Volatility Breakout Quantitative Methodology

1

Strategy Objective, Rationale & Market Regimes

Investment Objective: The Volatility Breakout strategy detects explosive directional price movements escaping tight consolidation channels, adjusted dynamically by Average True Range (ATR) volatility.

Foundational Economic & Behavioral Rationale: Based on the volatility expansion principle developed by J. Welles Wilder (1978, "New Concepts in Technical Trading Systems"). Financial time-series exhibit volatility clustering (Mandelbrot, 1963; Engle, 1982): periods of low volatility compress price into tight ranges, which inevitably resolve in violent, high-velocity directional breakouts.

Market Regime Suitability:

• Optimal Regimes

Transition periods from low-volatility compression to high-volatility expansion, earnings breakout cycles, and catalyst-driven momentum.

• Challenging Regimes

Low-liquidity erratic whip patterns and false breakout traps in tight trading ranges.

2

Market Data Inputs

The quantitative engine processes high-fidelity financial market data stored in high-performance QuestDB time-series tables across dual resolution layers:

1. End-of-Day (EOD) OHLCV Dataset (Primary)

Primary data source utilized for indicator calculations, mathematical factor modeling, and primary trade signal generation.

• Open, High, Low, Close (OHLC): Split & dividend adjusted.
• Volume: Total daily traded share volume.
• Universe Coverage: Active US equities spanning Russell 3000 and S&P 500.

2. 15-Minute Intraday Bar Dataset (Auxiliary)

High-frequency intraday bars utilized for auxiliary multi-timeframe confirmation and higher-timeframe regime alignment.

• 15m Interval Bars: Intraday pricing sequence.
• EMA(50, 200, 800) 15m: Triple Exponential Moving Average pattern detection.
• Purpose: Auxiliary signal evaluation and execution timing filter.

3

Indicators and Mathematical Formulations

The Volatility Breakout model establishes dynamic price channels expanded by multiples of the Average True Range (ATR):

$$\text{TR}(t) = \max\left(\text{High}_t - \text{Low}_t, |\text{High}_t - \text{Close}_{t-1}|, |\text{Low}_t - \text{Close}_{t-1}|\right)$$

$$\text{ATR}_K(t) = \frac{1}{K} \sum_{i=0}^{K-1} \text{TR}(t - i)$$

$$\text{Mid}_K(t) = \frac{\max_{i=1}^K(\text{Close}_{t-i}) + \min_{i=1}^K(\text{Close}_{t-i})}{2}$$

$$\text{UpperBand}(t) = \text{Mid}_K(t) + \left(M \times \text{ATR}_K(t)\right)$$

$$\text{LowerBand}(t) = \text{Mid}_K(t) - \left(M \times \text{ATR}_K(t)\right)$$

Default lookback window $K = 20$ days and ATR multiplier $M = 2.0$.

4

Entry & Exit Rules (Trade Execution Logic)

The systematic trade logic for Volatility Breakout triggers on channel boundary breaches:

• Long Entry Trigger (+1.0 Signal - Upside Breakout)

Condition: $\text{Close}(t) > \text{UpperBand}(t) = \text{Mid}_K(t) + \left(M \times \text{ATR}_K(t)\right)$. Price breaks above the dynamic volatility channel.

• Short Entry Trigger (-1.0 Signal - Downside Breakdown)

Condition: $\text{Close}(t) < \text{LowerBand}(t) = \text{Mid}_K(t) - \left(M \times \text{ATR}_K(t)\right)$. Price breaks below the dynamic volatility channel.

• Exit / Neutral Trigger (0.0 Signal)

Condition: Price pulls back inside the normal volatility channel ($\text{LowerBand}(t) \le \text{Close}(t) \le \text{UpperBand}(t)$).