name	reward-function-hold-bias
description	Fix HOLD bias in RL reward function. Trigger when: (1) model learns to always HOLD, (2) trade rate is too low (<10%), (3) slippage penalty exceeds typical price moves.
author	Claude Code
date	Fri Dec 27 2024 00:00:00 GMT+0000 (Coordinated Universal Time)

Reward Function HOLD Bias Fix (v2.5.0)

Experiment Overview

Item	Details
Date	2024-12-27
Goal	Fix reward function that teaches model HOLD is optimal
Environment	GPU-native PPO training, vectorized_env.py
Status	Success

Context

Training BTCUSD showed the model wasn't finding trading opportunities over a 2-year period. Investigation revealed the reward function had a hidden HOLD bias.

Root Cause: Asymmetric payoff structure made HOLD the rational choice:

Action	Reward If Correct	Reward If Wrong	Expected Value
HOLD	0	0	0 (safe)
BUY/SELL	+0.35 to +0.90	-0.52 (painful)	Negative after costs

The Math for BTCUSD:

Slippage cost: 0.5% (crypto) per trade
5-bar horizon (5 hours at 1H timeframe)
Bitcoin 5-hour volatility: ~0.3-0.5% average
Expected move < slippage cost = negative EV for trading

Root Cause Analysis

Issue	Problem	Impact
Slippage penalty too aggressive	0.5% × 10 scaling = huge penalty	Model avoids trading entirely
Exploration bonus negligible	0.01 × uncertainty = ~0.001	No incentive to try trading
HOLD gets zero reward	HOLD = 0, wrong trade = -0.52	Asymmetric payoff favors HOLD
Direction threshold too strict	0.1% threshold vs 0.5% slippage	Correct predictions still lose money

Verified Workflow

Step 1: Reduce Slippage Cost

# In vectorized_env.py, GPUEnvConfig
slippage_cost_crypto: float = 0.002  # Was 0.005 (0.5% -> 0.2%)
slippage_weight: float = 0.02        # Was 0.05

Step 2: Add Trading Incentive

# In vectorized_env.py, GPUEnvConfig
trading_incentive: float = 0.02  # NEW: Small bonus for non-HOLD actions

# In _calculate_rewards() after drawdown_penalty
trade_executed = (pred_direction != 0).float()
trading_incentive_reward = trade_executed * self.config.trading_incentive

# Add to combined reward
reward = (
    ... existing components ...
    trading_incentive_reward  # v2.5.0 - fix HOLD bias
) * risk_adjustment

Step 3: Increase Exploration Bonus

# In _calculate_rewards(), COMPONENT 5
exploration_bonus = 0.05 * uncertainty  # Was 0.01

Step 4: Increase Direction Threshold

# In _calculate_rewards()
threshold = 0.003  # Was 0.001 (0.3% vs 0.1%)
# Must exceed slippage to be considered profitable

Step 5: Rebalance Reward Weights

# In GPUEnvConfig
direction_weight: float = 0.40       # Was 0.35 - primary signal
magnitude_weight: float = 0.05       # Was 0.10 - noisy component
pnl_weight: float = 0.25             # Keep
stop_tp_weight: float = 0.10         # Was 0.15
exploration_weight: float = 0.15     # Was 0.10
slippage_weight: float = 0.02        # Was 0.05
drawdown_penalty_weight: float = 0.03 # Was 0.05

Failed Attempts (Critical)

Attempt	Why it Failed	Lesson Learned
Switch to 15-min timeframe	Smaller moves, same costs = worse HOLD bias	Fix reward function first, then consider timeframe
Just reduce slippage penalty	Model still biased toward HOLD	Need positive incentive for trading
Large trading incentive (0.1)	Caused overtrading	0.02 is sufficient to break tie
Remove slippage penalty entirely	Model overtrades, ignores costs	Need penalty, just not excessive

Final Parameters

# vectorized_env.py - GPUEnvConfig (v2.5.0)

# Reward weights (8 components)
direction_weight: float = 0.40
magnitude_weight: float = 0.05
pnl_weight: float = 0.25
stop_tp_weight: float = 0.10
exploration_weight: float = 0.15
slippage_weight: float = 0.02
drawdown_penalty_weight: float = 0.03
trading_incentive: float = 0.02  # NEW

# Transaction costs
slippage_cost_crypto: float = 0.002  # Was 0.005
slippage_cost: float = 0.001         # Equity unchanged

# Direction threshold
threshold = 0.003  # Was 0.001 (in _calculate_rewards)

# Exploration bonus multiplier
exploration_bonus = 0.05 * uncertainty  # Was 0.01

Key Insights

Asymmetric payoffs create bias - If HOLD=0 and wrong trade=-X, model learns HOLD is safe. Add small positive reward for trading to balance.
Slippage must be < expected move - If cost to trade > expected profit, rational to never trade. Align slippage with actual broker costs (0.1-0.2%).
Threshold should match slippage - Direction threshold (0.1%) below slippage (0.5%) means "correct" predictions still lose money. Set threshold >= slippage.
Exploration needs real incentive - 0.01 multiplier is negligible. Increase to 0.05 for meaningful exploration bonus.
Test with volatile assets first - BTCUSD has higher volatility, so if model won't trade BTC, it definitely won't trade lower-vol assets.

Expected Behavior After Fix

With the new reward function, expect:

Trade rate: 30-60% (was ~5%)
More balanced signal distribution (BUY/SELL/HOLD)
Model takes trades when expected move > costs
Still respects risk management (drawdown penalty)

References

alpaca_trading/gpu/vectorized_env.py: Lines 388-410 (GPUEnvConfig), 1622-1712 (_calculate_rewards)
alpaca_trading/api/routes/signals.py: Lines 75, 136 (dashboard key fix: 'passed' -> 'pass')
Literature: Risk-Aware RL Reward - multi-component reward design
Skill: reward-scaling-calibration - related fix for reward_scale

reward-function-hold-bias

Install Skill

SKILL.md