🔧 Other Indicators API Reference
Module: other_indicators
The other_indicators module provides technical analysis tools that do not fit neatly into the main categories like momentum, trend, or volatility.
These calculations often serve as foundational measures or are used as components of broader strategies.
📚 When to Use
Use these functions when you need to: - Calculate foundational metrics for risk, volatility, or bar strength - Analyze price movement range or return over a period - Incorporate less common but valuable technical measures into your models
🏗️ 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
return_on_investment
return_on_investment(
prices: List[float],
investment: float
) -> List[Tuple[float, float]]
Arguments
- prices: List of prices
- investment: Initial investment amount
Returns
List of tuples containing (final investment value, percentage return)
Example
from pytechnicalindicators import other_indicators as oi
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
]
roi = oi.bulk.return_on_investment(
prices,
investment=1000.0
)
print(f"Bulk Return on Investment: {roi}")
Output:
Bulk Return on Investment: [
(1020.0, 2.0), (1050.0, 2.941176470588235), (1030.0, -1.9047619047619049),
(1060.0, 2.912621359223301), (1080.0, 1.8867924528301887),
(1070.0, -0.9259259259259258), (1100.0, 2.803738317757009), (1120.0, 1.8181818181818181),
(1090.0, -2.6785714285714284), (1110.0, 1.834862385321101), (1130.0, 1.8018018018018018),
(1150.0, 1.7699115044247788), (1120.0, -2.608695652173913), (1140.0, 1.7857142857142856)
]
true_range
true_range(
close: List[float],
high: List[float],
low: List[float]
) -> List[float]
Arguments
- close: List of previous closes
- high: List of highs
- low: List of lows
Returns
List of True Range values
Example
from pytechnicalindicators import other_indicators as oi
close_prices = [
99.0, 101.0, 104.0, 102.0, 105.0, 107.0, 106.0, 109.0,
111.0, 108.0, 110.0, 112.0, 114.0, 111.0, 113.0
]
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
]
tr = oi.bulk.true_range(
close_prices,
high_prices,
low_prices
)
print(f"Bulk True Range: {tr}")
Output:
Bulki True Range: [10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0]
average_true_range
average_true_range(
close: List[float],
high: List[float],
low: List[float],
constant_model_type: str,
period: int
) -> List[float]
Arguments
- close: List of previous closes
- high: List of highs
- low: List of lows
- 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 ATR
Returns
List of Average True Range values
Example
from pytechnicalindicators import other_indicators as oi
close_prices = [
99.0, 101.0, 104.0, 102.0, 105.0, 107.0, 106.0, 109.0,
111.0, 108.0, 110.0, 112.0, 114.0, 111.0, 113.0
]
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
]
atr = oi.bulk.average_true_range(
close_prices,
high_prices,
low_prices,
constant_model_type="simple_moving_average",
period=14
)
print(f"Bulk Average True Range: {atr}")
Output:
Bulk Average True Range: [10.0, 10.0]
internal_bar_strength
internal_bar_strength(
high: List[float],
low: List[float],
close: List[float]
) -> List[float]
Arguments
- high: List of highs
- low: List of lows
- close: List of closing prices
Returns
List of internal bar strength values
Example
from pytechnicalindicators import other_indicators as oi
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
]
ibs = oi.bulk.internal_bar_strength(
high_prices,
low_prices,
close_prices
)
print(f"Bulk Internal Bar Strength: {ibs}")
Output:
Bulk Internal Bar Strength: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
positivity_indicator
positivity_indicator(
open: List[float],
previous_close: List[float],
signal_period: int,
constant_model_type: str
) -> List[Tuple[float, float]]
Arguments
- open: List of opening prices
- previous_close: List of closing prices
- signal_period: Period to calculate the signal
- constant_model_type: Choice of:
"simple_moving_average""smoothed_moving_average""exponential_moving_average""simple_moving_median""simple_moving_mode"
Returns
List of tuples containing (positivity indicator, signal line)
Example
from pytechnicalindicators import other_indicators as oi
open_prices = [
98.0, 101.5, 104.5, 102.5, 105.5, 107.5, 106.5, 109.5,
111.5, 108.5, 110.5, 112.5, 114.5, 111.5, 113.5
]
previous_close = [
99.0, 101.0, 104.0, 102.0, 105.0, 107.0, 106.0, 109.0,
111.0, 108.0, 110.0, 112.0, 114.0, 111.0, 113.0
]
pos_indicator = oi.bulk.positivity_indicator(
open_prices,
previous_close,
signal_period=5,
constant_model_type="simple_moving_average"
)
print(f"Bulk Positivity Indicator: {pos_indicator}")
Output:
Bulk Positivity Indicator: [
(0.4761904761904762, 0.18642085604811287), (0.46728971962616817, 0.48189900199354857),
(0.4716981132075472, 0.477228723644959), (0.45871559633027525, 0.47281799675716785),
(0.45045045045045046, 0.4648688711609834), (0.4629629629629629, 0.4622233685154808),
(0.45454545454545453, 0.45967451549933813), (0.4464285714285714, 0.45462060714354297),
(0.43859649122807015, 0.4505967861231019), (0.45045045045045046, 0.4505967861231019),
(0.4424778761061947, 0.44649976875174835)
]
🟢 Single Functions
return_on_investment
return_on_investment(
start_price: float,
end_price: float,
investment: float
) -> Tuple[float, float]
Arguments
- start_price: Initial price of the asset
- end_price: Final price of the asset
- investment: Amount invested at start
Returns
Tuple of (final investment value, percentage return)
Example
from pytechnicalindicators import other_indicators as oi
roi = oi.single.return_on_investment(
start_price=100.0,
end_price=114.0,
investment=1000.0
)
print(f"Single Return on Investment: {roi}")
Output:
Single Return on Investment: (1140.0, 14.000000000000002)
true_range
true_range(
close: float,
high: float,
low: float
) -> float
Arguments
- close: Previous period close
- high: Current period high
- low: Current period low
Returns
True Range value
Example
from pytechnicalindicators import other_indicators as oi
tr = oi.single.true_range(
close=113.0,
high=119.0,
low=109.0
)
print(f"Single True Range: {tr}")
Output:
Single True Range: 10.0
average_true_range
average_true_range(
close: List[float],
high: List[float],
low: List[float],
constant_model_type: str
) -> float
Arguments
- close: List of previous closes
- high: List of highs
- low: List of lows
- constant_model_type: Choice of:
"simple_moving_average""smoothed_moving_average""exponential_moving_average""simple_moving_median""simple_moving_mode"
Returns
Average True Range value
Example
from pytechnicalindicators import other_indicators as oi
close_prices = [
99.0, 101.0, 104.0, 102.0, 105.0, 107.0, 106.0, 109.0,
111.0, 108.0, 110.0, 112.0, 114.0, 111.0, 113.0
]
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
]
atr = oi.single.average_true_range(
close_prices,
high_prices,
low_prices,
constant_model_type="simple_moving_average"
)
print(f"Single Average True Range: {atr}")
Output:
Single Average True Range: 10.0
internal_bar_strength
internal_bar_strength(
high: float,
low: float,
close: float
) -> float
Arguments
- high: High price
- low: Low price
- close: Close price
Returns
Internal bar strength value
Example
from pytechnicalindicators import other_indicators as oi
ibs = oi.single.internal_bar_strength(
high=119.0,
low=109.0,
close=114.0
)
print(f"Single Internal Bar Strength: {ibs}")
Output:
Single Internal Bar Strength: 0.5
📝 Notes
- All input lists must be of type
List[float](Python list of floats) - True Range calculations require previous close, current high, and current low
- Internal Bar Strength measures where the close falls within the high-low range
- Return on Investment returns both absolute value and percentage return
- Positivity Indicator compares opening gaps to previous closes