first commit with basic time series operations

fincal/fincal.py

@@ -0,0 +1,126 @@
+import datetime
+import pandas as pd
+from typing import Union, Dict, List, Iterable, Any
+
+
+class TimeSeries:
+    def __init__(
+        self,
+        data=List[tuple],
+        date_format: str = '%Y-%m-%d',
+        frequency='infer'  # D, W, M, Q, H, Y
+    ):
+        self.time_series = [(datetime.datetime.strptime(i[0], date_format), i[1]) for i in data]
+        self.dates = {i[0] for i in self.time_series}
+
+#     def infer_frequency(self):
+#         sample_dates = [i[0] for i in self.time_series[:10]]
+#         for i in sample_dates
+    def __repr__(self):
+        if len(self.time_series) > 6:
+            printable_data_1 = self.time_series[:3]
+            printable_data_2 = self.time_series[-3:]
+            printable_str = "TimeSeries([{}\n\t...\n\t{}])".format(
+                ',\n\t'.join([str(i) for i in printable_data_1]),
+                ',\n\t'.join([str(i) for i in printable_data_2])
+            )
+        else:
+            printable_data = self.time_series
+            printable_str = "TimeSeries([{}])".format(',\n\t'.join([str(i) for i in printable_data]))
+        return printable_str
+
+    def __str__(self):
+        if len(self.time_series) > 6:
+            printable_data_1 = self.time_series[:3]
+            printable_data_2 = self.time_series[-3:]
+            printable_str = "[{}\n ...\n {}]".format(
+                ',\n '.join([str(i) for i in printable_data_1]),
+                ',\n '.join([str(i) for i in printable_data_2])
+            )
+        else:
+            printable_data = self.time_series
+            printable_str = "[{}]".format(',\n '.join([str(i) for i in printable_data]))
+        return printable_str
+
+    def ffill(self):
+        new_ts = []
+        for dt, val in self.time_series:
+            if dt == self.time_series[0][0]:
+                new_ts.append((dt, val))
+            else:
+                diff = (dt - prev_date).days
+                if diff != 1:
+                    for k in range(1, diff):
+                        new_ts.append((prev_date + datetime.timedelta(days=k), prev_val))
+                new_ts.append((dt, val))
+            prev_date = dt
+            prev_val = val
+        self.ffilled_time_series = new_ts
+        return self.ffilled_time_series
+
+    def bfill(self):
+        new_ts = []
+        for dt, val in self.time_series[::-1]:
+            if dt == self.time_series[-1][0]:
+                new_ts.append((dt, val))
+            else:
+                diff = (prev_date - dt).days
+                if diff != 1:
+                    for k in range(1, diff):
+                        new_ts.append((prev_date - datetime.timedelta(days=k), prev_val))
+                new_ts.append((dt, val))
+            prev_date = dt
+            prev_val = val
+        self.ffilled_time_series = new_ts[::-1]
+        return self.ffilled_time_series
+
+    def calculate_returns(
+        self,
+        as_on: datetime.date,
+        closest: str = 'previous',
+        compounding: bool = True,
+        years: int = 1
+    ) -> int:
+        """Method to calculate returns for a certain time-period as on a particular date
+            >>> calculate_returns(datetime.date(2020, 1, 1), years=1)
+        """
+
+        current = [(dt, val) for dt, val in self.time_series if dt == as_on][0]
+        if not current:
+            raise ValueError("As on date not found")
+
+        prev_date = as_on.replace(year=as_on.year-years)
+        if closest == 'previous':
+            previous = [(dt, val) for dt, val in self.time_series if dt <= prev_date][-1]
+        elif closest == 'next':
+            previous = [(dt, val) for dt, val in self.time_series if dt >= prev_date][0]
+#         print(current, previous)
+
+        returns = current[1]/previous[1]
+        if compounding:
+            returns = returns ** (1/years)
+        return returns - 1
+
+    def calculate_rolling_returns(
+        self,
+        from_date: datetime.date,
+        to_date: datetime.date,
+        frequency: str = 'd',
+        closest: str = 'previous',
+        compounding: bool = True,
+        years: int = 1
+    ) -> List[tuple]:
+        """Calculates the rolling return"""
+
+        datediff = (to_date - from_date).days
+        dates = []
+        for i in range(datediff):
+            if from_date + datetime.timedelta(days=i) in self.dates:
+                dates.append(from_date + datetime.timedelta(days=i))
+
+        rolling_returns = []
+        for i in dates:
+            returns = self.calculate_returns(as_on=i, compounding=compounding, years=years, closest=closest)
+            rolling_returns.append((i, returns))
+        self.rolling_returns = rolling_returns
+        return self.rolling_returns