discounted-thompson-sampling

name: discounted-thompson-sampling description: "Use when implementing Thompson sampling for non-stationary environments, handling regime changes in bandits, or tuning exploration-exploitation with decay" author: Claude date: 2026-02-06 version: v3.8.0

Discounted Thompson Sampling - Non-Stationary Bandit

Overview

Context

Standard Thompson Sampling assumes stationary reward distributions. In financial markets, reward distributions change with regime shifts (bull -> bear, low -> high volatility).

Old arm statistics become stale after regime changes, causing the bandit to make decisions based on outdated information.

Literature Source:

• "POW-dTS: Policy Weighting via Discounted Thompson Sampling", AI Review (Jul 2025)

Solution: Exponential Discounting

The Discounting Mechanism

Instead of treating all observations equally, we exponentially decay past observations:

# Standard Thompson Sampling
n += 1
mean = mean + (reward - mean) / n

# Discounted Thompson Sampling
effective_n = effective_n * discount + 1.0
sum_rewards = sum_rewards * discount + reward
mean = sum_rewards / effective_n

With discount=0.99:

• Observation from 100 steps ago has weight: 0.99^100 = 0.366
• Observation from 200 steps ago has weight: 0.99^200 = 0.134
• Recent observations dominate the mean estimate

Configuration Parameters

Usage Examples

from alpaca_trading.training.online_bandit import OnlineBandit

# Standard Thompson Sampling (original behavior)
bandit = OnlineBandit([0.5, 1.0, 1.5], discount=1.0)

# Discounted Thompson Sampling for non-stationarity
bandit = OnlineBandit([0.5, 1.0, 1.5], discount=0.99)

# More aggressive discounting for fast regime changes
bandit = OnlineBandit([0.5, 1.0, 1.5], discount=0.95)

Implementation Details

New State Variables

@dataclass
class ArmState:
    value: float
    n: int = 0                # Raw count (for diagnostics)
    mean: float = 0.0
    effective_n: float = 0.0  # Discounted count
    sum_rewards: float = 0.0  # Discounted sum

Update Logic

def update(self, arm_index: int, reward: float):
    a = self.state.arms[int(arm_index)]
    reward = float(reward)

    if self.discount < 1.0:
        # Discounted Thompson Sampling
        a.effective_n = a.effective_n * self.discount + 1.0
        a.sum_rewards = a.sum_rewards * self.discount + reward
        a.mean = a.sum_rewards / max(1.0, a.effective_n)
        a.n += 1  # Keep raw count for diagnostics
    else:
        # Standard Thompson Sampling (original behavior)
        a.n += 1
        a.mean += (reward - a.mean) / a.n
        a.effective_n = float(a.n)
        a.sum_rewards = a.mean * a.n

    self._save_state()

Selection with Effective N

def select_arm(self) -> int:
    samples = []
    for a in self.state.arms:
        # Use effective_n for discounted TS, or regular n for standard TS
        n_effective = a.effective_n if self.discount < 1.0 else float(a.n)
        sd = 1.0 / max(1.0, n_effective)
        samples.append(self.rng.normal(a.mean, sd))
    return int(np.argmax(samples))

New Utility Methods

# Get diagnostic statistics for all arms
stats = bandit.get_arm_stats()
# Returns: [{'value': 0.5, 'n': 10, 'effective_n': 8.2, 'mean': 0.65}, ...]

# Reset a specific arm after regime change
bandit.reset_arm(0)

# Reset all arms
bandit.reset_all()

Why Discounting Works

Regime Change Handling

After a regime change:

• Old observations are down-weighted automatically
• New observations quickly dominate the mean
• Bandit adapts to new reward distribution

Exploration Recovery

With discounting:

• effective_n decreases over time if arm isn't pulled
• Variance 1/effective_n increases
• Unpulled arms become exploratory again

Backward Compatibility

With discount=1.0 (default):

• Behavior identical to standard Thompson Sampling
• Existing state files load correctly
• New fields initialized from legacy data

Choosing the Discount Factor

Half-life = ln(0.5) / ln(discount) ≈ 0.693 / (1 - discount) for discount near 1.

Failed Attempts

Key Insights

Discount Factor Selection

• 0.99: Conservative, for markets with slow regime shifts
• 0.95: Moderate, for markets with monthly regime cycles
• 0.90: Aggressive, for highly volatile markets

Persistence

State files now include:

• effective_n: Discounted observation count
• sum_rewards: Discounted reward sum
• discount: Factor used (for validation)

Old state files are automatically upgraded with backward-compatible defaults.

Variance Scaling

In Thompson Sampling, variance scales as 1/n. With discounting, variance scales as 1/effective_n. This means:

• Older arms have higher variance (more exploration)
• Recently updated arms have lower variance (more exploitation)

Related Skills

• regime-aware-bandit - Strategy-level bandit for regime-based selection
• adaptive-predator-prey - Regime-aware predator-prey dynamics
• integrated-risk-manager - Unified risk management with regime awareness