如何用matplotlib.finance绘制无间隙日内1分钟OHLC K线并设置自定义刻度

阿华AIGC实验室

2026-5-15

解决跨时段股票K线无间隙+自定义X轴刻度的问题

我刚碰到过类似的期货跨交易日K线绘制需求，你的核心痛点在于candlestick2_ohlc是基于序列索引（0,1,2...）来绘制K线的，不是真实的时间轴，所以直接用mdates的定位器完全不生效——它只认matplotlib的日期数值，不认我们的序列位置。

下面是完整的解决方案，既能保留无间隙的K线，又能实现你要的整点主刻度、15分钟次刻度：

def generate_pseudo_data():
    # datetime index data
    idx = pd.date_range('2017-09-13 21:01:00', '2017-09-13 23:00:00', freq='1min')
    idx = idx.append(pd.date_range('2017-09-14 09:01:00', '2017-09-14 10:15:00', freq='1min'))
    idx = idx.append(pd.date_range('2017-09-14 10:31:00', '2017-09-14 11:30:00', freq='1min'))
    idx = idx.append(pd.date_range('2017-09-14 13:31:00', '2017-09-14 15:00:00', freq='1min'))
    # OHLC
    inc = np.random.randint(-2, 3, size=idx.shape).cumsum()
    opens = 3500 + inc
    closes = opens + np.random.randint(-3, 3, idx.shape)
    range_max = np.max(np.concatenate([opens.reshape(-1, 1), closes.reshape(-1, 1)], axis=1), axis=1)
    highs = range_max + np.random.randint(0, 5, size=idx.shape)
    range_min = np.min(np.concatenate([opens.reshape(-1, 1), closes.reshape(-1, 1)], axis=1), axis=1)
    lows = range_min - np.random.randint(0, 5, size=idx.shape)
    bar_df = pd.DataFrame({'open': opens, 'high': highs, 'low': lows, 'close': closes}, index=idx)
    return bar_df

from datetime import datetime, time
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.finance import candlestick2_ohlc, candlestick_ohlc
import matplotlib.dates as mdates
from matplotlib import ticker

bar_df = generate_pseudo_data()
fig, ax = plt.subplots(figsize=(12,6))
figManager = plt.get_current_fig_manager()
figManager.window.showMaximized()

# 绘制无间隙K线
candlestick2_ohlc(ax, bar_df.open, bar_df.high, bar_df.low, bar_df.close, 
                  width=0.6, colorup='r', colordown='c', alpha=1)

# -------------------------- 核心：自定义刻度配置 --------------------------
# 1. 准备所有K线的时间序列和对应的序列索引
x_dates = bar_df.index
x_indices = np.arange(len(x_dates))

# 2. 筛选主刻度位置：整点的K线对应的索引
major_positions = x_indices[x_dates.minute == 0]
# 3. 筛选次刻度位置：每15分钟的K线对应的索引（0/15/30/45分）
minor_positions = x_indices[x_dates.minute.isin([0,15,30,45])]

# 4. 定义主刻度定位器：返回整点的索引位置
def major_locator_func(value, pos):
    # 只返回在major_positions里的位置
    if value in major_positions:
        return value
    return None
ax.xaxis.set_major_locator(ticker.FuncLocator(major_locator_func))

# 5. 定义次刻度定位器：返回15分钟间隔的索引位置
def minor_locator_func(value, pos):
    if value in minor_positions:
        return value
    return None
ax.xaxis.set_minor_locator(ticker.FuncLocator(minor_locator_func))

# 6. 定义刻度格式化器：把索引转成时间字符串（比如22:00、09:15）
def date_formatter(value, pos):
    try:
        return x_dates[int(value)].strftime('%H:%M')
    except IndexError:
        return ''
ax.xaxis.set_major_formatter(ticker.FuncFormatter(date_formatter))
ax.xaxis.set_minor_formatter(ticker.FuncFormatter(date_formatter))

# 7. 美化：旋转主刻度标签，避免重叠，调整次刻度字体大小
plt.xticks(rotation=45)
ax.tick_params(axis='x', which='minor', labelsize=8, color='#999999')

# 调整布局，防止标签被截断
plt.tight_layout()
plt.show()