Indicator by Groups
Overlap
bbands
Defined by John Bollinger in the 80s. It measures volatility by defining
upper and lower bands at distance x standard deviations
Formula:
- midband = SimpleMovingAverage(close, period)
- topband = midband + devfactor * StandardDeviation(data, period)
- botband = midband - devfactor * StandardDeviation(data, period)
See:
- http://en.wikipedia.org/wiki/Bollinger_Bands
Aliases: BBANDS, BollingerBands, BOLLINGERBANDS
Inputs: close
Outputs: mid, top, bot
Params:
- period (default: 20)
Period to consider
- devs (default: 2.0)
Standard Deviations of Top/Bottom Bands
- _ma (default: sma)
Moving average to use
- _stdev (default: stddev)
Standard Deviation Calculation to use
TA-LIB (with compatibility flag "_talib=True"):
Change period to 5
dema
DEMA was first time introduced in 1994, in the article "Smoothing Data with
Faster Moving Averages" by Patrick G. Mulloy in "Technical Analysis of
Stocks & Commodities" magazine.
It attempts to reduce the inherent lag associated to Moving Averages
Formula:
- dema = (2.0 * ema(data, period)) - ema(ema(data, period), period)
See:
- https://en.wikipedia.org/wiki/Double_exponential_moving_average
Aliases: DEMA, DoubleExponentialMovingAverage
Inputs: close
Outputs: dema
Params:
- period (default: 30)
Period to consider
- _ma (default: ema)
Moving Average to use
ema
A Moving Average that smoothes data exponentially over time.
- Exponential Smotthing factor: alpha = 2 / (1 + period)
Formula
- prev = mean(data, period)
- movav = prev * (1.0 - alpha) + newdata * alpha
- (or alternatively # movav = prev + alpha(new - prev))
See also:
- http://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average
Aliases: EMA, ExponentialMovingAverage
Inputs: close
Outputs: ema
Params:
- period (default: 30)
Period for the moving average calculation
- _seed (default: 0)
Default to use average of periods as seed
ewma
This is **NOT** the `ema` or `ExponentialMovingAverage`. This is a wrap
around pandas.Series.ewm where `ewma` stands for `ExponentialWeigthedMean`
... to which later a function like `mean` is applied
Applying `mean` doesn't make it the `ExponentialMovingAverage` (aka `EMA`
or `ema`) because `ewma` in `pandas.Series` or `pandas.DataFrames` does not
support using a seed of the first n periods of an `ewm` of `span=n`
The purpose of this, is to be able to use this in place of the real `ema`
with parameters like `period` and `_seed` for compatibility.
Aliases: EWMA
Inputs: close
Outputs: ewma
Params:
- period (default: 30)
Default Period for the ewm calculation
- adjust (default: False)
Default calc individual terms like in `ema`
- _seed (default: 0)
(nop) for compatibility with `ema`
gdema
Described first by Tim Tillson in Stocks & Commodities V16:1, January 1998
for its use in the T3 indicator.
It generalizes the DEMA by using a "volume" factor to put weight on
the 1st or 2nd factor of the DEMA to smooth the output.
When "vfactor" is 1, the DEMA is the output. When "vfactor" is 0, the
output is s simple EMA.
Aliases: gd, GD, GDEMA, GeneralizedDEMA
Inputs: close
Outputs: gd
Params:
- period (default: 5)
Period to consider
- vfactor (default: 0.7)
volume factor
- _ma (default: ema)
Moving Average to use
ht_trendline
Ehlers': Hilber Transform Dominant Cycle Period
Formula:
- From *"Rocket Science for Traders: Digital Signal Processing Applications"*
See:
- https://www.amazon.com/Rocket-Science-Traders-Processing-Applications/dp/0471405671
Aliases: HT_TRENDLINE, HilberTransform_Trendline
Inputs: high, low
Outputs: trendline
kama
Defined by Perry Kaufman in his book `"Smarter Trading"`.
It is A Moving Average with a continuously scaled smoothing factor by
taking into account market direction and volatility. The smoothing factor
is calculated from 2 ExponetialMovingAverage smoothing factors, a fast one
and slow one.
If the market trends the value will tend to the fast ema smoothing
period. If the market doesn't trend it will move towards the slow EMA
smoothing period.
It is a subclass of SmoothingMovingAverage, overriding once to account for
the live nature of the smoothing factor
Formula:
- direction = close - close_period
- volatility = sumN(abs(close - close_n), period)
- effiency_ratio = abs(direction / volatility)
- smoothing constant fast = 2 / (fast_period + 1)
- smoothing constant slow = 2 / (slow_period + 1)
- smoothing factor = pow(efficienty_ratio * (fast - slow) + slow), 2)
- kama = ewm(data, alpha=smoothing factor, period=period)
Notice that "smoothing factor" isn't a single value, hence the
exponentially weighted moving average uses a value which changes for each
step of the calculation.
The standard seed is the simple moving average, use _seed=btalib.SEED_LAST
to apply the "last" known value of the input as the seed (for compatibility
this can be simply `True` or `1`)
Because the dynamic smoothing constant has a larger period (+1) than the
actual moving average, this average has alrady a seed value when the
calculation can start. Hence the seed value (eithre the simple moving
average or the last known value, is not seen because it can be calculated
before the actual period comes in effect)
See also:
- https://school.stockcharts.com/doku.php?id=technical_indicators:kaufman_s_adaptive_moving_average
- http://fxcodebase.com/wiki/index.php/Kaufman's_Adaptive_Moving_Average_(KAMA)
- http://www.metatrader5.com/en/terminal/help/analytics/indicators/trend_indicators/ama
- http://help.cqg.com/cqgic/default.htm#!Documents/adaptivemovingaverag2.htm
Aliases: KAMA, KaufmanAdaptiveMovingAverage
Inputs: close
Outputs: kama
Params:
- period (default: 30)
Period to consider
- fast (default: 2)
Fast exponential smoothing factor
- slow (default: 30)
Slow exponential smoothing factor
- _seed (default: 0)
Default to use average of n periods as seed
- _pvol (default: 1)
Lookback period for volatility calculation
TA-LIB (with compatibility flag "_talib=True"):
Apply las value as seed, instead of average of period values
mama
Quoting Ehlers: "The MESA Adaptive Moving Average (MAMA) adapts to price
movement in an entirely new and unique way. The adapation is based on the
rate change of phase as measured by the Hilbert Transform Discriminator"
Formula:
- The formula is overly complex. See the linked PDF from Ehlers himself
See:
- https://www.mesasoftware.com/papers/MAMA.pdf
Aliases: MAMA, MesaAdaptiveMovingAverage
Inputs: high, low
Outputs: mama, fama
Params:
- fastlimit (default: 0.5)
Fast Limit
- slowlimit (default: 0.05)
Fast Limit
mavp
Moving Average Variable Period.
It takes two inputs of equal length
- data (usually the "close"
- periods
It delivers for each timepoint "i", the value of the moving average
dictated by period[i], at point it (movingaverage[i])
Formula:
- mavp[i] = MovingAverage(data, period[i])[i]
See also:
- (None)
Aliases: MAVP, MovingAverageVariablePeriod
Inputs: close, periods
Outputs: mavp
Params:
- minperiod (default: 2)
Minimum allowed period for the moving averages
- maxperiod (default: 30)
Maximum allowed period for the moving averages
- _ma (default: sma)
Moving Average to use
midpoint
Calculates the middle price of the highest/lowest price across a period
Formula:
- midpoint = (Highest(close, period) + Lowest(close, period)) / 2.0
See:
- https://www.tradingtechnologies.com/xtrader-help/x-study/technical-indicator-definitions/midpoint-midpnt/
Aliases: MIDPOINT, MidPoint
Inputs: close
Outputs: midpoint
Params:
- period (default: 14)
Period to consider
midprice
Calculates the middle price of the highest high/lowest low across a period
Formula:
- midprice = (Highest(high, period) + Lowest(low, period)) / 2.0
See:
- https://www.tradingtechnologies.com/xtrader-help/x-study/technical-indicator-definitions/midprice-midpri/
Aliases: MIDPRICE, MidPrice
Inputs: high, low
Outputs: midprice
Params:
- period (default: 14)
Period to consider
sma
Non-weighted average of the last n periods
Formula:
- movav = Sum(data, period) / period
See also:
- http://en.wikipedia.org/wiki/Moving_average#Simple_moving_average
Aliases: SMA, SimpleMovingAverage
Inputs: close
Outputs: sma
Params:
- period (default: 30)
Period for the moving average calculation
smma
Smoothed Moving Average used by Wilder in his 1978 book `New Concepts in
Technical Trading`
Defined in his book originally as:
- new_value = (old_value * (period - 1) + new_data) / period
Which is a moving average that smoothes data exponentially over time.
- Exponential Smotthing factor: alpha = 1 / period
Formula
- prev = mean(data, period)
- movav = prev * (1.0 - alpha) + newdata * alpha
- (or alternatively # movav = prev + alpha(new - prev))
See also:
- http://en.wikipedia.org/wiki/Moving_average#Modified_moving_average
Aliases: SMMA, SmoothedMovingAverage, MMA, ModifiedMovingAverage
Inputs: close
Outputs: smma
Params:
- period (default: 30)
Period for the moving average calculation
- _seed (default: 0)
Default to use average of periods as seed
t3
Described first by Tim Tillson in Stocks & Commodities V16:1, January 1998.
It first generalizes the DEMA by using a "volume" factor to put weight on
the 1st or 2nd factor of the DEMA to smooth the output.
And it then passes the input three times (hence T3) through the generalized
dema to reduce phase lag.
The default behavir implements the quadractic equation MetaStock version
presented in the article, which is also ta-lib compatible.
Using the paramter `_gd` one can enable the GeneralizedDema Triple Filter
formulation shown in the article (which is expanded to the quadratic
version) by the auther. The initial results are similar but not the
same. The results converge aftera number of bars (period dependent) to at
lest 6 decimals of precision (period 5 => after 71 bars)
See also:
- https://www.fmlabs.com/reference/default.htm?url=T3.htm
- http://www.tradingpedia.com/forex-trading-indicators/t3-moving-average-indicator/
- https://www.tradingtechnologies.com/xtrader-help/x-study/technical-indicator-definitions/t3-t3/
Aliases: T3, TilsonsT3
Inputs: close
Outputs: t3
Params:
- period (default: 5)
Period to consider
- vfactor (default: 0.7)
Volume factor
- _ma (default: ema)
Moving Average to use
- _gd (default: False)
`True` use articl triple gd filter math formulation
tema
TEMA was first time introduced in 1994, in the article "Smoothing Data with
Faster Moving Averages" by Patrick G. Mulloy in "Technical Analysis of
Stocks & Commodities" magazine.
It attempts to reduce the inherent lag associated to Moving Averages
Formula:
- ema1 = ema(data, period)
- ema2 = ema(ema1, period)
- ema3 = ema(ema2, period)
- tema = 3 * ema1 - 3 * ema2 + ema3
See:
- https://en.wikipedia.org/wiki/Triple_exponential_moving_average
Aliases: TEMA, TripleExponentialMovingAverage
Inputs: close
Outputs: tema
Params:
- period (default: 30)
Period to consider
- _ma (default: ema)
Moving Average to use
trima
The Triangular Moving Average (TRIMA) is an average of an average, similar
to the `sma` but placing more weight on the middle values due to the second
smoothing.
Formula:
- if period is odd: p1 = p2 = (period + 1) // 2
- if period is even: p1, p2 = (p // 2) + 1, p //2
- trima = sma(sma(data, p2), p1)
See also:
- https://www.tradingtechnologies.com/xtrader-help/x-study/technical-indicator-definitions/triangular-moving-average-trima/
Aliases: TRIMA, TriangularMovingAverage
Inputs: close
Outputs: trima
Params:
- period (default: 30)
Period for the moving average calculation
- _ma (default: sma)
Moving average to use
wma
A Moving Average which gives an arithmetic weighting to values with the
newest having the more weight
Formula:
- weights = range(1, period + 1)
- coef = 2 / (period * (period + 1))
- movav = coef * Sum(weight[i] * data[period - i] for i in range(period))
See also:
- http://en.wikipedia.org/wiki/Moving_average#Weighted_moving_average
Aliases: WMA, WeightedMovingAverage
Inputs: close
Outputs: wma
Params:
- period (default: 30)
Period for the moving average calculation
- coef (default: 1.0)
Momentum
adx
Defined by J. Welles Wilder, Jr. in 1978 in his book *"New Concepts in
Technical Trading Systems"*.
Intended to measure trend strength and directionality
Formula:
- upmove = high - high(-1)
- downmove = low(-1) - low
- +dm = upmove if upmove > downmove and upmove > 0 else 0
- -dm = downmove if downmove > upmove and downmove > 0 else 0
- +di = 100 * SmoothedAccum(+dm, period) / SmoothAccum(truerange, period)
- -di = 100 * SmoothedAccum(-dm, period) / SmoothAccum(truerange, period)
- dx = 100 * abs(+di - -di) / (+di + -di)
- adx = MovingAverage(dx, period)
The moving average used is the one originally defined by Wilder,
the SmoothedMovingAverage
See also:
- https://school.stockcharts.com/doku.php?id=technical_indicators:average_directional_index_adx
- https://en.wikipedia.org/wiki/Average_directional_movement_index
Note:
- The `_alt` parameter changes the formula to use the
SmoothedMovingAverage in place of `SmoothAccum` as is interpreted by
several alternative sources (which also effectively implies that
`SmoothAccum(truerange, period) = ATR(high, low, close, period)`
Aliases: ADX, AverageDirectionalIndex, AVERAGEDIRECTIONALINDEX
Inputs: high, low, close
Outputs: adx
Params:
- period (default: 14)
- _period (default: 1)
- _pearly (default: False)
- _alt (default: False)
- _accum (default: smacc)
- _ma (default: smma)
TA-LIB (with compatibility flag "_talib=True"):
Increase period by 1, but keep 1-period early calculation from dm
intact
adxr
Defined by J. Welles Wilder, Jr. in 1978 in his book *"New Concepts in
Technical Trading Systems"*.
Intended to measure trend strength and directionality
Formula:
- adxr = (adx - adx(-period)) / 2.0
See also:
-
Aliases: ADXR, AverageDirectionalIndexRating
Inputs: high, low, close
Outputs: adxr
Params:
- period (default: 14)
- _period (default: 1)
- _pearly (default: False)
- _alt (default: False)
- _accum (default: smacc)
- _ma (default: smma)
- _prating (default: None)
Use as rating period instead of default period
TA-LIB (with compatibility flag "_talib=True"):
ta-lib uses period - 1 for the adxr formula when summing the current
adx and the adx from `period` ago
apo
Shows the difference between a short and long exponential moving
averages expressed in points, unlike the `ppo` which does in percent values
Formula:
- apo = ema(data, pfast) - ema(data, pslow)
See:
- http://www.metastock.com/Customer/Resources/TAAZ/?c=3&p=94
- https://www.tradingtechnologies.com/xtrader-help/x-study/technical-indicator-definitions/absolute-price-oscillator-apo/
Aliases: APO, AbsolutePriceOscillator, PriceOscillator
Inputs: close
Outputs: apo
Params:
- pfast (default: 12)
- pslow (default: 26)
- _ma (default: ema)
TA-LIB (with compatibility flag "_talib=True"):
Set moving average to sma instead of ema
aroon
Developed by Tushar Chande in 1995.
It tries to determine if a trend exists or not by calculating how far away
within a given period the last highs/lows are (AroonUp/AroonDown)
Formula:
- up = 100 * (period - distance to highest high) / period
- down = 100 * (period - distance to lowest low) / period
Note:
The lines oscillate between 0 and 100. That means that the "distance" to
the last highest or lowest must go from 0 to period so that the formula
can yield 0 and 100.
Hence the lookback period is period + 1, because the current bar is also
taken into account. And therefore this indicator needs an effective
lookback period of period + 1.
See:
- http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:aroon
Aliases: AROON, Aroon
Inputs: high, low
Outputs: aroondn, aroonup
Params:
- period (default: 14)
aroonosc
It is a variation of the AroonUpDown indicator which shows the current
difference between the AroonUp and AroonDown value, trying to present a
visualization which indicates which is stronger (greater than 0 -> AroonUp
and less than 0 -> AroonDown)
Formula:
- aroonosc = aroonup - aroondn
See:
- http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:aroon
Aliases: AROONOSC, AroonOscillator
Inputs: high, low
Outputs: aroonosc
Params:
- period (default: 14)
bop
Bop tries to determine the power of buyers vs sellers, by calculating the
power of being able to take the price to the extremes.
Igor Livshin introduced in August 2001 in the Stocks and Commodities
Magazine
Formula:
- bop = (close - open) / (high - low)
See also:
- https://www.interactivebrokers.com/en/software/tws/usersguidebook/technicalanalytics/balancePower.htm
- https://www.marketvolume.com/technicalanalysis/balanceofpower.asp
Aliases: BOP, BalanceOfPower
Inputs: open, high, low, close
Outputs: bop
cci
Introduced by Donald Lambert in 1980 to measure variations of the
"typical price" (see below) from its mean to identify extremes and
reversals
Formula:
- tp = typical_price = (high + low + close) / 3
- tpmean = MovingAverage(tp, period)
- deviation = tp - tpmean
- meandev = MeanDeviation(tp)
- cci = deviation / (meandeviation * factor)
See:
- https://en.wikipedia.org/wiki/Commodity_channel_index
Aliases: CCI, CommodityChannelIndex
Inputs: high, low, close
Outputs: cci
Params:
- period (default: 20)
Period to consider
- factor (default: 0.015)
Channel width factor
- _ma (default: sma)
Moving Average to sue
- _dev (default: mad)
Deviation to use (Def: Mean Abs Dev)
TA-LIB (with compatibility flag "_talib=True"):
Change period to 14
di
Defined by J. Welles Wilder, Jr. in 1978 in his book *"New Concepts in
Technical Trading Systems"*.
Intended to measure trend strength and directionality
Formula:
- upmove = high - high(-1)
- downmove = low(-1) - low
- +dm = upmove if upmove > downmove and upmove > 0 else 0
- -dm = downmove if downmove > upmove and downmove > 0 else 0
- +di = 100 * SmoothedAccum(+dm, period) / SmoothAccum(truerange, period)
- -di = 100 * SmoothedAccum(-dm, period) / SmoothAccum(truerange, period)
See also:
- https://school.stockcharts.com/doku.php?id=technical_indicators:average_directional_index_adx
- https://en.wikipedia.org/wiki/Average_directional_movement_index
Note:
- The `_alt` parameter changes the formula to use the
SmoothedMovingAverage in place of `SmoothAccum` as is interpreted by
several alternative sources (which also effectively implies that
`SmoothAccum(truerange, period) = ATR(high, low, close, period)`
Aliases: DI, DirectionalIndicator, DIRECTIONALINDICATOR
Inputs: high, low, close
Outputs: plusdi, minusdi
Params:
- period (default: 14)
- _period (default: 1)
- _pearly (default: False)
- _alt (default: False)
- _accum (default: smacc)
- _ma (default: smma)
TA-LIB (with compatibility flag "_talib=True"):
Increase period by 1, but keep 1-period early calculation from dm
intact
dm
Defined by J. Welles Wilder, Jr. in 1978 in his book *"New Concepts in
Technical Trading Systems"*.
Intended to measure trend strength and directionality
Formula:
- upmove = high - high(-1)
- downmove = low(-1) - low
- +dm1 = upmove if upmove > downmove and upmove > 0 else 0
- -dm1 = downmove if downmove > upmove and downmove > 0 else 0
- +dm = SmoothAccum(+dm1, period)
- -dm = SmoothAccum(-dm1, period)
See also:
- https://school.stockcharts.com/doku.php?id=technical_indicators:average_directional_index_adx
- https://en.wikipedia.org/wiki/Average_directional_movement_index
Note:
- Although the original definition from Welles Wilder for DirectionalMove
does not imply a period, `ta-lib` defines `plus_dm` (+dm) and
`minus_dm` (-dm) with a period over which they are accumulted. The
accumulation was defined within the scope of the `DirectionalIndicator`
(+di, -di) definition.
Aliases: DM, DirectionalMovement
Inputs: high, low
Outputs: plusdm, minusdm
Params:
- period (default: 14)
- _period (default: 1)
- _pearly (default: False)
- _alt (default: False)
- _accum (default: smacc)
- _ma (default: smma)
TA-LIB (with compatibility flag "_talib=True"):
Calculate seed 1-period too early and therefore deliver one period
too early
dx
Defined by J. Welles Wilder, Jr. in 1978 in his book *"New Concepts in
Technical Trading Systems"*.
Intended to measure trend strength and directionality
Formula:
- upmove = high - high(-1)
- downmove = low(-1) - low
- +dm = upmove if upmove > downmove and upmove > 0 else 0
- -dm = downmove if downmove > upmove and downmove > 0 else 0
- +di = 100 * SmoothedAccum(+dm, period) / SmoothAccum(truerange, period)
- -di = 100 * SmoothedAccum(-dm, period) / SmoothAccum(truerange, period)
- dx = 100 * abs(+di - -di) / (+di + -di)
See also:
- https://school.stockcharts.com/doku.php?id=technical_indicators:average_directional_index_adx
- https://en.wikipedia.org/wiki/Average_directional_movement_index
Note:
- The `_alt` parameter changes the formula to use the
SmoothedMovingAverage in place of `SmoothAccum` as is interpreted by
several alternative sources (which also effectively implies that
`SmoothAccum(truerange, period) = ATR(high, low, close, period)`
Aliases: DX, DirectionalIndex, DIRECTIONALINDEX
Inputs: high, low, close
Outputs: dx
Params:
- period (default: 14)
- _period (default: 1)
- _pearly (default: False)
- _alt (default: False)
- _accum (default: smacc)
- _ma (default: smma)
TA-LIB (with compatibility flag "_talib=True"):
Increase period by 1, but keep 1-period early calculation from dm
intact
macd
Moving Average Convergence Divergence. Defined by Gerald Appel in the 70s.
It measures the distance of a fast and a slow moving average to try to
identify the trend.
A second lagging moving average over the convergence-divergence should
provide a "signal" upon being crossed by the macd
Formula:
- macd = ma(data, pfast) - ma(data, pslow)
- signal = ma(macd, psignal)
- histogram = macd - signal
See:
- http://en.wikipedia.org/wiki/MACD
Aliases: MACD, MovingAverageConvergenceDivergence, MACDEXT, MACDFIX
Inputs: close
Outputs: macd, signal, histogram
Params:
- pfast (default: 12)
Fast moving average period
- pslow (default: 26)
Slow moving average period
- psignal (default: 9)
Signal smoothing period
- _ma (default: ema)
Moving average to use
- _masig (default: None)
Signal moving average (if `None`, same as others)
TA-LIB (with compatibility flag "_talib=True"):
Start fast ema calc delivery at the offset of the slow ema
mfi
Created by Gene Quong and Avrum Soudack to identify buying/selling
pressure by combining price and volume in the calculation.
Pressure is positive if the (typical) price increases and negative if it
decreases. The volume is the weight factor for how much pressure is being
exercised on the asset.
Formula:
- tp = typical_price = (high + low + close) / 3
- money_flow_raw = tp * volume
- flow_positive = Sum(money_flow_raw * (tp > tp(-1)), period)
- flow_netagive = Sum(money_flow_raw * (tp < tp(-1)), period)
- money_flow_ratio = flow_positive / flow_negative
- mfi 100 - 100 / (1 + money_flow_ratio)
See:
- https://school.stockcharts.com/doku.php?id=technical_indicators:money_flow_index_mfi
- https://www.investopedia.com/terms/m/mfi.asp
Aliases: MFI, MoneyFlowIndicator
Inputs: high, low, close, volume
Outputs: mfi
Params:
- period (default: 14)
Period to consider
minus_di
Defined by J. Welles Wilder, Jr. in 1978 in his book *"New Concepts in
Technical Trading Systems"*.
Intended to measure trend strength and directionality
Formula:
- upmove = high - high(-1)
- downmove = low(-1) - low
- -dm = downmove if downmove > upmove and downmove > 0 else 0
- -di = 100 * SmoothedAccum(-dm, period) / SmoothAccum(truerange, period)
See also:
- https://school.stockcharts.com/doku.php?id=technical_indicators:average_directional_index_adx
- https://en.wikipedia.org/wiki/Average_directional_movement_index
Note:
- The `_alt` parameter changes the formula to use the
SmoothedMovingAverage in place of `SmoothAccum` as is interpreted by
several alternative sources (which also effectively implies that
`SmoothAccum(truerange, period) = ATR(high, low, close, period)`
Aliases: MINUS_DI, MinusDirectionalIndicator
Inputs: high, low, close
Outputs: minusdi
Params:
- period (default: 14)
- _period (default: 1)
- _pearly (default: False)
- _alt (default: False)
- _accum (default: smacc)
- _ma (default: smma)
TA-LIB (with compatibility flag "_talib=True"):
Increase period by 1, but keep 1-period early calculation from dm
intact
minus_dm
Defined by J. Welles Wilder, Jr. in 1978 in his book *"New Concepts in
Technical Trading Systems"*.
Intended to measure trend strength and directionality
Formula:
- upmove = high - high(-1)
- downmove = low(-1) - low
- -dm1 = downmove if downmove > upmove and downmove > 0 else 0
- -dm = SmoothAccum(-dm1, period)
See also:
- https://school.stockcharts.com/doku.php?id=technical_indicators:average_directional_index_adx
- https://en.wikipedia.org/wiki/Average_directional_movement_index
Note:
- Although the original definition from Welles Wilder for DirectionalMove
does not imply a period, `ta-lib` defines `plus_dm` (+dm) and
`minus_dm` (-dm) with a period over which they are accumulted. The
accumulation was defined within the scope of the `DirectionalIndicator`
(+di, -di) definition.
Aliases: MINUS_DM, MinusDirectionalMovement
Inputs: high, low
Outputs: minusdm
Params:
- period (default: 14)
- _period (default: 1)
- _pearly (default: False)
- _alt (default: False)
- _accum (default: smacc)
- _ma (default: smma)
TA-LIB (with compatibility flag "_talib=True"):
Calculate seed 1-period too early and therefore deliver one period
too early
plus_di
Defined by J. Welles Wilder, Jr. in 1978 in his book *"New Concepts in
Technical Trading Systems"*.
Intended to measure trend strength and directionality
Formula:
- upmove = high - high(-1)
- downmove = low(-1) - low
- +dm = upmove if upmove > downmove and upmove > 0 else 0
- +di = 100 * SmoothedAccum(+dm, period) / SmoothAccum(truerange, period)
See also:
- https://school.stockcharts.com/doku.php?id=technical_indicators:average_directional_index_adx
- https://en.wikipedia.org/wiki/Average_directional_movement_index
Note:
- The `_alt` parameter changes the formula to use the
SmoothedMovingAverage in place of `SmoothAccum` as is interpreted by
several alternative sources (which also effectively implies that
`SmoothAccum(truerange, period) = ATR(high, low, close, period)`
Aliases: PLUS_DI, PlusDirectionalIndicator
Inputs: high, low, close
Outputs: plusdi
Params:
- period (default: 14)
- _period (default: 1)
- _pearly (default: False)
- _alt (default: False)
- _accum (default: smacc)
- _ma (default: smma)
TA-LIB (with compatibility flag "_talib=True"):
Increase period by 1, but keep 1-period early calculation from dm
intact
plus_dm
Defined by J. Welles Wilder, Jr. in 1978 in his book *"New Concepts in
Technical Trading Systems"*.
Intended to measure trend strength and directionality
Formula:
- upmove = high - high(-1)
- downmove = low(-1) - low
- +dm1 = upmove if upmove > downmove and upmove > 0 else 0
- +dm = SmoothAccum(+dm1, period)
See also:
- https://school.stockcharts.com/doku.php?id=technical_indicators:average_directional_index_adx
- https://en.wikipedia.org/wiki/Average_directional_movement_index
Note:
- Although the original definition from Welles Wilder for DirectionalMove
does not imply a period, `ta-lib` defines `plus_dm` (+dm) and
`minus_dm` (-dm) with a period over which they are accumulted. The
accumulation was defined within the scope of the `DirectionalIndicator`
(+di, -di) definition.
Aliases: PLUS_DM, PlusDirectionalMovement
Inputs: high, low
Outputs: plusdm
Params:
- period (default: 14)
- _period (default: 1)
- _pearly (default: False)
- _alt (default: False)
- _accum (default: smacc)
- _ma (default: smma)
TA-LIB (with compatibility flag "_talib=True"):
Calculate seed 1-period too early and therefore deliver one period
too early
ppo
Shows the difference between a fast and slow exponential moving
averages expressed in percentage. The MACD does the same but expressed in
absolute points.
Expressing the difference in percentage allows to compare the indicator at
different points in time when the underlying value has significatnly
different values.
Formula:
- ppo = 100.0 * (ema(data, pfast)/ema(data, pslow) - 1.0)
See:
- http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:price_oscillators_ppo
Aliases: PPO, PercentagePriceOscillator
Inputs: close
Outputs: ppo, signal, histogram
Params:
- pfast (default: 12)
- pslow (default: 26)
- _ma (default: ema)
- psignal (default: 9)
Signal smoothing period
- _masig (default: None)
Signal moving average (if `None`, same as others)
TA-LIB (with compatibility flag "_talib=True"):
Set moving average to sma instead of ema
ppofast
Shows the difference between a fast and slow exponential moving
averages expressed in percentage. The MACD does the same but expressed in
absolute points.
Expressing the difference in percentage allows to compare the indicator at
different points in time when the underlying value has significatnly
different values.
Most on-line literature shows the percentage calculation having the long
exponential moving average as the denominator. Some sources like MetaStock
use the fast one.
Formula:
- ppo = 100.0 * (1.0 - ema(data, pslow) / ema(data, pshort))
- Alternative = ppo = 100.0 - 100.0 * (ema_slow / ema_fast)
See:
- http://www.metastock.com/Customer/Resources/TAAZ/?c=3&p=94
Aliases: PPOFast, PercentagePriceOscillatorFast
Inputs: close
Outputs: ppo, signal, histogram
Params:
- pfast (default: 12)
- pslow (default: 26)
- _ma (default: ema)
- psignal (default: 9)
Signal smoothing period
- _masig (default: None)
Signal moving average (if `None`, same as others)
TA-LIB (with compatibility flag "_talib=True"):
Set moving average to sma instead of ema
roc
The ROC calculation compares the current price with the price n periods
ago, to determine momentum the as the percent change in price. This version
scales the percentage to 100.
- roc = 100.0 * (data / data(-period) - 1.0)
See:
- https://school.stockcharts.com/doku.php?id=technical_indicators:rate_of_change_roc_and_momentum
Aliases: rateofchange, ROC, RateOfChange
Inputs: close
Outputs: roc
Params:
- period (default: 12)
TA-LIB (with compatibility flag "_talib=True"):
Change period to 10
rocp
The ROC calculation compares the current price with the price n periods
ago, to determine momentum the as the percent change in price.
Formula:
- rocp = data / data(-period) - 1.0
See:
- https://school.stockcharts.com/doku.php?id=technical_indicators:rate_of_change_roc_and_momentum
Aliases: ROCP, RateOfChangePercentage
Inputs: close
Outputs: rocp
Params:
- period (default: 12)
TA-LIB (with compatibility flag "_talib=True"):
Change period to 10
rocr
The ROCR calculation compares the current price with the price n periods
ago as a ratio to determine momentum.
Formula:
- rocr = data / data(-period)
See:
- https://school.stockcharts.com/doku.php?id=technical_indicators:rate_of_change_roc_and_momentum
Aliases: ROCR, RateOfChangeRatio
Inputs: close
Outputs: rocr
Params:
- period (default: 12)
TA-LIB (with compatibility flag "_talib=True"):
Change period to 10
rocr100
The ROCR calculation compares the current price with the price n periods
ago as a ratio to determine momentum, scaled to 100.
Formula:
- rocr100 = 100.0 * data / data(-period)
See:
- https://school.stockcharts.com/doku.php?id=technical_indicators:rate_of_change_roc_and_momentum
Aliases: ROCR100, RateOfChangeRatio100
Inputs: close
Outputs: rocr100
Params:
- period (default: 12)
TA-LIB (with compatibility flag "_talib=True"):
Change period to 10
rsi
Defined by J. Welles Wilder, Jr. in 1978 in his book *"New Concepts in
Technical Trading Systems"*.
It measures momentum by calculating the ration of higher closes and
lower closes after having been smoothed by an average, normalizing
the result between 0 and 100
Formula:
- up = upday(data) # max(close - close(-1), 0.0)
- down = downday(data) # abs( min(close - close(-1), 0.0) )
- maup = movingaverage(up, period)
- madown = movingaverage(down, period)
- rs = maup / madown
- rsi = 100 - 100 / (1 + rs)
The moving average used is the one originally defined by Wilder,
the SmoothedMovingAverage
See:
- http://en.wikipedia.org/wiki/Relative_strength_index
Aliases: RSI, RelativeStrengthIndex
Inputs: close
Outputs: rsi
Params:
- period (default: 14)
Period to consider
- lookback (default: 1)
Lookback for up/down days
- _ma (default: smma)
Smoothing moving average
sar
Defined by J. Welles Wilder, Jr. in 1978 in his book *"New Concepts in
Technical Trading Systems"* for the RSI
SAR stands for *Stop and Reverse* and the indicator was meant as a signal
for entry (and reverse)
How to select the 1st signal is left unspecified in the book. Because the
inputs are "high" and "low", a 1-bar MinusDM is calculated, which accounts
for both downmove and upmove of high/low. This is also done by ta-lib
See:
- https://en.wikipedia.org/wiki/Parabolic_SAR
- http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:parabolic_sar
Aliases: SAR, psar, ParabolicStopAndReverse
Inputs: high, low
Outputs: sar
Params:
- af (default: 0.02)
Acceleration Factor
- afmax (default: 0.2)
Maximum Acceleration Factor
sarext
Defined by J. Welles Wilder, Jr. in 1978 in his book *"New Concepts in
Technical Trading Systems"* for the RSI
SAR stands for *Stop and Reverse* and the indicator was meant as a signal
for entry (and reverse)
How to select the 1st signal is left unspecified in the book. Because the
inputs are "high" and "low", a 1-bar MinusDM is calculated, which accounts
for both downmove and upmove of high/low. This is also done by ta-lib
Compared to the standard `sar`, one can define the initial trend, the value
of the initial `sar`, different acceleration factors for long and short,
and an offset to be applied to the sar when the trend is reversed
See:
- https://en.wikipedia.org/wiki/Parabolic_SAR
- http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:parabolic_sar
Aliases: SAR, psar, ParabolicStopAndReverse
Inputs: high, low
Outputs: sar
Params:
- startval (default: 0)
Start trend (0 auto, 1 long, -1 short)
- aflong (default: 0.02)
Acceleration Factor (Long)
- afmaxlong (default: 0.2)
Maximum Acceleration Factor (Long)
- afshort (default: 0.02)
Acceleration Factor (Long)
- afmaxshort (default: 0.2)
Maximum Acceleration Factor (Long)
- offsetonreverse (default: 0.0)
Offset to apply when reversing position
stochastic
By Dr. George Lane in the 50s. It compares a closing price to the price
range and tries to show convergence if the closing prices are close to the
extremes
- It will go up if closing prices are close to the highs
- It will go down if closing prices are close to the lows
It shows divergence if the extremes keep on growing/decreasing but closing
prices do not in the same manner (distance to the extremes increases)
Formula:
- hh = highest(high, period)
- ll = lowest(low, period)
- kfast = 100 * (close - ll) / (hh - ll)
- k = MovingAverage(kfast, pfast)
- d = MovingAverage(k, pslow)
See:
- http://en.wikipedia.org/wiki/Stochastic_oscillator
Aliases: stoch, Stochastic, STOCHASTIC, STOCH
Inputs: high, low, close
Outputs: k, d
Params:
- period (default: 14)
- pfast (default: 3)
- _ma (default: sma)
- pslow (default: 3)
Slow moving average period
- _maslow (default: None)
Slow moving average (if `None`, use same as fast)
TA-LIB (with compatibility flag "_talib=True"):
Change period to 5
stochf
By Dr. George Lane in the 50s. It compares a closing price to the price
range and tries to show convergence if the closing prices are close to the
extremes
- It will go up if closing prices are close to the highs
- It will go down if closing prices are close to the lows
It shows divergence if the extremes keep on growing/decreasing but closing
prices do not in the same manner (distance to the extremes increases)
Formula:
- hh = highest(high, period)
- ll = lowest(low, period)
- kfast = 100 * (close - ll) / (hh - ll)
- dfast = MovingAverage(kfast, pfast)
See:
- http://en.wikipedia.org/wiki/Stochastic_oscillator
Aliases: stochfast, StochasticFast, STOCHF
Inputs: high, low, close
Outputs: k, d
Params:
- period (default: 14)
Period to consider
- pfast (default: 3)
Fast smoothing period
- _ma (default: sma)
Moving average to use
TA-LIB (with compatibility flag "_talib=True"):
Change period to 5
stochrsi
Presented by Chande and Kroll the 1990 book: "The New Technical Trader".
The RSI is fed into a atochastic-like calculation to increase its
sensitivity.
The recommendation is to keep the period for looking for highest highes and
lowest lows the same as the for the RSI, but it can be played with for
experimentation.
Scaling to 100 is also suggested as a possiblity (the range is 0.0 => 1.0)
Formula:
- rsi = RSI(data, period)
- maxrsi = Highest(rsi, period)
- minrsi = Lowest(rsi, period)
- stochrsi = (rsi - minrsi) / (maxrsi - minrsi)
See
- https://school.stockcharts.com/doku.php?id=technical_indicators:stochrsi
Aliases: StochRsi, STOCHRSI
Inputs: close
Outputs: stochrsi
Params:
- period (default: 14)
Period to consider
- _philo (default: None)
Period for highest/lowest (None => period)
- _scale (default: 1.0)
Scale the result by this factor
TA-LIB (with compatibility flag "_talib=True"):
ta-lib uses internally the fast stochastic to calculate the stochrsi,
with these side-effects
- The scale changes from 0.0-1.0 to 0.0-100.0
- A 2nd output is returned (stochrsi as defined by its authors has
only 1 output)
- The highest/lowest period is no longer symmetric with the rsi
period
Compatibility does this
- Change the scale to 0.0-100.0
- Change the highest/lowest period 5
- Add a 2nd output named 'd' (as the 2nd output of the stochastic)
- Run a simple moving average on it of period 3
trix
Defined by Jack Hutson in the 80s and shows the Rate of Change (%) or slope
of a triple exponentially smoothed moving average
Formula:
- ema1 = EMA(data, period)
- ema2 = EMA(ema1, period)
- ema3 = EMA(ema2, period)
- trix = 100 * (ema3 / ema3(1) - 1)
Where -1 is the lookback period to consider the rate of change
The final formula can be simplified to: 100 * (ema3 / ema3(-1) - 1)
The moving average used is the one originally defined by Wilder,
the SmoothedMovingAverage
See:
- https://en.wikipedia.org/wiki/Trix_(technical_analysis)
- http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:trix
Aliases: Trix, TRIX
Inputs: close
Outputs: trix
Params:
- period (default: 30)
Period to consider
- _ma (default: ema)
Moving average to use
ultimateoscillator
Developed by Larry Williams. It is based on notion of buying or selling
pressure by considering where where a the closing price is within the true
range.
Formula (default values):
- bp = close - truelow(low, close) # buying pressure
- tr = truerange(high, low, close)
- av7 = Sum(bp, 7) / Sum(tr, 7)
- av14 = Sum(bp, 14) / Sum(tr, 14)
- av28= Sum(bp, 28) / Sum(tr, 28)
- uo = 100 * (4*av7 + 2*av14 + av18) / (4 + 2 + 1)
See:
- https://en.wikipedia.org/wiki/Ultimate_oscillator
- http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ultimate_oscillator
Aliases: ultosc, UltimateOscillator, ULTOSC
Inputs: high, low, close
Outputs: uo
Params:
- period1 (default: 7)
Faster oscillating period
- period2 (default: 14)
Medium oscillating period
- period3 (default: 28)
Slower oscillating period
- factor1 (default: 4.0)
Factor weight for the faster oscillation
- factor2 (default: 2.0)
Factor weight for the medium oscillation
- factor3 (default: 1.0)
Factor weight for the slower oscillation
williamsr
Developed by Larry Williams to show the relation of closing prices to
the highest-lowest range of a given period.
Known as Williams %R (but % is not allowed in Python identifiers)
Formula:
- num = highest(high, period) - close
- den = highest(high, period) - lowest(low, period)
- %R = -100.0 * (num / den)
See:
- http://en.wikipedia.org/wiki/Williams_%25R
- https://school.stockcharts.com/doku.php?id=technical_indicators:williams_r
Aliases: willr, WilliamsR, WILLIAMSR, WILLR
Inputs: high, low, close
Outputs: r
Params:
- period (default: 14)
Period to consider
Volume
ad
Originally the "Cumulative Money Flow Line" by Mark Chaikin, which attempts
to measure the incoming and outgoing flow of money by using the volume in
addition the standard price components.
Formula:
- mfm = ((close - high) + (close - low)) / (high - low)
- ergo: mfm = (2*close - high - low) / (high - low)
- mfv = mf * volume
- ad = cumulative_sum(mvf)
See also:
- https://en.wikipedia.org/wiki/Accumulation/distribution_index
- https://school.stockcharts.com/doku.php?id=technical_indicators:accumulation_distribution_line
Aliases: AD, adl, ADL, ChaikinAD, ChaikinADL, chaikinad
Inputs: high, low, close, volume
Outputs: ad
adosc
The Chaikin Oscillator applies a `MACD` formula to the
Accumulation/Distribution Line (`ad`) to calculate the momentum of the
money flow.
See also:
- https://en.wikipedia.org/wiki/Chaikin_Analytics#Chaikin_Oscillator
- https://school.stockcharts.com/doku.php?id=technical_indicators:chaikin_oscillator
- https://www.metastock.com/customer/resources/taaz/?p=41
Aliases: ADOSC, ChaikinADOSC, ChaikinOsc, ChaikinOscillator
Inputs: high, low, close, volume
Outputs: adosc
Params:
- pfast (default: 3)
Fast ema period
- pslow (default: 10)
Slow ema period
- _ma (default: ema)
Moving average to use
TA-LIB (with compatibility flag "_talib=True"):
Switch to `ewma` (i.e.: *exponential weighted moving mean* with no seed
instead of using a standard `ema` exponential moving average
obv
Originally called "continuous volume" by Woods and Vignola, it was later
named "on-balance volume" by Joseph Granville in his 1963 book Granville's
New Key to Stock Market Profits.
On Balance Volume (OBV) measures buying and selling pressure as a
cumulative indicator, adding volume on up days and subtracting it on down
days.
Formula:
- close > close(-1) => vol_pressure = +volume
- close < close(-1) => vol_pressure = -volume
- close == close(-1) => vol_pressure = 0
- obv = obv(-1) + vol_pressure
See also:
- https://en.wikipedia.org/wiki/On-balance_volume
- https://www.investopedia.com/terms/o/onbalancevolume.asp
- https://school.stockcharts.com/doku.php?id=technical_indicators:on_balance_volume_obv
Aliases: OBV, OnBalanceVolume
Inputs: close, volume
Outputs: obv
Params:
- _period (default: 1)
Period for `close` comparison
TA-LIB (with compatibility flag "_talib=True"):
Use first day volume as *positive* seed value
Volatility
atr
Defined by J. Welles Wilder, Jr. in 1978 in his book *"New Concepts in
Technical Trading Systems"*.
The idea is to take the close into account to calculate the range if it
yields a larger range than the daily range (High - Low)
Formula:
- truerange = max(high, close(-1)) - min(low, close(-1))
- atr = SmoothedMovingAverage(truerange, period)
See:
- http://en.wikipedia.org/wiki/Average_true_range
Aliases: ATR, AverageTrueRange
Inputs: high, low, close
Outputs: atr
Params:
- _period (default: 1)
- period (default: 14)
Period to consider
- _ma (default: smma)
Moving average to use
natr
Offers a normalized (against the `close`) version of the `atr`, which can
provide better values for comparison against different price ranges.
Formula:
- natr = 100.0 * atr / close
See:
- http://en.wikipedia.org/wiki/Average_true_range
Aliases: NATR, NormalizedAverageTrueRange
Inputs: high, low, close
Outputs: natr
Params:
- _period (default: 1)
- period (default: 14)
- _ma (default: smma)
truehigh
Defined by J. Welles Wilder, Jr. in 1978 in his book *"New Concepts in
Technical Trading Systems"* for the ATR
Records the "true high" which is the maximum of today's high and
yesterday's close
Formula:
- truehigh = max(high, close_prev)
See:
- http://en.wikipedia.org/wiki/Average_true_range
Aliases: TR, TrueRange, trange, TRANGE
Inputs: low, close
Outputs: truehi
Params:
- _period (default: 1)
Period to consider
truelow
Defined by J. Welles Wilder, Jr. in 1978 in his book *"New Concepts in
Technical Trading Systems"* for the ATR
Records the "true low" which is the minimum of today's low and
yesterday's close
Formula:
- truelow = min(low, close_prev)
See:
- http://en.wikipedia.org/wiki/Average_true_range
Aliases: TR, TrueRange, trange, TRANGE
Inputs: low, close
Outputs: truelo
Params:
- _period (default: 1)
Period to consider
truerange
Defined by J. Welles Wilder, Jr. in 1978 in his book New Concepts in
Technical Trading Systems.
Formula:
- max(high - low, abs(high - prev_close), abs(prev_close - low)
which can be simplified to
- truerange = max(high, prev_close) - min(low, prev_close)
See:
- http://en.wikipedia.org/wiki/Average_true_range
The idea is to take the previous close into account to calculate the range
if it yields a larger range than the daily range (High - Low)
Aliases: TR, TrueRange, trange, TRANGE
Inputs: high, low, close
Outputs: tr
Params:
- _period (default: 1)
Period for high/low vs close for truerange calc
avgprice
Returns the average of the 4 OHLC price components
Formula:
- avg = (open + high + low + close) / 3
See:
- (None)
Aliases: AvgPrice, AveragePrice, AVGPrice, AVGPRICE
Inputs: open, high, low, close
Outputs: avg
Params:
- divisor (default: 4.0)
Factor to use in the division
medprice
Delivers the Median Price
Formula:
- med = (high + low) / 2.0
See:
- https://www.metastock.com/customer/resources/taaz/?p=70
Aliases: WCLPrice, WeightedClosePrice, WCLPRICE, weightedcloseprice
Inputs: high, low
Outputs: med
Params:
- divisor (default: 2.0)
Factor to use in the division
typprice
Delivers the typical price
Formula:
- tp = (high + low + close) / 3
See:
- https://en.wikipedia.org/wiki/Typical_price
Aliases: typicalprice, TypicalPrice, TypPrice, TYPPRICE
Inputs: high, low, close
Outputs: tp
Params:
- divisor (default: 3.0)
Factor to use in the division
wclprice
Delivers the Weighted Close Price
Formula:
- wcl = (high + low + close * 2) / 4
See:
- https://www.metastock.com/customer/resources/taaz/?p=124
- http://www.ta-guru.com/Book/TechnicalAnalysis/TechnicalIndicators/WeightedClose.php5
Aliases: WCLPrice, WeightedClosePrice, WCLPRICE, weightedcloseprice
Inputs: high, low, close
Outputs: wcl
Params:
- weight (default: 2.0)
Weight Factor for close
- divisor (default: 4.0)
Factor to use in the division
Cycle
ht_dcperiod
Ehlers': Hilber Transform Dominant Cycle Period
Formula:
- From *"Rocket Science for Traders: Digital Signal Processing Applications"*
See:
- https://www.amazon.com/Rocket-Science-Traders-Processing-Applications/dp/0471405671
Aliases: HT_DCPERIOD, HilberTransform_DominantCyclePeriod
Inputs: high, low
Outputs: dcperiod
ht_dcphase
Ehlers': Hilber Transform Dominant Cycle Phase
Formula:
- From *"Rocket Science for Traders: Digital Signal Processing Applications"*
See:
- https://www.amazon.com/Rocket-Science-Traders-Processing-Applications/dp/0471405671
Aliases: HT_DCPHASE, HilberTransform_DominantCyclePhase
Inputs: high, low
Outputs: dcphase
ht_phasor
Ehlers': Hilbert Transform Phasor
Formula:
- From *"Rocket Science for Traders: Digital Signal Processing Applications"*
See:
- https://www.amazon.com/Rocket-Science-Traders-Processing-Applications/dp/0471405671
Aliases: HT_PHASOR, HilberTransform_Phasor
Inputs: high, low
Outputs: inphase, quadrature
ht_sine
Ehlers': Hilber Transform Sine
Formula:
- From *"Rocket Science for Traders: Digital Signal Processing Applications"*
See:
- https://www.amazon.com/Rocket-Science-Traders-Processing-Applications/dp/0471405671
Aliases: HT_SINE, HilberTransform_Sine
Inputs: high, low
Outputs: sine, leadsine
ht_trendmode
Ehlers': Hilber Transform Trend Mode
Formula:
- From *"Rocket Science for Traders: Digital Signal Processing Applications"*
See:
- https://www.amazon.com/Rocket-Science-Traders-Processing-Applications/dp/0471405671
Aliases: HT_TRENDMODE, HilberTransform_Trendmode
Inputs: high, low
Outputs: trendline
Statistic
beta
The description of the algorithm has been adapted from `ta-lib`
The Beta 'algorithm' is a measure of a stocks volatility vs from index. The
stock prices are given as the 1st input and the index prices are given in
the 2nd input. The size of the inputs should be equal. The algorithm is to
calculate the change between prices in both inputs and then 'plot' these
changes are points in the Euclidean plane. The x value of the point is
market return and the y value is the security return. The beta value is the
slope of a linear regression through these points. A beta of 1.0 is simple
the line y=x, so the stock varies precisely with the market. A beta of less
than 1.0 means the stock varies less thandd the market and a beta of more
than 1.0 means the stock varies more than market.
See:
- http://www.moneychimp.com/articles/risk/regression.htm
Aliases: BETA, Beta
Inputs: asset, market
Outputs: beta
Params:
- period (default: 5)
Period to consider
- _prets (default: 1)
Lookback period to calculate the returns
- _rets (default: True)
Calculate beta on returns
correl
Rolling Pearson correlation of the two inputs `asset1` and `asset2`
Note: the default inputs are named `high` and `low` to ensure an easy match
with a multiple input (dataframe). But they can be anything
See:
- https://en.wikipedia.org/wiki/Pearson_correlation_coefficient
Aliases: CORREL, Correl
Inputs: high, low
Outputs: correl
Params:
- period (default: 30)
Period to consider
- _prets (default: 1)
Lookback period to calculate the returns
- _rets (default: True)
Calculate beta on returns
linearreg
Linear Regression of the values of an asset (y-axis) along the "x" (period
window, x-axix) using the least squares method to find the best fit.
Formula:
- y = b + m * x
**This indicator returns: "b + m * x" (using x = period - 1)**
See:
- https://en.wikipedia.org/wiki/Ordinary_least_squares#Simple_linear_regression_model
- https://devarea.com/linear-regression-with-numpy/
Aliases: LINEARREG, LinearReg, linreg
Inputs: close
Outputs: linreg
Params:
- period (default: 14)
TA-LIB (with compatibility flag "_talib=True"):
`pminus1` is there for compatibility with the broken ta-lib behavior
If active, "p1" will be p - 1 and calculations will diverge by having
"0" as the 1st item of "x" ([0, period] instead of [1, period + 1])
The 1st behavior is used by ta-lib for `linearreg`,
`linearreg_intercept` and `tsf`.
The 2nd behavior is used by ta-lib for `linearreg_slope` and
`linearreg_angle`.
Intercept can be calculated as follows b = (Sum(y) - m * Sum(x)) / n.
In the default `ta-lib` behavior (and in the compatible one with this
method) this equation isn't true due to the
different period ranges used for the calculations.
linearreg_angle
Linear Regression of the values of an asset (y-axis) along the "x" (period
window, x-axix) using the least squares method to find the best fit.
Formula:
- y = b + m * x
**This indicator returns: "m" or (slope)** (expressed in degrees)
See:
- https://en.wikipedia.org/wiki/Ordinary_least_squares#Simple_linear_regression_model
- https://devarea.com/linear-regression-with-numpy/
Aliases: LINEARREG_ANGLE, LinearReg_Angle, linreg_angle
Inputs: close
Outputs: slope
Params:
- period (default: 14)
linearreg_intercept
Linear Regression of the values of an asset (y-axis) along the "x" (period
window, x-axix) using the least squares method to find the best fit.
Formula:
- y = b + m * x
**This indicator returns: "b" or (intercept)**
See:
- https://en.wikipedia.org/wiki/Ordinary_least_squares#Simple_linear_regression_model
- https://devarea.com/linear-regression-with-numpy/
Aliases: LINEARREG_INTERCEPT, LinearReg_Intercept, linreg_intercept
Inputs: close
Outputs: intercept
Params:
- period (default: 14)
TA-LIB (with compatibility flag "_talib=True"):
`pminus1` is there for compatibility with the broken ta-lib behavior
If active, "p1" will be p - 1 and calculations will diverge by having
"0" as the 1st item of "x" ([0, period] instead of [1, period + 1])
The 1st behavior is used by ta-lib for `linearreg`,
`linearreg_intercept` and `tsf`.
The 2nd behavior is used by ta-lib for `linearreg_slope` and
`linearreg_angle`.
Intercept can be calculated as follows b = (Sum(y) - m * Sum(x)) / n.
In the default `ta-lib` behavior (and in the compatible one with this
method) this equation isn't true due to the
different period ranges used for the calculations.
linearreg_slope
Linear Regression of the values of an asset (y-axis) along the "x" (period
window, x-axix) using the least squares method to find the best fit.
Formula:
- y = b + m * x
**This indicator returns: "m" or (slope)**
See:
- https://en.wikipedia.org/wiki/Ordinary_least_squares#Simple_linear_regression_model
- https://devarea.com/linear-regression-with-numpy/
Aliases: LINEARREG_SLOPE, LinearReg_Slope, linreg_slope
Inputs: close
Outputs: slope
Params:
- period (default: 14)
mad
Calculates the Mean Absolute Deviation ('mad') of the input over a given
period
See:
- https://en.wikipedia.org/wiki/Average_absolute_deviation
Aliases: MAD, meandev, MeanDev, MeanDeviation, MeanAbsDeviation
Inputs: close
Outputs: meandev
Params:
- period (default: 20)
Period to consider
- _ma (default: sma)
Moving Average to use
stddev
Calculates the standard deviation of the passed data for a given period,
considering the data to be the entire population
See:
- http://en.wikipedia.org/wiki/Standard_deviation
Aliases: StdDev, STDDEV, StandardDeviation, stddev_p, STDDEV_P
Inputs: close
Outputs: std
Params:
- period (default: 20)
Period to consider
- ddof (default: 0)
Degree of Freedom: 0 = population, > 0 sample
TA-LIB (with compatibility flag "_talib=True"):
Change period to 5
stddev_s
Calculates the standard deviation of the passed data for a given period,
considering the data to be a sample
See:
- http://en.wikipedia.org/wiki/Standard_deviation
Aliases: stddev_sample, STDDEV_S, StandardDeviationSample
Inputs: close
Outputs: std
Params:
- period (default: 20)
- ddof (default: 1)
Degree of Freedom: 0 = population, > 0 sample
TA-LIB (with compatibility flag "_talib=True"):
Change period to 5
tsf
Linear Regression of the values of an asset (y-axis) along the "x" (period
window, x-axix) using the least squares method to find the best fit.
Formula:
- y = b + m * x
**This indicator returns: "b + m * x" (using x = period)**
See:
- https://en.wikipedia.org/wiki/Ordinary_least_squares#Simple_linear_regression_model
- https://devarea.com/linear-regression-with-numpy/
Aliases: TSF, TimeSeriesForecast
Inputs: close
Outputs: tsf
Params:
- period (default: 14)
TA-LIB (with compatibility flag "_talib=True"):
`pminus1` is there for compatibility with the broken ta-lib behavior
If active, "p1" will be p - 1 and calculations will diverge by having
"0" as the 1st item of "x" ([0, period] instead of [1, period + 1])
The 1st behavior is used by ta-lib for `linearreg`,
`linearreg_intercept` and `tsf`.
The 2nd behavior is used by ta-lib for `linearreg_slope` and
`linearreg_angle`.
Intercept can be calculated as follows b = (Sum(y) - m * Sum(x)) / n.
In the default `ta-lib` behavior (and in the compatible one with this
method) this equation isn't true due to the
different period ranges used for the calculations.
var
Variance is the expectation of the squared deviation of a random variable
from its mean
See:
- https://en.wikipedia.org/wiki/Variance
Aliases: VAR, Var
Inputs: close
Outputs: var
Params:
- period (default: 5)
Period to consider
- ddof (default: 0)
Degree of Freedom: 0 = population, > 0 sample
var_s
Variance is the expectation of the squared deviation of a random variable
from its mean
This version considers the population is a sample (ddof=1)
See:
- https://en.wikipedia.org/wiki/Variance
Aliases: varsample, VARS, VarSample
Inputs: close
Outputs: var
Params:
- period (default: 5)
- ddof (default: 1)
Degree of Freedom: 0 = population, > 0 sample
acos
Calculates the `arccos` function of the input, i.e.: the inverse
trigonometric function of `cos`
Formula:
- acos = arccos(data)
See also:
- https://en.wikipedia.org/wiki/Inverse_trigonometric_functions
Aliases: ACOS, arccos, arccosine
Inputs: close
Outputs: acos
Params:
- degrees (default: False)
asin
Calculates the `arcsin` function of the input, i.e.: the inverse
trigonometric function of `sin`
Formula:
- asin = arcsin(data)
See also:
- https://en.wikipedia.org/wiki/Inverse_trigonometric_functions
Aliases: ASIN, arcsin, arcsine
Inputs: close
Outputs: asin
Params:
- degrees (default: False)
atan
Calculates the `arctan` function of the input, i.e.: the inverse
trigonometric function of `tan`
Formula:
- atan = arctan(data)
See also:
- https://en.wikipedia.org/wiki/Inverse_trigonometric_functions
Aliases: ATAN, arctan, arctangent
Inputs: close
Outputs: atan
Params:
- degrees (default: False)
ceil
Calculates the `ceil` function which maps the input to the least integer
greater than or equal to the input
Formula:
- ceil = ceil(data)
See also:
- https://en.wikipedia.org/wiki/Floor_and_ceiling_functions
Aliases: CEIL
Inputs: close
Outputs: ceil
cos
Calculates the `cosine` function of the input
Formula:
- cos = cosine(data)
See also:
- https://en.wikipedia.org/wiki/Trigonometric_functions
Aliases: COS, cosine
Inputs: close
Outputs: cos
Params:
- degrees (default: False)
cosh
Calculates the `secant` function of the input (1/cosine)
Formula:
- cosh = secant(data)
See also:
- https://en.wikipedia.org/wiki/Trigonometric_functions
Aliases: COSH, secant
Inputs: close
Outputs: cosh
Params:
- degrees (default: False)
exp
Calculates the natural logarithm of the input, i.e.: the logarithm with
base `e`
Formula:
- exp = exp(data)
See also:
- https://en.wikipedia.org/wiki/Exponential_function
Aliases: EXP
Inputs: close
Outputs: exp
floor
Calculates the `floor` function which maps the input to the greatest
integer least than or equal to the input
Formula:
- floor = floor(data)
See also:
- https://en.wikipedia.org/wiki/Floor_and_ceiling_functions
Aliases: FLOOR
Inputs: close
Outputs: floor
ln
Calculates the natural logarithm of the input, i.e.: the logarithm with
base `e`
Formula:
- ln = ln(data)
See also:
- https://en.wikipedia.org/wiki/Natural_logarithm
Aliases: LN, log, LOG
Inputs: close
Outputs: ln
log10
Calculates the common logarithm, i.e.: the logarithm with base 10, of the
input
Formula:
- log10 = log10(data)
See also:
- https://en.wikipedia.org/wiki/Common_logarithm
Aliases: LOG10
Inputs: close
Outputs: log10
sin
Calculates the `sine` function of the input
Formula:
- sin = sine(data)
See also:
- https://en.wikipedia.org/wiki/Trigonometric_functions
Aliases: SIN, sine
Inputs: close
Outputs: sin
Params:
- degrees (default: False)
sinh
Calculates the `cosecant` function of the input (1/sine)
Formula:
- sinh = cosecant(data)
See also:
- https://en.wikipedia.org/wiki/Trigonometric_functions
Aliases: SINH, cosecant
Inputs: close
Outputs: sinh
Params:
- degrees (default: False)
sqrt
Calculates the square root of the input
Formula:
- sqrt = sqrt(data)
See also:
- https://en.wikipedia.org/wiki/Square_root
Aliases: SQRT
Inputs: close
Outputs: sqrt
tan
Calculates the `tangent` function of the input
Formula:
- tan = tangent(data)
See also:
- https://en.wikipedia.org/wiki/Trigonometric_functions
Aliases: TAN, tangent
Inputs: close
Outputs: tan
Params:
- degrees (default: False)
tanh
Calculates the `cotangent` function of the input (1/tangent)
Formula:
- tanh = cotangent(data)
See also:
- https://en.wikipedia.org/wiki/Trigonometric_functions
Aliases: TANH, cotangent
Inputs: close
Outputs: tanh
Params:
- degrees (default: False)
Math Operators
add
Calculates the summation of the two inputs
Formula:
- add = data0 + data1
Aliases: ADD
Inputs: input1, input2
Outputs: add
div
Calculates the division of the two inputs
Formula:
- div = data0 / data1
Aliases: DIV
Inputs: input1, input2
Outputs: div
max
Rolling maximum over `period` of the input
Formula:
- max = max(data, period)
Aliases: highest, Highest, maxn, MaxN, MAX
Inputs: close
Outputs: max
Params:
- period (default: 30)
Period to consider
maxindex
Rolling index of the max value over a period
Formula:
- maxindex = data.index(max(data, period))
Aliases: MAXINDEX
Inputs: close
Outputs: maxindex
Params:
- period (default: 30)
Period to consider
- _absidx (default: False)
Return maxindex over the entire period
TA-LIB (with compatibility flag "_talib=True"):
ta-lib returns 0 as index during the warm-up period and then returns the
absolute index over the entire series and not over the window period
min
Rolling minimum over `period` of the input
Formula:
- min = min(data, period)
Aliases: lowest, Lowest, minn, MinN, MIN
Inputs: close
Outputs: min
Params:
- period (default: 30)
Period to consider
minindex
Rolling index of the max value over a period
Formula:
- maxindex = data.index(max(data, period))
Aliases: MININDEX
Inputs: close
Outputs: minindex
Params:
- period (default: 30)
Period to consider
- _absidx (default: False)
Return maxindex over the entire period
TA-LIB (with compatibility flag "_talib=True"):
ta-lib returns 0 as index during the warm-up period and then returns the
absolute index over the entire series and not over the window period
minmax
Rolling the minimum and maximo over `period` of the input
Formula:
- min = min(data, period)
- min = max(data, period)
Aliases: MINMAX
Inputs: close
Outputs: min, max
Params:
- period (default: 30)
Period to consider
minmaxindex
Rolling index of the max value over a period
Formula:
- maxindex = data.index(max(data, period))
Aliases: MINMAXINDEX
Inputs: close
Outputs: minindex, maxindex
Params:
- period (default: 30)
Period to consider
- _absidx (default: False)
Return maxindex over the entire period
TA-LIB (with compatibility flag "_talib=True"):
ta-lib returns 0 as index during the warm-up period and then returns
the absolute index over the entire series and not over the window
period
mult
Calculates the multiplication of the two inputs
Formula:
- mult = data0 * data1
Aliases: MULT
Inputs: input1, input2
Outputs: mult
sub
Calculates the subtraction of the two inputs
Formula:
- sub = data0 - data1
Aliases: SUB
Inputs: input1, input2
Outputs: sub
sum
Rolling sum over `period` of the input
Formula:
- sum = sum(data, period)
Aliases: sumn, Sum, SumN, SUM
Inputs: close
Outputs: sum
Params:
- period (default: 30)
Period to consider
Utils
crossdown
This indicator gives a signal if the 1st provided data crosses over the 2nd
indicator upwards
It does need to look into the current time index (0) and the previous time
index (-1) of both the 1st and 2nd data
Formula:
- crossdown = data0(-1) > data1(-1) and data0 < data1
Aliases: CrossDown
Inputs: crosser, crossed
Outputs: crossdown
Params:
- _strict (default: False)
crossover
This indicator gives a signal if the provided datas (2) cross up or down.
- `1.0` if the 1st data crosses the 2nd data upwards
- `-1.0` if the 1st data crosses the 2nd data downwards
It does need to look into the current time index (0) and the previous time
index (-1) of both the 1st and 2nd data
Formula:
- crossup = data0(-1) < data1(-1) and data0 > data1
- crossdown = data0(-1) > data1(-1) and data0 < data1
- crossover = crossup - crossdown
Aliases: CrossOver
Inputs: crosser, crossed
Outputs: crossover
Params:
- _strict (default: False)
crossup
This indicator gives a signal if the 1st provided data crosses over the 2nd
indicator upwards
It does need to look into the current time index (0) and the previous time
index (-1) of both the 1st and 2nd data
Formula:
- crossup = data0(-1) < data1(-1) and data0 > data1
Aliases: CrossUp
Inputs: crosser, crossed
Outputs: crossup
Params:
- _strict (default: False)
Pattern Recognition