Challenge: Signal Visualization with Moving Averages
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.
123456789101112131415161718192021222324252627282930313233343536373839404142import 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: 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.
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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.
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Can you explain how the crossover signals are detected in this code?
What do the buy and sell markers represent on the plot?
Can you walk me through the logic for calculating the moving averages?
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Completion rate forbedret til 4.76Challenge: Signal Visualization with Moving Averages
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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.
123456789101112131415161718192021222324252627282930313233343536373839404142import 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: 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.
Swipe to start coding
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.
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