Metadata-Version: 1.0
Name: statio
Version: 0.0.2
Summary: 

The statio statistical library in Python.


Home-page: http://github.com/taylortree/statio
Author: Mike Taylor
Author-email: mike@taylortree.com
License: MIT
Description: statio
        ======
        **statio** is a statistical Python libary geared towards running computations across
        a sliding window of values.
        
        :Download: http://pypi.python.org/pypi/statio/0.0.2
        :Source: https://github.com/TaylorTree/statio
        
        
        Usage Model
        -----------
        Most statistical libraries are based on a single point in time.  The -1 index of a list of values is the point in time in which the calculation is made.
        
        **statio** is based on multiple points in time.  Each index is considered a point in time in which the calculation is made.
        
        * Useful for simulation application types.
        * Useful for plotting or graphing applications types.
        
        
        Overview
        --------
        The major functions of **statio**:
        
        * **sum_values():**
        Builds a list of Running Sums over a sliding list of values.
        
        * **sma_values():**
        Builds a list of Simple Moving Averages over a sliding list of values.
        
        * **ema_values():**
        Builds a list of Exponential Moving Averages over a sliding list of values.
        
        * **wwma_values():**
        Builds a list of Welles Wilder Moving Averages over a sliding list of values.
        
        * **psa_values():**
        Builds a list of Power Sum Averages over a sliding list of values.
        
        * **varp_values():**
        Builds a list of Population Variances over a sliding list of values.
        
        * **var_values():**
        Builds a list of Sample Variances over a sliding list of values.
        
        * **stdp_values():**
        Builds a list of Population Standard Deviations over a sliding list of values.
        
        * **std_values():**
        Builds a list of Sample Standard Deviations over a sliding list of values.
        
        * **max_values():**
        Builds a list of the Maximum Values over a sliding list of values.
        
        * **min_values():**
        Builds a list of the Minimum Values over a sliding list of values.
        
        * **top_values():**
        Builds a list of the Top X Values over a sliding list of values.
        
        * **bottom_values():**
        Builds a list of the Bottom X Values over a sliding list of values.
        
        
        License
        -------
        Made available under the MIT License.
        
        
        Usage
        -----
        Import the library:
        
        >>> import statio
        
        1. Build list of running **sums** using a 3 period window:
        
        >>> values = [34, 30, 29, 34, 38, 25, 35]
        >>> statio.sum_values(values, 3)
        [34, 64, 93, 93, 101, 97, 98]
        
        2. Build list of **Simple Moving Averages** using a 3 period window:
        
        >>> values = [34, 30, 29, 34, 38, 25, 35]
        >>> results = statio.sma_values(values, 3)
        >>> ["%.2f" % x for x in results]
        ['34.00', '32.00', '31.00', '31.00', '33.67', '32.33', '32.67']
        
        3. Build list of **Exponential Moving Averages** using a 3 period window:
        
        >>> values = [34, 30, 29, 34, 38, 25, 35]
        >>> results = statio.ema_values(values, 3)
        >>> ["%.2f" % x for x in results]
        ['34.00', '32.00', '31.00', '32.50', '35.25', '30.13', '32.56']
        
        4. Build list of **Welles Wilder Averages** using a 3 period window:
        
        >>> values = [34, 30, 29, 34, 38, 25, 35]
        >>> results = statio.wwma_values(values, 3)
        >>> ["%.2f" % x for x in results]
        ['34.00', '32.00', '31.00', '32.00', '34.00', '31.00', '32.33']
        
        5. Build list of **Population Variances** using a 3 period window:
        
        >>> values = [34, 30, 29, 34, 38, 25, 35]
        >>> results = statio.varp_values(values, 3)
        >>> ["%.2f" % x for x in results]
        ['0.00', '4.00', '4.67', '4.67', '13.56', '29.56', '30.89']
        
        6. Build list of **Sample Variances** using a 3 period window:
        
        >>> values = [34, 30, 29, 34, 38, 25, 35]
        >>> results = statio.var_values(values, 3)
        >>> ["%.2f" % x for x in results]
        ['0.00', '8.00', '7.00', '7.00', '20.33', '44.33', '46.33']
        
        7. Build list of **Population Standard Deviations** using a 3 period window:
        
        >>> values = [34, 30, 29, 34, 38, 25, 35]
        >>> results = statio.stdp_values(values, 3)
        >>> ["%.2f" % x for x in results]
        ['0.00', '2.00', '2.16', '2.16', '3.68', '5.44', '5.56']
        
        8. Build list of **Sample Standard Deviations** using a 3 period window:
        
        >>> values = [34, 30, 29, 34, 38, 25, 35]
        >>> results = statio.std_values(values, 3)
        >>> ["%.2f" % x for x in results]
        ['0.00', '2.83', '2.65', '2.65', '4.51', '6.66', '6.81']
        
        9. Build list of the **Maximum Value** of 3 period window:
        
        >>> values = [34, 30, 29, 34, 38, 25, 35]
        >>> results = statio.max_values(values, 3)
        >>> ["%.2f" % x for x in results]
        ['34.00', '34.00', '34.00', '34.00', '38.00', '38.00', '38.00']
        
        10. Build list of the **Minimum Value** of 3 period window:
        
        >>> values = [34, 30, 29, 34, 38, 25, 35]
        >>> statio.min_values(values, 3)
        [34, 30, 29, 29, 29, 25, 25]
        
        11. Build list of the **Top X Values** of 3 period window:
        
        >>> values = [34, 30, 29, 34, 38, 25, 35]
        >>> statio.top_values(values, 3, 2)
        [[34], [30, 34], [30, 34], [30, 34], [34, 38], [34, 38], [35, 38]]
        
        12. Build list of the **Bottom X Values** of 3 period window:
        
        >>> values = [34, 30, 29, 34, 38, 25, 35]
        >>> statio.bottom_values(values, 3, 2)
        [[34], [30, 34], [29, 30], [29, 30], [29, 34], [25, 34], [25, 35]]
        
        
        Roadmap
        -------
        * Add median_values.
        * Add recentmax_values: the index of the most recent max value.
        * Add sincemax_values: the number of bars since recent max value.
        * Add recentmin_values: the index of the most recent min value.
        * Add sincemin_values: the number of bars since recent min value.
        * Add covariance, correlation, alpha, beta computations.
        
        
        For additional information, please email:
        mike@taylortree.com
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development :: Libraries :: Python Modules
