Decision Trees in Trading
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- Live Trading
- Learning Track
- Prerequisites
- Syllabus
- About author
- Testimonials
- Faqs
Live Trading
- Create a machine learning trading strategy using Decision Trees and ensemble methods
- Identify best trading indicators and create trading rules
- Create automated trading strategies
- Enhance your existing prediction models using advanced techniques
- Evaluate performance of trading strategies
- Apply and analyze strategies in the live markets without any installations or downloads

Skills Covered
learning track 4
This course is a part of the Learning Track: Machine Learning & Deep Learning in Trading Beginners
Course Fees
Full Learning Track
These courses are specially curated to help you with end-to-end learning of the subject.
Course Features
- Community
Faculty Support on Community
- Interactive Coding Exercises
Interactive Coding Practice
- Trade & Learn Together
Trade and Learn Together
- Get Certified
Get Certified
Prerequisites
You should have basic knowledge of machine learning algorithms and train and test datasets. These concepts are covered in our free course 'Introduction to Machine Learning'. Prior experience in programming is required to fully understand implementation of Artificial Intelligence techniques covered in the course. However, Python programming knowledge is optional. If you want to be able to code and implement the machine learning strategies in Python, you should be able to work with 'Dataframes' and 'Sklearn' library. Some of these skills are covered in the course 'Python for Trading'.
Syllabus
- Introduction To Decision TreesThis section introduces the topic and explains the basic structure of a decision tree. It also covers the concept of Decision Tree Inducers.IntroductionQuantra Features and GuidanceIntroduction To Decision TreesTypes Of Decision TreesVariables Of A Classification TreePrediction With A Classification TreeDecision Tree InducersSplit Parameter In A Decision TreeLeaf Parameter In A Decision TreeGreedy Approach In A Decision TreeDepth Of A Decision TreeTest on Introduction To Decision Trees
- Splitting, Stopping and Pruning Methods
Classification Model
- Live Trading on Blueshift
- Live Trading Template
- Regression Trees
- Parallel Ensemble Methods
- Sequential Ensemble Methods
Cross Validation and Hyperparameter Tuning
- Challenges in Live Trading
- Run Codes Locally on Your Machine
- Downloadable Code
Why quantra®?
- More in Less Time
Gain more in less time
- Expert Faculty
Get taught by practitioners
- Self-paced
Learn at your own pace
- Data & Strategy Models
Get data & strategy models to practice on your own
Faqs
- When will I have access to the course content, including videos and strategies?
You will gain access to the entire course content including videos and strategies, as soon as you complete the payment and successfully enroll in the course.
- Will I get a certificate at the completion of the course?
- Are there any webinars, live or classroom sessions available in the course?
- Is there any support available after I purchase the course?
- What are the system requirements to do this course?
- What is the admission criteria?
- Is there a refund available?
- Is the course downloadable?
- Can the python strategies provided in the course be immediately used for trading?
- I want to develop my own algorithmic trading strategy. Can I use a Quantra course notebook for the same?
- If I plug in the Quantra code to my trading system, am I sure to make money?
- Do you need to have knowledge of coding in order to learn through Quantra courses?
- What does "lifetime access" mean?