From 2f0f1e0e479b1ec354645dc6043f7599e4ed4703 Mon Sep 17 00:00:00 2001 From: Gourav Kumar Date: Mon, 21 Feb 2022 13:11:38 +0530 Subject: [PATCH] further testing --- testing.ipynb | 382 ++++++++++++++++++++++++++++++++++++++++++++------ 1 file changed, 338 insertions(+), 44 deletions(-) diff --git a/testing.ipynb b/testing.ipynb index 05fb9fa..b0a5121 100644 --- a/testing.ipynb +++ b/testing.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 14, + "execution_count": 1, "id": "3f7938c0-98e3-43b8-86e8-4f000cda7ce5", "metadata": {}, "outputs": [], @@ -16,28 +16,20 @@ }, { "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", + "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": 19, - "id": "4bc95ae0-8c33-4eab-acf9-e765d22979b8", + "execution_count": 3, + "id": "c52b0c2c-dd01-48dd-9ffa-3147ec9571ef", "metadata": {}, "outputs": [ { @@ -46,18 +38,7 @@ "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": [ @@ -67,47 +48,360 @@ "\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')" + "\t (datetime.datetime(2022, 2, 14, 0, 0), 82.533)], frequency='D')" ] }, - "execution_count": 20, + "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": 22, - "id": "dc469722-c816-4b57-8d91-7a3b865f86be", + "execution_count": 4, + "id": "9e8ff6c6-3a36-435a-ba87-5b9844c18779", "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:1\u001b[0m, in \u001b[0;36m\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" + "data": { + "text/plain": [ + "[(datetime.datetime(2021, 1, 1, 0, 0), 66.652),\n", + " (datetime.datetime(2020, 1, 1, 0, 0), 57.804)]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ts[['2021-01-01', '2020-01-01']]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4d927a61-0f90-4b47-89b7-0e0d3ab1b442", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Series([False, False, False, False, False, False, False, False, False, False])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s = ts.dates > '2020-01-01'\n", + "s[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "311d1c07-d827-4d69-855f-883e1198c162", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "bb625050-5d7b-45a9-9cde-ac6e599adea5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + " ... \n", + "2196 True\n", + "2197 True\n", + "2198 True\n", + "2199 True\n", + "2200 True\n", + "Length: 2201, dtype: bool" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "dc469722-c816-4b57-8d91-7a3b865f86be", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wall time: 14 ms\n" ] + }, + { + "data": { + "text/plain": [ + "[(datetime.datetime(2020, 1, 1, 0, 0), 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0.1496252444998356),\n", + " (datetime.datetime(2021, 1, 1, 0, 0), 0.15306899176527566)]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "%%time\n", - "ts.calculate_rolling_returns(from_date='2020-01-01', to_date='2021-01-01')" + "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", + "ts.calculate_rolling_returns(from_date, to_date)" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -121,7 +415,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.9.2" } }, "nbformat": 4,