Become a Quant Algorithmic Trading with Python and AI (2026)

This course is built from years of real-world experience solving one recurring problem: students want to build real trading bots, but get stuck between theory, complex APIs, and fragmented resources. In Become a Quant, I guide you step by step through the exact workflow used by professional quant developers—from Python fundamentals to machine learning, AI-assisted coding, and live-trading preparation on QuantConnect. Instead of overwhelming you with unnecessary theory, I focus on practical problem-solving: how to structure a trading algorithm, how to avoid common QuantConnect mistakes, how to debug, backtest, and improve strategies efficiently, and how to use AI tools as a productivity multiplier rather than a shortcut. Every concept is explained through clear examples, real trading logic, and progressive projects, so you always understand why you are doing something—not just what to type. By the end of the course, you won’t just “know Python” or “know QuantConnect” — you’ll be able to design, test, and prepare professional-grade algorithmic trading systems across multiple asset classes.

강의소개.상단개요.수강생

난이도 입문

수강기한 무제한

Python
Python
Machine Learning(ML)
Machine Learning(ML)
algorithmic-trading
algorithmic-trading
quantconnect
quantconnect
Python
Python
Machine Learning(ML)
Machine Learning(ML)
algorithmic-trading
algorithmic-trading
quantconnect
quantconnect

강의상세_배울수있는것_타이틀

  • Build complete algorithmic trading bots using Python and QuantConnect

  • Use AI tools to speed up development and create advanced trading features

  • Design, backtest, and evaluate trading strategies with professional metrics

  • Implement machine learning models (Random Forest & XGBoost) for price forecasting

  • Manage orders, portfolios, and risk across stocks, options, futures, forex, and crypto

  • Prepare trading systems for live deployment and real-world signal export

Become a Quant: Build AI-Powered Trading Bots with Python

Short description (for subtitle / preview):
Learn how professional quants design, backtest, and prepare algorithmic trading systems for stocks, options, futures, forex, and crypto using Python, AI, and machine learning.

Industries / Fields:
Quantitative Finance · Algorithmic Trading · FinTech · Data Science · Financial Engineering

Why I created this course:
After working with traders, finance students, and professionals who struggled with fragmented tutorials and overly theoretical material, I designed this course to remove confusion and focus on real, deployable skills.
This course reflects the exact workflow used by professional quant developers, including how AI tools are used in practice—not as shortcuts, but as productivity multipliers.

What You’ll Learn

Section (1): Core Keywords — Foundations of Algorithmic Trading

In this section, you’ll build a strong foundation in Python and algorithmic trading logic, even if you start with zero coding experience.

You will learn how to:

  • Understand and write Python code specifically for trading systems

  • Structure clean, scalable trading algorithms

  • Work with financial time series, candlestick data, and indicators

  • Use AI tools to accelerate coding while fully understanding the logic

  • Backtest strategies using professional historical market data

Section (2): Core Keywords — Advanced Trading, AI & Machine Learning

This section focuses on professional-level trading systems across multiple asset classes, enhanced with AI and machine learning.

You will learn how to:

  • Build trading bots for stocks, options, futures, forex, and crypto

  • Manage portfolios, orders, risk, and scheduled trading events

  • Access alternative data and consolidate high-frequency data

  • Train and deploy machine learning models (Random Forest & XGBoost)

  • Integrate ML predictions into real trading logic

  • Prepare strategies for live trading and real-world signal export

Before You Enroll

Prerequisites & Notices

Prior Knowledge:

  • No prior programming or quantitative trading experience is required.

  • The course starts from Python basics and gradually reaches an intermediate/advanced quant level.

  • Basic familiarity with financial markets is helpful but not mandatory.

Audio & Video Quality:

  • All lectures are recorded in high-resolution video with clear audio.

  • Code demonstrations are shown step by step with on-screen explanations.

Recommended Study Method:

  • Follow along by coding during the lectures.

  • Pause, experiment, and modify strategies to build real understanding.

  • Revisit backtests and performance reports to analyze results critically.

Questions & Support:

  • Students can ask questions in the course Q&A section.

  • Common issues and misunderstandings are addressed with clarifications and updates.

Course Updates:

  • The course may receive updates to reflect API changes, new features, or improved workflows.

Copyright & Intellectual Property Notice:

  • All course materials, including videos, slides, and source code, are protected by copyright.

  • Materials are for personal educational use only and may not be redistributed, resold, or republished without permission.

강의소개.콘텐츠.추천문구

학습 대상은 누구일까요?

  • Traders who are frustrated by manual trading and want to automate their strategies reliably

  • Finance students who understand theory but struggle to apply it to real market data

  • QuantConnect users overwhelmed by the API and looking for a structured, end-to-end guide

  • Finance professionals who want to test, validate, and propose data-driven strategies at work

  • Aspiring quants preparing for interviews or real trading roles across multiple asset classes

선수 지식, 필요할까요?

  • No prior programming or quantitative trading knowledge is required. The course starts from Python fundamentals and gradually builds up to: professional algorithmic trading logic, machine learning integration, and live-trading preparation. A basic understanding of financial markets is helpful but not mandatory. All required concepts are explained as they are used.

강의소개.지공자소개

Brendan is a UCLA grad and ex-Technology Investment Banker specialized within the AI/ML field. He has helped leading founders in the AI / no-code space implement growth strategies and exit businesses at 9 figure valuations.

He has a strong passion for helping anybody build complex apps with AI, even with no technical knowledge.

더보기

커리큘럼

전체

63개 ∙ (강의상세_런타임_시간 강의상세_런타임_분)

해당 강의에서 제공: [object Object]
강의 게시일: 
마지막 업데이트일: 

수강평

아직 충분한 평가를 받지 못한 강의입니다.
모두에게 도움이 되는 수강평의 주인공이 되어주세요!

Brendan LI님의 다른 강의

지식공유자님의 다른 강의를 만나보세요!

비슷한 강의

같은 분야의 다른 강의를 만나보세요!

강의상세.할인문구

$44.00

29%

$62.70