serial-data-handler-zxw 0.3.8

A simple library to handle time gaps in data , especially in AI 人工智能中时间数据预处理

process serial data for AI training

人工智能训练中,对于时间断层数据的处理工具包

• 根据断点,分配数据集,保证训练数据的连续性
• allocate data set according to breakpoints, to ensure the continuity of training data

code demo: 生成训练数据(避开数据断点)

# Attention:
# 传入的数据必须指定时间列(或其他顺序列)
# You must specify the time column (or other sequential column) of the incoming data
# 传入的数据必须已按顺序排列好
# The incoming data must be sorted in order

from datetime import timedelta
from serial_data_handler_zxw import 生成训练数据_避开时间断点, 时间列_三角函数化
import pandas as pd

csv_path = "/Volumes/time_serial_data.csv"
data = pd.read_csv(csv_path)

# specific the time column named '收盘时间' , 
# and set the gap is 2 minutes , 
# it means that if the gap between two adjacent data > 2 minutes, it will be considered as a breakpoint
# 生成训练数据
x = 生成训练数据_避开时间断点(data, column_timestamp='收盘时间', gap=timedelta(minutes=2))
print(x.断点)
训练数据index = x.数据划分_避开断点(input长度=100, output长度=100, step=1)
print(len(训练数据index))

# If your data interval < 1s, please do the corresponding multiplication conversion
# for example: 1ms data, you should multiply by 1000, convert to second-level data
# time column trigonometric function
# 如果您的数据间隔小于1秒,请做相应的乘法转换, 例如: 1毫秒的数据,请乘以1000,转换为秒级数据
# 时间列_三角函数化
data['收盘时间'] = pd.to_datetime(data['收盘时间'])
data['收盘时间'] = 时间列_三角函数化(data['收盘时间'], 周期=timedelta(days=1))
print(data['收盘时间'])

code demo: 多尺度时间数据的对齐

import pandas as pd
from datetime import datetime
from serial_data_handler_zxw import 时间序列_数据对齐

data = pd.read_csv('/Volumes/AI_1505056/量化交易/币安_K线数据_1d/BTCUSDT-1m-201909-202308.csv')
# to datetime
data['收盘时间'] = pd.to_datetime(data['收盘时间'])

# drop part of columns if it exits
data.drop(columns=['开盘时间戳', '收盘时间戳'], inplace=True)

#
数据预处理 = 时间序列_数据对齐(data, '收盘时间')
i = 数据预处理.查找_时间范围(datetime(2023, 8, 29, 16, 54, 0), 查找精度='1d')
print(i)

code demo3: pytorch的金融K线数据预处理

from serial_data_handler_zxw import 金融K线_AI数据预处理 as k_ai

# print help
k_ai.金融K线_AI数据预处理.help()

# 数据标准化
csv_file = "/Volumes/AI_1505056/量化交易/币安_K线数据/BTCUSDT-1m-201909-202308.csv"
x = k_ai.金融K线_AI数据预处理(csv_file, 100, 100)
xn = x.标准化(x.data)

# 适用dateset的__getitem__(i)的数据获取
x.get_训练or测试数据(i=0,data=xn,是训练集=True)

python setup.py sdist bdist_wheel twine upload dist/*