Database Management for Trading: Basic
- Skills Covered
- Learning Track
- Prerequisites
- Syllabus
- About author
- Testimonials
- Faqs
Skills Covered
Database Management
- SQLAlchemy
- SQLite
- Data Ingestion
- Data Querying
Python
- Pandas
- NumPy
- sqlalchemy
- threading
Market Data
- Historical and live market data
- Options chain data
- Bid-ask data
Course Features
CommunityFaculty Support on Community
- Interactive Coding Exercises
Interactive Coding Practice
- Capstone Project
Capstone Project Using Real Market Data
- Get Certified
Get Certified
learning track 1
This course is a part of the Learning Track: Algorithmic Trading for Beginners
Course Fees
Full Learning Track
These courses are specially curated to help you with end-to-end learning of the subject.
Prerequisite
No prior database experience is required. A basic understanding of Python, along with knowledge of data manipulation using Pandas and NumPy, is essential. Familiarity with financial markets and trading concepts is helpful, though not mandatory
After this course you’ll be able to
- Understand the importance of using databases for trading and learn about their structure and types.
- Learn how to create and manage databases using SQLite and SQLAlchemy.
- Source, structure, and store options chain data, live market data, and bid-ask data in databases.
- Develop skills to create databases based on data and query them for in-depth analysis.
- Apply multithreading and multiprocessing techniques to optimise database management for trading.
Syllabus
- Introduction to the CourseIn this section, you will go through a brief overview of the concepts taught in the course in a structured format and also explore the various features of Quantra Platform.
- Introduction to Market DataHow many times have you thought to yourself, I have a hypothesis or a strategy, but where do I get the data to test it? In this section, we will answer some of the most common questions as well as download equity, forex, and cryptocurrency data from a python library.Understanding Market Data and Storage Needs2m
Data Sources for Market Information5m
How to Get Market Data5m
Getting API Keys from Alpaca2m
Getting Data From Alpaca API5m
Resources for Downloading Financial Data2m
- Storing Data: CSV or DatabaseYou must be thinking, why do you need a database when your work can be done using a CSV, Excel or JSON file for storing data. In this section we will look at a few use cases and check whether you should use a CSV or a database would be more appropriate.Storing Data: CSV or Database2m
Handling Large Datasets5m
Data Storage Options5m
Data Integrity5m
Collaborative Data Sharing5m
Performance and Multi-Threading5m
Additional Reading on Storing Data: CSV or Database2m
Summary on Storing Data: CSV or Database2m
Test on Retrieval of Data and Use of Database16m
Introduction to a DatabaseIn this section, you will learn the components of a database as well as its types. You will also go through a brief overview of database components.
Introduction to a Database2m
Introduction to Databases5m
Data Organisation and Retrieval5m
Applications in Trading5m
Data Validation5m
Brief Overview of Database Components2m
Database Components5m
Table Structure5m
Unique Identifiers5m
Uniqueness in Data5m
- Primary and Foreign KeysIn this section, we will delve a bit deeper into the reason why database can retrieve data faster through the concept of primary and foreign keys.Primary Keys and Foreign Keys2m
Database Keys Fundamentals5m
Linking Tables with Keys5m
Data Organisation in Databases5m
Customising Primary Keys5m
Relational Database Examples5m
Test on Database Components and Keys18m
- Other Types of DatabasesDo you think there are only relational Databases? Not likely. In this section, you will look at two more types of databases and why are they important.Other Types of Databases2m
Time Series Databases5m
Indexing in Time Series Databases5m
Benefits of Time Series Databases5m
Relational Database Fundamentals5m
Benefit of Indexing in Time Series5m
NoSQL Databases2m
Data Classification5m
Source-Specific Fields5m
NoSQL Definition5m
FAQs on Other Types of Databases2m
Additional Reading on Other Types of Databases2m
Summary on Other Types of Databases2m
Test on Other Types of Databases14m
- Creating DatabaseLearn the different architectures used in databases, such as file-based, server-based, local, and cloud setups. Explore beginner-friendly tools like SQLite and combine them with SQLAlchemy to make your database easily migratable.Section Overview: Creating Database2m
The Beginner's Toolkit SQLite and SQLAlchemy2m
Why SQLite ?5m
Why Not SQLite?5m
Why SQLAlchemy?5m
Server-Based Database2m
Key Advantage of Server-Based Database5m
Why Host?5m
Seamless Database Transition.5m
- Brief Introduction to Database on CloudCloud databases become essential as systems scale beyond single machines, providing effortless scaling, remote access, automatic backups, and better performance for large datasets. You will see how you can transition to a cloud database.
Database on Cloud2m
Cloud Benefits5m
Key Advantage of Cloud Databases5m
Cloud Database Security5m
Initialising Database5m
Execute create_engine5m
SQLite and PostgreSQL5m
The Flexibility Provided by SQLAlchemy5m
FAQs on Creating Database2m
Additional Reading on Creating Database2m
Concepts of Database Types and Beginner Toolkits16m
Summary of Creating Database2m
- Schema DesignUnderstand how to structure tables, keys, and relationships for effective data storage.Introduction to Schema Design2m
Database Schema Fundamentals5m
Database Performance Optimisation5m
Data Quality and Constraints5m
Database Security and Access Control5m
Database Indexing and Query Performance5m
Common Schema Design Mistakes2m
Data Redundancy and Normalisation5m
Table Relationships and Keys5m
Cascade Operations5m
Column Design Principles5m
Normalisation Balance5m
FAQs on Schema Design2m
Additional Reading on Schema Design2m
- Creating the SchemaTurn your schema design learnings into real database tables. You'll define structure, keys, and indexes to make your SQLite database ready for the data.Section Overview on Creating the Schema2m
Understanding the Data2m
Why Datatypes?5m
Foreign Key in the Minute-level Data5m
Why Avoid Indexing All Columns?5m
Creating Tables with SQLAlchemy2m
SQLAlchemy Schema Steps Order?5m
SQLAlchemy Engine Object Role?5m
SQLAlchemy MetaData Importance?5m
Creating Tables5m
Defining Table Columns and Data Types5m
Creating Composite Primary Keys5m
Foreign Key Relationships5m
FAQs on Creating the Schema2m
Additional Reading on Creating the Schema2m
Summary on Creating the Schema2m
Test on Creating the Schema14m
- Inserting Data into a DatabaseOnce you have created the database, insitialised it and also created tables, you are now ready for the next step, which is inserting data in the database. In this section, you will see how to use real-world data and insert this data correctly in the database without any errors.How to Insert Data Into a Database2m
Order of Insertion of Data to Tables5m
Removal of Timezone Information5m
Begin Engine Connection5m
Inserting Data into Symbol Info Table5m
Database Connection and Table Reflection5m
SQLAlchemy and Data Insertion5m
Symbol Comparison Logic5m
Inserting and Validating Time Series Data5m
FAQs on Inserting Data into a Database2m
Summary on Inserting Data into a Database2m
Test on Inserting Data in Database10m
- Querying Data From the SQL DatabaseIn this section, you will understand how to retrieve data, or in database terms, query the database with the help of practical real-world examples.Querying Data From the SQL Database2m
Using LIMIT to Preview Data5m
Retrieving Unique Records5m
Query to Filter Data5m
Combining Tables with JOIN5m
LEFT JOIN and NULLs5m
Using Select and Where in Queries5m
Using Joins and Group By in Queries5m
Basic SQL Query with LIMIT5m
Using DISTINCT to Find Unique Values5m
Filtering Data with WHERE Clause5m
Resampling Data2m
- Structure of Options Chain DataLearn the structure of options chain data and how to use it effectively. Understand key components like strike, expiry, and Greeks, compare data formats across sources, and plan your own options chain database.Section Overview of the Structure of Options Chain Data2m
Structure of Options Chain Data2m
Understanding the Structure of Options Chain Data5m
Components of Call Option Data5m
Data Consistency Across Providers5m
Additional Columns in Options Chain Data5m
Initial Step for the Options Database5m
FAQs on the Structure of Options Chain Data2m
Additional Reading on Structure of Options Chain Data2m
Summary of the Structure of Options Chain Data2m
Test on the Structure of Options Chain Data14m
- Sourcing and Cleaning the Options Chain DataLearn to clean and structure raw options chain data by fixing column names, data types, missing values, and hidden formatting issues, ensuring it’s ready for accurate analysis and strategy building.Section Overview: Sourcing and Cleaning the Options Chain Data2m
Data Vendors10m
Sourcing US Options Data2m
Cleaning the Data2m
Importance of Data Cleaning5m
Identifying Unwanted Elements in Column Names5m
Standardising Column Name Casing5m
Resolving Data Inconsistencies2m
Purpose of Resolving Data Inconsistencies5m
Identifying Duplicate Rows5m
Importance of Data Type Validation5m
Handling Inconsistencies in Float-Type Columns5m
Extracting and Cleaning Options Data5m
Removing Leading Spaces from Column Names5m
Removing Square Brackets from Column Names5m
Converting Column Names to Lowercase5m
FAQs on Sourcing and Cleaning the Options Chain Data2m
Summary of Sourcing and Cleaning the Options Chain Data2m
Test on Sourcing and Cleaning the Options Chain Data14m
- Structuring and Filtering the Options DataLearn to structure and filter raw options data by categorising expiry types and removing illiquid or irrelevant contracts, preparing it for analysis and strategy development.Section Overview: Structuring and Filtering the Options Data2m
Classifying Options Data Based on Expiry2m
Purpose of Expiry Categorisation5m
Weekly Expiry Definition5m
LEAPS Classification5m
Filtering the Options Chain Data2m
Illiquid Contracts5m
Deep ITM/OTM Filtering5m
When the Bid or Ask Price is Zero5m
Filtering the Options Data5m
FAQs on Structuring and Filtering the Options Data2m
Additional Reading on Structuring and Filtering the Options Data2m
Summary of Structuring and Filtering the Options Data2m
Test on Structuring and Filtering the Options Data14m
- Storing Options Chain Data Learn how to organise and store options chain data efficiently in a SQL database, focusing on modular table design, translating Pandas DataFrames to SQL, and ensuring data integrity through best practices.
Section Overview: Storing Options Chain Data2m
Structure of Options Database2m
Single Table vs. Separate Tables5m
Database Structure Decision5m
SQL Data Types5m
Storing the Options Chain Data2m
Query Performance5m
SQL Table Structure5m
Use Case Alignment5m
Storing Options Data5m
Creating SQL Tables with Appropriate Columns and Data Types5m
FAQs on Storing Options Chain Data2m
Additional Reading on Storing Options Chain Data2m
Summary of Storing Options Chain Data2m
Test on Storing Options Chain Data14m
- Querying Options Chain DataLearn how to query structured options data from a SQL database using essential SQL queries to filter, select, and retrieve relevant data for backtesting and strategy analysis.Section Overview: Query Options Data from Database2m
Query Options Data from Database2m
Matching Multiple Exact Values5m
Retrieving Call Options Data5m
Advantages of Specific Column Selection5m
Query Options Data from Database5m
Creating a Parameterised SQL Query5m
Creating a Parameterised SQL Query with IN Clause5m
FAQs on Query Options Data from Database2m
Additional Reading on Query Options Data from Database2m
Summary of Query Options Data from Database2m
Test on Querying Options Chain Data10m
- Capstone ProjectIn this section, you will apply the knowledge you have gained in the course. You will pick up a capstone project where create an options database for 1-min frequency data.Getting Started2m
Problem Statement2m
Capstone Data Files2m
Capstone Solution2m
- When Should You Update Your Database?In this section, we ask ourselves if frequent update to the database is always a good thing or not.When Should You Update Your Database2m
Database Update Strategies5m
Real-Time Decision-Making and Database Performance5m
Timing Database Updates5m
FAQs on When Should You Update Your Database2m
Summary on When Should You Update Your Database2m
- Fetching Bid-Ask DataTo truly understand market behavior, it's not enough to see just the last trade. By fetching Level 1 and Level 2 bid-ask data, we gain a deeper look into price action—from the top of the order book to its full depth. Explore this section to see how it's done.Market Data Levels2m
Level 1 Market Data Contents5m
Level 2 Market Data Contents5m
Market Depth5m
Market Data Cost5m
Fetching Level 1 Data5m
Fetching Level 2 Data5m
Additional Reading on Fetching Bid-Ask Data2m
FAQs on Fetching Bid-Ask Dara2m
Summary on Fetching Bid-Ask Data2m
Test on Fetching Bid Ask Data12m
- MultithreadingExplore the concept of parallel processing, focusing on multithreading and multiprocessing techniques to speed up tasks like financial data retrieval and backtesting by executing multiple tasks simultaneously.Introduction To Parallel Processing2m
What is Parallel Processing?5m
Global Interpreter Lock5m
Task Suited for Multithreading5m
How to Implement Multithreading?5m
Starting Threads5m
Purpose of join()5m
Managing Threads2m
Managing Threads2m
Problems with Manual Threading5m
ThreadPoolExecutor5m
How to Implement Threadpoolexecutor?5m
FAQs on Multithreading2m
Additional Reading on Multithreading2m
Summary on Multithreading2m
- MultiprocessingIn the previous section, we explored one of the key parallel processing techniques—multithreading. Now, let’s turn up the intensity and dive into another powerful approach: multiprocessing. Curious how it can turbocharge your computation-heavy tasks? Let’s find out!Introduction to Multiprocessing2m
Benefit of Multiprocessing5m
Task Suited for Multiprocessing5m
Drawback of Using Multiprocessing5m
FAQs on Multiprocessing2m
Additional Reading on Multiprocessing2m
Summary on Multiprocessing2m
- Handling Corporate ActionsIn this section, we'll explore corporate actions and why they're crucial for maintaining accurate stock price data. From stock splits to dividends, understanding these events ensures your data stays reliable for analysis and decision-making.Handling Corporate Actions2m
Stock Split5m
Dividend5m
Stock Split Adjustment5m
Importance of Adjustment5m
How to Handle Corporate Actions?5m
FAQs on Handling Corporate Actions2m
Additional Reading on Handling Corporate Actions2m
Summary on Handling Corporate Actions2m
Test on Handling Corporate Actions10m
- 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
- Summary of the CourseIn this section, you will go through the summary of the course and access the data files and notebooks which were used in the course.Course Summary2m
Next Steps2m
Python Codes and Data2m
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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?
Yes, you will be awarded with a certification from QuantInsti after successfully completing the online learning units.
- 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 are required for this course. For best experience, use Chrome.
- What is the admission criteria?
There is no admission criterion. You are recommended to go through the prerequisites section and be aware of skill sets gained and required to learn 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 how to use these codes on your own system to practice further.
- Can the python strategies provided in the course be immediately used for trading?
We focus on teaching these 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 don't take any responsibility for the performance or any profit or losses that using these techniques results in.
- I want to develop my own algorithmic trading strategy. Can I use a Quantra course notebook for the same?
Quantra environment is a zero-installation solution to get beginners to start off with coding in Python. While learning you won't have to download or install anything! However, if you wish to later implement the learning on your system, you can definitely do that. All the notebooks in the Quantra portal are available for download at the end of each course and they can be run in the local system just the same as they run in the portal. The user can modify/tweak/rework all such code files as per his need. We encourage you to implement different concepts learnt from different learning tracks into your trading strategy to make it more suited to the real-world scenario.
- If I plug in the Quantra code to my trading system, am I sure to make money?
No. We provide you guidance on how to create strategy using different techniques and indicators, but no strategy is plug and play. A lot of effort is required to backtest any strategy, after which we fine-tune the strategy parameters and see the performance on paper trading before we finally implement the live execution of trades.
- 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.
- Do I need prior experience with databases to take this course?
No, this course is beginner-friendly. You’ll learn everything from scratch, including setting up databases using SQLite and interacting with them through Python and SQLAlchemy.
- What kind of trading data will I learn to store and manage?
You will learn to handle historical price data, real-time market data, bid-ask data, and options chain data—all structured efficiently within a database.
- How is this different from storing data in Excel or CSV files?
Unlike Excel or CSVs, databases allow for faster querying, better scalability, and safe multi-user access, crucial when managing large or real-time datasets in trading.
- Will I be using any specific tools or libraries in this course?
Yes, the course focuses on SQLite (a lightweight database), SQLAlchemy (Python ORM), and Python libraries like Pandas and NumPy. You’ll also explore multithreading and multiprocessing for efficiency.
- Is this course useful only for algorithmic traders?
While tailored for quants and algorithmic traders, anyone working with large trading datasets—analysts, developers, or portfolio managers—can benefit from learning database management.