diff --git a/testing.ipynb b/testing.ipynb
index c74d549..22839a5 100644
--- a/testing.ipynb
+++ b/testing.ipynb
@@ -485,7 +485,7 @@
    "source": [
     "import random\n",
     "import math\n",
-    "import fincal as fc\n",
+    "import pyfacts as pft\n",
     "from typing import List\n",
     "import datetime\n",
     "from dateutil.relativedelta import relativedelta"
@@ -536,7 +536,7 @@
     "\n",
     "\n",
     "def sample_data_generator(\n",
-    "    frequency: fc.Frequency,\n",
+    "    frequency: pft.Frequency,\n",
     "    num: int = 1000,\n",
     "    skip_weekends: bool = False,\n",
     "    mu: float = 0.1,\n",
@@ -571,11 +571,11 @@
     "    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",
+    "            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 = fc.create_date_series(start_date, end_date, frequency.symbol, skip_weekends=skip_weekends, eomonth=eomonth)\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"
@@ -583,40 +583,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 6,
    "id": "c85b5dd9-9a88-4608-ac58-1a141295f63f",
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = sample_data_generator(num=261, frequency=fc.AllFrequencies.W)\n",
-    "ts = fc.TimeSeries(data, \"W\")"
+    "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": 13,
+   "execution_count": 8,
    "id": "0488a4d0-bca1-4341-9fae-1fd254adc0dc",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "TimeSeries([(datetime.datetime(2017, 1, 1, 0, 0), 1003.03),\n",
-       "\t    (datetime.datetime(2017, 1, 8, 0, 0), 1002.71),\n",
-       "\t    (datetime.datetime(2017, 1, 15, 0, 0), 1008.77)\n",
-       "\t    ...\n",
-       "\t    (datetime.datetime(2021, 12, 12, 0, 0), 1107.21),\n",
-       "\t    (datetime.datetime(2021, 12, 19, 0, 0), 1106.66),\n",
-       "\t    (datetime.datetime(2021, 12, 26, 0, 0), 1104.32)], frequency='W')"
+       "1.020217253491451"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "ts"
+    "pft.beta(sts, mts)"
    ]
   },
   {
@@ -708,7 +704,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.2"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,