.gitignore

@@ -3,5 +3,4 @@
 .env
 *egg-info
 __pycache__
-.vscode
-.idea
+.vscode

Check3.py

@@ -1,21 +0,0 @@
-import datetime
-import math
-import random
-import time
-from typing import List
-
-from dateutil.relativedelta import relativedelta
-
-import pyfacts as pft
-
-data = [
-    ("2021-01-01", 10),
-    ("2021-02-01", 12),
-    ("2021-03-01", 14),
-    ("2021-04-01", 16),
-    ("2021-05-01", 18),
-    ("2021-06-01", 20),
-]
-
-ts = pft.TimeSeries(data)
-print(repr(ts))

check.py

@@ -1,118 +0,0 @@
-import datetime
-import math
-import random
-
-# import time
-from typing import List
-
-from dateutil.relativedelta import relativedelta
-
-import pyfacts as pft
-
-
-def create_prices(s0: float, mu: float, sigma: float, num_prices: int) -> list:
-    """Generates a price following a geometric brownian motion process based on the input of the arguments.
-
-        Since this function is used only to generate data for tests, the seed is fixed as 1234.
-        Many of the tests rely on exact values generated using this seed.
-        If the seed is changed, those tests will fail.
-
-    Parameters:
-    ------------
-    s0: float
-        Asset inital price.
-
-    mu: float
-        Interest rate expressed annual terms.
-
-    sigma: float
-        Volatility expressed annual terms.
-
-    num_prices: int
-        number of prices to generate
-
-    Returns:
-    --------
-        Returns a list of values generated using GBM algorithm
-    """
-
-    random.seed(1234)  # WARNING! Changing the seed will cause most tests to fail
-    all_values = []
-    for _ in range(num_prices):
-        s0 *= math.exp(
-            (mu - 0.5 * sigma**2) * (1.0 / 365.0) + sigma * math.sqrt(1.0 / 365.0) * random.gauss(mu=0, sigma=1)
-        )
-        all_values.append(round(s0, 2))
-
-    return all_values
-
-
-def sample_data_generator(
-    frequency: pft.Frequency,
-    num: int = 1000,
-    skip_weekends: bool = False,
-    mu: float = 0.1,
-    sigma: float = 0.05,
-    eomonth: bool = False,
-) -> List[tuple]:
-    """Creates TimeSeries data
-
-    Parameters:
-    -----------
-    frequency: Frequency
-        The frequency of the time series data to be generated.
-
-    num: int
-        Number of date: value pairs to be generated.
-
-    skip_weekends: bool
-        Whether weekends (saturday, sunday) should be skipped.
-        Gets used only if the frequency is daily.
-
-    mu: float
-        Mean return for the values.
-
-    sigma: float
-        standard deviation of the values.
-
-    Returns:
-    --------
-        Returns a TimeSeries object
-    """
-
-    start_date = datetime.datetime(2017, 1, 1)
-    timedelta_dict = {
-        frequency.freq_type: int(
-            frequency.value * num * (7 / 5 if frequency == pft.AllFrequencies.D and skip_weekends else 1)
-        )
-    }
-    end_date = start_date + relativedelta(**timedelta_dict)
-    dates = pft.create_date_series(start_date, end_date, frequency.symbol, skip_weekends=skip_weekends, eomonth=eomonth)
-    values = create_prices(1000, mu, sigma, num)
-    ts = list(zip(dates, values))
-    return ts
-
-
-market_data = sample_data_generator(num=3600, frequency=pft.AllFrequencies.D, skip_weekends=False)
-mts = pft.TimeSeries(market_data, "D")
-print(mts)
-
-# print("Datediff=", (mts.end_date - mts.start_date).days)
-# stock_data = sample_data_generator(num=3600, frequency=pft.AllFrequencies.D, skip_weekends=False, mu=0.12, sigma=0.15)
-# sts = pft.TimeSeries(stock_data, "D")
-# print(sts)
-
-# start = time.time()
-# alpha = pft.jensens_alpha(
-#     asset_data=sts, market_data=mts, risk_free_rate=0.052, return_period_unit="months", return_period_value=1
-# )
-
-# print(alpha)
-
-# print("Alpha calculation took", time.time() - start, "seconds")
-
-# print("Correlation=", pft.correlation(sts, mts))
-
-rr = mts.calculate_rolling_returns(frequency="D")
-
-print(117, rr[rr.values < 0.1])

check2.py

@@ -1,100 +0,0 @@
-import datetime
-import math
-import random
-import time
-from typing import List
-
-from dateutil.relativedelta import relativedelta
-
-import pyfacts as pft
-
-
-def create_prices(s0: float, mu: float, sigma: float, num_prices: int) -> list:
-    """Generates a price following a geometric brownian motion process based on the input of the arguments.
-
-        Since this function is used only to generate data for tests, the seed is fixed as 1234.
-        Many of the tests rely on exact values generated using this seed.
-        If the seed is changed, those tests will fail.
-
-    Parameters:
-    ------------
-    s0: float
-        Asset inital price.
-
-    mu: float
-        Interest rate expressed annual terms.
-
-    sigma: float
-        Volatility expressed annual terms.
-
-    num_prices: int
-        number of prices to generate
-
-    Returns:
-    --------
-        Returns a list of values generated using GBM algorithm
-    """
-
-    random.seed(1234)  # WARNING! Changing the seed will cause most tests to fail
-    all_values = []
-    for _ in range(num_prices):
-        s0 *= math.exp(
-            (mu - 0.5 * sigma**2) * (1.0 / 365.0) + sigma * math.sqrt(1.0 / 365.0) * random.gauss(mu=0, sigma=1)
-        )
-        all_values.append(round(s0, 2))
-
-    return all_values
-
-
-def sample_data_generator(
-    frequency: pft.Frequency,
-    num: int = 1000,
-    skip_weekends: bool = False,
-    mu: float = 0.1,
-    sigma: float = 0.05,
-    eomonth: bool = False,
-) -> List[tuple]:
-    """Creates TimeSeries data
-
-    Parameters:
-    -----------
-    frequency: Frequency
-        The frequency of the time series data to be generated.
-
-    num: int
-        Number of date: value pairs to be generated.
-
-    skip_weekends: bool
-        Whether weekends (saturday, sunday) should be skipped.
-        Gets used only if the frequency is daily.
-
-    mu: float
-        Mean return for the values.
-
-    sigma: float
-        standard deviation of the values.
-
-    Returns:
-    --------
-        Returns a TimeSeries object
-    """
-
-    start_date = datetime.datetime(2017, 1, 1)
-    timedelta_dict = {
-        frequency.freq_type: int(
-            frequency.value * num * (7 / 5 if frequency == pft.AllFrequencies.D and skip_weekends else 1)
-        )
-    }
-    end_date = start_date + relativedelta(**timedelta_dict)
-    dates = pft.create_date_series(start_date, end_date, frequency.symbol, skip_weekends=skip_weekends, eomonth=eomonth)
-    values = create_prices(1000, mu, sigma, num)
-    ts = list(zip(dates, values))
-    return ts
-
-
-market_data = sample_data_generator(num=3600, frequency=pft.AllFrequencies.D, skip_weekends=False)
-mts = pft.TimeSeries(market_data, "D")
-print(mts)
-
-sortino = pft.sortino_ratio(mts, risk_free_rate=0.05)
-print(sortino)

my_checks.py

@@ -1,26 +0,0 @@
-import datetime
-import time
-import timeit
-
-import pandas
-
-from pyfacts.pyfacts import AllFrequencies, TimeSeries, create_date_series
-
-dfd = pandas.read_csv("test_files/msft.csv")
-dfm = pandas.read_csv("test_files/nav_history_monthly.csv")
-dfq = pandas.read_csv("test_files/nav_history_quarterly.csv")
-
-data_d = [(i.date, i.nav) for i in dfd.itertuples()]
-data_m = [{"date": i.date, "value": i.nav} for i in dfm.itertuples()]
-data_q = {i.date: i.nav for i in dfq.itertuples()}
-data_q.update({"14-02-2022": 93.7})
-
-tsd = TimeSeries(data_d, frequency="D")
-tsm = TimeSeries(data_m, frequency="M", date_format="%d-%m-%Y")
-tsq = TimeSeries(data_q, frequency="Q", date_format="%d-%m-%Y")
-
-start = time.time()
-# ts.calculate_rolling_returns(datetime.datetime(2015, 1, 1), datetime.datetime(2022, 2, 1), years=1)
-bdata = tsq.bfill()
-# rr = tsd.calculate_rolling_returns(datetime.datetime(2022, 1, 1), datetime.datetime(2022, 2, 1), years=1)
-print(time.time() - start)

my_test.py

@@ -0,0 +1,26 @@
+import datetime
+import time
+import timeit
+
+import pandas
+
+from fincal.fincal import AllFrequencies, TimeSeries, create_date_series
+
+dfd = pandas.read_csv('test_files/msft.csv')
+dfm = pandas.read_csv('test_files/nav_history_monthly.csv')
+dfq = pandas.read_csv('test_files/nav_history_quarterly.csv')
+
+data_d = [(i.date, i.nav) for i in dfd.itertuples()]
+data_m = [{'date': i.date, 'value': i.nav} for i in dfm.itertuples()]
+data_q = {i.date: i.nav for i in dfq.itertuples()}
+data_q.update({'14-02-2022': 93.7})
+
+tsd = TimeSeries(data_d, frequency='D')
+tsm = TimeSeries(data_m, frequency='M', date_format='%d-%m-%Y')
+tsq = TimeSeries(data_q, frequency='Q', date_format='%d-%m-%Y')
+
+start = time.time()
+# ts.calculate_rolling_returns(datetime.datetime(2015, 1, 1), datetime.datetime(2022, 2, 1), years=1)
+bdata = tsq.bfill()
+# rr = tsd.calculate_rolling_returns(datetime.datetime(2022, 1, 1), datetime.datetime(2022, 2, 1), years=1)
+print(time.time() - start)

pyfacts/exceptions.py

@@ -13,9 +13,9 @@ class DateNotFoundError(Exception):
 class DateOutOfRangeError(Exception):
     """Exception to be raised when provided date is outside the range of dates in the time series"""
 
-    def __init__(self, date: datetime.datetime, type: Literal["min", "max"]) -> None:
-        if type == "min":
+    def __init__(self, date: datetime.datetime, type: Literal['min', 'max']) -> None:
+        if type == 'min':
             message = f"Provided date {date} is before the first date in the TimeSeries"
-        if type == "max":
+        if type == 'max':
             message = f"Provided date {date} is after the last date in the TimeSeries"
         super().__init__(message)

pyfacts/statistics.py

@@ -8,7 +8,7 @@ from typing import Literal
 from pyfacts.core import date_parser
 
 from .pyfacts import TimeSeries
-from .utils import _interval_to_years, covariance
+from .utils import _interval_to_years
 
 
 @date_parser(3, 4)
@@ -212,7 +212,7 @@ def beta(
     asset_rr = asset_data.calculate_rolling_returns(**common_params)
     market_rr = market_data.calculate_rolling_returns(**common_params)
 
-    cov = covariance(asset_rr.values, market_rr.values)
+    cov = statistics.covariance(asset_rr.values, market_rr.values)
     market_var = statistics.variance(market_rr.values)
 
     beta = cov / market_var

pyfacts/utils.py

@@ -1,7 +1,4 @@
-from __future__ import annotations
-
 import datetime
-import statistics
 from dataclasses import dataclass
 from typing import List, Literal, Mapping, Sequence, Tuple
 
@@ -190,36 +187,3 @@ def _is_eomonth(dates: Sequence[datetime.datetime], threshold: float = 0.7):
     eomonth_dates = [date.month != (date + relativedelta(days=1)).month for date in dates]
     eomonth_proportion = sum(eomonth_dates) / len(dates)
     return eomonth_proportion > threshold
-
-
-def covariance(series1: list, series2: list) -> float:
-    """Returns the covariance of two series
-
-        This is a compatibility function for Python versions prior to 3.10.
-        It will be replaced with statistics.covariance when support is dropped for versions <3.10.
-
-    Parameters
-    ----------
-    series1 : List
-        A list of numbers
-    series2 : list
-        A list of numbers
-
-    Returns
-    -------
-    float
-        Returns the covariance as a float value
-    """
-
-    n = len(series1)
-    if len(series2) != n:
-        raise ValueError("Lenght of both series must be same for covariance calcualtion.")
-    if n < 2:
-        raise ValueError("At least two data poitns are required for covariance calculation.")
-
-    mean1 = statistics.mean(series1)
-    mean2 = statistics.mean(series2)
-
-    xy = sum([(x - mean1) * (y - mean2) for x, y in zip(series1, series2)])
-
-    return xy / n

test.py

@@ -0,0 +1,34 @@
+# from fincal.core import FincalOptions
+import fincal as fc
+
+data = [
+    ("2022-01-01", 150),
+    ("2022-01-02", 152),
+    ("2022-01-03", 151),
+    ("2022-01-04", 154),
+    ("2022-01-05", 150),
+    ("2022-01-06", 157),
+    ("2022-01-07", 155),
+    ("2022-01-08", 158),
+    ("2022-01-09", 162),
+    ("2022-01-10", 160),
+    ("2022-01-11", 156),
+    ("2022-01-12", 162),
+    ("2023-01-01", 164),
+    ("2023-01-02", 161),
+    ("2023-01-03", 167),
+    ("2023-01-04", 168),
+]
+ts = fc.TimeSeries(data, frequency="D", date_format="%Y-%d-%m")
+print(ts)
+
+sharpe = fc.sharpe_ratio(
+    ts,
+    risk_free_rate=(1 + 0.15) ** (1 / 12) - 1,
+    from_date="2022-02-01",
+    to_date="2023-04-01",
+    frequency="M",
+    return_period_unit="months",
+    return_period_value=1,
+)
+print(f"{sharpe=}")

test2.py

@@ -0,0 +1,52 @@
+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(
+#           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)
+
+print(ts.get("2022-01-01"))
+
+print(ts.ffill())

tests/test_stats.py

@@ -169,7 +169,7 @@ class TestBeta:
         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) == 1.5997
+        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)
@@ -177,7 +177,7 @@ class TestBeta:
         sts = pft.TimeSeries(stock_data, "D")
         mts = pft.TimeSeries(market_data, "D")
         beta = pft.beta(sts, mts)
-        assert round(beta, 4) == 1.6287
+        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)
@@ -185,7 +185,7 @@ class TestBeta:
         sts = pft.TimeSeries(stock_data, "D")
         mts = pft.TimeSeries(market_data, "D")
         beta = pft.beta(sts, mts, frequency="M")
-        assert round(beta, 4) == 1.6137
+        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)
@@ -193,4 +193,4 @@ class TestBeta:
         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) == 1.5887
+        assert round(beta, 4) == 1.6023

tox.ini

@@ -1,10 +1,6 @@
 [tox]
-minversion = 3.8.0
-envlist = py38,py39,py310
+envlist = py39
 
 [testenv]
 deps = pytest
-commands = pytest
-
-[flake8]
-max-line-length=125
+commands = pytest