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ticker_plotter.py
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ticker_plotter.py
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import os
import pandas as pd
from toolbox import database
from toolbox import ticker_prices
import plotly.express as px
import plotly.graph_objects as go
def set_storage_path(database_path: str, make_dir=False):
"""
Params
------
database_path: str
Path to the database
make_dir: bool
If True, create the directory if it does not exist
Returns
-------
None
Notes
-----
This function is used to set the path to the database. The database is a
Examples
--------
from toolbox import ticker_price_analysis
ticker_price_analysis.set_storage_path('~/Desktop/database', make_dir=True)
"""
if make_dir:
if not os.path.exists(database_path):
os.makedirs(database_path)
database.set_storage_path(database_path)
ticker_prices.set_storage_path(database_path)
def get_figure(trend: pd.DataFrame, columns: list, title: str, yaxis_name: str = "Price ($)", key_name: str = "Type"):
"""
Parameters
----------
trend: pd.DataFrame
Dataframe containing the trend
columns: list
List of columns to plot
title: str
Title of the plot
yaxis_name: str
Name of the y-axis
key_name: str
Name of the key
Returns
-------
fig: plotly.graph_objects.Figure
Plotly figure
Notes
-----
This function is used to plot the trend of the data. The trend is a dataframe where the index is the date and the
columns are the different types of data. The columns are the different types of data.
Examples
--------
from toolbox import ticker_price_analysis
from toolbox import ticker_prices
ticker_price_analysis.set_storage_path('~/Desktop/database', make_dir=True)
ticker_prices.set_storage_path('~/Desktop/database')
trend = ticker_prices.get_ticker_historical_trend('AAPL')
fig = ticker_price_analysis.get_figure(trend, ['Close', 'Open'], 'AAPL')
fig.show()
fig.write_image(f"AAPL_trend.png")
"""
# First create Date column for the plot
trend = ticker_prices.interpolate(trend, create_dates_column=True)
return px.line(trend, x="Date", y=columns, title=title, color_discrete_sequence=px.colors.qualitative.Plotly,
labels={"value": yaxis_name, "variable": key_name})
def get_candlestick_figure(trend: pd.DataFrame, title: str, yaxis_name: str = "Price ($)"):
"""
Parameters
----------
trend: pd.DataFrame
Dataframe containing the trend
title: str
Title of the plot
yaxis_name: str
Name of the y-axis
Returns
-------
fig: plotly.graph_objects.Figure
Plotly figure
Notes
-----
This function is used to plot the trend of the data. The trend is a dataframe where the index is the date and the
columns are the different types of data. The columns are the different types of data.
Examples
--------
from toolbox import ticker_price_analysis
from toolbox import ticker_prices
ticker_price_analysis.set_storage_path('~/Desktop/database', make_dir=True)
ticker_prices.set_storage_path('~/Desktop/database')
trend = ticker_prices.get_ticker_historical_trend('AAPL')
fig = ticker_price_analysis.get_candlestick_figure(trend, 'AAPL')
fig.show()
fig.write_image(f"AAPL_candlestick.png")
"""
# First create Date column for the plot
trend = ticker_prices.interpolate(trend, create_dates_column=True)
fig = go.Figure(data=[go.Candlestick(x=trend['Date'],
open=trend['Open'],
high=trend['High'],
low=trend['Low'],
close=trend['Close'])])
fig.update_layout(
title=title,
yaxis_title=yaxis_name,
)
return fig