{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e40a5526-458a-4d11-8eaa-3b584f723738",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import fincal as fc\n",
    "import datetime\n",
    "from dateutil.relativedelta import relativedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "id": "fcc5f8f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 10.0),\n",
       "\t(datetime.datetime(2022, 1, 2, 0, 0), 12.0),\n",
       "\t(datetime.datetime(2022, 1, 3, 0, 0), 14.0),\n",
       "\t(datetime.datetime(2022, 1, 4, 0, 0), 16.0)], frequency='D')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = fc.TimeSeries(data, 'D')\n",
    "ts2 = fc.TimeSeries(data, 'D')\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c091da16-d3a2-4d5b-93da-099d67373932",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([datetime.datetime(2021, 1, 1, 0, 0), datetime.datetime(2021, 1, 2, 0, 0)], data_type='datetime')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fc.Series(['2021-01-01', '2021-01-02'], data_type='date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "77fc30d8-2843-40c4-9842-d943e6ef9813",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([11.0, 14.0, 17.0, 20.0], data_type='float')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.values + fc.Series([1, 2, 3, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8e812756",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "TimeSeries can be only expanded to a higher frequency",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[0;32mIn [8]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mts\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexpand\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mW\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mffill\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Documents/projects/fincal/fincal/fincal.py:624\u001b[0m, in \u001b[0;36mTimeSeries.expand\u001b[0;34m(self, to_frequency, method, skip_weekends, eomonth)\u001b[0m\n\u001b[1;32m    621\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 to_frequency \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mto_frequency\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    623\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m to_frequency\u001b[38;5;241m.\u001b[39mdays \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfrequency\u001b[38;5;241m.\u001b[39mdays:\n\u001b[0;32m--> 624\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTimeSeries can be only expanded to a higher frequency\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    626\u001b[0m new_dates \u001b[38;5;241m=\u001b[39m create_date_series(\n\u001b[1;32m    627\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstart_date,\n\u001b[1;32m    628\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mend_date,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    632\u001b[0m     ensure_coverage\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m    633\u001b[0m )\n\u001b[1;32m    635\u001b[0m closest: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprevious\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mffill\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnext\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
      "\u001b[0;31mValueError\u001b[0m: TimeSeries can be only expanded to a higher frequency"
     ]
    }
   ],
   "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": 2,
   "id": "9431eb8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from fincal.utils import _is_eomonth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "36eefec7-7dbf-4a28-ac50-2e502d9d6864",
   "metadata": {},
   "outputs": [],
   "source": [
    "weekly_data = [\n",
    "    ('2018-01-31', 26),\n",
    "    ('2018-02-28', 44),\n",
    "    ('2018-03-30', 40),\n",
    "    ('2018-04-30', 36),\n",
    "    ('2018-05-31', 31),\n",
    "    ('2018-06-30', 45),\n",
    "    ('2018-07-30', 31),\n",
    "    ('2018-08-31', 42),\n",
    "    ('2018-09-30', 40),\n",
    "    ('2018-10-30', 30),\n",
    "    ('2018-11-30', 35),\n",
    "    ('2018-12-31', 37),\n",
    "    ('2019-01-31', 31),\n",
    "    ('2019-02-28', 44),\n",
    "    ('2019-03-31', 31),\n",
    "    ('2019-04-29', 32),\n",
    "    ('2019-05-30', 39),\n",
    "    ('2019-06-30', 27),\n",
    "    ('2019-07-31', 35),\n",
    "    ('2019-08-31', 33),\n",
    "    ('2019-09-30', 29),\n",
    "    ('2019-10-30', 26),\n",
    "    ('2019-11-30', 39),\n",
    "    ('2019-12-30', 30),\n",
    "    ('2020-01-30', 29)\n",
    "]\n",
    "week_ts = fc.TimeSeries(weekly_data, 'W')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e1071f90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_is_eomonth(week_ts.dates)"
   ]
  },
  {
   "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": null,
   "id": "a549c5c0-c89a-4cc3-b396-c4afa77a9879",
   "metadata": {},
   "outputs": [],
   "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 57.5 ms, sys: 3.38 ms, total: 60.8 ms\n",
      "Wall time: 60.5 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "ts = fc.read_csv('test_files/msft.csv', frequency='D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b7c176d4-d89f-4bda-9d67-75463eb90468",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(datetime.datetime(2022, 2, 11, 0, 0), 295.040009)\n",
      "(datetime.datetime(2022, 2, 12, 0, 0), 296.0)\n",
      "(datetime.datetime(2022, 2, 13, 0, 0), 296.0)\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": 6,
   "id": "69c57754-a6fb-4881-9359-ba17c7fb8be5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.76 ms, sys: 123 µs, total: 1.88 ms\n",
      "Wall time: 1.88 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "ts['2022-02-12'] = 296"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7aa02023-406e-4700-801c-c06390ddf914",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.61 ms, sys: 68 µs, total: 3.68 ms\n",
      "Wall time: 3.7 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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "ts.max_drawdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "72cb4da4-1318-4b9b-b563-adac46accfb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from typing import Mapping\n",
    "isinstance(ts, Mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "96bbecbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import fincal as fc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "19199c92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['amfi_code', 'date', 'nav']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gourav/Documents/projects/fincal/fincal/core.py:308: UserWarning: The input data contains duplicate dates which have been ignored.\n",
      "  warnings.warn(\"The input data contains duplicate dates which have been ignored.\")\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",
       "\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), 82.533)], frequency='D')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = fc.read_csv('test_files/nav_history_daily - copy.csv', col_index=(1, 2), frequency='D', date_format='%d-%m-%y')\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "51c9ae9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12031455056454916"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fc.sharpe_ratio(\n",
    "    ts,\n",
    "    risk_free_rate=0.06,\n",
    "    from_date='2013-02-04',\n",
    "    to_date='2022-02-14',\n",
    "    return_period_unit='months',\n",
    "    return_period_value=1\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3fb7b59-eaa3-41a5-b1ab-89d63b69edb0",
   "metadata": {},
   "source": [
    "# Data generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aead3e77-2670-4541-846a-5537b01f3d2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import math\n",
    "import pyfacts as pft\n",
    "from typing import List\n",
    "import datetime\n",
    "from dateutil.relativedelta import relativedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f287e05f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_prices(s0: float, mu: float, sigma: float, num_prices: int) -> list:\n",
    "    \"\"\"Generates a price following a geometric brownian motion process based on the input of the arguments.\n",
    "\n",
    "        Since this function is used only to generate data for tests, the seed is fixed as 1234.\n",
    "        Many of the tests rely on exact values generated using this seed.\n",
    "        If the seed is changed, those tests will fail.\n",
    "\n",
    "    Parameters:\n",
    "    ------------\n",
    "    s0: float\n",
    "        Asset inital price.\n",
    "\n",
    "    mu: float\n",
    "        Interest rate expressed annual terms.\n",
    "\n",
    "    sigma: float\n",
    "        Volatility expressed annual terms.\n",
    "\n",
    "    num_prices: int\n",
    "        number of prices to generate\n",
    "\n",
    "    Returns:\n",
    "    --------\n",
    "        Returns a list of values generated using GBM algorithm\n",
    "    \"\"\"\n",
    "\n",
    "    random.seed(1234)  # WARNING! Changing the seed will cause most tests to fail\n",
    "    all_values = []\n",
    "    for _ in range(num_prices):\n",
    "        s0 *= math.exp(\n",
    "            (mu - 0.5 * sigma**2) * (1.0 / 365.0) + sigma * math.sqrt(1.0 / 365.0) * random.gauss(mu=0, sigma=1)\n",
    "        )\n",
    "        all_values.append(round(s0, 2))\n",
    "\n",
    "    return all_values\n",
    "\n",
    "\n",
    "def sample_data_generator(\n",
    "    frequency: pft.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 == pft.AllFrequencies.D and skip_weekends else 1)\n",
    "        )\n",
    "    }\n",
    "    end_date = start_date + relativedelta(**timedelta_dict)\n",
    "    dates = pft.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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c85b5dd9-9a88-4608-ac58-1a141295f63f",
   "metadata": {},
   "outputs": [],
   "source": [
    "market_data = sample_data_generator(num=3600, frequency=pft.AllFrequencies.D)\n",
    "mts = pft.TimeSeries(market_data, \"D\")\n",
    "stock_data = sample_data_generator(num=3600, frequency=pft.AllFrequencies.D, mu=0.12, sigma=0.05)\n",
    "sts = pft.TimeSeries(stock_data, 'D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0488a4d0-bca1-4341-9fae-1fd254adc0dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.020217253491451"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pft.beta(sts, mts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "04624145-4fce-484c-aa69-0d17d159b598",
   "metadata": {},
   "outputs": [],
   "source": [
    "tst = ts.transform('Q', 'mean', False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "75ed1666-5fc8-4707-bf42-62d44adcae18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bccd7d1c-2d57-444c-af68-290f476f2b05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(datetime.datetime(2017, 1, 1, 0, 0), 1010.4553846153846)\n",
      "(datetime.datetime(2017, 4, 1, 0, 0), 1019.34)\n",
      "(datetime.datetime(2017, 7, 1, 0, 0), 1015.3515384615384)\n",
      "(datetime.datetime(2017, 10, 1, 0, 0), 1031.2892857142858)\n",
      "(datetime.datetime(2018, 1, 1, 0, 0), 1054.7216666666666)\n",
      "(datetime.datetime(2018, 4, 1, 0, 0), 1059.736153846154)\n",
      "(datetime.datetime(2018, 7, 1, 0, 0), 1049.1100000000001)\n",
      "(datetime.datetime(2018, 10, 1, 0, 0), 1051.663076923077)\n",
      "(datetime.datetime(2019, 1, 1, 0, 0), 1062.2869230769231)\n",
      "(datetime.datetime(2019, 4, 1, 0, 0), 1059.7423076923076)\n",
      "(datetime.datetime(2019, 7, 1, 0, 0), 1050.7661538461539)\n",
      "(datetime.datetime(2019, 10, 1, 0, 0), 1045.2061538461537)\n",
      "(datetime.datetime(2020, 1, 1, 0, 0), 1046.11)\n",
      "(datetime.datetime(2020, 4, 1, 0, 0), 1053.126923076923)\n",
      "(datetime.datetime(2020, 7, 1, 0, 0), 1053.273846153846)\n",
      "(datetime.datetime(2020, 10, 1, 0, 0), 1064.2384615384615)\n",
      "(datetime.datetime(2021, 1, 1, 0, 0), 1073.1538461538462)\n",
      "(datetime.datetime(2021, 4, 1, 0, 0), 1094.3215384615385)\n",
      "(datetime.datetime(2021, 7, 1, 0, 0), 1104.3584615384616)\n",
      "(datetime.datetime(2021, 10, 1, 0, 0), 1112.806923076923)\n"
     ]
    }
   ],
   "source": [
    "for i in tst:\n",
    "    print(i)"
   ]
  }
 ],
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  "interpreter": {
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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