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# Wrapper Functions
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High-level wrapper functions that add multiple technical indicators to a DataFrame at once. These functions provide the most convenient way to perform comprehensive technical analysis by adding all indicators from specific categories or all categories simultaneously.
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## Capabilities
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### Add All Features
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Adds all 43 technical analysis features from all categories (Volume, Volatility, Trend, Momentum, Others) to the DataFrame in a single operation.
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```python { .api }
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def add_all_ta_features(df, open, high, low, close, volume, fillna=False, colprefix="", vectorized=False):
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    """
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    Add all technical analysis features to dataframe.
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    Parameters:
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    - df (DataFrame): Base dataframe to add features to
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    - open (str): Column name for 'Open' prices
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    - high (str): Column name for 'High' prices  
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    - low (str): Column name for 'Low' prices
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    - close (str): Column name for 'Close' prices
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    - volume (str): Column name for 'Volume' data
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    - fillna (bool): If True, fill NaN values with 0 (default: False)
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    - colprefix (str): Prefix to add to all new column names (default: "")
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    - vectorized (bool): If True, use only vectorized functions (default: False)
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    Returns:
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    DataFrame: Original DataFrame with all technical analysis features added
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    """
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```
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### Add Volume Features
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Adds all 9 volume-based technical indicators including Money Flow Index, On-Balance Volume, VWAP, and others.
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```python { .api }
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def add_volume_ta(df, high, low, close, volume, fillna=False, colprefix="", vectorized=False):
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    """
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    Add volume technical analysis features to dataframe.
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    Parameters:
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    - df (DataFrame): Base dataframe to add features to
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    - high (str): Column name for 'High' prices
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    - low (str): Column name for 'Low' prices
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    - close (str): Column name for 'Close' prices
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    - volume (str): Column name for 'Volume' data
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    - fillna (bool): If True, fill NaN values with 0 (default: False)
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    - colprefix (str): Prefix to add to all new column names (default: "")
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    - vectorized (bool): If True, use only vectorized functions (default: False)
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    Returns:
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    DataFrame: Original DataFrame with volume indicators added
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    """
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```
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### Add Volatility Features
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Adds all 5 volatility-based technical indicators including Bollinger Bands, Average True Range, Keltner Channel, and others.
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```python { .api }
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def add_volatility_ta(df, high, low, close, fillna=False, colprefix="", vectorized=False):
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    """
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    Add volatility technical analysis features to dataframe.
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    Parameters:
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    - df (DataFrame): Base dataframe to add features to
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    - high (str): Column name for 'High' prices
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    - low (str): Column name for 'Low' prices
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    - close (str): Column name for 'Close' prices
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    - fillna (bool): If True, fill NaN values with 0 (default: False)
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    - colprefix (str): Prefix to add to all new column names (default: "")
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    - vectorized (bool): If True, use only vectorized functions (default: False)
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    Returns:
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    DataFrame: Original DataFrame with volatility indicators added
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    """
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```
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### Add Trend Features
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Adds all 20 trend-based technical indicators including MACD, Moving Averages, ADX, Ichimoku, and others.
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```python { .api }
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def add_trend_ta(df, high, low, close, fillna=False, colprefix="", vectorized=False):
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    """
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    Add trend technical analysis features to dataframe.
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    Parameters:
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    - df (DataFrame): Base dataframe to add features to
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    - high (str): Column name for 'High' prices
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    - low (str): Column name for 'Low' prices
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    - close (str): Column name for 'Close' prices
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    - fillna (bool): If True, fill NaN values with 0 (default: False)
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    - colprefix (str): Prefix to add to all new column names (default: "")
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    - vectorized (bool): If True, use only vectorized functions (default: False)
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    Returns:
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    DataFrame: Original DataFrame with trend indicators added
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    """
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```
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### Add Momentum Features
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Adds all 11 momentum-based technical indicators including RSI, Stochastic Oscillator, Williams %R, and others.
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```python { .api }
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def add_momentum_ta(df, high, low, close, volume, fillna=False, colprefix="", vectorized=False):
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    """
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    Add momentum technical analysis features to dataframe.
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    Parameters:
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    - df (DataFrame): Base dataframe to add features to
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    - high (str): Column name for 'High' prices
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    - low (str): Column name for 'Low' prices
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    - close (str): Column name for 'Close' prices
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    - volume (str): Column name for 'Volume' data
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    - fillna (bool): If True, fill NaN values with 0 (default: False)
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    - colprefix (str): Prefix to add to all new column names (default: "")
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    - vectorized (bool): If True, use only vectorized functions (default: False)
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    Returns:
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    DataFrame: Original DataFrame with momentum indicators added
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    """
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```
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### Add Others Features
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Adds all 3 other financial analysis indicators for calculating various types of returns.
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```python { .api }
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def add_others_ta(df, close, fillna=False, colprefix=""):
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    """
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    Add others analysis features to dataframe.
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    Parameters:
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    - df (DataFrame): Base dataframe to add features to
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    - close (str): Column name for 'Close' prices
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    - fillna (bool): If True, fill NaN values with 0 (default: False)
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    - colprefix (str): Prefix to add to all new column names (default: "")
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    Returns:
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    DataFrame: Original DataFrame with return calculation features added
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    """
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```
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## Usage Examples
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### Complete Technical Analysis
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```python
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import pandas as pd
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import ta
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# Your OHLCV data
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df = pd.read_csv('stock_data.csv')
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# Add all 43 technical indicators at once
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df_complete = ta.add_all_ta_features(
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    df,
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    open='Open',
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    high='High', 
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    low='Low',
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    close='Close',
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    volume='Volume',
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    fillna=True  # Fill NaN values
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)
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print(f"Original columns: {len(df.columns)}")
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print(f"With indicators: {len(df_complete.columns)}")
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```
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### Category-Specific Analysis
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```python
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import ta
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# Add only trend indicators for trend analysis
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df_trend = ta.add_trend_ta(
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    df,
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    high='High',
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    low='Low', 
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    close='Close',
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    colprefix='trend_'  # Prefix all new columns
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)
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# Add only momentum indicators for momentum analysis  
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df_momentum = ta.add_momentum_ta(
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    df,
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    high='High',
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    low='Low',
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    close='Close', 
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    volume='Volume',
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    vectorized=True  # Use only vectorized functions
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)
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```
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### Combining Categories
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```python
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import ta
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# Start with base data
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result_df = df.copy()
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# Add indicators by category
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result_df = ta.add_volume_ta(result_df, 'High', 'Low', 'Close', 'Volume')
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result_df = ta.add_volatility_ta(result_df, 'High', 'Low', 'Close')
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result_df = ta.add_trend_ta(result_df, 'High', 'Low', 'Close')
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# Now result_df contains volume, volatility, and trend indicators
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```