Merge branch 'develop' into feat_readjust_entry

freqtrade/strategy/informative_decorator.py

@@ -23,7 +23,7 @@ class InformativeData:
 def informative(timeframe: str, asset: str = '',
                 fmt: Optional[Union[str, Callable[[Any], str]]] = None,
                 *,
-                candle_type: Optional[CandleType] = None,
+                candle_type: Optional[Union[CandleType, str]] = None,
                 ffill: bool = True) -> Callable[[PopulateIndicators], PopulateIndicators]:
     """
     A decorator for populate_indicators_Nn(self, dataframe, metadata), allowing these functions to

freqtrade/strategy/interface.py

@@ -3,7 +3,6 @@ IStrategy interface
 This module defines the interface to apply for strategies
 """
 import logging
-import warnings
 from abc import ABC, abstractmethod
 from datetime import datetime, timedelta, timezone
 from typing import Dict, List, Optional, Tuple, Union
@@ -44,14 +43,11 @@ class IStrategy(ABC, HyperStrategyMixin):
     """
     # Strategy interface version
     # Default to version 2
-    # Version 1 is the initial interface without metadata dict
+    # Version 1 is the initial interface without metadata dict - deprecated and no longer supported.
     # Version 2 populate_* include metadata dict
     # Version 3 - First version with short and leverage support
     INTERFACE_VERSION: int = 3
 
-    _populate_fun_len: int = 0
-    _buy_fun_len: int = 0
-    _sell_fun_len: int = 0
     _ft_params_from_file: Dict
     # associated minimal roi
     minimal_roi: Dict = {}
@@ -114,7 +110,7 @@ class IStrategy(ABC, HyperStrategyMixin):
     # Class level variables (intentional) containing
     # the dataprovider (dp) (access to other candles, historic data, ...)
     # and wallets - access to the current balance.
-    dp: Optional[DataProvider]
+    dp: DataProvider
     wallets: Optional[Wallets] = None
     # Filled from configuration
     stake_currency: str
@@ -359,7 +355,7 @@ class IStrategy(ABC, HyperStrategyMixin):
 
     def custom_exit_price(self, pair: str, trade: Trade,
                           current_time: datetime, proposed_rate: float,
-                          current_profit: float, **kwargs) -> float:
+                          current_profit: float, exit_tag: Optional[str], **kwargs) -> float:
         """
         Custom exit price logic, returning the new exit price.
 
@@ -372,6 +368,7 @@ class IStrategy(ABC, HyperStrategyMixin):
         :param current_time: datetime object, containing the current datetime
         :param proposed_rate: Rate, calculated based on pricing settings in exit_pricing.
         :param current_profit: Current profit (as ratio), calculated based on current_rate.
+        :param exit_tag: Exit reason.
         :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
         :return float: New exit price value if provided
         """
@@ -1116,12 +1113,7 @@ class IStrategy(ABC, HyperStrategyMixin):
             dataframe = _create_and_merge_informative_pair(
                 self, dataframe, metadata, inf_data, populate_fn)
 
-        if self._populate_fun_len == 2:
-            warnings.warn("deprecated - check out the Sample strategy to see "
-                          "the current function headers!", DeprecationWarning)
-            return self.populate_indicators(dataframe)  # type: ignore
-        else:
-            return self.populate_indicators(dataframe, metadata)
+        return self.populate_indicators(dataframe, metadata)
 
     def advise_entry(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         """
@@ -1135,12 +1127,7 @@ class IStrategy(ABC, HyperStrategyMixin):
 
         logger.debug(f"Populating enter signals for pair {metadata.get('pair')}.")
 
-        if self._buy_fun_len == 2:
-            warnings.warn("deprecated - check out the Sample strategy to see "
-                          "the current function headers!", DeprecationWarning)
-            df = self.populate_buy_trend(dataframe)  # type: ignore
-        else:
-            df = self.populate_entry_trend(dataframe, metadata)
+        df = self.populate_entry_trend(dataframe, metadata)
         if 'enter_long' not in df.columns:
             df = df.rename({'buy': 'enter_long', 'buy_tag': 'enter_tag'}, axis='columns')
 
@@ -1155,14 +1142,8 @@ class IStrategy(ABC, HyperStrategyMixin):
             currently traded pair
         :return: DataFrame with exit column
         """
-
         logger.debug(f"Populating exit signals for pair {metadata.get('pair')}.")
-        if self._sell_fun_len == 2:
-            warnings.warn("deprecated - check out the Sample strategy to see "
-                          "the current function headers!", DeprecationWarning)
-            df = self.populate_sell_trend(dataframe)  # type: ignore
-        else:
-            df = self.populate_exit_trend(dataframe, metadata)
+        df = self.populate_exit_trend(dataframe, metadata)
         if 'exit_long' not in df.columns:
             df = df.rename({'sell': 'exit_long'}, axis='columns')
         return df

freqtrade/strategy/strategy_helper.py

@@ -56,12 +56,18 @@ def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
 
     # Combine the 2 dataframes
     # all indicators on the informative sample MUST be calculated before this point
-    dataframe = pd.merge(dataframe, informative, left_on='date',
-                         right_on=date_merge, how='left')
+    if ffill:
+        # https://pandas.pydata.org/docs/user_guide/merging.html#timeseries-friendly-merging
+        # merge_ordered - ffill method is 2.5x faster than seperate ffill()
+        dataframe = pd.merge_ordered(dataframe, informative, fill_method="ffill", left_on='date',
+                                     right_on=date_merge, how='left')
+    else:
+        dataframe = pd.merge(dataframe, informative, left_on='date',
+                             right_on=date_merge, how='left')
     dataframe = dataframe.drop(date_merge, axis=1)
 
-    if ffill:
-        dataframe = dataframe.ffill()
+    # if ffill:
+    #     dataframe = dataframe.ffill()
 
     return dataframe