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~~$542~~ $82 – Learning Track: Automated Trading using Python and Interactive Brokers – QuantInsti

A complete end-to-end learning programme that starts by teaching basics in Python and ends in implementation of new algorithmic trading techniques in live markets. Highly recommended if you wish to multiply your portfolio and include historical data back-testing & discipline in your trades.

SKILLS COVERED

COURSE FEATURES

Lifetime Access to the course
Community support
Downloadable codes
Hands-on guided learning
Get Certified

LEARNING TRACK

Automated Trading using Python & Interactive Brokers

Mini Learning Track

These courses are often bought together for better understanding of connected concepts.

Specialization Bundle

This set of courses have shown to be most effective in learning and implementation of techniques in trading.

PREREQUISITES

It is expected that you have some trading experience and understand basic financial markets terminology like sell, buy, margin, entry, exit positions. You can complete the learning track without any Python knowledge and replicate the models in spreadsheets or any other trading software language you are comfortable with.

AFTER THIS COURSE YOU’LL BE ABLE TO

Automate your trading. Backtest your trading strategies ideas on historical data. Connect with broker by understanding the API structure. Learn to manage your portfolio and place orders.

Multiply your portfolio. Master different quantitative techniques used across different asset classes and options: Statistical Arbitrage, Options Pricing models, Time Series Modelling. Work with actual markets data to create prediction models using machine learning algorithms. Create 20+ new trading strategies.

Start your algorithmic trading desk/business. In a short time of around 40 hours, gain hands-on experience in using technology and mathematics in trading. Get trained by Quants, HFT traders and market practitioners and learn from their experience.

SYLLABUS

Course 1: Getting Started with Algorithmic Trading!

Introduction
Why Algorithmic Trading?
Available Platforms & Languages
Strategy Paradigms
Algorithmic Trading Platform
Regulations & Compliance (Optional Section)
Downloadable Resources

Course 2: Python For Trading!

Introduction to Python!
Python Data Structures
Data Analysis & Trading
Dealing with Financial Data
Backtesting
Performance Metrics
Python Installation and Automated Execution

Course 3: Trading using Options Sentiment Indicators

Introduction to Sentiment Trading
Breadth Measures
Option Trading Measures
Volatility Measures
Risks in Trading
Python Installation and Automated Execution
Conclusion and Downloadable Resources

Course 4: Quantitative Trading Strategies and Models

Quantitative Trading: An Introduction
Technical Trading Strategies
Econometric Models
Quantitative Trading Strategies for Options
Python Installation and Automated Execution
Summary

Course 5: Trading with Machine Learning: Classification and SVM

Introduction
Binary Classification
Multiclass Classification
Support Vector Machine
Prediction and Strategy
Python Installation and Automated Execution
Downloadable Code

Course 6: Automated Trading with IBridgePy using Interactive Brokers Platform

Course 7: Mean Reversion Strategies In Python

Stationarity of Time Series
Cointegration
Triplets
Half Life
Risk Management
Best Markets to Pair Trade
Index Arbitrage
Long Short Portfolio
Python Installation and Automated Execution
Summary

~~$542~~ $82 – Learning Track: Automated Trading using Python and Interactive Brokers – QuantInsti

Buy the Learning Track: Automated Trading using Python and Interactive Brokers – QuantInsti course at the best price at GBesy.. After your purchase, you will get access to the downloads page. You can download all the files associated in your order at here and we will also send a download notification email via your mail.

Unlock your full potential with Learning Track: Automated Trading using Python and Interactive Brokers – QuantInsti courses. our courses are designed to help you excel.

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Status	Available
Language	English
Author	Quantinsti

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