Python for Financial Analysis and Algorithmic Trading

$195 | Duration: 17 hours | Video: h264, 1280x720 | Audio: AAC, 44100 Hz, 2 Ch | 2.4 GB

Genre: eLearning | Language: English | Project Files

BESTSELLING | Last updated 8/2017

What Will I Learn?

Use NumPy to quickly work with Numerical Data

Use Pandas for Analyze and Visualize Data

Use Matplotlib to create custom plots

Learn how to use statsmodels for Time Series Analysis

Calculate Financial Statistics, such as Daily Returns, Cumulative Returns, Volatility, etc..

Use Exponentially Weighted Moving Averages

Use ARIMA models on Time Series Data

Calculate the Sharpe Ratio

Optimize Portfolio Allocations

Understand the Capital Asset Pricing Model

Learn about the Efficient Market Hypothesis

Conduct algorithmic Trading on Quantopian

Requirements

Some knowledge of programming (preferably Python)

Ability to Download Anaconda (Python) to your computer

Basic Statistics and Linear Algebra will be helpful

Description

Welcome to Python for Financial Analysis and Algorithmic Trading! Are you interested in how people use Python to conduct rigorous financial analysis and pursue algorithmic trading, then this is the right course for you!

This course will guide you through everything you need to know to use Python for Finance and Algorithmic Trading! We’ll start off by learning the fundamentals of Python, and then proceed to learn about the various core libraries used in the Py-Finance Ecosystem, including jupyter, numpy, pandas, matplotlib, statsmodels, zipline, Quantopian, and much more!

We’ll cover the following topics used by financial professionals:

Python Fundamentals

NumPy for High Speed Numerical Processing

Pandas for Efficient Data Analysis

Matplotlib for Data Visualization

Using pandas-datareader and Quandl for data ingestion

Pandas Time Series Analysis Techniques

Stock Returns Analysis

Cumulative Daily Returns

Volatility and Securities Risk

EWMA (Exponentially Weighted Moving Average)

Statsmodels

ETS (Error-Trend-Seasonality)

ARIMA (Auto-regressive Integrated Moving Averages)

Auto Correlation Plots and Partial Auto Correlation Plots

Sharpe Ratio

Portfolio Allocation Optimization

Efficient Frontier and Markowitz Optimization

Types of Funds

Order Books

Short Selling

Capital Asset Pricing Model

Stock Splits and Dividends

Efficient Market Hypothesis

Algorithmic Trading with Quantopian

Futures Trading

Who is the target audience?

Someone familiar with Python who wants to learn about Financial Analysis!

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