PyFacts/testing.ipynb

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   "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": [],
   "source": [
    "dfd = pd.read_csv('test_files/nav_history_daily - copy.csv')\n",
    "\n",
    "dfd = dfd[dfd['amfi_code'] == 118825].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c52b0c2c-dd01-48dd-9ffa-3147ec9571ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: 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), 82.53299999999999)], frequency='D')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = TimeSeries([(i.date, i.nav) for i in dfd.itertuples()], frequency='D')\n",
    "\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9e8ff6c6-3a36-435a-ba87-5b9844c18779",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(datetime.datetime(2022, 1, 31, 0, 0), 85.18),\n",
       " (datetime.datetime(2021, 5, 31, 0, 0), 74.85)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts[['2022-01-31', '2021-05-31']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4d927a61-0f90-4b47-89b7-0e0d3ab1b442",
   "metadata": {},
   "outputs": [],
   "source": [
    "s = ts.dates > '2020-01-01'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f90074f8-5173-49a9-a7d6-ceac01e92431",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TimeSeries([(datetime.datetime(2020, 1, 2, 0, 0), 58.285),\n",
       "\t    (datetime.datetime(2020, 1, 3, 0, 0), 58.056999999999995),\n",
       "\t    (datetime.datetime(2020, 1, 6, 0, 0), 56.938)\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')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts[s]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dc469722-c816-4b57-8d91-7a3b865f86be",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 15.6 ms\n",
      "Wall time: 13 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from_date = datetime.date(2020, 1, 1)\n",
    "to_date = datetime.date(2021, 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": 15,
   "id": "086d4377-d1b1-4e51-84c0-39dee28ef75e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(rr)"
   ]
  }
 ],
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