Mathew K AnalyticsLesson 57 · Finance and Stock Market Analytics
Designing Financial Analytics Dashboards: Principles, Tools, and Best Practices
In this lesson, we will learn how to build interactive finance dashboards using real stock market data. Dashboards help analysts and investors make quick,…
- CourseFinance and Stock Market Analytics
- Lesson57 of 16
- Video22 min
- FormatJupyter notebook · 17 code cells
What you'll learn
- Data concepts for financial dashboards
- Beginner Example 1: Filter by ticker
- Beginner Example 2: Create a basic price plot
- Beginner Example 3: Calculate daily returns
- Intermediate Example 1: Pivot to wide for multi-ticker displays
- Intermediate Example 2: Heatmap of daily returns
- Intermediate Example 3: Rolling volatility chart
- Advanced Example 1: Mini-dashboard - summary KPIs table
Data
No separate download needed — the notebook creates or downloads everything it uses.
📓 Full notebook
Download .ipynbDesigning Financial Analytics Dashboards#
- In this lesson, we will learn how to build interactive finance dashboards using real stock market data.
- Dashboards help analysts and investors make quick, informed decisions by visualising key market metrics and trends.
- You will use Python to fetch, transform, and visualise live dataessential skills in real-world financial analytics.
- By the end, you will be able to assemble and display valuable analytics for finance using live Python code.
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
import numpy as np
import yfinance as yf
import matplotlib.pyplot as plt
import seaborn as sns
Data concepts for financial dashboards#
- We will use real daily OHLCV (open, high, low, close, volume) data for multiple major stocks.
- The data is in long format: each row corresponds to one date and one ticker.
- Key columns include: Date, Ticker, Open, High, Low, Close, Volume.
- Dashboards display metrics like price trends, daily changes, sector breakdowns, and risk statistics.
- Common beginner mistakes:
- Using incorrectly reshaped data- Plotting with mismatched indexes- Failing to check for missing data
tickers = ['AAPL','MSFT','GOOGL','AMZN','TSLA']
ohlcv = yf.download(tickers, period='1y', auto_adjust=True, progress=False)
ohlcv = ohlcv.stack(future_stack=True).rename_axis(['Date','Ticker']).reset_index()
ohlcv.columns.name = None
print('Shape:', ohlcv.shape)
print(ohlcv.head(3))
Beginner Example 1: Filter by ticker#
- To analyse one company in detail, filter the long data using its ticker symbol.
- This lets us quickly extract all rows for a selected stock.
aapl = ohlcv[ohlcv['Ticker'] == 'AAPL']
print(aapl.head())
Beginner Example 2: Create a basic price plot#
- Line charts show changes in stock price over time.
- This is the most common way to visualise stock movement in a dashboard.
plt.figure(figsize=(10,5))
plt.plot(aapl['Date'], aapl['Close'], label='AAPL Close Price')
plt.title('AAPL Closing Price Over Last Year')
plt.xlabel('Date')
plt.ylabel('Close Price (USD)')
plt.legend()
plt.tight_layout()
plt.show()
Beginner Example 3: Calculate daily returns#
- Dashboards often show daily returns the percent change in closing price from one day to the next.
- This helps investors and analysts quickly spot volatility.
aapl['Daily_Return'] = aapl['Close'].pct_change()*100
print(aapl[['Date','Close','Daily_Return']].head(7))
Intermediate Example 1: Pivot to wide for multi-ticker displays#
- Dashboards often need to show price lines for multiple stocks simultaneously.
- Pivoting reshapes the data so each ticker is a column.
close_wide = ohlcv.pivot(index='Date', columns='Ticker', values='Close')
print(close_wide.head())
plt.figure(figsize=(10,6))
close_wide.plot(ax=plt.gca())
plt.title('Tech Stock Closing Prices Over Last Year')
plt.ylabel('Close Price (USD)')
plt.xlabel('Date')
plt.legend(loc='upper left')
plt.tight_layout()
plt.show()
Intermediate Example 2: Heatmap of daily returns#
- Correlation heatmaps helps in visualising how stocks move together.
- It is useful for quickly spotting diversification or concentration risk.
returns = close_wide.pct_change()*100
corr = returns.corr()
plt.figure(figsize=(7,6))
sns.heatmap(corr, annot=True, cmap='coolwarm', fmt='.2f')
plt.title('Correlation Heatmap of Daily Returns')
plt.show()
Intermediate Example 3: Rolling volatility chart#
- Volatility (moving standard deviation of returns) is a key measure in dashboards.
- Rolling volatility shows how risk changes over time.
window = 21 # roughly 1 trading month
volatility = returns.rolling(window).std()
plt.figure(figsize=(10,6))
volatility.plot(ax=plt.gca())
plt.title(f'{window}-Day Rolling Volatility of Daily Returns')
plt.ylabel('Volatility (%)')
plt.xlabel('Date')
plt.legend(loc='upper left')
plt.tight_layout()
plt.show()
Advanced Example 1: Mini-dashboard - summary KPIs table#
- Financial dashboards often display key summary metrics (KPIs) for decision making.
- Let us compute total returns, median volatility, and the maximum drawdown for each stock.
kpis = pd.DataFrame(index=close_wide.columns)
kpis['Total Return (%)'] = (close_wide.iloc[-1] / close_wide.iloc[0] - 1) * 100
kpis['Median Volatility'] = returns.rolling(21).std().median()
drawdown = close_wide / close_wide.cummax() - 1
kpis['Max Drawdown (%)'] = drawdown.min() * 100
print(kpis.round(2))
Advanced Example 2: Dashboard sector breakdown (mock portfolio)#
- Real dashboards often show how holdings are split by sector.
- We will use a simulated but realistic portfolio to create a sector allocation chart.
tickers = ['AAPL','MSFT','GOOGL','AMZN','TSLA','NVDA','META','NFLX','JPM','JNJ']
sector_map = {'AAPL':'Tech','MSFT':'Tech','GOOGL':'Tech','AMZN':'Consumer',
'TSLA':'Auto','NVDA':'Tech','META':'Tech','NFLX':'Media',
'JPM':'Finance','JNJ':'Health'}
np.random.seed(42)
data = yf.download(tickers, period='1y', auto_adjust=True, progress=False)['Close']
rows = []
for tk in tickers:
buy = round(float(data[tk].iloc[0]), 2)
cur = round(float(data[tk].iloc[-1]), 2)
rows.append({'ticker': tk, 'shares': int(np.random.randint(5, 100)),
'buy_price': buy, 'cur_price': cur, 'sector': sector_map[tk]})
df_portfolio = pd.DataFrame(rows)
df_portfolio['gain_pct'] = np.round((df_portfolio['cur_price'] - df_portfolio['buy_price']) / df_portfolio['buy_price'] * 100, 2)
print(df_portfolio.head())
sector_counts = df_portfolio.groupby('sector')['shares'].sum()
plt.figure(figsize=(7,5))
sector_counts.plot(kind='bar', color='teal')
plt.title('Portfolio Share Allocation by Sector')
plt.xlabel('Sector')
plt.ylabel('Total Shares')
plt.tight_layout()
plt.show()
Advanced Example 3: Export dashboard metrics to file#
- Saving key dashboard outputs like KPIs allows you to share or archive results.
- Let us export our KPI table to CSV for report use.
kpis.to_csv('dashboard_kpis.csv')
print('Saved KPI dashboard metrics to dashboard_kpis.csv')
Error Handling and Debugging#
- Beginners often struggle with missing data and mismatched date indexes in finance analytics.
- We show how to detect, handle, and resolve common issues.
print('Any NA in close_wide? ', close_wide.isna().values.any())
na_counts = close_wide.isna().sum()
if na_counts.any():
print('Tickers with NA:', na_counts[na_counts>0])
close_wide = close_wide.fillna(method='ffill')
print('Filled missing values with previous price')
Best Practices for Dashboard Code#
- Use functions to keep repeated code clean and reusable.
- Always check for missing data before plotting.
- Use clear chart titles and axis labels so others can understand your work.
- Set random seed to 42 when randomisation is involved so results are reproducible.
- Keep code and dashboard metrics in sync to prevent reporting errors.
def plot_dashboard_price(ticker):
df = ohlcv[ohlcv['Ticker'] == ticker]
plt.figure(figsize=(9,5))
plt.plot(df['Date'], df['Close'], label=f'{ticker} Close')
plt.title(f'{ticker} Dashboard Price Chart')
plt.xlabel('Date')
plt.ylabel('Close Price (USD)')
plt.legend()
plt.tight_layout()
plt.show()
plot_dashboard_price('GOOGL')
Tiny End-to-End Example: Dashboard Snapshots#
- You will now create a simple dashboard summary: plot, table, and sector split in three lines.
- This flows from fetching data to dashboardsall in one place.
# Dashboard: 1. Plot all price trends
close_wide.plot(figsize=(10,5), title='All Tech Closing Prices (1yr)')
plt.xlabel('Date'); plt.ylabel('USD'); plt.tight_layout(); plt.show()
# Dashboard: 2. Show key metrics
display(kpis.round(2))
# Dashboard: 3. Portfolio sector pie chart
sector_val = df_portfolio.groupby('sector').apply(lambda d: (d['shares']*d['cur_price']).sum())
sector_val.plot.pie(autopct='%1.1f%%', ylabel='', title='Portfolio Allocation by Sector')
plt.tight_layout(); plt.show()
Found this useful?
All lessons, notebooks and datasets here are free. If they helped you, a coffee keeps new lessons coming.
