Learn Challenge: Build a Custom Candlestick Plot | Visualizing Market Trends and Indicators

In this challenge, you will use matplotlib to create a custom candlestick chart from a hardcoded DataFrame containing OHLC (open, high, low, close) data for seven days. Candlestick charts are essential tools for traders, offering a clear visual representation of price movements and patterns over time. Each "candle" shows the open and close prices as well as the high and low for each trading period, helping you quickly spot bullish and bearish trends. Your goal is to plot each candle in green if the close is higher than the open (bullish) and in red if the close is lower than the open (bearish). Additionally, you will annotate the chart with the highest high and the lowest low values to highlight significant price levels.


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import pandas as pd
import matplotlib.pyplot as plt

# Hardcoded OHLC data for 7 days
data = {
    "Date": pd.date_range("2024-06-01", periods=7, freq="D"),
    "Open":  [100, 102, 101, 103, 102, 105, 104],
    "High":  [105, 104, 103, 106, 107, 108, 107],
    "Low":   [99, 100, 100, 102, 101, 104, 103],
    "Close": [104, 101, 103, 105, 106, 106, 104],
}
df = pd.DataFrame(data)

fig, ax = plt.subplots(figsize=(10, 6))

width = 0.6  # width of the candlestick body
for idx, row in df.iterrows():
    color = "green" if row["Close"] >= row["Open"] else "red"
    # Draw the wick (high-low line)
    ax.plot([idx, idx], [row["Low"], row["High"]], color="black", linewidth=1)
    # Draw the candle body (open-close rectangle)
    lower = min(row["Open"], row["Close"])
    height = abs(row["Close"] - row["Open"])
    rect = plt.Rectangle((idx - width/2, lower), width, height or 0.1, color=color, alpha=0.8)
    ax.add_patch(rect)

# Annotate highest high and lowest low
highest_high = df["High"].max()
highest_idx = df["High"].idxmax()
ax.annotate(f"High: {highest_high}", xy=(highest_idx, highest_high), xytext=(highest_idx, highest_high+0.5),
            arrowprops=dict(facecolor='blue', shrink=0.05), ha='center', color='blue')

lowest_low = df["Low"].min()
lowest_idx = df["Low"].idxmin()
ax.annotate(f"Low: {lowest_low}", xy=(lowest_idx, lowest_low), xytext=(lowest_idx, lowest_low-1),
            arrowprops=dict(facecolor='blue', shrink=0.05), ha='center', color='blue')

# Formatting the x-axis with dates
ax.set_xticks(range(len(df)))
ax.set_xticklabels(df["Date"].dt.strftime("%b %d"), rotation=45)
ax.set_title("Custom Candlestick Chart")
ax.set_xlabel("Date")
ax.set_ylabel("Price")
plt.tight_layout()
plt.show()

Note

Candlestick charts are widely used in technical analysis because they provide more information than simple line charts. By visualizing the relationship between open, close, high, and low prices, you can quickly identify market sentiment and potential reversal points.

For more on candlestick charting techniques, consider reading "Japanese Candlestick Charting Techniques" by Steve Nison.

Task

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Plot a candlestick chart using the provided DataFrame, following these steps:

Draw each candlestick with rectangles and lines using matplotlib.
Color each candle body green if close >= open, or red if close < open.
Annotate the chart with the highest high and lowest low values using ax.annotate.
Format the x-axis to display the dates from the DataFrame.

Solution

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import pandas as pd
import matplotlib.pyplot as plt

# Hardcoded OHLC data for 7 days
data = {
    "Date": pd.date_range("2024-06-01", periods=7, freq="D"),
    "Open":  [100, 102, 101, 103, 102, 105, 104],
    "High":  [105, 104, 103, 106, 107, 108, 107],
    "Low":   [99, 100, 100, 102, 101, 104, 103],
    "Close": [104, 101, 103, 105, 106, 106, 104],
}
df = pd.DataFrame(data)

fig, ax = plt.subplots(figsize=(10, 6))

width = 0.6  # width of the candlestick body
for idx, row in df.iterrows():
    color = "green" if row["Close"] >= row["Open"] else "red"
    # Draw the wick (high-low line)
    ax.plot([idx, idx], [row["Low"], row["High"]], color="black", linewidth=1)
    # Draw the candle body (open-close rectangle)
    lower = min(row["Open"], row["Close"])
    height = abs(row["Close"] - row["Open"])
    rect = plt.Rectangle((idx - width/2, lower), width, height or 0.1, color=color, alpha=0.8)
    ax.add_patch(rect)

# Annotate highest high and lowest low
highest_high = df["High"].max()
highest_idx = df["High"].idxmax()
ax.annotate(f"High: {highest_high}", xy=(highest_idx, highest_high), xytext=(highest_idx, highest_high+0.5),
            arrowprops=dict(facecolor='blue', shrink=0.05), ha='center', color='blue')

lowest_low = df["Low"].min()
lowest_idx = df["Low"].idxmin()
ax.annotate(f"Low: {lowest_low}", xy=(lowest_idx, lowest_low), xytext=(lowest_idx, lowest_low-1),
            arrowprops=dict(facecolor='blue', shrink=0.05), ha='center', color='blue')

# Formatting the x-axis with dates
ax.set_xticks(range(len(df)))
ax.set_xticklabels(df["Date"].dt.strftime("%b %d"), rotation=45)
ax.set_title("Custom Candlestick Chart")
ax.set_xlabel("Date")
ax.set_ylabel("Price")
plt.tight_layout()
plt.show()