Quantitative finance course catalogue
Browse Argos Academy modules, from Python foundations to portfolio research, strategy design and risk.
- Quantitative finance, from zero
No prerequisites. What markets, returns and risk are, and what a quant actually does — explained as if you knew nothing.
- Setting up
Install VSCode, Python and the working environment. First quest: run your first quant script.
- Introduction to smart beta
Beta, alpha and the space in between: history from Markowitz to factor ETFs, why the premia exist, what smart beta is not, and its documented limits.
- Essential mathematics
Every formula introduced by the problem it solves, with a worked numerical example. Returns, aggregation, estimation error, covariance, portfolio algebra, distributions and the mean-variance program.
- Factors, from theory to portfolio
Value, momentum, size, quality, low-vol, carry; Fama-French and Carhart models; score construction; long-only vs long/short portfolios; and an interactive lab separating real signal from chance.
- The pipeline, brick by brick
The core of the track: returns → robust covariance → risk aversion λ → optimisation → final weights, with an interactive λ demo.
- Code — from first script to a portfolio of strategies
Eleven progressive VSCode exercises: pandas toolkit, reusable backtest engine, then four strategy families coded from scratch — mean reversion, trend following, stock picking, pairs trading — plus vol targeting and combination.
- Project — full backtest
The capstone: code a momentum backtest from scratch, block by block, and match the expected results to the digit.
- Your first strategy on real data
The bridge between the track and the data workspace: download real ES minute bars, clean them, aggregate, write a strategy from a stated hypothesis, and run it through the robustness framework.
- Look-ahead — the bug that kills edges
The single most destructive error in quantitative research: using information that did not exist yet. Where it hides, how to hunt it, and why no statistic can detect it for you.
- Robustness — the 27 tests that kill a strategy
The decisive module: 27 tests in six families, from purging & embargo to permutation, PBO, capacity and kill-switch — every one run on the module 07 backtest, up to the verdict.
- Metrics & diagnostics
The full strategy dashboard: Sharpe, Sortino, Calmar, CAGR, PF, IC/IR, log equity vs benchmark, rolling metrics, P&L distribution, Monte Carlo.
- Data & library
Where to find usable data, how to load it cleanly, and the reference books in reading order.
- The authors library
38 practitioners and researchers organised by school of thought: trend following, system building, risk & sizing, factors, statistics/ML, options, market wisdom.
- Market finance foundations
Products, participants, microstructure and execution mechanics: what actually happens between your order and your fill.
- Derivatives
Pricing, Greeks, dynamic hedging and volatility surfaces: why an option is a bet on volatility, not on direction.
- Stat-Arb & Pair Trading
Cointegration, Ornstein-Uhlenbeck, signal construction and execution: the only family where the edge is provable before trading.
- Market Making
Where the spread comes from, how adverse selection kills you, how to manage inventory, and how to find a venue worth quoting on. With Avellaneda-Stoikov and batch auctions.
- Stochastic calculus
Brownian motion, geometric Brownian motion, Itô's lemma and the Black-Scholes PDE: the mathematical machinery under every derivatives model, derived rather than asserted.
- From backtest to live account
Capital, leverage and the Kelly criterion; broker and infrastructure choices; execution systems; why live performance always diverges from the backtest; and psychological preparation.
- Alpha R&D papers
Applied, reproducible research: SVM microstructure strategies, neural network option pricing, HMM regime detection. Each paper is a documented edge plus the code to replay it.
- Executable notebooks
Black-Scholes-Merton, Heston, Bates, Variance Ratio and neural pricing: runnable code, ready to paste into Jupyter or Colab.
- Options: the contract and the price
From the contract to Black-Scholes, built step by step: payoffs, arbitrage bounds, parity, replication, binomial tree, then the formula as the tree's continuous limit.
- The greeks and hedging
The nine sensitivities, measured first by finite differences then derived analytically, the volatility surface, and the proof that a hedged position's P&L depends only on the volatility gap.
- Option strategies
Fourteen structures with their diagrams, a generic engine to code them all, building from a real chain, autocall pricing, and an honest evaluation of volatility selling.