Push master

.gitignore

@@ -49,3 +49,5 @@ tags
 
 ./pretrain
 .idea/
+
+*.log

examples/benchmarks/MASTER/README.md

@@ -0,0 +1,16 @@
+## overview
+This is an alternative version of the MASTER benchmark. 
+
+paper: [MASTER: Market-Guided Stock Transformer for Stock Price Forecasting](https://arxiv.org/abs/2312.15235) 
+
+codes: [https://github.com/SJTU-Quant/MASTER](https://github.com/SJTU-Quant/MASTER)
+
+## run
+You can directly use the bash script to run the codes (you can set the `universe` and `only_backtest` flag in `run.sh`), this `main.py` will test the model with 10 random seeds:
+```
+bash run.sh
+```
+<!-- or you can just directly use `qrun` tp run the codes (note that you should modify your `qlib`, since we add or modify some files in `qlib/contrib/data/dataset.py`, `qlib/data/dataset/__init__.py`, `qlib/data/dataset/processor.py` and `qlib/contrib/model/pytorch_master.py`):
+```
+qrun workflow_config_master_Alpha158.yaml
+``` -->

examples/benchmarks/MASTER/main.py

@@ -0,0 +1,97 @@
+#  Copyright (c) Microsoft Corporation.
+#  Licensed under the MIT License.
+"""
+Qlib provides two kinds of interfaces. 
+(1) Users could define the Quant research workflow by a simple configuration.
+(2) Qlib is designed in a modularized way and supports creating research workflow by code just like building blocks.
+
+The interface of (1) is `qrun XXX.yaml`.  The interface of (2) is script like this, which nearly does the same thing as `qrun XXX.yaml`
+"""
+import sys
+from pathlib import Path
+DIRNAME = Path(__file__).absolute().resolve().parent
+sys.path.append(str(DIRNAME))
+sys.path.append(str(DIRNAME.parent.parent.parent))
+
+import qlib
+from qlib.constant import REG_CN
+from qlib.utils import init_instance_by_config
+from qlib.workflow import R
+from qlib.workflow.record_temp import SignalRecord, PortAnaRecord, SigAnaRecord
+from qlib.tests.data import GetData
+import yaml
+import argparse
+import os
+import pprint as pp
+import numpy as np
+
+def parse_args():
+    """parse arguments. You can add other arguments if needed."""
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--only_backtest", action="store_true", help="whether only backtest or not")
+    return parser.parse_args()
+
+if __name__ == "__main__":
+    args = parse_args()
+    # use default data
+    provider_uri = "~/.qlib/qlib_data/cn_data"  # target_dir
+    GetData().qlib_data(target_dir=provider_uri, region=REG_CN, exists_skip=True)
+    qlib.init(provider_uri=provider_uri, region=REG_CN)
+    with open("./workflow_config_master_Alpha158.yaml", 'r') as f:
+        basic_config = yaml.safe_load(f)
+    ###################################
+    # train model
+    ###################################
+
+    dataset = init_instance_by_config(basic_config['task']["dataset"])
+    if not os.path.exists('./model'):
+        os.mkdir("./model")
+
+    all_metrics = {
+        k: []
+        for k in [
+            "IC",
+            "ICIR",
+            "Rank IC",
+            "Rank ICIR",
+            "1day.excess_return_without_cost.annualized_return",
+            "1day.excess_return_without_cost.information_ratio",
+        ]
+    }
+
+    for seed in range(0, 10):
+        print("------------------------")
+        print(f"seed: {seed}")
+
+        basic_config['task']["model"]['kwargs']["seed"] = seed
+        model = init_instance_by_config(basic_config['task']["model"])
+
+        # start exp
+        if not args.only_backtest:
+            model.fit(dataset=dataset)
+        else:
+            model.load_model(f"./model/{basic_config['market']}master_{seed}.pkl")
+
+        with R.start(experiment_name=f"workflow_seed{seed}"):
+            # prediction
+            recorder = R.get_recorder()
+            sr = SignalRecord(model, dataset, recorder)
+            sr.generate()
+
+            # Signal Analysis
+            sar = SigAnaRecord(recorder)
+            sar.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, basic_config['port_analysis_config'], "day")
+            par.generate()
+
+            metrics = recorder.list_metrics()
+            print(metrics)
+            for k in all_metrics.keys():
+                all_metrics[k].append(metrics[k])
+            pp.pprint(all_metrics)
+
+    for k in all_metrics.keys():
+        print(f"{k}: {np.mean(all_metrics[k])} +- {np.std(all_metrics[k])}")

examples/benchmarks/MASTER/run.sh

@@ -0,0 +1,23 @@
+if [ ! -d "./logs" ]; then
+    mkdir ./logs
+fi
+if [ ! -d "./backtest" ]; then
+    mkdir ./backtest
+fi
+
+# set the config
+universe=csi300
+only_backtest=false
+
+sed -i "s/csi.../$universe/g" workflow_config_master_Alpha158.yaml
+if [ $universe == 'csi300' ]; then
+    sed -i "s/SH....../SH000300/g" workflow_config_master_Alpha158.yaml
+elif [ $universe == 'csi500' ]; then
+    sed -i "s/SH....../SH000905/g" workflow_config_master_Alpha158.yaml
+fi
+if $only_backtest; then
+    nohup python -u main.py --only_backtest > ./backtest/${universe}.log 2>&1 &
+else
+    nohup python -u main.py > ./logs/${universe}.log 2>&1 &
+fi
+echo $!

examples/benchmarks/MASTER/workflow_config_master_Alpha158.yaml

@@ -0,0 +1,95 @@
+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
+    infer_processors:
+        - class: RobustZScoreNorm
+          kwargs:
+              fields_group: feature
+              clip_outlier: true
+        - class: Fillna
+          kwargs:
+              fields_group: feature
+    learn_processors:
+        - class: DropnaLabel
+        - class: CSRankNorm
+          kwargs:
+              fields_group: label
+    label: ["Ref($close, -5) / Ref($close, -1) - 1"] 
+market_data_handler_config: &market_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
+    infer_processors:
+        - class: RobustZScoreNorm
+          kwargs:
+              fields_group: feature
+              clip_outlier: true
+        - class: Fillna
+          kwargs:
+              fields_group: feature
+port_analysis_config: &port_analysis_config
+    strategy:
+        class: TopkDropoutStrategy
+        module_path: qlib.contrib.strategy
+        kwargs:
+            signal: <PRED>
+            topk: 30
+            n_drop: 30
+    backtest:
+        start_time: 2017-01-01
+        end_time: 2020-08-01
+        account: 100000000
+        benchmark: *benchmark
+        exchange_kwargs:
+            deal_price: close
+task:
+    model:
+        class: MASTERModel
+        module_path: qlib.contrib.model.pytorch_master_ts
+        kwargs:
+            seed: 0
+            n_epochs: 40
+            lr: 0.000008
+            train_stop_loss_thred: 0.95
+            market: *market
+            benchmark: *benchmark
+            save_prefix: *market
+    dataset:
+        class: MASTERTSDatasetH
+        module_path: qlib.contrib.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]
+            step_len: 8
+            market_data_handler_config: *market_data_handler_config
+    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

qlib/contrib/data/dataset.py

@@ -7,7 +7,10 @@
 import numpy as np
 import pandas as pd
 
-from qlib.data.dataset import DatasetH
+from qlib.data.dataset import DatasetH, TSDatasetH, TSDataSampler
+from typing import Callable, Union, List, Tuple, Dict, Text, Optional
+from qlib.data.dataset.handler import DataHandler, DataHandlerLP
+from qlib.contrib.data.handler import check_transform_proc
 
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -351,3 +354,138 @@ def __iter__(self):
             }
 
         # end indice loop
+
+###################################################################################
+# lqa: for MASTER
+class marketDataHandler(DataHandlerLP):
+    """Market Data Handler for MASTER (see `examples/benchmarks/MASTER`)
+
+    Args:
+        instruments (str): instrument list
+        start_time (str): start time
+        end_time (str): end time
+        freq (str): data frequency
+        infer_processors (list): inference processors
+        learn_processors (list): learning processors
+        fit_start_time (str): fit start time
+        fit_end_time (str): fit end time
+        process_type (str): process type
+        filter_pipe (list): filter pipe
+        inst_processors (list): instrument processors
+    """
+    def __init__(
+        self,
+        instruments="csi300",
+        start_time=None,
+        end_time=None,
+        freq="day",
+        infer_processors=[],
+        learn_processors=[],
+        fit_start_time=None,
+        fit_end_time=None,
+        process_type=DataHandlerLP.PTYPE_A,
+        filter_pipe=None,
+        inst_processors=None,
+        **kwargs
+    ):
+        infer_processors = check_transform_proc(infer_processors, fit_start_time, fit_end_time)
+        learn_processors = check_transform_proc(learn_processors, fit_start_time, fit_end_time)
+
+        data_loader = {
+            "class": "QlibDataLoader",
+            "kwargs": {
+                "config": {
+                    "feature": self.get_feature_config(),
+                },
+                "filter_pipe": filter_pipe,
+                "freq": freq,
+                "inst_processors": inst_processors,
+            },
+        }
+        super().__init__(
+            instruments=instruments,
+            start_time=start_time,
+            end_time=end_time,
+            data_loader=data_loader,
+            infer_processors=infer_processors,
+            learn_processors=learn_processors,
+            process_type=process_type,
+            **kwargs
+        )
+
+    @staticmethod
+    def get_feature_config():
+        """
+        Get market feature (63-dimensional), which are csi100 index, csi300 index, csi500 index. 
+        The first list is the name to be shown for the feature, and the second list is the feature to fecth.
+        """
+        return (
+            ['Mask($close/Ref($close,1)-1, "sh000300")', 'Mask(Mean($close/Ref($close,1)-1,5), "sh000300")', 'Mask(Std($close/Ref($close,1)-1,5), "sh000300")', 'Mask(Mean($volume,5)/$volume, "sh000300")', 'Mask(Std($volume,5)/$volume, "sh000300")', 'Mask(Mean($close/Ref($close,1)-1,10), "sh000300")', 'Mask(Std($close/Ref($close,1)-1,10), "sh000300")', 'Mask(Mean($volume,10)/$volume, "sh000300")', 'Mask(Std($volume,10)/$volume, "sh000300")', 'Mask(Mean($close/Ref($close,1)-1,20), "sh000300")', 'Mask(Std($close/Ref($close,1)-1,20), "sh000300")', 'Mask(Mean($volume,20)/$volume, "sh000300")', 'Mask(Std($volume,20)/$volume, "sh000300")', 'Mask(Mean($close/Ref($close,1)-1,30), "sh000300")', 'Mask(Std($close/Ref($close,1)-1,30), "sh000300")', 'Mask(Mean($volume,30)/$volume, "sh000300")', 'Mask(Std($volume,30)/$volume, "sh000300")', 'Mask(Mean($close/Ref($close,1)-1,60), "sh000300")', 'Mask(Std($close/Ref($close,1)-1,60), "sh000300")', 'Mask(Mean($volume,60)/$volume, "sh000300")', 'Mask(Std($volume,60)/$volume, "sh000300")',
+                'Mask($close/Ref($close,1)-1, "sh000903")', 'Mask(Mean($close/Ref($close,1)-1,5), "sh000903")', 'Mask(Std($close/Ref($close,1)-1,5), "sh000903")', 'Mask(Mean($volume,5)/$volume, "sh000903")', 'Mask(Std($volume,5)/$volume, "sh000903")', 'Mask(Mean($close/Ref($close,1)-1,10), "sh000903")', 'Mask(Std($close/Ref($close,1)-1,10), "sh000903")', 'Mask(Mean($volume,10)/$volume, "sh000903")', 'Mask(Std($volume,10)/$volume, "sh000903")', 'Mask(Mean($close/Ref($close,1)-1,20), "sh000903")', 'Mask(Std($close/Ref($close,1)-1,20), "sh000903")', 'Mask(Mean($volume,20)/$volume, "sh000903")', 'Mask(Std($volume,20)/$volume, "sh000903")', 'Mask(Mean($close/Ref($close,1)-1,30), "sh000903")', 'Mask(Std($close/Ref($close,1)-1,30), "sh000903")', 'Mask(Mean($volume,30)/$volume, "sh000903")', 'Mask(Std($volume,30)/$volume, "sh000903")', 'Mask(Mean($close/Ref($close,1)-1,60), "sh000903")', 'Mask(Std($close/Ref($close,1)-1,60), "sh000903")', 'Mask(Mean($volume,60)/$volume, "sh000903")', 'Mask(Std($volume,60)/$volume, "sh000903")',
+                'Mask($close/Ref($close,1)-1, "sh000905")', 'Mask(Mean($close/Ref($close,1)-1,5), "sh000905")', 'Mask(Std($close/Ref($close,1)-1,5), "sh000905")', 'Mask(Mean($volume,5)/$volume, "sh000905")', 'Mask(Std($volume,5)/$volume, "sh000905")', 'Mask(Mean($close/Ref($close,1)-1,10), "sh000905")', 'Mask(Std($close/Ref($close,1)-1,10), "sh000905")', 'Mask(Mean($volume,10)/$volume, "sh000905")', 'Mask(Std($volume,10)/$volume, "sh000905")', 'Mask(Mean($close/Ref($close,1)-1,20), "sh000905")', 'Mask(Std($close/Ref($close,1)-1,20), "sh000905")', 'Mask(Mean($volume,20)/$volume, "sh000905")', 'Mask(Std($volume,20)/$volume, "sh000905")', 'Mask(Mean($close/Ref($close,1)-1,30), "sh000905")', 'Mask(Std($close/Ref($close,1)-1,30), "sh000905")', 'Mask(Mean($volume,30)/$volume, "sh000905")', 'Mask(Std($volume,30)/$volume, "sh000905")', 'Mask(Mean($close/Ref($close,1)-1,60), "sh000905")', 'Mask(Std($close/Ref($close,1)-1,60), "sh000905")', 'Mask(Mean($volume,60)/$volume, "sh000905")', 'Mask(Std($volume,60)/$volume, "sh000905")'],
+            ['Mask($close/Ref($close,1)-1, "sh000300")', 'Mask(Mean($close/Ref($close,1)-1,5), "sh000300")', 'Mask(Std($close/Ref($close,1)-1,5), "sh000300")', 'Mask(Mean($volume,5)/$volume, "sh000300")', 'Mask(Std($volume,5)/$volume, "sh000300")', 'Mask(Mean($close/Ref($close,1)-1,10), "sh000300")', 'Mask(Std($close/Ref($close,1)-1,10), "sh000300")', 'Mask(Mean($volume,10)/$volume, "sh000300")', 'Mask(Std($volume,10)/$volume, "sh000300")', 'Mask(Mean($close/Ref($close,1)-1,20), "sh000300")', 'Mask(Std($close/Ref($close,1)-1,20), "sh000300")', 'Mask(Mean($volume,20)/$volume, "sh000300")', 'Mask(Std($volume,20)/$volume, "sh000300")', 'Mask(Mean($close/Ref($close,1)-1,30), "sh000300")', 'Mask(Std($close/Ref($close,1)-1,30), "sh000300")', 'Mask(Mean($volume,30)/$volume, "sh000300")', 'Mask(Std($volume,30)/$volume, "sh000300")', 'Mask(Mean($close/Ref($close,1)-1,60), "sh000300")', 'Mask(Std($close/Ref($close,1)-1,60), "sh000300")', 'Mask(Mean($volume,60)/$volume, "sh000300")', 'Mask(Std($volume,60)/$volume, "sh000300")',
+                'Mask($close/Ref($close,1)-1, "sh000903")', 'Mask(Mean($close/Ref($close,1)-1,5), "sh000903")', 'Mask(Std($close/Ref($close,1)-1,5), "sh000903")', 'Mask(Mean($volume,5)/$volume, "sh000903")', 'Mask(Std($volume,5)/$volume, "sh000903")', 'Mask(Mean($close/Ref($close,1)-1,10), "sh000903")', 'Mask(Std($close/Ref($close,1)-1,10), "sh000903")', 'Mask(Mean($volume,10)/$volume, "sh000903")', 'Mask(Std($volume,10)/$volume, "sh000903")', 'Mask(Mean($close/Ref($close,1)-1,20), "sh000903")', 'Mask(Std($close/Ref($close,1)-1,20), "sh000903")', 'Mask(Mean($volume,20)/$volume, "sh000903")', 'Mask(Std($volume,20)/$volume, "sh000903")', 'Mask(Mean($close/Ref($close,1)-1,30), "sh000903")', 'Mask(Std($close/Ref($close,1)-1,30), "sh000903")', 'Mask(Mean($volume,30)/$volume, "sh000903")', 'Mask(Std($volume,30)/$volume, "sh000903")', 'Mask(Mean($close/Ref($close,1)-1,60), "sh000903")', 'Mask(Std($close/Ref($close,1)-1,60), "sh000903")', 'Mask(Mean($volume,60)/$volume, "sh000903")', 'Mask(Std($volume,60)/$volume, "sh000903")',
+                'Mask($close/Ref($close,1)-1, "sh000905")', 'Mask(Mean($close/Ref($close,1)-1,5), "sh000905")', 'Mask(Std($close/Ref($close,1)-1,5), "sh000905")', 'Mask(Mean($volume,5)/$volume, "sh000905")', 'Mask(Std($volume,5)/$volume, "sh000905")', 'Mask(Mean($close/Ref($close,1)-1,10), "sh000905")', 'Mask(Std($close/Ref($close,1)-1,10), "sh000905")', 'Mask(Mean($volume,10)/$volume, "sh000905")', 'Mask(Std($volume,10)/$volume, "sh000905")', 'Mask(Mean($close/Ref($close,1)-1,20), "sh000905")', 'Mask(Std($close/Ref($close,1)-1,20), "sh000905")', 'Mask(Mean($volume,20)/$volume, "sh000905")', 'Mask(Std($volume,20)/$volume, "sh000905")', 'Mask(Mean($close/Ref($close,1)-1,30), "sh000905")', 'Mask(Std($close/Ref($close,1)-1,30), "sh000905")', 'Mask(Mean($volume,30)/$volume, "sh000905")', 'Mask(Std($volume,30)/$volume, "sh000905")', 'Mask(Mean($close/Ref($close,1)-1,60), "sh000905")', 'Mask(Std($close/Ref($close,1)-1,60), "sh000905")', 'Mask(Mean($volume,60)/$volume, "sh000905")', 'Mask(Std($volume,60)/$volume, "sh000905")']
+        )
+
+class MASTERTSDatasetH(TSDatasetH):
+    """
+    MASTER Time Series Dataset with Handler
+
+    Args:
+        market_data_handler_config (dict): market data handler config
+    """
+    def __init__(
+        self,
+        market_data_handler_config = Dict,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        marketdl = marketDataHandler(**market_data_handler_config)
+        self.market_dataset = DatasetH(marketdl, segments = self.segments)
+
+
+    def get_market_information(
+        self,
+        slc: slice,
+    ) -> Union[List[pd.DataFrame], pd.DataFrame]:
+        return self.market_dataset.prepare(slc)
+
+    def _prepare_seg(self, slc: slice, **kwargs) -> TSDataSampler:
+        dtype = kwargs.pop("dtype", None)
+        if not isinstance(slc, slice):
+            slc = slice(*slc)
+        start, end = slc.start, slc.stop
+        flt_col = kwargs.pop("flt_col", None)
+        # TSDatasetH will retrieve more data for complete time-series
+
+        ext_slice = self._extend_slice(slc, self.cal, self.step_len)
+        only_label = kwargs.pop("only_label", False)
+        data = super(TSDatasetH, self)._prepare_seg(ext_slice, **kwargs)
+
+        ############################## Add market information ###########################
+        # If we only need label for testing, we do not need to add market information
+        if not only_label:
+            marketData = self.get_market_information(ext_slice)
+            cols = pd.MultiIndex.from_tuples([("feature", feature) for feature in marketData.columns])
+            marketData = pd.DataFrame(marketData.values, columns = cols, index = marketData.index)
+            data = data.iloc[:,:-1].join(marketData).join(data.iloc[:,-1])
+        #################################################################################
+        flt_kwargs = copy.deepcopy(kwargs)
+        if flt_col is not None:
+            flt_kwargs["col_set"] = flt_col
+            flt_data = super()._prepare_seg(ext_slice, **flt_kwargs)
+            assert len(flt_data.columns) == 1
+        else:
+            flt_data = None
+
+        tsds = TSDataSampler(
+            data=data,
+            start=start,
+            end=end,
+            step_len=self.step_len,
+            dtype=dtype,
+            flt_data=flt_data,
+            fillna_type = "ffill+bfill"
+        )
+        return tsds