Merge branch 'UserDict-trial' of http://192.168.0.114:3000/buddy/fincal into UserDict-trial

.ipynb_checkpoints/README-checkpoint.md

@@ -0,0 +1,15 @@
+# Fincal
+This module simplified handling of time-series data
+
+## The problem
+Time series data often have missing data points. These missing points mess things up when you are trying to do a comparison between two sections of a time series.
+
+To make things worse, most libraries don't allow comparison based on dates. Month to Month and year to year comparisons become difficult as they cannot be translated into number of days. However, these are commonly used metrics while looking at financial data.
+
+## The Solution
+Fincal aims to simplify things by allowing you to:
+* Compare time-series data based on dates
+* Easy way to work around missing dates by taking the closest data points
+* Completing series with missing data points using forward fill and backward fill
+
+## Examples

.ipynb_checkpoints/testing-checkpoint.ipynb

@@ -0,0 +1,129 @@
+{
+ "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
+}

.vscode/launch.json

@@ -0,0 +1,15 @@
+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python: Current File",
+            "type": "python",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal"
+        }
+    ]
+}

fincal/core.py

@@ -108,9 +108,9 @@ def _parse_date(date: str, date_format: str = None):
     try:
         date = datetime.datetime.strptime(date, date_format)
     except TypeError:
-        raise Exception("Date does not seem to be valid date-like string")
+        raise ValueError("Date does not seem to be valid date-like string")
     except ValueError:
-        raise Exception("Date could not be parsed. Have you set the correct date format in FincalOptions.date_format?")
+        raise ValueError("Date could not be parsed. Have you set the correct date format in FincalOptions.date_format?")
     return date
 
 
@@ -156,6 +156,12 @@ class Series(UserList):
     def __repr__(self):
         return f"{self.__class__.__name__}({self.data})"
 
+    def __getitem__(self, i):
+        if isinstance(i, slice):
+            return self.__class__(self.data[i], self.dtype)
+        else:
+            return self.data[i]
+
     def __gt__(self, other):
         if self.dtype == bool:
             raise TypeError("> not supported for boolean series")
@@ -299,7 +305,7 @@ class TimeSeriesCore(UserDict):
             else:
                 dates_to_return = [self.dates[i] for i, j in enumerate(key) if j]
                 data_to_return = [(key, self.data[key]) for key in dates_to_return]
-                return TimeSeriesCore(data_to_return, frequency=self.frequency.symbol)
+                return self.__class__(data_to_return, frequency=self.frequency.symbol)
 
         if isinstance(key, int):
             raise KeyError(f"{key}. For index based slicing, use .iloc[{key}]")

fincal/fincal.py

@@ -75,7 +75,7 @@ class TimeSeries(TimeSeriesCore):
             self.data = new_ts
             return None
 
-        return TimeSeries(new_ts, frequency=self.frequency.symbol)
+        return self.__class__(new_ts, frequency=self.frequency.symbol)
 
     def bfill(self, inplace: bool = False, limit: int = None) -> Union[TimeSeries, None]:
         """Backward fill missing dates in the time series
@@ -109,7 +109,7 @@ class TimeSeries(TimeSeriesCore):
             self.data = new_ts
             return None
 
-        return TimeSeries(new_ts, frequency=self.frequency.symbol)
+        return self.__class__(new_ts, frequency=self.frequency.symbol)
 
     def calculate_returns(
         self,

testing.ipynb

@@ -0,0 +1,192 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "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": 2,
+   "id": "4b8ccd5f-dfff-4202-82c4-f66a30c122b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dfd = pd.read_csv('test_files/nav_history_daily - copy.csv')\n",
+    "\n",
+    "dfd = dfd[dfd['amfi_code'] == 118825].reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c52b0c2c-dd01-48dd-9ffa-3147ec9571ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning: The input data contains duplicate dates which have been ignored.\n"
+     ]
+    },
+    {
+     "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": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ts = TimeSeries([(i.date, i.nav) for i in dfd.itertuples()], frequency='D')\n",
+    "\n",
+    "ts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9e8ff6c6-3a36-435a-ba87-5b9844c18779",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(datetime.datetime(2022, 1, 31, 0, 0), 85.18),\n",
+       " (datetime.datetime(2021, 5, 31, 0, 0), 74.85)]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ts[['2022-01-31', '2021-05-31']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4d927a61-0f90-4b47-89b7-0e0d3ab1b442",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = ts.dates > '2020-01-01'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f90074f8-5173-49a9-a7d6-ceac01e92431",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TimeSeries([(datetime.datetime(2020, 1, 2, 0, 0), 58.285),\n",
+       "\t    (datetime.datetime(2020, 1, 3, 0, 0), 58.056999999999995),\n",
+       "\t    (datetime.datetime(2020, 1, 6, 0, 0), 56.938)\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": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ts[s]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "dc469722-c816-4b57-8d91-7a3b865f86be",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: total: 15.6 ms\n",
+      "Wall time: 13 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "from_date = datetime.date(2020, 1, 1)\n",
+    "to_date = datetime.date(2021, 1, 1)\n",
+    "# print(ts.calculate_returns(to_date, years=7))\n",
+    "rr = ts.calculate_rolling_returns(from_date, to_date)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "086d4377-d1b1-4e51-84c0-39dee28ef75e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "list"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(rr)"
+   ]
+  }
+ ],
+ "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
+}