Welcome to the Quant Guild Library — a curated collection of Jupyter Notebooks and lecture videos diving deep into quantitative finance. Topics range from stochastic calculus and options pricing to trading strategies and AI in finance.
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Each folder contains a Jupyter Notebook and a corresponding lecture video by Roman Paolucci.
Latest:
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- 114. Modeling with the Law of Total Expectation
- 113. How to Trade the Cash Secured Put
- 112. Why your Trading Strategy Sucks
- 111. The Mathematical Delusion of Retail Trading
- 110. Coding an AI Bot to Trade Nvidia (From Scratch)
- 109. Vibe Coding an Options Trading System
- 108. Ultimate Options Trading Crash Course
- 107. How to Trade Options with the Black-Scholes Model
- 106. How to Trade the Covered Call
- 105. How to Read Options Chains
- 104. The Gaussian Cookbook for Aspiring Quants
- 103. Quant Finance in 3 Minutes
- 102. How Markovian Lifting Solves the Rough Volatility Problem
- 101. Stop Using the Sharpe Ratio Until You Watch This
- 100. Black-Litterman vs. Mean-Variance Portfolio Optimization in Python
- 99. Quant Ranks Retail Trading Mistakes that Blow Up Your Account
- 98. How to Get Historical Market Data with Interactive Brokers and Python
- 97. 3 Backtesting Pitfalls That Ruin Your Trading Strategy
- 96. I Bet You've Never Found Alpha (and I Can Prove It)
- 95. Trading Mean Reversion with Kalman Filters
- 94. Hawkes Processes for Quant Finance
- 93. Non-Stationarity and Why Market Timing Fails
- 92. Kalman Filters for Quant Finance
- 91. How Goldman Sachs Prices Variance Swaps
- 90. Quant Responds to and Corrects YouTube Comments
- 89. Black-Scholes Implied Volatility in 3 Minutes
- 88. How a Quant Manages a Portfolio
- 87. How to Quant Trade in 3 Minutes
- 86. 5 Projects that Made me a Quant
- 85. Quant Derives Volterra Process Discretization and Simulation
- 84. How to Build a Live Volatility Surface in Python (Interactive Brokers)
- 83. Quant Explains Risk-Neutral Option Pricing
- 82. Poisson Processes for Quant Finance
- 81. Why Most Traders Lose: Ergodicity for Quant Trading
- 80. The 5 Papers that Built Modern Quant Finance
- 79. My Approach to Solving Quant Interview Questions (Optiver Example)
- 78. Quant Explains Alpha in 3 Minutes
- 77. Profitable vs Tradable: Why Most Strategies Fail Live
- 76. I Built the Quant Roadmap
- 75. Quant Explains Backtesting with Poker
- 74. How to Build a Markov Chain Regime Switching Bot in Python with Interactive Brokers | Part 2
- 73. How to Price Options with Monte Carlo Simulation
- 72. How to Build a Markov Chain Regime Switching Bot in Python with Interactive Brokers | Part 1
- 71. Why Your Backtests are Wrong | Markov Property for Quant Trading
- 70. Non-Target to Quant: How to Get a Quant Job in 3 Steps
- 69. Quant Explains Algorithmic Market-Making
- 68. I Ranked the Best College Majors for Becoming a Quant
- 67. How Physics Accidentally Proved the Black-Scholes Model
- 66. How I Cracked the Quant Interview
- 65. Natural Language Processing (NLP) for Quant Trading
- 64. Books that Made Me a Quant
- 63. Neural Networks for Quant Finance
- 62. My Life as a Quant
- 61. Central Limit Theorem for Quant Finance
- 60. Is Trading Gambling? Quant Proves It's Not With Math & Logic
- 59. Brownian Motion for Quant Finance
- 58. Why Quant Models Break
- 57. Banks are Just Casinos (Quant Explains Why)
- 56. Quant Busts 3 Trading Myths with Math
- 55. How to Build an Earnings Event Trading Dashboard in Python (Interactive Brokers API)
- 54. Quant vs. Discretionary Trading
- 53. Do Emojis Predict Stock Returns? | Quant Guild at UCSD's Triton Quantitative Trading
- 52. Quant Proves Trading Can't Be Taught (But You CAN Learn This)
- 51. Hidden Markov Models for Quant Finance
- 50. Why Poker Pros Make the Best Traders (It's NOT Luck)
- 49. Markov Chains for Quant Finance
- 48. Why Trading Metrics are Misleading (Unless This is True)
- 47. Master Volatility with ARCH & GARCH Models
- 46. Is Trading Luck or Skill? Quant Debunks Trading Gurus with Math
- 45. How to Build an Options Volatility Trading Tool in Python with Interactive Brokerss
- 44. Time Series Analysis for Quant Finance
- 43. How to Trade Implied Volatility Crush
- 42. Quant on Trading and Investing
- 41. How to Build a Volatility Trading Dashboard in Python with Interactive Brokers
- 40. Quant Trader on Retail vs. Institutional Trading
- 39. Heston Stochastic Volatility Model and Fast Fourier Transforms
- 38. Finite Differences Option Pricing for Quant Finance
- 37. Stochastic Differential Equations for Quant Finance
- 36. How to Trade with the Kelly Criterion
- 35. What Does AI Actually Learn
- 34. How to Trade with an Edge
- 33. Why Monte Carlo Simulation Works
- 32. How to Price Exotic Options
- 31. Ito Integration Clearly and Visually Explained
- 30. Trading with the Black-Scholes Implied Volatility Surface
- 29. Ito's Lemma Clearly and Visually Explained
- 28. Gambler's Ruin Problem in Quant Trading
- 27. A Quant Derives the Karhunen-Loève Expansion of the Brownian Bridge in Continuous-Time
- 26. Is Quant Trading Gambling - Roulette, Poker, and Trading
- 25. How to Simulate Fractional Brownian Motion (fbM) via Davies-Harte
- 24. Trading with Violated Model Assumptions
- 23. How to Trade Option Implied Volatility
- 22. How to Trade
- 21. Expected Stock Returns Don't Exist
- 20. Why Portfolio Optimization Doesn't Work
- 19. Monte Carlo Simulation and Black-Scholes for Pricing Options
- 18. Why Quant Traders Care About Pricing
- 17. Analyzing Stock Returns with Principal Component Analysis in Python
- 16. Information and Stock Price Prediction
- 15. How to Build an AI Trading Bot in Python
- 14. Quant Investing for Beginners
- 13. Can AI Learn Black-Scholes
- 12. Equity Trading and Tariffs
- 11. Managing Option Portfolios with Black-Scholes Greeks
- 10. A Quant's Visual Guide to Progress
- 9. Delta Hedging and Black-Scholes Prices
- 8. Why is the Definition of a Derivative Useful
- 7. Martingale Volatility Trading
- 6. How to Trade with the Black-Scholes Model
- 5. I Made an Open-Source Market-Making Game to Practice Trading
- 4. Analyzing Trading Strategy Performance Over Time
- 3. How to Make & Lose Money Trading
- 2. Control Variates for Variance Reduction
- 1. Inverse Transform Method for Generating Random Variables
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