Momentum API Reference
Momentum Indicators Module
This module provides functions for calculating momentum-based indicators for financial time series analysis.
adx(data, period=14)
Calculate Average Directional Index (ADX).
ADX is a technical indicator used to determine the strength of a trend.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
data
|
ndarray
|
Input time series data |
required |
period
|
int
|
Number of periods for calculation |
14
|
Returns:
| Type | Description |
|---|---|
ndarray
|
ADX values |
cci(close, period=20, constant=0.015)
Calculate Commodity Channel Index (CCI) using close prices.
CCI measures the current price level relative to an average price level over a given period. This version uses only close prices instead of typical prices for simplified calculation.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
close
|
ndarray
|
Close prices |
required |
period
|
int
|
Number of periods for calculation |
20
|
constant
|
float
|
Scaling constant |
0.015
|
Returns:
| Type | Description |
|---|---|
ndarray
|
CCI values |
macd(data, fast_period=12, slow_period=26, signal_period=9)
Calculate Moving Average Convergence Divergence (MACD).
MACD is a trend-following momentum indicator that shows the relationship between two moving averages of a security's price.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
data
|
ndarray
|
Input time series data |
required |
fast_period
|
int
|
Period for the fast EMA |
12
|
slow_period
|
int
|
Period for the slow EMA |
26
|
signal_period
|
int
|
Period for the signal line (EMA of MACD line) |
9
|
Returns:
| Type | Description |
|---|---|
tuple of numpy.ndarray
|
Tuple containing (macd_line, signal_line, histogram) |
momentum(data, period=14)
Calculate momentum over a specified period.
Momentum measures the amount that a price has changed over a given period.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
data
|
ndarray
|
Input time series data |
required |
period
|
int
|
Number of periods to calculate momentum |
14
|
Returns:
| Type | Description |
|---|---|
ndarray
|
Momentum values |
percent_change(data, periods=1)
Calculate percentage change between consecutive periods.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
data
|
ndarray
|
Input time series data |
required |
periods
|
int
|
Number of periods to calculate change over |
1
|
Returns:
| Type | Description |
|---|---|
ndarray
|
Percentage change values |
roc(data, period=14)
Calculate Rate of Change (ROC) over a specified period.
ROC measures the percentage change in price over a given period.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
data
|
ndarray
|
Input time series data |
required |
period
|
int
|
Number of periods to calculate ROC |
14
|
Returns:
| Type | Description |
|---|---|
ndarray
|
ROC values in percentage |
rsi(data, period=14, smoothing_type='sma')
Calculate Relative Strength Index (RSI) over a specified period.
RSI measures the speed and change of price movements, indicating overbought (>70) or oversold (<30) conditions.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
data
|
ndarray
|
Input time series data |
required |
period
|
int
|
Number of periods to calculate RSI |
14
|
smoothing_type
|
str
|
Type of smoothing to use: 'sma' (Simple Moving Average) or 'ema' (Exponential Moving Average) |
'sma'
|
Returns:
| Type | Description |
|---|---|
ndarray
|
RSI values (0-100) |
stochastic_oscillator(close, high, low, k_period=14, d_period=3)
Calculate Stochastic Oscillator.
The Stochastic Oscillator is a momentum indicator that shows the location of the close relative to the high-low range over a set number of periods.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
close
|
ndarray
|
Close prices |
required |
high
|
ndarray
|
High prices. If None, assumes close contains close prices and high=low=close |
required |
low
|
ndarray
|
Low prices. If None, assumes close contains close prices and high=low=close |
required |
k_period
|
int
|
Number of periods for %K |
14
|
d_period
|
int
|
Number of periods for %D (moving average of %K) |
3
|
Returns:
| Type | Description |
|---|---|
tuple of numpy.ndarray
|
Tuple containing (%K, %D) |
tsi(data, long_period=25, short_period=13, signal_period=7)
Calculate True Strength Index (TSI).
TSI is a momentum oscillator that helps identify trends and reversals.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
data
|
ndarray
|
Input time series data |
required |
long_period
|
int
|
Long period for double smoothing |
25
|
short_period
|
int
|
Short period for double smoothing |
13
|
signal_period
|
int
|
Period for the signal line |
7
|
Returns:
| Type | Description |
|---|---|
tuple of numpy.ndarray
|
Tuple containing (tsi_line, signal_line) |
williams_r(close, high=None, low=None, period=14)
Calculate Williams %R.
Williams %R is a momentum indicator that measures overbought and oversold levels.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
close
|
ndarray
|
Close prices |
required |
high
|
ndarray
|
High prices. If None, assumes close contains close prices and high=low=close |
None
|
low
|
ndarray
|
Low prices. If None, assumes close contains close prices and high=low=close |
None
|
period
|
int
|
Number of periods for calculation |
14
|
Returns:
| Type | Description |
|---|---|
ndarray
|
Williams %R values (-100 to 0) |