📈 Moving Average API Reference

Module: moving_average

The moving_average module provides functions for calculating moving averages, a core component of many technical indicators and trading strategies.

🏗️ 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.

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🚀 Bulk Functions

moving_average

moving_average(
    prices: List[float],
    moving_average_type: str,
    period: int
) -> List[float]

Arguments

• prices: List of prices
• moving_average_type: Choice of:
  • "simple"
  • "smoothed"
  • "exponential"
• period: Period over which to calculate the moving average

Returns

List of moving averages

Example

from pytechnicalindicators import moving_average as ma

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
]

sma = ma.bulk.moving_average(
    prices,
    moving_average_type="simple",
    period=5
)

print(f"Bulk Moving Average: {sma}")

Output:

Bulk Moving Average: [103.2, 104.8, 105.8, 106.8, 108.6, 109.2, 109.8, 111.0, 112.0, 112.0, 113.0]

---

mcginley_dynamic

mcginley_dynamic(
    prices: List[float],
    previous_mcginley_dynamic: float,
    period: int
) -> List[float]

Arguments

• prices: List of prices
• previous_mcginley_dynamic: Previous McGinley dynamic (if none use 0.0)
• period: Period over which to calculate the McGinley dynamic

Returns

List of McGinley dynamics

Example

from pytechnicalindicators import moving_average as ma

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
]

mcginley = ma.bulk.mcginley_dynamic(
    prices,
    previous_mcginley_dynamic=0.0,
    period=10
)

print(f"Bulk McGinley Dynamic: {mcginley}")

Output:

Bulk McGinley Dynamic: [
    109.0, 109.1859705058081, 109.51842914892896, 109.9693107300164, 110.15804788110441, 110.49301136686225
]

---

🟢 Single Functions

moving_average

moving_average(
    prices: List[float],
    moving_average_type: str
) -> float

Arguments

• prices: List of prices
• moving_average_type: Choice of:
  • "simple"
  • "smoothed"
  • "exponential"

Returns

Moving average value

Example

from pytechnicalindicators import moving_average as ma

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
]

sma = ma.single.moving_average(
    prices,
    moving_average_type="simple"
)

print(f"Single Moving Average: {sma}")

Output:

Single Moving Average: 108.46666666666667

---

mcginley_dynamic

mcginley_dynamic(
    latest_price: float,
    previous_mcginley_dynamic: float,
    period: int
) -> float

Arguments

• latest_price: Most recent price
• previous_mcginley_dynamic: Previous McGinley dynamic (if none use 0.0)
• period: Length of the observed period

Returns

McGinley dynamic value

Example

from pytechnicalindicators import moving_average as ma

mcginley = ma.single.mcginley_dynamic(
    latest_price=114.0,
    previous_mcginley_dynamic=108.5,
    period=10
)

print(f"Single McGinley Dynamic: {mcginley}")

Output:

Single McGinley Dynamic: 108.95129678212406

---

📝 Notes

• All input lists must be of type List[float] (Python list of floats)
• Bulk functions require a period argument for window size
• McGinley Dynamic requires previous values for continuity
• Use 0.0 for previous_mcginley_dynamic if no previous value exists

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🔗 See Also

• API Reference Index