Quickstart Python

Quickstart: Python

A five-minute tour of the Wickra Python binding. By the end you will have run a batch RSI over a NumPy array, fed the same indicator one tick at a time, and read a multi-column MACD result correctly during warmup.

Install

pip install wickra

The published wheels target Python 3.9 – 3.12 on Linux x86_64, macOS (Intel + Apple Silicon), and Windows x86_64. The only runtime dependency is numpy >= 1.22. No system compiler, no C headers, no Rust toolchain are needed to install — Wickra ships pre-built native wheels.

Verify the install:

import wickra as ta
print(ta.__version__)

Batch: RSI over a NumPy array

Indicator.batch(prices) takes a 1-D numpy.ndarray of float64 closes and returns a 1-D numpy.ndarray of float64 outputs. Warmup steps come back as NaN so the result aligns 1:1 with your input prices and slots straight into a pandas column or a NumPy mask.

The first 15 prices below are the classic Wilder textbook example. RSI(14) emits its first value at index 14 (the 15th input) because it needs 14 diffs to seed Wilder's smoothing.

import numpy as np
import wickra as ta

prices = np.array([
    44.34, 44.09, 44.15, 43.61, 44.33, 44.83, 45.10, 45.42,
    45.84, 46.08, 45.89, 46.03, 45.61, 46.28, 46.28, 46.00,
    46.03, 46.41, 46.22, 45.64,
], dtype=float)

rsi = ta.RSI(14)
values = rsi.batch(prices)

print(values.dtype, values.shape)
print("warmup count:", int(np.isnan(values).sum()))
print("first value :", float(values[14]))
print("last value  :", float(values[-1]))

Running this prints:

float64 (20,)
warmup count: 14
first value : 70.46413502109705
last value  : 57.91502067008556

The exact first value 70.464 matches Wilder's published table; this is the same input/output pair the Rust test suite pins as classic_wilder_textbook_values in crates/wickra-core/src/indicators/rsi.rs.

Streaming: feed one price at a time

The same RSI instance can be driven tick-by-tick with update(). Each call is O(1) and returns either a float or None while the indicator is still warming up.

import wickra as ta

rsi = ta.RSI(14)
prices = [
    44.34, 44.09, 44.15, 43.61, 44.33, 44.83, 45.10, 45.42,
    45.84, 46.08, 45.89, 46.03, 45.61, 46.28, 46.28, 46.00,
    46.03, 46.41,
]

for tick, price in enumerate(prices, start=1):
    value = rsi.update(price)
    if value is not None:
        print(f"tick {tick:2d}  close={price:.2f}  rsi={value:.4f}")

Output:

tick 15  close=46.28  rsi=70.4641
tick 16  close=46.00  rsi=66.2496
tick 17  close=46.03  rsi=66.4809
tick 18  close=46.41  rsi=69.3469

Tick 15 is the first emission because RSI(14).warmup_period() == 15. Before that, update() returns None. After warmup the indicator never goes back to None: each subsequent tick produces a steady value.

The full set of streaming-state methods is:

Method	Returns	Notes
update(price)	float/None	O(1) state transition, None during warmup
batch(prices)	np.ndarray	replays update, NaN during warmup
reset()	None	returns to a freshly-constructed state
is_ready()	bool	True once the first value has been emitted
warmup_period()	int	inputs required before the first value

MACD: a multi-column indicator and its warmup NaNs

Some indicators emit several values at once. MACD returns three: the MACD line, the signal line, and the histogram. The Python batch reflects that shape directly — instead of a 1-D float64 vector you get a 2-D (n_rows, n_columns) array, and each warmup row is filled with NaN across every column. (Stochastic follows the same pattern with two columns, Bollinger Bands with four, Keltner/Donchian/ADX with three.)

import numpy as np
import wickra as ta

prices = np.linspace(100.0, 120.0, 40)
macd = ta.MACD(12, 26, 9)
out = macd.batch(prices)

print("shape       :", out.shape)
print("warmup rows :", int(np.isnan(out[:, 0]).sum()))
print("first ready :", int(np.argmax(~np.isnan(out[:, 0]))))
print("row 33      :", out[33])
print("row 39      :", out[39])

Output:

shape       : (40, 3)
warmup rows : 33
first ready : 33
row 33      : [ 3.58974359e+00  3.58974359e+00 -1.77635684e-15]
row 39      : [3.58974359e+00 3.58974359e+00 6.21724894e-15]

Two things to notice:

1. MACD(12, 26, 9).warmup_period() is slow + signal - 1 = 34, and indeed row 34 - 1 = 33 is the first row where every column is finite. Earlier rows are entirely NaN; you should not slice a partial row out and use, say, the signal column independently of the macd column.
2. Columns are positional — out[:, 0] is MACD, out[:, 1] is signal, out[:, 2] is histogram. The streaming form returns the same triple as a plain Python tuple: (macd, signal, histogram).

To filter a warmup-aware mask cleanly:

ready = ~np.isnan(out[:, 0])
clean_rows = out[ready]

ready is a single boolean column you can apply to every column at once because the warmup pattern is identical across all of them.

A deeper example

examples/python/backtest.py in the repo runs a full panel of indicators (RSI, EMA, Bollinger, MACD, ATR, ADX, OBV) over an OHLCV CSV and prints a summary. It's a good template for "I have historical data on disk, give me a table of indicator values" workflows; for live workflows, see examples/python/live_trading.py.

See also

• Quickstart: Rust — same API surface in Rust.
• Streaming vs Batch — why the streaming path is the primary one, not a convenience.
• Warmup Periods — the full table of warmup counts.
• Source: https://github.com/kingchenc/wickra