Merge branch 'develop' into backtest_fitlivepredictions
This commit is contained in:
Wagner Costa Santos
commit
f9c6c538be
@ -11,12 +11,14 @@
|
||||
"mounts": [
|
||||
"source=freqtrade-bashhistory,target=/home/ftuser/commandhistory,type=volume"
|
||||
],
|
||||
"workspaceMount": "source=${localWorkspaceFolder},target=/workspaces/freqtrade,type=bind,consistency=cached",
|
||||
// Uncomment to connect as a non-root user if you've added one. See https://aka.ms/vscode-remote/containers/non-root.
|
||||
"remoteUser": "ftuser",
|
||||
|
||||
"onCreateCommand": "pip install --user -e .",
|
||||
"postCreateCommand": "freqtrade create-userdir --userdir user_data/",
|
||||
|
||||
"workspaceFolder": "/freqtrade/",
|
||||
"workspaceFolder": "/workspaces/freqtrade",
|
||||
|
||||
"settings": {
|
||||
"terminal.integrated.shell.linux": "/bin/bash",
|
||||
|
||||
@ -39,7 +39,7 @@ Please read the [exchange specific notes](docs/exchanges.md) to learn about even
|
||||
|
||||
- [X] [Binance](https://www.binance.com/)
|
||||
- [X] [Gate.io](https://www.gate.io/ref/6266643)
|
||||
- [X] [OKX](https://okx.com/).
|
||||
- [X] [OKX](https://okx.com/)
|
||||
|
||||
Please make sure to read the [exchange specific notes](docs/exchanges.md), as well as the [trading with leverage](docs/leverage.md) documentation before diving in.
|
||||
|
||||
|
||||
@ -175,6 +175,10 @@ print(res)
|
||||
|
||||
## FTX
|
||||
|
||||
!!! Warning
|
||||
Due to the current situation, we can no longer recommend FTX.
|
||||
Please make sure to investigate the current situation before depositing any funds to FTX.
|
||||
|
||||
!!! Tip "Stoploss on Exchange"
|
||||
FTX supports `stoploss_on_exchange` and can use both stop-loss-market and stop-loss-limit orders. It provides great advantages, so we recommend to benefit from it.
|
||||
You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide which type of stoploss shall be used.
|
||||
|
||||
@ -32,7 +32,7 @@ Freqtrade is a free and open source crypto trading bot written in Python. It is
|
||||
- Run: Test your strategy with simulated money (Dry-Run mode) or deploy it with real money (Live-Trade mode).
|
||||
- Run using Edge (optional module): The concept is to find the best historical [trade expectancy](edge.md#expectancy) by markets based on variation of the stop-loss and then allow/reject markets to trade. The sizing of the trade is based on a risk of a percentage of your capital.
|
||||
- Control/Monitor: Use Telegram or a WebUI (start/stop the bot, show profit/loss, daily summary, current open trades results, etc.).
|
||||
- Analyse: Further analysis can be performed on either Backtesting data or Freqtrade trading history (SQL database), including automated standard plots, and methods to load the data into [interactive environments](data-analysis.md).
|
||||
- Analyze: Further analysis can be performed on either Backtesting data or Freqtrade trading history (SQL database), including automated standard plots, and methods to load the data into [interactive environments](data-analysis.md).
|
||||
|
||||
## Supported exchange marketplaces
|
||||
|
||||
@ -51,7 +51,7 @@ Please read the [exchange specific notes](exchanges.md) to learn about eventual,
|
||||
|
||||
- [X] [Binance](https://www.binance.com/)
|
||||
- [X] [Gate.io](https://www.gate.io/ref/6266643)
|
||||
- [X] [OKX](https://okx.com/).
|
||||
- [X] [OKX](https://okx.com/)
|
||||
|
||||
Please make sure to read the [exchange specific notes](exchanges.md), as well as the [trading with leverage](leverage.md) documentation before diving in.
|
||||
|
||||
|
||||
@ -21,7 +21,11 @@ class Bybit(Exchange):
|
||||
|
||||
_ft_has: Dict = {
|
||||
"ohlcv_candle_limit": 200,
|
||||
"ccxt_futures_name": "linear"
|
||||
"ccxt_futures_name": "linear",
|
||||
"ohlcv_has_history": False,
|
||||
}
|
||||
_ft_has_futures: Dict = {
|
||||
"ohlcv_has_history": True,
|
||||
}
|
||||
|
||||
_supported_trading_mode_margin_pairs: List[Tuple[TradingMode, MarginMode]] = [
|
||||
|
||||
93
freqtrade/freqai/base_models/FreqaiMultiOutputClassifier.py
Normal file
93
freqtrade/freqai/base_models/FreqaiMultiOutputClassifier.py
Normal file
@ -0,0 +1,93 @@
|
||||
import numpy as np
|
||||
from joblib import Parallel
|
||||
from sklearn.base import is_classifier
|
||||
from sklearn.multioutput import MultiOutputClassifier, _fit_estimator
|
||||
from sklearn.utils.fixes import delayed
|
||||
from sklearn.utils.multiclass import check_classification_targets
|
||||
from sklearn.utils.validation import has_fit_parameter
|
||||
|
||||
from freqtrade.exceptions import OperationalException
|
||||
|
||||
|
||||
class FreqaiMultiOutputClassifier(MultiOutputClassifier):
|
||||
|
||||
def fit(self, X, y, sample_weight=None, fit_params=None):
|
||||
"""Fit the model to data, separately for each output variable.
|
||||
Parameters
|
||||
----------
|
||||
X : {array-like, sparse matrix} of shape (n_samples, n_features)
|
||||
The input data.
|
||||
y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
|
||||
Multi-output targets. An indicator matrix turns on multilabel
|
||||
estimation.
|
||||
sample_weight : array-like of shape (n_samples,), default=None
|
||||
Sample weights. If `None`, then samples are equally weighted.
|
||||
Only supported if the underlying classifier supports sample
|
||||
weights.
|
||||
fit_params : A list of dicts for the fit_params
|
||||
Parameters passed to the ``estimator.fit`` method of each step.
|
||||
Each dict may contain same or different values (e.g. different
|
||||
eval_sets or init_models)
|
||||
.. versionadded:: 0.23
|
||||
Returns
|
||||
-------
|
||||
self : object
|
||||
Returns a fitted instance.
|
||||
"""
|
||||
|
||||
if not hasattr(self.estimator, "fit"):
|
||||
raise ValueError("The base estimator should implement a fit method")
|
||||
|
||||
y = self._validate_data(X="no_validation", y=y, multi_output=True)
|
||||
|
||||
if is_classifier(self):
|
||||
check_classification_targets(y)
|
||||
|
||||
if y.ndim == 1:
|
||||
raise ValueError(
|
||||
"y must have at least two dimensions for "
|
||||
"multi-output regression but has only one."
|
||||
)
|
||||
|
||||
if sample_weight is not None and not has_fit_parameter(
|
||||
self.estimator, "sample_weight"
|
||||
):
|
||||
raise ValueError("Underlying estimator does not support sample weights.")
|
||||
|
||||
if not fit_params:
|
||||
fit_params = [None] * y.shape[1]
|
||||
|
||||
self.estimators_ = Parallel(n_jobs=self.n_jobs)(
|
||||
delayed(_fit_estimator)(
|
||||
self.estimator, X, y[:, i], sample_weight, **fit_params[i]
|
||||
)
|
||||
for i in range(y.shape[1])
|
||||
)
|
||||
|
||||
self.classes_ = []
|
||||
for estimator in self.estimators_:
|
||||
self.classes_.extend(estimator.classes_)
|
||||
if len(set(self.classes_)) != len(self.classes_):
|
||||
raise OperationalException(f"Class labels must be unique across targets: "
|
||||
f"{self.classes_}")
|
||||
|
||||
if hasattr(self.estimators_[0], "n_features_in_"):
|
||||
self.n_features_in_ = self.estimators_[0].n_features_in_
|
||||
if hasattr(self.estimators_[0], "feature_names_in_"):
|
||||
self.feature_names_in_ = self.estimators_[0].feature_names_in_
|
||||
|
||||
return self
|
||||
|
||||
def predict_proba(self, X):
|
||||
"""
|
||||
Get predict_proba and stack arrays horizontally
|
||||
"""
|
||||
results = np.hstack(super().predict_proba(X))
|
||||
return np.squeeze(results)
|
||||
|
||||
def predict(self, X):
|
||||
"""
|
||||
Get predict and squeeze into 2D array
|
||||
"""
|
||||
results = super().predict(X)
|
||||
return np.squeeze(results)
|
||||
@ -87,6 +87,7 @@ class FreqaiDataDrawer:
|
||||
self.create_follower_dict()
|
||||
self.load_drawer_from_disk()
|
||||
self.load_historic_predictions_from_disk()
|
||||
self.metric_tracker: Dict[str, Dict[str, Dict[str, list]]] = {}
|
||||
self.load_metric_tracker_from_disk()
|
||||
self.training_queue: Dict[str, int] = {}
|
||||
self.history_lock = threading.Lock()
|
||||
@ -97,7 +98,6 @@ class FreqaiDataDrawer:
|
||||
self.empty_pair_dict: pair_info = {
|
||||
"model_filename": "", "trained_timestamp": 0,
|
||||
"data_path": "", "extras": {}}
|
||||
self.metric_tracker: Dict[str, Dict[str, Dict[str, list]]] = {}
|
||||
|
||||
def update_metric_tracker(self, metric: str, value: float, pair: str) -> None:
|
||||
"""
|
||||
@ -153,6 +153,7 @@ class FreqaiDataDrawer:
|
||||
if exists:
|
||||
with open(self.metric_tracker_path, "r") as fp:
|
||||
self.metric_tracker = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
|
||||
logger.info("Loading existing metric tracker from disk.")
|
||||
else:
|
||||
logger.info("Could not find existing metric tracker, starting from scratch")
|
||||
|
||||
|
||||
@ -0,0 +1,74 @@
|
||||
import logging
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict
|
||||
|
||||
from catboost import CatBoostClassifier, Pool
|
||||
|
||||
from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
|
||||
from freqtrade.freqai.base_models.FreqaiMultiOutputClassifier import FreqaiMultiOutputClassifier
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class CatboostClassifierMultiTarget(BaseClassifierModel):
|
||||
"""
|
||||
User created prediction model. The class needs to override three necessary
|
||||
functions, predict(), train(), fit(). The class inherits ModelHandler which
|
||||
has its own DataHandler where data is held, saved, loaded, and managed.
|
||||
"""
|
||||
|
||||
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
|
||||
"""
|
||||
User sets up the training and test data to fit their desired model here
|
||||
:param data_dictionary: the dictionary constructed by DataHandler to hold
|
||||
all the training and test data/labels.
|
||||
"""
|
||||
|
||||
cbc = CatBoostClassifier(
|
||||
allow_writing_files=True,
|
||||
loss_function='MultiClass',
|
||||
train_dir=Path(dk.data_path),
|
||||
**self.model_training_parameters,
|
||||
)
|
||||
|
||||
X = data_dictionary["train_features"]
|
||||
y = data_dictionary["train_labels"]
|
||||
|
||||
sample_weight = data_dictionary["train_weights"]
|
||||
|
||||
eval_sets = [None] * y.shape[1]
|
||||
|
||||
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
|
||||
eval_sets = [None] * data_dictionary['test_labels'].shape[1]
|
||||
|
||||
for i in range(data_dictionary['test_labels'].shape[1]):
|
||||
eval_sets[i] = Pool(
|
||||
data=data_dictionary["test_features"],
|
||||
label=data_dictionary["test_labels"].iloc[:, i],
|
||||
weight=data_dictionary["test_weights"],
|
||||
)
|
||||
|
||||
init_model = self.get_init_model(dk.pair)
|
||||
|
||||
if init_model:
|
||||
init_models = init_model.estimators_
|
||||
else:
|
||||
init_models = [None] * y.shape[1]
|
||||
|
||||
fit_params = []
|
||||
for i in range(len(eval_sets)):
|
||||
fit_params.append({
|
||||
'eval_set': eval_sets[i], 'init_model': init_models[i],
|
||||
'log_cout': sys.stdout, 'log_cerr': sys.stderr,
|
||||
})
|
||||
|
||||
model = FreqaiMultiOutputClassifier(estimator=cbc)
|
||||
thread_training = self.freqai_info.get('multitarget_parallel_training', False)
|
||||
if thread_training:
|
||||
model.n_jobs = y.shape[1]
|
||||
model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
|
||||
|
||||
return model
|
||||
@ -0,0 +1,64 @@
|
||||
import logging
|
||||
from typing import Any, Dict
|
||||
|
||||
from lightgbm import LGBMClassifier
|
||||
|
||||
from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
|
||||
from freqtrade.freqai.base_models.FreqaiMultiOutputClassifier import FreqaiMultiOutputClassifier
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class LightGBMClassifierMultiTarget(BaseClassifierModel):
|
||||
"""
|
||||
User created prediction model. The class needs to override three necessary
|
||||
functions, predict(), train(), fit(). The class inherits ModelHandler which
|
||||
has its own DataHandler where data is held, saved, loaded, and managed.
|
||||
"""
|
||||
|
||||
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
|
||||
"""
|
||||
User sets up the training and test data to fit their desired model here
|
||||
:param data_dictionary: the dictionary constructed by DataHandler to hold
|
||||
all the training and test data/labels.
|
||||
"""
|
||||
|
||||
lgb = LGBMClassifier(**self.model_training_parameters)
|
||||
|
||||
X = data_dictionary["train_features"]
|
||||
y = data_dictionary["train_labels"]
|
||||
sample_weight = data_dictionary["train_weights"]
|
||||
|
||||
eval_weights = None
|
||||
eval_sets = [None] * y.shape[1]
|
||||
|
||||
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
|
||||
eval_weights = [data_dictionary["test_weights"]]
|
||||
eval_sets = [(None, None)] * data_dictionary['test_labels'].shape[1] # type: ignore
|
||||
for i in range(data_dictionary['test_labels'].shape[1]):
|
||||
eval_sets[i] = ( # type: ignore
|
||||
data_dictionary["test_features"],
|
||||
data_dictionary["test_labels"].iloc[:, i]
|
||||
)
|
||||
|
||||
init_model = self.get_init_model(dk.pair)
|
||||
if init_model:
|
||||
init_models = init_model.estimators_
|
||||
else:
|
||||
init_models = [None] * y.shape[1]
|
||||
|
||||
fit_params = []
|
||||
for i in range(len(eval_sets)):
|
||||
fit_params.append(
|
||||
{'eval_set': eval_sets[i], 'eval_sample_weight': eval_weights,
|
||||
'init_model': init_models[i]})
|
||||
|
||||
model = FreqaiMultiOutputClassifier(estimator=lgb)
|
||||
thread_training = self.freqai_info.get('multitarget_parallel_training', False)
|
||||
if thread_training:
|
||||
model.n_jobs = y.shape[1]
|
||||
model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
|
||||
|
||||
return model
|
||||
@ -667,7 +667,7 @@ class LocalTrade():
|
||||
self.close(order.safe_price)
|
||||
else:
|
||||
self.recalc_trade_from_orders()
|
||||
elif order.ft_order_side == 'stoploss':
|
||||
elif order.ft_order_side == 'stoploss' and order.status not in ('canceled', 'open'):
|
||||
self.stoploss_order_id = None
|
||||
self.close_rate_requested = self.stop_loss
|
||||
self.exit_reason = ExitType.STOPLOSS_ON_EXCHANGE.value
|
||||
|
||||
@ -150,14 +150,20 @@ class Worker:
|
||||
if timeframe:
|
||||
next_tf = timeframe_to_next_date(timeframe)
|
||||
# Maximum throttling should be until new candle arrives
|
||||
# Offset of 0.2s is added to ensure a new candle has been issued.
|
||||
next_tf_with_offset = next_tf.timestamp() - time.time() + timeframe_offset
|
||||
# Offset is added to ensure a new candle has been issued.
|
||||
next_tft = next_tf.timestamp() - time.time()
|
||||
next_tf_with_offset = next_tft + timeframe_offset
|
||||
if next_tft < sleep_duration and sleep_duration < next_tf_with_offset:
|
||||
# Avoid hitting a new loop between the new candle and the candle with offset
|
||||
sleep_duration = next_tf_with_offset
|
||||
sleep_duration = min(sleep_duration, next_tf_with_offset)
|
||||
sleep_duration = max(sleep_duration, 0.0)
|
||||
# next_iter = datetime.now(timezone.utc) + timedelta(seconds=sleep_duration)
|
||||
|
||||
logger.debug(f"Throttling with '{func.__name__}()': sleep for {sleep_duration:.2f} s, "
|
||||
f"last iteration took {time_passed:.2f} s.")
|
||||
f"last iteration took {time_passed:.2f} s."
|
||||
# f"next: {next_iter}"
|
||||
)
|
||||
self._sleep(sleep_duration)
|
||||
return result
|
||||
|
||||
|
||||
@ -1,5 +1,6 @@
|
||||
# Include all requirements to run the bot.
|
||||
-r requirements.txt
|
||||
-r requirements-plot.txt
|
||||
|
||||
# Required for freqai
|
||||
scikit-learn==1.1.3
|
||||
|
||||
@ -2,7 +2,7 @@ numpy==1.23.4
|
||||
pandas==1.5.1
|
||||
pandas-ta==0.3.14b
|
||||
|
||||
ccxt==2.1.33
|
||||
ccxt==2.1.54
|
||||
# Pin cryptography for now due to rust build errors with piwheels
|
||||
cryptography==38.0.1
|
||||
aiohttp==3.8.3
|
||||
|
||||
@ -77,17 +77,19 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
@pytest.mark.parametrize('model', [
|
||||
'LightGBMRegressorMultiTarget',
|
||||
'XGBoostRegressorMultiTarget',
|
||||
'CatboostRegressorMultiTarget',
|
||||
@pytest.mark.parametrize('model, strat', [
|
||||
('LightGBMRegressorMultiTarget', "freqai_test_multimodel_strat"),
|
||||
('XGBoostRegressorMultiTarget', "freqai_test_multimodel_strat"),
|
||||
('CatboostRegressorMultiTarget', "freqai_test_multimodel_strat"),
|
||||
('LightGBMClassifierMultiTarget', "freqai_test_multimodel_classifier_strat"),
|
||||
('CatboostClassifierMultiTarget', "freqai_test_multimodel_classifier_strat")
|
||||
])
|
||||
def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model):
|
||||
if is_arm() and model == 'CatboostRegressorMultiTarget':
|
||||
def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model, strat):
|
||||
if is_arm() and 'Catboost' in model:
|
||||
pytest.skip("CatBoost is not supported on ARM")
|
||||
|
||||
freqai_conf.update({"timerange": "20180110-20180130"})
|
||||
freqai_conf.update({"strategy": "freqai_test_multimodel_strat"})
|
||||
freqai_conf.update({"strategy": strat})
|
||||
freqai_conf.update({"freqaimodel": model})
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
|
||||
@ -1460,6 +1460,7 @@ def test_api_strategies(botclient, tmpdir):
|
||||
'StrategyTestV3CustomEntryPrice',
|
||||
'StrategyTestV3Futures',
|
||||
'freqai_test_classifier',
|
||||
'freqai_test_multimodel_classifier_strat',
|
||||
'freqai_test_multimodel_strat',
|
||||
'freqai_test_strat'
|
||||
]}
|
||||
|
||||
138
tests/strategy/strats/freqai_test_multimodel_classifier_strat.py
Normal file
138
tests/strategy/strats/freqai_test_multimodel_classifier_strat.py
Normal file
@ -0,0 +1,138 @@
|
||||
import logging
|
||||
from functools import reduce
|
||||
|
||||
import numpy as np
|
||||
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_classifier_strat(IStrategy):
|
||||
"""
|
||||
Test strategy - used for testing freqAI multimodel functionalities.
|
||||
DO not use in production.
|
||||
"""
|
||||
|
||||
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 populate_any_indicators(
|
||||
self, pair, df, tf, informative=None, set_generalized_indicators=False
|
||||
):
|
||||
|
||||
coin = pair.split('/')[0]
|
||||
|
||||
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-up_or_down'] = np.where(df["close"].shift(-50) >
|
||||
df["close"], 'up', 'down')
|
||||
|
||||
df['&s-up_or_down2'] = np.where(df["close"].shift(-50) >
|
||||
df["close"], 'up2', 'down2')
|
||||
|
||||
return df
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
self.freqai_info = self.config["freqai"]
|
||||
|
||||
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
|
||||
@ -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) == 10
|
||||
assert len(strategies) == 11
|
||||
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) == 11
|
||||
assert len(strategies) == 12
|
||||
# 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]) == 10
|
||||
assert len([x for x in strategies if x['class'] is not None]) == 11
|
||||
|
||||
assert len([x for x in strategies if x['class'] is None]) == 1
|
||||
|
||||
|
||||
@ -113,6 +113,16 @@ def test_throttle_sleep_time(mocker, default_conf, caplog) -> None:
|
||||
# 300 (5m) - 60 (1m - see set time above) - 5 (duration of throttled_func) = 235
|
||||
assert 235.2 < sleep_mock.call_args[0][0] < 235.6
|
||||
|
||||
t.move_to("2022-09-01 05:04:51 +00:00")
|
||||
sleep_mock.reset_mock()
|
||||
# Offset of 5s, so we hit the sweet-spot between "candle" and "candle offset"
|
||||
# Which should not get a throttle iteration to avoid late candle fetching
|
||||
assert worker._throttle(throttled_func, throttle_secs=10, timeframe='5m',
|
||||
timeframe_offset=5, x=1.2) == 42
|
||||
assert sleep_mock.call_count == 1
|
||||
# Time is slightly bigger than throttle secs due to the high timeframe offset.
|
||||
assert 11.1 < sleep_mock.call_args[0][0] < 13.2
|
||||
|
||||
|
||||
def test_throttle_with_assets(mocker, default_conf) -> None:
|
||||
def throttled_func(nb_assets=-1):
|
||||
|
||||
Loading…
Reference in New Issue
Block a user