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🕯️ Candle Indicators API Reference

Module: candle_indicators

Candle indicators are technical indicators designed for use with candlestick price charts.
They help identify trends, volatility, and price action patterns commonly used in trading and analysis.

📚 When to Use

  • Analyze support/resistance, volatility bands, and price channels on candle charts.
  • Suitable for both traditional and crypto assets.

🏗️ 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

Bulk functions operate over a moving window and return a list of values for the entire data series.

moving_constant_envelopes

moving_constant_envelopes(
    prices: List[float],
    constant_model_type: str,
    difference: float,
    period: int
) -> List[Tuple[float, 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"
  • difference: Percent band width (e.g., 3.0 for ±3%)
  • period: Period over which to calculate the moving constant envelopes

Returns

List of tuples (lower envelope, constant model result, upper envelope)

Example

from pytechnicalindicators import candle_indicators as ci

prices = [
    100.0, 102.0, 103.0, 102.5, 102.8, 103.1, 103.8, 103.9, 
    104.4, 103.6, 103.1, 102.9, 103.3, 103.7
]

mces = ci.bulk.moving_constant_envelopes(
    prices,
    constant_model_type="simple_moving_average",
    difference=2.0,
    period=5
)

print(f"Bulk Moving Constant Envelopes: {mces}")

Output:

Bulk Moving Constant Envelopes: [
    (100.0188, 102.06, 104.1012), (100.62639999999999, 102.67999999999999, 104.7336), 
    (100.97919999999999, 103.03999999999999, 105.10079999999999), 
    (101.15559999999999, 103.22, 105.2844), (101.52799999999999, 103.6, 105.672), 
    (101.6848, 103.75999999999999, 105.83519999999999), 
    (101.68480000000002, 103.76000000000002, 105.83520000000001), (101.5084, 103.58, 105.6516), 
    (101.3908, 103.46, 105.52919999999999), (101.2536, 103.32000000000001, 105.38640000000001)
]

mcginley_dynamic_envelopes

mcginley_dynamic_envelopes(
    prices: List[float],
    difference: float,
    previous_mcginley_dynamic: float,
    period: int
) -> List[Tuple[float, float, float]]

Arguments

  • prices: List of prices
  • difference: Percent band width (e.g., 3.0 for ±3%)
  • previous_mcginley_dynamic: Previous McGinley dynamic (use 0.0 if none)
  • period: Period over which to calculate the McGinley dynamic envelopes

Returns

List of McGinley dynamic envelopes tuple (lower envelope, McGinley dynamic, upper envelope)

Example

from pytechnicalindicators import candle_indicators as ci

prices = [
    100.0, 102.0, 103.0, 102.5, 102.8, 103.1, 103.8, 103.9,
    104.4, 103.6, 103.1, 102.9, 103.3, 103.7
]

mdes = ci.bulk.mcginley_dynamic_envelopes(
    prices,
    difference=2.0,
    previous_mcginley_dynamic=0.0,
    period=5
)

print(f"Bulk McGinley Dynamic Envelopes: {mdes}")

Output

Bulk McGinley Dynamic Envelopes: [
    (100.744, 102.8, 104.856), (100.80211859724395, 102.85930469106525, 104.91649078488655), 
    (100.9799014916942, 103.04071580785123, 105.10153012400825), 
    (101.14281840437332, 103.20695755548297, 105.27109670659263), 
    (101.36614779143355, 103.43484468513627, 105.50354157883899), 
    (101.3983123111345, 103.46766562360663, 105.53701893607877), 
    (101.32521640748107, 103.39307796681742, 105.46093952615377), 
    (101.22670738127877, 103.29255855232527, 105.35840972337178), 
    (101.22816548479628, 103.29404641305743, 105.35992734131858), 
    (101.3064937650942, 103.37397322968796, 105.44145269428172)
]

moving_constant_bands

moving_constant_bands(
    prices: List[float],
    constant_model_type: str,
    deviation_model: str,
    deviation_multiplier: float,
    period: int
) -> List[Tuple[float, 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"
  • deviation_multiplier: Price deviation multiplier
  • period: Period over which to calculate the moving constant bands

Returns

List of Moving constant bands tuple (lower band, constant model result, upper band)

Example

from pytechnicalindicators import candle_indicators as ci

prices = [
    100.0, 102.0, 103.0, 102.5, 102.8, 103.1, 103.8, 103.9,
    104.4, 103.6, 103.1, 102.9, 103.3, 103.7
]

mcbs = ci.bulk.moving_constant_bands(
    prices,
    constant_model_type="simple_moving_average",
    deviation_model="standard_deviation",
    deviation_multiplier=2.0,
    period=5
)

print(f"Bulk Moving Constant Bands: {mcbs}")

Output

Bulk Moving Constant Bands: [
    (99.89260525053695, 102.06, 104.22739474946306), 
    (101.88602267034882, 102.67999999999999, 103.47397732965116), 
    (102.17651867420308, 103.03999999999999, 103.9034813257969), 
    (102.12163758258032, 103.22, 104.31836241741968), 
    (102.44761117672896, 103.6, 104.75238882327103), 
    (102.91525151672226, 103.75999999999999, 104.60474848327772), 
    (102.9152515167223, 103.76000000000002, 104.60474848327775), 
    (102.49630262526847, 103.58, 104.66369737473153), 
    (102.41233593170328, 103.46, 104.5076640682967), 
    (102.72133481811618, 103.32000000000001, 103.91866518188384)
]

mcginley_dynamic_bands

mcginley_dynamic_bands(
    prices: List[float],
    deviation_model: str,
    deviation_multiplier: float,
    previous_mcginley_dynamic: float,
    period: int
) -> List[Tuple[float, float, float]]

Arguments

  • prices: List of prices
  • deviation_model: Choice of:
    • "standard_deviation"
    • "mean_absolute_deviation"
    • "median_absolute_deviation"
    • "mode_absolute_deviation"
    • "ulcer_index"
  • deviation_multiplier: Price deviation multiplier
  • previous_mcginley_dynamic: Previous McGinley dynamic (0.0 if none)
  • period: Period over which to calculate the moving constant bands

Returns

List of McGinley dynamic bands tuple (lower band, McGinley dynamic, upper band)

Example

from pytechnicalindicators import candle_indicators as ci

prices = [
    100.0, 102.0, 103.0, 102.5, 102.8, 103.1, 103.8, 103.9,
    104.4, 103.6, 103.1, 102.9, 103.3, 103.7
]

mdbs = ci.bulk.mcginley_dynamic_bands(
    prices,
    deviation_model="standard_deviation",
    deviation_multiplier=2.0,
    previous_mcginley_dynamic=0.0,
    period=5
)

print(f"Bulk McGinley Dynamic Bands: {mdbs}")

Output

Bulk McGinley Dynamic Bands: [
    (100.63260525053694, 102.8, 104.96739474946305), 
    (102.06532736141408, 102.85930469106525, 103.65328202071642), 
    (102.17723448205432, 103.04071580785123, 103.90419713364814), 
    (102.1085951380633, 103.20695755548297, 104.30531997290265), 
    (102.28245586186523, 103.43484468513627, 104.58723350840731), 
    (102.62291714032891, 103.46766562360663, 104.31241410688436), 
    (102.54832948353969, 103.39307796681742, 104.23782645009514), 
    (102.20886117759375, 103.29255855232527, 104.3762559270568), 
    (102.24638234476072, 103.29404641305743, 104.34171048135414), 
    (102.77530804780413, 103.37397322968796, 103.9726384115718)
]

ichimoku_cloud

ichimoku_cloud(
    highs: List[float],
    lows: List[float],
    close: List[float],
    conversion_period: int,
    base_period: int,
    span_b_period: int
) -> List[Tuple[float, float, float, float, float]]

Arguments

  • highs: List of price highs
  • lows: List of price lows
  • close: List of closing prices
  • conversion_period: Period used to calculate the conversion line
  • base_period: Period used to calculate the base line
  • span_b_period: Period used to calculate the Span B line

Returns

A list of tuples, each tuple contains (leading span a, leading span b, base line, conversion line, relevant close price)

Example

from pytechnicalindicators import candle_indicators as ci

high_prices = [
    105.0, 103.0, 107.0, 101.0, 103.0, 100.0, 109.0, 105.0,
    110.0, 112.0, 111.0, 105.0, 106.0, 100.0, 103.0, 102.0, 98.0
]
low_prices = [
    97.0, 99.0, 98.0, 100.0, 95.0, 98.0, 99.0, 100.0, 102.0, 106.0,
    99.0, 101.0, 98.0, 93.0, 98.0, 91.0, 89.0
]
closing_prices = [
    100.0, 102.0, 103.0, 101.0, 99.0, 99.0, 102.0, 103.0, 106.0,
    107.0, 105.0, 104.0, 101.0, 97.0, 100.0, 96.0, 93.0
]

icloud = ci.bulk.ichimoku_cloud(
    high_prices,
    low_prices,
    closing_prices,
    conversion_period=5,
    base_period=10,
    span_b_period=15
)

print(f"Bulk Ichimoku Cloud: {icloud}")

Output

Bulk Ichimoku Cloud: [
    (102.25, 102.5, 102.5, 102.0, 99.0), (100.0, 101.5, 101.5, 98.5, 102.0), (99.0, 100.5, 100.5, 97.5, 103.0)
]

donchian_channels

donchian_channels(
    high: List[float],
    low: List[float],
    period: int
) -> List[Tuple[float, float, float]]

Arguments

  • high: List of price highs
  • low: List of price lows
  • period: Period over which to calculate the Donchian channels

Returns

List of Donchian channel tuples (lower, average, upper)

Example

from pytechnicalindicators import candle_indicators as ci

high_prices = [
    105.0, 103.0, 107.0, 101.0, 103.0, 100.0, 109.0, 105.0,
    110.0, 112.0, 111.0, 105.0, 106.0, 100.0, 103.0, 102.0, 98.0
]
low_prices = [
    97.0, 99.0, 98.0, 100.0, 95.0, 98.0, 99.0, 100.0, 102.0, 106.0,
    99.0, 101.0, 98.0, 93.0, 98.0, 91.0, 89.0
]

dc = ci.bulk.donchian_channels(
    high_prices,
    low_prices,
    period=5
)

print(f"Bulk Donchian Channels: {dc}")

Output

Bulk Donchian Channels: [
    (95.0, 101.0, 107.0), (95.0, 101.0, 107.0), (95.0, 102.0, 109.0), (95.0, 102.0, 109.0), 
    (95.0, 102.5, 110.0), (98.0, 105.0, 112.0), (99.0, 105.5, 112.0), (99.0, 105.5, 112.0), 
    (98.0, 105.0, 112.0), (93.0, 102.5, 112.0), (93.0, 102.0, 111.0), (91.0, 98.5, 106.0), 
    (89.0, 97.5, 106.0)
]

keltner_channel

keltner_channel(
    high: List[float],
    low: List[float],
    close: List[float],
    constant_model_type: str,
    atr_constant_model_type: str,
    multiplier: float,
    period: int
) -> List[Tuple[float, float, float]]

Arguments

  • highs: List of price highs
  • lows: List of price lows
  • close: List of closing prices
  • constant_model_type: Choice of:
    • "simple_moving_average"
    • "smoothed_moving_average"
    • "exponential_moving_average"
    • "simple_moving_median"
    • "simple_moving_mode"
  • atr_constant_model_type: Choice of:
    • "simple_moving_average"
    • "smoothed_moving_average"
    • "exponential_moving_average"
    • "simple_moving_median"
    • "simple_moving_mode"
  • multiplier: Multiplier for the ATR
  • period: Period over which to calculate the Keltner Channel

Returns

List of Keltner channel tuples

Example

from pytechnicalindicators import candle_indicators as ci

high_prices = [
    105.0, 103.0, 107.0, 101.0, 103.0, 100.0, 109.0, 105.0,
    110.0, 112.0, 111.0, 105.0, 106.0, 100.0, 103.0, 102.0, 98.0
]
low_prices = [
    97.0, 99.0, 98.0, 100.0, 95.0, 98.0, 99.0, 100.0, 102.0, 106.0,
    99.0, 101.0, 98.0, 93.0, 98.0, 91.0, 89.0
]
closing_prices = [
    100.0, 102.0, 103.0, 101.0, 99.0, 99.0, 102.0, 103.0, 106.0,
    107.0, 105.0, 104.0, 101.0, 97.0, 100.0, 96.0, 93.0
]

kc = ci.bulk.keltner_channel(
    high_prices,
    low_prices,
    closing_prices,
    constant_model_type="simple_moving_average",
    atr_constant_model_type="simple_moving_average",
    multiplier=2.0,
    period=10
)

print(f"Bulk Keltner Channel: {kc}")

Output

Bulk Keltner Channel: [
    (90.16666666666667, 102.36666666666667, 114.56666666666668),
    (89.8, 102.8, 115.8), (90.0, 103.0, 116.0), 
    (90.10000000000002, 102.90000000000002, 115.70000000000002), 
    (88.5, 102.5, 116.5), 
    (89.23333333333332, 102.63333333333333, 116.03333333333333), 
    (87.16666666666666, 102.36666666666666, 117.56666666666666),
    (86.36666666666667, 101.36666666666667, 116.36666666666667)
]

supertrend

supertrend(
    high: List[float],
    low: List[float],
    close: List[float],
    constant_model_type: str,
    multiplier: float,
    period: int
) -> List[float]

Arguments

  • highs: List of price highs
  • lows: List of price lows
  • close: List of closing prices
  • constant_model_type: Choice of:
    • "simple_moving_average"
    • "smoothed_moving_average"
    • "exponential_moving_average"
    • "simple_moving_median"
    • "simple_moving_mode"
  • multiplier: Multiplier for the ATR
  • period: Period over which to calculate the Keltner Channel

Returns

List of Super Trend indicators

Example

from pytechnicalindicators import candle_indicators as ci

high_prices = [
    105.0, 103.0, 107.0, 101.0, 103.0, 100.0, 109.0, 105.0,
    110.0, 112.0, 111.0, 105.0, 106.0, 100.0, 103.0, 102.0, 98.0
]
low_prices = [
    97.0, 99.0, 98.0, 100.0, 95.0, 98.0, 99.0, 100.0, 102.0, 106.0,
    99.0, 101.0, 98.0, 93.0, 98.0, 91.0, 89.0
]
closing_prices = [
    100.0, 102.0, 103.0, 101.0, 99.0, 99.0, 102.0, 103.0, 106.0,
    107.0, 105.0, 104.0, 101.0, 97.0, 100.0, 96.0, 93.0
]

sptrnd = ci.bulk.supertrend(
    high_prices,
    low_prices,
    closing_prices,
    constant_model_type="simple_moving_average",
    multiplier=2.0,
    period=10
)

print(f"Bulk Supertend : {sptrnd}")

Output

Bulk Supertend : [115.7, 116.5, 116.5, 116.3, 116.5, 115.9, 116.7, 115.5]

🟢 Single Functions

Single functions compute a single value for the latest available data slice.

moving_constant_envelopes

moving_constant_envelopes(
    prices: List[float],
    constant_model_type: str,
    difference: float
) -> Tuple[float, 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"
  • difference: Percent band width (e.g., 3.0 for ±3%)

Returns

Moving Constant Envelopes tuples (lower envelope, constant model result, upper envelope)

Example

from pytechnicalindicators import candle_indicators as ci

prices = [
    100.0, 102.0, 103.0, 102.5, 102.8, 103.1, 103.8, 103.9,
    104.4, 103.6, 103.1, 102.9, 103.3, 103.7
]

mce = ci.single.moving_constant_envelopes(
    prices,
    constant_model_type="simple_moving_average",
    difference=2.0,
)

print(f"Single Moving Constant Envelopes: {mce}")

Output:

Single Moving Constant Envelopes: (100.94699999999999, 103.00714285714285, 105.06728571428572)

mcginley_dynamic_envelopes

mcginley_dynamic_envelopes(
    prices: List[float],
    difference: float,
    previous_mcginley_dynamic: float
) -> Tuple[float, float, float]

Arguments

  • prices: List of prices
  • difference: Percent band width (e.g., 3.0 for ±3%)
  • previous_mcginley_dynamic: Previous McGinley dynamic (use 0.0 if none)

Returns

McGinley dynamic envelopes tuple (lower envelope, McGinley dynamic, upper envelope)

Example

from pytechnicalindicators import candle_indicators as ci

prices = [
    100.0, 102.0, 103.0, 102.5, 102.8, 103.1, 103.8, 103.9,
    104.4, 103.6, 103.1, 102.9, 103.3, 103.7
]

mde = ci.single.mcginley_dynamic_envelopes(
    prices,
    difference=2.0,
    previous_mcginley_dynamic=0.0,
)

print(f"Single McGinley Dynamic Envelopes: {mde}")

Output

Single McGinley Dynamic Envelopes: (101.626, 103.7, 105.774)

moving_constant_bands

moving_constant_bands(
    prices: List[float],
    constant_model_type: str,
    deviation_model: str,
    deviation_multiplier: float
) -> Tuple[float, 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"
  • deviation_multiplier: Price deviation multiplier

Returns

Moving constant bands tuple (lower band, constant model result, upper band)

Example

from pytechnicalindicators import candle_indicators as ci

prices = [
    100.0, 102.0, 103.0, 102.5, 102.8, 103.1, 103.8, 103.9,
    104.4, 103.6, 103.1, 102.9, 103.3, 103.7
]

mcb = ci.single.moving_constant_bands(
    prices,
    constant_model_type="simple_moving_average",
    deviation_model="standard_deviation",
    deviation_multiplier=2.0,
)

print(f"Single Moving Constant Bands: {mcb}")

Output

Single Moving Constant Bands: (100.95989482874084, 103.00714285714285, 105.05439088554486)

mcginley_dynamic_bands

mcginley_dynamic_bands(
    prices: List[float],
    deviation_model: str,
    deviation_multiplier: float,
    previous_mcginley_dynamic: float
) -> Tuple[float, float, float]

Arguments

  • prices: List of prices
  • deviation_model: Choice of:
    • "standard_deviation"
    • "mean_absolute_deviation"
    • "median_absolute_deviation"
    • "mode_absolute_deviation"
    • "ulcer_index"
  • deviation_multiplier: Price deviation multiplier
  • previous_mcginley_dynamic: Previous McGinley dynamic (0.0 if none)

Returns

McGinley dynamic bands tuple (lower band, McGinley dynamic, upper band)

Example

from pytechnicalindicators import candle_indicators as ci

prices = [
    100.0, 102.0, 103.0, 102.5, 102.8, 103.1, 103.8, 103.9,
    104.4, 103.6, 103.1, 102.9, 103.3, 103.7
]

mdb = ci.single.mcginley_dynamic_bands(
    prices,
    deviation_model="standard_deviation",
    deviation_multiplier=2.0,
    previous_mcginley_dynamic=0.0,
)

print(f"Single McGinley Dynamic Bands: {mdb}")

Output

Single McGinley Dynamic Bands: (101.652751971598, 103.7, 105.74724802840201)

ichimoku_cloud

ichimoku_cloud(
    highs: List[float],
    lows: List[float],
    close: List[float],
    conversion_period: int,
    base_period: int,
    span_b_period: int
) -> Tuple[float, float, float, float, float]

Arguments

  • highs: List of price highs
  • lows: List of price lows
  • close: List of closing prices
  • conversion_period: Period used to calculate the conversion line
  • base_period: Period used to calculate the base line
  • span_b_period: Period used to calculate the Span B line

Returns

(leading span a, leading span b, base line, conversion line, relevant close price)

Example

from pytechnicalindicators import candle_indicators as ci

high_prices = [
    105.0, 103.0, 107.0, 101.0, 103.0, 100.0, 109.0, 105.0,
    110.0, 112.0, 111.0, 105.0, 106.0, 100.0, 103.0, 102.0, 98.0
]
low_prices = [
    97.0, 99.0, 98.0, 100.0, 95.0, 98.0, 99.0, 100.0, 102.0, 106.0,
    99.0, 101.0, 98.0, 93.0, 98.0, 91.0, 89.0
]
closing_prices = [
    100.0, 102.0, 103.0, 101.0, 99.0, 99.0, 102.0, 103.0, 106.0,
    107.0, 105.0, 104.0, 101.0, 97.0, 100.0, 96.0, 93.0
]

icloud = ci.single.ichimoku_cloud(
    high_prices,
    low_prices,
    closing_prices,
    conversion_period=5,
    base_period=10,
    span_b_period=15
)

print(f"Single Ichimoku Cloud: {icloud}")

Output

Single Ichimoku Cloud: (99.0, 100.5, 100.5, 97.5, 103.0)

donchian_channels

donchian_channels(
    high: List[float],
    low: List[float]
) -> Tuple[float, float, float]

Arguments

  • high: List of price highs
  • low: List of price lows

Returns

Donchian channel tuple (lower, average, upper)

Example

from pytechnicalindicators import candle_indicators as ci

high_prices = [
    105.0, 103.0, 107.0, 101.0, 103.0, 100.0, 109.0, 105.0,
    110.0, 112.0, 111.0, 105.0, 106.0, 100.0, 103.0, 102.0, 98.0
]
low_prices = [
    97.0, 99.0, 98.0, 100.0, 95.0, 98.0, 99.0, 100.0, 102.0, 106.0,
    99.0, 101.0, 98.0, 93.0, 98.0, 91.0, 89.0
]

dc = ci.single.donchian_channels(
    high_prices,
    low_prices,
)

print(f"Single Donchian Channels: {dc}")

Output

Single Donchian Channels: (89.0, 100.5, 112.0)

keltner_channel

keltner_channel(
    high: List[float],
    low: List[float],
    close: List[float],
    constant_model_type: str,
    atr_constant_model_type: str,
    multiplier: float
) -> Tuple[float, float, float]

Arguments

  • highs: List of price highs
  • lows: List of price lows
  • close: List of closing prices
  • constant_model_type: Choice of:
    • "simple_moving_average"
    • "smoothed_moving_average"
    • "exponential_moving_average"
    • "simple_moving_median"
    • "simple_moving_mode"
  • atr_constant_model_type: Choice of:
    • "simple_moving_average"
    • "smoothed_moving_average"
    • "exponential_moving_average"
    • "simple_moving_median"
    • "simple_moving_mode"
  • multiplier: Multiplier for the ATR

Returns

Keltner channel tuple

Example

from pytechnicalindicators import candle_indicators as ci

high_prices = [
    105.0, 103.0, 107.0, 101.0, 103.0, 100.0, 109.0, 105.0,
    110.0, 112.0, 111.0, 105.0, 106.0, 100.0, 103.0, 102.0, 98.0
]
low_prices = [
    97.0, 99.0, 98.0, 100.0, 95.0, 98.0, 99.0, 100.0, 102.0, 106.0,
    99.0, 101.0, 98.0, 93.0, 98.0, 91.0, 89.0
]
closing_prices = [
    100.0, 102.0, 103.0, 101.0, 99.0, 99.0, 102.0, 103.0, 106.0,
    107.0, 105.0, 104.0, 101.0, 97.0, 100.0, 96.0, 93.0
]

kc = ci.single.keltner_channel(
    high_prices,
    low_prices,
    closing_prices,
    constant_model_type="simple_moving_average",
    atr_constant_model_type="simple_moving_average",
    multiplier=2.0,
)

print(f"Single Keltner Channel: {kc}")

Output

Single Keltner Channel: (87.4313725490196, 101.19607843137254, 114.96078431372548)

supertrend

supertrend(
    high: List[float],
    low: List[float],
    close: List[float],
    constant_model_type: str,
    multiplier: float
) -> float

Arguments

  • highs: List of price highs
  • lows: List of price lows
  • close: List of closing prices
  • constant_model_type: Choice of:
    • "simple_moving_average"
    • "smoothed_moving_average"
    • "exponential_moving_average"
    • "simple_moving_median"
    • "simple_moving_mode"
  • multiplier: Multiplier for the ATR

Returns

List of Super Trend indicators

Example

from pytechnicalindicators import candle_indicators as ci

high_prices = [
    105.0, 103.0, 107.0, 101.0, 103.0, 100.0, 109.0, 105.0,
    110.0, 112.0, 111.0, 105.0, 106.0, 100.0, 103.0, 102.0, 98.0
]
low_prices = [
    97.0, 99.0, 98.0, 100.0, 95.0, 98.0, 99.0, 100.0, 102.0, 106.0,
    99.0, 101.0, 98.0, 93.0, 98.0, 91.0, 89.0
]
closing_prices = [
    100.0, 102.0, 103.0, 101.0, 99.0, 99.0, 102.0, 103.0, 106.0,
    107.0, 105.0, 104.0, 101.0, 97.0, 100.0, 96.0, 93.0
]

sptrnd = ci.single.supertrend(
    high_prices,
    low_prices,
    closing_prices,
    constant_model_type="simple_moving_average",
    multiplier=2.0,
)

print(f"Single Supertend : {sptrnd}")

Output

Single Supertend : 114.26470588235294

🧩 Model Type Choices

Many functions accept model types as string arguments.
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 period argument for window size.
  • Some functions require previous values (previous_mcginley_dynamic).

🔗 See Also