Momentum Omega Quantitative Methodology - Technosight Documentation

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 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 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

Entry & Exit Rules (Trade Execution Logic)

The systematic trade logic for Momentum Omega balances trend strength against volatility risk:

• Long Entry Trigger (+1.0 Signal)

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 entry.

• Short Entry Trigger (-1.0 Signal)

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.

• Exit / Neutral Filter (0.0 Signal)

Condition: Score falls within the equilibrium band $[-0.25, +0.25]$.