⚡ Momentum Indicators API Reference
Module: momentum_indicators
The momentum_indicators module provides functions to measure the speed, strength, and direction of price movements in time series data.
These indicators are commonly used to identify overbought/oversold conditions, trend continuation, or potential reversals.
📚 When to Use
Use momentum indicators to: - Gauge the strength and velocity of price trends - Identify bullish or bearish momentum - Spot early signals for possible price reversals or continuations
🏗️ Structure
- single: Functions that return a single value for a slice of prices.
- bulk: Functions that compute values of a slice of prices over a period and return a vector.
🚀 Bulk Functions
relative_strength_index
relative_strength_index(
prices: List[float],
constant_model_type: str,
period: int
) -> List[float]
Arguments
- prices: List of prices
- constant_model_type: Choice of:
"simple_moving_average""smoothed_moving_average""exponential_moving_average""simple_moving_median""simple_moving_mode"
- period: Period over which to calculate the RSI
Returns
List of Relative Strength Index values
Example
from pytechnicalindicators import momentum_indicators as mi
prices = [
100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0,
112.0, 109.0, 111.0, 113.0, 115.0, 112.0, 114.0
]
rsi = mi.bulk.relative_strength_index(
prices,
constant_model_type="simple_moving_average",
period=5
)
print(f"Bulk RSI: {rsi}")
Output:
Bulk RSI: [
57.14285714285714, 57.14285714285714, 62.50000000000001, 72.72727272727272,
70.0, 55.55555555555556, 43.75, 39.99999999999999, 39.99999999999999,
39.99999999999999, 39.99999999999999
]
stochastic_oscillator
stochastic_oscillator(
prices: List[float],
period: int
) -> List[float]
Arguments
- prices: List of prices
- period: Period over which to calculate the stochastic oscillator
Returns
List of Stochastic Oscillator values
Example
from pytechnicalindicators import momentum_indicators as mi
prices = [
100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0,
112.0, 109.0, 111.0, 113.0, 115.0, 112.0, 114.0
]
stoch = mi.bulk.stochastic_oscillator(
prices,
period=5
)
print(f"Bulk Stochastic Oscillator: {stoch}")
Output:
Bulk Stochastic Oscillator: [100.0, 100.0, 80.0, 100.0, 100.0, 40.0, 80.0, 100.0, 100.0, 50.0, 75.0]
slow_stochastic
slow_stochastic(
stochastics: List[float],
constant_model_type: str,
period: int
) -> List[float]
Arguments
- stochastics: List of stochastic values
- constant_model_type: Choice of:
"simple_moving_average""smoothed_moving_average""exponential_moving_average""simple_moving_median""simple_moving_mode"
- period: Period over which to calculate the slow stochastic
Returns
List of Slow Stochastic values
Example
from pytechnicalindicators import momentum_indicators as mi
stochastics = [
80.0, 75.0, 70.0, 72.0, 68.0, 65.0, 70.0, 73.0,
75.0, 78.0, 80.0, 77.0, 74.0, 76.0, 79.0
]
slow_stoch = mi.bulk.slow_stochastic(
stochastics,
constant_model_type="simple_moving_average",
period=5
)
print(f"Bulk Slow Stochastic: {slow_stoch}")
Output:
Bulk Slow Stochastic: [73.0, 70.0, 69.0, 69.6, 70.2, 72.2, 75.2, 76.6, 76.8, 77.0, 77.2]
slowest_stochastic
slowest_stochastic(
slow_stochastics: List[float],
constant_model_type: str,
period: int
) -> List[float]
Arguments
- slow_stochastics: List of slow stochastic values
- constant_model_type: Choice of:
"simple_moving_average""smoothed_moving_average""exponential_moving_average""simple_moving_median""simple_moving_mode"
- period: Period over which to calculate the slowest stochastic
Returns
List of Slowest Stochastic values
Example
from pytechnicalindicators import momentum_indicators as mi
slow_stochastics = [
73.0, 70.0, 69.0, 69.6, 70.2, 72.2, 75.2, 76.6, 76.8, 77.0, 77.2
]
slowest_stoch = mi.bulk.slowest_stochastic(
slow_stochastics,
constant_model_type="simple_moving_average",
period=5
)
print(f"Bulk Slowest Stochastic: {slowest_stoch}")
Output:
Bulk Slowest Stochastic: [70.36, 70.2, 71.24, 72.75999999999999, 74.20000000000002, 75.56, 76.56]
williams_percent_r
williams_percent_r(
high: List[float],
low: List[float],
close: List[float],
period: int
) -> List[float]
Arguments
- high: List of high prices
- low: List of low prices
- close: List of closing prices
- period: Period over which to calculate Williams %R
Returns
List of Williams %R values
Example
from pytechnicalindicators import momentum_indicators as mi
high_prices = [
105.0, 107.0, 110.0, 108.0, 111.0, 113.0, 112.0, 115.0,
117.0, 114.0, 116.0, 118.0, 120.0, 117.0, 119.0
]
low_prices = [
95.0, 97.0, 100.0, 98.0, 101.0, 103.0, 102.0, 105.0,
107.0, 104.0, 106.0, 108.0, 110.0, 107.0, 109.0
]
close_prices = [
100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0,
112.0, 109.0, 111.0, 113.0, 115.0, 112.0, 114.0
]
williams_r = mi.bulk.williams_percent_r(
high_prices,
low_prices,
close_prices,
period=5
)
print(f"Bulk Williams %R: {williams_r}")
Output:
Bulk Williams %R: [
-31.25, -31.25, -40.0, -29.411764705882355, -31.25, -53.333333333333336, -40.0,
-35.714285714285715, -31.25, -50.0, -42.857142857142854
]
money_flow_index
money_flow_index(
prices: List[float],
volume: List[float],
period: int
) -> List[float]
Arguments
- prices: List of prices
- volume: List of volumes
- period: Period over which to calculate the MFI
Returns
List of Money Flow Index values
Example
from pytechnicalindicators import momentum_indicators as mi
prices = [
100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0,
112.0, 109.0, 111.0, 113.0, 115.0, 112.0, 114.0
]
volume = [
1000, 1200, 1500, 1100, 1300, 1400, 1250, 1600,
1800, 1350, 1450, 1550, 1700, 1400, 1500
]
mfi = mi.bulk.money_flow_index(
prices,
volume,
period=5
)
print(f"Bulk Money Flow Index: {mfi}")
Output:
Bulk Money Flow Index: [
78.66290018832392, 79.76062879599857, 53.912881261076386, 77.66179540709813,
79.8128443136367, 57.34244495064541, 78.54017792037334, 78.51354311163028,
78.32044198895028, 77.22254503195816, 77.55028992769704
]
rate_of_change
rate_of_change(
prices: List[float]
) -> List[float]
Arguments
- prices: List of prices
Returns
List of Rate of Change values
Example
from pytechnicalindicators import momentum_indicators as mi
prices = [
100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0,
112.0, 109.0, 111.0, 113.0, 115.0, 112.0, 114.0
]
roc = mi.bulk.rate_of_change(prices)
print(f"Bulk Rate of Change: {roc}")
Output:
Bulk Rate of Change: [
2.0, 2.941176470588235, -1.9047619047619049, 2.912621359223301, 1.8867924528301887,
-0.9259259259259258, 2.803738317757009, 1.8181818181818181, -2.6785714285714284,
1.834862385321101, 1.8018018018018018, 1.7699115044247788, -2.608695652173913,
1.7857142857142856
]
on_balance_volume
on_balance_volume(
prices: List[float],
volume: List[float],
previous_on_balance_volume: float
) -> List[float]
Arguments
- prices: List of prices
- volume: List of volumes
- previous_on_balance_volume: Previous OBV value (use 0.0 if none)
Returns
List of On Balance Volume values
Example
from pytechnicalindicators import momentum_indicators as mi
prices = [
100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0,
112.0, 109.0, 111.0, 113.0, 115.0, 112.0, 114.0
]
volume = [
1000, 1200, 1500, 1100, 1300, 1400, 1250, 1600,
1800, 1350, 1450, 1550, 1700, 1400, 1500
]
obv = mi.bulk.on_balance_volume(
prices,
volume,
previous_on_balance_volume=0.0
)
print(f"Bulk On Balance Volume: {obv}")
Output:
Bulk On Balance Volume: [
1200.0, 2700.0, 1600.0, 2900.0, 4300.0, 3050.0, 4650.0, 6450.0,
5100.0, 6550.0, 8100.0, 9800.0, 8400.0, 9900.0
]
commodity_channel_index
commodity_channel_index(
prices: List[float],
constant_model_type: str,
deviation_model: str,
constant_multiplier: float,
period: int
) -> List[float]
Arguments
- prices: List of prices
- constant_model_type: Choice of:
"simple_moving_average""smoothed_moving_average""exponential_moving_average""simple_moving_median""simple_moving_mode"
- deviation_model: Choice of:
"standard_deviation""mean_absolute_deviation""median_absolute_deviation""mode_absolute_deviation""ulcer_index"
- constant_multiplier: Scale factor (normally 0.015)
- period: Period over which to calculate the CCI
Returns
List of Commodity Channel Index values
Example
from pytechnicalindicators import momentum_indicators as mi
prices = [
100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0,
112.0, 109.0, 111.0, 113.0, 115.0, 112.0, 114.0
]
cci = mi.bulk.commodity_channel_index(
prices,
constant_model_type="simple_moving_average",
deviation_model="standard_deviation",
constant_multiplier=0.015,
period=5
)
print(f"Bulk Commodity Channel Index: {cci}")
Output:
Bulk Commodity Channel Index: [
87.4146757476247, 99.90248656871412, 46.49905549752783, 92.14596539534516,
105.22735830032957, -7.749842582921396, 46.49905549752783, 94.28090415820633,
100.0, 0.0, 47.14045207910316
]
mcginley_dynamic_commodity_channel_index
mcginley_dynamic_commodity_channel_index(
prices: List[float],
previous_mcginley_dynamic: float,
deviation_model: str,
constant_multiplier: float,
period: int
) -> List[Tuple[float, float]]
Arguments
- prices: List of prices
- previous_mcginley_dynamic: Previous McGinley dynamic (0.0 if none)
- deviation_model: Choice of:
"standard_deviation""mean_absolute_deviation""median_absolute_deviation""mode_absolute_deviation""ulcer_index"
- constant_multiplier: Scale factor (normally 0.015)
- period: Period over which to calculate the CCI
Returns
List of tuples with (Commodity Channel Index, McGinley Dynamic)
Example
from pytechnicalindicators import momentum_indicators as mi
prices = [
100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0,
112.0, 109.0, 111.0, 113.0, 115.0, 112.0, 114.0
]
md_cci = mi.bulk.mcginley_dynamic_commodity_channel_index(
prices,
previous_mcginley_dynamic=0.0,
deviation_model="standard_deviation",
constant_multiplier=0.015,
period=5
)
print(f"Bulk McGinley Dynamic CCI: {md_cci}")
Output:
Bulk McGinley Dynamic CCI: [
(0.0, 106.0), (50.850886978780416, 106.37118330162708), (19.606471661011987, 106.49401626029876),
(83.21929522843516, 107.11000103381146), (126.02325345778948, 107.92806406359112),
(33.551336476526465, 108.13414147145478), (91.04621305102224, 108.65037258300794),
(169.99349322038103, 109.39389344558838), (156.2681314408503, 110.31195605677449),
(45.67777102116869, 110.62966686936494), (130.69756142487142, 111.22748604095784)
]
macd_line
macd_line(
prices: List[float],
short_period: int,
short_period_model: str,
long_period: int,
long_period_model: str
) -> List[float]
Arguments
- prices: List of prices
- short_period: Length of the short period
- short_period_model: Choice of:
"simple_moving_average""smoothed_moving_average""exponential_moving_average""simple_moving_median""simple_moving_mode"
- long_period: Length of the long period
- long_period_model: Choice of:
"simple_moving_average""smoothed_moving_average""exponential_moving_average""simple_moving_median""simple_moving_mode"
Returns
List of MACD line values
Example
from pytechnicalindicators import momentum_indicators as mi
prices = [
100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0,
112.0, 109.0, 111.0, 113.0, 115.0, 112.0, 114.0
]
macd = mi.bulk.macd_line(
prices,
short_period=5,
short_period_model="exponential_moving_average",
long_period=10,
long_period_model="exponential_moving_average"
)
print(f"Bulk MACD Line: {macd}")
Output:
Bulk MACD Line: [
1.724347544444484, 1.4586781725636513, 1.6040869889352223,
1.8185082303110107, 1.0980111584955239, 1.1377855745084275
]
signal_line
signal_line(
macds: List[float],
constant_model_type: str,
period: int
) -> List[float]
Arguments
- macds: List of MACD values
- constant_model_type: Choice of:
"simple_moving_average""smoothed_moving_average""exponential_moving_average""simple_moving_median""simple_moving_mode"
- period: Period over which to calculate the signal line
Returns
List of Signal line values
Example
from pytechnicalindicators import momentum_indicators as mi
macds = [
-0.5, -0.3, 0.1, 0.4, 0.2, 0.6, 0.8, 0.5,
0.3, 0.7, 0.9, 1.1, 0.8, 0.6, 1.0
]
signal = mi.bulk.signal_line(
macds,
constant_model_type="exponential_moving_average",
period=9
)
print(f"Bulk Signal Line: {signal}")
Output:
Bulk Signal Line: [
0.37228892577740375, 0.4750370938526215, 0.597235628312796, 0.728793463675819,
0.755436755350888, 0.7367513886909434, 0.8018030953629876
]
mcginley_dynamic_macd_line
mcginley_dynamic_macd_line(
prices: List[float],
short_period: int,
previous_short_mcginley: float,
long_period: int,
previous_long_mcginley: float
) -> List[Tuple[float, float, float]]
Arguments
- prices: List of prices
- short_period: Length of the short period
- previous_short_mcginley: Previous short McGinley dynamic (0.0 if none)
- long_period: Length of the long period
- previous_long_mcginley: Previous long McGinley dynamic (0.0 if none)
Returns
List of tuples with (MACD, short McGinley dynamic, long McGinley dynamic)
Example
from pytechnicalindicators import momentum_indicators as mi
prices = [
100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0,
112.0, 109.0, 111.0, 113.0, 115.0, 112.0, 114.0
]
md_macd = mi.bulk.mcginley_dynamic_macd_line(
prices,
short_period=5,
previous_short_mcginley=0.0,
long_period=10,
previous_long_mcginley=0.0
)
print(f"Bulk McGinley Dynamic MACD: {md_macd}")
Output:
Bulk McGinley Dynamic MACD: [
(0.0, 109.0, 109.0), (0.18597050580808627, 109.37194101161619, 109.1859705058081),
(0.4903282630853596, 110.00875741201432, 109.51842914892896),
(0.8753505778212656, 110.84466130783767, 109.9693107300164),
(0.9082933415511008, 111.06634122265551, 110.15804788110441),
(1.1019577190823497, 111.5949690859446, 110.49301136686225)
]
chaikin_oscillator
chaikin_oscillator(
highs: List[float],
lows: List[float],
close: List[float],
volume: List[float],
short_period: int,
long_period: int,
previous_accumulation_distribution: float,
short_period_model: str,
long_period_model: str
) -> List[Tuple[float, float]]
Arguments
- highs: List of high prices
- lows: List of low prices
- close: List of closing prices
- volume: List of volumes
- short_period: Short period for AD calculation
- long_period: Long period for AD calculation
- previous_accumulation_distribution: Previous AD value (0.0 if none)
- short_period_model: Choice of:
"simple_moving_average""smoothed_moving_average""exponential_moving_average""simple_moving_median""simple_moving_mode"
- long_period_model: Choice of:
"simple_moving_average""smoothed_moving_average""exponential_moving_average""simple_moving_median""simple_moving_mode"
Returns
List of tuples with (Chaikin Oscillator, Accumulation Distribution)
Example
from pytechnicalindicators import momentum_indicators as mi
high_prices = [
105.0, 107.0, 110.0, 108.0, 111.0, 113.0, 112.0, 115.0,
117.0, 114.0, 116.0, 118.0, 120.0, 117.0, 119.0
]
low_prices = [
95.0, 97.0, 100.0, 98.0, 101.0, 103.0, 102.0, 105.0,
107.0, 104.0, 106.0, 108.0, 110.0, 107.0, 109.0
]
close_prices = [
100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0,
112.0, 109.0, 111.0, 113.0, 115.0, 112.0, 114.0
]
volume = [
1000, 1200, 1500, 1100, 1300, 1400, 1250, 1600,
1800, 1350, 1450, 1550, 1700, 1400, 1500
]
chaikin = mi.bulk.chaikin_oscillator(
high_prices,
low_prices,
close_prices,
volume,
short_period=5,
long_period=10,
previous_accumulation_distribution=0.0,
short_period_model="simple_moving_average",
long_period_model="simple_moving_average"
)
print(f"Bulk Chaikin Oscillator: {chaikin}")
Output:
Bulk Chaikin Oscillator: []
percentage_price_oscillator
percentage_price_oscillator(
prices: List[float],
short_period: int,
long_period: int,
constant_model_type: str
) -> List[float]
Arguments
- prices: List of prices
- short_period: Length of short period
- long_period: Length of long period
- constant_model_type: Choice of:
"simple_moving_average""smoothed_moving_average""exponential_moving_average""simple_moving_median""simple_moving_mode"
Returns
List of Percentage Price Oscillator values
Example
from pytechnicalindicators import momentum_indicators as mi
prices = [
100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0,
112.0, 109.0, 111.0, 113.0, 115.0, 112.0, 114.0
]
ppo = mi.bulk.percentage_price_oscillator(
prices,
short_period=5,
long_period=10,
constant_model_type="exponential_moving_average"
)
print(f"Bulk Percentage Price Oscillator: {ppo}")
Output:
Bulk Percentage Price Oscillator: [
1.5979390071606525, 1.340906021403326, 1.4601143417976372,
1.6371566789984267, 0.9847709201141644, 1.0142521932946695
]
chande_momentum_oscillator
chande_momentum_oscillator(
prices: List[float],
period: int
) -> List[float]
Arguments
- prices: List of prices
- period: Period over which to calculate the CMO
Returns
List of Chande Momentum Oscillator values
Example
from pytechnicalindicators import momentum_indicators as mi
prices = [
100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0,
112.0, 109.0, 111.0, 113.0, 115.0, 112.0, 114.0
]
cmo = mi.bulk.chande_momentum_oscillator(
prices,
period=10
)
print(f"Bulk Chande Momentum Oscillator: {cmo}")
Output:
Bulk Chande Momentum Oscillator: [42.857142857142854, 42.857142857142854, 40.0, 60.0, 30.0, 30.0]
🟢 Single Functions
relative_strength_index
relative_strength_index(
prices: List[float],
constant_model_type: str
) -> float
Arguments
- prices: List of prices
- constant_model_type: Choice of:
"simple_moving_average""smoothed_moving_average""exponential_moving_average""simple_moving_median""simple_moving_mode"
Returns
Relative Strength Index value
Example
from pytechnicalindicators import momentum_indicators as mi
prices = [
100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0,
112.0, 109.0, 111.0, 113.0, 115.0, 112.0, 114.0
]
rsi = mi.single.relative_strength_index(
prices,
constant_model_type="simple_moving_average"
)
print(f"Single RSI: {rsi}")
Output:
Single RSI: 50.54945054945055
stochastic_oscillator
stochastic_oscillator(
prices: List[float]
) -> float
Arguments
- prices: List of prices
Returns
Stochastic Oscillator value
Example
from pytechnicalindicators import momentum_indicators as mi
prices = [
100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0,
112.0, 109.0, 111.0, 113.0, 115.0, 112.0, 114.0
]
stoch = mi.single.stochastic_oscillator(prices)
print(f"Single Stochastic Oscillator: {stoch}")
Output:
Single Stochastic Oscillator: 93.33333333333333
slow_stochastic
slow_stochastic(
stochastics: List[float],
constant_model_type: str
) -> float
Arguments
- stochastics: List of stochastic values
- constant_model_type: Choice of:
"simple_moving_average""smoothed_moving_average""exponential_moving_average""simple_moving_median""simple_moving_mode"
Returns
Slow Stochastic value
Example
from pytechnicalindicators import momentum_indicators as mi
stochastics = [
80.0, 75.0, 70.0, 72.0, 68.0, 65.0, 70.0, 73.0,
75.0, 78.0, 80.0, 77.0, 74.0, 76.0, 79.0
]
slow_stoch = mi.single.slow_stochastic(
stochastics,
constant_model_type="simple_moving_average"
)
print(f"Single Slow Stochastic: {slow_stoch}")
Output:
Single Slow Stochastic: 74.13333333333334
slowest_stochastic
slowest_stochastic(
slow_stochastics: List[float],
constant_model_type: str
) -> float
Arguments
- slow_stochastics: List of slow stochastic values
- constant_model_type: Choice of:
"simple_moving_average""smoothed_moving_average""exponential_moving_average""simple_moving_median""simple_moving_mode"
Returns
Slowest Stochastic value
Example
from pytechnicalindicators import momentum_indicators as mi
slow_stochastics = [
75.0, 72.3, 71.7, 68.3, 67.7, 69.0, 72.7, 75.3,
77.7, 78.3, 76.3, 75.0, 76.3, 78.0
]
slowest_stoch = mi.single.slowest_stochastic(
slow_stochastics,
constant_model_type="simple_moving_average"
)
print(f"Single Slowest Stochastic: {slowest_stoch}")
Output:
Single Slowest Stochastic: 73.82857142857142
williams_percent_r
williams_percent_r(
high: List[float],
low: List[float],
close: float
) -> float
Arguments
- high: List of high prices
- low: List of low prices
- close: Closing price for the observed period
Returns
Williams %R value
Example
from pytechnicalindicators import momentum_indicators as mi
high_prices = [
105.0, 107.0, 110.0, 108.0, 111.0, 113.0, 112.0, 115.0,
117.0, 114.0, 116.0, 118.0, 120.0, 117.0, 119.0
]
low_prices = [
95.0, 97.0, 100.0, 98.0, 101.0, 103.0, 102.0, 105.0,
107.0, 104.0, 106.0, 108.0, 110.0, 107.0, 109.0
]
williams_r = mi.single.williams_percent_r(
high_prices,
low_prices,
close=114.0
)
print(f"Single Williams %R: {williams_r}")
Output:
Single Williams %R: -24.0
money_flow_index
money_flow_index(
prices: List[float],
volume: List[float]
) -> float
Arguments
- prices: List of prices
- volume: List of volumes
Returns
Money Flow Index value
Example
from pytechnicalindicators import momentum_indicators as mi
prices = [
100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0,
112.0, 109.0, 111.0, 113.0, 115.0, 112.0, 114.0
]
volume = [
1000, 1200, 1500, 1100, 1300, 1400, 1250, 1600,
1800, 1350, 1450, 1550, 1700, 1400, 1500
]
mfi = mi.single.money_flow_index(prices, volume)
print(f"Single Money Flow Index: {mfi}")
Output:
Single Money Flow Index: 74.95568383255306
rate_of_change
rate_of_change(
current_price: float,
previous_price: float
) -> float
Arguments
- current_price: Current price
- previous_price: Previous price
Returns
Rate of Change value
Example
from pytechnicalindicators import momentum_indicators as mi
roc = mi.single.rate_of_change(
current_price=114.0,
previous_price=100.0
)
print(f"Single Rate of Change: {roc}")
Output:
Single Rate of Change: 14.000000000000002
on_balance_volume
on_balance_volume(
current_price: float,
previous_price: float,
current_volume: float,
previous_on_balance_volume: float
) -> float
Arguments
- current_price: Current price
- previous_price: Previous price
- current_volume: Current volume
- previous_on_balance_volume: Previous OBV value (0.0 if none)
Returns
On Balance Volume value
Example
from pytechnicalindicators import momentum_indicators as mi
obv = mi.single.on_balance_volume(
current_price=114.0,
previous_price=112.0,
current_volume=1500.0,
previous_on_balance_volume=5000.0
)
print(f"Single On Balance Volume: {obv}")
Output:
Single On Balance Volume: 6500.0
commodity_channel_index
commodity_channel_index(
prices: List[float],
constant_model_type: str,
deviation_model: str,
constant_multiplier: float
) -> float
Arguments
- prices: List of prices
- constant_model_type: Choice of:
"simple_moving_average""smoothed_moving_average""exponential_moving_average""simple_moving_median""simple_moving_mode"
- deviation_model: Choice of:
"standard_deviation""mean_absolute_deviation""median_absolute_deviation""mode_absolute_deviation""ulcer_index"
- constant_multiplier: Scale factor (normally 0.015)
Returns
Commodity Channel Index value
Example
from pytechnicalindicators import momentum_indicators as mi
prices = [
100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0,
112.0, 109.0, 111.0, 113.0, 115.0, 112.0, 114.0
]
cci = mi.single.commodity_channel_index(
prices,
constant_model_type="simple_moving_average",
deviation_model="standard_deviation",
constant_multiplier=0.015
)
print(f"Single Commodity Channel Index: {cci}")
Output:
Single Commodity Channel Index: 83.64657763003788
mcginley_dynamic_commodity_channel_index
mcginley_dynamic_commodity_channel_index(
prices: List[float],
previous_mcginley_dynamic: float,
deviation_model: str,
constant_multiplier: float
) -> Tuple[float, float]
Arguments
- prices: List of prices
- previous_mcginley_dynamic: Previous McGinley dynamic (0.0 if none)
- deviation_model: Choice of:
"standard_deviation""mean_absolute_deviation""median_absolute_deviation""mode_absolute_deviation""ulcer_index"
- constant_multiplier: Scale factor (normally 0.015)
Returns
Tuple with (Commodity Channel Index, McGinley Dynamic)
Example
from pytechnicalindicators import momentum_indicators as mi
prices = [
100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0,
112.0, 109.0, 111.0, 113.0, 115.0, 112.0, 114.0
]
md_cci = mi.single.mcginley_dynamic_commodity_channel_index(
prices,
previous_mcginley_dynamic=0.0,
deviation_model="standard_deviation",
constant_multiplier=0.015
)
print(f"Single McGinley Dynamic CCI: {md_cci}")
Output:
Single McGinley Dynamic CCI: (0.0, 114.0)
macd_line
macd_line(
prices: List[float],
short_period: int,
short_period_model: str,
long_period_model: str
) -> float
Arguments
- prices: List of prices
- short_period: Length of the short period
- short_period_model: Choice of:
"simple_moving_average""smoothed_moving_average""exponential_moving_average""simple_moving_median""simple_moving_mode"
- long_period_model: Choice of:
"simple_moving_average""smoothed_moving_average""exponential_moving_average""simple_moving_median""simple_moving_mode"
Returns
MACD line value
Example
from pytechnicalindicators import momentum_indicators as mi
prices = [
100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0,
112.0, 109.0, 111.0, 113.0, 115.0, 112.0, 114.0
]
macd = mi.single.macd_line(
prices,
short_period=5,
short_period_model="exponential_moving_average",
long_period_model="exponential_moving_average"
)
print(f"Single MACD Line: {macd}")
Output:
Single MACD Line: 2.695637490797523
signal_line
signal_line(
macds: List[float],
constant_model_type: str
) -> float
Arguments
- macds: List of MACD values
- constant_model_type: Choice of:
"simple_moving_average""smoothed_moving_average""exponential_moving_average""simple_moving_median""simple_moving_mode"
Returns
Signal line value
Example
from pytechnicalindicators import momentum_indicators as mi
macds = [
-0.5, -0.3, 0.1, 0.4, 0.2, 0.6, 0.8, 0.5,
0.3, 0.7, 0.9, 1.1, 0.8, 0.6, 1.0
]
signal = mi.single.signal_line(
macds,
constant_model_type="exponential_moving_average"
)
print(f"Single Signal Line: {signal}")
Output:
Single Signal Line: 0.662108064232895
mcginley_dynamic_macd_line
mcginley_dynamic_macd_line(
prices: List[float],
short_period: int,
previous_short_mcginley: float,
previous_long_mcginley: float
) -> Tuple[float, float, float]
Arguments
- prices: List of prices
- short_period: Length of the short period
- previous_short_mcginley: Previous short McGinley dynamic (0.0 if none)
- previous_long_mcginley: Previous long McGinley dynamic (0.0 if none)
Returns
Tuple with (MACD, short McGinley dynamic, long McGinley dynamic)
Example
from pytechnicalindicators import momentum_indicators as mi
prices = [
100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0,
112.0, 109.0, 111.0, 113.0, 115.0, 112.0, 114.0
]
md_macd = mi.single.mcginley_dynamic_macd_line(
prices,
short_period=5,
previous_short_mcginley=0.0,
previous_long_mcginley=0.0
)
print(f"Single McGinley Dynamic MACD: {md_macd}")
Output:
Single McGinley Dynamic MACD: (0.0, 114.0, 114.0)
chaikin_oscillator
chaikin_oscillator(
highs: List[float],
lows: List[float],
close: List[float],
volume: List[float],
short_period: int,
previous_accumulation_distribution: float,
short_period_model: str,
long_period_model: str
) -> Tuple[float, float]
Arguments
- highs: List of high prices
- lows: List of low prices
- close: List of closing prices
- volume: List of volumes
- short_period: Short period for AD calculation
- previous_accumulation_distribution: Previous AD value (0.0 if none)
- short_period_model: Choice of:
"simple_moving_average""smoothed_moving_average""exponential_moving_average""simple_moving_median""simple_moving_mode"
- long_period_model: Choice of:
"simple_moving_average""smoothed_moving_average""exponential_moving_average""simple_moving_median""simple_moving_mode"
Returns
Tuple with (Chaikin Oscillator, Accumulation Distribution)
Example
from pytechnicalindicators import momentum_indicators as mi
high_prices = [
105.0, 107.0, 110.0, 108.0, 111.0, 113.0, 112.0, 115.0,
117.0, 114.0, 116.0, 118.0, 120.0, 117.0, 119.0
]
low_prices = [
95.0, 97.0, 100.0, 98.0, 101.0, 103.0, 102.0, 105.0,
107.0, 104.0, 106.0, 108.0, 110.0, 107.0, 109.0
]
close_prices = [
100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0,
112.0, 109.0, 111.0, 113.0, 115.0, 112.0, 114.0
]
volume = [
1000, 1200, 1500, 1100, 1300, 1400, 1250, 1600,
1800, 1350, 1450, 1550, 1700, 1400, 1500
]
chaikin = mi.single.chaikin_oscillator(
high_prices,
low_prices,
close_prices,
volume,
short_period=5,
previous_accumulation_distribution=0.0,
short_period_model="exponential_moving_average",
long_period_model="exponential_moving_average"
)
print(f"Single Chaikin Oscillator: {chaikin}")
Output:
Single Chaikin Oscillator: []
percentage_price_oscillator
percentage_price_oscillator(
prices: List[float],
short_period: int,
constant_model_type: str
) -> float
Arguments
- prices: List of prices
- short_period: Length of short period
- constant_model_type: Choice of:
"simple_moving_average""smoothed_moving_average""exponential_moving_average""simple_moving_median""simple_moving_mode"
Returns
Percentage Price Oscillator value
Example
from pytechnicalindicators import momentum_indicators as mi
prices = [
100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0,
112.0, 109.0, 111.0, 113.0, 115.0, 112.0, 114.0
]
ppo = mi.single.percentage_price_oscillator(
prices,
short_period=5,
constant_model_type="exponential_moving_average"
)
print(f"Single Percentage Price Oscillator: {ppo}")
Output:
Single Percentage Price Oscillator: 2.436802783365856
chande_momentum_oscillator
chande_momentum_oscillator(
prices: List[float]
) -> float
Arguments
- prices: List of prices
Returns
Chande Momentum Oscillator value
Example
from pytechnicalindicators import momentum_indicators as mi
prices = [
100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0,
112.0, 109.0, 111.0, 113.0, 115.0, 112.0, 114.0
]
cmo = mi.single.chande_momentum_oscillator(prices)
print(f"Single Chande Momentum Oscillator: {cmo}")
Output:
Single Chande Momentum Oscillator: 43.75
🧩 Model Type Choices
constant_model_type:
- "simple_moving_average"
- "smoothed_moving_average"
- "exponential_moving_average"
- "simple_moving_median"
- "simple_moving_mode"
deviation_model:
- "standard_deviation"
- "mean_absolute_deviation"
- "median_absolute_deviation"
- "mode_absolute_deviation"
- "ulcer_index"
📝 Notes
- All input lists must be of type
List[float](Python list of floats) - Bulk functions require a
periodargument for window size - Some functions require previous values for continuity
- Volume-based indicators require both price and volume data