shock: defining new lab environment and formulation

lab/outlet/objectives/penalties.py

@@ -0,0 +1,101 @@
+"""
+Standard objective components and penalties.
+
+This module provides common reward terms:
+- PnLObjective: Basic profit and loss
+- VolatilityPenalty: Penalize price volatility for UX
+- HoldingCostPenalty: Inventory holding cost
+- LostOpportunityCostPenalty: Stockout/missed fill cost
+- InventoryRiskPenalty: Quadratic inventory risk (market making)
+- SpreadCaptureReward: Bid-ask spread capture (market making)
+"""
+from __future__ import annotations
+import numpy as np
+from .base import BaseObjective
+from ..types import Quote, InstrumentSet, StepMetrics, HiddenState, Observation
+from ..math_util import inventory_penalty
+
+class PnLObjective(BaseObjective):
+    """Profit and loss reward (revenue - cost)."""
+
+    def reward(self, quote: Quote, instruments: InstrumentSet,
+               metrics: StepMetrics, hidden: HiddenState, obs: Observation) -> float:
+        return metrics.pnl
+
+    def breakdown(self, quote: Quote, instruments: InstrumentSet,
+                  metrics: StepMetrics, hidden: HiddenState, obs: Observation) -> dict[str, float]:
+        return {'pnl': metrics.pnl, 'revenue': metrics.revenue, 'cost': metrics.cost}
+
+class VolatilityPenalty(BaseObjective):
+    """Penalize price volatility for user experience."""
+
+    def __init__(self, scale: float = 1.0):
+        self.scale = scale
+
+    def reward(self, quote: Quote, instruments: InstrumentSet,
+               metrics: StepMetrics, hidden: HiddenState, obs: Observation) -> float:
+        return -self.scale * metrics.volatility
+
+    def breakdown(self, quote: Quote, instruments: InstrumentSet,
+                  metrics: StepMetrics, hidden: HiddenState, obs: Observation) -> dict[str, float]:
+        return {'volatility_penalty': -self.scale * metrics.volatility}
+
+class HoldingCostPenalty(BaseObjective):
+    """Penalty for inventory holding costs."""
+
+    def __init__(self, scale: float = 1.0):
+        self.scale = scale
+
+    def reward(self, quote: Quote, instruments: InstrumentSet,
+               metrics: StepMetrics, hidden: HiddenState, obs: Observation) -> float:
+        return -self.scale * metrics.position_cost
+
+    def breakdown(self, quote: Quote, instruments: InstrumentSet,
+                  metrics: StepMetrics, hidden: HiddenState, obs: Observation) -> dict[str, float]:
+        return {'holding_cost_penalty': -self.scale * metrics.position_cost}
+
+class LostOpportunityCostPenalty(BaseObjective):
+    """Penalty for lost sales due to stockouts or missed fills."""
+
+    def __init__(self, scale: float = 1.0):
+        self.scale = scale
+
+    def reward(self, quote: Quote, instruments: InstrumentSet,
+               metrics: StepMetrics, hidden: HiddenState, obs: Observation) -> float:
+        return -self.scale * metrics.lost_opportunity
+
+    def breakdown(self, quote: Quote, instruments: InstrumentSet,
+                  metrics: StepMetrics, hidden: HiddenState, obs: Observation) -> dict[str, float]:
+        return {'lost_opportunity_penalty': -self.scale * metrics.lost_opportunity}
+
+class InventoryRiskPenalty(BaseObjective):
+    """Quadratic inventory risk penalty (Avellaneda-Stoikov style).
+
+    Penalty = gamma * sigma^2 * q^2 / 2, where q is total position.
+    Encourages market makers to keep inventory near zero.
+    """
+
+    def __init__(self, gamma: float = 0.1, sigma: float = 1.0):
+        self.gamma = gamma
+        self.sigma = sigma
+
+    def reward(self, quote: Quote, instruments: InstrumentSet,
+               metrics: StepMetrics, hidden: HiddenState, obs: Observation) -> float:
+        if obs.position is None: return 0.0
+        q = np.sum(obs.position)
+        return -inventory_penalty(q, self.gamma, self.sigma)
+
+    def breakdown(self, quote: Quote, instruments: InstrumentSet,
+                  metrics: StepMetrics, hidden: HiddenState, obs: Observation) -> dict[str, float]:
+        return {'inventory_risk_penalty': self.reward(quote, instruments, metrics, hidden, obs)}
+
+class SpreadCaptureReward(BaseObjective):
+    """Reward for capturing bid-ask spread in market making."""
+
+    def reward(self, quote: Quote, instruments: InstrumentSet,
+               metrics: StepMetrics, hidden: HiddenState, obs: Observation) -> float:
+        return metrics.spread_capture
+
+    def breakdown(self, quote: Quote, instruments: InstrumentSet,
+                  metrics: StepMetrics, hidden: HiddenState, obs: Observation) -> dict[str, float]:
+        return {'spread_capture': metrics.spread_capture}