fincal/core.py

@@ -7,6 +7,7 @@ from collections import UserList
 from dataclasses import dataclass
 from numbers import Number
 from typing import Any, Callable, Iterable, List, Literal, Mapping, Sequence, Type
+from unittest import skip
 
 from dateutil.relativedelta import relativedelta
 

fincal/statistics.py

@@ -1,11 +1,9 @@
 import datetime
-import statistics
 from typing import Literal
 
 from fincal.core import date_parser
 
 from .fincal import TimeSeries
-from .utils import _interval_to_years
 
 
 @date_parser(3, 4)
@@ -23,13 +21,6 @@ def sharpe_ratio(
     closest: Literal["previous", "next"] = "previous",
     date_format: str = None,
 ):
-    interval_days = int(_interval_to_years(return_period_unit, return_period_value) * 365 + 1)
-
-    if from_date is None:
-        from_date = time_series_data.start_date + datetime.timedelta(days=interval_days)
-    if to_date is None:
-        to_date = time_series_data.end_date
-
     if risk_free_data is None and risk_free_rate is None:
         raise ValueError("At least one of risk_free_data or risk_free rate is required")
     elif risk_free_data is not None:
@@ -56,47 +47,3 @@ def sharpe_ratio(
 
     sharpe_ratio_value = excess_returns / sd
     return sharpe_ratio_value
-
-
-@date_parser(2, 3)
-def beta(
-    asset_data: TimeSeries,
-    market_data: TimeSeries,
-    from_date: str | datetime.datetime = None,
-    to_date: str | datetime.datetime = None,
-    frequency: Literal["D", "W", "M", "Q", "H", "Y"] = None,
-    return_period_unit: Literal["years", "months", "days"] = "years",
-    return_period_value: int = 1,
-    as_on_match: str = "closest",
-    prior_match: str = "closest",
-    closest: Literal["previous", "next"] = "previous",
-    date_format: str = None,
-):
-
-    interval_days = int(_interval_to_years(return_period_unit, return_period_value) * 365 + 1)
-
-    if from_date is None:
-        from_date = asset_data.start_date + datetime.timedelta(days=interval_days)
-    if to_date is None:
-        to_date = asset_data.end_date
-
-    common_params = {
-        "from_date": from_date,
-        "to_date": to_date,
-        "frequency": frequency,
-        "return_period_unit": return_period_unit,
-        "return_period_value": return_period_value,
-        "as_on_match": as_on_match,
-        "prior_match": prior_match,
-        "closest": closest,
-        "date_format": date_format,
-    }
-
-    asset_rr = asset_data.calculate_rolling_returns(**common_params)
-    market_rr = market_data.calculate_rolling_returns(**common_params)
-
-    cov = statistics.covariance(asset_rr.values, market_rr.values)
-    market_var = statistics.variance(market_rr.values)
-
-    beta = cov / market_var
-    return beta

tests/test_fincal.py

@@ -355,24 +355,6 @@ class TestReadCsv:
 
 
 class TestTransform:
-    def test_daily_to_weekly(self, create_test_data):
-        ts_data = create_test_data(AllFrequencies.D, num=782, skip_weekends=True)
-        ts = TimeSeries(ts_data, "D")
-        tst = ts.transform("W", "mean")
-        assert isinstance(tst, TimeSeries)
-        assert len(tst) == 157
-        assert "2017-01-30" in tst
-        assert tst.iloc[4] == (datetime.datetime(2017, 1, 30), 1021.19)
-
-    def test_daily_to_monthly(self, create_test_data):
-        ts_data = create_test_data(AllFrequencies.D, num=782, skip_weekends=False)
-        ts = TimeSeries(ts_data, "D")
-        tst = ts.transform("M", "mean")
-        assert isinstance(tst, TimeSeries)
-        assert len(tst) == 26
-        assert "2018-01-01" in tst
-        assert round(tst.iloc[12][1], 2) == 1146.1
-
     def test_daily_to_yearly(self, create_test_data):
         ts_data = create_test_data(AllFrequencies.D, num=782, skip_weekends=True)
         ts = TimeSeries(ts_data, "D")
@@ -404,18 +386,6 @@ class TestTransform:
         tst = ts.transform("Y", "mean")
         assert "2019-01-01" in tst
         assert round(tst.iloc[2][1], 2) == 1054.50
-        with pytest.raises(ValueError):
-            ts.transform("D", "mean")
-
-    def test_monthly_to_qty(self, create_test_data):
-        ts_data = create_test_data(AllFrequencies.M, num=36)
-        ts = TimeSeries(ts_data, "M")
-        tst = ts.transform("Q", "mean")
-        assert len(tst) == 12
-        assert "2018-10-01" in tst
-        assert tst.iloc[7] == (datetime.datetime(2018, 10, 1), 1021.19)
-        with pytest.raises(ValueError):
-            ts.transform("M", "sum")
 
 
 class TestReturnsAgain: