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Refactor documentation to remove "Zero-Allocation Design" sections across various trend indicators and implement a PowerShell script for automated cleanup

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- Updated mathematical foundations and performance profiles where necessary to maintain clarity and coherence.
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Miha Kralj
2025-12-21 14:37:44 -08:00
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commit a7b7207801
65 changed files with 1766 additions and 482 deletions
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docs/benchmarks.md
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# Benchmarks
Performance claims require measurement. We benchmark QuanTAlib against established libraries: TA-Lib and Tulip (industry-standard C libraries accessed via P/Invoke), Skender.Stock.Indicators and Ooples.FinancialIndicators (popular .NET implementations).
Performance claims require measurement. QuanTAlib is benchmarked against established libraries: TA-Lib and Tulip (industry-standard C libraries accessed via P/Invoke), Skender.Stock.Indicators and Ooples.FinancialIndicators (popular .NET implementations).
## Test Environment
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### Simple Moving Average (SMA)
QuanTAlib's Span mode calculates 500,000 SMA values in 318 microseconds with zero memory allocations. That's 0.64 nanoseconds per value. For context, a single L1 cache access takes approximately 1 nanosecond on modern CPUs — we're calculating moving averages faster than fetching data from the nearest cache level.
QuanTAlib's Span mode calculates 500,000 SMA values in 318 microseconds with zero memory allocations. That's 0.64 nanoseconds per value. For context, a single L1 cache access takes approximately 1 nanosecond on modern CPUs, so moving averages are being calculated faster than data can be fetched from the nearest cache level.
| Library | Mean Time | Allocations | Relative Speed |
| ------- | --------- | ----------- | -------------- |
@@ -76,7 +76,7 @@ Even QuanTAlib's slowest mode (Eventing with complete event infrastructure and 1
## Methodology
We use [BenchmarkDotNet](https://benchmarkdotnet.org/) for all performance testing. This ensures:
[BenchmarkDotNet](https://benchmarkdotnet.org/) is used for all performance testing. This ensures:
- Warmup iterations to stabilize JIT compilation
- Statistical analysis of results (mean, standard deviation)