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Add Risk Prediction Demo
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meng-ustc
179
examples/workflow_by_code_lgb_risk_demo.py
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179
examples/workflow_by_code_lgb_risk_demo.py
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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import sys
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from pathlib import Path
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import qlib
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from qlib.config import REG_CN
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from qlib.utils import exists_qlib_data, init_instance_by_config, flatten_dict
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from qlib.workflow import R
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from qlib.workflow.record_temp import SignalRecord, PortAnaRecord
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from qlib.data.dataset.handler import DataHandlerLP
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import seaborn as sns
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import matplotlib.pyplot as plt
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import math
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import pandas as pd
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from scipy.stats.stats import pearsonr
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import numpy as np
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if __name__ == "__main__":
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# use default data
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provider_uri = "~/.qlib/qlib_data/cn_data" # target_dir
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if not exists_qlib_data(provider_uri):
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print(f"Qlib data is not found in {provider_uri}")
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sys.path.append(str(Path(__file__).resolve().parent.parent.joinpath("scripts")))
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from get_data import GetData
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GetData().qlib_data(target_dir=provider_uri, region=REG_CN)
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qlib.init(provider_uri=provider_uri, region=REG_CN)
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market = "csi300"
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benchmark = "SH000300"
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###################################
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# train model
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###################################
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data_handler_config = {
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"start_time": "2008-01-01",
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"end_time": "2020-08-01",
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"fit_start_time": "2008-01-01",
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"fit_end_time": "2014-12-31",
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"instruments": market,
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"infer_processors": [
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{"class": "ProcessInf", "kwargs": {}},
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{"class": "ZScoreNorm", "kwargs": {"fields_group": "feature"}},
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{"class": "Fillna", "kwargs": {}},
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],
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"learn_processors": [{
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"class": "DropnaLabel", },
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],
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"label": (["Ref(Min($low, 5), -4)/$close - 1"], ["LABEL0"]) # the period for risk prediction is 5 days
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}
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task = {
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"model": {
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"class": "LGBModel",
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"module_path": "qlib.contrib.model.gbdt",
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"kwargs": {
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"loss": "mse",
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"colsample_bytree": 0.8999,
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"learning_rate": 0.02,
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"subsample": 0.7,
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"lambda_l1": 11.9668,
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"lambda_l2": 339.1301,
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"max_depth": 16,
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"num_leaves": 31,
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"num_threads": 20,
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},
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},
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"dataset": {
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"class": "DatasetH",
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"module_path": "qlib.data.dataset",
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"kwargs": {
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"handler": {
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"class": "Alpha360",
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"module_path": "qlib.contrib.data.handler",
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"kwargs": data_handler_config,
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},
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"segments": {
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"train": ("2008-01-01", "2014-12-31"),
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"valid": ("2015-01-01", "2016-12-31"),
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"test": ("2017-01-01", "2020-08-01"),
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},
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},
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},
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}
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port_analysis_config = {
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"strategy": {
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"class": "TopkDropoutStrategy",
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"module_path": "qlib.contrib.strategy.strategy",
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"kwargs": {
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"topk": 50,
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"n_drop": 5,
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},
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},
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"backtest": {
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"verbose": False,
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"limit_threshold": 0.095,
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"account": 100000000,
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"benchmark": benchmark,
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"deal_price": "close",
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"open_cost": 0.0005,
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"close_cost": 0.0015,
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"min_cost": 5,
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"return_order": True,
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},
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}
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# model initiaiton
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model = init_instance_by_config(task["model"])
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dataset = init_instance_by_config(task["dataset"])
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# NOTE: This line is optional
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# It demonstrates that the dataset can be used standalone.
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example_df = dataset.prepare("train")
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print(example_df.head())
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def heatmap(actual_risk, predicted_risk, step=0.02):
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"""
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plot the precision heatmap as a visualized evaluation for risk predition
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:param actual_risk: the LABEL0 of test samples
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:param predicted_risk: the predicted results of test samples
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:param step: the internal size of risk values on axis
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:return:
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"""
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num_step = math.ceil(-predicted_risk.min() / step)
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matrix = np.zeros((num_step, num_step), dtype=np.float)
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for pred_thresh in range(num_step):
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for act_thresh in range(num_step):
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actual_positive = actual_risk < -act_thresh*step
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predicted_alarm = predicted_risk < -pred_thresh*step
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num_alarm = predicted_alarm.sum()
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num_tp = (actual_positive & predicted_alarm).sum()
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matrix[pred_thresh, act_thresh] = num_tp / num_alarm
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axis_labels = ['{:.3f}'.format(-x * step) for x in range(num_step)]
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return matrix, axis_labels
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# start exp
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with R.start(experiment_name="workflow"):
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R.log_params(**flatten_dict(task))
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model.fit(dataset)
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# prediction
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actual_risk = dataset.prepare("test", col_set="label", data_key=DataHandlerLP.DK_I)['LABEL0']
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pred = model.predict(dataset)
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result_df = pd.concat((actual_risk, pred), axis=1)
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result_df.columns = ['Actual Risk', 'Predicted Risk']
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result_df.dropna(inplace=True)
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actual_risk, predicted_risk = result_df.iloc[:, 0], result_df.iloc[:, 1]
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corr = pearsonr(actual_risk, predicted_risk)[0]
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print('The correlation between predicted risk and actual risk is: {:.6f}'.format(corr))
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# visualized results
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fig, axes = plt.subplots(2, 2, figsize=(15, 10))
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sns.histplot(actual_risk, ax=axes[0, 0])
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axes[0, 0].set_title('Market: {} Actual Risk'.format(market))
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axes[0, 0].grid()
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sns.histplot(predicted_risk, ax=axes[0, 1])
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axes[0, 1].set_title('Feature: {} Predicted Risk'.format(task['dataset']['kwargs']['handler']['class']))
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axes[0, 1].grid()
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sns.scatterplot(data=result_df, ax=axes[1, 0], x='Actual Risk', y='Predicted Risk', s=20)
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axes[1, 0].set_title('Market: {} Feature: {} Corr: {:.5f}'.format(
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market, task['dataset']['kwargs']['handler']['class'], corr))
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axes[1, 0].grid()
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matrix, ax_labels = heatmap(actual_risk, predicted_risk)
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sns.heatmap(matrix, annot=True, fmt=".3f", xticklabels=ax_labels, yticklabels=ax_labels, ax=axes[1, 1],
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)
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axes[1, 1].set_xlabel('Predicted Alarm Threshold')
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axes[1, 1].set_ylabel('Actual Positive Threshold')
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axes[1, 1].set_title('Risk Prediction Precision Heatmap')
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plt.show()
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