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Merge branch 'develop' into move_datadownload

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This commit is contained in:
Matthias
2022-08-31 10:23:45 +00:00
parent bb3523f383 4aec2db14d
commit a88ffd2c9d
101 changed files with 3154 additions and 1523 deletions
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7
freqtrade/freqai/data_drawer.py
View File

@@ -421,7 +421,7 @@ class FreqaiDataDrawer:
)
# if self.live:
self.model_dictionary[dk.model_filename] = model
self.model_dictionary[coin] = model
self.pair_dict[coin]["model_filename"] = dk.model_filename
self.pair_dict[coin]["data_path"] = str(dk.data_path)
self.save_drawer_to_disk()
@@ -460,8 +460,8 @@ class FreqaiDataDrawer:
)
# try to access model in memory instead of loading object from disk to save time
if dk.live and dk.model_filename in self.model_dictionary:
model = self.model_dictionary[dk.model_filename]
if dk.live and coin in self.model_dictionary:
model = self.model_dictionary[coin]
elif not dk.keras:
model = load(dk.data_path / f"{dk.model_filename}_model.joblib")
else:
@@ -566,7 +566,6 @@ class FreqaiDataDrawer:
for training according to user defined train_period_days
metadata: dict = strategy furnished pair metadata
"""
with self.history_lock:
corr_dataframes: Dict[Any, Any] = {}
base_dataframes: Dict[Any, Any] = {}

112
freqtrade/freqai/data_kitchen.py
View File

@@ -166,9 +166,17 @@ class FreqaiDataKitchen:
train_labels = labels
train_weights = weights
return self.build_data_dictionary(
train_features, test_features, train_labels, test_labels, train_weights, test_weights
)
# Simplest way to reverse the order of training and test data:
if self.freqai_config['feature_parameters'].get('reverse_train_test_order', False):
return self.build_data_dictionary(
test_features, train_features, test_labels,
train_labels, test_weights, train_weights
)
else:
return self.build_data_dictionary(
train_features, test_features, train_labels,
test_labels, train_weights, test_weights
)
def filter_features(
self,
@@ -452,7 +460,6 @@ class FreqaiDataKitchen:
logger.info("reduced feature dimension by %s", n_components - n_keep_components)
logger.info("explained variance %f", np.sum(pca2.explained_variance_ratio_))
train_components = pca2.transform(self.data_dictionary["train_features"])
test_components = pca2.transform(self.data_dictionary["test_features"])
self.data_dictionary["train_features"] = pd.DataFrame(
data=train_components,
@@ -466,6 +473,7 @@ class FreqaiDataKitchen:
self.training_features_list = self.data_dictionary["train_features"].columns
if self.freqai_config.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
test_components = pca2.transform(self.data_dictionary["test_features"])
self.data_dictionary["test_features"] = pd.DataFrame(
data=test_components,
columns=["PC" + str(i) for i in range(0, n_keep_components)],
@@ -504,10 +512,25 @@ class FreqaiDataKitchen:
# logger.info("computing average mean distance for all training points")
pairwise = pairwise_distances(
self.data_dictionary["train_features"], n_jobs=self.thread_count)
avg_mean_dist = pairwise.mean(axis=1).mean()
# remove the diagonal distances which are itself distances ~0
np.fill_diagonal(pairwise, np.NaN)
pairwise = pairwise.reshape(-1, 1)
avg_mean_dist = pairwise[~np.isnan(pairwise)].mean()
return avg_mean_dist
def get_outlier_percentage(self, dropped_pts: npt.NDArray) -> float:
"""
Check if more than X% of points werer dropped during outlier detection.
"""
outlier_protection_pct = self.freqai_config["feature_parameters"].get(
"outlier_protection_percentage", 30)
outlier_pct = (dropped_pts.sum() / len(dropped_pts)) * 100
if outlier_pct >= outlier_protection_pct:
return outlier_pct
else:
return 0.0
def use_SVM_to_remove_outliers(self, predict: bool) -> None:
"""
Build/inference a Support Vector Machine to detect outliers
@@ -545,8 +568,17 @@ class FreqaiDataKitchen:
self.data_dictionary["train_features"]
)
y_pred = self.svm_model.predict(self.data_dictionary["train_features"])
dropped_points = np.where(y_pred == -1, 0, y_pred)
kept_points = np.where(y_pred == -1, 0, y_pred)
# keep_index = np.where(y_pred == 1)
outlier_pct = self.get_outlier_percentage(1 - kept_points)
if outlier_pct:
logger.warning(
f"SVM detected {outlier_pct:.2f}% of the points as outliers. "
f"Keeping original dataset."
)
self.svm_model = None
return
self.data_dictionary["train_features"] = self.data_dictionary["train_features"][
(y_pred == 1)
]
@@ -558,7 +590,7 @@ class FreqaiDataKitchen:
]
logger.info(
f"SVM tossed {len(y_pred) - dropped_points.sum()}"
f"SVM tossed {len(y_pred) - kept_points.sum()}"
f" train points from {len(y_pred)} total points."
)
@@ -567,7 +599,7 @@ class FreqaiDataKitchen:
# to reduce code duplication
if self.freqai_config['data_split_parameters'].get('test_size', 0.1) != 0:
y_pred = self.svm_model.predict(self.data_dictionary["test_features"])
dropped_points = np.where(y_pred == -1, 0, y_pred)
kept_points = np.where(y_pred == -1, 0, y_pred)
self.data_dictionary["test_features"] = self.data_dictionary["test_features"][
(y_pred == 1)
]
@@ -578,7 +610,7 @@ class FreqaiDataKitchen:
]
logger.info(
f"SVM tossed {len(y_pred) - dropped_points.sum()}"
f"SVM tossed {len(y_pred) - kept_points.sum()}"
f" test points from {len(y_pred)} total points."
)
@@ -596,7 +628,11 @@ class FreqaiDataKitchen:
is an outlier.
"""
from math import cos, sin
if predict:
if not self.data['DBSCAN_eps']:
return
train_ft_df = self.data_dictionary['train_features']
pred_ft_df = self.data_dictionary['prediction_features']
num_preds = len(pred_ft_df)
@@ -614,28 +650,61 @@ class FreqaiDataKitchen:
else:
MinPts = len(self.data_dictionary['train_features'].columns) * 2
# measure pairwise distances to train_features.shape[1]*2 nearest neighbours
def normalise_distances(distances):
normalised_distances = (distances - distances.min()) / \
(distances.max() - distances.min())
return normalised_distances
def rotate_point(origin, point, angle):
# rotate a point counterclockwise by a given angle (in radians)
# around a given origin
x = origin[0] + cos(angle) * (point[0] - origin[0]) - \
sin(angle) * (point[1] - origin[1])
y = origin[1] + sin(angle) * (point[0] - origin[0]) + \
cos(angle) * (point[1] - origin[1])
return (x, y)
MinPts = int(len(self.data_dictionary['train_features'].index) * 0.25)
# measure pairwise distances to nearest neighbours
neighbors = NearestNeighbors(
n_neighbors=MinPts, n_jobs=self.thread_count)
neighbors_fit = neighbors.fit(self.data_dictionary['train_features'])
distances, _ = neighbors_fit.kneighbors(self.data_dictionary['train_features'])
distances = np.sort(distances, axis=0)
index_ten_pct = int(len(distances[:, 1]) * 0.1)
distances = distances[index_ten_pct:, 1]
epsilon = distances[-1]
distances = np.sort(distances, axis=0).mean(axis=1)
normalised_distances = normalise_distances(distances)
x_range = np.linspace(0, 1, len(distances))
line = np.linspace(normalised_distances[0],
normalised_distances[-1], len(normalised_distances))
deflection = np.abs(normalised_distances - line)
max_deflection_loc = np.where(deflection == deflection.max())[0][0]
origin = x_range[max_deflection_loc], line[max_deflection_loc]
point = x_range[max_deflection_loc], normalised_distances[max_deflection_loc]
rot_angle = np.pi / 4
elbow_loc = rotate_point(origin, point, rot_angle)
epsilon = elbow_loc[1] * (distances[-1] - distances[0]) + distances[0]
clustering = DBSCAN(eps=epsilon, min_samples=MinPts,
n_jobs=int(self.thread_count)).fit(
self.data_dictionary['train_features']
)
logger.info(f'DBSCAN found eps of {epsilon}.')
logger.info(f'DBSCAN found eps of {epsilon:.2f}.')
self.data['DBSCAN_eps'] = epsilon
self.data['DBSCAN_min_samples'] = MinPts
dropped_points = np.where(clustering.labels_ == -1, 1, 0)
outlier_pct = self.get_outlier_percentage(dropped_points)
if outlier_pct:
logger.warning(
f"DBSCAN detected {outlier_pct:.2f}% of the points as outliers. "
f"Keeping original dataset."
)
self.data['DBSCAN_eps'] = 0
return
self.data_dictionary['train_features'] = self.data_dictionary['train_features'][
(clustering.labels_ != -1)
]
@@ -693,8 +762,8 @@ class FreqaiDataKitchen:
if (len(do_predict) - do_predict.sum()) > 0:
logger.info(
f"DI tossed {len(do_predict) - do_predict.sum():.2f} predictions for "
"being too far from training data"
f"DI tossed {len(do_predict) - do_predict.sum()} predictions for "
"being too far from training data."
)
self.do_predict += do_predict
@@ -866,13 +935,6 @@ class FreqaiDataKitchen:
data_load_timerange.stopts = int(time)
retrain = True
# logger.info(
# f"downloading data for "
# f"{(data_load_timerange.stopts-data_load_timerange.startts)/SECONDS_IN_DAY:.2f} "
# " days. "
# f"Extension of {additional_seconds/SECONDS_IN_DAY:.2f} days"
# )
return retrain, trained_timerange, data_load_timerange
def set_new_model_names(self, pair: str, trained_timerange: TimeRange):

37
freqtrade/freqai/freqai_interface.py
View File

@@ -80,12 +80,15 @@ class IFreqaiModel(ABC):
logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
self.CONV_WIDTH = self.freqai_info.get("conv_width", 2)
self.pair_it = 0
self.pair_it_train = 0
self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist"))
self.last_trade_database_summary: DataFrame = {}
self.current_trade_database_summary: DataFrame = {}
self.analysis_lock = Lock()
self.inference_time: float = 0
self.train_time: float = 0
self.begin_time: float = 0
self.begin_time_train: float = 0
self.base_tf_seconds = timeframe_to_seconds(self.config['timeframe'])
def assert_config(self, config: Dict[str, Any]) -> None:
@@ -128,11 +131,20 @@ class IFreqaiModel(ABC):
dk = self.start_backtesting(dataframe, metadata, self.dk)
dataframe = dk.remove_features_from_df(dk.return_dataframe)
del dk
self.clean_up()
if self.live:
self.inference_timer('stop')
return dataframe
def clean_up(self):
"""
Objects that should be handled by GC already between coins, but
are explicitly shown here to help demonstrate the non-persistence of these
objects.
"""
self.model = None
self.dk = None
@threaded
def start_scanning(self, strategy: IStrategy) -> None:
"""
@@ -159,9 +171,11 @@ class IFreqaiModel(ABC):
dk.set_paths(pair, new_trained_timerange.stopts)
if retrain:
self.train_timer('start')
self.train_model_in_series(
new_trained_timerange, pair, strategy, dk, data_load_timerange
)
self.train_timer('stop')
self.dd.save_historic_predictions_to_disk()
@@ -474,8 +488,7 @@ class IFreqaiModel(ABC):
data_load_timerange: TimeRange,
):
"""
Retrieve data and train model in single threaded mode (only used if model directory is empty
upon startup for dry/live )
Retrieve data and train model.
:param new_trained_timerange: TimeRange = the timerange to train the model on
:param metadata: dict = strategy provided metadata
:param strategy: IStrategy = user defined strategy object
@@ -606,6 +619,24 @@ class IFreqaiModel(ABC):
self.inference_time = 0
return
def train_timer(self, do='start'):
"""
Timer designed to track the cumulative time spent training the full pairlist in
FreqAI.
"""
if do == 'start':
self.pair_it_train += 1
self.begin_time_train = time.time()
elif do == 'stop':
end = time.time()
self.train_time += (end - self.begin_time_train)
if self.pair_it_train == self.total_pairs:
logger.info(
f'Total time spent training pairlist {self.train_time:.2f} seconds')
self.pair_it_train = 0
self.train_time = 0
return
# Following methods which are overridden by user made prediction models.
# See freqai/prediction_models/CatboostPredictionModel.py for an example.