u/AMGraduate564

Hey r/algotrading,

Over the past couple months pandas-ta-classic has had a huge wave of contributions land on main. Here's a rundown of what's new if you haven't checked in recently:

🕯️ 62 Native Candlestick Patterns (no TA-Lib required)

60 new cdl_*.py pattern files were added natively. Every pattern — Engulfing, Hammer, Morning Star, Three Black Crows, you name it — is now pure Python. TA-Lib is never used for CDL even if installed. Access all of them via df.ta.cdl_pattern(name="engulfing").

📈 30+ New Indicators

Trend / Momentum: adxr, dx, plus_dm, minus_dm, sarext, cpr (4 methods: classic/camarilla/fibonacci/woodie), lrsi, pmax, macdext, macdfix, stochf, fosc, rocp, rocr, rocr100, trixh, vwmacd

Overlap / MA: mama/fama, ht_trendline, tsf, mmar, rainbow, mavp

Hilbert Transform cycles: ht_dcperiod, ht_dcphase, ht_phasor, ht_sine, ht_trendmode — full HT family now supported

Volatility: Chandelier Exit (ce), avolume, cvi, hvol

Volume: vfi, emv, marketfi, vosc, wad

Stats / Math: beta, correl, md, stderr, linregangle, linregintercept, linregslope, edecay, new math namespace with add/sub/mult/div + rolling ops

Cycle: dsp (Detrended Synthetic Price)

⚡ Performance: Numba JIT + NumPy Vectorization

SSF, MCGD, HWMA, RSX, PSAR, Supertrend, QQE and others get optional @njit(cache=True) via numba
Install with: pip install pandas-ta-classic[performance]
Measured speedups: RSX 230×, HWMA 70×, MCGD 43×, SSF 42×, Supertrend 13×, QQE 10×, PSAR 6×
15 additional indicators got NumPy sliding_window_view vectorization (replacing slow .iloc loops)

🧪 Oracle / Parity Test Suites

New test_oracle_talib.py and test_oracle_tulipy.py validate results against TA-Lib and tulipy on shared SPY fixtures. Zero skipped tests — every divergence is explicitly documented.

🔧 Breaking Changes to be Aware Of

qqe() now returns 6 columns (was 3) — adds long band, short band, direction
linreg(angle=True) now returns degrees by default (was radians) to match TA-Lib
stdev/variance ddof now defaults to 0 (population, was 1 sample) to match TA-Lib

📦 Other Quality of Life

uv package manager fully documented alongside pip
Automatic version management via setuptools-scm (no more manual version bumps)
Dynamic Category dict — no more manually registering new indicators in _meta.py
Python version support follows a rolling 5-version policy (now includes 3.14)
Total indicator count: 224 (up from ~213)

GitHub: https://github.com/xgboosted/pandas-ta-classic
Install: pip install pandas-ta-classic or uv add pandas-ta-classic

Feedback and PRs welcome — especially on the oracle parity tests if you spot any formula divergences.