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Extract hyperopt_defaults_serializer to hyperopt_tools
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@ -29,7 +29,7 @@ from freqtrade.optimize.backtesting import Backtesting
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from freqtrade.optimize.hyperopt_auto import HyperOptAuto
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from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F401
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from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F401
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from freqtrade.optimize.hyperopt_tools import HyperoptTools
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from freqtrade.optimize.hyperopt_tools import HyperoptTools, hyperopt_parser
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from freqtrade.optimize.optimize_reports import generate_strategy_stats
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from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver, HyperOptResolver
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@ -163,13 +163,9 @@ class Hyperopt:
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While not a valid json object - this allows appending easily.
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:param epoch: result dictionary for this epoch.
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"""
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def default_parser(x):
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if isinstance(x, np.integer):
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return int(x)
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return str(x)
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epoch[FTHYPT_FILEVERSION] = 2
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with self.results_file.open('a') as f:
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rapidjson.dump(epoch, f, default=default_parser,
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rapidjson.dump(epoch, f, default=hyperopt_parser,
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number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN)
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f.write("\n")
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@ -2,9 +2,11 @@
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import io
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import logging
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from copy import deepcopy
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Any, Dict, List, Optional
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import numpy as np
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import rapidjson
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import tabulate
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from colorama import Fore, Style
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@ -20,6 +22,12 @@ logger = logging.getLogger(__name__)
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NON_OPT_PARAM_APPENDIX = " # value loaded from strategy"
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def hyperopt_parser(x):
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if isinstance(x, np.integer):
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return int(x)
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return str(x)
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class HyperoptTools():
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@staticmethod
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@ -48,9 +56,12 @@ class HyperoptTools():
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'strategy_name': strategy_name,
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'params': final_params,
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'ft_stratparam_v': 1,
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'export_time': datetime.now(timezone.utc),
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}
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logger.info(f"Dumping parameters to {filename}")
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rapidjson.dump(final_params, filename.open('w'), indent=2)
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rapidjson.dump(final_params, filename.open('w'), indent=2,
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default=hyperopt_parser, number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN
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)
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@staticmethod
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def try_export_params(config: Dict[str, Any], strategy_name: str, val: Dict):
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