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| author | CoprDistGit <infra@openeuler.org> | 2023-05-05 14:27:23 +0000 |
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| committer | CoprDistGit <infra@openeuler.org> | 2023-05-05 14:27:23 +0000 |
| commit | 8528844a21a43765e52e89417f8fd619ade8005e (patch) | |
| tree | c3dc0a3cbcb32bdfddb09e866493e0dfce5f7af1 | |
| parent | 2835a2f64223168658f094e9453de60aab7f0ad5 (diff) | |
automatic import of python-fundamentalanalysisopeneuler20.03
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | python-fundamentalanalysis.spec | 615 | ||||
| -rw-r--r-- | sources | 1 |
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@@ -0,0 +1 @@ +/fundamentalanalysis-0.2.14.tar.gz diff --git a/python-fundamentalanalysis.spec b/python-fundamentalanalysis.spec new file mode 100644 index 0000000..b0de115 --- /dev/null +++ b/python-fundamentalanalysis.spec @@ -0,0 +1,615 @@ +%global _empty_manifest_terminate_build 0 +Name: python-fundamentalanalysis +Version: 0.2.14 +Release: 1 +Summary: Fully-fledged Fundamental Analysis package capable of collecting 20 years of Company Profiles, Financial Statements, Ratios and Stock Data of 20.000+ companies. +License: MIT +URL: https://github.com/JerBouma/FundamentalAnalysis +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/19/99/727250aaa91a2cd291363dba950dfcb1967630a8cd29e209592ae1cf8656/fundamentalanalysis-0.2.14.tar.gz +BuildArch: noarch + + +%description +# Fundamental Analysis + +This package collects fundamentals and detailed company stock data from a large group of companies (20.000+) +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. + +To find symbols of specific sectors and/or industries have a look at my [Finance Database](https://github.com/JerBouma/FinanceDatabase) or +see a visualisation of the data on my [Fundamentals Quantifier website](https://github.com/JerBouma/FundamentalsQuantifier). + + + +[](https://www.buymeacoffee.com/jerbouma) +[](https://github.com/JerBouma/FundamentalAnalysis/issues) +[](https://github.com/JerBouma/FundamentalAnalysis/pulls) +[](https://pypi.org/project/FundamentalAnalysis/) +[](https://pypi.org/project/FundamentalAnalysis/) + +## 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 20.000 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. + - `balance_sheet_statement` - collects a complete balance sheet statement over time. This can be either quarterly + or annually. + - `cash_flow_statement` - collects a complete cash flow statement over time. This can be either quarterly + or annually. +- **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`. Use the `stock_data` function for information about + anything else. (ETFs, Mutual Funds, Options, Indices etc.) + - `stock_dividend` - gives complete information about the company's dividend which includes adjusted dividend, dividend, record date, payment date and declaration date over time. This function only allows company tickers and is limited to the companies found by calling `available_companies` from the details module. + +## 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/) +5. Start using this package. + +When you run out of daily requests (250), you have to upgrade to a Premium version. Note that I am in no way +affiliated with FinancialModellingPrep and never will be. + +## Example +To collect all annual data about a company, in this case MSFT, you can run the following code: + +```python +import fundamentalanalysis as fa + +ticker = "MSFT" +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") + +# Collect the Balance Sheet statements +balance_sheet_annually = fa.balance_sheet_statement(ticker, api_key, period="annual") + +# Collect the Income Statements +income_statement_annually = fa.income_statement(ticker, api_key, period="annual") + +# Collect the Cash Flow Statements +cash_flow_statement_annually = fa.cash_flow_statement(ticker, api_key, period="annual") + +# Show Key Metrics +key_metrics_annually = fa.key_metrics(ticker, api_key, period="annual") + +# Show a large set of in-depth ratios +financial_ratios_annually = fa.financial_ratios(ticker, api_key, period="annual") + +# Show the growth of the company +growth_annually = fa.financial_statement_growth(ticker, api_key, period="annual") + +# 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") + +# Download dividend history +dividends = fa.stock_dividend(ticker, api_key, begin="2000-01-01", end="2020-01-01") + +``` +Note that quarterly data is not available with a free API key. You should therefore not be able to run this code below without a subscription. + +```python +import fundamentalanalysis as fa + +ticker = "MSFT" +api_key = "YOUR API KEY HERE" + +# Obtain DCFs over time +dcf_quarterly = fa.discounted_cash_flow(ticker, api_key, period="quarter") + +# Collect the Balance Sheet statements +balance_sheet_quarterly = fa.balance_sheet_statement(ticker, api_key, period="quarter") + +# Collect the Income Statements +income_statement_quarterly = fa.income_statement(ticker, api_key, period="quarter") + +# Collect the Cash Flow Statements +cash_flow_statement_quarterly = fa.cash_flow_statement(ticker, api_key, period="quarter") + +# Show Key Metrics +key_metrics_quarterly = fa.key_metrics(ticker, api_key, period="quarter") + +# Show a large set of in-depth ratios +financial_ratios_quarterly = fa.financial_ratios(ticker, api_key, period="quarter") + +# Show the growth of the company +growth_quarterly = fa.financial_statement_growth(ticker, api_key, period="quarter") + +``` + +With this data you can do a complete analysis of the selected company, in this case Microsoft. 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) or use the [Finance Database](https://github.com/JerBouma/FinanceDatabase). 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. + +## Contribution + +I highly appreciate Pull Requests and Issues Reports as they can greatly improve the package. + +<a href="https://www.buymeacoffee.com/jerbouma" target="_blank"><img src="https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png" alt="Buy Me A Coffee" style="height: 41px !important;width: 174px !important;box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;-webkit-box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;" ></a> + + + + +%package -n python3-fundamentalanalysis +Summary: Fully-fledged Fundamental Analysis package capable of collecting 20 years of Company Profiles, Financial Statements, Ratios and Stock Data of 20.000+ companies. +Provides: python-fundamentalanalysis +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-fundamentalanalysis +# Fundamental Analysis + +This package collects fundamentals and detailed company stock data from a large group of companies (20.000+) +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. + +To find symbols of specific sectors and/or industries have a look at my [Finance Database](https://github.com/JerBouma/FinanceDatabase) or +see a visualisation of the data on my [Fundamentals Quantifier website](https://github.com/JerBouma/FundamentalsQuantifier). + + + +[](https://www.buymeacoffee.com/jerbouma) +[](https://github.com/JerBouma/FundamentalAnalysis/issues) +[](https://github.com/JerBouma/FundamentalAnalysis/pulls) +[](https://pypi.org/project/FundamentalAnalysis/) +[](https://pypi.org/project/FundamentalAnalysis/) + +## 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 20.000 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. + - `balance_sheet_statement` - collects a complete balance sheet statement over time. This can be either quarterly + or annually. + - `cash_flow_statement` - collects a complete cash flow statement over time. This can be either quarterly + or annually. +- **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`. Use the `stock_data` function for information about + anything else. (ETFs, Mutual Funds, Options, Indices etc.) + - `stock_dividend` - gives complete information about the company's dividend which includes adjusted dividend, dividend, record date, payment date and declaration date over time. This function only allows company tickers and is limited to the companies found by calling `available_companies` from the details module. + +## 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/) +5. Start using this package. + +When you run out of daily requests (250), you have to upgrade to a Premium version. Note that I am in no way +affiliated with FinancialModellingPrep and never will be. + +## Example +To collect all annual data about a company, in this case MSFT, you can run the following code: + +```python +import fundamentalanalysis as fa + +ticker = "MSFT" +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") + +# Collect the Balance Sheet statements +balance_sheet_annually = fa.balance_sheet_statement(ticker, api_key, period="annual") + +# Collect the Income Statements +income_statement_annually = fa.income_statement(ticker, api_key, period="annual") + +# Collect the Cash Flow Statements +cash_flow_statement_annually = fa.cash_flow_statement(ticker, api_key, period="annual") + +# Show Key Metrics +key_metrics_annually = fa.key_metrics(ticker, api_key, period="annual") + +# Show a large set of in-depth ratios +financial_ratios_annually = fa.financial_ratios(ticker, api_key, period="annual") + +# Show the growth of the company +growth_annually = fa.financial_statement_growth(ticker, api_key, period="annual") + +# 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") + +# Download dividend history +dividends = fa.stock_dividend(ticker, api_key, begin="2000-01-01", end="2020-01-01") + +``` +Note that quarterly data is not available with a free API key. You should therefore not be able to run this code below without a subscription. + +```python +import fundamentalanalysis as fa + +ticker = "MSFT" +api_key = "YOUR API KEY HERE" + +# Obtain DCFs over time +dcf_quarterly = fa.discounted_cash_flow(ticker, api_key, period="quarter") + +# Collect the Balance Sheet statements +balance_sheet_quarterly = fa.balance_sheet_statement(ticker, api_key, period="quarter") + +# Collect the Income Statements +income_statement_quarterly = fa.income_statement(ticker, api_key, period="quarter") + +# Collect the Cash Flow Statements +cash_flow_statement_quarterly = fa.cash_flow_statement(ticker, api_key, period="quarter") + +# Show Key Metrics +key_metrics_quarterly = fa.key_metrics(ticker, api_key, period="quarter") + +# Show a large set of in-depth ratios +financial_ratios_quarterly = fa.financial_ratios(ticker, api_key, period="quarter") + +# Show the growth of the company +growth_quarterly = fa.financial_statement_growth(ticker, api_key, period="quarter") + +``` + +With this data you can do a complete analysis of the selected company, in this case Microsoft. 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) or use the [Finance Database](https://github.com/JerBouma/FinanceDatabase). 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. + +## Contribution + +I highly appreciate Pull Requests and Issues Reports as they can greatly improve the package. + +<a href="https://www.buymeacoffee.com/jerbouma" target="_blank"><img src="https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png" alt="Buy Me A Coffee" style="height: 41px !important;width: 174px !important;box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;-webkit-box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;" ></a> + + + + +%package help +Summary: Development documents and examples for fundamentalanalysis +Provides: python3-fundamentalanalysis-doc +%description help +# Fundamental Analysis + +This package collects fundamentals and detailed company stock data from a large group of companies (20.000+) +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. + +To find symbols of specific sectors and/or industries have a look at my [Finance Database](https://github.com/JerBouma/FinanceDatabase) or +see a visualisation of the data on my [Fundamentals Quantifier website](https://github.com/JerBouma/FundamentalsQuantifier). + + + +[](https://www.buymeacoffee.com/jerbouma) +[](https://github.com/JerBouma/FundamentalAnalysis/issues) +[](https://github.com/JerBouma/FundamentalAnalysis/pulls) +[](https://pypi.org/project/FundamentalAnalysis/) +[](https://pypi.org/project/FundamentalAnalysis/) + +## 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 20.000 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. + - `balance_sheet_statement` - collects a complete balance sheet statement over time. This can be either quarterly + or annually. + - `cash_flow_statement` - collects a complete cash flow statement over time. This can be either quarterly + or annually. +- **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`. Use the `stock_data` function for information about + anything else. (ETFs, Mutual Funds, Options, Indices etc.) + - `stock_dividend` - gives complete information about the company's dividend which includes adjusted dividend, dividend, record date, payment date and declaration date over time. This function only allows company tickers and is limited to the companies found by calling `available_companies` from the details module. + +## 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/) +5. Start using this package. + +When you run out of daily requests (250), you have to upgrade to a Premium version. Note that I am in no way +affiliated with FinancialModellingPrep and never will be. + +## Example +To collect all annual data about a company, in this case MSFT, you can run the following code: + +```python +import fundamentalanalysis as fa + +ticker = "MSFT" +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") + +# Collect the Balance Sheet statements +balance_sheet_annually = fa.balance_sheet_statement(ticker, api_key, period="annual") + +# Collect the Income Statements +income_statement_annually = fa.income_statement(ticker, api_key, period="annual") + +# Collect the Cash Flow Statements +cash_flow_statement_annually = fa.cash_flow_statement(ticker, api_key, period="annual") + +# Show Key Metrics +key_metrics_annually = fa.key_metrics(ticker, api_key, period="annual") + +# Show a large set of in-depth ratios +financial_ratios_annually = fa.financial_ratios(ticker, api_key, period="annual") + +# Show the growth of the company +growth_annually = fa.financial_statement_growth(ticker, api_key, period="annual") + +# 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") + +# Download dividend history +dividends = fa.stock_dividend(ticker, api_key, begin="2000-01-01", end="2020-01-01") + +``` +Note that quarterly data is not available with a free API key. You should therefore not be able to run this code below without a subscription. + +```python +import fundamentalanalysis as fa + +ticker = "MSFT" +api_key = "YOUR API KEY HERE" + +# Obtain DCFs over time +dcf_quarterly = fa.discounted_cash_flow(ticker, api_key, period="quarter") + +# Collect the Balance Sheet statements +balance_sheet_quarterly = fa.balance_sheet_statement(ticker, api_key, period="quarter") + +# Collect the Income Statements +income_statement_quarterly = fa.income_statement(ticker, api_key, period="quarter") + +# Collect the Cash Flow Statements +cash_flow_statement_quarterly = fa.cash_flow_statement(ticker, api_key, period="quarter") + +# Show Key Metrics +key_metrics_quarterly = fa.key_metrics(ticker, api_key, period="quarter") + +# Show a large set of in-depth ratios +financial_ratios_quarterly = fa.financial_ratios(ticker, api_key, period="quarter") + +# Show the growth of the company +growth_quarterly = fa.financial_statement_growth(ticker, api_key, period="quarter") + +``` + +With this data you can do a complete analysis of the selected company, in this case Microsoft. 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) or use the [Finance Database](https://github.com/JerBouma/FinanceDatabase). 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. + +## Contribution + +I highly appreciate Pull Requests and Issues Reports as they can greatly improve the package. + +<a href="https://www.buymeacoffee.com/jerbouma" target="_blank"><img src="https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png" alt="Buy Me A Coffee" style="height: 41px !important;width: 174px !important;box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;-webkit-box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;" ></a> + + + + +%prep +%autosetup -n fundamentalanalysis-0.2.14 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-fundamentalanalysis -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.14-1 +- Package Spec generated @@ -0,0 +1 @@ +a3ac8bca2df55188cfc5346daf2d500e fundamentalanalysis-0.2.14.tar.gz |