Momentum Quantitative Methodology - Technosight Documentation

1

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).

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 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:

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

4

Entry & Exit Rules (Trade Execution Logic)

The systematic trade logic for Momentum executes on discrete daily bar close signals:

• Long Entry Trigger (+1.0 Signal)

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.

• Short Entry Trigger (-1.0 Signal)

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.

• Exit / Neutral Filter (0.0 Signal)

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