Algorithmic Trading & Quantitative Analysis Using Python

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

• Course Introduction
• What Is Covered in this Course?
• Course Prerequisites
• Is it For Me?
• How To Get Help
• Pandas Datareader - Introduction
• Pandas Datareader - Deep Dive
• Yahoofinancials Python Module - Intro
• Yahoofinancials Python Module - Deep Dive
• Intraday Data - Alphavantage Python Wrapper
• Web Scraping Intro
• Using Web Scraping to Extract Fundamental Data - I
• Using Web Scraping to Extract Stock Fundamental Data - II
• Updated Web-Scraping Code - Yahoo-Finance Webpage Changes
• Data Handling
• Basic Statistics - Familiarize Yourself With Your Data
• Rolling Operations - Data In Motion
• Visualization Basics - I
• Visualization Basics - II
• Technical Indicators - Intro
• MACD Overview
• MACD Implementation in Python
• ATR and Bollinger Bands Overview
• ATR and Bollinger Bands Implementation in Python
• RSI Overview and Excel Implementation
• RSI Implementation in Python
• ADX Overview
• ADX Implementation in Excel
• ADX Implementation in Python
• OBV Overview and Excel Implementation
• OBV Implementation in Python
• Slope in a Chart
• Slope Implementation in Python
• Renko Overview
• Renko Implementation in Python
• TA-Lib Introduction
• TA-Lib Installation & Application
• Introduction to Performance Measurement
• CAGR Overview
• CAGR Implementation in Python
• How to Measure Volatility
• Volatility Measures' Python Implementation
• Sharpe Ratio and Sortino Ratio
• Sharpe and Sortino in Python
• Maximum Drawdown and Calmar Ratio
• Maximum Drawdown and Calmar Ratio in Python
• Why Should I Backtest My Strategies?
• Strategy I - Portfolio Rebalancing
• Strategy I in Python
• Strategy II - Resistance Breakout
• Strategy II in Python
• Strategy III - Renko and OBV
• Strategy III - Renko and OBV
• Strategy IV - Renko and MACD
• Strategy IV in Python
• Value Investing Overview
• Introduction to Magic Formula
• Magic Formula Implementation in Python
• Introduction to Piotroski F-Score
• Piotroski F-Score Implementation in Python
• Automated/Algorithmic Trading Overview
• Using Time Module in Python
• FXCM Overview
• Introduction to FXCM Terminal
• FXCM API
• Building an Automated Trading System - part I
• Building an Automated Trading System - part II
• Building an Automated Trading System - part III
• 7 9 Automated Trading Script 4