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Remove freqAI model bridge in favor of `self.freqai`

This commit is contained in:
Matthias 2022-07-23 15:58:31 +02:00
parent c91e23dc50
commit 8fa6e8b4ba
4 changed files with 25 additions and 36 deletions
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12
freqtrade/freqai/strategy_bridge.py
View File

@ -1,12 +0,0 @@
from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
class CustomModel:
"""
A bridge between the user defined IFreqaiModel class
and the strategy.
"""
def __init__(self, config):
self.bridge = FreqaiModelResolver.load_freqaimodel(config)

9
freqtrade/strategy/interface.py
View File

@ -145,11 +145,20 @@ class IStrategy(ABC, HyperStrategyMixin):
informative_data.candle_type = config['candle_type_def']
self._ft_informative.append((informative_data, cls_method))
def load_freqAI_model(self) -> None:
if self.config.get('freqai', None):
# Import here to avoid importing this if freqAI is disabled
from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
self.freqai = FreqaiModelResolver.load_freqaimodel(self.config)
def ft_bot_start(self, **kwargs) -> None:
"""
Strategy init - runs after dataprovider has been added.
Must call bot_start()
"""
self.load_freqAI_model()
strategy_safe_wrapper(self.bot_start)()
self.ft_load_hyper_params(self.config.get('runmode') == RunMode.HYPEROPT)

30
freqtrade/templates/FreqaiExampleStrategy.py
View File

@ -7,7 +7,6 @@ from pandas import DataFrame
from technical import qtpylib
from freqtrade.exchange import timeframe_to_prev_date
from freqtrade.freqai.strategy_bridge import CustomModel
from freqtrade.persistence import Trade
from freqtrade.strategy import DecimalParameter, IntParameter, merge_informative_pair
from freqtrade.strategy.interface import IStrategy
@ -21,7 +20,7 @@ class FreqaiExampleStrategy(IStrategy):
Example strategy showing how the user connects their own
IFreqaiModel to the strategy. Namely, the user uses:
self.model = CustomModel(self.config)
self.model.bridge.start(dataframe, metadata)
self.freqai.start(dataframe, metadata)
to make predictions on their data. populate_any_indicators() automatically
generates the variety of features indicated by the user in the
@ -67,9 +66,6 @@ class FreqaiExampleStrategy(IStrategy):
informative_pairs.append((pair, tf))
return informative_pairs
def bot_start(self):
self.model = CustomModel(self.config)
def populate_any_indicators(
self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False
):
@ -88,7 +84,7 @@ class FreqaiExampleStrategy(IStrategy):
:coin: the name of the coin which will modify the feature names.
"""
with self.model.bridge.lock:
with self.freqai.lock:
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
@ -180,7 +176,7 @@ class FreqaiExampleStrategy(IStrategy):
# the target mean/std values for each of the labels created by user in
# `populate_any_indicators()` for each training period.
dataframe = self.model.bridge.start(dataframe, metadata, self)
dataframe = self.freqai.start(dataframe, metadata, self)
dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
@ -234,9 +230,9 @@ class FreqaiExampleStrategy(IStrategy):
follow_mode = self.config.get("freqai", {}).get("follow_mode", False)
if not follow_mode:
pair_dict = self.model.bridge.dd.pair_dict
pair_dict = self.freqai.dd.pair_dict
else:
pair_dict = self.model.bridge.dd.follower_dict
pair_dict = self.freqai.dd.follower_dict
entry_tag = trade.enter_tag
@ -244,12 +240,12 @@ class FreqaiExampleStrategy(IStrategy):
"prediction" + entry_tag not in pair_dict[pair]
or pair_dict[pair]["prediction" + entry_tag] > 0
):
with self.model.bridge.lock:
with self.freqai.lock:
pair_dict[pair]["prediction" + entry_tag] = abs(trade_candle["&-s_close"])
if not follow_mode:
self.model.bridge.dd.save_drawer_to_disk()
self.freqai.dd.save_drawer_to_disk()
else:
self.model.bridge.dd.save_follower_dict_to_disk()
self.freqai.dd.save_follower_dict_to_disk()
roi_price = pair_dict[pair]["prediction" + entry_tag]
roi_time = self.max_roi_time_long.value
@ -284,16 +280,16 @@ class FreqaiExampleStrategy(IStrategy):
entry_tag = trade.enter_tag
follow_mode = self.config.get("freqai", {}).get("follow_mode", False)
if not follow_mode:
pair_dict = self.model.bridge.dd.pair_dict
pair_dict = self.freqai.dd.pair_dict
else:
pair_dict = self.model.bridge.dd.follower_dict
pair_dict = self.freqai.dd.follower_dict
with self.model.bridge.lock:
with self.freqai.lock:
pair_dict[pair]["prediction" + entry_tag] = 0
if not follow_mode:
self.model.bridge.dd.save_drawer_to_disk()
self.freqai.dd.save_drawer_to_disk()
else:
self.model.bridge.dd.save_follower_dict_to_disk()
self.freqai.dd.save_follower_dict_to_disk()
return True

10
tests/strategy/strats/freqai_test_strat.py
View File

@ -5,7 +5,6 @@ import pandas as pd
import talib.abstract as ta
from pandas import DataFrame
from freqtrade.freqai.strategy_bridge import CustomModel
from freqtrade.strategy import DecimalParameter, IntParameter, merge_informative_pair
from freqtrade.strategy.interface import IStrategy
@ -18,7 +17,7 @@ class freqai_test_strat(IStrategy):
Example strategy showing how the user connects their own
IFreqaiModel to the strategy. Namely, the user uses:
self.model = CustomModel(self.config)
self.model.bridge.start(dataframe, metadata)
self.freqai.start(dataframe, metadata)
to make predictions on their data. populate_any_indicators() automatically
generates the variety of features indicated by the user in the
@ -64,9 +63,6 @@ class freqai_test_strat(IStrategy):
informative_pairs.append((pair, tf))
return informative_pairs
def bot_start(self):
self.model = CustomModel(self.config)
def populate_any_indicators(
self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False
):
@ -85,7 +81,7 @@ class freqai_test_strat(IStrategy):
:coin: the name of the coin which will modify the feature names.
"""
with self.model.bridge.lock:
with self.freqai.lock:
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
@ -146,7 +142,7 @@ class freqai_test_strat(IStrategy):
# the model will return 4 values, its prediction, an indication of whether or not the
# prediction should be accepted, the target mean/std values from the labels used during
# each training period.
dataframe = self.model.bridge.start(dataframe, metadata, self)
dataframe = self.freqai.start(dataframe, metadata, self)
dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25