QuanTAlib
Unverified ML strategy on Multi by mihakralj. BotFinder score 18 out of 100.
447 validated technical indicators for C#, Python and PineScript. O(1) streaming and SIMD batch. Cross-checked against TA-Lib, Tulip, Skender and pandas-ta.
Source: github
BotFinder analysis pending.
QuanTAlib
QuanTAlib 447 technical indicators. One library. Brutal architectural trade-offs for absolute speed. ⭐ Documentation pages → QuanTAlib exists because I got tired of validating other people's indicators. Every implementation is cross-checked against TA-Lib, Tulip, Skender, and Pandas-TA. Where they disagree, we went to the original papers. Where the papers disagree, we picked the math that doesn't lie. Same indicators, same results: C#, Python, and PineScript. How Fast? C# native AOT-compiled code spits out half a million bars of SMA in 288 microseconds. That is faster (per value) than a single L1 cache miss on any fancy new CPU. Achieved by trading object allocation for contiguous memory spans, slapping Fused Multiply-Add (FMA) on everything, and forcing SIMD vectorized paths. You want speed? We dictate the heap. | Library | SMA (500K bars) | Allocations | Relative time | | :--- | ---: | ---: | :--- | | QuanTAlib | 288 μs | 0 B | 1× | | TA-Lib (C++)| 365 μs | 32 B | 1.3× slower | | Wickra (Rust)| 1,562 μs | 4 MB | 5.4× slower | | Skender (C#)| 68,378 μs | 42 MB | 238× slower | | Oopl
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