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Arithmion plugin development
Arithmion usage
Quant onboarding
Validation deep-dive

Financial Markets

Behavioral Finance: Why Investors Make Predictable Mistakes
Capital Asset Pricing Model: Systematic Risk and Beta
Company Valuation: DCF, Multiples, and Intrinsic Value
Corporate Actions: Dividends, Stock Splits, and Corporate Events
Credit Risk and Ratings: Understanding Default Risk
Diversification: Reducing Risk Through Asset Allocation
ETFs, Mutual Funds, and Investment Funds
Financial Instruments: Stocks, Bonds, Derivatives, and Cash
Financial Mathematics: Returns, Percentages, and Growth Rates
Financial Regulations: Protecting Investors and Markets
Inflation: What It Is and Why It Matters
Interest Rates and the Role of Central Banks
Market Anomalies: Patterns That Challenge Market Efficiency
Market Efficiency Hypothesis: Can You Beat the Market?
Market Regimes: Bull Markets, Bear Markets, and Crashes
Opportunity Cost, Risk-Free Rate, and the Risk-Return Tradeoff
Order Types: Market Orders, Limit Orders, and Execution
Portfolio Theory: Diversification and the Efficient Frontier
Primary Markets vs. Secondary Markets: Where Securities Trade
Risk Management: Volatility, VaR, and Position Sizing
Supply, Demand, and Price Formation
Time Value of Money: Present Value, Future Value, and Discounting
What Are Bonds? Understanding Fixed Income Investing
What Are Options? The Right to Buy or Sell at a Set Price
What Is a Bond? Understanding Fixed Income Securities
What Is a Derivative? Forwards, Futures, Options, and Swaps
What Is a Stock? Understanding Equity Ownership
What Is an Order Book? How Buyers and Sellers Meet
What Is the Cryptocurrency Market? Bitcoin, Spot, and Derivatives
What Is the Foreign Exchange (FX) Market?
What Is the Stock Market? How Equity Trading Actually Works

Mathematics & Statistics

Linear Algebra
Probability
Statistics

Quant Trading Onboarding

Arithmion SDK Quickstart
Five Ways Backtests Lie
Python for Quant: Skip the Boring Parts
Vectorized vs Event-Driven Backtests
What Is Backtesting, Really?
What Quant Trading Actually Is (And Isn't)
Your Data Is Wrong. How to Fix It.
Your First Strategy: Moving Average Crossover

Advanced Quant Trading

CAPM and Market Beta
Carhart Four-Factor Model
Factor Crowding and Decay: Why Every Factor Dies and How to Extend Its Life
Factor Zoo and Beyond: Which Factors Actually Survive Transaction Costs
Fama-French Three-Factor Model
Mean Reversion Strategies: Pairs Trading, Stat Arb, and Why Capacity Is Limited
Momentum Strategies: The Most Robust Factor and How Not to Get Killed by It
What Are Alpha Factors

Machine Learning Foundations

Cross-Validation and Honest Evaluation
Decision Trees and Random Forests
Gradient Boosting: XGBoost and LightGBM
Linear and Logistic Regression
Neural Networks: The Honest Version
Overfitting and Regularization: The Central Problem of ML
Supervised Learning: Regression and Classification
What Is Machine Learning, Really?

ML × Quant Trading

Alpha Decay and Feature Half-Life
Cross-Validation in Time Series
Embedding Methods for Assets
Feature Engineering for Quant
ML for Return Prediction
ML Trading Strategy Lifecycle
Neural Networks for Time Series
Reinforcement Learning for Portfolios

Backtesting & Validation

Cross-Validation Methods for Quant Strategies: K-Fold vs Walk-Forward vs Purged vs CPCV
Live vs Backtest Performance: The Gap You Should Plan For
Multiple Testing and the Deflated Sharpe Ratio
Probability of Backtest Overfitting (PBO): How to Know Your Best Strategy Is Probably the Worst
Stress Testing and Regime Robustness: Will Your Strategy Survive 2027?
The White Reality Check and the SPA Test
Walk-Forward Analysis
What Is Backtesting and What Is Validation

Tools & Systems

Arithmion SDK Overview
Data Pipelines and Storage
Live Trading Infrastructure: From Backtest to Real Capital
Model Monitoring and Retraining: Keeping Alpha Alive
Monitoring and Alerting for Live Trading Strategies
Research to Production Workflow
Risk Management Systems for Quant Trading
The Arithmion Backtest Engine

Quant Methodology

Alpha Factor: What Makes a Quant Signal Actually Work
Cross-Sectional Stability: Is Your Factor's Edge Consistent Across Markets?
Factor Combination: How to Blend Multiple Alpha Signals
Factor Decay: Why Your Signal's Edge Disappears Over Time
Feature Engineering: Building the Right Inputs for Your Alpha Model
IC Analysis: How to Measure Whether Your Factor Actually Predicts
Label Quality: The Most Underappreciated Problem in Quant Finance
Momentum vs. Mean Reversion: Two Sides of the Same Coin
Regime Detection: Finding the Market States Your Factor Works In
Turnover Analysis: The Transaction Cost Reality Check

Arithmion Plugin SDK: Building Custom Integrations
Arithmion Quickstart: Getting Your First Alpha Factor Live
Building a DataSource Plugin: Connecting Any Data to Arithmion
LightGBM for Finance: Gradient Boosting That Actually Works on Returns
Notebook Limitations: Why Jupyter Alone Breaks Quant Workflows
NumPy Vectorization: Writing Quant Code That Runs 100x Faster
Python Pandas for Finance: Data Structures That Make Sense
VectorBT vs Backtrader: Choosing Your Backtesting Framework

Validation & Debunking

[MIGRATED] Deflated Sharpe Ratio — See Multiple Testing + Learning Track
[MIGRATED] Purged K-Fold CV — See Time Series CV + Learning Track
Backtest Overfitting: PBO and Combinatorial Purged Cross-Validation
Combinatorial Purged Cross-Validation (CPCV)
Multiple Testing: Bonferroni, Benjamini-Hochberg, and the False Discovery Rate
Probabilistic Backtest: A Bayesian View of Strategy Evaluation
Signal Vendor Due Diligence: Red Flags Before You Subscribe
The Cult of Sharpe: Why Your 2.1 Sharpe Is Probably Wrong
Time Series Cross-Validation: Purged K-Fold and Embargo

Walkthrough 01: Purged K-Fold Cross-Validation — From Novice to Proficient
Walkthrough 02: Combinatorial Purged CV — Testing Strategy Robustness
Walkthrough 03: Deflated Sharpe Ratio — Getting an Honest Performance Estimate
Walkthrough 04: Backtest Overfitting — The PBO Framework
Walkthrough 05: Probabilistic Backtest — Bayesian Thinking for Quants
Walkthrough 06: The Cult of Sharpe — Why Single Metrics Mislead
Walkthrough 07: Signal Vendor Due Diligence — Not Getting Scammed

Time Series (Depth Module)

ARIMA and the Box-Jenkins Methodology
Cointegration: The Engle-Granger Two-Step Method
Cointegration: The Johansen Method
GARCH Family Extensions: Asymmetry, Power, and Multivariate
Kalman Filter Applications: Alpha Signals, Beta, Dynamic Factors
Markov-Switching Models: Hamilton's Regime Model
State-Space Models: Representation and Kalman Filter
Stochastic Processes: The Foundations Underneath Everything Else
The ARMA Model: Derivation, Identification, and Estimation
The GARCH Model: From ARCH to Integrated GARCH
Unit Root Tests: ADF, Phillips-Perron, KPSS
VAR Models: Vector Autoregression

Stochastic (Depth Module)

Geometric Brownian Motion: Deriving the Workhorse of Equity Models
Hawkes Processes. Self-Exciting Point Processes for Finance
Hull-White and HJM: Time-Dependent Rates and the No-Arbitrage Condition
Itô's Lemma: The Chain Rule for Random Processes
Lévy Processes Overview. Why Normal Isn't Enough
Martingale Representation: Hedging as Projection
Monte Carlo Variance Reduction: Getting More Bang for Your Buck
Quasi-Monte Carlo. Low-Discrepancy Sequences for Faster Convergence
Risk-Neutral Pricing: Why Derivatives Don't Depend on Expected Returns
The Ornstein-Uhlenbeck Process: Mean Reversion Done Right
The Variance-Gamma Process: Returns With Skew and Kurtosis
Vasicek and CIR: Affine Term Structure Models

Risk Mathematics (Depth Module)

Backtesting VaR and ES: Kupiec, Christoffersen, and the Traffic Light
Clayton, Gumbel, and the Smile of Tail Dependence
CoVaR, MES, and SRISK: Measuring Systemic Risk
EVT for Tail Risk: Fitting GPD to Financial Losses
Expected Shortfall vs VaR: Why Coherence Wins
Extreme Value Theory: From the Fisher-Tippett Theorem to GPD
Liquidity-Adjusted VaR: Bid-Ask, Market Impact, and Time to Liquidate
SPAN and Portfolio Margining: How Derivative Margin Is Actually Computed
Stress Testing: CCAR, Historical Scenarios, and Reverse Stress Tests
The Gaussian Copula: Why Correlation Alone Is Not Enough
The Student-t Copula: Symmetric Tail Dependence Done Right
Value-at-Risk: Deriving the Quantile That's Used Everywhere

Options Pricing (Depth Module)

Black-Scholes Derivation: From Replicating Portfolio to Closed Form
From Binomial Trees to the Black-Scholes PDE: A Limit Argument
Greeks: The Complete Reference, From Delta to Veta
Implied Volatility Solvers: From Newton to Manifold-Free Methods
Interest Rate Models: Vasicek, CIR, Hull-White, HJM
Local Volatility: Dupire's Formula and the Forward Variance
Merton's Jump-Diffusion: Adding Discontinuities to GBM
Path-Dependent Options: Asian, Barrier, Lookback
The Heston Model: Two-State SDE to Semi-Analytical Pricing
The Volatility Smile: Why Implied Vol is a Curve, Not a Number
Variance Swaps and Vol Swaps: Fair Strike, Replicating Portfolios, Convexity
Volatility Surface Construction: From Quotes to an Arbitrage-Free Surface

Portfolio Construction (Depth Module)

Black-Litterman, From First Principles
CVaR Optimization, the Linear Programming Way
Factor Portfolio Construction
Hierarchical Risk Parity, the López de Prado Way
Mean-Variance Optimization, From Scratch
Multi-Period Portfolio Optimization, the Li-Ng Route
Resampled Efficiency, the Bootstrap Way
Risk Parity and the All Weather Logic
Robust Portfolio Optimization, the Goldfarb-Iyengar Way
The Minimum-Variance Portfolio, and Why It's the Only MVO You Should Deploy
Transaction Cost-Aware Portfolio Optimization
Why MVO Weights Are Unstable (And What To Do About It)

Microstructure (Depth Module)

Adverse Selection and Informed Trading: The Glosten-Milgrom Model
Almgren-Chriss Optimal Execution: The Mean-Variance Framework for Trading Trajectories
Amihud's Illiquidity Measure: The Price Impact per Dollar Traded
Avellaneda-Stoikov Market Making: The Reservation Price and Inventory Risk
Bid-Ask Spread Decomposition: Quoted, Realized, Effective, and the Roll Measure
Hasbrouck's Information Share: Measuring What a Trade Tells the Market
Implementation Shortfall: Perold's Fundamental Measure of Execution Cost
Kyle's Lambda and Information Asymmetry: The Auction Model of Price Impact
Limit Order Book Dynamics: Price-Time Priority and Queue Position
Order Types and Time-in-Force: A Practitioner's Map
The Square-Root Law of Market Impact: Why Impact Scales Like √Q
VWAP and TWAP Execution Algorithms: How They Work and When They Fail

Execution (Depth Module)

Aggressive vs Passive Execution: When to Cross the Spread and When to Wait
Broker Selection and Execution Quality: How to Tell If Your Broker Is Actually Doing the Job
Execution Quality Benchmarks: VWAP, TWAP, Arrival, and What They Actually Mean
Iceberg and Hidden Orders: Why They Exist and What They Tell You
Intraday Volume Profiles: The U-Shape, the Double-U, and Why Most of the Day Doesn't Matter
Liquidity Detection and Sourcing: Finding Size That Isn't Displayed
Liquidity-Seeking Algorithms: When to Use a Sniper, a Guerrilla, or a March
Multi-Asset Execution: Coordinated Trading Across Correlated Assets
Order Book Impact and Pre-Trade Cost Estimation
Post-Trade TCA and Attribution: Measuring What Happened and Why
Smart Order Routing: Where Your Order Actually Goes
Transaction Cost Analysis: Knowing What You Actually Paid

Backtest Statistics (Depth Module)

CPCV: Combinatorial Purged Cross-Validation, Properly Derived
CSCV: Combinatorially Symmetric Cross-Validation, Properly Derived
Deflated Sharpe Ratio: Full Derivation
Haircut Sharpe: Correcting for Autocorrelation
Hansen's SPA Test: Superior Predictive Ability, Properly Derived
Minimum Backtest Length (MBL): How Many Years Do You Need?
Multiple Testing Corrections: Bonferroni, Holm, and Benjamini-Hochberg
Probabilistic Sharpe Ratio (PSR): From CLT to Confidence
Probability of Backtest Overfitting (PBO): The Full Derivation
Romano-Wolf Step-Down: Bootstrap Multiple Testing with Power
The Sharpe Ratio, Properly Derived
White's Reality Check: Derivation and the Centering Question

Advanced Machine Learning (Depth Module)

Bagging Classifier for Imbalanced Classes
Cross-Validation in Finance
Entropy Features: Permutation and Sample
Fractional Differentiation: The Derivation
Meta-Labeling: Side and Size
Online Learning Algorithms
Purged k-Fold: The Derivation
Sample Weights: The Four Types
Structural Breaks Detection
Synthetic Data Generation: The Ornstein-Uhlenbeck Backtest
The Embargo Effect
Triple-Barrier Method

Empirical Methodology (Depth Module)

Data Snooping and Stepwise Testing: A P-Hacking Factory
Harvey-Liu-Zhu Multiple Testing: Raising the Factor Bar
In-Sample Tuning vs Out-of-Sample: The Overfitting Pattern
McLean-Pontiff Pre-Registered: The Replication That Publication Created
Multiple Testing in Factor Research: The Garden of Forking Paths
Out-of-Sample vs In-Sample: A False Dichotomy
Post-Publication Decay: What McLean and Pontiff Actually Measured
Reproducibility in Finance: When a Result Cannot Be Re-run
Selection Bias in Published Factors: The Harvey-Liu-Zhu Argument
The 7 Reasons Most Financial Research Fails
What Makes a Factor Real? Five Tests Beyond a Nice Backtest
White's Reality Check Revisited: Applying the Bootstrap

Learning / Advanced Machine Learning (Depth Module)

Advanced Machine Learning (Depth Module)

In progress — this article is outlined and will be published after the beta launch.

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