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30-Day Money-Back Guarantee
Finance & Accounting Investing & Trading Algorithmic Trading

Algorithmic Trading & Quantitative Analysis Using Python

Build fully automated trading system and Implement quantitative trading strategies using Python
Bestseller
Rating: 4.5 out of 54.5 (2,107 ratings)
16,178 students
Created by Mayank Rasu
Last updated 2/2021
English
English [Auto], French [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Algorithmic trading and quantitative analysis using python
  • Carrying out both technical analysis and fundamental analysis programatically
  • API trading
Curated for the Udemy for Business collection

Requirements

  • Intermediate level expertise in python
  • high school level familiarity with mathematics and statistics
  • Basic understanding of equity/forex trading

Description

Build a fully automated trading bot on a shoestring budget. Learn quantitative analysis of financial data using python. Automate steps like extracting data, performing technical and fundamental analysis, generating signals, backtesting, API integration etc. You will learn how to code and back test trading strategies using python. The course will also give an introduction to relevant python libraries required to perform quantitative analysis. The USP of this course is delving into API trading and familiarizing students with how to fully automate their trading strategies.


You can expect to gain the following skills from this course

  • Extracting daily and intraday data for free using APIs and web-scraping

  • Working with JSON data

  • Incorporating technical indicators using python

  • Performing thorough quantitative analysis of fundamental data

  • Value investing using quantitative methods

  • Visualization of time series data

  • Measuring the performance of your trading strategies

  • Incorporating and backtesting your strategies using python

  • API integration of your trading script

  • FXCM and OANDA API

  • Sentiment Analysis

Who this course is for:

  • traders looking to automate strategies and building automated trading stations, data scientists seeking to work with financial data, anyone curious about quantitative analysis

Featured review

Mellacheruvu Krishna Teja
Mellacheruvu Krishna Teja
4 courses
2 reviews
Rating: 5.0 out of 5a year ago
Highly recommended course for the people who wants to explore the finance with technology. The content was super cool and the instructor is really friendly, the way he explains the concepts is amazing. Thanks a lot Mayank!

Course content

11 sections • 106 lectures • 18h 22m total length

  • Preview04:31
  • Preview01:57
  • Preview01:39
  • Preview02:42
  • Preview02:27
  • Anaconda Distribution Intro
    03:36
  • Creating Virtual Environment (Optional)
    09:58

  • Data Gathering Intro
    05:00
  • yfinance Overview
    07:32
  • yfinance - Getting Data for Multiple Stocks
    14:16
  • yahoofinancials Library and Parsing JSON Data
    10:28
  • yahoofinancials - Getting Data for Multiple Stocks
    13:51
  • Alpha Vantage Python Library Intro
    07:53
  • Alpha Vantage - Getting Data for Multiple Tickers
    16:48
  • Web Scraping Intro
    09:23
  • Using Web Scraping to Extract Stock Fundamental Data - I
    16:02
  • Using Web Scraping to Extract Stock Fundamental Data - II
    23:43
  • Updated Web-Scraping Code - Yahoo-Finance Webpage Changes
    12:35
  • Other Free Data Resources
    00:30

  • Handling NaN Values
    17:00
  • Basic Statistics - Familiarize Yourself With Your Data
    10:46
  • Rolling Operations - Data In Motion
    15:01
  • Visualization Basics - I
    10:18
  • Visualization Basics - II
    13:51

  • Introduction to Technical Indicators
    09:11
  • MACD Overview
    08:31
  • MACD Implementation in Python
    11:10
  • ATR and Bollinger Bands Overview
    06:08
  • ATR and Bollinger Bands Implementation in Python
    14:00
  • RSI Overview and Excel Implementation
    11:58
  • RSI Implementation in Python
    11:27
  • ADX Overview
    04:13
  • ADX Implementation in Excel
    12:55
  • ADX Implementation in Python
    13:41
  • OBV Overview and Excel Implementation
    06:36
  • OBV Implementation in Python
    03:07
  • Slope in a Chart
    04:12
  • Slope Implementation in Python
    23:03
  • Renko Overview
    06:57
  • Renko Implementation in Python
    14:27
  • TA-Lib Introduction
    04:23
  • TA-Lib Installation and Application
    17:53

  • Introduction to Performance Measurement
    02:03
  • CAGR Overview
    04:03
  • CAGR Implementation in Python
    09:29
  • How to Measure Volatility
    04:17
  • Volatility Measures' Python Implementation
    02:27
  • Sharpe Ratio and Sortino Ratio
    04:32
  • Sharpe and Sortino in Python
    10:25
  • Maximum Drawdown and Calmar Ratio
    03:58
  • Maximum Drawdown and Calmar Ratio in Python
    11:11

  • Why Should I Backtest My Strategies?
    06:53
  • Strategy I - Portfolio Rebalancing
    07:03
  • Strategy I in Python
    28:20
  • Strategy II - Resistance Breakout
    08:27
  • Strategy II in Python -I
    18:41
  • Strategy II in Python -II
    28:05
  • Strategy III - Renko and OBV
    04:34
  • Strategy III in Python
    21:24
  • Strategy IV - Renko and MACD
    05:19
  • Strategy IV in Python
    12:54

  • Value Investing Overview
    04:56
  • Introduction to Magic Formula
    06:02
  • Magic Formula Implementation in Python
    24:16
  • Updated Python Code - Yahoo-Finance Webpage Changes
    00:17
  • Introduction to Piotroski F-Score
    07:20
  • Piotroski F-Score Implementation in Python
    27:08
  • Preview00:17

  • Automated/Algorithmic Trading Overview
    13:48
  • Using Time Module in Python
    12:44
  • FXCM Overview
    07:01
  • Introduction to FXCM Terminal
    12:34
  • FXCM API
    22:06
  • Building an Automated Trading System - part I
    08:08
  • Building an Automated Trading System - part II
    11:14
  • Building an Automated Trading System - part III
    11:23
  • Building an Automated Trading System - part IV
    10:50
  • OANDA Overview
    06:56
  • OANDA API
    25:57
  • SMA Crossover Strategy using OANDA API
    19:19

  • Why Cloud
    04:54
  • Launching AWS EC2 Instance
    19:57
  • Connecting To The EC2 Instance I
    15:11
  • Connecting To The EC2 Instance II
    07:48
  • Transferring Files to EC2 Instance
    12:11
  • Scheduling/Automating Your Scripts Using Crontab
    15:15
  • Keeping Track of Running Processes
    10:32
  • Using Screen Command with Crontab
    07:35
  • Shutting Down/Deleting EC2 Instance
    03:41

  • Why Sentiment Analysis
    06:00
  • Sentiment Analysis - Intuition
    08:09
  • Natural Language Processing Basics
    17:16
  • Lexicon Based Sentiment Analysis
    06:36
  • VADER Introduction
    11:35
  • Textblob Introduction
    07:41
  • Building a Sentiment Analyzer using VADER - Part I
    10:35
  • Building a Sentiment Analyzer using VADER - Part II
    18:21
  • Machine Learning Based Sentiment Analysis
    13:59
  • ML Feature Matrix & TF-IDF Introduction
    09:50
  • Building ML Based Sentiment Analyzer - Part I
    03:16
  • Building a ML Based Sentiment Analyzer - Part II
    16:59
  • Building a ML Based Sentiment Analyzer - Part III
    06:31
  • Sentiment Analysis Application - Opportunities & Challenges
    04:56

Instructor

Mayank Rasu
Experienced Quant Researcher and Educator
Mayank Rasu
  • 4.5 Instructor Rating
  • 3,152 Reviews
  • 21,222 Students
  • 5 Courses

​I have authored and published bestselling courses and books on finance and technology. I have a passion for simplifying seemingly complex topics such as algorithmic trading, machine learning, artificial intelligence and make them accessible to students with no technology/finance background.

I have more than a decade of experience working with global investment banks in the quantitative analysis/risk management areas. My work experience has made me a firm believer in taking data driven decisions while always being mindful of the qualitative aspects of my job. 

I did my undergraduate education in engineering and earned MBA and MFE degrees. I see myself as a life long learner and I deeply cherish the opportunity of sharing my knowledge with others.

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