shock: defining new lab environment and formulation

lab/outlet/gym_wrapper.py

@@ -0,0 +1,86 @@
+"""
+Gymnasium-compatible wrapper for the Quote-Control platform.
+
+Provides a standard Gym interface for RL training:
+- observation_space: Box space with flattened observation
+- action_space: Box space with price multipliers [0.5, 2.0]
+- reset(), step(), render(), close() methods
+
+Example:
+    >>> from lab.config import make_retail_platform
+    >>> from lab.outlet.gym_wrapper import QuoteGymEnv
+    >>> env = QuoteGymEnv(make_retail_platform())
+    >>> obs, info = env.reset()
+    >>> obs, reward, done, truncated, info = env.step(env.action_space.sample())
+"""
+from __future__ import annotations
+from typing import Any
+import numpy as np
+
+try:
+    import gymnasium as gym
+    from gymnasium import spaces
+    HAS_GYM = True
+except ImportError:
+    HAS_GYM = False
+
+from .platform import Platform, PlatformConfig
+from .types import Quote, InstrumentSet, StepResult
+
+class QuoteGymEnv:
+    """Gymnasium-compatible environment wrapper.
+
+    Wraps a Platform instance with standard Gym interface.
+    Actions are price multipliers in [0.5, 2.0] applied to reference prices.
+    Observations are flattened numpy arrays containing quotes, fills, exposures.
+    """
+
+    def __init__(self, platform: Platform):
+        if not HAS_GYM:
+            raise ImportError("gymnasium required for QuoteGymEnv")
+        self.platform = platform
+        self.n = platform.instruments.n
+        self._last_result: StepResult | None = None
+
+        # action space: price adjustments as multipliers [0.5, 2.0]
+        self.action_space = spaces.Box(low=0.5, high=2.0, shape=(self.n,), dtype=np.float32)
+
+        # observation space
+        obs_dim = self.n * 4  # quotes + fills + exposures + position
+        if platform.market:
+            obs_dim += self.n  # competitor quotes
+        self.observation_space = spaces.Box(low=-np.inf, high=np.inf,
+                                            shape=(obs_dim,), dtype=np.float32)
+
+    def reset(self, seed: int | None = None, options: dict | None = None) -> tuple[np.ndarray, dict]:
+        result = self.platform.reset(seed)
+        self._last_result = result
+        return result.obs.to_flat().astype(np.float32), result.info
+
+    def step(self, action: np.ndarray) -> tuple[np.ndarray, float, bool, bool, dict]:
+        # convert action (multipliers) to absolute prices
+        refs = self.platform.instruments.refs
+        prices = refs * action
+        result = self.platform.step(prices)
+        self._last_result = result
+        return (result.obs.to_flat().astype(np.float32), result.reward,
+                result.terminated, result.truncated, result.info)
+
+    def render(self) -> None:
+        if self._last_result:
+            m = self._last_result.metrics
+            print(f"t={self.platform._t} pnl={m.pnl:.2f} units={m.units_traded:.0f} "
+                  f"conv={m.conversion:.3f} vol={m.volatility:.3f}")
+
+    def close(self) -> None:
+        pass
+
+def make_env(platform: Platform) -> QuoteGymEnv:
+    return QuoteGymEnv(platform)
+
+if HAS_GYM:
+    # register if gymnasium available
+    try:
+        gym.register(id='QuoteControl-v0', entry_point='outlet.gym_wrapper:QuoteGymEnv')
+    except:
+        pass  # already registered or other issue