quantchdb 0.1.11

A Well-Encapsulated ClickHouse Database APIs Lib

quantchdb: A Well-Encapsulated ClickHouse Database APIs Lib

Quick Start

Install quantchdb:

pip install quantchdb==0.1.11  -i https://pypi.org/simple

An example of how to use quantchdb:

1. Import quantchdb

from quantchdb import ClickHouseDatabase
import pandas as pd
import numpy as np

2. Configure ClickHouseDatabase instance

# To connect your clickhouse database, you need to setup your config, in which the '.env' method is recommmended for security
config = {
            "host": os.getenv("DB_HOST", "localhost"),
            "port": int(os.getenv("DB_PORT", 9000)),
            "user": os.getenv("DB_USER", "default"),
            "password": os.getenv("DB_PASSWORD", ""),
            "database": os.getenv("DB_DATABASE", "default")
        }

# 'terminal_log' and 'file_log' control the log records. 'True' denotes the corresponding log method will be executed. You can control the logs' file path by the 'log_file' param. 
db = ClickHouseDatabase(config=config, terminal_log=True, file_log=False)

3. Functions

# Fetch data from clickhouse database
sql = "SELECT * FROM stocks.snap ORDER BY date DESC LIMIT 5"
df = db.fetch(sql)

# Execute SQL sentence
sql = f"""
CREATE TABLE IF NOT EXISTS etf.kline_1m(
	`exg` UInt8 NOT NULL COMMENT '交易所标识，沪市为1，深市为0， 北交所为2',
    `code` String NOT NULL COMMENT '股票代码',
    `date` Date NOT NULL COMMENT '日期',
    `date_time` DateTime('Asia/Shanghai') NOT NULL COMMENT '日期时间，最高精度为秒',
    `time_int` UInt32 NOT NULL COMMENT '从当日开始至当前时刻的毫秒数',
    `open` Float32 NULL COMMENT 'K线开始价格',
    `high` Float32 NULL COMMENT 'K线内最高价',
    `low` Float32 NULL COMMENT 'K线内最低价',
    `close` Float32 NULL COMMENT 'K线结束价格',
    `volume` UInt64 NULL COMMENT 'K线内成交量',
    `amount` Float32 NULL COMMENT 'K线内成交额'
)Engine = ReplacingMergeTree()
ORDER BY (code, date_time);
"""
db.execute(sql)

# Insert dataframe into clickhouse database. Before you insert your dataframe, you need to make sure the corresponding database and table are existed.
# Make sure the dtypes of DataFrame is consistent with dtypes of clickhouse table, or else insert_dataframe may failed.

file_path = "Your/Data/Path/kline_1m.csv"
dtype_dict = {
    'exg' : np.int,
    'code' : str,
    'open' : np.float32,
    'close' : np.float32,
    'high' : np.float32,
    'low' : np.float32,
    'amount' : np.float32
}
df = pd.read_csv(file_path, dtype=dtype_dict)

#Int type with NA need to deal with seperately
df['volume'] = pd.to_numeric(df['volume'], errors='coerce').astype('UInt64')

db.insert_dataframe(
            df=df,
            table_name="etf.kline_1m",
            datetime_cols=['date','date_time'],
            convert_tz=False
        )


# Create table from DataFrame and insert data into table automatically. This method is not recommanded, because data type inferred may be not suitable or even the sentence failed.

# You can use dtypes to make sure some columns have corrected dtypes and use other params to control the create sql sentence, though dtypes/engine/orderby/other have default values.

db.create_table_from_df(df=df, 
                        table_name='test.etf_kline_1m',
                        dtypes={'code': 'String',
                                'date':'Date',
                                'date_time' :'DateTime'},
                         engine='ReplacingMergeTree()', 
                         orderby='(code,date_time)',
                         other='PARTITION BY toYYYYMM(code)')