Readme details

README.md

@@ -9,11 +9,11 @@
 [![PYPI Version](https://img.shields.io/pypi/v/FinanceToolkit)](https://pypi.org/project/FinanceToolkit/)
 [![PYPI Downloads](https://static.pepy.tech/badge/financetoolkit/month)](https://pepy.tech/project/financetoolkit)
 
-While browsing a variety of websites, I kept finding that the same financial metric can greatly vary per source and so do the financial statements reported while little information is given how the metric was calculated.
+While browsing a variety of websites, I repeatedly observed significant fluctuations in the same financial metric among different sources. Similarly, the reported financial statements often didn't line up, and there was limited information on the methodology used to calculate each metric.
 
-For example, Microsoft's Price-to-Earnings (PE) ratio on the 6th of May, 2023 is reported to be 28.93 (Stockopedia), 32.05 (Morningstar), 32.66 (Macrotrends), 33.09 (Finance Charts), 33.66 (Y Charts), 33.67 (Wall Street Journal), 33.80 (Yahoo Finance) and 34.4 (Companies Market Cap). All of these calculations are correct, however the method applied varies leading to different results. Therefore, collecting data from multiple sources can lead to wrong interpretation of the results given that one source could be applying a different calculation method than another. And that is, if it is even freely available. Often the calculation is hidden behind a paid subscription.
+For example, Microsoft's Price-to-Earnings (PE) ratio on the 6th of May, 2023 is reported to be 28.93 (Stockopedia), 32.05 (Morningstar), 32.66 (Macrotrends), 33.09 (Finance Charts), 33.66 (Y Charts), 33.67 (Wall Street Journal), 33.80 (Yahoo Finance) and 34.4 (Companies Market Cap). All of these calculations are correct, however the method of calculation varies leading to different results. Therefore, collecting data from multiple sources can lead to wrong interpretation of the results given that one source could apply a different definition than another. And that is, if that definition is even available as often the underlying methods are hidden behind a paid subscription.
 
-**This is why I designed the FinanceToolkit**, this is an open-source toolkit in which all relevant financial ratios ([150+](#available-metrics)), indicators and performance measurements are written down in the most simplistic way allowing for complete transparency of the calculation method ([proof](https://github.com/JerBouma/FinanceToolkit/blob/main/financetoolkit/ratios/valuation_model.py)). This allows you to not have to rely on metrics from other providers and, given a financial statement, allow for efficient manual calculations. This leads to one uniform method of calculation being applied that is available and understood by everyone.
+**This is why I designed the FinanceToolkit**, this is an open-source toolkit in which all relevant financial ratios ([150+](#available-metrics)), indicators and performance measurements are written down in the most simplistic way allowing for complete transparency of the method of calculation ([proof](https://github.com/JerBouma/FinanceToolkit/blob/main/financetoolkit/ratios/valuation_model.py)). This enables you to avoid dependence on metrics from other providers that do not provide their methods. With a large selection of financial statements in hand, it facilitates streamlined calculations, promoting the adoption of a consistent and universally understood methods and formulas.
 
 The Finance Toolkit not only supports Equities. Even for Options, Currencies, Cryptocurrencies, ETFs, Mutual Funds, Indices, Money Markets, Commodities, Key Economic Indicators and more, the Finance Toolkit can be used to obtain historical data as well as important performance and risk measurements such as the Sharpe Ratio and Value at Risk.
 
@@ -97,7 +97,7 @@ profitability_ratios = companies.ratios.collect_profitability_ratios()
 # a Models example
 extended_dupont_analysis = companies.models.get_extended_dupont_analysis()
 
-# a Options example
+# an Options example
 all_greeks = companies.options.collect_all_greeks(expiration_time_range=180)
 
 # a Performance example
@@ -133,10 +133,9 @@ And below the cumulative returns are plotted which include the S&P 500 as benchm
 
 ![HistoricalData](https://github.com/JerBouma/FinanceToolkit/assets/46355364/cd7b5029-0e66-4592-9822-42b652e7deed)
 
-
 ### Obtaining Financial Statements
 
-Obtain an Income Statement on an annual or quarterly basis. This can also be an income statement (`companies.get_income_statement()`) or cash flow statement (`companies.get_cash_flow_statement()`). For example, the first 5 rows of the Income Statement for Apple are shown below.
+Obtain an Income Statement on an annual or quarterly basis. This can also be a balance statement (`companies.get_balance_sheet_statement()`) or cash flow statement (`companies.get_cash_flow_statement()`). For example, the first 5 rows of the Income Statement for Apple are shown below.
 
 |                                   |        2017 |        2018 |        2019 |        2020 |        2021 |        2022 |        2023 |
 |:----------------------------------|------------:|------------:|------------:|------------:|------------:|------------:|------------:|
@@ -201,7 +200,7 @@ Which can also be plotted together with Gamma, Theta and Vega as follows:
 
 ### Obtaining Performance Metrics
 
-Get the correlations with the factors as defined by Fama-and-French. These include market, size, value, operating profitability and investment. The beauty of all functionality here is that it can be based on any period as the function accepts the period 'weekly', 'monthly', 'quarterly' and 'yearly'. For example, this shows the quarterly correlations for Apple:
+Get the correlations with the factors as defined by Fama-and-French. These include market, size, value, operating profitability and investment. The beauty of all functionality here is that it can be based on any period as the function accepts the period `intraday`, `weekly`, `monthly`, `quarterly` and `yearly`. For example, this shows the quarterly correlations for Apple:
 
 |        |   Mkt-RF |     SMB |     HML |     RMW |     CMA |
 |:-------|---------:|--------:|--------:|--------:|--------:|