mirror of
https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
40 lines
3.3 KiB
Markdown
40 lines
3.3 KiB
Markdown
### Is QuanTAlib fast?
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Well, _no_, but actually *yes*. QuanTAlib works on an additive principle, meaning that even when served a full list of quotes, QuanTAlib will process them one item at the time, rolling forward throug the given time series of data.
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- If the last bar is still forming (parameter `update: true`), QuanTAlib can easily recalculates the last entry as often as needed without any need to recalculate the history.
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- If a new bar is added to the input, QuanTAlib will process that one item (default parameter `update: false`) and add that one result to the List. No recalculation of the history needed.
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So, if you test QuanTAlib with small set of 500 historical bars and calculate EMA(20) on it, the performance of QuanTAlib will be dead last compared to all other TA libraries.
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But when the system uses 10,000 or historical bars, updates the current data at every new tick, and adds a new bar every minute, QuanTAlib has no rivals; all other libraries will need to re-calculate the full length of the indicator on each update/addition to the time series, while QuanTAlib will just update the last entry or add a single new value to the series. No back calculations are performed - ever. Longer the input data series and more updates/additions it gets, the greater advantage there is for QuanTAlib.
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### Are results of QuanTAlib valid?
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QuanTAlib includes battery of tests to compare its results with four well-known and reviewed Technical Analysis libraries to assure accuracy and validity of results:
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- [TA-LIB](https://www.ta-lib.org/function.html)
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- [Skender Stock Indicators](https://dotnet.stockindicators.dev/)
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- [Pandas-TA](https://twopirllc.github.io/pandas-ta/)
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- [Tulip Indicators](https://tulipindicators.org/)
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Not all indicators are implemented by all libraries - and sometimes results of the four reference libraries disagree with each other. Indicators that return equivalent result set to **all four** libraries are marked with ⭐ on [the list of indicators](indicators.md) - these are indicators that can be trusted most. Each verified equivalency of results is marked with ✔️, and each discrepancy is marked with ❌.
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For each discrepancy the research was done to establish the reason and to select the implementation that is the most faithful to the original description of the indicator.
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_For example, CMO indicator was described by Tushar S. Chande in his book The New Technical Trader, where he includes the example of calculating 10-day CMO on a given data. QuanTAlib, Skender.NET and Tulip libraries can all replicate the results, while TA-LIB and Pandas-TA return something very different:_
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| #| Input | **QuanTAlib** | TA-LIB | Skender | Pandas-TA | Tulip |
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|--|:--:|:--:|:--:|:--:|:--:|:--:|
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| 0| 101.03|**0.00**| NaN| NaN| NaN| NaN|
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| 1| 101.03|**0.00**| NaN| NaN| NaN| NaN|
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| 2| 101.12|**100.00**| NaN| NaN| NaN| NaN|
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| 3| 101.97|**100.00**| NaN| NaN| NaN| NaN|
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| 4| 102.78|**100.00**| NaN| NaN| NaN| NaN|
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| 5| 103.00|**100.00**| NaN| NaN| NaN| NaN|
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| 6| 102.97|**96.87**| NaN| NaN| NaN| NaN|
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| 7| 103.06|**97.01**| NaN| NaN| NaN| NaN|
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| 8| 102.94|**85.91**| NaN| NaN| NaN| NaN|
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| 9| 102.72|**69.23**| NaN| NaN| NaN| NaN|
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|10| 102.75|**69.62**| 69.62| 69.62| ~55.22~| 69.62|
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|11| 102.91|**71.43**| ~71.62~| 71.43| ~60.09~| 71.43|
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|12| 102.97|**71.08**| ~72.42~| 71.08| ~61.93~| 71.08| |