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isolate data_drawer functions from data_kitchen, accommodate tests, add new test

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This commit is contained in:
robcaulk
2022-07-26 10:24:14 +02:00
parent 56b17e6f3c
commit e213d0ad55
12 changed files with 606 additions and 376 deletions
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12
tests/freqai/conftest.py
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@@ -1,12 +1,12 @@
from copy import deepcopy
from pathlib import Path
from unittest.mock import MagicMock
import pytest
from freqtrade.configuration import TimeRange
from freqtrade.data.dataprovider import DataProvider
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.data_drawer import FreqaiDataDrawer
from freqtrade.resolvers import StrategyResolver
from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
from tests.conftest import get_patched_exchange
@@ -57,11 +57,17 @@ def freqai_conf(default_conf, tmpdir):
def get_patched_data_kitchen(mocker, freqaiconf):
dd = mocker.patch('freqtrade.freqai.data_drawer', MagicMock())
dk = FreqaiDataKitchen(freqaiconf, dd)
# dd = mocker.patch('freqtrade.freqai.data_drawer', MagicMock())
dk = FreqaiDataKitchen(freqaiconf)
return dk
def get_patched_data_drawer(mocker, freqaiconf):
# dd = mocker.patch('freqtrade.freqai.data_drawer', MagicMock())
dd = FreqaiDataDrawer(freqaiconf)
return dd
def get_patched_freqai_strategy(mocker, freqaiconf):
strategy = StrategyResolver.load_strategy(freqaiconf)
strategy.ft_bot_start()

95
tests/freqai/test_freqai_datadrawer.py Normal file
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@@ -0,0 +1,95 @@
import shutil
from pathlib import Path
from freqtrade.configuration import TimeRange
from freqtrade.data.dataprovider import DataProvider
# from freqtrade.freqai.data_drawer import FreqaiDataDrawer
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from tests.conftest import get_patched_exchange
from tests.freqai.conftest import get_patched_freqai_strategy
def test_update_historic_data(mocker, freqai_conf):
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180114")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"])
dp_candles = len(strategy.dp.get_pair_dataframe("ADA/BTC", "5m"))
candle_difference = dp_candles - historic_candles
freqai.dd.update_historic_data(strategy, freqai.dk)
updated_historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"])
assert updated_historic_candles - historic_candles == candle_difference
shutil.rmtree(Path(freqai.dk.full_path))
def test_load_all_pairs_histories(mocker, freqai_conf):
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180114")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
assert len(freqai.dd.historic_data.keys()) == len(
freqai_conf.get("exchange", {}).get("pair_whitelist")
)
assert len(freqai.dd.historic_data["ADA/BTC"]) == len(
freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes")
)
shutil.rmtree(Path(freqai.dk.full_path))
def test_get_base_and_corr_dataframes(mocker, freqai_conf):
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180114")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
sub_timerange = TimeRange.parse_timerange("20180111-20180114")
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
num_tfs = len(
freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes")
)
assert len(base_df.keys()) == num_tfs
assert len(corr_df.keys()) == len(
freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_corr_pairlist")
)
assert len(corr_df["ADA/BTC"].keys()) == num_tfs
shutil.rmtree(Path(freqai.dk.full_path))
def test_use_strategy_to_populate_indicators(mocker, freqai_conf):
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180114")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
sub_timerange = TimeRange.parse_timerange("20180111-20180114")
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, 'LTC/BTC')
assert len(df.columns) == 45
shutil.rmtree(Path(freqai.dk.full_path))

92
tests/freqai/test_freqai_datakitchen.py
View File

@@ -4,13 +4,8 @@ from pathlib import Path
import pytest
from freqtrade.configuration import TimeRange
from freqtrade.data.dataprovider import DataProvider
# from freqtrade.freqai.data_drawer import FreqaiDataDrawer
from freqtrade.exceptions import OperationalException
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from tests.conftest import get_patched_exchange
from tests.freqai.conftest import get_patched_data_kitchen, get_patched_freqai_strategy
from tests.freqai.conftest import get_patched_data_kitchen
@pytest.mark.parametrize(
@@ -60,27 +55,6 @@ def test_split_timerange(
shutil.rmtree(Path(dk.full_path))
def test_update_historic_data(mocker, freqai_conf):
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
timerange = TimeRange.parse_timerange("20180110-20180114")
freqai.dk.load_all_pair_histories(timerange)
historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"])
dp_candles = len(strategy.dp.get_pair_dataframe("ADA/BTC", "5m"))
candle_difference = dp_candles - historic_candles
freqai.dk.update_historic_data(strategy)
updated_historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"])
assert updated_historic_candles - historic_candles == candle_difference
shutil.rmtree(Path(freqai.dk.full_path))
@pytest.mark.parametrize(
"timestamp, expected",
[
@@ -92,67 +66,3 @@ def test_check_if_model_expired(mocker, freqai_conf, timestamp, expected):
dk = get_patched_data_kitchen(mocker, freqai_conf)
assert dk.check_if_model_expired(timestamp) == expected
shutil.rmtree(Path(dk.full_path))
def test_load_all_pairs_histories(mocker, freqai_conf):
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
timerange = TimeRange.parse_timerange("20180110-20180114")
freqai.dk.load_all_pair_histories(timerange)
assert len(freqai.dd.historic_data.keys()) == len(
freqai_conf.get("exchange", {}).get("pair_whitelist")
)
assert len(freqai.dd.historic_data["ADA/BTC"]) == len(
freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes")
)
shutil.rmtree(Path(freqai.dk.full_path))
def test_get_base_and_corr_dataframes(mocker, freqai_conf):
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
timerange = TimeRange.parse_timerange("20180110-20180114")
freqai.dk.load_all_pair_histories(timerange)
sub_timerange = TimeRange.parse_timerange("20180111-20180114")
corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
num_tfs = len(
freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes")
)
assert len(base_df.keys()) == num_tfs
assert len(corr_df.keys()) == len(
freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_corr_pairlist")
)
assert len(corr_df["ADA/BTC"].keys()) == num_tfs
shutil.rmtree(Path(freqai.dk.full_path))
def test_use_strategy_to_populate_indicators(mocker, freqai_conf):
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
timerange = TimeRange.parse_timerange("20180110-20180114")
freqai.dk.load_all_pair_histories(timerange)
sub_timerange = TimeRange.parse_timerange("20180111-20180114")
corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, 'LTC/BTC')
assert len(df.columns) == 45
shutil.rmtree(Path(freqai.dk.full_path))

77
tests/freqai/test_freqai_interface.py
View File

@@ -1,5 +1,3 @@
# from unittest.mock import MagicMock
# from freqtrade.commands.optimize_commands import setup_optimize_configuration, start_edge
import platform
import shutil
from pathlib import Path
@@ -23,9 +21,9 @@ def test_train_model_in_series_LightGBM(mocker, freqai_conf):
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dk.load_all_pair_histories(timerange)
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
freqai.dd.pair_dict = MagicMock()
@@ -42,6 +40,36 @@ def test_train_model_in_series_LightGBM(mocker, freqai_conf):
shutil.rmtree(Path(freqai.dk.full_path))
def test_train_model_in_series_LightGBMMultiModel(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"strategy": "freqai_test_multimodel_strat"})
freqai_conf.update({"freqaimodel": "LightGBMPredictionMultiModel"})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
freqai.dd.pair_dict = MagicMock()
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
new_timerange = TimeRange.parse_timerange("20180120-20180130")
freqai.train_model_in_series(new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
assert len(freqai.dk.label_list) == 2
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").is_file()
shutil.rmtree(Path(freqai.dk.full_path))
@pytest.mark.skipif("arm" in platform.uname()[-1], reason="no ARM for Catboost ...")
def test_train_model_in_series_Catboost(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
@@ -54,9 +82,9 @@ def test_train_model_in_series_Catboost(mocker, freqai_conf):
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dk.load_all_pair_histories(timerange)
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
freqai.dd.pair_dict = MagicMock()
@@ -82,11 +110,11 @@ def test_start_backtesting(mocker, freqai_conf):
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = False
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dk.load_all_pair_histories(timerange)
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
@@ -108,11 +136,11 @@ def test_start_backtesting_subdaily_backtest_period(mocker, freqai_conf):
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = False
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dk.load_all_pair_histories(timerange)
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
@@ -132,11 +160,11 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = False
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dk.load_all_pair_histories(timerange)
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
@@ -155,11 +183,11 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = False
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dk.load_all_pair_histories(timerange)
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
freqai.start_backtesting(df, metadata, freqai.dk)
@@ -181,13 +209,12 @@ def test_follow_mode(mocker, freqai_conf):
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dk.load_all_pair_histories(timerange)
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
metadata = {"pair": "ADA/BTC"}
freqai.dd.set_pair_dict_info(metadata)
# freqai.dd.pair_dict = MagicMock()
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
new_timerange = TimeRange.parse_timerange("20180120-20180130")
@@ -209,9 +236,9 @@ def test_follow_mode(mocker, freqai_conf):
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd, freqai.live)
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.live)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dk.load_all_pair_histories(timerange)
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
df = strategy.dp.get_pair_dataframe('ADA/BTC', '5m')
freqai.start_live(df, metadata, strategy, freqai.dk)
@@ -232,9 +259,9 @@ def test_principal_component_analysis(mocker, freqai_conf):
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dk.load_all_pair_histories(timerange)
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
freqai.dd.pair_dict = MagicMock()

1
tests/rpc/test_rpc_apiserver.py
View File

@@ -1404,6 +1404,7 @@ def test_api_strategies(botclient):
'StrategyTestV3',
'StrategyTestV3Analysis',
'StrategyTestV3Futures',
'freqai_test_multimodel_strat',
'freqai_test_strat'
]}

188
tests/strategy/strats/freqai_test_multimodel_strat.py Normal file
View File

@@ -0,0 +1,188 @@
import logging
from functools import reduce
import pandas as pd
import talib.abstract as ta
from pandas import DataFrame
from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair
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'].
"""
minimal_roi = {"0": 0.1, "240": -1}
plot_config = {
"main_plot": {},
"subplots": {
"prediction": {"prediction": {"color": "blue"}},
"target_roi": {
"target_roi": {"color": "brown"},
},
"do_predict": {
"do_predict": {"color": "brown"},
},
},
}
process_only_new_candles = True
stoploss = -0.05
use_exit_signal = True
startup_candle_count: int = 300
can_short = False
linear_roi_offset = DecimalParameter(
0.00, 0.02, default=0.005, space="sell", optimize=False, load=True
)
max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=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
def populate_any_indicators(
self, metadata, pair, df, tf, informative=None, coin="", 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.
"""
with self.freqai.lock:
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
# first loop is automatically duplicating indicators for time periods
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
t = int(t)
informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
informative[f"%-{coin}raw_volume"] = informative["volume"]
informative[f"%-{coin}raw_price"] = informative["close"]
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
if n == 0:
continue
informative_shift = informative[indicators].shift(n)
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
informative = pd.concat((informative, informative_shift), axis=1)
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
skip_columns = [
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
]
df = df.drop(columns=skip_columns)
# Add generalized indicators here (because in live, it will call this
# function to populate indicators during training). Notice how we ensure not to
# add them multiple times
if set_generalized_indicators:
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
# user adds targets here by prepending them with &- (see convention below)
# If user wishes to use multiple targets, a multioutput prediction model
# needs to be used such as templates/CatboostPredictionMultiModel.py
df["&-s_close"] = (
df["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ df["close"]
- 1
)
df["&-s_range"] = (
df["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.max()
-
df["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.min()
)
return df
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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
dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
return dataframe
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"]]
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["sell_roi"]]
if enter_short_conditions:
df.loc[
reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
] = (1, "short")
return df
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"] * 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["target_roi"] * 0.25]
if exit_short_conditions:
df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
return df

6
tests/strategy/test_strategy_loading.py
View File

@@ -34,7 +34,7 @@ def test_search_all_strategies_no_failed():
directory = Path(__file__).parent / "strats"
strategies = StrategyResolver.search_all_objects(directory, enum_failed=False)
assert isinstance(strategies, list)
assert len(strategies) == 8
assert len(strategies) == 9
assert isinstance(strategies[0], dict)
@@ -42,10 +42,10 @@ def test_search_all_strategies_with_failed():
directory = Path(__file__).parent / "strats"
strategies = StrategyResolver.search_all_objects(directory, enum_failed=True)
assert isinstance(strategies, list)
assert len(strategies) == 9
assert len(strategies) == 10
# with enum_failed=True search_all_objects() shall find 2 good strategies
# and 1 which fails to load
assert len([x for x in strategies if x['class'] is not None]) == 8
assert len([x for x in strategies if x['class'] is not None]) == 9
assert len([x for x in strategies if x['class'] is None]) == 1