用Python绘制MACD、KDJ、布林线技术指标图

article/2025/9/17 22:35:49

背景介绍：

搭建程序化交易平台的过程需要研究技术指标。

环境：

OS：mac

PYTHON: 3.8

IDE: pycharm

步骤：

第一步：加载库

import pandas as pd
import pandas_datareader as web
from datetime import datetime, timedelta
import talib
import matplotlib.pyplot as plt
from mplfinance.original_flavor import candlestick_ohlc
import matplotlib.ticker as ticker

pandas: 处理股票数据

pandas_datareader：行情数据获取api，也可以用tushare或者从新浪等网站获取

datetime：获取日期

talib：生成技术指标

candlestick_ohlc: 绘制蜡烛图

matplotlib.ticker: 设置x轴刻度，如果不设置，matplotlib会将没有交易的日期也显示出来

第二步：获取数据

def stock(stock_code): #stock_code是股票代码，例子：上市 "600036.ss", 深市 "000001.sz"start_date = "2019-12-01"  #起始日期today = datetime.date(datetime.now())  #截止日期stock_info = web.get_data_yahoo(stock_code, start_date, today)  #获取行情数据，返回dataframestock_info = stock_info.reset_index()  #默认index是日期，这里要重置一下，为后面绘图做准备stock_info = stock_info.astype({"Date": str})    #将Date列的类型设置为str，为绘图做准备return stock_info

以下是获取招商银行（股票代码 "600036.ss"）的返回结果，一共6列，Date, High, Low, Open, Close, Volumn, Adj Close

第三步：获取技术指标数据

def get_indicators(stock_code):# 创建dataframedata = stock(stock_code)#获取macddata["macd"], data["macd_signal"], data["macd_hist"] = talib.MACD(data['Close'])#获取10日均线和30日均线data["ma10"] = talib.MA(data["Close"], timeperiod=10)data["ma30"] = talib.MA(data["Close"], timeperiod=30)#获取rsidata["rsi"] = talib.RSI(data["Close"])return data

talib有各种各样的技术指标，这里只获取了均线，macd和rsi，返回dataframe。

第四步：绘图

def plot_chart(data, title):fig = plt.figure()  #创建绘图区，包含四个子图fig.set_size_inches((20, 16))ax_candle = fig.add_axes((0, 0.72, 1, 0.32))   #蜡烛图子图ax_macd = fig.add_axes((0, 0.48, 1, 0.2), sharex=ax_candle)  #macd子图ax_rsi = fig.add_axes((0, 0.24, 1, 0.2), sharex=ax_candle)  #rsi子图ax_vol = fig.add_axes((0, 0, 1, 0.2), sharex=ax_candle)   #成交量子图ohlc = []   #存放行情数据，candlestick_ohlc需要传入固定格式的数据row_number = 0for date, row in data.iterrows():date, highp, lowp, openp, closep = row[:5]ohlc.append([row_number, openp, highp, lowp, closep])row_number = row_number+1date_tickers = data.Date.values #获取Date数据def format_date(x, pos=None):# 由于前面股票数据在 date 这个位置传入的都是int# 因此 x=0,1,2,...# date_tickers 是所有日期的字符串形式列表if x < 0 or x > len(date_tickers) - 1:return ''return date_tickers[int(x)]#绘制蜡烛图ax_candle.plot(data.index, data["ma7"], label="MA7")ax_candle.plot(data.index, data["ma8"], label="MA8")ax_candle.plot(data.index, data["ma25"], label="MA25")candlestick_ohlc(ax_candle, ohlc, colorup="g", colordown="r", width=0.8)ax_candle.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))ax_candle.xaxis.set_major_locator(ticker.MultipleLocator(6)) #设置间隔为6个交易日ax_candle.grid(True)ax_candle.set_title(title, fontsize=20)ax_candle.legend()#绘制MACDax_macd.plot(data.index, data["macd"], label="macd")ax_macd.bar(data.index, data["macd_hist"] * 3, label="hist")ax_macd.plot(data.index, data["macd_signal"], label="signal")ax_macd.set_title('MACD')ax_macd.legend()#绘制RSIax_rsi.set_ylabel("(%)")ax_rsi.plot(data.index, [70] * len(data.index), label="overbought")ax_rsi.plot(data.index, [30] * len(data.index), label="oversold")ax_rsi.plot(data.index, data["rsi"], label="rsi")ax_rsi.set_title('KDJ')ax_rsi.legend()#绘制成交量ax_vol.bar(data.index, data["Volume"] / 1000000)ax_vol.set_ylabel("(Million)")#保存图片到本地fig.savefig("/Users/answer/Desktop/investment/photos/" + title + ".png", bbox_inches="tight")#这里个人选择不要plt.show()，因为保存图片到本地的#plt.show()

第五步：批量绘制

def industry(dict):for key, value in dict.items():# d.iteritems: an iterator over the (key, value) itemsstock_info = get_indicators(key)plot_chart(stock_info, value)

这里传进去的参数是字典，预先定义了目标股票代码。用字典的好处是，能够把股票名称也传进去。

finance_list = {"600036.ss": "Zhaoshang Yinhang","002142.sz": "Ningbo Yinhang","000001.sz": "Pingan Yinhang","601318.ss": "Zhongguo Pingan"
}

下面是恒瑞医药的绘制效果。几个指标清晰可见。

其它：

绘图的时候最好把绘图独立窗口关掉

Reference:

那未必, 2017, 使用matplotlib绘制k线图(去掉空白日期), https://www.jianshu.com/p/c10e57ccc7ba

Melina Mackey, 2020, Building a Technical Analysis Chart with Python, https://medium.com/analytics-vidhya/building-a-technical-analysis-chart-with-python-17107b78b297