extract result-printing from hyperopt class
This commit is contained in:
Matthias
parent
b05de6d468
commit
76ca3c219f
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@ -17,7 +17,7 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
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"""
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List hyperopt epochs previously evaluated
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"""
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from freqtrade.optimize.hyperopt import Hyperopt
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from freqtrade.optimize.hyperopt_tools import HyperoptTools
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config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
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@ -47,7 +47,7 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
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config.get('hyperoptexportfilename'))
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# Previous evaluations
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epochs = Hyperopt.load_previous_results(results_file)
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epochs = HyperoptTools.load_previous_results(results_file)
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total_epochs = len(epochs)
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epochs = hyperopt_filter_epochs(epochs, filteroptions)
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@ -57,18 +57,19 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
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if not export_csv:
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try:
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print(Hyperopt.get_result_table(config, epochs, total_epochs,
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not filteroptions['only_best'], print_colorized, 0))
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print(HyperoptTools.get_result_table(config, epochs, total_epochs,
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not filteroptions['only_best'],
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print_colorized, 0))
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except KeyboardInterrupt:
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print('User interrupted..')
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if epochs and not no_details:
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sorted_epochs = sorted(epochs, key=itemgetter('loss'))
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results = sorted_epochs[0]
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Hyperopt.print_epoch_details(results, total_epochs, print_json, no_header)
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HyperoptTools.print_epoch_details(results, total_epochs, print_json, no_header)
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if epochs and export_csv:
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Hyperopt.export_csv_file(
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HyperoptTools.export_csv_file(
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config, epochs, total_epochs, not filteroptions['only_best'], export_csv
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)
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@ -77,7 +78,7 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
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"""
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Show details of a hyperopt epoch previously evaluated
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"""
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from freqtrade.optimize.hyperopt import Hyperopt
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from freqtrade.optimize.hyperopt_tools import HyperoptTools
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config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
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@ -105,7 +106,7 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
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}
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# Previous evaluations
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epochs = Hyperopt.load_previous_results(results_file)
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epochs = HyperoptTools.load_previous_results(results_file)
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total_epochs = len(epochs)
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epochs = hyperopt_filter_epochs(epochs, filteroptions)
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@ -124,8 +125,8 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
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if epochs:
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val = epochs[n]
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Hyperopt.print_epoch_details(val, total_epochs, print_json, no_header,
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header_str="Epoch details")
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HyperoptTools.print_epoch_details(val, total_epochs, print_json, no_header,
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header_str="Epoch details")
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def hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
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@ -4,36 +4,31 @@
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This module contains the hyperopt logic
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"""
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import io
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import locale
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import logging
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import random
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import warnings
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from collections import OrderedDict
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from datetime import datetime
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from math import ceil
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from operator import itemgetter
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from pathlib import Path
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from pprint import pformat
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from typing import Any, Dict, List, Optional
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import progressbar
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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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from colorama import init as colorama_init
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from joblib import Parallel, cpu_count, delayed, dump, load, wrap_non_picklable_objects
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from pandas import DataFrame, isna, json_normalize
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from pandas import DataFrame
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from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
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from freqtrade.data.converter import trim_dataframe
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from freqtrade.data.history import get_timerange
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from freqtrade.exceptions import OperationalException
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from freqtrade.misc import file_dump_json, plural, round_dict
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from freqtrade.misc import file_dump_json, plural
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from freqtrade.optimize.backtesting import Backtesting
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# Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
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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.resolvers.hyperopt_resolver import HyperOptLossResolver, HyperOptResolver
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from freqtrade.strategy import IStrategy
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@ -169,15 +164,6 @@ class Hyperopt:
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file_dump_json(latest_filename, {'latest_hyperopt': str(self.results_file.name)},
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log=False)
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@staticmethod
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def _read_results(results_file: Path) -> List:
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"""
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Read hyperopt results from file
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"""
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logger.info("Reading epochs from '%s'", results_file)
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data = load(results_file)
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return data
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def _get_params_details(self, params: Dict) -> Dict:
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"""
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Return the params for each space
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@ -200,102 +186,16 @@ class Hyperopt:
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return result
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@staticmethod
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def print_epoch_details(results, total_epochs: int, print_json: bool,
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no_header: bool = False, header_str: str = None) -> None:
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"""
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Display details of the hyperopt result
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"""
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params = results.get('params_details', {})
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# Default header string
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if header_str is None:
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header_str = "Best result"
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if not no_header:
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explanation_str = Hyperopt._format_explanation_string(results, total_epochs)
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print(f"\n{header_str}:\n\n{explanation_str}\n")
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if print_json:
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result_dict: Dict = {}
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for s in ['buy', 'sell', 'roi', 'stoploss', 'trailing']:
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Hyperopt._params_update_for_json(result_dict, params, s)
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print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
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else:
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Hyperopt._params_pretty_print(params, 'buy', "Buy hyperspace params:")
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Hyperopt._params_pretty_print(params, 'sell', "Sell hyperspace params:")
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Hyperopt._params_pretty_print(params, 'roi', "ROI table:")
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Hyperopt._params_pretty_print(params, 'stoploss', "Stoploss:")
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Hyperopt._params_pretty_print(params, 'trailing', "Trailing stop:")
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@staticmethod
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def _params_update_for_json(result_dict, params, space: str) -> None:
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if space in params:
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space_params = Hyperopt._space_params(params, space)
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if space in ['buy', 'sell']:
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result_dict.setdefault('params', {}).update(space_params)
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elif space == 'roi':
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# TODO: get rid of OrderedDict when support for python 3.6 will be
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# dropped (dicts keep the order as the language feature)
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# Convert keys in min_roi dict to strings because
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# rapidjson cannot dump dicts with integer keys...
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# OrderedDict is used to keep the numeric order of the items
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# in the dict.
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result_dict['minimal_roi'] = OrderedDict(
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(str(k), v) for k, v in space_params.items()
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)
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else: # 'stoploss', 'trailing'
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result_dict.update(space_params)
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@staticmethod
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def _params_pretty_print(params, space: str, header: str) -> None:
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if space in params:
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space_params = Hyperopt._space_params(params, space, 5)
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params_result = f"\n# {header}\n"
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if space == 'stoploss':
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params_result += f"stoploss = {space_params.get('stoploss')}"
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elif space == 'roi':
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# TODO: get rid of OrderedDict when support for python 3.6 will be
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# dropped (dicts keep the order as the language feature)
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minimal_roi_result = rapidjson.dumps(
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OrderedDict(
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(str(k), v) for k, v in space_params.items()
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),
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default=str, indent=4, number_mode=rapidjson.NM_NATIVE)
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params_result += f"minimal_roi = {minimal_roi_result}"
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elif space == 'trailing':
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for k, v in space_params.items():
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params_result += f'{k} = {v}\n'
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else:
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params_result += f"{space}_params = {pformat(space_params, indent=4)}"
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params_result = params_result.replace("}", "\n}").replace("{", "{\n ")
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params_result = params_result.replace("\n", "\n ")
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print(params_result)
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@staticmethod
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def _space_params(params, space: str, r: int = None) -> Dict:
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d = params[space]
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# Round floats to `r` digits after the decimal point if requested
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return round_dict(d, r) if r else d
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@staticmethod
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def is_best_loss(results, current_best_loss: float) -> bool:
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return results['loss'] < current_best_loss
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def print_results(self, results) -> None:
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"""
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Log results if it is better than any previous evaluation
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TODO: this should be moved to HyperoptTools too
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"""
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is_best = results['is_best']
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if self.print_all or is_best:
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print(
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self.get_result_table(
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HyperoptTools.get_result_table(
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self.config, results, self.total_epochs,
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self.print_all, self.print_colorized,
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self.hyperopt_table_header
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@ -303,166 +203,6 @@ class Hyperopt:
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)
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self.hyperopt_table_header = 2
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@staticmethod
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def _format_explanation_string(results, total_epochs) -> str:
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return (("*" if results['is_initial_point'] else " ") +
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f"{results['current_epoch']:5d}/{total_epochs}: " +
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f"{results['results_explanation']} " +
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f"Objective: {results['loss']:.5f}")
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@staticmethod
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def get_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
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print_colorized: bool, remove_header: int) -> str:
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"""
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Log result table
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"""
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if not results:
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return ''
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tabulate.PRESERVE_WHITESPACE = True
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trials = json_normalize(results, max_level=1)
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trials['Best'] = ''
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if 'results_metrics.winsdrawslosses' not in trials.columns:
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# Ensure compatibility with older versions of hyperopt results
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trials['results_metrics.winsdrawslosses'] = 'N/A'
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trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
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'results_metrics.winsdrawslosses',
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'results_metrics.avg_profit', 'results_metrics.total_profit',
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'results_metrics.profit', 'results_metrics.duration',
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'loss', 'is_initial_point', 'is_best']]
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trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
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'Total profit', 'Profit', 'Avg duration', 'Objective',
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'is_initial_point', 'is_best']
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trials['is_profit'] = False
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trials.loc[trials['is_initial_point'], 'Best'] = '* '
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trials.loc[trials['is_best'], 'Best'] = 'Best'
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trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
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trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
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trials['Trades'] = trials['Trades'].astype(str)
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trials['Epoch'] = trials['Epoch'].apply(
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lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
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)
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trials['Avg profit'] = trials['Avg profit'].apply(
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lambda x: '{:,.2f}%'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
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)
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trials['Avg duration'] = trials['Avg duration'].apply(
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lambda x: '{:,.1f} m'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
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)
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trials['Objective'] = trials['Objective'].apply(
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lambda x: '{:,.5f}'.format(x).rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
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)
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trials['Profit'] = trials.apply(
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lambda x: '{:,.8f} {} {}'.format(
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x['Total profit'], config['stake_currency'],
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'({:,.2f}%)'.format(x['Profit']).rjust(10, ' ')
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).rjust(25+len(config['stake_currency']))
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if x['Total profit'] != 0.0 else '--'.rjust(25+len(config['stake_currency'])),
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axis=1
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)
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trials = trials.drop(columns=['Total profit'])
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if print_colorized:
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for i in range(len(trials)):
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if trials.loc[i]['is_profit']:
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for j in range(len(trials.loc[i])-3):
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trials.iat[i, j] = "{}{}{}".format(Fore.GREEN,
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str(trials.loc[i][j]), Fore.RESET)
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if trials.loc[i]['is_best'] and highlight_best:
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for j in range(len(trials.loc[i])-3):
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trials.iat[i, j] = "{}{}{}".format(Style.BRIGHT,
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str(trials.loc[i][j]), Style.RESET_ALL)
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trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
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if remove_header > 0:
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table = tabulate.tabulate(
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trials.to_dict(orient='list'), tablefmt='orgtbl',
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headers='keys', stralign="right"
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)
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table = table.split("\n", remove_header)[remove_header]
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elif remove_header < 0:
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table = tabulate.tabulate(
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trials.to_dict(orient='list'), tablefmt='psql',
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headers='keys', stralign="right"
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)
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table = "\n".join(table.split("\n")[0:remove_header])
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else:
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table = tabulate.tabulate(
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trials.to_dict(orient='list'), tablefmt='psql',
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headers='keys', stralign="right"
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)
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return table
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@staticmethod
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def export_csv_file(config: dict, results: list, total_epochs: int, highlight_best: bool,
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csv_file: str) -> None:
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"""
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Log result to csv-file
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"""
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if not results:
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return
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# Verification for overwrite
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if Path(csv_file).is_file():
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logger.error(f"CSV file already exists: {csv_file}")
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return
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try:
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io.open(csv_file, 'w+').close()
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except IOError:
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logger.error(f"Failed to create CSV file: {csv_file}")
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return
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trials = json_normalize(results, max_level=1)
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trials['Best'] = ''
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trials['Stake currency'] = config['stake_currency']
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base_metrics = ['Best', 'current_epoch', 'results_metrics.trade_count',
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'results_metrics.avg_profit', 'results_metrics.median_profit',
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'results_metrics.total_profit',
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'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
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'loss', 'is_initial_point', 'is_best']
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param_metrics = [("params_dict."+param) for param in results[0]['params_dict'].keys()]
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trials = trials[base_metrics + param_metrics]
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base_columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Median profit', 'Total profit',
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'Stake currency', 'Profit', 'Avg duration', 'Objective',
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'is_initial_point', 'is_best']
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param_columns = list(results[0]['params_dict'].keys())
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trials.columns = base_columns + param_columns
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trials['is_profit'] = False
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trials.loc[trials['is_initial_point'], 'Best'] = '*'
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trials.loc[trials['is_best'], 'Best'] = 'Best'
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trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
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trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
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trials['Epoch'] = trials['Epoch'].astype(str)
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trials['Trades'] = trials['Trades'].astype(str)
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trials['Total profit'] = trials['Total profit'].apply(
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lambda x: '{:,.8f}'.format(x) if x != 0.0 else ""
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)
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trials['Profit'] = trials['Profit'].apply(
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lambda x: '{:,.2f}'.format(x) if not isna(x) else ""
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)
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trials['Avg profit'] = trials['Avg profit'].apply(
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lambda x: '{:,.2f}%'.format(x) if not isna(x) else ""
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)
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trials['Avg duration'] = trials['Avg duration'].apply(
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lambda x: '{:,.1f} m'.format(x) if not isna(x) else ""
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)
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trials['Objective'] = trials['Objective'].apply(
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lambda x: '{:,.5f}'.format(x) if x != 100000 else ""
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)
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trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
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trials.to_csv(csv_file, index=False, header=True, mode='w', encoding='UTF-8')
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logger.info(f"CSV file created: {csv_file}")
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def has_space(self, space: str) -> bool:
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"""
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Tell if the space value is contained in the configuration
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@ -626,22 +366,6 @@ class Hyperopt:
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return parallel(delayed(
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wrap_non_picklable_objects(self.generate_optimizer))(v, i) for v in asked)
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@staticmethod
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def load_previous_results(results_file: Path) -> List:
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"""
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Load data for epochs from the file if we have one
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"""
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epochs: List = []
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if results_file.is_file() and results_file.stat().st_size > 0:
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epochs = Hyperopt._read_results(results_file)
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# Detection of some old format, without 'is_best' field saved
|
||||
if epochs[0].get('is_best') is None:
|
||||
raise OperationalException(
|
||||
"The file with Hyperopt results is incompatible with this version "
|
||||
"of Freqtrade and cannot be loaded.")
|
||||
logger.info(f"Loaded {len(epochs)} previous evaluations from disk.")
|
||||
return epochs
|
||||
|
||||
def _set_random_state(self, random_state: Optional[int]) -> int:
|
||||
return random_state or random.randint(1, 2**16 - 1)
|
||||
|
||||
|
|
@ -734,7 +458,7 @@ class Hyperopt:
|
|||
|
||||
logger.debug(f"Optimizer epoch evaluated: {val}")
|
||||
|
||||
is_best = self.is_best_loss(val, self.current_best_loss)
|
||||
is_best = HyperoptTools.is_best_loss(val, self.current_best_loss)
|
||||
# This value is assigned here and not in the optimization method
|
||||
# to keep proper order in the list of results. That's because
|
||||
# evaluations can take different time. Here they are aligned in the
|
||||
|
|
@ -762,7 +486,7 @@ class Hyperopt:
|
|||
if self.epochs:
|
||||
sorted_epochs = sorted(self.epochs, key=itemgetter('loss'))
|
||||
best_epoch = sorted_epochs[0]
|
||||
self.print_epoch_details(best_epoch, self.total_epochs, self.print_json)
|
||||
HyperoptTools.print_epoch_details(best_epoch, self.total_epochs, self.print_json)
|
||||
else:
|
||||
# This is printed when Ctrl+C is pressed quickly, before first epochs have
|
||||
# a chance to be evaluated.
|
||||
|
|
|
|||
|
|
@ -0,0 +1,294 @@
|
|||
|
||||
import io
|
||||
import logging
|
||||
from collections import OrderedDict
|
||||
from pathlib import Path
|
||||
from pprint import pformat
|
||||
from typing import Dict, List
|
||||
|
||||
import rapidjson
|
||||
import tabulate
|
||||
from colorama import Fore, Style
|
||||
from joblib import load
|
||||
from pandas import isna, json_normalize
|
||||
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.misc import round_dict
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class HyperoptTools():
|
||||
|
||||
@staticmethod
|
||||
def _read_results(results_file: Path) -> List:
|
||||
"""
|
||||
Read hyperopt results from file
|
||||
"""
|
||||
logger.info("Reading epochs from '%s'", results_file)
|
||||
data = load(results_file)
|
||||
return data
|
||||
|
||||
@staticmethod
|
||||
def load_previous_results(results_file: Path) -> List:
|
||||
"""
|
||||
Load data for epochs from the file if we have one
|
||||
"""
|
||||
epochs: List = []
|
||||
if results_file.is_file() and results_file.stat().st_size > 0:
|
||||
epochs = HyperoptTools._read_results(results_file)
|
||||
# Detection of some old format, without 'is_best' field saved
|
||||
if epochs[0].get('is_best') is None:
|
||||
raise OperationalException(
|
||||
"The file with HyperoptTools results is incompatible with this version "
|
||||
"of Freqtrade and cannot be loaded.")
|
||||
logger.info(f"Loaded {len(epochs)} previous evaluations from disk.")
|
||||
return epochs
|
||||
|
||||
@staticmethod
|
||||
def print_epoch_details(results, total_epochs: int, print_json: bool,
|
||||
no_header: bool = False, header_str: str = None) -> None:
|
||||
"""
|
||||
Display details of the hyperopt result
|
||||
"""
|
||||
params = results.get('params_details', {})
|
||||
|
||||
# Default header string
|
||||
if header_str is None:
|
||||
header_str = "Best result"
|
||||
|
||||
if not no_header:
|
||||
explanation_str = HyperoptTools._format_explanation_string(results, total_epochs)
|
||||
print(f"\n{header_str}:\n\n{explanation_str}\n")
|
||||
|
||||
if print_json:
|
||||
result_dict: Dict = {}
|
||||
for s in ['buy', 'sell', 'roi', 'stoploss', 'trailing']:
|
||||
HyperoptTools._params_update_for_json(result_dict, params, s)
|
||||
print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
|
||||
|
||||
else:
|
||||
HyperoptTools._params_pretty_print(params, 'buy', "Buy hyperspace params:")
|
||||
HyperoptTools._params_pretty_print(params, 'sell', "Sell hyperspace params:")
|
||||
HyperoptTools._params_pretty_print(params, 'roi', "ROI table:")
|
||||
HyperoptTools._params_pretty_print(params, 'stoploss', "Stoploss:")
|
||||
HyperoptTools._params_pretty_print(params, 'trailing', "Trailing stop:")
|
||||
|
||||
@staticmethod
|
||||
def _params_update_for_json(result_dict, params, space: str) -> None:
|
||||
if space in params:
|
||||
space_params = HyperoptTools._space_params(params, space)
|
||||
if space in ['buy', 'sell']:
|
||||
result_dict.setdefault('params', {}).update(space_params)
|
||||
elif space == 'roi':
|
||||
# TODO: get rid of OrderedDict when support for python 3.6 will be
|
||||
# dropped (dicts keep the order as the language feature)
|
||||
|
||||
# Convert keys in min_roi dict to strings because
|
||||
# rapidjson cannot dump dicts with integer keys...
|
||||
# OrderedDict is used to keep the numeric order of the items
|
||||
# in the dict.
|
||||
result_dict['minimal_roi'] = OrderedDict(
|
||||
(str(k), v) for k, v in space_params.items()
|
||||
)
|
||||
else: # 'stoploss', 'trailing'
|
||||
result_dict.update(space_params)
|
||||
|
||||
@staticmethod
|
||||
def _params_pretty_print(params, space: str, header: str) -> None:
|
||||
if space in params:
|
||||
space_params = HyperoptTools._space_params(params, space, 5)
|
||||
params_result = f"\n# {header}\n"
|
||||
if space == 'stoploss':
|
||||
params_result += f"stoploss = {space_params.get('stoploss')}"
|
||||
elif space == 'roi':
|
||||
# TODO: get rid of OrderedDict when support for python 3.6 will be
|
||||
# dropped (dicts keep the order as the language feature)
|
||||
minimal_roi_result = rapidjson.dumps(
|
||||
OrderedDict(
|
||||
(str(k), v) for k, v in space_params.items()
|
||||
),
|
||||
default=str, indent=4, number_mode=rapidjson.NM_NATIVE)
|
||||
params_result += f"minimal_roi = {minimal_roi_result}"
|
||||
elif space == 'trailing':
|
||||
|
||||
for k, v in space_params.items():
|
||||
params_result += f'{k} = {v}\n'
|
||||
|
||||
else:
|
||||
params_result += f"{space}_params = {pformat(space_params, indent=4)}"
|
||||
params_result = params_result.replace("}", "\n}").replace("{", "{\n ")
|
||||
|
||||
params_result = params_result.replace("\n", "\n ")
|
||||
print(params_result)
|
||||
|
||||
@staticmethod
|
||||
def _space_params(params, space: str, r: int = None) -> Dict:
|
||||
d = params[space]
|
||||
# Round floats to `r` digits after the decimal point if requested
|
||||
return round_dict(d, r) if r else d
|
||||
|
||||
@staticmethod
|
||||
def is_best_loss(results, current_best_loss: float) -> bool:
|
||||
return results['loss'] < current_best_loss
|
||||
|
||||
@staticmethod
|
||||
def _format_explanation_string(results, total_epochs) -> str:
|
||||
return (("*" if results['is_initial_point'] else " ") +
|
||||
f"{results['current_epoch']:5d}/{total_epochs}: " +
|
||||
f"{results['results_explanation']} " +
|
||||
f"Objective: {results['loss']:.5f}")
|
||||
|
||||
@staticmethod
|
||||
def get_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
|
||||
print_colorized: bool, remove_header: int) -> str:
|
||||
"""
|
||||
Log result table
|
||||
"""
|
||||
if not results:
|
||||
return ''
|
||||
|
||||
tabulate.PRESERVE_WHITESPACE = True
|
||||
|
||||
trials = json_normalize(results, max_level=1)
|
||||
trials['Best'] = ''
|
||||
if 'results_metrics.winsdrawslosses' not in trials.columns:
|
||||
# Ensure compatibility with older versions of hyperopt results
|
||||
trials['results_metrics.winsdrawslosses'] = 'N/A'
|
||||
|
||||
trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
|
||||
'results_metrics.winsdrawslosses',
|
||||
'results_metrics.avg_profit', 'results_metrics.total_profit',
|
||||
'results_metrics.profit', 'results_metrics.duration',
|
||||
'loss', 'is_initial_point', 'is_best']]
|
||||
trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
|
||||
'Total profit', 'Profit', 'Avg duration', 'Objective',
|
||||
'is_initial_point', 'is_best']
|
||||
trials['is_profit'] = False
|
||||
trials.loc[trials['is_initial_point'], 'Best'] = '* '
|
||||
trials.loc[trials['is_best'], 'Best'] = 'Best'
|
||||
trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
|
||||
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
|
||||
trials['Trades'] = trials['Trades'].astype(str)
|
||||
|
||||
trials['Epoch'] = trials['Epoch'].apply(
|
||||
lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
|
||||
)
|
||||
trials['Avg profit'] = trials['Avg profit'].apply(
|
||||
lambda x: '{:,.2f}%'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
|
||||
)
|
||||
trials['Avg duration'] = trials['Avg duration'].apply(
|
||||
lambda x: '{:,.1f} m'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
|
||||
)
|
||||
trials['Objective'] = trials['Objective'].apply(
|
||||
lambda x: '{:,.5f}'.format(x).rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
|
||||
)
|
||||
|
||||
trials['Profit'] = trials.apply(
|
||||
lambda x: '{:,.8f} {} {}'.format(
|
||||
x['Total profit'], config['stake_currency'],
|
||||
'({:,.2f}%)'.format(x['Profit']).rjust(10, ' ')
|
||||
).rjust(25+len(config['stake_currency']))
|
||||
if x['Total profit'] != 0.0 else '--'.rjust(25+len(config['stake_currency'])),
|
||||
axis=1
|
||||
)
|
||||
trials = trials.drop(columns=['Total profit'])
|
||||
|
||||
if print_colorized:
|
||||
for i in range(len(trials)):
|
||||
if trials.loc[i]['is_profit']:
|
||||
for j in range(len(trials.loc[i])-3):
|
||||
trials.iat[i, j] = "{}{}{}".format(Fore.GREEN,
|
||||
str(trials.loc[i][j]), Fore.RESET)
|
||||
if trials.loc[i]['is_best'] and highlight_best:
|
||||
for j in range(len(trials.loc[i])-3):
|
||||
trials.iat[i, j] = "{}{}{}".format(Style.BRIGHT,
|
||||
str(trials.loc[i][j]), Style.RESET_ALL)
|
||||
|
||||
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
|
||||
if remove_header > 0:
|
||||
table = tabulate.tabulate(
|
||||
trials.to_dict(orient='list'), tablefmt='orgtbl',
|
||||
headers='keys', stralign="right"
|
||||
)
|
||||
|
||||
table = table.split("\n", remove_header)[remove_header]
|
||||
elif remove_header < 0:
|
||||
table = tabulate.tabulate(
|
||||
trials.to_dict(orient='list'), tablefmt='psql',
|
||||
headers='keys', stralign="right"
|
||||
)
|
||||
table = "\n".join(table.split("\n")[0:remove_header])
|
||||
else:
|
||||
table = tabulate.tabulate(
|
||||
trials.to_dict(orient='list'), tablefmt='psql',
|
||||
headers='keys', stralign="right"
|
||||
)
|
||||
return table
|
||||
|
||||
@staticmethod
|
||||
def export_csv_file(config: dict, results: list, total_epochs: int, highlight_best: bool,
|
||||
csv_file: str) -> None:
|
||||
"""
|
||||
Log result to csv-file
|
||||
"""
|
||||
if not results:
|
||||
return
|
||||
|
||||
# Verification for overwrite
|
||||
if Path(csv_file).is_file():
|
||||
logger.error(f"CSV file already exists: {csv_file}")
|
||||
return
|
||||
|
||||
try:
|
||||
io.open(csv_file, 'w+').close()
|
||||
except IOError:
|
||||
logger.error(f"Failed to create CSV file: {csv_file}")
|
||||
return
|
||||
|
||||
trials = json_normalize(results, max_level=1)
|
||||
trials['Best'] = ''
|
||||
trials['Stake currency'] = config['stake_currency']
|
||||
|
||||
base_metrics = ['Best', 'current_epoch', 'results_metrics.trade_count',
|
||||
'results_metrics.avg_profit', 'results_metrics.median_profit',
|
||||
'results_metrics.total_profit',
|
||||
'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
|
||||
'loss', 'is_initial_point', 'is_best']
|
||||
param_metrics = [("params_dict."+param) for param in results[0]['params_dict'].keys()]
|
||||
trials = trials[base_metrics + param_metrics]
|
||||
|
||||
base_columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Median profit', 'Total profit',
|
||||
'Stake currency', 'Profit', 'Avg duration', 'Objective',
|
||||
'is_initial_point', 'is_best']
|
||||
param_columns = list(results[0]['params_dict'].keys())
|
||||
trials.columns = base_columns + param_columns
|
||||
|
||||
trials['is_profit'] = False
|
||||
trials.loc[trials['is_initial_point'], 'Best'] = '*'
|
||||
trials.loc[trials['is_best'], 'Best'] = 'Best'
|
||||
trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
|
||||
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
|
||||
trials['Epoch'] = trials['Epoch'].astype(str)
|
||||
trials['Trades'] = trials['Trades'].astype(str)
|
||||
|
||||
trials['Total profit'] = trials['Total profit'].apply(
|
||||
lambda x: '{:,.8f}'.format(x) if x != 0.0 else ""
|
||||
)
|
||||
trials['Profit'] = trials['Profit'].apply(
|
||||
lambda x: '{:,.2f}'.format(x) if not isna(x) else ""
|
||||
)
|
||||
trials['Avg profit'] = trials['Avg profit'].apply(
|
||||
lambda x: '{:,.2f}%'.format(x) if not isna(x) else ""
|
||||
)
|
||||
trials['Avg duration'] = trials['Avg duration'].apply(
|
||||
lambda x: '{:,.1f} m'.format(x) if not isna(x) else ""
|
||||
)
|
||||
trials['Objective'] = trials['Objective'].apply(
|
||||
lambda x: '{:,.5f}'.format(x) if x != 100000 else ""
|
||||
)
|
||||
|
||||
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
|
||||
trials.to_csv(csv_file, index=False, header=True, mode='w', encoding='UTF-8')
|
||||
logger.info(f"CSV file created: {csv_file}")
|
||||
|
|
@ -920,7 +920,7 @@ def test_start_test_pairlist(mocker, caplog, tickers, default_conf, capsys):
|
|||
|
||||
def test_hyperopt_list(mocker, capsys, caplog, hyperopt_results):
|
||||
mocker.patch(
|
||||
'freqtrade.optimize.hyperopt.Hyperopt.load_previous_results',
|
||||
'freqtrade.optimize.hyperopt_tools.HyperoptTools.load_previous_results',
|
||||
MagicMock(return_value=hyperopt_results)
|
||||
)
|
||||
|
||||
|
|
@ -1152,7 +1152,7 @@ def test_hyperopt_list(mocker, capsys, caplog, hyperopt_results):
|
|||
|
||||
def test_hyperopt_show(mocker, capsys, hyperopt_results):
|
||||
mocker.patch(
|
||||
'freqtrade.optimize.hyperopt.Hyperopt.load_previous_results',
|
||||
'freqtrade.optimize.hyperopt_tools.HyperoptTools.load_previous_results',
|
||||
MagicMock(return_value=hyperopt_results)
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -16,6 +16,7 @@ from freqtrade.commands.optimize_commands import setup_optimize_configuration, s
|
|||
from freqtrade.data.history import load_data
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.optimize.hyperopt import Hyperopt
|
||||
from freqtrade.optimize.hyperopt_tools import HyperoptTools
|
||||
from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver
|
||||
from freqtrade.state import RunMode
|
||||
from tests.conftest import (get_args, log_has, log_has_re, patch_exchange,
|
||||
|
|
@ -336,9 +337,9 @@ def test_save_results_saves_epochs(mocker, hyperopt, testdatadir, caplog) -> Non
|
|||
|
||||
def test_read_results_returns_epochs(mocker, hyperopt, testdatadir, caplog) -> None:
|
||||
epochs = create_results(mocker, hyperopt, testdatadir)
|
||||
mock_load = mocker.patch('freqtrade.optimize.hyperopt.load', return_value=epochs)
|
||||
mock_load = mocker.patch('freqtrade.optimize.hyperopt_tools.load', return_value=epochs)
|
||||
results_file = testdatadir / 'optimize' / 'ut_results.pickle'
|
||||
hyperopt_epochs = hyperopt._read_results(results_file)
|
||||
hyperopt_epochs = HyperoptTools._read_results(results_file)
|
||||
assert log_has(f"Reading epochs from '{results_file}'", caplog)
|
||||
assert hyperopt_epochs == epochs
|
||||
mock_load.assert_called_once()
|
||||
|
|
@ -346,7 +347,7 @@ def test_read_results_returns_epochs(mocker, hyperopt, testdatadir, caplog) -> N
|
|||
|
||||
def test_load_previous_results(mocker, hyperopt, testdatadir, caplog) -> None:
|
||||
epochs = create_results(mocker, hyperopt, testdatadir)
|
||||
mock_load = mocker.patch('freqtrade.optimize.hyperopt.load', return_value=epochs)
|
||||
mock_load = mocker.patch('freqtrade.optimize.hyperopt_tools.load', return_value=epochs)
|
||||
mocker.patch.object(Path, 'is_file', MagicMock(return_value=True))
|
||||
statmock = MagicMock()
|
||||
statmock.st_size = 5
|
||||
|
|
@ -354,16 +355,16 @@ def test_load_previous_results(mocker, hyperopt, testdatadir, caplog) -> None:
|
|||
|
||||
results_file = testdatadir / 'optimize' / 'ut_results.pickle'
|
||||
|
||||
hyperopt_epochs = hyperopt.load_previous_results(results_file)
|
||||
hyperopt_epochs = HyperoptTools.load_previous_results(results_file)
|
||||
|
||||
assert hyperopt_epochs == epochs
|
||||
mock_load.assert_called_once()
|
||||
|
||||
del epochs[0]['is_best']
|
||||
mock_load = mocker.patch('freqtrade.optimize.hyperopt.load', return_value=epochs)
|
||||
mock_load = mocker.patch('freqtrade.optimize.hyperopt_tools.load', return_value=epochs)
|
||||
|
||||
with pytest.raises(OperationalException):
|
||||
hyperopt.load_previous_results(results_file)
|
||||
HyperoptTools.load_previous_results(results_file)
|
||||
|
||||
|
||||
def test_roi_table_generation(hyperopt) -> None:
|
||||
|
|
@ -453,7 +454,7 @@ def test_format_results(hyperopt):
|
|||
'is_initial_point': True,
|
||||
}
|
||||
|
||||
result = hyperopt._format_explanation_string(results, 1)
|
||||
result = HyperoptTools._format_explanation_string(results, 1)
|
||||
assert result.find(' 66.67%')
|
||||
assert result.find('Total profit 1.00000000 BTC')
|
||||
assert result.find('2.0000Σ %')
|
||||
|
|
@ -467,7 +468,7 @@ def test_format_results(hyperopt):
|
|||
df = pd.DataFrame.from_records(trades, columns=labels)
|
||||
results_metrics = hyperopt._calculate_results_metrics(df)
|
||||
results['total_profit'] = results_metrics['total_profit']
|
||||
result = hyperopt._format_explanation_string(results, 1)
|
||||
result = HyperoptTools._format_explanation_string(results, 1)
|
||||
assert result.find('Total profit 1.00000000 EUR')
|
||||
|
||||
|
||||
|
|
@ -1076,7 +1077,7 @@ def test_print_epoch_details(capsys):
|
|||
'is_best': True
|
||||
}
|
||||
|
||||
Hyperopt.print_epoch_details(test_result, 5, False, no_header=True)
|
||||
HyperoptTools.print_epoch_details(test_result, 5, False, no_header=True)
|
||||
captured = capsys.readouterr()
|
||||
assert '# Trailing stop:' in captured.out
|
||||
# re.match(r"Pairs for .*", captured.out)
|
||||
|
|
|
|||
Loading…
Reference in New Issue