Stock Market Analysis (Finance)

Goal

Given daily stock prices, compute:

Daily returns
Rolling averages
Volatility
Compare multiple stocks

Dataset columns

datedate, closeclose (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())

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))

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()

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()

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()

Volatility

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

Deliverable

Summarize:

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

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