Learn numpy , pandas , matplotlib , quantopian , finance , and more for algorithmic trading with Python! By Downloading The ython for Financial Analysis and Algorithmic Trading Udemy Course.
What you’ll 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
Welcome to Python for Financial Analysis and Algorithmic Trading! Are you thinking about how individuals use Python to conduct extensive financial analysis and pursue algorithmic trading, then this is the best Python for Financial Analysis and Algorithmic Trading course for you!
This Python for Financial Analysis and Algorithmic Trading course will direct you through whatever you require to know to use Python for Finance and Algorithmic Trading! We’ll start by discovering the fundamentals of Python, and then continue to learn more about the various core libraries used in the Py-Finance Ecosystem, consisting of jupyter, NumPy, pandas, matplotlib, statsmodels, zipline, Quantopian, and far more!
We’ll cover the following subjects utilized by monetary experts:
- 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).
- 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.
- Kinds of Funds.
- Order Books.
- Short Selling.
- Capital Asset Pricing Model.
- Stock Splits and Dividends.
- Efficient Market Hypothesis.
- Algorithmic Trading with Quantopian.
- Futures Trading.
Who this course is for:
- Someone familiar with Python who wants to learn about Financial Analysis!
Created by Jose Portilla
Last updated 12/2020
Size: 12.35 GB