PyFacts/.ipynb_checkpoints/testing-checkpoint.ipynb

{
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   "execution_count": 1,
   "id": "3f7938c0-98e3-43b8-86e8-4f000cda7ce5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import pandas as pd\n",
    "\n",
    "from fincal.fincal import TimeSeries\n",
    "from fincal.core import Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4b8ccd5f-dfff-4202-82c4-f66a30c122b6",
   "metadata": {},
   "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"
     ]
    },
    {
     "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": {},
     "output_type": "execute_result"
    }
   ],
   "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": 3,
   "id": "a0232e05-27c7-4d2d-a4bc-5dcf42666983",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "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",
    "dfd = pd.read_csv(\"test_files/msft.csv\")\n",
    "ts = fincal.TimeSeries([(i.date, i.nav) for i in dfd.itertuples()], frequency=\"D\")"
   ]
  }
 ],
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