TA-Numba: Technical Analysis Library with Numba Acceleration
TA-Numba is a Python library for financial technical analysis that provides dependency-free installation and high-performance computation through Numba JIT compilation. It offers both bulk processing for historical analysis and real-time streaming for live trading applications.
Below is the embedded research paper which details the architecture, implementation, and benchmark results of ta-numba, demonstrating its effectiveness in a quantitative research environment.
📊 Performance Comparison
Based on comprehensive benchmarks with 100,000 data points across multiple technical analysis libraries:
| Aspect | TA-Lib | ta-numba | ta | pandas | cython |
|---|---|---|---|---|---|
| Installation | C compiler required | pip install only | pip install only | pip install only | Compilation required |
| Average Performance | Fastest (baseline) | 4.3x slower | 857x slower | 94x slower | 2.5x slower |
| Best Cases | Fastest overall | MACD: 3.8x faster | All cases slower | All cases slower | Mixed results |
| Worst Cases | WMA, ADX fastest | WMA: 33x slower | PSAR: 8,837x slower | ATR: 13x slower | Variable performance |
| Dependency Issues | Frequent | None | None | Rare | Build-time only |
| Streaming Support | No | Yes (15.8x faster) | No | No | No |
⚡ Performance & Benchmarks
📊 Benchmark Methodology
Test Environment:
- Data Size: 100,000 price points
- Iterations: 3 runs per indicator per library
- Hardware: Standard development machine
- Libraries: ta-numba, ta-lib, ta, pandas, cython, NautilusTrader
Performance Analysis:
- ta-numba delivers substantial performance improvements over pure Python libraries
- TA-Lib maintains performance leadership in bulk processing
- ta-numba provides unique advantages in streaming scenarios
- Installation reliability varies significantly between libraries
📊 Comprehensive Benchmark Results (100K data points)
Complete Library Comparison:
Performance Comparison (Average Time per Run):
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Indicator | ta | ta-numba | ta-lib | pandas | cython | nautilus | Speedup vs ta | Speedup vs talib | Speedup vs pandas | Speedup vs cython | Speedup vs nautilus
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SMA | 0.001196s | 0.001082s | 0.000087s | 0.000713s | 0.000058s | 0.105247s | 1.11x | 0.08x | 0.66x | 0.05x | 97.29x
EMA | 0.000577s | 0.000112s | 0.000332s | 0.000493s | 0.000168s | 0.011398s | 5.16x | 2.97x | 4.41x | 1.50x | 101.92x
RSI | 0.002789s | 0.001355s | 0.000433s | 0.002412s | 0.001946s | 0.062416s | 2.06x | 0.32x | 1.78x | 1.44x | 46.06x
MACD | 0.001635s | 0.000642s | 0.002456s | 0.001860s | 0.000666s | 0.012047s | 2.55x | 3.83x | 2.90x | 1.04x | 18.77x
ATR | 0.205986s | 0.000672s | 0.002262s | 0.008719s | 0.001687s | 0.018718s | 306.60x | 3.37x | 12.98x | 2.51x | 27.86x
Bollinger Upper | 0.002052s | 0.001432s | 0.000341s | 0.002129s | 0.006004s | 0.214716s | 1.43x | 0.24x | 1.49x | 4.19x | 149.92x
OBV | 0.000685s | 0.000066s | 0.000224s | N/A | 0.000275s | 14.146200s | 10.43x | 3.42x | N/A | 4.19x | 215376.26x
MFI | 0.482099s | 0.002581s | 0.002374s | 0.003096s | 0.006168s | 0.021110s | 186.77x | 0.92x | 1.20x | 2.39x | 8.18x
WMA | 2.456998s | 0.003013s | 0.000092s | 0.126318s | 0.002411s | 0.339517s | 815.56x | 0.03x | 41.93x | 0.80x | 112.70x
VWEMA | 0.000908s | 0.000822s | 0.029710s | 0.002095s | 0.004002s | 0.058675s | 1.10x | 36.13x | 2.55x | 4.87x | 71.35x
ADX | 0.407531s | 0.003533s | 0.000643s | 0.012459s | 0.009984s | 0.002930s | 115.34x | 0.18x | 3.53x | 2.83x | 0.83x
PSAR | 4.123320s | 0.000467s | 0.000346s | 0.449931s | 0.001659s | 0.007989s | 8837.04x | 0.74x | 964.29x | 3.56x | 17.12x
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Summary Statistics:
Average speedup vs ta: 857.10x
Average speedup vs ta-lib: 4.35x
Average speedup vs pandas: 94.34x
Average speedup vs cython: 2.45x
Average speedup vs nautilus: 18002.35x
Identical results vs ta: 11/12
Identical results vs ta-lib: 4/12
Identical results vs cython: 5/12
Identical results vs nautilus: 3/12
📈 Performance Summary
Benchmark Results Analysis:
vs Pure Python Libraries:
- ta library: 857x average speedup (range: 1.1x to 8,837x)
- pandas: 94x average speedup (range: 0.66x to 964x)
- Consistent performance advantage across most indicators
vs Compiled Libraries:
- TA-Lib: 0.23x average performance (ta-numba is 4.3x slower on average)
- cython: 2.5x average speedup (mixed results depending on indicator)
- Performance varies significantly by indicator complexity
Streaming Performance:
- 15.8x faster than bulk recalculation methods
- Constant O(1) memory usage vs. O(n) growth
- Microsecond-level latency for real-time applications
Library Selection Criteria:
- Choose TA-Lib for: Maximum performance, stable environment, C compilation acceptable
- Choose ta-numba for: Reliable deployment, streaming requirements, Python-only environments
- Choose ta/pandas for: Simplicity, small datasets, existing pandas workflows
Real-Time Streaming Performance (per tick):
🚀 REAL-TIME STREAMING COMPARISON
============================================================
Simulating live market data feed with continuous price updates...
📊 Generating 100 warmup ticks...
🔥 Warming up JIT compilation...
📈 Initializing streaming indicators...
🎯 SIMULATING 10,000 LIVE MARKET TICKS...
------------------------------------------------------------
Progress: 10% | Avg Bulk: 0.039ms | Avg Streaming: 0.017ms | Speedup: 2.3x
Progress: 20% | Avg Bulk: 0.103ms | Avg Streaming: 0.018ms | Speedup: 5.8x
Progress: 30% | Avg Bulk: 0.174ms | Avg Streaming: 0.019ms | Speedup: 9.0x
Progress: 40% | Avg Bulk: 0.244ms | Avg Streaming: 0.021ms | Speedup: 11.6x
Progress: 50% | Avg Bulk: 0.313ms | Avg Streaming: 0.023ms | Speedup: 13.5x
Progress: 60% | Avg Bulk: 0.378ms | Avg Streaming: 0.023ms | Speedup: 16.2x
Progress: 70% | Avg Bulk: 0.447ms | Avg Streaming: 0.024ms | Speedup: 18.7x
Progress: 80% | Avg Bulk: 0.516ms | Avg Streaming: 0.024ms | Speedup: 21.7x
Progress: 90% | Avg Bulk: 0.589ms | Avg Streaming: 0.024ms | Speedup: 24.3x
Progress: 100% | Avg Bulk: 0.671ms | Avg Streaming: 0.026ms | Speedup: 26.1x
📊 FINAL RESULTS
============================================================
Total ticks processed: 10,000
Lookback window size: 10000
⏱️ TIMING STATISTICS (per tick):
Method Mean Median 95%ile 99%ile
-------------------------------------------------------
Bulk 0.347ms 0.346ms 0.673ms 0.699ms
Streaming 0.022ms 0.022ms 0.028ms 0.039ms
🚀 PERFORMANCE IMPROVEMENT:
Average speedup: 15.8x faster
Median speedup: 15.9x faster
💾 MEMORY USAGE COMPARISON:
Bulk approach: O(n) = 10000 * 8 bytes * 7 indicators = 546.9 KB
Streaming approach: O(1) = ~1 KB total (constant)
Memory efficiency: 547x less memory
⚡ LATENCY ANALYSIS:
Bulk 99th percentile: 0.699ms
Streaming 99th percentile: 0.039ms
For HFT (<1ms requirement): ✅ Bulk passes, ✅ Streaming passes