Digital Garden
A curated collection of resources, tools, and courses I wish I had when I started — for those aiming for the top 0.1%.
Resources
Books
Books that shaped how I think about markets.
Quantitative Equity Portfolio Management
Changed how I think about building portfolios — from constructing factors to assembling a real portfolio.
Principles
Not a quant book, but the mental models for decision-making and radical transparency apply everywhere.
Beginner
- Quantitative TradingErnest ChanStart here. Teaches you how to build and backtest a trading strategy from scratch. Best for anyone with basic Python who wants to understand the full pipeline.
- Machine TradingErnest ChanThe natural follow-up. Covers automated execution, risk management, and ML-driven strategies. Read after Quantitative Trading.
Advanced
- Active Portfolio ManagementGrinold & KahnThe industry bible for portfolio construction. Covers alpha modeling, risk forecasting, and the fundamental law of active management.
- Quantitative Equity Portfolio ManagementChincarini & KimBridges theory and practice. Great for understanding factor models and real-world portfolio optimization with constraints.
- Your Complete Guide to Factor-Based InvestingBerkin & SwedroeAccessible intro to why factors (value, momentum, size) have alpha. Pairs well with the multi-factor models papers.
Derivatives
- Options, Futures, and Other DerivativesJohn C. HullThe standard derivatives textbook. Covers BSM, Greeks, and exotic options. Essential if you want to trade or price any derivative.
- Option Volatility & PricingSheldon NatenbergMore practical than Hull. Focuses on volatility trading strategies and how market makers actually think about options.
Interview Prep
- Practical Guide to Quantitative Finance InterviewXinfeng ZhouCovers probability, brainteasers, and stochastic calculus problems. The most comprehensive quant interview book available.
- Quant Job Interview Questions and AnswersMark JoshiShorter and more focused. Good for drilling specific question types in the weeks before an interview.
Papers
Papers teach you how quants actually think — not textbook theory, but real methodology. Paste any keyword into Google Scholar or feed it to an LLM and ask for the top 5 most influential papers.
Start Here — 5 Papers Every Aspiring Quant Should Read
Search by Topic
Click a keyword to search Google Scholar. Sort by citation count.
Toolkit
Tools that made my life easier.
Skill Buildings
University curricula update slowly — the skills the market demands today aren’t waiting for next semester’s syllabus. In the age of AI, creativity and self-directed learning are the edge — not credentials. Here’s how I’m building skills without waiting for a classroom.
How I Learn
Project-first + AI-accelerated
I don’t take a course then apply it. I pick a target project, identify the skill gaps, use Claude to explain the theory, then implement immediately. Voice input with Typeless removes the friction of typing long prompts — I think out loud, Claude responds, I build. This loop is 5–10x faster than traditional coursework.
Project Ideas
Each project proves multiple skills at once — the highest ROI way to stand out.
Based on: Fama & French 1993
Replicate Fama-French 3-factor with 2010–2024 data. Test which factors still carry alpha vs. which have decayed. Include turnover and cost analysis.
Based on: Tetlock 2007, Loughran & McDonald 2011
Build a sentiment score from SEC EDGAR earnings transcripts. Test predictive power for next-quarter returns. Measure IC and combine with traditional factors.
Based on: Gatheral 2006, Carr & Wu 2009
Construct implied vol surface from options data. Detect mispricings via put-call parity and butterfly conditions. Backtest with realistic transaction costs.
Alpha Research Framework
Most people learn tools. Researchers learn process. This is the 5-step workflow I’m studying — adapted from the PandaAI Factor Competition champion (1st place in factor returns & overall rankings). I’m actively internalizing this framework and will annotate it as I apply each step.
Idea Generation
Where do alpha ideas come from?
- Industry exchange — Learn from practitioners’ real experience. If you haven’t built a track record, listen more than you criticize.
- AI-powered exploration — Use LLMs to scan for patterns across commodities, indices, and macro signals.
- Deep research — Read professional reports, track trends, and replicate findings patiently.
- Theory foundation — Study classic books and strategies to build bottom-up mental models. Academics aren’t useless — classics don’t expire.
- Self-discovery — Develop your own edge through original thinking. This is the most important long-term skill.
Data Layer
Prepare your raw materials.
- Basic processing — Handle missing values, outliers, and standardize data formats.
- AI-enhanced feature engineering — Use AI for multimodal data, market sentiment, and latent features.
- Start simple — Use well-documented, accessible datasets first. Don’t chase exotic alternative data until your pipeline is solid.
Factor Construction
Find what actually predicts returns.
- Statistical screening — Test significance, cross-correlation, and stability across time periods.
- Combine methods — Linear formulas + nonlinear factors + AI-mined features.
- Economic meaning — Every factor you keep must have intuition behind it — otherwise it’s curve-fitting disguised as research.
Strategy Development
Turn factors into a trading strategy.
- Prefer interpretable models — Multi-factor, linear regression. Complex models (neural nets) need simple model hedges.
- Build a strategy library — Time-series, cross-sectional, arbitrage, enhancement, and position sizing strategies.
- Calibrate on stable plateaus — Find parameter ranges that are robust, not optimal in a narrow window.
- Templatize your workflow — Don’t rebuild from scratch. Fix the pipeline, iterate only on core factor logic.
Strategy Validation
Stress-test before you deploy.
- AI adversarial testing — Feed your strategy and backtest results to an LLM. Ask it to challenge you from a senior investor’s perspective.
- Question everything — Overfitting? Out-of-sample failure? Did you ignore slippage and fees? Does it only work in specific regimes?
- Paper trading — Compare backtest vs. simulated live performance. Check slippage, execution quality, and real costs.
Key Principles
Quant is not a trading cheat code — it replaces emotion with rationality. AI is not a cognitive shortcut — it can’t understand markets for you or build your logic. It’s an accelerator and an amplifier.
Interview Prep Roadmap
First principles: the interview tests 5 things. Master them in this order.
Probability & Brainteasers
60% of interview weight. Start here.
- Green Book — Ch 2 (Probability), Ch 4 (Brainteasers), Ch 5 (Stochastic). Do every problem.
- Quantable — Filter by topic. 1500+ questions. Move here after Green Book.
- CoachQuant — Firm-specific questions + AI mock interviews.
- Key concepts: Bayes’ theorem, Markov chains, random walks, expected value, Monty Hall, birthday paradox, coupon collector.
Mental Math
Speed matters. Especially for trading firms.
- Zetamac — Baseline 50, target 70+. Daily practice.
- RFQJobs — Optiver-style drills + focus tests.
- RankYourBrain — Fractions and decimals speed.
Coding
LeetCode Medium level is the bar. Python preferred.
- NeetCode 150 — Structured DSA roadmap. Do all Medium.
- LeetCode — Focus: arrays, hash maps, DP, graphs. Skip Hard unless targeting Jane Street.
- For QR specifically: pandas manipulation, rolling statistics, vectorized operations > algorithmic puzzles.
Statistics & ML
For QR roles. Less emphasis for QT.
- Must-know: Linear regression (assumptions, diagnostics), hypothesis testing, MLE, bias-variance tradeoff, overfitting, cross-validation.
- Keigo Hayashi — Linear Regression Deep Dive — QR interview focused.
- Be ready to explain: your projects in statistical detail. Every Sharpe ratio, every methodology choice.
Finance & Market Intuition
Know the basics. Don’t need derivatives mastery for equity QR.
- Must-know: What is alpha/beta, Sharpe ratio, factor models, market microstructure basics, bid-ask spread.
- Greeks: Delta, gamma, theta, vega — at least conceptual understanding.
- Best source: Your own projects + SIM Fund experience > any textbook.
Timeline
QR internship: Start 6 months before recruiting season. Phase 1 & 2 daily from day one. Phase 3 & 4 ramp up in month 3. Phase 5 comes naturally from your projects.
Minimum prep: Green Book cover-to-cover + Quantable 200 questions + NeetCode 75 + know your projects cold.
For ASU Students
Free resources and tools specific to Arizona State University.
Course Guide for Aspiring Quants
Recommended ASU courses organized by topic. Click a course to search on ASU catalog.
Inspired by Dylan Chou (Yale, Hedge Fund Quant Researcher) and Coding Jesus (Quant Developer, 300K+ on YouTube).
LP, QP, SOCP, MIP — solve portfolio optimization under constraints.
Bootstrapping, MCMC, Gibbs sampling — approximate models without closed-form solutions.
Predict returns, volatility, or anything that gives an information edge.
Student Tools
Welcome to add all of them. I use most of them daily.
Fast-Track with CLEP Exams
Most ASU students don’t know you can skip a gen ed class entirely — no lectures, no homework — by passing one 90-minute exam. Score 50+ out of 80 and you earn 3–4 credits instantly. It’s free if you use ModernStates. See which exams ASU accepts →
Low-Stress A+ Classes
Stress-free iCourses so you can focus on what matters.
Heads Up: Hard Classes
These are notoriously difficult. Plan your semester around them.
