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Order execution open source (#1447)

Browse Source 
* Waiting for bin data

* Complete readme

* CI

* Add inst filter by time

* Update qlib/data/dataset/processor.py

* typo

* Fix time filter bug

* Add Filter and set Universe

* Complete data pipeline

* Fix Provider Logger Info Args

* Add DQN; a minor bugfix in ppo reward.

* update readme. modify assertion logic in strategy check.

* Fix Doc issues and fix black

* Fix pylint Error

---------

Co-authored-by: Young <afe.young@gmail.com>
Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
This commit is contained in:
Huoran Li
2023-03-13 12:06:28 +08:00
committed by GitHub
parent f98e04ca9d
commit 653c082e7a
24 changed files with 742 additions and 42 deletions
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8
qlib/contrib/data/handler.py
View File

@@ -56,7 +56,7 @@ class Alpha360(DataHandlerLP):
fit_start_time=None,
fit_end_time=None,
filter_pipe=None,
inst_processor=None,
inst_processors=None,
**kwargs
):
infer_processors = check_transform_proc(infer_processors, fit_start_time, fit_end_time)
@@ -71,7 +71,7 @@ class Alpha360(DataHandlerLP):
},
"filter_pipe": filter_pipe,
"freq": freq,
"inst_processor": inst_processor,
"inst_processors": inst_processors,
},
}
@@ -152,7 +152,7 @@ class Alpha158(DataHandlerLP):
fit_end_time=None,
process_type=DataHandlerLP.PTYPE_A,
filter_pipe=None,
inst_processor=None,
inst_processors=None,
**kwargs
):
infer_processors = check_transform_proc(infer_processors, fit_start_time, fit_end_time)
@@ -167,7 +167,7 @@ class Alpha158(DataHandlerLP):
},
"filter_pipe": filter_pipe,
"freq": freq,
"inst_processor": inst_processor,
"inst_processors": inst_processors,
},
}
super().__init__(

6
qlib/contrib/data/highfreq_handler.py
View File

@@ -115,6 +115,7 @@ class HighFreqGeneralHandler(DataHandlerLP):
day_length=240,
freq="1min",
columns=["$open", "$high", "$low", "$close", "$vwap"],
inst_processors=None,
):
self.day_length = day_length
self.columns = columns
@@ -128,6 +129,7 @@ class HighFreqGeneralHandler(DataHandlerLP):
"config": self.get_feature_config(),
"swap_level": False,
"freq": freq,
"inst_processors": inst_processors,
},
}
super().__init__(
@@ -257,6 +259,7 @@ class HighFreqGeneralBacktestHandler(DataHandler):
day_length=240,
freq="1min",
columns=["$close", "$vwap", "$volume"],
inst_processors=None,
):
self.day_length = day_length
self.columns = set(columns)
@@ -266,6 +269,7 @@ class HighFreqGeneralBacktestHandler(DataHandler):
"config": self.get_feature_config(),
"swap_level": False,
"freq": freq,
"inst_processors": inst_processors,
},
}
super().__init__(
@@ -311,6 +315,7 @@ class HighFreqOrderHandler(DataHandlerLP):
learn_processors=[],
fit_start_time=None,
fit_end_time=None,
inst_processors=None,
drop_raw=True,
):
@@ -323,6 +328,7 @@ class HighFreqOrderHandler(DataHandlerLP):
"config": self.get_feature_config(),
"swap_level": False,
"freq": "1min",
"inst_processors": inst_processors,
},
}
super().__init__(

38
qlib/contrib/data/highfreq_provider.py
View File

@@ -128,7 +128,7 @@ class HighFreqProvider:
raise ValueError("Must specify the path to save the dataset.") from e
if os.path.isfile(path):
start = time.time()
self.logger.info("Dataset exists, load from disk.", __name__)
self.logger.info(f"[{__name__}]Dataset exists, load from disk.")
# res = dataset.prepare(['train', 'valid', 'test'])
with open(path, "rb") as f:
@@ -137,11 +137,11 @@ class HighFreqProvider:
res = [data[i] for i in datasets]
else:
res = data.prepare(datasets)
self.logger.info(f"Data loaded, time cost: {time.time() - start:.2f}", __name__)
self.logger.info(f"[{__name__}]Data loaded, time cost: {time.time() - start:.2f}")
else:
if not os.path.exists(os.path.dirname(path)):
os.makedirs(os.path.dirname(path))
self.logger.info("Generating dataset", __name__)
self.logger.info(f"[{__name__}]Generating dataset")
start_time = time.time()
self._prepare_calender_cache()
dataset = init_instance_by_config(config)
@@ -160,7 +160,7 @@ class HighFreqProvider:
with open(path[:-4] + "test.pkl", "wb") as f:
pkl.dump(testset, f)
res = [data[i] for i in datasets]
self.logger.info(f"Data generated, time cost: {(time.time() - start_time):.2f}", __name__)
self.logger.info(f"[{__name__}]Data generated, time cost: {(time.time() - start_time):.2f}")
return res
def _gen_data(self, config, datasets=["train", "valid", "test"]):
@@ -170,7 +170,7 @@ class HighFreqProvider:
raise ValueError("Must specify the path to save the dataset.") from e
if os.path.isfile(path):
start = time.time()
self.logger.info("Dataset exists, load from disk.", __name__)
self.logger.info(f"[{__name__}]Dataset exists, load from disk.")
# res = dataset.prepare(['train', 'valid', 'test'])
with open(path, "rb") as f:
@@ -179,18 +179,18 @@ class HighFreqProvider:
res = [data[i] for i in datasets]
else:
res = data.prepare(datasets)
self.logger.info(f"Data loaded, time cost: {time.time() - start:.2f}", __name__)
self.logger.info(f"[{__name__}]Data loaded, time cost: {time.time() - start:.2f}")
else:
if not os.path.exists(os.path.dirname(path)):
os.makedirs(os.path.dirname(path))
self.logger.info("Generating dataset", __name__)
self.logger.info(f"[{__name__}]Generating dataset")
start_time = time.time()
self._prepare_calender_cache()
dataset = init_instance_by_config(config)
dataset.config(dump_all=True, recursive=True)
dataset.to_pickle(path)
res = dataset.prepare(datasets)
self.logger.info(f"Data generated, time cost: {(time.time() - start_time):.2f}", __name__)
self.logger.info(f"[{__name__}]Data generated, time cost: {(time.time() - start_time):.2f}")
return res
def _gen_dataset(self, config):
@@ -200,21 +200,21 @@ class HighFreqProvider:
raise ValueError("Must specify the path to save the dataset.") from e
if os.path.isfile(path):
start = time.time()
self.logger.info("Dataset exists, load from disk.", __name__)
self.logger.info(f"[{__name__}]Dataset exists, load from disk.")
with open(path, "rb") as f:
dataset = pkl.load(f)
self.logger.info(f"Data loaded, time cost: {time.time() - start:.2f}", __name__)
self.logger.info(f"[{__name__}]Data loaded, time cost: {time.time() - start:.2f}")
else:
start = time.time()
if not os.path.exists(os.path.dirname(path)):
os.makedirs(os.path.dirname(path))
self.logger.info("Generating dataset", __name__)
self.logger.info(f"[{__name__}]Generating dataset")
self._prepare_calender_cache()
dataset = init_instance_by_config(config)
self.logger.info(f"Dataset init, time cost: {time.time() - start:.2f}", __name__)
self.logger.info(f"[{__name__}]Dataset init, time cost: {time.time() - start:.2f}")
dataset.prepare(["train", "valid", "test"])
self.logger.info(f"Dataset prepared, time cost: {time.time() - start:.2f}", __name__)
self.logger.info(f"[{__name__}]Dataset prepared, time cost: {time.time() - start:.2f}")
dataset.config(dump_all=True, recursive=True)
dataset.to_pickle(path)
return dataset
@@ -227,15 +227,15 @@ class HighFreqProvider:
if os.path.isfile(path + "tmp_dataset.pkl"):
start = time.time()
self.logger.info("Dataset exists, load from disk.", __name__)
self.logger.info(f"[{__name__}]Dataset exists, load from disk.")
else:
start = time.time()
if not os.path.exists(os.path.dirname(path)):
os.makedirs(os.path.dirname(path))
self.logger.info("Generating dataset", __name__)
self.logger.info(f"[{__name__}]Generating dataset")
self._prepare_calender_cache()
dataset = init_instance_by_config(config)
self.logger.info(f"Dataset init, time cost: {time.time() - start:.2f}", __name__)
self.logger.info(f"[{__name__}]Dataset init, time cost: {time.time() - start:.2f}")
dataset.config(dump_all=False, recursive=True)
dataset.to_pickle(path + "tmp_dataset.pkl")
@@ -268,15 +268,15 @@ class HighFreqProvider:
if os.path.isfile(path + "tmp_dataset.pkl"):
start = time.time()
self.logger.info("Dataset exists, load from disk.", __name__)
self.logger.info(f"[{__name__}]Dataset exists, load from disk.")
else:
start = time.time()
if not os.path.exists(os.path.dirname(path)):
os.makedirs(os.path.dirname(path))
self.logger.info("Generating dataset", __name__)
self.logger.info(f"[{__name__}]Generating dataset")
self._prepare_calender_cache()
dataset = init_instance_by_config(config)
self.logger.info(f"Dataset init, time cost: {time.time() - start:.2f}", __name__)
self.logger.info(f"[{__name__}]Dataset init, time cost: {time.time() - start:.2f}")
dataset.config(dump_all=False, recursive=True)
dataset.to_pickle(path + "tmp_dataset.pkl")

22
qlib/data/dataset/loader.py
View File

@@ -153,7 +153,7 @@ class QlibDataLoader(DLWParser):
filter_pipe: List = None,
swap_level: bool = True,
freq: Union[str, dict] = "day",
inst_processor: dict = None,
inst_processors: Union[dict, list] = None,
):
"""
Parameters
@@ -167,16 +167,19 @@ class QlibDataLoader(DLWParser):
freq: dict or str
If type(config) == dict and type(freq) == str, load config data using freq.
If type(config) == dict and type(freq) == dict, load config[<group_name>] data using freq[<group_name>]
inst_processor: dict
If inst_processor is not None and type(config) == dict; load config[<group_name>] data using inst_processor[<group_name>]
inst_processors: dict | list
If inst_processors is not None and type(config) == dict; load config[<group_name>] data using inst_processors[<group_name>]
If inst_processors is a list, then it will be applied to all groups.
"""
self.filter_pipe = filter_pipe
self.swap_level = swap_level
self.freq = freq
# sample
self.inst_processor = inst_processor if inst_processor is not None else {}
assert isinstance(self.inst_processor, dict), f"inst_processor(={self.inst_processor}) must be dict"
self.inst_processors = inst_processors if inst_processors is not None else {}
assert isinstance(
self.inst_processors, (dict, list)
), f"inst_processors(={self.inst_processors}) must be dict or list"
super().__init__(config)
@@ -187,8 +190,8 @@ class QlibDataLoader(DLWParser):
if _gp not in freq:
raise ValueError(f"freq(={freq}) missing group(={_gp})")
assert (
self.inst_processor
), f"freq(={self.freq}), inst_processor(={self.inst_processor}) cannot be None/empty"
self.inst_processors
), f"freq(={self.freq}), inst_processors(={self.inst_processors}) cannot be None/empty"
def load_group_df(
self,
@@ -208,9 +211,10 @@ class QlibDataLoader(DLWParser):
warnings.warn("`filter_pipe` is not None, but it will not be used with `instruments` as list")
freq = self.freq[gp_name] if isinstance(self.freq, dict) else self.freq
df = D.features(
instruments, exprs, start_time, end_time, freq=freq, inst_processors=self.inst_processor.get(gp_name, [])
inst_processors = (
self.inst_processors if isinstance(self.inst_processors, list) else self.inst_processors.get(gp_name, [])
)
df = D.features(instruments, exprs, start_time, end_time, freq=freq, inst_processors=inst_processors)
df.columns = names
if self.swap_level:
df = df.swaplevel().sort_index() # NOTE: if swaplevel, return <datetime, instrument>

43
qlib/data/dataset/processor.py
View File

@@ -2,7 +2,7 @@
# Licensed under the MIT License.
import abc
from typing import Union, Text
from typing import Union, Text, Optional
import numpy as np
import pandas as pd
@@ -11,6 +11,8 @@ from ...constant import EPS
from .utils import fetch_df_by_index
from ...utils.serial import Serializable
from ...utils.paral import datetime_groupby_apply
from qlib.data.inst_processor import InstProcessor
from qlib.data import D
def get_group_columns(df: pd.DataFrame, group: Union[Text, None]):
@@ -378,3 +380,42 @@ class HashStockFormat(Processor):
from .storage import HashingStockStorage # pylint: disable=C0415
return HashingStockStorage.from_df(df)
class TimeRangeFlt(InstProcessor):
"""
This is a filter to filter stock.
Only keep the data that exist from start_time to end_time (the existence in the middle is not checked.)
WARNING: It may induce leakage!!!
"""
def __init__(
self,
start_time: Optional[Union[pd.Timestamp, str]] = None,
end_time: Optional[Union[pd.Timestamp, str]] = None,
freq: str = "day",
):
"""
Parameters
----------
start_time : Optional[Union[pd.Timestamp, str]]
The data must start earlier (or equal) than `start_time`
None indicates data will not be filtered based on `start_time`
end_time : Optional[Union[pd.Timestamp, str]]
similar to start_time
freq : str
The frequency of the calendar
"""
# Align to calendar before filtering
cal = D.calendar(start_time=start_time, end_time=end_time, freq=freq)
self.start_time = None if start_time is None else cal[0]
self.end_time = None if end_time is None else cal[-1]
def __call__(self, df: pd.DataFrame, instrument, *args, **kwargs):
if (
df.empty
or (self.start_time is None or df.index.min() <= self.start_time)
and (self.end_time is None or df.index.max() >= self.end_time)
):
return df
return df.head(0)

5
qlib/rl/contrib/backtest.py
View File

@@ -357,7 +357,10 @@ def backtest(backtest_config: dict, with_simulator: bool = False) -> pd.DataFram
if not output_path.exists():
os.makedirs(output_path)
res.to_csv(output_path / "summary.csv")
if "pa" in res.columns:
res["pa"] = res["pa"] * 10000.0 # align with training metrics
res.to_csv(output_path / "backtest_result.csv")
return res

54
qlib/rl/order_execution/policy.py
View File

@@ -12,11 +12,11 @@ import torch
import torch.nn as nn
from gym.spaces import Discrete
from tianshou.data import Batch, ReplayBuffer, to_torch
from tianshou.policy import BasePolicy, PPOPolicy
from tianshou.policy import BasePolicy, PPOPolicy, DQNPolicy
from qlib.rl.trainer.trainer import Trainer
__all__ = ["AllOne", "PPO"]
__all__ = ["AllOne", "PPO", "DQN"]
# baselines #
@@ -158,6 +158,56 @@ class PPO(PPOPolicy):
set_weight(self, Trainer.get_policy_state_dict(weight_file))
DQNModel = PPOActor # Reuse PPOActor.
class DQN(DQNPolicy):
"""A wrapper of tianshou DQNPolicy.
Differences:
- Auto-create model network. Supports discrete action space only.
- Support a ``weight_file`` that supports loading checkpoint.
"""
def __init__(
self,
network: nn.Module,
obs_space: gym.Space,
action_space: gym.Space,
lr: float,
weight_decay: float = 0.0,
discount_factor: float = 0.99,
estimation_step: int = 1,
target_update_freq: int = 0,
reward_normalization: bool = False,
is_double: bool = True,
clip_loss_grad: bool = False,
weight_file: Optional[Path] = None,
) -> None:
assert isinstance(action_space, Discrete)
model = DQNModel(network, action_space.n)
optimizer = torch.optim.Adam(
model.parameters(),
lr=lr,
weight_decay=weight_decay,
)
super().__init__(
model,
optimizer,
discount_factor=discount_factor,
estimation_step=estimation_step,
target_update_freq=target_update_freq,
reward_normalization=reward_normalization,
is_double=is_double,
clip_loss_grad=clip_loss_grad,
)
if weight_file is not None:
set_weight(self, Trainer.get_policy_state_dict(weight_file))
# utilities: these should be put in a separate (common) file. #

14
qlib/rl/order_execution/reward.py
View File

@@ -70,7 +70,19 @@ class PPOReward(Reward[SAOEState]):
def reward(self, simulator_state: SAOEState) -> float:
if simulator_state.cur_step == self.max_step - 1 or simulator_state.position < 1e-6:
vwap_price = cast(dict, simulator_state.metrics)["trade_price"]
if simulator_state.history_exec["deal_amount"].sum() == 0.0:
vwap_price = cast(
float,
np.average(simulator_state.history_exec["market_price"]),
)
else:
vwap_price = cast(
float,
np.average(
simulator_state.history_exec["market_price"],
weights=simulator_state.history_exec["deal_amount"],
),
)
twap_price = simulator_state.backtest_data.get_deal_price().mean()
if simulator_state.order.direction == OrderDir.SELL:

8
qlib/rl/order_execution/strategy.py
View File

@@ -7,6 +7,7 @@ import collections
from types import GeneratorType
from typing import Any, Callable, cast, Dict, Generator, List, Optional, Tuple, Union
import warnings
import numpy as np
import pandas as pd
import torch
@@ -137,7 +138,12 @@ class SAOEStateAdapter:
exec_vol[idx - last_step_range[0]] = order.deal_amount
if exec_vol.sum() > self.position and exec_vol.sum() > 0.0:
assert exec_vol.sum() < self.position + 1, f"{exec_vol} too large"
if exec_vol.sum() > self.position + 1.0:
warnings.warn(
f"Sum of execution volume is {exec_vol.sum()} which is larger than "
f"position + 1.0 = {self.position} + 1.0 = {self.position + 1.0}. "
f"All execution volume is scaled down linearly to ensure that their sum does not position."
)
exec_vol *= self.position / (exec_vol.sum())
market_volume = cast(