LSTM Basics

Lesson Overview

In "Machine Learning for Trading" → "LSTM Prediction", this lesson "LSTM Basics" builds knowledge you can apply immediately.

LSTM handles sequence dependency. Input: past N-day return sequence. Output: next-day direction. Keras/PyTorch demo on EUR/USD.

Core Concepts

These points align with the video walkthrough—take notes as you watch.

• ▸LSTM handles sequence dependency.
• ▸Input: past N-day return sequence.
• ▸Output: next-day direction.
• ▸Keras/PyTorch demo on EUR/USD.

Practical Application

After studying, complete the companion practice lesson to move from understanding to doing.

• ▸Follow along in Jupyter or VS Code
• ▸Log how parameter changes affect backtest results
• ▸Submit a reproducible analysis notebook

FAQ

• ▸Q: How does this lesson fit the course? A: Part of "LSTM Prediction"—follow the curriculum order.
• ▸Q: What do I need? A: Demo account and notes; quant lessons need a Python environment.
• ▸Q: How do I know I've mastered it? A: Pass the quiz and explain three key points of LSTM Basics on your own.