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Trend Following

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 Trend Following strategy exploits persistent directional price trends across multi-month horizons using dual simple moving average crossovers, capturing sustained market moves without predicting market tops or bottoms.

Foundational Economic & Behavioral Rationale: Pioneered by Richard Donchian (1960) and documented across centuries of market history by Hurst, Ooi, and Pedersen (2017, "A Century of Evidence on Trend-Following Investing"). Trend following capitalizes on capital inertia, systematic institutional rebalancing, and fundamental macro trends.

Market Regime Suitability:

• Optimal Regimes

Strong trending bull and bear markets, inflationary cycles, and macro regime transitions.

• Challenging Regimes

Choppy, non-directional sideways consolidation where frequent false crossovers generate whipsaw losses.

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 Trend Following model computes the relative percentage spread between fast and slow rolling Simple Moving Averages:

$$\text{SMA}_{\text{fast}}(t) = \frac{1}{F} \sum_{i=0}^{F-1} \text{Close}(t - i)$$

$$\text{SMA}_{\text{slow}}(t) = \frac{1}{S} \sum_{i=0}^{S-1} \text{Close}(t - i)$$

$$\text{Spread}(t) = \frac{\text{SMA}_{\text{fast}}(t) - \text{SMA}_{\text{slow}}(t)}{\text{SMA}_{\text{slow}}(t)}$$

Standard institutional parameter pairings: Fast period $F = 50$ days, Slow period $S = 200$ days.

Factor Classification & Scoring Thresholds (Model Logic)

The quantitative factor model classifies equities based on directional moving average crossovers:

• Positive Factor Classification (+1.0 Score - Bullish Moving Average Alignment)

Condition: Fast moving average exceeds slow moving average: $\text{SMA}_{\text{fast}}(t) > \text{SMA}_{\text{slow}}(t)$ ($\text{Spread} > 0.0$).

• Negative Factor Classification (-1.0 Score - Bearish Moving Average Alignment)

Condition: Fast moving average falls below slow moving average: $\text{SMA}_{\text{fast}}(t) < \text{SMA}_{\text{slow}}(t)$ ($\text{Spread} < 0.0$).

• Reversal / Shift Threshold

Condition: Factor state shifts immediately upon opposite moving average crossover.

Empirical Research Notes & Case Studies

Real-world historical simulations and market research examining Trend Following 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 →