使用图表库在 Python 中的烛台图表上叠加交易量配置文件

Question

我想在 python 中的烛台图表上绘制成交量剖面图，这将导致类似这样的结果。

我的主要 ohlc 数据将在 pandas 数据框中。

Date,       Open,  High,  Low,   Close
2019-10-18, 54.09, 54.62, 53.35, 53.78
2019-10-17, 52.99, 54.16, 52.62, 53.93
2019-10-16, 52.92, 53.74, 52.51, 53.36

然后我的卷信息将在另一个这样的数据框中。

Price, Volume
54.75, 150
54.50, 135
54.25, 140
54.00, 140
53.75, 125
53.50, 145
53.25, 130
53.00, 135
52.75, 155
52.50, 150

我尝试了我所知道的每个库，Matplotlib、Plotly、Bokeh。我试过简单地在烛台旁边绘制条形图，但缩放通常是关闭的。我有兴趣在 python 中使用任何标准的图表库，它们会以一种相当简单的方式产生这个结果。希望这里有人知道如何做到这一点。

Answer 1

好吧，我决定深入研究情节文档，看看我是否能找到一种方法来做到这一点。事实证明这没什么大不了的。我开始越来越喜欢 Plotly。

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Oct 23 22:17:44 2019

@author: TysonU
"""
from plotly.offline import plot
import plotly.graph_objects as go
import random
import pandas as pd

#Create random OHLC and Volume
high = 40
low = 20
dev = 1
days = 100

fake_market = []
for each in range(days):
    ohlc = []
    ohlc.append(each)
    if each == 0:
        o = random.randrange(low, high)
        ohlc.append(o)
    else:
        ohlc.append(c) #I know
    h = random.randrange(o, high)
    ohlc.append(h)
    l = random.randrange(low, o)
    ohlc.append(l)
    c = random.randrange(l, h)
    ohlc.append(c)
    fake_market.append(ohlc)

fake_volume = [[x, random.randrange(10, 30)] for x in range(low, (high+1))]
df = pd.DataFrame(fake_market, columns=["Date", "Open", "High", "Low", "Close"])
df2 = pd.DataFrame(fake_volume, columns=["Volume", "Price"])

#Do all the plotly stuff
fig = go.Figure(
    data=[
        go.Bar(
            x=[str(x) for x in df2.Price.to_list()],
            y=[str(x) for x in df2.Volume.to_list()],
            orientation="h",
            xaxis="x",
            yaxis="y",
            visible=True,
            showlegend=False
        ),
        go.Candlestick(
            x=[str(x) for x in df.Date.to_list()],
            open=[str(x) for x in df.Open.to_list()],
            high=[str(x) for x in df.High.to_list()],
            low=[str(x) for x in df.Low.to_list()],
            close=[str(x) for x in df.Close.to_list()],
            xaxis="x2",
            yaxis="y2",
            visible=True,
            showlegend=False
        )
    ],
    layout=go.Layout(
        title=go.layout.Title(text="Candlestick With Volume Profile"),
        xaxis=go.layout.XAxis(
            side="top",
            range=[0, 300],
            rangeslider=go.layout.xaxis.Rangeslider(visible=False),
            showticklabels=False
        ),
        yaxis=go.layout.YAxis(
            side="left",
            range=[low, high],
            showticklabels=False
        ),
        xaxis2=go.layout.XAxis(
            side="bottom",
            title="Date",
            rangeslider=go.layout.xaxis.Rangeslider(visible=False),
            overlaying="x"
        ),
        yaxis2=go.layout.YAxis(
            side="right",
            title="Price",
            range=[low, high],
            overlaying="y"
        )
    )
)
template = ["plotly", "plotly_white", "plotly_dark", "ggplot2", "seaborn", "none"]
fig.update_layout(template=template[2])

plot(fig)

不确定公开发布股票数据的法律是什么，因此我构建了一个简单的生成器来生成 OHLC 数据和交易量。真实的股票数据会让图表看起来不那么混乱。

我还没有做对的是酒吧所在的那一侧。目前它们位于左侧，但将它们放在右侧会很好。不过应该很容易。

嗯，希望这对某人有所帮助。祝你有美好的一天！

Answer 2

这对我有用：

def custom_round(x, base=5):
    return int(base * round(float(x)/base))

def round_and_group(data,base=5):
    df = data[['Last', 'Volume']].copy()
    #Round to nearest X
    df['Last'] = df['Last'].apply(lambda x: custom_round(x, base=base))
    # Remove the date index
    df = df.set_index('Last')
    df = df.groupby(['Last']).sum()
    return df

df=round_and_group(TTF,base=1)
df.reset_index(inplace=True)
plt.figure(figsize=(10,4), dpi=120) # 10 is width, 4 is height
ax1=plt.subplot(1,2,1)
plt.barh(df['Last'],df['Volume'], 2)
ax2= ax1.twiny()
candlestick2_ohlc(ax2,TTF['Open'],
                      TTF['High'],
                      TTF['Low'],
                      TTF['Last'],width = 0.6,colorup='g')