Learning Track: Quantitative Trading for Beginners
34 hours
Take your first step to become a quant trader in a structured and hands-on manner. Get an introduction to quantitative trading, followed by Python trading training. Learn about machine learning for trading through real-market data examples of Options, Forex, Crypto, Equities and other asset classes. Implement algorithmic trading strategies using brokers such as Interactive Brokers.
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FOUNDATION to INTERMEDIATE
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QuantInsti®
NSE Academy
Interactive Brokers ®
FXCM
Price Lifetime Access
₹0/-₹27166/-
- Live Trading
- Learning Track
- Prerequisites
- Syllabus
- About author
- Testimonials
- Faqs
Become a Quant Trader
Get a complete picture of the domain, including the what, how, why of algorithmic trading.
Explore Options trading strategy such as Bull-Call, Bear-Put, Protective Call, Protective Put and Iron Condor
Acquire Python coding skills to work with data structures to handle financial markets data and generate buy and sell trading signals.
Explore normal probability distribution, standard deviation and different types of volatility
Apply AI for trading stocks using different machine learning algorithms
Create and backtest momentum trading strategy using real Forex markets data in Python.
Track the status of your orders and your portfolio position on a real-time basis in Python. Practice questions to crack a quant interview.
Compute financial ratios such as return on equity, price to earnings, price to book value and analyze the long-term trend in revenue and PE ratio.
Fetch market and pricing data for stocks, stock indices, FX, Crypto and Futures.
Essential Skills for Quantitative Trading
Math & Core Concepts
- Historical Price Data
- Backtesting
- Automated Trading
- Volatility Types
- Normal Probability Distribution
Strategies
- Momentum Trading
- Bull Call Spread
- Iron Condor
- Machine Learning
- Crossover Strategy
Python Libraries
- NumPy & Pandas
- Matplotlib & DateTime
- IBridgePy
- Loops, Conditional Statements, Functions
- Python Data Types
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These courses are specially curated to help you with end-to-end learning of the subject.
Need help? Write to us at quantra@quantinsti.com or call us at
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Learn for Free - Our Mission Explained!
Discover Algorithmic Trading Without Any Barriers
At QuantInsti, our mission is to make algorithmic trading knowledge and technology accessible to everyone. Our vision is to empower individuals and institutions, enabling them to harness cutting-edge technology in financial markets, fostering growth and success. We offer comprehensive learning tracks, free fintech tools, hundreds of engaging webinars, and a vast repository of over 500 insightful blogs designed to equip aspiring traders with essential skills and resources.
For over 14 years, we've actively contributed as speakers and industry experts at academic and professional forums globally, helping shape the future of algorithmic trading. This free learning track exemplifies our commitment to accessibility and empowerment, helping you take your first step into the ever-evolving world of algorithmic trading. We appreciate you joining us on this exciting journey. Happy Learning!
Course Features
CommunityFaculty Support on Community
- Interactive Coding Exercises
Interactive Coding Practice
- Capstone Project
Capstone Project using Real Market Data
- Trade & Learn Together
Trade and Learn Together
- Get Certified
Get Certified
Prerequisites
None at all! This is a perfect combination of courses for beginners who have never coded or never traded in financial markets.
Syllabus
Python for Trading: Basic
- WelcomeThis section introduces the topic and explains the importance of Python.
Introduction2m 40s
If Algo, Then How Does Python Contribute?6m 27s
Quantra Features and Guidance3m 48s
FAQ2m
- Hello PythonThis familiarises you with the basic components of Python like variables references, operators, modules, packages, and libraries.
Python Environment2m 34s
Variables, Object References and Operators3m 36s
When X = Y, Then What?2m
When X != Y, Then What?2m
How to Use Jupyter Notebook?2m 5s
My First Python Code10m
Practice Print Statement5m
Learn Modules, Packages and Libraries3m 11s
File With .py Extension2m
Import Python Modules10m
Modules, Packages & Libraries2m
Importing Module5m
Install Packages in Python10m
Error if the Package is Not Installed2m
Syntax to Install a Package2m
Which Version of the Package is Installed?2m
Syntax to Install a Version?2m
Test on Python Modules16m
- ExpressionsIn this section, you will learn about an important concept of 'Time Value of Money', and the use of expressions in Python.
Introduction to Time Value of Money1m 51s
Calculate the Future Value2m
Calculate the Present Value2m
Learn Compounding in Time Value of Money2m 6s
Compounding With Monthly Coupons2m
Compounding With Quarterly Coupons2m
PDF: TVM Applications (Optional Read)10m
Use of Expressions10m
Print Future Value5m
Print Calculating Present Value5m
- Python Data StructuresThis section focuses on different Python data structures like lists, dictionaries, stacks, queues, graphs, trees, tuples and sets.What Are Lists?4m 6s
Syntax for Lists2m
Properties of List2m
Learn to Create Lists10m
Create Lists on Your Own5m
Print pop() From Lists5m
What Are Stacks, Queues, Graphs & Trees10m
Stacks & Queues2m
What Are Dictionaries?1m 55s
Access Dictionaries2m
Keys in Dictionaries2m
Learn to Create and Print Dictionaries10m
Create Dictionaries5m
Practice Printing Keys5m
What Are Tuples and Sets?2m 25s
Which is Valid for Data Structures?2m
Tuples2m
Learn to Create Tuples and Sets10m
Construct Tuple5m
Print Set Union function5m
Test on Expressions and Python Data Structures14m
- Importing Data and Data VisualisationThis section demonstrates how to import and visualise financial data using Python.
What is Time Series Data?3m 19s
Time Series: A Collection of Observations2m
Characteristics of Longitudinal & Panel Data2m
How to Import Time series data?3m 6s
Find: True/False for DataFrame2m
Correct Syntax for a DataFrame2m
Import Data from Web Sources10m
Download of Historical Data5m
Read Data from CSV Files10m
Practice read_csv()5m
How to Plot Market Data?3m 48s
Giving Title to the Graph2m
Function to Visualise the Graph2m
Data Visualization10m
Create 2D Plot5m
Plot the Grid5m
3D Plotting10m
What are Candlesticks?4m 14s
Assessment on Green Candlesticks2m
Advantage of Candlesticks2m
Candlesticks (Optional Read)10m
Test on Importing Data and Data Visualisation14m
- FunctionsIn this section, you will learn what Python functions are, how to define functions, what Lambda functions are and how to use them.
What Are Functions?4m 30s
Assessment: What Are Functions?2m
Syntax to Define a Function2m
Functions10m
Print Using Function5m
Call the Function5m
Lambda10m
Create Sum of Variables with Lamba5m
Multiplication of Variables With Lambda5m
- NumpyThis section shows how you can use the NumPy library to manipulate arrays by slicing, indexing, vectorization and broadcasting.
What Are NumPy Arrays?3m 51s
Assessment on NumPy2m
Put Syntax for Array Constructor2m
Introduction to Arrays10m
Use Numpy.arange ()2m
Create Array With Linspace5m
Create 2D Array5m
Indexing & Slicing Arrays10m
Indexing 1D Arrays2m
Indexing 2D Arrays2m
Slicing 1D Arrays2m
Slicing 2D Arrays2m
Practice Indexing5m
Practice Slicing5m
Vectorization & Broadcasting in Arrays10m
Scalar Vectorization2m
Array Comparison2m
Practice Using == Operator5m
Practice New Axis5m
- PandasThis section illustrates how to use the Pandas library for the manipulation of DataFrames.
Pandas and Data Manipulation4m 26s
Dropping/Deleting Columns2m
Dataframe Indexing2m
Introduction to Series10m
Assessment on Series.apply()2m
Practice Creating Series5m
Apply Method to a Series5m
DataFrame & Basic Functionality10m
Printing Columns2m
Practice Using DataFrame.head()5m
Create DataFrame5m
Descriptive Statistical Function10m
Dataframe Manipulation2m
Print Using mean()5m
Practice corr()5m
Indexing & Missing Values10m
loc()2m
iloc()5m
Grouping & Reshaping10m
Groupby Function5m
- Conditional Statements and LoopsIn this section, you will learn how to use conditional statements and loops in Python.What Are Conditional Statements and Loops?4m 18s
Assessment on 'if' Conditional Statement2m
What Do You Know About 'for loop'?2m
Introduction to Conditional Statements10m
If Conditional Statement5m
Introduction to Loops10m
For Loop5m
Getting Out of 'for Loop'5m
Test on Libraries, Functions, and Loops16m
- Buy and Hold StrategyIn this section, you will learn to create a buy and hold strategy in Python.
Buy and Hold Strategy10m
Frequently Asked Questions10m
- Live Trading on BlueshiftThis 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.Uninterrupted Learning Journey with Quantra2m
Section Overview2m 19s
Live Trading Overview2m 41s
Vectorised vs Event Driven2m
Process in Live Trading2m
Real-Time Data Source2m
Blueshift Code Structure2m 57s
Important API Methods10m
Schedule Strategy Logic2m
Fetch Historical Data2m
Place Orders2m
Backtest and Live Trade on Blueshift4m 5s
Additional Reading10m
Blueshift Data FAQs10m
- Live Trading TemplateBlueshift Live Trading TemplatePaper/Live Trading Buy and Hold Strategy10m
Sharpe Ratio5m
Strategy Returns5m
Maximum Drawdown5m
Ending Capital5m
Next Step5m
Paper Trading5m
Investment Style5m
Rebalancing Function5m
FAQs for Live Trading on Blueshift10m
- Run Codes Locally on Your MachineLearn to install the Python environment in your local machine.Python Installation Overview1m 59s
Flow Diagram10m
Install Anaconda on Windows10m
Install Anaconda on Mac10m
Know your Current Environment2m
Troubleshooting Anaconda Installation Problems10m
Creating a Python Environment10m
Changing Environments2m
Quantra Environment2m
Troubleshooting Tips For Setting Up Environment10m
How to Run Files in Downloadable Section?10m
Troubleshooting For Running Files in Downloadable Section10m
- Course SummaryCourse Summary and Next Steps10m
Python Code and Data2m
Stock Market Basics
- Financial MarketsFinancial markets are markets where traders trade in financial instruments. In this section, you will be introduced to financial markets and learn the need for it. You will also go through real examples of financial markets. Additionally, you will acquaint yourself with the course structure.Welcome To The Course!3m 45s
Financial Markets2m
Various Financial Markets2m
Financial Markets And The Web2m
Need For Financial Markets2m 29s
Choose The Correct2m
- Financial InstrumentsIn this section, you will be introduced to various instruments traded in the financial markets and what they are. There are hundreds of assets which can be traded in an exchange. We will also discuss the classification of these instruments along with necessary information. Then, you will dive deeper into one of these instruments; stocks and learn their characteristics, benefits, and risks of owning them.Introduction To Financial Instruments2m 39s
Debt Instruments2m
Stocks2m 39s
Stock Identifier2m
Stock Identifier On Multiple Exchanges2m
Owning A Stock2m
- Primary And Secondary MarketsThis section introduces you to two types of markets; primary and secondary, and discusses their functionality. You will also go through the process of Initial Public Offering(IPO).Primary And Secondary Markets3m 59s
Initial Public Offering(IPO)10m
Offering Of A Stock2m
Seller Of A Stock2m
Secondary Markets2m
Primary Markets2m
Test on Instruments, Primary and Secondary Markets14m
- Financial IntermediariesIn this section, you will learn about various intermediaries in the financial markets and their roles. We will discuss about brokers, stock exchanges, central securities depository, clearinghouses, and regulators. You will also be exposed to the trading workflow and gain knowledge of how markets can be accessed. Additionally, you will build an understanding of how each of these intermediaries work in tandem.Introduction To Various Intermediaries3m 33s
Demat Account2m
Choose The Incorrect2m
Brokers3m 8s
Services Provided By A Broker2m
Choose The Correct2m
Central Securities Depository2m 30s
Choose The Correct2m
Trade Settlement2m
Stock Exchange3m 37s
Exchanges And Other Entities2m
Choose The Incorrect2m
Facilities By Stock Exchanges2m
Clearinghouses2m 1s
Choose The Correct2m
Counterparty Risk2m
Financial Regulators2m 44s
Entities To Be Regulated2m
Choose The Correct2m
- Tracking MarketsKnowing how markets function is not enough. We as a trader also need to understand how they are performing. This is where you will learn about market indices and their type. This section also comprehends how indices are built to track a market. You will be presented with some of the real-life examples to grasp the concept.Market Indices5m 10s
Choose The Correct2m
Indices And Constituents2m
Broad-Based Indices2m
The World Index2m
Test on Intermediaries and Tracking Markets14m
- Market ParticipantsThis section of the course gives you information about the participants that operate in financial markets. You will be able to fathom speculators, hedgers, and arbitrageurs. Then, you will understand their roles and general characteristics in the context of financial markets.Introduction4m 21s
Identify The Odd2m
Speculators3m 8s
Choose The Correct2m
Hedgers3m 40s
Ways To Hedge2m
Arbitrageurs3m 12s
Traders Trading Price Inefficiencies2m
Arbitrage Duration2m
Market Makers2m
- Types Of MarketsIn this section, your understanding of markets gets deeper. It explains various types of financial markets and discusses the assets traded in those markets.Various Financial Markets5m 3s
Markets Without A Central Party2m
Raise Funds2m
Commodity Markets2m
OTC Markets2m
Stock Markets2m
- Market TerminologyThis section will explain various jargons used in day to day trading. You will be equipped with the terms used in the stock market and be able to understand the language of traders.Glossary4m 6s
Trending Markets2m
Downtrend Markets2m
Short Sell Transaction2m
Square Off Transaction2m
Test on Market Participants and Terminology14m
- SummaryIn this section, you will go through the different concepts you learnt throughout the course. It will help you connect the dots among all the theories we covered in the course.Summary2m 24s
Introduction to Machine Learning for Trading
- IntroductionThis section introduces the topic of machine learning and goes on to explain where it can be applied. 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.
Welcome to Machine Learning!2m 45s
Course Structure2m 26s
Course Learning Outcomes44s
Quantra Features and Guidance3m 48s
- Machine LearningThis section focuses on explaining the terminologies used in machine learning such as training and testing data sets.Terminologies of Machine Learning1m 40s
True/not True About Machine Learning2m
Where to Apply Machine Learning?2m
Uses of Feature Selection2m
- Types of Machine LearningThis section explains the types of machine learning such as supervised learning, unsupervised learning, and reinforcement learning along with their applications in general and trading.Types of Machine Learning - Part 12m 58s
Types of Machine Learning - Part 255s
Dividing the Dataset2m
What is Not Supervised Learning?2m
What is a Regression Problem?2m
Types of Machine Learning2m
How Do Humans Learn?2m
How Not to Do Unsupervised Learning?2m
Where Does Clustering Belong?2m
Additional Reading10m
- Supervised LearningThis section covers the important models in supervised learning such as regression models, classification models, and artificial neural networks.How to Use Jupyter Notebook?2m 5s
Linear Regression10m
What is the Equation for Linear Regression?2m
Find the Predicted Price2m
Logistic Regression10m
Output of Logistic Regression2m
KNN Classification10m
KNN Algorithm2m
Support Vector Machine10m
What is Not a Support Vector Machine?2m
What is a Hyperplane?2m
Do You Know Machine Learning Models?2m
Random Forest10m
Do You Know Random Forest?2m
Artificial Neural Network10m
Output of an Artificial Neural Network2m
Additional Reading10m
Test on Basics of Machine Learning and Supervised Learning20m
- Unsupervised LearningThis section covers the unsupervised learning model known as K-Means clustering and its relevance in trading.K-Means Clustering10m
How Well Do You Know K-means?2m
Statistical Arbitrage2m
Additional Reading10m
- Reinforcement LearningThis section explains the third type of machine learning which is reinforcement learning. It covers the architecture of reinforcement learning and how it can be utilised in the trading world.Reinforcement Learning Example- Part 12m 44s
Reinforcement Learning Example- Part 22m 8s
What is Optimal Execution Price?2m
Assumptions of Reinforcement Learning2m
What is Optimal Action?2m
What is a Reward?2m
Reinforcement Learning10m
Additional Reading10m
- Predict Trend Using ClassificationThis section explains how to code and backtest a trading strategy using the machine learning classification algorithm. It demonstrates the backtesting of a trading strategy on S&P 500 index using the Support Vector Classifier algorithm in Python.Stock Market Prediction1m 56s
Assumptions of Prediction Model2m
Feature Identification2m
Classification Algorithm2m 27s
Support Vector Classifier Strategy10m
Frequently Asked Questions10m
Name the Algorithm2m
What Type of Data Did We Use?2m
Length of the Test Data2m
Getting Started5m
Import SVM Module5m
Read Data From CSV File5m
Target Variable2m
Create Classifier Model5m
Predict Trading Signals5m
Additional Reading10m
- Live Trading on BlueshiftThis 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.Uninterrupted Learning Journey with Quantra2m
Section Overview2m 19s
Live Trading Overview2m 41s
Vectorised vs Event Driven2m
Process in Live Trading2m
Real-Time Data Source2m
Blueshift Code Structure2m 57s
Important API Methods10m
Schedule Strategy Logic2m
Fetch Historical Data2m
Place Orders2m
Backtest and Live Trade on Blueshift4m 5s
Additional Reading10m
Blueshift Data FAQs10m
- Live Trading TemplateBlueshift Live Trading TemplateSupport Vector Classifier Strategy Template10m
FAQs for Live Trading on Blueshift10m
- Natural Language ProcessingThis section focuses on the introduction of natural language processing and its application in trading. It explains how machines get closer to a human level understanding of the text from sources such as news, social media, etc. This interpretation of the text is converted into sentiment score and further used to generate trading signals.Natural Language Processing10m
What is NLP?2m
Positive Sentiment Score2m
Financial Data2m
Additional Reading10m
- Data & Feature EngineeringThis section explains why it is extremely important to remove several issues from raw data such as survivorship bias, and duplicates before feeding data to the machine learning algorithm. Then it explains the features and labels to take from this data which help increase the predictive power of the algorithm.Data & Feature Engineering2m 48s
Importance of Data Quality2m
What is Feature Engineering?2m
Additional Reading10m
Test on Unsupervised Learning, RL, NLP and, Data and Feature Engineering12m
- Introduction to Generative AIThis section will explore the key principles of Generative AI, its capabilities, and its applications in trading.Section Overview2m
How does Generative AI work?2m
Main Capabilities of Generative AI2m
Applications in Finance2m
Case Study2m
Traditional ML vs Gen AI5m
Text Generation5m
Principles of Generative AI5m
Speech to Text Model5m
Summary2m
Additional Reading2m
- Run Codes Locally on Your MachineLearn to install the Python environment in your local machine.Python Installation Overview1m 59s
Flow Diagram10m
Install Anaconda on Windows10m
Install Anaconda on Mac10m
Know your Current Environment2m
Troubleshooting Anaconda Installation Problems10m
Creating a Python Environment10m
Changing Environments2m
Quantra Environment2m
Troubleshooting Tips For Setting Up Environment10m
How to Run Files in Downloadable Section?10m
Troubleshooting For Running Files in Downloadable Section10m
- SummaryThis section concludes the course and provides a downloadable zipped folder with notebook and data file for easy access.Summary3m 23s
Frequently Asked Questions10m
Downloadable Code2m
Options Trading Strategies In Python: Basic
- Know Your Options!This section introduces the basic concepts of call and put options, along with the Python code payoff graphs.Introduction2m 58s
Quantra Features and Guidance2m 38s
Options Terminology3m 33s
Upfront Payment2m
Options Price2m
Put Options3m 50s
PnL of a Put Option2m
Break Even Point2m
How to Use Jupyter Notebook?2m 5s
Put Option Payoff7m
Put Option Payoff5m
Call Options2m 30s
Value of a Call Option2m
PnL of a Call Option2m
Call Option Payoff7m
Call Option Payoff5m
Call Option Payoff With Premium5m
Test on Options Types and Terms10m
- Options NomenclatureThis covers the types of options based on different parameters like exercisability, tradability etc. It also explains the important concepts of moneyness and put-call parity.Types of Options10m
Options Exercise Date2m
Moneyness3m 27s
Moneyness of Option2m
Intrinsic Value of Option2m
Time Value of Option2m
Open Interest and Volume10m
Open Interest2m
Calculate Open Interest2m
Calculate Volume2m
Pre-Reading on Put-Call Parity2m
Put-Call Parity4m 36s
Factors Affecting the Price of Option2m
Test on Options Nomenclature14m
- Types of VolatilityDifferent types of volatility like historical, implied and realised volatility are covered in this section. It also includes the Python code and calculation for historical volatility.Normal Probability Distribution10m
Daily Returns Distribution2m
Measure of Risk2m
Standard Normal Distribution2m
Volatility4m 31s
Measurement of Volatility2m
Implied Volatility2m
Historical Volatility Calculation7m
Compute Historical Volatility5m
Frequently Asked Questions10m
- Options Trading StrategiesThis section explains different options trading strategies like bull call, bear spread, protective put, Iron Condor strategy, and covered call strategy along with the Python code. It also acquaints one with the concept of hedging in options.
Delta Trading Strategies5m 50s
Bull Call Spread2m
Bear Put Spread2m
Maximum Loss2m
Bull Call Spread Payoff7m
Bear Put Spread Payoff7m
Maximum Profit5m
Maximum Loss5m
Hedging With Options4m 37s
Protective Put2m
Covered Call2m
Protective Put Payoff7m
Covered Call Payoff7m
Neutral Strategy: Iron Condor10m
Test on Volatility and Trading Strategies14m
- Run Codes Locally on Your MachineLearn to install the Python environment in your local machine.Uninterrupted Learning Journey with Quantra2m
Python Installation Overview1m 59s
Uninterrupted Learning Journey with Quantra2m
Flow Diagram10m
Install Anaconda on Windows10m
Install Anaconda on Mac10m
Know your Current Environment2m
Troubleshooting Anaconda Installation Problems10m
Creating a Python Environment10m
Changing Environments2m
Quantra Environment2m
Troubleshooting Tips For Setting Up Environment10m
How to Run Files in Downloadable Section?10m
Troubleshooting For Running Files in Downloadable Section10m
- Wrapping Up!This section summarises the course and provides downloadable strategy codes.
Automated Trading with IBridgePy using Interactive Brokers Platform
- Introduction to IBridgePyThis section introduces the topic of automation trading. It discusses the wrapper IBridgePy API which acts as a bridge between Interactive Brokers interface and Python console.
- Installation StepsYou learn to install all packages and softwares on your personal machine for later use. It includes Anaconda, Spyder interface, IBridgePy, IB Trader Workstation and IB Gateway. Although you would not need these installations to understand and complete the course, setting all system requirements is an important skill to automate your trading. Hence, the course starts with the same.Python Environment2m 13s
Elements in Python Environment2m
How to Use Spyder Interface?10m
Download and Setup IBridgePy on Windows10m
Download and Setup IBridgePy on Mac10m
Spyder Interface2m
IB TWS and IB Gateway Configuration Guide10m
- Code StructureIn this section, you will understand the code structure to create trading algorithms using IBridgePy.
Code Structure3m 11s
Initialize Function2m
Context Parameter2m
Handle Data Function2m
Handle Data Function Call2m
Schedule Function2m
Scheduling a Function2m
Schedule Function Parameters2m
Test on IBridgePy Installation and Code Structure14m
- Run the CodeIn this section, you will understand how to run the trading algorithms code on your local system using IBridgePy.Run the Code on Your Computer10m
IBridgePy Error FAQs10m
- Fetch DataThis section covers how to fetch real-time and historical data for different time frames from Interactive Brokers. You will also learn how to handle this data in Python.
How to Fetch Real-Time Data?4m 25s
Parameters of data.current Function2m
Function to Request Real-Time Quotes1m
Create Security Object1m
How to Fetch Historical Data?3m 45s
Demo Account Data Guide10m
Function to Fetch Historical Data1m
Parameters of data.history Function1m
Pull Data using data.history Function1m
Test on Fetching Data12m
- Orders ManagementThis section explains how to create orders & send different types of orders like market order, limit order, stop orders etc. for various instruments like stocks, futures, options, and currencies.
How to Place and Cancel Order?5m 1s
Valid Order Type2m
Limit Order2m
Cancel Order2m
Different Ways to Place an Order Guide10s
Function to Place Order2m
How to Retrieve Open Orders?3m 30s
Retrieve Open Orders2m
Retrieve Open Orders for a Specific Security2m
Properties of Order Object2m
Test on Order Management14m
- Portfolio ManagementIn this section, you will learn how to track the status of your orders and your portfolio position on a real-time basis.
Positions Tracking1m 48s
Fetch Positions in the Portfolio2m
Fetch Portfolio Value2m
Exit All Positions2m
- Trading Strategy ImplementationThis section illustrates how to build a simple moving average crossover strategy in Python.
Simple Moving Average Crossover4m 37s
Calculate Moving Average2m
Moving Average Condition2m
Order Status2m
Test on Portfolio Management and Trading Implementation12m
- Run Codes Locally on Your MachineLearn to install the Python environment in your local machine.Uninterrupted Learning Journey with Quantra2m
Python Installation Overview1m 59s
Flow Diagram10m
Install Anaconda on Windows10m
Install Anaconda on Mac10m
Know your Current Environment2m
Troubleshooting Anaconda Installation Problems10m
Creating a Python Environment10m
Changing Environments2m
Quantra Environment2m
Troubleshooting Tips For Setting Up Environment10m
How to Run Files in Downloadable Section?10m
Troubleshooting For Running Files in Downloadable Section10m
- Downloadable ResourcesYou can download the Python strategy codes at the end of the course.Python Codes and Data
Getting Market Data: Stocks, Crypto, News & Fundamental
- Course IntroductionThe first step in creating a trading strategy is to retrieve data. While there are a plethora of resources available for free or paid, it makes sense to use a trusted data provider. In this section, you will understand the importance of data and also acquaint yourself with the course structure.Introduction to the Course2m 16s
Course Structure2m 14s
Course Structure Flow Diagram10m
- Equity Price DataStock price analysis is one of the most important tools to make an informed decision while trading. In this section, you will learn how to fetch the various stock price data. You will learn how to download the adjusted and unadjusted price data of different frequencies such as 15 minutes, 1 hour and 1 day.Stock Daily Price Data5m
Download of Historical Data5m
Data Adjusted for Corporate Actions2m
Stock Index Data5m
List of Tickers in S&P5005m
Getting Close Data5m
Minute Price Data and Resampling Techniques5m
Resample Data2m
Aggregate function5m
Data from Different Geographies10m
Download Data from Yahoo! Finance10m
Additional Reading10m
- Forex Price DataIn this section, you will learn to obtain historical forex. The data is visualised in the Jupyter notebook.Forex Price Data10m
Minute Price Data for AUD/USD5m
Additional Reading10m
- Crypto DataIn this section, you will learn to obtain historical data for cryptocurrency assets. The data is visualised in the Jupyter notebook.Cryptocurrency Data5m
Additional Reading10m
- Futures DataIn this section, you will learn how to fetch data for continuous futures contracts and their limitations. You will learn about the proportional adjustment method to stitch and create a continuous futures contract.Futures Data5m
Futures Continuations10m
Additional Reading10m
- Options DataOptions are derivative contracts that derive their value from the underlying securities, such as stocks. In this section, you will learn to fetch options chain data for the US equities market from Yahoo Finance.Options Chain Data From Yahoo! Finance10m
Put Option Premium Vs Strike Price2m
Additional Reading10m
Getting Options Data for Different Geographies2m
- Stock Fundamental DataIn this section, you will learn how to fetch fundamental data like income statements, balance sheets and cash flow statements. And calculate fundamental ratios such as current ratio, return on equity and debt to equity ratio. You will then learn to fetch earnings calendar, analyst recommendations and institutional holders.Fundamental Data10m
Ratios from Fundamental Data10m
Other Company Data10m
Additional Reading10m
- Macro DataMacro data gives a bigger picture of how a country’s economy is performing. This section will take you through various sources such as FRED and yfinance to fetch these data. You will fetch the macro data such as GDP, Inflation and Rates.Macro Data10m
Gross Domestic Product of US5m
US Treasury Rate Chart2m
Additional Reading10m
- News DataInitially, traders and investors would go through the financial section of the newspapers and depending on the news, buy or sell their favourite assets. As the world moved online, consumption of news moved too. News is disseminated instantaneously and so are trading decisions. This section will help you fetch and aggregate the news from major digital platforms so that you can make your trading decisions faster.Fetch News Headlines5m
Additional Reading10m
- Data Quality Checks and Data CleaningThis section covers the data quality checks on the market data and methods used for data cleaning. This section also includes an assessment test on concepts covered in this course so far to test your learning.Basic Data Quality Checks and Data Cleaning10m
Count of Null Values5m
Drop the Missing Value5m
Count of Duplicate Values5m
INFO Method2m
Outliers2m
Additional Reading10m
Test on Getting Market Data12m
- Run Codes Locally on Your MachineLearn to install the Python environment in your local machine.Uninterrupted Learning Journey with Quantra2m
Python Installation Overview1m 59s
Flow Diagram10m
Install Anaconda on Windows10m
Install Anaconda on Mac10m
Know your Current Environment2m
Troubleshooting Anaconda Installation Problems10m
Creating a Python Environment10m
Changing Environments2m
Quantra Environment2m
Troubleshooting Tips For Setting Up Environment10m
How to Run Files in Downloadable Section?10m
Troubleshooting For Running Files in Downloadable Section10m
- ResourcesIn this section, you will be able to download all the strategy notebooks as a zip file. You can use these notebooks and modify their contents according to your needs.Course Summary and Next Steps10m
Python Codes and Data2m
Forex Trading using Python: Basics
- Introduction to Forex TradingThis section covers the basics of Forex trading and explains how macroeconomic factors affect the Forex market.
Introduction to Forex Trading5m 11s
Quantra Features and Guidance3m 48s
What is PIP?2m
What is Spread?2m
What is Spread Value?2m
Factors Affecting Forex Trading4m 28s
Interest Rate2m
Inflation2m
Participants in the Forex Market2m
- Momentum Trading StrategyThis section focuses on explaining the details of the momentum trading strategy, and coding and backtesting it in Quantra Blueshift.
Momentum Trading Strategy Overview2m 42s
What is Momentum Strategy?2m
Momentum Strategy Rationale2m
Momentum Strategy Logic2m
Ranking Criteria for Currency Pairs2m
Approach to Capital Allocation2m
Rebalance Frequency2m
Quantra Blueshift Functions10m
Code the Momentum Trading Strategy3m 20s
Quantra Blueshift Features2m
Initialize Function2m
Define Currency List5m
Syntax for Schedule Function2m
Fetch the Historical Data5m
Calculate Returns5m
Method to Place Order2m
Assessment Test On Forex Markets and Momentum Strategy12m
- Live Trading on BlueshiftThis section will walk you through the steps involved in taking your trading strategy live. You will learn about backtesting and the 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.Uninterrupted Learning Journey with Quantra2m
Section Overview2m 19s
Live Trading Overview2m 41s
Vectorised vs Event Driven2m
Process in Live Trading2m
Real-Time Data Source2m
Blueshift Code Structure2m 57s
Important API Methods10m
Schedule Strategy Logic2m
Fetch Historical Data2m
Place Orders2m
Backtest and Live Trade on Blueshift4m 5s
Additional Reading10m
Blueshift Data FAQs10m
- Live Trading TemplateBlueshift Live Trading TemplatePaper/Live Trading Long Short FX Strategy10m
FAQs for Live Trading on Blueshift10m
- Risk ManagementThis section demonstrates how to manage the intraday risk while trading in the Forex market.
Risk Management in Forex2m 49s
Downside of Leverage2m
Stop Loss2m
How to Allocate Capital?2m
Course Summary1m 55s
Assessment Test - II6m
Quant Interview Questions Preparation
- IntroductionThis section introduces the course and provides you with tips regarding resume creation for the position of a Quant Analyst, Quant Trader or Data Scientist.
Introduction2m 31s
Resume Tips
- AptitudeThis section aims to test you on different mathematical and logical problems which are apt for a quant interview.Mathematics Topics Overview10m
Interest Rates With Different Periods2m
Interest Rates With Same Period2m
Highest Growth2m
Shortest Distance2m
Podcast: Gaurav7m 16s
Leap Year2m
Ranking2m
Clock Problem2m
Next Term2m
Glass of Whiskey2m
Number of Socks2m
Sudoku2m
- ProbabilityThe concepts of probability are tested in this section, including mathematical problems related to cards, dice, coins, fair value etc.Card Problem2m
Marbles2m
Dice2m
Unbiased Coin2m
Biased Coin2m
Most Likely Event2m
Fair Value2m
- StatisticsStatistics, being an important area for a quant analyst, this section provides practice questions based on standard deviations, future value, returns etc.Standard Deviations2m
Expected Return2m
Predicted Return2m
Future Value2m
Types of Error2m
- Python BasicThis section aims to cover a diverse set of topics in Python that are relevant for a quant interview, including basic concepts like operators, lists, loops etc.Python Topics Overview10m
Logical Operators2m
Podcast: Rob2m 33s
Functions2m
Change the Iterative Inside the While Loop2m
String Intersection5m
Change the Iterative Outside the While Loop2m
If Statement2m
Properties of Lists2m
Lists of a Set of Numbers2m
Using Zip5m
Pop Function2m
Len of List2m
- Python AdvancedAlong with the basic concepts, you can also find practice questions on advanced concepts like tuples, regex, multithreading etc.MRO2m
File Rename2m
Reduce5m
Tuples2m
Indexing2m
Regex2m
Counter From Collections5m
*args and **kwargs2m
Monkey Patching2m
Multithreading2m
- Time SeriesThis section includes finance topics like ARIMA, exponential moving average, log linear trend etc.Finance Topics Overview10m
ARIMA2m
Podcast: Rajib11m 9s
Autocorrelation2m
Autoregressive Model2m
Patterns in Time Series2m
Example of Time Series2m
Exponential Moving Average2m
Properties of Stationarity2m
Stationarity Test2m
Log Linear Trend2m
Test on Mathematics Topics and Python12m
- Technical IndicatorsPractice questions related to technical indicators like volatility, momentum etc. in this section.Momentum Indicator2m
Indicator Values2m
Elliott Wave Theory2m
Volatility Indicator2m
- Asset Class and Quant TradingThis section covers diverse questions from the topics of arbitrage, asset class, portfolio diversity etc.Asset Class2m
Interest Rate Hike2m
Portfolio Diversity2m
R- Squared2m
Adjusted R-Squared2m
Carry Trade2m
Index Arbitrage2m
Statistical Arbitrage2m
Hedge Ratio2m
HFT2m
- Portfolio ManagementQuiz your knowledge on risk measures such as Sharpe Ratio and portfolio returns such as Asset Beta and Alpha.Carry Trade2m
Sharpe and Sortino Ratio2m
Asset Beta2m
Alpha2m
What Are Key Rate Durations2m
Yield Rate in Key Rate Durations2m
- Risk ManagementQuiz your knowledge on concepts such as VaR and topics which are usually asked in interviews.Market Risk2m
Value At Risk (VaR)2m
Hedge2m
Exchange Rate Risk2m
Interest Rate Swap2m
Currency Swap2m
VaR Methods2m
- OptionsIf you can answer all the questions of this section correctly, you can count yourself as an expert in Options trading: from core to advanced concepts are covered in 8 questions.Call Option2m
Breakeven Point2m
Derman Kani and Heston Model2m
Call Price2m
Options Price2m
Dispersion Trading2m
Delta Neutral Portfolio2m
Profit and Loss2m
- Machine LearningHear what an expert in Artificial Intelligence, in an HFT firm, has to say about his recruiting experience. Continue to test your knowledge and know whether you are ready to join a trading desk which employs machine learning techniques.
Podcast: Sameer5m 55s
Precision and Recall2m
Regularization2m
Separating Data2m
Model Accuracy and Model Performance2m
Machine Learning Approach2m
Bias or Variance2m
Sigmoid Function2m
Regression or Classification2m
Podcast: Dr. Ernest P Chan10m 56s
Test on Finance Topics and Machine Learning12m
- SummaryLearn to master the HR round and get a summary of all the skills required to crack a Quant interview.HR Interview10m
Summary10m
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.
NSE Academy
The National Stock Exchange of India Ltd. (NSE) is the world's largest derivatives exchange by the number of contracts traded in 2019 and the leading stock exchange in India. NSE is a pioneer in technology and ensures the reliability and performance of its systems through a culture of innovation and investment in technology. NSE conducts financial education workshops through NSE Academy to develop new generation investors.
Interactive Brokers ®
Interactive Brokers ® is a U.S. based brokerage firm founded in 1993. It is the largest electronic trading platform to trade equities, options and futures. Trader Workstation (TWS) supports over 60 order types, from the most basic limit order to advanced trading to the most complex algorithmic trading, to help you execute a wide variety of trading strategies. Best Online Broker 2019 and a Top Online Broker 1 for Nine Consecutive Years by Barron's. Get started by using a free demo account.
FXCM
FXCM or Forex Capital Markets, acts as a retail broker for foreign exchange market, which was founded in 1999. It offers access to the global forex trading market, with intuitive platform options, including award-winning Trading Station. Their customer service team, one of the best in the industry, is available 24/5, wherever you are in the world.
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
- Are there any webinars, live or classroom sessions available in the course?
No, there are no live or classroom sessions in the course. You can ask your queries on community and get responses from fellow learners and faculty members
- Is there any support available after I purchase the course?
Yes, you can ask your queries related to the course on the community: https://quantra.quantinsti.com/community
- What are the system requirements to do this course?
Fast-speed internet connection and a browser application is required for this course. For the best experience, use Chrome.
- What is the admission criteria?
There is no admission criterion. You are recommended to go through the prerequisites section, be aware of skill sets gained and to learn the most from the course.
- Is there a refund available?
We respect your time, and hence, we offer concise but effective short-term courses created under professional guidance. We try to offer the most value within the shortest time. There are a few courses on Quantra which are free of cost. Please check the price of the course before enrolling in it. Once a purchase is made, we offer complete course content. For paid courses, we follow a 'no refund' policy.
- Is the course downloadable?
Some of the course material is downloadable such as Python notebooks with strategy codes. We also guide you to use these codes on your own system for you to practice further.
- Can the Python strategies provided in the course be used immediately for trading?
We focus on teaching about quantitative and machine learning techniques and how learners can use them for developing their own strategies. You may or may not be able to directly use them in your own system. Please do note that we are not advising or offering any trading/investment services. The strategies are used for learning & understanding purposes and we do not take any responsibility for the performance or any profit or losses that using these techniques results in.
- Are you using real time Stock market data in the course?
No. We do not take examples from 'real time data', but you shall be learning to code through historical data. Please do note that there is a section at the end that will guide you on how to automate for real time live trading. You can get more detailed learning on how to automate trading strategies through our free course, 'Automated trading with IBridgePy using Interactive Brokers Platform'.
- Will I be getting a certificate post the completion of the programme?
Yes, you will get a certificate for each course separately within a few hours of completion of the course. The certificates are downloadable from your account tab "My Certificates".
- What does "lifetime access" mean?
Lifetime access means that once you enroll in the course, you will have unlimited access to all course materials, including videos, resources, readings, and other learning materials for as long as the course remains available online. There are no time limits or expiration dates on your access, allowing you to learn at your own pace and revisit the content whenever you need it, even after you've completed the course. It's important to note that "lifetime" refers to the lifetime of the course itself—if the platform or course is discontinued for any reason, we will inform you in advance. This will allow you enough time to download or access any course materials you need for future use.
- Is Python an essential skill for automated trading for beginners?
Python is not strictly essential for beginners in automated trading, but it is highly recommended. Here’s why:
Ease of Learning: Python has a beginner-friendly syntax, making it accessible even for those new to programming.
Versatility: Python is widely used in the finance industry for tasks like backtesting, data analysis, and building trading algorithms. Libraries like Pandas, NumPy, and TA-Lib make it easy to analyse financial data
Extensive Community Support: Python's popularity means a vast amount of tutorials, forums, and resources are available for troubleshooting and learning.
Integration with Broker APIs: Many brokers and trading platforms offer APIs with Python support, enabling seamless integration for order execution and real-time data analysis.
- What do quant traders do? Will I become a quant trader after the course?
Quant traders use mathematical models and algorithms to analyze market data and execute trades. They focus on creating and refining data-driven strategies for trading in financial markets. Completing these quantitative trading courses will equip you with the skills needed to pursue a career in this field.
- What is quantitative trading? Is this the right path for me?
Quantitative trading involves using statistical and mathematical models to make trading decisions. If you either enjoy working with data, have strong analytical skills or are comfortable with programming and finance, this could be a rewarding career path for you
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