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DataFrame transmission, strategy follower logic

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This commit is contained in:
Timothy Pogue
2022-08-21 22:45:36 -06:00
parent 739b68f8fd
commit 6f5478cc02
13 changed files with 332 additions and 79 deletions
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92
freqtrade/strategy/interface.py
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@@ -5,7 +5,7 @@ This module defines the interface to apply for strategies
import logging
from abc import ABC, abstractmethod
from datetime import datetime, timedelta, timezone
from typing import Dict, List, Optional, Tuple, Union
from typing import Callable, Dict, List, Optional, Tuple, Union
import arrow
from pandas import DataFrame
@@ -18,6 +18,7 @@ from freqtrade.enums.runmode import RunMode
from freqtrade.exceptions import OperationalException, StrategyError
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_next_date, timeframe_to_seconds
from freqtrade.persistence import Order, PairLocks, Trade
from freqtrade.rpc.replicate import ReplicateController
from freqtrade.strategy.hyper import HyperStrategyMixin
from freqtrade.strategy.informative_decorator import (InformativeData, PopulateIndicators,
_create_and_merge_informative_pair,
@@ -110,6 +111,7 @@ class IStrategy(ABC, HyperStrategyMixin):
# the dataprovider (dp) (access to other candles, historic data, ...)
# and wallets - access to the current balance.
dp: DataProvider
replicate_controller: Optional[ReplicateController]
wallets: Optional[Wallets] = None
# Filled from configuration
stake_currency: str
@@ -123,6 +125,7 @@ class IStrategy(ABC, HyperStrategyMixin):
self.config = config
# Dict to determine if analysis is necessary
self._last_candle_seen_per_pair: Dict[str, datetime] = {}
self._last_candle_seen_external: Dict[str, datetime] = {}
super().__init__(config)
# Gather informative pairs from @informative-decorated methods.
@@ -678,7 +681,12 @@ class IStrategy(ABC, HyperStrategyMixin):
lock_time = timeframe_to_next_date(self.timeframe, candle_date)
return PairLocks.is_pair_locked(pair, lock_time, side=side)
def analyze_ticker(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def analyze_ticker(
self,
dataframe: DataFrame,
metadata: dict,
populate_indicators: bool = True
) -> DataFrame:
"""
Parses the given candle (OHLCV) data and returns a populated DataFrame
add several TA indicators and entry order signal to it
@@ -687,12 +695,19 @@ class IStrategy(ABC, HyperStrategyMixin):
:return: DataFrame of candle (OHLCV) data with indicator data and signals added
"""
logger.debug("TA Analysis Launched")
dataframe = self.advise_indicators(dataframe, metadata)
if populate_indicators:
dataframe = self.advise_indicators(dataframe, metadata)
dataframe = self.advise_entry(dataframe, metadata)
dataframe = self.advise_exit(dataframe, metadata)
return dataframe
def _analyze_ticker_internal(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def _analyze_ticker_internal(
self,
dataframe: DataFrame,
metadata: dict,
external_data: bool = False,
finish_callback: Optional[Callable] = None,
) -> DataFrame:
"""
Parses the given candle (OHLCV) data and returns a populated DataFrame
add several TA indicators and buy signal to it
@@ -707,12 +722,19 @@ class IStrategy(ABC, HyperStrategyMixin):
# always run if process_only_new_candles is set to false
if (not self.process_only_new_candles or
self._last_candle_seen_per_pair.get(pair, None) != dataframe.iloc[-1]['date']):
populate_indicators = not external_data
# Defs that only make change on new candle data.
dataframe = self.analyze_ticker(dataframe, metadata)
dataframe = self.analyze_ticker(dataframe, metadata, populate_indicators)
self._last_candle_seen_per_pair[pair] = dataframe.iloc[-1]['date']
self.dp._set_cached_df(
pair, self.timeframe, dataframe,
candle_type=self.config.get('candle_type_def', CandleType.SPOT))
candle_type = self.config.get('candle_type_def', CandleType.SPOT)
self.dp._set_cached_df(pair, self.timeframe, dataframe, candle_type=candle_type)
if finish_callback:
finish_callback(pair, dataframe, self.timeframe, candle_type)
else:
logger.debug("Skipping TA Analysis for already analyzed candle")
dataframe[SignalType.ENTER_LONG.value] = 0
@@ -726,16 +748,25 @@ class IStrategy(ABC, HyperStrategyMixin):
return dataframe
def analyze_pair(self, pair: str) -> None:
def analyze_pair(
self,
pair: str,
external_data: bool = False,
finish_callback: Optional[Callable] = None,
) -> None:
"""
Fetch data for this pair from dataprovider and analyze.
Stores the dataframe into the dataprovider.
The analyzed dataframe is then accessible via `dp.get_analyzed_dataframe()`.
:param pair: Pair to analyze.
"""
dataframe = self.dp.ohlcv(
pair, self.timeframe, candle_type=self.config.get('candle_type_def', CandleType.SPOT)
)
candle_type = self.config.get('candle_type_def', CandleType.SPOT)
if not external_data:
dataframe = self.dp.ohlcv(pair, self.timeframe, candle_type)
else:
dataframe, last_analyzed = self.dp.get_external_df(pair, self.timeframe, candle_type)
if not isinstance(dataframe, DataFrame) or dataframe.empty:
logger.warning('Empty candle (OHLCV) data for pair %s', pair)
return
@@ -745,7 +776,7 @@ class IStrategy(ABC, HyperStrategyMixin):
dataframe = strategy_safe_wrapper(
self._analyze_ticker_internal, message=""
)(dataframe, {'pair': pair})
)(dataframe, {'pair': pair}, external_data, finish_callback)
self.assert_df(dataframe, df_len, df_close, df_date)
except StrategyError as error:
@@ -756,15 +787,43 @@ class IStrategy(ABC, HyperStrategyMixin):
logger.warning('Empty dataframe for pair %s', pair)
return
def analyze(self, pairs: List[str]) -> None:
def analyze(
self,
pairs: List[str],
finish_callback: Optional[Callable] = None
) -> None:
"""
Analyze all pairs using analyze_pair().
:param pairs: List of pairs to analyze
"""
for pair in pairs:
self.analyze_pair(pair, finish_callback=finish_callback)
def analyze_external(self, pairs: List[str], leader_pairs: List[str]) -> None:
"""
Analyze the pre-populated dataframes from the Leader
:param pairs: The active pair whitelist
:param leader_pairs: The list of pairs from the Leaders
"""
# Get the extra pairs not listed in Leader pairs, and process
# them normally.
# List order is not preserved when doing this!
# We use ^ instead of - for symmetric difference
# What do we do with these?
extra_pairs = list(set(pairs) ^ set(leader_pairs))
# These would be the pairs that we have trades in, which means
# we would have to analyze them normally
for pair in leader_pairs:
# Analyze the pairs, but get the dataframe from the external data
self.analyze_pair(pair, external_data=True)
for pair in extra_pairs:
self.analyze_pair(pair)
@staticmethod
@ staticmethod
def preserve_df(dataframe: DataFrame) -> Tuple[int, float, datetime]:
""" keep some data for dataframes """
return len(dataframe), dataframe["close"].iloc[-1], dataframe["date"].iloc[-1]
@@ -1185,6 +1244,9 @@ class IStrategy(ABC, HyperStrategyMixin):
dataframe = _create_and_merge_informative_pair(
self, dataframe, metadata, inf_data, populate_fn)
# If in follower mode, get analyzed dataframe from leader df's in dp
# otherise run populate_indicators
return self.populate_indicators(dataframe, metadata)
def advise_entry(self, dataframe: DataFrame, metadata: dict) -> DataFrame: