PyFacts/testing.ipynb

{
 "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": 2,
   "id": "aead3e77-2670-4541-846a-5537b01f3d2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import math\n",
    "import fincal as fc\n",
    "from typing import List\n",
    "import datetime\n",
    "from dateutil.relativedelta import relativedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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: fc.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 == fc.AllFrequencies.D and skip_weekends else 1)\n",
    "        )\n",
    "    }\n",
    "    end_date = start_date + relativedelta(**timedelta_dict)\n",
    "    dates = fc.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": 4,
   "id": "c85b5dd9-9a88-4608-ac58-1a141295f63f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = sample_data_generator(num=261, frequency=fc.AllFrequencies.W, mu=0.6, sigma=0.7)\n",
    "ts = fc.TimeSeries(data, \"W\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0488a4d0-bca1-4341-9fae-1fd254adc0dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TimeSeries([(datetime.datetime(2017, 1, 1, 0, 0), 1040.39),\n",
       "\t    (datetime.datetime(2017, 1, 8, 0, 0), 1032.83),\n",
       "\t    (datetime.datetime(2017, 1, 15, 0, 0), 1120.5)\n",
       "\t    ...\n",
       "\t    (datetime.datetime(2021, 12, 12, 0, 0), 2007.18),\n",
       "\t    (datetime.datetime(2021, 12, 19, 0, 0), 1987.49),\n",
       "\t    (datetime.datetime(2021, 12, 26, 0, 0), 1924.2)], frequency='W')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cd0eb38c",
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = fc.create_date_series(ts.start_date, ts.end_date, 'M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "69c48512",
   "metadata": {},
   "outputs": [],
   "source": [
    "prev_date = dates[0]\n",
    "for i in dates[1:]:\n",
    "    cur_date = i\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "43fa2254",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TimeSeries([(datetime.datetime(2017, 1, 8, 0, 0), 1032.83),\n",
       "\t(datetime.datetime(2017, 1, 15, 0, 0), 1120.5),\n",
       "\t(datetime.datetime(2017, 1, 22, 0, 0), 1125.86),\n",
       "\t(datetime.datetime(2017, 1, 29, 0, 0), 1178.74)], frequency='W')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts[(ts.dates < '2017-01-31') & (ts.dates > '2017-01-01')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "32a82399-e056-45d6-86a3-b9f0855aed27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([datetime.datetime(2017, 1, 1, 0, 0), datetime.datetime(2017, 2, 1, 0, 0), datetime.datetime(2017, 3, 1, 0, 0), datetime.datetime(2017, 4, 1, 0, 0), datetime.datetime(2017, 5, 1, 0, 0), datetime.datetime(2017, 6, 1, 0, 0), datetime.datetime(2017, 7, 1, 0, 0), datetime.datetime(2017, 8, 1, 0, 0), datetime.datetime(2017, 9, 1, 0, 0), datetime.datetime(2017, 10, 1, 0, 0), datetime.datetime(2017, 11, 1, 0, 0), datetime.datetime(2017, 12, 1, 0, 0), datetime.datetime(2018, 1, 1, 0, 0), datetime.datetime(2018, 2, 1, 0, 0), datetime.datetime(2018, 3, 1, 0, 0), datetime.datetime(2018, 4, 1, 0, 0), datetime.datetime(2018, 5, 1, 0, 0), datetime.datetime(2018, 6, 1, 0, 0), datetime.datetime(2018, 7, 1, 0, 0), datetime.datetime(2018, 8, 1, 0, 0), datetime.datetime(2018, 9, 1, 0, 0), datetime.datetime(2018, 10, 1, 0, 0), datetime.datetime(2018, 11, 1, 0, 0), datetime.datetime(2018, 12, 1, 0, 0), datetime.datetime(2019, 1, 1, 0, 0), datetime.datetime(2019, 2, 1, 0, 0), datetime.datetime(2019, 3, 1, 0, 0), datetime.datetime(2019, 4, 1, 0, 0), datetime.datetime(2019, 5, 1, 0, 0), datetime.datetime(2019, 6, 1, 0, 0), datetime.datetime(2019, 7, 1, 0, 0), datetime.datetime(2019, 8, 1, 0, 0), datetime.datetime(2019, 9, 1, 0, 0), datetime.datetime(2019, 10, 1, 0, 0), datetime.datetime(2019, 11, 1, 0, 0), datetime.datetime(2019, 12, 1, 0, 0), datetime.datetime(2020, 1, 1, 0, 0), datetime.datetime(2020, 2, 1, 0, 0), datetime.datetime(2020, 3, 1, 0, 0), datetime.datetime(2020, 4, 1, 0, 0), datetime.datetime(2020, 5, 1, 0, 0), datetime.datetime(2020, 6, 1, 0, 0), datetime.datetime(2020, 7, 1, 0, 0), datetime.datetime(2020, 8, 1, 0, 0), datetime.datetime(2020, 9, 1, 0, 0), datetime.datetime(2020, 10, 1, 0, 0), datetime.datetime(2020, 11, 1, 0, 0), datetime.datetime(2020, 12, 1, 0, 0), datetime.datetime(2021, 1, 1, 0, 0), datetime.datetime(2021, 2, 1, 0, 0), datetime.datetime(2021, 3, 1, 0, 0), datetime.datetime(2021, 4, 1, 0, 0), datetime.datetime(2021, 5, 1, 0, 0), datetime.datetime(2021, 6, 1, 0, 0), datetime.datetime(2021, 7, 1, 0, 0), datetime.datetime(2021, 8, 1, 0, 0), datetime.datetime(2021, 9, 1, 0, 0), datetime.datetime(2021, 10, 1, 0, 0), datetime.datetime(2021, 11, 1, 0, 0), datetime.datetime(2021, 12, 1, 0, 0)], data_type='datetime')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "352a71a3-5469-4464-8a93-17f4660822fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-01-01 00:00:00 1040.39\n",
      "2017-01-08 00:00:00 1032.83\n",
      "2017-01-15 00:00:00 1120.5\n",
      "2017-01-22 00:00:00 1125.86\n",
      "2017-01-29 00:00:00 1178.74\n",
      "2017-02-05 00:00:00 1158.8\n",
      "2017-02-12 00:00:00 1151.54\n",
      "2017-02-19 00:00:00 1137.19\n",
      "2017-02-26 00:00:00 1141.47\n",
      "2017-03-05 00:00:00 1112.37\n",
      "2017-03-12 00:00:00 1186.34\n",
      "2017-03-19 00:00:00 1202.35\n",
      "2017-03-26 00:00:00 1169.04\n",
      "2017-04-02 00:00:00 1220.55\n",
      "2017-04-09 00:00:00 1202.93\n",
      "2017-04-16 00:00:00 1187.57\n",
      "2017-04-23 00:00:00 1197.52\n",
      "2017-04-30 00:00:00 1218.61\n",
      "2017-05-07 00:00:00 1272.3\n",
      "2017-05-14 00:00:00 1278.49\n",
      "2017-05-21 00:00:00 1296.56\n",
      "2017-05-28 00:00:00 1294.03\n",
      "2017-06-04 00:00:00 1244.9\n",
      "2017-06-11 00:00:00 1233.13\n",
      "2017-06-18 00:00:00 1218.74\n",
      "2017-06-25 00:00:00 1208.5\n",
      "2017-07-02 00:00:00 1179.17\n",
      "2017-07-09 00:00:00 1164.11\n",
      "2017-07-16 00:00:00 1109.05\n",
      "2017-07-23 00:00:00 1090.0\n",
      "2017-07-30 00:00:00 1054.5\n",
      "2017-08-06 00:00:00 1059.84\n",
      "2017-08-13 00:00:00 1061.35\n",
      "2017-08-20 00:00:00 1157.0\n",
      "2017-08-27 00:00:00 1109.25\n",
      "2017-09-03 00:00:00 1124.86\n",
      "2017-09-10 00:00:00 1175.81\n",
      "2017-09-17 00:00:00 1183.18\n",
      "2017-09-24 00:00:00 1208.74\n",
      "2017-10-01 00:00:00 1210.82\n",
      "2017-10-08 00:00:00 1170.14\n",
      "2017-10-15 00:00:00 1178.4\n",
      "2017-10-22 00:00:00 1235.02\n",
      "2017-10-29 00:00:00 1256.52\n",
      "2017-11-05 00:00:00 1288.37\n",
      "2017-11-12 00:00:00 1342.41\n",
      "2017-11-19 00:00:00 1417.1\n",
      "2017-11-26 00:00:00 1518.11\n",
      "2017-12-03 00:00:00 1538.06\n",
      "2017-12-10 00:00:00 1405.51\n",
      "2017-12-17 00:00:00 1434.94\n",
      "2017-12-24 00:00:00 1471.6\n",
      "2017-12-31 00:00:00 1515.75\n",
      "2018-01-07 00:00:00 1528.28\n",
      "2018-01-14 00:00:00 1541.99\n",
      "2018-01-21 00:00:00 1510.71\n",
      "2018-01-28 00:00:00 1592.01\n",
      "2018-02-04 00:00:00 1718.11\n",
      "2018-02-11 00:00:00 1788.51\n",
      "2018-02-18 00:00:00 1895.84\n",
      "2018-02-25 00:00:00 1965.28\n",
      "2018-03-04 00:00:00 1985.45\n",
      "2018-03-11 00:00:00 1948.76\n",
      "2018-03-18 00:00:00 2004.36\n",
      "2018-03-25 00:00:00 2040.49\n",
      "2018-04-01 00:00:00 1966.17\n",
      "2018-04-08 00:00:00 1984.85\n",
      "2018-04-15 00:00:00 1908.0\n",
      "2018-04-22 00:00:00 1970.14\n",
      "2018-04-29 00:00:00 1840.56\n",
      "2018-05-06 00:00:00 1736.25\n",
      "2018-05-13 00:00:00 1779.54\n",
      "2018-05-20 00:00:00 1803.03\n",
      "2018-05-27 00:00:00 1754.64\n",
      "2018-06-03 00:00:00 1785.96\n",
      "2018-06-10 00:00:00 1817.42\n",
      "2018-06-17 00:00:00 1788.5\n",
      "2018-06-24 00:00:00 1803.23\n",
      "2018-07-01 00:00:00 1686.82\n",
      "2018-07-08 00:00:00 1666.94\n",
      "2018-07-15 00:00:00 1559.94\n",
      "2018-07-22 00:00:00 1571.29\n",
      "2018-07-29 00:00:00 1527.71\n",
      "2018-08-05 00:00:00 1463.91\n",
      "2018-08-12 00:00:00 1418.93\n",
      "2018-08-19 00:00:00 1488.85\n",
      "2018-08-26 00:00:00 1502.09\n",
      "2018-09-02 00:00:00 1473.89\n",
      "2018-09-09 00:00:00 1511.63\n",
      "2018-09-16 00:00:00 1489.29\n",
      "2018-09-23 00:00:00 1550.82\n",
      "2018-09-30 00:00:00 1645.07\n",
      "2018-10-07 00:00:00 1626.79\n",
      "2018-10-14 00:00:00 1527.51\n",
      "2018-10-21 00:00:00 1508.86\n",
      "2018-10-28 00:00:00 1517.32\n",
      "2018-11-04 00:00:00 1505.09\n",
      "2018-11-11 00:00:00 1517.15\n",
      "2018-11-18 00:00:00 1515.15\n",
      "2018-11-25 00:00:00 1531.76\n",
      "2018-12-02 00:00:00 1509.4\n",
      "2018-12-09 00:00:00 1509.87\n",
      "2018-12-16 00:00:00 1591.72\n",
      "2018-12-23 00:00:00 1556.62\n",
      "2018-12-30 00:00:00 1502.95\n",
      "2019-01-06 00:00:00 1630.62\n",
      "2019-01-13 00:00:00 1674.96\n",
      "2019-01-20 00:00:00 1695.86\n",
      "2019-01-27 00:00:00 1757.33\n",
      "2019-02-03 00:00:00 1789.38\n",
      "2019-02-10 00:00:00 1810.87\n",
      "2019-02-17 00:00:00 1877.5\n",
      "2019-02-24 00:00:00 1839.49\n",
      "2019-03-03 00:00:00 1710.78\n",
      "2019-03-10 00:00:00 1600.36\n",
      "2019-03-17 00:00:00 1601.57\n",
      "2019-03-24 00:00:00 1530.07\n",
      "2019-03-31 00:00:00 1618.94\n",
      "2019-04-07 00:00:00 1531.05\n",
      "2019-04-14 00:00:00 1524.31\n",
      "2019-04-21 00:00:00 1559.94\n",
      "2019-04-28 00:00:00 1630.43\n",
      "2019-05-05 00:00:00 1597.4\n",
      "2019-05-12 00:00:00 1703.88\n",
      "2019-05-19 00:00:00 1635.2\n",
      "2019-05-26 00:00:00 1621.76\n",
      "2019-06-02 00:00:00 1634.33\n",
      "2019-06-09 00:00:00 1562.77\n",
      "2019-06-16 00:00:00 1549.12\n",
      "2019-06-23 00:00:00 1576.87\n",
      "2019-06-30 00:00:00 1487.67\n",
      "2019-07-07 00:00:00 1404.03\n",
      "2019-07-14 00:00:00 1416.17\n",
      "2019-07-21 00:00:00 1401.12\n",
      "2019-07-28 00:00:00 1401.12\n",
      "2019-08-04 00:00:00 1405.16\n",
      "2019-08-11 00:00:00 1373.43\n",
      "2019-08-18 00:00:00 1312.29\n",
      "2019-08-25 00:00:00 1314.92\n",
      "2019-09-01 00:00:00 1289.19\n",
      "2019-09-08 00:00:00 1300.85\n",
      "2019-09-15 00:00:00 1352.79\n",
      "2019-09-22 00:00:00 1351.29\n",
      "2019-09-29 00:00:00 1319.38\n",
      "2019-10-06 00:00:00 1341.78\n",
      "2019-10-13 00:00:00 1300.52\n",
      "2019-10-20 00:00:00 1389.83\n",
      "2019-10-27 00:00:00 1302.28\n",
      "2019-11-03 00:00:00 1202.98\n",
      "2019-11-10 00:00:00 1181.17\n",
      "2019-11-17 00:00:00 1125.6\n",
      "2019-11-24 00:00:00 1156.62\n",
      "2019-12-01 00:00:00 1205.04\n",
      "2019-12-08 00:00:00 1138.9\n",
      "2019-12-15 00:00:00 1167.41\n",
      "2019-12-22 00:00:00 1187.0\n",
      "2019-12-29 00:00:00 1117.66\n",
      "2020-01-05 00:00:00 1162.87\n",
      "2020-01-12 00:00:00 1160.5\n",
      "2020-01-19 00:00:00 1182.43\n",
      "2020-01-26 00:00:00 1162.46\n",
      "2020-02-02 00:00:00 1184.64\n",
      "2020-02-09 00:00:00 1173.68\n",
      "2020-02-16 00:00:00 1120.14\n",
      "2020-02-23 00:00:00 1203.76\n",
      "2020-03-01 00:00:00 1234.59\n",
      "2020-03-08 00:00:00 1164.89\n",
      "2020-03-15 00:00:00 1193.34\n",
      "2020-03-22 00:00:00 1213.7\n",
      "2020-03-29 00:00:00 1248.24\n",
      "2020-04-05 00:00:00 1291.25\n",
      "2020-04-12 00:00:00 1313.79\n",
      "2020-04-19 00:00:00 1250.83\n",
      "2020-04-26 00:00:00 1282.44\n",
      "2020-05-03 00:00:00 1237.36\n",
      "2020-05-10 00:00:00 1244.45\n",
      "2020-05-17 00:00:00 1209.46\n",
      "2020-05-24 00:00:00 1215.62\n",
      "2020-05-31 00:00:00 1176.28\n",
      "2020-06-07 00:00:00 1220.93\n",
      "2020-06-14 00:00:00 1236.97\n",
      "2020-06-21 00:00:00 1292.12\n",
      "2020-06-28 00:00:00 1341.62\n",
      "2020-07-05 00:00:00 1319.86\n",
      "2020-07-12 00:00:00 1286.69\n",
      "2020-07-19 00:00:00 1228.97\n",
      "2020-07-26 00:00:00 1253.36\n",
      "2020-08-02 00:00:00 1186.4\n",
      "2020-08-09 00:00:00 1176.77\n",
      "2020-08-16 00:00:00 1160.93\n",
      "2020-08-23 00:00:00 1112.51\n",
      "2020-08-30 00:00:00 1168.84\n",
      "2020-09-06 00:00:00 1191.77\n",
      "2020-09-13 00:00:00 1202.53\n",
      "2020-09-20 00:00:00 1253.35\n",
      "2020-09-27 00:00:00 1220.74\n",
      "2020-10-04 00:00:00 1298.59\n",
      "2020-10-11 00:00:00 1289.99\n",
      "2020-10-18 00:00:00 1331.7\n",
      "2020-10-25 00:00:00 1331.88\n",
      "2020-11-01 00:00:00 1316.17\n",
      "2020-11-08 00:00:00 1278.24\n",
      "2020-11-15 00:00:00 1316.89\n",
      "2020-11-22 00:00:00 1304.82\n",
      "2020-11-29 00:00:00 1336.41\n",
      "2020-12-06 00:00:00 1424.98\n",
      "2020-12-13 00:00:00 1414.69\n",
      "2020-12-20 00:00:00 1480.55\n",
      "2020-12-27 00:00:00 1442.9\n",
      "2021-01-03 00:00:00 1415.72\n",
      "2021-01-10 00:00:00 1394.22\n",
      "2021-01-17 00:00:00 1434.96\n",
      "2021-01-24 00:00:00 1426.63\n",
      "2021-01-31 00:00:00 1518.6\n",
      "2021-02-07 00:00:00 1461.76\n",
      "2021-02-14 00:00:00 1427.69\n",
      "2021-02-21 00:00:00 1447.32\n",
      "2021-02-28 00:00:00 1412.62\n",
      "2021-03-07 00:00:00 1422.12\n",
      "2021-03-14 00:00:00 1433.03\n",
      "2021-03-21 00:00:00 1599.56\n",
      "2021-03-28 00:00:00 1643.38\n",
      "2021-04-04 00:00:00 1735.51\n",
      "2021-04-11 00:00:00 1721.82\n",
      "2021-04-18 00:00:00 1801.1\n",
      "2021-04-25 00:00:00 1798.19\n",
      "2021-05-02 00:00:00 1728.64\n",
      "2021-05-09 00:00:00 1761.09\n",
      "2021-05-16 00:00:00 1864.6\n",
      "2021-05-23 00:00:00 1915.55\n",
      "2021-05-30 00:00:00 1880.83\n",
      "2021-06-06 00:00:00 1985.8\n",
      "2021-06-13 00:00:00 2031.42\n",
      "2021-06-20 00:00:00 2005.34\n",
      "2021-06-27 00:00:00 1903.11\n",
      "2021-07-04 00:00:00 1902.17\n",
      "2021-07-11 00:00:00 1943.94\n",
      "2021-07-18 00:00:00 1848.16\n",
      "2021-07-25 00:00:00 1807.84\n",
      "2021-08-01 00:00:00 1885.75\n",
      "2021-08-08 00:00:00 1930.57\n",
      "2021-08-15 00:00:00 2004.8\n",
      "2021-08-22 00:00:00 2181.39\n",
      "2021-08-29 00:00:00 2112.61\n",
      "2021-09-05 00:00:00 2176.47\n",
      "2021-09-12 00:00:00 2255.73\n",
      "2021-09-19 00:00:00 2224.32\n",
      "2021-09-26 00:00:00 2195.41\n",
      "2021-10-03 00:00:00 2255.36\n",
      "2021-10-10 00:00:00 2238.18\n",
      "2021-10-17 00:00:00 2290.97\n",
      "2021-10-24 00:00:00 2380.52\n",
      "2021-10-31 00:00:00 2237.11\n",
      "2021-11-07 00:00:00 2234.85\n",
      "2021-11-14 00:00:00 2281.46\n",
      "2021-11-21 00:00:00 2225.14\n",
      "2021-11-28 00:00:00 2200.28\n",
      "2021-12-05 00:00:00 2104.56\n",
      "2021-12-12 00:00:00 2007.18\n",
      "2021-12-19 00:00:00 1987.49\n",
      "2021-12-26 00:00:00 1924.2\n"
     ]
    }
   ],
   "source": [
    "for i, j in ts:\n",
    "    print(i, j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "14c72558-0500-44e4-a893-628a4102ff53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-01-01 00:00:00 1099.664\n",
      "2017-02-01 00:00:00 1147.25\n",
      "2017-03-01 00:00:00 1167.5249999999999\n",
      "2017-04-01 00:00:00 1205.436\n",
      "2017-05-01 00:00:00 1285.345\n",
      "2017-06-01 00:00:00 1226.3175\n",
      "2017-07-01 00:00:00 1119.366\n",
      "2017-08-01 00:00:00 1096.86\n",
      "2017-09-01 00:00:00 1173.1475\n",
      "2017-10-01 00:00:00 1210.18\n",
      "2017-11-01 00:00:00 1391.4975\n",
      "2017-12-01 00:00:00 1473.172\n",
      "2018-01-01 00:00:00 1543.2475\n",
      "2018-02-01 00:00:00 1841.935\n",
      "2018-03-01 00:00:00 1994.7649999999999\n",
      "2018-04-01 00:00:00 1933.944\n",
      "2018-05-01 00:00:00 1768.365\n",
      "2018-06-01 00:00:00 1798.7775000000001\n",
      "2018-07-01 00:00:00 1602.54\n",
      "2018-08-01 00:00:00 1468.445\n",
      "2018-09-01 00:00:00 1534.14\n",
      "2018-10-01 00:00:00 1545.12\n",
      "2018-11-01 00:00:00 1517.2875\n",
      "2018-12-01 00:00:00 1534.112\n",
      "2019-01-01 00:00:00 1689.6924999999999\n",
      "2019-02-01 00:00:00 1829.31\n",
      "2019-03-01 00:00:00 1612.344\n",
      "2019-04-01 00:00:00 1561.4325\n",
      "2019-05-01 00:00:00 1639.56\n",
      "2019-06-01 00:00:00 1562.152\n",
      "2019-07-01 00:00:00 1405.61\n",
      "2019-08-01 00:00:00 1351.45\n",
      "2019-09-01 00:00:00 1322.7\n",
      "2019-10-01 00:00:00 1333.6025\n",
      "2019-11-01 00:00:00 1166.5925\n",
      "2019-12-01 00:00:00 1163.202\n",
      "2020-01-01 00:00:00 1167.065\n",
      "2020-02-01 00:00:00 1170.555\n",
      "2020-03-01 00:00:00 1210.952\n",
      "2020-04-01 00:00:00 1284.5774999999999\n",
      "2020-05-01 00:00:00 1216.634\n",
      "2020-06-01 00:00:00 1272.9099999999999\n",
      "2020-07-01 00:00:00 1272.22\n",
      "2020-08-01 00:00:00 1161.09\n",
      "2020-09-01 00:00:00 1217.0974999999999\n",
      "2020-10-01 00:00:00 1313.04\n",
      "2020-11-01 00:00:00 1310.506\n",
      "2020-12-01 00:00:00 1440.78\n",
      "2021-01-01 00:00:00 1438.026\n",
      "2021-02-01 00:00:00 1437.3474999999999\n",
      "2021-03-01 00:00:00 1524.5225\n",
      "2021-04-01 00:00:00 1764.155\n",
      "2021-05-01 00:00:00 1830.142\n",
      "2021-06-01 00:00:00 1981.4175\n",
      "2021-07-01 00:00:00 1875.5275000000001\n",
      "2021-08-01 00:00:00 2023.024\n",
      "2021-09-01 00:00:00 2212.9825\n",
      "2021-10-01 00:00:00 2280.428\n",
      "2021-11-01 00:00:00 2235.4325\n"
     ]
    }
   ],
   "source": [
    "prev_date = dates[0]\n",
    "for date in dates[1:]:\n",
    "    cur_ts = ts[(ts.dates < date) & (ts.dates >= prev_date)]\n",
    "    print(prev_date, cur_ts.mean())\n",
    "    prev_date = date"
   ]
  }
 ],
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