From 7bcd310652244400f9fa69bf4ba7f6a3dcf0f449 Mon Sep 17 00:00:00 2001 From: Gourav Kumar Date: Sun, 10 Apr 2022 14:10:18 +0530 Subject: [PATCH] Remove test check if instance is of type Mapping --- .DS_Store | Bin 0 -> 6148 bytes .ipynb_checkpoints/testing-checkpoint.ipynb | 216 +- test.py | 54 +- test2.py | 92 +- .../.ipynb_checkpoints/msft-checkpoint.csv | 7560 +++++++++++++++++ .../nav_history_daily-checkpoint.csv | 139 + .../nav_history_monthly-checkpoint.csv | 11 + test_files/nav_history_monthly.csv | 4 + testing.ipynb | 515 +- tests/test_core.py | 4 +- 10 files changed, 8241 insertions(+), 354 deletions(-) create mode 100644 .DS_Store create mode 100644 test_files/.ipynb_checkpoints/msft-checkpoint.csv create mode 100644 test_files/.ipynb_checkpoints/nav_history_daily-checkpoint.csv create mode 100644 test_files/.ipynb_checkpoints/nav_history_monthly-checkpoint.csv diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..44cf26401fbace68f69dfdebd31fd0369231c5f2 GIT binary patch literal 6148 zcmeHKJ5EC}5S)b+5i}_&eFbh{Md1V-07Mcbq$j0F=wFp{aWrN>1pcS z=p~5F1H@i9CNe^^q!N>A)nZuE8E=)>3&+Hy!>akPy4k8jvACV*w@8QeM2%8F3Y;o% zpUZ{U{}=i{{r@RRD=8oaZb|{0tzK74KB;Q!7M16^{S_ literal 0 HcmV?d00001 diff --git a/.ipynb_checkpoints/testing-checkpoint.ipynb b/.ipynb_checkpoints/testing-checkpoint.ipynb index 05fb9fa..7aa5d25 100644 --- a/.ipynb_checkpoints/testing-checkpoint.ipynb +++ b/.ipynb_checkpoints/testing-checkpoint.ipynb @@ -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", - "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", + "execution_count": 2, + "id": "4b8ccd5f-dfff-4202-82c4-f66a30c122b6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Warning: The input data contains duplicate dates which have been ignored.\n" + "CPU times: user 152 ms, sys: 284 ms, total: 436 ms\n", + "Wall time: 61.3 ms\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')" + "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": 20, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ts" + "%%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": 22, - "id": "dc469722-c816-4b57-8d91-7a3b865f86be", + "execution_count": 3, + "id": "a0232e05-27c7-4d2d-a4bc-5dcf42666983", "metadata": {}, "outputs": [ { "ename": "TypeError", - "evalue": "getattr(): attribute name must be string", + "evalue": "Type List cannot be instantiated; use list() instead", "output_type": "error", "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "File \u001b[1;32m:1\u001b[0m, in \u001b[0;36m\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" + "\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\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": [ + "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": 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(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, 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": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "FincalOptions.date_format = '%Y-%m-%d'\n", + "fincal.read_csv('test_files/msft.csv', frequency='M')" + ] + }, + { + "cell_type": "code", + "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": [ + { + "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, diff --git a/test.py b/test.py index 7634a5f..674c577 100644 --- a/test.py +++ b/test.py @@ -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)) diff --git a/test2.py b/test2.py index 2a5ce97..0d07c7c 100644 --- a/test2.py +++ b/test2.py @@ -1,58 +1,52 @@ -import pandas as pd +import time -from fincal.fincal import TimeSeries, create_date_series +from fincal.fincal import TimeSeries -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']]) +# 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)) -# 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) +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']]) - -# 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") # 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: + +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) -# 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", -# ) +print(ts.get("2022-01-01")) -# print(returns) - -volatility = ts.volatility(start_date="2018-01-01", end_date="2021-01-01") -print(volatility) +print(ts.ffill()) diff --git a/test_files/.ipynb_checkpoints/msft-checkpoint.csv b/test_files/.ipynb_checkpoints/msft-checkpoint.csv new file mode 100644 index 0000000..d312c42 --- /dev/null +++ b/test_files/.ipynb_checkpoints/msft-checkpoint.csv @@ -0,0 +1,7560 @@ +date,nav +1992-02-19,2.398438 +1992-02-20,2.447917 +1992-02-21,2.385417 +1992-02-24,2.393229 +1992-02-25,2.411458 +1992-02-26,2.541667 +1992-02-27,2.601563 +1992-02-28,2.572917 +1992-03-02,2.5625 +1992-03-03,2.567708 +1992-03-04,2.552083 +1992-03-05,2.526042 +1992-03-06,2.463542 +1992-03-09,2.479167 +1992-03-10,2.536458 +1992-03-11,2.505208 +1992-03-12,2.523438 +1992-03-13,2.520833 +1992-03-16,2.588542 +1992-03-17,2.666667 +1992-03-18,2.632813 +1992-03-19,2.65625 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a/test_files/.ipynb_checkpoints/nav_history_daily-checkpoint.csv b/test_files/.ipynb_checkpoints/nav_history_daily-checkpoint.csv new file mode 100644 index 0000000..73ecdf9 --- /dev/null +++ b/test_files/.ipynb_checkpoints/nav_history_daily-checkpoint.csv @@ -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 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b/test_files/.ipynb_checkpoints/nav_history_monthly-checkpoint.csv new file mode 100644 index 0000000..d0037ea --- /dev/null +++ b/test_files/.ipynb_checkpoints/nav_history_monthly-checkpoint.csv @@ -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 diff --git a/test_files/nav_history_monthly.csv b/test_files/nav_history_monthly.csv index d0037ea..aa5749b 100644 --- a/test_files/nav_history_monthly.csv +++ b/test_files/nav_history_monthly.csv @@ -1,3 +1,7 @@ + + + + amfi_code,date,nav 118825,31-03-2021,70.69 118825,30-04-2021,70.39 diff --git a/testing.ipynb b/testing.ipynb index c036e9b..5cc2b73 100644 --- a/testing.ipynb +++ b/testing.ipynb @@ -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, @@ -41,207 +28,45 @@ } ], "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']]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "ffd9665d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(datetime.datetime(2022, 1, 31, 0, 0), 310.980011)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ts['2022-01-31']" + "fc.TimeSeries" ] }, { "cell_type": "code", "execution_count": 3, - "id": "086d4377-d1b1-4e51-84c0-39dee28ef75e", - "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')" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%time\n", - "s = ts.dates >= '2022-01-01'\n", - "ts[s]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "e815edc9-3746-4192-814e-bd27b2771a0c", - "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)]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%time\n", - "ts.iloc[:10]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "dc469722-c816-4b57-8d91-7a3b865f86be", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: total: 297 ms\n", - "Wall time: 290 ms\n" - ] - } - ], - "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)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e5d357b4-4fe5-4a0a-8107-0ab6828d7c41", - "metadata": {}, - "outputs": [ - { - "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')" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rr" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "4bad2efa", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Series([1.0, 2.0, 3.0, 4.0, 5.0])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sr = Series([1, 2, 3, 4, 5], 'number')\n", - "sr" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "adceda69", + "id": "a54bfbdf", "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')" + "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": "68cf9f8c", + "id": "fcc5f8f1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(datetime.datetime(2021, 2, 1, 0, 0), 0.045454545454545414)" + "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": 5, @@ -250,38 +75,294 @@ } ], "source": [ - "ts.calculate_returns('2021-02-05', interval_type='months', interval_value=1, compounding=False)" + "ts = fc.TimeSeries(data, 'M')\n", + "ts" ] }, { "cell_type": "code", - "execution_count": 6, - "id": "a583347f", - "metadata": {}, - "outputs": [], - "source": [ - "D = {'a': 1, 'b': 2}" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "f79ac787", + "execution_count": 7, + "id": "c9e9cb1b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "dict_keys(['a', 'b'])" + "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": 10, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "D.keys()" + "ts['2022-01-04'] = 15\n", + "ts" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8e812756", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "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": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ts.expand('W', 'ffill')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "55918da9-2df6-4773-9ca0-e19b52c3ece2", + "metadata": {}, + "outputs": [ + { + "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": [ + "ts.shrink('Q', 'ffill')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "36eefec7-7dbf-4a28-ac50-2e502d9d6864", + "metadata": {}, + "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)]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "39bd8598-ab0f-4c81-8428-ad8248e686d3", + "metadata": {}, + "outputs": [], + "source": [ + "week_ts = fc.TimeSeries(weekly_data, 'W')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "d64dd3c6-4295-4301-90e4-5c74ea23c4af", + "metadata": {}, + "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": [ + "for i in week_ts.shrink('M', 'ffill', skip_weekends=True):\n", + " print(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "a549c5c0-c89a-4cc3-b396-c4afa77a9879", + "metadata": {}, + "outputs": [ + { + "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..parse_dates..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\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..parse_dates..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" + ] + } + ], + "source": [ + "week_ts.sync(ts)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "4755aea3-3655-4651-91d2-8e54c24303bc", + "metadata": {}, + "outputs": [], + "source": [ + "import fincal as fc" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "bd9887b3-d98a-4c80-8f95-ef7b7f19ded4", + "metadata": {}, + "outputs": [ + { + "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')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b7c176d4-d89f-4bda-9d67-75463eb90468", + "metadata": {}, + "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": [ + "for i in ts.tail(8):\n", + " print(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "69c57754-a6fb-4881-9359-ba17c7fb8be5", + "metadata": {}, + "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": [ + "%%time\n", + "ts['2022-02-12'] = 295" + ] + }, + { + "cell_type": "code", + "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": [ + "{'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": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%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, diff --git a/tests/test_core.py b/tests/test_core.py index 1e72f08..72f2ee3 100644 --- a/tests/test_core.py +++ b/tests/test_core.py @@ -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: