From d29ae2692f2b548a97b9721a4710412716d1adb4 Mon Sep 17 00:00:00 2001 From: jeroen Date: Sun, 10 Sep 2023 18:17:03 +0200 Subject: [PATCH] Release of version 1.3.3 --- README.md | 26 ++++++++++++++------------ pyproject.toml | 2 +- 2 files changed, 15 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index c667e78b..1a31065c 100644 --- a/README.md +++ b/README.md @@ -74,18 +74,20 @@ ___ Within this package the following things are included: -- Company profiles (`get_profile`), including country, sector, ISIN and general characteristics (from FinancialModelingPrep) -- Company quotes (`get_quote`), including 52 week highs and lows, volume metrics and current shares outstanding (from FinancialModelingPrep) -- Company ratings (`get_rating`), based on key indicators like PE and DE ratios (from FinancialModelingPrep) -- Historical market data (`get_historical_data`), which can be retrieved on a daily, weekly, monthly and yearly basis. This includes OHLC, dividends, returns, cumulative returns and volatility calculations for each corresponding period. (from Yahoo Finance) -- Treasury Rates (`get_treasury_data`) for several months and several years over the last 3 months which allows yield curves to be constructed (from FinancialModelingPrep) -- Analyst Estimates (`get_analyst_estimates`) that show the expected EPS and Revenue from the past and future from a range of analysts (from FinancialModelingPrep) -- Earnings Calendar (`get_earnings_calendar`) which shows the exact dates earnings are released in the past and in the future including expectations (from FinancialModelingPrep) -- Revenue Geographic Segmentation (`get_revenue_geographic_segmentation`) which shows the revenue per company from each country and Revenue Product Segmentation (`get_revenue_product_segmenttion`) which shows the revenue per company from each product (from FinancialModelingPrep) -- Balance Sheet Statements (`get_balance_sheet_statement`), Income Statements (`get_income_statement`), Cash Flow Statements (`get_cash_flow_statement`) and Statistics Statement (`get_statistics_statement`), obtainable from FinancialModelingPrep or the source of your choosing through custom input. These functions are accompanied with a normalization function so that for any source, the same ratio analysis can be performed. Please see [this Jupyter Notebook](https://www.jeroenbouma.com/projects/financetoolkit/external-datasets) that explains how to use a custom source. -- Efficiency ratios (`ratios.collect_efficiency_ratios`), liquidity ratios (`ratios.collect_liquidity_ratios`), profitability ratios (`ratios._collect_profitability_ratios`), solvency ratios (`ratios.collect_solvency_ratios`) and valuation ratios (`ratios.collect_valuation_ratios`) functionality that automatically calculates the most important ratios based on the inputted balance sheet, income and cash flow statements. Any of the underlying ratios can also be called individually such as `ratios.get_return_on_equity`. Next to that, it is also possible to input your own custom ratios (`ratios.collect_custom_ratios`). See also [this Notebook](https://www.jeroenbouma.com/projects/financetoolkit/custom-ratios) or [this section](#defining-custom-ratios) for more information. -- Models like DUPONT analysis (`models.get_extended_dupont_analysis`) or Enterprise Breakdown (`models.get_enterprise_value_breakdown`) that can be used to perform in-depth financial analysis through a single function. These functions combine much of the functionality throughout the Toolkit to provide advanced calculations. -- Technical indicators like Relative Strength Index (`technicals.get_relative_strength_index`), Exponential Moving Average (`models.get_exponential_moving_average`) and Bollinger Bands (`technicals.get_bollinger_bands`) that can be used to perform in-depth momentum and trend analysis. These functions allow for the calculation of technical indicators based on the historical market data. +- **Company profiles** (`get_profile`), including country, sector, ISIN and general characteristics (from FinancialModelingPrep) +- **Company quotes** (`get_quote`), including 52 week highs and lows, volume metrics and current shares outstanding (from FinancialModelingPrep) +- **Company ratings** (`get_rating`), based on key indicators like PE and DE ratios (from FinancialModelingPrep) +- **Historical market data** (`get_historical_data`), which can be retrieved on a daily, weekly, monthly, quarterly and yearly basis. This includes OHLC, dividends, returns, cumulative returns and volatility calculations for each corresponding period. (from Yahoo Finance) +- **Treasury Rates** (`get_treasury_data`) for several months and several years over the last 3 months which allows yield curves to be constructed (from Yahoo Finance) +- **Analyst Estimates** (`get_analyst_estimates`) that show the expected EPS and Revenue from the past and future from a range of analysts (from FinancialModelingPrep) +- **Earnings Calendar**(`get_earnings_calendar`) which shows the exact dates earnings are released in the past and in the future including expectations (from FinancialModelingPrep) +- **Revenue Geographic Segmentation** (`get_revenue_geographic_segmentation`) which shows the revenue per company from each country and **Revenue Product Segmentation** (`get_revenue_product_segmenttion`) which shows the revenue per company from each product (from FinancialModelingPrep) +- **Balance Sheet Statements** (`get_balance_sheet_statement`), **Income Statements** (`get_income_statement`), **Cash Flow Statements** (`get_cash_flow_statement`) and **Statistics Statements** (`get_statistics_statement`), obtainable from FinancialModelingPrep or the source of your choosing through custom input. These functions are accompanied with a normalization function so that for any source, the same ratio analysis can be performed. Please see [this Jupyter Notebook](https://www.jeroenbouma.com/projects/financetoolkit/external-datasets) that explains how to use a custom source. +- **Efficiency ratios** (`ratios.collect_efficiency_ratios`), **liquidity ratios** (`ratios.collect_liquidity_ratios`), **profitability ratios** (`ratios._collect_profitability_ratios`), **solvency ratios** (`ratios.collect_solvency_ratios`) and **valuation ratios** (`ratios.collect_valuation_ratios`) functionality that automatically calculates the most important ratios (50+) based on the inputted balance sheet, income and cash flow statements. Any of the underlying ratios can also be called individually such as `ratios.get_return_on_equity`. Next to that, it is also possible to input your own **custom ratios** (`ratios.collect_custom_ratios`). See also [this Notebook](https://www.jeroenbouma.com/projects/financetoolkit/custom-ratios) or [this section](#defining-custom-ratios) for more information. +- **Models** like DUPONT analysis (`models.get_extended_dupont_analysis`) or Enterprise Breakdown (`models.get_enterprise_value_breakdown`) that can be used to perform in-depth financial analysis through a single function. These functions combine much of the functionality throughout the Toolkit to provide advanced calculations. +- **Performance metrics** like Jensens Alpha (`performance.get_jensens_alpha`), Capital Asset Pricing Model (CAPM) (`performance.get_capital_asset_pricing_model`) and (Rolling) Sharpe Ratio (`performance.get_sharpe_ratio`) that can be used to understand how each company is performing versus the benchmark and compared to each other. +- **Risk metrics** like Value at Risk (`risk.get_value_at_risk`) and Conditional Value at Risk (`risk.get_conditional_value_at_risk`) that can be used to understand the risk profile of each company and how it compares to the benchmark. +- **Technical indicators** like Relative Strength Index (`technicals.get_relative_strength_index`), Exponential Moving Average (`technicals.get_exponential_moving_average`) and Bollinger Bands (`technicals.get_bollinger_bands`) that can be used to perform in-depth momentum and trend analysis. These functions allow for the calculation of technical indicators based on the historical market data. The dependencies of the package are on purpose *very slim* so that it will work well with any combination of packages and not result in conflicts. diff --git a/pyproject.toml b/pyproject.toml index 2007d473..31c21fb2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "financetoolkit" -version = "1.3.13" +version = "1.3.3" description = "Transparent and Efficient Financial Analysis" license = "MIT" authors = ["Jeroen Bouma"]