GARCH Volatility : notre 60e strategie utilise les maths pour predire quand le marche se trompe

The market is often wrong about risk.

When Bitcoin drops 5% in a day, everyone panics. Volatility spikes. Fear Index goes to 20. Twitter screams "CRASH!"

But our GARCH model says: "Actually, predicted volatility for tomorrow is lower than what the market currently prices. The fear is overblown."

That's the prediction premium — the gap between what GARCH predicts and what the market shows. When this gap is large enough, and RSI confirms oversold + Bollinger Bands show price at the lower band — we buy.

How GARCH works (simplified)

GARCH(1,1) stands for Generalized Autoregressive Conditional Heteroskedasticity. In plain English:

1. Look at recent price returns (last 60 candles)
2. Calculate how volatile they were (realized volatility)
3. Predict tomorrow's volatility using the formula:
4. σ²_tomorrow = ω + α × (last_return²) + β × (σ²_today)
5. Where α ≈ 0.10 (weight of last shock) and β ≈ 0.85 (persistence of volatility)
6. Compare predicted vs realized
7. Predicted >> Realized = market is calm but GARCH sees risk coming → wait
8. Predicted << Realized = market is panicking but GARCH says it'll calm down → buy the dip

No machine learning. No API calls. No cloud. Pure math running locally on the server, $0 cost.

The strategy in the arena

GARCH Volatility is Strategy #60 in the Strategy Arena, competing live against 59 other AI and quantitative strategies on real Bitcoin data.

Entry conditions (all 3 must be true): - Prediction premium < -1.2 (market overpricing risk) - RSI(14) < 35 (oversold confirmation) - Price below Bollinger lower band (technical confirmation)

Exit conditions: - Take profit: +5% - Stop loss: -2.5% (note: TP > SL = positive expected value) - Trailing stop: 1.5% after +2% gain - Volatility normalization: premium returns to 0

Risk management: - Position size: 65% of capital (not all-in) - Cooldown: 12 candles between trades - Only long (no shorting in the arena)

Inspired by freeCodeCamp

This strategy was inspired by Lachezar Haralampiev's Quant Course on freeCodeCamp, which teaches 3 quantitative strategies including GARCH for intraday trading. We adapted the concept for our 10-minute BTC arena.

How it connects to our systems

GARCH Volatility benefits from the full Strategy Arena intelligence stack:

• Nutrition Filter — once it has enough trades, the filter will grade it and decide if it's healthy enough to teach Meta Intelligence
• Strategy Health Check — graded on 7 frameworks (Risk-Reward, Consistency, Robustness...)
• Evolution Lab — Darwin Engine could evolve GARCH parameters (α, β, thresholds) overnight using the Karpathy autoresearch pattern
• Knowledge Graph — connected to 126 AI nodes

Why quantitative strategies matter

Most strategies in the arena are AI-designed (Claude, Grok, GPT built them). GARCH is different — it's math-designed. No AI decided these parameters. The formula comes from Robert Engle (Nobel Prize 2003) and Tim Bollerslev (1986).

Having both AI and math strategies in the same arena is the whole point: which approach trades better? Watch them compete on the live dashboard.

GARCH Volatility is Strategy #60. It joins the arena with $10,000 virtual capital, zero trades, and a Nobel Prize-winning formula. Let's see how math does against AI.