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Miha Kralj
2023-01-08 19:28:45 -08:00
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@@ -39,12 +39,6 @@ QuanTAlib uses validation tests with four other TA libraries to assure accuracy
- [Pandas-TA](https://twopirllc.github.io/pandas-ta/)
- [Tulip Indicators](https://tulipindicators.org/)
### Performance
### Questions
Is QuanTAlib fast? Well, _no_, but actually *yes*. QuanTAlib works on an additive principle, meaning that even when served a full list of quotes, QuanTAlib will process one item at the time, rolling forward throug time series of data.
- If the last bar is still forming (parameter `update: true`), QuanTAlib easily recalculates the last entry as often as needed without any need to recalculate the history.
- 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.
If you feed QuanTAlib 500 historical bars and calculate EMA(20) on it, the performance of QuanTAlib will be dead last compared to all other TA libraries.
But when system uses 10,000 historical bars, does updates to the current/last bar every tick, and adds a new bar every minute, QuanTAlib has no rivals; all other libraries need to re-calculate the full length of the array on each update/addition to the time series. Longer the series and more updates/additions it gets, more advantage for QuanTAlib.
[Some most common questions addressed](QA.md)