Merge branch 'develop' of https://github.com/theluxaz/freqtrade into main

# Conflicts:
#	freqtrade/freqtradebot.py
#	freqtrade/optimize/backtesting.py

freqtrade/optimize/backtesting.py

@@ -11,7 +11,7 @@ from typing import Any, Dict, List, Optional, Tuple
 
 from pandas import DataFrame
 
-from freqtrade.configuration import TimeRange, remove_credentials, validate_config_consistency
+from freqtrade.configuration import TimeRange, validate_config_consistency
 from freqtrade.constants import DATETIME_PRINT_FORMAT
 from freqtrade.data import history
 from freqtrade.data.btanalysis import trade_list_to_dataframe
@@ -61,8 +61,7 @@ class Backtesting:
         self.config = config
         self.results: Optional[Dict[str, Any]] = None
 
-        # Reset keys for backtesting
-        remove_credentials(self.config)
+        config['dry_run'] = True
         self.strategylist: List[IStrategy] = []
         self.all_results: Dict[str, Dict] = {}
 
@@ -86,7 +85,7 @@ class Backtesting:
                                        "configuration or as cli argument `--timeframe 5m`")
         self.timeframe = str(self.config.get('timeframe'))
         self.timeframe_min = timeframe_to_minutes(self.timeframe)
-
+        self.init_backtest_detail()
         self.pairlists = PairListManager(self.exchange, self.config)
         if 'VolumePairList' in self.pairlists.name_list:
             raise OperationalException("VolumePairList not allowed for backtesting.")
@@ -109,14 +108,6 @@ class Backtesting:
         else:
             self.fee = self.exchange.get_fee(symbol=self.pairlists.whitelist[0])
 
-        Trade.use_db = False
-        Trade.reset_trades()
-        PairLocks.timeframe = self.config['timeframe']
-        PairLocks.use_db = False
-        PairLocks.reset_locks()
-
-        self.wallets = Wallets(self.config, self.exchange, log=False)
-
         self.timerange = TimeRange.parse_timerange(
             None if self.config.get('timerange') is None else str(self.config.get('timerange')))
 
@@ -125,9 +116,7 @@ class Backtesting:
         # Add maximum startup candle count to configuration for informative pairs support
         self.config['startup_candle_count'] = self.required_startup
         self.exchange.validate_required_startup_candles(self.required_startup, self.timeframe)
-
-        self.progress = BTProgress()
-        self.abort = False
+        self.init_backtest()
 
     def __del__(self):
         self.cleanup()
@@ -137,6 +126,28 @@ class Backtesting:
         PairLocks.use_db = True
         Trade.use_db = True
 
+    def init_backtest_detail(self):
+        # Load detail timeframe if specified
+        self.timeframe_detail = str(self.config.get('timeframe_detail', ''))
+        if self.timeframe_detail:
+            self.timeframe_detail_min = timeframe_to_minutes(self.timeframe_detail)
+            if self.timeframe_min <= self.timeframe_detail_min:
+                raise OperationalException(
+                    "Detail timeframe must be smaller than strategy timeframe.")
+
+        else:
+            self.timeframe_detail_min = 0
+        self.detail_data: Dict[str, DataFrame] = {}
+
+    def init_backtest(self):
+
+        self.prepare_backtest(False)
+
+        self.wallets = Wallets(self.config, self.exchange, log=False)
+
+        self.progress = BTProgress()
+        self.abort = False
+
     def _set_strategy(self, strategy: IStrategy):
         """
         Load strategy into backtesting
@@ -144,7 +155,7 @@ class Backtesting:
         self.strategy: IStrategy = strategy
         strategy.dp = self.dataprovider
         # Attach Wallets to Strategy baseclass
-        IStrategy.wallets = self.wallets
+        strategy.wallets = self.wallets
         # Set stoploss_on_exchange to false for backtesting,
         # since a "perfect" stoploss-sell is assumed anyway
         # And the regular "stoploss" function would not apply to that case
@@ -188,6 +199,23 @@ class Backtesting:
         self.progress.set_new_value(1)
         return data, self.timerange
 
+    def load_bt_data_detail(self) -> None:
+        """
+        Loads backtest detail data (smaller timeframe) if necessary.
+        """
+        if self.timeframe_detail:
+            self.detail_data = history.load_data(
+                datadir=self.config['datadir'],
+                pairs=self.pairlists.whitelist,
+                timeframe=self.timeframe_detail,
+                timerange=self.timerange,
+                startup_candles=0,
+                fail_without_data=True,
+                data_format=self.config.get('dataformat_ohlcv', 'json'),
+            )
+        else:
+            self.detail_data = {}
+
     def prepare_backtest(self, enable_protections):
         """
         Backtesting setup method - called once for every call to "backtest()".
@@ -199,7 +227,8 @@ class Backtesting:
         Trade.reset_trades()
         self.rejected_trades = 0
         self.dataprovider.clear_cache()
-        self._load_protections(self.strategy)
+        if enable_protections:
+            self._load_protections(self.strategy)
 
     def check_abort(self):
         """
@@ -320,10 +349,8 @@ class Backtesting:
         else:
             return sell_row[OPEN_IDX]
 
-    def _get_sell_trade_entry(self, trade: LocalTrade, sell_row: Tuple) -> Optional[LocalTrade]:
-
-
-
+    def _get_sell_trade_entry_for_candle(self, trade: LocalTrade,
+                                         sell_row: Tuple) -> Optional[LocalTrade]:
         sell_candle_time = sell_row[DATE_IDX].to_pydatetime()
         sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX],  # type: ignore
                                          sell_candle_time, sell_row[BUY_IDX],
@@ -353,6 +380,32 @@ class Backtesting:
 
         return None
 
+    def _get_sell_trade_entry(self, trade: LocalTrade, sell_row: Tuple) -> Optional[LocalTrade]:
+        if self.timeframe_detail and trade.pair in self.detail_data:
+            sell_candle_time = sell_row[DATE_IDX].to_pydatetime()
+            sell_candle_end = sell_candle_time + timedelta(minutes=self.timeframe_min)
+
+            detail_data = self.detail_data[trade.pair]
+            detail_data = detail_data.loc[
+                (detail_data['date'] >= sell_candle_time) &
+                (detail_data['date'] < sell_candle_end)
+             ].copy()
+            if len(detail_data) == 0:
+                # Fall back to "regular" data if no detail data was found for this candle
+                return self._get_sell_trade_entry_for_candle(trade, sell_row)
+            detail_data.loc[:, 'buy'] = sell_row[BUY_IDX]
+            detail_data.loc[:, 'sell'] = sell_row[SELL_IDX]
+            headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
+            for det_row in detail_data[headers].values.tolist():
+                res = self._get_sell_trade_entry_for_candle(trade, det_row)
+                if res:
+                    return res
+
+            return None
+
+        else:
+            return self._get_sell_trade_entry_for_candle(trade, sell_row)
+
     def _enter_trade(self, pair: str, row: List) -> Optional[LocalTrade]:
         try:
             stake_amount = self.wallets.get_trade_stake_amount(pair, None)
@@ -601,6 +654,7 @@ class Backtesting:
         data: Dict[str, Any] = {}
 
         data, timerange = self.load_bt_data()
+        self.load_bt_data_detail()
         logger.info("Dataload complete. Calculating indicators")
 
         for strat in self.strategylist:

freqtrade/optimize/edge_cli.py

@@ -7,7 +7,8 @@ import logging
 from typing import Any, Dict
 
 from freqtrade import constants
-from freqtrade.configuration import TimeRange, remove_credentials, validate_config_consistency
+from freqtrade.configuration import TimeRange, validate_config_consistency
+from freqtrade.data.dataprovider import DataProvider
 from freqtrade.edge import Edge
 from freqtrade.optimize.optimize_reports import generate_edge_table
 from freqtrade.resolvers import ExchangeResolver, StrategyResolver
@@ -28,11 +29,12 @@ class EdgeCli:
     def __init__(self, config: Dict[str, Any]) -> None:
         self.config = config
 
-        # Reset keys for edge
-        remove_credentials(self.config)
+        # Ensure using dry-run
+        self.config['dry_run'] = True
         self.config['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT
         self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
         self.strategy = StrategyResolver.load_strategy(self.config)
+        self.strategy.dp = DataProvider(config, None)
 
         validate_config_consistency(self.config)
 

freqtrade/optimize/hyperopt.py

@@ -22,6 +22,7 @@ from pandas import DataFrame
 from freqtrade.constants import DATETIME_PRINT_FORMAT, FTHYPT_FILEVERSION, LAST_BT_RESULT_FN
 from freqtrade.data.converter import trim_dataframes
 from freqtrade.data.history import get_timerange
+from freqtrade.exceptions import OperationalException
 from freqtrade.misc import deep_merge_dicts, file_dump_json, plural
 from freqtrade.optimize.backtesting import Backtesting
 # Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
@@ -30,7 +31,7 @@ from freqtrade.optimize.hyperopt_interface import IHyperOpt  # noqa: F401
 from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss  # noqa: F401
 from freqtrade.optimize.hyperopt_tools import HyperoptTools, hyperopt_serializer
 from freqtrade.optimize.optimize_reports import generate_strategy_stats
-from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver, HyperOptResolver
+from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver
 
 
 # Suppress scikit-learn FutureWarnings from skopt
@@ -44,7 +45,7 @@ progressbar.streams.wrap_stdout()
 logger = logging.getLogger(__name__)
 
 
-INITIAL_POINTS = 30
+INITIAL_POINTS = 5
 
 # Keep no more than SKOPT_MODEL_QUEUE_SIZE models
 # in the skopt model queue, to optimize memory consumption
@@ -78,10 +79,10 @@ class Hyperopt:
 
         if not self.config.get('hyperopt'):
             self.custom_hyperopt = HyperOptAuto(self.config)
-            self.auto_hyperopt = True
         else:
-            self.custom_hyperopt = HyperOptResolver.load_hyperopt(self.config)
-            self.auto_hyperopt = False
+            raise OperationalException(
+                "Using separate Hyperopt files has been removed in 2021.9. Please convert "
+                "your existing Hyperopt file to the new Hyperoptable strategy interface")
 
         self.backtesting._set_strategy(self.backtesting.strategylist[0])
         self.custom_hyperopt.strategy = self.backtesting.strategy
@@ -103,31 +104,6 @@ class Hyperopt:
         self.num_epochs_saved = 0
         self.current_best_epoch: Optional[Dict[str, Any]] = None
 
-        if not self.auto_hyperopt:
-            # Populate "fallback" functions here
-            # (hasattr is slow so should not be run during "regular" operations)
-            if hasattr(self.custom_hyperopt, 'populate_indicators'):
-                logger.warning(
-                    "DEPRECATED: Using `populate_indicators()` in the hyperopt file is deprecated. "
-                    "Please move these methods to your strategy."
-                    )
-                self.backtesting.strategy.populate_indicators = (  # type: ignore
-                    self.custom_hyperopt.populate_indicators)  # type: ignore
-            if hasattr(self.custom_hyperopt, 'populate_buy_trend'):
-                logger.warning(
-                    "DEPRECATED: Using `populate_buy_trend()` in the hyperopt file is deprecated. "
-                    "Please move these methods to your strategy."
-                )
-                self.backtesting.strategy.populate_buy_trend = (  # type: ignore
-                    self.custom_hyperopt.populate_buy_trend)  # type: ignore
-            if hasattr(self.custom_hyperopt, 'populate_sell_trend'):
-                logger.warning(
-                    "DEPRECATED: Using `populate_sell_trend()` in the hyperopt file is deprecated. "
-                    "Please move these methods to your strategy."
-                )
-                self.backtesting.strategy.populate_sell_trend = (  # type: ignore
-                    self.custom_hyperopt.populate_sell_trend)  # type: ignore
-
         # Use max_open_trades for hyperopt as well, except --disable-max-market-positions is set
         if self.config.get('use_max_market_positions', True):
             self.max_open_trades = self.config['max_open_trades']
@@ -256,7 +232,7 @@ class Hyperopt:
         """
         Assign the dimensions in the hyperoptimization space.
         """
-        if self.auto_hyperopt and HyperoptTools.has_space(self.config, 'protection'):
+        if HyperoptTools.has_space(self.config, 'protection'):
             # Protections can only be optimized when using the Parameter interface
             logger.debug("Hyperopt has 'protection' space")
             # Enable Protections if protection space is selected.
@@ -265,7 +241,7 @@ class Hyperopt:
 
         if HyperoptTools.has_space(self.config, 'buy'):
             logger.debug("Hyperopt has 'buy' space")
-            self.buy_space = self.custom_hyperopt.indicator_space()
+            self.buy_space = self.custom_hyperopt.buy_indicator_space()
 
         if HyperoptTools.has_space(self.config, 'sell'):
             logger.debug("Hyperopt has 'sell' space")
@@ -285,6 +261,15 @@ class Hyperopt:
         self.dimensions = (self.buy_space + self.sell_space + self.protection_space
                            + self.roi_space + self.stoploss_space + self.trailing_space)
 
+    def assign_params(self, params_dict: Dict, category: str) -> None:
+        """
+        Assign hyperoptable parameters
+        """
+        for attr_name, attr in self.backtesting.strategy.enumerate_parameters(category):
+            if attr.optimize:
+                # noinspection PyProtectedMember
+                attr.value = params_dict[attr_name]
+
     def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict:
         """
         Used Optimize function.
@@ -296,18 +281,13 @@ class Hyperopt:
 
         # Apply parameters
         if HyperoptTools.has_space(self.config, 'buy'):
-            self.backtesting.strategy.advise_buy = (  # type: ignore
-                self.custom_hyperopt.buy_strategy_generator(params_dict))
+            self.assign_params(params_dict, 'buy')
 
         if HyperoptTools.has_space(self.config, 'sell'):
-            self.backtesting.strategy.advise_sell = (  # type: ignore
-                self.custom_hyperopt.sell_strategy_generator(params_dict))
+            self.assign_params(params_dict, 'sell')
 
         if HyperoptTools.has_space(self.config, 'protection'):
-            for attr_name, attr in self.backtesting.strategy.enumerate_parameters('protection'):
-                if attr.optimize:
-                    # noinspection PyProtectedMember
-                    attr.value = params_dict[attr_name]
+            self.assign_params(params_dict, 'protection')
 
         if HyperoptTools.has_space(self.config, 'roi'):
             self.backtesting.strategy.minimal_roi = (  # type: ignore
@@ -385,10 +365,20 @@ class Hyperopt:
         }
 
     def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer:
+        estimator = self.custom_hyperopt.generate_estimator()
+
+        acq_optimizer = "sampling"
+        if isinstance(estimator, str):
+            if estimator not in ("GP", "RF", "ET", "GBRT"):
+                raise OperationalException(f"Estimator {estimator} not supported.")
+            else:
+                acq_optimizer = "auto"
+
+        logger.info(f"Using estimator {estimator}.")
         return Optimizer(
             dimensions,
-            base_estimator="ET",
-            acq_optimizer="auto",
+            base_estimator=estimator,
+            acq_optimizer=acq_optimizer,
             n_initial_points=INITIAL_POINTS,
             acq_optimizer_kwargs={'n_jobs': cpu_count},
             random_state=self.random_state,
@@ -517,11 +507,10 @@ class Hyperopt:
                     f"saved to '{self.results_file}'.")
 
         if self.current_best_epoch:
-            if self.auto_hyperopt:
-                HyperoptTools.try_export_params(
-                    self.config,
-                    self.backtesting.strategy.get_strategy_name(),
-                    self.current_best_epoch)
+            HyperoptTools.try_export_params(
+                self.config,
+                self.backtesting.strategy.get_strategy_name(),
+                self.current_best_epoch)
 
             HyperoptTools.show_epoch_details(self.current_best_epoch, self.total_epochs,
                                              self.print_json)

freqtrade/optimize/hyperopt_auto.py

@@ -4,15 +4,23 @@ This module implements a convenience auto-hyperopt class, which can be used toge
  that implement IHyperStrategy interface.
 """
 from contextlib import suppress
-from typing import Any, Callable, Dict, List
+from typing import Callable, Dict, List
 
-from pandas import DataFrame
+from freqtrade.exceptions import OperationalException
 
 
 with suppress(ImportError):
     from skopt.space import Dimension
 
-from freqtrade.optimize.hyperopt_interface import IHyperOpt
+from freqtrade.optimize.hyperopt_interface import EstimatorType, IHyperOpt
+
+
+def _format_exception_message(space: str) -> str:
+    raise OperationalException(
+        f"The '{space}' space is included into the hyperoptimization "
+        f"but no parameter for this space was not found in your Strategy. "
+        f"Please make sure to have parameters for this space enabled for optimization "
+        f"or remove the '{space}' space from hyperoptimization.")
 
 
 class HyperOptAuto(IHyperOpt):
@@ -22,26 +30,6 @@ class HyperOptAuto(IHyperOpt):
      sell_indicator_space methods, but other hyperopt methods can be overridden as well.
     """
 
-    def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable:
-        def populate_buy_trend(dataframe: DataFrame, metadata: dict):
-            for attr_name, attr in self.strategy.enumerate_parameters('buy'):
-                if attr.optimize:
-                    # noinspection PyProtectedMember
-                    attr.value = params[attr_name]
-            return self.strategy.populate_buy_trend(dataframe, metadata)
-
-        return populate_buy_trend
-
-    def sell_strategy_generator(self, params: Dict[str, Any]) -> Callable:
-        def populate_sell_trend(dataframe: DataFrame, metadata: dict):
-            for attr_name, attr in self.strategy.enumerate_parameters('sell'):
-                if attr.optimize:
-                    # noinspection PyProtectedMember
-                    attr.value = params[attr_name]
-            return self.strategy.populate_sell_trend(dataframe, metadata)
-
-        return populate_sell_trend
-
     def _get_func(self, name) -> Callable:
         """
         Return a function defined in Strategy.HyperOpt class, or one defined in super() class.
@@ -60,21 +48,22 @@ class HyperOptAuto(IHyperOpt):
             if attr.optimize:
                 yield attr.get_space(attr_name)
 
-    def _get_indicator_space(self, category, fallback_method_name):
+    def _get_indicator_space(self, category):
+        # TODO: is this necessary, or can we call "generate_space" directly?
         indicator_space = list(self._generate_indicator_space(category))
         if len(indicator_space) > 0:
             return indicator_space
         else:
-            return self._get_func(fallback_method_name)()
+            _format_exception_message(category)
 
-    def indicator_space(self) -> List['Dimension']:
-        return self._get_indicator_space('buy', 'indicator_space')
+    def buy_indicator_space(self) -> List['Dimension']:
+        return self._get_indicator_space('buy')
 
     def sell_indicator_space(self) -> List['Dimension']:
-        return self._get_indicator_space('sell', 'sell_indicator_space')
+        return self._get_indicator_space('sell')
 
     def protection_space(self) -> List['Dimension']:
-        return self._get_indicator_space('protection', 'protection_space')
+        return self._get_indicator_space('protection')
 
     def generate_roi_table(self, params: Dict) -> Dict[int, float]:
         return self._get_func('generate_roi_table')(params)
@@ -90,3 +79,6 @@ class HyperOptAuto(IHyperOpt):
 
     def trailing_space(self) -> List['Dimension']:
         return self._get_func('trailing_space')()
+
+    def generate_estimator(self) -> EstimatorType:
+        return self._get_func('generate_estimator')()

freqtrade/optimize/hyperopt_interface.py

@@ -5,11 +5,11 @@ This module defines the interface to apply for hyperopt
 import logging
 import math
 from abc import ABC
-from typing import Any, Callable, Dict, List
+from typing import Dict, List, Union
 
+from sklearn.base import RegressorMixin
 from skopt.space import Categorical, Dimension, Integer
 
-from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_minutes
 from freqtrade.misc import round_dict
 from freqtrade.optimize.space import SKDecimal
@@ -18,12 +18,7 @@ from freqtrade.strategy import IStrategy
 
 logger = logging.getLogger(__name__)
 
-
-def _format_exception_message(method: str, space: str) -> str:
-    return (f"The '{space}' space is included into the hyperoptimization "
-            f"but {method}() method is not found in your "
-            f"custom Hyperopt class. You should either implement this "
-            f"method or remove the '{space}' space from hyperoptimization.")
+EstimatorType = Union[RegressorMixin, str]
 
 
 class IHyperOpt(ABC):
@@ -45,36 +40,13 @@ class IHyperOpt(ABC):
         IHyperOpt.ticker_interval = str(config['timeframe'])  # DEPRECATED
         IHyperOpt.timeframe = str(config['timeframe'])
 
-    def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable:
+    def generate_estimator(self) -> EstimatorType:
         """
-        Create a buy strategy generator.
+        Return base_estimator.
+        Can be any of "GP", "RF", "ET", "GBRT" or an instance of a class
+        inheriting from RegressorMixin (from sklearn).
         """
-        raise OperationalException(_format_exception_message('buy_strategy_generator', 'buy'))
-
-    def sell_strategy_generator(self, params: Dict[str, Any]) -> Callable:
-        """
-        Create a sell strategy generator.
-        """
-        raise OperationalException(_format_exception_message('sell_strategy_generator', 'sell'))
-
-    def protection_space(self) -> List[Dimension]:
-        """
-        Create a protection space.
-        Only supported by the Parameter interface.
-        """
-        raise OperationalException(_format_exception_message('indicator_space', 'protection'))
-
-    def indicator_space(self) -> List[Dimension]:
-        """
-        Create an indicator space.
-        """
-        raise OperationalException(_format_exception_message('indicator_space', 'buy'))
-
-    def sell_indicator_space(self) -> List[Dimension]:
-        """
-        Create a sell indicator space.
-        """
-        raise OperationalException(_format_exception_message('sell_indicator_space', 'sell'))
+        return 'ET'
 
     def generate_roi_table(self, params: Dict) -> Dict[int, float]:
         """

freqtrade/optimize/hyperopt_loss_max_drawdown.py

@@ -0,0 +1,41 @@
+"""
+MaxDrawDownHyperOptLoss
+
+This module defines the alternative HyperOptLoss class which can be used for
+Hyperoptimization.
+"""
+from datetime import datetime
+
+from pandas import DataFrame
+
+from freqtrade.data.btanalysis import calculate_max_drawdown
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+
+class MaxDrawDownHyperOptLoss(IHyperOptLoss):
+
+    """
+    Defines the loss function for hyperopt.
+
+    This implementation optimizes for max draw down and profit
+    Less max drawdown more profit -> Lower return value
+    """
+
+    @staticmethod
+    def hyperopt_loss_function(results: DataFrame, trade_count: int,
+                               min_date: datetime, max_date: datetime,
+                               *args, **kwargs) -> float:
+
+        """
+        Objective function.
+
+        Uses profit ratio weighted max_drawdown when drawdown is available.
+        Otherwise directly optimizes profit ratio.
+        """
+        total_profit = results['profit_abs'].sum()
+        try:
+            max_drawdown = calculate_max_drawdown(results, value_col='profit_abs')
+        except ValueError:
+            # No losing trade, therefore no drawdown.
+            return -total_profit
+        return -total_profit / max_drawdown[0]

freqtrade/optimize/hyperopt_tools.py

@@ -7,6 +7,7 @@ from pathlib import Path
 from typing import Any, Dict, Iterator, List, Optional, Tuple
 
 import numpy as np
+import pandas as pd
 import rapidjson
 import tabulate
 from colorama import Fore, Style
@@ -298,8 +299,8 @@ class HyperoptTools():
                 f"Objective: {results['loss']:.5f}")
 
     @staticmethod
-    def prepare_trials_columns(trials, legacy_mode: bool, has_drawdown: bool) -> str:
-
+    def prepare_trials_columns(trials: pd.DataFrame, legacy_mode: bool,
+                               has_drawdown: bool) -> pd.DataFrame:
         trials['Best'] = ''
 
         if 'results_metrics.winsdrawslosses' not in trials.columns:
@@ -435,8 +436,7 @@ class HyperoptTools():
         return table
 
     @staticmethod
-    def export_csv_file(config: dict, results: list, total_epochs: int, highlight_best: bool,
-                        csv_file: str) -> None:
+    def export_csv_file(config: dict, results: list, csv_file: str) -> None:
         """
         Log result to csv-file
         """

freqtrade/optimize/optimize_reports.py

@@ -464,6 +464,7 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
         'max_open_trades_setting': (config['max_open_trades']
                                     if config['max_open_trades'] != float('inf') else -1),
         'timeframe': config['timeframe'],
+        'timeframe_detail': config.get('timeframe_detail', ''),
         'timerange': config.get('timerange', ''),
         'enable_protections': config.get('enable_protections', False),
         'strategy_name': strategy,