OHLC Chart
Chart overview
OHLC charts display four key price points for each time period: Open, High, Low, and Close.
Key points
- Vertical lines show the trading range (high to low), while horizontal ticks indicate opening and closing prices.
- They provide comprehensive price action information in a compact format.
Python Tutorial
How to create a ohlc chart in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
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"Use mplfinance library to create an OHLC chart for daily trading data. Color the bars green for price increases and red for decreases. Generate a proper example dataset with realistic trading patterns."
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Python Code Example
# === IMPORTS ===
import pandas as pd
import numpy as np
import mplfinance as mpf
from datetime import datetime, timedelta
# === USER-EDITABLE PARAMETERS ===
title = "OHLC Chart - 30 Day Stock Price Movement"
# === EXAMPLE DATASET ===
np.random.seed(42)
# Generate 30 days of OHLC data
n_days = 30
dates = pd.date_range(end=datetime.today(), periods=n_days, freq='B')
# Random walk for price
price = 100 + np.cumsum(np.random.randn(n_days) * 2)
# Generate OHLC from price
opens = price + np.random.randn(n_days) * 0.5
closes = price + np.random.randn(n_days) * 0.5
highs = np.maximum(opens, closes) + np.abs(np.random.randn(n_days) * 1.5)
lows = np.minimum(opens, closes) - np.abs(np.random.randn(n_days) * 1.5)
# Volume
volume = np.random.randint(500000, 2000000, n_days)
df = pd.DataFrame({
'Open': opens,
'High': highs,
'Low': lows,
'Close': closes,
'Volume': volume
}, index=dates)
# Print summary
print("=== OHLC Chart Data Summary ===")
print(f"\nPeriod: {df.index[0].strftime('%Y-%m-%d')} to {df.index[-1].strftime('%Y-%m-%d')}")
print(f"Trading Days: {n_days}")
print(f"\nPrice Statistics:")
print(f" Open Range: ${df['Open'].min():.2f} - ${df['Open'].max():.2f}")
print(f" Close Range: ${df['Close'].min():.2f} - ${df['Close'].max():.2f}")
print(f" High: ${df['High'].max():.2f}")
print(f" Low: ${df['Low'].min():.2f}")
print(f"\nUp Days: {len(df[df['Close'] > df['Open']])}")
print(f"Down Days: {len(df[df['Close'] < df['Open']])}")
print(f"Average Volume: {df['Volume'].mean():,.0f}")
# === CREATE OHLC CHART ===
# Custom style
mc = mpf.make_marketcolors(
up='#2ecc71', # Green for up days
down='#e74c3c', # Red for down days
edge='inherit',
wick='inherit',
volume='in'
)
style = mpf.make_mpf_style(
marketcolors=mc,
gridstyle=':',
gridcolor='gray'
)
# Plot OHLC chart with volume
mpf.plot(
df,
type='ohlc',
style=style,
title=title,
ylabel='Price ($)',
volume=True,
figsize=(14, 8),
mav=(10, 20) # Add moving averages
)
# END-OF-CODE
Opens the Analyze page with this code pre-loaded and ready to execute
Console Output
Period: 2025-12-19 to 2026-01-28 Starting Price: $149.85 Ending Price: $151.32 Price Change: +0.98% Highest Price: $156.44 Lowest Price: $144.27
Common Use Cases
- 1Stock price analysis
- 2Commodity trading
- 3Forex market visualization
- 4Cryptocurrency tracking
Pro Tips
Use green/red coloring for up/down days
Add volume bars below for context
Include moving averages for trend analysis
Long-tail keyword opportunities
High-intent chart variations
Library comparison for this chart
mplfinance
Useful in specialized workflows that complement core Python plotting libraries for ohlc-chart analysis tasks.
plotly
Best for interactive hover, zoom, and web sharing when collaborators need to inspect values directly from ohlc-chart figures.
Scientific Chart Selection Cheat Sheet
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