code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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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 |
def update(self, trade_calendar: TradeCalendarManager) -> Optional[BaseTradeDecision]:
"""
Be called at the **start** of each step.
This function is design for following purpose
1) Leave a hook for the strategy who make `self` decision to update the decision itself
2) Update som... |
Be called at the **start** of each step.
This function is design for following purpose
1) Leave a hook for the strategy who make `self` decision to update the decision itself
2) Update some information from the inner executor calendar
Parameters
----------
trad... | update | python | microsoft/qlib | qlib/backtest/decision.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/decision.py | MIT |
def mod_inner_decision(self, inner_trade_decision: BaseTradeDecision) -> None:
"""
This method will be called on the inner_trade_decision after it is generated.
`inner_trade_decision` will be changed **inplace**.
Motivation of the `mod_inner_decision`
- Leave a hook for outer de... |
This method will be called on the inner_trade_decision after it is generated.
`inner_trade_decision` will be changed **inplace**.
Motivation of the `mod_inner_decision`
- Leave a hook for outer decision to affect the decision generated by the inner strategy
- e.g. the outmo... | mod_inner_decision | python | microsoft/qlib | qlib/backtest/decision.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/decision.py | MIT |
def check_stock_suspended(
self,
stock_id: str,
start_time: pd.Timestamp,
end_time: pd.Timestamp,
) -> bool:
"""if stock is suspended(hence not tradable), True will be returned"""
# is suspended
if stock_id in self.quote.get_all_stock():
# suspende... | if stock is suspended(hence not tradable), True will be returned | check_stock_suspended | python | microsoft/qlib | qlib/backtest/exchange.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/exchange.py | MIT |
def deal_order(
self,
order: Order,
trade_account: Account | None = None,
position: BasePosition | None = None,
dealt_order_amount: Dict[str, float] = defaultdict(float),
) -> Tuple[float, float, float]:
"""
Deal order when the actual transaction
the r... |
Deal order when the actual transaction
the results section in `Order` will be changed.
:param order: Deal the order.
:param trade_account: Trade account to be updated after dealing the order.
:param position: position to be updated after dealing the order.
:param dealt_... | deal_order | python | microsoft/qlib | qlib/backtest/exchange.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/exchange.py | MIT |
def get_volume(
self,
stock_id: str,
start_time: pd.Timestamp,
end_time: pd.Timestamp,
method: Optional[str] = "sum",
) -> Union[None, int, float, bool, IndexData]:
"""get the total deal volume of stock with `stock_id` between the time interval [start_time, end_time)"... | get the total deal volume of stock with `stock_id` between the time interval [start_time, end_time) | get_volume | python | microsoft/qlib | qlib/backtest/exchange.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/exchange.py | MIT |
def get_factor(
self,
stock_id: str,
start_time: pd.Timestamp,
end_time: pd.Timestamp,
) -> Optional[float]:
"""
Returns
-------
Optional[float]:
`None`: if the stock is suspended `None` may be returned
`float`: return factor if... |
Returns
-------
Optional[float]:
`None`: if the stock is suspended `None` may be returned
`float`: return factor if the factor exists
| get_factor | python | microsoft/qlib | qlib/backtest/exchange.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/exchange.py | MIT |
def generate_amount_position_from_weight_position(
self,
weight_position: dict,
cash: float,
start_time: pd.Timestamp,
end_time: pd.Timestamp,
direction: OrderDir = OrderDir.BUY,
) -> dict:
"""
Generates the target position according to the weight and ... |
Generates the target position according to the weight and the cash.
NOTE: All the cash will be assigned to the tradable stock.
Parameter:
weight_position : dict {stock_id : weight}; allocate cash by weight_position
among then, weight must be in this range: 0 < weight < 1
... | generate_amount_position_from_weight_position | python | microsoft/qlib | qlib/backtest/exchange.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/exchange.py | MIT |
def get_real_deal_amount(self, current_amount: float, target_amount: float, factor: float | None = None) -> float:
"""
Calculate the real adjust deal amount when considering the trading unit
:param current_amount:
:param target_amount:
:param factor:
:return real_deal_am... |
Calculate the real adjust deal amount when considering the trading unit
:param current_amount:
:param target_amount:
:param factor:
:return real_deal_amount; Positive deal_amount indicates buying more stock.
| get_real_deal_amount | python | microsoft/qlib | qlib/backtest/exchange.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/exchange.py | MIT |
def generate_order_for_target_amount_position(
self,
target_position: dict,
current_position: dict,
start_time: pd.Timestamp,
end_time: pd.Timestamp,
) -> List[Order]:
"""
Note: some future information is used in this function
Parameter:
target... |
Note: some future information is used in this function
Parameter:
target_position : dict { stock_id : amount }
current_position : dict { stock_id : amount}
trade_unit : trade_unit
down sample : for amount 321 and trade_unit 100, deal_amount is 300
deal order on t... | generate_order_for_target_amount_position | python | microsoft/qlib | qlib/backtest/exchange.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/exchange.py | MIT |
def calculate_amount_position_value(
self,
amount_dict: dict,
start_time: pd.Timestamp,
end_time: pd.Timestamp,
only_tradable: bool = False,
direction: OrderDir = OrderDir.SELL,
) -> float:
"""Parameter
position : Position()
amount_dict : {stoc... | Parameter
position : Position()
amount_dict : {stock_id : amount}
direction : the direction of the deal price for estimating the amount
# NOTE:
This function is used for calculating current position value.
So the default direction is se... | calculate_amount_position_value | python | microsoft/qlib | qlib/backtest/exchange.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/exchange.py | MIT |
def _get_factor_or_raise_error(
self,
factor: float | None = None,
stock_id: str | None = None,
start_time: pd.Timestamp = None,
end_time: pd.Timestamp = None,
) -> float:
"""Please refer to the docs of get_amount_of_trade_unit"""
if factor is None:
... | Please refer to the docs of get_amount_of_trade_unit | _get_factor_or_raise_error | python | microsoft/qlib | qlib/backtest/exchange.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/exchange.py | MIT |
def get_amount_of_trade_unit(
self,
factor: float | None = None,
stock_id: str | None = None,
start_time: pd.Timestamp = None,
end_time: pd.Timestamp = None,
) -> Optional[float]:
"""
get the trade unit of amount based on **factor**
the factor can be g... |
get the trade unit of amount based on **factor**
the factor can be given directly or calculated in given time range and stock id.
`factor` has higher priority than `stock_id`, `start_time` and `end_time`
Parameters
----------
factor : float
the adjusted facto... | get_amount_of_trade_unit | python | microsoft/qlib | qlib/backtest/exchange.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/exchange.py | MIT |
def round_amount_by_trade_unit(
self,
deal_amount: float,
factor: float | None = None,
stock_id: str | None = None,
start_time: pd.Timestamp = None,
end_time: pd.Timestamp = None,
) -> float:
"""Parameter
Please refer to the docs of get_amount_of_trade... | Parameter
Please refer to the docs of get_amount_of_trade_unit
deal_amount : float, adjusted amount
factor : float, adjusted factor
return : float, real amount
| round_amount_by_trade_unit | python | microsoft/qlib | qlib/backtest/exchange.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/exchange.py | MIT |
def _calc_trade_info_by_order(
self,
order: Order,
position: Optional[BasePosition],
dealt_order_amount: dict,
) -> Tuple[float, float, float]:
"""
Calculation of trade info
**NOTE**: Order will be changed in this function
:param order:
:param ... |
Calculation of trade info
**NOTE**: Order will be changed in this function
:param order:
:param position: Position
:param dealt_order_amount: the dealt order amount dict with the format of {stock_id: float}
:return: trade_price, trade_val, trade_cost
| _calc_trade_info_by_order | python | microsoft/qlib | qlib/backtest/exchange.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/exchange.py | MIT |
def __init__(
self,
time_per_step: str,
start_time: Union[str, pd.Timestamp] = None,
end_time: Union[str, pd.Timestamp] = None,
indicator_config: dict = {},
generate_portfolio_metrics: bool = False,
verbose: bool = False,
track_data: bool = False,
... |
Parameters
----------
time_per_step : str
trade time per trading step, used for generate the trade calendar
show_indicator: bool, optional
whether to show indicators, :
- 'pa', the price advantage
- 'pos', the positive rate
- '... | __init__ | python | microsoft/qlib | qlib/backtest/executor.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/executor.py | MIT |
def reset(self, common_infra: CommonInfrastructure | None = None, **kwargs: Any) -> None:
"""
- reset `start_time` and `end_time`, used in trade calendar
- reset `common_infra`, used to reset `trade_account`, `trade_exchange`, .etc
"""
if "start_time" in kwargs or "end_time" in ... |
- reset `start_time` and `end_time`, used in trade calendar
- reset `common_infra`, used to reset `trade_account`, `trade_exchange`, .etc
| reset | python | microsoft/qlib | qlib/backtest/executor.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/executor.py | MIT |
def execute(self, trade_decision: BaseTradeDecision, level: int = 0) -> List[object]:
"""execute the trade decision and return the executed result
NOTE: this function is never used directly in the framework. Should we delete it?
Parameters
----------
trade_decision : BaseTradeD... | execute the trade decision and return the executed result
NOTE: this function is never used directly in the framework. Should we delete it?
Parameters
----------
trade_decision : BaseTradeDecision
level : int
the level of current executor
Returns
-... | execute | python | microsoft/qlib | qlib/backtest/executor.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/executor.py | MIT |
def _collect_data(
self,
trade_decision: BaseTradeDecision,
level: int = 0,
) -> Union[Generator[Any, Any, Tuple[List[object], dict]], Tuple[List[object], dict]]:
"""
Please refer to the doc of collect_data
The only difference between `_collect_data` and `collect_data... |
Please refer to the doc of collect_data
The only difference between `_collect_data` and `collect_data` is that some common steps are moved into
collect_data
Parameters
----------
Please refer to the doc of collect_data
Returns
-------
Tuple[Lis... | _collect_data | python | microsoft/qlib | qlib/backtest/executor.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/executor.py | MIT |
def collect_data(
self,
trade_decision: BaseTradeDecision,
return_value: dict | None = None,
level: int = 0,
) -> Generator[Any, Any, List[object]]:
"""Generator for collecting the trade decision data for rl training
his function will make a step forward
Par... | Generator for collecting the trade decision data for rl training
his function will make a step forward
Parameters
----------
trade_decision : BaseTradeDecision
level : int
the level of current executor. 0 indicates the top level
return_value : dict
... | collect_data | python | microsoft/qlib | qlib/backtest/executor.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/executor.py | MIT |
def reset_common_infra(self, common_infra: CommonInfrastructure, copy_trade_account: bool = False) -> None:
"""
reset infrastructure for trading
- reset inner_strategy and inner_executor common infra
"""
# NOTE: please refer to the docs of BaseExecutor.reset_common_infra for ... |
reset infrastructure for trading
- reset inner_strategy and inner_executor common infra
| reset_common_infra | python | microsoft/qlib | qlib/backtest/executor.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/executor.py | MIT |
def __init__(
self,
time_per_step: str,
start_time: Union[str, pd.Timestamp] = None,
end_time: Union[str, pd.Timestamp] = None,
indicator_config: dict = {},
generate_portfolio_metrics: bool = False,
verbose: bool = False,
track_data: bool = False,
... |
Parameters
----------
trade_type: str
please refer to the doc of `TT_SERIAL` & `TT_PARAL`
| __init__ | python | microsoft/qlib | qlib/backtest/executor.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/executor.py | MIT |
def _get_order_iterator(self, trade_decision: BaseTradeDecision) -> List[Order]:
"""
Parameters
----------
trade_decision : BaseTradeDecision
the trade decision given by the strategy
Returns
-------
List[Order]:
get a list orders accordin... |
Parameters
----------
trade_decision : BaseTradeDecision
the trade decision given by the strategy
Returns
-------
List[Order]:
get a list orders according to `self.trade_type`
| _get_order_iterator | python | microsoft/qlib | qlib/backtest/executor.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/executor.py | MIT |
def get_data(
self,
stock_id: str,
start_time: Union[pd.Timestamp, str],
end_time: Union[pd.Timestamp, str],
field: Union[str],
method: Optional[str] = None,
) -> Union[None, int, float, bool, IndexData]:
"""get the specific field of stock data during start ti... | get the specific field of stock data during start time and end_time,
and apply method to the data.
Example:
.. code-block::
$close $volume
instrument datetime
SH600000 2010-01-04 86.778313 16162960.0
... | get_data | python | microsoft/qlib | qlib/backtest/high_performance_ds.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/high_performance_ds.py | MIT |
def __init__(self, quote_df: pd.DataFrame, freq: str, region: str = "cn") -> None:
"""NumpyQuote
Parameters
----------
quote_df : pd.DataFrame
the init dataframe from qlib.
self.data : Dict(stock_id, IndexData.DataFrame)
"""
super().__init__(quote_df=... | NumpyQuote
Parameters
----------
quote_df : pd.DataFrame
the init dataframe from qlib.
self.data : Dict(stock_id, IndexData.DataFrame)
| __init__ | python | microsoft/qlib | qlib/backtest/high_performance_ds.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/high_performance_ds.py | MIT |
def transfer(self, func: Callable, new_col: str = None) -> Optional[BaseSingleMetric]:
"""compute new metric with existing metrics.
Parameters
----------
func : Callable
the func of computing new metric.
the kwargs of func will be replaced with metric data by nam... | compute new metric with existing metrics.
Parameters
----------
func : Callable
the func of computing new metric.
the kwargs of func will be replaced with metric data by name in this function.
e.g.
def func(pa):
return (pa ... | transfer | python | microsoft/qlib | qlib/backtest/high_performance_ds.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/high_performance_ds.py | MIT |
def sum_all_indicators(
order_indicator: BaseOrderIndicator,
indicators: List[BaseOrderIndicator],
metrics: Union[str, List[str]],
fill_value: float = 0,
) -> None:
"""sum indicators with the same metrics.
and assign to the order_indicator(BaseOrderIndicator).
... | sum indicators with the same metrics.
and assign to the order_indicator(BaseOrderIndicator).
NOTE: indicators could be a empty list when orders in lower level all fail.
Parameters
----------
order_indicator : BaseOrderIndicator
the order indicator to assign.
... | sum_all_indicators | python | microsoft/qlib | qlib/backtest/high_performance_ds.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/high_performance_ds.py | MIT |
def __init__(self, cash: float = 0, position_dict: Dict[str, Union[Dict[str, float], float]] = {}) -> None:
"""Init position by cash and position_dict.
Parameters
----------
cash : float, optional
initial cash in account, by default 0
position_dict : Dict[
... | Init position by cash and position_dict.
Parameters
----------
cash : float, optional
initial cash in account, by default 0
position_dict : Dict[
stock_id,
Union[
int, # it is equal to {... | __init__ | python | microsoft/qlib | qlib/backtest/position.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/position.py | MIT |
def fill_stock_value(self, start_time: Union[str, pd.Timestamp], freq: str, last_days: int = 30) -> None:
"""fill the stock value by the close price of latest last_days from qlib.
Parameters
----------
start_time :
the start time of backtest.
freq : str
F... | fill the stock value by the close price of latest last_days from qlib.
Parameters
----------
start_time :
the start time of backtest.
freq : str
Frequency
last_days : int, optional
the days to get the latest close price, by default 30.
... | fill_stock_value | python | microsoft/qlib | qlib/backtest/position.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/position.py | MIT |
def _init_stock(self, stock_id: str, amount: float, price: float | None = None) -> None:
"""
initialization the stock in current position
Parameters
----------
stock_id :
the id of the stock
amount : float
the amount of the stock
price :
... |
initialization the stock in current position
Parameters
----------
stock_id :
the id of the stock
amount : float
the amount of the stock
price :
the price when buying the init stock
| _init_stock | python | microsoft/qlib | qlib/backtest/position.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/position.py | MIT |
def get_stock_count(self, code: str, bar: str) -> float:
"""the days the account has been hold, it may be used in some special strategies"""
if f"count_{bar}" in self.position[code]:
return self.position[code][f"count_{bar}"]
else:
return 0 | the days the account has been hold, it may be used in some special strategies | get_stock_count | python | microsoft/qlib | qlib/backtest/position.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/position.py | MIT |
def get_stock_amount_dict(self) -> dict:
"""generate stock amount dict {stock_id : amount of stock}"""
d = {}
stock_list = self.get_stock_list()
for stock_code in stock_list:
d[stock_code] = self.get_stock_amount(code=stock_code)
return d | generate stock amount dict {stock_id : amount of stock} | get_stock_amount_dict | python | microsoft/qlib | qlib/backtest/position.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/position.py | MIT |
def get_stock_weight_dict(self, only_stock: bool = False) -> dict:
"""get_stock_weight_dict
generate stock weight dict {stock_id : value weight of stock in the position}
it is meaningful in the beginning or the end of each trade date
:param only_stock: If only_stock=True, the weight of ... | get_stock_weight_dict
generate stock weight dict {stock_id : value weight of stock in the position}
it is meaningful in the beginning or the end of each trade date
:param only_stock: If only_stock=True, the weight of each stock in total stock will be returned
If only_... | get_stock_weight_dict | python | microsoft/qlib | qlib/backtest/position.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/position.py | MIT |
def get_benchmark_weight(
bench,
start_date=None,
end_date=None,
path=None,
freq="day",
):
"""get_benchmark_weight
get the stock weight distribution of the benchmark
:param bench:
:param start_date:
:param end_date:
:param path:
:param freq:
:return: The weight dis... | get_benchmark_weight
get the stock weight distribution of the benchmark
:param bench:
:param start_date:
:param end_date:
:param path:
:param freq:
:return: The weight distribution of the the benchmark described by a pandas dataframe
Every row corresponds to a trading day.
... | get_benchmark_weight | python | microsoft/qlib | qlib/backtest/profit_attribution.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/profit_attribution.py | MIT |
def get_stock_weight_df(positions):
"""get_stock_weight_df
:param positions: Given a positions from backtest result.
:return: A weight distribution for the position
"""
stock_weight = []
index = []
for date in sorted(positions.keys()):
pos = positions[date]
if isinst... | get_stock_weight_df
:param positions: Given a positions from backtest result.
:return: A weight distribution for the position
| get_stock_weight_df | python | microsoft/qlib | qlib/backtest/profit_attribution.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/profit_attribution.py | MIT |
def decompose_portofolio_weight(stock_weight_df, stock_group_df):
"""decompose_portofolio_weight
'''
:param stock_weight_df: a pandas dataframe to describe the portofolio by weight.
every row corresponds to a day
every column corresponds to a stock.
... | decompose_portofolio_weight
'''
:param stock_weight_df: a pandas dataframe to describe the portofolio by weight.
every row corresponds to a day
every column corresponds to a stock.
Here is an example below.
code SH600004 S... | decompose_portofolio_weight | python | microsoft/qlib | qlib/backtest/profit_attribution.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/profit_attribution.py | MIT |
def decompose_portofolio(stock_weight_df, stock_group_df, stock_ret_df):
"""
:param stock_weight_df: a pandas dataframe to describe the portofolio by weight.
every row corresponds to a day
every column corresponds to a stock.
Here is an example below.... |
:param stock_weight_df: a pandas dataframe to describe the portofolio by weight.
every row corresponds to a day
every column corresponds to a stock.
Here is an example below.
code SH600004 SH600006 SH600017 SH600022 SH60002... | decompose_portofolio | python | microsoft/qlib | qlib/backtest/profit_attribution.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/profit_attribution.py | MIT |
def get_daily_bin_group(bench_values, stock_values, group_n):
"""get_daily_bin_group
Group the values of the stocks of benchmark into several bins in a day.
Put the stocks into these bins.
:param bench_values: A series contains the value of stocks in benchmark.
The index is the... | get_daily_bin_group
Group the values of the stocks of benchmark into several bins in a day.
Put the stocks into these bins.
:param bench_values: A series contains the value of stocks in benchmark.
The index is the stock code.
:param stock_values: A series contains the value of ... | get_daily_bin_group | python | microsoft/qlib | qlib/backtest/profit_attribution.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/profit_attribution.py | MIT |
def brinson_pa(
positions,
bench="SH000905",
group_field="industry",
group_method="category",
group_n=None,
deal_price="vwap",
freq="day",
):
"""brinson profit attribution
:param positions: The position produced by the backtest class
:param bench: The benchmark for comparing. TO... | brinson profit attribution
:param positions: The position produced by the backtest class
:param bench: The benchmark for comparing. TODO: if no benchmark is set, the equal-weighted is used.
:param group_field: The field used to set the group for assets allocation.
`industry` and `ma... | brinson_pa | python | microsoft/qlib | qlib/backtest/profit_attribution.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/profit_attribution.py | MIT |
def load_portfolio_metrics(self, path: str) -> None:
"""load pm from a file
should have format like
columns = ['account', 'return', 'total_turnover', 'turnover', 'cost', 'total_cost', 'value', 'cash', 'bench']
:param
path: str/ pathlib.Path()
"""
with ... | load pm from a file
should have format like
columns = ['account', 'return', 'total_turnover', 'turnover', 'cost', 'total_cost', 'value', 'cash', 'bench']
:param
path: str/ pathlib.Path()
| load_portfolio_metrics | python | microsoft/qlib | qlib/backtest/report.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/report.py | MIT |
def _get_base_vol_pri(
self,
inst: str,
trade_start_time: pd.Timestamp,
trade_end_time: pd.Timestamp,
direction: OrderDir,
decision: BaseTradeDecision,
trade_exchange: Exchange,
pa_config: dict = {},
) -> Tuple[Optional[float], Optional[float]]:
... |
Get the base volume and price information
All the base price values are rooted from this function
| _get_base_vol_pri | python | microsoft/qlib | qlib/backtest/report.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/report.py | MIT |
def _agg_base_price(
self,
inner_order_indicators: List[BaseOrderIndicator],
decision_list: List[Tuple[BaseTradeDecision, pd.Timestamp, pd.Timestamp]],
trade_exchange: Exchange,
pa_config: dict = {},
) -> None:
"""
# NOTE:!!!!
# Strong assumption!!!!!!... |
# NOTE:!!!!
# Strong assumption!!!!!!
# the correctness of the base_price relies on that the **same** exchange is used
Parameters
----------
inner_order_indicators : List[BaseOrderIndicator]
the indicators of account of inner executor
decision_list: ... | _agg_base_price | python | microsoft/qlib | qlib/backtest/report.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/report.py | MIT |
def get_signal(self, start_time: pd.Timestamp, end_time: pd.Timestamp) -> Union[pd.Series, pd.DataFrame, None]:
"""
get the signal at the end of the decision step(from `start_time` to `end_time`)
Returns
-------
Union[pd.Series, pd.DataFrame, None]:
returns None if n... |
get the signal at the end of the decision step(from `start_time` to `end_time`)
Returns
-------
Union[pd.Series, pd.DataFrame, None]:
returns None if no signal in the specific day
| get_signal | python | microsoft/qlib | qlib/backtest/signal.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/signal.py | MIT |
def _update_model(self) -> None:
"""
When using online data, update model in each bar as the following steps:
- update dataset with online data, the dataset should support online update
- make the latest prediction scores of the new bar
- update the pred score into th... |
When using online data, update model in each bar as the following steps:
- update dataset with online data, the dataset should support online update
- make the latest prediction scores of the new bar
- update the pred score into the latest prediction
| _update_model | python | microsoft/qlib | qlib/backtest/signal.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/signal.py | MIT |
def create_signal_from(
obj: Union[Signal, Tuple[BaseModel, Dataset], List, Dict, Text, pd.Series, pd.DataFrame],
) -> Signal:
"""
create signal from diverse information
This method will choose the right method to create a signal based on `obj`
Please refer to the code below.
"""
if isinstan... |
create signal from diverse information
This method will choose the right method to create a signal based on `obj`
Please refer to the code below.
| create_signal_from | python | microsoft/qlib | qlib/backtest/signal.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/signal.py | MIT |
def __init__(
self,
freq: str,
start_time: Union[str, pd.Timestamp] = None,
end_time: Union[str, pd.Timestamp] = None,
level_infra: LevelInfrastructure | None = None,
) -> None:
"""
Parameters
----------
freq : str
frequency of trad... |
Parameters
----------
freq : str
frequency of trading calendar, also trade time per trading step
start_time : Union[str, pd.Timestamp], optional
closed start of the trading calendar, by default None
If `start_time` is None, it must be reset before tra... | __init__ | python | microsoft/qlib | qlib/backtest/utils.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/utils.py | MIT |
def reset(
self,
freq: str,
start_time: Union[str, pd.Timestamp] = None,
end_time: Union[str, pd.Timestamp] = None,
) -> None:
"""
Please refer to the docs of `__init__`
Reset the trade calendar
- self.trade_len : The total count for trading step
... |
Please refer to the docs of `__init__`
Reset the trade calendar
- self.trade_len : The total count for trading step
- self.trade_step : The number of trading step finished, self.trade_step can be
[0, 1, 2, ..., self.trade_len - 1]
| reset | python | microsoft/qlib | qlib/backtest/utils.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/utils.py | MIT |
def get_step_time(self, trade_step: int | None = None, shift: int = 0) -> Tuple[pd.Timestamp, pd.Timestamp]:
"""
Get the left and right endpoints of the trade_step'th trading interval
About the endpoints:
- Qlib uses the closed interval in time-series data selection, which has the s... |
Get the left and right endpoints of the trade_step'th trading interval
About the endpoints:
- Qlib uses the closed interval in time-series data selection, which has the same performance as
pandas.Series.loc
# - The returned right endpoints should minus 1 seconds bec... | get_step_time | python | microsoft/qlib | qlib/backtest/utils.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/utils.py | MIT |
def get_range_idx(self, start_time: pd.Timestamp, end_time: pd.Timestamp) -> Tuple[int, int]:
"""
get the range index which involve start_time~end_time (both sides are closed)
Parameters
----------
start_time : pd.Timestamp
end_time : pd.Timestamp
Returns
... |
get the range index which involve start_time~end_time (both sides are closed)
Parameters
----------
start_time : pd.Timestamp
end_time : pd.Timestamp
Returns
-------
Tuple[int, int]:
the index of the range. **the left and right are closed*... | get_range_idx | python | microsoft/qlib | qlib/backtest/utils.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/utils.py | MIT |
def create_account_instance(
start_time: Union[pd.Timestamp, str],
end_time: Union[pd.Timestamp, str],
benchmark: Optional[str],
account: Union[float, int, dict],
pos_type: str = "Position",
) -> Account:
"""
# TODO: is very strange pass benchmark_config in the account (maybe for report)
... |
# TODO: is very strange pass benchmark_config in the account (maybe for report)
# There should be a post-step to process the report.
Parameters
----------
start_time
start time of the benchmark
end_time
end time of the benchmark
benchmark : str
the benchmark for rep... | create_account_instance | python | microsoft/qlib | qlib/backtest/__init__.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/__init__.py | MIT |
def backtest(
start_time: Union[pd.Timestamp, str],
end_time: Union[pd.Timestamp, str],
strategy: Union[str, dict, object, Path],
executor: Union[str, dict, object, Path],
benchmark: str = "SH000300",
account: Union[float, int, dict] = 1e9,
exchange_kwargs: dict = {},
pos_type: str = "Po... | initialize the strategy and executor, then backtest function for the interaction of the outermost strategy and
executor in the nested decision execution
Parameters
----------
start_time : Union[pd.Timestamp, str]
closed start time for backtest
**NOTE**: This will be applied to the outmo... | backtest | python | microsoft/qlib | qlib/backtest/__init__.py | https://github.com/microsoft/qlib/blob/master/qlib/backtest/__init__.py | MIT |
Subsets and Splits
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SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
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