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kingchenc 8b4a847d24 docs(wiki): add Cookbook, TA-Lib migration table and FAQ

Three content gaps in the wiki: there was no migration story for users
porting from TA-Lib, no strategy cookbook, and no FAQ. Add all three as
self-contained pages and link them from Home.md's "Wiki contents".

* docs/wiki/TA-Lib-Migration.md — full one-to-one mapping table from
  every common talib.X(...) call to the equivalent Wickra expression,
  plus a "what Wickra has that TA-Lib does not" / "what TA-Lib has that
  Wickra does not (yet)" delta.
* docs/wiki/Cookbook.md — seven concrete strategy recipes (RSI mean
  reversion, MACD histogram crossover, Bollinger breakout, ADX-gated
  trend, multi-timeframe confirmation, SuperTrend trailing stop,
  Chain<EMA, RSI>) with Rust or Python snippets.
* docs/wiki/FAQ.md — common questions on warmup, NaN handling, thread
  safety, installation, performance and comparing Wickra to TA-Lib /
  pandas-ta / talipp / finta.

Also extend the [Unreleased] CHANGELOG entry that records the
examples/<lang>/ restructure with the wiki additions; Home.md gains
three new bullets under "Wiki contents".

2026-05-23 00:23:00 +02:00

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Migrating from TA-Lib

A quick lookup table for users porting code from TA-Lib (the C library, or its Python binding talib) to Wickra. Replace talib.X(...) with the matching Wickra expression and the rest of your code keeps working.

Argument-order conventions

The two libraries take the same numeric arguments but differ in shape:

TA-Lib (Python) is functional and pass-by-array. talib.RSI(close, n) is a recompute-everything call: it walks the entire close series each time, even when you only want the latest value.
Wickra is a state machine. wickra.RSI(n) returns an instance; you call .batch(close) for the full series or .update(price) one price at a time. The same instance, fed one price per minute, drives a live trading bot — see Streaming vs Batch.

Multi-output indicators (MACD, Bollinger Bands, Stochastic, ADX, Aroon, Keltner, Donchian, SuperTrend, …) return a tuple from update and a 2-D NumPy array (one column per output) from batch.

Mapping table

TA-Lib	Wickra (Python)
`talib.SMA(close, n)`	`wickra.SMA(n).batch(close)`
`talib.EMA(close, n)`	`wickra.EMA(n).batch(close)`
`talib.WMA(close, n)`	`wickra.WMA(n).batch(close)`
`talib.DEMA(close, n)`	`wickra.DEMA(n).batch(close)`
`talib.TEMA(close, n)`	`wickra.TEMA(n).batch(close)`
`talib.KAMA(close, n)`	`wickra.KAMA(n).batch(close)`
`talib.T3(close, n, vfactor)`	`wickra.T3(n, vfactor).batch(close)`
`talib.RSI(close, n)`	`wickra.RSI(n).batch(close)`
`talib.STOCH(high, low, close, k, smooth, d)`	`wickra.Stochastic(k_period, d_period).batch(high, low, close)` → shape `(n, 2)`
`talib.STOCHRSI(close, n, k, d)`	`wickra.StochRSI(rsi_period, stoch_period).batch(close)`
`talib.CCI(high, low, close, n)`	`wickra.CCI(n).batch(high, low, close)`
`talib.WILLR(high, low, close, n)`	`wickra.WilliamsR(n).batch(high, low, close)`
`talib.MFI(high, low, close, volume, n)`	`wickra.MFI(n).batch(high, low, close, volume)`
`talib.ROC(close, n)`	`wickra.ROC(n).batch(close)`
`talib.MOM(close, n)`	`wickra.MOM(n).batch(close)`
`talib.CMO(close, n)`	`wickra.CMO(n).batch(close)`
`talib.MACD(close, fast, slow, signal)`	`wickra.MACD(fast, slow, signal).batch(close)` → shape `(n, 3)`
`talib.PPO(close, fast, slow)`	`wickra.PPO(fast, slow).batch(close)`
`talib.APO(close, fast, slow)`	`wickra.PPO(fast, slow).batch(close)` (PPO is APO scaled to percent)
`talib.TRIX(close, n)`	`wickra.TRIX(n).batch(close)`
`talib.ADX(high, low, close, n)`	`wickra.ADX(n).batch(high, low, close)` → shape `(n, 3)` (`+DI`, `−DI`, `ADX`)
`talib.AROON(high, low, n)`	`wickra.Aroon(n).batch(high, low, close)` → shape `(n, 2)`
`talib.AROONOSC(high, low, n)`	`wickra.AroonOscillator(n).batch(high, low, close)`
`talib.BBANDS(close, n, dev_up, dev_dn)`	`wickra.BollingerBands(n, multiplier).batch(close)` → shape `(n, 4)` (`upper`, `middle`, `lower`, `stddev`)
`talib.ATR(high, low, close, n)`	`wickra.ATR(n).batch(high, low, close)`
`talib.NATR(high, low, close, n)`	`wickra.NATR(n).batch(high, low, close)`
`talib.STDDEV(close, n)`	`wickra.StdDev(n).batch(close)`
`talib.TRANGE(high, low, close)`	`wickra.TrueRange().batch(high, low, close)`
`talib.OBV(close, volume)`	`wickra.OBV().batch(close, volume)`
`talib.AD(high, low, close, volume)`	`wickra.ADL().batch(high, low, close, volume)`
`talib.ADOSC(high, low, close, volume, fast, slow)`	`wickra.ChaikinOscillator(fast, slow).batch(high, low, close, volume)`
`talib.SAR(high, low, accel, max)`	`wickra.PSAR(accel_start, accel_step, accel_max).batch(high, low, close)`
`talib.LINEARREG(close, n)`	`wickra.LinearRegression(n).batch(close)`
`talib.LINEARREG_SLOPE(close, n)`	`wickra.LinRegSlope(n).batch(close)`
`talib.LINEARREG_ANGLE(close, n)`	`wickra.LinRegAngle(n).batch(close)`
`talib.TYPPRICE(high, low, close)`	`wickra.TypicalPrice().batch(high, low, close)`
`talib.MEDPRICE(high, low)`	`wickra.MedianPrice().batch(high, low, close)`
`talib.WCLPRICE(high, low, close)`	`wickra.WeightedClose().batch(high, low, close)`
`talib.ULTOSC(high, low, close, p1, p2, p3)`	`wickra.UltimateOscillator(p1, p2, p3).batch(high, low, close)`

What Wickra has that TA-Lib does not

Trailing stops — SuperTrend, ChandelierExit, ChandeKrollStop, AtrTrailingStop (TA-Lib only has SAR).
Volume oscillators — ChaikinMoneyFlow, ForceIndex, EaseOfMovement, VolumePriceTrend, plus the windowed RollingVwap.
Other modern indicators — Choppiness Index, Vertical Horizontal Filter, Coppock, PMO, Z-Score, Mass Index, Vortex, TSI, Smma, Trima, Zlema, Vwma, BollingerBandwidth, %B.

What TA-Lib has that Wickra does not (yet)

Pattern recognition (CDL* candlestick patterns).
Hilbert-transform-based indicators (HT_DCPERIOD, HT_TRENDLINE, …).
A few trivial transforms (AVGPRICE, MIDPOINT, MIDPRICE).

If you need one of these, open an issue — most are short additions on top of the existing engine.

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