Learn Challenge: Signal Visualization with Moving Averages | Visualizing Market Trends and Indicators

In real-world trading, visualizing moving averages and their crossovers can help you spot potential buy or sell signals. You will work with a hardcoded set of closing prices, calculate two moving averages, and plot both together. Your goal is to clearly mark every point where the short-term average (3-day) crosses above or below the long-term average (5-day), highlighting possible trading signals.


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import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

# Hardcoded closing prices for 15 days
data = {
    "date": pd.date_range("2024-01-01", periods=15, freq="D"),
    "close": [100, 102, 101, 105, 108, 110, 107, 106, 108, 109, 111, 113, 112, 114, 115]
}
df = pd.DataFrame(data)
df.set_index("date", inplace=True)

# Calculate 3-day and 5-day simple moving averages
df["SMA_3"] = df["close"].rolling(window=3).mean()
df["SMA_5"] = df["close"].rolling(window=5).mean()

# Find crossover points
df["signal"] = 0
df["signal"][df["SMA_3"] > df["SMA_5"]] = 1
df["signal"][df["SMA_3"] < df["SMA_5"]] = -1
df["crossover"] = df["signal"].diff()

# Plotting
plt.figure(figsize=(12, 6))
plt.plot(df.index, df["close"], label="Close Price", color="black", linewidth=2)
plt.plot(df.index, df["SMA_3"], label="3-Day SMA", color="blue", linestyle="--")
plt.plot(df.index, df["SMA_5"], label="5-Day SMA", color="red", linestyle="-.")

# Mark crossover points
buy_signals = df[df["crossover"] == 2]
sell_signals = df[df["crossover"] == -2]

plt.scatter(buy_signals.index, buy_signals["close"], marker="^", color="green", s=100, label="Buy Signal (3>5)")
plt.scatter(sell_signals.index, sell_signals["close"], marker="v", color="red", s=100, label="Sell Signal (3<5)")

plt.title("Moving Average Crossover Signals")
plt.xlabel("Date")
plt.ylabel("Price")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

Definition

Definition: A moving average crossover occurs when a short-term moving average crosses above (bullish) or below (bearish) a longer-term moving average. Traders often use these crossovers to generate buy or sell signals.

Task

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Plot the closing prices and both moving averages using the provided hardcoded data. Then, mark all crossover points with buy (when the 3-day SMA crosses above the 5-day SMA) and sell (when the 3-day crosses below the 5-day) markers. Your plot should:

Display the close price, 3-day SMA, and 5-day SMA.
Mark every crossover with a green upward triangle for buy signals.
Mark every crossover with a red downward triangle for sell signals.
Include a legend and axis labels.

Solution

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Section 2. Chapter 7

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import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

# Hardcoded closing prices for 15 days
data = {
    "date": pd.date_range("2024-01-01", periods=15, freq="D"),
    "close": [100, 102, 101, 105, 108, 110, 107, 106, 108, 109, 111, 113, 112, 114, 115]
}
df = pd.DataFrame(data)
df.set_index("date", inplace=True)

# Calculate 3-day and 5-day simple moving averages
df["SMA_3"] = df["close"].rolling(window=3).mean()
df["SMA_5"] = df["close"].rolling(window=5).mean()

# Find crossover points
df["signal"] = 0
df["signal"][df["SMA_3"] > df["SMA_5"]] = 1
df["signal"][df["SMA_3"] < df["SMA_5"]] = -1
df["crossover"] = df["signal"].diff()

# Plotting
plt.figure(figsize=(12, 6))
plt.plot(df.index, df["close"], label="Close Price", color="black", linewidth=2)
plt.plot(df.index, df["SMA_3"], label="3-Day SMA", color="blue", linestyle="--")
plt.plot(df.index, df["SMA_5"], label="5-Day SMA", color="red", linestyle="-.")

# Mark crossover points
buy_signals = df[df["crossover"] == 2]
sell_signals = df[df["crossover"] == -2]

plt.scatter(buy_signals.index, buy_signals["close"], marker="^", color="green", s=100, label="Buy Signal (3>5)")
plt.scatter(sell_signals.index, sell_signals["close"], marker="v", color="red", s=100, label="Sell Signal (3<5)")

plt.title("Moving Average Crossover Signals")
plt.xlabel("Date")
plt.ylabel("Price")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()