DonchianChannels

Description

Trend-following envelope. The upper line is the rolling max of high, the lower line is the rolling min of low, and the midline is their average:

\[\begin{split} \begin{aligned} \text{upper}[t] &= \max(\text{high},\ w\ \text{bars}) \\ \text{lower}[t] &= \min(\text{low},\ w\ \text{bars}) \\ \text{mid}[t] &= (\text{upper} + \text{lower}) / 2 \end{aligned} \end{split}\]

2-input, 3-output (FunctorBase<_, 2, 3>). Inputs (high, low); outputs (lower, mid, upper). First valid at sample index window_size - 1.

Composes two detail::MonotonicDeque instances. Amortised O(1) per step. Bit-exact to pandas-ta-classic.donchian.

NaN handling

Policy: ignore. A NaN in any input at index t causes the function to skip that step: output at t is NaN and internal state is unchanged. Subsequent finite samples are processed as if step t had not occurred.

Examples

Usage example

import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from screamer import DonchianChannels

np.random.seed(0)
close = 100*np.exp(np.cumsum(np.random.normal(0, 0.01, size=300)))
open_ = np.concatenate([[close[0]], close[:-1]])
wick = np.abs(np.random.normal(0, 0.4, size=300))
high = np.maximum(open_, close) + wick
low  = np.minimum(open_, close) - wick
out = np.asarray(DonchianChannels(20)(high, low))

fig = make_subplots(rows=2, cols=1, shared_xaxes=True,
                    row_heights=[0.55, 0.45], vertical_spacing=0.08)
fig.add_trace(go.Scatter(y=high, name="high", line=dict(color="#888")), row=1, col=1)
fig.add_trace(go.Scatter(y=low, name="low", line=dict(color="#bbb")), row=1, col=1)
fig.add_trace(go.Scatter(y=out[:, 2], name="upper", line=dict(color="red")), row=2, col=1)
fig.add_trace(go.Scatter(y=out[:, 1], name="mid", line=dict(color="gray")), row=2, col=1)
fig.add_trace(go.Scatter(y=out[:, 0], name="lower", line=dict(color="green")), row=2, col=1)
fig.update_layout(title="Donchian channels (DonchianChannels)",
                  margin=dict(l=20, r=20, t=60, b=20),
                  legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1))
fig.update_yaxes(title_text="high / low", row=1, col=1)
fig.update_yaxes(title_text="channel bands", row=2, col=1)
fig.show()