Quant Trading using Machine Learning (Udemy) на языке Python

Складчина: Quant Trading using Machine Learning (Udemy) на языке Python

Видеокурс (на английском языке) Quant Trading using Machine Learning (Udemy) - Количественный трейдинг с использованием алгоритмов машинного обучения

Автор Loony Corn
Последнее обновление 7/2017.
11 часов видео, 67 лекций.
Почти 40 000 студентов; 5 071 отзывов о курсе со средней оценкой 4.3.
Со скидкой 80% - всего за 10 USD.
Требуются начальные знания языка Python (в курсе используется версия 2.7).

Чему я научусь?
Разработка количественных торговых систем с использованием методов машинного обучения.
Сравнение систем на основе значения коэффициента Шарпа.
Используемые алгоритмы: случайный лес (random forest), k ближайших соседей (K-Nearest Neighbors), градиентный бустинг деревьев решений (Gradient Boosted trees).
Выделение значимых признаков (Feature engineering), подбор параметров (parameter tuning), как избежать переобучения (overfitting).
Весь путь от получения исходных данных до конечного продукта.
Работа с базой данных MySQL для хранения исторических данных
Библиотеки Python : Pandas, Scikit-Learn, XGBoost, Hyperopt

Учебный план курса:

Спойлер

Лекций: 67 11:08:02
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You, This Course and Us 02:00
You, This Course and Us 02:00
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Developing Trading Strategies in Excel 01:10:36
Are markets efficient or inefficient? 10:27
Momentum Investing 11:31
Mean Reversion 06:30
Evaluating Trading Strategies - Risk And Return 16:22
Evaluating Trading Strategies - The Sharpe Ratio 10:16
The 2 Step process - Modeling and Backtesting 03:48
Developing a Trading Strategy in Excel 11:42
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Setting up your Development Environment 52:26
Installing Anaconda for Python 09:00
Installing Pycharm - a Python IDE 03:55
MySQL Introduced and Installed (Mac OS X) 07:03
MySQL Server Configuration and MySQL Workbench (Mac OS X) 17:32
MySQL Installation (Windows) 06:31
[For Linux/Mac OS Shell Newbies] Path and other Environment Variables 08:25
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Setting up a Price Database 02:22:57
Programmatically Downloading Historical Price Data 06:23
Manually download data for 10 years 00:22
CodeAlong - Dowloading Price data from Yahoo Finance 14:39
CodeAlong - Downloading a URL in Python 07:38
CodeAlong - Downloading Price data from the NSE 13:55
Manually download data for 10 years 00:22
CodeAlong - Unzip and process the downloaded files 05:21
CodeAlong - Download Historical Data for 10 years 06:26
Inserting the Downloaded files into a Database 10:10
CodeAlong - Bulk loading downloaded files into MySQL tables 15:12
Data Preparation 04:16
CodeAlong - Data Preparation 12:43
Adjusting for Corporate Actions 08:41
CodeAlong - Adjusting for Corporate Actions 1 15:29
CodeAlong - Adjusting for Corporate Actions 2 08:47
CodeAlong - Inserting Index prices into MySQL 05:40
CodeAlong - Constructing a Calendar Features table in MySQL 06:53
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Decision Trees, Ensemble Learning and Random Forests 02:45:27
Planting the seed - What are Decision Trees? 17:00
Growing the Tree - Decision Tree Learning 18:03
Branching out - Information Gain 18:51
Decision Tree Algorithms 07:49
Overfitting - The Bane of Machine Learning 19:03
Overfitting Continued 11:19
Cross Validation 18:55
Regularization 07:18
The Wisdom Of Crowds - Ensemble Learning 16:39
Ensemble Learning continued - Bagging, Boosting and Stacking 18:02
Random Forests - Much more than trees 12:28
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A Trading Strategy as Machine Learning Classification 15:51
Defining the problem - Machine Learning Classification 15:51
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Feature Engineering 01:20:10
Know the basics - A Pandas tutorial 11:41
CodeAlong - Fetching Data from MySQL 18:34
CodeAlong - Constructing some simple features 07:27
CodeAlong - Constructing a Momentum Feature 08:42
CodeAlong - Constructing a Jump Features 05:52
CodeAlong - Assigning Labels 03:12
CodeAlong - Putting it all together 18:08
CodeAlong - Include support features from other tickers 06:34
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Engineering a Complex Feature - A Categorical Variable with Past Trends 10:35
Engineering a Categorical Variable 03:49
CodeAlong - Engineering a Categorical Variable 06:46
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Building a Machine Learning Classifier in Python 36:54
Introducing Scikit-Learn 03:33
Introducing RandomForestClassifier 09:25
Training and Testing a Machine Learning Classifier 15:01
Compare Results from different Strategies 05:44
Using Class probabilities for predictions 03:11
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Nearest Neighbors Classifier 11:05
A Nearest Neighbors Classifier 06:49
CodeAlong - A nearest neighbors Classifier 04:16
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Gradient Boosted Trees 33:50
What are Gradient Boosted Trees? 12:38
Introducing XGBoost - A python library for GBT 11:51
CodeAlong - Parameter Tuning for Gradient Boosted Classifiers 09:21
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Introduction to Quant Trading 46:12
Financial Markets - Who are the players? 16:38
What is a Stock Market Index? 03:13
The Mechanics of Trading - Long vs Short positions 11:56
Futures Contracts 14:25