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Modify comment in new test-strategies to point out their purpose

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This commit is contained in:
Matthias 2022-08-04 07:17:26 +02:00
parent eae82d0222
commit 778833f90e
2 changed files with 4 additions and 52 deletions
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28
tests/strategy/strats/freqai_test_multimodel_strat.py
View File

@ -13,13 +13,8 @@ logger = logging.getLogger(__name__)
class freqai_test_multimodel_strat(IStrategy):
"""
Example strategy showing how the user connects their own
IFreqaiModel to the strategy. Namely, the user uses:
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
canonical freqtrade configuration file under config['freqai'].
Test strategy - used for testing freqAI multimodel functionalities.
DO not use in production.
"""
minimal_roi = {"0": 0.1, "240": -1}
@ -64,20 +59,6 @@ class freqai_test_multimodel_strat(IStrategy):
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
"""
Function designed to automatically generate, name and merge features
from user indicated timeframes in the configuration file. User controls the indicators
passed to the training/prediction by prepending indicators with `'%-' + coin `
(see convention below). I.e. user should not prepend any supporting metrics
(e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
model.
:params:
:pair: pair to be used as informative
:df: strategy dataframe which will receive merges from informatives
:tf: timeframe of the dataframe which will modify the feature names
:informative: the dataframe associated with the informative pair
:coin: the name of the coin which will modify the feature names.
"""
coin = pair.split('/')[0]
@ -149,11 +130,6 @@ class freqai_test_multimodel_strat(IStrategy):
self.freqai_info = self.config["freqai"]
# All indicators must be populated by populate_any_indicators() for live functionality
# to work correctly.
# 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.freqai.start(dataframe, metadata, self)
dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25

28
tests/strategy/strats/freqai_test_strat.py
View File

@ -13,13 +13,8 @@ logger = logging.getLogger(__name__)
class freqai_test_strat(IStrategy):
"""
Example strategy showing how the user connects their own
IFreqaiModel to the strategy. Namely, the user uses:
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
canonical freqtrade configuration file under config['freqai'].
Test strategy - used for testing freqAI functionalities.
DO not use in production.
"""
minimal_roi = {"0": 0.1, "240": -1}
@ -64,20 +59,6 @@ class freqai_test_strat(IStrategy):
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
"""
Function designed to automatically generate, name and merge features
from user indicated timeframes in the configuration file. User controls the indicators
passed to the training/prediction by prepending indicators with `'%-' + coin `
(see convention below). I.e. user should not prepend any supporting metrics
(e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
model.
:params:
:pair: pair to be used as informative
:df: strategy dataframe which will receive merges from informatives
:tf: timeframe of the dataframe which will modify the feature names
:informative: the dataframe associated with the informative pair
:coin: the name of the coin which will modify the feature names.
"""
coin = pair.split('/')[0]
@ -137,11 +118,6 @@ class freqai_test_strat(IStrategy):
self.freqai_info = self.config["freqai"]
# All indicators must be populated by populate_any_indicators() for live functionality
# to work correctly.
# 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.freqai.start(dataframe, metadata, self)
dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25