忽略“分钟/小时”数据的 Plotly 烛台中的非交易日（假期/删除缺口）
Ignore Non Trading days (Holidays / remove gaps) in Plotly candlesticks for "Minutes / Hours) data
但我不知道在哪里使用该参数（来自 matplotlib 的新鲜内容仅适用于烛台）
通过一些链接，我发现 运行 下面的代码在 Day 数据上，我可以很容易地消除差距：
dt_all = pd.date_range(start=stocks.iloc[0,0],end=stocks.iloc[-1,0], freq = f'{freq}min')
dt_obs = [d.strftime("%Y-%m-%d %H:%M:%S") for d in pd.to_datetime(stocks.DATE)]
dt_breaks = [d for d in dt_all.strftime("%Y-%m-%d %H:%M:%S").tolist() if not d in dt_obs]
range_selector = dict(buttons = list([
dict(count = 5, label = '5Min', step = 'minute', stepmode = 'backward'),
dict(count = 15, label = '15Min', step = 'minute', stepmode = 'backward'),
dict(count = 75, label = '75M', step = 'minute', stepmode = 'backward'),
dict(count = 1, label = '1D', step = 'day', stepmode = 'backward'),
dict(step = 'all')]))
candle = go.Figure(data = [go.Candlestick(opacity = 0.9,x = stocks['Date'], name = 'X',
open = stocks['Open'],
high = stocks['High'],
low = stocks['Low'],
close = stocks['Close']),])
candle.update_xaxes(
title_text = 'Date',
rangeslider_visible = True,
rangebreaks=[dict(values=dt_breaks)],
range_selector = range_selector)
但我有 5 分钟的数据：
DATE OPEN HIGH LOW CLOSE 52W H 52W L SYMBOL
374 2022-01-14 15:25:00+05:30 720.25 722.35 720.25 721.55 NaN NaN BHARTIARTL
373 2022-01-14 15:20:00+05:30 720.30 720.45 719.45 720.25 NaN NaN BHARTIARTL
372 2022-01-14 15:15:00+05:30 720.75 720.90 720.15 720.30 NaN NaN BHARTIARTL
371 2022-01-14 15:10:00+05:30 720.35 720.90 720.20 720.70 NaN NaN BHARTIARTL
370 2022-01-14 15:05:00+05:30 720.70 720.90 720.05 720.20 NaN NaN BHARTIARTL
... ... ... ... ... ... ... ... ...
4 2022-01-10 09:35:00+05:30 706.05 707.15 705.65 706.55 NaN NaN BHARTIARTL
3 2022-01-10 09:30:00+05:30 705.90 706.40 705.05 706.05 NaN NaN BHARTIARTL
2 2022-01-10 09:25:00+05:30 707.10 707.95 705.60 705.60 NaN NaN BHARTIARTL
1 2022-01-10 09:20:00+05:30 709.00 709.40 706.15 707.10 NaN NaN BHARTIARTL
0 2022-01-10 09:15:00+05:30 705.40 709.00 705.40 708.55 NaN NaN BHARTIARTL
使用上面的代码得到的结果为：
在这种情况下可以做什么？
还有其他更适合您的用例的答案。这 可以 以 5 分钟为间隔有点棘手。只需考虑时间戳的格式，并仔细执行以下步骤：
- 找出从第一次观察到最后一次观察的所有个时间间隔
- 找出您在该完整时间轴中发生的观察结果
- 隔离其余日期并将它们包含在 x 轴的
rangebreaks 属性中
- 调整
rangebreaks 的 dvalues 属性以匹配 your time interval in milliseconds 与 fig.update_xaxes(rangebreaks=[dict(dvalue = 5*60*1000, values=dt_breaks)])
基本代码元素：
# grab first and last observations from df.date and make a continuous date range from that
dt_all = pd.date_range(start=df['Date'].iloc[0],end=df['Date'].iloc[-1], freq = '5min')
# check which dates from your source that also accur in the continuous date range
dt_obs = [d.strftime("%Y-%m-%d %H:%M:%S") for d in df['Date']]
# isolate missing timestamps
dt_breaks = [d for d in dt_all.strftime("%Y-%m-%d %H:%M:%S").tolist() if not d in dt_obs]
# adjust xaxis for rangebreaks
fig.update_xaxes(rangebreaks=[dict(dvalue = 5*60*1000, values=dt_breaks)])
情节 1：显示缺少时间戳
图 2：未显示缺少的时间戳
完整代码：
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
import numpy as np
# sample data
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv').tail(90)
df = df[df.columns[:6]]
df['Date'] = pd.date_range("2018-01-01", periods=len(df), freq="5min")
df.columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume']
df = df.tail(10)
# remove some data
np.random.seed(0)
remove_n = 4
drop_indices = np.random.choice(df.index, remove_n, replace=False)
df = df.drop(drop_indices)
# plotly candlestick figure
fig = go.Figure(data=[go.Candlestick(
x=df['Date'],
open=df['Open'], high=df['High'],
low=df['Low'], close=df['Close'],
)])
# grab first and last observations from df.date and make a continuous date range from that
dt_all = pd.date_range(start=df['Date'].iloc[0],end=df['Date'].iloc[-1], freq = '5min')
# check which dates from your source that also accur in the continuous date range
dt_obs = [d.strftime("%Y-%m-%d %H:%M:%S") for d in df['Date']]
# isolate missing timestamps
dt_breaks = [d for d in dt_all.strftime("%Y-%m-%d %H:%M:%S").tolist() if not d in dt_obs]
dt_breaks = pd.to_datetime(dt_breaks)
fig.show()
fig.update_xaxes(rangebreaks=[dict(dvalue = 5*60*1000, values=dt_breaks)] )
print(fig.layout.xaxis.rangebreaks)
fig.show()
matplotlib 的新鲜内容仅适用于烛台）
通过一些链接，我发现 运行 下面的代码在 Day 数据上，我可以很容易地消除差距：
dt_all = pd.date_range(start=stocks.iloc[0,0],end=stocks.iloc[-1,0], freq = f'{freq}min')
dt_obs = [d.strftime("%Y-%m-%d %H:%M:%S") for d in pd.to_datetime(stocks.DATE)]
dt_breaks = [d for d in dt_all.strftime("%Y-%m-%d %H:%M:%S").tolist() if not d in dt_obs]
range_selector = dict(buttons = list([
dict(count = 5, label = '5Min', step = 'minute', stepmode = 'backward'),
dict(count = 15, label = '15Min', step = 'minute', stepmode = 'backward'),
dict(count = 75, label = '75M', step = 'minute', stepmode = 'backward'),
dict(count = 1, label = '1D', step = 'day', stepmode = 'backward'),
dict(step = 'all')]))
candle = go.Figure(data = [go.Candlestick(opacity = 0.9,x = stocks['Date'], name = 'X',
open = stocks['Open'],
high = stocks['High'],
low = stocks['Low'],
close = stocks['Close']),])
candle.update_xaxes(
title_text = 'Date',
rangeslider_visible = True,
rangebreaks=[dict(values=dt_breaks)],
range_selector = range_selector)
但我有 5 分钟的数据：
DATE OPEN HIGH LOW CLOSE 52W H 52W L SYMBOL
374 2022-01-14 15:25:00+05:30 720.25 722.35 720.25 721.55 NaN NaN BHARTIARTL
373 2022-01-14 15:20:00+05:30 720.30 720.45 719.45 720.25 NaN NaN BHARTIARTL
372 2022-01-14 15:15:00+05:30 720.75 720.90 720.15 720.30 NaN NaN BHARTIARTL
371 2022-01-14 15:10:00+05:30 720.35 720.90 720.20 720.70 NaN NaN BHARTIARTL
370 2022-01-14 15:05:00+05:30 720.70 720.90 720.05 720.20 NaN NaN BHARTIARTL
... ... ... ... ... ... ... ... ...
4 2022-01-10 09:35:00+05:30 706.05 707.15 705.65 706.55 NaN NaN BHARTIARTL
3 2022-01-10 09:30:00+05:30 705.90 706.40 705.05 706.05 NaN NaN BHARTIARTL
2 2022-01-10 09:25:00+05:30 707.10 707.95 705.60 705.60 NaN NaN BHARTIARTL
1 2022-01-10 09:20:00+05:30 709.00 709.40 706.15 707.10 NaN NaN BHARTIARTL
0 2022-01-10 09:15:00+05:30 705.40 709.00 705.40 708.55 NaN NaN BHARTIARTL
使用上面的代码得到的结果为：
- 找出从第一次观察到最后一次观察的所有个时间间隔
- 找出您在该完整时间轴中发生的观察结果
- 隔离其余日期并将它们包含在 x 轴的
rangebreaks属性中 - 调整
rangebreaks的dvalues属性以匹配 your time interval in milliseconds 与fig.update_xaxes(rangebreaks=[dict(dvalue = 5*60*1000, values=dt_breaks)])
基本代码元素：
# grab first and last observations from df.date and make a continuous date range from that
dt_all = pd.date_range(start=df['Date'].iloc[0],end=df['Date'].iloc[-1], freq = '5min')
# check which dates from your source that also accur in the continuous date range
dt_obs = [d.strftime("%Y-%m-%d %H:%M:%S") for d in df['Date']]
# isolate missing timestamps
dt_breaks = [d for d in dt_all.strftime("%Y-%m-%d %H:%M:%S").tolist() if not d in dt_obs]
# adjust xaxis for rangebreaks
fig.update_xaxes(rangebreaks=[dict(dvalue = 5*60*1000, values=dt_breaks)])
情节 1：显示缺少时间戳
图 2：未显示缺少的时间戳
完整代码：
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
import numpy as np
# sample data
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv').tail(90)
df = df[df.columns[:6]]
df['Date'] = pd.date_range("2018-01-01", periods=len(df), freq="5min")
df.columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume']
df = df.tail(10)
# remove some data
np.random.seed(0)
remove_n = 4
drop_indices = np.random.choice(df.index, remove_n, replace=False)
df = df.drop(drop_indices)
# plotly candlestick figure
fig = go.Figure(data=[go.Candlestick(
x=df['Date'],
open=df['Open'], high=df['High'],
low=df['Low'], close=df['Close'],
)])
# grab first and last observations from df.date and make a continuous date range from that
dt_all = pd.date_range(start=df['Date'].iloc[0],end=df['Date'].iloc[-1], freq = '5min')
# check which dates from your source that also accur in the continuous date range
dt_obs = [d.strftime("%Y-%m-%d %H:%M:%S") for d in df['Date']]
# isolate missing timestamps
dt_breaks = [d for d in dt_all.strftime("%Y-%m-%d %H:%M:%S").tolist() if not d in dt_obs]
dt_breaks = pd.to_datetime(dt_breaks)
fig.show()
fig.update_xaxes(rangebreaks=[dict(dvalue = 5*60*1000, values=dt_breaks)] )
print(fig.layout.xaxis.rangebreaks)
fig.show()