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{"sheet":"Case1","index":1,"n_rows":1,"n_cols":1,"rows_kept":1,"cut":false,"hidden":false,"merged":[]}
["Sygefravær"]

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["Sygefravær"]

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["Sygefravær"]

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["Sygefravær"]

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["Sygefravær"]

{"sheet":"1Definitions","index":6,"n_rows":156,"n_cols":1,"rows_kept":156,"cut":false,"hidden":false,"merged":[]}
["Investment Management"]
[]
["4-point summary of Investment Management:"]
[]
["1. Core Challenge"]
["The main problem is allocating capital efficiently under uncertainty, balancing return and risk in complex, noisy markets influenced by many unpredictable factors and behavioral biases."]
[]
["2. Main Objective"]
["The goal is to maximize risk-adjusted returns while preserving capital, by generating alpha, managing risk, ensuring consistency, optimizing allocation, and maintaining liquidity."]
[]
["3. Key Components"]
["Investment management relies on five core functions:"]
["signal generation, portfolio construction, risk management, execution (cost control), and continuous monitoring & adaptation."]
[]
["4. What Drives Success"]
["Success depends on building a robust, adaptive system—not just prediction—where disciplined risk control, efficient execution, and integration of multiple signals matter more than any single model."]
[]
["Problem Definition"]
[]
["Investment management faces a fundamental challenge:"]
[]
["How to allocate capital efficiently under uncertainty in order to generate returns while managing risk."]
[]
["Financial markets are inherently noisy, dynamic, and influenced by countless factors such as macroeconomic conditions, behavioral biases, geopolitical events, and company‑specific developments. Investors must make decisions with incomplete information, where future returns are uncertain and often unpredictable."]
[]
["Additionally, key problems include:"]
[]
["Information overload: Massive volumes of financial data, often contradictory"]
["Market inefficiencies vs. competition: Identifying true opportunities is difficult due to crowded markets"]
["Risk exposure: Volatility, drawdowns, and tail risks can significantly impact performance"]
["Behavioral biases: Overconfidence, loss aversion, and herd behavior can distort decision‑making"]
[]
["Ultimately, the core problem is balancing return optimization and risk control in an uncertain and evolving environment."]
[]
[]
[]
["Clear Objective"]
[]
["The primary objective of investment management is:"]
[]
["To maximize risk-adjusted returns over a defined investment horizon while preserving capital."]
[]
["This objective can be broken down into several key goals:"]
[]
["1. Generate alpha (excess returns)"]
["Achieve returns above a benchmark through superior analysis, strategy, or execution."]
[]
["2. Manage risk effectively"]
["Control volatility and limit drawdowns using diversification, hedging, and portfolio construction techniques."]
[]
["3. Ensure consistency and robustness"]
["Deliver stable performance across different market conditions, including bull, bear, and sideways markets."]
[]
["4. Optimize capital allocation"]
["Allocate resources across assets, strategies, or factors to achieve the most efficient portfolio."]
[]
["5. Maintain liquidity and flexibility"]
["Ensure positions can be adjusted as new information becomes available."]
[]
["In modern investment practice, success is not measured purely by return, but by:"]
[]
["Sharpe ratio (return per unit of risk)"]
["drawdown control"]
["consistency over time"]
[]
[]
[]
["Business Review – What to Solve"]
[]
["From a business perspective, investment management must solve a set of operational and strategic challenges:"]
[]
["1. Signal Generation (Alpha Creation)"]
[]
["Identify reliable sources of predictive information (alpha)."]
["This includes:"]
[]
["fundamental analysis"]
["technical signals"]
["quantitative models"]
["cross‑sectional ranking"]
[]
["The key challenge is distinguishing true signal from noise."]
[]
[]
[]
["2. Portfolio Construction"]
[]
["Transform signals into positions."]
[]
["This involves:"]
[]
["diversification across assets"]
["position sizing"]
["balancing long and short exposure"]
["correlation management"]
[]
["Poor portfolio construction can destroy even strong signals."]
[]
[]
[]
["3. Risk Management"]
[]
["Protect against adverse outcomes."]
[]
["Key elements:"]
[]
["volatility targeting"]
["drawdown control"]
["stress testing"]
["scenario analysis"]
[]
["Risk management is often more important than prediction accuracy."]
[]
[]
[]
["4. Execution & Costs"]
[]
["Implement trades efficiently."]
[]
["Challenges include:"]
[]
["transaction costs"]
["slippage"]
["market impact"]
[]
["Even profitable strategies can fail if execution is inefficient."]
[]
[]
[]
["5. Monitoring & Adaptation"]
[]
["Markets change — strategies must evolve."]
[]
["This includes:"]
[]
["performance tracking"]
["detecting model decay"]
["retraining or adjusting models"]
["responding to regime shifts"]
[]
["Failure to adapt leads to performance degradation over time."]
[]
[]
[]
["Conclusion"]
[]
["Investment management is a complex system that integrates:"]
[]
["data analysis"]
["decision-making under uncertainty"]
["risk control"]
["capital allocation"]
[]
["The ultimate goal is not simply to predict markets, but to build a robust, adaptive system that consistently extracts value while controlling risk."]
[]
["In practice, successful investment management depends less on any single model or indicator and more on the integration of signals, disciplined risk management, and a scalable process."]

{"sheet":"2datamanagement","index":7,"n_rows":178,"n_cols":1,"rows_kept":178,"cut":false,"hidden":false,"merged":[]}
["Data Management"]
[]
["4-point summary of Data Management:"]
[]
["1. Data Acquisition Foundation"]
["Data management starts with collecting reliable data from sources like market, fundamental, and alternative data. Key factors include frequency, coverage, latency, and consistency, supported by automated and standardized pipelines."]
[]
["2. Data Quality is Critical"]
["High-quality data must be complete, accurate, consistent, and timely. Cleaning steps (handling missing values, outliers, and alignment) are essential to avoid distorted models and incorrect conclusions."]
[]
["3. Major Risks & Pitfalls"]
["Common dangers include look-ahead bias, data leakage, survivorship bias, time misalignment, over-filtering, and regime dependency—each can lead to misleading results and failed strategies."]
[]
["4. Impact on Performance"]
["Data quality directly determines system performance. Strong data management enables reliable models, realistic backtests, and stable live trading, while poor data handling is a leading cause of failure in investment systems."]
[]
["Data Acquisition"]
[]
["Data acquisition is the foundation of any investment or analytical system. It involves sourcing, collecting, and organizing data from relevant providers in a consistent and reliable manner."]
[]
["Typical sources include:"]
[]
["Market data: prices, volumes, order books (e.g., Yahoo Finance, Bloomberg)"]
["Fundamental data: earnings, balance sheets, macro indicators"]
["Alternative data: news, sentiment, ESG, satellite data"]
[]
["Key considerations:"]
[]
["Frequency (daily, intraday, real‑time)"]
["Coverage (asset classes, geographies)"]
["Latency (how fast data becomes available)"]
["Consistency (same structure across assets and time)"]
[]
["A robust acquisition pipeline should ensure:"]
[]
["automated downloads or API integration"]
["standardized formats (e.g., timestamps, column names)"]
["version control and storage (databases or data lakes)"]
[]
["Without reliable data acquisition, all downstream analysis becomes unreliable."]
[]
[]
[]
["Data Quality"]
[]
["High-performing investment systems depend heavily on data integrity and accuracy."]
[]
["Key elements of data quality include:"]
[]
["✅ Completeness"]
[]
["No missing or sparse data points"]
["Sufficient historical depth for model training"]
[]
["✅ Accuracy"]
[]
["Correct pricing, volumes, and indicators"]
["Adjusted data (splits, dividends)"]
[]
["✅ Consistency"]
[]
["Uniform formatting across assets"]
["Proper alignment of timestamps"]
[]
["✅ Timeliness"]
[]
["Data available at the correct time"]
["No delays that would distort backtests or live trading"]
[]
[]
[]
["Common quality issues:"]
[]
["Missing values (NaNs) → may distort models"]
["Outliers → extreme values due to bad ticks"]
["Survivorship bias → only including current assets"]
["Look‑ahead bias → using future data unintentionally"]
[]
["Data cleaning steps typically include:"]
[]
["filling or removing NaNs"]
["clipping or filtering outliers"]
["aligning datasets across time"]
["validating values against expected ranges"]
[]
[]
[]
["Data Pitfalls (Critical Risks)"]
[]
["Poor data management is one of the biggest hidden risks in investment systems. The most common pitfalls include:"]
[]
[]
[]
["⚠️ 1. Look‑ahead Bias"]
[]
["Using data that would not have been available at the time of decision-making."]
[]
["Example:"]
[]
["using tomorrow’s earnings or returns in today’s prediction"]
[]
["👉 Leads to unrealistic performance and overfitted models"]
[]
[]
[]
["⚠️ 2. Data Leakage"]
[]
["Information from the future or target variable indirectly leaks into features."]
[]
["Example:"]
[]
["using indicators that incorporate future pricing"]
[]
["👉 Causes models to appear accurate but fail in real trading"]
[]
[]
[]
["⚠️ 3. Survivorship Bias"]
[]
["Ignoring assets that no longer exist (e.g., delisted stocks)."]
[]
["👉 Results:"]
[]
["overly optimistic backtests"]
["underestimated risk"]
[]
[]
[]
["⚠️ 4. Over‑filtering / Data Scarcity"]
[]
["Removing too much data (e.g., strict thresholds)."]
[]
["👉 Leads to:"]
[]
["small datasets"]
["unstable models"]
["poor generalization"]
[]
[]
[]
["⚠️ 5. Misalignment of Time Series"]
[]
["Different assets or features not aligned on the same timestamps."]
[]
["👉 Result:"]
[]
["incorrect training signals"]
["distorted relationships"]
[]
[]
[]
["⚠️ 6. Regime Dependency"]
[]
["Data patterns change over time."]
[]
["👉 Models trained on one regime may fail in another:"]
[]
["bull vs bear markets"]
["high vs low volatility"]
[]
[]
[]
["Conclusion"]
[]
["Data management is not just a technical task — it is a core driver of performance in investment systems."]
[]
["A strong data management process ensures:"]
[]
["reliable and clean inputs"]
["unbiased model training"]
["realistic backtesting"]
["stable live execution"]
[]
["In practice, many failures in quantitative investing do not come from poor models, but from poor data handling."]
[]
["The quality of your results is directly limited by the quality of your data."]
[]
["Successful systems treat data as a first-class asset, investing heavily in its acquisition, validation, and ongoing monitoring."]

{"sheet":"3alphagen","index":8,"n_rows":143,"n_cols":1,"rows_kept":143,"cut":false,"hidden":false,"merged":[]}
["Alpha Generation"]
[]
["4-point summary of Investment Management:"]
[]
["1. Core Problem"]
["Investment management is about allocating capital efficiently under uncertainty, balancing return and risk in noisy, complex markets affected by data overload, competition, and behavioral biases."]
[]
["2. Primary Objective"]
["The goal is to maximize risk-adjusted returns while preserving capital, achieved through generating alpha, managing risk, ensuring consistency, optimizing allocation, and maintaining liquidity."]
[]
["3. Key Functions"]
["Success depends on five core areas: signal generation (finding alpha), portfolio construction (positioning and diversification), risk management (controlling losses), execution (minimizing costs), and continuous monitoring/adaptation."]
[]
["4. Practical Reality"]
["Effective investment management relies on a robust, adaptive system—not just prediction—where disciplined risk control, integration of signals, and process consistency matter more than any single model."]
[]
[]
["Overview"]
[]
["Alpha generation refers to the process of identifying and exploiting inefficiencies in financial markets to achieve returns above a benchmark (excess returns). In a competitive and information-rich environment, generating alpha is challenging because markets tend to become efficient over time, quickly incorporating available information into prices."]
[]
["At its core:"]
[]
["Alpha = Skill + Information + Execution"]
[]
["Successful alpha generation requires combining data, models, and disciplined portfolio construction into a consistent and repeatable process."]
[]
[]
[]
["Types of Alpha"]
[]
["Alpha can be broadly categorized by source, time horizon, and strategy style."]
[]
[]
[]
["🔹 1. Market-Based vs. Relative Alpha"]
[]
["✅ Directional (Market-Based) Alpha"]
[]
["Profits from predicting market direction"]
["Example: forecasting whether a stock or index will rise"]
[]
["👉 Challenge: highly noisy and difficult to predict"]
[]
[]
[]
["✅ Relative / Cross-Sectional Alpha"]
[]
["Profits from ranking assets relative to each other"]
["Example: going long top-performing stocks and short underperformers"]
[]
["👉 More robust and widely used in quantitative strategies"]
[]
[]
[]
["🔹 2. Time Horizon"]
[]
["✅ Short-term (High Frequency / Intraday)"]
[]
["Exploits order flow, liquidity imbalances"]
["Requires high data frequency"]
[]
[]
[]
["✅ Medium-term (Days to Weeks)"]
[]
["Momentum, mean reversion signals"]
["Most common in quant strategies"]
[]
[]
[]
["✅ Long-term (Multi-month / Years)"]
[]
["Fundamentals, macro trends"]
["Slower but often more stable"]
[]
[]
[]
["🔹 3. Structural vs. Tactical Alpha"]
[]
["✅ Structural Alpha"]
[]
["Based on persistent inefficiencies (e.g., risk premia)"]
["Examples:"]
["value"]
["momentum"]
["low volatility"]
[]
[]
[]
["✅ Tactical Alpha"]
[]
["Short-lived opportunities"]
["Examples:"]
["news events"]
["earnings surprises"]
["market dislocations"]
[]
[]
[]
["Macro vs. Micro Alpha"]
[]
[]
[]
["🔹 Macro Alpha"]
[]
["Macro alpha focuses on large-scale economic and market variables."]
[]
["Examples:"]
[]
["interest rates"]
["inflation"]
["economic growth"]
["central bank policy"]
[]
["Strategies:"]
[]
["global macro"]
["asset allocation"]
["currency trading"]
[]
["👉 Characteristics:"]
[]
["lower frequency"]
["driven by economic cycles"]
["often directional"]
[]
[]
[]
["🔹 Micro Alpha"]
[]
["Micro alpha focuses on individual securities or small groups."]
[]
["Examples:"]
[]
["company earnings signals"]
["price momentum"]
["technical indicators"]
["cross-sectional rankings"]
[]
["Strategies:"]
[]
["equity long/short"]

{"sheet":"4risk","index":9,"n_rows":257,"n_cols":2,"rows_kept":257,"cut":false,"hidden":false,"merged":[]}
["Risk Management"]
[]
["4-point summary of Risk Management:"]
[]
["1. Core Objective"]
["Risk management focuses on preserving capital while generating consistent, risk-adjusted returns by measuring, controlling, and allocating risk—not eliminating it—across both portfolio and position levels."]
[]
["2. Portfolio-Level Risk Tools"]
["Key methods include Value-at-Risk (VaR) for expected losses, Expected Shortfall (ES) for tail risk, and stress testing to evaluate performance under extreme market scenarios."]
[]
["3. Position-Level Control"]
["Effective performance depends on position sizing, stop-loss rules, and volatility adjustment, which help limit losses, stabilize exposure, and reduce the risk of ruin."]
[]
["4. Practical Framework & Goal"]
["Modern strategies integrate tools like risk parity, drawdown control, and regime-based adjustments, with the ultimate goal of controlling downside, ensuring survival, and enabling long-term compounding rather than perfect prediction."]
[]
[]
["Overview"]
[]
["Risk management in investment management focuses on:"]
[]
["Preserving capital while allowing for consistent, risk‑adjusted return generation."]
[]
["Rather than eliminating risk, the goal is to measure, control, and allocate risk efficiently across positions and portfolios. Effective risk management operates at two levels:"]
[]
["Top-down (portfolio risk)"]
["Bottom-up (position level / money management)"]
[]
[]
[]
["Portfolio Risk Management"]
[]
["✅ Value-at-Risk (VaR)"]
[]
["Value-at-Risk estimates the maximum expected loss over a given time horizon at a specified confidence level."]
[]
["Example:"]
[]
["1‑day VaR (95%) = −2%"]
["→ There is a 5% probability of losing more than 2% in one day"]
[]
["Strengths:"]
[]
["Simple, widely used"]
["Provides clear risk limits"]
[]
["Limitations:"]
[]
["Does not capture extreme tail losses"]
["Assumes historical relationships remain stable"]
[]
[]
[]
["✅ Expected Shortfall (ES)"]
[]
["Expected Shortfall (also called Conditional VaR) measures:"]
[]
["The average loss in the worst-case scenarios beyond the VaR threshold"]
[]
["Example:"]
[]
["If VaR = −2%, ES may be −4%"]
["→ loss if things go badly"]
[]
["Advantages:"]
[]
["Better captures tail risk"]
["More realistic for stress situations"]
[]
["👉 ES is considered a more robust measure than VaR in modern risk frameworks."]
[]
[]
[]
["✅ Stress Testing"]
[]
["Stress testing evaluates performance under extreme or hypothetical scenarios, such as:"]
[]
["market crashes (e.g., 2008, COVID shock)"]
["volatility spikes"]
["liquidity crises"]
[]
["Examples:"]
[]
["equities down −20%"]
["rates increase sharply"]
["correlation breakdown"]
[]
["Purpose:"]
[]
["identify hidden vulnerabilities"]
["test robustness under non‑normal conditions"]
[]
[]
[]
["Bottom-Up Risk Management (Money Management)"]
[]
["While portfolio metrics measure aggregate risk, performance is driven by position-level control."]
[]
[]
[]
["✅ Position Sizing"]
[]
["Position sizing determines how much capital to allocate to each trade."]
[]
["Common approaches:"]
[]
["Equal weight → simple but inefficient"]
["Volatility scaling:"]
[]
["Python"]
["position = target_risk / volatility"]
["Show more lines"]
[]
["👉 Ensures each position contributes equal risk"]
[]
[]
[]
["✅ Stop-Loss / Exit Rules"]
[]
["Stops protect against large losses:"]
[]
["Fixed stop-loss (e.g., −5%)"]
["Trailing stop → locks in gains"]
["time-based exits"]
[]
["Key idea:"]
[]
["Cut losses quickly, let winners run"]
[]
["However, overly tight stops can:"]
[]
["increase noise trades"]
["reduce profitability"]
[]
[]
[]
["✅ Volatility-Adjusted Risk"]
[]
["Instead of fixed position sizes:"]
[]
["Python"]
["position_size ∝ 1 / volatility"]
["Show more lines"]
[]
["👉 Results:"]
[]
["stable risk exposure"]
["reduced drawdowns"]
["smoother equity curve"]
[]
[]
[]
["✅ Risk of Ruin"]
[]
["Risk of ruin measures:"]
[]
["The probability of losing enough capital to destroy the strategy"]
[]
["Key drivers:"]
[]
["win rate"]
["risk per trade"]
["payoff ratio"]
[]
["Insight:"]
[]
["High leverage + large drawdowns → high ruin probability"]
["Small consistent risk → survival"]
[]
[]
[]
["Modern Risk Management Frameworks"]
[]
[]
[]
["✅ Risk Parity"]
[]
["Allocates capital so each asset contributes equal risk:"]
[]
["Python"]
["weight ∝ 1 / volatility"]
["``"]
["Show more lines"]
[]
["👉 Diversification across risk, not capital"]
[]
[]
[]
["✅ Maximum Drawdown Control"]
[]
["Limits total portfolio decline:"]
[]
["Python"]
["drawdown = equity / peak - 1"]
["Show more lines"]
[]
["If threshold exceeded:"]
[]
["reduce exposure"]
["pause trading"]
[]
[]
[]
["✅ Kelly Criterion (Advanced)"]
[]
["Optimal sizing based on edge:"]
[]
["Python"]
["f* = (win_rate × payoff - loss_rate) / payoff"]
["Show more lines"]
[]
["👉 Maximizes long-term growth"]
[]
["BUT:"]
[]
["very sensitive to estimation error"]
["often used in fractional form (e.g., half Kelly)"]
[]
[]
[]
["✅ Regime-Based Risk Adjustment"]
[]
["Adjust exposure based on market conditions:"]
[]
["high volatility → reduce exposure"]
["strong trends → increase risk"]
[]
[]
[]
["Integration: Portfolio + Position Risk"]
[]
["A robust system integrates:"]
[]
["Layer","Role"]
["Portfolio (VaR, ES)","aggregate risk control"]
["Stress testing","extreme scenario protection"]
["Position sizing","local risk allocation"]
["Stops & volatility scaling","dynamic adjustment"]
["Risk of ruin","survival constraint"]
[]
[]
["Conclusion"]
[]
["Effective risk management is not about avoiding losses, but about:"]
[]
["✅ controlling downside"]
["✅ ensuring survival"]
["✅ stabilizing returns"]
[]
["The most successful investment systems rely less on prediction accuracy and more on:"]
[]
["disciplined position sizing"]
["robust portfolio construction"]
["strict risk controls"]
[]
["In practice, the goal is not to be right all the time,"]
["but to ensure that losses are limited and gains can compound over time."]

{"sheet":"5pfcon","index":10,"n_rows":256,"n_cols":1,"rows_kept":256,"cut":false,"hidden":false,"merged":[]}
["Portfolio Construction"]
[]
["4-point summary of Portfolio Construction:"]
[]
["1. Purpose & Importance"]
["Portfolio construction converts investment signals into actual capital allocation, balancing return, risk, diversification, and robustness. Even strong signals can fail without disciplined portfolio design."]
[]
["2. Limits of Classical Approaches"]
["Mean–variance optimization (MVO) aims to maximize return for a given risk but is impractical due to sensitivity to inputs, estimation errors, extreme allocations, and unrealistic assumptions about market behavior."]
[]
["3. Modern Approaches"]
["More robust methods include risk parity, factor-based allocation, minimum variance, equal risk contribution, and Black–Litterman models, which emphasize stability, diversification, and reduced reliance on precise forecasts."]
[]
["4. Key Insights for Success"]
["Portfolio performance is driven more by risk allocation and diversification than individual asset selection. Simpler, robust strategies and attention to correlations, tail risk, and adaptability lead to more resilient portfolios."]
[]
["Overview"]
[]
["Portfolio construction is the process of translating investment signals into a coherent allocation of capital across assets. While alpha generation identifies opportunities, portfolio construction determines how these opportunities are expressed."]
[]
["A strong signal combined with poor portfolio construction can lead to poor performance, while disciplined construction can significantly enhance returns and reduce risk."]
[]
["The key challenge is to balance return, risk, diversification, and robustness in a dynamic market environment."]
[]
[]
[]
["Classical Approach: Mean–Variance Optimization"]
[]
["The traditional framework, introduced by Harry Markowitz, is mean–variance optimization (MVO). It seeks to:"]
[]
["maximize expected return,"]
["while minimizing portfolio variance (risk)."]
[]
["Mathematically:"]
[]
["maximize:    expected return"]
["minimize:    variance (covariance structure)"]
[]
["This framework produces an “efficient frontier” of optimal portfolios."]
[]
[]
[]
["Limitations of Mean–Variance"]
[]
["Despite its theoretical elegance, MVO has major practical weaknesses:"]
[]
["⚠️ 1. Input Sensitivity"]
[]
["Small changes in expected returns or covariance lead to large and unstable weight changes."]
[]
["⚠️ 2. Estimation Error"]
[]
["Expected returns are extremely hard to estimate accurately. Errors dominate results."]
[]
["⚠️ 3. Extreme Allocations"]
[]
["MVO often produces:"]
[]
["concentrated portfolios"]
["unrealistic leverage"]
[]
["⚠️ 4. Assumption of Normality"]
[]
["Variance assumes symmetric risk, but real markets exhibit:"]
[]
["fat tails"]
["skewness"]
["extreme events"]
[]
["👉 In practice, pure mean–variance optimization is rarely used directly."]
[]
[]
[]
["Why Portfolio Construction is Critical"]
[]
["Portfolio construction is powerful because of non-linear diversification effects:"]
[]
["✅ Diversification is not additive"]
[]
["Adding assets does not just average returns—it can:"]
[]
["significantly reduce overall volatility"]
["smooth drawdowns"]
["improve Sharpe ratio"]
[]
["Even small, weak signals can become powerful when combined and diversified properly."]
[]
[]
[]
["Modern Portfolio Construction Approaches"]
[]
[]
[]
["✅ 1. Risk Parity"]
[]
["Allocates capital so that each asset contributes equal risk, not equal weight:"]
[]
["weight ∝ 1 / volatility"]
[]
["Benefits:"]
[]
["stable exposure"]
["better diversification"]
["reduced dependence on forecasts"]
[]
[]
[]
["✅ 2. Factor-Based Allocation"]
[]
["Instead of allocating to assets, allocate to systematic risk factors:"]
[]
["momentum"]
["value"]
["volatility"]
["quality"]
[]
["Advantage:"]
[]
["captures underlying drivers of returns"]
["reduces redundancy"]
[]
[]
[]
["✅ 3. Minimum Variance / Robust Optimization"]
[]
["Focuses on:"]
[]
["minimizing variance using stable covariance estimates"]
["reducing sensitivity to noisy inputs"]
[]
["Often combined with:"]
[]
["shrinkage techniques"]
["constraints on weights"]
[]
[]
[]
["✅ 4. Equal Risk Contribution (ERC)"]
[]
["Extends risk parity:"]
[]
["ensures each position contributes equally to total portfolio risk"]
[]
["👉 More stable than traditional optimization"]
[]
[]
[]
["✅ 5. Bayesian / Black–Litterman Models"]
[]
["Improves mean–variance by:"]
[]
["incorporating prior beliefs"]
["combining model output with market equilibrium"]
[]
["Result:"]
[]
["more realistic allocations"]
["less extreme weights"]
[]
[]
[]
["✅ 6. Machine Learning-Based Allocation"]
[]
["Modern approaches use:"]
[]
["reinforcement learning"]
["optimization based on simulated outcomes"]
["nonlinear allocation models"]
[]
["👉 Still emerging, but promising for complex multi-asset systems"]
[]
[]
[]
["Bottom-Up vs. Top-Down Integration"]
[]
["Effective portfolio construction combines:"]
[]
["🔹 Bottom-Up (Position Level)"]
[]
["position sizing"]
["volatility scaling"]
["signal strength weighting"]
[]
["🔹 Top-Down (Portfolio Level)"]
[]
["diversification"]
["risk budgeting"]
["correlation control"]
[]
[]
[]
["Recent Insights and Discoveries"]
[]
[]
[]
["✅ 1. Correlation is unstable"]
[]
["correlations change dramatically during stress"]
["diversification benefits can collapse in crises"]
[]
["👉 Dynamic correlation modeling is essential"]
[]
[]
[]
["✅ 2. Diversification across strategies matters"]
[]
["Not just assets — but:"]
[]
["models"]
["signals"]
["time horizons"]
[]
["👉 “Alpha diversification” is as important as asset diversification"]
[]
[]
[]
["✅ 3. Risk dominates returns"]
[]
["Studies show that:"]
[]
["most portfolio performance is driven by risk allocation, not selection"]
[]
[]
[]
["✅ 4. Simpler often works better"]
[]
["equal weight or simple risk parity often outperform complex optimization"]
[]
["👉 because they avoid estimation error"]
[]
[]
[]
["✅ 5. Tail risk matters more than variance"]
[]
["Modern approaches focus on:"]
[]
["drawdown"]
["expected shortfall"]
[]
["rather than just variance"]
[]
[]
[]
["Conclusion"]
[]
["Portfolio construction is a critical component of investment management, often more impactful than the underlying alpha model."]
[]
["While classical mean–variance optimization provides a foundation, modern practice emphasizes:"]
[]
["✅ robustness over precision"]
["✅ diversification across assets and signals"]
["✅ risk-based allocation"]
["✅ adaptability to changing market conditions"]
[]
["The goal is not to build the “optimal” portfolio for one scenario,"]
["but a resilient portfolio that performs across many uncertain futures."]

{"sheet":"6backtest","index":11,"n_rows":281,"n_cols":1,"rows_kept":281,"cut":false,"hidden":false,"merged":[]}
["Backtesting"]
[]
["4-point summary of Backtesting:"]
[]
["1. Purpose & Process"]
["Backtesting evaluates an investment strategy on historical data to test robustness (not to prove success). It follows a pipeline: define rules → prepare clean data → generate signals → simulate trades → evaluate performance → validate results."]
[]
["2. Key Risks & Biases"]
["Common pitfalls include look-ahead bias, survivorship bias, data leakage, overfitting, and ignoring transaction costs. These can make strategies appear better than they truly are if not handled properly."]
[]
["3. Validation & Testing Methods"]
["Reliable backtests use out-of-sample testing, walk-forward analysis, stress testing, and Monte Carlo methods to ensure strategies work across different time periods and market conditions."]
[]
["4. Reality vs. Backtest"]
["Live trading usually underperforms backtests due to costs, slippage, and market changes. Consistency between backtest and live results requires realistic assumptions, identical implementation, and ongoing monitoring."]
[]
["Overview"]
[]
["Backtesting is the process of evaluating an investment strategy using historical data to estimate how it would have performed in the past. It is a critical step in the investment lifecycle, bridging the gap between idea generation and live deployment."]
[]
["The goal of backtesting is not to prove that a strategy works, but to test robustness, identify weaknesses, and approximate real-world behavior."]
[]
["Basic Workflow"]
[]
["A standard backtesting pipeline follows these steps:"]
[]
["✅ 1. Define Strategy & Hypothesis"]
[]
["Specify the signal (e.g., momentum, mean reversion)"]
["Define entry, exit, and position sizing rules"]
[]
[]
[]
["✅ 2. Data Preparation"]
[]
["Acquire clean, aligned, bias-free data"]
["Handle missing values, corporate actions, and timestamps"]
[]
[]
[]
["✅ 3. Feature Engineering & Signals"]
[]
["Transform raw data into model inputs"]
["Generate trading signals or rankings"]
[]
[]
[]
["✅ 4. Simulation Engine"]
[]
["Apply signals to historical data"]
["simulate trades including positions, returns, and costs"]
[]
[]
[]
["✅ 5. Performance Evaluation"]
[]
["Compute metrics such as:"]
[]
["total return"]
["Sharpe ratio"]
["drawdown"]
["turnover and costs"]
[]
[]
[]
["✅ 6. Validation"]
[]
["out-of-sample testing"]
["walk-forward analysis"]
["robustness checks"]
[]
[]
[]
["Avoiding Common Pitfalls"]
[]
["Backtests often fail due to hidden biases rather than bad strategies."]
[]
[]
[]
["⚠️ 1. Look‑Ahead Bias"]
[]
["Using information not available at the decision time."]
[]
["✅ Fix: strictly use lagged data (predict t using t−1)"]
[]
[]
[]
["⚠️ 2. Survivorship Bias"]
[]
["Using only assets that exist today"]
[]
["✅ Fix: include delisted assets"]
[]
[]
[]
["⚠️ 3. Data Leakage"]
[]
["Future information embedded in features"]
[]
["✅ Fix: isolate training/test strictly and avoid overlaps"]
[]
[]
[]
["⚠️ 4. Overfitting"]
[]
["Too many parameters tuned to past data"]
[]
["✅ Fix:"]
[]
["keep models simple"]
["use separate validation sets"]
[]
[]
[]
["⚠️ 5. Ignoring Costs"]
[]
["Transaction costs and slippage often kill profitability"]
[]
["✅ Fix:"]
[]
["include realistic trading costs"]
["simulate execution delays"]
[]
[]
[]
["Testing Methodologies"]
[]
[]
[]
["✅ In-Sample vs Out-of-Sample"]
[]
["train on one period"]
["test on unseen data"]
[]
[]
[]
["✅ Walk-Forward Testing"]
[]
["repeatedly retrain and test forward in time"]
["mimics real trading"]
[]
[]
[]
["✅ Stress Testing"]
[]
["test strategy under extreme market scenarios"]
["crash periods, volatility spikes"]
[]
[]
[]
["✅ Monte Carlo Resampling"]
[]
["shuffle or resample returns"]
["test robustness of outcomes"]
[]
[]
[]
["Sandbox (Paper Trading) vs Live Trading"]
[]
[]
[]
["✅ Sandbox / Paper Trading"]
[]
["strategy runs on live data without real capital"]
["validates:"]
["execution logic"]
["latency"]
["signal stability"]
[]
[]
[]
["✅ Live Portfolio"]
[]
["The final step is deployment with real capital."]
[]
["Key requirement:"]
[]
["Live results must track the backtest within expected error bounds"]
[]
[]
[]
["Important checks:"]
[]
["signal consistency (same trades?)"]
["slippage differences"]
["cost assumptions vs reality"]
["latency effects"]
[]
[]
[]
["Ensuring Backtest ↔ Live Consistency"]
[]
["To avoid divergence:"]
[]
["✅ Use identical code paths"]
[]
["same signal computation"]
["same position sizing"]
[]
[]
[]
["✅ Include realistic assumptions"]
[]
["execution delay (T+1 trading)"]
["slippage models"]
["liquidity constraints"]
[]
[]
[]
["✅ Monitor drift"]
[]
["Track:"]
[]
["live_return - expected_return"]
[]
["👉 diagnose deviations early"]
[]
[]
[]
["Recent Insights & Best Practices"]
[]
[]
[]
["✅ 1. Simplicity Outperforms Complexity"]
[]
["Simpler models tend to:"]
[]
["generalize better"]
["avoid overfitting"]
[]
[]
[]
["✅ 2. Walk-Forward is Mandatory"]
[]
["Single train/test split is insufficient"]
["→ rolling validation is standard"]
[]
[]
[]
["✅ 3. Cross-Sectional Testing is More Robust"]
[]
["Ranking across assets is:"]
[]
["more stable"]
["less noisy than time-series prediction"]
[]
[]
[]
["✅ 4. Distribution Matters More Than Mean"]
[]
["Focus on:"]
[]
["drawdowns"]
["tail risk"]
[]
["not just average returns"]
[]
[]
[]
["✅ 5. Backtest ≠ Reality"]
[]
["Even good backtests will degrade in live trading"]
[]
["👉 Expect difference:"]
[]
["Sharpe drop (30–50%)"]
["higher costs"]
["regime changes"]
[]
[]
[]
["Conclusion"]
[]
["Backtesting is essential but must be approached with discipline:"]
[]
["use clean, unbiased data"]
["apply realistic assumptions"]
["validate across time and scenarios"]
["test robustness, not just performance"]
[]
["A good backtest is not one that looks perfect, but one that survives scrutiny and behaves consistently in live trading."]

{"sheet":"7exe","index":12,"n_rows":283,"n_cols":1,"rows_kept":283,"cut":false,"hidden":false,"merged":[]}
["Trade Execution"]
[]
["4-point summary of Trade Execution:"]
[]
["1. Role & Importance"]
["Trade execution converts portfolio decisions into real trades and directly determines realized returns. Poor execution (costs, slippage, impact) can significantly reduce or even eliminate theoretical profits."]
[]
["2. Key Cost Drivers"]
["The main factors are slippage, transaction costs, and market impact, all of which compound over time—especially in high-frequency or high-turnover strategies."]
[]
["3. Execution Methods"]
["Strategies range from market and limit orders to advanced algorithmic execution (TWAP, VWAP, implementation shortfall), which aim to optimize timing, reduce costs, and minimize market impact."]
[]
["4. Best Practices & Insight"]
["Successful execution requires realistic cost modeling, matching strategy to liquidity, controlling turnover, and continuous monitoring. The key insight: execution is where strategy meets reality, and often determines success more than the signal itself."]
[]
["Overview"]
[]
["Trade execution is the process of converting portfolio decisions into actual market trades. While often overlooked, execution is a critical component of investment performance."]
[]
["Even the best alpha signals can fail if trades are executed inefficiently."]
[]
["Execution determines the realized returns, as opposed to the theoretical returns from backtests. The gap between these is largely driven by transaction costs, slippage, and market impact."]
[]
[]
[]
["Core Components of Execution"]
[]
[]
[]
["✅ Slippage"]
[]
["Slippage refers to the difference between the expected price of a trade and the actual execution price."]
[]
["Types of slippage:"]
[]
["Timing slippage: delay between signal and execution"]
["Market slippage: price movement during order execution"]
["Liquidity slippage: insufficient depth at desired price"]
[]
["Example:"]
[]
["Expected buy price: 100"]
["Actual execution: 100.5"]
["→ Slippage = +0.5%"]
[]
[]
[]
["Impact:"]
[]
["Small slippage per trade compounds significantly over time"]
["High-turnover strategies are especially sensitive"]
[]
[]
[]
["✅ Transaction Costs"]
[]
["Costs include:"]
[]
["Commissions (broker fees)"]
["Bid–ask spread (implicit cost)"]
["Taxes and fees"]
[]
["Ignoring costs leads to overstated backtest performance."]
[]
[]
[]
["✅ Market Impact"]
[]
["Large orders can move the market itself, especially in illiquid assets."]
[]
["Buying pushes prices up"]
["Selling pushes prices down"]
[]
["👉 This creates a feedback effect:"]
[]
["the act of trading reduces profitability"]
[]
[]
[]
["Execution Strategies"]
[]
[]
[]
["✅ Market Orders"]
[]
["Execute immediately at best available price"]
["High certainty of execution"]
[]
["❌ Downsides:"]
[]
["higher slippage"]
["poor price control"]
[]
[]
[]
["✅ Limit Orders"]
[]
["Specify maximum/minimum price"]
["better price control"]
[]
["❌ Downsides:"]
[]
["risk of non-execution"]
["missed opportunities"]
[]
[]
[]
["✅ Algorithmic Execution (Algo Trading)"]
[]
["Modern trading relies heavily on execution algorithms:"]
[]
[]
[]
["🔹 TWAP (Time-Weighted Average Price)"]
[]
["splits order evenly over time"]
["reduces market impact"]
[]
[]
[]
["🔹 VWAP (Volume-Weighted Average Price)"]
[]
["executes according to market volume"]
["trades more when liquidity is high"]
[]
[]
[]
["🔹 Implementation Shortfall"]
[]
["minimizes difference between decision price and execution price"]
[]
["👉 Focuses on cost vs timing trade-off"]
[]
[]
[]
["Algo Trading in Modern Markets"]
[]
["Algorithmic trading systems:"]
[]
["automate order placement"]
["optimize execution timing"]
["react to real-time market conditions"]
[]
["They are used for:"]
[]
["execution optimization"]
["high-frequency strategies"]
["statistical arbitrage"]
[]
[]
[]
["✅ Smart Order Routing (SOR)"]
[]
["Routes orders across multiple venues to:"]
[]
["find best price"]
["minimize costs"]
[]
[]
[]
["✅ Liquidity Detection"]
[]
["Algorithms estimate where liquidity is available to:"]
[]
["avoid large price movements"]
["execute quietly"]
[]
[]
[]
["Modern Execution Concepts"]
[]
[]
[]
["✅ Trade Scheduling"]
[]
["Breaking large orders into smaller pieces over time to reduce:"]
[]
["market impact"]
["signaling risk"]
[]
[]
[]
["✅ Latency and Speed"]
[]
["In some strategies:"]
[]
["milliseconds matter"]
[]
["Low latency improves:"]
[]
["execution quality"]
["price accuracy"]
[]
[]
[]
["✅ Dark Pools"]
[]
["Private trading venues where:"]
[]
["orders are hidden"]
["market impact is reduced"]
[]
[]
[]
["✅ Adaptive Execution"]
[]
["Modern systems dynamically adjust based on:"]
[]
["volatility"]
["liquidity"]
["market conditions"]
[]
[]
[]
["Execution vs Strategy Performance"]
[]
["There are two performance layers:"]
[]
["Theoretical return (model)  "]
["− Execution costs  "]
[{"$f":" Realized return","v":{"$error":"#NAME?"}}]
[]
["👉 The difference can be 50%+ of profits in active strategies"]
[]
[]
[]
["Best Practices in Execution"]
[]
[]
[]
["✅ 1. Model realistic costs in backtests"]
[]
["include slippage and spreads"]
[]
[]
[]
["✅ 2. Delay execution realistically"]
[]
["trade at next bar, not same bar"]
[]
[]
[]
["✅ 3. Control turnover"]
[]
["fewer trades → lower costs"]
[]
[]
[]
["✅ 4. Match strategy to liquidity"]
[]
["avoid illiquid assets for large strategies"]
[]
[]
[]
["✅ 5. Monitor live vs expected execution"]
[]
["track slippage deviations"]
[]
[]
[]
["Key Insight"]
[]
["Execution is where theory meets reality."]
[]
["Many strategies fail not because the signal is wrong, but because:"]
[]
["costs are too high"]
["execution is inefficient"]
["liquidity is insufficient"]
[]
[]
[]
["Conclusion"]
[]
["Trade execution is a critical layer in investment management, translating strategy into realized performance. Modern execution relies on:"]
[]
["✅ algorithmic trading"]
["✅ cost-aware strategies"]
["✅ liquidity management"]
["✅ continuous monitoring"]
[]
["Successful systems treat execution not as an afterthought, but as a core part of the investment process, ensuring that theoretical alpha is preserved in real trading."]

{"sheet":"8perf","index":13,"n_rows":236,"n_cols":1,"rows_kept":236,"cut":false,"hidden":false,"merged":[]}
["Performance Measurement"]
[]
["4-point summary of Performance Measurement:"]
[]
["1. Purpose & Focus"]
["Performance measurement evaluates investment results beyond raw returns by analyzing risk-adjusted performance, sources of return, and whether true value (alpha) was generated."]
[]
["2. Alpha vs Beta"]
["Returns are split into beta (market-driven) and alpha (skill-based). True investment skill is measured by the ability to generate consistent alpha, not just market exposure."]
[]
["3. Attribution & Drivers of Return"]
["Tools like Brinson–Beebower attribution and factor models (e.g., Fama–French) break performance into allocation, selection, and factor exposure—helping distinguish real skill from systematic effects."]
[]
["4. Risk-Adjusted Evaluation & Insights"]
["Metrics like Sharpe ratio, Information ratio, and drawdown assess performance relative to risk. In practice, consistent, stable returns, diversification, and managing tail risk matter more than high average returns."]
[]
["Overview"]
[]
["Performance measurement evaluates how well an investment strategy or portfolio performs relative to its objectives and benchmarks. It goes beyond raw returns, focusing on:"]
[]
["Where returns come from, how much risk was taken, and whether value (alpha) was truly created."]
[]
["Key components include:"]
[]
["total return"]
["risk-adjusted return (Sharpe, Information Ratio)"]
["attribution (sources of return)"]
["alpha vs beta decomposition"]
[]
[]
[]
["Alpha and Beta Separation"]
[]
["A fundamental concept in performance measurement is separating returns into:"]
[]
["✅ Beta (Systematic Return)"]
[]
["return explained by market exposure"]
["e.g., equity market, interest rates, macro factors"]
[]
[]
[]
["✅ Alpha (Excess Return)"]
[]
["return beyond what can be explained by beta"]
["reflects skill, not market movement"]
[]
["Total Return = Beta (Market) + Alpha (Skill)"]
[]
["👉 True investment skill is measured by consistent alpha generation, not just riding the market."]
[]
[]
[]
["Performance Attribution"]
[]
["Performance attribution explains:"]
[]
["Why a portfolio performed the way it did"]
[]
[]
[]
["✅ 1. Brinson–Beebower Attribution"]
[]
["Widely used in portfolio management, it decomposes performance into:"]
[]
["🔹 Allocation Effect"]
[]
["Impact of allocating capital across sectors or asset classes"]
["(e.g., overweight tech)"]
[]
[]
[]
["🔹 Selection Effect"]
[]
["Ability to pick better securities within a segment"]
["(e.g., choosing strong tech stocks)"]
[]
[]
[]
["🔹 Interaction Effect"]
[]
["Combined effect of allocation and selection decisions"]
[]
[]
[]
["Summary:"]
[]
["Total Excess Return = Allocation + Selection + Interaction"]
[]
["👉 Key insight: Brinson–Beebower showed that asset allocation drives a large portion of performance variability."]
[]
[]
[]
["✅ 2. Factor Attribution (Fama–French and Extensions)"]
[]
["Factor models explain returns using systematic drivers."]
[]
[]
[]
["🔹 Fama–French 3-Factor Model:"]
[]
["Market (MKT)"]
["Size (SMB: small minus big)"]
["Value (HML: high minus low)"]
[]
[]
[]
["🔹 Extended models (5-factor, etc.):"]
[]
["Profitability"]
["Investment"]
["Momentum (often added separately)"]
[]
[]
[]
["Purpose:"]
[]
["identify which factors drive returns"]
["distinguish alpha from factor exposure"]
[]
[]
[]
["Interpretation:"]
[]
["Return = Market + Factors + Residual (Alpha)"]
[]
["👉 If returns are fully explained by factors → no true alpha"]
[]
[]
[]
["Risk-Adjusted Performance Metrics"]
[]
["Performance must always be evaluated relative to risk."]
[]
[]
[]
["✅ Sharpe Ratio"]
[]
["Measures return per unit of total volatility:"]
[]
["Sharpe = (Return − Risk-free) / Volatility"]
[]
[]
[]
["✅ Information Ratio"]
[]
["Measures excess return relative to benchmark:"]
[]
["IR = Active Return / Tracking Error"]
[]
[]
[]
["✅ Maximum Drawdown"]
[]
["Largest peak-to-trough loss"]
[]
["👉 Critical for evaluating downside risk"]
[]
[]
[]
["Modern Performance Insights"]
[]
[]
[]
["✅ 1. Alpha is Rare and Decays"]
[]
["many strategies appear to generate alpha"]
["few persist over time"]
[]
["👉 continuous monitoring is essential"]
[]
[]
[]
["✅ 2. Factor Exposures Dominate Returns"]
[]
["Studies show that:"]
[]
["much performance is due to factor exposure"]
["true alpha is often small"]
[]
[]
[]
["✅ 3. Diversification Across Signals Matters"]
[]
["Performance improves when combining:"]
[]
["multiple strategies"]
["uncorrelated signals"]
[]
["👉 “alpha diversification” is key"]
[]
[]
[]
["✅ 4. Distribution Matters More than Average"]
[]
["Focus is shifting from:"]
[]
["mean return"]
[]
["to:"]
[]
["drawdowns"]
["tail risk (Expected Shortfall)"]
[]
[]
[]
["✅ 5. Consistency Over Magnitude"]
[]
["Investors increasingly value:"]
[]
["stable returns"]
["low volatility"]
[]
["over:"]
[]
["occasional large gains"]
[]
[]
[]
["Conclusion"]
[]
["Performance measurement is not just about returns—it is about understanding:"]
[]
["where returns come from"]
["how much risk was taken"]
["whether results are repeatable"]
[]
["Key frameworks such as:"]
[]
["Brinson–Beebower attribution"]
["Fama–French factor models"]
["alpha vs beta decomposition"]
[]
["allow investors to distinguish skill from exposure."]
[]
["The ultimate goal is not just to generate returns, but to produce consistent, risk-adjusted performance driven by genuine alpha."]