QuantInsti – Getting Started with Algorithmic Trading!

Description

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QuantInsti – Getting Started with Algorithmic Trading!

This course builds a foundation in Algorithmic Trading and is perfect for those who want to get a complete picture of the domain. It includes the what, how, and why of algorithmic trading. Different algorithmic trading strategies and regulations for setting up an algorithmic trading business are included.

LEVEL
Beginner

AUTHOR
Multi Commodity Exchange

LIVE TRADING

• Explain what Algorithmic Trading is and its advantages
• Describe different strategy paradigms & modelling ideas
• List requirements for setting up an algorithmic trading platform
• Describe regulatory requirements for US and Indian markets
• Paper trade and live trade your strategies without any installations or downloads

LEARNING TRACK 1
This course is a part of the Learning Track: Algorithmic Trading for Beginners

FOUNDATION

• Getting Started with Algorithmic Trading!
• Python for Trading: Basic
• Getting Market Data: Stocks, Crypto, News & Fundamental

BEGINNER

• Day Trading Strategies for Beginners
• Volatility Trading Strategies for Beginners
• Backtesting Trading Strategies

INTERMEDIATE

• Event Driven Trading Strategies
• Financial Time Series Analysis for Trading
• Statistical Arbitrage Trading

SYLLABUS

Introduction
This section introduces the topic of Algorithmic Trading. You will be guided through the detailed course structure and the various concepts covered in the course. You will also explore various features that are available to you on Quantra.

• Introduction
• Course Structure Flow Diagram
• Course Structure
• Quantra Features & Guidance

What is Algorithmic Trading?

This section introduces the topic of Algorithmic Trading and states important concepts like Direct Market Access(DMA), advantages of colocation, HFT, and quantitative trading.

• What is Algorithmic Trading?
• Algorithmic Trading System Fail
• Quantitative Analysis
• Direct Market Access
• What is DMA?
• What is High-frequency trading?
• Characteristics of HFT

Why Algorithmic Trading?

This section aims to help you understand the benefits of Algorithmic Trading and why it is preferred over traditional trading techniques. It also explains the concept of backtesting and how it can help in optimising your algorithmic trading strategies.

• Why Go Algo (part 1)
• Know About Algorithmic Trading
• What is Colocation?
• Advantages of Colocation
• Low Latency in Algorithmic Trading
• Why Go Algo (part 2)
• Research Backtesting
• Know ‘In Sample Backtesting’
• Understanding Backtesting
• Why Go Algo (part 3)
• How to Start Algorithmic Trading
• Advantages of Algorithmic Trading

Available Platforms & Languages

This section discusses some of the platforms and programming languages available to trade algorithmically.

• Available Platforms & Languages
• Programming Language for Algo Trading
• Cost of Using Programming Language

Strategy Paradigms

This section explains some algorithmic trading strategies. It includes concepts like market making, momentum-based trading strategies, trend-following strategies, arbitrage strategies, and Machine Learning used in algorithmic trading strategies.

• Types of Algorithmic Trading Strategies
• Market Making Strategy
• Know How Market Makers Profit
• Characteristics of Illiquid Securities
• Benefit of Market Making
• Inventory Risk
• Adverse Selection Spread
• Statistical Arbitrage
• Arbitrage Trading Strategies
• Pair Trading Follows
• When is Pair Trading Profitable?
• Momentum Based Strategies
• Using Momentum Based Strategies
• Using Trend Following Strategies
• Machine Readable News & Ml
• Examples of Scheduled News Event
• How Machine Learning Models Work?

Algorithmic Trading Platform

This section explains the various components of the system architecture of an algorithmic trading platform like Market Data Adapter (MDA), Complex Events Processing engine (CEP), and Order Management/routing System (OMS).

• Algorithmic Trading Platform
• How Market Data Adapter Works?
• Why Use CEP Engine?
• What is OMS?
• Core of Algorithmic Trading System

Introduction to Python

This section will help you update your knowledge of Python with simple exercises on implementing functions, and manipulating dataframes using Numpy and Pandas libraries. The Quantra environment ensures that you don’t have to install anything for the Jupyter notebooks to function.

• Need for Python
• Preference for Python
• Functionality of Python
• How to Use Jupyter Notebook?
• Print Statement
• My First Jupyter Notebook
• Getting Started with Interactive Exercises
• Operations and Functions in Python
• Divide Two Numbers
• Pandas Dataframe
• Function Call
• DataFrame Axis Label
• DataFrame and Basic Functionality
• DataFrame Syntax
• Dropping/Deleting Columns
• Create Pandas DataFrame
• DataFrame Indexing
• Print Columns
• Access Elements of a DataFrame
• Add New Column to a DataFrame
• Set Column as Index
• Add Values of a Column
• Additional Reading

Financial Market Data and Visualisation

An important component of a successful strategy is the data set used. In this section, you will learn how to import the correct data from various web resources, so that you can work on your own unique strategy.

• Importing Data
• Correct Syntax for Importing Stock Data
• Importing Time Series Data
• Import Data from Yahoo! Finance
• Data Visualisation
• Plot Line Graph
• Plot Bar Graph
• Additional Reading

Moving Average Crossover Strategy

Learn how to use moving average crossover strategy in Python. This unit covers the calculation of strategy returns as well as generation of buy and sell signals.

• Moving Average Crossover Strategy
• Why Use Short Term Average
• Calculate Rolling Average
• Buy or Sell?
• What is the Sharpe ratio?
• Moving Average Crossover Strategy
• Print the Message
• Import Module
• Read Data File
• Calculate Daily Change
• Create a New Column With NaN
• Calculate Short Term Moving Average
• Calculate Long Term Moving Average
• Generate Buy and Sell Signals
• Calculate Strategy Returns

Live Trading on Blueshift

This section will walk you through the steps involved in taking your trading strategy live. You will learn about backtesting and live trading platform, Blueshift. You will learn about code structure, various functions used to create a strategy and finally, paper or live trade on Blueshift.

• Section Overview
• Live Trading Overview
• Vectorised vs Event Driven
• Process in Live Trading
• Real-Time Data Source
• Blueshift Code Structure
• Important API Methods
• Schedule Strategy Logic
• Fetch Historical Data
• Place Orders
• Backtest and Live Trade on Blueshift
• Additional Reading

Live Trading Template

Blueshift Live Trading Template

• Paper/Live Trading Moving Average Crossover

Regulations & Compliance (Optional Section)

This section takes you through the regulations and compliance requirements like audit requirements, control parameters, and more.

• Regulations & Compliance
• Additional Reading

Run Codes Locally on Your Machine

Learn to install the Python environment in your local machine.

• Python Installation Overview
• Flow Diagram
• Install Anaconda on Windows
• Install Anaconda on Mac
• Know your Current Environment
• Troubleshooting Anaconda Installation Problems
• Creating a Python Environment
• Changing Environments
• Quantra Environment
• Troubleshooting Tips For Setting Up Environment
• How to Run Files in Downloadable Section?
• Troubleshooting For Running Files in Downloadable Section

Summary

This consists of the summary of the entire course and downloadable e-book on ‘Getting Started With Algorithmic Trading’ for reference.

• Setting Up a Trading Desk FAQs
• Downloadable E-book
• Summary
• Python Codes and Data

WHY QUANTRA®?

• Gain more in less time
• Get taught by practitioners
• Learn at your own pace
• Get data & strategy models to practice on your own

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