Candlestick Plotting with Python Libraries

This post was written primarily because I wanted to learn how quarto’s computations work along with interactivity of Bokeh and Plotly in python, also found out that as of now we cannot contorl the size of output images produced with matplotlib (hence them appearing large in this post).

Introduction

In this post, we will be looking at creating candlestick charts, which are integral to traders’ daily routines, using Python libraries such as Mplfinance, Plotly and Bokeh, and look at customizing the plots, change themes and tweak some colors.
As this post is written on Quarto, it enables viewers to interact with the output plots. You’ll be able to pan and zoom with the charts created with plotly and bokeh!
So, now lets head on to fetch our data first.

Getting Data

We will make use of the yfinance library. Let’s fetch the Open-High-Low-Close and Volume data for Apple (AAPL) for the past 5 months.

Our data looks like this:

import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

import yfinance as yf
import pandas as pd


data = yf.download("AAPL", period="5mo",auto_adjust=True)
data.head()

[*********************100%%**********************]  1 of 1 completed

	Open	High	Low	Close	Volume
Date
2024-02-06	186.369081	188.812642	186.279321	188.802673	43490800
2024-02-07	190.139164	190.548090	188.114498	188.912399	53439000
2024-02-08	188.892437	189.042037	186.857803	187.825256	40962000
2024-02-09	188.394466	189.732663	187.745353	188.594208	45155200
2024-02-12	188.164784	188.414445	186.536987	186.896500	41781900

Plots

Now we will head straight into plotting. We will start with mplfinance, a special module for finance charts of matplotlib.

mplfinance

Creating candlestick charts with mplfinance is a simple one line code:

import mplfinance as mpf

mpf.plot(data, type="candle", volume=True, style="yahoo",
         figsize=(6,3),title="AAPL Candlestick with Mplfinance",
        ylabel="Price [USD]")

This plot uses the “yahoo” style. We can check the available style using the command mpf.available_styles(). Now let us look at a few other available styles below:

mc_list = ["binance","blueskies","starsandstripes","brasil"]

fig = mpf.figure(figsize=(6,6))
ax_dict = {}

for i,st in enumerate(mc_list):
    ax_dict[i]=fig.add_subplot(2,2,i+1, style=st)
    mpf.plot(data[:50], type ="candle", ax=ax_dict[i], axtitle=(st),xrotation=0)

And now we will tweak and play with the candle colors and background. First we will set the base mplstyle to dark_background (for reference, check here). Then we will set marketcolors for the chart:

mc = mpf.make_marketcolors(up="g",down="r",inherit=True)
s = mpf.make_mpf_style(base_mpl_style="dark_background",marketcolors=mc)
mpf.plot(data, type="candle", volume=True, style=s,
         figsize=(6,3),title="Customised Dark theme plot on mplfinance",
        ylabel="Price [USD]")

For looking further stye changes using mplfinance, one might check their example notebook

We will move ahead and try hands on some interactive plotting libraries.

Plotly

Plotly is perhaps the most used interactive visualization library in python. It offers a great deal of customization. With Plotly, we will make two types of charts, OHLC and Volume plotted together in first one, separate in the next.

import plotly.graph_objects as go
from plotly.subplots import make_subplots

fig = make_subplots(specs=[[{"secondary_y": True}]])

fig.add_trace(go.Candlestick(x=data.index, open=data["Open"],
                              high=data["High"], low=data["Low"],
                             close=data["Close"], name = "OHLC"), secondary_y=False)

fig.add_trace(go.Bar(x=data.index, y=data['Volume'], opacity=0.3,
                     name= "Volume"), secondary_y=True)

fig.update_layout(title="AAPL Candlestick with Plotly", width=900, height=400)
fig.update_yaxes(title="Price [USD]", secondary_y=False, showgrid=True)
fig.update_yaxes(title="Volume", secondary_y=True, showgrid=False)

fig.show()

In the next one, we hide the range slider for better viewing and separate the volume bar chart:

fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.05, row_width=[0.2, 0.7])

fig.add_trace(go.Candlestick(x=data.index, open=data["Open"],
                              high=data["High"], low=data["Low"],
                             close=data["Close"], name = "OHLC"), row=1, col=1)

fig.add_trace(go.Bar(x=data.index, y=data['Volume'], opacity=0.3,
                     name= "Volume"), row=2, col=1)

fig.update_layout(title="AAPL Candlestick with Plotly", width=900, height=500)

fig.update(layout_xaxis_rangeslider_visible=False)
fig.show()

Plotly as well offers multiple themes and customizations, for now will use the dark theme and just change the colors of volume bars for the first plot.

fig = make_subplots(specs=[[{"secondary_y": True}]])

fig.add_trace(go.Candlestick(x=data.index, open=data["Open"],
                              high=data["High"], low=data["Low"],
                             close=data["Close"], name = "OHLC"), secondary_y=False)

fig.add_trace(go.Bar(x=data.index, y=data['Volume'], opacity=0.3,
                     name= "Volume", marker_color="gray"), secondary_y=True)

fig.update_layout(title="AAPL Candlestick with Plotly", width=900, height=500, xaxis={"rangeslider":{"visible":False}}, template="plotly_dark")
fig.update_yaxes(title="Price [USD]", secondary_y=False, showgrid=True)
fig.update_yaxes(title="Volume", secondary_y=True, showgrid=False)

fig.show()

Bokeh plot

Quoting:

Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. Bokeh can help anyone who would like to quickly and easily make interactive plots, dashboards, and data applications.

Bokeh additionally has great documentation and user guide. Now let’s look at a candlestick chart created using Bokeh:

from bokeh.layouts import column
from bokeh.plotting import figure, show, output_notebook
from bokeh.models import ColumnDataSource, RangeTool, BoxAnnotation, HoverTool
from bokeh.resources import INLINE


output_notebook(resources=INLINE)

inc= data.Close > data.Open
dec = data.Open > data.Close
TOOLS = "pan,wheel_zoom,box_zoom,reset,save"
width_set = pd.Timedelta("16H")


fig = figure(x_axis_type="datetime", tools=TOOLS, width=900, height=350,
             title="AAPL Candlestick with Bokeh")

fig.segment(data.index, data.High, data.index, data.Low, color="black")
fig.vbar(data.index[dec], width_set, data.Open[dec], data.Close[dec], color="red")
fig.vbar(data.index[inc], width_set, data.Open[inc], data.Close[inc], color="green")
fig.yaxis.axis_label="Price [USD]"

vol = figure(x_axis_type="datetime", tools=TOOLS, width=900, height=150, x_range=fig.x_range)
vol.vbar(data.index, width=width_set, top=data.Volume, fill_color="grey", alpha=0.5)
vol.yaxis.axis_label="Volume"

show(column(fig,vol))

Loading BokehJS ...

Great! We will now change to dark theme. Check out : bokeh themes.

from bokeh.plotting import curdoc
from bokeh.themes import Theme
output_notebook(resources=INLINE)

inc= data.Close > data.Open
dec = data.Open > data.Close
TOOLS = "pan,wheel_zoom,box_zoom,reset,save"
width_set = pd.Timedelta("16H")


fig = figure(x_axis_type="datetime", tools=TOOLS, width=900, height=400,
             title="AAPL Candlestick with Bokeh")

fig.segment(data.index, data.High, data.index, data.Low, color="white")
fig.vbar(data.index[dec], width_set, data.Open[dec], data.Close[dec], color="red")
fig.vbar(data.index[inc], width_set, data.Open[inc], data.Close[inc], color="green")
fig.yaxis.axis_label="Price [USD]"

doc = curdoc()
doc.theme = "dark_minimal"
doc.add_root(fig)
show(fig)

Loading BokehJS ...

That really looks good. If you are going through the code here, you can see how simple it would be to change the colors of bars. One can further add tooltips that will display the OHLC data on hovering over the candles.

One might further look at libraries like Altair, ggplot. One great library that I just found while writing this post is Holoviews

Have fun plotting!