A collection of 48 technical indicators. Suggestions are welcome.
Accumulation/Distribution - acc_dist
Average Directional Index - adx
Average True Range - atr
Average True Range Percent - atr_percent
Bollinger Bands - bollinger_bands
Camarilla Pivot Points - camarilla_pivots
Chaikin Oscillator - chaikin_oscillator
Choppiness Index - choppiness
Classic Pivot Points - classic_pivots
Commodity Channel Index - cci
Coppock Curve - coppock
Demark Pivot Points - demark_pivots
Donchian Channels - donchian_channels
Ease of Movement - ease_of_movement
Exponential Moving Average - ema
Fibonacci Moving Average - fma
Fibonacci Pivot Points - fibonacci_pivots
Force Index - force_index
Historical Volatility - hvol
Hull Moving Average - hma
Kaufman's Adaptive Moving Average - kama
Keltner Channels - keltner_channels
KST Oscillator - kst
Log Returns - returns(ret_method='log')
MACD - macd
Mass Index - mass_index
Momentum - momentum
Money Flow Index - money_flow_index
On-Balance Volume - obv
Parabolic Stop-and-Reverse - parabolic_sar
Rate of Change - rate_of_change
Relative Strength Index - rsi
RSI-Stochastic Oscillator - rsi_stochastic
Simple Moving Average - sma
Simple Returns - returns(ret_method='simple')
Stochastic Oscillator - stochastic
Stochastic-RSI Oscillator - stochastic_rsi
Supertrend - supertrend
Traditional Pivot Points - trad_pivots
Triangular RSI - triangular_rsi
True Range - true_range
TRIX - trix
True Strength Index - tsi
Ultimate Oscillator - ultimate_oscillator
Vortex Indicator - vortex
Weighted Moving Average - wma
Wilder's Moving Average - wilders_ma
Woodie Pivot Points - woodie_pivots
Data should be in open/high/low/close/volume format in a Pandas DataFrame with the date as the index.
ohlc = float
volume = int
date = Datetime
Data Example:
python 3.9
numpy 1.21.5
pandas 1.4.4
numba 0.55.1