AD

Description

Chaikin Accumulation/Distribution Line:

\[ \text{CLV}[t] = \dfrac{(C - L) - (H - C)}{H - L} \qquad \text{AD}[t] = \text{AD}[t-1] + \text{CLV}\ \cdot\ V[t] \]

The "close location value" is in [-1, +1]: +1 means close at the high (full accumulation), -1 at the low (full distribution). When high == low the CLV is undefined and the AD line is unchanged (TA-Lib's convention).

4-input, 1-output on (high, low, close, volume). Cumulative; no window. Bit-exact to talib.AD.

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 AD

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 = AD()(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="AD line", line=dict(color="red")), row=2, col=1)
fig.update_layout(title="Accumulation/distribution line (AD)",
                  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="AD line", row=2, col=1)
fig.show()