DESCRIPTION:
View attachment Allquant-Asset Allocation.mp4
Previously known as ALL-WEATHER INVESTING VIA QUANTITATIVE MODELING IN EXCEL – AllQuant
COURSE OVERVIEW
This program institutionalizes risk parity portfolio construction—the methodology pioneered by Ray Dalio at Bridgewater Associates—into a deployable Excel-based system. Participants build an all-weather portfolio model that dynamically allocates capital across asset classes based on risk contribution rather than capital weighting. The curriculum emphasizes resilience during market dislocations while requiring approximately five minutes of daily operation time. No programming, chart reading, or continuous market monitoring is required.
Core Value Proposition: Construct a quantitative, risk-balanced portfolio that delivers equity-like returns with 30-40% lower volatility than traditional 60/40 allocations, using only free data sources and Excel.
LEARNING OBJECTIVES
Upon completion, participants will demonstrate competency in:
Risk Parity Mathematics: Equalizing marginal risk contributions across asset classes rather than using nominal weights
All-Weather Portfolio Theory: Building allocations that perform across inflation, deflation, growth, and recession regimes
Quantitative Investing Framework: Distinguishing systematic, risk-based allocation from conventional capital-weighted approaches
Leverage Mechanics: Applying prudent leverage to low-volatility assets to achieve risk-balanced target returns
Rebalancing Optimization: Calendar-based versus drift-based rebalancing triggers
Transaction Cost Integration: Modeling ETF expense ratios and trade commissions for realistic performance attribution
Performance Metrics: Computing Sharpe ratio, risk-adjusted returns, and maximum drawdown for leveraged portfolios
VBA Automation: Scripting data updates and weight optimization to minimize manual intervention
COURSE CONTENT STRUCTURE
Total Duration: Approximately 8 hours across 7 sections
SECTION 1: INTRODUCTION (30 minutes)Pitfalls of buy-and-hold and traditional 60/40 allocations
Risk parity philosophy: why equal risk matters more than equal capital
Course roadmap and performance expectations
SECTION 2: CONCEPT OF ALL-WEATHER INVESTING (90 minutes)Bridgewater's all-weather framework: four economic regimes (rising/falling growth, rising/falling inflation)
Asset class behavior across regimes: equities, bonds, commodities, gold
Risk contribution versus capital contribution: mathematical intuition
Leverage rationale: why borrowing to increase low-risk asset exposure is rational
Weaknesses analysis: interest rate regime changes, leverage-induced drawdowns
SECTION 3: EXCEL CRASH COURSE (60 minutes)Critical functions: VLOOKUP, INDEX/MATCH, array formulas, conditional logic
Data structuring for time-series analysis across multiple asset classes
VBA fundamentals: recording and editing macros for automation
Error checking protocols for leveraged portfolio models
SECTION 4: FINANCIAL MATHEMATICS (90 minutes)Logarithmic returns for volatility calculation
Rolling volatility and correlation matrix estimation
Risk contribution calculation: covariance matrix decomposition
Leverage modeling: margin requirements, borrowing costs, leverage drag
Sharpe ratio derivation: handling leveraged portfolios and risk-free rates
SECTION 5: BUILDING THE ALL-WEATHER RISK PARITY MODEL (180 minutes)Data acquisition: Yahoo Finance bulk download for equity, bond, commodity ETFs
Covariance matrix construction: rolling 60-day window optimization
Risk budget calibration: equalizing 25% risk contribution per asset class
Leverage implementation: applying 2:1 leverage to bond positions via mathematical modeling
Rebalancing logic: 10% drift threshold versus calendar-month rebalancing
Transaction cost integration: ETF expense ratios (0.05-0.15%) and commission drag
SECTION 6: RISK PARITY OPERATIONS (30 minutes)Daily workflow: 5-minute data update and signal verification protocol
Risk monitoring: tracking leverage utilization and margin cushion
Performance logging: separating beta returns from risk parity alpha
Crisis protocol: deleveraging triggers during correlation breakdown (e.g., March 2020)
SECTION 7: BONUS: VBA SCRIPTS (30 minutes)Automated data refresh: button-click update across all asset classes
Weight optimization: Solver automation for risk budget rebalancing
Error handling: managing missing data and corporate actions
DELIVERABLES & RESOURCES
Fully Completed Model File: Live-ready Excel workbook with risk parity allocation engine, covariance matrix calculator, and leverage monitor
VBA Automation Scripts: Pre-written macros for daily data updates and weight optimization
Guided Build Templates: Step-by-step worksheets with partial completion scaffolding
Practice Exercises: Risk contribution mathematics problem sets with detailed solutions
Performance Analytics Worksheet: Portfolio-level Sharpe ratio, drawdown, and risk-adjusted return calculator
Decision Dashboard: Interactive interface showing current weights, target risk budgets, and rebalancing alerts
TARGET AUDIENCE PROFILE
Optimal Fit:
Investment advisors constructing resilient portfolios for high-net-worth clients
Portfolio managers at family offices seeking institutional-grade asset allocation without Bloomberg terminals
Sophisticated self-directed investors managing ₹50+ lakh portfolios requiring systematic risk management
Risk officers evaluating portfolio construction frameworks for pension funds or endowments
Quantitative analysts building strategic asset allocation models for asset management firms
Suboptimal Fit:
Individuals seeking high-return stock picking strategies (focus is risk reduction)
Participants uncomfortable with mathematical modeling of leverage and correlation
Investors unable to maintain daily 5-minute discipline (model requires consistent updates)
Traders requiring intraday tactical signals (strategy operates on daily/monthly frequencies)
PREREQUISITES & TECHNICAL REQUIREMENTS
Intellectual Prerequisites:
Portfolio theory: understanding of correlation, covariance, efficient frontier concepts
Statistics: mean, standard deviation, percentile calculations
Derivatives basics: ETF structures, margin mechanics (for leverage module)
Algebraic comfort: matrix multiplication concepts (simplified in Excel)
Technical Prerequisites:
Microsoft Excel 2016 or later with VBA macros enabled
Stable internet connection for daily Yahoo Finance data retrieval
No prior VBA or programming knowledge required
Software Provision: All analysis uses free resources; no mandatory data subscriptions
INSTRUCTOR BIOGRAPHIES
ENG GUAN – CO-FOUNDER & LEAD INSTRUCTOR
Quantitative investment practitioner with 15+ years spanning sovereign wealth funds, investment banks, proprietary trading desks, and multi-strategy hedge funds. Most recent role: key Portfolio Manager at a Singapore-based multi-strategy hedge fund, managing cross-asset systematic strategies with direct P&L responsibility. Holds 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, institutional risk mandates. Sovereign wealth fund background provides long-horizon capital preservation principles essential for strategic asset allocation.
PATRICK LING – CO-FOUNDER & SENIOR INSTRUCTOR
15+ years comprehensive investment industry experience across private banking (UBS), investment banking (Goldman Sachs), and hedge fund portfolio management. As a key Portfolio Manager at the same Singapore-based multi-strategy hedge fund, he co-managed systematic equity strategies and developed proprietary risk analytics. Holds MSc in Wealth Management integrating quantitative techniques with high-net-worth client portfolio construction.
Pedagogical Edge: Private banking experience translates quantitative allocation concepts into executable processes for non-institutional investors. Hedge fund tenure provides insight into multi-strategy portfolio integration and factor diversification—critical context for preventing over-leverage in risk parity implementation.
Joint Credibility: Both instructors maintain parallel practitioner careers, ensuring curriculum evolves with current industry standards rather than academic abstraction.
METHODOLOGICAL APPROACH
The course employs a "build-operate-stress test" framework. Participants construct the risk parity model, operate it through four economic regimes (1990s inflation, 2000s growth, 2008 crisis, 2020 pandemic), then stress-test leverage limits and correlation breakdowns. Each section concludes with scenario validation: what happens to the model during bond bear markets or equity crashes.
Instruction emphasizes risk-based thinking over return forecasting, teaching participants to view assets through marginal risk contribution lens rather than expected return lens.
Time Commitment: Video instruction totals 8 hours; practical implementation requires estimated additional 4-6 hours for independent model building and parameter calibration. Five-minute daily operation assumes stable model and reliable data feeds.
STRATEGY SCOPE & LIMITATIONS
Geographic Application: Explicit model calibrated for U.S. asset classes (equity ETFs: SPY, QQQ; bond ETFs: TLT, IEF; commodities: GLD, DBC) to ensure data availability. Mathematical architecture is transferable to Indian asset classes (Nifty 50 ETFs, Gilt funds, Gold ETFs) where historical data exists.
Capacity Considerations: Risk parity portfolios require minimum capital of ₹25 lakh for effective implementation across multiple ETFs while keeping transaction costs below 0.10% annually.
Performance Expectations: Model targets 10-12% annual returns with 10-12% volatility (Sharpe ratio ≈ 0.9-1.0), significantly improving on traditional 60/40's 8-10% returns with 12-15% volatility. During 2008 and 2020 crises, backtests show drawdowns of 15-20% versus 40-50% for 60/40 portfolios.
Key Limitations:
Leverage risk: Bond leverage creates 30-40% faster drawdowns during interest rate spikes (e.g., 2022 bond bear market)
Correlation breakdown: During liquidity crises, all asset correlations → 1.0, temporarily defeating risk parity diversification
Regime dependency: Strategy underperforms during strong equity bull markets when 100% equity allocation would dominate
BOTTOM-LINE ASSESSMENT
This program delivers exactly what it specifies: a hedge fund-caliber risk parity allocation system built entirely in Excel, with VBA automation and crisis-tested risk controls. The instructors' practitioner credentials provide rare authenticity, and the curriculum addresses the most dangerous aspects of risk parity (leverage, correlation breakdown) rather than presenting a sanitized version.
Critical Differentiator: Unlike academic risk parity courses, this explicitly teaches leverage implementation and contingency planning for correlation failures—the two factors that separate successful institutional implementation from retail disasters.
For investment advisors, portfolio managers, and sophisticated HNW investors seeking systematic, risk-balanced allocation without Bloomberg or Python infrastructure, this represents a professionally rigorous, operationally viable solution. The primary risk is behavioral: maintaining leveraged bond positions during rate spikes requires conviction that most retail investors lack.
SOURCE: purchase from a reseller
Original seller site:
https://www.allquant.co/
View attachment Allquant-Asset Allocation.mp4
Previously known as ALL-WEATHER INVESTING VIA QUANTITATIVE MODELING IN EXCEL – AllQuant
COURSE OVERVIEW
This program institutionalizes risk parity portfolio construction—the methodology pioneered by Ray Dalio at Bridgewater Associates—into a deployable Excel-based system. Participants build an all-weather portfolio model that dynamically allocates capital across asset classes based on risk contribution rather than capital weighting. The curriculum emphasizes resilience during market dislocations while requiring approximately five minutes of daily operation time. No programming, chart reading, or continuous market monitoring is required.
Core Value Proposition: Construct a quantitative, risk-balanced portfolio that delivers equity-like returns with 30-40% lower volatility than traditional 60/40 allocations, using only free data sources and Excel.
LEARNING OBJECTIVES
Upon completion, participants will demonstrate competency in:
Risk Parity Mathematics: Equalizing marginal risk contributions across asset classes rather than using nominal weights
All-Weather Portfolio Theory: Building allocations that perform across inflation, deflation, growth, and recession regimes
Quantitative Investing Framework: Distinguishing systematic, risk-based allocation from conventional capital-weighted approaches
Leverage Mechanics: Applying prudent leverage to low-volatility assets to achieve risk-balanced target returns
Rebalancing Optimization: Calendar-based versus drift-based rebalancing triggers
Transaction Cost Integration: Modeling ETF expense ratios and trade commissions for realistic performance attribution
Performance Metrics: Computing Sharpe ratio, risk-adjusted returns, and maximum drawdown for leveraged portfolios
VBA Automation: Scripting data updates and weight optimization to minimize manual intervention
COURSE CONTENT STRUCTURE
Total Duration: Approximately 8 hours across 7 sections
SECTION 1: INTRODUCTION (30 minutes)Pitfalls of buy-and-hold and traditional 60/40 allocations
Risk parity philosophy: why equal risk matters more than equal capital
Course roadmap and performance expectations
SECTION 2: CONCEPT OF ALL-WEATHER INVESTING (90 minutes)Bridgewater's all-weather framework: four economic regimes (rising/falling growth, rising/falling inflation)
Asset class behavior across regimes: equities, bonds, commodities, gold
Risk contribution versus capital contribution: mathematical intuition
Leverage rationale: why borrowing to increase low-risk asset exposure is rational
Weaknesses analysis: interest rate regime changes, leverage-induced drawdowns
SECTION 3: EXCEL CRASH COURSE (60 minutes)Critical functions: VLOOKUP, INDEX/MATCH, array formulas, conditional logic
Data structuring for time-series analysis across multiple asset classes
VBA fundamentals: recording and editing macros for automation
Error checking protocols for leveraged portfolio models
SECTION 4: FINANCIAL MATHEMATICS (90 minutes)Logarithmic returns for volatility calculation
Rolling volatility and correlation matrix estimation
Risk contribution calculation: covariance matrix decomposition
Leverage modeling: margin requirements, borrowing costs, leverage drag
Sharpe ratio derivation: handling leveraged portfolios and risk-free rates
SECTION 5: BUILDING THE ALL-WEATHER RISK PARITY MODEL (180 minutes)Data acquisition: Yahoo Finance bulk download for equity, bond, commodity ETFs
Covariance matrix construction: rolling 60-day window optimization
Risk budget calibration: equalizing 25% risk contribution per asset class
Leverage implementation: applying 2:1 leverage to bond positions via mathematical modeling
Rebalancing logic: 10% drift threshold versus calendar-month rebalancing
Transaction cost integration: ETF expense ratios (0.05-0.15%) and commission drag
SECTION 6: RISK PARITY OPERATIONS (30 minutes)Daily workflow: 5-minute data update and signal verification protocol
Risk monitoring: tracking leverage utilization and margin cushion
Performance logging: separating beta returns from risk parity alpha
Crisis protocol: deleveraging triggers during correlation breakdown (e.g., March 2020)
SECTION 7: BONUS: VBA SCRIPTS (30 minutes)Automated data refresh: button-click update across all asset classes
Weight optimization: Solver automation for risk budget rebalancing
Error handling: managing missing data and corporate actions
DELIVERABLES & RESOURCES
Fully Completed Model File: Live-ready Excel workbook with risk parity allocation engine, covariance matrix calculator, and leverage monitor
VBA Automation Scripts: Pre-written macros for daily data updates and weight optimization
Guided Build Templates: Step-by-step worksheets with partial completion scaffolding
Practice Exercises: Risk contribution mathematics problem sets with detailed solutions
Performance Analytics Worksheet: Portfolio-level Sharpe ratio, drawdown, and risk-adjusted return calculator
Decision Dashboard: Interactive interface showing current weights, target risk budgets, and rebalancing alerts
TARGET AUDIENCE PROFILE
Optimal Fit:
Investment advisors constructing resilient portfolios for high-net-worth clients
Portfolio managers at family offices seeking institutional-grade asset allocation without Bloomberg terminals
Sophisticated self-directed investors managing ₹50+ lakh portfolios requiring systematic risk management
Risk officers evaluating portfolio construction frameworks for pension funds or endowments
Quantitative analysts building strategic asset allocation models for asset management firms
Suboptimal Fit:
Individuals seeking high-return stock picking strategies (focus is risk reduction)
Participants uncomfortable with mathematical modeling of leverage and correlation
Investors unable to maintain daily 5-minute discipline (model requires consistent updates)
Traders requiring intraday tactical signals (strategy operates on daily/monthly frequencies)
PREREQUISITES & TECHNICAL REQUIREMENTS
Intellectual Prerequisites:
Portfolio theory: understanding of correlation, covariance, efficient frontier concepts
Statistics: mean, standard deviation, percentile calculations
Derivatives basics: ETF structures, margin mechanics (for leverage module)
Algebraic comfort: matrix multiplication concepts (simplified in Excel)
Technical Prerequisites:
Microsoft Excel 2016 or later with VBA macros enabled
Stable internet connection for daily Yahoo Finance data retrieval
No prior VBA or programming knowledge required
Software Provision: All analysis uses free resources; no mandatory data subscriptions
INSTRUCTOR BIOGRAPHIES
ENG GUAN – CO-FOUNDER & LEAD INSTRUCTOR
Quantitative investment practitioner with 15+ years spanning sovereign wealth funds, investment banks, proprietary trading desks, and multi-strategy hedge funds. Most recent role: key Portfolio Manager at a Singapore-based multi-strategy hedge fund, managing cross-asset systematic strategies with direct P&L responsibility. Holds 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, institutional risk mandates. Sovereign wealth fund background provides long-horizon capital preservation principles essential for strategic asset allocation.
PATRICK LING – CO-FOUNDER & SENIOR INSTRUCTOR
15+ years comprehensive investment industry experience across private banking (UBS), investment banking (Goldman Sachs), and hedge fund portfolio management. As a key Portfolio Manager at the same Singapore-based multi-strategy hedge fund, he co-managed systematic equity strategies and developed proprietary risk analytics. Holds MSc in Wealth Management integrating quantitative techniques with high-net-worth client portfolio construction.
Pedagogical Edge: Private banking experience translates quantitative allocation concepts into executable processes for non-institutional investors. Hedge fund tenure provides insight into multi-strategy portfolio integration and factor diversification—critical context for preventing over-leverage in risk parity implementation.
Joint Credibility: Both instructors maintain parallel practitioner careers, ensuring curriculum evolves with current industry standards rather than academic abstraction.
METHODOLOGICAL APPROACH
The course employs a "build-operate-stress test" framework. Participants construct the risk parity model, operate it through four economic regimes (1990s inflation, 2000s growth, 2008 crisis, 2020 pandemic), then stress-test leverage limits and correlation breakdowns. Each section concludes with scenario validation: what happens to the model during bond bear markets or equity crashes.
Instruction emphasizes risk-based thinking over return forecasting, teaching participants to view assets through marginal risk contribution lens rather than expected return lens.
Time Commitment: Video instruction totals 8 hours; practical implementation requires estimated additional 4-6 hours for independent model building and parameter calibration. Five-minute daily operation assumes stable model and reliable data feeds.
STRATEGY SCOPE & LIMITATIONS
Geographic Application: Explicit model calibrated for U.S. asset classes (equity ETFs: SPY, QQQ; bond ETFs: TLT, IEF; commodities: GLD, DBC) to ensure data availability. Mathematical architecture is transferable to Indian asset classes (Nifty 50 ETFs, Gilt funds, Gold ETFs) where historical data exists.
Capacity Considerations: Risk parity portfolios require minimum capital of ₹25 lakh for effective implementation across multiple ETFs while keeping transaction costs below 0.10% annually.
Performance Expectations: Model targets 10-12% annual returns with 10-12% volatility (Sharpe ratio ≈ 0.9-1.0), significantly improving on traditional 60/40's 8-10% returns with 12-15% volatility. During 2008 and 2020 crises, backtests show drawdowns of 15-20% versus 40-50% for 60/40 portfolios.
Key Limitations:
Leverage risk: Bond leverage creates 30-40% faster drawdowns during interest rate spikes (e.g., 2022 bond bear market)
Correlation breakdown: During liquidity crises, all asset correlations → 1.0, temporarily defeating risk parity diversification
Regime dependency: Strategy underperforms during strong equity bull markets when 100% equity allocation would dominate
BOTTOM-LINE ASSESSMENT
This program delivers exactly what it specifies: a hedge fund-caliber risk parity allocation system built entirely in Excel, with VBA automation and crisis-tested risk controls. The instructors' practitioner credentials provide rare authenticity, and the curriculum addresses the most dangerous aspects of risk parity (leverage, correlation breakdown) rather than presenting a sanitized version.
Critical Differentiator: Unlike academic risk parity courses, this explicitly teaches leverage implementation and contingency planning for correlation failures—the two factors that separate successful institutional implementation from retail disasters.
For investment advisors, portfolio managers, and sophisticated HNW investors seeking systematic, risk-balanced allocation without Bloomberg or Python infrastructure, this represents a professionally rigorous, operationally viable solution. The primary risk is behavioral: maintaining leveraged bond positions during rate spikes requires conviction that most retail investors lack.
SOURCE: purchase from a reseller
Original seller site:
https://www.allquant.co/