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>

examples/portfolio/README.md

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+# Portfolio Optimization Strategy
+
+## Introduction
+
+In `qlib/examples/benchmarks` we have various **alpha** models that predict
+the stock returns. We also use a simple rule based `TopkDropoutStrategy` to
+evaluate the investing performance of these models. However, such a strategy
+is too simple to control the portfolio risk like correlation and volatility.
+
+To this end, an optimization based strategy should be used to for the
+trade-off between return and risk. In this doc, we will show how to use
+`EnhancedIndexingStrategy` to maximize portfolio return while minimizing
+tracking error relative to a benchmark.
+
+
+## Preparation
+
+We use China stock market data for our example.
+
+1. Prepare CSI300 weight:
+
+   ```bash
+   wget http://fintech.msra.cn/stock_data/downloads/csi300_weight.zip
+   unzip -d ~/.qlib/qlib_data/cn_data csi300_weight.zip
+   rm -f csi300_weight.zip
+   ```
+
+2. Prepare risk model data:
+
+   ```bash
+   python prepare_riskdata.py
+   ```
+
+Here we use a **Statistical Risk Model** implemented in `qlib.model.riskmodel`.
+However users are strongly recommended to use other risk models for better quality:
+* **Fundamental Risk Model** like MSCI BARRA
+* [Deep Risk Model](https://arxiv.org/abs/2107.05201)
+
+
+## End-to-End Workflow
+
+You can finish workflow with `EnhancedIndexingStrategy` by running
+`qrun config_enhanced_indexing.yaml`.
+
+In this config, we mainly changed the strategy section compared to
+`qlib/examples/benchmarks/workflow_config_lightgbm_Alpha158.yaml`.

examples/portfolio/config_enhanced_indexing.yaml

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+qlib_init:
+    provider_uri: "~/.qlib/qlib_data/cn_data"
+    region: cn
+market: &market csi300
+benchmark: &benchmark SH000300
+data_handler_config: &data_handler_config
+    start_time: 2008-01-01
+    end_time: 2020-08-01
+    fit_start_time: 2008-01-01
+    fit_end_time: 2014-12-31
+    instruments: *market
+port_analysis_config: &port_analysis_config
+    strategy:
+        class: EnhancedIndexingStrategy
+        module_path: qlib.contrib.strategy
+        kwargs:
+            model: <MODEL>
+            dataset: <DATASET>
+            riskmodel_root: ./riskdata
+    backtest:
+        start_time: 2017-01-01
+        end_time: 2020-08-01
+        account: 100000000
+        benchmark: *benchmark
+        exchange_kwargs:
+            limit_threshold: 0.095
+            deal_price: close
+            open_cost: 0.0005
+            close_cost: 0.0015
+            min_cost: 5
+task:
+    model:
+        class: LGBModel
+        module_path: qlib.contrib.model.gbdt
+        kwargs:
+            loss: mse
+            colsample_bytree: 0.8879
+            learning_rate: 0.2
+            subsample: 0.8789
+            lambda_l1: 205.6999
+            lambda_l2: 580.9768
+            max_depth: 8
+            num_leaves: 210
+            num_threads: 20
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: Alpha158
+                module_path: qlib.contrib.data.handler
+                kwargs: *data_handler_config
+            segments:
+                train: [2008-01-01, 2014-12-31]
+                valid: [2015-01-01, 2016-12-31]
+                test: [2017-01-01, 2020-08-01]
+    record:
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+            model: <MODEL>
+            dataset: <DATASET>
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+            ana_long_short: False
+            ann_scaler: 252
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+            config: *port_analysis_config

examples/portfolio/prepare_riskdata.py

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+# 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()