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💪 Strength Indicators API Reference

Module: strength_indicators

The strength_indicators module provides functions to assess the strength and conviction of price movements and trends using volume and price-based calculations.

📚 When to Use

Use these indicators to: - Analyze volume-price relationships - Gauge market conviction - Identify trend strength

🏗️ 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

accumulation_distribution

accumulation_distribution(
    highs: List[float],
    lows: List[float],
    close: List[float],
    volume: List[float],
    previous_accumulation_distribution: float
) -> List[float]

Arguments

  • highs: List of high prices
  • lows: List of low prices
  • close: List of closing prices
  • volume: List of volumes
  • previous_accumulation_distribution: Previous AD value (use 0.0 if none)

Returns

List of Accumulation Distribution values

Example

from pytechnicalindicators import strength_indicators as si

highs = [103.0, 102.0, 105.0]
lows = [99.0, 99.0, 100.0]
close = [102.0, 100.0, 103.0]
volume = [1000.0, 1500.0, 1200.0]

ad = si.bulk.accumulation_distribution(
    highs, lows, close, volume, previous_accumulation_distribution=0.0
)
print(f"Bulk Accumulation Distribution: {ad}")

Output:

Bulk Accumulation Distribution: [500.0, 0.0, 240.0]

positive_volume_index

positive_volume_index(
    close: List[float],
    volume: List[float],
    previous_volume_index: float
) -> List[float]

Arguments

  • close: List of closing prices
  • volume: List of volumes
  • previous_volume_index: Previous PVI value (use 0.0 if none)

Returns

List of Positive Volume Index values

Example

from pytechnicalindicators import strength_indicators as si

close = [100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0, 112.0, 109.0]
volume = [1000, 1200, 1500, 1100, 1300, 1400, 1250, 1600, 1800, 1350]

pvi = si.bulk.positive_volume_index(
    close, volume, previous_volume_index=0.0
)
print(f"Bulk Positive Volume Index: {pvi}")

Output:

Bulk Positive Volume Index: [
    0.0204, 0.021, 0.021, 0.021611650485436895, 0.022019417475728158, 0.022019417475728158, 
    0.02263678432084203, 0.023048362217584613, 0.023048362217584613
]

negative_volume_index

negative_volume_index(
    close: List[float],
    volume: List[float],
    previous_volume_index: float
) -> List[float]

Arguments

  • close: List of closing prices
  • volume: List of volumes
  • previous_volume_index: Previous NVI value (use 0.0 if none)

Returns

List of Negative Volume Index values

Example

from pytechnicalindicators import strength_indicators as si

close = [100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0, 112.0, 109.0]
volume = [1000, 1200, 1500, 1100, 1300, 1400, 1250, 1600, 1800, 1350]

nvi = si.bulk.negative_volume_index(
    close, volume, previous_volume_index=0.0
)
print(f"Bulk Negative Volume Index: {nvi}")

Output:

Bulk Negative Volume Index: [
    0.0, 0.0, -0.01868480725623583, -0.01868480725623583, -0.01868480725623583, -0.018511799781641053, 
    -0.018511799781641053, -0.018511799781641053, -0.018015948001775667
]

relative_vigor_index

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

Arguments

  • open: List of opening prices
  • high: List of highs
  • low: List of 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"
  • period: Period over which to calculate the RVI

Returns

List of Relative Vigor Index values

Example

from pytechnicalindicators import strength_indicators as si

open_prices = [98.0, 101.5, 104.5, 102.5, 105.5, 107.5, 106.5, 109.5, 111.5, 108.5]
high_prices = [105.0, 107.0, 110.0, 108.0, 111.0, 113.0, 112.0, 115.0, 117.0, 114.0]
low_prices = [95.0, 97.0, 100.0, 98.0, 101.0, 103.0, 102.0, 105.0, 107.0, 104.0]
close_prices = [100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0, 112.0, 109.0]

rvi = si.bulk.relative_vigor_index(
    open_prices, high_prices, low_prices, close_prices,
    constant_model_type="simple_moving_average",
    period=5
)
print(f"Bulk Relative Vigor Index: {rvi}")

Output:

Bulk Relative Vigor Index: [0.0625, 0.05, 0.05, 0.05, 0.05, 0.05]

🟢 Single Functions

accumulation_distribution

accumulation_distribution(
    high: float,
    low: float,
    close: float,
    volume: float,
    previous_accumulation_distribution: float
) -> float

Arguments

  • high: High price
  • low: Low price
  • close: Close price
  • volume: Volume
  • previous_accumulation_distribution: Previous AD value (use 0.0 if none)

Returns

Accumulation Distribution value

Example

from pytechnicalindicators import strength_indicators as si

ad = si.single.accumulation_distribution(
    high=103.0, low=99.0, close=102.0, volume=1600.0, previous_accumulation_distribution=0.0
)
print(f"Single Accumulation Distribution: {ad}")

Output:

Single Accumulation Distribution: 800.0

volume_index

volume_index(
    current_close: float,
    previous_close: float,
    previous_volume_index: float
) -> float

Arguments

  • current_close: Current close price
  • previous_close: Previous close price
  • previous_volume_index: Previous PVI/NVI value (use 0.0 if none)

Returns

Volume Index value

Example

from pytechnicalindicators import strength_indicators as si

vi = si.single.volume_index(
    current_close=110.0, previous_close=108.0, previous_volume_index=0.0
)
print(f"Single Volume Index: {vi}")

Output:

Single Volume Index: 0.01886145404663923

relative_vigor_index

relative_vigor_index(
    open: List[float],
    high: List[float],
    low: List[float],
    close: List[float],
    constant_model_type: str
) -> float

Arguments

  • open: List of opening prices
  • high: List of highs
  • low: List of 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"

Returns

Relative Vigor Index value

Example

from pytechnicalindicators import strength_indicators as si

open_prices = [98.0, 101.5, 104.5, 102.5, 105.5, 107.5, 106.5, 109.5, 111.5, 108.5]
high_prices = [105.0, 107.0, 110.0, 108.0, 111.0, 113.0, 112.0, 115.0, 117.0, 114.0]
low_prices = [95.0, 97.0, 100.0, 98.0, 101.0, 103.0, 102.0, 105.0, 107.0, 104.0]
close_prices = [100.0, 102.0, 105.0, 103.0, 106.0, 108.0, 107.0, 110.0, 112.0, 109.0]

rvi = si.single.relative_vigor_index(
    open_prices, high_prices, low_prices, close_prices,
    constant_model_type="simple_moving_average"
)
print(f"Single Relative Vigor Index: {rvi}")

Output:

Single Relative Vigor Index: 0.05357142857142857

🧩 Model Type Choices

constant_model_type:
- "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., for AD, PVI, NVI)
  • Volume Index is used generically for both Positive and Negative Volume Index calculations

🔗 See Also