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Volatility Breakout

Technical Specification, Mathematical Formulation, Market Data Inputs, and Quantitative Factor Logic.

Reports & Scenarios Guide →
Strategy Docs: Momentum Momentum Omega Mean Reversion Statistical Arbitrage Trend Following Volatility Breakout
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 quantitative model scoring.

  • • 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 regime evaluation and multi-timeframe confirmation 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

Factor Classification & Scoring Thresholds (Model Logic)

The quantitative factor model classifies equities based on channel boundary breaches:

• Positive Factor Classification (+1.0 Score - Upside Expansion)

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.

• Negative Factor Classification (-1.0 Score - Downside Compression)

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.

• Equilibrium / Neutral State (0.0 Score - Within Band)

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

Empirical Research Notes & Case Studies

Real-world historical simulations and market research examining Volatility Breakout factor performance.

Backtesting 15 min read

Signals and themes from quant strategies after last week’s earnings: FTNT, F5 and healthcare

We reviewed 300 signals from our six quant strategies together. Strategies split into two families (trend and reversion) that read last week’s earnings in opposite ways. The trend family (Momentum, Momentum Omega, Trend Following, Volatility Breakout) is buying the stocks that jumped, such as Grid Dynamics (+24.5% on 30 Oct) and Regeneron (+11.5%). The reversion family (Mean Reversion, Statistical Arbitrage) is shorting them and buying the stocks that fell, such as Reddit (−22%). The most valuable signals are the ones both families back: F5 and Fortinet, each long across both families with no votes against them, each down about 20% after beating on the quarter and guiding to slower growth. Beyond the stocks, two themes stand out: healthcare catalysts lead momentum, and the market is selling companies that guide lower or pay heavily for AI.

Nov 02, 2026 Read Analysis →
Semiconductors 12 min read

Intel Inc (INTC) earnings: data center jumps 78%, but Q4 guidance cut triggers 25% sell-off

Intel’s Q3 print delivered a stark contrast between a surging Data Center business (+78% YoY to $7.3B) and a severe guidance reset. While Q3 beat top- and bottom-line estimates ($16.5B revenue, $0.52 non-GAAP EPS), Q4 revenue guidance ($15.0B–$16.0B vs. $17.1B consensus) sent shares down ~25% pre-market. Management highlighted supply bottlenecks in substrate packaging, a memory-driven PC downturn running into H1 2027, and a $300M revenue hit from the Fab 28 explosion in Israel. With capex ramping for 18A/14A and 2027 expected to be cash-flow negative, we adjust the score from -2/10 to -1/10 Neutral.

Oct 30, 2026 Read Analysis →