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📈 Trend Indicators API Reference

Module: trend_indicators

The trend_indicators module provides functions to analyze and quantify price trends in time series data.

Trend indicators are used to determine the direction, strength, and potential reversals of market trends.

📚 When to Use

Use trend indicators to: - Identify the presence and direction of a trend (uptrend, downtrend, or sideways) - Spot trend reversals or trend exhaustion - Confirm other technical signals

🏗️ 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

aroon_up

aroon_up(
    highs: List[float],
    period: int
) -> List[float]

Arguments

  • highs: List of highs
  • period: Period over which to calculate the Aroon up

Returns

List of Aroon Up values

Example

from pytechnicalindicators import trend_indicators as ti

highs = [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]
aroon_up = ti.bulk.aroon_up(highs, period=5)
print(f"Bulk Aroon Up: {aroon_up}")

Output:

Bulk Aroon Up: [100.0, 100.0, 75.0, 100.0, 100.0, 75.0, 50.0, 100.0, 100.0, 75.0, 50.0]

aroon_down

aroon_down(
    lows: List[float],
    period: int
) -> List[float]

Arguments

  • lows: List of lows
  • period: Period over which to calculate the Aroon down

Returns

List of Aroon Down values

Example

from pytechnicalindicators import trend_indicators as ti

lows = [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]
aroon_down = ti.bulk.aroon_down(lows, period=5)
print(f"Bulk Aroon Down: {aroon_down}")

Output:

Bulk Aroon Down: [0.0, 0.0, 25.0, 0.0, 0.0, 25.0, 0.0, 50.0, 25.0, 0.0, 0.0]

aroon_oscillator

aroon_oscillator(
    aroon_up: List[float],
    aroon_down: List[float]
) -> List[float]

Arguments

  • aroon_up: List of Aroon Up values
  • aroon_down: List of Aroon Down values

Returns

List of Aroon Oscillator values

Example

from pytechnicalindicators import trend_indicators as ti

aroon_up = [100.0, 80.0, 60.0, 40.0, 20.0]
aroon_down = [20.0, 40.0, 60.0, 80.0, 100.0]
osc = ti.bulk.aroon_oscillator(aroon_up, aroon_down)
print(f"Bulk Aroon Oscillator: {osc}")

Output:

Bulk Aroon Oscillator: [80.0, 40.0, 0.0, -40.0, -80.0]

aroon_indicator

aroon_indicator(
    highs: List[float],
    lows: List[float],
    period: int
) -> List[Tuple[float, float, float]]

Arguments

  • highs: List of highs
  • lows: List of lows
  • period: Period over which to calculate the Aroon indicator

Returns

List of Aroon indicator tuples (Aroon Up, Aroon Down, Aroon Oscillator)

Example

from pytechnicalindicators import trend_indicators as ti

highs = [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]
lows = [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]
aroon = ti.bulk.aroon_indicator(highs, lows, period=5)
print(f"Bulk Aroon Indicator: {aroon}")

Output:

Bulk Aroon Indicator: [
    (100.0, 0.0, 100.0), (100.0, 0.0, 100.0), (75.0, 25.0, 50.0), (100.0, 0.0, 100.0), 
    (100.0, 0.0, 100.0), (75.0, 25.0, 50.0), (50.0, 0.0, 50.0), (100.0, 50.0, 50.0), 
    (100.0, 25.0, 75.0), (75.0, 0.0, 75.0), (50.0, 0.0, 50.0)
]

parabolic_time_price_system

parabolic_time_price_system(
    highs: List[float],
    lows: List[float],
    af_start: float,
    af_step: float,
    af_max: float,
    position: str,
    previous_sar: float
) -> List[float]

Arguments

  • highs: List of highs
  • lows: List of lows
  • af_start: Initial acceleration factor
  • af_step: Acceleration factor increment (default 0.02)
  • af_max: Maximum acceleration factor (default 0.2)
  • position: "long" or "short"
  • previous_sar: Previous SAR value (use 0.0 if none)

Returns

List of SAR values

Example

from pytechnicalindicators import trend_indicators as ti

highs = [105.0, 107.0, 110.0, 108.0, 111.0, 113.0, 112.0, 115.0, 117.0, 114.0]
lows = [95.0, 97.0, 100.0, 98.0, 101.0, 103.0, 102.0, 105.0, 107.0, 104.0]

sar = ti.bulk.parabolic_time_price_system(
    highs, lows, af_start=0.02, af_step=0.02, af_max=0.2, position="long", previous_sar=0.0
)
print(f"Bulk Parabolic Time Price System: {sar}")

Output:

Bulk Parabolic Time Price System: [
    95.0, 95.0, 95.3, 95.594, 95.90212, 96.2440776, 96.579196048, 96.94761212704, 97.3486598844992, 97.74168668680922
]

directional_movement_system

directional_movement_system(
    highs: List[float],
    lows: List[float],
    close: List[float],
    period: int,
    constant_model_type: str
) -> List[Tuple[float, float, float, float]]

Arguments

  • highs: List of highs
  • lows: List of lows
  • close: List of close prices
  • period: Period for calculation
  • constant_model_type: Choice of:
    • "simple_moving_average"
    • "smoothed_moving_average"
    • "exponential_moving_average"
    • "simple_moving_median"
    • "simple_moving_mode"

Returns

List of Directional Movement System tuples (+DI, -DI, ADX, ADXR)

Example

from pytechnicalindicators import trend_indicators as ti

highs = [105.0, 107.0, 110.0, 108.0, 111.0, 113.0, 112.0, 115.0, 117.0, 114.0]
lows = [95.0, 97.0, 100.0, 98.0, 101.0, 103.0, 102.0, 105.0, 107.0, 104.0]
close = [100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0, 112.0, 109.0]

dms = ti.bulk.directional_movement_system(
    highs, lows, close, period=3, constant_model_type="simple_moving_average"
)
print(f"Bulk Directional Movement System: {dms}")

Output:

Bulk Directional Movement System: [
    (16.666666666666664, 3.3333333333333335, 58.73015873015871, 51.984126984126966), 
    (16.666666666666664, 3.3333333333333335, 66.66666666666666, 59.92063492063491), 
    (16.666666666666664, 10.0, 52.77777777777777, 55.75396825396824)
]

volume_price_trend

volume_price_trend(
    prices: List[float],
    volumes: List[float],
    previous_vpt: float
) -> List[float]

Arguments

  • prices: List of prices
  • volumes: List of volumes
  • previous_vpt: Previous VPT value (use 0.0 if none)

Returns

List of VPT values

Example

from pytechnicalindicators import trend_indicators as ti

prices = [100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0, 112.0, 109.0]
volumes = [1000, 1200, 1500, 1100, 1300, 1400, 1250, 1600, 1800]

vpt = ti.bulk.volume_price_trend(
    prices, volumes, previous_vpt=0.0
)
print(f"Bulk Volume Price Trend: {vpt}")

Output:

Bulk Volume Price Trend: [
    20.0, 55.294117647058826, 26.722689075630253, 58.761524027086566, 83.28982591387901, 
    70.32686295091605, 105.37359192287866, 134.46450101378775, 86.25021529950205
]

true_strength_index

true_strength_index(
    prices: List[float],
    first_constant_model: str,
    first_period: int,
    second_constant_model: str,
    second_period: int
) -> List[float]

Arguments

  • prices: List of prices
  • first_constant_model: Choice of:
    • "simple_moving_average"
    • "smoothed_moving_average"
    • "exponential_moving_average"
    • "simple_moving_median"
    • "simple_moving_mode"
  • first_period: Period for first smoothing
  • second_constant_model: Choice of:
    • "simple_moving_average"
    • "smoothed_moving_average"
    • "exponential_moving_average"
    • "simple_moving_median"
    • "simple_moving_mode"
  • second_period: Period for second smoothing

Returns

List of TSI values

Example

from pytechnicalindicators import trend_indicators as ti

prices = [100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0, 112.0, 109.0]

tsi = ti.bulk.true_strength_index(
    prices,
    first_constant_model="exponential_moving_average",
    first_period=3,
    second_constant_model="exponential_moving_average",
    second_period=5
)
print(f"Bulk True Strength Index: {tsi}")

Output:

Bulk True Strength Index: [0.5758338577721839, 0.7186215618084096, 0.2882438316400581]

🟢 Single Functions

aroon_up

aroon_up(
    highs: List[float]
) -> float

Arguments

  • highs: List of highs

Returns

Aroon Up value

Example

from pytechnicalindicators import trend_indicators as ti

highs = [105.0, 107.0, 110.0, 108.0, 111.0]
aroon_up = ti.single.aroon_up(highs)
print(f"Single Aroon Up: {aroon_up}")

Output:

Single Aroon Up: 100.0

aroon_down

aroon_down(
    lows: List[float]
) -> float

Arguments

  • lows: List of lows

Returns

Aroon Down value

Example

from pytechnicalindicators import trend_indicators as ti

lows = [95.0, 97.0, 100.0, 98.0, 101.0]
aroon_down = ti.single.aroon_down(lows)
print(f"Single Aroon Down: {aroon_down}")

Output:

Single Aroon Down: 0.0

aroon_oscillator

aroon_oscillator(
    aroon_up: float,
    aroon_down: float
) -> float

Arguments

  • aroon_up: Aroon Up value
  • aroon_down: Aroon Down value

Returns

Aroon Oscillator value

Example

from pytechnicalindicators import trend_indicators as ti

osc = ti.single.aroon_oscillator(80.0, 20.0)
print(f"Single Aroon Oscillator: {osc}")

Output:

Single Aroon Oscillator: 60.0

aroon_indicator

aroon_indicator(
    highs: List[float],
    lows: List[float]
) -> Tuple[float, float, float]

Arguments

  • highs: List of highs
  • lows: List of lows

Returns

Aroon indicator tuple (Aroon Up, Aroon Down, Aroon Oscillator)

Example

from pytechnicalindicators import trend_indicators as ti

highs = [105.0, 107.0, 110.0, 108.0, 111.0]
lows = [95.0, 97.0, 100.0, 98.0, 101.0]
aroon = ti.single.aroon_indicator(highs, lows)
print(f"Single Aroon Indicator: {aroon}")

Output:

Single Aroon Indicator: (100.0, 0.0, 100.0)

long_parabolic_time_price_system

long_parabolic_time_price_system(
    previous_sar: float,
    extreme_point: float,
    af: float,
    low: float
) -> float

Arguments

  • previous_sar: Previous SAR value (if none use period low)
  • extreme_point: Highest high for the period
  • af: Acceleration factor (default 0.02)
  • low: Lowest low for t or t-1

Returns

SAR value

Example

from pytechnicalindicators import trend_indicators as ti

sar = ti.single.long_parabolic_time_price_system(
    previous_sar=98.0, extreme_point=111.0, af=0.02, low=95.0
)
print(f"Single Long Parabolic SAR: {sar}")

Output:

Single Long Parabolic SAR: 95.0

short_parabolic_time_price_system

short_parabolic_time_price_system(
    previous_sar: float,
    extreme_point: float,
    af: float,
    high: float
) -> float

Arguments

  • previous_sar: Previous SAR value (if none use period high)
  • extreme_point: Lowest low for the period
  • af: Acceleration factor (default 0.02)
  • high: Highest high for t or t-1

Returns

SAR value

Example

from pytechnicalindicators import trend_indicators as ti

sar = ti.single.short_parabolic_time_price_system(
    previous_sar=112.0, extreme_point=95.0, af=0.02, high=119.0
)
print(f"Single Short Parabolic SAR: {sar}")

Output:

Single Short Parabolic SAR: 119.0

volume_price_trend

volume_price_trend(
    current_price: float,
    previous_price: float,
    current_volume: float,
    previous_vpt: float
) -> float

Arguments

  • current_price: Current price
  • previous_price: Previous price
  • current_volume: Current volume
  • previous_vpt: Previous VPT value (use 0.0 if none)

Returns

VPT value

Example

from pytechnicalindicators import trend_indicators as ti

vpt = ti.single.volume_price_trend(
    current_price=114.0, previous_price=112.0, current_volume=1500.0, previous_vpt=0.0
)
print(f"Single Volume Price Trend: {vpt}")

Output:

Single Volume Price Trend: 26.785714285714285

true_strength_index

true_strength_index(
    prices: List[float],
    first_period: int,
    first_constant_model: str,
    second_constant_model: str
) -> float

Arguments

  • prices: List of prices
  • first_period: Period for first smoothing
  • first_constant_model: Choice of:
    • "simple_moving_average"
    • "smoothed_moving_average"
    • "exponential_moving_average"
    • "simple_moving_median"
    • "simple_moving_mode"
  • second_constant_model: Choice of:
    • "simple_moving_average"
    • "smoothed_moving_average"
    • "exponential_moving_average"
    • "simple_moving_median"
    • "simple_moving_mode"

Returns

TSI value

Example

from pytechnicalindicators import trend_indicators as ti

prices = [100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0, 112.0, 109.0]
tsi = ti.single.true_strength_index(
    prices,
    first_period=5,
    first_constant_model="exponential_moving_average",
    second_constant_model="exponential_moving_average"
)
print(f"Single True Strength Index: {tsi}")

Output:

Single True Strength Index: 0.38660105755017404

🧩 Model Type Choices

constant_model_type / first_constant_model / second_constant_model:
- "simple_moving_average" - "smoothed_moving_average" - "exponential_moving_average" - "simple_moving_median" - "simple_moving_mode"

📝 Notes

  • All input lists must be of type List[float] (Python list of floats)
  • Bulk functions require a period argument for window size where applicable
  • Use 0.0 for previous values if no prior value exists (e.g., SAR, VPT)
  • Parabolic SAR and DMS support both "long" and "short" positions where specified

🔗 See Also