Algorithmic Trading & Quantitative Analysis 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


Homepage

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