如何用matplotlib画K线或在mpf中叠加布林带完成加密货币扫描器绘图

首先安装依赖：
pip install mplfinance
mpf原生支持K线渲染，叠加自定义指标的逻辑非常简洁，你原有代码里的指标计算、买卖点判断逻辑都无需修改，仅替换绘图部分的代码即可，完整修改后的循环内代码如下：

import yfinance as yf
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import mplfinance as mpf

with open('symbols.csv') as f:
    symbols = f.read().splitlines()
    for symbol in symbols:
        df = yf.download(symbol, start='2020-01-01')
        # 原有指标计算逻辑不变
        df['SMA'] = df.Close.rolling(window=20).mean()
        df['stddev'] = df.Close.rolling(window=20).std()
        df['Upper'] = df.SMA + 2* df.stddev
        df['Lower'] = df.SMA - 2* df.stddev
        df['Buy_Signal'] = np.where(df.Lower > df.Close, True, False)
        df['Sell_Signal'] = np.where(df.Upper < df.Close, True, False)

        buys = []
        sells = []
        open_pos = False
        for i in range(len(df)):
            if df.Lower[i] > df.Close[i]:
                if not open_pos:
                    buys.append(i)
                    open_pos = True
            elif df.Upper[i] < df.Close[i]:
                if open_pos:
                    sells.append(i)
                    open_pos = False

        # --------------------------替换原有绘图逻辑--------------------------
        # 准备叠加的指标：布林带上下轨、SMA、买卖信号
        addplots = [
            # 布林带上下轨和SMA
            mpf.make_addplot(df['SMA'], color='orange'),
            mpf.make_addplot(df['Upper'], color='blue'),
            mpf.make_addplot(df['Lower'], color='blue'),
            # 填充布林带区域
            mpf.make_addplot(df['Upper'], fill_between=dict(y1=df['Lower'].values, alpha=0.3, color='gray')),
            # 买卖信号
            mpf.make_addplot(df.iloc[buys]['Close'], type='scatter', marker='^', markersize=100, color='g'),
            mpf.make_addplot(df.iloc[sells]['Close'], type='scatter', marker='v', markersize=100, color='r')
        ]
        # 绘制K线+叠加指标
        mpf.plot(df, type='candle', addplot=addplots, title=f'{symbol} 走势', figsize=(12,6), style='yahoo')
        # --------------------------绘图逻辑结束--------------------------

        # 原有收益计算逻辑不变
        merged = pd.concat([df.iloc[buys].Close, df.iloc[sells].Close], axis=1)
        merged.columns = ['Buys', 'Sells']
        totalprofit = merged.shift(-1).Sells - merged.Buys
        relprofits = (merged.shift(-1).Sells - merged.Buys) / merged.Buys
        print(f"{symbol} 平均收益率: {relprofits.mean()}")

不需要安装额外库，自己实现K线的渲染逻辑，直接替换原有绘图部分即可：

# 原有逻辑不变，仅替换plt绘图部分
plt.figure(figsize=(12, 6))

# --------------------------新增K线绘制逻辑--------------------------
width = 0.6  # K线实体宽度
width2 = 0.05  # K线影线宽度
up = df[df.Close >= df.Open]
down = df[df.Close < df.Open]

# 绘制上涨K线（绿色）
plt.bar(up.index, up.Close-up.Open, width, bottom=up.Open, color='g')
plt.bar(up.index, up.High-up.Close, width2, bottom=up.Close, color='g')
plt.bar(up.index, up.Low-up.Open, width2, bottom=up.Open, color='g')

# 绘制下跌K线（红色）
plt.bar(down.index, down.Close-down.Open, width, bottom=down.Open, color='r')
plt.bar(down.index, down.High-down.Open, width2, bottom=down.Open, color='r')
plt.bar(down.index, down.Low-down.Close, width2, bottom=down.Close, color='r')
# --------------------------K线绘制结束--------------------------

# 原有布林带、买卖信号逻辑不变
plt.scatter(df.iloc[buys].index, df.iloc[buys].Close, marker = '^', color ='g', s=100)
plt.scatter(df.iloc[sells].index, df.iloc[sells].Close, marker = 'v', color ='r', s=100)
plt.plot(df[['SMA', 'Upper', 'Lower']])
plt.fill_between(df.index, df.Upper, df.Lower, color='grey', alpha=0.3)
plt.legend(['SMA', 'Upper', 'Lower'])
plt.xticks(rotation=45)
plt.show()

两种方案都可以满足需求，方案1代码更简洁、渲染性能更好，适合大量K线的场景；方案2不需要额外依赖，自定义程度更高。

内容的提问来源于stack exchange，提问作者Jellyfish