"""
Mansfield Stock Charts
======================
This module provides functionality for generating and plotting Mansfield Stock
Charts and Mansfield Relative Strength (RSM) charts based on the methods
outlined in Stan Weinstein's book "Secrets for Profiting in Bull and Bear
Markets." It includes two main classes for creating visualizations:
1. StockChart:
- Generates a Mansfield Stock Chart for a given stock symbol compared to a
reference index.
- Allows customization of chart elements such as moving averages, chart
templates, and hiding non-trading periods.
2. RelativeStrengthLines:
- Plots the Mansfield Relative Strength (RSM) of multiple stocks compared
to a reference index.
- Allows comparison of multiple stocks’ RS values against a reference index
with customization options for the time period, data interval, and moving
average type.
Usage:
------
To generate a Mansfield Stock Chart:
>>> StockChart.plot('TSLA', interval='1wk')
To generate a Relative Strength Lines chart for multiple stocks:
>>> RelativeStrengthLines.plot(['NVDA', 'MSFT', 'META'], interval='1wk')
See Also:
---------
- `Mansfield relative strength | TrendSpider Store
<https://trendspider.com/trading-tools-store/indicators/
mansfield-relative-strength/>`_
"""
__software__ = "Mansfield Stock Charts"
__version__ = "2.4"
__author__ = "York <york.jong@gmail.com>"
__date__ = "2024/08/24 (initial version) ~ 2024/09/12 (last revision)"
__all__ = [
'StockChart',
'RelativeStrengthLines',
]
import numpy as np
import pandas as pd
import yfinance as yf
import plotly.graph_objs as go
from plotly.subplots import make_subplots
import plotly.express as px
from .. import tw
from .. import file_utils
from . import fig_utils as futil
from ..utils import MarketColorStyle, decide_market_color_style
from .. import stock_indices as si
from ..ta import simple_moving_average, exponential_moving_average
from ..rsm import mansfield_relative_strength
[docs]
class StockChart:
"""A class for generating and plotting Mansfield Stock Charts based on Stan
Weinstein's methods outlined in the book "Secrets for Profiting in Bull and
Bear Markets."
"""
[docs]
@staticmethod
def plot(symbol, period='2y', interval='1wk', ticker_ref=None, ma='SMA',
market_color_style=MarketColorStyle.AUTO,
template='plotly', hides_nontrading=True, out_dir='out'):
"""Plot a Mansfield Stock Chart for a given stock symbol and time
period.
Parameters
----------
symbol: str
the stock symbol.
period: str, optional
the period data to download. Valid values are 1y, 2y, 5y, 10y, ytd,
max. Default is '2y'.
- y -- years
- ytd -- year to date
- max -- all data
interval: str, optional
the interval of an OHLC item. Valid values are 1d, 1wk, 1mo, 3mo.
Default is '1wk'.
- d -- days
- wk -- weeks
- mo -- months
ticker_ref: str, optional
The ticker symbol of the reference index. If None, defaults to S&P
500 ('^GSPC') or Taiwan Weighted Index ('^TWII') if the first stock
is a Taiwan stock.
ma: str, optional
Moving average type ('SMA', 'EMA'). Default to 'SMA'.
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'.
Raises
------
ValueError
If an unsupported interval is provided.
"""
ticker = tw.as_yfinance(symbol)
if not ticker_ref:
ticker_ref = '^GSPC' # S&P 500 Index
if tw.is_taiwan_stock(ticker):
ticker_ref = '^TWII' # Taiwan Weighted Index
# Set moving average windows based on the interval
try:
rs_window = { '1d': 252, '1wk': 52, '1mo': 12 }[interval]
ma_windows = {
'1d': [50, 150, 200],
'1wk': [10, 30, 40],
'1mo': [3, 8, 10],
}[interval]
except KeyError:
raise ValueError("Invalid interval. "
"Must be '1d', '1wk', or '1mo'.")
vma_window, *_ = ma_windows
# Select the MA function based on the 'ma' parameter
try:
ma_func = {
'SMA': simple_moving_average,
'EMA': exponential_moving_average,
}[ma]
except KeyError:
raise ValueError("Invalid ma type. Must be 'SMA' or 'EMA'.")
# Fetch data for stock and index
df = yf.download([ticker_ref, ticker], period=period, interval=interval)
df_ref = df.xs(ticker_ref, level='Ticker', axis=1)
df = df.xs(ticker, level='Ticker', axis=1)
# Calculate Mansfield Relative Strength (RSM)
df['RSM'] = mansfield_relative_strength(df['Close'], df_ref['Close'],
rs_window, ma=ma)
df[f'RS {ticker_ref}'] = 0
# Calculate moving averages for stock
ma = ma.replace('SMA', 'MA')
for window in ma_windows:
df[f'{ma}{window}'] = ma_func(df['Close'], window)
# Calculate volume MA
df[f'Vol {ma}{vma_window}'] = ma_func(df['Volume'], vma_window)
# Create subplots
fig = make_subplots(rows=3, cols=1, shared_xaxes=True,
vertical_spacing=0.02, row_heights=[0.5, 0.3, 0.2])
# colors of candlesticks
mc_style = decide_market_color_style(ticker, market_color_style)
mc_colors = futil.get_candlestick_colors(mc_style)
# colors of volume bars
cl = futil.get_volume_colors(mc_style)
vol_colors = [cl['up'] if c >= o
else cl['down'] for o, c in zip(df['Open'], df['Close'])]
# Plot the figure
price_row, rsm_row, vol_row = 1, 2, 3
traces = [
# Stock Price and Moving Averages
(go.Candlestick(
x=df.index, open=df['Open'], high=df['High'],
low=df['Low'], close=df['Close'], name='Candle', **mc_colors),
price_row),
*[(go.Scatter(x=df.index, y=df[f'{ma}{window}'],
name=f'{ma}{window}'), price_row)
for window in ma_windows],
# RSM and zero line
(go.Scatter(x=df.index, y=df['RSM'], name='RS',
line=dict(color='green', width=2)), rsm_row),
(go.Scatter(x=df.index, y=df[f'RS {ticker_ref}'],
mode='lines', name=si.get_name(ticker_ref),
line=dict(dash='dash', color='gray')), rsm_row),
# Volume and Volume MA
(go.Bar(x=df.index, y=df['Volume'], name='Volume',
marker_color=vol_colors, opacity=0.5), vol_row),
(go.Scatter(x=df.index, y=df[f'Vol {ma}{vma_window}'],
name=f'Vol {ma}{vma_window}',
line=dict(color='purple', width=2)), vol_row),
]
for trace, row in traces:
fig.add_trace(trace, row=row, col=1)
# Convert datetime index to string format suitable for display
df.index = df.index.strftime('%Y-%m-%d')
# Update layout
fig.update_layout(
title=f'Mansfield Stock Charts: {symbol} - {interval} '
f'({df.index[0]} to {df.index[-1]})',
title_x=0.5, title_y=0.92,
xaxis=dict(anchor='free'),
yaxis=dict(title=f"Price ({yf.Ticker(ticker).info['currency']})",
side='right'),
xaxis2=dict(anchor='free'),
yaxis2=dict(title='Relative Strength', side='right'),
yaxis3=dict(title='Volume', side='right'),
legend=dict(yanchor='bottom', y=0.01, xanchor="left", x=0.01),
height=1000,
xaxis_rangeslider_visible=False,
template=template,
)
if hides_nontrading:
futil.hide_nontrading_periods(fig, df, interval)
# For Crosshair cursor
futil.add_crosshair_cursor(fig)
futil.add_hovermode_menu(fig)
# 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], 'RSM')
fig.write_html(f'{out_dir}/{fn}.html')
[docs]
class RelativeStrengthLines:
"""
A class for plotting Mansfield Relative Strength (RSM) lines of multiple
stocks compared to a reference index.
This class generates an interactive Plotly chart that compares the Relative
Strength (RS) of multiple stocks against a reference index (e.g., S&P 500
or Taiwan Weighted Index). It provides a visualization of how each stock's
RS changes over time relative to the reference index, with options to
customize the time period, data interval, and moving average type.
"""
[docs]
@staticmethod
def plot(symbols, period='2y', interval='1d', ticker_ref=None, ma='SMA',
template='plotly_dark', colorway=px.colors.qualitative.Set3,
hides_nontrading=True, out_dir='out'):
"""
Plot the Mansfield Relative Strength (RSM) of multiple stocks compared
to a reference index.
This function generates an interactive Plotly chart that compares the
RS values of the specified stocks against a reference index (e.g.,
S&P 500 or Taiwan Weighted Index). The chart includes RS lines for
each stock and can be customized based on the selected period and
interval. The resulting plot is saved as an HTML file in the specified
output directory.
Parameters
------------
symbols: list of str
List of stock symbols to compare. Can include both US and Taiwan
stocks.
period: str, optional
the period data to download. . Defaults to '2y'. Valid values are
6mo, 1y, 2y, 5y, 10y, ytd, max.
- mo -- monthes
- y -- years
- ytd -- year to date
- max -- all data
interval: str, optional
The interval for data points ('1d' for daily, '1wk' for weekly;
default is '1d').
ticker_ref: str, optional
The ticker symbol of the reference index. If None, defaults to S&P
500 ('^GSPC') or Taiwan Weighted Index ('^TWII') if the first stock
is a Taiwan stock.
ma: str, optional
Moving average type ('SMA', 'EMA'). Default to 'SMA'.
template: str, optional:
The Plotly template to use for styling the chart.
Defaults to 'plotly_dark'.
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.
colorway: list or None
Sets the default trace colors for the plot. If None, Plotly's
default color sequence will be used. You can pass a list of custom
colors or choose from Plotly's predefined color sequences.
By default, this is set to `px.colors.qualitative.Set3`, which
consists of 24 vibrant colors.
Useful predefined color sequences include:
- px.colors.qualitative.Light24 (24 colors, vibrant and varied)
- px.colors.qualitative.Dark24 (24 colors, darker tones)
- px.colors.qualitative.Pastel (26 colors, soft pastel tones)
- px.colors.qualitative.Bold (26 colors, bold and distinct)
- px.colors.qualitative.Alphabet (26 colors, one for each letter)
- px.colors.qualitative.Set3 (12 colors, good for categorical data)
- px.colors.qualitative.G10 (10 colors, general use)
- px.colors.qualitative.T10 (10 colors, clear and bright)
- px.colors.qualitative.Plotly (10 colors, default Plotly colors)
hides_nontrading: bool, optional
Whether to hide non-trading periods on the plot. Defaults to True.
out_dir: str, optional
Directory to save the HTML file. Defaults to 'out'.
Returns
--------
None
The function generates a plot and saves it as an HTML file.
Example
--------
>>> symbols = ['NVDA', 'MSFT', 'META', 'AAPL', 'TSM']
>>> plot(symbols)
"""
if not ticker_ref:
ticker_ref = '^GSPC' # S&P 500 Index
if tw.is_taiwan_stock(tw.as_yfinance(symbols[0])):
ticker_ref = '^TWII' # Taiwan Weighted Index
# Set moving average windows based on the interval
try:
rs_window = { '1d': 252, '1wk': 52, '1mo': 12 }[interval]
except KeyError:
raise ValueError("Invalid interval. Must be '1d', '1wk', or '1mo'.")
# Fetch data for stocks and index
tickers = [tw.as_yfinance(s) for s in symbols]
df = yf.download([ticker_ref]+tickers, period=period, interval=interval)
df = df.xs('Close', level='Price', axis=1)
# Plot the figure
fig = go.Figure()
for ticker, symbol in zip(tickers, symbols):
rs = mansfield_relative_strength(df[ticker], df[ticker_ref],
rs_window, ma=ma)
fig.add_trace(go.Scatter(x=rs.index, y=rs, mode='lines',
name=si.get_name(symbol)))
df[f'RS {ticker_ref}'] = 0
fig.add_trace(go.Scatter(x=df.index, y=df[f'RS {ticker_ref}'],
mode='lines', name=si.get_name(ticker_ref),
line=dict(dash='dash', color='gray')))
# Convert datetime index to string format suitable for display
df.index = df.index.strftime('%Y-%m-%d')
# Update layout
fig.update_layout(
title=f'Mansfield Relative Strength Comparison - {interval} '
f'({df.index[0]} to {df.index[-1]})',
title_x=0.5, title_y=0.87,
yaxis=dict(title='Relative Strength '
f'(Compared to {si.get_name(ticker_ref)})',
side='right'),
#height=600,
legend_title='Stocks',
legend=dict(yanchor='top', y=.98, xanchor="left", x=0.01),
xaxis_rangeslider_visible=False,
template=template,
)
if colorway:
fig.update_layout(colorway=colorway)
if hides_nontrading:
futil.hide_nontrading_periods(fig, df, interval)
# For Crosshair cursor
futil.add_crosshair_cursor(fig)
futil.add_hovermode_menu(fig, x=0, y=1.1)
# 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('stocks', interval, df.index[-1],
'RsmLines')
fig.write_html(f'{out_dir}/{fn}.html')
if __name__ == '__main__':
#StockChart.plot('TSLA', interval='1d')
#StockChart.plot('TSLA', interval='1wk')
#StockChart.plot('聯發科', interval='1wk')
#symbols = ['羅昇', '昆盈', '穎漢', '光聖', '所羅門']
#RelativeStrengthLines.plot(symbols, interval='1d')
symbols = ['SOXX', 'DVY', 'IWB','IWM', 'IWV', 'IJR',
'ITB', 'IHI', 'IYC', 'ITA', 'IAK']
RelativeStrengthLines.plot(symbols, interval='1d',
colorway=px.colors.qualitative.Set3)