Learn Working with OHLC Data | Financial Data Analysis with Python

Understanding the structure and significance of Open-High-Low-Close (OHLC) data is essential for analyzing financial markets and building trading strategies. OHLC data summarizes the price action of a security for a specific time period—often a day—by recording four key values: the opening price, the highest price, the lowest price, and the closing price. This format provides a concise yet comprehensive view of market activity, allowing you to spot trends, volatility, and key support or resistance levels. Technical analysts rely heavily on OHLC data to identify patterns, gauge market sentiment, and design rules-based trading strategies. For example, chart patterns like candlesticks and bar charts are built from OHLC data, helping traders make informed decisions based on price movements within each period.


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import pandas as pd

# Create a DataFrame with OHLC data for a fictional stock over 5 days
data = {
    "open": [100, 102, 101, 103, 104],
    "high": [105, 104, 103, 106, 108],
    "low": [99, 101, 100, 102, 103],
    "close": [104, 103, 102, 105, 107]
}
ohlc_df = pd.DataFrame(data, index=pd.date_range("2024-06-01", periods=5, freq="D"))
print(ohlc_df)

With OHLC data organized, you can extract actionable insights for trading. One common calculation is the daily price range, which is simply the difference between the high and low prices for each day. The daily range is a direct measure of volatility for that period—wider ranges indicate more active or volatile trading sessions, while narrower ranges suggest consolidation or less price movement.

Another useful analysis involves classifying each day as bullish or bearish. A bullish day occurs when the closing price is higher than the opening price, signaling upward momentum during the session. Conversely, a bearish day is when the close is below the open, indicating downward movement. By identifying bullish and bearish days, you can quickly assess market sentiment and spot trends that may inform entry or exit decisions.


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# Calculate daily range (high - low)
ohlc_df["range"] = ohlc_df["high"] - ohlc_df["low"]

# Identify bullish days (close > open)
ohlc_df["bullish"] = ohlc_df["close"] > ohlc_df["open"]

print(ohlc_df)

1. What does a bullish day mean in the context of OHLC data?

2. How can the daily range (high-low) be useful for traders?

Everything was clear?

Thanks for your feedback!

Section 1. Chapter 4

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              1234567891011
            
import pandas as pd

# Create a DataFrame with OHLC data for a fictional stock over 5 days
data = {
    "open": [100, 102, 101, 103, 104],
    "high": [105, 104, 103, 106, 108],
    "low": [99, 101, 100, 102, 103],
    "close": [104, 103, 102, 105, 107]
}
ohlc_df = pd.DataFrame(data, index=pd.date_range("2024-06-01", periods=5, freq="D"))
print(ohlc_df)


              1234567
            
# Calculate daily range (high - low)
ohlc_df["range"] = ohlc_df["high"] - ohlc_df["low"]

# Identify bullish days (close > open)
ohlc_df["bullish"] = ohlc_df["close"] > ohlc_df["open"]

print(ohlc_df)

1. What does a bullish day mean in the context of OHLC data?

2. How can the daily range (high-low) be useful for traders?

Everything was clear?

Thanks for your feedback!

Section 1. Chapter 4