Home / Strategies / Momentum / Quantitative Methodology

Momentum

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

Strategy Docs: Momentum Momentum Omega Mean Reversion Statistical Arbitrage Trend Following Volatility Breakout

Strategy Objective, Rationale & Market Regimes

Investment Objective: The Momentum strategy identifies and systematically allocates capital to US equities exhibiting robust historical price outperformance over medium-to-long term lookback horizons, seeking to capture excess returns driven by trend continuation.

Foundational Economic & Behavioral Rationale: Documented extensively by Jegadeesh and Titman (1993, "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency") and Asness, Moskowitz, and Pedersen (2013, "Value and Momentum Everywhere"), momentum is one of the most pervasive anomalies in modern finance. The anomaly stems from two core investor biases:

Underreaction: Initial underreaction by market participants to positive fundamental surprises and accelerating earnings growth.
Delayed Overreaction & Herd Behavior: Institutional capital gradually follows rising prices, creating sustained serial positive autocorrelation in returns.

Market Regime Suitability:

• Optimal Regimes

Sustained bull markets, economic expansions, growth-driven macro cycles, and high dispersion equity environments.

• Challenging Regimes

Sharp macro inflection points, sudden liquidity shocks, and high-frequency volatile range consolidations (momentum crashes).

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.

Indicators and Mathematical Formulations

The Momentum model calculates cumulative historical price return over an $N$-day lookback horizon:

$$R_N(t) = \frac{\text{Close}(t) - \text{Close}(t - N)}{\text{Close}(t - N)}$$

Where $N \in \{252, 189, 126, 63, 42\}$ represents trading day lookbacks across parametric sweep configurations.

Cross-Sectional Percentile Normalization: At each timestamp $t$ across the active universe of assets $\mathcal{U}$, raw returns are ranked into quantiles:

$$q_i(t) = \text{QuantileRank}(R_{N, i}(t), \mathcal{U}(t)) \in [0.0, 1.0]$$

Factor Classification & Scoring Thresholds (Model Logic)

The quantitative factor model classifies equities into categorical states based on objective mathematical criteria:

• Positive Factor Classification (+1.0 Score)

Condition: The asset's $N$-day cumulative return $R_N(t)$ ranks in the top 20th percentile ($q \ge 0.80$) cross-sectionally across the active universe, or satisfies $R_N(t) > 0$ under directional absolute mode.

• Negative Factor Classification (-1.0 Score)

Condition: The asset's $N$-day cumulative return $R_N(t)$ ranks in the bottom 20th percentile ($q \le 0.20$), or satisfies $R_N(t) < 0$ under directional absolute mode.

• Equilibrium / Neutral State (0.0 Score)

Condition: When an asset's percentile rank falls within the neutral middle band ($0.20 < q < 0.80$), the score is set to Neutral ($0.0$).

Empirical Research Notes & Case Studies

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

All Research Articles →

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 →