Challenge: Analyze OHLC Patterns
In this challenge, you will work with a small, hardcoded pandas DataFrame representing OHLC (Open, High, Low, Close) data for a stock over seven consecutive trading days. Your goal is to analyze the data to uncover specific price patterns that are of interest to traders.
Specifically, you will:
- Calculate the daily range for each day;
- Identify all days where the closing price was higher than both the open and the previous day's close.
This kind of analysis can help you spot bullish momentum in price action, which is a core skill for any trader.
123456789101112131415161718192021222324252627282930313233import pandas as pd # Create a sample OHLC DataFrame for 7 days data = { "Date": [ "2024-06-01", "2024-06-02", "2024-06-03", "2024-06-04", "2024-06-05", "2024-06-06", "2024-06-07" ], "Open": [100, 102, 101, 103, 104, 105, 104], "High": [103, 104, 103, 105, 106, 107, 106], "Low": [99, 101, 100, 102, 103, 104, 103], "Close": [102, 103, 102, 104, 105, 106, 105] } df = pd.DataFrame(data) df["Date"] = pd.to_datetime(df["Date"]) df.set_index("Date", inplace=True) # Calculate daily range (High - Low) df["Range"] = df["High"] - df["Low"] # Identify days where Close > Open and Close > previous day's Close df["Prev_Close"] = df["Close"].shift(1) mask = (df["Close"] > df["Open"]) & (df["Close"] > df["Prev_Close"]) # Output the dates matching the pattern bullish_days = df[mask].index.strftime("%Y-%m-%d").tolist() print("Dates where Close > Open and Close > previous Close:") print(bullish_days)
By running this code, you will see which dates meet the specified criteria. Calculating the daily range gives you a sense of volatility, while identifying days with strong closes compared to both the open and the previous close highlights persistent upward momentum. This approach is frequently used to spot short-term bullish signals in trading.
Swipe to start coding
Create a pandas DataFrame containing OHLC data for 7 consecutive days with the following columns: Date, Open, High, Low, and Close. The dates should be in order and the data should be hardcoded, not loaded from a file or the internet.
Then, for each day:
- Calculate the daily range (high minus low) and add it as a new column called
Range. - Identify all days where the closing price is higher than both the open and the previous day's close.
- Output a list of strings with the dates (in
YYYY-MM-DDformat) that match this pattern.
Your code should:
- Use only the
pandaslibrary for data manipulation; - Print the list of dates that meet the criteria.
Lösung
Danke für Ihr Feedback!
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Fragen Sie alles oder probieren Sie eine der vorgeschlagenen Fragen, um unser Gespräch zu beginnen
Can you explain what the daily range tells us about the stock's behavior?
How can I modify the criteria to find bearish days instead?
What other patterns can I look for using this OHLC data?
Großartig!
Completion Rate verbessert auf 4.76Challenge: Analyze OHLC Patterns
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In this challenge, you will work with a small, hardcoded pandas DataFrame representing OHLC (Open, High, Low, Close) data for a stock over seven consecutive trading days. Your goal is to analyze the data to uncover specific price patterns that are of interest to traders.
Specifically, you will:
- Calculate the daily range for each day;
- Identify all days where the closing price was higher than both the open and the previous day's close.
This kind of analysis can help you spot bullish momentum in price action, which is a core skill for any trader.
123456789101112131415161718192021222324252627282930313233import pandas as pd # Create a sample OHLC DataFrame for 7 days data = { "Date": [ "2024-06-01", "2024-06-02", "2024-06-03", "2024-06-04", "2024-06-05", "2024-06-06", "2024-06-07" ], "Open": [100, 102, 101, 103, 104, 105, 104], "High": [103, 104, 103, 105, 106, 107, 106], "Low": [99, 101, 100, 102, 103, 104, 103], "Close": [102, 103, 102, 104, 105, 106, 105] } df = pd.DataFrame(data) df["Date"] = pd.to_datetime(df["Date"]) df.set_index("Date", inplace=True) # Calculate daily range (High - Low) df["Range"] = df["High"] - df["Low"] # Identify days where Close > Open and Close > previous day's Close df["Prev_Close"] = df["Close"].shift(1) mask = (df["Close"] > df["Open"]) & (df["Close"] > df["Prev_Close"]) # Output the dates matching the pattern bullish_days = df[mask].index.strftime("%Y-%m-%d").tolist() print("Dates where Close > Open and Close > previous Close:") print(bullish_days)
By running this code, you will see which dates meet the specified criteria. Calculating the daily range gives you a sense of volatility, while identifying days with strong closes compared to both the open and the previous close highlights persistent upward momentum. This approach is frequently used to spot short-term bullish signals in trading.
Swipe to start coding
Create a pandas DataFrame containing OHLC data for 7 consecutive days with the following columns: Date, Open, High, Low, and Close. The dates should be in order and the data should be hardcoded, not loaded from a file or the internet.
Then, for each day:
- Calculate the daily range (high minus low) and add it as a new column called
Range. - Identify all days where the closing price is higher than both the open and the previous day's close.
- Output a list of strings with the dates (in
YYYY-MM-DDformat) that match this pattern.
Your code should:
- Use only the
pandaslibrary for data manipulation; - Print the list of dates that meet the criteria.
Lösung
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