code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
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def run(
self,
times=1,
models=None,
dataset="Alpha360",
universe="",
exclude=False,
qlib_uri: str = "git+https://github.com/microsoft/qlib#egg=pyqlib",
exp_folder_name: str = "run_all_model_records",
wait_before_rm_env: bool = False,
wait_... |
Please be aware that this function can only work under Linux. MacOS and Windows will be supported in the future.
Any PR to enhance this method is highly welcomed. Besides, this script doesn't support parallel running the same model
for multiple times, and this will be fixed in the future develo... | run | python | microsoft/qlib | examples/run_all_model.py | https://github.com/microsoft/qlib/blob/master/examples/run_all_model.py | MIT |
def process_qlib_data(df, dataset, fillna=False):
"""Prepare data to fit the TFT model.
Args:
df: Original DataFrame.
fillna: Whether to fill the data with the mean values.
Returns:
Transformed DataFrame.
"""
# Several features selected manually
feature_col = DATASET_SETTING... | Prepare data to fit the TFT model.
Args:
df: Original DataFrame.
fillna: Whether to fill the data with the mean values.
Returns:
Transformed DataFrame.
| process_qlib_data | python | microsoft/qlib | examples/benchmarks/TFT/tft.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/tft.py | MIT |
def process_predicted(df, col_name):
"""Transform the TFT predicted data into Qlib format.
Args:
df: Original DataFrame.
fillna: New column name.
Returns:
Transformed DataFrame.
"""
df_res = df.copy()
df_res = df_res.rename(columns={"forecast_time": "datetime", "identifier":... | Transform the TFT predicted data into Qlib format.
Args:
df: Original DataFrame.
fillna: New column name.
Returns:
Transformed DataFrame.
| process_predicted | python | microsoft/qlib | examples/benchmarks/TFT/tft.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/tft.py | MIT |
def to_pickle(self, path: Union[Path, str]):
"""
Tensorflow model can't be dumped directly.
So the data should be save separately
**TODO**: Please implement the function to load the files
Parameters
----------
path : Union[Path, str]
the target path ... |
Tensorflow model can't be dumped directly.
So the data should be save separately
**TODO**: Please implement the function to load the files
Parameters
----------
path : Union[Path, str]
the target path to be dumped
| to_pickle | python | microsoft/qlib | examples/benchmarks/TFT/tft.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/tft.py | MIT |
def get_column_definition(self):
"""Returns formatted column definition in order expected by the TFT."""
column_definition = self._column_definition
# Sanity checks first.
# Ensure only one ID and time column exist
def _check_single_column(input_type):
length = len(... | Returns formatted column definition in order expected by the TFT. | get_column_definition | python | microsoft/qlib | examples/benchmarks/TFT/data_formatters/base.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/data_formatters/base.py | MIT |
def _get_tft_input_indices(self):
"""Returns the relevant indexes and input sizes required by TFT."""
# Functions
def _extract_tuples_from_data_type(data_type, defn):
return [tup for tup in defn if tup[1] == data_type and tup[2] not in {InputTypes.ID, InputTypes.TIME}]
def ... | Returns the relevant indexes and input sizes required by TFT. | _get_tft_input_indices | python | microsoft/qlib | examples/benchmarks/TFT/data_formatters/base.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/data_formatters/base.py | MIT |
def get_experiment_params(self):
"""Returns fixed model parameters for experiments."""
required_keys = [
"total_time_steps",
"num_encoder_steps",
"num_epochs",
"early_stopping_patience",
"multiprocessing_workers",
]
fixed_para... | Returns fixed model parameters for experiments. | get_experiment_params | python | microsoft/qlib | examples/benchmarks/TFT/data_formatters/base.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/data_formatters/base.py | MIT |
def split_data(self, df, valid_boundary=2016, test_boundary=2018):
"""Splits data frame into training-validation-test data frames.
This also calibrates scaling object, and transforms data for each split.
Args:
df: Source data frame to split.
valid_boundary: Starting year fo... | Splits data frame into training-validation-test data frames.
This also calibrates scaling object, and transforms data for each split.
Args:
df: Source data frame to split.
valid_boundary: Starting year for validation data
test_boundary: Starting year for test data
... | split_data | python | microsoft/qlib | examples/benchmarks/TFT/data_formatters/qlib_Alpha158.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/data_formatters/qlib_Alpha158.py | MIT |
def set_scalers(self, df):
"""Calibrates scalers using the data supplied.
Args:
df: Data to use to calibrate scalers.
"""
print("Setting scalers with training data...")
column_definitions = self.get_column_definition()
id_column = utils.get_single_col_by_input... | Calibrates scalers using the data supplied.
Args:
df: Data to use to calibrate scalers.
| set_scalers | python | microsoft/qlib | examples/benchmarks/TFT/data_formatters/qlib_Alpha158.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/data_formatters/qlib_Alpha158.py | MIT |
def transform_inputs(self, df):
"""Performs feature transformations.
This includes both feature engineering, preprocessing and normalisation.
Args:
df: Data frame to transform.
Returns:
Transformed data frame.
"""
output = df.copy()
if sel... | Performs feature transformations.
This includes both feature engineering, preprocessing and normalisation.
Args:
df: Data frame to transform.
Returns:
Transformed data frame.
| transform_inputs | python | microsoft/qlib | examples/benchmarks/TFT/data_formatters/qlib_Alpha158.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/data_formatters/qlib_Alpha158.py | MIT |
def format_predictions(self, predictions):
"""Reverts any normalisation to give predictions in original scale.
Args:
predictions: Dataframe of model predictions.
Returns:
Data frame of unnormalised predictions.
"""
output = predictions.copy()
column... | Reverts any normalisation to give predictions in original scale.
Args:
predictions: Dataframe of model predictions.
Returns:
Data frame of unnormalised predictions.
| format_predictions | python | microsoft/qlib | examples/benchmarks/TFT/data_formatters/qlib_Alpha158.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/data_formatters/qlib_Alpha158.py | MIT |
def __init__(self, experiment="volatility", root_folder=None):
"""Creates configs based on default experiment chosen.
Args:
experiment: Name of experiment.
root_folder: Root folder to save all outputs of training.
"""
if experiment not in self.default_experiments:
... | Creates configs based on default experiment chosen.
Args:
experiment: Name of experiment.
root_folder: Root folder to save all outputs of training.
| __init__ | python | microsoft/qlib | examples/benchmarks/TFT/expt_settings/configs.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/expt_settings/configs.py | MIT |
def make_data_formatter(self):
"""Gets a data formatter object for experiment.
Returns:
Default DataFormatter per experiment.
"""
data_formatter_class = {
"Alpha158": data_formatters.qlib_Alpha158.Alpha158Formatter,
}
return data_formatter_class[s... | Gets a data formatter object for experiment.
Returns:
Default DataFormatter per experiment.
| make_data_formatter | python | microsoft/qlib | examples/benchmarks/TFT/expt_settings/configs.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/expt_settings/configs.py | MIT |
def __init__(self, param_ranges, fixed_params, model_folder, override_w_fixed_params=True):
"""Instantiates model.
Args:
param_ranges: Discrete hyperparameter range for random search.
fixed_params: Fixed model parameters per experiment.
model_folder: Folder to store optimi... | Instantiates model.
Args:
param_ranges: Discrete hyperparameter range for random search.
fixed_params: Fixed model parameters per experiment.
model_folder: Folder to store optimisation artifacts.
override_w_fixed_params: Whether to override serialsed fixed model
... | __init__ | python | microsoft/qlib | examples/benchmarks/TFT/libs/hyperparam_opt.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/hyperparam_opt.py | MIT |
def load_results(self):
"""Loads results from previous hyperparameter optimisation.
Returns:
A boolean indicating if previous results can be loaded.
"""
print("Loading results from", self.hyperparam_folder)
results_file = os.path.join(self.hyperparam_folder, "results.... | Loads results from previous hyperparameter optimisation.
Returns:
A boolean indicating if previous results can be loaded.
| load_results | python | microsoft/qlib | examples/benchmarks/TFT/libs/hyperparam_opt.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/hyperparam_opt.py | MIT |
def _get_params_from_name(self, name):
"""Returns previously saved parameters given a key."""
params = self.saved_params
selected_params = dict(params[name])
if self._override_w_fixed_params:
for k in self.fixed_params:
selected_params[k] = self.fixed_params... | Returns previously saved parameters given a key. | _get_params_from_name | python | microsoft/qlib | examples/benchmarks/TFT/libs/hyperparam_opt.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/hyperparam_opt.py | MIT |
def clear(self):
"""Clears all previous results and saved parameters."""
shutil.rmtree(self.hyperparam_folder)
os.makedirs(self.hyperparam_folder)
self.results = pd.DataFrame()
self.saved_params = pd.DataFrame() | Clears all previous results and saved parameters. | clear | python | microsoft/qlib | examples/benchmarks/TFT/libs/hyperparam_opt.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/hyperparam_opt.py | MIT |
def _check_params(self, params):
"""Checks that parameter map is properly defined."""
valid_fields = list(self.param_ranges.keys()) + list(self.fixed_params.keys())
invalid_fields = [k for k in params if k not in valid_fields]
missing_fields = [k for k in valid_fields if k not in params... | Checks that parameter map is properly defined. | _check_params | python | microsoft/qlib | examples/benchmarks/TFT/libs/hyperparam_opt.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/hyperparam_opt.py | MIT |
def _get_name(self, params):
"""Returns a unique key for the supplied set of params."""
self._check_params(params)
fields = list(params.keys())
fields.sort()
return "_".join([str(params[k]) for k in fields]) | Returns a unique key for the supplied set of params. | _get_name | python | microsoft/qlib | examples/benchmarks/TFT/libs/hyperparam_opt.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/hyperparam_opt.py | MIT |
def get_next_parameters(self, ranges_to_skip=None):
"""Returns the next set of parameters to optimise.
Args:
ranges_to_skip: Explicitly defines a set of keys to skip.
"""
if ranges_to_skip is None:
ranges_to_skip = set(self.results.index)
if not isinstance... | Returns the next set of parameters to optimise.
Args:
ranges_to_skip: Explicitly defines a set of keys to skip.
| get_next_parameters | python | microsoft/qlib | examples/benchmarks/TFT/libs/hyperparam_opt.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/hyperparam_opt.py | MIT |
def _get_next():
"""Returns next hyperparameter set per try."""
parameters = {k: np.random.choice(self.param_ranges[k]) for k in param_range_keys}
# Adds fixed params
for k in self.fixed_params:
parameters[k] = self.fixed_params[k]
return pa... | Returns next hyperparameter set per try. | _get_next | python | microsoft/qlib | examples/benchmarks/TFT/libs/hyperparam_opt.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/hyperparam_opt.py | MIT |
def update_score(self, parameters, loss, model, info=""):
"""Updates the results from last optimisation run.
Args:
parameters: Hyperparameters used in optimisation.
loss: Validation loss obtained.
model: Model to serialised if required.
info: Any ancillary inform... | Updates the results from last optimisation run.
Args:
parameters: Hyperparameters used in optimisation.
loss: Validation loss obtained.
model: Model to serialised if required.
info: Any ancillary information to tag on to results.
Returns:
Boolean flag ... | update_score | python | microsoft/qlib | examples/benchmarks/TFT/libs/hyperparam_opt.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/hyperparam_opt.py | MIT |
def __init__(
self,
param_ranges,
fixed_params,
root_model_folder,
worker_number,
search_iterations=1000,
num_iterations_per_worker=5,
clear_serialised_params=False,
):
"""Instantiates optimisation manager.
This hyperparameter optimisa... | Instantiates optimisation manager.
This hyperparameter optimisation pre-generates #search_iterations
hyperparameter combinations and serialises them
at the start. At runtime, each worker goes through their own set of
parameter ranges. The pregeneration
allows for multiple worker... | __init__ | python | microsoft/qlib | examples/benchmarks/TFT/libs/hyperparam_opt.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/hyperparam_opt.py | MIT |
def get_next_parameters(self):
"""Returns next dictionary of hyperparameters to optimise."""
param_name = self.worker_search_queue.pop()
params = self.global_hyperparam_df.loc[param_name, :].to_dict()
# Always override!
for k in self.fixed_params:
print("Overriding ... | Returns next dictionary of hyperparameters to optimise. | get_next_parameters | python | microsoft/qlib | examples/benchmarks/TFT/libs/hyperparam_opt.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/hyperparam_opt.py | MIT |
def load_serialised_hyperparam_df(self):
"""Loads serialsed hyperparameter ranges from file.
Returns:
DataFrame containing hyperparameter combinations.
"""
print(
"Loading params for {} search iterations form {}".format(
self.total_search_iterations... | Loads serialsed hyperparameter ranges from file.
Returns:
DataFrame containing hyperparameter combinations.
| load_serialised_hyperparam_df | python | microsoft/qlib | examples/benchmarks/TFT/libs/hyperparam_opt.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/hyperparam_opt.py | MIT |
def update_serialised_hyperparam_df(self):
"""Regenerates hyperparameter combinations and saves to file.
Returns:
DataFrame containing hyperparameter combinations.
"""
search_df = self._generate_full_hyperparam_df()
print(
"Serialising params for {} search... | Regenerates hyperparameter combinations and saves to file.
Returns:
DataFrame containing hyperparameter combinations.
| update_serialised_hyperparam_df | python | microsoft/qlib | examples/benchmarks/TFT/libs/hyperparam_opt.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/hyperparam_opt.py | MIT |
def _generate_full_hyperparam_df(self):
"""Generates actual hyperparameter combinations.
Returns:
DataFrame containing hyperparameter combinations.
"""
np.random.seed(131) # for reproducibility of hyperparam list
name_list = []
param_list = []
for _ ... | Generates actual hyperparameter combinations.
Returns:
DataFrame containing hyperparameter combinations.
| _generate_full_hyperparam_df | python | microsoft/qlib | examples/benchmarks/TFT/libs/hyperparam_opt.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/hyperparam_opt.py | MIT |
def clear(self): # reset when cleared
"""Clears results for hyperparameter manager and resets."""
super().clear()
self.worker_search_queue = self._get_worker_search_queue() | Clears results for hyperparameter manager and resets. | clear | python | microsoft/qlib | examples/benchmarks/TFT/libs/hyperparam_opt.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/hyperparam_opt.py | MIT |
def load_results(self):
"""Load results from file and queue parameter combinations to try.
Returns:
Boolean indicating if results were successfully loaded.
"""
success = super().load_results()
if success:
self.worker_search_queue = self._get_worker_search_... | Load results from file and queue parameter combinations to try.
Returns:
Boolean indicating if results were successfully loaded.
| load_results | python | microsoft/qlib | examples/benchmarks/TFT/libs/hyperparam_opt.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/hyperparam_opt.py | MIT |
def _get_worker_search_queue(self):
"""Generates the queue of param combinations for current worker.
Returns:
Queue of hyperparameter combinations outstanding.
"""
global_df = self.assign_worker_numbers(self.global_hyperparam_df)
worker_df = global_df[global_df["worker... | Generates the queue of param combinations for current worker.
Returns:
Queue of hyperparameter combinations outstanding.
| _get_worker_search_queue | python | microsoft/qlib | examples/benchmarks/TFT/libs/hyperparam_opt.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/hyperparam_opt.py | MIT |
def assign_worker_numbers(self, df):
"""Updates parameter combinations with the index of the worker used.
Args:
df: DataFrame of parameter combinations.
Returns:
Updated DataFrame with worker number.
"""
output = df.copy()
n = self.total_search_iter... | Updates parameter combinations with the index of the worker used.
Args:
df: DataFrame of parameter combinations.
Returns:
Updated DataFrame with worker number.
| assign_worker_numbers | python | microsoft/qlib | examples/benchmarks/TFT/libs/hyperparam_opt.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/hyperparam_opt.py | MIT |
def linear_layer(size, activation=None, use_time_distributed=False, use_bias=True):
"""Returns simple Keras linear layer.
Args:
size: Output size
activation: Activation function to apply if required
use_time_distributed: Whether to apply layer across time
use_bias: Whether bias should b... | Returns simple Keras linear layer.
Args:
size: Output size
activation: Activation function to apply if required
use_time_distributed: Whether to apply layer across time
use_bias: Whether bias should be included in layer
| linear_layer | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def apply_mlp(
inputs, hidden_size, output_size, output_activation=None, hidden_activation="tanh", use_time_distributed=False
):
"""Applies simple feed-forward network to an input.
Args:
inputs: MLP inputs
hidden_size: Hidden state size
output_size: Output size of MLP
output_activat... | Applies simple feed-forward network to an input.
Args:
inputs: MLP inputs
hidden_size: Hidden state size
output_size: Output size of MLP
output_activation: Activation function to apply on output
hidden_activation: Activation function to apply on input
use_time_distributed: Wheth... | apply_mlp | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def apply_gating_layer(x, hidden_layer_size, dropout_rate=None, use_time_distributed=True, activation=None):
"""Applies a Gated Linear Unit (GLU) to an input.
Args:
x: Input to gating layer
hidden_layer_size: Dimension of GLU
dropout_rate: Dropout rate to apply if any
use_time_distribut... | Applies a Gated Linear Unit (GLU) to an input.
Args:
x: Input to gating layer
hidden_layer_size: Dimension of GLU
dropout_rate: Dropout rate to apply if any
use_time_distributed: Whether to apply across time
activation: Activation function to apply to the linear feature transform if
... | apply_gating_layer | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def add_and_norm(x_list):
"""Applies skip connection followed by layer normalisation.
Args:
x_list: List of inputs to sum for skip connection
Returns:
Tensor output from layer.
"""
tmp = Add()(x_list)
tmp = LayerNorm()(tmp)
return tmp | Applies skip connection followed by layer normalisation.
Args:
x_list: List of inputs to sum for skip connection
Returns:
Tensor output from layer.
| add_and_norm | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def gated_residual_network(
x,
hidden_layer_size,
output_size=None,
dropout_rate=None,
use_time_distributed=True,
additional_context=None,
return_gate=False,
):
"""Applies the gated residual network (GRN) as defined in paper.
Args:
x: Network inputs
hidden_layer_size: In... | Applies the gated residual network (GRN) as defined in paper.
Args:
x: Network inputs
hidden_layer_size: Internal state size
output_size: Size of output layer
dropout_rate: Dropout rate if dropout is applied
use_time_distributed: Whether to apply network across time dimension
ad... | gated_residual_network | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def get_decoder_mask(self_attn_inputs):
"""Returns causal mask to apply for self-attention layer.
Args:
self_attn_inputs: Inputs to self attention layer to determine mask shape
"""
len_s = tf.shape(self_attn_inputs)[1]
bs = tf.shape(self_attn_inputs)[:1]
mask = K.cumsum(tf.eye(len_s, batc... | Returns causal mask to apply for self-attention layer.
Args:
self_attn_inputs: Inputs to self attention layer to determine mask shape
| get_decoder_mask | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def __call__(self, q, k, v, mask):
"""Applies scaled dot product attention.
Args:
q: Queries
k: Keys
v: Values
mask: Masking if required -- sets softmax to very large value
Returns:
Tuple of (layer outputs, attention weights)
"""
... | Applies scaled dot product attention.
Args:
q: Queries
k: Keys
v: Values
mask: Masking if required -- sets softmax to very large value
Returns:
Tuple of (layer outputs, attention weights)
| __call__ | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def __init__(self, n_head, d_model, dropout):
"""Initialises layer.
Args:
n_head: Number of heads
d_model: TFT state dimensionality
dropout: Dropout discard rate
"""
self.n_head = n_head
self.d_k = self.d_v = d_k = d_v = d_model // n_head
s... | Initialises layer.
Args:
n_head: Number of heads
d_model: TFT state dimensionality
dropout: Dropout discard rate
| __init__ | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def __call__(self, q, k, v, mask=None):
"""Applies interpretable multihead attention.
Using T to denote the number of time steps fed into the transformer.
Args:
q: Query tensor of shape=(?, T, d_model)
k: Key of shape=(?, T, d_model)
v: Values of shape=(?, T, d_mo... | Applies interpretable multihead attention.
Using T to denote the number of time steps fed into the transformer.
Args:
q: Query tensor of shape=(?, T, d_model)
k: Key of shape=(?, T, d_model)
v: Values of shape=(?, T, d_model)
mask: Masking if required with shape... | __call__ | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def __init__(self, raw_params, use_cudnn=False):
"""Builds TFT from parameters.
Args:
raw_params: Parameters to define TFT
use_cudnn: Whether to use CUDNN GPU optimised LSTM
"""
self.name = self.__class__.__name__
params = dict(raw_params) # copy locally
... | Builds TFT from parameters.
Args:
raw_params: Parameters to define TFT
use_cudnn: Whether to use CUDNN GPU optimised LSTM
| __init__ | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def get_tft_embeddings(self, all_inputs):
"""Transforms raw inputs to embeddings.
Applies linear transformation onto continuous variables and uses embeddings
for categorical variables.
Args:
all_inputs: Inputs to transform
Returns:
Tensors for transformed i... | Transforms raw inputs to embeddings.
Applies linear transformation onto continuous variables and uses embeddings
for categorical variables.
Args:
all_inputs: Inputs to transform
Returns:
Tensors for transformed inputs.
| get_tft_embeddings | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def cache_batched_data(self, data, cache_key, num_samples=-1):
"""Batches and caches data once for using during training.
Args:
data: Data to batch and cache
cache_key: Key used for cache
num_samples: Maximum number of samples to extract (-1 to use all data)
"""
... | Batches and caches data once for using during training.
Args:
data: Data to batch and cache
cache_key: Key used for cache
num_samples: Maximum number of samples to extract (-1 to use all data)
| cache_batched_data | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def _batch_sampled_data(self, data, max_samples):
"""Samples segments into a compatible format.
Args:
data: Sources data to sample and batch
max_samples: Maximum number of samples in batch
Returns:
Dictionary of batched data with the maximum samples specified.
... | Samples segments into a compatible format.
Args:
data: Sources data to sample and batch
max_samples: Maximum number of samples in batch
Returns:
Dictionary of batched data with the maximum samples specified.
| _batch_sampled_data | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def _batch_data(self, data):
"""Batches data for training.
Converts raw dataframe from a 2-D tabular format to a batched 3-D array
to feed into Keras model.
Args:
data: DataFrame to batch
Returns:
Batched Numpy array with shape=(?, self.time_steps, self.inp... | Batches data for training.
Converts raw dataframe from a 2-D tabular format to a batched 3-D array
to feed into Keras model.
Args:
data: DataFrame to batch
Returns:
Batched Numpy array with shape=(?, self.time_steps, self.input_size)
| _batch_data | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def _build_base_graph(self):
"""Returns graph defining layers of the TFT."""
# Size definitions.
time_steps = self.time_steps
combined_input_size = self.input_size
encoder_steps = self.num_encoder_steps
# Inputs.
all_inputs = tf.keras.layers.Input(
s... | Returns graph defining layers of the TFT. | _build_base_graph | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def static_combine_and_mask(embedding):
"""Applies variable selection network to static inputs.
Args:
embedding: Transformed static inputs
Returns:
Tensor output for variable selection network
"""
# Add temporal features
... | Applies variable selection network to static inputs.
Args:
embedding: Transformed static inputs
Returns:
Tensor output for variable selection network
| static_combine_and_mask | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def lstm_combine_and_mask(embedding):
"""Apply temporal variable selection networks.
Args:
embedding: Transformed inputs.
Returns:
Processed tensor outputs.
"""
# Add temporal features
_, time_steps, embedding_dim, nu... | Apply temporal variable selection networks.
Args:
embedding: Transformed inputs.
Returns:
Processed tensor outputs.
| lstm_combine_and_mask | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def get_lstm(return_state):
"""Returns LSTM cell initialized with default parameters."""
if self.use_cudnn:
lstm = tf.keras.layers.CuDNNLSTM(
self.hidden_layer_size,
return_sequences=True,
return_state=return_state,
... | Returns LSTM cell initialized with default parameters. | get_lstm | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def build_model(self):
"""Build model and defines training losses.
Returns:
Fully defined Keras model.
"""
with tf.variable_scope(self.name):
transformer_layer, all_inputs, attention_components = self._build_base_graph()
outputs = tf.keras.layers.Time... | Build model and defines training losses.
Returns:
Fully defined Keras model.
| build_model | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def quantile_loss(self, a, b):
"""Returns quantile loss for specified quantiles.
Args:
a: Targets
b: Predictions
"""
quantiles_used = set(self.quantiles)
loss = 0.0
... | Returns quantile loss for specified quantiles.
Args:
a: Targets
b: Predictions
| quantile_loss | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def fit(self, train_df=None, valid_df=None):
"""Fits deep neural network for given training and validation data.
Args:
train_df: DataFrame for training data
valid_df: DataFrame for validation data
"""
print("*** Fitting {} ***".format(self.name))
# Add rele... | Fits deep neural network for given training and validation data.
Args:
train_df: DataFrame for training data
valid_df: DataFrame for validation data
| fit | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def evaluate(self, data=None, eval_metric="loss"):
"""Applies evaluation metric to the training data.
Args:
data: Dataframe for evaluation
eval_metric: Evaluation metic to return, based on model definition.
Returns:
Computed evaluation loss.
"""
i... | Applies evaluation metric to the training data.
Args:
data: Dataframe for evaluation
eval_metric: Evaluation metic to return, based on model definition.
Returns:
Computed evaluation loss.
| evaluate | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def predict(self, df, return_targets=False):
"""Computes predictions for a given input dataset.
Args:
df: Input dataframe
return_targets: Whether to also return outputs aligned with predictions to
facilitate evaluation
Returns:
Input dataframe or tuple... | Computes predictions for a given input dataset.
Args:
df: Input dataframe
return_targets: Whether to also return outputs aligned with predictions to
facilitate evaluation
Returns:
Input dataframe or tuple of (input dataframe, aligned output dataframe).
... | predict | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def get_attention(self, df):
"""Computes TFT attention weights for a given dataset.
Args:
df: Input dataframe
Returns:
Dictionary of numpy arrays for temporal attention weights and variable
selection weights, along with their identifiers and time indices
... | Computes TFT attention weights for a given dataset.
Args:
df: Input dataframe
Returns:
Dictionary of numpy arrays for temporal attention weights and variable
selection weights, along with their identifiers and time indices
| get_attention | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def get_batch_attention_weights(input_batch):
"""Returns weights for a given minibatch of data."""
input_placeholder = self._input_placeholder
attention_weights = {}
for k in self._attention_components:
attention_weight = tf.keras.backend.get_session().run... | Returns weights for a given minibatch of data. | get_batch_attention_weights | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def reset_temp_folder(self):
"""Deletes and recreates folder with temporary Keras training outputs."""
print("Resetting temp folder...")
utils.create_folder_if_not_exist(self._temp_folder)
shutil.rmtree(self._temp_folder)
os.makedirs(self._temp_folder) | Deletes and recreates folder with temporary Keras training outputs. | reset_temp_folder | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def save(self, model_folder):
"""Saves optimal TFT weights.
Args:
model_folder: Location to serialze model.
"""
# Allows for direct serialisation of tensorflow variables to avoid spurious
# issue with Keras that leads to different performance evaluation results
... | Saves optimal TFT weights.
Args:
model_folder: Location to serialze model.
| save | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def load(self, model_folder, use_keras_loadings=False):
"""Loads TFT weights.
Args:
model_folder: Folder containing serialized models.
use_keras_loadings: Whether to load from Keras checkpoint.
Returns:
"""
if use_keras_loadings:
# Loads tempora... | Loads TFT weights.
Args:
model_folder: Folder containing serialized models.
use_keras_loadings: Whether to load from Keras checkpoint.
Returns:
| load | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def get_hyperparm_choices(cls):
"""Returns hyperparameter ranges for random search."""
return {
"dropout_rate": [0.1, 0.2, 0.3, 0.4, 0.5, 0.7, 0.9],
"hidden_layer_size": [10, 20, 40, 80, 160, 240, 320],
"minibatch_size": [64, 128, 256],
"learning_rate": [1... | Returns hyperparameter ranges for random search. | get_hyperparm_choices | python | microsoft/qlib | examples/benchmarks/TFT/libs/tft_model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/tft_model.py | MIT |
def get_single_col_by_input_type(input_type, column_definition):
"""Returns name of single column.
Args:
input_type: Input type of column to extract
column_definition: Column definition list for experiment
"""
l = [tup[0] for tup in column_definition if tup[2] == input_type]
if len(l)... | Returns name of single column.
Args:
input_type: Input type of column to extract
column_definition: Column definition list for experiment
| get_single_col_by_input_type | python | microsoft/qlib | examples/benchmarks/TFT/libs/utils.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/utils.py | MIT |
def tensorflow_quantile_loss(y, y_pred, quantile):
"""Computes quantile loss for tensorflow.
Standard quantile loss as defined in the "Training Procedure" section of
the main TFT paper
Args:
y: Targets
y_pred: Predictions
quantile: Quantile to use for loss calculations (between 0 & 1... | Computes quantile loss for tensorflow.
Standard quantile loss as defined in the "Training Procedure" section of
the main TFT paper
Args:
y: Targets
y_pred: Predictions
quantile: Quantile to use for loss calculations (between 0 & 1)
Returns:
Tensor for quantile loss.
| tensorflow_quantile_loss | python | microsoft/qlib | examples/benchmarks/TFT/libs/utils.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/utils.py | MIT |
def numpy_normalised_quantile_loss(y, y_pred, quantile):
"""Computes normalised quantile loss for numpy arrays.
Uses the q-Risk metric as defined in the "Training Procedure" section of the
main TFT paper.
Args:
y: Targets
y_pred: Predictions
quantile: Quantile to use for loss calcula... | Computes normalised quantile loss for numpy arrays.
Uses the q-Risk metric as defined in the "Training Procedure" section of the
main TFT paper.
Args:
y: Targets
y_pred: Predictions
quantile: Quantile to use for loss calculations (between 0 & 1)
Returns:
Float for normalised q... | numpy_normalised_quantile_loss | python | microsoft/qlib | examples/benchmarks/TFT/libs/utils.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/utils.py | MIT |
def get_default_tensorflow_config(tf_device="gpu", gpu_id=0):
"""Creates tensorflow config for graphs to run on CPU or GPU.
Specifies whether to run graph on gpu or cpu and which GPU ID to use for multi
GPU machines.
Args:
tf_device: 'cpu' or 'gpu'
gpu_id: GPU ID to use if relevant
Re... | Creates tensorflow config for graphs to run on CPU or GPU.
Specifies whether to run graph on gpu or cpu and which GPU ID to use for multi
GPU machines.
Args:
tf_device: 'cpu' or 'gpu'
gpu_id: GPU ID to use if relevant
Returns:
Tensorflow config.
| get_default_tensorflow_config | python | microsoft/qlib | examples/benchmarks/TFT/libs/utils.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/utils.py | MIT |
def save(tf_session, model_folder, cp_name, scope=None):
"""Saves Tensorflow graph to checkpoint.
Saves all trainiable variables under a given variable scope to checkpoint.
Args:
tf_session: Session containing graph
model_folder: Folder to save models
cp_name: Name of Tensorflow checkpoi... | Saves Tensorflow graph to checkpoint.
Saves all trainiable variables under a given variable scope to checkpoint.
Args:
tf_session: Session containing graph
model_folder: Folder to save models
cp_name: Name of Tensorflow checkpoint
scope: Variable scope containing variables to save
| save | python | microsoft/qlib | examples/benchmarks/TFT/libs/utils.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/utils.py | MIT |
def load(tf_session, model_folder, cp_name, scope=None, verbose=False):
"""Loads Tensorflow graph from checkpoint.
Args:
tf_session: Session to load graph into
model_folder: Folder containing serialised model
cp_name: Name of Tensorflow checkpoint
scope: Variable scope to use.
ver... | Loads Tensorflow graph from checkpoint.
Args:
tf_session: Session to load graph into
model_folder: Folder containing serialised model
cp_name: Name of Tensorflow checkpoint
scope: Variable scope to use.
verbose: Whether to print additional debugging information.
| load | python | microsoft/qlib | examples/benchmarks/TFT/libs/utils.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/utils.py | MIT |
def print_weights_in_checkpoint(model_folder, cp_name):
"""Prints all weights in Tensorflow checkpoint.
Args:
model_folder: Folder containing checkpoint
cp_name: Name of checkpoint
Returns:
"""
load_path = os.path.join(model_folder, "{0}.ckpt".format(cp_name))
print_tensors_in_ch... | Prints all weights in Tensorflow checkpoint.
Args:
model_folder: Folder containing checkpoint
cp_name: Name of checkpoint
Returns:
| print_weights_in_checkpoint | python | microsoft/qlib | examples/benchmarks/TFT/libs/utils.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TFT/libs/utils.py | MIT |
def _create_ts_slices(index, seq_len):
"""
create time series slices from pandas index
Args:
index (pd.MultiIndex): pandas multiindex with <instrument, datetime> order
seq_len (int): sequence length
"""
assert index.is_lexsorted(), "index should be sorted"
# number of dates for... |
create time series slices from pandas index
Args:
index (pd.MultiIndex): pandas multiindex with <instrument, datetime> order
seq_len (int): sequence length
| _create_ts_slices | python | microsoft/qlib | examples/benchmarks/TRA/src/dataset.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TRA/src/dataset.py | MIT |
def _get_date_parse_fn(target):
"""get date parse function
This method is used to parse date arguments as target type.
Example:
get_date_parse_fn('20120101')('2017-01-01') => '20170101'
get_date_parse_fn(20120101)('2017-01-01') => 20170101
"""
if isinstance(target, pd.Timestamp):
... | get date parse function
This method is used to parse date arguments as target type.
Example:
get_date_parse_fn('20120101')('2017-01-01') => '20170101'
get_date_parse_fn(20120101)('2017-01-01') => 20170101
| _get_date_parse_fn | python | microsoft/qlib | examples/benchmarks/TRA/src/dataset.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TRA/src/dataset.py | MIT |
def shoot_infs(inp_tensor):
"""Replaces inf by maximum of tensor"""
mask_inf = torch.isinf(inp_tensor)
ind_inf = torch.nonzero(mask_inf, as_tuple=False)
if len(ind_inf) > 0:
for ind in ind_inf:
if len(ind) == 2:
inp_tensor[ind[0], ind[1]] = 0
elif len(ind)... | Replaces inf by maximum of tensor | shoot_infs | python | microsoft/qlib | examples/benchmarks/TRA/src/model.py | https://github.com/microsoft/qlib/blob/master/examples/benchmarks/TRA/src/model.py | MIT |
def get_data(self):
"""use dataset to get highreq data"""
self._init_qlib()
self._prepare_calender_cache()
dataset = init_instance_by_config(self.task["dataset"])
xtrain, xtest = dataset.prepare(["train", "test"])
print(xtrain, xtest)
dataset_backtest = init_ins... | use dataset to get highreq data | get_data | python | microsoft/qlib | examples/highfreq/workflow.py | https://github.com/microsoft/qlib/blob/master/examples/highfreq/workflow.py | MIT |
def dump_and_load_dataset(self):
"""dump and load dataset state on disk"""
self._init_qlib()
self._prepare_calender_cache()
dataset = init_instance_by_config(self.task["dataset"])
dataset_backtest = init_instance_by_config(self.task["dataset_backtest"])
##=============du... | dump and load dataset state on disk | dump_and_load_dataset | python | microsoft/qlib | examples/highfreq/workflow.py | https://github.com/microsoft/qlib/blob/master/examples/highfreq/workflow.py | MIT |
def backtest_only_daily(self):
"""
This backtest is used for comparing the nested execution and single layer execution
Due to the low quality daily-level and miniute-level data, they are hardly comparable.
So it is used for detecting serious bugs which make the results different greatly.... |
This backtest is used for comparing the nested execution and single layer execution
Due to the low quality daily-level and miniute-level data, they are hardly comparable.
So it is used for detecting serious bugs which make the results different greatly.
.. code-block:: shell
... | backtest_only_daily | python | microsoft/qlib | examples/nested_decision_execution/workflow.py | https://github.com/microsoft/qlib/blob/master/examples/nested_decision_execution/workflow.py | MIT |
def __init__(
self,
provider_uri="~/.qlib/qlib_data/cn_data",
region="cn",
exp_name="rolling_exp",
task_url="mongodb://10.0.0.4:27017/", # not necessary when using TrainerR or DelayTrainerR
task_db_name="rolling_db", # not necessary when using TrainerR or DelayTrainerR
... |
Init OnlineManagerExample.
Args:
provider_uri (str, optional): the provider uri. Defaults to "~/.qlib/qlib_data/cn_data".
region (str, optional): the stock region. Defaults to "cn".
exp_name (str, optional): the experiment name. Defaults to "rolling_exp".
... | __init__ | python | microsoft/qlib | examples/online_srv/online_management_simulate.py | https://github.com/microsoft/qlib/blob/master/examples/online_srv/online_management_simulate.py | MIT |
def add_one_stock_daily_data(filepath, type, exchange_place, arc, date):
"""
exchange_place: "SZ" OR "SH"
type: "tick", "orderbook", ...
filepath: the path of csv
arc: arclink created by a process
"""
code = os.path.split(filepath)[-1].split(".csv")[0]
if exchange_place == "SH" and code[... |
exchange_place: "SZ" OR "SH"
type: "tick", "orderbook", ...
filepath: the path of csv
arc: arclink created by a process
| add_one_stock_daily_data | python | microsoft/qlib | examples/orderbook_data/create_dataset.py | https://github.com/microsoft/qlib/blob/master/examples/orderbook_data/create_dataset.py | MIT |
def __init__(self, provider_uri: Union[str, Path, dict], mount_path: Union[str, Path, dict]):
"""
The relation of `provider_uri` and `mount_path`
- `mount_path` is used only if provider_uri is an NFS path
- otherwise, provider_uri will be used for accessing data
... |
The relation of `provider_uri` and `mount_path`
- `mount_path` is used only if provider_uri is an NFS path
- otherwise, provider_uri will be used for accessing data
| __init__ | python | microsoft/qlib | qlib/config.py | https://github.com/microsoft/qlib/blob/master/qlib/config.py | MIT |
def get_data_uri(self, freq: Optional[Union[str, Freq]] = None) -> Path:
"""
please refer DataPathManager's __init__ and class doc
"""
if freq is not None:
freq = str(freq) # converting Freq to string
if freq is None or freq not in self.provid... |
please refer DataPathManager's __init__ and class doc
| get_data_uri | python | microsoft/qlib | qlib/config.py | https://github.com/microsoft/qlib/blob/master/qlib/config.py | MIT |
def set(self, default_conf: str = "client", **kwargs):
"""
configure qlib based on the input parameters
The configuration will act like a dictionary.
Normally, it literally is replaced the value according to the keys.
However, sometimes it is hard for users to set the config wh... |
configure qlib based on the input parameters
The configuration will act like a dictionary.
Normally, it literally is replaced the value according to the keys.
However, sometimes it is hard for users to set the config when the configuration is nested and complicated
So this AP... | set | python | microsoft/qlib | qlib/config.py | https://github.com/microsoft/qlib/blob/master/qlib/config.py | MIT |
def get_kernels(self, freq: str):
"""get number of processors given frequency"""
if isinstance(self["kernels"], Callable):
return self["kernels"](freq)
return self["kernels"] | get number of processors given frequency | get_kernels | python | microsoft/qlib | qlib/config.py | https://github.com/microsoft/qlib/blob/master/qlib/config.py | MIT |
def __call__(self, module_name, level: Optional[int] = None) -> QlibLogger:
"""
Get a logger for a specific module.
:param module_name: str
Logic module name.
:param level: int
:return: Logger
Logger object.
"""
if level is None:
... |
Get a logger for a specific module.
:param module_name: str
Logic module name.
:param level: int
:return: Logger
Logger object.
| __call__ | python | microsoft/qlib | qlib/log.py | https://github.com/microsoft/qlib/blob/master/qlib/log.py | MIT |
def set_time_mark(cls):
"""
Set a time mark with current time, and this time mark will push into a stack.
:return: float
A timestamp for current time.
"""
_time = time()
cls.time_marks.append(_time)
return _time |
Set a time mark with current time, and this time mark will push into a stack.
:return: float
A timestamp for current time.
| set_time_mark | python | microsoft/qlib | qlib/log.py | https://github.com/microsoft/qlib/blob/master/qlib/log.py | MIT |
def log_cost_time(cls, info="Done"):
"""
Get last time mark from stack, calculate time diff with current time, and log time diff and info.
:param info: str
Info that will be logged into stdout.
"""
cost_time = time() - cls.time_marks.pop()
cls.timer_logger.inf... |
Get last time mark from stack, calculate time diff with current time, and log time diff and info.
:param info: str
Info that will be logged into stdout.
| log_cost_time | python | microsoft/qlib | qlib/log.py | https://github.com/microsoft/qlib/blob/master/qlib/log.py | MIT |
def logt(cls, name="", show_start=False):
"""logt.
Log the time of the inside code
Parameters
----------
name :
name
show_start :
show_start
"""
if show_start:
cls.timer_logger.info(f"{name} Begin")
cls.set_time... | logt.
Log the time of the inside code
Parameters
----------
name :
name
show_start :
show_start
| logt | python | microsoft/qlib | qlib/log.py | https://github.com/microsoft/qlib/blob/master/qlib/log.py | MIT |
def set_global_logger_level(level: int, return_orig_handler_level: bool = False):
"""set qlib.xxx logger handlers level
Parameters
----------
level: int
logger level
return_orig_handler_level: bool
return origin handler level map
Examples
---------
.. code-block::... | set qlib.xxx logger handlers level
Parameters
----------
level: int
logger level
return_orig_handler_level: bool
return origin handler level map
Examples
---------
.. code-block:: python
import qlib
import logging
from qlib.log imp... | set_global_logger_level | python | microsoft/qlib | qlib/log.py | https://github.com/microsoft/qlib/blob/master/qlib/log.py | MIT |
def set_global_logger_level_cm(level: int):
"""set qlib.xxx logger handlers level to use contextmanager
Parameters
----------
level: int
logger level
Examples
---------
.. code-block:: python
import qlib
import logging
from qlib.log import ... | set qlib.xxx logger handlers level to use contextmanager
Parameters
----------
level: int
logger level
Examples
---------
.. code-block:: python
import qlib
import logging
from qlib.log import get_module_logger, set_global_logger_level_cm
... | set_global_logger_level_cm | python | microsoft/qlib | qlib/log.py | https://github.com/microsoft/qlib/blob/master/qlib/log.py | MIT |
def init(default_conf="client", **kwargs):
"""
Parameters
----------
default_conf: str
the default value is client. Accepted values: client/server.
**kwargs :
clear_mem_cache: str
the default value is True;
Will the memory cache be clear.
It is of... |
Parameters
----------
default_conf: str
the default value is client. Accepted values: client/server.
**kwargs :
clear_mem_cache: str
the default value is True;
Will the memory cache be clear.
It is often used to improve performance when init will be ... | init | python | microsoft/qlib | qlib/__init__.py | https://github.com/microsoft/qlib/blob/master/qlib/__init__.py | MIT |
def init_from_yaml_conf(conf_path, **kwargs):
"""init_from_yaml_conf
:param conf_path: A path to the qlib config in yml format
"""
if conf_path is None:
config = {}
else:
with open(conf_path) as f:
yaml = YAML(typ="safe", pure=True)
config = yaml.load(f)
... | init_from_yaml_conf
:param conf_path: A path to the qlib config in yml format
| init_from_yaml_conf | python | microsoft/qlib | qlib/__init__.py | https://github.com/microsoft/qlib/blob/master/qlib/__init__.py | MIT |
def get_project_path(config_name="config.yaml", cur_path: Union[Path, str, None] = None) -> Path:
"""
If users are building a project follow the following pattern.
- Qlib is a sub folder in project path
- There is a file named `config.yaml` in qlib.
For example:
If your project file system ... |
If users are building a project follow the following pattern.
- Qlib is a sub folder in project path
- There is a file named `config.yaml` in qlib.
For example:
If your project file system structure follows such a pattern
<project_path>/
- config.yaml
-... | get_project_path | python | microsoft/qlib | qlib/__init__.py | https://github.com/microsoft/qlib/blob/master/qlib/__init__.py | MIT |
def auto_init(**kwargs):
"""
This function will init qlib automatically with following priority
- Find the project configuration and init qlib
- The parsing process will be affected by the `conf_type` of the configuration file
- Init qlib with default config
- Skip initialization if already ... |
This function will init qlib automatically with following priority
- Find the project configuration and init qlib
- The parsing process will be affected by the `conf_type` of the configuration file
- Init qlib with default config
- Skip initialization if already initialized
:**kwargs: it m... | auto_init | python | microsoft/qlib | qlib/__init__.py | https://github.com/microsoft/qlib/blob/master/qlib/__init__.py | MIT |
def __init__(
self,
init_cash: float = 1e9,
position_dict: dict = {},
freq: str = "day",
benchmark_config: dict = {},
pos_type: str = "Position",
port_metr_enabled: bool = True,
) -> None:
"""the trade account of backtest.
Parameters
-... | the trade account of backtest.
Parameters
----------
init_cash : float, optional
initial cash, by default 1e9
position_dict : Dict[
stock_id,
Union[
int, # it is equal to {"amount": int}... | __init__ | python | microsoft/qlib | qlib/backtest/account.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/account.py | MIT |
def reset(
self, freq: str | None = None, benchmark_config: dict | None = None, port_metr_enabled: bool | None = None
) -> None:
"""reset freq and report of account
Parameters
----------
freq : str, optional
frequency of account & report, by default None
... | reset freq and report of account
Parameters
----------
freq : str, optional
frequency of account & report, by default None
benchmark_config : {}, optional
benchmark config of report, by default None
port_metr_enabled: bool
| reset | python | microsoft/qlib | qlib/backtest/account.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/account.py | MIT |
def update_current_position(
self,
trade_start_time: pd.Timestamp,
trade_end_time: pd.Timestamp,
trade_exchange: Exchange,
) -> None:
"""
Update current to make rtn consistent with earning at the end of bar, and update holding bar count of stock
"""
# ... |
Update current to make rtn consistent with earning at the end of bar, and update holding bar count of stock
| update_current_position | python | microsoft/qlib | qlib/backtest/account.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/account.py | MIT |
def update_indicator(
self,
trade_start_time: pd.Timestamp,
trade_exchange: Exchange,
atomic: bool,
outer_trade_decision: BaseTradeDecision,
trade_info: list = [],
inner_order_indicators: List[BaseOrderIndicator] = [],
decision_list: List[Tuple[BaseTradeDe... | update trade indicators and order indicators in each bar end | update_indicator | python | microsoft/qlib | qlib/backtest/account.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/account.py | MIT |
def update_bar_end(
self,
trade_start_time: pd.Timestamp,
trade_end_time: pd.Timestamp,
trade_exchange: Exchange,
atomic: bool,
outer_trade_decision: BaseTradeDecision,
trade_info: list = [],
inner_order_indicators: List[BaseOrderIndicator] = [],
d... | update account at each trading bar step
Parameters
----------
trade_start_time : pd.Timestamp
closed start time of step
trade_end_time : pd.Timestamp
closed end time of step
trade_exchange : Exchange
trading exchange, used to update current
... | update_bar_end | python | microsoft/qlib | qlib/backtest/account.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/account.py | MIT |
def get_portfolio_metrics(self) -> Tuple[pd.DataFrame, dict]:
"""get the history portfolio_metrics and positions instance"""
if self.is_port_metr_enabled():
assert self.portfolio_metrics is not None
_portfolio_metrics = self.portfolio_metrics.generate_portfolio_metrics_dataframe(... | get the history portfolio_metrics and positions instance | get_portfolio_metrics | python | microsoft/qlib | qlib/backtest/account.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/account.py | MIT |
def backtest_loop(
start_time: Union[pd.Timestamp, str],
end_time: Union[pd.Timestamp, str],
trade_strategy: BaseStrategy,
trade_executor: BaseExecutor,
) -> Tuple[PORT_METRIC, INDICATOR_METRIC]:
"""backtest function for the interaction of the outermost strategy and executor in the nested decision e... | backtest function for the interaction of the outermost strategy and executor in the nested decision execution
please refer to the docs of `collect_data_loop`
Returns
-------
portfolio_dict: PORT_METRIC
it records the trading portfolio_metrics information
indicator_dict: INDICATOR_METRIC
... | backtest_loop | python | microsoft/qlib | qlib/backtest/backtest.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/backtest.py | MIT |
def collect_data_loop(
start_time: Union[pd.Timestamp, str],
end_time: Union[pd.Timestamp, str],
trade_strategy: BaseStrategy,
trade_executor: BaseExecutor,
return_value: dict | None = None,
) -> Generator[BaseTradeDecision, Optional[BaseTradeDecision], None]:
"""Generator for collecting the tra... | Generator for collecting the trade decision data for rl training
Parameters
----------
start_time : Union[pd.Timestamp, str]
closed start time for backtest
**NOTE**: This will be applied to the outmost executor's calendar.
end_time : Union[pd.Timestamp, str]
closed end time for ... | collect_data_loop | python | microsoft/qlib | qlib/backtest/backtest.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/backtest.py | MIT |
def create(
code: str,
amount: float,
direction: OrderDir,
start_time: Union[str, pd.Timestamp] = None,
end_time: Union[str, pd.Timestamp] = None,
) -> Order:
"""
help to create a order
# TODO: create order for unadjusted amount order
Paramet... |
help to create a order
# TODO: create order for unadjusted amount order
Parameters
----------
code : str
the id of the instrument
amount : float
**adjusted trading amount**
direction : OrderDir
trading direction
star... | create | python | microsoft/qlib | qlib/backtest/decision.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/decision.py | MIT |
def __init__(self, start_time: str | time, end_time: str | time) -> None:
"""
This is a callable class.
**NOTE**:
- It is designed for minute-bar for intra-day trading!!!!!
- Both start_time and end_time are **closed** in the range
Parameters
----------
... |
This is a callable class.
**NOTE**:
- It is designed for minute-bar for intra-day trading!!!!!
- Both start_time and end_time are **closed** in the range
Parameters
----------
start_time : str | time
e.g. "9:30"
end_time : str | time
... | __init__ | python | microsoft/qlib | qlib/backtest/decision.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/decision.py | MIT |
def __init__(self, strategy: BaseStrategy, trade_range: Union[Tuple[int, int], TradeRange, None] = None) -> None:
"""
Parameters
----------
strategy : BaseStrategy
The strategy who make the decision
trade_range: Union[Tuple[int, int], Callable] (optional)
... |
Parameters
----------
strategy : BaseStrategy
The strategy who make the decision
trade_range: Union[Tuple[int, int], Callable] (optional)
The index range for underlying strategy.
Here are two examples of trade_range for each type
1) Tupl... | __init__ | python | microsoft/qlib | qlib/backtest/decision.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/decision.py | MIT |
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