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Implement OPD policy #1461
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Implement OPD policy #1461
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fd26077
minor
Arthur-Null c3c4334
Merge remote-tracking branch 'upstream/main' into opd
Arthur-Null a99910e
Add opd interpreter
Arthur-Null c50ba2f
log
Arthur-Null 856f35f
Merge branch 'main' into opd
Arthur-Null dbba68a
update pickle_styled
Arthur-Null 8c9edd6
update pickle_styled
Arthur-Null 216a6b1
update data provider
Arthur-Null 52ee999
logger
Arthur-Null a0bc203
Merge branch 'opd' into yucfan/opd
Arthur-Null 62228f5
minor
Arthur-Null b44e03f
debug
Arthur-Null 6aeb267
OPDT network
Arthur-Null 36dd47d
OPD policy
Arthur-Null aa42812
data
Arthur-Null 28f0ff3
OPD entry
d3d1b5b
Merge branch 'main' into yucfan/opd
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# Copyright (c) Microsoft Corporation. | ||
# Licensed under the MIT License. | ||
import argparse | ||
import os | ||
import random | ||
from pathlib import Path | ||
from typing import cast, List, Optional | ||
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import numpy as np | ||
import pandas as pd | ||
import qlib | ||
import torch | ||
import yaml | ||
from qlib.backtest import Order | ||
from qlib.backtest.decision import OrderDir | ||
from qlib.constant import ONE_MIN | ||
from qlib.rl.data.pickle_styled import load_simple_intraday_backtest_data | ||
from qlib.rl.interpreter import ActionInterpreter, StateInterpreter | ||
from qlib.rl.order_execution import SingleAssetOrderExecutionSimple | ||
from qlib.rl.reward import Reward | ||
from qlib.rl.trainer import Checkpoint, backtest, train | ||
from qlib.rl.trainer.callbacks import Callback, EarlyStopping, MetricsWriter | ||
from qlib.rl.utils.log import CsvWriter, ActionWriter | ||
from qlib.utils import init_instance_by_config | ||
from tianshou.policy import BasePolicy | ||
from torch import nn | ||
from torch.utils.data import Dataset | ||
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def seed_everything(seed: int) -> None: | ||
torch.manual_seed(seed) | ||
torch.cuda.manual_seed_all(seed) | ||
np.random.seed(seed) | ||
random.seed(seed) | ||
torch.backends.cudnn.deterministic = True | ||
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def _read_orders(order_dir: Path) -> pd.DataFrame: | ||
if os.path.isfile(order_dir): | ||
return pd.read_pickle(order_dir) | ||
else: | ||
orders = [] | ||
for file in order_dir.iterdir(): | ||
order_data = pd.read_pickle(file) | ||
orders.append(order_data) | ||
return pd.concat(orders) | ||
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class LazyLoadDataset(Dataset): | ||
def __init__( | ||
self, | ||
order_file_path: Path, | ||
data_dir: Path, | ||
default_start_time_index: int, | ||
default_end_time_index: int, | ||
) -> None: | ||
self._default_start_time_index = default_start_time_index | ||
self._default_end_time_index = default_end_time_index | ||
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self._order_file_path = order_file_path | ||
self._order_df = _read_orders(order_file_path).reset_index() | ||
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self._data_dir = data_dir | ||
self._ticks_index: Optional[pd.DatetimeIndex] = None | ||
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def __len__(self) -> int: | ||
return len(self._order_df) | ||
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def __getitem__(self, index: int) -> Order: | ||
row = self._order_df.iloc[index] | ||
date = pd.Timestamp(str(row["date"])) | ||
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if self._ticks_index is None: | ||
# TODO: We only load ticks index once based on the assumption that ticks index of different dates | ||
# TODO: in one experiment are all the same. If that assumption is not hold, we need to load ticks index | ||
# TODO: of all dates. | ||
backtest_data = load_simple_intraday_backtest_data( | ||
data_dir=self._data_dir, | ||
stock_id=row["instrument"], | ||
date=date, | ||
) | ||
self._ticks_index = [t - date for t in backtest_data.get_time_index()] | ||
|
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order = Order( | ||
stock_id=row["instrument"], | ||
amount=row["amount"], | ||
direction=OrderDir(int(row["order_type"])), | ||
start_time=date + self._ticks_index[self._default_start_time_index], | ||
end_time=date + self._ticks_index[self._default_end_time_index - 1] + ONE_MIN, | ||
) | ||
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return order | ||
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def train_and_test( | ||
env_config: dict, | ||
simulator_config: dict, | ||
trainer_config: dict, | ||
data_config: dict, | ||
state_interpreter: StateInterpreter, | ||
action_interpreter: ActionInterpreter, | ||
policy: BasePolicy, | ||
reward: Reward, | ||
run_backtest: bool, | ||
) -> None: | ||
qlib.init() | ||
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order_root_path = Path(data_config["source"]["order_dir"]) | ||
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data_granularity = simulator_config.get("data_granularity", 1) | ||
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def _simulator_factory_simple(order: Order) -> SingleAssetOrderExecutionSimple: | ||
return SingleAssetOrderExecutionSimple( | ||
order=order, | ||
data_dir=Path(data_config["source"]["data_dir"]), | ||
ticks_per_step=simulator_config["time_per_step"], | ||
data_granularity=data_granularity, | ||
deal_price_type=data_config["source"].get("deal_price_column", "close"), | ||
vol_threshold=simulator_config["vol_limit"], | ||
) | ||
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assert data_config["source"]["default_start_time_index"] % data_granularity == 0 | ||
assert data_config["source"]["default_end_time_index"] % data_granularity == 0 | ||
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train_dataset, valid_dataset, test_dataset = [ | ||
LazyLoadDataset( | ||
order_file_path=order_root_path / tag, | ||
data_dir=Path(data_config["source"]["data_dir"]), | ||
default_start_time_index=data_config["source"]["default_start_time_index"] // data_granularity, | ||
default_end_time_index=data_config["source"]["default_end_time_index"] // data_granularity, | ||
) | ||
for tag in ("train", "valid", "all") | ||
] | ||
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if "checkpoint_path" in trainer_config: | ||
callbacks: List[Callback] = [] | ||
callbacks.append(MetricsWriter(dirpath=Path(trainer_config["checkpoint_path"]))) | ||
callbacks.append( | ||
Checkpoint( | ||
dirpath=Path(trainer_config["checkpoint_path"]) / "checkpoints", | ||
every_n_iters=trainer_config.get("checkpoint_every_n_iters", 1), | ||
save_latest="copy", | ||
), | ||
) | ||
if "earlystop_patience" in trainer_config: | ||
callbacks.append( | ||
EarlyStopping( | ||
patience=trainer_config["earlystop_patience"], | ||
monitor="val/pa", | ||
) | ||
) | ||
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trainer_kwargs = { | ||
"max_iters": trainer_config["max_epoch"], | ||
"finite_env_type": env_config["parallel_mode"], | ||
"concurrency": env_config["concurrency"], | ||
"val_every_n_iters": trainer_config.get("val_every_n_epoch", None), | ||
"callbacks": callbacks, | ||
} | ||
vessel_kwargs = { | ||
"episode_per_iter": trainer_config["episode_per_collect"], | ||
"update_kwargs": { | ||
"batch_size": trainer_config["batch_size"], | ||
"repeat": trainer_config["repeat_per_collect"], | ||
}, | ||
"val_initial_states": valid_dataset, | ||
} | ||
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train( | ||
simulator_fn=_simulator_factory_simple, | ||
state_interpreter=state_interpreter, | ||
action_interpreter=action_interpreter, | ||
policy=policy, | ||
reward=reward, | ||
initial_states=cast(List[Order], train_dataset), | ||
trainer_kwargs=trainer_kwargs, | ||
vessel_kwargs=vessel_kwargs, | ||
) | ||
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if run_backtest: | ||
backtest( | ||
simulator_fn=_simulator_factory_simple, | ||
state_interpreter=state_interpreter, | ||
action_interpreter=action_interpreter, | ||
initial_states=test_dataset, | ||
policy=policy, | ||
logger=[CsvWriter(Path(trainer_config["checkpoint_path"])), ActionWriter(Path(trainer_config["checkpoint_path"]))], | ||
# logger = CsvWriter(Path(trainer_config["checkpoint_path"]), loglevel=10), | ||
reward=reward, | ||
finite_env_type=trainer_kwargs["finite_env_type"], | ||
concurrency=trainer_kwargs["concurrency"], | ||
) | ||
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def main(config: dict, run_backtest: bool) -> None: | ||
if "seed" in config["runtime"]: | ||
seed_everything(config["runtime"]["seed"]) | ||
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state_config = config["state_interpreter"] | ||
state_interpreter: StateInterpreter = init_instance_by_config(state_config) | ||
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action_interpreter: ActionInterpreter = init_instance_by_config(config["action_interpreter"]) | ||
reward: Reward = init_instance_by_config(config["reward"]) | ||
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# Create torch network | ||
if "kwargs" not in config["network"]: | ||
config["network"]["kwargs"] = {} | ||
config["network"]["kwargs"].update({"obs_space": state_interpreter.observation_space}) | ||
network: nn.Module = init_instance_by_config(config["network"]) | ||
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# Create policy | ||
config["policy"]["kwargs"].update( | ||
{ | ||
"network": network, | ||
"obs_space": state_interpreter.observation_space, | ||
"action_space": action_interpreter.action_space, | ||
} | ||
) | ||
policy: BasePolicy = init_instance_by_config(config["policy"]) | ||
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use_cuda = config["runtime"].get("use_cuda", False) | ||
if use_cuda: | ||
policy.cuda() | ||
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train_and_test( | ||
env_config=config["env"], | ||
simulator_config=config["simulator"], | ||
data_config=config["data"], | ||
trainer_config=config["trainer"], | ||
action_interpreter=action_interpreter, | ||
state_interpreter=state_interpreter, | ||
policy=policy, | ||
reward=reward, | ||
run_backtest=run_backtest, | ||
) | ||
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if __name__ == "__main__": | ||
import warnings | ||
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warnings.filterwarnings("ignore", category=DeprecationWarning) | ||
warnings.filterwarnings("ignore", category=RuntimeWarning) | ||
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parser = argparse.ArgumentParser() | ||
parser.add_argument("--config_path", type=str, required=True, help="Path to the config file") | ||
parser.add_argument("--run_backtest", action="store_true", help="Run backtest workflow after training is finished") | ||
args = parser.parse_args() | ||
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with open(args.config_path, "r") as input_stream: | ||
config = yaml.safe_load(input_stream) | ||
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main(config, run_backtest=args.run_backtest) |
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Why can't opdt reuse this example?
https://github.com/microsoft/qlib/blob/main/examples/rl_order_execution/README.md#training
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And user can try the model simply by running
python -m qlib.rl.contrib.train_onpolicy --config_path exp_configs/XXXX.yml --run_backtest