improved Readme

README.md

@@ -1,20 +1,56 @@
-# Fincal
-This module simplified handling of time-series data
+# PyFacts
+PyFacts stands for Python library for Financial analysis and computations on time series. It is a library which makes it simple to work with time series data.
+
+Most libraries, and languages like SQL, work with rows. Operations are performed by rows and not by dates. For instance, to calculate 1-year rolling returns in SQL, you are forced to use either a lag of 365/252 rows, leading to an approximation, or slow and cumbersome joins. PyFacts solves this by allowing you to work with dates and time intervals. Hence, to calculate 1-year returns, you will be specifying a lag of 1-year and the library will do the grunt work of finding the most appropriate observations to calculate these returns on.
 
 ## The problem
-Time series data often have missing data points. These missing points mess things up when you are trying to do a comparison between two sections of a time series.
-
-To make things worse, most libraries don't allow comparison based on dates. Month to Month and year to year comparisons become difficult as they cannot be translated into number of days. However, these are commonly used metrics while looking at financial data.
+Libraries and languages usually don't allow comparison based on dates. Calculating month on month or year on year returns are always cumbersome as users are forced to rely on row lags. However, data always have inconsistencies, especially financial data. Markets don't work on weekends, there are off days, data doesn't get released on a few days a year, data availability is patchy when dealing with 40-year old data. All these problems are exacerbated when you are forced to make calculations using lag.
 
 ## The Solution
-Fincal aims to simplify things by allowing you to:
-* Compare time-series data based on dates
+PyFacts aims to simplify things by allowing you to:
+* Compare time-series data based on dates and time-period-based lag
 * Easy way to work around missing dates by taking the closest data points
 * Completing series with missing data points using forward fill and backward fill
+* Use friendly dates everywhere written as a simple string
 
-## Examples
+## Creating a time series
+Time series data can be created from a dictionary, a list of lists/tuples/dicts, or by reading a csv file.
 
+Example:
+```
+>>> import pyfacts as pft
 
+>>> time_series_data = [
+...    ('2021-01-01', 10),
+...    ('2021-02-01', 12),
+...    ('2021-03-01', 14),
+...    ('2021-04-01', 16),
+...    ('2021-05-01', 18),
+...    ('2021-06-01', 20)
+...]
+
+>>> ts = fc.TimeSeries(time_series_data)
+```
+
+### Sample usage
+```
+>>> ts.calculate_returns(as_on='2021-04-01', return_period_unit='months', return_period_value=3, annual_compounded_returns=False)
+(datetime.datetime(2021, 4, 1, 0, 0), 0.6)
+
+>>> ts.calculate_returns(as_on='2021-04-15', return_period_unit='months', return_period_value=3, annual_compounded_returns=False)
+(datetime.datetime(2021, 4, 1, 0, 0), 0.6)
+```
+
+### Working with dates
+With PyFacts, you never have to go into the hassle of creating datetime objects for your time series. PyFacts will parse any date passed to it as string. The default format is ISO format, i.e., YYYY-MM-DD. However, you can use your preferred format simply by specifying it in the options in datetime library compatible format, after importing the library. For example, to use DD-MM-YYY format:
+
+```
+>>> import pyfacts as pft
+>>> fc.PyfactsOptions.date_format = '%d-%m-%Y'
+```
+Now the library will automatically parse all dates as DD-MM-YYYY
+
+If you happen to have any one situation where you need to use a different format, all methods accept a date_format parameter to override the default.
 
 ## To-do
 
@@ -23,16 +59,17 @@ Fincal aims to simplify things by allowing you to:
 - [ ] Create emtpy TimeSeries object
 - [x] Read from CSV
 - [ ] Write to CSV
-- [ ] Convert to dict
-- [ ] Convert to list of dicts
-### Fincal features
+- [x] Convert to dict
+- [x] Convert to list of tuples
+
+### pyfacts features
 - [x] Sync two TimeSeries
 - [x] Average rolling return
 - [x] Sharpe ratio
 - [x] Jensen's Alpha
 - [x] Beta
 - [ ] Sortino ratio
-- [ ] Correlation & R-squared
+- [x] Correlation & R-squared
 - [ ] Treynor ratio
 - [x] Max drawdown
 - [ ] Moving average

tests/test_utils.py

@@ -1,7 +1,7 @@
 import datetime
 
 import pytest
-from fincal.utils import _interval_to_years, _parse_date
+from pyfacts.utils import _interval_to_years, _parse_date
 
 
 class TestParseDate: