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Remove test check if instance is of type Mapping

own-class
Gourav Kumar 2 years ago
parent
2ee8f928af
commit
7bcd310652
10 changed files with 8160 additions and 273 deletions
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  1. BIN
      .DS_Store
  2. 188
      .ipynb_checkpoints/testing-checkpoint.ipynb
  3. 54
      test.py
  4. 98
      test2.py
  5. 7560
      test_files/.ipynb_checkpoints/msft-checkpoint.csv
  6. 139
      test_files/.ipynb_checkpoints/nav_history_daily-checkpoint.csv
  7. 11
      test_files/.ipynb_checkpoints/nav_history_monthly-checkpoint.csv
  8. 4
      test_files/nav_history_monthly.csv
  9. 353
      testing.ipynb
  10. 4
      tests/test_core.py

BIN
.DS_Store
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188
.ipynb_checkpoints/testing-checkpoint.ipynb
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@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 1,
"id": "3f7938c0-98e3-43b8-86e8-4f000cda7ce5",
"metadata": {},
"outputs": [],
@ -16,92 +16,196 @@
},
{
"cell_type": "code",
"execution_count": 16,
"id": "757eafc2-f804-4e7e-a3b8-2d09cd62e646",
"execution_count": 2,
"id": "4b8ccd5f-dfff-4202-82c4-f66a30c122b6",
"metadata": {},
"outputs": [],
"source": [
"dfd = pd.read_csv('test_files/nav_history_daily - copy.csv')"
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 152 ms, sys: 284 ms, total: 436 ms\n",
"Wall time: 61.3 ms\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "59b3d4a9-8ef4-4652-9e20-1bac69ab4ff9",
"data": {
"text/plain": [
"TimeSeries([(datetime.datetime(2021, 5, 28, 0, 0), 249.679993),\n",
"\t(datetime.datetime(2022, 1, 31, 0, 0), 310.980011)], frequency='D')"
]
},
"execution_count": 2,
"metadata": {},
"outputs": [],
"output_type": "execute_result"
}
],
"source": [
"dfd = dfd[dfd['amfi_code'] == 118825].reset_index(drop=True)"
"%%time\n",
"dfd = pd.read_csv('test_files/msft.csv')\n",
"# dfd = dfd[dfd['amfi_code'] == 118825].reset_index(drop=True)\n",
"ts = TimeSeries([(i.date, i.nav) for i in dfd.itertuples()], frequency='D')\n",
"repr(ts)\n",
"ts[['2022-01-31', '2021-05-28']]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "4bc95ae0-8c33-4eab-acf9-e765d22979b8",
"execution_count": 3,
"id": "a0232e05-27c7-4d2d-a4bc-5dcf42666983",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Warning: The input data contains duplicate dates which have been ignored.\n"
"ename": "TypeError",
"evalue": "Type List cannot be instantiated; use list() instead",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"Input \u001b[0;32mIn [3]\u001b[0m, in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfincal\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Frequency\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m List, Tuple\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcreate_test_data\u001b[39m(\n\u001b[1;32m 6\u001b[0m frequency: Frequency,\n\u001b[1;32m 7\u001b[0m num: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1000\u001b[39m,\n\u001b[1;32m 8\u001b[0m skip_weekends: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 9\u001b[0m mu: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.1\u001b[39m,\n\u001b[1;32m 10\u001b[0m sigma: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.05\u001b[39m,\n\u001b[1;32m 11\u001b[0m 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---> 12\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[43mList\u001b[49m\u001b[43m(\u001b[49m\u001b[43mTuple\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 13\u001b[0m \u001b[38;5;124;03m\"\"\"Creates TimeSeries data\u001b[39;00m\n\u001b[1;32m 14\u001b[0m \n\u001b[1;32m 15\u001b[0m \u001b[38;5;124;03m Parameters:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[38;5;124;03m Returns a TimeSeries object\u001b[39;00m\n\u001b[1;32m 36\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 38\u001b[0m start_date \u001b[38;5;241m=\u001b[39m datetime\u001b[38;5;241m.\u001b[39mdatetime(\u001b[38;5;241m2017\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m)\n",
"File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/typing.py:941\u001b[0m, in \u001b[0;36m_BaseGenericAlias.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 939\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 940\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_inst:\n\u001b[0;32m--> 941\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mType \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m cannot be instantiated; \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 942\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muse \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__origin__\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m() instead\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 943\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__origin__(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 944\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
"\u001b[0;31mTypeError\u001b[0m: Type List cannot be instantiated; use list() instead"
]
}
],
"source": [
"ts = TimeSeries([(i.date, i.nav) for i in dfd.itertuples()], frequency='D')"
"from fincal.fincal import create_date_series\n",
"from fincal.core import Frequency\n",
"from typing import List, Tuple\n",
"\n",
"def create_test_data(\n",
" frequency: Frequency,\n",
" num: int = 1000,\n",
" skip_weekends: bool = False,\n",
" mu: float = 0.1,\n",
" sigma: float = 0.05,\n",
" eomonth: bool = False,\n",
") -> List[Tuple]:\n",
" \"\"\"Creates TimeSeries data\n",
"\n",
" Parameters:\n",
" -----------\n",
" frequency: Frequency\n",
" The frequency of the time series data to be generated.\n",
"\n",
" num: int\n",
" Number of date: value pairs to be generated.\n",
"\n",
" skip_weekends: bool\n",
" Whether weekends (saturday, sunday) should be skipped.\n",
" Gets used only if the frequency is daily.\n",
"\n",
" mu: float\n",
" Mean return for the values.\n",
"\n",
" sigma: float\n",
" standard deviation of the values.\n",
"\n",
" Returns:\n",
" --------\n",
" Returns a TimeSeries object\n",
" \"\"\"\n",
"\n",
" start_date = datetime.datetime(2017, 1, 1)\n",
" timedelta_dict = {\n",
" frequency.freq_type: int(\n",
" frequency.value * num * (7 / 5 if frequency == AllFrequencies.D and skip_weekends else 1)\n",
" )\n",
" }\n",
" end_date = start_date + relativedelta(**timedelta_dict)\n",
" dates = create_date_series(start_date, end_date, frequency.symbol, skip_weekends=skip_weekends, eomonth=eomonth)\n",
" values = create_prices(1000, mu, sigma, num)\n",
" ts = list(zip(dates, values))\n",
" return ts"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "f2c3218c-3984-43d6-8638-41a74a9d0b58",
"execution_count": null,
"id": "53dbc8a6-d7b1-4d82-ac3d-ee3908ff086d",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 1,
"id": "aa1584d5-1df0-4661-aeeb-5e8c424de06d",
"metadata": {},
"outputs": [],
"source": [
"from fincal import fincal\n",
"from fincal.core import FincalOptions\n",
"import csv"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "7d51fca1-f731-47c8-99c9-6e199cfeca92",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['date', 'nav']\n",
"CPU times: user 47.7 ms, sys: 3.16 ms, total: 50.9 ms\n",
"Wall time: 50.3 ms\n"
]
},
{
"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",
"TimeSeries([(datetime.datetime(1992, 2, 19, 0, 0), '2.398438'),\n",
"\t (datetime.datetime(1992, 2, 20, 0, 0), '2.447917'),\n",
"\t (datetime.datetime(1992, 2, 21, 0, 0), '2.385417')\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')"
"\t (datetime.datetime(2022, 2, 16, 0, 0), '299.5'),\n",
"\t (datetime.datetime(2022, 2, 17, 0, 0), '290.730011'),\n",
"\t (datetime.datetime(2022, 2, 18, 0, 0), '287.929993')], frequency='M')"
]
},
"execution_count": 20,
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts"
"%%time\n",
"FincalOptions.date_format = '%Y-%m-%d'\n",
"fincal.read_csv('test_files/msft.csv', frequency='M')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "dc469722-c816-4b57-8d91-7a3b865f86be",
"execution_count": 9,
"id": "b689f64c-6764-45b5-bccf-f23b351f6419",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "6c9b2dd7-9983-40cd-8ac4-3530a3892f17",
"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"
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 61.4 ms, sys: 2.35 ms, total: 63.7 ms\n",
"Wall time: 62.6 ms\n"
]
}
],
"source": [
"%%time\n",
"ts.calculate_rolling_returns(from_date='2020-01-01', to_date='2021-01-01')"
"dfd = pd.read_csv(\"test_files/msft.csv\")\n",
"ts = fincal.TimeSeries([(i.date, i.nav) for i in dfd.itertuples()], frequency=\"D\")"
]
}
],
@ -121,7 +225,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
"version": "3.10.2"
}
},
"nbformat": 4,

54
test.py
View File

@ -1,35 +1,29 @@
# type: ignore
import datetime
import time
import pandas as pd
# from fincal.core import FincalOptions
from fincal.fincal import TimeSeries
df = pd.read_csv('test_files/nav_history_daily.csv')
df = df.sort_values(by=['amfi_code', 'date']) # type: ignore
data_list = [(i.date, i.nav) for i in df[df.amfi_code == 118825].itertuples()]
data = [
("2022-01-01", 10),
("2022-01-02", 12),
("2022-01-03", 14),
("2022-01-04", 16),
("2022-01-06", 18),
("2022-01-07", 20),
("2022-01-09", 22),
("2022-01-10", 24),
("2022-01-11", 26),
("2022-01-13", 28),
("2022-01-14", 30),
("2022-01-15", 32),
("2022-01-16", 34),
]
ts = TimeSeries(data, frequency="D")
print(ts)
start = time.time()
ts_data = TimeSeries(data_list, frequency='M')
print(f"Instantiation took {round((time.time() - start)*1000, 2)} ms")
# ts_data.fill_missing_days()
start = time.time()
# ts_data.calculate_returns(as_on=datetime.datetime(2022, 1, 4), closest='next', years=1)
rr = ts_data.calculate_rolling_returns(datetime.datetime(2015, 1, 1),
datetime.datetime(2022, 1, 21),
frequency='M',
as_on_match='next',
prior_match='previous',
closest='previous',
years=1)
data = [("2022-01-01", 220), ("2022-01-08", 230), ("2022-01-15", 240)]
ts2 = TimeSeries(data, frequency="W")
print(ts2)
# ffill_data = ts_data.bfill()
print(f"Calculation took {round((time.time() - start)*1000, 2)} ms")
rr.sort()
for i in rr[:10]:
synced_ts = ts.sync(ts2)
print("---------\n")
for i in synced_ts:
print(i)
# print(ffill_data)
# print(ts_data)
# print(repr(ts_data))

98
test2.py
View File

@ -1,58 +1,52 @@
import pandas as pd
from fincal.fincal import TimeSeries, create_date_series
dfd = pd.read_csv("test_files/nav_history_daily - Copy.csv")
dfd = dfd[dfd["amfi_code"] == 118825].reset_index(drop=True)
ts = TimeSeries([(i.date, i.nav) for i in dfd.itertuples()], frequency="D")
repr(ts)
# print(ts[['2022-01-31', '2021-05-28']])
# rr = ts.calculate_rolling_returns(from_date='2021-01-01', to_date='2022-01-01', frequency='D', interval_type='days', interval_value=30, compounding=False)
# data = [
# ("2020-01-01", 10),
# ("2020-02-01", 12),
# ("2020-03-01", 14),
# ("2020-04-01", 16),
# ("2020-05-01", 18),
# ("2020-06-01", 20),
# ("2020-07-01", 22),
# ("2020-08-01", 24),
# ("2020-09-01", 26),
# ("2020-10-01", 28),
# ("2020-11-01", 30),
# ("2020-12-01", 32),
# ("2021-01-01", 34),
# ]
# ts = TimeSeries(data, frequency="M")
import time
from fincal.fincal import TimeSeries
# start = time.time()
# dfd = pd.read_csv("test_files/msft.csv") # , dtype=dict(nav=str))
# # dfd = dfd[dfd["amfi_code"] == 118825].reset_index(drop=True)
# print("instantiation took", round((time.time() - start) * 1000, 2), "ms")
# ts = TimeSeries([(i.date, i.nav) for i in dfd.itertuples()], frequency="D")
# print(repr(ts))
start = time.time()
# mdd = ts.max_drawdown()
# print(mdd)
# print("max drawdown calc took", round((time.time() - start) * 1000, 2), "ms")
# # print(ts[['2022-01-31', '2021-05-28']])
# rr = ts.calculate_rolling_returns(
# "2020-02-01",
# "2021-01-01",
# if_not_found="nan",
# compounding=False,
# interval_type="months",
# interval_value=1,
# as_on_match="exact",
# from_date='2021-01-01',
# to_date='2022-01-01',
# frequency='D',
# interval_type='days',
# interval_value=30,
# compounding=False
# )
# for i in rr:
# print(i)
# returns = ts.calculate_returns(
# "2020-04-25",
# return_actual_date=True,
# closest_max_days=15,
# compounding=True,
# interval_type="days",
# interval_value=90,
# closest="previous",
# if_not_found="fail",
# )
data = [
("2022-01-01", 10),
# ("2022-01-08", 12),
("2022-01-15", 14),
("2022-01-22", 16)
# ("2020-02-07", 18),
# ("2020-02-14", 20),
# ("2020-02-21", 22),
# ("2020-02-28", 24),
# ("2020-03-01", 26),
# ("2020-03-01", 28),
# ("2020-03-01", 30),
# ("2020-03-01", 32),
# ("2021-03-01", 34),
]
ts = TimeSeries(data, "W")
# ts_expanded = ts.expand("D", "ffill", skip_weekends=True)
# for i in ts_expanded:
# print(i)
# print(returns)
print(ts.get("2022-01-01"))
volatility = ts.volatility(start_date="2018-01-01", end_date="2021-01-01")
print(volatility)
print(ts.ffill())

7560
test_files/.ipynb_checkpoints/msft-checkpoint.csv
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File diff suppressed because it is too large

139
test_files/.ipynb_checkpoints/nav_history_daily-checkpoint.csv
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@ -0,0 +1,139 @@
amfi_code,date,nav
118825,01-11-2021,87.925
119528,02-11-2021,378.51
118825,02-11-2021,87.885
119528,03-11-2021,377.79
118825,03-11-2021,87.553
119528,08-11-2021,383.13
118825,08-11-2021,88.743
119528,09-11-2021,383.06
118825,09-11-2021,88.793
119528,10-11-2021,382.71
118825,10-11-2021,88.723
118825,10-11-2021,88.78
119528,11-11-2021,379.28
118825,11-11-2021,88.205
119528,12-11-2021,383.94
118825,12-11-2021,89.025
119528,15-11-2021,383.31
118825,15-11-2021,89.182
119528,16-11-2021,381.08
118825,16-11-2021,88.569
119528,17-11-2021,379.17
118825,17-11-2021,88.09
119528,18-11-2021,375.09
118825,18-11-2021,87.202
119528,22-11-2021,368.16
118825,22-11-2021,85.382
119528,23-11-2021,370.64
118825,23-11-2021,85.978
119528,24-11-2021,369.91
118825,24-11-2021,85.635
119528,25-11-2021,371.33
118825,25-11-2021,86.212
119528,26-11-2021,360.66
118825,26-11-2021,83.748
119528,29-11-2021,360.05
118825,29-11-2021,83.523
119528,30-11-2021,359.8
118825,30-11-2021,83.475
119528,01-12-2021,362.35
118825,01-12-2021,84.269
119528,02-12-2021,366.09
118825,02-12-2021,85.105
119528,03-12-2021,363.11
118825,03-12-2021,84.507
119528,06-12-2021,357.21
118825,06-12-2021,83.113
119528,07-12-2021,362.63
118825,07-12-2021,84.429
119528,08-12-2021,368.73
118825,08-12-2021,85.935
119528,09-12-2021,369.49
118825,09-12-2021,86.045
119528,10-12-2021,369.44
118825,10-12-2021,86.058
119528,13-12-2021,367.6
118825,13-12-2021,85.632
119528,14-12-2021,366.36
118825,14-12-2021,85.502
119528,15-12-2021,364.34
118825,15-12-2021,84.989
119528,16-12-2021,363.73
118825,16-12-2021,84.972
119528,17-12-2021,358.17
118825,17-12-2021,83.83
119528,20-12-2021,349.98
118825,20-12-2021,81.817
119528,21-12-2021,353.71
118825,21-12-2021,82.746
119528,22-12-2021,357.93
118825,22-12-2021,83.776
119528,23-12-2021,360.68
118825,23-12-2021,84.297
119528,24-12-2021,359.11
118825,24-12-2021,83.903
119528,27-12-2021,360.71
118825,27-12-2021,84.227
119528,28-12-2021,363.81
118825,28-12-2021,85.044
119528,29-12-2021,363.2
118825,29-12-2021,85.03
119528,30-12-2021,363.31
118825,30-12-2021,85.047
119528,31-12-2021,366.98
118825,31-12-2021,85.759
119528,03-01-2022,371.76
118825,03-01-2022,87.111
119528,04-01-2022,374.22
118825,04-01-2022,87.804
119528,05-01-2022,376.31
118825,05-01-2022,88.162
119528,06-01-2022,373.64
118825,06-01-2022,87.541
119528,07-01-2022,374.68
118825,07-01-2022,87.818
119528,10-01-2022,378.47
118825,10-01-2022,88.622
119528,11-01-2022,379.34
118825,11-01-2022,88.678
119528,12-01-2022,382.86
118825,12-01-2022,89.332
119528,13-01-2022,383.68
118825,13-01-2022,89.553
119528,14-01-2022,384.02
118825,14-01-2022,89.729
119528,17-01-2022,384.36
118825,17-01-2022,89.733
119528,18-01-2022,380
118825,18-01-2022,88.781
119528,19-01-2022,377.24
118825,19-01-2022,88.059
119528,20-01-2022,374.45
118825,20-01-2022,87.361
119528,21-01-2022,369.86
118825,21-01-2022,86.22
119528,24-01-2022,361.01
118825,24-01-2022,83.907
119528,25-01-2022,364.63
118825,25-01-2022,84.763
119528,27-01-2022,361.95
118825,27-01-2022,83.876
119528,28-01-2022,361.91
118825,28-01-2022,83.829
119528,31-01-2022,367.31
118825,31-01-2022,85.18
119528,04-02-2022,371.01
118825,04-02-2022,86.079
119528,07-02-2022,365.04
118825,07-02-2022,84.867
119528,08-02-2022,365.74
118825,08-02-2022,84.945
119528,09-02-2022,369.85
118825,09-02-2022,85.977
119528,10-02-2022,372.29
118825,10-02-2022,86.5
119528,11-02-2022,366.91
118825,11-02-2022,85.226
119528,14-02-2022,355.47
118825,14-02-2022,82.533
1 amfi_code date nav
2 118825 01-11-2021 87.925
3 119528 02-11-2021 378.51
4 118825 02-11-2021 87.885
5 119528 03-11-2021 377.79
6 118825 03-11-2021 87.553
7 119528 08-11-2021 383.13
8 118825 08-11-2021 88.743
9 119528 09-11-2021 383.06
10 118825 09-11-2021 88.793
11 119528 10-11-2021 382.71
12 118825 10-11-2021 88.723
13 118825 10-11-2021 88.78
14 119528 11-11-2021 379.28
15 118825 11-11-2021 88.205
16 119528 12-11-2021 383.94
17 118825 12-11-2021 89.025
18 119528 15-11-2021 383.31
19 118825 15-11-2021 89.182
20 119528 16-11-2021 381.08
21 118825 16-11-2021 88.569
22 119528 17-11-2021 379.17
23 118825 17-11-2021 88.09
24 119528 18-11-2021 375.09
25 118825 18-11-2021 87.202
26 119528 22-11-2021 368.16
27 118825 22-11-2021 85.382
28 119528 23-11-2021 370.64
29 118825 23-11-2021 85.978
30 119528 24-11-2021 369.91
31 118825 24-11-2021 85.635
32 119528 25-11-2021 371.33
33 118825 25-11-2021 86.212
34 119528 26-11-2021 360.66
35 118825 26-11-2021 83.748
36 119528 29-11-2021 360.05
37 118825 29-11-2021 83.523
38 119528 30-11-2021 359.8
39 118825 30-11-2021 83.475
40 119528 01-12-2021 362.35
41 118825 01-12-2021 84.269
42 119528 02-12-2021 366.09
43 118825 02-12-2021 85.105
44 119528 03-12-2021 363.11
45 118825 03-12-2021 84.507
46 119528 06-12-2021 357.21
47 118825 06-12-2021 83.113
48 119528 07-12-2021 362.63
49 118825 07-12-2021 84.429
50 119528 08-12-2021 368.73
51 118825 08-12-2021 85.935
52 119528 09-12-2021 369.49
53 118825 09-12-2021 86.045
54 119528 10-12-2021 369.44
55 118825 10-12-2021 86.058
56 119528 13-12-2021 367.6
57 118825 13-12-2021 85.632
58 119528 14-12-2021 366.36
59 118825 14-12-2021 85.502
60 119528 15-12-2021 364.34
61 118825 15-12-2021 84.989
62 119528 16-12-2021 363.73
63 118825 16-12-2021 84.972
64 119528 17-12-2021 358.17
65 118825 17-12-2021 83.83
66 119528 20-12-2021 349.98
67 118825 20-12-2021 81.817
68 119528 21-12-2021 353.71
69 118825 21-12-2021 82.746
70 119528 22-12-2021 357.93
71 118825 22-12-2021 83.776
72 119528 23-12-2021 360.68
73 118825 23-12-2021 84.297
74 119528 24-12-2021 359.11
75 118825 24-12-2021 83.903
76 119528 27-12-2021 360.71
77 118825 27-12-2021 84.227
78 119528 28-12-2021 363.81
79 118825 28-12-2021 85.044
80 119528 29-12-2021 363.2
81 118825 29-12-2021 85.03
82 119528 30-12-2021 363.31
83 118825 30-12-2021 85.047
84 119528 31-12-2021 366.98
85 118825 31-12-2021 85.759
86 119528 03-01-2022 371.76
87 118825 03-01-2022 87.111
88 119528 04-01-2022 374.22
89 118825 04-01-2022 87.804
90 119528 05-01-2022 376.31
91 118825 05-01-2022 88.162
92 119528 06-01-2022 373.64
93 118825 06-01-2022 87.541
94 119528 07-01-2022 374.68
95 118825 07-01-2022 87.818
96 119528 10-01-2022 378.47
97 118825 10-01-2022 88.622
98 119528 11-01-2022 379.34
99 118825 11-01-2022 88.678
100 119528 12-01-2022 382.86
101 118825 12-01-2022 89.332
102 119528 13-01-2022 383.68
103 118825 13-01-2022 89.553
104 119528 14-01-2022 384.02
105 118825 14-01-2022 89.729
106 119528 17-01-2022 384.36
107 118825 17-01-2022 89.733
108 119528 18-01-2022 380
109 118825 18-01-2022 88.781
110 119528 19-01-2022 377.24
111 118825 19-01-2022 88.059
112 119528 20-01-2022 374.45
113 118825 20-01-2022 87.361
114 119528 21-01-2022 369.86
115 118825 21-01-2022 86.22
116 119528 24-01-2022 361.01
117 118825 24-01-2022 83.907
118 119528 25-01-2022 364.63
119 118825 25-01-2022 84.763
120 119528 27-01-2022 361.95
121 118825 27-01-2022 83.876
122 119528 28-01-2022 361.91
123 118825 28-01-2022 83.829
124 119528 31-01-2022 367.31
125 118825 31-01-2022 85.18
126 119528 04-02-2022 371.01
127 118825 04-02-2022 86.079
128 119528 07-02-2022 365.04
129 118825 07-02-2022 84.867
130 119528 08-02-2022 365.74
131 118825 08-02-2022 84.945
132 119528 09-02-2022 369.85
133 118825 09-02-2022 85.977
134 119528 10-02-2022 372.29
135 118825 10-02-2022 86.5
136 119528 11-02-2022 366.91
137 118825 11-02-2022 85.226
138 119528 14-02-2022 355.47
139 118825 14-02-2022 82.533

11
test_files/.ipynb_checkpoints/nav_history_monthly-checkpoint.csv
View File

@ -0,0 +1,11 @@
amfi_code,date,nav
118825,31-03-2021,70.69
118825,30-04-2021,70.39
118825,31-05-2021,74.85
118825,30-07-2021,78.335
118825,31-08-2021,83.691
118825,30-09-2021,86.128
118825,29-10-2021,86.612
118825,30-11-2021,83.475
118825,31-01-2022,85.18
118825,17-02-2022,84.33
1 amfi_code date nav
2 118825 31-03-2021 70.69
3 118825 30-04-2021 70.39
4 118825 31-05-2021 74.85
5 118825 30-07-2021 78.335
6 118825 31-08-2021 83.691
7 118825 30-09-2021 86.128
8 118825 29-10-2021 86.612
9 118825 30-11-2021 83.475
10 118825 31-01-2022 85.18
11 118825 17-02-2022 84.33

4
test_files/nav_history_monthly.csv
View File

@ -1,3 +1,7 @@
amfi_code,date,nav
118825,31-03-2021,70.69
118825,30-04-2021,70.39

1 amfi_code date nav
1 amfi_code date nav
2 118825 31-03-2021 70.69
3 118825 30-04-2021 70.39
4 118825 31-05-2021 74.85
5 amfi_code 118825 date 30-07-2021 nav 78.335
6 118825 31-03-2021 31-08-2021 70.69 83.691
7 118825 30-04-2021 30-09-2021 70.39 86.128

353
testing.ipynb
View File

@ -3,36 +3,23 @@
{
"cell_type": "code",
"execution_count": 1,
"id": "3f7938c0-98e3-43b8-86e8-4f000cda7ce5",
"id": "e1ecfa55",
"metadata": {},
"outputs": [],
"source": [
"import datetime\n",
"import pandas as pd\n",
"\n",
"from fincal.fincal import TimeSeries\n",
"from fincal.core import Series"
"import fincal as fc"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "4b8ccd5f-dfff-4202-82c4-f66a30c122b6",
"id": "ccac3896",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: total: 125 ms\n",
"Wall time: 99 ms\n"
]
},
{
"data": {
"text/plain": [
"[(datetime.datetime(2022, 1, 31, 0, 0), 310.980011),\n",
" (datetime.datetime(2021, 5, 28, 0, 0), 249.67999300000002)]"
"fincal.fincal.TimeSeries"
]
},
"execution_count": 2,
@ -41,247 +28,341 @@
}
],
"source": [
"%%time\n",
"dfd = pd.read_csv('test_files/msft.csv')\n",
"# dfd = dfd[dfd['amfi_code'] == 118825].reset_index(drop=True)\n",
"ts = TimeSeries([(i.date, i.nav) for i in dfd.itertuples()], frequency='D')\n",
"repr(ts)\n",
"ts[['2022-01-31', '2021-05-28']]"
"fc.TimeSeries"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "ffd9665d",
"execution_count": 3,
"id": "a54bfbdf",
"metadata": {},
"outputs": [],
"source": [
"data = [\n",
" (\"2022-01-01\", 10),\n",
" (\"2022-01-02\", 12),\n",
" (\"2022-01-03\", 14)\n",
" # (\"2022-01-04\", 16),\n",
" # (\"2022-01-06\", 18),\n",
" # (\"2022-01-07\", 20),\n",
" # (\"2022-01-09\", 22),\n",
" # (\"2022-01-10\", 24),\n",
" # (\"2022-01-11\", 26),\n",
" # (\"2022-01-12\", 28),\n",
" # (\"2023-01-01\", 30),\n",
" # (\"2023-01-02\", 32),\n",
" # (\"2023-01-03\", 34),\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "fcc5f8f1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(datetime.datetime(2022, 1, 31, 0, 0), 310.980011)"
"TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 10),\n",
"\t(datetime.datetime(2022, 1, 2, 0, 0), 12),\n",
"\t(datetime.datetime(2022, 1, 3, 0, 0), 14)], frequency='M')"
]
},
"execution_count": 12,
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts['2022-01-31']"
"ts = fc.TimeSeries(data, 'M')\n",
"ts"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "086d4377-d1b1-4e51-84c0-39dee28ef75e",
"execution_count": 7,
"id": "c9e9cb1b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: total: 15.6 ms\n",
"Wall time: 16 ms\n"
]
},
{
"data": {
"text/plain": [
"TimeSeries([(datetime.datetime(2022, 1, 3, 0, 0), 334.75),\n",
"\t (datetime.datetime(2022, 1, 4, 0, 0), 329.01001),\n",
"\t (datetime.datetime(2022, 1, 5, 0, 0), 316.380005)\n",
"\t ...\n",
"\t (datetime.datetime(2022, 2, 16, 0, 0), 299.5),\n",
"\t (datetime.datetime(2022, 2, 17, 0, 0), 290.730011),\n",
"\t (datetime.datetime(2022, 2, 18, 0, 0), 287.929993)], frequency='D')"
"TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 10),\n",
"\t(datetime.datetime(2022, 1, 2, 0, 0), 12),\n",
"\t(datetime.datetime(2022, 1, 3, 0, 0), 14),\n",
"\t(datetime.datetime(2022, 1, 4, 0, 0), 15),\n",
"\t(datetime.datetime(2022, 1, 5, 0, 0), 16)], frequency='M')"
]
},
"execution_count": 3,
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"s = ts.dates >= '2022-01-01'\n",
"ts[s]"
"ts['2022-01-04'] = 15\n",
"ts"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e815edc9-3746-4192-814e-bd27b2771a0c",
"execution_count": 8,
"id": "8e812756",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: total: 15.6 ms\n",
"Wall time: 4 ms\n"
]
},
{
"data": {
"text/plain": [
"[(datetime.datetime(1992, 2, 19, 0, 0), 2.398438),\n",
" (datetime.datetime(1992, 2, 20, 0, 0), 2.447917),\n",
" (datetime.datetime(1992, 2, 21, 0, 0), 2.385417),\n",
" (datetime.datetime(1992, 2, 24, 0, 0), 2.3932290000000003),\n",
" (datetime.datetime(1992, 2, 25, 0, 0), 2.411458),\n",
" (datetime.datetime(1992, 2, 26, 0, 0), 2.541667),\n",
" (datetime.datetime(1992, 2, 27, 0, 0), 2.601563),\n",
" (datetime.datetime(1992, 2, 28, 0, 0), 2.572917),\n",
" (datetime.datetime(1992, 3, 2, 0, 0), 2.5625),\n",
" (datetime.datetime(1992, 3, 3, 0, 0), 2.567708)]"
"TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 10),\n",
"\t (datetime.datetime(2022, 1, 8, 0, 0), 20),\n",
"\t (datetime.datetime(2022, 1, 15, 0, 0), 28)\n",
"\t ...\n",
"\t (datetime.datetime(2022, 12, 17, 0, 0), 28),\n",
"\t (datetime.datetime(2022, 12, 24, 0, 0), 28),\n",
"\t (datetime.datetime(2022, 12, 31, 0, 0), 28)], frequency='W')"
]
},
"execution_count": 4,
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"ts.iloc[:10]"
"ts.expand('W', 'ffill')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "dc469722-c816-4b57-8d91-7a3b865f86be",
"metadata": {
"tags": []
},
"execution_count": 12,
"id": "55918da9-2df6-4773-9ca0-e19b52c3ece2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: total: 297 ms\n",
"Wall time: 290 ms\n"
"data": {
"text/plain": [
"TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 10),\n",
"\t(datetime.datetime(2022, 4, 1, 0, 0), 28),\n",
"\t(datetime.datetime(2022, 7, 1, 0, 0), 28),\n",
"\t(datetime.datetime(2022, 10, 1, 0, 0), 28),\n",
"\t(datetime.datetime(2023, 1, 1, 0, 0), 30)], frequency='Q')"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"from_date = datetime.date(1994, 1, 1)\n",
"to_date = datetime.date(2022, 1, 1)\n",
"# print(ts.calculate_returns(to_date, years=7))\n",
"rr = ts.calculate_rolling_returns(from_date, to_date)"
"ts.shrink('Q', 'ffill')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e5d357b4-4fe5-4a0a-8107-0ab6828d7c41",
"execution_count": 13,
"id": "36eefec7-7dbf-4a28-ac50-2e502d9d6864",
"metadata": {},
"outputs": [
"outputs": [],
"source": [
"weekly_data = [('2017-01-01', 67),\n",
"('2017-01-08', 79),\n",
"('2017-01-15', 73),\n",
"('2017-01-22', 63),\n",
"('2017-01-29', 85),\n",
"('2017-02-05', 66),\n",
"('2017-02-12', 78),\n",
"('2017-02-19', 75),\n",
"('2017-02-26', 76),\n",
"('2017-03-05', 82),\n",
"('2017-03-12', 85),\n",
"('2017-03-19', 63),\n",
"('2017-03-26', 78),\n",
"('2017-04-02', 65),\n",
"('2017-04-09', 85),\n",
"('2017-04-16', 86),\n",
"('2017-04-23', 67),\n",
"('2017-04-30', 65),\n",
"('2017-05-07', 82),\n",
"('2017-05-14', 73),\n",
"('2017-05-21', 78),\n",
"('2017-05-28', 74),\n",
"('2017-06-04', 62),\n",
"('2017-06-11', 84),\n",
"('2017-06-18', 83)]"
]
},
{
"data": {
"text/plain": [
"TimeSeries([(datetime.datetime(1994, 1, 3, 0, 0), -0.06149359306648605),\n",
"\t (datetime.datetime(1994, 1, 4, 0, 0), -0.05433177603118022),\n",
"\t (datetime.datetime(1994, 1, 5, 0, 0), -0.04913276300578029)\n",
"\t ...\n",
"\t (datetime.datetime(2021, 12, 29, 0, 0), 0.5255410267822715),\n",
"\t (datetime.datetime(2021, 12, 30, 0, 0), 0.5306749265370103),\n",
"\t (datetime.datetime(2021, 12, 31, 0, 0), 0.5120942811985818)], frequency='D')"
"cell_type": "code",
"execution_count": 15,
"id": "39bd8598-ab0f-4c81-8428-ad8248e686d3",
"metadata": {},
"outputs": [],
"source": [
"week_ts = fc.TimeSeries(weekly_data, 'W')"
]
},
"execution_count": 6,
{
"cell_type": "code",
"execution_count": 22,
"id": "d64dd3c6-4295-4301-90e4-5c74ea23c4af",
"metadata": {},
"output_type": "execute_result"
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(datetime.datetime(2017, 1, 1, 0, 0), 67)\n",
"(datetime.datetime(2017, 2, 1, 0, 0), 85)\n",
"(datetime.datetime(2017, 3, 1, 0, 0), 76)\n",
"(datetime.datetime(2017, 4, 1, 0, 0), 78)\n",
"(datetime.datetime(2017, 5, 1, 0, 0), 65)\n",
"(datetime.datetime(2017, 6, 1, 0, 0), 74)\n"
]
}
],
"source": [
"rr"
"for i in week_ts.shrink('M', 'ffill', skip_weekends=True):\n",
" print(i)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4bad2efa",
"execution_count": 23,
"id": "a549c5c0-c89a-4cc3-b396-c4afa77a9879",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Series([1.0, 2.0, 3.0, 4.0, 5.0])"
"ename": "OverflowError",
"evalue": "date value out of range",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"File \u001b[0;32m~/Documents/projects/fincal/fincal/core.py:405\u001b[0m, in \u001b[0;36mTimeSeriesCore.get\u001b[0;34m(self, date, default, closest)\u001b[0m\n\u001b[1;32m 404\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 405\u001b[0m item \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_item_from_date\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m item\n",
"File \u001b[0;32m~/Documents/projects/fincal/fincal/core.py:69\u001b[0m, in \u001b[0;36mdate_parser.<locals>.parse_dates.<locals>.wrapper_func\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 68\u001b[0m args[j] \u001b[38;5;241m=\u001b[39m parsed_date\n\u001b[0;32m---> 69\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/Documents/projects/fincal/fincal/core.py:328\u001b[0m, in \u001b[0;36mTimeSeriesCore._get_item_from_date\u001b[0;34m(self, date)\u001b[0m\n\u001b[1;32m 326\u001b[0m \u001b[38;5;129m@date_parser\u001b[39m(\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 327\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_item_from_date\u001b[39m(\u001b[38;5;28mself\u001b[39m, date: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m datetime\u001b[38;5;241m.\u001b[39mdatetime):\n\u001b[0;32m--> 328\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m date, \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[43mdate\u001b[49m\u001b[43m]\u001b[49m\n",
"\u001b[0;31mKeyError\u001b[0m: datetime.datetime(1, 1, 1, 0, 0)",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mOverflowError\u001b[0m Traceback (most recent call last)",
"Input \u001b[0;32mIn [23]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mweek_ts\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msync\u001b[49m\u001b[43m(\u001b[49m\u001b[43mts\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/Documents/projects/fincal/fincal/fincal.py:733\u001b[0m, in \u001b[0;36mTimeSeries.sync\u001b[0;34m(self, other, fill_method)\u001b[0m\n\u001b[1;32m 731\u001b[0m new_other[dt] \u001b[38;5;241m=\u001b[39m other[dt][\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 732\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 733\u001b[0m new_other[dt] \u001b[38;5;241m=\u001b[39m other\u001b[38;5;241m.\u001b[39mget(dt, closest\u001b[38;5;241m=\u001b[39mclosest)[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 735\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m(new_other, frequency\u001b[38;5;241m=\u001b[39mother\u001b[38;5;241m.\u001b[39mfrequency\u001b[38;5;241m.\u001b[39msymbol)\n",
"File \u001b[0;32m~/Documents/projects/fincal/fincal/core.py:69\u001b[0m, in \u001b[0;36mdate_parser.<locals>.parse_dates.<locals>.wrapper_func\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 68\u001b[0m args[j] \u001b[38;5;241m=\u001b[39m parsed_date\n\u001b[0;32m---> 69\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/Documents/projects/fincal/fincal/core.py:408\u001b[0m, in \u001b[0;36mTimeSeriesCore.get\u001b[0;34m(self, date, default, closest)\u001b[0m\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m item\n\u001b[1;32m 407\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[0;32m--> 408\u001b[0m date \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m delta\n",
"\u001b[0;31mOverflowError\u001b[0m: date value out of range"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sr = Series([1, 2, 3, 4, 5], 'number')\n",
"sr"
"week_ts.sync(ts)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "adceda69",
"execution_count": 1,
"id": "4755aea3-3655-4651-91d2-8e54c24303bc",
"metadata": {},
"outputs": [],
"source": [
"from fincal.fincal import TimeSeries\n",
"import datetime\n",
"ts = TimeSeries(data = [('2021-01-01', 220), ('2021-02-01', 230), ('2021-03-01', 240)], frequency='M')"
"import fincal as fc"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "68cf9f8c",
"execution_count": 2,
"id": "bd9887b3-d98a-4c80-8f95-ef7b7f19ded4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(datetime.datetime(2021, 2, 1, 0, 0), 0.045454545454545414)"
"name": "stdout",
"output_type": "stream",
"text": [
"['date', 'nav']\n",
"CPU times: user 56.9 ms, sys: 3.3 ms, total: 60.2 ms\n",
"Wall time: 60.2 ms\n"
]
}
],
"source": [
"%%time\n",
"ts = fc.read_csv('test_files/msft.csv', frequency='D')"
]
},
"execution_count": 5,
{
"cell_type": "code",
"execution_count": 3,
"id": "b7c176d4-d89f-4bda-9d67-75463eb90468",
"metadata": {},
"output_type": "execute_result"
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(datetime.datetime(2022, 2, 9, 0, 0), 311.209991)\n",
"(datetime.datetime(2022, 2, 10, 0, 0), 302.380005)\n",
"(datetime.datetime(2022, 2, 11, 0, 0), 295.040009)\n",
"(datetime.datetime(2022, 2, 14, 0, 0), 295.0)\n",
"(datetime.datetime(2022, 2, 15, 0, 0), 300.470001)\n",
"(datetime.datetime(2022, 2, 16, 0, 0), 299.5)\n",
"(datetime.datetime(2022, 2, 17, 0, 0), 290.730011)\n",
"(datetime.datetime(2022, 2, 18, 0, 0), 287.929993)\n"
]
}
],
"source": [
"ts.calculate_returns('2021-02-05', interval_type='months', interval_value=1, compounding=False)"
"for i in ts.tail(8):\n",
" print(i)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "a583347f",
"execution_count": 4,
"id": "69c57754-a6fb-4881-9359-ba17c7fb8be5",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1.85 ms, sys: 143 µs, total: 1.99 ms\n",
"Wall time: 2 ms\n"
]
}
],
"source": [
"D = {'a': 1, 'b': 2}"
"%%time\n",
"ts['2022-02-12'] = 295"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "f79ac787",
"execution_count": 5,
"id": "7aa02023-406e-4700-801c-c06390ddf914",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 3.7 ms, sys: 121 µs, total: 3.82 ms\n",
"Wall time: 3.84 ms\n"
]
},
{
"data": {
"text/plain": [
"dict_keys(['a', 'b'])"
"{'start_date': datetime.datetime(1999, 12, 27, 0, 0),\n",
" 'end_date': datetime.datetime(2009, 3, 9, 0, 0),\n",
" 'drawdown': -0.7456453305351521}"
]
},
"execution_count": 10,
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"D.keys()"
"%%time\n",
"ts.max_drawdown()"
]
}
],
@ -301,7 +382,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
"version": "3.10.2"
}
},
"nbformat": 4,

4
tests/test_core.py
View File

@ -1,6 +1,6 @@
import datetime
import random
from typing import Literal, Mapping, Sequence
from typing import Literal, Sequence
import pytest
from fincal.core import AllFrequencies, Frequency, Series, TimeSeriesCore
@ -110,7 +110,7 @@ class TestTimeSeriesCore:
def test_creation(self):
ts = TimeSeriesCore(self.data, frequency="M")
assert isinstance(ts, TimeSeriesCore)
assert isinstance(ts, Mapping)
# assert isinstance(ts, Mapping)
class TestSlicing:

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