Udemy – Become a Quant: Algorithmic Trading with Python and AI 2026-2

Updated August 10, 2026 9.98 GB

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Descriptions

Become a Quant: Algorithmic Trading with Python and AI, Welcome to Become a Quant: Algorithmic Trading with Python and AI. This hands‑on, step‑by‑step program teaches you to build, backtest and deploy trading bots across multiple asset classes — stocks, options, futures, forex and crypto — using QuantConnect as the execution and backtesting platform. The course covers the full algorithmic trading workflow: Python essentials for trading, data ingestion and consolidation, indicator engineering, portfolio and risk management, order handling and scheduled events, plus advanced features like market scanners, financial‑statement and sentiment analysis, and managing options/futures expiries and Greeks. You will also learn to accelerate development with AI copilots and integrate machine‑learning forecasts (Random Forest, XGBoost) into live strategies and signal exports for real trading.

What you’ll learn

Multi-Asset: Build trading bots for stocks, options, futures, forex, and crypto!
Machine Learning: Build Random Forest and XGBoost models to analyze market data and integrate them into your trading bots!
Algorithm Structure: Learn every component of a trading bot from position sizing and risk management to order handling!
Generative AI: Accelerate algorithm development and create advanced functionalities using AI!
Complex Asset Classes: Learn contract expiry management for options and futures, plus learn to handle the options Greeks!
Sophisticated Functionalities: Learn to code advanced tools like market scanners, news sentiment analysis, and scheduled events!
Live-Trading Deployment: Learn to deploy, monitor, and manage trading bots in live markets!
Backtesting: Learn to backtest trading strategies using real-world historical market data!

Who this course is for

QuantConnect users seeking a complete, step-by-step tutorial that covers every core API module.
Traders who want to reduce their workload by automating their trading decisions.
Finance students who want to supplement their learning and apply classroom theory in real-world market environments.
Finance professionals who want to impress their boss and get ahead of their peers by testing and proposing high-quality trading strategies their firm can use.
Aspiring quants who need the skills to code trading algorithms across multiple asset classes to excel in job interviews and prove their capabilities on the job.