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![GitHub forks](https://img.shields.io/github/forks/rmcmillan34/algorithmic-trading-learning-roadmap)
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![GitHub contributors](https://img.shields.io/github/contributors/rmcmillan34/algorithmic-trading-learning-roadmap)
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Welcome to the Algorithmic Trading Learning Roadmap repository! This repository provides a structured, comprehensive roadmap for developing expertise in the core skills needed to become a proficient algorithmic trader. It includes resources, certifications, and project ideas across various fields that intersect in the world of algorithmic trading, such as AI, data science, finance, software engineering, cloud computing, and more.
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Welcome to the Algorithmic Trading Learning Roadmap repository! This repository provides a structured, comprehensive roadmap for developing expertise in the core skills needed to become a proficient in the quantitative space. It includes resources, certifications, and project ideas across various fields that intersect in the world of quant, such as mathematics, AI, data science, finance, software engineering, cloud computing, and more.
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## Repository Overview
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This repository is organized in a hierarchical folder structure, each top-level folder focusing on a key area of knowledge essential for algorithmic trading. Each top-level folder contains topics and Markdown files listing curated resources, recommended certifications, project ideas, and guidelines to help build foundational and advanced skills in that topic.
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This repository is organised in a hierarchical folder structure. Each top-level folder focuses on a key area of knowledge essential for algorithmic trading. Each top-level folder contains topics and Markdown files listing curated resources, recommended certifications, project ideas, and guidelines to help build foundational and advanced skills in that topic.
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## Table of Contents
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- [Purpose](#purpose)
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- [License](#license)
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## Purpose
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After recently deciding that I wanted to undertake the challenge of becoming a quantitative developer by occupation and algorithmic trader by hobby, I noticed there was a lot of information across many domains that I would need to get my head around. I also noticed that there was alot of overlap between the roles of a an algorithmic trader, quantitative developer, quantitative researcher and quantitative trader.
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After recently deciding that I wanted to undertake the challenge of becoming a quantitative developer by occupation, and algorithmic trader by hobby, I noticed there was a lot of information across many domains that I would need to get my head around. I also came to understand that there was some overlap between the roles of an algorithmic trader, quantitative developer, quantitative researcher and quantitative trader. As such, I wanted to document what I have learned about the space and provide a single location for others to find the resources I have come across.
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I personally learn best when I have a structured list outlining what I need to accomplish. The purpose of this repository is to provide myself and anyone who is on a similar path a self-paced, modular, curated learning template. The following key domains are what I found relevant:
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Personally, I learn best when I have a structured list outlining what I need to accomplish. The purpose of this repository is to provide myself and anyone who is on a similar path a self-paced, modular, curated learning template. The following key domains are what I found relevant:
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- **Artificial Intelligence**: Applying machine learning techniques to create predictive models for trading.
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- **Algorithmic Trading**: Developing, testing, and optimising trading algorithms.
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- **General Skills**: Technical Writing, Databases, Project management, and more.
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- **Software Engineering**: Writing clean, efficient code and following best practices for project deployment and version control.
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This repository is designed to be used as a local repository, to organise digital resources and learning materials, adaptable for individual use or collaboration, and suitable for any level of experience.
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This repository is designed to be used as local storage, to organise digital resources and learning materials, adaptable for individual use or collaboration, and suitable for any level of experience.
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## Why Build This Roadmap?
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After completing more than 15 years as an electronics technician, I realised that I am ready for a change, and since I’m yet to hit the lottery, **algorithmic trading felt like the next best shot** at financial independence and reclaiming time back with the added benefit of needing less luck and more skill.
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After completing more than 15 years as an electronics technician, I have realised that I am ready for a change, and since I’m yet to hit the lottery, **algorithmic trading felt like the next best shot** at financial independence and reclaiming time back with the added benefit of needing less luck and more skill.
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After, researching and dabbling, I accumulated **hundreds of gigabytes of books, courses and materials**, I knew that I needed a system to avoid drowning in content. I also wanted to find the **best possible resources** without wasting time, noting there was so much to learn. So I’ve created this repository to gather and organise them all in one place.
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..And this is what I came up with, a **modular learning system** I’m building to keep myself accountable, organised, and always moving toward my goal.
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If you’re also chasing financial freedom through algorithmic trading, or a career change into a quant finance role — and want a roadmap/information that respects both depth and structure, then feel free to download this repo and go forth.
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If you’re also chasing financial freedom through algorithmic trading, or a career change into a quant finance role — and want a roadmap/information that respects both depth and structure, then feel free to look around, download this repo, check out the resources and go forth.
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> **Feel free to star the repo, share it with others, or open a pull request to improve any section.**
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## Usage Guide
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Across all of the domains, there is enough breadth that I am certain that noone could learn all of it, so this is not intended to be a start at the start and work through to the end of each and every domain. If you want a guided path depending on your target role/goal, start here:
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Across all of the domains, there is enough breadth that I am certain that no one could learn all of it, in a realistic timeframe. Therefore, this is not intended to be a start at the start and work through to the end of every domain. If you want a guided path depending on your target role/goal, start here:
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- **Core Skill Stack**[roadmap/personas/core/README.md](roadmap/personas/core/README.md)
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- **Algorithmic Trader**[roadmap/personas/algorithmic-trader/README.md](roadmap/personas/algorithmic-trader/README.md)
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- **Quantitative Developer**[roadmap/personas/quantitative-developer/README.md](roadmap/personas/quantitative-developer/README
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- **Quantitative Developer**[roadmap/personas/quantitative-developer/README.md](roadmap/personas/quantitative-developer/README.md)
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- **Quantitative Researcher**[roadmap/personas/quantitative-researcher/README.md](roadmap/personas/quantitative-researcher/README.md)
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- **Quantitative Trader**[roadmap/personas/quantitative-trader/README.md](roadmap/personas/quantitative-trader/README.md)
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- `C++`: Primary language used in Quantitative Development
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- `Python`: Primary language for algorithmic trading projects.
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- `R`: Primary language used for data analysis by Quantitative Researchers
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- `Jupyter Notebooks`: For data science and machine learning experimentation.
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- `Jupyter Notebooks`: For prototyping, and data science and machine learning experimentation.
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- `Git`: For version control and collaboration.
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- `Cloud Services`: Set up accounts on AWS, GCP, or Azure for cloud projects.
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- `Anki`: For spaced repetition learning and flashcards.
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- `Obsidian`: For note-taking and organising resources.
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- `Trello`: For project management and tracking progress.
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## Contributing
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We welcome contributions to expand and improve the resources referenced in this repository! To contribute:
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I welcome contributions to expand and improve the resources referenced in this repository! To contribute:
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1. **Fork this Repository**:
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- Create a new branch for your contribution, sourcing from the appropriate category branch.

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