pyfacts/pyfacts.py

@@ -140,7 +140,7 @@ class TimeSeries(TimeSeriesCore):
         self,
         data: List[Iterable] | Mapping,
         frequency: Literal["D", "W", "M", "Q", "H", "Y"] = None,
-        validate_frequency: bool = True,
+        validate_frequency: bool = False,
         date_format: str = "%Y-%m-%d",
     ):
         """Instantiate a TimeSeriesCore object"""
@@ -448,7 +448,7 @@ class TimeSeries(TimeSeriesCore):
             )
             rolling_returns.append(returns)
         rolling_returns.sort()
-        return self.__class__(rolling_returns, frequency.symbol)
+        return self.__class__(rolling_returns, self.frequency.symbol)
 
     @date_parser(1, 2)
     def volatility(

pyfacts/statistics.py

@@ -1,5 +1,3 @@
-from __future__ import annotations
-
 import datetime
 import statistics
 from typing import Literal

testing.ipynb

@@ -485,7 +485,7 @@
    "source": [
     "import random\n",
     "import math\n",
-    "import pyfacts as pft\n",
+    "import fincal as fc\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: pft.Frequency,\n",
+    "    frequency: fc.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 == pft.AllFrequencies.D and skip_weekends else 1)\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 = pft.create_date_series(start_date, end_date, frequency.symbol, skip_weekends=skip_weekends, eomonth=eomonth)\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"
@@ -583,36 +583,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "id": "c85b5dd9-9a88-4608-ac58-1a141295f63f",
    "metadata": {},
    "outputs": [],
    "source": [
-    "market_data = sample_data_generator(num=3600, frequency=pft.AllFrequencies.D)\n",
+    "data = sample_data_generator(num=261, frequency=fc.AllFrequencies.W)\n",
-    "mts = pft.TimeSeries(market_data, \"D\")\n",
+    "ts = fc.TimeSeries(data, \"W\")"
-    "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,
+   "execution_count": 13,
    "id": "0488a4d0-bca1-4341-9fae-1fd254adc0dc",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "1.020217253491451"
+       "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')"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pft.beta(sts, mts)"
+    "ts"
    ]
   },
   {
@@ -704,7 +708,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.10.2"
   }
  },
  "nbformat": 4,

tests/test_stats.py

@@ -82,37 +82,3 @@ class TestSharpe:
             return_period_value=12,
         )
         assert round(sharpe_ratio, 4) == 0.3199
-
-
-class TestBeta:
-    def test_beta_daily_freq(self, create_test_data):
-        market_data = create_test_data(num=3600, frequency=pft.AllFrequencies.D)
-        stock_data = create_test_data(num=3600, frequency=pft.AllFrequencies.D, mu=0.12, sigma=0.08)
-        sts = pft.TimeSeries(stock_data, "D")
-        mts = pft.TimeSeries(market_data, "D")
-        beta = pft.beta(sts, mts, frequency="D", return_period_unit="days", return_period_value=1)
-        assert round(beta, 4) == 1.6001
-
-    def test_beta_daily_freq_daily_returns(self, create_test_data):
-        market_data = create_test_data(num=3600, frequency=pft.AllFrequencies.D)
-        stock_data = create_test_data(num=3600, frequency=pft.AllFrequencies.D, mu=0.12, sigma=0.08)
-        sts = pft.TimeSeries(stock_data, "D")
-        mts = pft.TimeSeries(market_data, "D")
-        beta = pft.beta(sts, mts)
-        assert round(beta, 4) == 1.6292
-
-    def test_beta_monthly_freq(self, create_test_data):
-        market_data = create_test_data(num=3600, frequency=pft.AllFrequencies.D)
-        stock_data = create_test_data(num=3600, frequency=pft.AllFrequencies.D, mu=0.12, sigma=0.08)
-        sts = pft.TimeSeries(stock_data, "D")
-        mts = pft.TimeSeries(market_data, "D")
-        beta = pft.beta(sts, mts, frequency="M")
-        assert round(beta, 4) == 1.629
-
-    def test_beta_monthly_freq_monthly_returns(self, create_test_data):
-        market_data = create_test_data(num=3600, frequency=pft.AllFrequencies.D)
-        stock_data = create_test_data(num=3600, frequency=pft.AllFrequencies.D, mu=0.12, sigma=0.08)
-        sts = pft.TimeSeries(stock_data, "D")
-        mts = pft.TimeSeries(market_data, "D")
-        beta = pft.beta(sts, mts, frequency="M", return_period_unit="months", return_period_value=1)
-        assert round(beta, 4) == 1.6023