TREND FOLLOWING (STOCK TRADING) VIA QUANTITATIVE MODELING IN EXCEL – AllQuant
COURSE OVERVIEW
This program provides institutional-grade instruction in trend following methodologies as practiced within hedge fund environments, adapted for implementation via Microsoft Excel. Participants construct a fully operational, live-deployable trend following model through guided Excel implementation, acquiring robust frameworks for generating resilient equity portfolios during bear market regimes.
Core Value Proposition: Acquire a systematic, rules-based investment process requiring approximately five minutes of daily operation. The model architecture emphasizes defensive positioning, adaptive market response, and rigorous performance analytics.
LEARNING OBJECTIVES
Upon completion, participants will demonstrate competency in:
- Quantitative Investment Principles: Distinguishing systematic, data-driven approaches from conventional fundamental and technical analysis paradigms
- Universe Selection Criteria: Applying objective filters to identify equities suitable for trend following protocols
- Trend Signal Construction: Calculating and validating momentum-based entry/exit signals using price time series data
- Risk Metrics Implementation: Computing volatility, drawdown, Sharpe ratio, and risk-adjusted returns using financial mathematics
- Transaction Cost Integration: Modeling slippage, commissions, and market impact within performance simulations
- Leverage Mechanics: Understanding margin requirements, borrowing costs, and position sizing for amplified exposure
- Performance Analytics: Developing tracking dashboards for real-time strategy monitoring and decision support
- Operational Discipline: Maintaining model-driven execution without behavioral interference during market stress
COURSE CONTENT STRUCTURE
Total Duration: Approximately 7 hours across 7 sections
- SECTION 1: INTRODUCTION (15 minutes)
- SECTION 2: CONCEPT OF TREND FOLLOWING (90 minutes)
- Empirical foundations: time-series momentum and serial correlation in equity returns
- Signal generation: moving average crossovers, breakout channels, and volatility-adjusted position sizing
- Portfolio construction: equal-risk contribution versus equal-weight methodologies
- Weaknesses analysis: regime changes, whipsaw environments, and convergence risks
- Defensive characteristics: drawdown mitigation during systematic market declines
- SECTION 3: EXCEL CRASH COURSE (45 minutes)
- Critical functions: VLOOKUP, INDEX/MATCH, array formulas, conditional logic
- Data structuring best practices: time series alignment, handling missing data, dynamic ranges
- Visualizations: building interactive charts for signal visualization and performance attribution
- Error checking and model audit protocols
- SECTION 4: FINANCIAL MATHEMATICS (60 minutes)
- Logarithmic versus arithmetic returns: when and why
- Compounding mechanics and wealth trajectories
- Volatility estimation: rolling standard deviation and exponential weighting
- Sharpe ratio derivation: risk-free rate treatment and annualization conventions
- Drawdown mathematics: peak-to-trough calculations and recovery time analysis
- Leverage modeling: margin multiplication effects and interest expense integration
- SECTION 5: BUILDING THE TREND FOLLOWING MODEL (150 minutes)
- Data acquisition: bulk download procedures from Yahoo Finance API
- Signal engineering: constructing dual moving average crossover systems with adaptive parameters
- Position sizing: volatility targeting and dynamic leverage application
- Trading logic: entry triggers, exit conditions, and rebalance frequency optimization
- Cost integration: incorporating bid-ask spreads and brokerage commissions
- Backtesting engine: vectorized simulation across historical data with survival bias correction
- SECTION 6: TREND FOLLOWING OPERATIONS (45 minutes)
- Daily workflow: data update procedures (5-minute protocol)
- Signal interpretation: translating model output into actionable orders
- Risk monitoring: tracking exposure, leverage utilization, and margin requirements
- Performance logging: maintaining audit trails for continuous improvement
- Scenario analysis: stress-testing against historical crisis periods (2008, 2020)
- SECTION 7: HANDLING DOW COMPONENTS CHANGES (30 minutes)
- Corporate actions: delistings, mergers, spin-offs, and ticker changes
- Database hygiene: maintaining point-in-time accurate constituent lists
- Rebalancing implications: managing positions in exiting/entering securities
DELIVERABLES & RESOURCES
- Fully Completed Model File: Live-ready Excel workbook with all formulas, data connections, and performance dashboards
- Guided Build Templates: Step-by-step worksheets with partial completion to scaffold learning
- Practice Exercises: Financial mathematics problem sets with detailed solution keys
- Bulk Data Tool: VBA-enabled Excel file for automated Yahoo Finance price history retrieval
- Performance Analytics Worksheet: Pre-built metrics calculator for Sharpe ratio, drawdown, and return attribution
- Decision Dashboard: Interactive summary interface for real-time signal extraction
TARGET AUDIENCE PROFILE
- Optimal Fit:
- Investment professionals seeking systematic strategy implementation skills without programming prerequisites
- Self-directed investors managing personal portfolios exceeding ₹25 lakh who require institutional-grade risk management
- Finance students and CFA candidates pursuing practical quantitative modeling experience
- Portfolio managers at family offices and smaller funds constrained by technology budgets
- Suboptimal Fit:
PREREQUISITES & TECHNICAL REQUIREMENTS
- Intellectual Prerequisites:
- Foundational understanding of equity market mechanics (order types, settlements, margin)
- Basic statistics: mean, standard deviation, normal distribution concepts
- Arithmetic and algebraic comfort level sufficient for formula manipulation
- Technical Prerequisites:
- Microsoft Excel 2016 or later (Windows or Mac) with VBA macros enabled
- Stable internet connection for data downloads
- No prior VBA or Python knowledge required
- Software Provision: All analysis conducted using free resources; no mandatory subscriptions to data vendors or analytical platforms
INSTRUCTOR BIOGRAPHIES
ENG GUAN – CO-FOUNDER & LEAD INSTRUCTOR
Eng Guan is a quantitative investment practitioner with 15+ years of experience spanning sovereign wealth funds, investment banks, proprietary trading desks, and multi-strategy hedge funds. His most recent role was as key Portfolio Manager at a Singapore-based multi-strategy hedge fund, where he managed cross-asset systematic strategies with direct P&L responsibility. He holds an MSc in Financial Engineering specializing in derivatives pricing and optimal execution algorithms.
Pedagogical Edge: Direct hedge fund implementation experience ensures instruction reflects operational realities: transaction cost management, leverage constraints, and institutional risk mandates. His sovereign wealth fund background provides long-horizon capital preservation principles essential for strategy viability.
PATRICK LING – CO-FOUNDER & SENIOR INSTRUCTOR
Patrick Ling brings 15+ years of comprehensive investment industry experience across private banking (UBS), investment banking (Goldman Sachs), and hedge fund portfolio management. In his last role as key Portfolio Manager at the same Singapore-based multi-strategy hedge fund, he co-managed systematic equity strategies and developed proprietary risk analytics. He holds an MSc in Wealth Management, integrating quantitative techniques with high-net-worth client portfolio construction.
Pedagogical Edge: Private banking experience translates complex quantitative concepts into executable processes for non-institutional investors. His hedge fund tenure provides insight into multi-strategy portfolio integration and factor diversification—critical context for preventing over-reliance on trend following as a single alpha source.
Joint Credibility: Both instructors maintain parallel careers in quantitative finance, ensuring curriculum evolves with current practitioner standards rather than academic abstraction.
METHODOLOGICAL APPROACH
The course employs a "build-operate-improve" framework. Participants first construct a simplified version of the model, operate it through historical simulations, then iteratively enhance complexity (adding leverage, transaction costs, and dynamic universe selection). Each section concludes with validation checkpoints where learners test their model against known outcomes before proceeding.
Instruction explicitly addresses why trend following works (behavioral biases, institutional frictions) and when it fails (regime shifts, central bank interventions). This prevents blind faith in backtested results and cultivates adaptive mindset essential for long-term strategy viability.
Time Commitment Structure: While total video instruction is 7 hours, practical implementation requires an estimated additional 4-6 hours of independent model building and data validation to achieve deployment readiness. The five-minute daily operation time assumes model is fully constructed and stable.
STRATEGY SCOPE & LIMITATIONS
Geographic Application: The explicit model is calibrated for U.S. equities (Dow Jones components) to ensure data availability and pedagogical clarity. However, the mathematical architecture is jurisdiction-agnostic; participants can adapt the framework to Nifty 50, FTSE constituents, or other liquid equity universes with available price data.
Asset Class Constraints: Course focuses exclusively on single-stock trend following; does not cover futures trend following, commodity trading advisors (CTAs), or cross-asset momentum. Fixed income, currency, and commodity applications require separate parameter calibration not addressed in core curriculum.
Performance Expectations: The model is designed for risk mitigation first, return enhancement second. Participants should expect drawdown reduction of 20-30% versus buy-and-hold during bear markets, with modest outperformance (1-3% annually) in secular uptrends. The strategy is not engineered for absolute return generation in flat or rising volatility regimes.
BOTTOM-LINE ASSESSMENT
This program delivers exactly what it specifies: a hedge fund-validated trend following system built entirely in Excel, with no hidden costs or technical barriers. The instructors' practitioner credentials provide rare authenticity, and the curriculum structure supports genuine implementation rather than theoretical understanding.
Primary limitation is the modest edge of trend following itself—not instruction quality. For investors seeking systematic discipline and downside protection rather than speculative alpha, this represents a professionally rigorous, operationally viable solution.
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