Introduction

This is the Python binding for msd. msd is a high-performance financial time series database.

For high level usage, it provides MsdClient class, which uses requests and a DataFrame (pandas or polars) as the data format. You should install requests, pandas or polars manually.

High level API return DataFrame (pandas or polars) as the data format.

Installation

pip install pymsd

High Level Usage

install requests by pip install requests
choose a DataFrame library, pandas or polars, and install it by pip install pandas or pip install polars
create a MsdClient instance by pymsd.create_msd_pandas or pymsd.create_msd_polars with the url of msd server
use the MsdClient.load method to query data from msd

Low Level Usage

The transport layer is based on HTTP, and the data format can be JSON or Binary. The Binary format is more efficient and recommended for non-browser clients, binary format is parsed by pymsd._msd native library.

Because of the HTTP request library is very common, this package does not provide a client, instead it provides parse_reader and parse_reader_async functions to parse the HTTP response. With these functions, you can use any HTTP request library to query data from msd. For example, you can use requests for synchronous requests, and aiohttp for asynchronous requests.

It also provides pymsd.query and pymsd.query_async functions to query data from msd, which just demonstrate how to use pymsd.parse_reader and pymsd.parse_reader_async. When your want use these functions, your should install requests or aiohttp manually.

Performance

The performance of parse_reader and parse_reader_async is just same as the Rust based client, with about 1~2% overhead. For a test node, it can query about 6M rows of 1800 different symbols in about 1 second. The following table is the result of pytest .

RESULT_OBJECTS = 1789
RESULT_ROWS = 6245835
SQL_TO_TEST = "select * from kline where obj='SH60*'"

Name (time in ms)	Min	Max	Mean	StdDev	Median	IQR	Outliers	OPS	Rounds	Iterations
test_query_many_ndarray	972.8022 (1.0)	978.1467 (1.0)	976.1578 (1.0)	2.1991 (1.0)	975.8558 (1.0)	3.0612 (1.0)	1;0	1.0244 (1.0)	5	1
test_query_many_dataframe	972.8057 (1.00)	987.1984 (1.01)	980.0452 (1.00)	6.8454 (3.11)	980.4594 (1.00)	13.2980 (4.34)	2;0	1.0204 (1.00)	5	1
test_query_many_polars	973.1088 (1.00)	995.1073 (1.02)	982.3909 (1.01)	9.5757 (4.35)	979.7399 (1.00)	16.7033 (5.46)	1;0	1.0179 (0.99)	5	1
test_query_concat_polars	991.4861 (1.02)	999.8344 (1.02)	994.1573 (1.02)	3.3793 (1.54)	993.7752 (1.02)	3.8383 (1.25)	1;0	1.0059 (0.98)	5	1
test_query_concat_pandas	1,161.1306 (1.19)	1,186.2676 (1.21)	1,172.4941 (1.20)	11.3836 (5.18)	1,167.3264 (1.20)	20.0729 (6.56)	1;0	0.8529 (0.83)	5	1

see the test_query.py for more details.

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