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        "author": "Mike Taylor",
        "author_email": "mike@taylortree.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "statio\n======\n**statio** is a statistical Python libary geared towards running computations across\na sliding window of values.\n\n:Download: http://pypi.python.org/pypi/statio/0.0.2\n:Source: https://github.com/TaylorTree/statio\n\n\nUsage Model\n-----------\nMost 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.\n\n**statio** is based on multiple points in time.  Each index is considered a point in time in which the calculation is made.\n\n* Useful for simulation application types.\n* Useful for plotting or graphing applications types.\n\n\nOverview\n--------\nThe major functions of **statio**:\n\n* **sum_values():**\n    Builds a list of Running Sums over a sliding list of values.\n\n* **sma_values():**\n    Builds a list of Simple Moving Averages over a sliding list of values.\n\n* **ema_values():**\n    Builds a list of Exponential Moving Averages over a sliding list of values.\n\n* **wwma_values():**\n    Builds a list of Welles Wilder Moving Averages over a sliding list of values.\n\n* **psa_values():**\n    Builds a list of Power Sum Averages over a sliding list of values.\n\n* **varp_values():**\n    Builds a list of Population Variances over a sliding list of values.\n\n* **var_values():**\n    Builds a list of Sample Variances over a sliding list of values.\n\n* **stdp_values():**\n    Builds a list of Population Standard Deviations over a sliding list of values.\n\n* **std_values():**\n    Builds a list of Sample Standard Deviations over a sliding list of values.\n\n* **max_values():**\n    Builds a list of the Maximum Values over a sliding list of values.\n\n* **min_values():**\n    Builds a list of the Minimum Values over a sliding list of values.\n    \n* **top_values():**\n    Builds a list of the Top X Values over a sliding list of values.\n\n* **bottom_values():**\n    Builds a list of the Bottom X Values over a sliding list of values.\n\n\nLicense\n-------\nMade available under the MIT License.\n\n\nUsage\n-----\nImport the library:\n\n>>> import statio\n\n1. Build list of running **sums** using a 3 period window:\n\n>>> values = [34, 30, 29, 34, 38, 25, 35]\n>>> statio.sum_values(values, 3)\n[34, 64, 93, 93, 101, 97, 98]\n\n2. Build list of **Simple Moving Averages** using a 3 period window:\n\n>>> values = [34, 30, 29, 34, 38, 25, 35]\n>>> results = statio.sma_values(values, 3)\n>>> [\"%.2f\" % x for x in results]\n['34.00', '32.00', '31.00', '31.00', '33.67', '32.33', '32.67']\n\n3. Build list of **Exponential Moving Averages** using a 3 period window:\n\n>>> values = [34, 30, 29, 34, 38, 25, 35]\n>>> results = statio.ema_values(values, 3)\n>>> [\"%.2f\" % x for x in results]\n['34.00', '32.00', '31.00', '32.50', '35.25', '30.13', '32.56']\n\n4. Build list of **Welles Wilder Averages** using a 3 period window:\n\n>>> values = [34, 30, 29, 34, 38, 25, 35]\n>>> results = statio.wwma_values(values, 3)\n>>> [\"%.2f\" % x for x in results]\n['34.00', '32.00', '31.00', '32.00', '34.00', '31.00', '32.33']\n\n5. Build list of **Population Variances** using a 3 period window:\n\n>>> values = [34, 30, 29, 34, 38, 25, 35]\n>>> results = statio.varp_values(values, 3)\n>>> [\"%.2f\" % x for x in results]\n['0.00', '4.00', '4.67', '4.67', '13.56', '29.56', '30.89']\n\n6. Build list of **Sample Variances** using a 3 period window:\n\n>>> values = [34, 30, 29, 34, 38, 25, 35]\n>>> results = statio.var_values(values, 3)\n>>> [\"%.2f\" % x for x in results]\n['0.00', '8.00', '7.00', '7.00', '20.33', '44.33', '46.33']\n\n7. Build list of **Population Standard Deviations** using a 3 period window:\n\n>>> values = [34, 30, 29, 34, 38, 25, 35]\n>>> results = statio.stdp_values(values, 3)\n>>> [\"%.2f\" % x for x in results]\n['0.00', '2.00', '2.16', '2.16', '3.68', '5.44', '5.56']\n\n8. Build list of **Sample Standard Deviations** using a 3 period window:\n\n>>> values = [34, 30, 29, 34, 38, 25, 35]\n>>> results = statio.std_values(values, 3)\n>>> [\"%.2f\" % x for x in results]\n['0.00', '2.83', '2.65', '2.65', '4.51', '6.66', '6.81']\n\n9. Build list of the **Maximum Value** of 3 period window:\n\n>>> values = [34, 30, 29, 34, 38, 25, 35]\n>>> results = statio.max_values(values, 3)\n>>> [\"%.2f\" % x for x in results]\n['34.00', '34.00', '34.00', '34.00', '38.00', '38.00', '38.00']\n\n10. Build list of the **Minimum Value** of 3 period window:\n\n>>> values = [34, 30, 29, 34, 38, 25, 35]\n>>> statio.min_values(values, 3)\n[34, 30, 29, 29, 29, 25, 25]\n\n11. Build list of the **Top X Values** of 3 period window:\n\n>>> values = [34, 30, 29, 34, 38, 25, 35]\n>>> statio.top_values(values, 3, 2)\n[[34], [30, 34], [30, 34], [30, 34], [34, 38], [34, 38], [35, 38]]\n\n12. Build list of the **Bottom X Values** of 3 period window:\n\n>>> values = [34, 30, 29, 34, 38, 25, 35]\n>>> statio.bottom_values(values, 3, 2)\n[[34], [30, 34], [29, 30], [29, 30], [29, 34], [25, 34], [25, 35]]\n\n\nRoadmap\n-------\n* Add median_values.\n* Add recentmax_values: the index of the most recent max value.\n* Add sincemax_values: the number of bars since recent max value.\n* Add recentmin_values: the index of the most recent min value.\n* Add sincemin_values: the number of bars since recent min value.\n* Add covariance, correlation, alpha, beta computations.\n\n\nFor additional information, please email:\n    mike@taylortree.com",
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