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447 lines
16 KiB
Markdown
447 lines
16 KiB
Markdown
[](https://pypi.org/project/pandas_ta/)
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[](https://pypi.org/project/pandas_ta/)
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[](https://pypi.org/project/pandas_ta/)
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[](https://pypistats.org/packages/pandas_ta)
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# __Technical Analysis Library in Python 3.7__
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__Pandas Technical Analysis__ (Pandas TA) is an easy to use library that is built upon Python's Pandas library with more than 100 Indicators and Utility functions. These indicators are commonly used for financial time series datasets with columns or labels similar to: datetime, open, high, low, close, volume, et al. Many commonly used indicators are included, such as: _Simple Moving Average_ (*SMA*) _Moving Average Convergence Divergence_ (*MACD*), _Hull Exponential Moving Average_ (*HMA*), _Bollinger Bands_ (*BBANDS*), _On-Balance Volume_ (*OBV*), _Aroon & Aroon Oscillator_ (*AROON*) and more.
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This version contains both the orignal code branch as well as a newly refactored branch with the option to use [Pandas DataFrame Extension](https://pandas.pydata.org/pandas-docs/stable/extending.html) mode.
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All the indicators return a named Series or a DataFrame in uppercase underscore parameter format. For example, MACD(fast=12, slow=26, signal=9) will return a DataFrame with columns: ['MACD_12_26_9', 'MACDh_12_26_9', 'MACDs_12_26_9'].
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## __Features__
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* Has 100+ indicators and utility functions.
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* Option to use __multiprocessing__ when using df.ta.strategy(). See below.
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* Example Jupyter Notebooks under the [examples](https://github.com/twopirllc/pandas-ta/tree/master/examples) directory, including how to create Custom Strategies using the new [__Strategy__ Class](https://github.com/twopirllc/pandas-ta/tree/master/examples/PandaTA_Strategy_Examples.ipynb)
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* A new 'ta' method called 'strategy'. By default, it runs __all__ the indicators.
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* Abbreviated Indicator names as listed below.
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* __Extended Pandas DataFrame__ as 'ta'.
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* Easily add prefixes or suffixes or both to columns names.
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* Categories similar to [TA-lib](https://github.com/mrjbq7/ta-lib/tree/master/docs/func_groups) and tightly correlated with TA Lib in testing.
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## __Recent Changes__
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* A __Strategy__ Class to help name and group your favorite indicators.
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* An experimental and independent __Watchlist__ Class located in the [Examples](https://github.com/twopirllc/pandas-ta/tree/master/examples/watchlist.py) Directory that can be used in conjunction with the new __Strategy__ Class.
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* Improved the calculation performance of indicators: _Exponential Moving Averagage_
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and _Weighted Moving Average_.
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* Removed internal core optimizations when running ```df.ta.strategy('all')``` with multiprocessing. See the ```ta.strategy()``` method for more details.
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## What is a Pandas DataFrame Extension?
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A [Pandas DataFrame Extension](https://pandas.pydata.org/pandas-docs/stable/extending.html), extends a DataFrame allowing one to add more functionality and features to Pandas to suit your needs. As such, it is now easier to run Technical Analysis on existing Financial Time Series without leaving the current DataFrame. This extension by default returns the Indicator result or it can append the result to the existing DataFrame by including the parameter 'append=True' in the method call. Examples below.
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# __Getting Started and Examples__
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## __Installation__ (python 3)
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```sh
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$ pip install pandas_ta
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```
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## __Latest Version__
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```sh
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$ pip install -U git+https://github.com/twopirllc/pandas-ta
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```
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## __Quick Start__ using the DataFrame Extension
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```python
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import pandas as pd
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import pandas_ta as ta
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# Load data
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df = pd.read_csv('symbol.csv', sep=',')
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# Calculate Returns and append to the df DataFrame
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df.ta.log_return(cumulative=True, append=True)
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df.ta.percent_return(cumulative=True, append=True)
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# New Columns with results
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df.columns
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# Take a peek
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df.tail()
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# vv Continue Post Processing vv
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```
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## __Module and Indicator Help__
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```python
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import pandas as pd
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import pandas_ta as ta
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# Help about this, 'ta', extension
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help(pd.DataFrame().ta)
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# List of all indicators
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pd.DataFrame().ta.indicators()
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# Help about the log_return indicator
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help(ta.log_return)
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```
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## New Class: __Strategy__
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### What is a Pandas TA Strategy?
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A _Strategy_ is a simple way to name and group your favorite TA indicators. Technically, a _Strategy_ is a simple Data Class to contain list of indicators and their parameters. __Note__: _Strategy_ is experimental and subject to change. Pandas TA comes with two basic Strategies: __AllStrategy__ and __CommonStrategy__.
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* See the [Pandas TA Strategy Examples](https://github.com/twopirllc/pandas-ta/tree/master/examples/PandasTA_Strategy_Examples.ipynb) Notebook for more Examples including _Indicator Composition/Chaining_.
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### Strategy Requirements:
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- _name_: Some short memorable string. _Note_: Case-insensitive "All" is reserved.
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- _ta_: A list of dicts containing keyword arguments to identify the indicator and the indicator's arguments
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### Optional Requirements:
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- _description_: A more detailed description of what the Strategy tries to capture. Default: None
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- _created_: At datetime string of when it was created. Default: Automatically generated.
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#### Things to note:
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- A Strategy will __fail__ when consumed by Pandas TA if there is no {"kind": "indicator name"} attribute. __Remember__ to check your spelling.
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#### Brief Examples
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```python
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# Builtin All Default Strategy
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AllStrategy = Strategy(
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name="All",
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description="All the indicators with their default settings. Pandas TA default.",
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ta=None
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)
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# Builtin Default (Example) Strategy.
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CommonStrategy = Strategy(
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name="Common Price and Volume SMAs",
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description="Common Price SMAs: 10, 20, 50, 200 and Volume SMA: 20.",
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ta=[
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{"kind": "sma", "length": 10},
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{"kind": "sma", "length": 20},
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{"kind": "sma", "length": 50},
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{"kind": "sma", "length": 200},
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{"kind": "sma", "close": "volume", "length": 20, "prefix": "VOL"}
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]
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)
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# Your Custom Strategy or whatever your TA composition
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CustomStrategy = ta.Strategy(
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name="Momo and Volatility",
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description="SMA 50,200, BBANDS, RSI, MACD and Volume SMA 20",
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ta=[
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{"kind": "sma", "length": 50},
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{"kind": "sma", "length": 200},
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{"kind": "bbands", "length": 20},
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{"kind": "rsi"},
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{"kind": "macd", "fast": 8, "slow": 21},
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{"kind": "sma", "close": "volume", "length": 20, "prefix": "VOLUME"},
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]
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)
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```
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## __DataFrame Method__: _strategy_ with Multiprocessing
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The new __Pandas (TA)__ method __strategy__ is used to facilitate bulk indicator processing. By default, running ```df.ta.strategy()``` will append __all
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applicable__ indicators to DataFrame ```df```. Utility methods like ```above```, ```below``` et al are not included.
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* The ```ta.strategy()``` method is still __under development__. Future iterations will allow you to load a ```ta.json``` config file with your specific strategy name and parameters to automatically run you bulk indicators.
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```python
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# This property only effects df.ta.strategy(). When set to True,
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# it enables multiprocessing when processing "ALL" the indicators.
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# Default is False
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df.ta.mp = True
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# Runs and appends all indicators to the current DataFrame by default
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# The resultant DataFrame will be large.
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df.ta.strategy()
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# Or equivalently use name='all'
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df.ta.strategy(name='all')
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# Use verbose if you want to make sure it is running.
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df.ta.strategy(verbose=True)
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# Use timed if you want to see how long it takes to run.
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df.ta.strategy(timed=True)
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# You can change the number of cores to use. The default is the the number of
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# cpus you have. Not utilizing all your cores will result in quicker results.
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# For instance if you have 4 CPUs, then cores=2 will be quicker.
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df.ta.strategy(cores=2)
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# Maybe you do not want certain indicators.
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# Just exclude (a list of) them.
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df.ta.strategy(exclude=['bop', 'mom', 'percent_return', 'wcp', 'pvi'], verbose=True)
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# Perhaps you want to use different values for indicators.
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# This will run ALL indicators that have fast or slow as parameters.
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# Check your results and exclude as necessary.
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df.ta.strategy(fast=10, slow=50, verbose=True)
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# Sanity check. Make sure all the columns are there
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df.columns
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```
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### Running a Builtin, Categorical or Custom Strategy
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While the _Strategy_ Class it has not been fully integrated with the __strategy__ method yet. For now, the following can be done to implement your Custom Strategy.
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```python
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# Running the builtin CommonStrategy as mentioned above
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df.ta.strategy(ta.CommonStrategy)
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# Available categories
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ta.categories
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# Running a Categorical Strategy only requires the Category name
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df.ta.strategy(name="Momentum") # Default values for all Momentum indicators
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df.ta.strategy(name="overlap", length=27) # Override all 'length' attributes
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# Or create your own Custom Strategy
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CustomStrategy = ta.Strategy(
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name="Momo and Volatility",
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description="SMA 50,200, BBANDS, RSI, MACD and Volume SMA 20",
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ta=[
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{"kind": "sma", "length": 50},
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{"kind": "sma", "length": 200},
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{"kind": "bbands", "length": 20},
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{"kind": "rsi"},
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{"kind": "macd", "fast": 8, "slow": 21},
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{"kind": "sma", "close": "volume", "length": 20, "prefix": "VOLUME"},
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]
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)
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# To run "Custom Strategy"
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df.ta.strategy(CustomStrategy)
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# Or pass in the name and ta atributes of the "Custom Strategy"
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df.ta.strategy(name=CustomStrategy.name, ta=CustomStrategy.ta)
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# Sanity check. Make sure all the columns are there
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df.columns
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```
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## __DataFrame kwargs__: _prefix_ and _suffix_
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```python
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prehl2 = df.ta.hl2(prefix="pre")
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print(prehl2.name) # "pre_HL2"
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endhl2 = df.ta.hl2(suffix="post")
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print(endhl2.name) # "HL2_post"
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bothhl2 = df.ta.hl2(prefix="pre", suffix="post")
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print(bothhl2.name) # "pre_HL2_post"
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```
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## __DataFrame Properties__: _reverse_ & _datetime_ordered_
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```python
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# The 'reverse' is a helper property that returns the DataFrame
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# in reverse order
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df = df.ta.reverse
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# The 'datetime_ordered' property returns True if the DataFrame
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# index is of Pandas datetime64 and df.index[0] < df.index[-1]
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# Otherwise it returns False
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time_series_in_order = df.ta.datetime_ordered
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```
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## __DataFrame Property__: *adjusted*
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```python
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# Set ta to default to an adjusted column, 'adj_close', overriding default 'close'
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df.ta.adjusted = 'adj_close'
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df.ta.sma(length=10, append=True)
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# To reset back to 'close', set adjusted back to None
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df.ta.adjusted = None
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```
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# __Technical Analysis Indicators__ (_by Category_)
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## _Candles_ (2)
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* _Doji_: **cdl_doji**
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* _Heikin-Ashi_: **ha**
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## _Momentum_ (27)
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* _Awesome Oscillator_: **ao**
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* _Absolute Price Oscillator_: **apo**
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* _Bias_: **bias**
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* _Balance of Power_: **bop**
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* _BRAR_: **brar**
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* _Commodity Channel Index_: **cci**
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* _Center of Gravity_: **cg**
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* _Chande Momentum Oscillator_: **cmo**
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* _Coppock Curve_: **coppock**
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* _Fisher Transform_: **fisher**
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* _Inertia_: **inertia**
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* _KDJ_: **kdj**
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* _KST Oscillator_: **kst**
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* _Moving Average Convergence Divergence_: **macd**
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* _Momentum_: **mom**
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* _Percentage Price Oscillator_: **ppo**
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* _Psychological Line_: **psl**
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* _Percentage Volume Oscillator_: **pvo**
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* _Rate of Change_: **roc**
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* _Relative Strength Index_: **rsi**
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* _Relative Vigor Index_: **rvgi**
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* _Slope_: **slope*
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* _Stochastic Oscillator_: **stoch**
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* _Trix_: **trix**
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* _True strength index_: **tsi**
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* _Ultimate Oscillator_: **uo**
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* _Williams %R_: **willr**
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| _Moving Average Convergence Divergence_ (MACD) |
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|:--------:|
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## _Overlap_ (26)
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* _Double Exponential Moving Average_: **dema**
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* _Exponential Moving Average_: **ema**
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* _Fibonacci's Weighted Moving Average_: **fwma**
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* _High-Low Average_: **hl2**
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* _High-Low-Close Average_: **hlc3**
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* Commonly known as 'Typical Price' in Technical Analysis literature
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* _Hull Exponential Moving Average_: **hma**
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* _Ichimoku Kinkō Hyō_: **ichimoku**
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* Use: help(ta.ichimoku). Returns two DataFrames.
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* _Kaufman's Adaptive Moving Average_: **kama**
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* _Linear Regression_: **linreg**
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* _Midpoint_: **midpoint**
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* _Midprice_: **midprice**
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* _Open-High-Low-Close Average_: **ohlc4**
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* _Pascal's Weighted Moving Average_: **pwma**
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* _William's Moving Average_: **rma**
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* _Sine Weighted Moving Average_: **sinwma**
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* _Simple Moving Average_: **sma**
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* _Supertrend_: **supertrend**
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* _Symmetric Weighted Moving Average_: **swma**
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* _T3 Moving Average_: **t3**
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* _Triple Exponential Moving Average_: **tema**
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* _Triangular Moving Average_: **trima**
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* _Volume Weighted Average Price_: **vwap**
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* _Volume Weighted Moving Average_: **vwma**
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* _Weighted Closing Price_: **wcp**
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* _Weighted Moving Average_: **wma**
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* _Zero Lag Moving Average_: **zlma**
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| _Simple Moving Averages_ (SMA) and _Bollinger Bands_ (BBANDS) |
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|:--------:|
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## _Performance_ (3)
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Use parameter: cumulative=**True** for cumulative results.
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* _Log Return_: **log_return**
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* _Percent Return_: **percent_return**
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* _Trend Return_: **trend_return**
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| _Percent Return_ (Cumulative) with _Simple Moving Average_ (SMA) |
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|:--------:|
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## _Statistics_ (9)
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* _Entropy_: **entropy**
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* _Kurtosis_: **kurtosis**
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* _Mean Absolute Deviation_: **mad**
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* _Median_: **median**
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* _Quantile_: **quantile**
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* _Skew_: **skew**
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* _Standard Deviation_: **stdev**
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* _Variance_: **variance**
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* _Z Score_: **zscore**
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| _Z Score_ |
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|:--------:|
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## _Trend_ (14)
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* _Average Directional Movement Index_: **adx**
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* _Archer Moving Averages Trends_: **amat**
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* _Aroon & Aroon Oscillator_: **aroon**
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* _Choppiness Index_: **chop**
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* _Chande Kroll Stop_: **cksp**
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* _Decreasing_: **decreasing**
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* _Detrended Price Oscillator_: **dpo**
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* _Increasing_: **increasing**
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* _Linear Decay_: **linear_decay**
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* _Long Run_: **long_run**
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* _Parabolic Stop and Reverse_: **psar**
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* _Q Stick_: **qstick**
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* _Short Run_: **short_run**
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* _Vortex_: **vortex**
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| _Average Directional Movement Index_ (ADX) |
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|:--------:|
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## _Utility_ (5)
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* _Above_: **above**
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* _Above Value_: **above_value**
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* _Below_: **below**
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* _Below Value_: **below_value**
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* _Cross_: **cross**
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## _Volatility_ (11)
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* _Aberration_: **aberration**
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* _Acceleration Bands_: **accbands**
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* _Average True Range_: **atr**
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* _Bollinger Bands_: **bbands**
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* _Donchian Channel_: **donchian**
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* _Keltner Channel_: **kc**
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* _Mass Index_: **massi**
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* _Normalized Average True Range_: **natr**
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* _Price Distance_: **pdist**
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* _Relative Volatility Index_: **rvi**
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* _True Range_: **true_range**
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| _Average True Range_ (ATR) |
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|:--------:|
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## _Volume_ (13)
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* _Accumulation/Distribution Index_: **ad**
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* _Accumulation/Distribution Oscillator_: **adosc**
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* _Archer On-Balance Volume_: **aobv**
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* _Chaikin Money Flow_: **cmf**
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* _Elder's Force Index_: **efi**
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* _Ease of Movement_: **eom**
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* _Money Flow Index_: **mfi**
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* _Negative Volume Index_: **nvi**
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* _On-Balance Volume_: **obv**
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* _Positive Volume Index_: **pvi**
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* _Price-Volume_: **pvol**
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* _Price Volume Trend_: **pvt**
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* _Volume Profile_: **vp**
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| _On-Balance Volume_ (OBV) |
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|:--------:|
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# Contributors
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* [allahyarzadeh](https://github.com/allahyarzadeh)
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* [FGU1](https://github.com/FGU1)
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* [lluissalord](https://github.com/lluissalord)
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# Inspiration
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* TradingView: http://www.tradingview.com
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* Original TA-LIB: http://ta-lib.org/
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Please leave any comments, feedback, suggestions, or indicator requests. |