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be4646b4b7
* Intermediate version * Fix yaml template & Successfully run rolling * Be compatible with benchmark * Get same results with previous linear model * Black formatting * Update black * Update the placeholder mechanism * Update CI * Update CI * Upgrade Black * Fix CI and simplify code * Fix CI * Move the data processing caching mechanism into utils. * Adjusting DDG-DA * Organize import
53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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import os
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import numpy as np
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import pandas as pd
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from qlib.data import D
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from qlib.model.riskmodel import StructuredCovEstimator
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def prepare_data(riskdata_root="./riskdata", T=240, start_time="2016-01-01"):
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universe = D.features(D.instruments("csi300"), ["$close"], start_time=start_time).swaplevel().sort_index()
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price_all = (
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D.features(D.instruments("all"), ["$close"], start_time=start_time).squeeze().unstack(level="instrument")
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)
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# StructuredCovEstimator is a statistical risk model
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riskmodel = StructuredCovEstimator()
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for i in range(T - 1, len(price_all)):
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date = price_all.index[i]
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ref_date = price_all.index[i - T + 1]
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print(date)
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codes = universe.loc[date].index
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price = price_all.loc[ref_date:date, codes]
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# calculate return and remove extreme return
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ret = price.pct_change()
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ret.clip(ret.quantile(0.025), ret.quantile(0.975), axis=1, inplace=True)
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# run risk model
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F, cov_b, var_u = riskmodel.predict(ret, is_price=False, return_decomposed_components=True)
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# save risk data
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root = riskdata_root + "/" + date.strftime("%Y%m%d")
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os.makedirs(root, exist_ok=True)
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pd.DataFrame(F, index=codes).to_pickle(root + "/factor_exp.pkl")
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pd.DataFrame(cov_b).to_pickle(root + "/factor_cov.pkl")
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# for specific_risk we follow the convention to save volatility
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pd.Series(np.sqrt(var_u), index=codes).to_pickle(root + "/specific_risk.pkl")
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if __name__ == "__main__":
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import qlib
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qlib.init(provider_uri="~/.qlib/qlib_data/cn_data")
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prepare_data()
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