`ROC`#

Description#

ROC(k) is the rate of change over k steps, expressed as a percentage:

\[ \text{ROC}[t] = 100 \cdot \frac{x[t] - x[t-k]}{x[t-k]} \]

The three rate-of-change variants in this library, all matching their TA-Lib namesakes:

Class	Formula	Returns
`ROC`	\(100 \cdot (x[t] - x[t-k]) / x[t-k]\)	percentage (e.g. `5.0` = +5%)
`ROCP`	\((x[t] - x[t-k]) / x[t-k]\)	fraction (e.g. `0.05` = +5%); same as `Return`
`ROCR`	\(x[t] / x[t-k]\)	ratio (e.g. `1.05`)

Parameters:

window_size (int, positive): the lookback k.

NaN handling: NaN for the first k samples (no x[t-k] yet). Also NaN if x[t-k] == 0 (division by zero).

NaN handling#

Policy: propagate. Input NaN values are stored in the lookback. Output is NaN at any index where the function's positional formula references a NaN input; recovery happens once the NaN slides out of the lookback.

Examples#

Usage example#

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

np.random.seed(0)
price = 100 * np.exp(np.cumsum(np.random.normal(0.0005, 0.02, size=300)))
roc = ROC(window_size=20)(price)        # percent change over the last 20 bars

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=price, mode="lines", name="price"), row=1, col=1)
fig.add_trace(go.Scatter(y=roc, mode="lines", name="ROC",
                         line=dict(color="red")), row=2, col=1)
fig.update_layout(title="Rate of change over 20 bars (ROC)",
                  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="price", row=1, col=1)
fig.update_yaxes(title_text="percent", row=2, col=1)
fig.show()

Reference#

Equivalent to talib.ROC(x, timeperiod=k). Bit-exact match to ~1e-14 (cross-validated in tests/test_third_party_alignment.py).

ROC#