added sharpe ratio

fincal/__init__.py

@@ -1,3 +1,4 @@
 from .core import *
 from .fincal import *
+from .statistics import *
 from .utils import *

fincal/fincal.py

@@ -476,7 +476,7 @@ class TimeSeries(TimeSeriesCore):
             Only used when annualizing volatility for a time series with daily frequency.
             If not provided, will use the value in FincalOptions.traded_days.
 
-        Remaining options are passed on to rolling_return function.
+        Remaining options are passed on to calculate_rolling_returns function.
 
         Returns:
         -------
@@ -715,6 +715,7 @@ class TimeSeries(TimeSeriesCore):
         This will ensure that both time series have the same frequency and same set of dates.
         The frequency will be set to the higher of the two objects.
         Dates will be taken from the class on which the method is called.
+        Values will be taken from the other class.
 
         Parameters:
         -----------
@@ -751,6 +752,11 @@ class TimeSeries(TimeSeriesCore):
 
         return self.__class__(new_other, frequency=other.frequency.symbol)
 
+    def mean(self) -> float:
+        """Calculates the mean value of the time series data"""
+
+        return statistics.mean(self.values)
+
 
 def _preprocess_csv(file_path: str | pathlib.Path, delimiter: str = ",", encoding: str = "utf-8") -> List[list]:
     """Preprocess csv data"""

fincal/statistics.py

@@ -0,0 +1,22 @@
+from .fincal import TimeSeries
+
+
+def sharpe_ratio(
+    time_series_data: TimeSeries, risk_free_data: TimeSeries = None, risk_free_rate: float = None, **kwargs
+):
+    pass
+
+    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")
+
+    returns_ts = time_series_data.calculate_rolling_returns(**kwargs)
+
+    if risk_free_data is not None:
+        risk_free_data = returns_ts.sync(risk_free_data)
+    else:
+        risk_free_data = risk_free_rate
+
+    excess_returns = returns_ts - risk_free_data
+    sd = time_series_data.volatility(**kwargs)
+    sharpe_ratio = excess_returns.mean() / sd
+    return sharpe_ratio

test.py

@@ -1,29 +1,34 @@
 # from fincal.core import FincalOptions
-from fincal.fincal import TimeSeries
+import fincal as fc
 
 data = [
-    ("2022-01-01", 10),
+    ("2022-01-01", 150),
-    ("2022-01-02", 12),
+    ("2022-01-02", 152),
-    ("2022-01-03", 14),
+    ("2022-01-03", 151),
-    ("2022-01-04", 16),
+    ("2022-01-04", 154),
-    ("2022-01-06", 18),
+    ("2022-01-05", 150),
-    ("2022-01-07", 20),
+    ("2022-01-06", 157),
-    ("2022-01-09", 22),
+    ("2022-01-07", 155),
-    ("2022-01-10", 24),
+    ("2022-01-08", 158),
-    ("2022-01-11", 26),
+    ("2022-01-09", 162),
-    ("2022-01-13", 28),
+    ("2022-01-10", 160),
-    ("2022-01-14", 30),
+    ("2022-01-11", 156),
-    ("2022-01-15", 32),
+    ("2022-01-12", 162),
-    ("2022-01-16", 34),
+    ("2023-01-01", 164),
+    ("2023-01-02", 161),
+    ("2023-01-03", 167),
+    ("2023-01-04", 168),
 ]
-ts = TimeSeries(data, frequency="D")
+ts = fc.TimeSeries(data, frequency="D", date_format="%Y-%d-%m")
 print(ts)
 
-data = [("2022-01-01", 220), ("2022-01-08", 230), ("2022-01-15", 240)]
+sharpe = fc.sharpe_ratio(
-ts2 = TimeSeries(data, frequency="W")
+    ts,
-print(ts2)
+    risk_free_rate=(1 + 0.15) ** (1 / 12) - 1,
-
+    from_date="2022-02-01",
-synced_ts = ts.sync(ts2)
+    to_date="2023-04-01",
-print("---------\n")
+    frequency="M",
-for i in synced_ts:
+    return_period_unit="months",
-    print(i)
+    return_period_value=1,
+)
+print(f"{sharpe=}")