prevent overwriting errors

This commit is contained in:
Gourav Kumar 2022-02-22 11:27:39 +05:30
parent b4d5291572
commit 6c006cb6a4

View File

@ -2,7 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 1,
"id": "3f7938c0-98e3-43b8-86e8-4f000cda7ce5", "id": "3f7938c0-98e3-43b8-86e8-4f000cda7ce5",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -16,160 +16,107 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 2,
"id": "4b8ccd5f-dfff-4202-82c4-f66a30c122b6", "id": "4b8ccd5f-dfff-4202-82c4-f66a30c122b6",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"[(datetime.datetime(2022, 1, 31, 0, 0), 310.980011),\n",
" (datetime.datetime(2021, 5, 28, 0, 0), 249.679993)]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"dfd = pd.read_csv('test_files/nav_history_daily - copy.csv')\n", "dfd = pd.read_csv('test_files/msft.csv')\n",
"dfd = dfd[dfd['amfi_code'] == 118825].reset_index(drop=True)" "# dfd = dfd[dfd['amfi_code'] == 118825].reset_index(drop=True)\n",
"ts = TimeSeries([(i.date, i.nav) for i in dfd.itertuples()], frequency='D')\n",
"repr(ts)\n",
"ts[['2022-01-31', '2021-05-28']]"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 3,
"id": "c52b0c2c-dd01-48dd-9ffa-3147ec9571ef", "id": "086d4377-d1b1-4e51-84c0-39dee28ef75e",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Warning: The input data contains duplicate dates which have been ignored.\n" "Wall time: 17 ms\n"
] ]
}, },
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"TimeSeries([(datetime.datetime(2013, 1, 2, 0, 0), 18.972),\n", "TimeSeries([(datetime.datetime(2022, 1, 3, 0, 0), 334.75),\n",
"\t (datetime.datetime(2013, 1, 3, 0, 0), 19.011),\n", "\t (datetime.datetime(2022, 1, 4, 0, 0), 329.01001),\n",
"\t (datetime.datetime(2013, 1, 4, 0, 0), 19.008)\n", "\t (datetime.datetime(2022, 1, 5, 0, 0), 316.380005)\n",
"\t ...\n", "\t ...\n",
"\t (datetime.datetime(2022, 2, 10, 0, 0), 86.5),\n", "\t (datetime.datetime(2022, 2, 16, 0, 0), 299.5),\n",
"\t (datetime.datetime(2022, 2, 11, 0, 0), 85.226),\n", "\t (datetime.datetime(2022, 2, 17, 0, 0), 290.730011),\n",
"\t (datetime.datetime(2022, 2, 14, 0, 0), 82.53299999999999)], frequency='D')" "\t (datetime.datetime(2022, 2, 18, 0, 0), 287.929993)], frequency='D')"
] ]
}, },
"execution_count": 12, "execution_count": 3,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"ts = TimeSeries([(i.date, i.nav) for i in dfd.itertuples()], frequency='D')\n", "%%time\n",
"ts" "s = ts.dates >= '2022-01-01'\n",
]
},
{
"cell_type": "code",
"execution_count": 6,
"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": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts[['2022-01-31', '2021-05-31']]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "086d4377-d1b1-4e51-84c0-39dee28ef75e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"TimeSeries([(datetime.datetime(2021, 2, 15, 0, 0), 73.483),\n",
"\t (datetime.datetime(2021, 2, 16, 0, 0), 73.237),\n",
"\t (datetime.datetime(2021, 2, 17, 0, 0), 72.98)\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": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts[ts.dates>'2021-02-14']"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "6f1226a3-2327-435b-88e7-fd0fdcc8cc1c",
"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": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = ts.dates > '2020-01-01'\n",
"ts[s]" "ts[s]"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 4,
"id": "e815edc9-3746-4192-814e-bd27b2771a0c", "id": "e815edc9-3746-4192-814e-bd27b2771a0c",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 5.97 ms\n"
]
},
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"[(datetime.datetime(2013, 1, 2, 0, 0), 18.972),\n", "[(datetime.datetime(1992, 2, 19, 0, 0), 2.398438),\n",
" (datetime.datetime(2013, 1, 3, 0, 0), 19.011),\n", " (datetime.datetime(1992, 2, 20, 0, 0), 2.447917),\n",
" (datetime.datetime(2013, 1, 4, 0, 0), 19.008),\n", " (datetime.datetime(1992, 2, 21, 0, 0), 2.385417),\n",
" (datetime.datetime(2013, 1, 7, 0, 0), 18.95),\n", " (datetime.datetime(1992, 2, 24, 0, 0), 2.393229),\n",
" (datetime.datetime(2013, 1, 8, 0, 0), 18.954),\n", " (datetime.datetime(1992, 2, 25, 0, 0), 2.411458),\n",
" (datetime.datetime(2013, 1, 9, 0, 0), 18.94),\n", " (datetime.datetime(1992, 2, 26, 0, 0), 2.541667),\n",
" (datetime.datetime(2013, 1, 10, 0, 0), 18.957),\n", " (datetime.datetime(1992, 2, 27, 0, 0), 2.601563),\n",
" (datetime.datetime(2013, 1, 11, 0, 0), 18.948),\n", " (datetime.datetime(1992, 2, 28, 0, 0), 2.572917),\n",
" (datetime.datetime(2013, 1, 14, 0, 0), 19.177),\n", " (datetime.datetime(1992, 3, 2, 0, 0), 2.5625),\n",
" (datetime.datetime(2013, 1, 15, 0, 0), 19.272000000000002)]" " (datetime.datetime(1992, 3, 3, 0, 0), 2.567708)]"
] ]
}, },
"execution_count": 10, "execution_count": 4,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"%%time\n",
"ts.iloc[:10]" "ts.iloc[:10]"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 5,
"id": "dc469722-c816-4b57-8d91-7a3b865f86be", "id": "dc469722-c816-4b57-8d91-7a3b865f86be",
"metadata": { "metadata": {
"tags": [] "tags": []
@ -179,26 +126,66 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"CPU times: total: 15.6 ms\n", "Wall time: 311 ms\n"
"Wall time: 10 ms\n"
] ]
} }
], ],
"source": [ "source": [
"%%time\n", "%%time\n",
"from_date = datetime.date(2020, 1, 1)\n", "from_date = datetime.date(1994, 1, 1)\n",
"to_date = datetime.date(2021, 1, 1)\n", "to_date = datetime.date(2022, 1, 1)\n",
"# print(ts.calculate_returns(to_date, years=7))\n", "# print(ts.calculate_returns(to_date, years=7))\n",
"rr = ts.calculate_rolling_returns(from_date, to_date)" "rr = ts.calculate_rolling_returns(from_date, to_date)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 6,
"id": "e5d357b4-4fe5-4a0a-8107-0ab6828d7c41", "id": "e5d357b4-4fe5-4a0a-8107-0ab6828d7c41",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
"source": [] {
"data": {
"text/plain": [
"TimeSeries([(datetime.datetime(1994, 1, 3, 0, 0), -0.06149359306648605),\n",
"\t (datetime.datetime(1994, 1, 4, 0, 0), -0.05433177603118022),\n",
"\t (datetime.datetime(1994, 1, 5, 0, 0), -0.04913276300578029)\n",
"\t ...\n",
"\t (datetime.datetime(2021, 12, 29, 0, 0), 0.5255410267822715),\n",
"\t (datetime.datetime(2021, 12, 30, 0, 0), 0.5306749265370103),\n",
"\t (datetime.datetime(2021, 12, 31, 0, 0), 0.5120942811985818)], frequency='D')"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rr"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4bad2efa",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Series([1.0, 2.0, 3.0, 4.0, 5.0])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sr = Series([1, 2, 3, 4, 5], 'number')\n",
"sr"
]
} }
], ],
"metadata": { "metadata": {
@ -217,7 +204,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.8.3" "version": "3.9.2"
} }
}, },
"nbformat": 4, "nbformat": 4,