Final Python Cheat Sheet for Market Data Analysis in 2025

With Automated Conditional Email Alerts

Author

Ayrat Murtazin
August 27, 2025

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Volatility Forecasting with ARCH Model

A Hands-On Python Tutorial Using the arch Library

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Once upon a time, Excel was the king of financial analysis. A few thousand rows and some basic formulas used to handle most market data tasks. But in 2025, with billions of data points, real-time trading algorithms, and AI-driven strategies, Excel is outpaced. Enter Python and its powerhouse library, pandas, the go-to tools for analysts navigating today’s data-driven markets.

In 2025, finance is buzzing with trends like AI-powered investing, sustainable finance (ReFi), and blockchain-based systems. Whether you’re a trader, data scientist, or curious beginner, gone are the days where you could analyse manually and break into the market. This guide will show you how to use pandas to analyze stock market data, compute key indicators, and visualize trends by tapping into these cutting-edge trends. We’ll use NVIDIA (NVDA) stock data as a case study, given its volatility in the AI-driven market. Let’s dive in.

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Why Python and Pandas in 2025?

Markets are evolving fast. Artificial intelligence (AI) is revolutionizing trading with predictive models, while regenerative finance (ReFi) is channeling investments into sustainable assets via blockchain’s transparency. Meanwhile, cloud-based banking and decentralized finance (DeFi) are reshaping how data is accessed and analyzed. Pandas is perfect for this landscape because it:

  • Scales Effortlessly: Handles millions of rows with ease.

  • Masters Time Series: Aligns dates and computes rolling metrics for stock prices.

  • Integrates with AI: Pairs with libraries like scikit-learn for predictive modeling.

  • Supports Diverse Data: Merges market data with ESG (Environmental, Social, Governance) metrics for sustainable investing.

This tutorial will show you how to analyze NVIDIA’s stock data, a company at the heart of the AI boom, and connect your skills to 2025’s hottest finance trends.

1. Set Up Your Environment

To get started, install these Python libraries:

  • pandas: For data manipulation.

  • yfinance: To fetch stock data from Yahoo Finance.

  • matplotlib: For visualizations.

  • smtplib and email: For sending email alerts.

To install, run this command in your terminal:

pip install pandas yfinance matplotlib

Note: smtplib and email are built into Python’s standard library.

2. Load Historical Stock Data

We’ll fetch NVIDIA (NVDA) stock data from Yahoo Finance to analyze its price trends, which are influenced by AI demand in 2025.

import pandas as pd
import yfinance as yf
import matplotlib.pyplot as plt
import math

# Download NVDA data from Jan 1, 2023 to Aug 21, 2025
data = yf.download("NVDA", start="2023-01-01", end="2025-08-21")

# Check for successful data load
if data.empty:
    print("Error: No data downloaded. Check ticker or internet connection.")
else:
    print("Data loaded successfully!")

This stores daily stock metrics (e.g., Open, Close, Volume) in a pandas DataFrame, a table-like structure optimized for analysis. In 2025, tools like yfinance are critical for accessing real-time data to power AI-driven trading bots.

3. Inspect the Data

Let’s peek at the data to understand its structure.

# Display the first 5 rows
print(data.head())

Output:

[*********************100%***********************]  1 of 1 completed
Data loaded successfully!
Price           Close       High        Low       Open     Volume
Ticker           NVDA       NVDA       NVDA       NVDA       NVDA
Date
2023-01-03  14.302285  14.982681  14.083480  14.837809  401277000
2023-01-04  14.735902  14.839810  14.228354  14.554064  431324000
2023-01-05  14.252332  14.551066  14.135435  14.478131  389168000
2023-01-06  14.845804  14.996670  14.021537  14.461146  405044000
2023-01-09  15.614120  16.041740  15.127553  15.270426  504231000

The Close price reflects the stock’s end-of-day value. This data is the foundation for analyzing trends like AI-driven stock volatility.

4. Calculate Key Market Indicators

Let’s compute metrics used by traders and AI algorithms:

  • Simple Moving Averages (SMA): To spot price trends.

  • Historical Volatility: To measure risk, critical for 2025’s volatile markets.

  • Percentile Rank: To gauge if a stock is overbought or oversold.

# Calculate 20-day and 50-day SMAs
data['SMA20'] = data['Close'].rolling(window=20).mean()
data['SMA50'] = data['Close'].rolling(window=50).mean()

# Calculate annualized volatility (21-day window)
data['vol'] = data['Close'].pct_change().rolling(window=21).std() * math.sqrt(252)

# Calculate percentile rank of closing prices (21-day window)
data['pct_rank'] = data['Close'].rolling(window=21).rank(pct=True)

  • SMA: The 20-day SMA tracks short-term trends, while the 50-day SMA shows longer-term patterns. Crossovers signal potential buy/sell opportunities, often used in automated trading systems.

  • Volatility: We annualize the 21-day standard deviation of daily returns (√252 for trading days). High volatility in AI stocks like NVIDIA reflects market uncertainty.

  • Percentile Rank: A value near 1.0 suggests a stock is at a historical high, useful for spotting overbought conditions in ReFi or tech portfolios.

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5. Visualize Trends with Plots

Visuals are key to spotting patterns. Let’s plot NVIDIA’s price and SMAs.

# Plot closing price and moving averages
to_plot = ["Close", "SMA20", "SMA50"]
data[to_plot].plot(title="NVDA Stock Price and Moving Averages (2023-2025)")
plt.xlabel("Date")
plt.ylabel("Price (USD)")
plt.grid(True)
plt.show()

Plot 1 — NVDA Price and SMA

What to Look For: A 20-day SMA crossing above the 50-day SMA signals a bullish trend, common in AI-driven stocks like NVIDIA. A cross below suggests a bearish trend.

Next, plot volatility alongside price to assess risk.

# Plot price and volatility on dual axes
fig, ax1 = plt.subplots()
data["Close"].plot(ax=ax1, label="Close Price", color="blue")
ax1.set_xlabel("Date")
ax1.set_ylabel("Price (USD)", color="blue")
ax1.tick_params(axis="y", labelcolor="blue")
ax1.grid(True)

ax2 = ax1.twinx()
data["vol"].plot(ax=ax2, label="Volatility", color="orange")
ax2.set_ylabel("Annualized Volatility", color="orange")
ax2.tick_params(axis="y", labelcolor="orange")

plt.title("NVDA Price and Volatility (2023-2025)")
fig.legend(loc="upper left")
plt.show()

Plot 2 — NVDA Price and Volatility

What to Look For: Spikes in volatility often align with AI sector news (e.g., chip demand surges) or market shocks. Low volatility may indicate stability, appealing for ReFi investors.

Finally, plot the percentile rank.

# Plot percentile rank
data["pct_rank"].plot(title="NVDA Closing Price Percentile Rank (21-Day)")
plt.xlabel("Date")
plt.ylabel("Percentile Rank")
plt.grid(True)
plt.show()

Plot 3 — NVDA Closing Price Percentile Rank

What to Look For: A rank near 1.0 suggests NVIDIA is overbought (potential sell signal), while near 0.0 indicates oversold conditions, useful for timing investments in sustainable or AI-focused portfolios.

6. Set Up Email Notifications

Let’s add email alerts for two conditions:

  • Price crosses 50-day SMA: Signals a potential trend change.

  • Volatility exceeds 0.5: Indicates high risk, common in AI stocks like NVIDIA.

def send_email(subject, body):
    sender = "[email protected]"  # Replace with your email
    receiver = "[email protected]"  # Replace with recipient email
    password = "your_app_password"  # Use Gmail app-specific password (enable 2FA, generate at myaccount.google.com/security)
    msg = MIMEText(body)
    msg['Subject'] = subject
    msg['From'] = sender
    msg['To'] = receiver
    try:
        with smtplib.SMTP('smtp.gmail.com', 587) as server:
            server.starttls()
            server.login(sender, password)
            server.send_message(msg)
        print("Email sent successfully!")
    except Exception as e:
        print(f"Error sending email: {e}")

# Check conditions on the latest data point
latest = data.iloc[-1]
if latest['Close'] > latest['SMA50']:
    send_email("NVDA Alert: Price Above 50-Day SMA", 
               f"NVDA price (${latest['Close']:.2f}) crossed above 50-day SMA (${latest['SMA50']:.2f}) on {data.index[-1].date()}.")
if latest['vol'] > 0.5:
    send_email("NVDA Alert: High Volatility", 
               f"NVDA volatility ({latest['vol']:.2f}) exceeds threshold on {data.index[-1].date()}.")

Setup Notes:

This system ensures you’re alerted instantly, aligning with 2025’s need for rapid responses in AI-driven markets.

7. Tying It to 2025 Trends

Your analysis doesn’t stop here. In 2025, finance is shaped by:

  • AI and Machine Learning: Use pandas to preprocess data for AI models predicting stock prices. Try integrating scikit-learn for regression or classification.

  • Sustainable Investing (ReFi): Combine stock data with ESG metrics (e.g., from Bloomberg) to evaluate NVIDIA’s sustainability, aligning with ReFi’s rise.

  • Blockchain and DeFi: Analyze tokenized assets or crypto-related stocks (e.g., COIN) using similar pandas techniques, leveraging blockchain’s transparency.

Next Steps

  • Swap “NVDA” for another ticker (e.g., “TSLA” or “COIN”) to explore different sectors.

  • Experiment with windows (e.g., 10-day SMA or 30-day volatility) to test sensitivity.

  • Add indicators like RSI or Bollinger Bands for deeper insights.
    Automate your analysis with a daily script, syncing with cloud-based APIs.

Explore pandas’ documentation for more advanced features like merging data, time series analysis, and more.

Why This Matters in 2025

Pandas empowers you to analyze markets at scale, from AI-driven stocks to sustainable investments. With 65% of CFOs using AI for tasks like forecasting and 52% saving $100,000+ annually through automation, these skills are in high demand. Whether you’re optimizing portfolios or building trading bots, Python is your edge in a fast-moving world.