'])}"
)
- logger.info(f" support_policy: {col_weighting['support_policy']}")
- logger.info(
- f" min_pivot_equivalent_count: {col_weighting['min_pivot_equivalent_count']}"
- )
- logger.info(
- f" min_positive_label_weight_fraction: {format_number(col_weighting['min_positive_label_weight_fraction'])}"
- )
- logger.info(
- f" min_effective_sample_size: {format_number(col_weighting['min_effective_sample_size'])}"
- )
col_smoothing = get_label_column_config(
label_col, label_smoothing["default"], label_smoothing["columns"]
)
logger.info(" Smoothing:")
- logger.info(f" method: {col_smoothing['method']}")
- logger.info(f" window_candles: {col_smoothing['window_candles']}")
- logger.info(f" beta: {format_number(col_smoothing['beta'])}")
- logger.info(f" polyorder: {col_smoothing['polyorder']}")
- logger.info(f" mode: {col_smoothing['mode']}")
- logger.info(f" sigma: {format_number(col_smoothing['sigma'])}")
-
method = col_smoothing["method"]
+ logger.info(f" method: {method}")
+ if method != SMOOTHING_METHODS[0]: # "none"
+ logger.info(f" window_candles: {col_smoothing['window_candles']}")
+ if method in SMOOTHING_METHODS[2:4]: # Kaiser families
+ logger.info(f" beta: {format_number(col_smoothing['beta'])}")
+ if method == QuickAdapterV3._SMOOTHING_SAVGOL:
+ logger.info(f" polyorder: {col_smoothing['polyorder']}")
+ if method in SMOOTHING_METHOD_MODES:
+ logger.info(f" mode: {col_smoothing['mode']}")
+ if method == SMOOTHING_METHODS[8]: # "gaussian_filter1d"
+ logger.info(f" sigma: {format_number(col_smoothing['sigma'])}")
+
if col_weighting["strategy"] != QuickAdapterV3._WEIGHT_NONE and (
method == QuickAdapterV3._SMOOTHING_SMM
or (method == QuickAdapterV3._SMOOTHING_SAVGOL and col_smoothing["polyorder"] >= 2)
logger.info("Reversal Confirmation:")
logger.info(
- f" lookback_period_candles: {self.reversal_confirmation['lookback_period_candles']}"
+ f" lookback_period_candles: {reversal_confirmation['lookback_period_candles']}"
)
+ logger.info(f" decay_fraction: {format_number(reversal_confirmation['decay_fraction'])}")
logger.info(
- f" decay_fraction: {format_number(self.reversal_confirmation['decay_fraction'])}"
+ f" min_natr_multiplier_fraction: {format_number(reversal_confirmation['min_natr_multiplier_fraction'])}"
)
logger.info(
- f" min_natr_multiplier_fraction: {format_number(self.reversal_confirmation['min_natr_multiplier_fraction'])}"
- )
- logger.info(
- f" max_natr_multiplier_fraction: {format_number(self.reversal_confirmation['max_natr_multiplier_fraction'])}"
+ f" max_natr_multiplier_fraction: {format_number(reversal_confirmation['max_natr_multiplier_fraction'])}"
)
logger.info("Exit Pricing:")
- logger.info(f" trade_natr_method: {self.trade_natr_method}")
+ logger.info(f" trade_natr_method: {trade_natr_method}")
logger.info(
" final_take_profit_retracement_fraction: "
- f"{format_number(self.final_take_profit_retracement_fraction)}"
+ f"{format_number(final_take_profit_retracement_fraction)}"
)
+ logger.info(f" take_profit_stage_natr_multiplier_fraction_series: {fraction_series}")
logger.info("Custom Stoploss:")
logger.info(
logger.info("Partial Take-Profit Stages:")
for stage, (
- natr_multiplier_fraction,
stake_percent,
color,
) in QuickAdapterV3.partial_exit_stages.items():
logger.info(
- f" stage {stage}: natr_multiplier_fraction={format_number(natr_multiplier_fraction)}, stake_percent={format_number(stake_percent)}, color={color}"
+ f" stage {stage}: natr_multiplier_fraction="
+ f"{format_number(partial_exit_stage_fractions[stage])}, "
+ f"stake_percent={format_number(stake_percent)}, color={color}"
)
+ logger.info("Final Exit:")
logger.info(
- f"Final Exit: natr_multiplier_fraction={format_number(QuickAdapterV3._FINAL_EXIT_STAGE_PARAMS[0])}, stake_percent={format_number(QuickAdapterV3._FINAL_EXIT_STAGE_PARAMS[1])}, color={QuickAdapterV3._FINAL_EXIT_STAGE_PARAMS[2]}"
+ f" natr_multiplier_fraction: {format_number(QuickAdapterV3._FINAL_EXIT_STAGE_PARAMS[0])}"
)
+ logger.info(f" stake_percent: {format_number(QuickAdapterV3._FINAL_EXIT_STAGE_PARAMS[1])}")
+ logger.info(f" color: {QuickAdapterV3._FINAL_EXIT_STAGE_PARAMS[2]}")
logger.info("Protections:")
- if self.protections:
- for protection in self.protections:
+ if protections:
+ for protection in protections:
method = protection.get("method", "Unknown")
protection_params = {k: v for k, v in protection.items() if k != "method"}
logger.info(
def get_take_profit_target(
self, df: DataFrame, trade: Trade, exit_stage: int
) -> tuple[float, float] | None:
- natr_multiplier_fraction = (
- QuickAdapterV3.partial_exit_stages[exit_stage][0]
- if exit_stage in QuickAdapterV3.partial_exit_stages
- else QuickAdapterV3._FINAL_EXIT_STAGE_PARAMS[0]
- )
+ natr_multiplier_fraction = self.get_exit_stage_natr_multiplier_fraction(exit_stage)
take_profit_distance = self.get_take_profit_distance(df, trade, natr_multiplier_fraction)
if not is_finite_number(take_profit_distance) or take_profit_distance <= 0:
return None
),
)
if trade_partial_exit:
- trade_stake_percent = QuickAdapterV3.partial_exit_stages[trade_exit_stage][1]
+ trade_stake_percent = QuickAdapterV3.partial_exit_stages[trade_exit_stage][0]
trade_partial_stake_amount = trade_stake_percent * trade.stake_amount
tag = (
f"{QuickAdapterV3._TAKE_PROFIT_ORDER_TAG_PREFIX}"
"end": end_date,
"y_start": partial_take_profit_price,
"y_end": partial_take_profit_price,
- "color": QuickAdapterV3.partial_exit_stages[take_profit_stage][2],
+ "color": QuickAdapterV3.partial_exit_stages[take_profit_stage][1],
"line_style": "solid",
"width": 1,
"label": f"Partial Take-Profit Stage {take_profit_stage}",
repositories / freqai-strategies.git / commitdiff
