Stock data analysis and volatility tracking using Preswald
Amrutha Gujjar••5 min readCategory: Use Case
Analyzing stock market trends across multiple securities required manual data processing, cleaning, and visualization, making financial analysis slow and error-prone.
We needed a real-time stock analytics dashboard that could:
✅ Load raw stock data directly from CSV files
✅ Normalize stock prices for better comparability
✅ Track volatility differences between stocks
✅ Provide interactive visualizations
Instead of writing complex Python scripts and manually generating reports, we turned to Preswald to automate the entire process.
The Challenge: Manual and Inefficient Stock Data Analysis
Our team faced several challenges when working with stock data:
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📊 Data Cleaning Complexity – Stock prices have inconsistent formatting, missing values, and require standardization.
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⏳ Slow Comparisons – Manually comparing multiple stocks took too much time.
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📈 Lack of Interactivity – Static Excel charts made deeper insights difficult.
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⚠️ Volatility Tracking Issues – Identifying risk factors across different securities required additional calculations.
We needed a fast, automated, and interactive solution that would:
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Standardize stock price data for meaningful comparisons
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Track and visualize stock volatility over time
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Enable real-time analysis without manual intervention
Why We Chose Preswald
Preswald provided an end-to-end solution for stock data processing and analysis:
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✅ Automated CSV data ingestion – No more copy-pasting from Excel.
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✅ Real-time filtering and visualizations – Instantly compare stocks interactively.
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✅ Volatility tracking tools – Calculate and visualize volatility trends dynamically.
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✅ One-command deployment – Share dashboards instantly in the cloud.
The Implementation: From CSV Files to Automated Stock Dashboards
Step 1: Installing Preswald
We started by installing Preswald:
pip install preswald
Step 2: Setting Up the Stock Analysis App
We initialized a new Preswald project:
preswald init stock_dashboard
cd stock_dashboard
This generated a pre-configured project structure, including:
-
hello.py– The main stock analytics script. -
preswald.toml– Configuration settings. -
secrets.toml– Secure storage for credentials.
Step 3: Loading Stock Data from CSV
Instead of manually cleaning and loading data, we used Preswald to automate CSV ingestion:
import pandas as pd
from preswald import text, plotly
# Dashboard Title
text("# Analyzing Stock Data Cleaning and Volatility")
text("Exploring how standardization affects financial data analysis.")
# Load data
df = pd.read_csv("data/stock_data.csv")
df = df.reset_index()
✅ No manual data uploads – Preswald reads CSVs dynamically.
Step 4: Comparing Stock Prices Over Time
To compare stock performance, we melted the data and created an interactive line chart:
import plotly.express as px
df_melted = df.melt(
id_vars=["index"],
value_vars=[col for col in df.columns if col != "index"],
var_name="Stock",
value_name="Price",
)
fig1 = px.line(
df_melted,
x="index",
y="Price",
color="Stock",
title="Stock Price Comparison<br> <sup>Top 10 Stocks By Market Cap Over The Past Year</sup>",
template="plotly_white",
)
text("## Raw Data")
plotly(fig1)
✅ Interactive stock comparison – Users can analyze trends dynamically.
Step 5: Calculating Volatility Differences Between Stocks
Stock price volatility varies significantly. To track volatility differences, we:
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Calculated percentage returns
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Measured standard deviation (volatility)
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Created a heatmap to compare volatilities
import numpy as np
# Compute percentage returns
returns = df.pct_change().dropna()
# Compute volatility
volatilities = returns.std()
# Create volatility difference matrix
tickers = df.columns
volatility_diff = np.zeros((len(tickers), len(tickers)))
for i, ticker1 in enumerate(tickers):
for j, ticker2 in enumerate(tickers):
vol_diff = abs(volatilities[ticker1] - volatilities[ticker2])
volatility_diff[i, j] = vol_diff
df_volatility = pd.DataFrame(volatility_diff, index=tickers, columns=tickers)
fig2 = px.imshow(
df_volatility,
labels={"x": "Stock", "y": "Stock", "color": "Volatility Difference (stdev)"},
title="Stock Volatility Comparison Heatmap",
color_continuous_scale="Viridis",
)
plotly(fig2)
✅ Clear volatility differences – Easily identify risk levels across securities.
Step 6: Normalizing Data with Logarithmic Returns
To improve accuracy, we calculated logarithmic returns:
# Compute log returns
log_returns = np.log(df / df.shift(1)).dropna()
# Plot log returns over time
log_returns_melted = log_returns.reset_index().melt(
id_vars=["index"], var_name="Stock", value_name="Return"
)
fig3 = px.line(
log_returns_melted,
x="index",
y="Return",
color="Stock",
title="Logarithmic Returns Over Time",
template="plotly_white",
)
plotly(fig3)
✅ Improved data standardization – More accurate risk calculations.
Step 7: Rolling Volatility Analysis Over Time
We also calculated rolling window volatility to track trends:
# Compute rolling volatility (30-day window)
rolling_volatility = returns.rolling(window=30).std().dropna()
# Melt DataFrame for plotting
rolling_volatility_melted = rolling_volatility.reset_index().melt(
id_vars=["index"], var_name="Stock", value_name="Volatility"
)
fig4 = px.line(
rolling_volatility_melted,
x="index",
y="Volatility",
color="Stock",
title="30-Day Rolling Volatility",
template="plotly_white",
)
plotly(fig4)
✅ Track market risk fluctuations dynamically.
Step 8: Deploying the Dashboard in One Command
To share our interactive stock analytics tool, we deployed it to the cloud:
preswald deploy --target structured
📍 Now live at:
🌐 https://stock-dashboard.preswald.app
✅ Anyone can now interact with real-time stock analytics without Python installed.
The Results: A Fully Automated Stock Data Analysis Pipeline
🚀 Eliminated manual stock data processing
🚀 Automated volatility tracking and comparisons
🚀 Enabled real-time stock trend visualizations
🚀 Deployed a shareable cloud dashboard in minutes
Final Thoughts: Preswald for Financial Data Analysis
If your team struggles with analyzing stock data manually, Preswald is a game-changer. It allows you to build interactive financial dashboards without complex frontend development.
Try Preswald today!
