update env & strategy, add workflow

examples/highfreq/backtest/workflow.py

@@ -0,0 +1,135 @@
+#  Copyright (c) Microsoft Corporation.
+#  Licensed under the MIT License.
+
+import sys
+from pathlib import Path
+
+import qlib
+import pandas as pd
+from qlib.config import REG_CN
+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, PortAnaRecord
+from qlib.tests.data import GetData
+
+if __name__ == "__main__":
+
+    # use default data
+    provider_uri = "~/.qlib/qlib_data/cn_data"  # target_dir
+    if not exists_qlib_data(provider_uri):
+        print(f"Qlib data is not found in {provider_uri}")
+        GetData().qlib_data(target_dir=provider_uri, region=REG_CN)
+
+    qlib.init(provider_uri=provider_uri, region=REG_CN)
+
+    market = "csi300"
+    benchmark = "SH000300"
+
+    ###################################
+    # train model
+    ###################################
+    
+    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,
+    }
+
+    task = {
+        "model": {
+            "class": "LGBModel",
+            "module_path": "qlib.contrib.model.gbdt",
+            "kwargs": {
+                "loss": "mse",
+                "colsample_bytree": 0.8879,
+                "learning_rate": 0.0421,
+                "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"),
+                },
+            },
+        },
+    }
+        # model initialization
+    model = init_instance_by_config(task["model"])
+    dataset = init_instance_by_config(task["dataset"])
+    model.fit(dataset)
+
+    trade_start_time = "2017-01-01"
+    trade_end_time = "2020-08-01"
+    trade_exchange = get_exchange(start_time=trade_start_time, end_time=trade_end_time)
+
+    backtest_config={
+        "strategy": {
+            "class": "TopkDropoutStrategy",
+            "module_path": "qlib.contrib.strategy.dl_strategy",
+            "kwargs": {
+                "step_bar": "day",
+                "model": model,
+                "dataset": dataset,
+                "trade_exchange": trade_exchange,
+                "topk": 50,
+                "n_drop": 5,
+            },
+        },
+        "env":{
+            "class": "SplitEnv",
+            "module_path": "qlib.backtest.env",
+            "kwargs": {
+                "step_bar": "day",
+                "sub_env": {
+                    "class": "SimulatorEnv",
+                    "module_path": "qlib.backtest.env",
+                    "kwargs": {
+                        "step_bar": "1min",
+                        "trade_exchange": trade_exchange,
+                    }
+                },
+                "sub_strategy": {
+                    "class": "SBBStrategyEMA",
+                    "module_path": "qlib.contrib.strategy.rule_strategy",
+                    "kwargs": {
+                        "step_bar": "1min",
+                    }
+                }
+            }
+        }
+    }
+
+
+        # prediction
+        recorder = R.get_recorder()
+        sr = SignalRecord(model, dataset, recorder)
+        sr.generate()
+
+        # backtest. If users want to use backtest based on their own prediction,
+        # please refer to https://qlib.readthedocs.io/en/latest/component/recorder.html#record-template.
+        par = PortAnaRecord(recorder, port_analysis_config)
+        par.generate()