support optimization based strategy (#754)

* support optimization based strategy * fix riskdata not found & update doc * refactor signal_strategy * add portfolio example * Update examples/portfolio/prepare_riskdata.py Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> * fix typo Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> * fix typo Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> * update doc * fix riskmodel doc Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
2026-07-12 23:36:54 +08:00 · 2021-12-28 18:44:20 +08:00
parent 4709909782
commit 1b8f0b4575
14 changed files with 668 additions and 262 deletions
--- a/examples/portfolio/prepare_riskdata.py
+++ b/examples/portfolio/prepare_riskdata.py
@@ -0,0 +1,55 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+import os
+import numpy as np
+import pandas as pd
+
+from qlib.data import D
+from qlib.model.riskmodel import StructuredCovEstimator
+
+
+def prepare_data(riskdata_root="./riskdata", T=240, start_time="2016-01-01"):
+
+    universe = D.features(D.instruments("csi300"), ["$close"], start_time=start_time).swaplevel().sort_index()
+
+    price_all = (
+        D.features(D.instruments("all"), ["$close"], start_time=start_time).squeeze().unstack(level="instrument")
+    )
+
+    # StructuredCovEstimator is a statistical risk model
+    riskmodel = StructuredCovEstimator()
+
+    for i in range(T - 1, len(price_all)):
+
+        date = price_all.index[i]
+        ref_date = price_all.index[i - T + 1]
+
+        print(date)
+
+        codes = universe.loc[date].index
+        price = price_all.loc[ref_date:date, codes]
+
+        # calculate return and remove extreme return
+        ret = price.pct_change()
+        ret.clip(ret.quantile(0.025), ret.quantile(0.975), axis=1, inplace=True)
+
+        # run risk model
+        F, cov_b, var_u = riskmodel.predict(ret, is_price=False, return_decomposed_components=True)
+
+        # save risk data
+        root = riskdata_root + "/" + date.strftime("%Y%m%d")
+        os.makedirs(root, exist_ok=True)
+
+        pd.DataFrame(F, index=codes).to_pickle(root + "/factor_exp.pkl")
+        pd.DataFrame(cov_b).to_pickle(root + "/factor_cov.pkl")
+        # for specific_risk we follow the convention to save volatility
+        pd.Series(np.sqrt(var_u), index=codes).to_pickle(root + "/specific_risk.pkl")
+
+
+if __name__ == "__main__":
+
+    import qlib
+
+    qlib.init(provider_uri="~/.qlib/qlib_data/cn_data")
+
+    prepare_data()