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Momentum Omega

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 Momentum Omega strategy evaluates momentum magnitude normalized by downside volatility and historical trend stability. It identifies trend continuation opportunities while detecting early momentum exhaustion and trend divergence.

Foundational Economic & Behavioral Rationale: Traditional momentum strategies suffer when high-beta or erratic stocks generate large nominal gains accompanied by severe drawdown risk. Inspired by the Omega Ratio (Keating & Shadwick, 2002), Momentum Omega calculates the mathematical differential between established long-term trend momentum and short-term recent price action, normalized strictly by rolling price volatility.

Market Regime Suitability:

• Optimal Regimes

Mature bull markets, mixed/volatile economic cycles, and quality-driven equity regimes where downside risk mitigation provides alpha.

• Challenging Regimes

Extended low-volatility speculative rallies where high-risk unconstrained momentum temporarily outperforms risk-adjusted factors.

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 Momentum Omega model computes the volatility-normalized differential between established long-term trend returns and short-term recent price action:

$$R_{\text{long}}(t) = \frac{\text{Close}(t - W_{\text{short}}) - \text{Close}(t - W_{\text{long}})}{\text{Close}(t - W_{\text{long}})}$$
$$R_{\text{short}}(t) = \frac{\text{Close}(t - W_{\text{short}}) - \text{Close}(t)}{\text{Close}(t - W_{\text{short}})}$$
$$\sigma_{W_{\text{vol}}}(t) = \sqrt{\frac{1}{W_{\text{vol}}-1} \sum_{k=0}^{W_{\text{vol}}-1} \left(r_{t-k} - \bar{r}\right)^2}$$
$$\text{Score}_{\text{Omega}}(t) = \frac{R_{\text{long}}(t) - R_{\text{short}}(t)}{\sigma_{W_{\text{vol}}}(t) + \epsilon}$$

Default baseline parameters: \(W_{\text{long}} = 252\) days (1 year), \(W_{\text{short}} = 21\) days (1 month), \(W_{\text{vol}} = 126\) days (6 months).

4

Factor Classification & Scoring Thresholds (Model Logic)

The quantitative factor model balances trend strength against volatility risk:

• Positive Factor Classification (+1.0 Score)

Condition: \(\text{Score}_{\text{Omega}}(t) \ge \text{Threshold}_{\text{long}}\) (default \(\ge +0.25\)). Indicates that long-term momentum is robust and short-term volatility-normalized pullback presents a high-probability continuation structure.

• Negative Factor Classification (-1.0 Score)

Condition: \(\text{Score}_{\text{Omega}}(t) \le \text{Threshold}_{\text{short}}\) (default \(\le -0.25\)). Indicates severe short-term parabolic exhaustion or structural downward acceleration.

• Equilibrium / Neutral State (0.0 Score)

Condition: Score falls within the equilibrium band \([-0.25, +0.25]\).

Empirical Research Notes & Case Studies

Real-world historical simulations and market research examining Momentum Omega 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 →