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建立時間： 2026-03-04 來源： https://x.com/gemchange_ltd/status/2028904166895112617
Summary
A structured, 18-month self-study roadmap to become a quant, framed as non-skippable video-game levels: probability, statistics, linear algebra, calculus/optimization, and stochastic calculus — each with textbooks, homework, and Python code. It then covers prediction-market pricing (LMSR as softmax), the four quant archetypes and their compensation, the interview gauntlet, a full Python/C++ toolbox, and a reading list.
這是一份結構化的 18 個月自學路線圖，將成為 quant 的過程比喻成不可跳級的遊戲關卡：機率、統計、線性代數、微積分/最佳化、隨機微積分——每關都附教科書、作業與 Python 程式碼。接著涵蓋預測市場定價（LMSR 即 softmax）、四種 quant 原型及其薪酬、面試關卡、完整的 Python/C++ 工具箱與閱讀清單。
Key Points
- Quant trading is math, not stock-picking: probability (Bayes, EV/variance), statistics (hypothesis testing, multiple-comparisons/Bonferroni, regression alpha, MLE), linear algebra (covariance, eigendecomposition/PCA, Markowitz), calculus/optimization, stochastic calculus (Brownian motion, Itô’s lemma, deriving Black-Scholes).
- Each level has concrete homework with code (law of large numbers, MLE t-fit, PCA, Markowitz via cvxpy, Black-Scholes vs Monte Carlo).
- LMSR (Hanson) prices prediction markets via softmax with bounded market-maker loss b·ln(n).
- Four archetypes: Quant Researcher, Quant Developer, Quant Trader, Risk Quant; AI/ML quant hiring up 88% YoY; top-tier new-grad comp 500K+.
- Toolbox: pandas/polars, numpy/scipy, xgboost/lightgbm, pytorch, cvxpy, QuantLib, statsmodels; data via yfinance/Polygon/Bloomberg.
Insights
The author’s three closing lessons are the durable takeaways: estimation error (overfitting noisy parameter estimates) is the real enemy; tools have democratized but conviction hasn’t (edge lives in unique data/models/execution, not pip installs); and the math is the moat because AI can write code but can’t substitute for deriving why Itô’s lemma carries an extra term. The “noisy BS vs not-BS” refrain is really a repeated warning about signal-vs-noise discipline at every level.
Connections
Raw Excerpt
The math is the moat. AI can write code and suggest strategies. But the ability to derive why Itô’s lemma has an extra term … that mathematical fluency separates quants who build edge from quants who borrow it. And borrowed edge expires.