Merge branch 'develop' into feat/externalsignals

freqtrade/commands/cli_options.py

@@ -455,8 +455,6 @@ AVAILABLE_CLI_OPTIONS = {
         '-t', '--timeframes',
         help='Specify which tickers to download. Space-separated list. '
         'Default: `1m 5m`.',
-        choices=['1m', '3m', '5m', '15m', '30m', '1h', '2h', '4h',
-                 '6h', '8h', '12h', '1d', '3d', '1w', '2w', '1M', '1y'],
         default=['1m', '5m'],
         nargs='+',
     ),

freqtrade/exchange/exchange.py

@@ -205,7 +205,7 @@ class Exchange:
         logger.debug("Exchange object destroyed, closing async loop")
         if (self._api_async and inspect.iscoroutinefunction(self._api_async.close)
                 and self._api_async.session):
-            logger.info("Closing async ccxt session.")
+            logger.debug("Closing async ccxt session.")
             self.loop.run_until_complete(self._api_async.close())
 
     def validate_config(self, config):
@@ -446,6 +446,15 @@ class Exchange:
         contract_size = self.get_contract_size(pair)
         return contracts_to_amount(num_contracts, contract_size)
 
+    def amount_to_contract_precision(self, pair: str, amount: float) -> float:
+        """
+        Helper wrapper around amount_to_contract_precision
+        """
+        contract_size = self.get_contract_size(pair)
+
+        return amount_to_contract_precision(amount, self.get_precision_amount(pair),
+                                            self.precisionMode, contract_size)
+
     def set_sandbox(self, api: ccxt.Exchange, exchange_config: dict, name: str) -> None:
         if exchange_config.get('sandbox'):
             if api.urls.get('test'):
@@ -2500,8 +2509,13 @@ class Exchange:
             cache=False,
             drop_incomplete=False,
         )
-        funding_rates = candle_histories[funding_comb]
-        mark_rates = candle_histories[mark_comb]
+        try:
+            # we can't assume we always get histories - for example during exchange downtimes
+            funding_rates = candle_histories[funding_comb]
+            mark_rates = candle_histories[mark_comb]
+        except KeyError:
+            raise ExchangeError("Could not find funding rates.") from None
+
         funding_mark_rates = self.combine_funding_and_mark(
             funding_rates=funding_rates, mark_rates=mark_rates)
 
@@ -2581,6 +2595,8 @@ class Exchange:
         :param is_short: trade direction
         :param amount: Trade amount
         :param open_date: Open date of the trade
+        :return: funding fee since open_date
+        :raies: ExchangeError if something goes wrong.
         """
         if self.trading_mode == TradingMode.FUTURES:
             if self._config['dry_run']:

freqtrade/freqai/data_kitchen.py

@@ -1,7 +1,8 @@
 import copy
-import datetime
 import logging
 import shutil
+from datetime import datetime, timezone
+from math import cos, sin
 from pathlib import Path
 from typing import Any, Dict, List, Tuple
 
@@ -9,6 +10,7 @@ import numpy as np
 import numpy.typing as npt
 import pandas as pd
 from pandas import DataFrame
+from scipy import stats
 from sklearn import linear_model
 from sklearn.cluster import DBSCAN
 from sklearn.metrics.pairwise import pairwise_distances
@@ -360,7 +362,7 @@ class FreqaiDataKitchen:
 
     def denormalize_labels_from_metadata(self, df: DataFrame) -> DataFrame:
         """
-        Normalize a set of data using the mean and standard deviation from
+        Denormalize a set of data using the mean and standard deviation from
         the associated training data.
         :param df: Dataframe of predictions to be denormalized
         """
@@ -399,7 +401,7 @@ class FreqaiDataKitchen:
         config_timerange = TimeRange.parse_timerange(self.config["timerange"])
         if config_timerange.stopts == 0:
             config_timerange.stopts = int(
-                datetime.datetime.now(tz=datetime.timezone.utc).timestamp()
+                datetime.now(tz=timezone.utc).timestamp()
             )
         timerange_train = copy.deepcopy(full_timerange)
         timerange_backtest = copy.deepcopy(full_timerange)
@@ -416,8 +418,8 @@ class FreqaiDataKitchen:
             timerange_train.stopts = timerange_train.startts + train_period_days
 
             first = False
-            start = datetime.datetime.utcfromtimestamp(timerange_train.startts)
-            stop = datetime.datetime.utcfromtimestamp(timerange_train.stopts)
+            start = datetime.fromtimestamp(timerange_train.startts, tz=timezone.utc)
+            stop = datetime.fromtimestamp(timerange_train.stopts, tz=timezone.utc)
             tr_training_list.append(start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d"))
             tr_training_list_timerange.append(copy.deepcopy(timerange_train))
 
@@ -430,8 +432,8 @@ class FreqaiDataKitchen:
             if timerange_backtest.stopts > config_timerange.stopts:
                 timerange_backtest.stopts = config_timerange.stopts
 
-            start = datetime.datetime.utcfromtimestamp(timerange_backtest.startts)
-            stop = datetime.datetime.utcfromtimestamp(timerange_backtest.stopts)
+            start = datetime.fromtimestamp(timerange_backtest.startts, tz=timezone.utc)
+            stop = datetime.fromtimestamp(timerange_backtest.stopts, tz=timezone.utc)
             tr_backtesting_list.append(start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d"))
             tr_backtesting_list_timerange.append(copy.deepcopy(timerange_backtest))
 
@@ -451,8 +453,8 @@ class FreqaiDataKitchen:
                    it is sliced down to just the present training period.
         """
 
-        start = datetime.datetime.fromtimestamp(timerange.startts, tz=datetime.timezone.utc)
-        stop = datetime.datetime.fromtimestamp(timerange.stopts, tz=datetime.timezone.utc)
+        start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
+        stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
         df = df.loc[df["date"] >= start, :]
         if not self.live:
             df = df.loc[df["date"] < stop, :]
@@ -653,8 +655,6 @@ class FreqaiDataKitchen:
         is an outlier.
         """
 
-        from math import cos, sin
-
         if predict:
             if not self.data['DBSCAN_eps']:
                 return
@@ -747,6 +747,111 @@ class FreqaiDataKitchen:
 
         return
 
+    def compute_inlier_metric(self, set_='train') -> None:
+        """
+
+        Compute inlier metric from backwards distance distributions.
+        This metric defines how well features from a timepoint fit
+        into previous timepoints.
+        """
+
+        no_prev_pts = self.freqai_config["feature_parameters"]["inlier_metric_window"]
+
+        if set_ == 'train':
+            compute_df = copy.deepcopy(self.data_dictionary['train_features'])
+        elif set_ == 'test':
+            compute_df = copy.deepcopy(self.data_dictionary['test_features'])
+        else:
+            compute_df = copy.deepcopy(self.data_dictionary['prediction_features'])
+
+        compute_df_reindexed = compute_df.reindex(
+            index=np.flip(compute_df.index)
+        )
+
+        pairwise = pd.DataFrame(
+            np.triu(
+                pairwise_distances(compute_df_reindexed, n_jobs=self.thread_count)
+            ),
+            columns=compute_df_reindexed.index,
+            index=compute_df_reindexed.index
+        )
+        pairwise = pairwise.round(5)
+
+        column_labels = [
+            '{}{}'.format('d', i) for i in range(1, no_prev_pts + 1)
+        ]
+        distances = pd.DataFrame(
+            columns=column_labels, index=compute_df.index
+        )
+
+        for index in compute_df.index[no_prev_pts:]:
+            current_row = pairwise.loc[[index]]
+            current_row_no_zeros = current_row.loc[
+                :, (current_row != 0).any(axis=0)
+            ]
+            distances.loc[[index]] = current_row_no_zeros.iloc[
+                :, :no_prev_pts
+            ]
+        distances = distances.replace([np.inf, -np.inf], np.nan)
+        drop_index = pd.isnull(distances).any(1)
+        distances = distances[drop_index == 0]
+
+        inliers = pd.DataFrame(index=distances.index)
+        for key in distances.keys():
+            current_distances = distances[key].dropna()
+            fit_params = stats.weibull_min.fit(current_distances)
+            quantiles = stats.weibull_min.cdf(current_distances, *fit_params)
+
+            df_inlier = pd.DataFrame(
+                {key: quantiles}, index=distances.index
+            )
+            inliers = pd.concat(
+                [inliers, df_inlier], axis=1
+            )
+
+        inlier_metric = pd.DataFrame(
+            data=inliers.sum(axis=1) / no_prev_pts,
+            columns=['inlier_metric'],
+            index=compute_df.index
+        )
+
+        inlier_metric = (2 * (inlier_metric - inlier_metric.min()) /
+                         (inlier_metric.max() - inlier_metric.min()) - 1)
+
+        if set_ in ('train', 'test'):
+            inlier_metric = inlier_metric.iloc[no_prev_pts:]
+            compute_df = compute_df.iloc[no_prev_pts:]
+            self.remove_beginning_points_from_data_dict(set_, no_prev_pts)
+            self.data_dictionary[f'{set_}_features'] = pd.concat(
+                [compute_df, inlier_metric], axis=1)
+        else:
+            self.data_dictionary['prediction_features'] = pd.concat(
+                [compute_df, inlier_metric], axis=1)
+            self.data_dictionary['prediction_features'].fillna(0, inplace=True)
+
+        logger.info('Inlier metric computed and added to features.')
+
+        return None
+
+    def remove_beginning_points_from_data_dict(self, set_='train', no_prev_pts: int = 10):
+        features = self.data_dictionary[f'{set_}_features']
+        weights = self.data_dictionary[f'{set_}_weights']
+        labels = self.data_dictionary[f'{set_}_labels']
+        self.data_dictionary[f'{set_}_weights'] = weights[no_prev_pts:]
+        self.data_dictionary[f'{set_}_features'] = features.iloc[no_prev_pts:]
+        self.data_dictionary[f'{set_}_labels'] = labels.iloc[no_prev_pts:]
+
+    def add_noise_to_training_features(self) -> None:
+        """
+        Add noise to train features to reduce the risk of overfitting.
+        """
+        mu = 0  # no shift
+        sigma = self.freqai_config["feature_parameters"]["noise_standard_deviation"]
+        compute_df = self.data_dictionary['train_features']
+        noise = np.random.normal(mu, sigma, [compute_df.shape[0], compute_df.shape[1]])
+        self.data_dictionary['train_features'] += noise
+        return
+
     def find_features(self, dataframe: DataFrame) -> None:
         """
         Find features in the strategy provided dataframe
@@ -872,14 +977,14 @@ class FreqaiDataKitchen:
                                        "Please indicate the end date of your desired backtesting. "
                                        "timerange.")
             # backtest_timerange.stopts = int(
-            #     datetime.datetime.now(tz=datetime.timezone.utc).timestamp()
+            #     datetime.now(tz=timezone.utc).timestamp()
             # )
 
         backtest_timerange.startts = (
             backtest_timerange.startts - backtest_period_days * SECONDS_IN_DAY
         )
-        start = datetime.datetime.utcfromtimestamp(backtest_timerange.startts)
-        stop = datetime.datetime.utcfromtimestamp(backtest_timerange.stopts)
+        start = datetime.fromtimestamp(backtest_timerange.startts, tz=timezone.utc)
+        stop = datetime.fromtimestamp(backtest_timerange.stopts, tz=timezone.utc)
         full_timerange = start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d")
 
         self.full_path = Path(
@@ -905,7 +1010,7 @@ class FreqaiDataKitchen:
         :return:
             bool = If the model is expired or not.
         """
-        time = datetime.datetime.now(tz=datetime.timezone.utc).timestamp()
+        time = datetime.now(tz=timezone.utc).timestamp()
         elapsed_time = (time - trained_timestamp) / 3600  # hours
         max_time = self.freqai_config.get("expiration_hours", 0)
         if max_time > 0:
@@ -917,7 +1022,7 @@ class FreqaiDataKitchen:
         self, trained_timestamp: int
     ) -> Tuple[bool, TimeRange, TimeRange]:
 
-        time = datetime.datetime.now(tz=datetime.timezone.utc).timestamp()
+        time = datetime.now(tz=timezone.utc).timestamp()
         trained_timerange = TimeRange()
         data_load_timerange = TimeRange()
 

freqtrade/freqai/freqai_interface.py

@@ -1,10 +1,9 @@
-# import contextlib
-import datetime
 import logging
 import shutil
 import threading
 import time
 from abc import ABC, abstractmethod
+from datetime import datetime, timezone
 from pathlib import Path
 from threading import Lock
 from typing import Any, Dict, List, Tuple
@@ -59,7 +58,6 @@ class IFreqaiModel(ABC):
             "data_split_parameters", {})
         self.model_training_parameters: Dict[str, Any] = config.get("freqai", {}).get(
             "model_training_parameters", {})
-        self.feature_parameters = config.get("freqai", {}).get("feature_parameters")
         self.retrain = False
         self.first = True
         self.set_full_path()
@@ -70,11 +68,14 @@ class IFreqaiModel(ABC):
         self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
         self.identifier: str = self.freqai_info.get("identifier", "no_id_provided")
         self.scanning = False
+        self.ft_params = self.freqai_info["feature_parameters"]
         self.keras: bool = self.freqai_info.get("keras", False)
-        if self.keras and self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0):
-            self.freqai_info["feature_parameters"]["DI_threshold"] = 0
+        if self.keras and self.ft_params.get("DI_threshold", 0):
+            self.ft_params["DI_threshold"] = 0
             logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
         self.CONV_WIDTH = self.freqai_info.get("conv_width", 2)
+        if self.ft_params.get("inlier_metric_window", 0):
+            self.CONV_WIDTH = self.ft_params.get("inlier_metric_window", 0) * 2
         self.pair_it = 0
         self.pair_it_train = 0
         self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist"))
@@ -189,7 +190,7 @@ class IFreqaiModel(ABC):
 
                 if retrain:
                     self.train_timer('start')
-                    self.train_model_in_series(
+                    self.extract_data_and_train_model(
                         new_trained_timerange, pair, strategy, dk, data_load_timerange
                     )
                     self.train_timer('stop')
@@ -229,12 +230,12 @@ class IFreqaiModel(ABC):
             dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe)
 
             trained_timestamp = tr_train
-            tr_train_startts_str = datetime.datetime.utcfromtimestamp(tr_train.startts).strftime(
-                "%Y-%m-%d %H:%M:%S"
-            )
-            tr_train_stopts_str = datetime.datetime.utcfromtimestamp(tr_train.stopts).strftime(
-                "%Y-%m-%d %H:%M:%S"
-            )
+            tr_train_startts_str = datetime.fromtimestamp(
+                                                tr_train.startts,
+                                                tz=timezone.utc).strftime("%Y-%m-%d %H:%M:%S")
+            tr_train_stopts_str = datetime.fromtimestamp(
+                                                tr_train.stopts,
+                                                tz=timezone.utc).strftime("%Y-%m-%d %H:%M:%S")
             logger.info(
                 f"Training {metadata['pair']}, {self.pair_it}/{self.total_pairs} pairs"
                 f" from {tr_train_startts_str} to {tr_train_stopts_str}, {train_it}/{total_trains} "
@@ -419,24 +420,30 @@ class IFreqaiModel(ABC):
 
     def data_cleaning_train(self, dk: FreqaiDataKitchen) -> None:
         """
-        Base data cleaning method for train
-        Any function inside this method should drop training data points from the filtered_dataframe
-        based on user decided logic. See FreqaiDataKitchen::use_SVM_to_remove_outliers() for an
-        example of how outlier data points are dropped from the dataframe used for training.
+        Base data cleaning method for train.
+        Functions here improve/modify the input data by identifying outliers,
+        computing additional metrics, adding noise, reducing dimensionality etc.
         """
 
-        if self.freqai_info["feature_parameters"].get(
+        ft_params = self.freqai_info["feature_parameters"]
+
+        if ft_params.get('inlier_metric_window', 0):
+            dk.compute_inlier_metric(set_='train')
+            if self.freqai_info["data_split_parameters"]["test_size"] > 0:
+                dk.compute_inlier_metric(set_='test')
+
+        if ft_params.get(
             "principal_component_analysis", False
         ):
             dk.principal_component_analysis()
 
-        if self.freqai_info["feature_parameters"].get("use_SVM_to_remove_outliers", False):
+        if ft_params.get("use_SVM_to_remove_outliers", False):
             dk.use_SVM_to_remove_outliers(predict=False)
 
-        if self.freqai_info["feature_parameters"].get("DI_threshold", 0):
+        if ft_params.get("DI_threshold", 0):
             dk.data["avg_mean_dist"] = dk.compute_distances()
 
-        if self.freqai_info["feature_parameters"].get("use_DBSCAN_to_remove_outliers", False):
+        if ft_params.get("use_DBSCAN_to_remove_outliers", False):
             if dk.pair in self.dd.old_DBSCAN_eps:
                 eps = self.dd.old_DBSCAN_eps[dk.pair]
             else:
@@ -444,29 +451,31 @@ class IFreqaiModel(ABC):
             dk.use_DBSCAN_to_remove_outliers(predict=False, eps=eps)
             self.dd.old_DBSCAN_eps[dk.pair] = dk.data['DBSCAN_eps']
 
+        if self.freqai_info["feature_parameters"].get('noise_standard_deviation', 0):
+            dk.add_noise_to_training_features()
+
     def data_cleaning_predict(self, dk: FreqaiDataKitchen, dataframe: DataFrame) -> None:
         """
         Base data cleaning method for predict.
-        These functions each modify dk.do_predict, which is a dataframe with equal length
-        to the number of candles coming from and returning to the strategy. Inside do_predict,
-         1 allows prediction and < 0 signals to the strategy that the model is not confident in
-         the prediction.
-         See FreqaiDataKitchen::remove_outliers() for an example
-        of how the do_predict vector is modified. do_predict is ultimately passed back to strategy
-        for buy signals.
+        Functions here are complementary to the functions of data_cleaning_train.
         """
-        if self.freqai_info["feature_parameters"].get(
+        ft_params = self.freqai_info["feature_parameters"]
+
+        if ft_params.get('inlier_metric_window', 0):
+            dk.compute_inlier_metric(set_='predict')
+
+        if ft_params.get(
             "principal_component_analysis", False
         ):
-            dk.pca_transform(dataframe)
+            dk.pca_transform(self.dk.data_dictionary['prediction_features'])
 
-        if self.freqai_info["feature_parameters"].get("use_SVM_to_remove_outliers", False):
+        if ft_params.get("use_SVM_to_remove_outliers", False):
             dk.use_SVM_to_remove_outliers(predict=True)
 
-        if self.freqai_info["feature_parameters"].get("DI_threshold", 0):
+        if ft_params.get("DI_threshold", 0):
             dk.check_if_pred_in_training_spaces()
 
-        if self.freqai_info["feature_parameters"].get("use_DBSCAN_to_remove_outliers", False):
+        if ft_params.get("use_DBSCAN_to_remove_outliers", False):
             dk.use_DBSCAN_to_remove_outliers(predict=True)
 
     def model_exists(
@@ -502,7 +511,7 @@ class IFreqaiModel(ABC):
             Path(self.full_path, Path(self.config["config_files"][0]).name),
         )
 
-    def train_model_in_series(
+    def extract_data_and_train_model(
         self,
         new_trained_timerange: TimeRange,
         pair: str,
@@ -594,7 +603,7 @@ class IFreqaiModel(ABC):
 
         # # for keras type models, the conv_window needs to be prepended so
         # # viewing is correct in frequi
-        if self.freqai_info.get('keras', False):
+        if self.freqai_info.get('keras', False) or self.ft_params.get('inlier_metric_window', 0):
             n_lost_points = self.freqai_info.get('conv_width', 2)
             zeros_df = DataFrame(np.zeros((n_lost_points, len(hist_preds_df.columns))),
                                  columns=hist_preds_df.columns)

freqtrade/freqtradebot.py

@@ -294,16 +294,17 @@ class FreqtradeBot(LoggingMixin):
     def update_funding_fees(self):
         if self.trading_mode == TradingMode.FUTURES:
             trades = Trade.get_open_trades()
-            for trade in trades:
-                funding_fees = self.exchange.get_funding_fees(
-                    pair=trade.pair,
-                    amount=trade.amount,
-                    is_short=trade.is_short,
-                    open_date=trade.date_last_filled_utc
-                )
-                trade.funding_fees = funding_fees
-        else:
-            return 0.0
+            try:
+                for trade in trades:
+                    funding_fees = self.exchange.get_funding_fees(
+                        pair=trade.pair,
+                        amount=trade.amount,
+                        is_short=trade.is_short,
+                        open_date=trade.date_last_filled_utc
+                    )
+                    trade.funding_fees = funding_fees
+            except ExchangeError:
+                logger.warning("Could not update funding fees for open trades.")
 
     def startup_backpopulate_precision(self):
 
@@ -596,7 +597,9 @@ class FreqtradeBot(LoggingMixin):
 
         if stake_amount is not None and stake_amount < 0.0:
             # We should decrease our position
-            amount = abs(float(FtPrecise(stake_amount) / FtPrecise(current_exit_rate)))
+            amount = self.exchange.amount_to_contract_precision(
+                trade.pair,
+                abs(float(FtPrecise(stake_amount) / FtPrecise(current_exit_rate))))
             if amount > trade.amount:
                 # This is currently ineffective as remaining would become < min tradable
                 # Fixing this would require checking for 0.0 there -
@@ -605,9 +608,14 @@ class FreqtradeBot(LoggingMixin):
                     f"Adjusting amount to trade.amount as it is higher. {amount} > {trade.amount}")
                 amount = trade.amount
 
+            if amount == 0.0:
+                logger.info("Amount to sell is 0.0 due to exchange limits - not selling.")
+                return
+
             remaining = (trade.amount - amount) * current_exit_rate
             if remaining < min_exit_stake:
-                logger.info(f'Remaining amount of {remaining} would be too small.')
+                logger.info(f"Remaining amount of {remaining} would be smaller "
+                            f"than the minimum of {min_exit_stake}.")
                 return
 
             self.execute_trade_exit(trade, current_exit_rate, exit_check=ExitCheckTuple(
@@ -677,14 +685,12 @@ class FreqtradeBot(LoggingMixin):
         if not stake_amount:
             return False
 
-        if pos_adjust:
-            logger.info(f"Position adjust: about to create a new order for {pair} with stake: "
-                        f"{stake_amount} for {trade}")
-        else:
-            logger.info(
-                f"{name} signal found: about create a new trade for {pair} with stake_amount: "
-                f"{stake_amount} ...")
-
+        msg = (f"Position adjust: about to create a new order for {pair} with stake: "
+               f"{stake_amount} for {trade}" if pos_adjust
+               else
+               f"{name} signal found: about create a new trade for {pair} with stake_amount: "
+               f"{stake_amount} ...")
+        logger.info(msg)
         amount = (stake_amount / enter_limit_requested) * leverage
         order_type = ordertype or self.strategy.order_types['entry']
 
@@ -747,8 +753,13 @@ class FreqtradeBot(LoggingMixin):
 
         # This is a new trade
         if trade is None:
-            funding_fees = self.exchange.get_funding_fees(
-                pair=pair, amount=amount, is_short=is_short, open_date=open_date)
+            funding_fees = 0.0
+            try:
+                funding_fees = self.exchange.get_funding_fees(
+                    pair=pair, amount=amount, is_short=is_short, open_date=open_date)
+            except ExchangeError:
+                logger.warning("Could not find funding fee.")
+
             trade = Trade(
                 pair=pair,
                 base_currency=base_currency,
@@ -925,7 +936,7 @@ class FreqtradeBot(LoggingMixin):
             'stake_amount': trade.stake_amount,
             'stake_currency': self.config['stake_currency'],
             'fiat_currency': self.config.get('fiat_display_currency', None),
-            'amount': order.safe_amount_after_fee,
+            'amount': order.safe_amount_after_fee if fill else order.amount,
             'open_date': trade.open_date or datetime.utcnow(),
             'current_rate': current_rate,
             'sub_trade': sub_trade,
@@ -1499,12 +1510,16 @@ class FreqtradeBot(LoggingMixin):
         :param exit_check: CheckTuple with signal and reason
         :return: True if it succeeds False
         """
-        trade.funding_fees = self.exchange.get_funding_fees(
-            pair=trade.pair,
-            amount=trade.amount,
-            is_short=trade.is_short,
-            open_date=trade.date_last_filled_utc,
-        )
+        try:
+            trade.funding_fees = self.exchange.get_funding_fees(
+                pair=trade.pair,
+                amount=trade.amount,
+                is_short=trade.is_short,
+                open_date=trade.date_last_filled_utc,
+            )
+        except ExchangeError:
+            logger.warning("Could not update funding fee.")
+
         exit_type = 'exit'
         exit_reason = exit_tag or exit_check.exit_reason
         if exit_check.exit_type in (

freqtrade/optimize/backtesting.py

@@ -537,7 +537,11 @@ class Backtesting:
                     return pos_trade
 
         if stake_amount is not None and stake_amount < 0.0:
-            amount = abs(stake_amount) / current_rate
+            amount = amount_to_contract_precision(
+                abs(stake_amount) / current_rate, trade.amount_precision,
+                self.precision_mode, trade.contract_size)
+            if amount == 0.0:
+                return trade
             if amount > trade.amount:
                 # This is currently ineffective as remaining would become < min tradable
                 amount = trade.amount

freqtrade/strategy/strategy_helper.py

@@ -1,3 +1,5 @@
+from typing import Optional
+
 import pandas as pd
 
 from freqtrade.exchange import timeframe_to_minutes
@@ -6,7 +8,8 @@ from freqtrade.exchange import timeframe_to_minutes
 def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
                            timeframe: str, timeframe_inf: str, ffill: bool = True,
                            append_timeframe: bool = True,
-                           date_column: str = 'date') -> pd.DataFrame:
+                           date_column: str = 'date',
+                           suffix: Optional[str] = None) -> pd.DataFrame:
     """
     Correctly merge informative samples to the original dataframe, avoiding lookahead bias.
 
@@ -28,6 +31,8 @@ def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
     :param ffill: Forwardfill missing values - optional but usually required
     :param append_timeframe: Rename columns by appending timeframe.
     :param date_column: A custom date column name.
+    :param suffix: A string suffix to add at the end of the informative columns. If specified,
+                   append_timeframe must be false.
     :return: Merged dataframe
     :raise: ValueError if the secondary timeframe is shorter than the dataframe timeframe
     """
@@ -50,10 +55,16 @@ def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
 
     # Rename columns to be unique
     date_merge = 'date_merge'
-    if append_timeframe:
+    if suffix and append_timeframe:
+        raise ValueError("You can not specify `append_timeframe` as True and a `suffix`.")
+    elif append_timeframe:
         date_merge = f'date_merge_{timeframe_inf}'
         informative.columns = [f"{col}_{timeframe_inf}" for col in informative.columns]
 
+    elif suffix:
+        date_merge = f'date_merge_{suffix}'
+        informative.columns = [f"{col}_{suffix}" for col in informative.columns]
+
     # Combine the 2 dataframes
     # all indicators on the informative sample MUST be calculated before this point
     if ffill:

freqtrade/templates/FreqaiExampleStrategy.py

@@ -92,12 +92,10 @@ class FreqaiExampleStrategy(IStrategy):
             t = int(t)
             informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
             informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
-            informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
+            informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
             informative[f"%-{coin}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
             informative[f"%-{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
 
-            informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
-
             bollinger = qtpylib.bollinger_bands(
                 qtpylib.typical_price(informative), window=t, stds=2.2
             )

freqtrade/templates/FreqaiHybridExampleStrategy.py

@@ -135,7 +135,7 @@ class FreqaiExampleHybridStrategy(IStrategy):
             t = int(t)
             informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
             informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
-            informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
+            informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
             informative[f"%-{coin}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
             informative[f"%-{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
             informative[f"%-{coin}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)