Split classes in riskmodel.py & optimizer.py into seperate files.

tests/test_enhanced_indexing.py

@@ -2,32 +2,39 @@
 # Licensed under the MIT License.
 
 import sys
+import math
 import shutil
 import unittest
-from pathlib import Path
-
 import numpy as np
 import pandas as pd
+from tqdm import tqdm
+from pathlib import Path
 
 import qlib
-from qlib.config import REG_CN, C
+from qlib.config import C
-from qlib.utils import drop_nan_by_y_index
+from qlib.utils import init_instance_by_config, flatten_dict
-from qlib.contrib.model.gbdt import LGBModel
-from qlib.contrib.data.handler import Alpha158
-from qlib.contrib.strategy.strategy import TopkDropoutStrategy
-from qlib.contrib.evaluate import (
-    backtest as normal_backtest,
-    risk_analysis,
-)
-from qlib.utils import exists_qlib_data, init_instance_by_config, flatten_dict
 from qlib.workflow import R
-from qlib.workflow.record_temp import SignalRecord, SigAnaRecord, PortAnaRecord
+from qlib.config import REG_CN
-from qlib.tests.data import GetData
+from qlib.workflow.record_temp import SignalRecord, SigAnaRecord
 from qlib.tests import TestAutoData
+from qlib.portfolio.optimizer import EnhancedIndexingOptimizer
+from qlib.model.riskmodel import StructuredCovEstimator
+from qlib.data.dataset.loader import QlibDataLoader
+from qlib.data.dataset.handler import DataHandler
+from qlib.data import D
+from qlib.utils import exists_qlib_data, init_instance_by_config
 
+market = "all"
+trade_gap = 21
+label_config = "Ref($close, -{}) / Ref($close, -1) - 1".format(trade_gap)  # reconstruct portfolio once a month
 
-market = "csi300"
+provider_uri = "~/.qlib_ei/qlib_data/cn_data"  # target_dir
-benchmark = "SH000300"
+if not exists_qlib_data(provider_uri):
+    print(f"Qlib data is not found in {provider_uri}")
+    sys.path.append(str(Path.cwd().parent.joinpath("scripts")))
+    from get_data import GetData
+    GetData().qlib_data(target_dir=provider_uri, region=REG_CN)
+qlib.init(provider_uri=provider_uri, region=REG_CN)
 
 ###################################
 # train model
@@ -36,8 +43,9 @@ 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",
+    "fit_end_time": "2014-11-30",
     "instruments": market,
+    "label": [label_config]
 }
 
 task = {
@@ -53,7 +61,7 @@ task = {
             "lambda_l2": 580.9768,
             "max_depth": 8,
             "num_leaves": 210,
-            "num_threads": 20,
+            "num_threads": 32,
         },
     },
     "dataset": {
@@ -66,37 +74,104 @@ task = {
                 "kwargs": data_handler_config,
             },
             "segments": {
-                "train": ("2008-01-01", "2014-12-31"),
+                "train": ("2008-01-01", "2014-11-30"),
-                "valid": ("2015-01-01", "2016-12-31"),
+                "valid": ("2015-01-01", "2016-11-30"),
-                "test": ("2017-01-01", "2020-08-01"),
+                "test": ("2017-01-01", "2018-01-01"),
             },
         },
     },
 }
 
-port_analysis_config = {
+
-    "strategy": {
+class CSI300:
-        "class": "TopkDropoutStrategy",
+    """Simulate CSI300 as the Benchmark for Enhanced Indexing to Track"""
-        "module_path": "qlib.contrib.strategy.strategy",
+
-        "kwargs": {
+    def __init__(self):
-            "topk": 50,
+        # provider_uri = '/nfs_data/qlib_data/ycz_daily/qlib'
-            "n_drop": 5,
+        # qlib.init(provider_uri=provider_uri, region=REG_CN, dataset_cache=None, expression_cache=None)
-        },
+        self.csi_weight = D.features(D.instruments('csi300'), ['$csi300_weight'])
-    },
+
-    "backtest": {
+    def __call__(self, pd_index, trade_date):
-        "verbose": False,
+        weights = np.zeros(len(pd_index))
-        "limit_threshold": 0.095,
+
-        "account": 100000000,
+        for idx, instrument in enumerate(pd_index):
-        "benchmark": benchmark,
+            if (instrument, trade_date) in self.csi_weight.index:
-        "deal_price": "close",
+                weight = self.csi_weight.loc[(instrument, trade_date)].values[0]
-        "open_cost": 0.0005,
+                if not math.isnan(weight):
-        "close_cost": 0.0015,
+                    weights[idx] = weight
-        "min_cost": 5,
+
-    },
+        assert weights.sum() > 0, ' Fetch CSI Weights Error!'
-}
+        weights = weights / weights.sum()
+
+        return weights
+
+
+class EnhancedIndexingStrategy:
+    """Enhanced Indexing Strategy"""
+
+    def __init__(self):
+        self.benchmark = CSI300()
+
+        provider_uri = "~/.qlib_ei/qlib_data/cn_data"
+        qlib.init(provider_uri=provider_uri, region=REG_CN)
+
+        self.data_handler = DataHandler(market, "2015-01-01", "2019-01-01", QlibDataLoader(["$close"]))
+        self.label_handler = DataHandler(market, "2015-01-01", "2019-01-01", QlibDataLoader([label_config]))
+        self.cov_estimator = StructuredCovEstimator()
+        self.optimizer = EnhancedIndexingOptimizer(lamb=0.1, delta=0.4, bench_dev=0.03, max_iters=50000)
+
+    def update(self, score_series, current, pred_date):
+        """
+        Parameters
+        -----------
+        score_series : pd.Series
+            stock_id , score.
+        current : Position()
+            current of account.
+        trade_exchange : Exchange()
+            exchange.
+        trade_date : pd.Timestamp
+            date.
+        """
+        print(score_series)
+        score_series = score_series.dropna()
+
+        # portfolio init weight
+        init_weight = current.reindex(score_series.index, fill_value=0).values.squeeze()
+        init_weight_sum = init_weight.sum()
+        if init_weight_sum > 0:
+            init_weight /= init_weight_sum
+
+        # covariance estimation
+        selector = (self.data_handler.get_range_selector(pred_date, 252), score_series.index)
+        price = self.data_handler.fetch(selector, level=None, squeeze=True)
+        F, cov_b, var_u = self.cov_estimator.predict(price, return_decomposed_components=True)
+
+        # optimize target portfolio
+        w_bench = self.benchmark(score_series.index, pred_date)
+        passed_init_weight = init_weight if init_weight_sum > 0 else None
+        # print(F)
+        # print(cov_b)
+        # print(var_u)
+        # print(passed_init_weight)
+        # print(w_bench)
+        target_weight = self.optimizer(score_series.values, F, cov_b, var_u, passed_init_weight, w_bench)
+        # print(target_weight)
+        target = pd.DataFrame(data=target_weight, index=score_series.index)
+
+        active_weights = target_weight - w_bench
+        selector = (self.label_handler.get_range_selector(pred_date, 1), score_series.index)
+        label = self.label_handler.fetch(selector, level=None, squeeze=True)
+        alpha = 0
+        for instrument, weight in zip(score_series.index, active_weights):
+            delta = label.loc[(pred_date, instrument)]
+            alpha += weight * (0 if math.isnan(delta) else delta)
+
+        print(alpha)
+
+        return alpha, target
 
 
-# train
 def train():
     """train model
 
@@ -108,7 +183,7 @@ def train():
             model performance
     """
 
-    # model initiaiton
+    # model initiation
     model = init_instance_by_config(task["model"])
     dataset = init_instance_by_config(task["dataset"])
 
@@ -133,29 +208,42 @@ def train():
     return pred_score, {"ic": ic, "ric": ric}, rid
 
 
-def backtest_analysis(pred, rid):
+def backtest_analysis(scores):
-    """backtest and analysis
+    """backtest enhanced indexing
 
     Parameters
     ----------
-    pred : pandas.DataFrame
+        scores: pandas.DataFrame
-        predict scores
+                predict scores
-    rid : str
-        the id of the recorder to be used in this function
 
     Returns
     -------
-    analysis : pandas.DataFrame
+        sharpe_ratio: floating-point
-        the analysis result
+            sharpe ratio of the enhanced indexing portfolio
-
     """
-    recorder = R.get_recorder(experiment_name="workflow", recorder_id=rid)
+
-    # backtest
+    # backtest and analysis
-    par = PortAnaRecord(recorder, port_analysis_config)
+    with R.start(experiment_name="backtest_analysis"):
-    par.generate()
+        strategy = EnhancedIndexingStrategy()
-    analysis_df = par.load(par.get_path("port_analysis.pkl"))
+        dates = scores.index.get_level_values(0).unique()
-    print(analysis_df)
+
-    return analysis_df
+        alphas = []
+        current = pd.DataFrame()
+        gap_between_next_trade = 0
+        for date in tqdm(dates):
+            if gap_between_next_trade == 0:
+                score_series = scores.loc[date]
+                alpha, current = strategy.update(score_series, current, date)
+                alphas.append(alpha)
+                gap_between_next_trade = trade_gap
+            else:
+                gap_between_next_trade -= 1
+
+        alphas = np.array(alphas)
+        sharpe_ratio = alphas.mean() / np.std(alphas)
+        print('Sharpe:', sharpe_ratio)
+
+        return sharpe_ratio
 
 
 class TestAllFlow(TestAutoData):
@@ -174,10 +262,10 @@ class TestAllFlow(TestAutoData):
         self.assertGreaterEqual(ic_ric["ric"].all(), 0, "train failed")
 
     def test_1_backtest(self):
-        analyze_df = backtest_analysis(TestAllFlow.PRED_SCORE, TestAllFlow.RID)
+        sharpe_ratio = backtest_analysis(TestAllFlow.PRED_SCORE)
         self.assertGreaterEqual(
-            analyze_df.loc(axis=0)["excess_return_with_cost", "annualized_return"].values[0],
+            sharpe_ratio,
-            0.10,
+            0.90,
             "backtest failed",
         )