Stock Market Analysis (Finance)

Goal

Given daily stock prices, compute:

• Daily returns
• Rolling averages
• Volatility
• Compare multiple stocks

Analysis pipeline

diagram Stock analysis pipeline mermaid

  flowchart LR
  A["Raw prices
(date, close)"] --> B["Returns
(pct_change)"]
  B --> C["Smooth
(20/50-day MA)"]
  C --> D["Visualize
(price + MA lines)"]
  D --> E["Volatility
(rolling std)"]

From raw daily prices to a smoothed trend with a volatility measure.

From the book

The whole point of this final chapter, McKinney says, is that the same handful of tools — load, derive a column, group or roll, then plot — solves very different problems. pct_change() for daily returns and .rolling(20).mean() for a moving average are just vectorized versions of “compare today to yesterday” and “average the last N days,” so you never need a manual loop over rows.

Dataset columns

• date, close (and optionally open/high/low/volume)

Step 1: Load and parse

Load prices

import pandas as pd
 
df = pd.read_csv("data/stock.csv")
df["date"] = pd.to_datetime(df["date"], errors="coerce")
df = df.sort_values("date")
print(df.head())

Step 2: Daily returns

Returns

df["return"] = df["close"].pct_change()
print(df[["date", "close", "return"]].head(10))

Step 3: Moving averages

Moving averages

df["ma20"] = df["close"].rolling(20).mean()
df["ma50"] = df["close"].rolling(50).mean()

Step 4: Plot

Plot price + MA

import matplotlib.pyplot as plt
 
plt.figure(figsize=(10, 4))
plt.plot(df["date"], df["close"], label="close")
plt.plot(df["date"], df["ma20"], label="MA20")
plt.plot(df["date"], df["ma50"], label="MA50")
plt.title("Price and moving averages")
plt.legend()
plt.xticks(rotation=20)
plt.tight_layout()
plt.show()

Step 5: Volatility (rolling std)

Volatility

df["volatility_20"] = df["return"].rolling(20).std()

Visualize it

sketch Close price with moving averages p5.js Pause Restart

The raw close price (blue) vs its smoother 20-day (amber) and 50-day (green) averages.

Deliverable

Summarize:

• Trend periods
• High-volatility periods and possible reasons
• Compare performance across stocks (optional)

🧪 Try It Yourself

Exercise 1 – Daily returns

pch.exerciseTag pch.exerciseTitle

Exercise 2 – Moving average

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Exercise 3 – Rolling volatility

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