ta-py 2.0.0

Fastest zero-dependency pure-Python technical analysis library — full functional parity with ta.js 2.0

Technical Analysis (ta.py)

A zero-dependency, pure-Python technical analysis library. 151 indicators, full type hints, and full functional parity with ta.js 2.0 — its sibling JavaScript library. No NumPy, no pandas, no C extensions, no compilers required.

---

NOTE

ta_py runs anywhere CPython does. There is nothing to compile and nothing to vendor — pip install ta_py is the entire installation.

from ta_py import sma, rsi
sma([1, 2, 3, 4, 5, 6, 10], 6)   # [3.5, 5]

The only multi-process feature is ta_py.multi.sim (Monte Carlo via stdlib multiprocessing); it falls back to the sequential ta_py.sim automatically when run inside an environment that can't spawn workers.

Install

pip install ta_py

Requires Python 3.10 or newer. No other runtime dependencies.

Usage

import ta_py as ta

ta.sma([1, 2, 3, 4, 5, 6, 10], 6)                              # [3.5, 5]
ta.aroon.up([5, 4, 5, 2], 3)                                   # [100.0, 50.0]
ta.bands([1, 2, 3, 4, 5, 6], length=5, deviations=2)

Or import individual functions:

from ta_py import rsi, macd, atr, bands

Every function is also re-exported on the top-level ta_py module. The ta_py.aroon, ta_py.random, and ta_py.multi namespaces mirror their ta.js counterparts.

Why pure Python?

ta_py is the only TA library on PyPI that combines zero runtime dependencies, full pure-Python implementation, and full ta.js 2.0 indicator coverage. That combination matters when:

• Install pain is a deal-breaker. No C compiler, no system libraries, no platform-specific wheels. Works on Alpine containers, AWS Lambda, locked-down corporate Python builds, fresh Windows installs — anywhere pip works.
• Footprint matters. A few hundred KB on disk vs. ~50–100 MB for numpy + pandas. Important for serverless cold starts, container images, and edge deploys.
• You're chasing the latest CPython. Pure-Python libraries ship the day a new Python release does. C/Cython libraries lag months — pandas-ta doesn't yet support Python 3.14, and TA-Lib regularly delays wheels for new interpreters.
• You can't or won't add native dependencies. Compliance reviews, supply-chain audits, vendoring requirements: every line of ta_py is auditable Python source. No prebuilt binaries, no setup.py running compilers.
• You target alternative runtimes. PyPy, GraalPy, and Pyodide (Python in the browser via WASM) all run pure-Python code. Most native-extension TA libraries don't port.

If you need raw throughput across millions of bars in tight loops and you're comfortable installing a C extension, use TA-Lib. ta_py exists for the much larger case where indicator math is one step in a bigger pipeline and "fast enough on a 10k-bar series, with no install pain" is the right trade.

Performance notes

Internal sliding-window O(N) rewrites in 2.0 measurably beat naïve list-comprehension implementations of the same indicators (e.g. Bollinger Bands ~5× faster than a textbook Python implementation, standard deviation ~13× faster). The bench/ sub-project contains a regression-tracking harness used during development; it is not a marketing benchmark and does not claim parity with C-backed libraries.

What's new in 2.0.0

• 39 new indicators for full ta.js 2.0 parity: DM family (pdm / mdm / pdi / mdi / dx / adx / adxr / natr), cci, stoch_rsi, kdj, cmf, vortex, kvo, ult, trix, ppo, apo, mass, dema / tema / trima / t3 / zlema / vidya, linear regression family (lr_slope / lr_intercept / lr_angle / tsf), std_series, adl, nvi / pvi, emv, dpo, ulcer, fibnumbers, plus ta_py.random.* and ta_py.aroon.* namespaces.
• Multi-process Monte Carlo via ta_py.multi.sim — typically 2–3× faster than the sequential ta_py.sim on 8-core hardware.
• Sliding-window O(N) rewrites of hot paths: std, bands, sma, smma, wma, ema, vwma, variance, envelope, mfi, rsi, stoch, pr, don.
• 6 algorithm/output-shape breaks vs 1.17.0 — atr, smma, mfi, rsi, wrsi (now an alias of rsi), stoch. See MIGRATION.md for before/after examples.
• PEP 561 marker (py.typed) ships in the wheel so type-checkers pick up ta_py's annotations automatically.

See CHANGELOG.md for the full per-batch breakdown.

Examples

Per-indicator usage with input/output samples lives in EXAMPLES.md.

Moving Averages

• SMA · SMMA · WMA · EMA · DEMA · TEMA · TRIMA · T3 · ZLEMA · VIDYA · Hull · LSMA · VWMA · VWWMA · WSMA · PWMA · HWMA · KAMA · CWMA

Indicators

• MACD · MACD signal · MACD bars · RSI · Wilder's RSI · TSI · BOP · Force Index · ASI · Alligator · Williams %R · Stochastics · Stoch RSI · Fibonacci Retracement · Bollinger Bandwidth · Ichimoku · ATR · NATR · Aroon Up · Aroon Down · Aroon namespace · MFI · CMF · ROC · Coppock · KST · OBV · NVI · PVI · ADL · EMV · VWAP · Fractals · Crossover · Momentum · HalfTrend · ZigZag · PSAR · SuperTrend · Elder Ray · HV · RVI · RVI signal · RSI Divergence · Universal Divergence · TRIX · CCI · DM family · DI family · DX · ADX · ADXR · PPO · APO · DPO · Mass Index · Ulcer Index · Vortex · KDJ · KVO

Oscillators

• Alligator Osc · Chande Momentum · Chaikin · Aroon Osc · Awesome · Accelerator · Fisher · Ultimate

Bands

• Bollinger · Keltner · Donchian · Fibonacci Bollinger · Envelope

Statistics

• Std · Std series · LR slope · LR intercept · LR angle · TSF · Variance · Normal CDF · Inverse Normal · Monte Carlo · Multi-process Monte Carlo · Percentile · Correlation · Covariance · Pct Difference · Expected Return · Abnormal Return · Kelly · Permutations · Winratio · Avg Win · Avg Loss · Drawdown · Median · Recent High · Recent Low · MAD · AAD · Std Error · SSD · Log · Exp · Normalize · Denormalize · Normalize Pair · Normalize From · Standardize · Z-Score · K-means · MSE · Cumulative · Sum · Return Positive · Return Negative · P-Value · Martingale · Anti-Martingale · Expected Trails

Chart Types

• Heikin Ashi · Renko

Random

• Seeded PRNG

Miscellaneous

• Fibonacci Sequence · Times Up · Times Down

Experimental

• Support Line · Resistance Line · Divergence State

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you'd like to change. Please update tests as appropriate — see CONTRIBUTING.md for the test/lint workflow.

License

MIT

Maintained By

ta.py is an open-source project created and actively maintained by Nino Kroesen.

This library was built with portability in mind for high-frequency data and algorithmic trading systems where install footprint, dependency surface, and supply-chain auditability matter as much as raw throughput. If you'd like to see these indicators in action within a live quantitative trading environment, check out Bitvested.

---