ADOSC

Description

Chaikin A/D Oscillator: difference of two EMAs of the AD line.

\[ \text{ADOSC}[t] = \text{EMA}(\text{AD},\ \text{fast})[t] - \text{EMA}(\text{AD},\ \text{slow})[t] \]

4-input, 1-output on (high, low, close, volume). Default (fast=3, slow=10) matches TA-Lib.

The underlying EMA is screamer.EwMean (pandas adjust=True), so ADOSC inherits the same documented divergence from TA-Lib's ADOSC as DEMA/TEMA/MACD/TRIX. The class matches the explicit pandas-composition reference bit-exactly. See docs/conventions.md for the divergence detail.

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 ADOSC

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
volume = np.random.uniform(1e5, 5e5, size=300)
out = ADOSC(3, 10)(high, low, close, volume)

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=close, name="close", line=dict(color="royalblue")), row=1, col=1)
fig.add_trace(go.Scatter(y=out, name="ADOSC(3,10)", line=dict(color="red")), row=2, col=1)
fig.update_layout(title="Accumulation/distribution oscillator (ADOSC)",
                  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="close", row=1, col=1)
fig.update_yaxes(title_text="ADOSC", row=2, col=1)
fig.show()