Coursera Machine Learning for Trading 専門講座

Start Your Career in Machine Learning for Trading. Learn the machine learning techniques used in quantitative trading.

Index

Specialization : Machine Learning for Trading Specialization

Course 1 : ......
Course 2 : ......
Course 3 : ......

Course 1 : Introduction to Trading, Machine Learning & GCP

Wk 1 : Introduction to Trading with Machine Learning on Google Cloud
Wk 2 : Supervised Learning with BigQuery ML
Wk 3 : Time Series and ARIMA Modeling
Wk 4 : Introduction to Neural Networks and Deep Learning

Course 2 : Using Machine Learning in Trading and Finance

Wk 1 : Introduction to Quantitative Trading and TensorFlow
- Wk 1.1 : Introduction to TensorFlow, Trading, ML
  - Wk 1.1.1 : 動画: Introduction to Course
  - Wk 1.1.2 : 学習用教材: Welcome to Using Machine Learning in Trading and Finance
- Wk 1.2 : Understand Quantitative Trading Strategies
  - Wk 1.2.1 : 動画: Basic Trading Strategy Entries and Exits Endogenous Exogenous
  - Wk 1.2.2 : 動画: Basic Trading Strategy Building a Trading Model
  - Wk 1.2.3 : 動画: Advanced Concepts in Trading Strategies
  - Wk 1.2.4 : テスト: Understand Quantitative Strategies
- Wk 1.3 : Introduction to TensorFlow
  - Wk 1.3.1 : 動画: Overview
  - Wk 1.3.2 : 動画: Introduction to TensorFlow
  - Wk 1.3.3 : 動画: TensorFlow API Hierarchy
  - Wk 1.3.4 : 動画: Components of tensorflow Tensors and Variables
  - Wk 1.3.5 : 動画: Getting Started with Google Cloud Platform and Qwiklabs
  - Wk 1.3.6 : 動画: Lab Intro Writing low-level TensorFlow programs
  - Wk 1.3.7 : 評価済みの外部ツール: Lab: Writing low-level TensorFlow Programs
  - Wk 1.3.8 : 動画: Working in-memory and with files
  - Wk 1.3.9 : 動画: Training on Large Datasets with tf.data API
  - Wk 1.3.10 : 動画: Getting the data ready for model training
  - Wk 1.3.11 : 動画: Embeddings
  - Wk 1.3.12 : 動画: Lab Intro Manipulating data with TensorFlow Dataset API
  - Wk 1.3.13 : 評価済みの外部ツール: Lab: Manipulating data with TensorFlow Dataset API
Wk 2 : Training neural networks with Tensorflow 2 and Keras
- Wk 2.1 : Overview of Neural Networks and Introduction to Keras APIs
  - Wk 2.1.1 : 動画: Overview
  - Wk 2.1.2 : 動画: Activation functions
  - Wk 2.1.3 : [動画: Activation functions: Pitfalls to avoid in Backpropagation(https://www.youtube.com/watch?v=ztYFTRqI-Tw)
  - Wk 2.1.4 : 動画: Neural Networks with Keras Sequential API
  - Wk 2.1.5 : 動画: Serving models in the cloud
  - Wk 2.1.6 : 評価済みの外部ツール: Lab Intro : Keras Sequential API
  - Wk 2.1.7 : 動画: Neural Networks with Keras Functional API
  - Wk 2.1.8 : 動画: Regularization: The Basics
  - Wk 2.1.9 : 動画: Regularization: L1, L2, and Early Stopping
  - Wk 2.1.10 : 動画: Regularization: Dropout
  - Wk 2.1.11 : 評価済みの外部ツール: Lab Intro: Keras Functional API
  - Wk 2.1.12 : 動画: Recap
Wk 3 : Build a Momentum-based Trading System
- Wk 3.1 : Identify momentum-based factors
  - Wk 3.1.1 : 動画: Introduction to Momentum Trading
  - Wk 3.1.2 : 動画: Introduction to Hurst
  - Wk 3.1.3 : 学習用教材: Hurst Exponent and Trading Signals Derived from Market Time Series
- Wk 3.2 : Build a trading model that uses momentum factors
  - Wk 3.2.1 : 動画: Building a Momentum Trading Model
  - Wk 3.2.2 : 動画: Define the Problem
  - Wk 3.2.3 : 動画: Collect the Data
  - Wk 3.2.4 : 動画: Creating Features
  - Wk 3.2.5 : 動画: Split the Data
  - Wk 3.2.6 : 動画: Selecting a Machine Learning Algorithm
  - Wk 3.2.7 : 動画: Backtest on Unseen Data
  - Wk 3.2.8 : 動画: Understanding the Code: Simple ML Strategies to Generate Trading Signal
  - Wk 3.2.9 : ディスカッションのプロンプト: Compare interpretability versus explanatory power of the momentum factor
  - Wk 3.2.10 : 動画: Lab Intro: Momentum Trading
  - Wk 3.2.11 : 評価済みの外部ツール: Lab: Momentum Strategies
  - Wk 3.2.12 : 動画: Momentum Trading Lab Solution
  - Wk 3.2.13 : 未評価の外部ツール: Optional Lab: Improve Momentum Trading strategies using Hurst
Wk 4 : Build a Pair Trading Strategy Prediction Model
- Wk 4.1 : Picking Pairs
  - Wk 4.1.1 : 動画: Introduction to Pair Trading
  - Wk 4.1.2 : 動画: Picking Pairs
  - Wk 4.1.3 : 動画: Picking Pairs with Clustering
- Wk 4.2 : Trading Strategy
  - Wk 4.2.1 : 動画: How to implement a Pair Trading Strategy
  - Wk 4.2.2 : 動画: Evaluate Results of a Pair Trade
- Wk 4.3 : Backtesting and Avoiding Overfitting
  - Wk 4.3.1 : 動画: Backtesting and Avoiding Overfitting
  - Wk 4.3.2 : 動画: Next Steps: Imrovements to your Pair Strategy
  - Wk 4.3.3 : 動画: Lab Intro: Pairs Trading
  - Wk 4.3.4 : 評価済みの外部ツール: Lab: Pairs Trading Strategy
  - Wk 4.3.5 : 動画: Lab Solution: Pairs Trading
- Wk 4.4 : Optimize momentum trading model to minimize costs
  - Wk 4.4.1 : 動画: Kalman Filter Introduction
  - Wk 4.4.2 : 動画: Kalman Filter Trading Applications
  - Wk 4.4.3 : テスト: Pairs Trading Strategy concepts
  - Wk 4.4.4 : 未評価の外部ツール: Optional Lab: Estimate parameters using Kalman Filters

Course 3 : Reinforcement Learning for Trading Strategies

Wk 1 : Introduction to Course and Reinforcement Learning
Wk 2 : Neural Network Based Reinforcement Learning
Wk 3 : Portfolio Optimization

Miscellaneous

There have a RMarkdown file and also a copy of pdf and png format certificate of accomplishment in every single course (folders inside repo).

![Awarded on 08 June 2022](.......png

Above png file and feel free to download .......pdf.

Coursera Verified Certificate

Certificate of Specialization : ......

Cert 1 : ......
Cert 2 : ......
Cert 3 : ......
Cert 4 : ......
University : New York Institute of Finance

Reference

ML for Trading - 2nd Edition (GitHub) ❤️‍🔥
算法交易中的机器学习系列（一） ❤️‍🔥
Machine Learning for Algorithmic Trading (Official) ❤️‍🔥
ML for Trading (Exchange) ❤️‍🔥
Machine Learning for Algorithmic Trading (2nd Edition).pdf ❤️‍🔥

纽约金融学院（ニューヨーク金融研究所）

hopperhuang/Coursera-Machine-Learning-for-Trading