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QuantInsti – Quantitative Trading Strategies and Models

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QuantInsti – Quantitative Trading Strategies and Models
Quantitative Trading Strategies and Models

Recommended course for those starting their journey in quantitative trading. It includes basic technical trading strategies, like the trend based strategy and the Bollinger bands strategy. These strategies can be traded on the live markets as well! You also learn the basics of Time Series Analysis, ARIMA and GARCH models, gamma scalping, options contracts and how to make delta-neutral portfolios.
LIVE TRADING

 Explain the difference between Quantitative trading and Technical trading
 Code 3 different trading strategies based on technical indicators
 Explain the econometric models such as ARIMA and GARCH
 Explain the BSM Options pricing model and the options’ greeks
 Apply and analyze strategies in the live markets without any installations or downloads

SKILLS COVERED
Time Series Analysis

Heteroskedasticity

Autoregressive Integrated Moving Average (ARIMA)

Generalized Autoregressive Conditional Heteroskedasticity(GARCH)

Black Scholes Model
Math Concepts

Functions

Statistics

Regression

Standard Deviation
Python

Iexfinance

Matplotlib

TA-Lib

Datetime

Quantrautil
PREREQUISITES

It is expected that you have some financial markets experience and understand terms like sell, buy, margin, entry, exit positions. Some familiarity with options and technical indicators will help you in better understanding of the concepts. If you want to be able to code strategies in Python, then experience to store, visualise and manage data using Pandas and DataFrame is required. These skills are covered in our course ‘Python for Trading’. However, you can do this course without any Python knowledge and replicate the models in spreadsheets or any other trading software language you are comfortable with.
SYLLABUS

Introduction to Quantitative Trading

This section defines the term ‘Quantitative Trading’ and discusses the components of a quantitative trading model.

Introduction to Quantitative Trading 3m 57s

Quantra Features and Guidance 3m 48s

Definition of Quantitative Trading 3m 49s

Components of a Quantitative Trading Model 2m

Properties of a Quantitative Trading Model 2m

Factors of High Frequency Trading 2m
Technical Trading Strategies

This section helps you understand concepts like Candlestick chart, support and resistance levels, absolute price change, and also explains technical indicators like Fibonacci Retracement, Stochastic Oscillator, and Williams %R. It also demonstrates the codes for Volume Reversal Strategy and Trend Based Strategy.

Technical Trading Strategies: Primer I 10m

Volume Reversals and Fibonacci Retracements 4m 45s

How to Use Jupyter Notebook? 1m 54s

Volume Reversal Strategy 7m

Frequently Asked Questions 10m

Getting Started with Python codes 5m

Importing Python Libraries 5m

Importing CSV Data 5m

Calculating Fibonacci Ratio 2m

Strategy Buy Signal using Williams % R indica 2m

Technical Trading Strategies: Primer II 10m

Introduction to Trend and Volatility 6m 29s

Trend Based Strategy 7m

Trimming the Data 5m

Calculating Strategy Returns 5m

Interpreting Volatility using Bollinger Band 2m

Generating a Strategy Buy Signal 2m

Analysing Price Breakouts with Bollinger Band 2m 56s

Bollinger Bands Strategy 7m

Standard Deviation of Returns 5m

Counting the Number of Trades 5m

Bollinger Band Fake Breakout 2m

Consistency in Breakouts 2m
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 2m 19s

Live Trading Overview 2m 41s

Vectorised vs Event Driven 2m

Process in Live Trading 2m

Real-Time Data Source 2m

Blueshift Code Structure 2m 57s

Important API Methods 10m

Schedule Strategy Logic 2m

Fetch Historical Data 2m

Place Orders 2m

Backtest and Live Trade on Blueshift 4m 5s

Additional Reading 10m

Blueshift Data FAQs 10m
Live Trading Template

Blueshift Live Trading Template

Paper/Live Trading Volume Reversal Strategy 10m

Paper/Live Trading Trend Based Strategy 10m

Paper/Live Trading Bollinger Bands Strategy 10m

FAQs for Live Trading on Blueshift 10m
Econometric Models

This section demonstrates the building of ARIMA and GARCH models. It also explains the concepts of Heteroskedasticity, Autocorrelation, and Log-linear trend.

Linear Regression Forecasting Equation 10m

Predicting the Dependent Variable ‘y’ 2m

Assumptions of LR 2m

Errors and Residuals in Linear Regression 10m

Impact of the Equation: SST = RSS + SSE 2m

Interpreting the Regression Score 2m

Introduction to Heteroskedasticity & Autocorr 5m 35s

Conditional/Unconditional Heteroskedasticity 2m

Positive and Negative Autocorrelation 2m

Time Series & Autoregressive Model 5m 45s

Log-Linear Trend Model equations 2m

Autoregressive model vs. Linear regression mo 2m

Understanding the ARIMA Model 5m 50s

Components of the ARIMA model 2m

Representing exponential growth with ARIMA mo 2m

Implementation of ARIMA Model 10m

Examples of ACF and PACF 10m

Lag in Autocorrelation function 2m

Correlation/Interdependence in ACF vs. PACF 2m

Predicting volatility using GARCH Model 5m 27s

Variance of Residuals in ARCH Model 2m

GARCH Prediction Variable 2m

Implementation of GARCH Model 10m
Quantitative Trading Strategies for Options  

This section explains how to build a delta-neutral portfolio and trade using Greeks. It also explains the BSM model in Options.

Introduction to Options 10m

Call Option 2m

Put Option 2m

Black-Scholes-Merton Option Pricing model 10m

Factors influencing the BSM option pricing model 2m

Assumptions in BSM 2m

Introduction to Options Greeks 7m 28s

Calculating Delta of a Call option 2m

Impact of Volatility on Delta of an option 2m

Calculating Portfolio Delta 2m

Building a Delta Neutral portfolio with Gamma 7m 55s

Calculating PnL 2m

Using Gamma Scalping to Solve Negative Theta 6m 4s

Negative Gamma Exposure 2m

Positive Gamma Exposure 2m

Test on Quantitative Trading Strategies 10m
Run Codes Locally on Your Machine

Learn to install the Python environment in your local machine.

Python Installation Overview 2m 18s

Flow Diagram 10m

Install Anaconda on Windows 10m

Install Anaconda on Mac 10m

Know your Current Environment 2m

Troubleshooting Anaconda Installation Problems 10m

Creating a Python Environment 10m

Changing Environments 2m

Quantra Environment 2m

Troubleshooting Tips For Setting Up Environment 10m

How to Run Files in Downloadable Section? 10m

Troubleshooting For Running Files in Downloadable Section 10m
Summary

This contains of the course summary and downloadable strategy codes.

Course Recap 3m 57s

Python Codes and Data
ABOUT AUTHOR

QuantInsti®

QuantInsti is the world’s leading algorithmic and quantitative trading research & training institute with registered users in 190+ countries and territories. An initiative by founders of iRage, one of India’s top HFT firms, QuantInsti has been helping its users grow in this domain through its learning & financial applications based ecosystem for 10+ years.
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

REVIEWS
NISHAD SEERAJ – Data Scientist, Trinidad & Tobago

The course syllabus really appealed to me. It was more or less what I was looking for. Since I have a background in Finance, I was familiar with the theory concepts, I thought I might struggle with the programming part, but that did not happen because the course has a stepwise and detailed explanation of the codes. This is what convinced me to go ahead with the course. I also liked the Blueshift platform, although initially, the code for me was not the easiest thing to grasp, but line-by-line explanation really helped. It was a great bonus for me. All in all, this course has solid theory, solid practice exercises, and overall it was a great experience. After completing this course, I have a much better knowledge of programming compared to before.
ANIL BIYANI – Stock Market Trader, India

I learnt a lot. As an experienced trader, I felt I knew a lot. But after going through this course, I feel I am still a student and need to learn more. Thanks.
RAJAN VARGHESE – India

Quantitative Trading Strategies and Models gave me a basic understanding of the predictions methods and their various aspects. Also, the option delta, gamma, theta, and its relationship between the option price and the underlying price is very well explained.