A high-performance technical analysis library with JIT compilation and streaming capabilities for real-time trading.
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.
pip install without system-level dependenciesBased 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 Trade-offs:
Installation Reliability:
Real-Time Processing:
# TA-Lib installation requirements:
# - C compiler (Visual Studio, GCC, or Clang)
# - System-level TA-Lib library
# - Compatible NumPy version
# - Platform-specific build tools
# ta-numba installation:
pip install ta-numba
# Dependencies: numpy, numba (automatically resolved)
pip install ta-numba
Dependencies: numpy, numba (automatically installed)
Perfect for backtesting and historical analysis:
import ta_numba.bulk as bulk
import numpy as np
# Your price data
close_prices = np.array([100, 102, 101, 103, 105, 104, 106])
# Calculate indicators on entire dataset
sma_20 = bulk.trend.sma(close_prices, window=20)
rsi_14 = bulk.momentum.rsi(close_prices, window=14)
macd_line, macd_signal, macd_hist = bulk.trend.macd(close_prices)
# Warm up JIT compilation for faster subsequent calls
import ta_numba.warmup
ta_numba.warmup.warmup_all() # Optional but recommended
Perfect for live market data and real-time trading:
import ta_numba.stream as stream
# Create streaming indicators
sma = stream.SMA(window=20)
rsi = stream.RSI(window=14)
macd = stream.MACD(fast=12, slow=26, signal=9)
# Process live price updates
def on_new_price(price):
sma_value = sma.update(price)
rsi_value = rsi.update(price)
macd_values = macd.update(price)
if sma.is_ready:
print(f"SMA: {sma_value:.2f}")
if rsi.is_ready:
print(f"RSI: {rsi_value:.2f}")
if macd.is_ready:
print(f"MACD: {macd_values}")
# Simulate live data
for price in [100, 102, 101, 103, 105]:
on_new_price(price)
For existing ta library users:
# Same as original ta library
import ta_numba.trend as trend
import ta_numba.momentum as momentum
sma_values = trend.sma(close_prices, window=20)
rsi_values = momentum.rsi(close_prices, window=14)
Real-time indicators with O(1) updates and constant memory usage:
Trend Indicators (11)
SMA, EMA, WMA - Moving averagesMACD - Moving Average Convergence DivergenceADX - Average Directional IndexTRIX - Triple Exponential AverageCCI - Commodity Channel IndexDPO - Detrended Price OscillatorAroon - Aroon OscillatorParabolicSAR - Parabolic Stop and ReverseVortexIndicator - Vortex IndicatorMomentum Indicators (10)
RSI - Relative Strength IndexStochastic - Stochastic OscillatorStochasticRSI - Stochastic RSIWilliamsR - Williams %RTSI - True Strength IndexUltimateOscillator - Ultimate OscillatorAwesomeOscillator - Awesome OscillatorKAMA - Kaufman's Adaptive Moving AveragePPO - Percentage Price OscillatorROC - Rate of ChangeVolatility Indicators (9)
ATR - Average True RangeBollingerBands - Bollinger BandsKeltnerChannel - Keltner ChannelDonchianChannel - Donchian ChannelStandardDeviation - Rolling Standard DeviationVariance - Rolling VarianceTrueRange - True RangeHistoricalVolatility - Historical VolatilityUlcerIndex - Ulcer IndexVolume Indicators (10)
MoneyFlowIndex - Money Flow IndexAccDistIndex - Accumulation/Distribution IndexOnBalanceVolume - On Balance VolumeChaikinMoneyFlow - Chaikin Money FlowForceIndex - Force IndexEaseOfMovement - Ease of MovementVolumePriceTrend - Volume Price TrendNegativeVolumeIndex - Negative Volume IndexVWAP - Volume Weighted Average PriceVWEMA - Volume Weighted Exponential Moving AverageAll functions accept NumPy arrays for maximum performance.
ta_numba.volume.money_flow_indexta_numba.volume.acc_dist_indexta_numba.volume.on_balance_volumeta_numba.volume.chaikin_money_flowta_numba.volume.force_indexta_numba.volume.ease_of_movementta_numba.volume.volume_price_trendta_numba.volume.negative_volume_indexta_numba.volume.volume_weighted_average_priceta_numba.volume.volume_weighted_exponential_moving_averageta_numba.volatility.average_true_rangeta_numba.volatility.bollinger_bandsta_numba.volatility.keltner_channelta_numba.volatility.donchian_channelta_numba.volatility.ulcer_indexta_numba.trend.smata_numba.trend.emata_numba.trend.wmata_numba.trend.macdta_numba.trend.adxta_numba.trend.vortex_indicatorta_numba.trend.trixta_numba.trend.mass_indexta_numba.trend.ccita_numba.trend.dpota_numba.trend.kstta_numba.trend.ichimokuta_numba.trend.parabolic_sarta_numba.trend.schaff_trend_cycleta_numba.trend.aroonta_numba.momentum.rsita_numba.momentum.stochrsita_numba.momentum.tsita_numba.momentum.ultimate_oscillatorta_numba.momentum.stochta_numba.momentum.williams_rta_numba.momentum.awesome_oscillatorta_numba.momentum.kamata_numba.momentum.rocta_numba.momentum.ppota_numba.momentum.pvota_numba.others.daily_returnta_numba.others.daily_log_returnta_numba.others.cumulative_returnta_numba.others.compound_log_returnTest Environment:
Performance Analysis:
Complete Library Comparison:
Performance Comparison (Average Time per Run):
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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
Benchmark Results Analysis:
vs Pure Python Libraries:
vs Compiled Libraries:
Streaming Performance:
Library Selection Criteria:
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
Choose TA-Lib when:
Choose ta-numba when:
Choose ta/pandas when:
# Traditional approach (recalculates entire array each time)
def update_traditional(new_price, history, window=20):
history.append(new_price)
return talib.SMA(np.array(history), window)[-1] # 0.347ms per update
# ta-numba streaming (O(1) update)
sma = stream.SMA(window=20)
def update_streaming(new_price):
return sma.update(new_price) # 0.022ms per update - 15.8x faster!
Generating sample data of size 200000 with seed None...
Sample data generated.
--- Warming up Numba functions (JIT Compilation) ---
Warm-up complete.
--- Running Benchmarks (5 loops each) ---
Discrepancy for TRIX:
Mean Absolute Difference: 0.007569
Zero Status: Normal
First 5 differing values (Index, TA, Numba):
43, -0.035422, -0.054566
44, -0.021285, -0.035508
45, 0.001522, -0.006502
46, 0.002349, -0.005051
47, 0.012865, 0.008175
Discrepancy for MI:
Mean Absolute Difference: 6.323e-06
Zero Status: Normal
First 5 differing values (Index, TA, Numba):
40, 24.923410, 25.163110
41, 25.099305, 25.298424
42, 25.092817, 25.256278
43, 25.031762, 25.163705
44, 25.043005, 25.150111
Discrepancy for STC:
Mean Absolute Difference: 4.276e-06
Zero Status: Normal
First 5 differing values (Index, TA, Numba):
71, 16.939844, 17.313568
72, 8.469922, 8.656784
73, 4.234961, 4.328392
74, 15.217372, 15.289315
75, 28.028082, 28.091572
Discrepancy for TSI:
Mean Absolute Difference: 0.0004937
Zero Status: Normal
First 5 differing values (Index, TA, Numba):
37, 8.232642, 1.088498
38, 7.628686, 0.899511
39, 6.338255, -0.030883
40, 6.326458, 0.355236
41, 3.863873, -1.721345
--- Benchmark Results (Average Time per Run) ---
-----------------------------------------------------------
Indicator | `ta` Library | Numba Version | Speedup
-----------------------------------------------------------
MFI | 1.187933s | 0.005150s | 230.65x
ADI | 0.001475s | 0.000434s | 3.40x
OBV | 0.001602s | 0.000122s | 13.08x
CMF | 0.004253s | 0.001713s | 2.48x
FI | 0.001479s | 0.000609s | 2.43x
EOM | 0.001648s | 0.000172s | 9.58x
VPT | 0.002104s | 0.000451s | 4.66x
NVI | 3.244231s | 0.001093s | 2967.43x
VWAP | 0.003858s | 0.001392s | 2.77x
VWEMA | 0.005218s | 0.002011s | 2.60x
ATR | 0.419494s | 0.001130s | 371.32x
BB | 0.004472s | 0.003196s | 1.40x
KC | 0.005683s | 0.007647s | 0.74x
DC | 0.006115s | 0.009956s | 0.61x
UI | 0.398492s | 0.007430s | 53.63x
SMA | 0.001696s | 0.002453s | 0.69x
EMA | 0.001192s | 0.000444s | 2.69x
WMA | 5.459586s | 0.006479s | 842.68x
MACD | 0.003275s | 0.001290s | 2.54x
ADX | 0.883612s | 0.007472s | 118.25x
Vortex | 0.016811s | 0.007960s | 2.11x
TRIX | 0.004868s | 0.001166s | 4.18x
MI | 0.003594s | 0.008942s | 0.40x
CCI | 1.055140s | 0.007558s | 139.60x
DPO | 0.001935s | 0.002446s | 0.79x
KST | 0.011884s | 0.031931s | 0.37x
Ichimoku | 0.013384s | 0.027892s | 0.48x
PSAR | 9.464796s | 0.001216s | 7783.20x
STC | 0.018517s | 0.019506s | 0.95x
Aroon | 0.402076s | 0.005702s | 70.52x
RSI | 0.004719s | 0.002710s | 1.74x
StochRSI | 0.012424s | 0.014490s | 0.86x
TSI | 0.004547s | 0.001771s | 2.57x
UO | 0.034889s | 0.014549s | 2.40x
Stoch | 0.006982s | 0.011224s | 0.62x
WR | 0.006880s | 0.009031s | 0.76x
AO | 0.003143s | 0.004481s | 0.70x
KAMA | 0.130242s | 0.001560s | 83.47x
ROC | 0.000777s | 0.000344s | 2.26x
PPO | 0.003494s | 0.001294s | 2.70x
PVO | 0.003904s | 0.001216s | 3.21x
DR | 0.000662s | 0.000300s | 2.21x
DLR | 0.000803s | 0.001611s | 0.50x
CR | 0.000388s | 0.000184s | 2.11x
CLR | 11.993333s | 1.936194s | 6.19x
```
-----------------------------------------------------------
--- Zero Value Status for All Indicators ---
Normal (non-zero values): 44 indicators
MFI, ADI, OBV, CMF, FI, EOM, VPT, NVI, VWAP, VWEMA,
ATR, BB, KC, DC, UI, ... and 29 more
All 44 indicators have normal non-zero values!
--- Discrepancy Report ---
Indicator Status MAD Zero Status
TRIX Different 0.007569 Normal
MI Different 6.323000e-06 Normal
STC Different 4.276000e-06 Normal
TSI Different 4.937000e-04 Normal
-----------------------------------------------------------
SMA, EMA, RSI vs SMAStreaming)ta_numba.warmup.warmup_all() for all indicatorsta_numba.bulk and ta_numba.stream for clarity# Old way (still supported)
import ta_numba.trend as trend
sma_values = trend.sma(prices, window=20)
# New recommended way - Bulk processing
import ta_numba.bulk as bulk
sma_values = bulk.trend.sma(prices, window=20)
# New feature - Streaming
import ta_numba.stream as stream
sma = stream.SMA(window=20)
for price in live_prices:
current_sma = sma.update(price)
# From pandas
df['sma'] = df['close'].rolling(20).mean()
# To ta-numba bulk
sma_values = bulk.trend.sma(df['close'].values, window=20)
# From ta-lib streaming simulation
# (multiple function calls for updates)
# To ta-numba streaming
sma = stream.SMA(window=20)
current_value = sma.update(new_price)
# Recommended startup sequence for production
import ta_numba.warmup
import ta_numba.bulk as bulk
import ta_numba.stream as stream
# Warm up all indicators (do this once at startup)
ta_numba.warmup.warmup_all()
# Now all subsequent calls are fast
def process_historical_data(prices):
return bulk.trend.sma(prices, window=20)
def process_live_data():
sma = stream.SMA(window=20)
for price in live_feed:
yield sma.update(price)
# Dockerfile optimization
FROM python:3.11
RUN pip install ta-numba
# Pre-compile indicators at build time
RUN python -c "import ta_numba.warmup; ta_numba.warmup.warmup_all()"
# Your application will start faster
COPY . .
CMD ["python", "your_trading_app.py"]
import ta_numba.bulk as bulk
import time
# Process 1M price points
prices = np.random.randn(1_000_000).cumsum() + 100
start = time.time()
sma_values = bulk.trend.sma(prices, window=50)
rsi_values = bulk.momentum.rsi(prices, window=14)
macd_line, macd_signal, macd_hist = bulk.trend.macd(prices)
elapsed = time.time() - start
print(f"Processed 1M points in {elapsed:.3f}s")
# Output: Processed 1M points in 0.045s
import ta_numba.stream as stream
# Set up indicators
indicators = {
'sma_20': stream.SMA(window=20),
'sma_50': stream.SMA(window=50),
'rsi': stream.RSI(window=14),
'macd': stream.MACD()
}
def on_price_update(price):
signals = {}
for name, indicator in indicators.items():
signals[name] = indicator.update(price)
# Generate trading signals
if all(ind.is_ready for ind in indicators.values()):
if signals['sma_20'] > signals['sma_50']:
return "BUY_SIGNAL"
elif signals['rsi'] > 70:
return "SELL_SIGNAL"
return "HOLD"
# Process live feed (microsecond latency)
for price in live_price_feed:
signal = on_price_update(price) # ~2-5 microseconds
## **Acknowledgements**
## **🙏 Acknowledgements**
This library builds upon the excellent work of several projects:
- **[Technical Analysis Library (ta)](https://github.com/bukosabino/ta)** by Darío López Padial - API design and calculation logic foundation
- **[Numba](https://numba.pydata.org/)** - JIT compilation technology that makes the performance possible
- **[NumPy](https://numpy.org/)** - Fundamental array operations and mathematical functions
ta-numba extends the original ta library with high-performance Numba compilation and adds real-time streaming capabilities while maintaining mathematical accuracy and API compatibility.
## **📊 Mathematical Documentation**
All indicator implementations are based on established formulas documented in: [`ta-numba.pdf`](ta-numba.pdf)
This document provides:
- Precise mathematical definitions for all indicators
- Implementation details and edge case handling
- Verification against reference implementations
- Performance optimization techniques used
## **🤝 Contributing**
We welcome contributions! Whether it's:
- 🐛 Bug reports and fixes
- 📈 New indicator implementations
- ⚡ Performance optimizations
- 📚 Documentation improvements
- 🧪 Test coverage expansion
Please see our [contributing guidelines](CONTRIBUTING.md) for details.
## **📝 License**
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
---
**⭐ If ta-numba helps your trading or research, please give us a star on GitHub!**
FAQs
A high-performance technical analysis library with JIT compilation and streaming capabilities for real-time trading.
We found that ta-numba demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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