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changed return TimeSeries to return self.__class__

return_period-0.1
Gourav Kumar 2 years ago
parent
b99f0d67c1
commit
03283f7ed4
5 changed files with 201 additions and 273 deletions
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  1. 15
      .ipynb_checkpoints/README-checkpoint.md
  2. 129
      .ipynb_checkpoints/testing-checkpoint.ipynb
  3. 15
      .vscode/launch.json
  4. 4
      fincal/fincal.py
  5. 311
      testing.ipynb

15
.ipynb_checkpoints/README-checkpoint.md
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@ -0,0 +1,15 @@
# Fincal
This module simplified handling of time-series data
## 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.
## The Solution
Fincal aims to simplify things by allowing you to:
* Compare time-series data based on dates
* 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
## Examples

129
.ipynb_checkpoints/testing-checkpoint.ipynb
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@ -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
}

15
.vscode/launch.json
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@ -0,0 +1,15 @@
{
// Use IntelliSense to learn about possible attributes.
// Hover to view descriptions of existing attributes.
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
"version": "0.2.0",
"configurations": [
{
"name": "Python: Current File",
"type": "python",
"request": "launch",
"program": "${file}",
"console": "integratedTerminal"
}
]
}

4
fincal/fincal.py
View File

@ -75,7 +75,7 @@ class TimeSeries(TimeSeriesCore):
self.data = new_ts
return None
return TimeSeries(new_ts, frequency=self.frequency.symbol)
return self.__class__(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.data = new_ts
return None
return TimeSeries(new_ts, frequency=self.frequency.symbol)
return self.__class__(new_ts, frequency=self.frequency.symbol)
def calculate_returns(
self,

311
testing.ipynb
View File

@ -48,7 +48,7 @@
"\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), 82.533)], frequency='D')"
"\t (datetime.datetime(2022, 2, 14, 0, 0), 82.53299999999999)], frequency='D')"
]
},
"execution_count": 3,
@ -71,8 +71,8 @@
{
"data": {
"text/plain": [
"[(datetime.datetime(2021, 1, 1, 0, 0), 66.652),\n",
" (datetime.datetime(2020, 1, 1, 0, 0), 57.804)]"
"[(datetime.datetime(2022, 1, 31, 0, 0), 85.18),\n",
" (datetime.datetime(2021, 5, 31, 0, 0), 74.85)]"
]
},
"execution_count": 4,
@ -81,7 +81,7 @@
}
],
"source": [
"ts[['2021-01-01', '2020-01-01']]"
"ts[['2022-01-31', '2021-05-31']]"
]
},
{
@ -89,71 +89,43 @@
"execution_count": 5,
"id": "4d927a61-0f90-4b47-89b7-0e0d3ab1b442",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Series([False, False, False, False, False, False, False, False, False, False])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = ts.dates > '2020-01-01'\n",
"s[:10]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "311d1c07-d827-4d69-855f-883e1198c162",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
"s = ts.dates > '2020-01-01'"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "bb625050-5d7b-45a9-9cde-ac6e599adea5",
"execution_count": 6,
"id": "f90074f8-5173-49a9-a7d6-ceac01e92431",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 False\n",
"1 False\n",
"2 False\n",
"3 False\n",
"4 False\n",
" ... \n",
"2196 True\n",
"2197 True\n",
"2198 True\n",
"2199 True\n",
"2200 True\n",
"Length: 2201, dtype: bool"
"TimeSeries([(datetime.datetime(2020, 1, 2, 0, 0), 58.285),\n",
"\t (datetime.datetime(2020, 1, 3, 0, 0), 58.056999999999995),\n",
"\t (datetime.datetime(2020, 1, 6, 0, 0), 56.938)\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": 10,
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.Series(s)"
"ts[s]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 14,
"id": "dc469722-c816-4b57-8d91-7a3b865f86be",
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [
@ -161,247 +133,44 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 14 ms\n"
"CPU times: total: 15.6 ms\n",
"Wall time: 13 ms\n"
]
},
}
],
"source": [
"%%time\n",
"from_date = datetime.date(2020, 1, 1)\n",
"to_date = datetime.date(2021, 1, 1)\n",
"# print(ts.calculate_returns(to_date, years=7))\n",
"rr = ts.calculate_rolling_returns(from_date, to_date)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "086d4377-d1b1-4e51-84c0-39dee28ef75e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[(datetime.datetime(2020, 1, 1, 0, 0), 0.13778442642311628),\n",
" (datetime.datetime(2020, 1, 2, 0, 0), 0.15907011891977874),\n",
" (datetime.datetime(2020, 1, 3, 0, 0), 0.16528842679940592),\n",
" (datetime.datetime(2020, 1, 6, 0, 0), 0.13725881835976517),\n",
" (datetime.datetime(2020, 1, 7, 0, 0), 0.1361567661029075),\n",
" (datetime.datetime(2020, 1, 8, 0, 0), 0.12943599493029145),\n",
" (datetime.datetime(2020, 1, 9, 0, 0), 0.14162066246056781),\n",
" (datetime.datetime(2020, 1, 10, 0, 0), 0.14866092589666624),\n",
" (datetime.datetime(2020, 1, 13, 0, 0), 0.15898777321998292),\n",
" (datetime.datetime(2020, 1, 14, 0, 0), 0.16791720569210877),\n",
" (datetime.datetime(2020, 1, 15, 0, 0), 0.15701288705571237),\n",
" (datetime.datetime(2020, 1, 16, 0, 0), 0.15895332557726816),\n",
" (datetime.datetime(2020, 1, 17, 0, 0), 0.1597678687747972),\n",
" (datetime.datetime(2020, 1, 20, 0, 0), 0.1517952575115793),\n",
" (datetime.datetime(2020, 1, 21, 0, 0), 0.14416159380188165),\n",
" (datetime.datetime(2020, 1, 22, 0, 0), 0.1388367359038718),\n",
" (datetime.datetime(2020, 1, 23, 0, 0), 0.15628915183454306),\n",
" (datetime.datetime(2020, 1, 24, 0, 0), 0.16305428514615228),\n",
" (datetime.datetime(2020, 1, 27, 0, 0), 0.1622537810406386),\n",
" (datetime.datetime(2020, 1, 28, 0, 0), 0.16702450881101893),\n",
" (datetime.datetime(2020, 1, 29, 0, 0), 0.1718259672423219),\n",
" (datetime.datetime(2020, 1, 30, 0, 0), 0.15395125479716287),\n",
" (datetime.datetime(2020, 1, 31, 0, 0), 0.13100540412138484),\n",
" (datetime.datetime(2020, 2, 3, 0, 0), 0.10046175704475502),\n",
" (datetime.datetime(2020, 2, 4, 0, 0), 0.12408874488808119),\n",
" (datetime.datetime(2020, 2, 5, 0, 0), 0.13495745562947903),\n",
" (datetime.datetime(2020, 2, 6, 0, 0), 0.1316705790297339),\n",
" (datetime.datetime(2020, 2, 7, 0, 0), 0.1293798434480471),\n",
" (datetime.datetime(2020, 2, 10, 0, 0), 0.13417161807378752),\n",
" (datetime.datetime(2020, 2, 11, 0, 0), 0.14703664006986616),\n",
" (datetime.datetime(2020, 2, 12, 0, 0), 0.15665338645418325),\n",
" (datetime.datetime(2020, 2, 13, 0, 0), 0.16157913712294203),\n",
" (datetime.datetime(2020, 2, 14, 0, 0), 0.15528598971722363),\n",
" (datetime.datetime(2020, 2, 17, 0, 0), 0.15469223007063593),\n",
" (datetime.datetime(2020, 2, 18, 0, 0), 0.1616112297833383),\n",
" (datetime.datetime(2020, 2, 19, 0, 0), 0.1795321518161237),\n",
" (datetime.datetime(2020, 2, 20, 0, 0), 0.16892628011136668),\n",
" (datetime.datetime(2020, 2, 24, 0, 0), 0.14102204408817642),\n",
" (datetime.datetime(2020, 2, 25, 0, 0), 0.1287774793593952),\n",
" (datetime.datetime(2020, 2, 26, 0, 0), 0.11844857467280234),\n",
" (datetime.datetime(2020, 2, 27, 0, 0), 0.11330677290836633),\n",
" (datetime.datetime(2020, 2, 28, 0, 0), 0.07688934948979576),\n",
" (datetime.datetime(2020, 3, 2, 0, 0), 0.06323384067997617),\n",
" (datetime.datetime(2020, 3, 3, 0, 0), 0.08272385847005337),\n",
" (datetime.datetime(2020, 3, 4, 0, 0), 0.07610199644198445),\n",
" (datetime.datetime(2020, 3, 5, 0, 0), 0.06134216421544747),\n",
" (datetime.datetime(2020, 3, 6, 0, 0), 0.0307951704490399),\n",
" (datetime.datetime(2020, 3, 9, 0, 0), -0.014902463666744414),\n",
" (datetime.datetime(2020, 3, 11, 0, 0), -0.02972061472425558),\n",
" (datetime.datetime(2020, 3, 12, 0, 0), -0.1201765519331679),\n",
" (datetime.datetime(2020, 3, 13, 0, 0), -0.08575267799689612),\n",
" (datetime.datetime(2020, 3, 16, 0, 0), -0.15648316950777152),\n",
" (datetime.datetime(2020, 3, 17, 0, 0), -0.17358618226925038),\n",
" (datetime.datetime(2020, 3, 18, 0, 0), -0.21612661547106204),\n",
" (datetime.datetime(2020, 3, 19, 0, 0), -0.2452152219243373),\n",
" (datetime.datetime(2020, 3, 20, 0, 0), -0.20796112876097927),\n",
" (datetime.datetime(2020, 3, 23, 0, 0), -0.30970261339741667),\n",
" (datetime.datetime(2020, 3, 24, 0, 0), -0.2908531090417543),\n",
" (datetime.datetime(2020, 3, 25, 0, 0), -0.24436994526204125),\n",
" (datetime.datetime(2020, 3, 26, 0, 0), -0.22233453129679548),\n",
" (datetime.datetime(2020, 3, 27, 0, 0), -0.21821047890707101),\n",
" (datetime.datetime(2020, 3, 30, 0, 0), -0.2572613339750828),\n",
" (datetime.datetime(2020, 3, 31, 0, 0), -0.23261195549549218),\n",
" (datetime.datetime(2020, 4, 1, 0, 0), -0.2608607426811047),\n",
" (datetime.datetime(2020, 4, 3, 0, 0), -0.27431740614334477),\n",
" (datetime.datetime(2020, 4, 7, 0, 0), -0.2134398339479976),\n",
" (datetime.datetime(2020, 4, 8, 0, 0), -0.20755173891175982),\n",
" (datetime.datetime(2020, 4, 9, 0, 0), -0.18395973278558087),\n",
" (datetime.datetime(2020, 4, 13, 0, 0), -0.19529673178409412),\n",
" (datetime.datetime(2020, 4, 15, 0, 0), -0.20106882889523636),\n",
" (datetime.datetime(2020, 4, 16, 0, 0), -0.1970295580918935),\n",
" (datetime.datetime(2020, 4, 17, 0, 0), -0.1732330992098331),\n",
" (datetime.datetime(2020, 4, 20, 0, 0), -0.16734041380324138),\n",
" (datetime.datetime(2020, 4, 21, 0, 0), -0.19352467752012048),\n",
" (datetime.datetime(2020, 4, 23, 0, 0), -0.15569230769230757),\n",
" (datetime.datetime(2020, 4, 24, 0, 0), -0.17741518697626335),\n",
" (datetime.datetime(2020, 4, 27, 0, 0), -0.1667833069357988),\n",
" (datetime.datetime(2020, 4, 28, 0, 0), -0.1604110648642676),\n",
" (datetime.datetime(2020, 4, 29, 0, 0), -0.14832958856679812),\n",
" (datetime.datetime(2020, 4, 30, 0, 0), -0.1300687474725194),\n",
" (datetime.datetime(2020, 5, 4, 0, 0), -0.17366232326032705),\n",
" (datetime.datetime(2020, 5, 5, 0, 0), -0.1822936881988726),\n",
" (datetime.datetime(2020, 5, 6, 0, 0), -0.1722083628104405),\n",
" (datetime.datetime(2020, 5, 7, 0, 0), -0.17076925965564504),\n",
" (datetime.datetime(2020, 5, 8, 0, 0), -0.15704080857208003),\n",
" (datetime.datetime(2020, 5, 11, 0, 0), -0.1536333632218556),\n",
" (datetime.datetime(2020, 5, 12, 0, 0), -0.15922315279394084),\n",
" (datetime.datetime(2020, 5, 13, 0, 0), -0.12893477713422308),\n",
" (datetime.datetime(2020, 5, 14, 0, 0), -0.1515904189772027),\n",
" (datetime.datetime(2020, 5, 15, 0, 0), -0.1482976040353089),\n",
" (datetime.datetime(2020, 5, 18, 0, 0), -0.19486496766831485),\n",
" (datetime.datetime(2020, 5, 19, 0, 0), -0.19478889311525294),\n",
" (datetime.datetime(2020, 5, 20, 0, 0), -0.20504325418737346),\n",
" (datetime.datetime(2020, 5, 21, 0, 0), -0.1923849185691976),\n",
" (datetime.datetime(2020, 5, 22, 0, 0), -0.20265491640901256),\n",
" (datetime.datetime(2020, 5, 26, 0, 0), -0.21261575969288427),\n",
" (datetime.datetime(2020, 5, 27, 0, 0), -0.1977462346949832),\n",
" (datetime.datetime(2020, 5, 28, 0, 0), -0.18100478037341028),\n",
" (datetime.datetime(2020, 5, 29, 0, 0), -0.16737799894662286),\n",
" (datetime.datetime(2020, 6, 1, 0, 0), -0.1486066432934544),\n",
" (datetime.datetime(2020, 6, 2, 0, 0), -0.1406549318682908),\n",
" (datetime.datetime(2020, 6, 3, 0, 0), -0.1455950320312639),\n",
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" (datetime.datetime(2020, 6, 24, 0, 0), -0.09426049373672252),\n",
" (datetime.datetime(2020, 6, 25, 0, 0), -0.10081348053457295),\n",
" (datetime.datetime(2020, 6, 26, 0, 0), -0.09618408922470667),\n",
" (datetime.datetime(2020, 6, 29, 0, 0), -0.10191913883401882),\n",
" (datetime.datetime(2020, 6, 30, 0, 0), -0.10248092640582795),\n",
" (datetime.datetime(2020, 7, 1, 0, 0), -0.09865244649419913),\n",
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" (datetime.datetime(2020, 7, 3, 0, 0), -0.08705088880518042),\n",
" (datetime.datetime(2020, 7, 6, 0, 0), -0.06379048033036305),\n",
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" (datetime.datetime(2020, 7, 13, 0, 0), -0.04068015895018928),\n",
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" (datetime.datetime(2020, 8, 10, 0, 0), 0.048723986751670445),\n",
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" (datetime.datetime(2020, 8, 13, 0, 0), 0.07394496484839053),\n",
" (datetime.datetime(2020, 8, 14, 0, 0), 0.05147838833490859),\n",
" (datetime.datetime(2020, 8, 17, 0, 0), 0.05283004347993381),\n",
" (datetime.datetime(2020, 8, 18, 0, 0), 0.06824040940397857),\n",
" (datetime.datetime(2020, 8, 19, 0, 0), 0.07100728132024359),\n",
" (datetime.datetime(2020, 10, 8, 0, 0), 0.09372370534689822),\n",
" (datetime.datetime(2020, 10, 9, 0, 0), 0.08128249892061357),\n",
" (datetime.datetime(2020, 10, 12, 0, 0), 0.08653955068906938),\n",
" (datetime.datetime(2020, 10, 13, 0, 0), 0.08506701906739678),\n",
" (datetime.datetime(2020, 10, 14, 0, 0), 0.08405050922620161),\n",
" (datetime.datetime(2020, 10, 15, 0, 0), 0.05063125897469378),\n",
" (datetime.datetime(2020, 10, 16, 0, 0), 0.060133007954397355),\n",
" (datetime.datetime(2020, 10, 19, 0, 0), 0.04674158943297102),\n",
" (datetime.datetime(2020, 10, 20, 0, 0), 0.04907684448660876),\n",
" (datetime.datetime(2020, 10, 21, 0, 0), 0.052287820185360934),\n",
" (datetime.datetime(2020, 10, 22, 0, 0), 0.05187688373367427),\n",
" (datetime.datetime(2020, 10, 23, 0, 0), 0.05667055904247564),\n",
" (datetime.datetime(2020, 10, 26, 0, 0), 0.04027605345797136),\n",
" (datetime.datetime(2020, 10, 27, 0, 0), 0.04849193018330533),\n",
" (datetime.datetime(2020, 10, 28, 0, 0), 0.03806689549404818),\n",
" (datetime.datetime(2020, 10, 29, 0, 0), 0.014816143497757839),\n",
" (datetime.datetime(2020, 10, 30, 0, 0), 0.00481137623180139),\n",
" (datetime.datetime(2020, 11, 2, 0, 0), -0.00014131778837667142),\n",
" (datetime.datetime(2020, 11, 3, 0, 0), 0.010563504681151636),\n",
" (datetime.datetime(2020, 11, 4, 0, 0), 0.01794663654972828),\n",
" (datetime.datetime(2020, 11, 5, 0, 0), 0.0394424147838115),\n",
" (datetime.datetime(2020, 11, 6, 0, 0), 0.043924408336276866),\n",
" (datetime.datetime(2020, 11, 9, 0, 0), 0.06426903481460511),\n",
" (datetime.datetime(2020, 11, 10, 0, 0), 0.07422870082083222),\n",
" (datetime.datetime(2020, 11, 11, 0, 0), 0.08174632645483015),\n",
" (datetime.datetime(2020, 11, 12, 0, 0), 0.07851042385019369),\n",
" (datetime.datetime(2020, 11, 13, 0, 0), 0.0942456954233204),\n",
" (datetime.datetime(2020, 11, 17, 0, 0), 0.09901112922662514),\n",
" (datetime.datetime(2020, 11, 18, 0, 0), 0.10079208973472964),\n",
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" (datetime.datetime(2020, 11, 20, 0, 0), 0.07989130434782621),\n",
" (datetime.datetime(2020, 11, 23, 0, 0), 0.0914305886506046),\n",
" (datetime.datetime(2020, 11, 24, 0, 0), 0.10378607360338887),\n",
" (datetime.datetime(2020, 11, 25, 0, 0), 0.07314267788989381),\n",
" (datetime.datetime(2020, 11, 26, 0, 0), 0.08476710029374734),\n",
" (datetime.datetime(2020, 11, 27, 0, 0), 0.07934822345629589),\n",
" (datetime.datetime(2020, 12, 1, 0, 0), 0.09014280738418656),\n",
" (datetime.datetime(2020, 12, 2, 0, 0), 0.09430073533264638),\n",
" (datetime.datetime(2020, 12, 3, 0, 0), 0.10218081653420374),\n",
" (datetime.datetime(2020, 12, 4, 0, 0), 0.10882414661443751),\n",
" (datetime.datetime(2020, 12, 7, 0, 0), 0.13058534603084881),\n",
" (datetime.datetime(2020, 12, 8, 0, 0), 0.13247583879573832),\n",
" (datetime.datetime(2020, 12, 9, 0, 0), 0.14204505331544381),\n",
" (datetime.datetime(2020, 12, 10, 0, 0), 0.14810928106435184),\n",
" (datetime.datetime(2020, 12, 11, 0, 0), 0.14509213323883752),\n",
" (datetime.datetime(2020, 12, 14, 0, 0), 0.12969764486762192),\n",
" (datetime.datetime(2020, 12, 15, 0, 0), 0.1272258873087433),\n",
" (datetime.datetime(2020, 12, 16, 0, 0), 0.1384985949417905),\n",
" (datetime.datetime(2020, 12, 17, 0, 0), 0.1336538294659746),\n",
" (datetime.datetime(2020, 12, 18, 0, 0), 0.13410538706550335),\n",
" (datetime.datetime(2020, 12, 21, 0, 0), 0.09376186695204902),\n",
" (datetime.datetime(2020, 12, 22, 0, 0), 0.1060517140194015),\n",
" (datetime.datetime(2020, 12, 23, 0, 0), 0.12163516362002835),\n",
" (datetime.datetime(2020, 12, 24, 0, 0), 0.1345174146595043),\n",
" (datetime.datetime(2020, 12, 28, 0, 0), 0.13883233842862563),\n",
" (datetime.datetime(2020, 12, 29, 0, 0), 0.14235188571822932),\n",
" (datetime.datetime(2020, 12, 30, 0, 0), 0.1433421220424269),\n",
" (datetime.datetime(2020, 12, 31, 0, 0), 0.1496252444998356),\n",
" (datetime.datetime(2021, 1, 1, 0, 0), 0.15306899176527566)]"
"list"
]
},
"execution_count": 9,
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"from_date = datetime.date(2020, 1, 1)\n",
"to_date = datetime.date(2021, 1, 1)\n",
"# print(ts.calculate_returns(to_date, years=7))\n",
"ts.calculate_rolling_returns(from_date, to_date)"
"type(rr)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@ -415,7 +184,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.2"
"version": "3.8.3"
}
},
"nbformat": 4,

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