Merge branch 'develop' into pr/wizrds/7303

freqtrade/templates/FreqaiExampleStrategy.py

@@ -45,20 +45,7 @@ class FreqaiExampleStrategy(IStrategy):
     std_dev_multiplier_buy = CategoricalParameter(
         [0.75, 1, 1.25, 1.5, 1.75], default=1.25, space="buy", optimize=True)
     std_dev_multiplier_sell = CategoricalParameter(
-        [0.1, 0.25, 0.4], space="sell", default=0.2, optimize=True)
-
-    def informative_pairs(self):
-        whitelist_pairs = self.dp.current_whitelist()
-        corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
-        informative_pairs = []
-        for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
-            for pair in whitelist_pairs:
-                informative_pairs.append((pair, tf))
-            for pair in corr_pairs:
-                if pair in whitelist_pairs:
-                    continue  # avoid duplication
-                informative_pairs.append((pair, tf))
-        return informative_pairs
+        [0.75, 1, 1.25, 1.5, 1.75], space="sell", default=1.25, optimize=True)
 
     def populate_any_indicators(
         self, pair, df, tf, informative=None, set_generalized_indicators=False
@@ -183,25 +170,31 @@ class FreqaiExampleStrategy(IStrategy):
 
         dataframe = self.freqai.start(dataframe, metadata, self)
         for val in self.std_dev_multiplier_buy.range:
-            dataframe[f'target_roi_{val}'] = dataframe["&-s_close_mean"] + \
-                dataframe["&-s_close_std"] * val
+            dataframe[f'target_roi_{val}'] = (
+                dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * val
+                )
         for val in self.std_dev_multiplier_sell.range:
-            dataframe[f'sell_roi_{val}'] = dataframe["&-s_close_mean"] - \
-                dataframe["&-s_close_std"] * val
+            dataframe[f'sell_roi_{val}'] = (
+                dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * val
+                )
         return dataframe
 
     def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
 
-        enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"]
-                                 > df[f"target_roi_{self.std_dev_multiplier_buy.value}"]]
+        enter_long_conditions = [
+            df["do_predict"] == 1,
+            df["&-s_close"] > df[f"target_roi_{self.std_dev_multiplier_buy.value}"],
+            ]
 
         if enter_long_conditions:
             df.loc[
                 reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
             ] = (1, "long")
 
-        enter_short_conditions = [df["do_predict"] == 1, df["&-s_close"]
-                                  < df[f"sell_roi_{self.std_dev_multiplier_sell.value}"]]
+        enter_short_conditions = [
+            df["do_predict"] == 1,
+            df["&-s_close"] < df[f"sell_roi_{self.std_dev_multiplier_sell.value}"],
+            ]
 
         if enter_short_conditions:
             df.loc[
@@ -211,13 +204,17 @@ class FreqaiExampleStrategy(IStrategy):
         return df
 
     def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
-        exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] <
-                                df[f"sell_roi_{self.std_dev_multiplier_sell.value}"] * 0.25]
+        exit_long_conditions = [
+            df["do_predict"] == 1,
+            df["&-s_close"] < df[f"sell_roi_{self.std_dev_multiplier_sell.value}"] * 0.25,
+            ]
         if exit_long_conditions:
             df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
 
-        exit_short_conditions = [df["do_predict"] == 1, df["&-s_close"] >
-                                 df[f"target_roi_{self.std_dev_multiplier_buy.value}"] * 0.25]
+        exit_short_conditions = [
+            df["do_predict"] == 1,
+            df["&-s_close"] > df[f"target_roi_{self.std_dev_multiplier_buy.value}"] * 0.25,
+            ]
         if exit_short_conditions:
             df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
 

freqtrade/templates/FreqaiHybridExampleStrategy.py

@@ -95,20 +95,6 @@ class FreqaiExampleHybridStrategy(IStrategy):
     short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True)
     exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
 
-    # FreqAI required function, leave as is or add additional informatives to existing structure.
-    def informative_pairs(self):
-        whitelist_pairs = self.dp.current_whitelist()
-        corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
-        informative_pairs = []
-        for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
-            for pair in whitelist_pairs:
-                informative_pairs.append((pair, tf))
-            for pair in corr_pairs:
-                if pair in whitelist_pairs:
-                    continue  # avoid duplication
-                informative_pairs.append((pair, tf))
-        return informative_pairs
-
     # FreqAI required function, user can add or remove indicators, but general structure
     # must stay the same.
     def populate_any_indicators(

freqtrade/templates/base_strategy.py.j2

@@ -1,21 +1,21 @@
 # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
 # flake8: noqa: F401
-
+# isort: skip_file
 # --- Do not remove these libs ---
-import numpy as np  # noqa
-import pandas as pd  # noqa
-from pandas import DataFrame  # noqa
-from datetime import datetime  # noqa
-from typing import Optional, Union  # noqa
+import numpy as np
+import pandas as pd
+from pandas import DataFrame
+from datetime import datetime
+from typing import Optional, Union
 
 from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
-                                IStrategy, IntParameter)
+                                IntParameter, IStrategy, merge_informative_pair)
 
 # --------------------------------
 # Add your lib to import here
 import talib.abstract as ta
 import pandas_ta as pta
-import freqtrade.vendor.qtpylib.indicators as qtpylib
+from technical import qtpylib
 
 
 class {{ strategy }}(IStrategy):

freqtrade/templates/sample_hyperopt_loss.py

@@ -4,6 +4,7 @@ from typing import Dict
 
 from pandas import DataFrame
 
+from freqtrade.constants import Config
 from freqtrade.optimize.hyperopt import IHyperOptLoss
 
 
@@ -36,7 +37,7 @@ class SampleHyperOptLoss(IHyperOptLoss):
     @staticmethod
     def hyperopt_loss_function(results: DataFrame, trade_count: int,
                                min_date: datetime, max_date: datetime,
-                               config: Dict, processed: Dict[str, DataFrame],
+                               config: Config, processed: Dict[str, DataFrame],
                                *args, **kwargs) -> float:
         """
         Objective function, returns smaller number for better results