Build fully automated trading system and Implement quantitative trading strategies using Python

What you'll learn
Algorithmic trading and quantitative analysis using python
Carrying out both technical analysis and fundamental analysis programatically
API trading
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 courseExtracting daily and intraday data for free using APIs and web-scrapingWorking with JSON dataIncorporating technical indicators using pythonPerforming thorough quantitative analysis of fundamental dataValue investing using quantitative methodsVisualization of time series dataMeasuring the performance of your trading strategiesIncorporating and backtesting your strategies using pythonAPI integration of your trading scriptFXCM and OANDA APISentiment Analysis

Overview

Section 1: Introduction

Lecture 1 What Is Covered in this Course?

Lecture 2 Course Prerequisites

Lecture 3 Is This For Me?

Lecture 4 How To Get Help

Lecture 5 Anaconda Distribution Intro

Lecture 6 Creating Virtual Environment (Optional)

Section 2: Getting Data

Lecture 7 Data Gathering Intro

Lecture 8 yfinance Overview

Lecture 9 yfinance - Getting Data for Multiple Stocks

Lecture 10 yahoofinancials Library and Parsing JSON Data

Lecture 11 yahoofinancials - Getting Data for Multiple Stocks

Lecture 12 Alpha Vantage Python Library Intro

Lecture 13 Alpha Vantage - Getting Data for Multiple Tickers

Lecture 14 Other Free Data Resources

Section 3: Web Scraping to Extract Financial Data

Lecture 15 Web Scraping Vs API Based Data Extraction

Lecture 16 HTML Intro

Lecture 17 Web Scraping Financial Data Using Python - I

Lecture 18 Web Scraping Financial Data Using Python - II

Lecture 19 Web Scraping Financial Data Using Python - III

Section 4: Basic Data Handling and Operations

Lecture 20 Handling NaN Values

Lecture 21 Basic Statistics - Familiarize Yourself With Your Data

Lecture 22 Rolling Operations - Data In Motion

Lecture 23 Visualization Basics - I

Lecture 24 Visualization Basics - II

Section 5: Technical Indicators

Lecture 25 Introduction to Technical Indicators

Lecture 26 Introduction to Charting

Lecture 27 MACD Overview

Lecture 28 MACD Implementation in Python

Lecture 29 ATR and Bollinger Bands Overview

Lecture 30 ATR Implementation in Python

Lecture 31 Bollinger Bands Implementation in Python

Lecture 32 RSI Overview and Excel Implementation

Lecture 33 RSI Implementation in Python

Lecture 34 ADX Overview

Lecture 35 ADX Implementation in Excel

Lecture 36 ADX Implementation in Python

Lecture 37 Renko Overview

Lecture 38 Renko Implementation in Python

Lecture 39 TA-Lib Introduction

Lecture 40 TA-Lib Installation and Application

Section 6: Performance Measurement - KPIs

Lecture 41 Introduction to Performance Measurement

Lecture 42 CAGR Overview

Lecture 43 CAGR Implementation in Python

Lecture 44 How to Measure Volatility

Lecture 45 Volatility Measures' Python Implementation

Lecture 46 Sharpe Ratio and Sortino Ratio

Lecture 47 Sharpe and Sortino in Python

Lecture 48 Maximum Drawdown and Calmar Ratio

Lecture 49 Maximum Drawdown and Calmar Ratio in Python

Section 7: Backtest Your Strategies

Lecture 50 Why Should I Backtest My Strategies?

Lecture 51 Strategy I - Portfolio Rebalancing

Lecture 52 Strategy I in Python

Lecture 53 Strategy II - Resistance Breakout

Lecture 54 Strategy II in Python -I

Lecture 55 Strategy II in Python -II

Lecture 56 Strategy III - Renko and OBV

Lecture 57 Strategy III in Python

Lecture 58 Strategy IV - Renko and MACD

Lecture 59 Strategy IV in Python

Section 8: Value Investing

Lecture 60 Value Investing Overview

Lecture 61 Introduction to Magic Formula

Lecture 62 Magic Formula Implementation in Python

Lecture 63 Updated Python Code - Yahoo-Finance Webpage Changes

Lecture 64 Introduction to Piotroski F-Score

Lecture 65 Piotroski F-Score Implementation in Python

Lecture 66 Updated Python Code - Yahoo-Finance Webpage Changes

Section 9: Building Automated Trading System on a Shoestring Budget

Lecture 67 Automated/Algorithmic Trading Overview

Lecture 68 Using Time Module in Python

Lecture 69 FXCM Overview

Lecture 70 Introduction to FXCM Terminal

Lecture 71 FXCM API

Lecture 72 Building an Automated Trading System - part I

Lecture 73 Building an Automated Trading System - part II

Lecture 74 Building an Automated Trading System - part III

Lecture 75 Building an Automated Trading System - part IV

Lecture 76 OANDA Overview

Lecture 77 OANDA API

Lecture 78 SMA Crossover Strategy using OANDA API

Section 10: Bonus Section: Running Your Algorithms in Cloud

Lecture 79 Why Cloud

Lecture 80 Launching AWS EC2 Instance

Lecture 81 Connecting To The EC2 Instance I

Lecture 82 Connecting To The EC2 Instance II

Lecture 83 Transferring Files to EC2 Instance

Lecture 84 Scheduling/Automating Your Scripts Using Crontab

Lecture 85 Keeping Track of Running Processes

Lecture 86 Using Screen Command with Crontab

Lecture 87 Shutting Down/Deleting EC2 Instance

Section 11: Bonus Section: Sentiment Analysis

Lecture 88 Why Sentiment Analysis

Lecture 89 Sentiment Analysis - Intuition

Lecture 90 Natural Language Processing Basics

Lecture 91 Lexicon Based Sentiment Analysis

Lecture 92 VADER Introduction

Lecture 93 Textblob Introduction

Lecture 94 Building a Sentiment Analyzer using VADER - Part I

Lecture 95 Building a Sentiment Analyzer using VADER - Part II

Lecture 96 Machine Learning Based Sentiment Analysis

Lecture 97 ML Feature Matrix & TF-IDF Introduction

Lecture 98 Building ML Based Sentiment Analyzer - Part I

Lecture 99 Building a ML Based Sentiment Analyzer - Part II

Lecture 100 Building a ML Based Sentiment Analyzer - Part III

Lecture 101 Sentiment Analysis Application - Opportunities & Challenges

Section 12: Archived Lectures

Lecture 102 Archived Lectures - Important Note

Lecture 103 Pandas Datareader Overview

Lecture 104 Getting Data Using Pandas Datareader

Lecture 105 OBV Overview and Excel Implementation

Lecture 106 OBV Implementation in Python

Lecture 107 Slope in a Chart

Lecture 108 Slope Implementation in Python

Lecture 109 Web Scraping Intro

Lecture 110 Important Note - Yahoo Finance Web Scraping

Lecture 111 Using Web Scraping to Extract Stock Fundamental Data - I

Lecture 112 Using Web Scraping to Extract Stock Fundamental Data - II

Lecture 113 Updated Web-Scraping Code - Yahoo-Finance Webpage Changes

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