A Python library for working with time series data. It comes with common financial functions built-in.
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"source": [
"import datetime\n",
"import pandas as pd\n",
"\n",
"from fincal.fincal import TimeSeries\n",
"from fincal.core import Series"
]
},
{
"cell_type": "code",
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"id": "4b8ccd5f-dfff-4202-82c4-f66a30c122b6",
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"data": {
"text/plain": [
"[(datetime.datetime(2022, 1, 31, 0, 0), 310.980011),\n",
" (datetime.datetime(2021, 5, 28, 0, 0), 249.679993)]"
]
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"execution_count": 2,
"metadata": {},
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],
"source": [
"dfd = pd.read_csv('test_files/msft.csv')\n",
"# 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",
"execution_count": 3,
"id": "086d4377-d1b1-4e51-84c0-39dee28ef75e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 17 ms\n"
]
},
{
"data": {
"text/plain": [
"TimeSeries([(datetime.datetime(2022, 1, 3, 0, 0), 334.75),\n",
"\t (datetime.datetime(2022, 1, 4, 0, 0), 329.01001),\n",
"\t (datetime.datetime(2022, 1, 5, 0, 0), 316.380005)\n",
"\t ...\n",
"\t (datetime.datetime(2022, 2, 16, 0, 0), 299.5),\n",
"\t (datetime.datetime(2022, 2, 17, 0, 0), 290.730011),\n",
"\t (datetime.datetime(2022, 2, 18, 0, 0), 287.929993)], frequency='D')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"s = ts.dates >= '2022-01-01'\n",
"ts[s]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e815edc9-3746-4192-814e-bd27b2771a0c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 5.97 ms\n"
]
},
{
"data": {
"text/plain": [
"[(datetime.datetime(1992, 2, 19, 0, 0), 2.398438),\n",
" (datetime.datetime(1992, 2, 20, 0, 0), 2.447917),\n",
" (datetime.datetime(1992, 2, 21, 0, 0), 2.385417),\n",
" (datetime.datetime(1992, 2, 24, 0, 0), 2.393229),\n",
" (datetime.datetime(1992, 2, 25, 0, 0), 2.411458),\n",
" (datetime.datetime(1992, 2, 26, 0, 0), 2.541667),\n",
" (datetime.datetime(1992, 2, 27, 0, 0), 2.601563),\n",
" (datetime.datetime(1992, 2, 28, 0, 0), 2.572917),\n",
" (datetime.datetime(1992, 3, 2, 0, 0), 2.5625),\n",
" (datetime.datetime(1992, 3, 3, 0, 0), 2.567708)]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"ts.iloc[:10]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "dc469722-c816-4b57-8d91-7a3b865f86be",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 311 ms\n"
]
}
],
"source": [
"%%time\n",
"from_date = datetime.date(1994, 1, 1)\n",
"to_date = datetime.date(2022, 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": 6,
"id": "e5d357b4-4fe5-4a0a-8107-0ab6828d7c41",
"metadata": {},
"outputs": [
{
"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"
]
}
],
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"kernelspec": {
"display_name": "Python 3 (ipykernel)",
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