Q: ValueError: could not convert string to float: 'macd' I am getting a ValueError with the message could not convert string to float: 'macd' when trying to train a Random Forest classifier on my trading data. The code involves computing technical indicators such as RSI and MACD, and the target variable is computed as the sign of the difference in closing prices. The error occurs when calling the fit method of the classifier. I suspect the issue might be related to the column names of the input data, but I'm not sure how to resolve it. Can someone please help me understand and fix this error?
code:
    # Compute technical indicators
    df['rsi'] = ta.RSI(df, timeperiod=14)
    df['macd'], df['macdsignal'], df['macdhist'] = ta.MACD(df)

    # Compute the target variable (1 for buy, -1 for sell, 0 for hold)
    df['target'] = np.sign(df['close'].diff().shift(-1))

    # Drop the last row (since the target variable is NaN for the last row)
    df.dropna(inplace=True)

    if len(df) == 0:
        print('Error: Insufficient OHLCV data to run the trading strategy')
        return

    # Split the data into training and testing sets
    X_train = df.iloc[:-50, :-1]
    y_train = df.iloc[:-50, -1]
    X_test = df.iloc[-50:, :-1]
    y_test = df.iloc[-50:, -1]

    # Train a Random Forest classifier on the training set
    clf = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=0)
    clf.fit(X_train.values, y_train.values.ravel())

error:
ValueError: could not convert string to float: 'macd'
I tried to modify the column names of the input data to ensure they are all in the correct format for training the classifier, but this did not resolve the issue. I was expecting the code to successfully train the classifier on the input data and output the predictions of the trading strategy. Can someone please help me understand and fix this error?
