Algorithmic Trading & Quantitative Analysis Using Python

Mayank Rasu, Experienced Quant Researcher

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69 Lessons (12h 22m)
    • 1. Course Introduction

      4:19
    • 2. What Is Covered in this Course?

      4:31
    • 3. Course Prerequisites

      1:57
    • 4. Is it For Me?

      1:39
    • 5. How To Get Help

      2:42
    • 6. Pandas Datareader - Introduction

      9:23
    • 7. Pandas Datareader - Deep Dive

      11:40
    • 8. Yahoofinancials Python Module - Intro

      11:45
    • 9. Yahoofinancials Python Module - Deep Dive

      21:16
    • 10. Intraday Data - Alphavantage Python Wrapper

      10:36
    • 11. Web Scraping Intro

      9:23
    • 12. Using Web Scraping to Extract Fundamental Data - I

      16:02
    • 13. Using Web Scraping to Extract Stock Fundamental Data - II

      23:43
    • 14. Updated Web-Scraping Code - Yahoo-Finance Webpage Changes

      12:35
    • 15. Data Handling

      11:24
    • 16. Basic Statistics - Familiarize Yourself With Your Data

      10:46
    • 17. Rolling Operations - Data In Motion

      15:01
    • 18. Visualization Basics - I

      10:18
    • 19. Visualization Basics - II

      13:51
    • 20. Technical Indicators - Intro

      9:11
    • 21. MACD Overview

      8:31
    • 22. MACD Implementation in Python

      11:10
    • 23. ATR and Bollinger Bands Overview

      6:08
    • 24. ATR and Bollinger Bands Implementation in Python

      14:00
    • 25. RSI Overview and Excel Implementation

      11:58
    • 26. RSI Implementation in Python

      11:27
    • 27. ADX Overview

      4:13
    • 28. ADX Implementation in Excel

      12:55
    • 29. ADX Implementation in Python

      13:40
    • 30. OBV Overview and Excel Implementation

      6:36
    • 31. OBV Implementation in Python

      3:07
    • 32. Slope in a Chart

      4:12
    • 33. Slope Implementation in Python

      23:03
    • 34. Renko Overview

      6:57
    • 35. Renko Implementation in Python

      14:27
    • 36. TA-Lib Introduction

      4:23
    • 37. TA-Lib Installation & Application

      17:53
    • 38. Introduction to Performance Measurement

      2:03
    • 39. CAGR Overview

      4:03
    • 40. CAGR Implementation in Python

      9:29
    • 41. How to Measure Volatility

      4:17
    • 42. Volatility Measures' Python Implementation

      2:27
    • 43. Sharpe Ratio and Sortino Ratio

      4:32
    • 44. Sharpe and Sortino in Python

      10:25
    • 45. Maximum Drawdown and Calmar Ratio

      3:58
    • 46. Maximum Drawdown and Calmar Ratio in Python

      11:10
    • 47. Why Should I Backtest My Strategies?

      6:53
    • 48. Strategy I - Portfolio Rebalancing

      7:03
    • 49. Strategy I in Python

      28:20
    • 50. Strategy II - Resistance Breakout

      8:27
    • 51. Strategy II in Python

      28:47
    • 52. Strategy III - Renko and OBV

      4:34
    • 53. Strategy III - Renko and OBV

      21:24
    • 54. Strategy IV - Renko and MACD

      5:19
    • 55. Strategy IV in Python

      12:54
    • 56. Value Investing Overview

      4:56
    • 57. Introduction to Magic Formula

      6:02
    • 58. Magic Formula Implementation in Python

      24:16
    • 59. Introduction to Piotroski F-Score

      7:20
    • 60. Piotroski F-Score Implementation in Python

      27:08
    • 61. Automated/Algorithmic Trading Overview

      13:48
    • 62. Using Time Module in Python

      12:44
    • 63. FXCM Overview

      7:01
    • 64. Introduction to FXCM Terminal

      12:34
    • 65. FXCM API

      22:06
    • 66. Building an Automated Trading System - part I

      8:08
    • 67. Building an Automated Trading System - part II

      11:14
    • 68. Building an Automated Trading System - part III

      11:23
    • 69. 7 9 Automated Trading Script 4

      10:50
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About This Class

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