### 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 #

##### 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 #

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*

##### 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.

##### 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*

##### 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 #

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

##### 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 #

##### abs #

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

##### acos #

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

##### ad #

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

##### add #

* Add *adds two arrays together.

##### 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*

##### adx #

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

##### 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.

##### ao #

measures the momentum of the market.*Awesome Oscillator*

##### apo #

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

##### aroon #

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

and `Aroon-Down`

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

##### aroonosc #

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

and `Aroon-Down`

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

##### asin #

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

##### atan #

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

##### atr #

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

##### avgprice #

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

##### 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*

##### bop #

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

##### 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*

##### 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*

##### ceil #

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

##### 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*

##### cos #

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

##### cosh #

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

##### crossany #

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

##### 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*

##### 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.

##### dema #

`Double Exponential Moving Average`

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

##### di #

`Directional Indicator`

comprises `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.

##### div #

`Vector Division`

divides the provided inputs.

##### dm #

`Directional Movement`

draws `positive directional movement`

and `negative directional movement`

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

##### dpo #

`Detrended Price Oscillator`

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

##### dx #

`Directional Movement Index`

, which is also referred to as `dmi`

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

##### edecay #

`Exponential Decay`

is almost the same as decay but faster for the same period.

##### ema #

`Exponential Moving Average`

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

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

##### emv #

`Ease of Movement`

investigates the relationship between price fluctuations and trading volume.

##### exp #

`Vector Exponential`

calculates `e`

raised to the power of each input element.

##### fisher #

`Fisher`

is an unpopular indicator that, collaborating with other indicators, can identify price reversals.

##### floor #

The `Vector Floor`

of a value is the largest integer less than or equal to it.

##### fosc #

`Forecast Oscillator`

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

function.

##### hma #

`Hull Moving Average`

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

##### kama #

`Kaufman Adaptive Moving Average`

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.

##### kvo #

`Klinger Volume Oscillator`

forecasts market reversals by comparing the volume to the price.

##### lag #

The `Lag`

indicator produces lag to its input.

##### Laguerrefilter #

`Laguerre filter`

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.

##### last_profit #

`Last signal 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.

##### linreg #

`Linear Regression`

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

##### linregintercept #

`Linear Regression Intercept`

returns the height of the linear regression line for the first input bar in the moving period.

##### linregslope #

`Linear Regression Slope`

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 `n`

is the period specified by the trader.

##### ln #

`Vector Natural Log`

calculates the natural logarithm for each element in an input array.

##### log10 #

`Vector Base-10 Log`

calculates the base-10 logarithm for each element in an input array.

##### macd #

`Moving Average Convergence Divergence`

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.

##### marketfi #

`Market Facilitation Index`

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.

##### mass #

`Mass Index`

detects market trend reversals.

##### max #

`Maximum In Period`

returns the maximum value in the last `n`

bars.

##### md #

`Mean Deviation Over Period`

computes the absolute mean deviation over a period.

##### medprice #

`Median Price`

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

##### mfi #

The `Money Flow Index`

measures the trading pressure by monitoring both the price and volume and returns a value between 0 and 100.

##### min #

`Minimum In Period`

returns the minimum value in the last `n`

bars.

##### mom #

`Momentum`

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

bar from the last.

##### msw #

`Mesa Sine Wave`

detects whether the market is in a cycle mode or a trend mode.

##### mul #

`Mul`

takes two input arrays and multiplies them.

##### natr #

`Normalized Average True Range`

is a normalized version of the `average true range`

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

##### nvi #

The `Negative Volume Index`

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 `nvi`

indicator gets affected by them; the `nvi`

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

##### obv #

`On Balance Volume`

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

##### ppo #

`Percentage Price Oscillator`

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

##### predict #

`Predict`

block aims to leverage machine learning to predict the next price movement. Care: This block is still learning and may produce 100% accurate predictions initially.

##### psar #

`Parabolic SAR`

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

stands for `stop and reverse`

, which describes its application nicely.

##### pvi #

`Positive Volume Index`

is the same as `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.

##### roc #

`Rate of Change`

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

periods ago. The formula is:

`[current price - price n periods ago]/price n periods ago * 100`

##### rocr #

`Rate of Change Ratio`

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

periods ago. The formula is:

`current price/price n periods ago`

##### round #

`Vector Round`

returns the closest integer for each element in an array.

##### rsi #

`Relative Strength Index`

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

##### sin #

`Vector Sine`

computes the Trigonometric sine of each element in an array.

##### sinh #

`Vector Hyperbolic Sine`

computes the Trigonometric hyperbolic sine of each element in an array.

##### sma #

`Simple Moving Average`

shows the direction of the price changes over a period by calculating the average price value.

##### sqrt #

`Vector Square Root`

computes the square root of each element in an array.

##### stddev #

`Standard Deviation Over Period`

measures the difference between the current price and the average price over a period.

##### stderr #

`Standard Error Over Period`

shows how different the population mean is from the sample mean.

##### stoch #

`Stochastic Oscillator`

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

##### stochrsi #

`Stochastic RSI`

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.

##### stocktoflow #

`Stock to flow`

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.

##### sub #

`Sub`

returns the subtraction of the two inputs.

##### SuperTrend #

`Supertrend`

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

##### SuperTrendKO #

`SuperTrendKO`

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

##### sum #

`Sum Over Period`

returns the sum of the last `n`

bars.

##### tan #

`Vector Tangent`

calculates the Trigonometric tangent of each element in an array.

##### tanh #

`Vector Hyperbolic Tangent`

calculates the Trigonometric hyperbolic tangent of each element in an array.

##### tema #

`Triple Exponential Moving Average`

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.

##### todeg #

`Vector Degree Conversion`

converts an array of radians into an array of degrees.

##### torad #

`Vector Radian Conversion`

converts an array of degrees into an array of radians.

##### tr #

`True range`

is the maximum of the following values:

Subtraction of the high and low prices of the same day.

The absolute value of the subtraction of a day’s high price and the previous day’s close price.

The absolute value of the subtraction of a day’s low price and the previous day’s close price.

##### trima #

`Triangular Moving Average`

is the same as 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.

##### trix #

`Trix`

shows the percentage change of a triple-smoothed ema (applying an ema three times).

##### trunc #

`Vector Truncate`

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

##### tsf #

`Time Series Forecast`

, 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.

##### typprice #

`Typical Price`

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

##### ultosc #

`Ultimate Oscillator`

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

##### var #

`Variance Over Period`

measures the variation by calculating the average of squared deviations from the mean.

##### vhf #

`Vertical Horizontal Filter`

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

##### vidya #

`Variable Index Dynamic Average`

calculates an ema with a dynamic period depending on the market volatility.

##### volatility #

`Annualized Historical Volatility`

measures the deviation of the annual average stock price over a period.

##### 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.

##### vwma #

`Volume Weighted Moving Average`

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.

##### wad #

`Williams Accumulation/Distribution`

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.

##### wcprice #

`Weighted Close Price`

is simply the average of high, low, and doubled closing prices.

##### wilders #

`Wilder's Smoothing`

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

##### willr #

`Williams %R`

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

##### wma #

`Weighted Moving Average`

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.

##### zlema #

`Zero-Lag Exponential Moving Average`

follows the same goal as dema and tema. It eliminates the lags to improve the speed and track the price more closely.