Metadata-Version: 2.1
Name: FundamentalAnalysis
Version: 0.2.2
Summary: Fully-fledged Fundamental Analysis package capable of collecting 10 years of Company Profiles,    Financial Statements, Ratios and Stock Data of 13.000+ companies.
Home-page: https://github.com/JerBouma/FundamentalAnalysis
Author: JerBouma
Author-email: jer.bouma@gmail.com
License: MIT
Description: # Fundamental Analysis
        This package collects fundamentals and detailed company stock data from a large group of companies (13.000+)
        from FinancialModelingPrep and uses Yahoo Finance to obtain stock data for any financial instrument. It allows
        the user to do most of the essential fundamental analysis. It also gives the possibility to quickly compare
        multiple companies or do a sector analysis.
        
        See a visualisation of the data on my
        [Fundamentals Quantifier website](https://github.com/JerBouma/FundamentalsQuantifier).
        
        ![](images/FundamentalAnalysis.png)
        
        ## Functions
        Here you can find a list of the available functions within this package separated per module. 
        - **details**
            - `available companies` - shows the complete list of companies that are available for fundamental data
            gathering including current price and the exchange the company is listed on. This is an extensive list with
            well over 13.000 companies.
            - `profile` - gives information about, among other things, the industry, sector exchange
            and company description.
            - `quote` - provides actual information about the company which is, among other things, the day high,
            market cap, open and close price and price-to-equity ratio.
            - `enterprise` - displays stock price, number of shares, market capitalization and 
            enterprise value over time.
            - `rating` - based on specific ratios, provides information whether the company is a (strong) buy,
            neutral or a (strong) sell.
            - `discounted_cash_flow` - calculates the discounted cash flow of a company over time including the
            DCF of today.
            - `earnings_calendar` - displays information about earnings date of a large selection of symbols this year
            including the expected PE ratio.
        - **financial_statement**
            - `income_statement` - collects a complete income statement over time. This can be either quarterly
            or annually. It is limited to 10 years (or 40 quarters).
            - `balance_sheet_statement` - collects a complete balance sheet statement over time. This can be either quarterly
            or annually. It is limited to 10 years (or 40 quarters).
            - `cash_flow_statement` - collects a complete cash flow statement over time. This can be either quarterly
            or annually. It is limited to 10 years (or 40 quarters).
        - **ratios**
            - `key_metrics` - lists the key metrics (in total 57 metrics) of a company over time (annual
            and quarterly). This includes, among other things, Return on Equity (ROE), Working Capital,
            Current Ratio and Debt to Assets.
            - `financial_ratios` - includes in-depth ratios (in total 57 ratios) of a company over time (annual
            and quarterly). This contains, among other things, Price-to-Book Ratio, Payout Ratio and Operating Cycle.
            - `financial_statement_growth` - measures the growth of several financial statement items and ratios over
            time (annual and quarterly). These are, among other things, Revenue Growth (3, 5 and 10 years),
            inventory growth and operating cash flow growth (3, 5 and 10 years).
        - **stock_data**
            - `stock_data` - collects all stock data (including Close, Adjusted Close, High, Low, Open and Volume) of
            the provided ticker. This can be any financial instrument.
            - `stock_data_detailed` - collects an expansive amount of stock data (including Close, Adjusted Close,
             High, Low, Open, Volume, Unadjusted Volume, Absolute Change, Percentage Change, Volume Weighted
             Average Price (VWAP), Date Label and Change over Time). The data collection is limited to
             the companies listed in the function `available companies`.
        
        ## Installation
        1. `pip install FundamentalAnalysis`
            * Alternatively, download this repository.
        2. (within Python) `import FundamentalAnalysis as fa`
        
        To be able to use this package you need an API Key from FinancialModellingPrep. Follow the following instructions to
        obtain a _free_ API Key. Note that these keys are limited to 250 requests per account. There is no time limit.
        1. Go to [FinancialModellingPrep's API](https://financialmodelingprep.com/developer/docs/)
        2. Under "Get your Free API Key Today!" click on "Get my API KEY here"
        3. Sign-up to the website and select the Free Plan
        4. Obtain the API Key as found [here](https://financialmodelingprep.com/developer/docs/)
        4. Start using this package.
        
        When you run out of requests (250), you have to upgrade to a Premium version or create a new account. Note that I am
        in no way affiliated with FinancialModellingPrep and will never be.
        
        ## Example
        
        ```
        import FundamentalAnalysis as fa
        
        ticker = "AAPL"
        api_key = "YOUR API KEY HERE"
        
        # Show the available companies
        companies = fa.available_companies(api_key)
        
        # Collect general company information
        profile = fa.profile(ticker, api_key)
        
        # Collect recent company quotes
        quotes = fa.quote(ticker, api_key)
        
        # Collect market cap and enterprise value
        entreprise_value = fa.enterprise(ticker, api_key)
        
        # Show recommendations of Analysts
        ratings = fa.rating(ticker, api_key)
        
        # Obtain DCFs over time
        dcf_annually = fa.discounted_cash_flow(ticker, api_key, period="annual")
        dcf_quarterly = fa.discounted_cash_flow(ticker, api_key, period="quarter")
        
        # Collect the Balance Sheet statements
        balance_sheet_annually = fa.balance_sheet_statement(ticker, api_key, period="annual")
        balance_sheet_quarterly = fa.balance_sheet_statement(ticker, api_key, period="quarter")
        
        # Collect the Income Statements
        income_statement_annually = fa.income_statement(ticker, api_key, period="annual")
        income_statement_quarterly = fa.income_statement(ticker, api_key, period="quarter")
        
        # Collect the Cash Flow Statements
        cash_flow_statement_annually = fa.cash_flow_statement(ticker, api_key, period="annual")
        cash_flow_statement_quarterly = fa.cash_flow_statement(ticker, api_key, period="quarter")
        
        # Show Key Metrics
        key_metrics_annually = fa.key_metrics(ticker, api_key, period="annual")
        key_metrics_quarterly = fa.key_metrics(ticker, api_key, period="quarter")
        
        # Show a large set of in-depth ratios
        financial_ratios_annually = fa.financial_ratios(ticker, api_key, period="annual")
        financial_ratios_quarterly = fa.financial_ratios(ticker, api_key, period="quarter")
        
        # Show the growth of the company
        growth_annually = fa.financial_statement_growth(ticker, api_key, period="annual")
        growth_quarterly = fa.financial_statement_growth(ticker, api_key, period="quarter")
        
        # Download general stock data
        stock_data = fa.stock_data(ticker, period="ytd", interval="1d")
        
        # Download detailed stock data
        stock_data_detailed = fa.stock_data_detailed(ticker, api_key, begin="2000-01-01", end="2020-01-01")
        
        ```
        
        With this data you can do a complete analysis of the selected company, in this case Apple. However, by looping
        over a large selection of companies you are able to collect a bulk of data. Therefore, by  entering a specific sector
        (for example, all tickers of the Semi-Conducter industry) you can quickly quantify the sector and look for
        key performers.
        
        To find companies belonging to a specific sector or industry, please have a look at the JSON files
        [here](https://github.com/JerBouma/FundamentalsQuantifier/tree/master/data). Alternatively, you can have a 
        look at the [Fundamentals Quantifier](https://fundamentals-quantifier.herokuapp.com/), a website that I have written
        to visually compare any selection of companies.
Keywords: fundamental,analysis,finance
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Topic :: Office/Business :: Financial :: Investment
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
