Reward Function v3.3.0 - Risk-Aware Composite Reward
Overview
| Item | Details |
|---|---|
| Date | 2025-01-21 |
| Goal | Fix reward hacking and P&L gradient saturation |
| Files | alpaca_trading/gpu/vectorized_env.py, alpaca_trading/gpu/ppo_trainer_native.py |
| Status | Success |
Context
v2.5.0 introduced trading_incentive (+0.02 for non-HOLD actions) to fix HOLD bias.
However, this created the opposite problem: reward hacking via overtrading.
Additional issues:
- •Direction weight (40%) dominated despite being binary (+1/-1)
- •P&L weight (25%) too low despite being the actual objective
- •P&L saturated via
tanh(pnl * 10)- destroyed gradient signal beyond ±0.1% moves - •Exploration was limited due to low entropy coefficient (0.005)
Solution: Rebalanced Risk-Aware Reward
Weight Changes
| Component | v2.5.0 | v3.3.0 | Rationale |
|---|---|---|---|
| direction_weight | 0.40 | 0.15 | Binary signal, shouldn't dominate |
| pnl_weight | 0.25 | 0.40 | Primary trading objective |
| magnitude_weight | 0.05 | 0.05 | Unchanged |
| exploration_weight | 0.15 | 0.15 | Unchanged |
| slippage_cost | 0.02 | 0.02 | Unchanged |
| drawdown_penalty | 0.03 | 0.03 | Unchanged |
| stop_tp_weight | 0.10 | 0.10 | Unchanged |
| trading_incentive | 0.02 | REMOVED | Caused reward hacking |
P&L Reward Fix
python
# OLD (v2.5.0) - saturates quickly, destroys gradients pnl_reward = torch.tanh(pnl * 10) # Clips at ±1 for |pnl| > 0.1% # NEW (v3.3.0) - linear with wider range, preserves gradients pnl_reward = torch.clamp(pnl * 100, -2.0, 2.0) # Linear until ±2% move
Why this matters:
- •
tanh(pnl * 10)reaches 0.999 at pnl=0.3% - no gradient for larger moves - •
clamp(pnl * 100, -2, 2)preserves linear gradient until ±2% moves - •Larger moves still get proportionally larger rewards
PPO Hyperparameters
| Parameter | v2.5.0 | v3.3.0 | Rationale |
|---|---|---|---|
| gamma | 0.995 | 0.991 | Shorter horizon for hourly trading |
| ent_coef | 0.005 | 0.015 | 3x increase for exploration |
Code Location
python
# vectorized_env.py - GPUEnvConfig dataclass (~line 479)
@dataclass
class GPUEnvConfig:
# v3.3.0: Risk-Aware Composite Reward weights
direction_weight: float = 0.15 # Reduced from 0.40
magnitude_weight: float = 0.05 # Unchanged
pnl_weight: float = 0.40 # Increased from 0.25
exploration_weight: float = 0.15 # Unchanged
slippage_cost_equity: float = 0.001 # Unchanged
slippage_cost_crypto: float = 0.002 # Unchanged
drawdown_penalty: float = 0.03 # Unchanged
stop_tp_weight: float = 0.10 # Unchanged
# REMOVED: trading_incentive (caused reward hacking)
python
# ppo_trainer_native.py - NativePPOConfig dataclass (~line 120)
@dataclass
class NativePPOConfig:
gamma: float = 0.991 # v3.3.0: Reduced from 0.995
ent_coef: float = 0.015 # v3.3.0: 3x increase from 0.005
Failed Attempts
| Attempt | Why it Failed | Lesson Learned |
|---|---|---|
| trading_incentive = 0.02 | Created overtrading, model collected bonus constantly | Remove entirely, use proper P&L weighting |
| tanh(pnl * 10) | Gradients vanish at ±0.1%, couldn't learn from larger moves | Use linear clamp with wider range |
| direction_weight = 0.40 | Binary signal dominated continuous P&L | Reduce to 0.15, prioritize P&L |
| ent_coef = 0.005 | Insufficient exploration, model converged to local optima | 3x increase to 0.015 |
| gamma = 0.995 | Too long horizon for hourly trading decisions | Reduce to 0.991 for shorter horizon |
Key Insights
Trading Incentive Creates Reward Hacking
The model learned: "Any trade gives +0.02, slippage penalty is ~0.02, so trade constantly and hope for winners." This is classic reward hacking - optimizing the surrogate (incentive) rather than the true objective (P&L).
P&L Should Be Primary Objective
Direction is a weak proxy - being "right" about direction but wrong about magnitude loses money. P&L directly measures what we want: profit.
Entropy Coefficient Balances Exploration
- •Too low (0.005): Model converges quickly but to local optima
- •Too high (>0.05): Model explores randomly, never converges
- •Sweet spot (0.015): Enough exploration to find good strategies
Gamma Affects Time Horizon
- •gamma=0.995 means 50% credit at step 139 (hourly = 6+ days)
- •gamma=0.991 means 50% credit at step 77 (hourly = 3+ days)
- •Shorter horizon more appropriate for hourly trading
Related Skills
- •
reward-function-hold-bias- Historical context on HOLD bias fix - •
reward-scaling-calibration- reward_scale=0.001 calibration - •
training-improvements-v245- LR warmup and validation improvements