Quantitative Finance With Python

Learn to Analyze Financial Markets using Python, Data Science, Machine Learning and Technical Analysis.

What you'll learn

Develop a solid understanding about different Financial Markets like Stock Market, Forex Market, Bond Market and Commodity market.

Learn to Predict Stock Prices and Market Trends using Machine Learning.

You will learn to analyze different Financial Assets using the tools and concepts of Technical Analysis like support, resistance and moving averages.

Manage Risk and learn the art of optimal money management and portfolio diversification using Kelly Criterion.

This course will teach you about different Financial Theories like Efficient Market Hypothesis, Random Walk Theory and Modern Portfolio Theory.

Learn to Evaluate the risk and volatility adjusted return of a portfolio using Sharpe Ratio.

Learn to Predict Stock Prices using LSTM Neural Network.

Learn the complex concepts of Financial Derivatives like Futures and Options in a simplified manner.

Learn to develop and backtest trading strategies in python.

This course will explain the advanced concepts of pair trading, arbitrage and algorithmic trading in a simple manner.

Requirements

This course expects viewers to have some basic knowledge of Python, Data Science and Machine Learning.

No knowledge or background in Finance is assumed.

Description

Interested in a lucrative and rewarding position in quantitative finance? Are you a professional working in finance or an individual working in Data Science and want to bridge the gap between Finance and Data Science and become a full on quant?The role of a quantitative analyst in an investment bank, hedge fund, or financial company is an attractive career option for many quantitatively skilled professionals working in finance or other fields like data science, technology or engineering. If this describes you, what you need to move to the next level is a gateway to the quantitative finance knowledge required for this role that builds on the technical foundations you have already mastered.This course is designed to be exactly such a gateway into the quant world. If you succeed in this course you will become a master of quantitative finance and the financial engineering.This Course covers a variety of topics like:Stock MarketsCommodity MarketForex TradingCryptocurrencyTechnical AnalysisFinancial DerivativesFuturesOptionsTime Value of MoneyModern Portfolio TheoryEfficient Market HypothesisStock Price Prediction using Machine LearningStock Price Prediction using LSTM Neural Networks (Deep Learning)Gold Price Prediction using Machine LearningDevelop and Backtest Trading Strategies in PythonTechnical Indicators like Moving Averages and RSI.Algorithmic Trading.Advanced Trading Methodologies like Arbitrage and Pair Trading.Random Walk Theory.Capital Asset Pricing Model.Sharpe Ratio.Python for Finance.Correlation between different stocks and asset classes.Candle Stick Charts.Working with Financial and OHLC Data for stocks.Optimal Position Sizing using Kelly Criterion.Diversification and Risk Management.

Overview

Section 1: Introduction and Course Overview

Lecture 1 Introduction and Welcome Video

Lecture 2 What will you Learn in this Course ?

Section 2: Financial Markets

Lecture 3 Introduction to Financial Markets Part 1

Lecture 4 Introduction to Financial Markets Part 2

Lecture 5 Type Of Analysis in Financial Markets

Lecture 6 Time Value of Money

Lecture 7 Capital Asset Pricing Model (CAPM)

Lecture 8 Modern Portfolio Theory (MPT)

Lecture 9 Efficient Market Hypothesis

Lecture 10 Random Walk Theory

Lecture 11 Correlation in Finance

Lecture 12 Stock Correlation Matrix

Lecture 13 Artbitrage Trading

Lecture 14 Pair Trading

Lecture 15 Algo Trading

Lecture 16 Kelly Criterion

Lecture 17 Sharpe Ratio

Section 3: Python For Finance

Lecture 18 Working with OHLC Data for Stocks

Lecture 19 Plot CandleStick Chart with Python

Lecture 20 Simple Moving Average (SMA) in Python

Lecture 21 Exponential Moving Average (EMA) in Python

Section 4: Financial Derivates

Lecture 22 Introduction to Financial Derivatives

Lecture 23 Futures (Financial Derivatives)

Lecture 24 Options (Financial Derivatives)

Lecture 25 Black Scholes Model

Section 5: Technical Analysis

Lecture 26 Introduction to Technical Analysis

Lecture 27 Finding Support and Resistance

Lecture 28 Chart Patterns

Lecture 29 Moving Average

Lecture 30 Relative Strength Index (RSI) Indicator

Lecture 31 Dow Theory

Section 6: Develop and Backtest Trading Strategies in Python

Lecture 32 Practical Case Study on Amazon Stock

Section 7: Machine Learning in Finance

Lecture 33 Gold Price Prediction using Machine Learning

Lecture 34 Stock Price Prediction using Machine Learning

Lecture 35 Apple Stock Prediction using Linear Regression

Section 8: Stock Price Prediction using LSTM

Lecture 36 Microsoft Stock Price Prediction using LSTM

Anyone who wants to learn about Quantitative Finance using Python, Data Science and Machine Learning.,People preparing for CFA and FRM exams will find this course helpful.,Investors and Traders looking to level up their Financial Analysis game by leveraging the power of Data Science.