Python For Financial Analysis And Algorithmic Trading

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25
December
2024

Python For Financial Analysis And Algorithmic Trading
Python For Financial Analysis And Algorithmic Trading
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English (US) | Size: 6.42 GB | Duration: 16h 39m


Learn numpy , pandas , matplotlib , quantopian , finance , and more for algorithmic trading with Python!

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
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 FundamentalsNumPy for High Speed Numerical ProcessingPandas for Efficient Data AnalysisMatplotlib for Data VisualizationUsing pandas-datareader and Quandl for data ingestionPandas Time Series Analysis TechniquesStock Returns AnalysisCumulative Daily ReturnsVolatility and Securities RiskEWMA (Exponentially Weighted Moving Average)StatsmodelsETS (Error-Trend-Seasonality)ARIMA (Auto-regressive Integrated Moving Averages)Auto Correlation Plots and Partial Auto Correlation PlotsSharpe RatioPortfolio Allocation Optimization Efficient Frontier and Markowitz OptimizationTypes of FundsOrder BooksShort SellingCapital Asset Pricing ModelStock Splits and DividendsEfficient Market HypothesisAlgorithmic Trading with QuantopianFutures Trading

Who this course is for:
Someone familiar with Python who wants to learn about Financial Analysis!

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