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# Price Transform
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Functions that transform OHLC data into standardized price representations for further analysis. These transformations combine multiple price points into single representative values, providing different perspectives on price action.
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## Capabilities
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### Average Price
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Calculates the arithmetic mean of open, high, low, and close prices, providing a balanced representation of price activity.
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```python { .api }
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def AVGPRICE(open, high, low, close):
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    """
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    Average Price
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    Formula: (open + high + low + close) / 4
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    Parameters:
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    - open: array-like, open prices
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    - high: array-like, high prices
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    - low: array-like, low prices
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    - close: array-like, close prices
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    Returns:
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    numpy.ndarray: Average price values
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    """
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```
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### Median Price
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Calculates the midpoint between high and low prices, representing the middle of the trading range.
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```python { .api }
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def MEDPRICE(high, low):
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    """
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    Median Price
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    Formula: (high + low) / 2
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    Parameters:
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    - high: array-like, high prices
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    - low: array-like, low prices
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    Returns:
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    numpy.ndarray: Median price values (midpoint of range)
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    """
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```
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### Typical Price
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Calculates the average of high, low, and close prices, giving equal weight to the three key price points.
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```python { .api }
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def TYPPRICE(high, low, close):
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    """
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    Typical Price
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    Formula: (high + low + close) / 3
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    Parameters:
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    - high: array-like, high prices
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    - low: array-like, low prices
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    - close: array-like, close prices
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    Returns:
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    numpy.ndarray: Typical price values
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    """
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```
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### Weighted Close Price
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Calculates a weighted average emphasizing the close price, which is often considered the most important price of the period.
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```python { .api }
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def WCLPRICE(high, low, close):
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    """
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    Weighted Close Price
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    Formula: (high + low + 2*close) / 4
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    Parameters:
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    - high: array-like, high prices
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    - low: array-like, low prices
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    - close: array-like, close prices
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    Returns:
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    numpy.ndarray: Weighted close price values
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    """
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```
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## Usage Examples
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```python
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import talib
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import numpy as np
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# Sample OHLC data
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open_prices = np.array([100.0, 101.0, 102.0, 101.5, 103.0])
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high_prices = np.array([100.8, 102.2, 102.5, 102.0, 103.5])
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low_prices = np.array([99.2, 100.5, 101.0, 100.8, 102.5])
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close_prices = np.array([100.5, 101.8, 101.2, 102.2, 103.2])
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# Calculate different price transforms
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avg_price = talib.AVGPRICE(open_prices, high_prices, low_prices, close_prices)
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med_price = talib.MEDPRICE(high_prices, low_prices)
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typ_price = talib.TYPPRICE(high_prices, low_prices, close_prices)
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wcl_price = talib.WCLPRICE(high_prices, low_prices, close_prices)
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print("Latest prices:")
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print(f"Average Price: {avg_price[-1]:.2f}")
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print(f"Median Price: {med_price[-1]:.2f}")
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print(f"Typical Price: {typ_price[-1]:.2f}")
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print(f"Weighted Close Price: {wcl_price[-1]:.2f}")
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# These transformed prices are commonly used as:
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# - Input to other technical indicators instead of close price
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# - Basis for pivot point calculations
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# - Representative price for volume-weighted calculations
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# - Smoothed price series for trend analysis
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```
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## Common Use Cases
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- **Indicator Input**: Many traders use typical price or weighted close price as input to moving averages and other indicators instead of just close price
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- **Pivot Points**: Average price and typical price are often used in pivot point calculations
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- **Volume Analysis**: Typical price is commonly used in volume-weighted price calculations  
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- **Price Smoothing**: These transforms can help reduce noise in price data while preserving important characteristics