Leer Challenge: Visualize Volatility Clusters | Visualizing Market Trends and Indicators

You will practice visualizing volatility clusters using a small set of closing price data. This exercise will help you spot periods of unusual price movement, which is critical for risk management and trading decisions. Start by using a hardcoded pandas DataFrame containing closing prices. Calculate daily returns, then compute a 3-day rolling standard deviation to measure short-term volatility. Plot both the closing prices and rolling volatility together with matplotlib, using twin axes for clarity. Highlight periods where the rolling volatility rises above a chosen threshold to clearly identify volatility clusters.


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import pandas as pd
import matplotlib.pyplot as plt

# Hardcoded closing prices
data = {
    "date": pd.date_range(start="2024-01-01", periods=15, freq="D"),
    "close": [
        100, 102, 101, 103, 102, 105, 107, 106, 104, 108,
        110, 112, 111, 115, 117
    ]
}
df = pd.DataFrame(data)
df.set_index("date", inplace=True)

# Calculate daily returns
df["returns"] = df["close"].pct_change()

# Calculate 3-day rolling volatility (standard deviation)
df["volatility"] = df["returns"].rolling(window=3).std()

# Set volatility threshold
vol_threshold = 0.015

# Plot price and volatility
fig, ax1 = plt.subplots(figsize=(10, 5))

color_price = "tab:blue"
ax1.set_xlabel("Date")
ax1.set_ylabel("Close Price", color=color_price)
ax1.plot(df.index, df["close"], color=color_price, label="Close Price")
ax1.tick_params(axis="y", labelcolor=color_price)

# Create twin axis for volatility
ax2 = ax1.twinx()
color_vol = "tab:red"
ax2.set_ylabel("3-Day Rolling Volatility", color=color_vol)
ax2.plot(df.index, df["volatility"], color=color_vol, label="Volatility", linestyle="--")
ax2.tick_params(axis="y", labelcolor=color_vol)

# Highlight periods where volatility exceeds threshold
high_vol = df["volatility"] > vol_threshold
ax2.fill_between(df.index, 0, df["volatility"], where=high_vol, color="orange", alpha=0.3, label="High Volatility")

# Add legends
lines1, labels1 = ax1.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax1.legend(lines1 + lines2, labels1 + labels2, loc="upper left")

plt.title("Close Price and 3-Day Rolling Volatility")
plt.tight_layout()
plt.show()

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Calculate and visualize volatility clusters using the following steps:

Create a DataFrame with columns "date" and "close" using the provided price data.
Compute daily returns as the percentage change of the "close" price.
Calculate a 3-day rolling standard deviation of the returns to estimate short-term volatility.
Set a volatility threshold of 0.015.
Plot both the "close" price and "volatility" on the same chart using twin y-axes.
Highlight periods where volatility exceeds the threshold.
Your plot should clearly show both the price and volatility, with high-volatility periods visible.

Use only the libraries and methods shown in the example.

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import pandas as pd
import matplotlib.pyplot as plt

# Hardcoded closing prices
data = {
    "date": pd.date_range(start="2024-01-01", periods=15, freq="D"),
    "close": [
        100, 102, 101, 103, 102, 105, 107, 106, 104, 108,
        110, 112, 111, 115, 117
    ]
}
df = pd.DataFrame(data)
df.set_index("date", inplace=True)

# Calculate daily returns
df["returns"] = df["close"].pct_change()

# Calculate 3-day rolling volatility (standard deviation)
df["volatility"] = df["returns"].rolling(window=3).std()

# Set volatility threshold
vol_threshold = 0.015

# Plot price and volatility
fig, ax1 = plt.subplots(figsize=(10, 5))

color_price = "tab:blue"
ax1.set_xlabel("Date")
ax1.set_ylabel("Close Price", color=color_price)
ax1.plot(df.index, df["close"], color=color_price, label="Close Price")
ax1.tick_params(axis="y", labelcolor=color_price)

# Create twin axis for volatility
ax2 = ax1.twinx()
color_vol = "tab:red"
ax2.set_ylabel("3-Day Rolling Volatility", color=color_vol)
ax2.plot(df.index, df["volatility"], color=color_vol, label="Volatility", linestyle="--")
ax2.tick_params(axis="y", labelcolor=color_vol)

# Highlight periods where volatility exceeds threshold
high_vol = df["volatility"] > vol_threshold
ax2.fill_between(df.index, 0, df["volatility"], where=high_vol, color="orange", alpha=0.3, label="High Volatility")

# Add legends
lines1, labels1 = ax1.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax1.legend(lines1 + lines2, labels1 + labels2, loc="upper left")

plt.title("Close Price and 3-Day Rolling Volatility")
plt.tight_layout()
plt.show()