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Refine RL todos (#1332)

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* Refine several todos

* CI issues

* Remove Dropna limitation of `quote_df` in Exchange  (#1334)

* Remove Dropna limitation of `quote_df` of Exchange

* Impreove docstring

* Fix type error when expression is specified (#1335)

* Refine fill_missing_data()

* Remove several TODO comments

* Add back env for interpreters

* Change Literal import

* Resolve PR comments

* Move  to SAOEState

* Add Trainer.get_policy_state_dict()

* Mypy issue

Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
This commit is contained in:
Huoran Li
2022-11-10 21:10:11 +08:00
committed by GitHub
parent 49a5bccfec
commit 35794846ff
20 changed files with 461 additions and 530 deletions
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6
tests/rl/test_qlib_simulator.py
View File

@@ -7,11 +7,11 @@ from typing import Tuple
import pandas as pd
import pytest
from qlib.backtest.decision import Order, OrderDir, TradeRangeByTime
from qlib.backtest.decision import Order, OrderDir
from qlib.backtest.executor import SimulatorExecutor
from qlib.rl.order_execution import CategoricalActionInterpreter
from qlib.rl.order_execution.simulator_qlib import SingleAssetOrderExecution
from qlib.rl.utils.env_wrapper import CollectDataEnvWrapper
TOTAL_POSITION = 2100.0
@@ -183,8 +183,6 @@ def test_interpreter() -> None:
order = get_order()
simulator = get_simulator(order)
interpreter_action = CategoricalActionInterpreter(values=NUM_EXECUTION)
interpreter_action.env = CollectDataEnvWrapper()
interpreter_action.env.reset()
NUM_STEPS = 7
state = simulator.get_state()

11
tests/rl/test_saoe_simple.py
View File

@@ -20,7 +20,6 @@ from qlib.rl.data.pickle_styled import PickleProcessedDataProvider
from qlib.rl.order_execution import *
from qlib.rl.trainer import backtest, train
from qlib.rl.utils import ConsoleWriter, CsvWriter, EnvWrapperStatus
from qlib.rl.utils.env_wrapper import CollectDataEnvWrapper
pytestmark = pytest.mark.skipif(sys.version_info < (3, 8), reason="Pickle styled data only supports Python >= 3.8")
@@ -186,10 +185,6 @@ def test_interpreter():
assert np.sum(obs["data_processed"][60:]) == 0
# second step: action
interpreter_action.env = CollectDataEnvWrapper()
interpreter_action_twap.env = CollectDataEnvWrapper()
interpreter_action.env.reset()
interpreter_action_twap.env.reset()
action = interpreter_action(simulator.get_state(), 1)
assert action == 15 / 20
@@ -260,8 +255,6 @@ def test_twap_strategy(finite_env_type):
state_interp = FullHistoryStateInterpreter(13, 390, 5, PickleProcessedDataProvider(FEATURE_DATA_DIR))
action_interp = TwapRelativeActionInterpreter()
action_interp.env = CollectDataEnvWrapper()
action_interp.env.reset()
policy = AllOne(state_interp.observation_space, action_interp.action_space)
csv_writer = CsvWriter(Path(__file__).parent / ".output")
@@ -291,8 +284,6 @@ def test_cn_ppo_strategy():
state_interp = FullHistoryStateInterpreter(8, 240, 6, PickleProcessedDataProvider(CN_FEATURE_DATA_DIR))
action_interp = CategoricalActionInterpreter(4)
action_interp.env = CollectDataEnvWrapper()
action_interp.env.reset()
network = Recurrent(state_interp.observation_space)
policy = PPO(network, state_interp.observation_space, action_interp.action_space, 1e-4)
policy.load_state_dict(torch.load(CN_POLICY_WEIGHTS_DIR / "ppo_recurrent_30min.pth", map_location="cpu"))
@@ -324,8 +315,6 @@ def test_ppo_train():
state_interp = FullHistoryStateInterpreter(8, 240, 6, PickleProcessedDataProvider(CN_FEATURE_DATA_DIR))
action_interp = CategoricalActionInterpreter(4)
action_interp.env = CollectDataEnvWrapper()
action_interp.env.reset()
network = Recurrent(state_interp.observation_space)
policy = PPO(network, state_interp.observation_space, action_interp.action_space, 1e-4)