### Trading Indicators #

Unlock a spectrum of technical insights with Indicator Blocks, offering an extensive range of indicators, from straightforward moving averages to intricate calculations. This section empowers users to integrate these indicators seamlessly into their strategies, enhancing their ability to discern market trends and pinpoint potential entry or exit points with precision.

To create a new Indicator block, double click in the blueprint screen, or click the `+`

button in the menu. In the search bar, type: “indicator/” followed by the abbreviations below, or simply type the abbreviation below.

Remember, Different strategies require different types of indicators. The best trading indicators complement one another, without duplicating information.

### Some of our favourites #

Indicator: ticker

##### Last Signal Profit: last_profit #

facilitates the calculation of profit since the last trading signal, providing users with insights into the performance of their strategy over a specific time frame. Useful for creating stop triggers.*Last signal profit*

##### Candlestick Pattern Detection: candlestick #

aids in detecting Candlestick Patterns, such as bull, bear, hammers, shooting stars, and more, assisting users in identifying key market trends and potential reversal signals based on candlestick formations.*Candlestick Pattern Detection*

##### Relative Strength Index: rsi #

* Relative Strength Index *measures the speed and rate of change in price movements within the market; it oscillates between zero and 100. It provides insights into whether an asset is overbought or oversold, helping users identify potential trend reversals and market conditions.

##### Simple Moving Average: sma #

calculates the average price of an asset over a specified number of periods, providing a smooth trend line. It is useful for identifying general market direction.*Simple Moving Average*

##### Exponential Moving Average: ema #

is similar but gives more weight to recent prices, making it more responsive to short-term price changes. It is beneficial for capturing more immediate market trends.*Exponential Moving Average*

##### SuperTrend: SuperTrend #

calculates the Supertrend value based on the market’s price and volatility, helping users determine the current trend’s direction.*Supertrend*

##### SuperTrendKO: SuperTrendKO #

Similar to Supertrend, SupertrendKO is a modified version that factors in market noise and aims to provide more accurate trend signals by minimising false positives.*SuperTrendKO*

### The Full Trading Indicator List #

##### Vector Absolute Value: abs #

* Vector Absolute Value *calculates the absolute value of each element in an array.

##### Vector Arccosine: acos #

calculates the Trigonometric arccosine of each element in an array.*Vector Arccosine*

##### Accumulation Distribution Line: ad #

determines the trend of a stock, using the relation between the volume flow and the stock’s price.*Accumulation Distribution Line*

##### Add: add #

* Add *adds two arrays together.

##### Accumulation Distribution Oscillator: adosc #

is calculated by taking an exponential moving average of short periods of accumulation distribution line subtracted from an exponential moving average of long periods of accumulation distribution line.*Accumulation Distribution Oscillator*

##### Average Directional Movement Index: adx #

shows the strength of a trend through a value in a range of 0 to 100.*Average Directional Movement Index*

##### Average Directional Movement Index Rating: adxr #

is the same as the average directional movement index but is smoother. This indicator gets less affected than *Average Directional Movement Index Rating*`adx`

from the fast short-term market oscillations.

##### Awesome Oscillator: ao #

measures the momentum of the market.*Awesome Oscillator*

##### Absolute Price Oscillator: apo #

is the difference between the short-period exponential moving average and the long-period exponential moving average.*Absolute Price Oscillator*

##### Aroon: aroon #

comprises two indicators: *Aroon*`Aroon-Up`

and `Aroon-Down`

. Aroon can identify the beginning of a trend, its strength, and any changes.

##### Aroon Oscillator: aroonosc #

is the difference between *Aroon Oscillator*`Aroon-Up`

and `Aroon-Down`

indicators, and the output would be a value between 0 and 100.

##### Vector Arcsine: asin #

calculates the trigonometric arcsine of each element in an array.*Vector Arcsine*

##### Vector Arctangent: atan #

calculates the trigonometric arctangent of each element in an array.*Vector Arctangent*

##### Average True Range: atr #

measures market volatility over a stock’s price range for a specified period.*Average True Range*

##### Average Price: avgprice #

shows the mean of open, high, low, and close prices of a stock.*Average Price*

##### Bollinger Bands: bbands #

contains the upper, middle, and lower bands. The middle one is a moving average indicator, and the upper and lower bands are on the sides of the middle one. The value of the standard deviations determines the distance between the middle band and the upper and lower ones.*Bollinger Bands*

##### Balance of Power: bop #

evaluates the strength of buyers and sellers in the market.*Balance of Power*

##### Candlestick Pattern Detection: candlestick #

aids in detecting Candlestick Patterns, such as bull, bear, hammers, shooting stars, and more, assisting users in identifying key market trends and potential reversal signals based on candlestick formations.*Candlestick Pattern Detection*

##### Commodity Channel Index: cci #

would be high when prices are far above the average and would be low when prices are far below it. So cci can identify overbought and oversold areas of price action. Besides that, it gets used to discover reversals and divergences.*Commodity Channel Index*

##### Vector Ceiling: ceil #

shows the smallest integer from the elements of an array.*Vector Ceiling*

##### Chande Momentum Oscillator: cmo #

calculates the price of momentum on bullish or/and bearish days. In other words, it computes the difference between the sum of higher closes and the sum of lower closes, dividing by the sum of all price movements.*Chande Momentum Oscillator*

##### Vector Cosine: cos #

calculates the trigonometric cosine of each element in an array.*Vector Cosine*

##### Vector Hyperbolic Cosine: cosh #

calculates the trigonometric hyperbolic cosine of each element in an array.*Vector Hyperbolic Cosine*

##### Cross Any: crossany #

continuously detects whether the inputs are crossing each other.*Crossany*

##### Cross Over: crossover #

continuously detects whether the first input is crossing over the other one. It means, against the crossany indicator, the only situation that matters is when the first input would place above the other one.*Crossover*

##### Chaikins Volatility: cvi #

* Chaikins Volatility* calculates the difference between the high and low prices for each period.

##### Decay: decay #

saves an array of recent signals. It is a useful indicator, especially in machine learning algorithms.*Decay*

##### Double Exponential Moving Average: dema #

is the same as the exponential moving average, but due to allocating more weight to recent data points, delivers fewer lag data.*Double Exponential Moving Average*

##### Directional Indicator: di #

comprises *Directional Indicator*`positive directional indicator`

and `negative directional indicator`

lines that show the price trend movement. Crossing these two lines propagates the buy and sell signals; If the positive line crosses up through the negative one, it is a Buy signal, and vice versa.

##### Vector Division: div #

divides the provided inputs.*Vector Division*

##### Directional Movement: dm #

draws *Directional Movement*`positive directional movement`

and `negative directional movement`

lines. They get calculated using the prior high and low prices.

##### Detrended Price Oscillator: dpo #

removes price trends to make it easier to identify peaks and troughs. Thus, estimating the cycle lengths using the indicator is much simpler.*Detrended Price Oscillator*

##### Directional Movement Index: dx #

, which is also referred to as *Directional Movement Index*`dmi`

, contains two directional movement lines and the average directional movement index indicator.

##### Exponential Decay: edecay #

is almost the same as decay but faster for the same period.*Exponential Decay*

##### Exponential Moving Average: ema #

shows the direction of the price changes over a period. EMA is like a *Exponential Moving Average*`Simple Moving Average`

, but where the SMA directly calculates the average price values, EMA applies more weight to the recent prices.

##### Ease of Movement: emv #

investigates the relationship between price fluctuations and trading volume.*Ease of Movement*

##### Vector Exponential: exp #

returns the exponential for each number in the input arary. That is, it calculates Euler’s constant, e, raised to the power of each input element.*Vector Exponential*

##### Fisher Transform: fisher #

*Fis** her Transform* aims to enhance the predictability of turning points in a price series by making prices more normally distributed. This transformation makes it easier to identify extreme values and potential reversals

##### The Vector Floor: floor #

*The Vector Floor* of a value is the largest integer less than or equal to it.

##### Forecast Oscillator: fosc #

predicts the upcoming stock’s price by monitoring the difference between the current stock’s price and a linear regression price resulting from the *Forecast Oscillator*`Time Series Forecast`

function.

##### Hull Moving Average: hma #

is an improved moving average that removes the lags (and thus is super fast) and is smoother than the other traditional moving average indicators.*Hull Moving Average*

##### Kaufman Adaptive Moving Average: kama #

reduces false signals by eliminating short-term price fluctuations. In other words, kama removes the market noises, so if the market volatility is low, it will heel the current market price.*Kaufman Adaptive Moving Average*

##### Klinger Volume Oscillator: kvo #

forecasts market reversals by comparing the volume to the price.*Klinger Volume Oscillator*

##### Lag Block: lag #

* Lag block* delays the input data by a specified amount. For example, with a lag of 1 on 15-minute candles, it outputs data from the previous candle. This is useful for comparing current values with past ones.

##### Laguerre Filter: laguerrefilter #

is used to smooth price data and identify trends. It applies a Laguerre filter algorithm to market data, reducing noise and providing a clearer representation of the underlying trend.*Laguerre filter*

##### Last signal profit: last_profit #

facilitates the calculation of profit since the last trading signal, providing users with insights into the performance of their strategy over a specific time frame.*Last signal profit*

##### Linear Regression: linreg #

plots the ending values of linear regression lines for a specific number of bars.*Linear Regression*

##### Linear Regression Intercept: linregintercept #

returns the height of the linear regression line for the first input bar in the moving period.*Linear Regression Intercept*

##### Linear Regression Slope: linregslope #

determines the direction of trend strength. The indicator determines the slope for each bar using the current bar and the n-1 previous bars where *Linear Regression Slope*`n`

is the period specified by the trader.

##### Vector Natural Log: ln #

calculates the natural logarithm for each element in an input array.*Vector Natural Log*

##### Vector Base-10 Log: log10 #

calculates the base-10 logarithm for each element in an input array.*Vector Base-10 Log*

##### Moving Average Convergence Divergence: macd #

determines the direction of the stock price. Consider not using this indicator for detecting trend reversals since it can detect them only after they happen. It is not usually used to identify overbought or oversold conditions as well.*Moving Average Convergence Divergence*

##### Market Facilitation Index: marketfi #

measures the trend strength and predicts the starting of a trend when it is about to occur. It calculates the price movement per volume unit.*Market Facilitation Index*

##### Mass Index detects: mass #

* Mass Index detects* helps traders identify potential trend reversals by measuring the expansion and contraction of the trading range (the difference between the high and low prices) over a specified period using

*Exponential Moving Averages*.

##### Maximum In Period: max #

returns the maximum value in the last *Maximum In Period*`n`

bars.

##### Mean Deviation Over Period: md #

computes the absolute mean deviation over a period.*Mean Deviation Over Period*

##### Median Price: medprice #

computes the mean of the high and low prices for a bar.*Median Price*

##### The Money Flow Index: mfi #

*The Money Flow Index* measures the trading pressure by monitoring both the price and volume and returns a value between 0 and 100.

##### Minimum In Period: min #

* Minimum In Period *returns the minimum value in the last

`n`

bars.##### Momentum: mom #

computes the change between the current price and the price of the *Momentum*`n-th`

bar from the last.

##### Mesa Sine Wave: msw #

detects whether the market is in a cycle mode or a trend mode.*Mesa Sine Wave*

##### Vector Multiplication: mul #

*Vector Multiplication* takes two input arrays and multiplies them.

##### Normalized Average True Range: natr #

is a normalized version of the *Normalized Average True Range*`average true range`

and gets calculated with the following formula: NATR = (ATR / Close) * 100.

##### Negative Volume Index: nvi #

is a cumulative indicator and is sensitive to the market volume. It argued that high market volume is because of uninformative traders, so it doesn’t care about the high-volume days. On low-volume days, informed traders are more active, and therefore *Negative Volume Index*`nvi`

indicator gets affected by them; the `nvi`

value will rise on positive price changes and will fall on negative price changes.

##### On Balance Volume: obv #

is a cumulative indicator that calculates buying and selling pressures. It increases on up days and decreases on down days.*On Balance Volume*

##### Percentage Price Oscillator: ppo #

calculates the difference between two exponential moving averages with different periods divided by the longer one.*Percentage Price Oscillator*

##### Predict: predict #

block aims to leverage machine learning to predict the next price movement. *Predict*** Care: **This block is still in the process of learning and has not been fully trained. Until such time, it may not produce accurate predictions.

##### Parabolic SAR: psar #

helps to figure out stop points and potential reversals in trends. Indeed *Parabolic SAR*`SAR`

stands for `stop and reverse`

, which describes its application nicely.

##### Positive Volume Index: pvi #

is the same as *Positive Volume Index**Negative Volume Index* `nvi`

– and often gets used in conjunction with it – but is sensitive to high-volume days.

##### Qstick: qstick #

as a momentum indicator applies a simple moving average on the difference between the stock close and open prices.*Qstick*

##### Rate of Change: roc #

computes the percentage change between the current price and the price *Rate of Change*`n`

periods ago.

##### Rate of Change Ratio: rocr #

`Rate of Change Ratio`

computes the change between the current price and the price `n`

periods ago.

##### Vector Round: round #

returns the closest integer for each element in an array.*Vector Round*

##### Relative Strength Index: rsi #

measures the speed and rate of change in price movements within the market; it oscillates between zero and 100.*Relative Strength Index*

##### Vector Sine: sin #

computes the Trigonometric sine of each element in an array.*Vector Sine*

##### Vector Hyperbolic Sine: sinh #

computes the Trigonometric hyperbolic sine of each element in an array.*Vector Hyperbolic Sine*

##### Simple Moving Average: sma #

shows the direction of the price changes over a period by calculating the average price value.*Simple Moving Average*

##### Vector Square Root: sqrt #

computes the square root of each element in an array.*Vector Square Root*

##### Standard Deviation Over Period: stddev #

measures the difference between the current price and the average price over a period.*Standard Deviation Over Period*

##### Standard Error Over Period: stderr #

shows how different the population mean is from the sample mean.*Standard Error Over Period*

##### Stochastic Oscillator: stoch #

compares the last close price to the highest and lowest prices over a period and ranges from zero to 100.*Stochastic Oscillator*

##### Stochastic RSI: stochrsi #

is a combination of two indicators: stoch and rsi. Actually, it’s applying a stoch indicator on a rsi indicator, which means it’s a measure of rsi relative to its high/low range over a period.*Stochastic RSI*

##### Stock to flow: stocktoflow #

assesses the scarcity of a particular asset, often applied to cryptocurrencies like Bitcoin. It compares the existing stock (current supply) to the flow (new production), offering insights into the asset’s potential value and market dynamics.*Stock to flow*

##### Vector Subtraction: sub #

*Vector Subtraction* returns the subtraction of the two inputs (a – b).

##### Super Trend: SuperTrend #

calculates the Super Trend value based on the market’s price and volatility, helping users determine the current trend’s direction.*Super Trend*

##### Super Trend KO: SuperTrendKO #

Similar to *Super Trend KO**Super Trend*, *Super Trend KO* is a modified version that factors in market noise and aims to provide more accurate trend signals by minimizing false positives.

##### Sum Over Period: sum #

returns the sum of the last *Sum Over Period*`n`

bars.

##### Vector Tangent: tan #

calculates the Trigonometric tangent of each element in an array.*Vector Tangent*

##### Vector Hyperbolic Tangent: tanh #

calculates the Trigonometric hyperbolic tangent of each element in an array.*Vector Hyperbolic Tangent*

##### Triple Exponential Moving Average: tema #

is a high-speed moving average with smoother data. It reduces the lags by placing more weight on the recent data and thus is more appropriate for short-term trading.*Triple Exponential Moving Average*

##### Vector Degree Conversion: todeg #

converts an array of radians into an array of degrees.*Vector Degree Conversion*

##### Vector Radian Conversion: torad #

converts an array of degrees into an array of radians.*Vector Radian Conversion*

##### True Range: tr #

returns the greater value of:*True Range*

The daySubtraction of the high and low prices of the same day.

- Day’s high minus day’s low
- The absolute value of the day’s high minus the previous day’s close
- The absolute value of the day’s low minus the previous day’s close

##### Triangular Moving Average: trima #

is the same as *Triangular Moving Average**Simple Moving Average*, sma, but it’s averaged twice; In other words, trima is a sma that applies to another sma. This approach leads to a smoother line that places more weight on the middle bars.

##### Triple Exponential Moving Average: trix #

*Triple Exponential Moving Average *shows the percentage change of a triple-smoothed ema (applying an ema three times).

##### Vector Truncate: trunc #

returns only the integer part of a number for each element in an array.*Vector Truncate*

##### Time Series Forecast: tsf #

, as expected from the name, predicts future trends based on past data. It is more sensitive to sudden price changes compared to the moving average indicators.*Time Series Forecast*

##### Typical Price: typprice #

computes the arithmetic mean of the high, low, and close prices.*Typical Price*

##### Ultimate Oscillator: ultosc #

measures buying pressure by considering three different time frames. These periods (7, 14, 28) describe short, medium, and long-term market trends.*Ultimate Oscillator*

##### Variance Over Period: var #

measures the variation by calculating the average of squared deviations from the mean.*Variance Over Period*

##### Vertical Horizontal Filter: vhf #

monitors the price movements and indicates the prices phase, that they are in the trading or the congestion phase.*Vertical Horizontal Filter*

##### Variable Index Dynamic Average: vidya #

calculates an ema with a dynamic period depending on the market volatility.*Variable Index Dynamic Average*

##### Annualized Historical Volatility: volatility #

measures the deviation of the annual average stock price over a period.*Annualized Historical Volatility*

##### Volume Oscillator: vosc #

*Volume Oscillator *calculates the difference between a fast volume moving average and a slow volume moving average. Monitoring volume changes in this manner has more technical importance than monitoring volume itself.

##### Volume Weighted Moving Average: vwma #

is just like most moving average indicators but considers the market volume in its calculations. It actually gives more weight to the high-volume prices than the low-volume prices.*Volume Weighted Moving Average*

##### Williams Accumulation/Distribution: wad #

is the accumulated sum of accumulation and distribution price changes. Accumulation and distribution describe a market controlled by buyers and sellers, respectively. Indeed, the wad indicator measures the positive and negative market pressures.*Williams Accumulation/Distribution*

##### Weighted Close Price: wcprice #

is simply the average of high, low, and doubled closing prices.*Weighted Close Price*

##### Wilder’s Smoothing: wilders #

is the same as ema, but wilder’s smoothing uses a different smoothing factor, which leads to a slower response to price changes.*Wilder's Smoothing*

##### Williams %R: willr #

identifies overbought and oversold markets by comparing the position of the most recent closing price to the highest and lowest prices over a period.*Williams %R*

##### Weighted Moving Average: wma #

is the same as sma, but puts more weight on the recent data. This way, it responds faster to price changes and will stay closer to the market price.*Weighted Moving Average*

##### Zero-Lag Exponential Moving Average: zlema #

follows the same goal as dema and tema. It eliminates the lags to improve the speed and track the price more closely.*Zero-Lag Exponential Moving Average*