Qlib RL framework (stage 2) - trainer

qlib/rl/order_execution/__init__.py

@@ -9,4 +9,5 @@
 from .interpreter import *
 from .network import *
 from .policy import *
+from .reward import *
 from .simulator_simple import *

qlib/rl/order_execution/reward.py

@@ -0,0 +1,46 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+from __future__ import annotations
+
+from typing import cast
+
+import numpy as np
+from qlib.rl.reward import Reward
+
+from .simulator_simple import SAOEState, SAOEMetrics
+
+__all__ = ["PAPenaltyReward"]
+
+
+class PAPenaltyReward(Reward[SAOEState]):
+    """Encourage higher PAs, but penalize stacking all the amounts within a very short time.
+    Formally, for each time step, the reward is :math:`(PA_t * vol_t / target - vol_t^2 * penalty)`.
+
+    Parameters
+    ----------
+    penalty
+        The penalty for large volume in a short time.
+    """
+
+    def __init__(self, penalty: float = 100.0):
+        self.penalty = penalty
+
+    def reward(self, simulator_state: SAOEState) -> float:
+        whole_order = simulator_state.order.amount
+        assert whole_order > 0
+        last_step = cast(SAOEMetrics, simulator_state.history_steps.reset_index().iloc[-1].to_dict())
+        pa = last_step["pa"] * last_step["amount"] / whole_order
+
+        # Inspect the "break-down" of the latest step: trading amount at every tick
+        last_step_breakdown = simulator_state.history_exec.loc[last_step["datetime"] :]
+        penalty = -self.penalty * ((last_step_breakdown["amount"] / whole_order) ** 2).sum()
+
+        reward = pa + penalty
+
+        # Throw error in case of NaN
+        assert not (np.isnan(reward) or np.isinf(reward)), f"Invalid reward for simulator state: {simulator_state}"
+
+        self.log("reward/pa", pa)
+        self.log("reward/penalty", penalty)
+        return reward

qlib/rl/order_execution/simulator_simple.py

@@ -131,11 +131,14 @@ class SingleAssetOrderExecution(Simulator[Order, SAOEState, float]):
     """
 
     history_exec: pd.DataFrame
-    """All execution history at every possible time ticks. See :class:`SAOEMetrics` for available columns."""
+    """All execution history at every possible time ticks. See :class:`SAOEMetrics` for available columns.
+    Index is ``datetime``.
+    """
 
     history_steps: pd.DataFrame
     """Positions at each step. The position before first step is also recorded.
-    See :class:`SAOEMetrics` for available columns."""
+    See :class:`SAOEMetrics` for available columns.
+    Index is ``datetime``, which is the **starting** time of each step."""
 
     metrics: SAOEMetrics | None
     """Metrics. Only available when done."""

qlib/rl/trainer/__init__.py

@@ -0,0 +1,9 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+"""Train, test, inference utilities."""
+
+from .api import backtest, train
+from .callbacks import EarlyStopping, Checkpoint
+from .trainer import Trainer
+from .vessel import TrainingVessel, TrainingVesselBase

qlib/rl/trainer/api.py

@@ -0,0 +1,118 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+from __future__ import annotations
+
+from typing import Callable, Sequence, cast, Any
+
+from tianshou.policy import BasePolicy
+
+from qlib.rl.simulator import InitialStateType, Simulator
+from qlib.rl.interpreter import StateInterpreter, ActionInterpreter
+from qlib.rl.reward import Reward
+from qlib.rl.utils import FiniteEnvType, LogWriter
+
+from .vessel import TrainingVessel
+from .trainer import Trainer
+
+
+def train(
+    simulator_fn: Callable[[InitialStateType], Simulator],
+    state_interpreter: StateInterpreter,
+    action_interpreter: ActionInterpreter,
+    initial_states: Sequence[InitialStateType],
+    policy: BasePolicy,
+    reward: Reward,
+    vessel_kwargs: dict[str, Any],
+    trainer_kwargs: dict[str, Any],
+) -> None:
+    """Train a policy with the parallelism provided by RL framework.
+
+    Experimental API. Parameters might change shortly.
+
+    Parameters
+    ----------
+    simulator_fn
+        Callable receiving initial seed, returning a simulator.
+    state_interpreter
+        Interprets the state of simulators.
+    action_interpreter
+        Interprets the policy actions.
+    initial_states
+        Initial states to iterate over. Every state will be run exactly once.
+    policy
+        Policy to train against.
+    reward
+        Reward function.
+    vessel_kwargs
+        Keyword arguments passed to :class:`TrainingVessel`, like ``episode_per_iter``.
+    trainer_kwargs
+        Keyword arguments passed to :class:`Trainer`, like ``finite_env_type``, ``concurrency``.
+    """
+
+    vessel = TrainingVessel(
+        simulator_fn=simulator_fn,
+        state_interpreter=state_interpreter,
+        action_interpreter=action_interpreter,
+        policy=policy,
+        train_initial_states=initial_states,
+        reward=reward,  # ignore none
+        **vessel_kwargs,
+    )
+    trainer = Trainer(**trainer_kwargs)
+    trainer.fit(vessel)
+
+
+def backtest(
+    simulator_fn: Callable[[InitialStateType], Simulator],
+    state_interpreter: StateInterpreter,
+    action_interpreter: ActionInterpreter,
+    initial_states: Sequence[InitialStateType],
+    policy: BasePolicy,
+    logger: LogWriter | list[LogWriter],
+    reward: Reward | None = None,
+    finite_env_type: FiniteEnvType = "subproc",
+    concurrency: int = 2,
+) -> None:
+    """Backtest with the parallelism provided by RL framework.
+
+    Experimental API. Parameters might change shortly.
+
+    Parameters
+    ----------
+    simulator_fn
+        Callable receiving initial seed, returning a simulator.
+    state_interpreter
+        Interprets the state of simulators.
+    action_interpreter
+        Interprets the policy actions.
+    initial_states
+        Initial states to iterate over. Every state will be run exactly once.
+    policy
+        Policy to test against.
+    logger
+        Logger to record the backtest results. Logger must be present because
+        without logger, all information will be lost.
+    reward
+        Optional reward function. For backtest, this is for testing the rewards
+        and logging them only.
+    finite_env_type
+        Type of finite env implementation.
+    concurrency
+        Parallel workers.
+    """
+
+    vessel = TrainingVessel(
+        simulator_fn=simulator_fn,
+        state_interpreter=state_interpreter,
+        action_interpreter=action_interpreter,
+        policy=policy,
+        test_initial_states=initial_states,
+        reward=cast(Reward, reward),  # ignore none
+    )
+    trainer = Trainer(
+        finite_env_type=finite_env_type,
+        concurrency=concurrency,
+        loggers=logger,
+    )
+    trainer.test(vessel)