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    Python for Financial Analysis and Algorithmic Trading

    Posted By: Sigha
    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: 5.28 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!


    Python for Financial Analysis and Algorithmic Trading


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