What you'll learn

Use Matplotlib to create custom plots

Use NumPy to quickly work with Numerical Data

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Use ARIMA models on Series Data

Optimize Portfolio Allocations

Learn about the Efficient Market Hypothesis

Use Pandas for Analyze and Visualize Data

Learn how to use statsmodels for Series Analysis

Use Exponentially Weighted Moving Averages

Calculate the Sharpe Ratio

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 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

Got Python If you're serious about financial markets and algorithmic trading, then you're going to need it. Python is a computer programming language that is used by institutions and investors alike every day for a range of purposes, including quantitative research, i.e. data exploration and analysis, and for prototyping, testing, and executing trading algorithms. In the recent past, however, only the big institutional players had the money and tech know-how to harness the benefits of algorithmic trading, but the s they are a-cha'. Before we dig deeper into the finer points of Python and how to get started in algorithmic trading with Trality, let's take a brief trip back to the future.




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