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beyondsimulationsbeyondsimulations
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update for lecture 9
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‎.gitignore‎

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/_freeze/
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_quarto_internal_scss_error.scss
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.DS_Store
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*/.DS_Store
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*/.DS_Store
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.web/

‎assignments/assignment-2.qmd‎

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@@ -32,7 +32,7 @@ In this excercise, you will create a program that is able to redact secret infor
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```{python}
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# Secret information redactor
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# TODO: Create a program that is able to redact secret information in a text.
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# Your code here
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# YOUR CODE HERE
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```
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# Dice roll simulator
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```{python}
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# Dice roll simulator
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# TODO: Create a program that is able to simulate a dice roll.
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# Your code here
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# YOUR CODE HERE
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```
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# Regression analysis
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:::{.callout-note}
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You will either have to to this excercise or the next one about dashboards.
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:::
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In this excercise, you will create a program that performs linear regression on a dataset with housing prices. The dataset can be found in the git repository under `assignments/hamburg_housing_prices.csv`. The dataset contains the following columns:
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- Price: The price of the house in Euros.
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```{python}
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# Regression analysis
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# TODO: Create a program that performs linear regression analysis on the dataset.
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# Your code here
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# YOUR CODE HERE
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```
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# Dashboards
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:::{.callout-note}
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You will either have to to this excercise or the previous one about regression analysis.
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:::
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In this exercise, you will create a program that visualizes a data set of your choice in a dashboard. For this excercise, you can choose any data set of your interest. The data should be visualized in a dashboard with at least two plots. You can use one of the dashboard libraries we discussed in the lecture.
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:::{.callout-note}
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The dashboard should be programmed in a separate file I can call for evaluation.
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:::
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```{python}
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# Dashboards
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# TODO: Create a program that visualizes a data set of your choice in a dashboard.
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# YOUR CODE HERE
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```

assignments/generate-housing-prices.py renamed to assignments/create-data/generate-housing-prices.py

File renamed without changes.

‎part-09/dashboards/stocks_panel.py‎

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import panel as pn
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import yfinance as yf
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from bokeh.plotting import figure
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from bokeh.models import HoverTool, CrosshairTool
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from datetime import datetime
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import pandas as pd
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# Enable Panel extensions
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pn.extension(sizing_mode="stretch_width")
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# Cache function for data loading
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@pn.cache(ttl=3600)
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def load_data(ticker_symbol):
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try:
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stock = yf.Ticker(ticker_symbol)
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df = stock.history(period="2y")
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if df.empty:
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raise ValueError("No data available for this ticker")
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return df, None
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except Exception as e:
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return None, str(e)
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def create_candlestick_plot(df, ticker_symbol):
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# Create Bokeh figure
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p = figure(
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width=800,
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height=400,
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title=f'{ticker_symbol} Stock Price',
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x_axis_type='datetime',
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tools='pan,wheel_zoom,box_zoom,reset,save'
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)
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# Add hover tool with formatters
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hover = HoverTool(
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tooltips=[
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('Date', '@x{%F}'),
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('Open', '$@{Open}{0,0.00}'),
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('High', '$@{High}{0,0.00}'),
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('Low', '$@{Low}{0,0.00}'),
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('Close', '$@{Close}{0,0.00}')
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],
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formatters={"@x": "datetime"}
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)
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# Add tools
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p.add_tools(hover)
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p.add_tools(CrosshairTool())
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p.add_tools(CrosshairTool())
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# Calculate candlestick colors
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inc = df.Close > df.Open
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dec = df.Open > df.Close
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# Add candlestick chart
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p.segment(df.index, df.High, df.index, df.Low, color="black")
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p.vbar(
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x=df.index[inc],
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top=df.Open[inc],
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bottom=df.Close[inc],
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width=24*60*60*1000, # One day in milliseconds
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fill_color="green",
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line_color="green"
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)
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p.vbar(
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x=df.index[dec],
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top=df.Open[dec],
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bottom=df.Close[dec],
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width=24*60*60*1000,
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fill_color="red",
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line_color="red"
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)
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return p
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def create_volume_plot(df):
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# Create volume subplot
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v = figure(
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width=800,
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height=200,
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x_axis_type='datetime',
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title='Volume'
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)
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v.vbar(
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x=df.index,
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top=df.Volume,
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width=24*60*60*1000,
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color='gray',
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alpha=0.5
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)
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return v
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# Create error text widget with visibility control
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error_text = pn.widgets.StaticText(name='Error', styles={'color': 'red'}, visible=False)
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def update_plots(event):
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# Show loading indicator
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loading.visible = True
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error_text.visible = False # Hide error message at start
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error_text.value = ""
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try:
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# Validate ticker symbol
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if not ticker.value or len(ticker.value) < 1:
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raise ValueError("Please enter a valid ticker symbol")
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# Get data
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df, error = load_data(ticker.value)
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if error:
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raise ValueError(error)
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days = int(timeframe.value[1])
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filtered_df = df.iloc[-days:]
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# Update plots
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main_plot.object = create_candlestick_plot(filtered_df, ticker.value)
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volume_plot.object = create_volume_plot(filtered_df)
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# Update metrics with better formatting using iloc
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latest_close = filtered_df['Close'].iloc[-1]
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prev_close = filtered_df['Close'].iloc[-2]
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daily_change_val = ((latest_close - prev_close) / prev_close * 100)
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current_price.value = f"${latest_close:.2f}"
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daily_change.value = f"{daily_change_val:+.2f}%" # Added plus sign for positive values
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highest_price.value = f"${filtered_df['High'].max():.2f}"
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lowest_price.value = f"${filtered_df['Low'].min():.2f}"
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# Style daily change based on value
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daily_change.styles = {'color': 'green' if daily_change_val > 0 else 'red'}
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except Exception as e:
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error_text.value = str(e)
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error_text.visible = True # Only show when there's an error
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error_text.styles = {'color': 'red'}
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finally:
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loading.visible = False
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# Create widgets
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ticker = pn.widgets.TextInput(
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name='Stock Ticker',
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value='REMEDY.HE',
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)
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timeframe = pn.widgets.Select(
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name='Timeframe',
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options=[
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('1 Month', 30),
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('3 Months', 90),
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('6 Months', 180),
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('1 Year', 365),
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('2 Years', 730)
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],
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value=('1 Month', 30)
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)
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# Create additional widgets
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loading = pn.indicators.LoadingSpinner(value=True, size=25)
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# Update metrics styling
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current_price = pn.widgets.StaticText(name='Current Price', styles={'font-weight': 'bold'})
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daily_change = pn.widgets.StaticText(name='Daily Change', styles={'font-weight': 'bold'})
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highest_price = pn.widgets.StaticText(name='Highest Price')
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lowest_price = pn.widgets.StaticText(name='Lowest Price')
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error_text = pn.widgets.StaticText(name='Error', styles={'color': 'red'})
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# Update layout with loading indicator
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metrics = pn.Row(
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pn.Column(current_price, daily_change),
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pn.Column(highest_price, lowest_price),
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loading
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)
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# Create plot panes (initially empty)
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main_plot = pn.pane.Bokeh()
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volume_plot = pn.pane.Bokeh()
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# Watch for changes
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ticker.param.watch(update_plots, 'value')
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timeframe.param.watch(update_plots, 'value')
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# Layout
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sidebar = pn.Column(
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pn.pane.Markdown('# Settings'),
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ticker,
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timeframe,
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width=300
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)
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metrics = pn.Row(
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pn.Column(current_price, daily_change),
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pn.Column(highest_price),
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pn.Column(lowest_price)
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)
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main_content = pn.Column(
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pn.pane.Markdown('# Stock Price Analysis'),
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main_plot,
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volume_plot,
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metrics,
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error_text, # It will only be visible when there's an error
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pn.pane.Markdown('---\nData provided by Yahoo Finance')
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)
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# Create template
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template = pn.template.FastListTemplate(
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title='Stock Analyzer',
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sidebar=[sidebar],
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main=[main_content],
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)
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# Initial update
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update_plots(None)
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# Serve the dashboard
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template.servable()

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