Quant Roadmap

Quant Guild Roadmap by Roman Paolucci · fuente original
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Quant Roadmap Instructions

This is the Quant Guild roadmap, created by Roman Paolucci, a recommended order of study with clear goals at intermediate stages.

Your objective is to learn and be capable of applying the tools you learn in the real world - to do this you must understand NOT memorize or just "try and pass" a class.

is always available with adaptive practice, async and live courses, lessons, and more to provide you with materials so you can spend more time learning and practicing and less time searching for materials.

🚀 Master Quant Skills on Quant Guild

Stages to Learning Something New

3 Stages to Learning Something
1.) Studying It (Lecture, Reading, etc.)
2.) Practice It (Problem Sets)
3.) Applying It (Real World Problems)

Quant Guild has you covered for (1 & 2), step 3 is all about coming up with your own projects applying what you've learned to the real world. Experience is quite literally the best way to learn.

Where Do I Find Resources If They Aren't Listed?

I likely have a free YouTube Video dedicated to the subject.

Check Out Quant Guild on YouTube

Many of these topics are found in courses in High School and College. You should be enrolling in a quantitative degree program where you can master the relevant skills in each discipline, you can then supplement your knowledge gaps (these will always naturally arise no matter how hard you study) with material (like on Quant Guild). It is a marathon, not a sprint, remember this and keep learning.

How Much Do I Need To Learn?

Do I Need to Learn Everything?

This depends on your goal, you don't have to learn everything, but I would encourage you to learn as much as possible. I would recommend the following minimum levels for each role. . .

Quant Trader

  • Math: Level 2
  • Computer Science: Level 2
  • Machine Learning: Level 2
  • Probability & Statistics: Mastery

Quant Developer

  • Math: Level 2
  • Computer Science: Mastery
  • Machine Learning: Level 3
  • Probability & Statistics: Level 2

Quant Researcher

  • Math: Mastery
  • Computer Science: Level 3
  • Machine Learning: Level 3
  • Probability & Statistics: Mastery
Quant Roadmap
LEVEL 1
Computer Science
  • Programming Fundamentals and Tools
  • Basic Programming (Python/Java/JavaScript syntax)
  • Variables, Data Types, and Operators
  • Control Flow (If/Else, Loops)
  • Basic Data Structures (Lists, Arrays, Dictionaries, Objects)
  • Functions and Procedural Programming
  • Command Line Interface (CLI) usage
  • Basic Git and version control
  • Introduction to HTML/CSS/JS for UI concepts
🚀 Learn This Here (Quant Coding Course)
Mathematics
  • Algebra and Geometry
  • Solving Linear and Quadratic Equations
  • Functions and their Graphs
  • Polynomials and Rational Functions
  • Exponents and Logarithms
  • Coordinate Geometry
  • Basic Proofs
🚀 Learn This Here (Math Level I)
Finance & Economics
  • Market and Macroeconomic Basics
  • Stocks, Bonds, Mutual Funds
  • Saving vs. Investing
  • Time Value of Money (Future Value, Present Value)
  • Supply and Demand (Microeconomics basics)
  • Inflation and Interest Rates
  • Gross Domestic Product (GDP)
  • Monetary vs. Fiscal Policy
🚀 Learn This Here (Finance Level I)
Probability & Statistics
  • Core Statistical and Probabilistic Concepts
  • Mean, Median, Mode, Range
  • Variance and Standard Deviation
  • Sample Spaces and Events
  • Conditional Probability
  • Combinatorics (Permutations and Combinations)
  • Set Theory Basics
🚀 Learn This Here (Probability Level I)

Level 1 Learning Goals

If you LEARN you can DO

At this point, you should be able to. . .

LEVEL 2
Computer Science
  • Advanced DS/Algo and System Concepts
  • Advanced DS: Trees (BST, AVL), Heaps, Graphs, Hash Tables
  • Sorting Algorithms (Merge, Quick, Heap sort)
  • Searching Algorithms (DFS, BFS, Binary Search)
  • Dynamic Programming (Basic problems)
  • Time/Space Complexity (O(n) notation)
  • Object-Oriented Programming (OOP) principles
  • Database Fundamentals (SQL, Relational Databases)
Mathematics
  • Calculus and Linear Algebra
  • Limits, Derivatives, and Integrals
  • Optimization (Finding Minima/Maxima)
  • Multivariate Calculus (Partial Derivatives, Gradients)
  • Vectors, Matrices, and Matrix Operations
  • Eigenvalues and Eigenvectors
  • Solving Systems of Linear Equations
🚀 Learn This Here (Math Level I, II, III)
Probability & Statistics
  • Probability Distributions and Inference
  • Random Variables (Discrete and Continuous)
  • Common Distributions (Binomial, Poisson, Normal, T-distribution)
  • Central Limit Theorem
  • Hypothesis Testing (T-tests, Z-tests)
  • Confidence Intervals
  • Linear Regression (Model assumptions, residual analysis, R^2)
🚀 Learn This Here (Probability Level II)
Machine Learning
  • Core Machine Learning Principles
  • Supervised vs. Unsupervised Learning
  • Bias-Variance Tradeoff
  • Cross-Validation and Model Evaluation Metrics
  • Linear/Logistic Regression
  • Decision Trees and Ensemble Methods (Random Forests, Gradient Boosting)
  • Support Vector Machines (SVM)
  • K-Nearest Neighbors (KNN)
  • Introduction to Neural Networks (Perceptrons, Backpropagation)
Finance & Economics
  • Financial Instruments and Modern Theory Critique
  • Options, Futures, Swaps (Derivatives Basics)
  • Fixed Income (Yield, Duration, Convexity)
  • Foreign Exchange (FX)
  • Capital Asset Pricing Model (CAPM)
  • Efficient Market Hypothesis (EMH)
  • The concept of Alpha (alpha) and its existence
  • Valuation (DCF) and Risk Management basics
🚀 Learn This Here (Finance Level I, II, III)

Level 2 Learning Goals

Understanding allows you to think critically and create NEW extensions

At this point, you should be able to. . .

LEVEL 3
Computer Science
  • Advanced Search, Optimization, and Systems
  • Numerical Optimization Techniques (Gradient Descent variants, Newton's method)
  • Simulated Annealing and Genetic Algorithms (Stochastic Search)
  • Low-Latency Architecture (Market data pipelines, execution systems)
  • Concurrency and Parallelism
  • Distributed Systems (Microservices, message queues)
Mathematics
  • Differential Equations and Stochastic Calculus
  • Ordinary and Partial Differential Equations (PDEs)
  • Solving the Black-Scholes-Merton PDE
  • Brownian Motion and Geometric Brownian Motion (GBM)
  • Itô's Lemma (The fundamental theorem of stochastic calculus)
  • Stochastic Differential Equations (SDEs)
  • Monte Carlo Simulation (for pricing and risk)
  • Finite Difference Methods
🚀 Learn This Here (Math & Probability Level IV, V)
Probability & Statistics
  • Time Series and Advanced Statistics
  • Autoregressive (AR) and Moving Average (MA) models
  • ARMA/ARIMA/GARCH models for volatility modeling
  • Stationarity, Unit Root Tests
  • Bayesian Inference (Prior, Likelihood, Posterior)
  • Nonparametric Methods
  • Robust Statistics (Handling outliers and non-normal data)
Machine Learning
  • ML for Time Series and Advanced Concepts
  • Features Engineering specific to finance (Volume, Volatility, Order Book Data)
  • Walk-Forward Validation and rigorous backtesting (avoiding lookahead bias)
  • Reinforcement Learning (RL) (Policy Search, Q-Learning)
  • Deep Learning (RNNs, LSTMs for sequence data)
  • Causal Inference
  • Methods for combining Alpha (Ensemble models, Cross-sectional vs. Time-series approaches)
Finance & Economics
  • Behavioral Finance and Advanced Risk Management
  • Cognitive Biases and Market Anomalies
  • Value at Risk (VaR) and Conditional VaR (CVaR)
  • Tail Risk and Black Swan events
  • Market Microstructure (Order types, Latency Arbitrage, Market Impact)
🚀 Learn This Here (Finance Level IV, V)

Level 3 Learning Goals

New quantitative ideas come from MASTERY of foundational material

At this point, you should be able to. . .

Fuente: roadmap.sh/r/quant-roadmap-bzunq · Autor: Roman Paolucci (Quant Guild). Esta página local replica la estructura del roadmap original con fines de estudio personal.
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