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🚀 Rust-backed pandas-shaped DataFrame for live OHLCV data: 254 indicators, incremental O(lookback) refresh, NumPy/Torch output.

12 starsRust

Volas – Technical Indicators for Python

by kaelzhang·Jul 27, 2026·1 point·1 comment
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AI Analysis

●●●BangerWizardrySolve My Problem

Incremental updates are 360x faster than pandas by refreshing only the stale tail.

Strengths
  • •Rust kernels tuned to assembly instructions eliminate pandas runtime overhead completely.
  • •Pandas-shaped API allows dropping into existing OHLCV workflows with just an import change.
  • •Reproducible benchmarks show consistent wins against TA-Lib on 139 of 157 covered indicators.
Weaknesses
  • •Narrow focus on OHLCV data limits utility for general-purpose data science workflows.
  • •Missing some advanced pandas indexing features required for complex multi-axis operations.
Category
Data
Target Audience

Quantitative traders and financial data engineers

Similar To

Polars · TA-Lib · Pandas

Post Description

I wanted it faster to calculate technical indicators than Pandas, so I built volas, 254 indicators, similar APIs to Pandas, but much much much faster.

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