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233 lines
9.9 KiB
233 lines
9.9 KiB
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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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": 2,
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"id": "4b8ccd5f-dfff-4202-82c4-f66a30c122b6",
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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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"CPU times: user 152 ms, sys: 284 ms, total: 436 ms\n",
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"Wall time: 61.3 ms\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"TimeSeries([(datetime.datetime(2021, 5, 28, 0, 0), 249.679993),\n",
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"\t(datetime.datetime(2022, 1, 31, 0, 0), 310.980011)], frequency='D')"
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]
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},
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"execution_count": 2,
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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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"%%time\n",
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"dfd = pd.read_csv('test_files/msft.csv')\n",
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"# dfd = dfd[dfd['amfi_code'] == 118825].reset_index(drop=True)\n",
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"ts = TimeSeries([(i.date, i.nav) for i in dfd.itertuples()], frequency='D')\n",
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"repr(ts)\n",
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"ts[['2022-01-31', '2021-05-28']]"
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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": 3,
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"id": "a0232e05-27c7-4d2d-a4bc-5dcf42666983",
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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": "Type List cannot be instantiated; use list() instead",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
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"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",
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"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",
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"\u001b[0;31mTypeError\u001b[0m: Type List cannot be instantiated; use list() instead"
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]
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}
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],
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"source": [
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"from fincal.fincal import create_date_series\n",
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"from fincal.core import Frequency\n",
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"from typing import List, Tuple\n",
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"\n",
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"def create_test_data(\n",
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" frequency: Frequency,\n",
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" num: int = 1000,\n",
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" skip_weekends: bool = False,\n",
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" mu: float = 0.1,\n",
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" sigma: float = 0.05,\n",
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" eomonth: bool = False,\n",
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") -> List[Tuple]:\n",
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" \"\"\"Creates TimeSeries data\n",
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"\n",
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" Parameters:\n",
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" -----------\n",
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" frequency: Frequency\n",
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" The frequency of the time series data to be generated.\n",
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"\n",
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" num: int\n",
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" Number of date: value pairs to be generated.\n",
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"\n",
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" skip_weekends: bool\n",
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" Whether weekends (saturday, sunday) should be skipped.\n",
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" Gets used only if the frequency is daily.\n",
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"\n",
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" mu: float\n",
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" Mean return for the values.\n",
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"\n",
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" sigma: float\n",
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" standard deviation of the values.\n",
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"\n",
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" Returns:\n",
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" --------\n",
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" Returns a TimeSeries object\n",
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" \"\"\"\n",
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"\n",
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" start_date = datetime.datetime(2017, 1, 1)\n",
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" timedelta_dict = {\n",
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" frequency.freq_type: int(\n",
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" frequency.value * num * (7 / 5 if frequency == AllFrequencies.D and skip_weekends else 1)\n",
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" )\n",
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" }\n",
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" end_date = start_date + relativedelta(**timedelta_dict)\n",
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" dates = create_date_series(start_date, end_date, frequency.symbol, skip_weekends=skip_weekends, eomonth=eomonth)\n",
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" values = create_prices(1000, mu, sigma, num)\n",
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" ts = list(zip(dates, values))\n",
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" return 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": null,
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"id": "53dbc8a6-d7b1-4d82-ac3d-ee3908ff086d",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "aa1584d5-1df0-4661-aeeb-5e8c424de06d",
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"metadata": {},
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"outputs": [],
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"source": [
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"from fincal import fincal\n",
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"from fincal.core import FincalOptions\n",
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"import 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": 8,
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"id": "7d51fca1-f731-47c8-99c9-6e199cfeca92",
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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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"['date', 'nav']\n",
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"CPU times: user 47.7 ms, sys: 3.16 ms, total: 50.9 ms\n",
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"Wall time: 50.3 ms\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"TimeSeries([(datetime.datetime(1992, 2, 19, 0, 0), '2.398438'),\n",
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"\t (datetime.datetime(1992, 2, 20, 0, 0), '2.447917'),\n",
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"\t (datetime.datetime(1992, 2, 21, 0, 0), '2.385417')\n",
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"\t ...\n",
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"\t (datetime.datetime(2022, 2, 16, 0, 0), '299.5'),\n",
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"\t (datetime.datetime(2022, 2, 17, 0, 0), '290.730011'),\n",
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"\t (datetime.datetime(2022, 2, 18, 0, 0), '287.929993')], frequency='M')"
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]
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},
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"execution_count": 8,
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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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"%%time\n",
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"FincalOptions.date_format = '%Y-%m-%d'\n",
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"fincal.read_csv('test_files/msft.csv', frequency='M')"
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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": 9,
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"id": "b689f64c-6764-45b5-bccf-f23b351f6419",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd"
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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": 11,
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"id": "6c9b2dd7-9983-40cd-8ac4-3530a3892f17",
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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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"CPU times: user 61.4 ms, sys: 2.35 ms, total: 63.7 ms\n",
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"Wall time: 62.6 ms\n"
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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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"dfd = pd.read_csv(\"test_files/msft.csv\")\n",
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"ts = fincal.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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"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": "3.10.2"
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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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