Compare commits
No commits in common. "cd05250e8afb125a0eff0e62cff3a10aaa57762d" and "58524aae7e2262ee00541d96afa8fb19a9e71197" have entirely different histories.
cd05250e8a
...
58524aae7e
118
fincal/core.py
118
fincal/core.py
@ -1,6 +1,5 @@
|
|||||||
import datetime
|
import datetime
|
||||||
from dataclasses import dataclass
|
from dataclasses import dataclass
|
||||||
from numbers import Number
|
|
||||||
from typing import Iterable, List, Literal, Mapping, Sequence, Tuple, Union
|
from typing import Iterable, List, Literal, Mapping, Sequence, Tuple, Union
|
||||||
|
|
||||||
|
|
||||||
@ -112,67 +111,6 @@ class IndexSlicer:
|
|||||||
return item
|
return item
|
||||||
|
|
||||||
|
|
||||||
class Series:
|
|
||||||
def __init__(self, data):
|
|
||||||
if not isinstance(data, Sequence):
|
|
||||||
raise TypeError("Series only supports creation using Sequence types")
|
|
||||||
|
|
||||||
if isinstance(data[0], bool):
|
|
||||||
self.data = data
|
|
||||||
self.dtype = bool
|
|
||||||
elif isinstance(data[0], Number):
|
|
||||||
self.dtype = float
|
|
||||||
self.data = [float(i) for i in data]
|
|
||||||
elif isinstance(data[0], str):
|
|
||||||
try:
|
|
||||||
data = [datetime.datetime.strptime(i, FincalOptions.date_format) for i in data]
|
|
||||||
self.dtype = datetime.datetime
|
|
||||||
except ValueError:
|
|
||||||
raise TypeError("Series does not support string data type")
|
|
||||||
elif isinstance(data[0], datetime.datetime):
|
|
||||||
self.dtype = datetime.datetime
|
|
||||||
self.data = data
|
|
||||||
else:
|
|
||||||
raise TypeError(f"Cannot create series object from {type(data).__name__} of {type(data[0]).__name__}")
|
|
||||||
|
|
||||||
def __repr__(self):
|
|
||||||
return f"{self.__class__.__name__}({self.data})"
|
|
||||||
|
|
||||||
def __getitem__(self, n):
|
|
||||||
return self.data[n]
|
|
||||||
|
|
||||||
def __len__(self):
|
|
||||||
return len(self.data)
|
|
||||||
|
|
||||||
def __gt__(self, other):
|
|
||||||
if self.dtype == bool:
|
|
||||||
raise TypeError("> not supported for boolean series")
|
|
||||||
|
|
||||||
if self.dtype == float and isinstance(other, Number) or isinstance(other, self.dtype):
|
|
||||||
gt = Series([i > other for i in self.data])
|
|
||||||
else:
|
|
||||||
raise Exception(f"Cannot compare type {self.dtype.__name__} to {type(other).__name__}")
|
|
||||||
|
|
||||||
return gt
|
|
||||||
|
|
||||||
def __lt__(self, other):
|
|
||||||
if self.dtype == bool:
|
|
||||||
raise TypeError("< not supported for boolean series")
|
|
||||||
|
|
||||||
if self.dtype == float and isinstance(other, Number) or isinstance(other, self.dtype):
|
|
||||||
lt = Series([i < other for i in self.data])
|
|
||||||
else:
|
|
||||||
raise Exception(f"Cannot compare type {self.dtype.__name__} to {type(other).__name__}")
|
|
||||||
return lt
|
|
||||||
|
|
||||||
def __eq__(self, other):
|
|
||||||
if self.dtype == float and isinstance(other, Number) or isinstance(other, self.dtype):
|
|
||||||
eq = Series([i == other for i in self.data])
|
|
||||||
else:
|
|
||||||
raise Exception(f"Cannot compare type {self.dtype.__name__} to {type(other).__name__}")
|
|
||||||
return eq
|
|
||||||
|
|
||||||
|
|
||||||
class TimeSeriesCore:
|
class TimeSeriesCore:
|
||||||
"""Defines the core building blocks of a TimeSeries object"""
|
"""Defines the core building blocks of a TimeSeries object"""
|
||||||
|
|
||||||
@ -204,36 +142,12 @@ class TimeSeriesCore:
|
|||||||
data = _preprocess_timeseries(data, date_format=date_format)
|
data = _preprocess_timeseries(data, date_format=date_format)
|
||||||
|
|
||||||
self.time_series = dict(data)
|
self.time_series = dict(data)
|
||||||
|
self.dates = list(self.time_series)
|
||||||
if len(self.time_series) != len(data):
|
if len(self.time_series) != len(data):
|
||||||
print("Warning: The input data contains duplicate dates which have been ignored.")
|
print("Warning: The input data contains duplicate dates which have been ignored.")
|
||||||
|
self.start_date = self.dates[0]
|
||||||
|
self.end_date = self.dates[-1]
|
||||||
self.frequency = getattr(AllFrequencies, frequency)
|
self.frequency = getattr(AllFrequencies, frequency)
|
||||||
self.iter_num = -1
|
|
||||||
self._dates = None
|
|
||||||
self._values = None
|
|
||||||
self._start_date = None
|
|
||||||
self._end_date = None
|
|
||||||
|
|
||||||
@property
|
|
||||||
def dates(self):
|
|
||||||
if self._dates is None or len(self._dates) != len(self.time_series):
|
|
||||||
self._dates = list(self.time_series.keys())
|
|
||||||
|
|
||||||
return Series(self._dates)
|
|
||||||
|
|
||||||
@property
|
|
||||||
def values(self):
|
|
||||||
if self._values is None or len(self._values) != len(self.time_series):
|
|
||||||
self._values = list(self.time_series.values())
|
|
||||||
|
|
||||||
return Series(self._values)
|
|
||||||
|
|
||||||
@property
|
|
||||||
def start_date(self):
|
|
||||||
return self.dates[0]
|
|
||||||
|
|
||||||
@property
|
|
||||||
def end_date(self):
|
|
||||||
return self.dates[-1]
|
|
||||||
|
|
||||||
def _get_printable_slice(self, n: int):
|
def _get_printable_slice(self, n: int):
|
||||||
"""Returns a slice of the dataframe from beginning and end"""
|
"""Returns a slice of the dataframe from beginning and end"""
|
||||||
@ -279,25 +193,11 @@ class TimeSeriesCore:
|
|||||||
return printable_str
|
return printable_str
|
||||||
|
|
||||||
def __getitem__(self, key):
|
def __getitem__(self, key):
|
||||||
if isinstance(key, Series):
|
|
||||||
if not key.dtype == bool:
|
|
||||||
raise ValueError(f"Cannot slice {self.__class__.__name__} using a Series of {key.dtype.__name__}")
|
|
||||||
elif len(key) != len(self.dates):
|
|
||||||
raise Exception(f"Length of Series: {len(key)} did not match length of object: {len(self.dates)}")
|
|
||||||
else:
|
|
||||||
dates_to_return = [self.dates[i] for i, j in enumerate(key) if j]
|
|
||||||
data_to_return = [(key, self.time_series[key]) for key in dates_to_return]
|
|
||||||
return TimeSeriesCore(data_to_return)
|
|
||||||
|
|
||||||
if isinstance(key, int):
|
if isinstance(key, int):
|
||||||
raise KeyError(f"{key}. For index based slicing, use .iloc[{key}]")
|
raise KeyError(f"{key}. For index based slicing, use .iloc[{key}]")
|
||||||
elif isinstance(key, datetime.datetime):
|
elif isinstance(key, datetime.datetime):
|
||||||
item = (key, self.time_series[key])
|
item = (key, self.time_series[key])
|
||||||
if isinstance(key, str):
|
if isinstance(key, str):
|
||||||
if key == 'dates':
|
|
||||||
return self.dates
|
|
||||||
elif key == 'values':
|
|
||||||
return list(self.time_series.values())
|
|
||||||
try:
|
try:
|
||||||
dt_key = datetime.datetime.strptime(key, FincalOptions.date_format)
|
dt_key = datetime.datetime.strptime(key, FincalOptions.date_format)
|
||||||
item = (dt_key, self.time_series[dt_key])
|
item = (dt_key, self.time_series[dt_key])
|
||||||
@ -315,18 +215,6 @@ class TimeSeriesCore:
|
|||||||
def __len__(self):
|
def __len__(self):
|
||||||
return len(self.time_series)
|
return len(self.time_series)
|
||||||
|
|
||||||
def __iter__(self):
|
|
||||||
self.n = 0
|
|
||||||
return self
|
|
||||||
|
|
||||||
def __next__(self):
|
|
||||||
if self.n >= len(self.dates):
|
|
||||||
raise StopIteration
|
|
||||||
else:
|
|
||||||
key = self.dates[self.n]
|
|
||||||
self.n += 1
|
|
||||||
return key, self.time_series[key]
|
|
||||||
|
|
||||||
def head(self, n: int = 6):
|
def head(self, n: int = 6):
|
||||||
"""Returns the first n items of the TimeSeries object"""
|
"""Returns the first n items of the TimeSeries object"""
|
||||||
|
|
||||||
|
|||||||
@ -75,7 +75,7 @@ class TimeSeries(TimeSeriesCore):
|
|||||||
self.time_series = new_ts
|
self.time_series = new_ts
|
||||||
return None
|
return None
|
||||||
|
|
||||||
return TimeSeries(new_ts, frequency=self.frequency.symbol)
|
return new_ts
|
||||||
|
|
||||||
def bfill(self, inplace: bool = False, limit: int = None) -> Union[TimeSeries, None]:
|
def bfill(self, inplace: bool = False, limit: int = None) -> Union[TimeSeries, None]:
|
||||||
"""Backward fill missing dates in the time series
|
"""Backward fill missing dates in the time series
|
||||||
@ -109,7 +109,7 @@ class TimeSeries(TimeSeriesCore):
|
|||||||
self.time_series = new_ts
|
self.time_series = new_ts
|
||||||
return None
|
return None
|
||||||
|
|
||||||
return TimeSeries(new_ts, frequency=self.frequency.symbol)
|
return new_ts
|
||||||
|
|
||||||
def calculate_returns(
|
def calculate_returns(
|
||||||
self,
|
self,
|
||||||
|
|||||||
129
testing.ipynb
129
testing.ipynb
@ -1,129 +0,0 @@
|
|||||||
{
|
|
||||||
"cells": [
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 14,
|
|
||||||
"id": "3f7938c0-98e3-43b8-86e8-4f000cda7ce5",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"import datetime\n",
|
|
||||||
"import pandas as pd\n",
|
|
||||||
"\n",
|
|
||||||
"from fincal.fincal import TimeSeries\n",
|
|
||||||
"from fincal.core import Series"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 16,
|
|
||||||
"id": "757eafc2-f804-4e7e-a3b8-2d09cd62e646",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"dfd = pd.read_csv('test_files/nav_history_daily - copy.csv')"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 17,
|
|
||||||
"id": "59b3d4a9-8ef4-4652-9e20-1bac69ab4ff9",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"dfd = dfd[dfd['amfi_code'] == 118825].reset_index(drop=True)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 19,
|
|
||||||
"id": "4bc95ae0-8c33-4eab-acf9-e765d22979b8",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"Warning: The input data contains duplicate dates which have been ignored.\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"ts = TimeSeries([(i.date, i.nav) for i in dfd.itertuples()], frequency='D')"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 20,
|
|
||||||
"id": "f2c3218c-3984-43d6-8638-41a74a9d0b58",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"TimeSeries([(datetime.datetime(2013, 1, 2, 0, 0), 18.972),\n",
|
|
||||||
"\t (datetime.datetime(2013, 1, 3, 0, 0), 19.011),\n",
|
|
||||||
"\t (datetime.datetime(2013, 1, 4, 0, 0), 19.008)\n",
|
|
||||||
"\t ...\n",
|
|
||||||
"\t (datetime.datetime(2022, 2, 10, 0, 0), 86.5),\n",
|
|
||||||
"\t (datetime.datetime(2022, 2, 11, 0, 0), 85.226),\n",
|
|
||||||
"\t (datetime.datetime(2022, 2, 14, 0, 0), 82.53299999999999)], frequency='D')"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 20,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"ts"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 22,
|
|
||||||
"id": "dc469722-c816-4b57-8d91-7a3b865f86be",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"ename": "TypeError",
|
|
||||||
"evalue": "getattr(): attribute name must be string",
|
|
||||||
"output_type": "error",
|
|
||||||
"traceback": [
|
|
||||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
|
||||||
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
|
|
||||||
"File \u001b[1;32m<timed eval>:1\u001b[0m, in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n",
|
|
||||||
"File \u001b[1;32mD:\\Documents\\Projects\\fincal\\fincal\\fincal.py:203\u001b[0m, in \u001b[0;36mTimeSeries.calculate_rolling_returns\u001b[1;34m(self, from_date, to_date, frequency, as_on_match, prior_match, closest, compounding, years)\u001b[0m\n\u001b[0;32m 200\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n\u001b[0;32m 201\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid argument for frequency \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfrequency\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 203\u001b[0m dates \u001b[38;5;241m=\u001b[39m \u001b[43mcreate_date_series\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfrom_date\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mto_date\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrequency\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 204\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m frequency \u001b[38;5;241m==\u001b[39m AllFrequencies\u001b[38;5;241m.\u001b[39mD:\n\u001b[0;32m 205\u001b[0m dates \u001b[38;5;241m=\u001b[39m [i \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m dates \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtime_series]\n",
|
|
||||||
"File \u001b[1;32mD:\\Documents\\Projects\\fincal\\fincal\\fincal.py:16\u001b[0m, in \u001b[0;36mcreate_date_series\u001b[1;34m(start_date, end_date, frequency, eomonth)\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcreate_date_series\u001b[39m(\n\u001b[0;32m 12\u001b[0m start_date: datetime\u001b[38;5;241m.\u001b[39mdatetime, end_date: datetime\u001b[38;5;241m.\u001b[39mdatetime, frequency: \u001b[38;5;28mstr\u001b[39m, eomonth: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m 13\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m List[datetime\u001b[38;5;241m.\u001b[39mdatetime]:\n\u001b[0;32m 14\u001b[0m \u001b[38;5;124;03m\"\"\"Creates a date series using a frequency\"\"\"\u001b[39;00m\n\u001b[1;32m---> 16\u001b[0m frequency \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mAllFrequencies\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrequency\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 17\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m eomonth \u001b[38;5;129;01mand\u001b[39;00m frequency\u001b[38;5;241m.\u001b[39mdays \u001b[38;5;241m<\u001b[39m AllFrequencies\u001b[38;5;241m.\u001b[39mM\u001b[38;5;241m.\u001b[39mdays:\n\u001b[0;32m 18\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meomonth cannot be set to True if frequency is higher than \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mAllFrequencies\u001b[38;5;241m.\u001b[39mM\u001b[38;5;241m.\u001b[39mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
|
|
||||||
"\u001b[1;31mTypeError\u001b[0m: getattr(): attribute name must be string"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"%%time\n",
|
|
||||||
"ts.calculate_rolling_returns(from_date='2020-01-01', to_date='2021-01-01')"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"metadata": {
|
|
||||||
"kernelspec": {
|
|
||||||
"display_name": "Python 3 (ipykernel)",
|
|
||||||
"language": "python",
|
|
||||||
"name": "python3"
|
|
||||||
},
|
|
||||||
"language_info": {
|
|
||||||
"codemirror_mode": {
|
|
||||||
"name": "ipython",
|
|
||||||
"version": 3
|
|
||||||
},
|
|
||||||
"file_extension": ".py",
|
|
||||||
"mimetype": "text/x-python",
|
|
||||||
"name": "python",
|
|
||||||
"nbconvert_exporter": "python",
|
|
||||||
"pygments_lexer": "ipython3",
|
|
||||||
"version": "3.8.3"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"nbformat": 4,
|
|
||||||
"nbformat_minor": 5
|
|
||||||
}
|
|
||||||
Loading…
Reference in New Issue
Block a user