API Reference
Comprehensive documentation for all modules, classes, and functions available in PyTechnicalIndicators, as well as a full indicator list.
Quick Navigation
- Candle Indicators - Moving constant bands, envelopes, Ichimoku Cloud...
- Chart Trends - Peaks, valleys, overall trends, break down trends...
- Correlation Indicators - Correlate asset prices
- Momentum Indicators - RSI, MACD, stochastic oscillators, rate of change...
- Moving Average - Moving average, McGinley Dynamic
- Other Indicators - True Range, internal bar strength, return on investment...
- Standard Indicators - Simple, smoother, exponential moving average, RSI, MACD, Bollinger bands
- Strength Indicators - Relative vigor index, +ve/-ve volumw inswz, accumulation distribution
- Trend Indicators - Aroon up/down, oscillator, and indicator, volume price trend...
- Volatility Indicators - Ulcer index, volatiltiy sytem
Shared Types & Conventions
Many indicator functions use shared model types and enums as string arguments.
Constant Model Types
Accepted strings:
"simple","ma","simple_moving_average""smoothed","sma","smoothed_moving_average""exponential","ema","exponential_moving_average""median","smm","simple_moving_median""mode","simple_moving_mode"
Deviation Models
Accepted strings:
"standard","std","standard_deviation""mean","mean_absolute_deviation""median","median_absolute_deviation""mode","mode_absolute_deviation""ulcer","ulcer_index"
Moving Average Types
Accepted strings:
"simple""smoothed""exponential"
Position Types
Accepted strings:
"long""short"
Error Handling:
If you pass an invalid string, the API raises a Python ValueError with a list of valid options.
Package Structure
PyTechnicalIndicators is organized into indicator categories and modules:
import pytechnicalindicators
pytechnicalindicators.candle_indicators
pytechnicalindicators.chart_trends
pytechnicalindicators.correlation_indicators
pytechnicalindicators.momentum_indicators
pytechnicalindicators.moving_average
pytechnicalindicators.other_indicators
pytechnicalindicators.standard_indicators
pytechnicalindicators.strength_indicators
pytechnicalindicators.trend_indicators
pytechnicalindicators.volatility_indicators
Each submodule contains indicator functions, often organized into:
- single (single value for latest data slice)
- bulk (vectorized/moving window results)
Example usage:
from pytechnicalindicators.candle_indicators.bulk import supertrend
result = supertrend(high, low, close, "simple_moving_average", 3.0, 14)
Complete Indicators List
Welcome to the comprehensive catalog of PyTechnicalIndicators! This page lists all 60+ technical indicators available in the library, organized by category for easy navigation.
Candle Indicators
Candle indicators analyze price patterns and chart formations from OHLC charts.
| Indicator | Function Name | Description | Input Data | Parameters |
|---|---|---|---|---|
| Moving Constant Envelopes | moving_constant_envelopes() |
Price channels | Prices | difference, constant_model_type, period (bulk only) |
| Mcginley Dynamic Envelopes | mcginley_dynamic_envelopes() |
Price channels using the McGinley Dynamic | Prices | difference, previous_mcginley_dynamic, period (bulk only) |
| Moving Constant Bands | moving_constant_bands() |
Price channels with deviation | Prices | constant_model_type, deviation_model, deviation_multiplier, period (bulk only) |
| Mcginley Dynamic Bands | mcginley_dynamic_bands() |
Price channels using the McGinley Dynamic and deviation | Prices | deviation_model, deviation_multiplier, previous_mcginley_dynamic, period (bulk only) |
| Ichimoku Cloud | ichimoku_cloud() |
Complete Ichimoku system | High, Low, Close | conversion_period, base_period, span_b_period |
| Donchian Channels | donchian_channels() |
Highest high, lowest low channels | High, Low prices | period (bulk only) |
| Keltner Channels | keltner_channels() |
ATR-based price channels | High, Low, Close | constant_model_type, atr_constant_model_type, multiplier, period (bulk only) |
| Supertrend | supertrend() |
Trend-following indicator | High, Low, Close | constant_model_type, multiplier, period (bulk only) |
Example Usage
import pytechnicalindicators as pti
# Simple Moving Average with Standard Deviation Bands
bb = pti.candle_indicators.bulk.moving_constant_bands(close, period=20, constant_model_type="simple_moving_average", deviation_model="standard_deviation", deviation_multiplier=2)
# Ichimoku Cloud
ichimoku = pti.candle_indicators.bulk.ichimoku_cloud(highs, lows, closes, conversion_period=9, conversion_period=26, span_b_period=52)
# Donchian Channels
donchian = pti.candle_indicators.bulk.donchian_channels(highs, lows, period=20)
# Keltner Channels
keltner = pti.candle_indicators.single.keltner_channels(highs, lows, closes, multiplier=2i, constant_model_type="simple_moving_average", atr_constant_model_type="simple_moving_average")
# Supertrend
supertrend = pti.candle_indicators.single.supertrend(highs, lows, closes, constant_model_type="simple_moving_average", multiplier=3)
Chart Trends
Chart trends detect trend in price charts
| Indicator | Function Name | Description | Input Data | Parameters |
|---|---|---|---|---|
| Peaks | peaks() |
Get all peaks for a period | Prices | period, closest_neighbor |
| Valleys | valleys() |
Get all valleys for a period | Prices | period, closest_neighbor |
| Peak Trends | peak_trend() |
Calculate the trend of the peaks | Prices | period |
| Valley Trends | valley_trend() |
Calculate the trend of the valleys | Prices | period |
| Overall Trend | overall_trend() |
Calculates the overall trend of prices | Prices | |
| Breakdown Trends | break_down_trends() |
Splits the price trends into differrent parts | Prices | max_outliers, soft_r_squared_minimum, soft_r_squared_maximum, hard_r_squared_minimum, hard_r_squared_maximum, soft_standard_error_multiplier, hard_standard_error_multiplier, soft_reduced_chi_squared_multiplier, hard_reduced_chi_squared_multiplier |
Example Usage
# Get all peaks within 5 periods of each other within a 10 period window
peaks = pti.chart_trends.peaks(prices, period=10, closest_neighbor=5)
# Get the trend of valleys over a 5 period window
valley_trend = valley_trend(prices, period=5)
Correlation Indicators
Correlation indicators determine the correlation between two assets
| Indicator | Function Name | Description | Input Data | Parameters |
|---|---|---|---|---|
| Correlate Asset Prices | correlate_asset_prices() |
Calculate the correlation between two assests | Prices of both assets | constant_model_type, deviation_model, period (bulk only) |
Example Usage
# Correlation between a and b over the past 5 periods
corr = pti.correlation_indicators.bulk(prices_asset_a, prices_asset_b, constant_model_type="simple_moving_average", deviation_model="standard_deviation", period=5)
Momentum Indicators
Momentum indicators measure the speed and strength of price movements.
| Indicator | Function Name | Description | Input Data | Parameters |
|---|---|---|---|---|
| RSI | relative_strength_index() |
Relative Strength Index | Prices | constant_model_type, period (bulk only) |
| Stochastic Oscillator | stochastic_oscillator() |
Stochastic oscillator | Prices | period (bulk only) |
| Slow Stochastic Oscillator | slow_stochastic() |
Slow stochastic oscillator | Stochastics | constant_model_type, period (bulk only) |
| Slowest Stochastic Oscillator | slowest_stochastic() |
Slowest stochastic oscillator | Slow stochastics | constant_model_type, period (bulk only) |
| MACD | macd_line() |
Moving Average Convergence Divergence | Prices | short_period, short_period_model, long_period_model, long_period (bulk only) |
| Signal Line | signal_line() |
Signal line for the MACD | MACDs | constant_model_type, period (bulk only) |
| McGinley Dynamic MACD | macd_line() |
MACD using McGinley Dynamic | Prices | short_period, previous_short_mcginley, previous_long_mcginley, long_period (bulk only) |
| Williams %R | williams_percent_r() |
Williams Percent Range | High, Low, Close | period (bulk only) |
| RoC | rate_of_change() |
Rate of change | Prices | |
| MFI | money_flow_index() |
Money Flow Index | Prices, Volume | period (bulk only) |
| Commodity Channel Index | commodity_channel_index() |
CCI oscillator | Prices | constant_model_type, deviation_model, constant_multiplier, period (bulk only) |
| McGinley Dynamic CCI | mcginley_dynamic_commodity_channel_index() |
CCI oscillator using the McGinley Dynamic | Prices | previous_mcginley_dynamic, deviation_model, constant_multiplier, period (bulk only) |
| OBV | on_balance_volume() |
On Balance Volume | Prices, Volume | previous_on_balance_volume |
| Chaikin Oscillator | chaikin_oscillator() |
Chaikin Oscillator (CO) | High, Low, Close, Volume | short_period, previous_accumulation_distribution, short_period_model, long_period_model, long_period (bulk only) |
| PPO | percentage_price_oscillator() |
Percentage Price Oscillator | Prices | short_period, constant_model_type, long_period (bulk only) |
| CMO | chande_momentum_oscillator() |
Chande Momentum Oscillator | Prices | period (bulk only) |
Example Usage
# RSI
rsi = pti.momentum_indicators.bulk.relative_strength_index(close, constant_model_type="simple_moving_average", period=14)
# MACD
macd = pti.momentum_indicators.bulk.macd_line(close, short_period=12, long_period=26, short_period_model="simple_moving_average", long_period_model="simple_moving_average")
signal = pti.momentum_indicators.bulk.signal_line(macd, period=9)
# Stochastic
stoch = pti.momentum_indicators.bulk.stochastic_oscillator(close, period=14)
# Williams %R
williams = pti.momentum_indicators.single.williams_r(highs, lows, close)
# Money Flow Index
mfi = pti.momentum_indicators.single.money_flow_index(close, volumes)
Moving Average
Calculate various moving averages and the McGinley dynamic.
| Indicator | Function Name | Description | Input Data | Parameters |
|---|---|---|---|---|
| Moving Average | moving_average() |
Generic Moving Average | Prices | moving_average_type, period (bulk only) |
| McGinley Dynamic | mcginley_dynamic() |
McGinley Dynamic | Prices | previous_mcginley_dynamic, period |
Example Usage
# Different MAs for the past 5 periods
simple_ma = pti.moving_average.bulk.moving_average(prices, moving_average_type="simple", period=5)
smoothed_ma = pti.moving_average.bulk.moving_average(prices, moving_average_type="smoothed", period=5)
exponential_ma = pti.moving_average.bulk.moving_average(prices, moving_average_type="exponential", period=5)
# McGinley Dynamic
bulk_mcginley = pti.moving_average.bulk.mcginley_dynamic(prices, previous_mcginley_dynamic=0.0, period=5)
next_mcginley = pti.moving_average.single.mcginley_dynamic(next_price, previous_mcginley_dynamic=bulk_mcginley[-1], period=5
Other Indicators
Indicators that don't fall into a specific category.
| Indicator | Function Name | Description | Input Data | Parameters |
|---|---|---|---|---|
| Return on Investment | return_on_investment() |
ROI | Prices | investment |
| True Range | true_range() |
True Range (TR) | High, Low, Close | |
| Average True Range | average_true_range() |
Average True Range (ATR) | High, Low, Close | constant_model_type, period (bulk only) |
| Internal Bar Strength | internal_bar_strength() |
Internal Bar Strength (IBS) | High, Low, Close | |
| Positivity Indicator | positivity_indicator() |
Positivity Indicator | Open, Close | signal_period, constant_model_type |
Example Usage
# Calculate the Return on an 1000 investment
roi = pti.other_indicators.bulk.return_on_investment(prices, investment=1000)
# Calculate the internal bar strengrh for the last bar
ibs = pti.other_indicators.single.internal_bar_strength(high[-1], low[-1], close[-1]
Standard Indicators
Indicators with the defaults pre set to match they traditionally are.
| Indicator | Function Name | Description | Input Data | Parameters |
|---|---|---|---|---|
| Simple Moving Average | simple_moving_average() |
Simple Moving Average | Prices | period (bulk only) |
| Smoothed Moving Average | smoothed_moving_average() |
Smoothed Moving Average | Prices | period (bulk only) |
| Exponential Moving Average | exponential_moving_average() |
Exponential Moving Average | Prices | period (bulk only) |
| Bollinger Bands | bollinger_bands() |
Bollinger Bands | Prices | |
| MACD | macd() |
MACD, signal line, and histogram | Prices | |
| RSI | rsi() |
Relative Strength Index | Prices |
Example Usage
rsi = pti.standard_indicators.bulk.rsi(prices)
macd, signal, hist = pti.standard_indicators.bulk.macd(prices)
Strength Indicators
Strength indicators analyze market participation and buying/selling pressure.
| Indicator | Function Name | Description | Input Data | Parameters |
|---|---|---|---|---|
| Accumulation/Distribution | accumulation_distribution() |
A/D Line | High, Low, Close, Volume | previous_accumulation_distribution |
| Volume Index single only | volume_index() |
current_price, previous_price |
previous_volume_index |
|
| Positive Volume Index bulk only | positive_volume_index() |
PVI volume indicator | Close, Volume | previous_volume_index |
| Negative Volume Index bulk only | negative_volume_index() |
NVI volume indicator | Close, Volume | previous_volume_index |
| Relative Vigor Index | relative_vigor_index() |
RVI strength indicator | Open, High, Low, Close | constant_model_type, period (bulk only) |
Example Usage
# Volume Index Indicators
pvi = pti.strength_indicators.bulk.positive_volume_index(close, volume, previous_volume_index=0.0)
nvi = pti.strength_indicators.bulk.negative_volume_index(close, volume, previous_volume_index=0.0)
# Relative Vigor Index
rvi = pti.strength_indicators.single.relative_vigor_index(open, high, low, close, constant_model_type="simple_moving_average")
Trend Indicators
Trend indicators help identify the direction and strength of market trends.
| Indicator | Function Name | Description | Input Data | Parameters |
|---|---|---|---|---|
| Aroon Oscillator | aroon_oscillator() |
Measures trend strength and direction | Aroon Ups, Aroon Downs | |
| Aroon Up | aroon_up() |
Uptrend strength indicator | High prices | period (bulk only) |
| Aroon Down | aroon_down() |
Downtrend strength indicator | Low prices | period (bulk only) |
| Aroon Indicator | aroon_indicator() |
Measures trend strength and direction | High, Low prices | period (bulk only) |
| Long Parabolic Time Price System single only | long_parabolic_time_price_system() |
Long Stop and Reverse trend indicator | previous_sar, extreme_point, af, low |
|
| Short Parabolic Time Price System single only | short_parabolic_time_price_system() |
Short Stop and Reverse trend indicator | previous_sar, extreme_point, af, high |
|
| Parabolic SaR bulk only | parabolic_time_price_system() |
Stop and Reverse trend indicator | High, Low prices | af_start, af_step, af_max, position, previous_sar |
| True Strength Index | true_strength_index() |
Double-smoothed momentum oscillator | Prices | first_constant_model, second_constant_model, first_period, second_period (bulk only) |
| Directional Movement System bulk only | directional_movement_system () |
Directional Movement System | High, Low, Close | period, constant_model_type |
| Volume Price Trend | volume_price_trend() |
Volume Price Trend (VPT) | Prices, Volumes | previous_vpt |
Example Usage
# Aroon Indicators
aroon_up = pti.trend_indicators.bulk.aroon_up(highs, period=14)
aroon_down = pti.trend_indicators.bulk.aroon_down(lows, period=14)
aroon_oscillator = pti.trend_indicators.bulk.aroon_oscillator(aroon_up, aroon_down)
# Parabolic SAR
psar = pti.trend_indicators.bulk.parabolic_time_price_system(highs, lows, af_start=0.02, af_step=0.02, af_max=0.2, position="long", previous_sar=0)
Volatility Indicators
Volatility indicators measure market volatility and price range movements.
| Indicator | Function Name | Description | Input Data | Parameters |
|---|---|---|---|---|
| Ulcer Index | ulcer_index() |
Downside volatility measure | Prices | period (bulk only) |
| Volatility System bulk only | volatility_system() |
Volatility-based signals | High, Low, Close | period, constant_multiplier, constant_model_type |
Example Usage
# Ulcer Index
ui = pti.volatility_indicators.bulk.ulcer_index(close, period=14)
# Volatility System
vs = pti.volatility_indicators.bulk.volatility_system(high, low, close, period=14, constant_multiplier=2.0, constant_model_type="simple_moving_average")
Parameter Conventions
| Parameter Type | Description | Example |
|---|---|---|
| period | Number of periods for calculation | period=14 |
| constant_model_type | Type of constant model to use | constant_model_type="exponential_moving_average" |
| constant_multiplier | Multiplier for the constant model used | constant_multiplier=2.0 |
| deviation_model | Type of deviation model to use | deviation_model="median_aboslute_deviation" |
| deviation_multiplier | Multiplier for the deviation model | deviation_multiplier=2.0 |
Data Requirements
Data Format
# All data should be provided as Python lists
closes = [100.0, 101.5, 99.8, 102.3, 103.1]
highs = [101.0, 102.0, 100.5, 103.0, 104.0]
lows = [99.5, 100.8, 99.0, 101.5, 102.0]
volumes = [10000, 12000, 9500, 15000, 11000]
🎉 That's all 60+ indicators available in PyTechnicalIndicators!