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Algorithmic Trading using Angel One's Smartapi

Mayank Rasu

Angel One is a leading stock broker in India providing free API access. I have created this course based on feedback from my existing students who were not happy about the monthly API charges levied by Zerodha. Angel One's SmartAPI provides the same set of functionality provided by any other brokers through their RESTful APIs and by the end of this course you will be highly conversant with this API and should be able to design and deploy your fully automated strategies on Angel One's platform.

Read more

Angel One is a leading stock broker in India providing free API access. I have created this course based on feedback from my existing students who were not happy about the monthly API charges levied by Zerodha. Angel One's SmartAPI provides the same set of functionality provided by any other brokers through their RESTful APIs and by the end of this course you will be highly conversant with this API and should be able to design and deploy your fully automated strategies on Angel One's platform.

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Design and deploy trading strategies on Angel One's Automate every step of your strategy including authentication, extracting data, performing technical analysis, generating signals, risk management etc. Gain a thorough understanding of Restful APIs and smartapi python wrapper. Learn how to deploy your strategies on cloud.

You can expect to gain the following skills from this course

  • API trading

  • Harnessing streaming tick level data

  • Incorporating technical indicators using python

  • backtesting and live testing your strategy

  • End to End strategy design and deployment

  • AWS EC2

  • Sqlite database management

Course Prerequisites

  • Basic Python proficiency (Should be familiar with python data types, data structures, loops, functions etc...basic stuff)

  • Access to Angel One's demat/trading account

  • High school level mathematical proficiency

  • Familiarity with trading/investing in the Indian market.

Enroll now

What's inside

Learning objectives

  • Algorithmic trading
  • Angel one smartapi
  • Api trading
  • Aws ec2

Syllabus

Introduction
Angel One Intro & Account Set Up
Referral Code (Thank You For Your Support)
Creating SmartAPI Trading App
Read more
Available Resources
Anaconda Python Distribution (Optional)
Creating Virtual Environment (Optional)
Authentication & Creating Trading Session
Very Important - smartapi package name change
Enabling TOTP Based Login (Mandatory for SmartAPI)
TOTP Based Authentication
Historical Data
Instrument Token Lookup
Generic Historical Data Function
Extracting Historical Data Without using Dates
Getting Historical Data Over Extended Duration
Extracting Historical Data for Multiple Tickers
Technical Indicators
Technical Indicators - Intro
MACD - Intro
MACD - Implementation
MACD Implementation Using Custom EMA
ATR & Bollinger Bands - Intro
Bollinger Bands Implementation
ATR Implementation
RSI - Intro
RSI Implementation
Stochastic - Intro
Stochastic Implementation
Streaming Real Time Data
Websocket vs HTTP connection
Very Important - Changes in SmartAPI Streaming Framework
Implementing Streaming Data Function
Streaming Multiple Tokens
Filtering Ticks
Streaming using Websocket2.0 (beta)
Storing Streaming Ticks in SQL Database
Order API
Intro to the Order API
Placing Limit & Market Orders
Cancelling Orders
Modifying Orders
Getting Order Book
Advanced Order API
Stop Loss Orders
Getting LTP for Advanced Orders
Bracket/Robo Orders
Bracket Orders - Follow Up
GTT (Good Till Triggered) Orders
Modifying and Cancelling GTT Rules
Live-Testing Order Book Based Strategy
Order Book Based Strategies
Important (Please Read) - Changes Due to Websocket2.0
Live Testing Order Book Strategy - I
Live Testing Order Book Strategy - II
Live Testing Order Book Strategy - III
Live Testing Order Book Strategy - IV
Single Thread Architecture - Drawback
Multi Threading in Python - Intro
Implementing Multi Threading for Live Testing
KPIs for Live Testing - I
KPIs for Live Testing - II
Backtesting ORB Strategy
What & Why of Backtesting
Backtesting - Intraday KPIs Intro
Open Range Breakout Strategy
ORB Backtesting Approach
ORB Backtesting Implementation - I
ORB Backtesting Implementation - II
ORB Backtesting Implementation - III
ORB Backtesting KPI Calculation
Deploying Strategy in Live Market
Deploying ORB Strategy - I
Deploying ORB Strategy - II
Deploying ORB Strategy - III
Live Trading Demo
Deploying Strategy on Cloud
Why Cloud
Launching AWS EC2 Instance
Connection to the EC2 Instance - I
Connection to the EC2 Instance - II
Transferring Files to EC2 Instance
Scheduling/Automating Scripts using Crontab
Keeping Track of Running Processes
Using Screen Command with Crontab
Shutting Down/Terminating EC2 Instance
Extracting Option Chains Algorithmically

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Algorithmic Trading using Angel One's Smartapi with these activities:
Review Python Fundamentals
Strengthen your Python foundation to better understand the code examples and implement your own algorithmic trading strategies.
Show steps
  • Review basic Python syntax and data structures.
  • Practice writing simple functions and loops.
  • Work through online Python tutorials or exercises.
Review Python Fundamentals
Strengthen your Python foundation to better understand the code examples and implement your own algorithmic trading strategies.
Show steps
  • Review basic Python syntax and data structures like lists, dictionaries, and tuples.
  • Practice writing functions and using control flow statements (if/else, loops).
  • Work through online Python tutorials or exercises focusing on the basics.
Mastering Algorithmic Trading
Supplement your learning with a comprehensive guide to algorithmic trading.
View Putting Knowledge to Work on Amazon
Show steps
  • Read the chapters relevant to the current course modules.
  • Implement some of the strategies discussed in the book.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Review 'Python for Finance' by Yves Hilpisch
Gain a deeper understanding of Python's application in finance, including data analysis and algorithmic trading.
View Python for Finance on Amazon
Show steps
  • Read the chapters related to data analysis and algorithmic trading.
  • Experiment with the code examples provided in the book.
  • Apply the concepts learned to your own trading strategies.
Backtest Simple Strategies
Reinforce your understanding of backtesting by implementing and testing simple trading strategies using historical data.
Show steps
  • Choose a simple trading strategy (e.g., moving average crossover).
  • Write Python code to backtest the strategy using historical data from Angel One's SmartAPI.
  • Analyze the backtesting results and identify areas for improvement.
Practice API Calls with a Mock API
Familiarize yourself with making API requests and handling responses before working with the live Angel One API.
Show steps
  • Find a free mock API service online (e.g., Mockable.io).
  • Simulate the Angel One API endpoints using the mock API.
  • Write Python code to make requests to the mock API and parse the responses.
Document Your Trading Strategy
Solidify your understanding by documenting your trading strategy, including the rationale, implementation details, and backtesting results.
Show steps
  • Write a detailed description of your trading strategy.
  • Explain the rationale behind your strategy and the indicators you are using.
  • Document the implementation details, including the code and data sources.
  • Summarize the backtesting results and discuss the strengths and weaknesses of your strategy.
Review 'Algorithmic Trading: Winning Strategies and Their Rationale' by Ernest P. Chan
Explore different algorithmic trading strategies and their underlying principles to improve your own strategy design.
View Hands-On AI Trading with Python, QuantConnect... on Amazon
Show steps
  • Read the chapters on different trading strategies and their rationale.
  • Analyze the backtesting results presented in the book.
  • Adapt the strategies to the Indian market and the Angel One SmartAPI.
Automated Trading Bot
Apply your knowledge by building a fully automated trading bot that executes trades based on your chosen strategy.
Show steps
  • Design the architecture of your trading bot.
  • Implement the core logic for generating trading signals and placing orders.
  • Integrate your bot with Angel One's SmartAPI.
  • Test your bot in a paper trading environment before deploying it live.
Develop a Simple Trading Bot
Apply your knowledge to build a basic trading bot that executes simple buy/sell orders based on predefined rules.
Show steps
  • Define a simple trading strategy (e.g., moving average crossover).
  • Implement the strategy using the Angel One SmartAPI.
  • Test the bot in a paper trading environment.
  • Monitor the bot's performance and make adjustments as needed.
Create a Backtesting Report
Document the backtesting process and results of a specific trading strategy to analyze its performance.
Show steps
  • Choose a trading strategy to backtest.
  • Gather historical data for the chosen strategy.
  • Implement the backtesting logic in Python.
  • Generate a report summarizing the backtesting results, including key performance indicators (KPIs).
Contribute to a SmartAPI Wrapper Library
Enhance your understanding of the SmartAPI by contributing to an open-source wrapper library.
Show steps
  • Find an open-source SmartAPI wrapper library on GitHub.
  • Identify a bug or missing feature in the library.
  • Implement the fix or feature and submit a pull request.

Career center

Learners who complete Algorithmic Trading using Angel One's Smartapi will develop knowledge and skills that may be useful to these careers:
Algorithmic Trader
The role of an algorithmic trader involves designing, developing, and deploying automated trading systems. This Algorithmic Trading course helps build a foundation for this career by providing hands-on experience with API trading using Angel One's SmartAPI. You'll learn to automate every step of a trading strategy, from authentication and data extraction to technical analysis and risk management. You'll also gain expertise in using Python, a crucial skill for algorithmic traders, to incorporate technical indicators, backtest strategies, deploy these strategies on the cloud, and manage data using SQLite. The course's focus on end-to-end strategy design and deployment using a specific API makes it particularly relevant for aspiring algorithmic traders.
See salaries and explore the career path for Algorithmic Trader
Systematic Trader
Systematic traders develop and implement rule-based trading strategies that are executed automatically. This Algorithmic Trading course helps build a foundation for this role by providing experience in every step of automated strategy design and deployment. This includes authentication, data extraction, technical analysis, risk management, real-time data streaming, and order API implementation. Systematic traders will find the course's focus on practical application, backtesting strategies, and cloud deployment particularly appealing. The use of Angel One's SmartAPI provides a real-world context that will be extremely valuable.
See salaries and explore the career path for Systematic Trader
Proprietary Trader
Proprietary traders, or prop traders, trade financial instruments using a firm's capital to generate profits. This Algorithmic Trading course helps build a foundation for this role by providing hands-on experience in designing, backtesting, and deploying automated trading strategies. Prop traders can leverage the course's coverage of API trading using Angel One's SmartAPI, the implementation of technical indicators using Python, and the deployment of strategies on the cloud to develop a trading edge. The focus on intraday KPIs and the entire process of automated trading strategy development will be useful for a prop trader hoping to boost their profit.
See salaries and explore the career path for Proprietary Trader
Trading Strategist
Trading strategists develop and implement trading strategies based on market analysis, technical indicators, and quantitative models. This Algorithmic Trading course helps build a strong foundation for this role by providing practical experience with API trading, backtesting, and live testing strategies based on real-time data. Trading strategists will find the curriculum focused on real-time data streaming, order API implementation, and deployment on cloud platforms particularly relevant. The knowledge gained in this course enables trading strategists to design, test, and deploy effective algorithmic trading strategies.
See salaries and explore the career path for Trading Strategist
Trading System Developer
A trading system developer specializes in creating and maintaining automated trading systems. This Algorithmic Trading course helps build a foundation for this role, offering training with the Angel One SmartAPI to automate the design and deployment of trading strategies. You'll gain expertise in extracting data, performing technical analysis, generating signals, and implementing risk management techniques. The course emphasizes the practical aspects of trading system development, including backtesting, live testing, and deployment on cloud platforms, while incorporating data structures for trading signals.
See salaries and explore the career path for Trading System Developer
Trading Automation Engineer
Trading automation engineers are responsible for building and maintaining the infrastructure that supports algorithmic trading systems. This Algorithmic Trading course helps build a foundation in API integration, data handling, and system deployment. Those in trading automation will find the AWS EC2 modules helpful. You'll learn how to use Python to automate order placement, manage real-time data streams, and integrate with databases, gaining skills that are useful for automating trading processes. This course sets you up to build a solid foundation in the engineering part of algorithm trading.
See salaries and explore the career path for Trading Automation Engineer
Financial Software Developer
Financial software developers build and maintain the software systems used in the finance industry, including trading platforms and risk management tools. This Algorithmic Trading course helps build a foundation for this career by providing hands-on experience with API integration, data handling, and automated trading strategies. The course teaches how to use Python to interact with the Angel One SmartAPI, extract historical data, implement technical indicators, and automate order placement. You'll also learn how to deploy strategies on the cloud using AWS EC2 and manage data using SQL databases. The course's focus on practical application and real-world API usage makes it highly relevant for financial software developers.
See salaries and explore the career path for Financial Software Developer
Financial Engineer
Financial engineers apply mathematical and computational methods to solve financial problems, such as pricing derivatives, managing risk, and developing trading strategies. This Algorithmic Trading course helps build a foundation for this role by providing practical experience in designing and backtesting trading strategies using real-world API data. The course's syllabus includes technical indicator implementation, data handling, and cloud deployment. All these things provide practical experience necessary for financial engineers to develop and implement strategies. The course's focus on Python and automated trading makes it particularly appealing to those seeking a career as a financial engineer.
See salaries and explore the career path for Financial Engineer
Quantitative Analyst
A quantitative analyst, or quant, uses mathematical and statistical models to analyze financial markets and develop trading strategies. This Algorithmic Trading course may be useful for aspiring quants because it provides practical experience in designing and backtesting trading strategies using real-world API data. The course's syllabus includes various aspects of technical analysis, such as MACD, Bollinger Bands, ATR, RSI, and Stochastic indicators. Furthermore, the course covers how to use Python to analyze data, develop trading signals, and backtest strategies, which are essential skills for any quant. The skills learned in this course are particularly valuable for quants looking to develop algorithmic trading strategies.
See salaries and explore the career path for Quantitative Analyst
Hedge Fund Analyst
Hedge fund analysts conduct research and analysis to support investment decisions within hedge funds. This Algorithmic Trading course helps build a foundation for this role by providing hands-on experience with API trading, backtesting methodologies, and strategy deployment. Hedge fund analysts benefit from the course's use of Python to implement technical indicators and automate trading processes, as well as how to deploy trading strategies on cloud platforms. The course's focus on intraday KPIs and Open Range Breakout strategy backtesting is directly applicable to the fast-paced environment of hedge fund analysis.
See salaries and explore the career path for Hedge Fund Analyst
Data Scientist
Data scientists use statistical techniques, machine learning, and data visualization to analyze large datasets and extract meaningful insights. While not exclusively for financial data, this Algorithmic Trading course may be useful by providing experience with time-series data analysis, feature engineering (through technical indicators), and model backtesting. The course teaches how to use Python and SQL databases to store and analyze trading data. Specifically, this course could be useful for a data scientist interested in exploring financial markets and the development of trading strategies or to broaden pattern recognition expertise.
See salaries and explore the career path for Data Scientist
Portfolio Manager
Portfolio managers make investment decisions and manage investment portfolios to achieve specific financial goals for clients. This Algorithmic Trading course may be useful because it grants a solid foundation in systematic trading strategies and the use of APIs to access real-time market data, and this knowledge can inform portfolio management decisions. The course's focus on backtesting, risk management, and strategy design may be valuable in evaluating and incorporating algorithmic strategies into a broader investment portfolio. The understanding of cloud deployment for trading strategies may also be useful in evaluating technology infrastructure for investment management.
See salaries and explore the career path for Portfolio Manager
Investment Analyst
Investment analysts research companies, industries, and financial markets to provide recommendations to investment firms or individual investors. This Algorithmic Trading course may be useful, as it provides understanding of the tools and techniques used in automated trading, including API usage and technical analysis. While an investment analyst may not directly implement algorithmic strategies, knowledge of these methodologies can enhance their understanding of market dynamics and improve their ability to evaluate investment opportunities. The course's coverage of technical indicators and backtesting principles may be particularly useful for an investment analyst.
See salaries and explore the career path for Investment Analyst
Market Maker
Market makers provide liquidity in financial markets by quoting buy and sell prices for specific securities. This Algorithmic Trading course may be used to learn about automated trading strategies and API integration, which are crucial for efficient order execution and inventory management. The course's emphasis on real-time data streaming and order API functionality may be particularly useful for market makers looking to enhance their trading infrastructure. By studying the methods to maintain a constant presence in the market, a market maker can guarantee less volatility of the instruments that they oversee.
See salaries and explore the career path for Market Maker
Risk Manager
Risk managers identify, assess, and mitigate financial risks within an organization. While risk managers do not typically build trading strategies, this Algorithmic Trading course may be useful since its syllabus includes a focus on backtesting and key performance indicators (KPIs). Understanding how models are tested and validated can inform the risk assessment process when evaluating algorithmic trading strategies. The course can help risk managers better understand the dynamics and potential pitfalls of automated trading systems, especially concerning real-time market dynamics.
See salaries and explore the career path for Risk Manager

Reading list

We've selected three books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Algorithmic Trading using Angel One's Smartapi.
Cover image
Putting Knowledge to Work
Save
Provides a comprehensive overview of algorithmic trading strategies and techniques. It covers topics such as backtesting, risk management, and order execution. It valuable resource for understanding the theoretical underpinnings of the strategies you will be implementing in this course. This book can be used as a reference to expand on the topics covered in the course.
Putting Knowledge to Work
Paperback
Check
Putting Knowledge to Work
Kindle Edition
Check
Cover image
Python for Finance
Save
Provides a comprehensive overview of using Python for financial analysis. It covers topics such as data analysis, visualization, and algorithmic trading. It valuable resource for understanding the practical applications of Python in the financial industry and complements the course material well. This book is commonly used by finance professionals.
Python for Finance
Paperback
Check
Python for Finance
Kindle Edition
Check
Cover image
Hands-On AI Trading with Python, QuantConnect and...
Save
Delves into various algorithmic trading strategies and the rationale behind them. It provides insights into strategy development, backtesting, and risk management. While not specific to Angel One's API, the concepts are broadly applicable and can enhance your understanding of algorithmic trading principles. This book is more valuable as additional reading than as a current reference.
Hands-On AI Trading with Python, QuantConnect and...
Paperback
Check
Hands-On AI Trading with Python, QuantConnect and...
Kindle Edition
Check

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Effort
11.5 total hours
Via
Udemy
Instructor
Mayank Rasu
Language
English
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