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Merge remote-tracking branch 'microsoft/main' into online_srv

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lzh222333
2021-04-29 05:23:42 +00:00
parent 67c5740c83 5a7f9ef720
commit 2b7ffa100f
29 changed files with 570 additions and 48 deletions
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7
qlib/contrib/backtest/backtest.py
View File

@@ -15,7 +15,8 @@ LOG = get_module_logger("backtest")
def backtest(pred, strategy, executor, trade_exchange, shift, verbose, account, benchmark, return_order):
"""Parameters
"""
Parameters
----------
pred : pandas.DataFrame
predict should has <datetime, instrument> index and one `score` column
@@ -124,7 +125,9 @@ def backtest(pred, strategy, executor, trade_exchange, shift, verbose, account,
def update_account(trade_account, trade_info, trade_exchange, trade_date):
"""Update the account and strategy
"""
Update the account and strategy
Parameters
----------
trade_account : Account()

2
qlib/contrib/backtest/position.py
View File

@@ -128,7 +128,7 @@ class Position:
return self.position["cash"]
def get_stock_amount_dict(self):
"""generate stock amount dict {stock_id : amount of stock} """
"""generate stock amount dict {stock_id : amount of stock}"""
d = {}
stock_list = self.get_stock_list()
for stock_code in stock_list:

53
qlib/contrib/eva/alpha.py
View File

@@ -8,6 +8,59 @@ import pandas as pd
from typing import Tuple
def calc_long_short_prec(
pred: pd.Series, label: pd.Series, date_col="datetime", quantile: float = 0.2, dropna=False, is_alpha=False
) -> Tuple[pd.Series, pd.Series]:
"""
calculate the precision for long and short operation
:param pred/label: index is **pd.MultiIndex**, index name is **[datetime, instruments]**; columns names is **[score]**.
.. code-block:: python
score
datetime instrument
2020-12-01 09:30:00 SH600068 0.553634
SH600195 0.550017
SH600276 0.540321
SH600584 0.517297
SH600715 0.544674
label :
label
date_col :
date_col
Returns
-------
(pd.Series, pd.Series)
long precision and short precision in time level
"""
if is_alpha:
label = label - label.mean(level=date_col)
if int(1 / quantile) >= len(label.index.get_level_values(1).unique()):
raise ValueError("Need more instruments to calculate precision")
df = pd.DataFrame({"pred": pred, "label": label})
if dropna:
df.dropna(inplace=True)
group = df.groupby(level=date_col)
N = lambda x: int(len(x) * quantile)
# find the top/low quantile of prediction and treat them as long and short target
long = group.apply(lambda x: x.nlargest(N(x), columns="pred").label).reset_index(level=0, drop=True)
short = group.apply(lambda x: x.nsmallest(N(x), columns="pred").label).reset_index(level=0, drop=True)
groupll = long.groupby(date_col)
l_dom = groupll.apply(lambda x: x > 0)
l_c = groupll.count()
groups = short.groupby(date_col)
s_dom = groups.apply(lambda x: x < 0)
s_c = groups.count()
return (l_dom.groupby(date_col).sum() / l_c), (s_dom.groupby(date_col).sum() / s_c)
def calc_ic(pred: pd.Series, label: pd.Series, date_col="datetime", dropna=False) -> Tuple[pd.Series, pd.Series]:
"""calc_ic.

157
qlib/contrib/model/highfreq_gdbt_model.py Normal file
View File

@@ -0,0 +1,157 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import numpy as np
import pandas as pd
import lightgbm as lgb
from qlib.model.base import ModelFT
from qlib.data.dataset import DatasetH
from qlib.data.dataset.handler import DataHandlerLP
import warnings
class HFLGBModel(ModelFT):
"""LightGBM Model for high frequency prediction"""
def __init__(self, loss="mse", **kwargs):
if loss not in {"mse", "binary"}:
raise NotImplementedError
self.params = {"objective": loss, "verbosity": -1}
self.params.update(kwargs)
self.model = None
def _cal_signal_metrics(self, y_test, l_cut, r_cut):
"""
Calcaute the signal metrics by daily level
"""
up_pre, down_pre = [], []
up_alpha_ll, down_alpha_ll = [], []
for date in y_test.index.get_level_values(0).unique():
df_res = y_test.loc[date].sort_values("pred")
if int(l_cut * len(df_res)) < 10:
warnings.warn("Warning: threhold is too low or instruments number is not enough")
continue
top = df_res.iloc[: int(l_cut * len(df_res))]
bottom = df_res.iloc[int(r_cut * len(df_res)) :]
down_precision = len(top[top[top.columns[0]] < 0]) / (len(top))
up_precision = len(bottom[bottom[top.columns[0]] > 0]) / (len(bottom))
down_alpha = top[top.columns[0]].mean()
up_alpha = bottom[bottom.columns[0]].mean()
up_pre.append(up_precision)
down_pre.append(down_precision)
up_alpha_ll.append(up_alpha)
down_alpha_ll.append(down_alpha)
return (
np.array(up_pre).mean(),
np.array(down_pre).mean(),
np.array(up_alpha_ll).mean(),
np.array(down_alpha_ll).mean(),
)
def hf_signal_test(self, dataset: DatasetH, threhold=0.2):
"""
Test the sigal in high frequency test set
"""
if self.model == None:
raise ValueError("Model hasn't been trained yet")
df_test = dataset.prepare("test", col_set=["feature", "label"], data_key=DataHandlerLP.DK_I)
df_test.dropna(inplace=True)
x_test, y_test = df_test["feature"], df_test["label"]
# Convert label into alpha
y_test[y_test.columns[0]] = y_test[y_test.columns[0]] - y_test[y_test.columns[0]].mean(level=0)
res = pd.Series(self.model.predict(x_test.values), index=x_test.index)
y_test["pred"] = res
up_p, down_p, up_a, down_a = self._cal_signal_metrics(y_test, threhold, 1 - threhold)
print("===============================")
print("High frequency signal test")
print("===============================")
print("Test set precision: ")
print("Positive precision: {}, Negative precision: {}".format(up_p, down_p))
print("Test Alpha Average in test set: ")
print("Positive average alpha: {}, Negative average alpha: {}".format(up_a, down_a))
def _prepare_data(self, dataset: DatasetH):
df_train, df_valid = dataset.prepare(
["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
)
x_train, y_train = df_train["feature"], df_train["label"]
x_valid, y_valid = df_train["feature"], df_valid["label"]
if y_train.values.ndim == 2 and y_train.values.shape[1] == 1:
l_name = df_train["label"].columns[0]
# Convert label into alpha
df_train["label"][l_name] = df_train["label"][l_name] - df_train["label"][l_name].mean(level=0)
df_valid["label"][l_name] = df_valid["label"][l_name] - df_valid["label"][l_name].mean(level=0)
mapping_fn = lambda x: 0 if x < 0 else 1
df_train["label_c"] = df_train["label"][l_name].apply(mapping_fn)
df_valid["label_c"] = df_valid["label"][l_name].apply(mapping_fn)
x_train, y_train = df_train["feature"], df_train["label_c"].values
x_valid, y_valid = df_valid["feature"], df_valid["label_c"].values
else:
raise ValueError("LightGBM doesn't support multi-label training")
dtrain = lgb.Dataset(x_train.values, label=y_train)
dvalid = lgb.Dataset(x_valid.values, label=y_valid)
return dtrain, dvalid
def fit(
self,
dataset: DatasetH,
num_boost_round=1000,
early_stopping_rounds=50,
verbose_eval=20,
evals_result=dict(),
**kwargs
):
dtrain, dvalid = self._prepare_data(dataset)
self.model = lgb.train(
self.params,
dtrain,
num_boost_round=num_boost_round,
valid_sets=[dtrain, dvalid],
valid_names=["train", "valid"],
early_stopping_rounds=early_stopping_rounds,
verbose_eval=verbose_eval,
evals_result=evals_result,
**kwargs
)
evals_result["train"] = list(evals_result["train"].values())[0]
evals_result["valid"] = list(evals_result["valid"].values())[0]
def predict(self, dataset):
if self.model is None:
raise ValueError("model is not fitted yet!")
x_test = dataset.prepare("test", col_set="feature", data_key=DataHandlerLP.DK_I)
return pd.Series(self.model.predict(x_test.values), index=x_test.index)
def finetune(self, dataset: DatasetH, num_boost_round=10, verbose_eval=20):
"""
finetune model
Parameters
----------
dataset : DatasetH
dataset for finetuning
num_boost_round : int
number of round to finetune model
verbose_eval : int
verbose level
"""
# Based on existing model and finetune by train more rounds
dtrain, _ = self._prepare_data(dataset)
self.model = lgb.train(
self.params,
dtrain,
num_boost_round=num_boost_round,
init_model=self.model,
valid_sets=[dtrain],
valid_names=["train"],
verbose_eval=verbose_eval,
)

2
qlib/contrib/report/analysis_position/cumulative_return.py
View File

@@ -214,7 +214,7 @@ def cumulative_return_graph(
features_df = D.features(D.instruments('csi500'), ['Ref($close, -1)/$close - 1'], pred_df_dates.min(), pred_df_dates.max())
features_df.columns = ['label']
qcr.cumulative_return_graph(positions, report_normal_df, features_df)
qcr.analysis_position.cumulative_return_graph(positions, report_normal_df, features_df)
Graph desc:

2
qlib/contrib/report/analysis_position/rank_label.py
View File

@@ -94,7 +94,7 @@ def rank_label_graph(
features_df = D.features(D.instruments('csi500'), ['Ref($close, -1)/$close-1'], pred_df_dates.min(), pred_df_dates.max())
features_df.columns = ['label']
qcr.rank_label_graph(positions, features_df, pred_df_dates.min(), pred_df_dates.max())
qcr.analysis_position.rank_label_graph(positions, features_df, pred_df_dates.min(), pred_df_dates.max())
:param position: position data; **qlib.contrib.backtest.backtest.backtest** result.

2
qlib/contrib/report/analysis_position/report.py
View File

@@ -186,7 +186,7 @@ def report_graph(report_df: pd.DataFrame, show_notebook: bool = True) -> [list,
report_normal_df, _ = backtest(pred_df, strategy, **bparas)
qcr.report_graph(report_normal_df)
qcr.analysis_position.report_graph(report_normal_df)
:param report_df: **df.index.name** must be **date**, **df.columns** must contain **return**, **turnover**, **cost**, **bench**.

2
qlib/contrib/report/graph.py
View File

@@ -18,7 +18,7 @@ from ...utils import get_module_by_module_path
class BaseGraph:
""""""
""" """
_name = None

5
qlib/contrib/workflow/record_temp.py
View File

@@ -1,10 +1,11 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import logging
import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error
from typing import Dict, Text, Any
import numpy as np
from ...contrib.eva.alpha import calc_ic
from ...workflow.record_temp import RecordTemp
@@ -12,7 +13,7 @@ from ...workflow.record_temp import SignalRecord
from ...data import dataset as qlib_dataset
from ...log import get_module_logger
logger = get_module_logger("workflow", "INFO")
logger = get_module_logger("workflow", logging.INFO)
class MultiSegRecord(RecordTemp):

6
qlib/data/data.py
View File

@@ -522,6 +522,9 @@ class LocalCalendarProvider(CalendarProvider):
# if future calendar not exists, return current calendar
if not os.path.exists(fname):
get_module_logger("data").warning(f"{freq}_future.txt not exists, return current calendar!")
get_module_logger("data").warning(
"You can get future calendar by referring to the following document: https://github.com/microsoft/qlib/blob/main/scripts/data_collector/contrib/README.md"
)
fname = self._uri_cal.format(freq)
else:
fname = self._uri_cal.format(freq)
@@ -1016,7 +1019,8 @@ class ClientProvider(BaseProvider):
self.logger = get_module_logger(self.__class__.__name__)
if isinstance(Cal, ClientCalendarProvider):
Cal.set_conn(self.client)
Inst.set_conn(self.client)
if isinstance(Inst, ClientInstrumentProvider):
Inst.set_conn(self.client)
if hasattr(DatasetD, "provider"):
DatasetD.provider.set_conn(self.client)
else:

4
qlib/data/dataset/processor.py
View File

@@ -130,7 +130,7 @@ class FilterCol(Processor):
class TanhProcess(Processor):
""" Use tanh to process noise data"""
"""Use tanh to process noise data"""
def __call__(self, df):
def tanh_denoise(data):
@@ -145,7 +145,7 @@ class TanhProcess(Processor):
class ProcessInf(Processor):
"""Process infinity """
"""Process infinity"""
def __call__(self, df):
def replace_inf(data):

34
qlib/log.py
View File

@@ -12,7 +12,37 @@ from contextlib import contextmanager
from .config import C
def get_module_logger(module_name, level: Optional[int] = None):
class MetaLogger(type):
def __new__(cls, name, bases, dict):
wrapper_dict = logging.Logger.__dict__.copy()
wrapper_dict.update(dict)
wrapper_dict["__doc__"] = logging.Logger.__doc__
return type.__new__(cls, name, bases, wrapper_dict)
class QlibLogger(metaclass=MetaLogger):
"""
Customized logger for Qlib.
"""
def __init__(self, module_name):
self.module_name = module_name
self.level = 0
@property
def logger(self):
logger = logging.getLogger(self.module_name)
logger.setLevel(self.level)
return logger
def setLevel(self, level):
self.level = level
def __getattr__(self, name):
return self.logger.__getattribute__(name)
def get_module_logger(module_name, level: Optional[int] = None) -> logging.Logger:
"""
Get a logger for a specific module.
@@ -27,7 +57,7 @@ def get_module_logger(module_name, level: Optional[int] = None):
module_name = "qlib.{}".format(module_name)
# Get logger.
module_logger = logging.getLogger(module_name)
module_logger = QlibLogger(module_name)
module_logger.setLevel(level)
return module_logger

4
qlib/model/base.py
View File

@@ -11,11 +11,11 @@ class BaseModel(Serializable, metaclass=abc.ABCMeta):
@abc.abstractmethod
def predict(self, *args, **kwargs) -> object:
""" Make predictions after modeling things """
"""Make predictions after modeling things"""
pass
def __call__(self, *args, **kwargs) -> object:
""" leverage Python syntactic sugar to make the models' behaviors like functions """
"""leverage Python syntactic sugar to make the models' behaviors like functions"""
return self.predict(*args, **kwargs)

4
qlib/portfolio/optimizer/base.py
View File

@@ -5,9 +5,9 @@ import abc
class BaseOptimizer(abc.ABC):
""" Construct portfolio with a optimization related method """
"""Construct portfolio with a optimization related method"""
@abc.abstractmethod
def __call__(self, *args, **kwargs) -> object:
""" Generate a optimized portfolio allocation """
"""Generate a optimized portfolio allocation"""
pass

4
qlib/workflow/exp.py
View File

@@ -1,14 +1,14 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import mlflow
import mlflow, logging
from mlflow.entities import ViewType
from mlflow.exceptions import MlflowException
from pathlib import Path
from .recorder import Recorder, MLflowRecorder
from ..log import get_module_logger
logger = get_module_logger("workflow", "INFO")
logger = get_module_logger("workflow", logging.INFO)
class Experiment:

4
qlib/workflow/expm.py
View File

@@ -4,7 +4,7 @@
import mlflow
from mlflow.exceptions import MlflowException
from mlflow.entities import ViewType
import os
import os, logging
from pathlib import Path
from contextlib import contextmanager
from typing import Optional, Text
@@ -14,7 +14,7 @@ from ..config import C
from .recorder import Recorder
from ..log import get_module_logger
logger = get_module_logger("workflow", "INFO")
logger = get_module_logger("workflow", logging.INFO)
class ExpManager:

60
qlib/workflow/record_temp.py
View File

@@ -1,7 +1,7 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import re
import re, logging
import pandas as pd
from pathlib import Path
from pprint import pprint
@@ -13,10 +13,10 @@ from ..data.dataset.handler import DataHandlerLP
from ..utils import init_instance_by_config, get_module_by_module_path
from ..log import get_module_logger
from ..utils import flatten_dict
from ..contrib.eva.alpha import calc_ic, calc_long_short_return
from ..contrib.eva.alpha import calc_ic, calc_long_short_return, calc_long_short_prec
from ..contrib.strategy.strategy import BaseStrategy
logger = get_module_logger("workflow", "INFO")
logger = get_module_logger("workflow", logging.INFO)
class RecordTemp:
@@ -166,6 +166,60 @@ class SignalRecord(RecordTemp):
return super().load(name)
class HFSignalRecord(SignalRecord):
"""
This is the Signal Analysis Record class that generates the analysis results such as IC and IR. This class inherits the ``RecordTemp`` class.
"""
artifact_path = "hg_sig_analysis"
def __init__(self, recorder, **kwargs):
super().__init__(recorder=recorder)
def generate(self):
pred = self.load("pred.pkl")
raw_label = self.load("label.pkl")
long_pre, short_pre = calc_long_short_prec(pred.iloc[:, 0], raw_label.iloc[:, 0], is_alpha=True)
ic, ric = calc_ic(pred.iloc[:, 0], raw_label.iloc[:, 0])
metrics = {
"IC": ic.mean(),
"ICIR": ic.mean() / ic.std(),
"Rank IC": ric.mean(),
"Rank ICIR": ric.mean() / ric.std(),
"Long precision": long_pre.mean(),
"Short precision": short_pre.mean(),
}
objects = {"ic.pkl": ic, "ric.pkl": ric}
objects.update({"long_pre.pkl": long_pre, "short_pre.pkl": short_pre})
long_short_r, long_avg_r = calc_long_short_return(pred.iloc[:, 0], raw_label.iloc[:, 0])
metrics.update(
{
"Long-Short Average Return": long_short_r.mean(),
"Long-Short Average Sharpe": long_short_r.mean() / long_short_r.std(),
}
)
objects.update(
{
"long_short_r.pkl": long_short_r,
"long_avg_r.pkl": long_avg_r,
}
)
self.recorder.log_metrics(**metrics)
self.recorder.save_objects(**objects, artifact_path=self.get_path())
pprint(metrics)
def list(self):
paths = [
self.get_path("ic.pkl"),
self.get_path("ric.pkl"),
self.get_path("long_pre.pkl"),
self.get_path("short_pre.pkl"),
self.get_path("long_short_r.pkl"),
self.get_path("long_avg_r.pkl"),
]
return paths
class SigAnaRecord(SignalRecord):
"""
This is the Signal Analysis Record class that generates the analysis results such as IC and IR. This class inherits the ``RecordTemp`` class.

4
qlib/workflow/recorder.py
View File

@@ -1,14 +1,14 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import mlflow
import mlflow, logging
import shutil, os, pickle, tempfile, codecs, pickle
from pathlib import Path
from datetime import datetime
from ..utils.objm import FileManager
from ..log import get_module_logger
logger = get_module_logger("workflow", "INFO")
logger = get_module_logger("workflow", logging.INFO)
class Recorder:

4
qlib/workflow/utils.py
View File

@@ -1,12 +1,12 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import sys, traceback, signal, atexit
import sys, traceback, signal, atexit, logging
from . import R
from .recorder import Recorder
from ..log import get_module_logger
logger = get_module_logger("workflow", "INFO")
logger = get_module_logger("workflow", logging.INFO)
# function to handle the experiment when unusual program ending occurs