Why I stopped trading and started tendering with AI

Описание и применение

Автор отказался от предсказания рынка и перешёл к стратегии «тендеринга» с ИИ, используя скоринг на основе скользящих средних и волатильности, а также PPO‑RL для динамической настройки риск‑тольеранса.

Задача

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

Результат

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.

Рабочий процесс

• 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.

Доказательства

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

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

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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.

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