如何在 Matplotlib 中创建烛台图？

烛台图，通常称为日本烛台图，是一种实时显示股票、衍生品和其他金融工具价格变动的金融图表，只需检查四个基本组成部分。开盘价、最高价、最低价和收盘价是四个关键要素，烛台图已被使用。它是世界上最古老的图表之一。

制作烛台图的语法如下。在这里，我们使用 plt.bar 方法来构建烛台图。

语法：

plt.bar(up.index,up.close-up.open,bottom=up.open,color)

where,

• “up” dataframe will store the stock_prices when the closing stock price is greater than or equal to the opening stock prices

plt.bar(down.index,down.close-down.open,bottom=down.open,color)

 where ,

• “down” dataframe will store the stock_prices when the closing stock price is lesser than the opening stock prices

示例 1：用上下棒绘制 4 列的Python程序

Python3

import pandas as pd
import matplotlib.pyplot as plt
  
# DataFrame to represent opening , closing, high 
# and low prices of a stock for a week
stock_prices = pd.DataFrame({'open': [36, 56, 45, 29, 65, 66, 67],
                             'close': [29, 72, 11, 4, 23, 68, 45],
                             'high': [42, 73, 61, 62, 73, 56, 55],
                             'low': [22, 11, 10, 2, 13, 24, 25]},
                            index=pd.date_range(
                              "2021-11-10", periods=7, freq="d"))
  
  
plt.figure()
  
# "up" dataframe will store the stock_prices 
# when the closing stock price is greater
# than or equal to the opening stock prices
up = stock_prices[stock_prices.close >= stock_prices.open]
  
# "down" dataframe will store the stock_prices
# when the closing stock price is
# lesser than the opening stock prices
down = stock_prices[stock_prices.close < stock_prices.open]
  
# When the stock prices have decreased, then it
# will be represented by blue color candlestick
col1 = 'blue'
  
# When the stock prices have increased, then it 
# will be represented by green color candlestick
col2 = 'green'
  
# Setting width of candlestick elements
width = .3
width2 = .03
  
# Plotting up prices of the stock
plt.bar(up.index, up.close-up.open, width, bottom=up.open, color=col1)
plt.bar(up.index, up.high-up.close, width2, bottom=up.close, color=col1)
plt.bar(up.index, up.low-up.open, width2, bottom=up.open, color=col1)
  
# Plotting down prices of the stock
plt.bar(down.index, down.close-down.open, width, bottom=down.open, color=col2)
plt.bar(down.index, down.high-down.open, width2, bottom=down.open, color=col2)
plt.bar(down.index, down.low-down.close, width2, bottom=down.close, color=col2)
  
# rotating the x-axis tick labels at 30degree 
# towards right
plt.xticks(rotation=30, ha='right')
  
# displaying candlestick chart of stock data 
# of a week
plt.show()

输出：

我们还可以使用 mpl_finance 模块制作烛台图。要使用 mpl_finance 我们需要先安装它，这可以通过使用代码来完成。我们必须安装 mpl_finance。

pip install mpl_finance

语法：

candlestick_ohlc(ax, ohlc.values, width, colorup, colordown)

where

• ac is the axis
• values are the input values
• width is the width of each candle stick
• colorup is the color for up sticks
• colordown is the color for down sticks

示例 2：这里，我们定义了一个包含 5 个参数的股票价格数据集，即开盘价、收盘价、最高价、最低价和指数（即日期），然后使用 pandas.to DateTime 转换日期，然后使用 pandas。 astype 将所有数据转换为float()。

Python3

# Importing all the required libraries
  
import matplotlib.pyplot as plt
from mpl_finance import candlestick_ohlc
import pandas as pd
import matplotlib.dates as mpl_dates
import numpy as np
import datetime
  
  
# Defining a dataframe showing stock prices 
# of a week
stock_prices = pd.DataFrame({'date': np.array([datetime.datetime(2021, 11, i+1)
                                               for i in range(7)]),
                             'open': [36, 56, 45, 29, 65, 66, 67],
                             'close': [29, 72, 11, 4, 23, 68, 45],
                             'high': [42, 73, 61, 62, 73, 56, 55],
                             'low': [22, 11, 10, 2, 13, 24, 25]})
  
ohlc = stock_prices.loc[:, ['date', 'open', 'high', 'low', 'close']]
ohlc['date'] = pd.to_datetime(ohlc['date'])
ohlc['date'] = ohlc['date'].apply(mpl_dates.date2num)
ohlc = ohlc.astype(float)
  
# Creating Subplots
fig, ax = plt.subplots()
  
candlestick_ohlc(ax, ohlc.values, width=0.6, colorup='blue',
                 colordown='green', alpha=0.4)
  
# Setting labels & titles
ax.set_xlabel('Date')
ax.set_ylabel('Price')
fig.suptitle('Stock Prices of a week')
  
# Formatting Date
date_format = mpl_dates.DateFormatter('%d-%m-%Y')
ax.xaxis.set_major_formatter(date_format)
fig.autofmt_xdate()
  
fig.tight_layout()
  
plt.show()

输出：

我们也可以使用数据框而不是自己定义它。数据集可以通过点击这里下载。

示例 3：

Python3

import matplotlib.pyplot as plt
from mpl_finance import candlestick_ohlc
import pandas as pd
import matplotlib.dates as mpl_dates
import numpy as np
import datetime
  
  
# Extracting Data for plotting
data = pd.read_csv("C:/Users/aparn/Desktop/data.csv")
ohlc = data.loc[:, ['Date', 'Open', 'High', 'Low', 'Close']]
  
# Converting date into datetime format
ohlc['Date'] = pd.to_datetime(ohlc['Date'])
ohlc['Date'] = ohlc['Date'].apply(mpl_dates.date2num)
ohlc = ohlc.astype(float)
  
# Creating Subplots
fig, ax = plt.subplots()
  
candlestick_ohlc(ax, ohlc.values, width=0.6,
                 colorup='green', colordown='red', alpha=0.8)
  
# Setting labels & titles
ax.set_xlabel('Date')
ax.set_ylabel('Price')
fig.suptitle('Daily Candlestick Chart of NIFTY50')
  
# Formatting Date
date_format = mpl_dates.DateFormatter('%d-%m-%Y')
ax.xaxis.set_major_formatter(date_format)
fig.autofmt_xdate()
  
fig.tight_layout()
  
plt.show()

输出：