diff --git a/.DS_Store b/.DS_Store
new file mode 100644
index 0000000..44cf264
Binary files /dev/null and b/.DS_Store differ
diff --git a/.ipynb_checkpoints/testing-checkpoint.ipynb b/.ipynb_checkpoints/testing-checkpoint.ipynb
index 05fb9fa..7aa5d25 100644
--- a/.ipynb_checkpoints/testing-checkpoint.ipynb
+++ b/.ipynb_checkpoints/testing-checkpoint.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 1,
    "id": "3f7938c0-98e3-43b8-86e8-4f000cda7ce5",
    "metadata": {},
    "outputs": [],
@@ -16,92 +16,196 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "757eafc2-f804-4e7e-a3b8-2d09cd62e646",
+   "execution_count": 2,
+   "id": "4b8ccd5f-dfff-4202-82c4-f66a30c122b6",
    "metadata": {},
-   "outputs": [],
+   "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": [
-    "dfd = pd.read_csv('test_files/nav_history_daily - copy.csv')"
+    "%%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": 17,
-   "id": "59b3d4a9-8ef4-4652-9e20-1bac69ab4ff9",
+   "execution_count": 3,
+   "id": "a0232e05-27c7-4d2d-a4bc-5dcf42666983",
    "metadata": {},
-   "outputs": [],
+   "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": [
-    "dfd = dfd[dfd['amfi_code'] == 118825].reset_index(drop=True)"
+    "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": 19,
-   "id": "4bc95ae0-8c33-4eab-acf9-e765d22979b8",
+   "execution_count": null,
+   "id": "53dbc8a6-d7b1-4d82-ac3d-ee3908ff086d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Warning: The input data contains duplicate dates which have been ignored.\n"
-     ]
-    }
-   ],
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "aa1584d5-1df0-4661-aeeb-5e8c424de06d",
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "ts = TimeSeries([(i.date, i.nav) for i in dfd.itertuples()], frequency='D')"
+    "from fincal import fincal\n",
+    "from fincal.core import FincalOptions\n",
+    "import csv"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "f2c3218c-3984-43d6-8638-41a74a9d0b58",
+   "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(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",
+       "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, 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')"
+       "\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": 20,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "ts"
+    "%%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": 22,
-   "id": "dc469722-c816-4b57-8d91-7a3b865f86be",
+   "execution_count": 11,
+   "id": "6c9b2dd7-9983-40cd-8ac4-3530a3892f17",
    "metadata": {},
    "outputs": [
     {
-     "ename": "TypeError",
-     "evalue": "getattr(): attribute name must be string",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "File \u001b[1;32m<timed eval>:1\u001b[0m, in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n",
-      "File \u001b[1;32mD:\\Documents\\Projects\\fincal\\fincal\\fincal.py:203\u001b[0m, in \u001b[0;36mTimeSeries.calculate_rolling_returns\u001b[1;34m(self, from_date, to_date, frequency, as_on_match, prior_match, closest, compounding, years)\u001b[0m\n\u001b[0;32m    200\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n\u001b[0;32m    201\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 frequency \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfrequency\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 203\u001b[0m dates \u001b[38;5;241m=\u001b[39m \u001b[43mcreate_date_series\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfrom_date\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mto_date\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrequency\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    204\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m frequency \u001b[38;5;241m==\u001b[39m AllFrequencies\u001b[38;5;241m.\u001b[39mD:\n\u001b[0;32m    205\u001b[0m     dates \u001b[38;5;241m=\u001b[39m [i \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m dates \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtime_series]\n",
-      "File \u001b[1;32mD:\\Documents\\Projects\\fincal\\fincal\\fincal.py:16\u001b[0m, in \u001b[0;36mcreate_date_series\u001b[1;34m(start_date, end_date, frequency, eomonth)\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcreate_date_series\u001b[39m(\n\u001b[0;32m     12\u001b[0m     start_date: datetime\u001b[38;5;241m.\u001b[39mdatetime, end_date: datetime\u001b[38;5;241m.\u001b[39mdatetime, frequency: \u001b[38;5;28mstr\u001b[39m, 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     13\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m List[datetime\u001b[38;5;241m.\u001b[39mdatetime]:\n\u001b[0;32m     14\u001b[0m     \u001b[38;5;124;03m\"\"\"Creates a date series using a frequency\"\"\"\u001b[39;00m\n\u001b[1;32m---> 16\u001b[0m     frequency \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mAllFrequencies\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrequency\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     17\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m eomonth \u001b[38;5;129;01mand\u001b[39;00m frequency\u001b[38;5;241m.\u001b[39mdays \u001b[38;5;241m<\u001b[39m AllFrequencies\u001b[38;5;241m.\u001b[39mM\u001b[38;5;241m.\u001b[39mdays:\n\u001b[0;32m     18\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;124meomonth cannot be set to True if frequency is higher than \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mAllFrequencies\u001b[38;5;241m.\u001b[39mM\u001b[38;5;241m.\u001b[39mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
-      "\u001b[1;31mTypeError\u001b[0m: getattr(): attribute name must be string"
+     "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",
-    "ts.calculate_rolling_returns(from_date='2020-01-01', to_date='2021-01-01')"
+    "dfd = pd.read_csv(\"test_files/msft.csv\")\n",
+    "ts = fincal.TimeSeries([(i.date, i.nav) for i in dfd.itertuples()], frequency=\"D\")"
    ]
   }
  ],
@@ -121,7 +225,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.10.2"
   }
  },
  "nbformat": 4,
diff --git a/test.py b/test.py
index 7634a5f..674c577 100644
--- a/test.py
+++ b/test.py
@@ -1,35 +1,29 @@
-# type: ignore
-
-import datetime
-import time
-
-import pandas as pd
-
+# from fincal.core import FincalOptions
 from fincal.fincal import TimeSeries
 
-df = pd.read_csv('test_files/nav_history_daily.csv')
-df = df.sort_values(by=['amfi_code', 'date'])  # type: ignore
-data_list = [(i.date, i.nav) for i in df[df.amfi_code == 118825].itertuples()]
+data = [
+    ("2022-01-01", 10),
+    ("2022-01-02", 12),
+    ("2022-01-03", 14),
+    ("2022-01-04", 16),
+    ("2022-01-06", 18),
+    ("2022-01-07", 20),
+    ("2022-01-09", 22),
+    ("2022-01-10", 24),
+    ("2022-01-11", 26),
+    ("2022-01-13", 28),
+    ("2022-01-14", 30),
+    ("2022-01-15", 32),
+    ("2022-01-16", 34),
+]
+ts = TimeSeries(data, frequency="D")
+print(ts)
 
-start = time.time()
-ts_data = TimeSeries(data_list, frequency='M')
-print(f"Instantiation took {round((time.time() - start)*1000, 2)} ms")
-# ts_data.fill_missing_days()
-start = time.time()
-# ts_data.calculate_returns(as_on=datetime.datetime(2022, 1, 4), closest='next', years=1)
-rr = ts_data.calculate_rolling_returns(datetime.datetime(2015, 1, 1),
-                                       datetime.datetime(2022, 1, 21),
-                                       frequency='M',
-                                       as_on_match='next',
-                                       prior_match='previous',
-                                       closest='previous',
-                                       years=1)
+data = [("2022-01-01", 220), ("2022-01-08", 230), ("2022-01-15", 240)]
+ts2 = TimeSeries(data, frequency="W")
+print(ts2)
 
-# ffill_data = ts_data.bfill()
-print(f"Calculation took {round((time.time() - start)*1000, 2)} ms")
-rr.sort()
-for i in rr[:10]:
+synced_ts = ts.sync(ts2)
+print("---------\n")
+for i in synced_ts:
     print(i)
-# print(ffill_data)
-# print(ts_data)
-# print(repr(ts_data))
diff --git a/test2.py b/test2.py
index 2a5ce97..0d07c7c 100644
--- a/test2.py
+++ b/test2.py
@@ -1,58 +1,52 @@
-import pandas as pd
-
-from fincal.fincal import TimeSeries, create_date_series
-
-dfd = pd.read_csv("test_files/nav_history_daily - Copy.csv")
-dfd = dfd[dfd["amfi_code"] == 118825].reset_index(drop=True)
-ts = TimeSeries([(i.date, i.nav) for i in dfd.itertuples()], frequency="D")
-repr(ts)
-# print(ts[['2022-01-31', '2021-05-28']])
-
-# rr = ts.calculate_rolling_returns(from_date='2021-01-01', to_date='2022-01-01', frequency='D', interval_type='days', interval_value=30, compounding=False)
-
-
-# data = [
-#     ("2020-01-01", 10),
-#     ("2020-02-01", 12),
-#     ("2020-03-01", 14),
-#     ("2020-04-01", 16),
-#     ("2020-05-01", 18),
-#     ("2020-06-01", 20),
-#     ("2020-07-01", 22),
-#     ("2020-08-01", 24),
-#     ("2020-09-01", 26),
-#     ("2020-10-01", 28),
-#     ("2020-11-01", 30),
-#     ("2020-12-01", 32),
-#     ("2021-01-01", 34),
-# ]
-
-# ts = TimeSeries(data, frequency="M")
+import time
+
+from fincal.fincal import TimeSeries
+
+# start = time.time()
+# dfd = pd.read_csv("test_files/msft.csv")  # , dtype=dict(nav=str))
+# # dfd = dfd[dfd["amfi_code"] == 118825].reset_index(drop=True)
+# print("instantiation took", round((time.time() - start) * 1000, 2), "ms")
+# ts = TimeSeries([(i.date, i.nav) for i in dfd.itertuples()], frequency="D")
+# print(repr(ts))
+
+start = time.time()
+# mdd = ts.max_drawdown()
+# print(mdd)
+# print("max drawdown calc took", round((time.time() - start) * 1000, 2), "ms")
+# # print(ts[['2022-01-31', '2021-05-28']])
+
 # rr = ts.calculate_rolling_returns(
-#     "2020-02-01",
-#     "2021-01-01",
-#     if_not_found="nan",
-#     compounding=False,
-#     interval_type="months",
-#     interval_value=1,
-#     as_on_match="exact",
-# )
-
-# for i in rr:
+#           from_date='2021-01-01',
+#           to_date='2022-01-01',
+#           frequency='D',
+#           interval_type='days',
+#           interval_value=30,
+#           compounding=False
+#       )
+
+
+data = [
+    ("2022-01-01", 10),
+    # ("2022-01-08", 12),
+    ("2022-01-15", 14),
+    ("2022-01-22", 16)
+    # ("2020-02-07", 18),
+    # ("2020-02-14", 20),
+    # ("2020-02-21", 22),
+    # ("2020-02-28", 24),
+    # ("2020-03-01", 26),
+    # ("2020-03-01", 28),
+    # ("2020-03-01", 30),
+    # ("2020-03-01", 32),
+    # ("2021-03-01", 34),
+]
+
+ts = TimeSeries(data, "W")
+# ts_expanded = ts.expand("D", "ffill", skip_weekends=True)
+
+# for i in ts_expanded:
 #     print(i)
 
-# returns = ts.calculate_returns(
-#     "2020-04-25",
-#     return_actual_date=True,
-#     closest_max_days=15,
-#     compounding=True,
-#     interval_type="days",
-#     interval_value=90,
-#     closest="previous",
-#     if_not_found="fail",
-# )
-
-# print(returns)
-
-volatility = ts.volatility(start_date="2018-01-01", end_date="2021-01-01")
-print(volatility)
+print(ts.get("2022-01-01"))
+
+print(ts.ffill())
diff --git a/test_files/.ipynb_checkpoints/msft-checkpoint.csv b/test_files/.ipynb_checkpoints/msft-checkpoint.csv
new file mode 100644
index 0000000..d312c42
--- /dev/null
+++ b/test_files/.ipynb_checkpoints/msft-checkpoint.csv
@@ -0,0 +1,7560 @@
+date,nav
+1992-02-19,2.398438
+1992-02-20,2.447917
+1992-02-21,2.385417
+1992-02-24,2.393229
+1992-02-25,2.411458
+1992-02-26,2.541667
+1992-02-27,2.601563
+1992-02-28,2.572917
+1992-03-02,2.5625
+1992-03-03,2.567708
+1992-03-04,2.552083
+1992-03-05,2.526042
+1992-03-06,2.463542
+1992-03-09,2.479167
+1992-03-10,2.536458
+1992-03-11,2.505208
+1992-03-12,2.523438
+1992-03-13,2.520833
+1992-03-16,2.588542
+1992-03-17,2.666667
+1992-03-18,2.632813
+1992-03-19,2.65625
+1992-03-20,2.677083
+1992-03-23,2.677083
+1992-03-24,2.65625
+1992-03-25,2.677083
+1992-03-26,2.604167
+1992-03-27,2.526042
+1992-03-30,2.5
+1992-03-31,2.46875
+1992-04-01,2.510417
+1992-04-02,2.4375
+1992-04-03,2.447917
+1992-04-06,2.526042
+1992-04-07,2.416667
+1992-04-08,2.442708
+1992-04-09,2.5
+1992-04-10,2.416667
+1992-04-13,2.447917
+1992-04-14,2.4375
+1992-04-15,2.684896
+1992-04-16,2.658854
+1992-04-20,2.460938
+1992-04-21,2.419271
+1992-04-22,2.450521
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+1992-04-28,2.270833
+1992-04-29,2.320313
+1992-04-30,2.296875
+1992-05-01,2.369792
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+1992-06-25,2.1875
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diff --git a/test_files/.ipynb_checkpoints/nav_history_daily-checkpoint.csv b/test_files/.ipynb_checkpoints/nav_history_daily-checkpoint.csv
new file mode 100644
index 0000000..73ecdf9
--- /dev/null
+++ b/test_files/.ipynb_checkpoints/nav_history_daily-checkpoint.csv
@@ -0,0 +1,139 @@
+amfi_code,date,nav
+118825,01-11-2021,87.925
+119528,02-11-2021,378.51
+118825,02-11-2021,87.885
+119528,03-11-2021,377.79
+118825,03-11-2021,87.553
+119528,08-11-2021,383.13
+118825,08-11-2021,88.743
+119528,09-11-2021,383.06
+118825,09-11-2021,88.793
+119528,10-11-2021,382.71
+118825,10-11-2021,88.723
+118825,10-11-2021,88.78
+119528,11-11-2021,379.28
+118825,11-11-2021,88.205
+119528,12-11-2021,383.94
+118825,12-11-2021,89.025
+119528,15-11-2021,383.31
+118825,15-11-2021,89.182
+119528,16-11-2021,381.08
+118825,16-11-2021,88.569
+119528,17-11-2021,379.17
+118825,17-11-2021,88.09
+119528,18-11-2021,375.09
+118825,18-11-2021,87.202
+119528,22-11-2021,368.16
+118825,22-11-2021,85.382
+119528,23-11-2021,370.64
+118825,23-11-2021,85.978
+119528,24-11-2021,369.91
+118825,24-11-2021,85.635
+119528,25-11-2021,371.33
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+119528,26-11-2021,360.66
+118825,26-11-2021,83.748
+119528,29-11-2021,360.05
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+119528,30-11-2021,359.8
+118825,30-11-2021,83.475
+119528,01-12-2021,362.35
+118825,01-12-2021,84.269
+119528,02-12-2021,366.09
+118825,02-12-2021,85.105
+119528,03-12-2021,363.11
+118825,03-12-2021,84.507
+119528,06-12-2021,357.21
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+119528,07-12-2021,362.63
+118825,07-12-2021,84.429
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+118825,10-12-2021,86.058
+119528,13-12-2021,367.6
+118825,13-12-2021,85.632
+119528,14-12-2021,366.36
+118825,14-12-2021,85.502
+119528,15-12-2021,364.34
+118825,15-12-2021,84.989
+119528,16-12-2021,363.73
+118825,16-12-2021,84.972
+119528,17-12-2021,358.17
+118825,17-12-2021,83.83
+119528,20-12-2021,349.98
+118825,20-12-2021,81.817
+119528,21-12-2021,353.71
+118825,21-12-2021,82.746
+119528,22-12-2021,357.93
+118825,22-12-2021,83.776
+119528,23-12-2021,360.68
+118825,23-12-2021,84.297
+119528,24-12-2021,359.11
+118825,24-12-2021,83.903
+119528,27-12-2021,360.71
+118825,27-12-2021,84.227
+119528,28-12-2021,363.81
+118825,28-12-2021,85.044
+119528,29-12-2021,363.2
+118825,29-12-2021,85.03
+119528,30-12-2021,363.31
+118825,30-12-2021,85.047
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+118825,31-12-2021,85.759
+119528,03-01-2022,371.76
+118825,03-01-2022,87.111
+119528,04-01-2022,374.22
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+119528,24-01-2022,361.01
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+118825,25-01-2022,84.763
+119528,27-01-2022,361.95
+118825,27-01-2022,83.876
+119528,28-01-2022,361.91
+118825,28-01-2022,83.829
+119528,31-01-2022,367.31
+118825,31-01-2022,85.18
+119528,04-02-2022,371.01
+118825,04-02-2022,86.079
+119528,07-02-2022,365.04
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+119528,09-02-2022,369.85
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+119528,10-02-2022,372.29
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+118825,11-02-2022,85.226
+119528,14-02-2022,355.47
+118825,14-02-2022,82.533
diff --git a/test_files/.ipynb_checkpoints/nav_history_monthly-checkpoint.csv b/test_files/.ipynb_checkpoints/nav_history_monthly-checkpoint.csv
new file mode 100644
index 0000000..d0037ea
--- /dev/null
+++ b/test_files/.ipynb_checkpoints/nav_history_monthly-checkpoint.csv
@@ -0,0 +1,11 @@
+amfi_code,date,nav
+118825,31-03-2021,70.69
+118825,30-04-2021,70.39
+118825,31-05-2021,74.85
+118825,30-07-2021,78.335
+118825,31-08-2021,83.691
+118825,30-09-2021,86.128
+118825,29-10-2021,86.612
+118825,30-11-2021,83.475
+118825,31-01-2022,85.18
+118825,17-02-2022,84.33
diff --git a/test_files/nav_history_monthly.csv b/test_files/nav_history_monthly.csv
index d0037ea..aa5749b 100644
--- a/test_files/nav_history_monthly.csv
+++ b/test_files/nav_history_monthly.csv
@@ -1,3 +1,7 @@
+
+
+
+
 amfi_code,date,nav
 118825,31-03-2021,70.69
 118825,30-04-2021,70.39
diff --git a/testing.ipynb b/testing.ipynb
index c036e9b..5cc2b73 100644
--- a/testing.ipynb
+++ b/testing.ipynb
@@ -3,36 +3,23 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "3f7938c0-98e3-43b8-86e8-4f000cda7ce5",
+   "id": "e1ecfa55",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import datetime\n",
-    "import pandas as pd\n",
-    "\n",
-    "from fincal.fincal import TimeSeries\n",
-    "from fincal.core import Series"
+    "import fincal as fc"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "4b8ccd5f-dfff-4202-82c4-f66a30c122b6",
+   "id": "ccac3896",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: total: 125 ms\n",
-      "Wall time: 99 ms\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "[(datetime.datetime(2022, 1, 31, 0, 0), 310.980011),\n",
-       " (datetime.datetime(2021, 5, 28, 0, 0), 249.67999300000002)]"
+       "fincal.fincal.TimeSeries"
       ]
      },
      "execution_count": 2,
@@ -41,247 +28,341 @@
     }
    ],
    "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']]"
+    "fc.TimeSeries"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "ffd9665d",
+   "execution_count": 3,
+   "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": 5,
+   "id": "fcc5f8f1",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(datetime.datetime(2022, 1, 31, 0, 0), 310.980011)"
+       "TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 10),\n",
+       "\t(datetime.datetime(2022, 1, 2, 0, 0), 12),\n",
+       "\t(datetime.datetime(2022, 1, 3, 0, 0), 14)], frequency='M')"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "ts['2022-01-31']"
+    "ts = fc.TimeSeries(data, 'M')\n",
+    "ts"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "086d4377-d1b1-4e51-84c0-39dee28ef75e",
+   "execution_count": 7,
+   "id": "c9e9cb1b",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: total: 15.6 ms\n",
-      "Wall time: 16 ms\n"
-     ]
-    },
+     "data": {
+      "text/plain": [
+       "TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 10),\n",
+       "\t(datetime.datetime(2022, 1, 2, 0, 0), 12),\n",
+       "\t(datetime.datetime(2022, 1, 3, 0, 0), 14),\n",
+       "\t(datetime.datetime(2022, 1, 4, 0, 0), 15),\n",
+       "\t(datetime.datetime(2022, 1, 5, 0, 0), 16)], frequency='M')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ts['2022-01-04'] = 15\n",
+    "ts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8e812756",
+   "metadata": {},
+   "outputs": [
     {
      "data": {
       "text/plain": [
-       "TimeSeries([(datetime.datetime(2022, 1, 3, 0, 0), 334.75),\n",
-       "\t    (datetime.datetime(2022, 1, 4, 0, 0), 329.01001),\n",
-       "\t    (datetime.datetime(2022, 1, 5, 0, 0), 316.380005)\n",
+       "TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 10),\n",
+       "\t    (datetime.datetime(2022, 1, 8, 0, 0), 20),\n",
+       "\t    (datetime.datetime(2022, 1, 15, 0, 0), 28)\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='D')"
+       "\t    (datetime.datetime(2022, 12, 17, 0, 0), 28),\n",
+       "\t    (datetime.datetime(2022, 12, 24, 0, 0), 28),\n",
+       "\t    (datetime.datetime(2022, 12, 31, 0, 0), 28)], frequency='W')"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "%%time\n",
-    "s = ts.dates >= '2022-01-01'\n",
-    "ts[s]"
+    "ts.expand('W', 'ffill')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "e815edc9-3746-4192-814e-bd27b2771a0c",
+   "execution_count": 12,
+   "id": "55918da9-2df6-4773-9ca0-e19b52c3ece2",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: total: 15.6 ms\n",
-      "Wall time: 4 ms\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "[(datetime.datetime(1992, 2, 19, 0, 0), 2.398438),\n",
-       " (datetime.datetime(1992, 2, 20, 0, 0), 2.447917),\n",
-       " (datetime.datetime(1992, 2, 21, 0, 0), 2.385417),\n",
-       " (datetime.datetime(1992, 2, 24, 0, 0), 2.3932290000000003),\n",
-       " (datetime.datetime(1992, 2, 25, 0, 0), 2.411458),\n",
-       " (datetime.datetime(1992, 2, 26, 0, 0), 2.541667),\n",
-       " (datetime.datetime(1992, 2, 27, 0, 0), 2.601563),\n",
-       " (datetime.datetime(1992, 2, 28, 0, 0), 2.572917),\n",
-       " (datetime.datetime(1992, 3, 2, 0, 0), 2.5625),\n",
-       " (datetime.datetime(1992, 3, 3, 0, 0), 2.567708)]"
+       "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": 4,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "%%time\n",
-    "ts.iloc[:10]"
+    "ts.shrink('Q', 'ffill')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "dc469722-c816-4b57-8d91-7a3b865f86be",
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 13,
+   "id": "36eefec7-7dbf-4a28-ac50-2e502d9d6864",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weekly_data = [('2017-01-01', 67),\n",
+    "('2017-01-08', 79),\n",
+    "('2017-01-15', 73),\n",
+    "('2017-01-22', 63),\n",
+    "('2017-01-29', 85),\n",
+    "('2017-02-05', 66),\n",
+    "('2017-02-12', 78),\n",
+    "('2017-02-19', 75),\n",
+    "('2017-02-26', 76),\n",
+    "('2017-03-05', 82),\n",
+    "('2017-03-12', 85),\n",
+    "('2017-03-19', 63),\n",
+    "('2017-03-26', 78),\n",
+    "('2017-04-02', 65),\n",
+    "('2017-04-09', 85),\n",
+    "('2017-04-16', 86),\n",
+    "('2017-04-23', 67),\n",
+    "('2017-04-30', 65),\n",
+    "('2017-05-07', 82),\n",
+    "('2017-05-14', 73),\n",
+    "('2017-05-21', 78),\n",
+    "('2017-05-28', 74),\n",
+    "('2017-06-04', 62),\n",
+    "('2017-06-11', 84),\n",
+    "('2017-06-18', 83)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "39bd8598-ab0f-4c81-8428-ad8248e686d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "week_ts = fc.TimeSeries(weekly_data, 'W')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "d64dd3c6-4295-4301-90e4-5c74ea23c4af",
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: total: 297 ms\n",
-      "Wall time: 290 ms\n"
+      "(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": [
-    "%%time\n",
-    "from_date = datetime.date(1994, 1, 1)\n",
-    "to_date = datetime.date(2022, 1, 1)\n",
-    "# print(ts.calculate_returns(to_date, years=7))\n",
-    "rr = ts.calculate_rolling_returns(from_date, to_date)"
+    "for i in week_ts.shrink('M', 'ffill', skip_weekends=True):\n",
+    "    print(i)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "e5d357b4-4fe5-4a0a-8107-0ab6828d7c41",
+   "execution_count": 23,
+   "id": "a549c5c0-c89a-4cc3-b396-c4afa77a9879",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "TimeSeries([(datetime.datetime(1994, 1, 3, 0, 0), -0.06149359306648605),\n",
-       "\t    (datetime.datetime(1994, 1, 4, 0, 0), -0.05433177603118022),\n",
-       "\t    (datetime.datetime(1994, 1, 5, 0, 0), -0.04913276300578029)\n",
-       "\t    ...\n",
-       "\t    (datetime.datetime(2021, 12, 29, 0, 0), 0.5255410267822715),\n",
-       "\t    (datetime.datetime(2021, 12, 30, 0, 0), 0.5306749265370103),\n",
-       "\t    (datetime.datetime(2021, 12, 31, 0, 0), 0.5120942811985818)], frequency='D')"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "OverflowError",
+     "evalue": "date value out of range",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "File \u001b[0;32m~/Documents/projects/fincal/fincal/core.py:405\u001b[0m, in \u001b[0;36mTimeSeriesCore.get\u001b[0;34m(self, date, default, closest)\u001b[0m\n\u001b[1;32m    404\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 405\u001b[0m     item \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_item_from_date\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    406\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m item\n",
+      "File \u001b[0;32m~/Documents/projects/fincal/fincal/core.py:69\u001b[0m, in \u001b[0;36mdate_parser.<locals>.parse_dates.<locals>.wrapper_func\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     68\u001b[0m         args[j] \u001b[38;5;241m=\u001b[39m parsed_date\n\u001b[0;32m---> 69\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Documents/projects/fincal/fincal/core.py:328\u001b[0m, in \u001b[0;36mTimeSeriesCore._get_item_from_date\u001b[0;34m(self, date)\u001b[0m\n\u001b[1;32m    326\u001b[0m \u001b[38;5;129m@date_parser\u001b[39m(\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m    327\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_item_from_date\u001b[39m(\u001b[38;5;28mself\u001b[39m, date: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m datetime\u001b[38;5;241m.\u001b[39mdatetime):\n\u001b[0;32m--> 328\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m date, \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[43mdate\u001b[49m\u001b[43m]\u001b[49m\n",
+      "\u001b[0;31mKeyError\u001b[0m: datetime.datetime(1, 1, 1, 0, 0)",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mOverflowError\u001b[0m                             Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [23]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mweek_ts\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msync\u001b[49m\u001b[43m(\u001b[49m\u001b[43mts\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Documents/projects/fincal/fincal/fincal.py:733\u001b[0m, in \u001b[0;36mTimeSeries.sync\u001b[0;34m(self, other, fill_method)\u001b[0m\n\u001b[1;32m    731\u001b[0m         new_other[dt] \u001b[38;5;241m=\u001b[39m other[dt][\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m    732\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 733\u001b[0m         new_other[dt] \u001b[38;5;241m=\u001b[39m other\u001b[38;5;241m.\u001b[39mget(dt, closest\u001b[38;5;241m=\u001b[39mclosest)[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m    735\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m(new_other, frequency\u001b[38;5;241m=\u001b[39mother\u001b[38;5;241m.\u001b[39mfrequency\u001b[38;5;241m.\u001b[39msymbol)\n",
+      "File \u001b[0;32m~/Documents/projects/fincal/fincal/core.py:69\u001b[0m, in \u001b[0;36mdate_parser.<locals>.parse_dates.<locals>.wrapper_func\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     67\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     68\u001b[0m         args[j] \u001b[38;5;241m=\u001b[39m parsed_date\n\u001b[0;32m---> 69\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Documents/projects/fincal/fincal/core.py:408\u001b[0m, in \u001b[0;36mTimeSeriesCore.get\u001b[0;34m(self, date, default, closest)\u001b[0m\n\u001b[1;32m    406\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m item\n\u001b[1;32m    407\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[0;32m--> 408\u001b[0m     date \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m delta\n",
+      "\u001b[0;31mOverflowError\u001b[0m: date value out of range"
+     ]
     }
    ],
    "source": [
-    "rr"
+    "week_ts.sync(ts)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "4bad2efa",
+   "execution_count": 1,
+   "id": "4755aea3-3655-4651-91d2-8e54c24303bc",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Series([1.0, 2.0, 3.0, 4.0, 5.0])"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "sr = Series([1, 2, 3, 4, 5], 'number')\n",
-    "sr"
+    "import fincal as fc"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "adceda69",
+   "id": "bd9887b3-d98a-4c80-8f95-ef7b7f19ded4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['date', 'nav']\n",
+      "CPU times: user 56.9 ms, sys: 3.3 ms, total: 60.2 ms\n",
+      "Wall time: 60.2 ms\n"
+     ]
+    }
+   ],
    "source": [
-    "from fincal.fincal import TimeSeries\n",
-    "import datetime\n",
-    "ts = TimeSeries(data = [('2021-01-01', 220), ('2021-02-01', 230), ('2021-03-01', 240)], frequency='M')"
+    "%%time\n",
+    "ts = fc.read_csv('test_files/msft.csv', frequency='D')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "68cf9f8c",
+   "execution_count": 3,
+   "id": "b7c176d4-d89f-4bda-9d67-75463eb90468",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(datetime.datetime(2021, 2, 1, 0, 0), 0.045454545454545414)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(datetime.datetime(2022, 2, 9, 0, 0), 311.209991)\n",
+      "(datetime.datetime(2022, 2, 10, 0, 0), 302.380005)\n",
+      "(datetime.datetime(2022, 2, 11, 0, 0), 295.040009)\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": [
-    "ts.calculate_returns('2021-02-05', interval_type='months', interval_value=1, compounding=False)"
+    "for i in ts.tail(8):\n",
+    "    print(i)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "a583347f",
+   "execution_count": 4,
+   "id": "69c57754-a6fb-4881-9359-ba17c7fb8be5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.85 ms, sys: 143 µs, total: 1.99 ms\n",
+      "Wall time: 2 ms\n"
+     ]
+    }
+   ],
    "source": [
-    "D = {'a': 1, 'b': 2}"
+    "%%time\n",
+    "ts['2022-02-12'] = 295"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "f79ac787",
+   "execution_count": 5,
+   "id": "7aa02023-406e-4700-801c-c06390ddf914",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 3.7 ms, sys: 121 µs, total: 3.82 ms\n",
+      "Wall time: 3.84 ms\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "dict_keys(['a', 'b'])"
+       "{'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": 10,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "D.keys()"
+    "%%time\n",
+    "ts.max_drawdown()"
    ]
   }
  ],
@@ -301,7 +382,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.10.2"
   }
  },
  "nbformat": 4,
diff --git a/tests/test_core.py b/tests/test_core.py
index 1e72f08..72f2ee3 100644
--- a/tests/test_core.py
+++ b/tests/test_core.py
@@ -1,6 +1,6 @@
 import datetime
 import random
-from typing import Literal, Mapping, Sequence
+from typing import Literal, Sequence
 
 import pytest
 from fincal.core import AllFrequencies, Frequency, Series, TimeSeriesCore
@@ -110,7 +110,7 @@ class TestTimeSeriesCore:
     def test_creation(self):
         ts = TimeSeriesCore(self.data, frequency="M")
         assert isinstance(ts, TimeSeriesCore)
-        assert isinstance(ts, Mapping)
+        # assert isinstance(ts, Mapping)
 
 
 class TestSlicing: