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Play the Markets Like a Professional by Integrating Machine Learning into Your Funding Methods! This on-line coaching course takes a totally sensible strategy to making use of Machine Learning methods to Quant Trading. The main target is on virtually making use of Machine Learning methods to develop subtle Quant Trading fashions.
Loonycorn – Machine Learning – Quant Trading
Play the Markets Like a Professional by Integrating Machine Learning into Your Funding Methods! This on-line coaching course takes a totally sensible strategy to making use of Machine Learning methods to Quant Trading. The main target is on virtually making use of Machine Learning methods to develop subtle Quant Trading fashions. From organising your personal historic value database in MySQL, to writing a whole lot of traces of Python code, the main target is on doing from the get-go.
Monetary markets are fickle beasts that may be extraordinarily tough to navigate for the common investor. This Quant Trading Utilizing Machine Learning course will introduce you to machine studying, a area of examine that offers computer systems the power to study with out being explicitly programmed, whereas instructing you the best way to apply these methods to quantitative buying and selling. Utilizing Python libraries, you’ll uncover the best way to construct subtle monetary fashions that can higher inform your investing choices. Supplemental Materials included!
Desk of Contents
- INTRODUCTION
- You, This Course, and Us! 00:02:01
- DEVELOPING TRADING STRATEGIES IN EXCEL
- Are markets environment friendly or inefficient? 00:10:27
- Momentum Investing 00:11:31
- Imply Reversion 00:06:30
- Evaluating Trading Methods – Danger and Return 00:16:22
- Evaluating Trading Methods – The Sharpe Ratio 00:10:16
- The two Step course of – Modeling and Backtesting 00:03:49
- Growing a Trading Technique in Excel 00:11:42
- SETTING UP YOUR DEVELOPMENT ENVIRONMENT
- Putting in Anaconda for Python 00:09:00
- Putting in Pycharm – a Python IDE 00:03:55
- MySQL Launched and Put in – Mac OS X 00:07:04
- MySQL Server Configuration and MySQL Workbench – Mac OS X 00:17:32
- MySQL Set up – Home windows 00:06:32
- For Linux-Mac OS Shell Newbies – Path and Different Setting Variables 00:08:26
- SETTING UP A PRICE DATABASE
- Programmatically Downloading Historic Value Information 00:06:24
- Code Alongside – Downloading Value Information from Yahoo Finance 00:14:40
- Code Alongside – Downloading a URL in Python 00:07:39
- Code Alongside – Downloading Value Information from the NSE 00:13:55
- Code Alongside – Unzip and Course of the Downloaded Recordsdata 00:05:22
- Manually obtain knowledge for 10 years 00:01:00
- Code Alongside – Obtain Historic Information for 10 years 00:06:26
- Inserting the Downloaded Recordsdata right into a Database 00:10:11
- Code Alongside – Bulk Loading Downloaded Recordsdata into MySQL Tables 00:15:13
- Information Preparation 00:04:16
- Code Alongside – Information Preparation 00:12:43
- Adjusting for Company Actions 00:08:41
- Code Alongside – Adjusting for Company Actions 1 00:15:29
- Code Alongside – Adjusting for Company Actions 2 00:08:47
- Code Alongside – Inserting Index Costs into MySQL 00:05:41
- Code Alongside – Setting up a Calendar Options Desk in MySQL 00:06:54
- DECISION TREES, ENSEMBLE LEARNING AND RANDOM FORESTS
- Planting the seed – What are Resolution Timber 00:17:02
- Rising the Tree – Resolution Tree Learning 00:18:04
- Branching out – Info Acquire 00:18:51
- Resolution Tree Algorithms 00:07:51
- Overfitting – The Bane of Machine Learning 00:19:04
- Overfitting Continued 00:11:20
- Cross-Validation 00:18:55
- Regularization 00:07:18
- The Knowledge of Crowds – Ensemble Learning 00:16:39
- Ensemble Learning continued – Bagging, Boosting and Stacking 00:18:03
- Random Forests – A lot Extra Than Timber 00:12:28
- A TRADING STRATEGY AS MACHINE LEARNING CLASSIFICATION
- Defining the Drawback – Machine Learning Classification 00:15:51
- FEATURE ENGINEERING
- Know the fundamentals – A Pandas tutorial 00:11:42
- Code Alongside – Fetching Information from MySQL 00:18:35
- Code Alongside – Setting up Some Easy Options 00:07:28
- Code Alongside – Setting up a Momentum Characteristic 00:08:42
- Code Alongside – Setting up a Leap Characteristic 00:05:52
- Code Alongside – Assigning Labels 00:03:13
- Code Alongside – Placing It All Collectively 00:18:08
- Code Alongside – Embody Assist Options from Different Tickers 00:06:34
- ENGINEERING A COMPLEX FEATURE – A CATEGORICAL VARIABLE WITH PAST TRENDS
- Engineering a Categorical Variable 00:03:49
- Code Alongside – Engineering a Categorical Variable 00:06:47
- BUILDING A MACHINE LEARNING CLASSIFIER IN PYTHON
- Introducing Scikit-Be taught 00:03:33
- Introducing RandomForestClassifier 00:09:26
- Coaching and Testing a Machine Learning Classifier 00:15:01
- Evaluate Outcomes from Totally different Methods 00:05:45
- Utilizing Class Chances for Predictions 00:03:11
- NEAREST NEIGHBORS CLASSIFIER
- A Nearest Neighbors Classifier 00:06:50
- Code Alongside – A Nearest Neighbors Classifier 00:04:16
- GRADIENT BOOSTED TREES
- What are Gradient Boosted Timber 00:12:38
- Introducing XGBoost – A Python Library for GBT 00:11:51
- Code Alongside – Parameter Tuning for Gradient Boosted Classifiers 00:09:21
- INTRODUCTION TO QUANT TRADING
- Monetary Markets – Who Are the Gamers 00:16:38
- What’s a Inventory Market Index 00:03:14
- The Mechanics of Trading – Lengthy Vs Brief Positions 00:11:56
- Futures Contracts 00:14:26
Learn extra: http://archive.is/Gg4Vo
Course Features
- Lecture 0
- Quiz 0
- Duration Lifetime access
- Skill level All levels
- Students 0
- Assessments Yes