Why I stopped trading and started tendering with AI

说明与用途

作者从预测交易转向 AI 驱动的投标策略，使用移动平均和波动率评分，并通过 PPO 强化学习动态调整风险容忍度。

问题

Consistently predicting market movements is difficult and fragile; small changes in volatility, regulation, or news can break trading strategies.

结果

The transition from prediction‑centric trading to AI‑driven tendering reduced sensitivity to market regime changes, introduced risk‑aware scoring, and leveraged RL to adapt risk tolerance, potentially improving sustainability and profitability.

工作流

• 1. Compute short and long moving averages from price data.
• 2. Calculate 20‑day volatility as a risk indicator.
• 3. Assign a tender score: reward higher volatility on buy signals, penalize on sell signals.
• 4. Compare score to risk_tolerance to decide BUY, SELL, or HOLD.
• 5. Return signal and score for further filtering.
• 6. Use a PPO RL agent to predict adjustments to risk_tolerance based on market state features.
• 7. Execute trades with position size scaled by tender score when signal is BUY or SELL.

证据

I poured countless hours (and frankly, too much money) into building systems that promised to consistently outperform the market.

来源

Instead of predicting the market, I started focusing on responding to it in a more strategic, less frantic way.

来源

I used a Proximal Policy Optimization (PPO) agent, trained on historical market data, to dynamically adjust the risk_tolerance and weighting of the tender score components.

来源