import pandas as pd



def calculate_heikin_ashi(df):

 """

 Calculates Heikin-Ashi candles from OHLC data.

 

 Parameters:

 df (DataFrame): Pandas DataFrame with columns ['Open', 'High', 'Low', 'Close']

 

 Returns:

 DataFrame: New DataFrame with Heikin-Ashi candles ['HA_Open', 'HA_High', 'HA_Low', 'HA_Close']

 """



 ha_df = df.copy() # Create a copy to avoid modifying the original DataFrame



 # Calculate HA_Close

 ha_df["HA_Close"] = (ha_df["Open"] + ha_df["High"] + ha_df["Low"] + ha_df["Close"]) / 4



 # Initialize HA_Open with the first Open value

 ha_df["HA_Open"] = 0

 ha_df.iloc[0, ha_df.columns.get_loc("HA_Open")] = (ha_df.iloc[0]["Open"] + ha_df.iloc[0]["Close"]) / 2



 # Calculate HA_Open for the rest of the rows

 for i in range(1, len(ha_df)):

 ha_df.iloc[i, ha_df.columns.get_loc("HA_Open")] = (

 ha_df.iloc[i - 1]["HA_Open"] + ha_df.iloc[i - 1]["HA_Close"]

 ) / 2



 # Calculate HA_High and HA_Low

 ha_df["HA_High"] = ha_df[["High", "HA_Open", "HA_Close"]].max(axis=1)

 ha_df["HA_Low"] = ha_df[["Low", "HA_Open", "HA_Close"]].min(axis=1)



 return ha_df[["HA_Open", "HA_High", "HA_Low", "HA_Close"]]



# Example Usage:

# Create sample OHLC data

data = {

 "Open": [100, 102, 104, 106, 108],

 "High": [103, 105, 107, 109, 111],

 "Low": [99, 101, 103, 105, 107],

 "Close": [102, 104, 106, 108, 110],

}



df = pd.DataFrame(data)



# Calculate Heikin-Ashi candles

ha_df = calculate_heikin_ashi(df)

print(ha_df)