now testing using Jupyter Notebook
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testing.ipynb
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129
testing.ipynb
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "3f7938c0-98e3-43b8-86e8-4f000cda7ce5",
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"metadata": {},
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"outputs": [],
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"source": [
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"import datetime\n",
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"import pandas as pd\n",
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"\n",
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"from fincal.fincal import TimeSeries\n",
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"from fincal.core import Series"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "757eafc2-f804-4e7e-a3b8-2d09cd62e646",
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"metadata": {},
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"outputs": [],
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"source": [
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"dfd = pd.read_csv('test_files/nav_history_daily - copy.csv')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "59b3d4a9-8ef4-4652-9e20-1bac69ab4ff9",
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"metadata": {},
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"outputs": [],
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"source": [
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"dfd = dfd[dfd['amfi_code'] == 118825].reset_index(drop=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"id": "4bc95ae0-8c33-4eab-acf9-e765d22979b8",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Warning: The input data contains duplicate dates which have been ignored.\n"
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]
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}
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],
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"source": [
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"ts = TimeSeries([(i.date, i.nav) for i in dfd.itertuples()], frequency='D')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"id": "f2c3218c-3984-43d6-8638-41a74a9d0b58",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"TimeSeries([(datetime.datetime(2013, 1, 2, 0, 0), 18.972),\n",
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"\t (datetime.datetime(2013, 1, 3, 0, 0), 19.011),\n",
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"\t (datetime.datetime(2013, 1, 4, 0, 0), 19.008)\n",
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"\t ...\n",
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"\t (datetime.datetime(2022, 2, 10, 0, 0), 86.5),\n",
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"\t (datetime.datetime(2022, 2, 11, 0, 0), 85.226),\n",
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"\t (datetime.datetime(2022, 2, 14, 0, 0), 82.53299999999999)], frequency='D')"
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]
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},
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"execution_count": 20,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"ts"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"id": "dc469722-c816-4b57-8d91-7a3b865f86be",
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"metadata": {},
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"outputs": [
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{
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"ename": "TypeError",
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"evalue": "getattr(): attribute name must be string",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
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"File \u001b[1;32m<timed eval>:1\u001b[0m, in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n",
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"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",
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"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",
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"\u001b[1;31mTypeError\u001b[0m: getattr(): attribute name must be string"
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]
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}
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],
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"source": [
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"%%time\n",
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"ts.calculate_rolling_returns(from_date='2020-01-01', to_date='2021-01-01')"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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