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Foundations of Probability

  • What is Probability?
  • Theoretical vs Empirical Probability
  • Three Views of Probability
  • Sample Space and Events
  • Axioms of Probability
  • Independence and Expectation
  • Variance and Standard Deviation
  • Covariance and Correlation
  • Key Inequalities

Set Theory & Combinatorics

  • Set Operations in Probability
  • Counting Methods
  • Advanced Counting

Conditional & Bayesian Probability

  • Conditional Probability
  • Bayes' Theorem
  • Law of Total Probability

Random Variables & Distributions

  • What is a Random Variable?
  • Discrete vs Continuous
  • PDFs and CDFs
  • Expectation, Variance, and Moments

Discrete Distributions

  • Bernoulli and Binomial
  • Poisson and Geometric
  • Negative Binomial and Hypergeometric

Continuous Distributions

  • Uniform and Normal
  • Exponential, Gamma, Beta
  • Heavy-Tailed Distributions

Limit Theorems

  • Law of Large Numbers
  • Central Limit Theorem
  • Convergence in Probability vs Distribution

Frequentist Inference

  • Confidence Intervals
  • Hypothesis Testing
  • p-values and Statistical Decisions
  • Type I and Type II Errors
  • Power and Effect Size
  • Bootstrapping and Resampling

Advanced Probability Tools

  • Law of the Unconscious Statistician
  • Moment Generating Functions
  • Characteristic Functions
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Bayesian Inference

  • Bayesian Philosophy
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Regression Analysis

  • Ordinary Least Squares
  • Multiple Linear Regression
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  • Regularization
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Multivariate Statistics

  • Joint, Marginal, and Conditional
  • Multivariate Normal
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  • Correlation vs Causation
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Stochastic Processes

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Simulation & Approximation

  • Monte Carlo Simulation
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Time Series

  • Stationarity and Autocorrelation
  • AR, MA, and ARIMA
  • GARCH and Volatility Clustering
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Information Theory

  • Shannon Entropy
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Linear Algebra

  • Vectors, Norms, and Inner Products
  • Matrix Operations
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Calculus & Optimization

  • Multivariate Calculus
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  • Stochastic Calculus Primer

Machine Learning Fundamentals

  • Supervised vs Unsupervised
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Deep Learning

  • Feedforward Networks
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  • Loss Functions

Options Pricing

  • Payoffs and Put–Call Parity
  • Risk-Neutral Valuation
  • Binomial Trees
  • Black–Scholes
  • The Greeks
  • Volatility Smile and Surface
  • Exotic Options

Portfolio Theory

  • Mean–Variance Optimization
  • CAPM and Factor Models
  • Sharpe, Sortino, and Information Ratio
  • Black–Litterman
  • Risk Parity

Trading & Risk Applications

  • Value-at-Risk
  • Expected Shortfall
  • Backtesting
  • Market Making Basics
  • Execution and Market Microstructure
  • Statistical Arbitrage
Study Guide/Options Pricing
Section 21 · Lesson 21.101

The Greeks

Delta, gamma, vega, theta, rho — sensitivities every trader watches.

The Greeks measure how an option's price responds to changes in its inputs.

  • Delta Δ=∂V/∂S\Delta = \partial V / \partial SΔ=∂V/∂S: how much the price moves per unit of underlying. The first-order risk; hedge by buying/selling the underlying.
  • Gamma Γ=∂2V/∂S2\Gamma = \partial^2 V / \partial S^2Γ=∂2V/∂S2: how delta changes as SSS moves. High gamma means delta-hedging needs frequent rebalancing.
  • Vega ν=∂V/∂σ\nu = \partial V / \partial \sigmaν=∂V/∂σ: sensitivity to volatility.
  • Theta Θ=∂V/∂t\Theta = \partial V / \partial tΘ=∂V/∂t: time decay. Long options bleed theta; short options earn it.
  • Rho ρ=∂V/∂r\rho = \partial V / \partial rρ=∂V/∂r: sensitivity to interest rates. Usually small for short-dated options.

Greek hedging is the daily activity of an options trader. A market maker quoting a delta-neutral book is short gamma, long theta, and earning a spread for warehousing the risk; managing the rebalancing cost is most of the job.

If you sell an at-the-money straddle, you have:

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