now testing using Jupyter Notebook

ts['dates'] and ts['values'] keys implemted

fincal/core.py

@@ -1,5 +1,6 @@
 import datetime
 from dataclasses import dataclass
+from numbers import Number
 from typing import Iterable, List, Literal, Mapping, Sequence, Tuple, Union
 
 
@@ -111,6 +112,67 @@ class IndexSlicer:
         return item
 
 
+class Series:
+    def __init__(self, data):
+        if not isinstance(data, Sequence):
+            raise TypeError("Series only supports creation using Sequence types")
+
+        if isinstance(data[0], bool):
+            self.data = data
+            self.dtype = bool
+        elif isinstance(data[0], Number):
+            self.dtype = float
+            self.data = [float(i) for i in data]
+        elif isinstance(data[0], str):
+            try:
+                data = [datetime.datetime.strptime(i, FincalOptions.date_format) for i in data]
+                self.dtype = datetime.datetime
+            except ValueError:
+                raise TypeError("Series does not support string data type")
+        elif isinstance(data[0], datetime.datetime):
+            self.dtype = datetime.datetime
+            self.data = data
+        else:
+            raise TypeError(f"Cannot create series object from {type(data).__name__} of {type(data[0]).__name__}")
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}({self.data})"
+
+    def __getitem__(self, n):
+        return self.data[n]
+
+    def __len__(self):
+        return len(self.data)
+
+    def __gt__(self, other):
+        if self.dtype == bool:
+            raise TypeError("> not supported for boolean series")
+
+        if self.dtype == float and isinstance(other, Number) or isinstance(other, self.dtype):
+            gt = Series([i > other for i in self.data])
+        else:
+            raise Exception(f"Cannot compare type {self.dtype.__name__} to {type(other).__name__}")
+
+        return gt
+
+    def __lt__(self, other):
+        if self.dtype == bool:
+            raise TypeError("< not supported for boolean series")
+
+        if self.dtype == float and isinstance(other, Number) or isinstance(other, self.dtype):
+            lt = Series([i < other for i in self.data])
+        else:
+            raise Exception(f"Cannot compare type {self.dtype.__name__} to {type(other).__name__}")
+        return lt
+
+    def __eq__(self, other):
+        if self.dtype == float and isinstance(other, Number) or isinstance(other, self.dtype):
+            eq = Series([i == other for i in self.data])
+        else:
+            raise Exception(f"Cannot compare type {self.dtype.__name__} to {type(other).__name__}")
+        return eq
+
+
 class TimeSeriesCore:
     """Defines the core building blocks of a TimeSeries object"""
 
@@ -142,12 +204,36 @@ class TimeSeriesCore:
         data = _preprocess_timeseries(data, date_format=date_format)
 
         self.time_series = dict(data)
-        self.dates = list(self.time_series)
         if len(self.time_series) != len(data):
             print("Warning: The input data contains duplicate dates which have been ignored.")
-        self.start_date = self.dates[0]
-        self.end_date = self.dates[-1]
         self.frequency = getattr(AllFrequencies, frequency)
+        self.iter_num = -1
+        self._dates = None
+        self._values = None
+        self._start_date = None
+        self._end_date = None
+
+    @property
+    def dates(self):
+        if self._dates is None or len(self._dates) != len(self.time_series):
+            self._dates = list(self.time_series.keys())
+
+        return Series(self._dates)
+
+    @property
+    def values(self):
+        if self._values is None or len(self._values) != len(self.time_series):
+            self._values = list(self.time_series.values())
+
+        return Series(self._values)
+
+    @property
+    def start_date(self):
+        return self.dates[0]
+
+    @property
+    def end_date(self):
+        return self.dates[-1]
 
     def _get_printable_slice(self, n: int):
         """Returns a slice of the dataframe from beginning and end"""
@@ -193,11 +279,25 @@ class TimeSeriesCore:
         return printable_str
 
     def __getitem__(self, key):
+        if isinstance(key, Series):
+            if not key.dtype == bool:
+                raise ValueError(f"Cannot slice {self.__class__.__name__} using a Series of {key.dtype.__name__}")
+            elif len(key) != len(self.dates):
+                raise Exception(f"Length of Series: {len(key)} did not match length of object: {len(self.dates)}")
+            else:
+                dates_to_return = [self.dates[i] for i, j in enumerate(key) if j]
+                data_to_return = [(key, self.time_series[key]) for key in dates_to_return]
+                return TimeSeriesCore(data_to_return)
+
         if isinstance(key, int):
             raise KeyError(f"{key}. For index based slicing, use .iloc[{key}]")
         elif isinstance(key, datetime.datetime):
             item = (key, self.time_series[key])
         if isinstance(key, str):
+            if key == 'dates':
+                return self.dates
+            elif key == 'values':
+                return list(self.time_series.values())
             try:
                 dt_key = datetime.datetime.strptime(key, FincalOptions.date_format)
                 item = (dt_key, self.time_series[dt_key])
@@ -215,6 +315,18 @@ class TimeSeriesCore:
     def __len__(self):
         return len(self.time_series)
 
+    def __iter__(self):
+        self.n = 0
+        return self
+
+    def __next__(self):
+        if self.n >= len(self.dates):
+            raise StopIteration
+        else:
+            key = self.dates[self.n]
+            self.n += 1
+            return key, self.time_series[key]
+
     def head(self, n: int = 6):
         """Returns the first n items of the TimeSeries object"""
 

fincal/fincal.py

@@ -75,7 +75,7 @@ class TimeSeries(TimeSeriesCore):
             self.time_series = new_ts
             return None
 
-        return new_ts
+        return TimeSeries(new_ts, frequency=self.frequency.symbol)
 
     def bfill(self, inplace: bool = False, limit: int = None) -> Union[TimeSeries, None]:
         """Backward fill missing dates in the time series
@@ -109,7 +109,7 @@ class TimeSeries(TimeSeriesCore):
             self.time_series = new_ts
             return None
 
-        return new_ts
+        return TimeSeries(new_ts, frequency=self.frequency.symbol)
 
     def calculate_returns(
         self,

testing.ipynb

@@ -0,0 +1,129 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "3f7938c0-98e3-43b8-86e8-4f000cda7ce5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datetime\n",
+    "import pandas as pd\n",
+    "\n",
+    "from fincal.fincal import TimeSeries\n",
+    "from fincal.core import Series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "757eafc2-f804-4e7e-a3b8-2d09cd62e646",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dfd = pd.read_csv('test_files/nav_history_daily - copy.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "59b3d4a9-8ef4-4652-9e20-1bac69ab4ff9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dfd = dfd[dfd['amfi_code'] == 118825].reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "4bc95ae0-8c33-4eab-acf9-e765d22979b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning: The input data contains duplicate dates which have been ignored.\n"
+     ]
+    }
+   ],
+   "source": [
+    "ts = TimeSeries([(i.date, i.nav) for i in dfd.itertuples()], frequency='D')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "f2c3218c-3984-43d6-8638-41a74a9d0b58",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TimeSeries([(datetime.datetime(2013, 1, 2, 0, 0), 18.972),\n",
+       "\t    (datetime.datetime(2013, 1, 3, 0, 0), 19.011),\n",
+       "\t    (datetime.datetime(2013, 1, 4, 0, 0), 19.008)\n",
+       "\t    ...\n",
+       "\t    (datetime.datetime(2022, 2, 10, 0, 0), 86.5),\n",
+       "\t    (datetime.datetime(2022, 2, 11, 0, 0), 85.226),\n",
+       "\t    (datetime.datetime(2022, 2, 14, 0, 0), 82.53299999999999)], frequency='D')"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "dc469722-c816-4b57-8d91-7a3b865f86be",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "getattr(): attribute name must be string",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "File \u001b[1;32m<timed eval>:1\u001b[0m, in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n",
+      "File \u001b[1;32mD:\\Documents\\Projects\\fincal\\fincal\\fincal.py:203\u001b[0m, in \u001b[0;36mTimeSeries.calculate_rolling_returns\u001b[1;34m(self, from_date, to_date, frequency, as_on_match, prior_match, closest, compounding, years)\u001b[0m\n\u001b[0;32m    200\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n\u001b[0;32m    201\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid argument for frequency \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfrequency\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 203\u001b[0m dates \u001b[38;5;241m=\u001b[39m \u001b[43mcreate_date_series\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfrom_date\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mto_date\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrequency\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    204\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m frequency \u001b[38;5;241m==\u001b[39m AllFrequencies\u001b[38;5;241m.\u001b[39mD:\n\u001b[0;32m    205\u001b[0m     dates \u001b[38;5;241m=\u001b[39m [i \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m dates \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtime_series]\n",
+      "File \u001b[1;32mD:\\Documents\\Projects\\fincal\\fincal\\fincal.py:16\u001b[0m, in \u001b[0;36mcreate_date_series\u001b[1;34m(start_date, end_date, frequency, eomonth)\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcreate_date_series\u001b[39m(\n\u001b[0;32m     12\u001b[0m     start_date: datetime\u001b[38;5;241m.\u001b[39mdatetime, end_date: datetime\u001b[38;5;241m.\u001b[39mdatetime, frequency: \u001b[38;5;28mstr\u001b[39m, eomonth: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m     13\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m List[datetime\u001b[38;5;241m.\u001b[39mdatetime]:\n\u001b[0;32m     14\u001b[0m     \u001b[38;5;124;03m\"\"\"Creates a date series using a frequency\"\"\"\u001b[39;00m\n\u001b[1;32m---> 16\u001b[0m     frequency \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mAllFrequencies\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrequency\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     17\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m eomonth \u001b[38;5;129;01mand\u001b[39;00m frequency\u001b[38;5;241m.\u001b[39mdays \u001b[38;5;241m<\u001b[39m AllFrequencies\u001b[38;5;241m.\u001b[39mM\u001b[38;5;241m.\u001b[39mdays:\n\u001b[0;32m     18\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meomonth cannot be set to True if frequency is higher than \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mAllFrequencies\u001b[38;5;241m.\u001b[39mM\u001b[38;5;241m.\u001b[39mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[1;31mTypeError\u001b[0m: getattr(): attribute name must be string"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "ts.calculate_rolling_returns(from_date='2020-01-01', to_date='2021-01-01')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}