mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-08-15 12:45:08 +08:00
ENH cdl and cdl_pattern with talib compat added DOC cdl_pattern info + notebooks BUG yf extra output removed
This commit is contained in:
Kevin Johnson
+2
-1
@@ -143,4 +143,5 @@ data/SPY_D_TV2.csv
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data/SPY_D_TV3.csv
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data/TV_5min.csv
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data/tulip.csv
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examples/*.csv
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examples/*.csv
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examples/Chande_Kroll_Stop.ipynb
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@@ -15,7 +15,10 @@ Pandas TA - A Technical Analysis Library in Python 3
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_Pandas Technical Analysis_ (**Pandas TA**) is an easy to use library that leverages the Pandas library with more than 130 Indicators and Utility functions. 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**), _Squeeze_ (**squeeze**) and **_many more_**.
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_Pandas Technical Analysis_ (**Pandas TA**) is an easy to use library that leverages the Pandas library with more than 130 Indicators and Utility functions and more than 60 TA Lib Candlestick Patterns. 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**), _Squeeze_ (**squeeze**) and **_many more_**.
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**Note:** _TA Lib_ must be installed to use **all** the Candlestick Patterns. ```pip install TA-Lib```. If _TA Lib_ is not installed, then only the builtin Candlestick Patterns will be available.
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<br/>
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@@ -36,7 +39,7 @@ _Pandas Technical Analysis_ (**Pandas TA**) is an easy to use library that lever
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* [DataFrame Properties](#dataframe-properties)
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* [DataFrame Methods](#dataframe-methods)
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* [Indicators by Category](#indicators-by-category)
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* [Candles](#candles-3)
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* [Candles](#candles-63)
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* [Cycles](#cycles-1)
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* [Momentum](#momentum-37)
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* [Overlap](#overlap-31)
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@@ -63,13 +66,22 @@ _Pandas Technical Analysis_ (**Pandas TA**) is an easy to use library that lever
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# **Features**
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* Has 130+ indicators and utility functions.
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* With **TA Lib** installed there are an additional 63 Chart Patterns available.
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* Indicators are tightly correlated with the de facto [TA Lib](https://mrjbq7.github.io/ta-lib/) if they share common indicators.
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* Have the need for speed? By using the DataFrame _strategy_ method, you get **multiprocessing** for free!
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* Easily add _prefixes_ or _suffixes_ or both to columns names. Useful for Custom Chained Strategies.
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* Easily add _prefixes_ or _suffixes_ or _both_ to columns names. Useful for Custom Chained Strategies.
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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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* Potential Data Leaks: **ichimoku** and **dpo**. See indicator list below for details.
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* **UNDER DEVELOPMENT:** Performance Metrics
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* **UNDER DEVELOPMENT:** Easy Downloading of _ohlcv_ data using [yfinance](https://github.com/ranaroussi/yfinance). See ```help(ta.ticker)``` and ```help(ta.yf)```
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<br/>
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**Under Development**
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===================
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**Pandas TA** checks if the user has some common trading packages installed including but not limited to: [**TA Lib**](https://mrjbq7.github.io/ta-lib/), [**Vector BT**](https://github.com/polakowo/vectorbt), [**YFinance**](https://github.com/ranaroussi/yfinance) ... Much of which is experimental and likely to break until it stabilizes more.
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* If **TA Lib** installed, existing indicators will _eventually_ get a **TA Lib** version.
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* Easy Downloading of _ohlcv_ data using [yfinance](https://github.com/ranaroussi/yfinance). See ```help(ta.ticker)``` and ```help(ta.yf)``` and examples below.
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* Hopefully soon a Pandas TA _YAML_ configuration file contained in ```~/pandas_ta/``` can be implemented. To see the proposed specification and leave comments and suggestions on it's implementation, see Issue [#258](https://github.com/twopirllc/pandas-ta/issues/258).
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* Some Common Performance Metrics
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<br/>
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@@ -85,7 +97,7 @@ $ pip install pandas_ta
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Latest Version
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--------------
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Best choice! Version: *0.2.67b*
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Best choice! Version: *0.2.68b*
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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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@@ -556,79 +568,90 @@ help(ta.yf)
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# **Indicators** (_by Category_)
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### **Candles** (63)
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* _Patterns_: cdl_pattern (Patterns that aren't bold, need TA-Lib to be installed: "```pip install TA-Lib```")
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* 2crows
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* 3blackcrows
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* 3inside
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* 3linestrike
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* 3outside
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* 3starsinsouth
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* 3whitesoldiers
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* abandonedbaby
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* advanceblock
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* belthold
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* breakaway
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* closingmarubozu
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* concealbabyswall
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* counterattack
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* darkcloudcover
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* **doji**
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* dojistar
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* dragonflydoji
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* engulfing
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* eveningdojistar
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* eveningstar
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* gapsidesidewhite
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* gravestonedoji
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* hammer
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* hangingman
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* harami
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* haramicross
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* highwave
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* hikkake
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* hikkakemod
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* homingpigeon
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* identical3crows
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* inneck
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* **inside**
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* invertedhammer
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* kicking
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* kickingbylength
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* ladderbottom
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* longleggeddoji
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* longline
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* marubozu
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* matchinglow
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* mathold
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* morningdojistar
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* morningstar
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* onneck
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* piercing
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* rickshawman
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* risefall3methods
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* separatinglines
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* shootingstar
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* shortline
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* spinningtop
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* stalledpattern
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* sticksandwich
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* takuri
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* tasukigap
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* thrusting
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* tristar
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* unique3river
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* upsidegap2crows
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* xsidegap3methods
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_Candle Patterns_: ```ta.cdl_pattern``` or ```ta.cdl```
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Patterns that are **not bold**, require TA-Lib to be installed: ```pip install TA-Lib```
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* 2crows
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* 3blackcrows
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* 3inside
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* 3linestrike
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* 3outside
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* 3starsinsouth
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* 3whitesoldiers
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* abandonedbaby
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* advanceblock
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* belthold
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* breakaway
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* closingmarubozu
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* concealbabyswall
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* counterattack
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* darkcloudcover
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* **doji**
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* dojistar
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* dragonflydoji
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* engulfing
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* eveningdojistar
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* eveningstar
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* gapsidesidewhite
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* gravestonedoji
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* hammer
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* hangingman
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* harami
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* haramicross
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* highwave
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* hikkake
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* hikkakemod
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* homingpigeon
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* identical3crows
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* inneck
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* **inside**
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* invertedhammer
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* kicking
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* kickingbylength
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* ladderbottom
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* longleggeddoji
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* longline
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* marubozu
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* matchinglow
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* mathold
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* morningdojistar
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* morningstar
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* onneck
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* piercing
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* rickshawman
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* risefall3methods
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* separatinglines
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* shootingstar
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* shortline
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* spinningtop
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* stalledpattern
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* sticksandwich
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* takuri
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* tasukigap
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* thrusting
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* tristar
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* unique3river
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* upsidegap2crows
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* xsidegap3methods
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* _Heikin-Ashi_: **ha**
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```python
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# Get all candle patterns (This is the default behaviour)
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df = df.ta.cdl_pattern(name="all")
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# Or
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df.ta.cdl("all", append=True) # = df.ta.cdl_pattern("all", append=True)
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# Get only one pattern
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df = df.ta.cdl_pattern(name="doji")
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# Or
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df.ta.cdl("doji", append=True)
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# Get some patterns
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df = df.ta.cdl_pattern(name=["doji", "inside"])
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# Or
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df.ta.cdl(["doji", "inside"], append=True)
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```
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<br/>
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@@ -878,6 +901,7 @@ result = ta.cagr(df.close)
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## **New Indicators**
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* _Arnaud Legoux Moving Average_ (**alma**) uses the curve of the Normal (Gauss) distribution to allow regulating the smoothness and high sensitivity of the indicator. See: ```help(ta.alma)```
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trading account, or fund. See: ```help(ta.drawdown)```
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* _Candle Patterns_ (**cdl_pattern**) If TA Lib is installed, then all those Candle Patterns are available. See the list and examples above on how to call the patterns. See: ```help(ta.cdl_pattern)```
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* _Even Better Sinewave_ (**ebsw**) measures market cycles and uses a low pass filter to remove noise. See: ```help(ta.ebsw)```
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* _Tom DeMark's Sequential_ (**td_seq**) attempts to identify a price point where an uptrend or a downtrend exhausts itself and reverses. Currently exlcuded from ```df.ta.strategy()``` for performance reasons. See: ```help(ta.td_seq)```
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<br/>
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File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
+14
-11
@@ -64,9 +64,12 @@
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"output_type": "stream",
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"text": [
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"Pandas TA - Technical Analysis Indicators - v0.2.64b0\n",
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"Total Indicators: 134\n",
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"Total Indicators: 196\n",
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"Abbreviations:\n",
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" aberration, above, above_value, accbands, ad, adosc, adx, alma, amat, ao, aobv, apo, aroon, atr, bbands, below, below_value, bias, bop, brar, cci, cdl_doji, cdl_inside, cfo, cg, chop, cksp, cmf, cmo, coppock, cross, cross_value, decay, decreasing, dema, donchian, dpo, ebsw, efi, ema, entropy, eom, er, eri, fisher, fwma, ha, hilo, hl2, hlc3, hma, hwc, hwma, ichimoku, increasing, inertia, kama, kc, kdj, kst, kurtosis, linreg, log_return, long_run, macd, mad, massi, mcgd, median, mfi, midpoint, midprice, mom, natr, nvi, obv, ohlc4, pdist, percent_return, pgo, ppo, psar, psl, pvi, pvo, pvol, pvr, pvt, pwma, qqe, qstick, quantile, rma, roc, rsi, rsx, rvgi, rvi, short_run, sinwma, skew, slope, sma, smi, squeeze, ssf, stdev, stoch, stochrsi, supertrend, swma, t3, td_seq, tema, thermo, trend_return, trima, trix, true_range, tsi, ttm_trend, ui, uo, variance, vidya, vortex, vp, vwap, vwma, wcp, willr, wma, zlma, zscore\n"
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" aberration, above, above_value, accbands, ad, adosc, adx, alma, amat, ao, aobv, apo, aroon, atr, bbands, below, below_value, bias, bop, brar, cci, cdl, cdl_pattern, cfo, cg, chop, cksp, cmf, cmo, coppock, cross, cross_value, decay, decreasing, dema, donchian, dpo, ebsw, efi, ema, entropy, eom, er, eri, fisher, fwma, ha, hilo, hl2, hlc3, hma, hwc, hwma, ichimoku, increasing, inertia, kama, kc, kdj, kst, kurtosis, linreg, log_return, long_run, macd, mad, massi, mcgd, median, mfi, midpoint, midprice, mom, natr, nvi, obv, ohlc4, pdist, percent_return, pgo, ppo, psar, psl, pvi, pvo, pvol, pvr, pvt, pwma, qqe, qstick, quantile, rma, roc, rsi, rsx, rvgi, rvi, short_run, sinwma, skew, slope, sma, smi, squeeze, ssf, stdev, stoch, stochrsi, supertrend, swma, t3, td_seq, tema, thermo, trend_return, trima, trix, true_range, tsi, ttm_trend, ui, uo, variance, vidya, vortex, vp, vwap, vwma, wcp, willr, wma, zlma, zscore\n",
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"\n",
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"Candle Patterns:\n",
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" 2crows, 3blackcrows, 3inside, 3linestrike, 3outside, 3starsinsouth, 3whitesoldiers, abandonedbaby, advanceblock, belthold, breakaway, closingmarubozu, concealbabyswall, counterattack, darkcloudcover, doji, dojistar, dragonflydoji, engulfing, eveningdojistar, eveningstar, gapsidesidewhite, gravestonedoji, hammer, hangingman, harami, haramicross, highwave, hikkake, hikkakemod, homingpigeon, identical3crows, inneck, inside, invertedhammer, kicking, kickingbylength, ladderbottom, longleggeddoji, longline, marubozu, matchinglow, mathold, morningdojistar, morningstar, onneck, piercing, rickshawman, risefall3methods, separatinglines, shootingstar, shortline, spinningtop, stalledpattern, sticksandwich, takuri, tasukigap, thrusting, tristar, unique3river, upsidegap2crows, xsidegap3methods\n"
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]
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}
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],
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@@ -926,7 +929,7 @@
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"[i] Multiprocessing 5 indicators with 7 chunks and 8/8 cpus.\n",
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"[i] Total indicators: 5\n",
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"[i] Columns added: 5\n",
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"[i] Last Run: Saturday April 10, 2021, NYSE: 5:38:28, Local: 9:38:28 PDT, Day 100/365 (27.0%)\n"
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"[i] Last Run: Saturday April 10, 2021, NYSE: 12:37:31, Local: 16:37:31 PDT, Day 100/365 (27.0%)\n"
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]
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},
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{
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@@ -954,7 +957,7 @@
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{
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"data": {
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"text/plain": [
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"<__main__.Chart at 0x12ffc83d0>"
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"<__main__.Chart at 0x132188e20>"
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]
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},
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"execution_count": 11,
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@@ -1064,7 +1067,7 @@
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{
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"data": {
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"text/plain": [
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"<matplotlib.lines.Line2D at 0x130be2700>"
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"<matplotlib.lines.Line2D at 0x132e4c550>"
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]
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},
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"execution_count": 13,
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@@ -1103,7 +1106,7 @@
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{
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"data": {
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"text/plain": [
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"<matplotlib.lines.Line2D at 0x1308b2cd0>"
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"<matplotlib.lines.Line2D at 0x132ebf850>"
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]
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},
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"execution_count": 14,
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{
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"data": {
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"text/plain": [
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"<matplotlib.lines.Line2D at 0x130cb4580>"
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"<matplotlib.lines.Line2D at 0x132e85dc0>"
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]
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},
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"execution_count": 15,
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{
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"data": {
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"text/plain": [
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"<__main__.Chart at 0x130ceb6d0>"
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"<__main__.Chart at 0x132f42820>"
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]
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},
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"execution_count": 17,
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{
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"data": {
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"text/plain": [
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"<__main__.Chart at 0x130d2f190>"
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"<__main__.Chart at 0x132fd0d60>"
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]
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},
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"execution_count": 18,
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@@ -1322,7 +1325,7 @@
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{
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"data": {
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"text/plain": [
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"<__main__.Chart at 0x13147fd30>"
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"<__main__.Chart at 0x132936820>"
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]
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"execution_count": 19,
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{
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"data": {
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"text/plain": [
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]
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},
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"execution_count": 20,
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@@ -40,7 +40,7 @@ Imports = {
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Category = {
|
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# Candles
|
||||
"candles": [
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"cdl_pattern", "ha"
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"cdl", "cdl_pattern", "ha"
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],
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# Cycles
|
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"cycles": ["ebsw"],
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@@ -2,4 +2,4 @@
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from .ha import ha
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from .cdl_doji import cdl_doji
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from .cdl_inside import cdl_inside
|
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from .cdl_pattern import cdl_pattern, ALL_PATTERNS as CDL_PATTERN_NAMES
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from .cdl_pattern import cdl_pattern, cdl, ALL_PATTERNS as CDL_PATTERN_NAMES
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|
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@@ -5,8 +5,6 @@ from pandas import Series, DataFrame
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from . import cdl_doji, cdl_inside
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from pandas_ta.utils import get_offset, verify_series
|
||||
from pandas_ta import Imports
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||||
if Imports["talib"]:
|
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import talib.abstract as tala
|
||||
|
||||
|
||||
ALL_PATTERNS = [
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||||
@@ -45,11 +43,15 @@ def cdl_pattern(open_, high, low, close, name: Union[str, Sequence[str]]="all",
|
||||
if type(name) is str:
|
||||
name = [name]
|
||||
|
||||
if Imports["talib"]:
|
||||
import talib.abstract as tala
|
||||
|
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result = {}
|
||||
for n in name:
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if n not in ALL_PATTERNS:
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print(f"[X] There is no candle pattern named {n} available!")
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continue
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if n in pta_patterns:
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pattern_result = pta_patterns[n](open_, high, low, close, offset=offset, scalar=scalar, **kwargs)
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result[pattern_result.name] = pattern_result
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@@ -58,7 +60,7 @@ def cdl_pattern(open_, high, low, close, name: Union[str, Sequence[str]]="all",
|
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print(f"[X] Please install TA-Lib to use {n}. (pip install TA-Lib)")
|
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continue
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|
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pattern_func = tala.Function("CDL" + n.upper())
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pattern_func = tala.Function(f"CDL{n.upper()}")
|
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pattern_result = Series(pattern_func(open_, high, low, close, **kwargs) / 100 * scalar)
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pattern_result.index = close.index
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@@ -72,10 +74,9 @@ def cdl_pattern(open_, high, low, close, name: Union[str, Sequence[str]]="all",
|
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if "fill_method" in kwargs:
|
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pattern_result.fillna(method=kwargs["fill_method"], inplace=True)
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|
||||
result["CDL_" + n.upper()] = pattern_result
|
||||
result[f"CDL_{n.upper()}"] = pattern_result
|
||||
|
||||
if len(result) == 0:
|
||||
return
|
||||
if len(result) == 0: return
|
||||
|
||||
# Prepare DataFrame to return
|
||||
df = DataFrame(result)
|
||||
@@ -89,6 +90,23 @@ cdl_pattern.__doc__ = \
|
||||
|
||||
A wrapper around all candle patterns.
|
||||
|
||||
Examples:
|
||||
|
||||
Get all candle patterns (This is the default behaviour)
|
||||
>>> df = df.ta.cdl_pattern(name="all")
|
||||
Or
|
||||
>>> df.ta.cdl("all", append=True) # = df.ta.cdl_pattern("all", append=True)
|
||||
|
||||
Get only one pattern
|
||||
>>> df = df.ta.cdl_pattern(name="doji")
|
||||
Or
|
||||
>>> df.ta.cdl("doji", append=True)
|
||||
|
||||
Get some patterns
|
||||
>>> df = df.ta.cdl_pattern(name=["doji", "inside"])
|
||||
Or
|
||||
>>> df.ta.cdl(["doji", "inside"], append=True)
|
||||
|
||||
Args:
|
||||
open_ (pd.Series): Series of 'open's
|
||||
high (pd.Series): Series of 'high's
|
||||
@@ -105,3 +123,5 @@ Kwargs:
|
||||
Returns:
|
||||
pd.DataFrame: one column for each pattern.
|
||||
"""
|
||||
|
||||
cdl = cdl_pattern
|
||||
+6
-2
@@ -1,6 +1,7 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
from dataclasses import dataclass, field
|
||||
from multiprocessing import cpu_count, Pool
|
||||
from pandas_ta.candles.cdl_pattern import ALL_PATTERNS
|
||||
from time import perf_counter
|
||||
from typing import List, Tuple
|
||||
|
||||
@@ -597,9 +598,9 @@ class AnalysisIndicators(BasePandasObject):
|
||||
|
||||
total_indicators = len(ta_indicators)
|
||||
header = f"Pandas TA - Technical Analysis Indicators - v{self.version}"
|
||||
s = f"{header}\nTotal Indicators: {total_indicators}\n"
|
||||
s = f"{header}\nTotal Indicators: {total_indicators + len(ALL_PATTERNS)}\n"
|
||||
if total_indicators > 0:
|
||||
print(f"{s}Abbreviations:\n {', '.join(ta_indicators)}")
|
||||
print(f"{s}Abbreviations:\n {', '.join(ta_indicators)}\n\nCandle Patterns:\n {', '.join(ALL_PATTERNS)}")
|
||||
else:
|
||||
print(s)
|
||||
|
||||
@@ -640,6 +641,7 @@ class AnalysisIndicators(BasePandasObject):
|
||||
"above_value",
|
||||
"below",
|
||||
"below_value",
|
||||
"cdl", # Alias for "cdl_pattern"
|
||||
"cross",
|
||||
"cross_value",
|
||||
# "data", # reserved
|
||||
@@ -840,6 +842,8 @@ class AnalysisIndicators(BasePandasObject):
|
||||
result = cdl_pattern(open_=open_, high=high, low=low, close=close, name=name, offset=offset, **kwargs)
|
||||
return self._post_process(result, **kwargs)
|
||||
|
||||
cdl = cdl_pattern
|
||||
|
||||
def ha(self, offset=None, **kwargs):
|
||||
open_ = self._get_column(kwargs.pop("open", "open"))
|
||||
high = self._get_column(kwargs.pop("high", "high"))
|
||||
|
||||
@@ -125,7 +125,7 @@ def yf(ticker: str, **kwargs):
|
||||
# Ticker Info & Chart History
|
||||
yfd = yfra.Ticker(ticker)
|
||||
df = yfd.history(period=period, interval=interval, proxy=proxy, **kwargs)
|
||||
print(f"[X] df[{type(df)}:{df.empty}]\n{df}\n")
|
||||
|
||||
if df.empty: return
|
||||
df.name = ticker
|
||||
|
||||
@@ -134,18 +134,16 @@ def yf(ticker: str, **kwargs):
|
||||
except KeyError as ke:
|
||||
print(f"[X] Ticker '{ticker}' not found.")
|
||||
return
|
||||
print(f"[X] ticker_info[{type(ticker_info)}:{len(ticker_info.keys())}]\n{ticker_info}\n")
|
||||
# print(f"[X] ticker_info[{type(ticker_info)}:{len(ticker_info.keys())}]\n{ticker_info}\n")
|
||||
|
||||
try:
|
||||
infodf = DataFrame.from_dict(ticker_info, orient="index")
|
||||
except TypeError as te:
|
||||
print(f"[X] TypeError: {te}")
|
||||
# else:
|
||||
# infodf = DataFrame(ticker_info)
|
||||
|
||||
print(f"[X] infodf.empty: {infodf.empty}")
|
||||
# print(f"[X] infodf.empty: {infodf.empty}")
|
||||
if infodf.empty: return
|
||||
print(f"[X] infodf[{type(infodf)}:{len(infodf.keys())}]\n{infodf}\n")
|
||||
# print(f"[X] infodf[{type(infodf)}:{len(infodf.keys())}]\n{infodf}\n")
|
||||
infodf.name, infodf.columns = ticker, [ticker]
|
||||
|
||||
# Dividends and Splits
|
||||
@@ -156,9 +154,6 @@ def yf(ticker: str, **kwargs):
|
||||
description = kwargs.pop("desc", False)
|
||||
snd_length = kwargs.pop("snd", 5)
|
||||
|
||||
[print(f"{_[0]}: {_[1]}") for _ in sorted(ticker_info.items())]
|
||||
print()
|
||||
|
||||
print("\n==== Company Information " + div)
|
||||
print(f"{ticker_info['longName']} ({ticker_info['shortName']}) [{ticker_info['symbol']}]")
|
||||
print(f"[i] {type(ticker_info['longBusinessSummary'])}: {ticker_info['longBusinessSummary']}")
|
||||
|
||||
@@ -18,7 +18,7 @@ setup(
|
||||
"pandas_ta.volatility",
|
||||
"pandas_ta.volume"
|
||||
],
|
||||
version=".".join(("0", "2", "67b")),
|
||||
version=".".join(("0", "2", "68b")),
|
||||
description=long_description,
|
||||
long_description=long_description,
|
||||
author="Kevin Johnson",
|
||||
|
||||
@@ -19,12 +19,12 @@ class TestCandleExtension(TestCase):
|
||||
|
||||
|
||||
def test_cdl_doji_ext(self):
|
||||
self.data.ta.cdl_doji(append=True)
|
||||
self.data.ta.cdl_pattern("doji", append=True)
|
||||
self.assertIsInstance(self.data, DataFrame)
|
||||
self.assertEqual(self.data.columns[-1], "CDL_DOJI_10_0.1")
|
||||
|
||||
def test_cdl_inside_ext(self):
|
||||
self.data.ta.cdl_inside(append=True)
|
||||
self.data.ta.cdl_pattern("inside", append=True)
|
||||
self.assertIsInstance(self.data, DataFrame)
|
||||
self.assertEqual(self.data.columns[-1], "CDL_INSIDE")
|
||||
|
||||
|
||||
@@ -41,7 +41,6 @@ class TestTrendExtension(TestCase):
|
||||
def test_cksp_ext(self):
|
||||
self.data.ta.cksp(tvmode=False, append=True)
|
||||
self.assertIsInstance(self.data, DataFrame)
|
||||
print(self.data.columns[-3:])
|
||||
self.assertEqual(list(self.data.columns[-2:]), ["CKSPl_10_3_20", "CKSPs_10_3_20"])
|
||||
|
||||
def test_cksp_tv_ext(self):
|
||||
|
||||
@@ -106,15 +106,18 @@ class TestStrategyMethods(TestCase):
|
||||
# @skip
|
||||
def test_custom_a(self):
|
||||
self.category = "Custom A"
|
||||
print()
|
||||
print(self.category)
|
||||
|
||||
momo_bands_sma_ta = [
|
||||
{"kind": "cdl_pattern", "name": "tristar"}, # 1
|
||||
{"kind": "rsi"}, # 1
|
||||
{"kind": "macd"}, # 3
|
||||
{"kind": "sma", "length": 50}, # 1
|
||||
{"kind": "sma", "length": 200 }, # 1
|
||||
{"kind": "bbands", "length": 20}, # 3
|
||||
{"kind": "log_return", "cumulative": True}, # 1
|
||||
{"kind": "ema", "close": "CUMLOGRET_1", "length": 5, "suffix": "CLR"}
|
||||
{"kind": "ema", "close": "CUMLOGRET_1", "length": 5, "suffix": "CLR"} # 1
|
||||
]
|
||||
|
||||
custom = pandas_ta.Strategy(
|
||||
@@ -123,6 +126,7 @@ class TestStrategyMethods(TestCase):
|
||||
"Common indicators with specific lengths and a chained indicator", # description
|
||||
)
|
||||
self.data.ta.strategy(custom, verbose=verbose, timed=strategy_timed)
|
||||
self.assertEqual(len(self.data.columns), 15)
|
||||
|
||||
# @skip
|
||||
def test_custom_args_tuple(self):
|
||||
|
||||
Reference in New Issue
Block a user