Refine TT workflow
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D-X-Y
parent
511e2443d0
commit
349d9fcc9f
@ -21,60 +21,59 @@ from qlib.workflow import R
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class QResult:
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def __init__(self):
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self._result = defaultdict(list)
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def __init__(self):
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self._result = defaultdict(list)
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def append(self, key, value):
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self._result[key].append(value)
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def append(self, key, value):
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self._result[key].append(value)
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@property
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def result(self):
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return self._result
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@property
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def result(self):
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return self._result
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def update(self, metrics, filter_keys=None):
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for key, value in metrics.items():
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if filter_keys is not None and key in filter_keys:
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key = filter_keys[key]
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elif filter_keys is not None:
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continue
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self.append(key, value)
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def update(self, metrics, filter_keys=None):
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for key, value in metrics.items():
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if filter_keys is not None and key in filter_keys:
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key = filter_keys[key]
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elif filter_keys is not None:
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continue
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self.append(key, value)
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@staticmethod
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def full_str(xstr, space):
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xformat = "{:" + str(space) + "s}"
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return xformat.format(str(xstr))
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@staticmethod
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def full_str(xstr, space):
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xformat = '{:' + str(space) + 's}'
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return xformat.format(str(xstr))
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def info(self, keys: List[Text], separate: Text = '', space: int = 25, show=True):
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avaliable_keys = []
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for key in keys:
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if key not in self.result:
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print('There are invalid key [{:}].'.format(key))
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else:
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avaliable_keys.append(key)
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head_str = separate.join([self.full_str(x, space) for x in avaliable_keys])
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values = []
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for key in avaliable_keys:
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current_values = self._result[key]
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mean = np.mean(current_values)
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std = np.std(current_values)
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values.append('{:.4f} $\pm$ {:.4f}'.format(mean, std))
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value_str = separate.join([self.full_str(x, space) for x in values])
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if show:
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print(head_str)
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print(value_str)
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else:
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return head_str, value_str
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def info(self, keys: List[Text], separate: Text = "", space: int = 25, show=True):
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avaliable_keys = []
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for key in keys:
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if key not in self.result:
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print("There are invalid key [{:}].".format(key))
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else:
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avaliable_keys.append(key)
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head_str = separate.join([self.full_str(x, space) for x in avaliable_keys])
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values = []
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for key in avaliable_keys:
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current_values = self._result[key]
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mean = np.mean(current_values)
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std = np.std(current_values)
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values.append("{:.4f} $\pm$ {:.4f}".format(mean, std))
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value_str = separate.join([self.full_str(x, space) for x in values])
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if show:
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print(head_str)
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print(value_str)
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else:
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return head_str, value_str
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def compare_results(heads, values, names, space=10):
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for idx, x in enumerate(heads):
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assert x == heads[0], '[{:}] {:} vs {:}'.format(idx, x, heads[0])
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new_head = QResult.full_str('Name', space) + heads[0]
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print(new_head)
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for name, value in zip(names, values):
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xline = QResult.full_str(name, space) + value
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print(xline)
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for idx, x in enumerate(heads):
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assert x == heads[0], "[{:}] {:} vs {:}".format(idx, x, heads[0])
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new_head = QResult.full_str("Name", space) + heads[0]
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print(new_head)
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for name, value in zip(names, values):
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xline = QResult.full_str(name, space) + value
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print(xline)
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def filter_finished(recorders):
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@ -92,20 +91,22 @@ def main(xargs):
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R.reset_default_uri(xargs.save_dir)
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experiments = R.list_experiments()
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key_map = {"IC": "IC",
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"ICIR": "ICIR",
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"Rank IC": "Rank_IC",
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"Rank ICIR": "Rank_ICIR",
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"excess_return_with_cost.annualized_return": "Annualized_Return",
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"excess_return_with_cost.information_ratio": "Information_Ratio",
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"excess_return_with_cost.max_drawdown": "Max_Drawdown"}
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key_map = {
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"IC": "IC",
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"ICIR": "ICIR",
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"Rank IC": "Rank_IC",
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"Rank ICIR": "Rank_ICIR",
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"excess_return_with_cost.annualized_return": "Annualized_Return",
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"excess_return_with_cost.information_ratio": "Information_Ratio",
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"excess_return_with_cost.max_drawdown": "Max_Drawdown",
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}
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all_keys = list(key_map.values())
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print("There are {:} experiments.".format(len(experiments)))
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head_strs, value_strs, names = [], [], []
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for idx, (key, experiment) in enumerate(experiments.items()):
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if experiment.id == '0':
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continue
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if experiment.id == "0":
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continue
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recorders = experiment.list_recorders()
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recorders, not_finished = filter_finished(recorders)
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print(
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@ -115,7 +116,7 @@ def main(xargs):
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)
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result = QResult()
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for recorder_id, recorder in recorders.items():
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result.update(recorder.list_metrics(), key_map)
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result.update(recorder.list_metrics(), key_map)
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head_str, value_str = result.info(all_keys, show=False)
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head_strs.append(head_str)
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value_strs.append(value_str)
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@ -4,7 +4,7 @@
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# Refer to:
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# - https://github.com/microsoft/qlib/blob/main/examples/workflow_by_code.ipynb
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# - https://github.com/microsoft/qlib/blob/main/examples/workflow_by_code.py
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# python exps/trading/workflow_tt.py --market all
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# python exps/trading/workflow_tt.py --market all --gpu 1
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#####################################################
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import sys, argparse
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from pathlib import Path
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@ -13,6 +13,10 @@ lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
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if str(lib_dir) not in sys.path:
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sys.path.insert(0, str(lib_dir))
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from procedures.q_exps import update_gpu
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from procedures.q_exps import update_market
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from procedures.q_exps import run_exp
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import qlib
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from qlib.config import C
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from qlib.config import REG_CN
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@ -100,44 +104,23 @@ def main(xargs):
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},
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]
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task = dict(model=model_config, dataset=dataset_config, record=record_config)
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provider_uri = "~/.qlib/qlib_data/cn_data"
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qlib.init(provider_uri=provider_uri, region=REG_CN)
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# start exp to train model
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with R.start(experiment_name="tt_model", uri=xargs.save_dir + "-" + xargs.market):
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set_log_basic_config(R.get_recorder().root_uri / "log.log")
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model = init_instance_by_config(model_config)
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dataset = init_instance_by_config(dataset_config)
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R.log_params(**flatten_dict(task))
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model.fit(dataset)
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R.save_objects(trained_model=model)
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# prediction
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recorder = R.get_recorder()
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print(recorder)
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for record in task["record"]:
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record = record.copy()
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if record["class"] == "SignalRecord":
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srconf = {"model": model, "dataset": dataset, "recorder": recorder}
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record["kwargs"].update(srconf)
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sr = init_instance_by_config(record)
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sr.generate()
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else:
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rconf = {"recorder": recorder}
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record["kwargs"].update(rconf)
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ar = init_instance_by_config(record)
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ar.generate()
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dataset = init_instance_by_config(dataset_config)
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for irun in range(xargs.times):
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xmodel_config = model_config.copy()
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xmodel_config = update_gpu(xmodel_config, xags.gpu)
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task = dict(model=xmodel_config, dataset=dataset_config, record=record_config)
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run_exp(task_config, dataset, "Transformer", "recorder-{:02d}-{:02d}".format(irun, xargs.times), xargs.save_dir)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser("Vanilla Transformable Transformer")
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parser.add_argument("--save_dir", type=str, default="./outputs/tt-ml-runs", help="The checkpoint directory.")
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parser.add_argument("--times", type=int, default=10, help="The repeated run times.")
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parser.add_argument("--gpu", type=int, default=0, help="The GPU ID used for train / test.")
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parser.add_argument("--market", type=str, default="csi300", help="The market indicator.")
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args = parser.parse_args()
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provider_uri = "~/.qlib/qlib_data/cn_data"
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qlib.init(provider_uri=provider_uri, region=REG_CN)
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main(args)
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63
lib/procedures/q_exps.py
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63
lib/procedures/q_exps.py
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@ -0,0 +1,63 @@
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.02 #
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#####################################################
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import qlib
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from qlib.utils import init_instance_by_config
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from qlib.workflow import R
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from qlib.utils import flatten_dict
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from qlib.log import set_log_basic_config
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def update_gpu(config, gpu):
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config = config.copy()
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if "task" in config and "GPU" in config["task"]["model"]:
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config["task"]["model"]["GPU"] = gpu
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elif "model" in config and "GPU" in config["model"]:
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config["model"]["GPU"] = gpu
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elif "GPU" in config:
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config["GPU"] = gpu
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return config
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def update_market(config, market):
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config = config.copy()
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config["market"] = market
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config["data_handler_config"]["instruments"] = market
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return config
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def run_exp(task_config, dataset, experiment_name, recorder_name, uri):
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# model initiaiton
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print("")
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print("[{:}] - [{:}]: {:}".format(experiment_name, recorder_name, uri))
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print("dataset={:}".format(dataset))
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model = init_instance_by_config(task_config["model"])
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# start exp
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with R.start(experiment_name=experiment_name, recorder_name=recorder_name, uri=uri):
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log_file = R.get_recorder().root_uri / "{:}.log".format(experiment_name)
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set_log_basic_config(log_file)
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# train model
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R.log_params(**flatten_dict(task_config))
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model.fit(dataset)
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recorder = R.get_recorder()
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R.save_objects(**{"model.pkl": model})
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# generate records: prediction, backtest, and analysis
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for record in task_config["record"]:
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record = record.copy()
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if record["class"] == "SignalRecord":
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srconf = {"model": model, "dataset": dataset, "recorder": recorder}
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record["kwargs"].update(srconf)
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sr = init_instance_by_config(record)
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sr.generate()
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else:
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rconf = {"recorder": recorder}
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record["kwargs"].update(rconf)
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ar = init_instance_by_config(record)
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ar.generate()
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