Plotting Time Series Data
Visualizing financial time series data is a fundamental skill for any analyst aiming to understand market behavior and make informed decisions. By plotting price data over time, you can quickly spot patterns, trends, and anomalies that may not be obvious from raw numbers alone. Common chart types used by analysts include line plots, candlestick charts, and bar charts. Among these, the line plot is especially popular for showing the evolution of a stock's price or index value over a period, helping you grasp both short-term fluctuations and long-term trends at a glance.
1234567891011121314151617181920import pandas as pd import matplotlib.pyplot as plt import numpy as np # Generate sample data for two stocks dates = pd.date_range(start="2023-01-01", end="2023-06-30", freq="B") # Business days over six months np.random.seed(42) price1 = np.cumsum(np.random.normal(0.1, 1, len(dates))) + 100 # Simulated stock 1 price2 = np.cumsum(np.random.normal(0.05, 1.2, len(dates))) + 80 # Simulated stock 2 df = pd.DataFrame({"Stock A": price1, "Stock B": price2}, index=dates) # Plot the daily closing prices plt.figure(figsize=(12, 6)) plt.plot(df.index, df["Stock A"], label="Stock A") plt.plot(df.index, df["Stock B"], label="Stock B") plt.xlabel("Date") plt.ylabel("Closing Price ($)") plt.legend() plt.show()
When you interpret a line plot of financial time series, you are looking for the overall direction of price movement—whether it is trending upward, downward, or staying relatively flat. Sustained increases in the line indicate an uptrend, while persistent declines point to a downtrend. Sideways movement suggests a range-bound market. By plotting multiple stocks on the same chart, you can directly compare their performance over the same period. This allows you to see which stock outperformed, underperformed, or moved similarly, making it easier to spot correlations or divergences between assets.
1234567891011# Customize the plot with gridlines, title, and different line styles plt.figure(figsize=(12, 6)) plt.plot(df.index, df["Stock A"], label="Stock A", linestyle="-", color="blue") plt.plot(df.index, df["Stock B"], label="Stock B", linestyle="--", color="orange") plt.xlabel("Date") plt.ylabel("Closing Price ($)") plt.title("Daily Closing Prices: Stock A vs Stock B (Jan-Jun 2023)") plt.grid(True, linestyle=":", linewidth=0.7) plt.legend() plt.show()
1. What is the primary purpose of a line plot in financial analysis?
2. How can you compare the performance of two stocks using a single plot?
3. Which matplotlib function is used to add a legend to a plot?
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Visualizing financial time series data is a fundamental skill for any analyst aiming to understand market behavior and make informed decisions. By plotting price data over time, you can quickly spot patterns, trends, and anomalies that may not be obvious from raw numbers alone. Common chart types used by analysts include line plots, candlestick charts, and bar charts. Among these, the line plot is especially popular for showing the evolution of a stock's price or index value over a period, helping you grasp both short-term fluctuations and long-term trends at a glance.
1234567891011121314151617181920import pandas as pd import matplotlib.pyplot as plt import numpy as np # Generate sample data for two stocks dates = pd.date_range(start="2023-01-01", end="2023-06-30", freq="B") # Business days over six months np.random.seed(42) price1 = np.cumsum(np.random.normal(0.1, 1, len(dates))) + 100 # Simulated stock 1 price2 = np.cumsum(np.random.normal(0.05, 1.2, len(dates))) + 80 # Simulated stock 2 df = pd.DataFrame({"Stock A": price1, "Stock B": price2}, index=dates) # Plot the daily closing prices plt.figure(figsize=(12, 6)) plt.plot(df.index, df["Stock A"], label="Stock A") plt.plot(df.index, df["Stock B"], label="Stock B") plt.xlabel("Date") plt.ylabel("Closing Price ($)") plt.legend() plt.show()
When you interpret a line plot of financial time series, you are looking for the overall direction of price movement—whether it is trending upward, downward, or staying relatively flat. Sustained increases in the line indicate an uptrend, while persistent declines point to a downtrend. Sideways movement suggests a range-bound market. By plotting multiple stocks on the same chart, you can directly compare their performance over the same period. This allows you to see which stock outperformed, underperformed, or moved similarly, making it easier to spot correlations or divergences between assets.
1234567891011# Customize the plot with gridlines, title, and different line styles plt.figure(figsize=(12, 6)) plt.plot(df.index, df["Stock A"], label="Stock A", linestyle="-", color="blue") plt.plot(df.index, df["Stock B"], label="Stock B", linestyle="--", color="orange") plt.xlabel("Date") plt.ylabel("Closing Price ($)") plt.title("Daily Closing Prices: Stock A vs Stock B (Jan-Jun 2023)") plt.grid(True, linestyle=":", linewidth=0.7) plt.legend() plt.show()
1. What is the primary purpose of a line plot in financial analysis?
2. How can you compare the performance of two stocks using a single plot?
3. Which matplotlib function is used to add a legend to a plot?
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