Which training environments is this compatible with?

While designed for GPU-native PPO training in vectorized environments, the reward logic can be applied to any RL framework where trading frequency is lower than desired.

What are the recommended slippage settings for crypto?

The skill suggests reducing slippage costs from 0.5% to 0.2% and lowering the slippage weight to 0.02 to prevent the penalty from overwhelming the learning signal.

What is HOLD bias in RL trading?

HOLD bias occurs when an RL model learns that 'doing nothing' is the most rational action because the costs of trading (like slippage) consistently outweigh the expected rewards, resulting in a model that never enters positions.

How does this skill fix low trade rates?

It implements a multi-step fix: reducing excessive slippage penalties, adding a small 'trading incentive' bonus for non-HOLD actions, and increasing the exploration bonus to encourage the model to find profitable signals.

RL Trading Reward Function Optimizer

Name: RL Trading Reward Function Optimizer
Author: smith6jt-cop

bysmith6jt-cop

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데이터 과학 및 ML

Eliminates HOLD bias in reinforcement learning trading models by calibrating reward functions and slippage penalties.

This skill provides specialized logic and parameter configurations to fix the common 'HOLD bias' in Reinforcement Learning (RL) trading agents. It specifically addresses scenarios where models learn that staying out of the market is the only rational choice due to asymmetric payoffs, excessive slippage penalties, or insufficient exploration incentives. By adjusting trading incentives, direction thresholds, and rebalancing reward weights, it helps models identify profitable entry points and achieve realistic trade rates (30-60%) in volatile environments like BTCUSD.

주요 기능

01Multi-component reward weight rebalancing for PPO training

02Automated calibration of slippage and transaction cost penalties

030 GitHub stars

04Introduction of trading incentives to balance asymmetric payoffs

05Directional threshold optimization to ensure profit exceeds costs

06Dynamic exploration bonus scaling based on model uncertainty

사용 사례

01Correcting models with low trade rates (<10%) in volatile markets

02Fine-tuning reward functions to prevent 'rational' inactivity in RL agents

03Optimizing GPU-native PPO training environments for crypto trading

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add smith6jt-cop/skills_registry reward-function-hold-bias

For use in Claude.ai and ChatGPT

Download Skill