"""
Visualize a profile chart (eigher Volume Profile or Turnover Profile) with
4-section layout for a given stock.
"""
__software__ = "Volume Profile with Plotly 2x2 subplots"
__version__ = "2.5"
__author__ = "York <york.jong@gmail.com>"
__date__ = "2023/02/02 (initial version) ~ 2024/08/20 (last revision)"
__all__ = [
'Volume', # Volume Profile, i.e., PBV (Price-by-Volume) or Volume-by-Price
'Turnover', # Turnover Profile
]
import yfinance as yf
import pandas as pd
import plotly.graph_objs as go
from plotly.subplots import make_subplots
from .. import tw
from .. import file_utils
from . import fig_utils as futil
from ..utils import MarketColorStyle, decide_market_color_style
def _plot(df, ticker, market_color_style, profile_field='Volume',
period='1y', interval='1d',
ma_nitems=(5, 10, 20, 50, 150), vma_nitems=50, total_bins=42,
template='plotly', hides_nontrading=True):
# Initialize empty plot with marginal subplots
fig = make_subplots(
rows=2,
cols=2,
column_width=[0.8, 0.2],
row_heights=[0.7, 0.3],
#shared_xaxes="columns",
#shared_yaxes="rows",
#subplot_titles=["Price", "Price Bins", "Volume", ""]
horizontal_spacing=0.01,
vertical_spacing=0.03,
figure=go.Figure(layout=go.Layout(height=720))
)
# Plot the candlestick chart
mc_style = decide_market_color_style(ticker, market_color_style)
mc_colors = futil.get_candlestick_colors(mc_style)
candlestick = go.Candlestick(
x=df.index,
open=df['Open'], high=df['High'], low=df['Low'], close=df['Close'],
name='Candle',
**mc_colors
)
fig.add_trace(candlestick, row=1, col=1)
# Add moving averages to the figure
colors = ('orange', 'red', 'green', 'blue', 'cyan', 'magenta', 'yellow')
for d, c in zip(ma_nitems, colors):
df[f'ma{d}'] = df['Close'].rolling(window=d).mean()
ma = go.Scatter(x=df.index, y=df[f'ma{d}'], name=f'MA {d}',
line=dict(color=f'{c}', width=2), opacity=0.5)
fig.add_trace(ma, row=1, col=1)
# Add volume trace to 2nd row
cl = futil.get_volume_colors(mc_style)
colors = [cl['up'] if c >= o
else cl['down'] for o, c in zip(df['Open'], df['Close'])]
volume = go.Bar(x=df.index, y=df['Volume'], name='Volume',
marker_color=colors)
fig.add_trace(volume, row=2, col=1)
# Add moving average volume to 2nd row
df[f'vma{vma_nitems}'] = df['Volume'].rolling(window=vma_nitems).mean()
vma = go.Scatter(x=df.index, y=df[f'vma{vma_nitems}'],
name=f'VMA {vma_nitems}',
line=dict(color='purple', width=2))
fig.add_trace(vma, row=2, col=1)
# Add Price by Volume (Volume Profile) chart
bin_size = (max(df['High']) - min(df['Low'])) / total_bins
bin_round = lambda x: bin_size * round(x / bin_size)
bin = df[profile_field].groupby(
df['Close'].apply(lambda x: bin_round(x))).sum()
fig.add_trace(
go.Bar(
y=bin.keys(), # Price
x=bin.values, # Bin Comulative Volume
text=bin, # (price, volume) pairs
name="Price Bins",
orientation="h", # 'v', 'h'
marker_color="brown",
texttemplate="%{x:3.2f}",
hoverinfo="y", # 'x', 'y', 'x+y'
opacity=0.5
),
row=1, col=2
)
# Convert datetime index to string format suitable for display
if interval.endswith('m') or interval.endswith('h'):
df.index = df.index.strftime('%Y-%m-%d %H:%M')
else:
df.index = df.index.strftime('%Y-%m-%d')
# Update layout
fig.update_layout(
legend=dict(yanchor='top', xanchor="left", x=1.069),
xaxis=dict(anchor='free'),
yaxis=dict(anchor='x3', side='left', title='Price'),
xaxis2=dict(title=f'Bin Comulative {profile_field}'),
yaxis2=dict(side='right', title='Price'), # Bin Price
yaxis3=dict(side='left', title='Volume'),
xaxis_rangeslider_visible=False,
template=template,
)
# Update the layout to set the same range for both y-axes
# This ensures that both price axes have the same scale and range
y_range = [min(df['Close']) * 0.95, max(df['Close']) * 1.05]
fig.update_layout(
yaxis=dict(range=y_range),
yaxis2=dict(range=y_range)
)
if hides_nontrading:
futil.hide_nontrading_periods(fig, df, interval)
# For Crosshair cursor
futil.add_crosshair_cursor(fig)
futil.add_hovermode_menu(fig)
return fig
[docs]
class Volume:
"""Volume Profile, i.e., PBV (Price-by-Volume) or Volume-by-Price
"""
[docs]
@staticmethod
def plot(symbol='TSLA', period='1y', interval='1d',
ma_nitems=(5, 10, 20, 50, 150), vma_nitems=50, total_bins=42,
market_color_style=MarketColorStyle.AUTO,
template='plotly', hides_nontrading=True, out_dir='out'):
"""Plot a price-by-volume, PBV (also called volume profile) figure for
a given stock. This figure shows the volume distribution across price
levels for a stock.
Here the PBV is overlaied with the price subplot. This figure consists
of two subplots: a price subplot and a volume subplot. The former
includes candlestick, moving average lines, while the latter includes
trading volume bar chart and volume moving average line.
Parameters
----------
symbol: str
the stock symbol.
period: str, optional
the period data to download. Valid values are 1d, 5d, 1mo, 3mo, 6mo,
1y, 2y, 5y, 10y, ytd, max. Default is '1y'.
- d -- days
- mo -- monthes
- y -- years
- ytd -- year to date
- max -- all data
interval: str, optional
the interval of an OHLC item. Valid values are 1m, 2m, 5m, 15m, 30m,
60m, 90m, 1h, 1d, 5d, 1wk, 1mo, 3mo. Default is '1d'.
- m -- minutes
- h -- hours
- wk -- weeks
- mo -- monthes
Intraday data cannot extend last 60 days:
- 1m - max 7 days within last 30 days
- up to 90m - max 60 days
- 60m, 1h - max 730 days (yes 1h is technically < 90m but this what
Yahoo does)
ma_nitems: sequence of int
a sequence to list the number of data items to calclate moving
averges.
vma_nitems: int
the number of data items to calculate the volume moving average.
total_bins: int
the number of bins to calculate comulative volume for bins.
market_color_style: MarketColorStyle, optional
Color style for market data visualization. Default is
MarketColorStyle.AUTO.
template: str, optional:
The Plotly template to use for styling the chart.
Defaults to 'plotly'. Available templates include:
- 'plotly': Default Plotly template with interactive plots.
- 'plotly_white': Light theme with a white background.
- 'plotly_dark': Dark theme for the chart background.
- 'ggplot2': Style similar to ggplot2 from R.
- 'seaborn': Style similar to Seaborn in Python.
- 'simple_white': Minimal white style with no gridlines.
- 'presentation': Designed for presentations with a clean look.
- 'xgridoff': Plot with x-axis gridlines turned off.
- 'ygridoff': Plot with y-axis gridlines turned off.
For more details on templates, refer to Plotly's official
documentation.
hides_nontrading: bool, optional
Whether to hide non-trading periods. Default is True.
out_dir: str, optional
Directory to save the output HTML file. Default is 'out'.
"""
# Download stock data
ticker = tw.as_yfinance(symbol)
df = yf.Ticker(ticker).history(period=period, interval=interval)
# Plot
fig = _plot(df, ticker, market_color_style, 'Volume',
period, interval, ma_nitems, vma_nitems,
total_bins, template, hides_nontrading)
fig.update_layout(
title=f'{symbol} - {interval} ({df.index[0]} to {df.index[-1]})',
title_x=0.5, title_y=.9,
)
# Show the figure
fig.show()
# Write the figure to an HTML file
out_dir = file_utils.make_dir(out_dir)
fn = file_utils.gen_fn_info(symbol, interval, df.index[-1],
'volume_prf')
fig.write_html(f'{out_dir}/{fn}.html')
[docs]
class Turnover:
'''Turnover Profile
Here "turnover" means "trading value" (= price * volume)
'''
[docs]
@staticmethod
def plot(symbol='TSLA', period='1y', interval='1d',
ma_nitems=(5, 10, 20, 50, 150), vma_nitems=50, total_bins=42,
market_color_style=MarketColorStyle.AUTO,
template='plotly', hides_nontrading=True, out_dir='out'):
"""Plot a price-by-volume, PBV (also called volume profile) figure for
a given stock. This figure shows the volume distribution across price
levels for a stock.
Here the PBV is overlaied with the price subplot. This figure consists
of two subplots: a price subplot and a volume subplot. The former
includes candlestick, moving average lines, while the latter includes
trading volume bar chart and volume moving average line.
Parameters
----------
symbol: str
the stock symbol.
period: str, optional
the period data to download. Valid values are 1d, 5d, 1mo, 3mo, 6mo,
1y, 2y, 5y, 10y, ytd, max. Default is '1y'.
- d -- days
- mo -- monthes
- y -- years
- ytd -- year to date
- max -- all data
interval: str, optional
the interval of an OHLC item. Valid values are 1m, 2m, 5m, 15m, 30m,
60m, 90m, 1h, 1d, 5d, 1wk, 1mo, 3mo. Default is '1d'.
- m -- minutes
- h -- hours
- wk -- weeks
- mo -- monthes
Intraday data cannot extend last 60 days:
- 1m - max 7 days within last 30 days
- up to 90m - max 60 days
- 60m, 1h - max 730 days (yes 1h is technically < 90m but this what
Yahoo does)
ma_nitems: sequence of int
a sequence to list the number of data items to calclate moving
averges.
vma_nitems: int
the number of data items to calculate the volume moving average.
total_bins: int
the number of bins to calculate comulative volume for bins.
market_color_style: MarketColorStyle, optional
Color style for market data visualization. Default is
MarketColorStyle.AUTO.
template: str, optional:
The Plotly template to use for styling the chart.
Defaults to 'plotly'. Available templates include:
- 'plotly': Default Plotly template with interactive plots.
- 'plotly_white': Light theme with a white background.
- 'plotly_dark': Dark theme for the chart background.
- 'ggplot2': Style similar to ggplot2 from R.
- 'seaborn': Style similar to Seaborn in Python.
- 'simple_white': Minimal white style with no gridlines.
- 'presentation': Designed for presentations with a clean look.
- 'xgridoff': Plot with x-axis gridlines turned off.
- 'ygridoff': Plot with y-axis gridlines turned off.
For more details on templates, refer to Plotly's official
documentation.
hides_nontrading: bool, optional
Whether to hide non-trading periods. Default is True.
out_dir: str, optional
Directory to save the output HTML file. Default is 'out'.
"""
# Download stock data
ticker = tw.as_yfinance(symbol)
df = yf.Ticker(ticker).history(period=period, interval=interval)
df['Turnover'] = df['Close'] * df['Volume']
# Plot
fig = _plot(df, ticker, market_color_style, 'Turnover',
period, interval, ma_nitems, vma_nitems,
total_bins, template, hides_nontrading)
fig.update_layout(
title=f'{symbol} - {interval} ({df.index[0]} to {df.index[-1]})',
title_x=0.5, title_y=.9,
)
# Show the figure
fig.show()
# Write the figure to an HTML file
out_dir = file_utils.make_dir(out_dir)
fn = file_utils.gen_fn_info(symbol, interval, df.index[-1],
'turnover_prf')
fig.write_html(f'{out_dir}/{fn}.html')
if __name__ == '__main__':
Volume.plot('TSLA')
Turnover.plot('TSLA')