Oscillator Toolkit (Expo)█ Overview
The Oscillators Toolkit stands at the forefront of technical trading tools, offering a comprehensive suite of sophisticated, adaptive, and unique oscillators. This toolkit has been thoughtfully designed to cater to all trading styles, ensuring versatility and utility for every trader. The toolkit features our flagship oscillators, including the WaveTrend Momentum, Leading RSI, Momentum Oscillator, and Bellcurves. Furthermore, it offers many great features such as trend recognition, market impulses, and trend changes; all consolidated into a single, easy-to-use indicator.
Access to these high-quality oscillators and tools can elevate your trading strategy, providing you with insightful market analysis and potential trading opportunities. In addition, these tools help traders and investors to identify and interpret various market trends, momentum, and volatility patterns more efficiently.
The Oscillator toolkit works in any market and timeframe for discretionary analysis and includes many oscillators and features:
█ Oscillators
WaveTrend Momentum
The WaveTrend Momentum oscillator is a significant component of the toolkit. It factors in both the direction and the momentum of market trends. The waves within this system are both quick and responsive, operating independently to offer the most pertinent insights at the most opportune moments. Their rapid response time ensures that traders receive timely information, which is essential in the fast-paced, dynamic world of trading.
Example of how to use the WaveTrend Momentum Oscialltor
The WaveTrend Momentum is proficient at identifying trend reversals and pullbacks, allowing traders to enter or exit trades at optimal moments.
Leading RSI
The Leading Relative Strength Index (RSI) is a type of momentum oscillator that is commonly used in technical analysis to predict price movements. As the name suggests, it is an advanced form of the traditional Relative Strength Index (RSI), and it provides traders with more timely signals for market entries and exits.
The Leading RSI works on similar principles but is designed to provide signals ahead of the traditional RSI. This is achieved through more advanced mathematical modeling and calculations, which aim to identify shifts in market momentum before they happen. It takes into account not only the current price action but also considers historical data in a way that can foresee changes in trend directions.
Example of how to use the Leading RSI
The Leading RSI is an enhanced version of the traditional Relative Strength Index, offering more timely indications of divergences and overbought or oversold market conditions.
Momentum Oscillator
This oscillator measures the amount that a security's price has changed over a given time span. It is an excellent tool for understanding the strength of a trend and its potential endurance. When the momentum oscillator rises, it suggests that the price is moving upwards and vice versa.
The Momentum Oscillator is an advanced technical analysis tool that helps traders identify the rate of change or the momentum of the market. It is typically used to determine the strength or speed at which the price of an asset increases or decreases for a set of returns. This oscillator is considered 'fast-moving' and 'sensitive' because it responds quickly to changes in price momentum. The fast-moving nature of this oscillator helps traders to get early signals for potential market entry or exit points.
The Momentum Oscillator analyzes the current price compared to the previous price and adds two additional layers of analysis: 'Buy & Sell moves' and 'Extremes.'
Buy & Sell Moves: This layer of the oscillator helps identify the buying and selling pressure in the market. This can provide traders with valuable information about the possible direction of future price moves. When there is high buying pressure (demand), the price tends to rise, and when there is high selling pressure (supply), the price tends to fall.
Extremes: This layer helps to identify extreme overbought or oversold conditions. When the oscillator enters the overbought territory, it could indicate that the price is at a high and could potentially reverse. Conversely, if the oscillator enters the oversold territory, it could suggest that the price is at a low and could potentially rebound.
Example of how to use the Momentum Oscillator
The Momentum Oscillator is a sensitive and fast-moving oscillator that adapts quickly to price changes while keeping track of the long-term momentum, making it easier to spot buying or selling opportunities in trends.
Bellcurves
The Bellcurves indicator is a powerful tool for traders that uses statistical analysis to help identify potential market reversals and key support and resistance levels by leveraging the principles of statistical analysis to measure market impulses. The concept behind this tool is the normal distribution, also known as the bell curve, which is a fundamental statistical concept signifying that data tends to cluster around the average or mean value. The "impulses" in the market context refer to significant price movements driven by a high volume of trading activity. These are typically sharp and swift moves either upwards (bullish impulse) or downwards (bearish impulse). These impulses often signify a strong sentiment in the market and can result at the beginning of a new trend or the continuation of an existing one.
In effect, the Bellcurve indicator is designed to filter out minor price fluctuations or 'noise,' allowing traders to focus solely on significant market impulses. This makes it easier for traders to identify key market movements.
Example of how to use the Bellcurve
The Bellcurves uses the principles of statistical analysis to identify significant market impulses and potential market reversals.
█ Why is this Oscillator Toolkit Needed?
The Oscillator Toolkit is a vital asset for traders for several reasons:
Insight into Market Trends: The Oscillator Toolkit provides valuable insight into current market trends. This includes understanding whether the market is bullish (rising) or bearish (falling), as well as identifying potential future price movements.
Identification of Overbought or Oversold Conditions: Oscillators like those in the toolkit can help traders identify when an asset is overbought (potentially overvalued) or oversold (potentially undervalued). This can signal potential market reversals.
Confirmation of Price Patterns: The oscillators in the toolkit can confirm price patterns and trends. For example, if a price pattern suggests a bullish trend, an oscillator can help confirm this by showing rising momentum.
Versatility Across Markets and Timeframes: The Oscillator Toolkit is designed to work across a variety of markets, including stocks, forex, commodities, and cryptocurrencies. It's also effective across different timeframes, from short-term day trading to longer-term investment strategies.
Timely Trade Signals: By providing real-time insights into market conditions and price momentum, the Oscillator Toolkit offers timely signals for trade entries and exits.
Enhancing Trading Strategy: Every trader has a unique approach to the market. The Oscillator Toolkit, with its suite of different oscillators, provides a robust set of tools that can be customized to enhance any trading strategy, whether it's a trend following, swing trading, scalping, or any other approach.
█ Any Alert Function Call
This function allows traders to combine any feature and create customized alerts. These alerts can be set for various conditions and customized according to the trader's strategy or preferences.
█ How are the Oscillators calculated? - Overview
The Toolkit combines many of our existing premium indicators and new technical analysis algorithms to analyze the market. This overview covers how the main features are calculated.
WaveTrend Momentum
The WaveTrend Momentum oscillator operates at its core by comparing the current price to previous prices. If the current price is higher than the previous price, the oscillator value will rise, indicating an uptrend. Conversely, if the current price is lower than the previous price, the oscillator value will fall, indicating a downtrend. To make it unique and useful normalized weighting functions are added.
Leading RSI
The Leading RSI is based on the traditional Relative Strength Index, with an added exploration function that takes into account historical price movements.
Momentum Oscillator
The Momentum oscillator measures how quickly the price is changing, on average, over a certain period, relative to the variability of the price over that same period. It gives higher values when the price is changing rapidly in one direction and lower values when the price is fluctuating or changing more slowly. In addition, other functions, such as market extremes and buying/selling pressure, are factored in.
Bellcurves
The Bellcurves assume that some common historical price data is normally distributed, and once these patterns or moves are found the in the price data, a Bellcurve is formed.
█ In conclusion , the Oscillator Toolkit is an advanced, versatile, and indispensable asset for traders across various markets and timeframes. This innovative collection includes different oscillators, including the WaveTrend Momentum, Leading RSI, Momentum Oscillator, and the Bellcurves Indicator, each serving a unique function in providing valuable insights into the market's behavior.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
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GKD-C Variety Stepped, Variety Filter [Loxx]Giga Kaleidoscope GKD-C Variety Stepped, Variety Filter is a Confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ Giga Kaleidoscope Modularized Trading System
What is Loxx's "Giga Kaleidoscope Modularized Trading System"?
The Giga Kaleidoscope Modularized Trading System is a trading system built on the philosophy of the NNFX (No Nonsense Forex) algorithmic trading.
What is the NNFX algorithmic trading strategy?
The NNFX (No-Nonsense Forex) trading system is a comprehensive approach to Forex trading that is designed to simplify the process and remove the confusion and complexity that often surrounds trading. The system was developed by a Forex trader who goes by the pseudonym "VP" and has gained a significant following in the Forex community.
The NNFX trading system is based on a set of rules and guidelines that help traders make objective and informed decisions. These rules cover all aspects of trading, including market analysis, trade entry, stop loss placement, and trade management.
Here are the main components of the NNFX trading system:
1. Trading Philosophy: The NNFX trading system is based on the idea that successful trading requires a comprehensive understanding of the market, objective analysis, and strict risk management. The system aims to remove subjective elements from trading and focuses on objective rules and guidelines.
2. Technical Analysis: The NNFX trading system relies heavily on technical analysis and uses a range of indicators to identify high-probability trading opportunities. The system uses a combination of trend-following and mean-reverting strategies to identify trades.
3. Market Structure: The NNFX trading system emphasizes the importance of understanding the market structure, including price action, support and resistance levels, and market cycles. The system uses a range of tools to identify the market structure, including trend lines, channels, and moving averages.
4. Trade Entry: The NNFX trading system has strict rules for trade entry. The system uses a combination of technical indicators to identify high-probability trades, and traders must meet specific criteria to enter a trade.
5. Stop Loss Placement: The NNFX trading system places a significant emphasis on risk management and requires traders to place a stop loss order on every trade. The system uses a combination of technical analysis and market structure to determine the appropriate stop loss level.
6. Trade Management: The NNFX trading system has specific rules for managing open trades. The system aims to minimize risk and maximize profit by using a combination of trailing stops, take profit levels, and position sizing.
Overall, the NNFX trading system is designed to be a straightforward and easy-to-follow approach to Forex trading that can be applied by traders of all skill levels.
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the Stochastic Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data between modules. Data is passed between each module as described below:
GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-C(Continuation) => GKD-E => GKD-BT
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Variety Stepped, Variety Filter as shown on the chart above
Confirmation 2: Williams Percent Range
Continuation: Fisher Transform
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain.
Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm)
Standard Entry
1. GKD-C Confirmation 1 Signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Continuation Entry
1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously
2. GKD-B Baseline hasn't crossed since entry signal trigger
3. GKD-C Confirmation Continuation Indicator signals
4. GKD-C Confirmation 1 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 2 agrees
1-Candle Rule Standard Entry
1. GKD-C Confirmation 1 signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
1-Candle Rule Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
PullBack Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is beyond 1.0x Volatility of Baseline
Next Candle:
1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
█ GKD-C Variety Stepped, Variety Filter
Variety Stepped, Variety Filter is an indicator that uses various types of stepping behavior to reduce false signals. This indicator includes 5+ volatility stepping types and 60+ moving averages.
Stepping calculations
First off, you can filter by both price and/or MA output. Both price and MA output can be filtered/stepped in their own way. You'll see two selectors in the input settings. Default is ATR ATR. Here's how stepping works in simple terms: if the price/MA output doesn't move by X deviations, then revert to the price/MA output one bar back.
ATR
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma σ or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis we usually use it to measure the level of current volatility .
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA , we can call it EMA deviation. And added to that, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
See how this compares to Standard Devaition here:
Adaptive Deviation
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
For this indicator, I used a manual recreation of the quantile function in Pine Script. This is so users have a full inside view into how this is calculated.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is widely used indicator in many occasions for technical analysis . It is calculated as the RMA of true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range
See how this compares to ATR here:
ER-Adaptive ATR
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation ( SD ). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
For Pine Coders, this is equivalent of using ta.dev()
Included Filters
Adaptive Moving Average - AMA
ADXvma - Average Directional Volatility Moving Average
Ahrens Moving Average
Alexander Moving Average - ALXMA
Deviation Scaled Moving Average - DSMA
Donchian
Double Exponential Moving Average - DEMA
Double Smoothed Exponential Moving Average - DSEMA
Double Smoothed FEMA - DSFEMA
Double Smoothed Range Weighted EMA - DSRWEMA
Double Smoothed Wilders EMA - DSWEMA
Double Weighted Moving Average - DWMA
Ehlers Optimal Tracking Filter - EOTF
Exponential Moving Average - EMA
Fast Exponential Moving Average - FEMA
Fractal Adaptive Moving Average - FRAMA
Generalized DEMA - GDEMA
Generalized Double DEMA - GDDEMA
Hull Moving Average (Type 1) - HMA1
Hull Moving Average (Type 2) - HMA2
Hull Moving Average (Type 3) - HMA3
Hull Moving Average (Type 4) - HMA4
IE /2 - Early T3 by Tim Tilson
Integral of Linear Regression Slope - ILRS
Instantaneous Trendline
Kalman Filter
Kaufman Adaptive Moving Average - KAMA
Laguerre Filter
Leader Exponential Moving Average
Linear Regression Value - LSMA ( Least Squares Moving Average )
Linear Weighted Moving Average - LWMA
McGinley Dynamic
McNicholl EMA
Non-Lag Moving Average
Ocean NMA Moving Average - ONMAMA
Parabolic Weighted Moving Average
Probability Density Function Moving Average - PDFMA
Quadratic Regression Moving Average - QRMA
Regularized EMA - REMA
Range Weighted EMA - RWEMA
Recursive Moving Trendline
Simple Decycler - SDEC
Simple Jurik Moving Average - SJMA
Simple Moving Average - SMA
Sine Weighted Moving Average
Smoothed LWMA - SLWMA
Smoothed Moving Average - SMMA
Smoother
Super Smoother
T3
Three-pole Ehlers Butterworth
Three-pole Ehlers Smoother
Triangular Moving Average - TMA
Triple Exponential Moving Average - TEMA
Two-pole Ehlers Butterworth
Two-pole Ehlers smoother
Variable Index Dynamic Average - VIDYA
Variable Moving Average - VMA
Volume Weighted EMA - VEMA
Volume Weighted Moving Average - VWMA
Zero-Lag DEMA - Zero Lag Exponential Moving Average
Zero-Lag Moving Average
Zero Lag TEMA - Zero Lag Triple Exponential Moving Average
Adaptive Moving Average - AMA
Description. The Adaptive Moving Average (AMA) is a moving average that changes its sensitivity to price moves depending on the calculated volatility . It becomes more sensitive during periods when the price is moving smoothly in a certain direction and becomes less sensitive when the price is volatile.
ADXvma - Average Directional Volatility Moving Average
Linnsoft's ADXvma formula is a volatility-based moving average, with the volatility being determined by the value of the ADX indicator.
The ADXvma has the SMA in Chande's CMO replaced with an EMA , it then uses a few more layers of EMA smoothing before the "Volatility Index" is calculated.
A side effect is, those additional layers slow down the ADXvma when you compare it to Chande's Variable Index Dynamic Average VIDYA .
The ADXVMA provides support during uptrends and resistance during downtrends and will stay flat for longer, but will create some of the most accurate market signals when it decides to move.
Ahrens Moving Average
Richard D. Ahrens's Moving Average promises "Smoother Data" that isn't influenced by the occasional price spike. It works by using the Open and the Close in his formula so that the only time the Ahrens Moving Average will change is when the candlestick is either making new highs or new lows.
Alexander Moving Average - ALXMA
This Moving Average uses an elaborate smoothing formula and utilizes a 7 period Moving Average. It corresponds to fitting a second-order polynomial to seven consecutive observations. This moving average is rarely used in trading but is interesting as this Moving Average has been applied to diffusion indexes that tend to be very volatile.
Deviation Scaled Moving Average - DSMA
The Deviation-Scaled Moving Average is a data smoothing technique that acts like an exponential moving average with a dynamic smoothing coefficient. The smoothing coefficient is automatically updated based on the magnitude of price changes. In the Deviation-Scaled Moving Average, the standard deviation from the mean is chosen to be the measure of this magnitude. The resulting indicator provides substantial smoothing of the data even when price changes are small while quickly adapting to these changes.
Donchian
Donchian Channels are three lines generated by moving average calculations that comprise an indicator formed by upper and lower bands around a midrange or median band. The upper band marks the highest price of a security over N periods while the lower band marks the lowest price of a security over N periods.
Double Exponential Moving Average - DEMA
The Double Exponential Moving Average ( DEMA ) combines a smoothed EMA and a single EMA to provide a low-lag indicator. It's primary purpose is to reduce the amount of "lagging entry" opportunities, and like all Moving Averages, the DEMA confirms uptrends whenever price crosses on top of it and closes above it, and confirms downtrends when the price crosses under it and closes below it - but with significantly less lag.
Double Smoothed Exponential Moving Average - DSEMA
The Double Smoothed Exponential Moving Average is a lot less laggy compared to a traditional EMA . It's also considered a leading indicator compared to the EMA , and is best utilized whenever smoothness and speed of reaction to market changes are required.
Double Smoothed FEMA - DSFEMA
Same as the Double Exponential Moving Average ( DEMA ), but uses a faster version of EMA for its calculation.
Double Smoothed Range Weighted EMA - DSRWEMA
Range weighted exponential moving average ( EMA ) is, unlike the "regular" range weighted average calculated in a different way. Even though the basis - the range weighting - is the same, the way how it is calculated is completely different. By definition this type of EMA is calculated as a ratio of EMA of price*weight / EMA of weight. And the results are very different and the two should be considered as completely different types of averages. The higher than EMA to price changes responsiveness when the ranges increase remains in this EMA too and in those cases this EMA is clearly leading the "regular" EMA . This version includes double smoothing.
Double Smoothed Wilders EMA - DSWEMA
Welles Wilder was frequently using one "special" case of EMA ( Exponential Moving Average ) that is due to that fact (that he used it) sometimes called Wilder's EMA . This version is adding double smoothing to Wilder's EMA in order to make it "faster" (it is more responsive to market prices than the original) and is still keeping very smooth values.
Double Weighted Moving Average - DWMA
Double weighted moving average is an LWMA (Linear Weighted Moving Average ). Instead of doing one cycle for calculating the LWMA, the indicator is made to cycle the loop 2 times. That produces a smoother values than the original LWMA
Ehlers Optimal Tracking Filter - EOTF
The Elher's Optimum Tracking Filter quickly adjusts rapid shifts in the price and yet is relatively smooth when the price has a sideways action. The operation of this filter is similar to Kaufman’s Adaptive Moving
Average
Exponential Moving Average - EMA
The EMA places more significance on recent data points and moves closer to price than the SMA ( Simple Moving Average ). It reacts faster to volatility due to its emphasis on recent data and is known for its ability to give greater weight to recent and more relevant data. The EMA is therefore seen as an enhancement over the SMA .
Fast Exponential Moving Average - FEMA
An Exponential Moving Average with a short look-back period.
Fractal Adaptive Moving Average - FRAMA
The Fractal Adaptive Moving Average by John Ehlers is an intelligent adaptive Moving Average which takes the importance of price changes into account and follows price closely enough to display significant moves whilst remaining flat if price ranges. The FRAMA does this by dynamically adjusting the look-back period based on the market's fractal geometry.
Generalized DEMA - GDEMA
The double exponential moving average ( DEMA ), was developed by Patrick Mulloy in an attempt to reduce the amount of lag time found in traditional moving averages. It was first introduced in the February 1994 issue of the magazine Technical Analysis of Stocks & Commodities in Mulloy's article "Smoothing Data with Faster Moving Averages.". Instead of using fixed multiplication factor in the final DEMA formula, the generalized version allows you to change it. By varying the "volume factor" form 0 to 1 you apply different multiplications and thus producing DEMA with different "speed" - the higher the volume factor is the "faster" the DEMA will be (but also the slope of it will be less smooth). The volume factor is limited in the calculation to 1 since any volume factor that is larger than 1 is increasing the overshooting to the extent that some volume factors usage makes the indicator unusable.
Generalized Double DEMA - GDDEMA
The double exponential moving average ( DEMA ), was developed by Patrick Mulloy in an attempt to reduce the amount of lag time found in traditional moving averages. It was first introduced in the February 1994 issue of the magazine Technical Analysis of Stocks & Commodities in Mulloy's article "Smoothing Data with Faster Moving Averages''. This is an extension of the Generalized DEMA using Tim Tillsons (the inventor of T3) idea, and is using GDEMA of GDEMA for calculation (which is the "middle step" of T3 calculation). Since there are no versions showing that middle step, this version covers that too. The result is smoother than Generalized DEMA , but is less smooth than T3 - one has to do some experimenting in order to find the optimal way to use it, but in any case, since it is "faster" than the T3 (Tim Tillson T3) and still smooth, it looks like a good compromise between speed and smoothness.
Hull Moving Average (Type 1) - HMA1
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses SMA for smoothing.
Hull Moving Average (Type 2) - HMA2
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses EMA for smoothing.
Hull Moving Average (Type 3) - HMA3
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses LWMA for smoothing.
Hull Moving Average (Type 4) - HMA4
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses SMMA for smoothing.
IE /2 - Early T3 by Tim Tilson and T3 new
T3 is basically an EMA on steroids, You can read about T3 here:
T3 Striped
Integral of Linear Regression Slope - ILRS
A Moving Average where the slope of a linear regression line is simply integrated as it is fitted in a moving window of length N (natural numbers in maths) across the data. The derivative of ILRS is the linear regression slope. ILRS is not the same as a SMA ( Simple Moving Average ) of length N, which is actually the midpoint of the linear regression line as it moves across the data.
Instantaneous Trendline
The Instantaneous Trendline is created by removing the dominant cycle component from the price information which makes this Moving Average suitable for medium to long-term trading.
Kalman Filter
Kalman filter is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies. This means that the filter was originally designed to work with noisy data. Also, it is able to work with incomplete data. Another advantage is that it is designed for and applied in dynamic systems; our price chart belongs to such systems. This version is true to the original design of the trade-ready Kalman Filter where velocity is the triggering mechanism.
Kalman Filter is a more accurate smoothing/prediction algorithm than the moving average because it is adaptive: it accounts for estimation errors and tries to adjust its predictions from the information it learned in the previous stage. Theoretically, Kalman Filter consists of measurement and transition components.
Kaufman Adaptive Moving Average - KAMA
Developed by Perry Kaufman, Kaufman's Adaptive Moving Average ( KAMA ) is a moving average designed to account for market noise or volatility . KAMA will closely follow prices when the price swings are relatively small and the noise is low.
Laguerre Filter
The Laguerre Filter is a smoothing filter which is based on Laguerre polynomials. The filter requires the current price, three prior prices, a user defined factor called Alpha to fill its calculation.
Adjusting the Alpha coefficient is used to increase or decrease its lag and its smoothness.
Leader Exponential Moving Average
The Leader EMA was created by Giorgos E. Siligardos who created a Moving Average which was able to eliminate lag altogether whilst maintaining some smoothness. It was first described during his research paper "MACD Leader" where he applied this to the MACD to improve its signals and remove its lagging issue. This filter uses his leading MACD's "modified EMA" and can be used as a zero lag filter.
Linear Regression Value - LSMA ( Least Squares Moving Average )
LSMA as a Moving Average is based on plotting the end point of the linear regression line. It compares the current value to the prior value and a determination is made of a possible trend, eg. the linear regression line is pointing up or down.
Linear Weighted Moving Average - LWMA
LWMA reacts to price quicker than the SMA and EMA . Although it's similar to the Simple Moving Average , the difference is that a weight coefficient is multiplied to the price which means the most recent price has the highest weighting, and each prior price has progressively less weight. The weights drop in a linear fashion.
McGinley Dynamic
John McGinley created this Moving Average to track prices better than traditional Moving Averages. It does this by incorporating an automatic adjustment factor into its formula, which speeds (or slows) the indicator in trending, or ranging, markets.
McNicholl EMA
Dennis McNicholl developed this Moving Average to use as his center line for his "Better Bollinger Bands" indicator and was successful because it responded better to volatility changes over the standard SMA and managed to avoid common whipsaws.
Non-lag moving average
The Non Lag Moving average follows price closely and gives very quick signals as well as early signals of price change. As a standalone Moving Average, it should not be used on its own, but as an additional confluence tool for early signals.
Ocean NMA Moving Average - ONMAMA
Created by Jim Sloman, the NMA is a moving average that automatically adjusts to volatility without being programmed to do so. For more info, read his guide "Ocean Theory, an Introduction"
Parabolic Weighted Moving Average
The Parabolic Weighted Moving Average is a variation of the Linear Weighted Moving Average . The Linear Weighted Moving Average calculates the average by assigning different weights to each element in its calculation. The Parabolic Weighted Moving Average is a variation that allows weights to be changed to form a parabolic curve. It is done simply by using the Power parameter of this indicator.
Probability Density Function Moving Average - PDFMA
Probability density function based MA is a sort of weighted moving average that uses probability density function to calculate the weights. By its nature it is similar to a lot of digital filters.
Quadratic Regression Moving Average - QRMA
A quadratic regression is the process of finding the equation of the parabola that best fits a set of data. This moving average is an obscure concept that was posted to Forex forums in around 2008.
Regularized EMA - REMA
The regularized exponential moving average (REMA) by Chris Satchwell is a variation on the EMA (see Exponential Moving Average ) designed to be smoother but not introduce too much extra lag.
Range Weighted EMA - RWEMA
This indicator is a variation of the range weighted EMA . The variation comes from a possible need to make that indicator a bit less "noisy" when it comes to slope changes. The method used for calculating this variation is the method described by Lee Leibfarth in his article "Trading With An Adaptive Price Zone".
Recursive Moving Trendline
Dennis Meyers's Recursive Moving Trendline uses a recursive (repeated application of a rule) polynomial fit, a technique that uses a small number of past values estimations of price and today's price to predict tomorrow's price.
Simple Decycler - SDEC
The Ehlers Simple Decycler study is a virtually zero-lag technical indicator proposed by John F. Ehlers . The original idea behind this study (and several others created by John F. Ehlers ) is that market data can be considered a continuum of cycle periods with different cycle amplitudes. Thus, trending periods can be considered segments of longer cycles, or, in other words, low-frequency segments. Applying the right filter might help identify these segments.
Simple Loxx Moving Average - SLMA
A three stage moving average combining an adaptive EMA , a Kalman Filter, and a Kauffman adaptive filter.
Simple Moving Average - SMA
The SMA calculates the average of a range of prices by adding recent prices and then dividing that figure by the number of time periods in the calculation average. It is the most basic Moving Average which is seen as a reliable tool for starting off with Moving Average studies. As reliable as it may be, the basic moving average will work better when it's enhanced into an EMA .
Sine Weighted Moving Average
The Sine Weighted Moving Average assigns the most weight at the middle of the data set. It does this by weighting from the first half of a Sine Wave Cycle and the most weighting is given to the data in the middle of that data set. The Sine WMA closely resembles the TMA (Triangular Moving Average).
Smoothed LWMA - SLWMA
A smoothed version of the LWMA
Smoothed Moving Average - SMMA
The Smoothed Moving Average is similar to the Simple Moving Average ( SMA ), but aims to reduce noise rather than reduce lag. SMMA takes all prices into account and uses a long lookback period. Due to this, it's seen as an accurate yet laggy Moving Average.
Smoother
The Smoother filter is a faster-reacting smoothing technique which generates considerably less lag than the SMMA ( Smoothed Moving Average ). It gives earlier signals but can also create false signals due to its earlier reactions. This filter is sometimes wrongly mistaken for the superior Jurik Smoothing algorithm.
Super Smoother
The Super Smoother filter uses John Ehlers’s “Super Smoother” which consists of a Two pole Butterworth filter combined with a 2-bar SMA ( Simple Moving Average ) that suppresses the 22050 Hz Nyquist frequency: A characteristic of a sampler, which converts a continuous function or signal into a discrete sequence.
Three-pole Ehlers Butterworth
The 3 pole Ehlers Butterworth (as well as the Two pole Butterworth) are both superior alternatives to the EMA and SMA . They aim at producing less lag whilst maintaining accuracy. The 2 pole filter will give you a better approximation for price, whereas the 3 pole filter has superior smoothing.
Three-pole Ehlers Smoother
The 3 pole Ehlers smoother works almost as close to price as the above mentioned 3 Pole Ehlers Butterworth. It acts as a strong baseline for signals but removes some noise. Side by side, it hardly differs from the Three Pole Ehlers Butterworth but when examined closely, it has better overshoot reduction compared to the 3 pole Ehlers Butterworth.
Triangular Moving Average - TMA
The TMA is similar to the EMA but uses a different weighting scheme. Exponential and weighted Moving Averages will assign weight to the most recent price data. Simple moving averages will assign the weight equally across all the price data. With a TMA (Triangular Moving Average), it is double smoother (averaged twice) so the majority of the weight is assigned to the middle portion of the data.
Triple Exponential Moving Average - TEMA
The TEMA uses multiple EMA calculations as well as subtracting lag to create a tool which can be used for scalping pullbacks. As it follows price closely, its signals are considered very noisy and should only be used in extremely fast-paced trading conditions.
Two-pole Ehlers Butterworth
The 2 pole Ehlers Butterworth (as well as the three pole Butterworth mentioned above) is another filter that cuts out the noise and follows the price closely. The 2 pole is seen as a faster, leading filter over the 3 pole and follows price a bit more closely. Analysts will utilize both a 2 pole and a 3 pole Butterworth on the same chart using the same period, but having both on chart allows its crosses to be traded.
Two-pole Ehlers Smoother
A smoother version of the Two pole Ehlers Butterworth. This filter is the faster version out of the 3 pole Ehlers Butterworth. It does a decent job at cutting out market noise whilst emphasizing a closer following to price over the 3 pole Ehlers .
Variable Index Dynamic Average - VIDYA
Variable Index Dynamic Average Technical Indicator ( VIDYA ) was developed by Tushar Chande. It is an original method of calculating the Exponential Moving Average ( EMA ) with the dynamically changing period of averaging.
Variable Moving Average - VMA
The Variable Moving Average (VMA) is a study that uses an Exponential Moving Average being able to automatically adjust its smoothing factor according to the market volatility .
Volume Weighted EMA - VEMA
An EMA that uses a volume and price weighted calculation instead of the standard price input.
Volume Weighted Moving Average - VWMA
A Volume Weighted Moving Average is a moving average where more weight is given to bars with heavy volume than with light volume . Thus the value of the moving average will be closer to where most trading actually happened than it otherwise would be without being volume weighted.
Zero-Lag DEMA - Zero Lag Double Exponential Moving Average
John Ehlers's Zero Lag DEMA's aim is to eliminate the inherent lag associated with all trend following indicators which average a price over time. Because this is a Double Exponential Moving Average with Zero Lag, it has a tendency to overshoot and create a lot of false signals for swing trading. It can however be used for quick scalping or as a secondary indicator for confluence.
Zero-Lag Moving Average
The Zero Lag Moving Average is described by its creator, John Ehlers , as a Moving Average with absolutely no delay. And it's for this reason that this filter will cause a lot of abrupt signals which will not be ideal for medium to long-term traders. This filter is designed to follow price as close as possible whilst de-lagging data instead of basing it on regular data. The way this is done is by attempting to remove the cumulative effect of the Moving Average.
Zero-Lag TEMA - Zero Lag Triple Exponential Moving Average
Just like the Zero Lag DEMA , this filter will give you the fastest signals out of all the Zero Lag Moving Averages. This is useful for scalping but dangerous for medium to long-term traders, especially during market Volatility and news events. Having no lag, this filter also has no smoothing in its signals and can cause some very bizarre behavior when applied to certain indicators.
Requirements
Inputs
Confirmation 1 and Solo Confirmation: GKD-V Volatility / Volume indicator
Confirmation 2: GKD-C Confirmation indicator
Outputs
Confirmation 2 and Solo Confirmation Complex: GKD-E Exit indicator
Confirmation 1: GKD-C Confirmation indicator
Continuation: GKD-E Exit indicator
Solo Confirmation Simple: GKD-BT Backtest strategy
Additional features will be added in future releases.
GKD-B Baseline [Loxx]Giga Kaleidoscope Baseline is a Baseline module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
What is Loxx's "Giga Kaleidoscope Modularized Trading System"?
The Giga Kaleidoscope Modularized Trading System is a trading system built on the philosophy of the NNFX (No Nonsense Forex) algorithmic trading.
What is an NNFX algorithmic trading strategy?
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend (such as "Baseline" shown on the chart above)
3. Confirmation 1 - a technical indicator used to identify trend. This should agree with the "Baseline"
4. Confirmation 2 - a technical indicator used to identify trend. This filters/verifies the trend identified by "Baseline" and "Confirmation 1"
5. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown.
6. Exit - a technical indicator used to determine when trend is exhausted.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 module (Confirmation 1/2, Numbers 3 and 4 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 5 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 6 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data between modules. Data is passed between each module as described below:
GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-E => GKD-BT
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles
Baseline: Hull Moving Average as shown on the chart above
Volatility/Volume: Jurik Volty
Confirmation 1: Vortex
Confirmation 2: Fisher Transform
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain.
Now that you have a general understanding of the NNFX algorithm and the GKD trading system. let's go over what's inside the GKD-B Baseline itself.
GKD Baseline Special Features and Notable Inputs
GKD Baseline v1.0 includes 63 different moving averages:
Adaptive Moving Average - AMA
ADXvma - Average Directional Volatility Moving Average
Ahrens Moving Average
Alexander Moving Average - ALXMA
Deviation Scaled Moving Average - DSMA
Donchian
Double Exponential Moving Average - DEMA
Double Smoothed Exponential Moving Average - DSEMA
Double Smoothed FEMA - DSFEMA
Double Smoothed Range Weighted EMA - DSRWEMA
Double Smoothed Wilders EMA - DSWEMA
Double Weighted Moving Average - DWMA
Ehlers Optimal Tracking Filter - EOTF
Exponential Moving Average - EMA
Fast Exponential Moving Average - FEMA
Fractal Adaptive Moving Average - FRAMA
Generalized DEMA - GDEMA
Generalized Double DEMA - GDDEMA
Hull Moving Average (Type 1) - HMA1
Hull Moving Average (Type 2) - HMA2
Hull Moving Average (Type 3) - HMA3
Hull Moving Average (Type 4) - HMA4
IE /2 - Early T3 by Tim Tilson
Integral of Linear Regression Slope - ILRS
Instantaneous Trendline
Kalman Filter
Kaufman Adaptive Moving Average - KAMA
Laguerre Filter
Leader Exponential Moving Average
Linear Regression Value - LSMA ( Least Squares Moving Average )
Linear Weighted Moving Average - LWMA
McGinley Dynamic
McNicholl EMA
Non-Lag Moving Average
Ocean NMA Moving Average - ONMAMA
Parabolic Weighted Moving Average
Probability Density Function Moving Average - PDFMA
Quadratic Regression Moving Average - QRMA
Regularized EMA - REMA
Range Weighted EMA - RWEMA
Recursive Moving Trendline
Simple Decycler - SDEC
Simple Jurik Moving Average - SJMA
Simple Moving Average - SMA
Sine Weighted Moving Average
Smoothed LWMA - SLWMA
Smoothed Moving Average - SMMA
Smoother
Super Smoother
T3
Three-pole Ehlers Butterworth
Three-pole Ehlers Smoother
Triangular Moving Average - TMA
Triple Exponential Moving Average - TEMA
Two-pole Ehlers Butterworth
Two-pole Ehlers smoother
Variable Index Dynamic Average - VIDYA
Variable Moving Average - VMA
Volume Weighted EMA - VEMA
Volume Weighted Moving Average - VWMA
Zero-Lag DEMA - Zero Lag Exponential Moving Average
Zero-Lag Moving Average
Zero Lag TEMA - Zero Lag Triple Exponential Moving Average
Adaptive Moving Average - AMA
Description. The Adaptive Moving Average (AMA) is a moving average that changes its sensitivity to price moves depending on the calculated volatility. It becomes more sensitive during periods when the price is moving smoothly in a certain direction and becomes less sensitive when the price is volatile.
ADXvma - Average Directional Volatility Moving Average
Linnsoft's ADXvma formula is a volatility-based moving average, with the volatility being determined by the value of the ADX indicator.
The ADXvma has the SMA in Chande's CMO replaced with an EMA , it then uses a few more layers of EMA smoothing before the "Volatility Index" is calculated.
A side effect is, those additional layers slow down the ADXvma when you compare it to Chande's Variable Index Dynamic Average VIDYA .
The ADXVMA provides support during uptrends and resistance during downtrends and will stay flat for longer, but will create some of the most accurate market signals when it decides to move.
Ahrens Moving Average
Richard D. Ahrens's Moving Average promises "Smoother Data" that isn't influenced by the occasional price spike. It works by using the Open and the Close in his formula so that the only time the Ahrens Moving Average will change is when the candlestick is either making new highs or new lows.
Alexander Moving Average - ALXMA
This Moving Average uses an elaborate smoothing formula and utilizes a 7 period Moving Average. It corresponds to fitting a second-order polynomial to seven consecutive observations. This moving average is rarely used in trading but is interesting as this Moving Average has been applied to diffusion indexes that tend to be very volatile.
Deviation Scaled Moving Average - DSMA
The Deviation-Scaled Moving Average is a data smoothing technique that acts like an exponential moving average with a dynamic smoothing coefficient. The smoothing coefficient is automatically updated based on the magnitude of price changes. In the Deviation-Scaled Moving Average, the standard deviation from the mean is chosen to be the measure of this magnitude. The resulting indicator provides substantial smoothing of the data even when price changes are small while quickly adapting to these changes.
Donchian
Donchian Channels are three lines generated by moving average calculations that comprise an indicator formed by upper and lower bands around a midrange or median band. The upper band marks the highest price of a security over N periods while the lower band marks the lowest price of a security over N periods.
Double Exponential Moving Average - DEMA
The Double Exponential Moving Average ( DEMA ) combines a smoothed EMA and a single EMA to provide a low-lag indicator. It's primary purpose is to reduce the amount of "lagging entry" opportunities, and like all Moving Averages, the DEMA confirms uptrends whenever price crosses on top of it and closes above it, and confirms downtrends when the price crosses under it and closes below it - but with significantly less lag.
Double Smoothed Exponential Moving Average - DSEMA
The Double Smoothed Exponential Moving Average is a lot less laggy compared to a traditional EMA . It's also considered a leading indicator compared to the EMA , and is best utilized whenever smoothness and speed of reaction to market changes are required.
Double Smoothed FEMA - DSFEMA
Same as the Double Exponential Moving Average (DEMA), but uses a faster version of EMA for its calculation.
Double Smoothed Range Weighted EMA - DSRWEMA
Range weighted exponential moving average (EMA) is, unlike the "regular" range weighted average calculated in a different way. Even though the basis - the range weighting - is the same, the way how it is calculated is completely different. By definition this type of EMA is calculated as a ratio of EMA of price*weight / EMA of weight. And the results are very different and the two should be considered as completely different types of averages. The higher than EMA to price changes responsiveness when the ranges increase remains in this EMA too and in those cases this EMA is clearly leading the "regular" EMA. This version includes double smoothing.
Double Smoothed Wilders EMA - DSWEMA
Welles Wilder was frequently using one "special" case of EMA (Exponential Moving Average) that is due to that fact (that he used it) sometimes called Wilder's EMA. This version is adding double smoothing to Wilder's EMA in order to make it "faster" (it is more responsive to market prices than the original) and is still keeping very smooth values.
Double Weighted Moving Average - DWMA
Double weighted moving average is an LWMA (Linear Weighted Moving Average). Instead of doing one cycle for calculating the LWMA, the indicator is made to cycle the loop 2 times. That produces a smoother values than the original LWMA
Ehlers Optimal Tracking Filter - EOTF
The Elher's Optimum Tracking Filter quickly adjusts rapid shifts in the price and yet is relatively smooth when the price has a sideways action. The operation of this filter is similar to Kaufman’s Adaptive Moving
Average
Exponential Moving Average - EMA
The EMA places more significance on recent data points and moves closer to price than the SMA ( Simple Moving Average ). It reacts faster to volatility due to its emphasis on recent data and is known for its ability to give greater weight to recent and more relevant data. The EMA is therefore seen as an enhancement over the SMA .
Fast Exponential Moving Average - FEMA
An Exponential Moving Average with a short look-back period.
Fractal Adaptive Moving Average - FRAMA
The Fractal Adaptive Moving Average by John Ehlers is an intelligent adaptive Moving Average which takes the importance of price changes into account and follows price closely enough to display significant moves whilst remaining flat if price ranges. The FRAMA does this by dynamically adjusting the look-back period based on the market's fractal geometry.
Generalized DEMA - GDEMA
The double exponential moving average (DEMA), was developed by Patrick Mulloy in an attempt to reduce the amount of lag time found in traditional moving averages. It was first introduced in the February 1994 issue of the magazine Technical Analysis of Stocks & Commodities in Mulloy's article "Smoothing Data with Faster Moving Averages.". Instead of using fixed multiplication factor in the final DEMA formula, the generalized version allows you to change it. By varying the "volume factor" form 0 to 1 you apply different multiplications and thus producing DEMA with different "speed" - the higher the volume factor is the "faster" the DEMA will be (but also the slope of it will be less smooth). The volume factor is limited in the calculation to 1 since any volume factor that is larger than 1 is increasing the overshooting to the extent that some volume factors usage makes the indicator unusable.
Generalized Double DEMA - GDDEMA
The double exponential moving average (DEMA), was developed by Patrick Mulloy in an attempt to reduce the amount of lag time found in traditional moving averages. It was first introduced in the February 1994 issue of the magazine Technical Analysis of Stocks & Commodities in Mulloy's article "Smoothing Data with Faster Moving Averages''. This is an extension of the Generalized DEMA using Tim Tillsons (the inventor of T3) idea, and is using GDEMA of GDEMA for calculation (which is the "middle step" of T3 calculation). Since there are no versions showing that middle step, this version covers that too. The result is smoother than Generalized DEMA, but is less smooth than T3 - one has to do some experimenting in order to find the optimal way to use it, but in any case, since it is "faster" than the T3 (Tim Tillson T3) and still smooth, it looks like a good compromise between speed and smoothness.
Hull Moving Average (Type 1) - HMA1
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses SMA for smoothing.
Hull Moving Average (Type 2) - HMA2
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses EMA for smoothing.
Hull Moving Average (Type 3) - HMA3
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses LWMA for smoothing.
Hull Moving Average (Type 4) - HMA4
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses SMMA for smoothing.
IE /2 - Early T3 by Tim Tilson and T3 new
T3 is basically an EMA on steroids, You can read about T3 here:
Integral of Linear Regression Slope - ILRS
A Moving Average where the slope of a linear regression line is simply integrated as it is fitted in a moving window of length N (natural numbers in maths) across the data. The derivative of ILRS is the linear regression slope. ILRS is not the same as a SMA ( Simple Moving Average ) of length N, which is actually the midpoint of the linear regression line as it moves across the data.
Instantaneous Trendline
The Instantaneous Trendline is created by removing the dominant cycle component from the price information which makes this Moving Average suitable for medium to long-term trading.
Kalman Filter
Kalman filter is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies. This means that the filter was originally designed to work with noisy data. Also, it is able to work with incomplete data. Another advantage is that it is designed for and applied in dynamic systems; our price chart belongs to such systems. This version is true to the original design of the trade-ready Kalman Filter where velocity is the triggering mechanism.
Kalman Filter is a more accurate smoothing/prediction algorithm than the moving average because it is adaptive: it accounts for estimation errors and tries to adjust its predictions from the information it learned in the previous stage. Theoretically, Kalman Filter consists of measurement and transition components.
Kaufman Adaptive Moving Average - KAMA
Developed by Perry Kaufman, Kaufman's Adaptive Moving Average (KAMA) is a moving average designed to account for market noise or volatility. KAMA will closely follow prices when the price swings are relatively small and the noise is low.
Laguerre Filter
The Laguerre Filter is a smoothing filter which is based on Laguerre polynomials. The filter requires the current price, three prior prices, a user defined factor called Alpha to fill its calculation.
Adjusting the Alpha coefficient is used to increase or decrease its lag and its smoothness.
Leader Exponential Moving Average
The Leader EMA was created by Giorgos E. Siligardos who created a Moving Average which was able to eliminate lag altogether whilst maintaining some smoothness. It was first described during his research paper "MACD Leader" where he applied this to the MACD to improve its signals and remove its lagging issue. This filter uses his leading MACD's "modified EMA" and can be used as a zero lag filter.
Linear Regression Value - LSMA ( Least Squares Moving Average )
LSMA as a Moving Average is based on plotting the end point of the linear regression line. It compares the current value to the prior value and a determination is made of a possible trend, eg. the linear regression line is pointing up or down.
Linear Weighted Moving Average - LWMA
LWMA reacts to price quicker than the SMA and EMA . Although it's similar to the Simple Moving Average , the difference is that a weight coefficient is multiplied to the price which means the most recent price has the highest weighting, and each prior price has progressively less weight. The weights drop in a linear fashion.
McGinley Dynamic
John McGinley created this Moving Average to track prices better than traditional Moving Averages. It does this by incorporating an automatic adjustment factor into its formula, which speeds (or slows) the indicator in trending, or ranging, markets.
McNicholl EMA
Dennis McNicholl developed this Moving Average to use as his center line for his "Better Bollinger Bands" indicator and was successful because it responded better to volatility changes over the standard SMA and managed to avoid common whipsaws.
Non-lag moving average
The Non Lag Moving average follows price closely and gives very quick signals as well as early signals of price change. As a standalone Moving Average, it should not be used on its own, but as an additional confluence tool for early signals.
Ocean NMA Moving Average - ONMAMA
Created by Jim Sloman, the NMA is a moving average that automatically adjusts to volatility without being programmed to do so. For more info, read his guide "Ocean Theory, an Introduction"
Parabolic Weighted Moving Average
The Parabolic Weighted Moving Average is a variation of the Linear Weighted Moving Average . The Linear Weighted Moving Average calculates the average by assigning different weights to each element in its calculation. The Parabolic Weighted Moving Average is a variation that allows weights to be changed to form a parabolic curve. It is done simply by using the Power parameter of this indicator.
Probability Density Function Moving Average - PDFMA
Probability density function based MA is a sort of weighted moving average that uses probability density function to calculate the weights. By its nature it is similar to a lot of digital filters.
Quadratic Regression Moving Average - QRMA
A quadratic regression is the process of finding the equation of the parabola that best fits a set of data. This moving average is an obscure concept that was posted to Forex forums in around 2008.
Regularized EMA - REMA
The regularized exponential moving average (REMA) by Chris Satchwell is a variation on the EMA (see Exponential Moving Average) designed to be smoother but not introduce too much extra lag.
Range Weighted EMA - RWEMA
This indicator is a variation of the range weighted EMA. The variation comes from a possible need to make that indicator a bit less "noisy" when it comes to slope changes. The method used for calculating this variation is the method described by Lee Leibfarth in his article "Trading With An Adaptive Price Zone".
Recursive Moving Trendline
Dennis Meyers's Recursive Moving Trendline uses a recursive (repeated application of a rule) polynomial fit, a technique that uses a small number of past values estimations of price and today's price to predict tomorrow's price.
Simple Decycler - SDEC
The Ehlers Simple Decycler study is a virtually zero-lag technical indicator proposed by John F. Ehlers. The original idea behind this study (and several others created by John F. Ehlers) is that market data can be considered a continuum of cycle periods with different cycle amplitudes. Thus, trending periods can be considered segments of longer cycles, or, in other words, low-frequency segments. Applying the right filter might help identify these segments.
Simple Loxx Moving Average - SLMA
A three stage moving average combining an adaptive EMA, a Kalman Filter, and a Kauffman adaptive filter.
Simple Moving Average - SMA
The SMA calculates the average of a range of prices by adding recent prices and then dividing that figure by the number of time periods in the calculation average. It is the most basic Moving Average which is seen as a reliable tool for starting off with Moving Average studies. As reliable as it may be, the basic moving average will work better when it's enhanced into an EMA .
Sine Weighted Moving Average
The Sine Weighted Moving Average assigns the most weight at the middle of the data set. It does this by weighting from the first half of a Sine Wave Cycle and the most weighting is given to the data in the middle of that data set. The Sine WMA closely resembles the TMA (Triangular Moving Average).
Smoothed LWMA - SLWMA
A smoothed version of the LWMA
Smoothed Moving Average - SMMA
The Smoothed Moving Average is similar to the Simple Moving Average ( SMA ), but aims to reduce noise rather than reduce lag. SMMA takes all prices into account and uses a long lookback period. Due to this, it's seen as an accurate yet laggy Moving Average.
Smoother
The Smoother filter is a faster-reacting smoothing technique which generates considerably less lag than the SMMA ( Smoothed Moving Average ). It gives earlier signals but can also create false signals due to its earlier reactions. This filter is sometimes wrongly mistaken for the superior Jurik Smoothing algorithm.
Super Smoother
The Super Smoother filter uses John Ehlers’s “Super Smoother” which consists of a Two pole Butterworth filter combined with a 2-bar SMA ( Simple Moving Average ) that suppresses the 22050 Hz Nyquist frequency: A characteristic of a sampler, which converts a continuous function or signal into a discrete sequence.
Three-pole Ehlers Butterworth
The 3 pole Ehlers Butterworth (as well as the Two pole Butterworth) are both superior alternatives to the EMA and SMA . They aim at producing less lag whilst maintaining accuracy. The 2 pole filter will give you a better approximation for price, whereas the 3 pole filter has superior smoothing.
Three-pole Ehlers smoother
The 3 pole Ehlers smoother works almost as close to price as the above mentioned 3 Pole Ehlers Butterworth. It acts as a strong baseline for signals but removes some noise. Side by side, it hardly differs from the Three Pole Ehlers Butterworth but when examined closely, it has better overshoot reduction compared to the 3 pole Ehlers Butterworth.
Triangular Moving Average - TMA
The TMA is similar to the EMA but uses a different weighting scheme. Exponential and weighted Moving Averages will assign weight to the most recent price data. Simple moving averages will assign the weight equally across all the price data. With a TMA (Triangular Moving Average), it is double smoother (averaged twice) so the majority of the weight is assigned to the middle portion of the data.
Triple Exponential Moving Average - TEMA
The TEMA uses multiple EMA calculations as well as subtracting lag to create a tool which can be used for scalping pullbacks. As it follows price closely, its signals are considered very noisy and should only be used in extremely fast-paced trading conditions.
Two-pole Ehlers Butterworth
The 2 pole Ehlers Butterworth (as well as the three pole Butterworth mentioned above) is another filter that cuts out the noise and follows the price closely. The 2 pole is seen as a faster, leading filter over the 3 pole and follows price a bit more closely. Analysts will utilize both a 2 pole and a 3 pole Butterworth on the same chart using the same period, but having both on chart allows its crosses to be traded.
Two-pole Ehlers smoother
A smoother version of the Two pole Ehlers Butterworth. This filter is the faster version out of the 3 pole Ehlers Butterworth. It does a decent job at cutting out market noise whilst emphasizing a closer following to price over the 3 pole Ehlers .
Variable Index Dynamic Average - VIDYA
Variable Index Dynamic Average Technical Indicator ( VIDYA ) was developed by Tushar Chande. It is an original method of calculating the Exponential Moving Average ( EMA ) with the dynamically changing period of averaging.
Variable Moving Average - VMA
The Variable Moving Average (VMA) is a study that uses an Exponential Moving Average being able to automatically adjust its smoothing factor according to the market volatility.
Volume Weighted EMA - VEMA
An EMA that uses a volume and price weighted calculation instead of the standard price input.
Volume Weighted Moving Average - VWMA
A Volume Weighted Moving Average is a moving average where more weight is given to bars with heavy volume than with light volume. Thus the value of the moving average will be closer to where most trading actually happened than it otherwise would be without being volume weighted.
Zero-Lag DEMA - Zero Lag Double Exponential Moving Average
John Ehlers's Zero Lag DEMA's aim is to eliminate the inherent lag associated with all trend following indicators which average a price over time. Because this is a Double Exponential Moving Average with Zero Lag, it has a tendency to overshoot and create a lot of false signals for swing trading. It can however be used for quick scalping or as a secondary indicator for confluence.
Zero-Lag Moving Average
The Zero Lag Moving Average is described by its creator, John Ehlers , as a Moving Average with absolutely no delay. And it's for this reason that this filter will cause a lot of abrupt signals which will not be ideal for medium to long-term traders. This filter is designed to follow price as close as possible whilst de-lagging data instead of basing it on regular data. The way this is done is by attempting to remove the cumulative effect of the Moving Average.
Zero-Lag TEMA - Zero Lag Triple Exponential Moving Average
Just like the Zero Lag DEMA , this filter will give you the fastest signals out of all the Zero Lag Moving Averages. This is useful for scalping but dangerous for medium to long-term traders, especially during market Volatility and news events. Having no lag, this filter also has no smoothing in its signals and can cause some very bizarre behavior when applied to certain indicators.
Exotic Triggers
This version of Baseline allows the user to select from exotic or source triggers. An exotic trigger determines trend by either slope or some other mechanism that is special to each moving average. A source trigger is one of 32 different source types from Loxx's Exotic Source Types. You can read about these source types here:
Volatility Goldie Locks Zone
This volatility filter is the standard first pass filter that is used for all NNFX systems despite the additional volatility/volume filter used in step 5. For this filter, price must fall into a range of maximum and minimum values calculated using multiples of volatility. Unlike the standard NNFX systems, this version of volatility filtering is separated from the core Baseline and uses it's own moving average with Loxx's Exotic Source Types. The green and red dots at the top of the chart denote whether a candle qualifies for a either or long or short respectively. The green and red triangles at the bottom of the chart denote whether the trigger has crossed up or down and qualifies inside the Goldie Locks zone. White coloring of the Goldie Locks Zone mean line is where volatility is too low to trade.
Volatility Types Included
v1.0 Included Volatility
Close-to-Close
Close-to-Close volatility is a classic and most commonly used volatility measure, sometimes referred to as historical volatility .
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a bigger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility calculated using only stock's closing prices. It is the simplest volatility estimator. But in many cases, it is not precise enough. Stock prices could jump considerably during a trading session, and return to the open value at the end. That means that a big amount of price information is not taken into account by close-to-close volatility .
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. That is useful as close to close prices could show little difference while large price movements could have happened during the day. Thus Parkinson's volatility is considered to be more precise and requires less data for calculation than the close-close volatility .
One drawback of this estimator is that it doesn't take into account price movements after market close. Hence it systematically undervalues volatility . That drawback is taken into account in the Garman-Klass's volatility estimator.
Garman-Klass
Garman Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing price. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change is a process of continuous diffusion (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremums.
Researchers Rogers and Satchel have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates drift term (mean return not equal to zero). As a result, it provides a better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. It means an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
We can think of the Yang-Zhang volatility as the combination of the overnight (close-to-open volatility ) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility . It considered being 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator consists of using the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e. it assumes that the underlying asset follows a GBM process with zero drift. Therefore the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, the main applications being technical analysis and volatility modeling.
The moving average is designed as such that older observations are given lower weights. The weights fall exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility . It's the standard deviation of ln(close/close(1))
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by θ.
θavg(var ;M) + (1 − θ) avg (var ;N) = 2θvar/(M+1-(M-1)L) + 2(1-θ)var/(M+1-(M-1)L)
Solving for θ can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg (var; N) against avg (var; M) - avg (var; N) and using the resulting beta estimate as θ.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Additional features will be added in future releases.
This indicator is only available to ALGX Trading VIP group members . You can see the Author's Instructions below to get more information on how to get access.
Unicorn QuantDeeply customizable trading algorithm with instant backtesting. It emulates real trading and displays all the actions it takes on the chart. For example, it shows when to enter or partially close a position, move Stop-Loss to breakeven, etc. The user can replicate these actions in their trading terminal in real time. The algorithm uses up to three Take-Profit levels, and a Stop-Loss level that can move in a trade to protect the floating profit.
The script can send real-time alerts to the user’s Email and to the cell phone via notifications in the TradingView app.
The indicator is designed to be used on all timeframes, including lower ones for intraday trading and scalping.
HOW TO USE
Set the Stop-Loss and up to three Take-Profit levels. Choose the rules for moving the Stop-Loss level in a trade. Adjust the sensitivity of the trading signals. And check the backtest result in the Instant Backtesting dashboard. If the performance of the strategy satisfies you, proceed with the forward testing or live trading.
When using this script, please, keep in mind that past results do not necessarily reflect future results and there are many factors that influence trading results.
FEATURES
Trading Signals
The feature calculates Buy and Sell signals for trend or swing trading. The user can change the Sensitivity parameter to control the frequency of the signals. This allows them to be adjusted for different markets and timeframes.
Position Manager
To make the Position Manager setup as easy as possible, the algorithm calculates Stop-Loss and Take-Profit levels in Average True Range (ATR) units. They are self-adjusting for any market and timeframe, since they account for its average volatility .
You don't have to worry about what market you are trading - Forex, Stocks, Crypto, etc. With the self-adjusting Stop-Loss and Take-Profit, you can find settings that work for one market and use the same numerical values as a starting point for a completely different market.
Instant Backtesting
After changing any settings, you can immediately see the performance of the strategy on the Instant Backtesting panel. Two metrics are displayed there - the percentage of profitable trades and the total return. This information, as well as the historical trades shown on the chart, will help you quickly and easily evaluate the settings.
SETTINGS
TRADING SIGNALS
Sensitivity - controls the sensitivity of the trading signals algorithm. It determines the frequency of the trading signals. The higher the value of this parameter, the less trading signals you get and the longer trends the algorithm tries to catch. The lower the sensitivity value, the more signals you receive. This can be useful if you want to profit from small price movements.
POSITION MANAGER
SL - sets the Stop-Loss level measured in ATR units.
TP1, TP2, TP3 - set the Take-Profit levels measured in the ATR units.
Close % at TP1, Close % at TP2, Close % at TP3 - set portions of the open position (as a percentage of the initial order size) to close at each of the TP levels.
At TP1 move SL to, At TP2 move SL to - set the rules for moving the Stop-Loss level in an open trade to protect the floating profit.
Show Open Position Dashboard - turns on/off a dashboard that shows the current Stop-Loss and Take-Profit levels for the open position.
BACKTESTING
Use Starting Date - turns on/off the starting date for the strategy and backtests. When off, all available historical data is used.
Starting Date - sets the starting date for the strategy and backtests.
Show Instant Backtesting Dashboard - turns on/off a dashboard that shows the current strategy performance: the percentage of profitable trades and total return.
Leverage - sets the leverage that the strategy uses.
Auto Fibonacci Retracement - Real-Time (Expo)█ Fibonacci retracement is a popular technical analysis method to draw support and resistance levels. The Fibonacci levels are calculated between 2 swing points (high/low) and divided by the key Fibonacci coefficients equal to 23.6%, 38.2%, 50%, 61.8%, and 100%. The percentage represents how much of a prior move the price has retraced.
█ Our Auto Fibonacci Retracement indicator analyzes the market in real-time and draws Fibonacci levels automatically for you on the chart. Real-time fib levels use the current swing points, which gives you a huge advantage when using them in your trading. You can always be sure that the levels are calculated from the correct swing high and low, regardless of the current trend. The algorithm has a trend filter and shifts the swing points if there is a trend change.
The user can set the preferred swing move to scalping, trend trading, or swing trading. This way, you can use our automatic fib indicator to do any trading. The auto fib works on any market and timeframe and displays the most important levels in real-time for you.
█ This Auto Fib Retracement indicator for TradingView is powerful since it does the job for you in real-time. Apply it to the chart, set the swing move to fit your trading style, and leave it on the chart. The indicator does the rest for you. The auto Fibonacci indicator calculates and plots the levels for you in any market and timeframe. In addition, it even changes the swing points based on the current trend direction, allowing traders to get the correct Fibonacci levels in every trend.
█ How does the Auto Fib Draw the levels?
The algorithm analyzes the recent price action and examines the current trend; based on the trend direction, two significant swings (high and low) are identified, and Fibonacci levels will then be plotted automatically on the chart. If the algorithm has identified an uptrend, it will calculate the Fibonacci levels from the swing low and up to the swing high. Similarly, if the algorithm has identified a downtrend, it will calculate the Fibonacci levels from the swing high and down to the swing low.
█ HOW TO USE
The levels allow for a quick and easy understanding of the current Fibonacci levels and help traders anticipate and react when the price levels are tested. In addition, the levels are often used for entries to determine stop-loss levels and to set profit targets. It's also common for traders to use Fibonacci levels to identify resistance and support levels.
Traders can set alerts when the levels are tested.
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Disclaimer
Copyright by Zeiierman.
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Moving Average Filters Add-on w/ Expanded Source Types [Loxx]Moving Average Filters Add-on w/ Expanded Source Types is a conglomeration of specialized and traditional moving averages that will be used in most of indicators that I publish moving forward. There are 39 moving averages included in this indicator as well as expanded source types including traditional Heiken Ashi and Better Heiken Ashi candles. You can read about the expanded source types clicking here . About half of these moving averages are closed source on other trading platforms. This indicator serves as a reference point for future public/private, open/closed source indicators that I publish to TradingView. Information about these moving averages was gleaned from various forex and trading forums and platforms as well as TASC publications and other assorted research publications.
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Included moving averages
ADXvma - Average Directional Volatility Moving Average
Linnsoft's ADXvma formula is a volatility-based moving average, with the volatility being determined by the value of the ADX indicator.
The ADXvma has the SMA in Chande's CMO replaced with an EMA, it then uses a few more layers of EMA smoothing before the "Volatility Index" is calculated.
A side effect is, those additional layers slow down the ADXvma when you compare it to Chande's Variable Index Dynamic Average VIDYA.
The ADXVMA provides support during uptrends and resistance during downtrends and will stay flat for longer, but will create some of the most accurate market signals when it decides to move.
Ahrens Moving Average
Richard D. Ahrens's Moving Average promises "Smoother Data" that isn't influenced by the occasional price spike. It works by using the Open and the Close in his formula so that the only time the Ahrens Moving Average will change is when the candlestick is either making new highs or new lows.
Alexander Moving Average - ALXMA
This Moving Average uses an elaborate smoothing formula and utilizes a 7 period Moving Average. It corresponds to fitting a second-order polynomial to seven consecutive observations. This moving average is rarely used in trading but is interesting as this Moving Average has been applied to diffusion indexes that tend to be very volatile.
Double Exponential Moving Average - DEMA
The Double Exponential Moving Average (DEMA) combines a smoothed EMA and a single EMA to provide a low-lag indicator. It's primary purpose is to reduce the amount of "lagging entry" opportunities, and like all Moving Averages, the DEMA confirms uptrends whenever price crosses on top of it and closes above it, and confirms downtrends when the price crosses under it and closes below it - but with significantly less lag.
Double Smoothed Exponential Moving Average - DSEMA
The Double Smoothed Exponential Moving Average is a lot less laggy compared to a traditional EMA. It's also considered a leading indicator compared to the EMA, and is best utilized whenever smoothness and speed of reaction to market changes are required.
Exponential Moving Average - EMA
The EMA places more significance on recent data points and moves closer to price than the SMA (Simple Moving Average). It reacts faster to volatility due to its emphasis on recent data and is known for its ability to give greater weight to recent and more relevant data. The EMA is therefore seen as an enhancement over the SMA.
Fast Exponential Moving Average - FEMA
An Exponential Moving Average with a short look-back period.
Fractal Adaptive Moving Average - FRAMA
The Fractal Adaptive Moving Average by John Ehlers is an intelligent adaptive Moving Average which takes the importance of price changes into account and follows price closely enough to display significant moves whilst remaining flat if price ranges. The FRAMA does this by dynamically adjusting the look-back period based on the market's fractal geometry.
Hull Moving Average - HMA
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points.
IE/2 - Early T3 by Tim Tilson
The IE/2 is a Moving Average that uses Linear Regression slope in its calculation to help with smoothing. It's a worthy Moving Average on it's own, even though it is the precursor and very early version of the famous "T3 Indicator".
Integral of Linear Regression Slope - ILRS
A Moving Average where the slope of a linear regression line is simply integrated as it is fitted in a moving window of length N (natural numbers in maths) across the data. The derivative of ILRS is the linear regression slope. ILRS is not the same as a SMA (Simple Moving Average) of length N, which is actually the midpoint of the linear regression line as it moves across the data.
Instantaneous Trendline
The Instantaneous Trendline is created by removing the dominant cycle component from the price information which makes this Moving Average suitable for medium to long-term trading.
Laguerre Filter
The Laguerre Filter is a smoothing filter which is based on Laguerre polynomials. The filter requires the current price, three prior prices, a user defined factor called Alpha to fill its calculation.
Adjusting the Alpha coefficient is used to increase or decrease its lag and it's smoothness.
Leader Exponential Moving Average
The Leader EMA was created by Giorgos E. Siligardos who created a Moving Average which was able to eliminate lag altogether whilst maintaining some smoothness. It was first described during his research paper "MACD Leader" where he applied this to the MACD to improve its signals and remove its lagging issue. This filter uses his leading MACD's "modified EMA" and can be used as a zero lag filter.
Linear Regression Value - LSMA (Least Squares Moving Average)
LSMA as a Moving Average is based on plotting the end point of the linear regression line. It compares the current value to the prior value and a determination is made of a possible trend, eg. the linear regression line is pointing up or down.
Linear Weighted Moving Average - LWMA
LWMA reacts to price quicker than the SMA and EMA. Although it's similar to the Simple Moving Average, the difference is that a weight coefficient is multiplied to the price which means the most recent price has the highest weighting, and each prior price has progressively less weight. The weights drop in a linear fashion.
McGinley Dynamic
John McGinley created this Moving Average to track price better than traditional Moving Averages. It does this by incorporating an automatic adjustment factor into its formula, which speeds (or slows) the indicator in trending, or ranging, markets.
McNicholl EMA
Dennis McNicholl developed this Moving Average to use as his center line for his "Better Bollinger Bands" indicator and was successful because it responded better to volatility changes over the standard SMA and managed to avoid common whipsaws.
Non lag moving average
The Non Lag Moving average follows price closely and gives very quick signals as well as early signals of price change. As a standalone Moving Average, it should not be used on its own, but as an additional confluence tool for early signals.
Parabolic Weighted Moving Average
The Parabolic Weighted Moving Average is a variation of the Linear Weighted Moving Average. The Linear Weighted Moving Average calculates the average by assigning different weight to each element in its calculation. The Parabolic Weighted Moving Average is a variation that allows weights to be changed to form a parabolic curve. It is done simply by using the Power parameter of this indicator.
Recursive Moving Trendline
Dennis Meyers's Recursive Moving Trendline uses a recursive (repeated application of a rule) polynomial fit, a technique that uses a small number of past values estimations of price and today's price to predict tomorrows price.
Simple Moving Average - SMA
The SMA calculates the average of a range of prices by adding recent prices and then dividing that figure by the number of time periods in the calculation average. It is the most basic Moving Average which is seen as a reliable tool for starting off with Moving Average studies. As reliable as it may be, the basic moving average will work better when it's enhanced into an EMA.
Sine Weighted Moving Average
The Sine Weighted Moving Average assigns the most weight at the middle of the data set. It does this by weighting from the first half of a Sine Wave Cycle and the most weighting is given to the data in the middle of that data set. The Sine WMA closely resembles the TMA (Triangular Moving Average).
Smoothed Moving Average - SMMA
The Smoothed Moving Average is similar to the Simple Moving Average (SMA), but aims to reduce noise rather than reduce lag. SMMA takes all prices into account and uses a long lookback period. Due to this, it's seen a an accurate yet laggy Moving Average.
Smoother
The Smoother filter is a faster-reacting smoothing technique which generates considerably less lag than the SMMA (Smoothed Moving Average). It gives earlier signals but can also create false signals due to its earlier reactions. This filter is sometimes wrongly mistaken for the superior Jurik Smoothing algorithm.
Super Smoother
The Super Smoother filter uses John Ehlers’s “Super Smoother” which consists of a a Two pole Butterworth filter combined with a 2-bar SMA (Simple Moving Average) that suppresses the 22050 Hz Nyquist frequency: A characteristic of a sampler, which converts a continuous function or signal into a discrete sequence.
Three pole Ehlers Butterworth
The 3 pole Ehlers Butterworth (as well as the Two pole Butterworth) are both superior alternatives to the EMA and SMA. They aim at producing less lag whilst maintaining accuracy. The 2 pole filter will give you a better approximation for price, whereas the 3 pole filter has superior smoothing.
Three pole Ehlers smoother
The 3 pole Ehlers smoother works almost as close to price as the above mentioned 3 Pole Ehlers Butterworth. It acts as a strong baseline for signals but removes some noise. Side by side, it hardly differs from the Three Pole Ehlers Butterworth but when examined closely, it has better overshoot reduction compared to the 3 pole Ehlers Butterworth.
Triangular Moving Average - TMA
The TMA is similar to the EMA but uses a different weighting scheme. Exponential and weighted Moving Averages will assign weight to the most recent price data. Simple moving averages will assign the weight equally across all the price data. With a TMA (Triangular Moving Average), it is double smoother (averaged twice) so the majority of the weight is assigned to the middle portion of the data.
The TMA and Sine Weighted Moving Average Filter are almost identical at times.
Triple Exponential Moving Average - TEMA
The TEMA uses multiple EMA calculations as well as subtracting lag to create a tool which can be used for scalping pullbacks. As it follows price closely, it's signals are considered very noisy and should only be used in extremely fast-paced trading conditions.
Two pole Ehlers Butterworth
The 2 pole Ehlers Butterworth (as well as the three pole Butterworth mentioned above) is another filter that cuts out the noise and follows the price closely. The 2 pole is seen as a faster, leading filter over the 3 pole and follows price a bit more closely. Analysts will utilize both a 2 pole and a 3 pole Butterworth on the same chart using the same period, but having both on chart allows its crosses to be traded.
Two pole Ehlers smoother
A smoother version of the Two pole Ehlers Butterworth. This filter is the faster version out of the 3 pole Ehlers Butterworth. It does a decent job at cutting out market noise whilst emphasizing a closer following to price over the 3 pole Ehlers.
Volume Weighted EMA - VEMA
Utilizing tick volume in MT4 (or real volume in MT5), this EMA will use the Volume reading in its decision to plot its moves. The more Volume it detects on a move, the more authority (confirmation) it has. And this EMA uses those Volume readings to plot its movements.
Studies show that tick volume and real volume have a very strong correlation, so using this filter in MT4 or MT5 produces very similar results and readings.
Zero Lag DEMA - Zero Lag Double Exponential Moving Average
John Ehlers's Zero Lag DEMA's aim is to eliminate the inherent lag associated with all trend following indicators which average a price over time. Because this is a Double Exponential Moving Average with Zero Lag, it has a tendency to overshoot and create a lot of false signals for swing trading. It can however be used for quick scalping or as a secondary indicator for confluence.
Zero Lag Moving Average
The Zero Lag Moving Average is described by its creator, John Ehlers, as a Moving Average with absolutely no delay. And it's for this reason that this filter will cause a lot of abrupt signals which will not be ideal for medium to long-term traders. This filter is designed to follow price as close as possible whilst de-lagging data instead of basing it on regular data. The way this is done is by attempting to remove the cumulative effect of the Moving Average.
Zero Lag TEMA - Zero Lag Triple Exponential Moving Average
Just like the Zero Lag DEMA, this filter will give you the fastest signals out of all the Zero Lag Moving Averages. This is useful for scalping but dangerous for medium to long-term traders, especially during market Volatility and news events. Having no lag, this filter also has no smoothing in its signals and can cause some very bizarre behavior when applied to certain indicators.
________________________________________________________________
What are Heiken Ashi "better" candles?
The "better formula" was proposed in an article/memo by BNP-Paribas (In Warrants & Zertifikate, No. 8, August 2004 (a monthly German magazine published by BNP Paribas, Frankfurt), there is an article by Sebastian Schmidt about further development (smoothing) of Heikin-Ashi chart.)
They proposed to use the following:
(Open+Close)/2+(((Close-Open)/( High-Low ))*ABS((Close-Open)/2))
instead of using :
haClose = (O+H+L+C)/4
According to that document the HA representation using their proposed formula is better than the traditional formula.
What are traditional Heiken-Ashi candles?
The Heikin-Ashi technique averages price data to create a Japanese candlestick chart that filters out market noise.
Heikin-Ashi charts, developed by Munehisa Homma in the 1700s, share some characteristics with standard candlestick charts but differ based on the values used to create each candle. Instead of using the open, high, low, and close like standard candlestick charts, the Heikin-Ashi technique uses a modified formula based on two-period averages. This gives the chart a smoother appearance, making it easier to spots trends and reversals, but also obscures gaps and some price data.
Expanded generic source types:
Close = close
Open = open
High = high
Low = low
Median = hl2
Typical = hlc3
Weighted = hlcc4
Average = ohlc4
Average Median Body = (open+close)/2
Trend Biased = (see code, too complex to explain here)
Trend Biased (extreme) = (see code, too complex to explain here)
Included:
-Toggle bar color on/off
-Toggle signal line on/off
TPRC - Time-based Price Range Channel [Free]You define a time range (hours and minutes) and based on this, the indicator draws the price range (high / low) as a channel in your chart - projected into the future and, if desired, also for past days. You are completely free to choose the time range and NOT limited to trading sessions.
In addition, further lines are drawn below / above the price range channel at a distance that you can define (based on the price range).
These lines can serve as target levels, support and resistance lines.
What functions does this free version of the indicator offer?
Selection of the time range for which a price range is to be determined and based on this a price range channel is to be created
Display of 3 additional lines above / below the price range channel
Distance between the lines: height of the price range
Display of the price range channels for the past 3 days as well as for the current day.
Lines are shown in gray
For the past days, only those lines are displayed that are required due to the distance to the price. This will make your chart cleaner.
(Details about the premium version can be found on TradingView: )
How can this indicator be used?
The time-based price range channel and the additional lines can serve as support and resistance lines.
Whether you are enthusiastic about scalping, swing trading or another type of trading,… “TPRC - Time-based Price Range Channel” could therefore support you. Try it out. I want to invite you to experiment and thereby adapt “TPRC” to your own way of trading.
Due to the free choice with regard to the time span, for example “opening range (break-out)” strategies and the like are conceivable. Much has been written or published as a video on the subjects of "Price Range Trading", "Range Trading", "Opening Range Breakout Trading" and the like. Research on this is recommended to every interested trader. I would be happy to provide a list of interesting articles on this topic - just send me a short message.
Due to the implementation and the functions, the focus is definitely on intraday trading strategies.
For which timeframe is this indicator intended?
This indicator was developed for Chart Time Intervals between 1 and 120 minutes, whereby the following Chart Time Intervals have proven themselves and successfully withstand tests: 1, 2, 5, 10, 15, 30, 60, 90
What do I need to consider?
It may be advisable to add further indicators and an analysis of the market structure in order to confirm the signals issued by the indicator. Please note that when you make adjustments to any strategy, you always carry out particularly detailed tests.
Will this indicator be further developed and will I receive free updates?
All my indicators are of course constantly updated and, if possible and with the aim of the indicator justifiable, supplemented by user requests.
An example of the use of this indicator (here with the premium version)
#revision: dv699
RobocanThis script is equipped with
🔵 Robo 2
It offers strategic trading entry and exit points. Truly unique tool for technical analysis for the financial market as it includes calculation of specific metrics like MACD, ATR and RSI.
🔵 Bull & Bear
The signal can be a fairly valuable tool. Momentum is one of those aspects of the market that is crucial to understanding price movements, yet it is so hard to get a solid grip on. It can be used in some instances to generate quality signals but much like with any signal generating indicator, it should be used with caution.
When indicator gives you " Bull " signal , short term momentum is now rising faster than the long term momentum. This can present a bullish buying opportunity.
When indicator gives you "Bear " signal, short term momentum is now falling faster then the long term momentum. This can present a bearish selling opportunity.
🔵 Robo's Cloud
The indicator inspired from Ichimoku CLoud, it uses an unique formula to generate clouds on its own system!
" BUY or ENTER "when the price breaks the Cloud in the direction of the breakout (UP ) and the cloud turns to green colour. Stay in the market until the cloud turns to red colour. Let's assume that You are a swing trader and use 1D candles as long as The candle is above the "green " cloud , you should continue with a trend! No need to hurry to sell until you see the " red " cloud.
🔵 Super Robo
It can perform greatly in a bull and bear market
It's unique algorithm find profitable coins based on "Early Bird + Buy 2 + Volume "gives you ENTRY and EXIT ideas
It works perfectly on the 1W - 3D - 1D charts
🔵 Hell & Moon
When the “Moon or Hell “closes below top of the closing price, a Moon - Buy signal is generated
It works perfectly on the 1W - 1D - 3H charts
🔵 Early Bird Signals
Being an early bird rather than a night owl will naturally lead you to become more successful in trading. There is no secret magic formula to success; this is something you must accept. Trading success is the result of a ‘simple’ list made up of four things: hard work, timing, persistence , and a good dose of Early Bird signals.
it provides high risk & high reward opportunities.
Dont use more than 3 Robo signals at the same time on the chart. Why?
Example, Robo 2 already included 3 different indicators in the formula.
Robo 2 : Truly unique tool for technical analysis for the financial market as it includes calculation of specific metrics like SAR + MACD + Price Movement that gives you ENTRY and EXIT ideas ( Buy 2 & Sell 2 )
If you use more than 3 robo signals, you try to use around " 10 - 12 " different indicators at the same time!
DON'T DO IT!
To get maximum results from your robo advisors, follow the advice below ;
A ) 3 robo signals
B ) 3 robo signals + 1 side strategy
A or B + Pick one bonus below
Dynamic Support Resistance,
Fibonacci Levels
Pivot Support Resistance
Robo signals :
Robo 1
Robo 2
Super EngineeringRobo
Robo 3
Robo 4
Bull & Bear
Hell & Moon
Early Bird
EngineeringRobo's cloud
Ultimate MA crossover strategy
Side strategies :
McGinley Dynamic
Bollinger Bands Strategy
MA 20 & MA 50
MA 50 & MA 200
EMA Trendlines
Robo ( 2 + 3 ) shows you that if the signals are covering each other. So, It is good to keep open it when you use Robo 2 and Robo 3 at the same time.
If you are following any signals, you should always wait for the candle close before buying or selling.
The signal can come and go anytime during the live candle. ALL indicators do that, that is not considered repainting.
Repainting is when a signal appears, the candle is closed, and when you refresh the chart it disappeared. It is logical that until the candle is closed the signal is not decided yet, hence the alert setup as Once per bar Close.
Deluxe never repaints! Yes, you heard it right: you will never have to worry about signal changing after the candle is closed.
________________________________________________________________________Timeframes_____________________________________________________________________
Our recommendations to get the best results:
Swing Trading Crypto : Use 1D Time Frame Candles
Swing Trading Stocks : Use 1W Time Frame Candles
Swing Trading Commodities : Use 1W Time Frame Candles
Day Trading Crypto : Use 3H Time Frame Candles
Day Trading Stocks : Use 1D Time Frame Candles
Day Trading Commodities : Use 1D Time Frame Candles
Not recommended any other time frames.
It gives you all the tools and information you need for day-to-day trading and investing, while also keeping a great buy and sell signals! No excuse to lose in any financial market anymore! Try now!
How can you add the algorithm into your chart?
1. Login to TradingView.com
2. From the homepage, click on ‘Chart’ in the top navigation bar
3. Select “Indicators” on the top-center-middle panel
4. In the indicator library, type "Robocan "
5. Use the website link below to obtain access to this indicator
INTRADAY/SWING TRADING - 3 EMASEstimados/as inversores:
Diagramé este indicador para hacer tradings de corto o muy corto plazo.
Es un indicador que a simple vista ayuda al usuario a entrar en posiciones de Compra o de Venta.
Este indicador es un sistema de 3 EMAS. La primera, la de color verde es una EMA de 4 periodos. La segunda, la de color amarillo es una EMA de 9 periodos. Y por último, la de color rojo es una EMA de 18 periodos.
Por otro lado tiene señales de Compra y de Venta las cuales tienen una alta eficacia y eficiencia.
Las señales de BUY (Compra) se dan cuando la EMA verde cruza al alza a la EMA roja. Las señales de SELL (Venta) se dan cuando la EMA roja cruza a la baja a la EMA verde.
En algunas ocasiones, estos cruces se pueden producir muy rápido generando unas falsas entradas en compra o en venta según corresponda.
Para subsanar esto, es importante que se utilice este sistema de BUY y SELL con las columnas de color verde o rojo según corresponda según se ve el gráfico.
El fondo de color verde se da cuando la EMA verde y la EMA amarilla se encuentran por encima de la EMA roja. Sin embargo, cuando la EMA roja se encuentra por encima de la EMA verde y de la EMA amarilla el fondo es de color rojo.
Es importante remarcar que si la EMA verde está por encima de la EMA roja pero la EMA amarilla se encuentra por debajo de la EMA roja, en el gráfico no se va a ver ningún color de fondo. Por otro lado, cuando la EMA verde este por debajo de la EMA roja, pero la EMA amarilla todavía se encuentre por encima de la EMA roja, tampoco va a poder verse ningún tipo de color de fondo.
En resumidas cuentas:
COMPRA-BUY -> Cuando aparezca la señal de BUY y además, esta señal se complemente con un fondo de color VERDE, entonces debemos entrar en LONG. Para cerrar la operación, de manera ganadora, tenemos que esperar a que desaparezca el color de fondo VERDE.
VENTA-SELL -> Cuando aparezca la señal de SELL y además, esta señal se complemente con un fondo de color ROJO, entonces, debemos entrar en SHORT. Para cerrar la operación, de manera ganadora, tenemos que esperar a que desparezca el color de fondo ROJO.
RECOMENDACIÓN: Siempre tener presente que cada inversor tiene una aversión al riesgo distinta. Por favor, cada uno que use este indicador, primero haga una gestión de riesgo y utilice SIEMPRE Stop Loss luego de abrir una posición ya sea estipulando que el precio va a subir o a bajar, es decir, entrando en LONG o en SHORT.
Espero que este indicador les sirva.
Saludos a todos.
DEAR INVESTORS:
I plotted this indicator for short or very short term trading.
It is an indicator that at a glance helps the user to enter Buy or Sell positions.
This indicator is a 3 EMAS system. The first, the green one, is a 4-period EMA . The second one, the one in yellow, is a 9-period EMA . And finally, the one in red is an EMA of 18 periods.
On the other hand, it has Buy and Sell signals which are highly effective and efficient.
The BUY signals are given when the green EMA crosses higher than the red EMA . SELL (Sell) signals are given when the red EMA crosses down to the green EMA .
On some occasions, these crosses can occur very quickly, generating false tickets for purchase or sale as appropriate.
To correct this, it is important that this system of BUY and SELL is used with the green or red columns as appropriate as the graph is seen.
The green colored background occurs when the green EMA and the yellow EMA are above the red EMA . However, when the red EMA is above the green EMA and the yellow EMA the bottom is red.
It is important to note that if the green EMA is above the red EMA but the yellow EMA is below the red EMA , no background color will be seen on the chart. On the other hand, when the green EMA is below the red EMA , but the yellow EMA is still above the red EMA , you will not be able to see any kind of background color either.
In short:
BUY-BUY -> When the BUY signal appears and this signal is complemented by a GREEN background, then we must enter LONG. To close the operation, in a winning way, we have to wait for the GREEN background color to disappear.
VENTA-SELL -> When the SELL signal appears and also this signal is complemented with a RED background, then, we must enter SHORT. To close the operation, in a winning way, we have to wait for the RED background color to disappear.
RECOMMENDATION: Always keep in mind that each investor has a different aversion to risk. Please, everyone who uses this indicator, first do a risk management and ALWAYS use Stop Loss after opening a position either by stipulating that the price is going to rise or fall, that is, entering LONG or SHORT.
I hope this indicator helps you.
Greetings to all.
[blackcat] L2 Ehlers Fisherized Deviation Scaled OscillatorLevel: 2
Background
John F. Ehlers introuced Fisherized Deviation Scaled Oscillator in Oct, 2018.
Function
In “Probability—Probably A Good Thing To Know,” John Ehlers introduces a procedure for measuring an indicator’s probability distribution to determine if it can be used as part of a reversion-to-the-mean trading strategy. Dr. Ehlers demonstrates this method with several of his existing indicators and presents a new indicator that he calls a deviation-scaled oscillator with Fisher transform. It charts the probability density of an oscillator to evaluate its applicability to swing trading.
Key Signal
FisherFilt --> Ehlers Fisherized Deviation Scaled Oscillator fast line
Trigger --> Ehlers Fisherized Deviation Scaled Oscillator slow line
Pros and Cons
100% John F. Ehlers definition translation, even variable names are the same. This help readers who would like to use pine to read his book.
Remarks
The 91th script for Blackcat1402 John F. Ehlers Week publication.
Readme
In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts.
The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works.
Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element.
Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level.
Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated.
Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop.
Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
Stock Analysis SoftwareStock Analysis Software is a full trading setup and style that is meant for swing trading stocks, but can also be used for Forex, cryptocurrencies, indices and commodities. Whatever your choice of trading style (Intraday, Scalping, Swing trading, Investing) or trading instrument is (FX, Futures, Cryptos, Stocks) I can tailor it for you specifically. For example if you want to use it for trading Forex intraday I will show you how to use it for that.
The software consists of 11 indicators, 7 are custom and 4 are common and well known indicators available on Tradingview. The system itself is part software and part learning my specific pattern finding techniques. There is no one without the other. This is a complete system
This trading system is something I have developed over the last 10 years through extensive research and development and is now available on this platform.
The indicators used are mostly screening for trend breakouts, support and resistance, specific candle patterns, overextended, volume spikes and more.
This is a system that can be taught easily if one is motivated to learn.
The setup includes a video guide and a live one-on-one full breakdown on how to use it to your benefit.
Trade Crusher: Swing and Day Trade IndicatorHow to use the indicator
Add to favorites/apply to chart.
The indicator can be used for both Swing trade and Intra-day trading.
Swing trading:
--Use with background colors
--Input: 30 or 36
--Time frame: Daily or Weekly
--Buy only when background is aqua
--Sell only when background is red
--Use with bars or candles (use candles without borders to avoid confusion). I suggest to just use bars.
--Place buy orders above the 1st or 2nd blue bar after black bars. The background must be aqua.
--Ignore yellow bars with aqua background. They are shake out bars at the beginning or a trend and warnings of reversal
towards the end of trend.
--Place sell orders bellow the 1st or 2nd yellow bar after black bars. The background must be red.
--Ignore blue bars with red background (same as above).
--Black bars are nothing: Pullbacks/Chop
Day Trading:
--DO NOT use background colors. Un-click.
--Input: 10
--Time frame: 5 minutes
--Use with bars or candles (use candles without borders to avoid confusion). I suggest to just use bars.
--Place buy orders above the 1st or 2nd blue bar after black bars.
--Place sell orders bellow the 1st or 2nd yellow bar after black bars.
--Utilize some sort of scanner that can identify stocks with heavy pre-market volume (news, earnings, etc)
***
Use stop losses however you normally do. Take profits however you normally do.
I do not suggest using with other indicators as you may just paralyze your brain, however, if you find something that works, drop a comment.
Best of luck
TrendShikari NTS - StudyTrendShikari NTS is a Nifty Index, Swing trading system with great profitability. This is the STUDY file for you to generate E-mail / SMS signal alerts (based on your TV plan) and to see crisp and clear graphical Daily trade level plotting. For seeing backtest results and next day trading levels in advance use the STRATEGY file from indicator library. Access to this system will be limited. See my profile status field to see how you can gain access.
Salient Features
1. Daily Bar System. System analyzes a Daily chart of NIFTY to give signals with average holding period of 5 days.
2. Automatic Long and Short signal generation. No need to draw waves / lines and other fancy stuff on your charts to analyze NIFTY any more.
3. Backtester Results Available - Thanks to TradingView, backtest results for previous years (from 1990) are available right in the charting platform for NIFTY.
Having a good trading system is one thing and trading it to make money is a whole different ball game. One thing you must always do if you want to mimic the backtest results in live trading is to follow the rules mentioned below as if your life depends on it.
Trading Rules
1. Each day the system gives you a Long and Short trading level. You go Long on NIFTY when the Daily Long level is breached and you go Short on NIFTY when the Daily Short Level is breached.
2. Trade using Nifty Options, In the Money calls, one strike below the nearest strike price for going Long using Call Option or one strike above the nearest strike price for going Short using Put Option.
3. Preset exit and entry orders of appropriate option contracts every day at market open. To set the levels see the difference in Nifty spot price and the trading levels given by system and then multiply it with 0.8 to give an approximate order trigger price in both directions for the corresponding option contracts.
4. Book profit when Nifty moves significantly along signal direction. Every time NIFTY moves 100 points in your direction you exit the current option contract and enter a trade in the next strike price in the same direction.
5. Rollover before expiry. Its important that you rollover (ideally one day before the expiry day) your Option contact positions by exiting the current month contract and take a new position in the next month contract of the same type and strike price of the current month contract.
6. Trade only Nifty using this system. Also Daily chart has to be used for trading. System parameters have been tested and optimized for Nifty Index Daily patterns only and hence is likely to give stated results with Nifty Daily chart only.
7. Trade all signals. Don't pick and choose or add your own or someone else's analysis to filter the signals. Take confidence from the objective backtest results and not any subjective interpretations.
8. Trade with only that amount of money you can afford to loose. Initial capital that you need to have to trade one lot of NIFTY Option using this system should be at least INR 150000. You need only INR 7500 - 15000 to open a position and the rest is the margin of safety you need to have in your trading account to account for drawdowns in trading. You can add the capital in a staggered need to basis to your trading account. But make sure you have the initial capital mentioned above at your disposal, if need be.
As always your thoughts and inputs are welcome. Happy Trading !!!
AI Predictive Market + FVG + MSS v6AI Predictive Market + FVG + MSS v6
This powerful TradingView indicator combines advanced AI predictive scoring with market structure and price action analysis to provide clear trading insights. Designed for traders who want both signals and visualization, it helps identify potential market reversals, trend continuations, and key levels with precision.
Key Features:
- AI Predictive Score: Calculates a normalized predictive score based on trend, momentum, and volatility for real-time market bias.
- Buy/Sell Signals with Take-Profit Labels: Highlights actionable entries along with dynamic ATR-based target levels for clarity.
- Fair Value Gap (FVG) Detection: Shows bullish and bearish price gaps for potential reversal or continuation zones.
- Market Structure Shift (MSS): Marks higher highs, higher lows, lower highs, and lower lows for structural trend analysis.
- Multi-Timeframe EMA Filters: Confirms trends using higher and macro timeframe EMAs to reduce false signals.
- Optional MACD Confirmation: Adds additional momentum validation for buy/sell decisions.
- Background & Bar Coloring: Quickly identifies bullish, bearish, or neutral market conditions.
- Dynamic Thresholds: Visual threshold lines for predictive score to gauge signal strength.
- Performance HUD: Displays real-time bias, predictive score, and trend strength.
- Safe & Optimized for Pine v6: Fully compatible with TradingView’s latest Pine Script v6, with label-safe implementation and zero errors.
Who is this for:
Traders who want a comprehensive visual trading tool that combines AI scoring, market structure, and price action analysis for clearer decision-making. Ideal for intraday and swing trading.
Usage Tips:
- Use signals in conjunction with multi-timeframe trend analysis.
- Combine FVG and MSS insights for potential reversal or continuation trades.
- Adjust ATR multipliers and sensitivity to match your preferred risk/reward and market conditions.
DrFX MACD-RSI Reversal Algo with Dynamic ZonesOverview
This indicator identifies high-probability reversal points by combining MACD momentum crossovers with RSI trend confirmation, enhanced by dynamically calculated support and resistance zones. Unlike standard MACD crossover systems that generate numerous false signals in ranging markets, this approach adds three layers of confirmation: RSI directional bias, adaptive volatility zones, and Kalman-filtered zone boundaries to improve signal reliability. All parameters have been systematically optimized through extensive backtesting across multiple instruments and timeframes to maximize signal quality while maintaining practical usability.
Core Methodology
1. MACD Momentum Detection System
The indicator uses a customized MACD configuration (20-period fast, 50-period slow, 12-period signal smoothing) that has been optimized to be slower than the standard 12/26/9 setup. This longer timeframe reduces noise and focuses on more significant trend changes rather than short-term fluctuations.
Why These Specific MACD Parameters:
Through systematic testing across Forex majors, Gold, and indices over 2+ years of data, the 20/50/12 combination was selected because it:
Reduces false crossovers by approximately 45% compared to standard 12/26/9
Maintains responsiveness to genuine trend changes (average lag: 3-5 bars vs 2-3 bars for standard settings)
Produces optimal signal-to-noise ratio on H1-D1 timeframes
Aligns crossover timing with RSI momentum shifts more consistently
Signal Generation Logic:
Buy Signal: MACD line crosses above signal line (momentum shifts bullish)
Sell Signal: MACD line crosses below signal line (momentum shifts bearish)
The MACD histogram's absolute value determines the "power" or strength of the current momentum, which is used for visual gradient effects and can help traders assess signal conviction.
2. RSI Trend Confirmation Layer
A 14-period RSI adds directional context to MACD crossovers by measuring whether price momentum aligns with the signal. The RSI value is normalized by subtracting 50, creating a zero-centered oscillator where:
Positive values indicate bullish bias (RSI > 50)
Negative values indicate bearish bias (RSI < 50)
Signal Classification System:
The combination of MACD crossover direction and RSI bias creates four signal types:
Strong Buy (Large green triangle): MACD crosses up + RSI > 50 = Bullish reversal with momentum confirmation
Buy (Small green triangle): MACD crosses up + RSI ≤ 50 = Bullish reversal without full momentum (weaker signal)
Strong Sell (Large red triangle): MACD crosses down + RSI < 50 = Bearish reversal with momentum confirmation
Sell (Small red triangle): MACD crosses down + RSI ≥ 0 = Bearish reversal without full momentum (weaker signal)
This tiered approach allows traders to prioritize "Strong" signals while still being aware of weaker setup opportunities.
3. Dynamic Support and Resistance Zone System
The indicator calculates adaptive support and resistance zones using a multi-step process with optimized parameters:
Step A - Volatility Band Creation:
Uses ATR (Average True Range) with 10-bar period (optimized for balance between responsiveness and stability)
Calculates midpoint as (high + low) / 2
Creates upper and lower bands: midpoint ± (ATR × 5.0 multiplier)
Why ATR Period = 10 and Multiplier = 5.0:
These values were optimized through testing across volatile (Gold, Crypto) and stable (Forex majors, indices) instruments. The 10-period captures recent volatility without excessive lag, while the 5.0 multiplier ensures zones encompass approximately 85-90% of price action in normal conditions, leaving breakouts as the significant 10-15% of moves that generate reversal signals.
Step B - Swing Level Integration:
Identifies 20-period swing high (resistance reference)
Identifies 20-period swing low (support reference)
Combines these swing levels with the volatility bands to create zone boundaries
The 20-period lookback was selected because it captures 1-4 weeks of price structure on daily charts (20 trading days ≈ 1 month), or 3-4 hours on M15 charts, providing meaningful structural levels without looking too far back.
Step C - Kalman Filter Smoothing:
The raw zone boundaries are smoothed using a Kalman filter algorithm with optimized parameters Q=0.01 (process noise) and R=0.1 (measurement noise).
Why These Kalman Parameters:
Through iterative testing, Q=0.01 and R=0.1 provide the optimal balance:
Q=0.01 (low process noise): Assumes zone levels change gradually, preventing overreaction to single-bar spikes
R=0.1 (moderate measurement noise): Acknowledges that raw ATR calculations contain some noise, requiring smoothing
Q/R ratio of 1:10: Produces 1-2 bar lag in zone adaptation while filtering out 70-80% of false level breaks
The Kalman filter is a recursive algorithm that estimates the true position of a moving target from noisy measurements. In this context, it prevents the support/resistance zones from jumping erratically on each bar while still tracking genuine level shifts. The result is stable, predictable zone boundaries that move smoothly rather than making sudden adjustments.
4. Optional Zone Filter
Traders can enable an additional filter requiring:
Buy signals: Price must be above the support zone (confirming breakout potential)
Sell signals: Price must be below the resistance zone (confirming breakdown potential)
This filter eliminates signals that occur within the consolidation zones, focusing only on breakout opportunities. Testing shows this filter improves signal win rate by 12-18% but reduces signal frequency by approximately 40%.
5. Visual Momentum Feedback
Bar colors provide real-time feedback on trend strength:
Green gradient: Bullish (MACD histogram positive and rising + RSI > 50) - intensity increases with histogram strength
Red gradient: Bearish (MACD histogram negative and falling + RSI < 50) - intensity increases with histogram strength
Mixed colors: Consolidation phase (MACD and RSI not aligned) - transitions from red to green based on histogram power
The gradient range (default: 2000) was optimized to provide clear visual distinction between strong and weak momentum states across different instruments. Lower values create more dramatic color changes; higher values create subtler gradients.
Parameter Optimization Methodology
Optimization Process:
All default parameters were systematically tested using the following methodology:
Instrument Selection: EURUSD, GBPUSD, XAUUSD (Gold), SPX500, BTCUSD
Timeframes Tested: M15, H1, H4, D1
Data Range: 2+ years of historical data per instrument (2021-2024)
Optimization Criteria:
Signal quality (win rate on Strong signals)
Signal frequency (minimum 50 signals per year on D1, scaling proportionally for shorter timeframes)
Risk-reward ratio (average winning signal move vs average losing signal move)
Drawdown characteristics (consecutive losing signals)
Robustness across different market regimes (trending, ranging, volatile)
Testing Methodology:
Walk-forward analysis (optimize on 12 months, test on following 6 months, roll forward)
Out-of-sample validation on instruments not used in initial optimization
Stress testing during high-volatility periods (2022 inflation spike, 2023 banking crisis, COVID-19 crash)
Optimization Results:
The current default settings represent the "sweet spot" across all tested instruments:
MACD 20/50/12: Produced most consistent results across 5 instruments vs alternatives (15/45/9, 25/60/15, standard 12/26/9)
RSI 14: Standard period performed best; shorter periods (7, 10) produced excessive noise
ATR Period 10, Multiplier 5.0: Best balance of zone stability and adaptability
Kalman Q=0.01, R=0.1: Optimal smoothing without excessive lag
Swing Lookback 20: Captured relevant structure without looking too far back
Gradient Range 2000: Provided clear visual feedback across instruments without requiring adjustment
Important Optimization Disclosure:
These optimized parameters work well across multiple markets and timeframes but are not guaranteed to be optimal for all instruments or future market conditions. The settings represent a generalist approach prioritizing robustness over maximum performance on any single asset. Traders using this indicator on specific instruments may benefit from fine-tuning parameters to their particular market.
Why This Combination Works
Standard MACD crossovers generate excessive signals in sideways markets because momentum oscillates frequently around the zero line. By requiring RSI confirmation, the indicator ensures that signals occur in the direction of the prevailing momentum, reducing counter-trend whipsaws by approximately 40-50%.
The dynamic zone system addresses another weakness of pure oscillator strategies: they don't account for price structure. By overlaying support/resistance zones, traders can distinguish between:
Signals occurring at established levels (higher probability)
Signals occurring mid-range (lower probability)
The Kalman filter smoothing is crucial because raw ATR bands can be choppy, causing zones to flash on and off the chart. The filtered zones remain stable enough for traders to use as actual reference levels rather than just visual noise.
How to Use This Indicator
Signal Interpretation Hierarchy:
Highest Priority: Strong Buy/Sell signals occurring at zone boundaries (confluence of momentum, trend, and structure)
Medium Priority: Strong Buy/Sell signals within zones (momentum + trend confirmation, but no structural support)
Lower Priority: Regular Buy/Sell signals at any location (divergent momentum, weaker setup)
Recommended Workflow:
Wait for a Strong Buy or Strong Sell signal (large triangle)
Verify price is near a support/resistance zone (or enable the zone filter)
Confirm bar color gradient shows intensifying momentum
Enter on signal bar close or on next bar open
Place stop loss beyond the opposite zone boundary
Target the opposite zone or use trailing stop once price enters profit zone
Parameter Adjustment by Asset:
While the default optimized settings work across multiple markets, traders can fine-tune for specific instruments:
Forex Majors: Default settings work well; consider 15/35/9 MACD for faster signals on M15-H1
Gold/Metals: Increase ATR multiplier to 6-7 for wider zones; use 25/60/15 MACD for smoother signals
Indices: Reduce volatility period to 5-7 bars; keep default MACD
Cryptocurrencies: Increase ATR multiplier to 7-10 for extreme volatility; consider 14/35/7 MACD
Timeframe Recommendations:
M15-H1: Best for intraday reversal trading
H4-D1: Best for swing trading major turns (optimized primarily for these timeframes)
Weekly: Generates infrequent but high-quality macro reversal signals
Understanding the Visual Elements
Chart Overlays:
Blue shaded zone: Dynamic support area (safe zone for longs)
Red shaded zone: Dynamic resistance area (safe zone for shorts)
Green triangles: Buy signals (large = strong, small = regular)
Red triangles: Sell signals (large = strong, small = regular)
Bar Colors:
Bright green: Strong bullish momentum (both MACD and RSI bullish)
Dark green: Moderate bullish momentum
Bright red: Strong bearish momentum (both MACD and RSI bearish)
Dark red: Moderate bearish momentum
Mixed/transitional colors: Consolidation or conflicting indicators
What Makes This Original
While MACD, RSI, and ATR are standard indicators, this script's originality comes from:
The Kalman filter implementation for zone smoothing - not commonly applied to support/resistance in Pine Script
The four-tier signal classification system that combines MACD crossover direction with RSI positioning to create distinct signal strengths
The hybrid zone calculation merging ATR volatility bands with swing high/low levels, then applying recursive filtering
The gradient bar coloring system that visualizes momentum intensity rather than simple binary color switches
The zone-filtered alert system that optionally requires structural confirmation for signal validity
The comprehensive multi-asset optimization process resulting in robust default parameters that work across instruments and timeframes
The combination transforms basic crossover signals into a context-aware reversal detection system that accounts for trend, momentum, and market structure simultaneously.
Practical Application Examples
Scenario 1 - Trending Market:
Price in uptrend, bounces off blue support zone
Strong Buy signal appears (MACD crosses up, RSI > 50)
Bar color shifts to bright green
Action: Enter long, stop below support zone, target resistance zone
Scenario 2 - Range-Bound Market:
Price oscillating between zones
Regular Buy signal appears mid-range (MACD up, RSI < 50)
Bar color mixed/transitional
Action: Skip signal or wait for Strong signal at zone boundary
Scenario 3 - False Breakout:
Price breaks above resistance zone briefly
Strong Sell signal appears (MACD crosses down, RSI < 50)
Bar color shifts to red
Action: Short opportunity on failed breakout
Alert System
The indicator includes built-in alerts with detailed information:
Symbol and timeframe identification
Current price level
Signal type (Buy or Sell)
Optional zone filtering applied
Alerts fire once per bar close (not on every tick) to prevent spam and ensure confirmed signals.
Important Notes
This is a reversal indicator, not a trend-following system - works best for catching turning points, not riding established trends
All default parameters have been optimized across multiple instruments and timeframes, but past performance does not guarantee future results
Strong signals have approximately 60-70% reliability in optimized testing; regular signals approximately 45-55% (varies by market and regime)
Zone filtering significantly improves signal quality but reduces frequency (roughly 40% fewer signals)
The Kalman filter introduces minor lag (1-2 bars) in zone adaptation - this is intentional to prevent false level breaks
Performance degrades during low-volatility periods when MACD oscillates frequently around the zero line
Not suitable for news events or gap trading - designed for technical reversal scenarios
While parameters are optimized, traders should still practice proper risk management and validate signals with price action context
Customization Tips
For More Signals (Less Selective):
Reduce MACD slow length to 35-40
Disable zone filter
Reduce ATR multiplier to 3-4
For Fewer, Higher-Quality Signals:
Increase MACD slow length to 60-70
Enable zone filter
Increase ATR multiplier to 6-8
Focus only on Strong Buy/Sell signals
Note on Customization:
The default optimized settings represent a balanced approach. Deviating significantly from these parameters may improve performance on specific instruments but could reduce robustness across different market conditions.
Double Stochastic & RSI Signals (Custom by TitikSona)This custom TradingView indicator combines two Stochastic oscillators with RSI to generate clear Buy and Sell signals on the chart. It is designed for traders who want a multi-timeframe confirmation using momentum and overbought/oversold conditions.
Features:
Dual Stochastic Oscillators: Two independent Stochastics (%K and %D) with customizable periods for flexible analysis.
RSI Filter: Confirms signals by checking if RSI is within a defined range.
Buy & Sell Signals:
Green triangle under the bar indicates a Buy signal.
Red triangle above the bar indicates a Sell signal.
Chart Labels: Displays indicator values (%K, %D, RSI) directly on the chart when signals appear.
Info Table: Shows real-time indicator values, signal status, market condition (Overbought/Oversold/Normal), and price.
Alerts: Set alerts for Buy and Sell signals directly from the indicator.
Inputs:
K & D periods and slowing for both Stochastics
RSI period and upper/lower levels
Usage:
Buy when both Stochastics are oversold and RSI is within the defined range.
Sell when both Stochastics are overbought and RSI is within the defined range.
Wait when conditions are not met.
Ideal for scalping, swing trading, day trading, and momentum strategies.
Diablo Flow v6 (stable build)⚙️ 1️⃣ Add It to Your Chart
Copy the final Pine script → go to TradingView → Pine Editor → New → Paste → Save → Add to Chart.
Make sure you’re on a 5m, 15m, or 1H chart (for day or swing trading).
You’ll see:
Green bars / background = bullish trend
Red bars / background = bearish trend
“BUY” or “SELL” labels when all internal conditions align
🔍 2️⃣ Understand What Each Component Means
Visual Meaning
Green bars / lime background Bullish trend confirmed (EMA & Supertrend aligned)
Red bars / red background Bearish trend confirmed
Gray / neutral No clear momentum (avoid trades)
BUY / SELL labels Signal when trend + RSI + MACD + Volume all confirm
EMA Fast (Teal) Short-term momentum line
EMA Slow (Orange) Trend direction filter
Supertrend Line (Green/Red) Dynamic support/resistance
🎯 3️⃣ Trading Rules
Entry Setup
✅ BUY (Long)
A “BUY” label appears
Bars are green
Price is above the fast EMA
RSI is > 50
MACD histogram > 0
Volume spike confirmed (relative to recent average)
🔴 SELL (Short)
A “SELL” label appears
Bars are red
Price is below fast EMA
RSI is < 50
MACD histogram < 0
Volume spike confirmed
Entry Timing
After a signal appears:
Wait for candle close to confirm it (don’t enter mid-candle).
On next candle, enter in same direction.
Optional confirmation: use VWAP or Volume Profile:
Only buy if price is above VWAP.
Only short if below VWAP.
Stop-Loss & Take-Profit
💥 Conservative setup (Intraday):
Stop-Loss: below previous swing low (for long) / above swing high (for short).
TP1: 1× ATR (average true range).
TP2: 2× ATR or next resistance/support level.
💥 Aggressive setup (Scalping):
Stop = below last green bar (for long) or above last red bar (for short).
Exit on opposite “SELL”/“BUY” signal.
🧩 4️⃣ Filters to Avoid False Signals
Use higher-timeframe confirmation:
If trading 5m → confirm 15m trend direction.
If trading 15m → confirm 1H trend direction.
Only trade signals in the direction of higher TF trend.
📊 5️⃣ Backtest / Optimize
Open TradingView’s “Strategy Tester” tab (you can ask me for a strategy version next).
Tune these parameters:
EMA Fast/Slow (try 10/30 or 20/50)
ATR Mult (2.0–3.0)
Vol Mult (1.2–2.0)
RSI Bull/Bear thresholds (55/45 for stronger filters)
🧠 6️⃣ Psychology of the System
It’s a trend-following + momentum confirmation system.
Works best in volatile, directional sessions (NY, London, or US futures open).
Avoid using it in flat, low-volume premarket conditions.
🪄 Example: ES / NQ Futures
Timeframe: 5m
Setup: “BUY” label at 9:45 ET with strong volume, background lime.
Entry: Long next candle close.
Exit: Opposite “SELL” label or +10 pts (whichever first).
Stop: Below last red candle.
✅ Summary of Workflow
Step What to Do
1 Wait for BUY/SELL label + bar color confirmation
2 Confirm with VWAP or higher timeframe
3 Enter on next candle close
4 Place stop beyond Supertrend/ATR
5 Take profit at 1×–2× ATR or opposite signal
ULTIMATE Smart Trading Pro 🔥
## 🇬🇧 ENGLISH
### 📊 The Most Complete All-in-One Trading Indicator
**ULTIMATE Smart Trading Pro** combines the best technical analysis tools and Smart Money Concepts into a single powerful and intelligent indicator. Designed for serious traders who want a real edge in the markets.
---
### ✨ KEY FEATURES
#### 💰 **SMART MONEY CONCEPTS**
- **Order Blocks**: Automatically detects institutional zones where "smart money" enters positions
- **Break of Structure (BOS)**: Identifies structure breaks to confirm trend changes
- **Liquidity Zones**: Spots equal highs/lows areas where institutions hunt stops
- **Market Structure**: Visually displays bullish (green background) or bearish (red background) structure
#### 📈 **ADVANCED TECHNICAL INDICATORS**
- **RSI with Auto Divergences**: Classic RSI + automatic detection of bullish and bearish divergences
- **MACD with Signals**: Identifies bullish and bearish crossovers in real-time
- **Dynamic Support & Resistance**: Adaptive zones with intelligent scoring based on volume, multiple touches, and ATR
- **Fair Value Gaps (FVG)**: Detects unfilled price gaps (imbalance zones)
#### 📐 **AUTOMATIC TOOLS**
- **Auto Fibonacci**: Automatically calculates Fibonacci retracement levels on the last major trend
- **Pivot Points**: Daily, Weekly, or Monthly pivot points (PP, R1, R2, S1, S2)
- **Pattern Finder**: Automatically detects candlestick patterns (Hammer, Shooting Star, Engulfing, Morning/Evening Star) and chart patterns (Double Top/Bottom)
---
### 🎯 HOW TO USE IT
#### Quick Setup:
1. **Add the indicator** to your chart
2. **Open Settings** and enable/disable modules as needed
3. **Adjust parameters** for your trading style (scalping, swing, day trading)
#### Optimal Trading Setup:
🔥 **ULTRA STRONG Signal** when you have:
- An institutional **Order Block**
- Aligned with a **Support/Resistance** tested 3+ times
- An unfilled **FVG** nearby
- An **RSI divergence** confirming the reversal
- On a key **Fibonacci** level (50%, 61.8%, or 78.6%)
- Favorable market structure (green background for buys, red for sells)
---
### 💡 UNIQUE ADVANTAGES
✅ **Adaptive Intelligence**: Automatically adjusts to market volatility (ATR)
✅ **Volume Filters**: Validates important levels with volume confirmation
✅ **Multi-Timeframe Ready**: Works on all timeframes (1m to 1M)
✅ **Complete Alerts**: Notifications for all important signals
✅ **Clear Interface**: Emojis and colored labels for quick identification
✅ **Intelligent Scoring**: Levels ranked by importance (🔴🔴🔴 = very strong)
✅ **100% Customizable**: Enable only what you need
---
### 🎨 SYMBOL LEGEND
**Smart Money:**
- 🟢 OB = Bullish Order Block
- 🔴 OB = Bearish Order Block
- BOS ↑/↓ = Break of Structure
- 💧 LIQ = Liquidity Zone
**Candlestick Patterns:**
- 🔨 = Hammer (bullish signal)
- ⭐ = Shooting Star (bearish signal)
- 📈 = Bullish Engulfing
- 📉 = Bearish Engulfing
- 🌅 = Morning Star (bullish reversal)
- 🌆 = Evening Star (bearish reversal)
**Indicators:**
- 🚀 MACD ↑ = Bullish crossover
- 📉 MACD ↓ = Bearish crossover
- ⚠️ DIV = Bearish RSI divergence
- ✅ DIV = Bullish RSI divergence
**Support & Resistance:**
- 🟢/🔴 S1, R1 = Support/Resistance
- 🟢🟢🟢/🔴🔴🔴 = VERY strong level (3+ touches)
- (×N) = Number of times touched
---
### ⚙️ RECOMMENDED SETTINGS
**For Scalping (1m - 5m):**
- SR Lookback: 15
- Structure Strength: 3
- RSI: 14
- Volume Filter: ON
**For Day Trading (15m - 1H):**
- SR Lookback: 20
- Structure Strength: 5
- RSI: 14
- All filters: ON
**For Swing Trading (4H - Daily):**
- SR Lookback: 30
- Structure Strength: 7
- Pattern Lookback: 100
- Fibonacci: ON
---
### 🚨 DISCLAIMER
This indicator is a decision support tool. It does not guarantee profits and does not constitute financial advice. Always test on a demo account before real use. Trading involves significant risks.
---
## 📞 SUPPORT & UPDATES
For questions, suggestions, or bug reports, please comment below or contact the author.
**Version:** 1.0
**Last Updated:** October 2025
**Compatible:** TradingView Pine Script v6
---
### 🌟 If you find this indicator useful, please give it a 👍 and share it with other traders!
**Happy Trading! 🚀📈**
Supply & Demand Zones [QuantAlgo]🟢 Overview
The Supply & Demand (Support & Resistance) Zones indicator identifies price levels where significant buying and selling pressure historically emerged, using swing point analysis and pattern recognition to mark high-probability reversal and continuation areas. Unlike conventional support/resistance tools that draw arbitrary horizontal lines, this indicator can automatically detect structural zones, offering traders systematic entry and exit levels where institutional order flow likely congregates across any market or timeframe.
🟢 How to Use
# Zone Types:
Green/Demand Zones: Support areas where buying pressure historically emerged, representing potential long entry opportunities where price may bounce or consolidate before moving higher. These zones mark levels where buyers previously overcame sellers.
Red/Supply Zones: Resistance areas where selling pressure historically dominated, indicating potential short entry opportunities where price may reverse or stall before declining. These zones identify levels where sellers previously overwhelmed buyers.
# Zone Pattern Types:
Wick Rejection Zones: Zones created from candles with exceptionally long wicks showing violent price rejection. A demand rejection occurs when price drops sharply but closes well above the low, forming a long lower wick (relative to the total candle range) that demonstrates buyers aggressively defending that level. A supply rejection shows price spiking higher but closing well below the high, with the long upper wick proving sellers rejected that price aggressively. These zones often represent major institutional orders that absorbed significant market pressure. The rejection wick ratio setting controls how prominent the wick must be (higher ratios require more dramatic rejections and produce fewer but higher-quality zones).
Continuation Demand Zones: Areas where price rallied upward, paused in a brief consolidation base, then rallied again. This pattern confirms strong buying continuation (the consolidation represents profit-taking or minor pullbacks that failed to attract meaningful selling). When price returns to these zones, buyers who missed the initial rally often provide support, making them high-probability long entries within established uptrends. These zones follow the classic Rally-Base-Rally structure, demonstrating that buyers remain in control even during temporary pauses.
Reversal Demand Zones: Zones where price dropped, formed a consolidation base, then reversed into a rally. This structure marks potential trend reversals or major swing lows where buyers finally overwhelmed sellers after a decline. The base period represents accumulation by stronger hands, and these zones frequently appear at market bottoms or as significant pullback support within larger uptrends, signaling shifts in market control. These zones follow the Drop-Base-Rally pattern, showing the moment when selling pressure exhausted and buying interest emerged.
Continuation Supply Zones: Areas where price dropped, consolidated briefly, then dropped again. This pattern demonstrates strong selling continuation (the pause represents temporary buying attempts that failed to generate meaningful recovery). When price returns to these zones, sellers who missed the initial decline often provide resistance, creating short entry opportunities within established downtrends. These zones follow the Drop-Base-Drop structure, confirming that sellers maintain dominance even during temporary consolidations.
Reversal Supply Zones: Zones where price rallied upward, formed a consolidation base, then reversed into a decline. This formation identifies potential trend reversals or major swing highs where sellers overcame buyers after an advance. The base period often represents distribution by institutional participants, and these zones commonly appear at market tops or as key pullback resistance within larger downtrends, marking transfers of market control from buyers to sellers. These zones follow the Rally-Base-Drop pattern, capturing the transition point when buying exhaustion meets aggressive selling.
# Zone Mitigation Methods:
Wick Mitigation: Zones become invalidated immediately upon first contact by any wick. This assumes zones work only on their initial test, reflecting the belief that institutional orders concentrated at these levels get completely filled on first touch. Best for traders seeking only the highest-probability, untested zones and willing to accept that zones invalidate frequently in volatile markets. When price touches a zone boundary with even a single wick, that zone is considered "used up" and becomes mitigated.
Close Mitigation: Zones remain valid through wick penetration but become invalidated only when a candle closes through the zone boundary. This method allows price to briefly probe the zone with wicks while requiring actual commitment (a close) for invalidation. Suitable for traders who recognize that zones can withstand initial tests and prefer filtering out false breakouts caused by temporary volatility or liquidity hunts. A zone stays active as long as candles close within or outside it, regardless of wick penetration, until a close occurs beyond the boundary.
Full Body Mitigation: Zones stay valid until an entire candle body exists completely beyond the zone boundary, meaning both the open and close must be outside the zone. This approach maintains zone validity through partial penetrations, accommodating the reality that institutional zones can absorb considerable price action before exhausting. Ideal for volatile markets or traders who believe zones represent price ranges rather than precise levels, and who want zones to persist through aggressive but ultimately rejected breakout attempts. Only when both the open and close of a candle are beyond the zone does it become mitigated.
🟢 Pro Tips for Trading and Investing
→ Preset Selection: Choose presets matching your preferred timeframe - Scalping (M1-M30) for aggressive detection on minute charts, Intraday (H1-H12) for balanced filtering on hourly timeframes, or Swing Trading (1D+) for strict filtering on daily charts. Each preset automatically optimizes swing length, zone strength, and max zone counts for the selected timeframe.
→ Input Calibration: Adjust Swing Length based on market speed (lower values 3-7 for fast markets, higher values 12-20 for slower markets). Set Minimum Zone Strength according to asset volatility (0.05-0.15% for low-volatility assets, 0.25-0.5% for high-volatility assets). Tune Rejection Wick Ratio higher (0.6-0.8) for strict wick filtering or lower (0.3-0.5) to capture more subtle rejections.
→ Zone Pattern Toggle Strategy: Pattern types are mutually exclusive - enable Continuation OR Reversal patterns for each zone type, not both together. Recommended combinations: For trend trading, enable Rejection + Continuation (2-4 toggles total). For reversal trading, enable Rejection + Reversal (2-4 toggles). For scalping, enable only Rejection zones (1-2 toggles). Maximum 3-4 active toggles provides optimal chart clarity. A simple Wick Rejection toggle can also work on virtually any market and timeframe.
→ Mitigation Method Selection: Use Wick mitigation in clean trending markets for strict zone invalidation on first touch. Use Close mitigation in moderate volatility to filter out temporary spikes. Use Full Body mitigation in highly volatile markets to keep zones active through whipsaws and false breakouts.
→ Alert Configuration: Utilize built-in alerts for new zone creation, zone touches, and zone breaks. New zone alerts notify when fresh supply/demand areas form. Zone touch alerts signal potential entry opportunities as price reaches zones. Zone break alerts indicate when levels fail, signaling possible trend acceleration or structure changes.
First Passage Time - Distribution AnalysisThe First Passage Time (FPT) Distribution Analysis indicator is a sophisticated probabilistic tool that answers one of the most critical questions in trading: "How long will it take for price to reach my target, and what are the odds of getting there first?"
Unlike traditional technical indicators that focus on what might happen, this indicator tells you when it's likely to happen.
Mathematical Foundation: First Passage Time Theory
What is First Passage Time?
First Passage Time (FPT) is a concept in stochastic processes that measures the time it takes for a random process to reach a specific threshold for the first time. Originally developed in physics and mathematics, FPT has applications in:
Quantitative Finance: Option pricing, risk management, and algorithmic trading
Neuroscience: Modeling neural firing patterns
Biology: Population dynamics and disease spread
Engineering: Reliability analysis and failure prediction
The Mathematics Behind It
This indicator uses Geometric Brownian Motion (GBM), the same stochastic model used in the Black-Scholes option pricing formula:
dS = μS dt + σS dW
Where:
S = Asset price
μ = Drift (trend component)
σ = Volatility (uncertainty component)
dW = Wiener process (random walk)
Through Monte Carlo simulation, the indicator runs 1,000+ price path simulations to statistically determine:
When each threshold (+X% or -X%) is likely to be hit
Which threshold is hit first (directional bias)
How often each scenario occurs (probability distribution)
🎯 How This Indicator Works
Core Algorithm Workflow:
Calculate Historical Statistics
Measures recent price volatility (standard deviation of log returns)
Calculates drift (average directional movement)
Annualizes these metrics for meaningful comparison
Run Monte Carlo Simulations
Generates 1,000+ random price paths based on historical behavior
Tracks when each path hits the upside (+X%) or downside (-X%) threshold
Records which threshold was hit first in each simulation
Aggregate Statistical Results
Calculates percentile distributions (10th, 25th, 50th, 75th, 90th)
Computes "first hit" probabilities (upside vs downside)
Determines average and median time-to-target
Visual Representation
Displays thresholds as horizontal lines
Shows gradient risk zones (purple-to-blue)
Provides comprehensive statistics table
📈 Use Cases
1. Options Trading
Selling Options: Determine if your strike price is likely to be hit before expiration
Buying Options: Estimate probability of reaching profit targets within your time window
Time Decay Management: Compare expected time-to-target vs theta decay
Example: You're considering selling a 30-day call option 5% out of the money. The indicator shows there's a 72% chance price hits +5% within 12 days. This tells you the trade has high assignment risk.
2. Swing Trading
Entry Timing: Wait for higher probability setups when directional bias is strong
Target Setting: Use median time-to-target to set realistic profit expectations
Stop Loss Placement: Understand probability of hitting your stop before target
Example: The indicator shows 85% upside probability with median time of 3.2 days. You can confidently enter long positions with appropriate position sizing.
3. Risk Management
Position Sizing: Larger positions when probability heavily favors one direction
Portfolio Allocation: Reduce exposure when probabilities are near 50/50 (high uncertainty)
Hedge Timing: Know when to add protective positions based on downside probability
Example: Indicator shows 55% upside vs 45% downside—nearly neutral. This signals high uncertainty, suggesting reduced position size or wait for better setup.
4. Market Regime Detection
Trending Markets: High directional bias (70%+ one direction)
Range-bound Markets: Balanced probabilities (45-55% both directions)
Volatility Regimes: Compare actual vs theoretical minimum time
Example: Consistent 90%+ bullish bias across multiple timeframes confirms strong uptrend—stay long and avoid counter-trend trades.
First Hit Rate (Most Important!)
Shows which threshold is likely to be hit FIRST:
Upside %: Probability of hitting upside target before downside
Downside %: Probability of hitting downside target before upside
These always sum to 100%
⚠️ Warning: If you see "Low Hit Rate" warning, increase this parameter!
Advanced Parameters
Drift Mode
Allows you to explore different scenarios:
Historical: Uses actual recent trend (default—most realistic)
Zero (Neutral): Assumes no trend, only volatility (symmetric probabilities)
50% Reduced: Dampens trend effect (conservative scenario)
Use Case: Switch to "Zero (Neutral)" to see what happens in a pure volatility environment, useful for range-bound markets.
Distribution Type
Percentile: Shows 10%, 25%, 50%, 75%, 90% levels (recommended for most users)
Sigma: Shows standard deviation levels (1σ, 2σ)—useful for statistical analysis
⚠️ Important Limitations & Best Practices
Limitations
Assumes GBM: Real markets have fat tails, jumps, and regime changes not captured by GBM
Historical Parameters: Uses recent volatility/drift—may not predict regime shifts
No Fundamental Events: Cannot predict earnings, news, or macro shocks
Computational: Runs only on last bar—doesn't give historical signals
Remember: Probabilities are not certainties. Use this indicator as part of a comprehensive trading plan with proper risk management.
Created by: Henrique Centieiro. feedback is more than welcome!
Reversal Nexus Pro Suite — Smart Scalper/Swing Trader/Hybrid 📝 Description
The Reversal Suite (5–15m) is a dynamic price-action-driven indicator built for scalpers and intraday traders who want to catch high-probability reversals with precision.
This system combines SFP (Swing Failure Patterns), Volume Climax filters, EMA bias, and momentum confirmation logic — all customizable to match your personal trading style.
The default configuration is tuned for NASDAQ futures (NQ1!) and similar indices on 5–15-minute charts, but it can adapt seamlessly to crypto, forex, and equities.
⚙️ How It Works
The indicator looks for exhaustion points in price where:
Volume Climax confirms liquidity sweeps,
EMA bias determines directional filters (single or dual-EMA),
Reclaim and rejection mechanics confirm structure shifts,
Momentum thrust ensures strength on reversal confirmation.
Each setup requires multi-factor alignment to reduce noise and increase signal precision.
🧩 Default Custom Settings (Recommended Start)
Setting Value Description
Mode Custom Enables full manual control
Signals must align within N bars 6 Forces confluence across recent bars
TP1 / TP2 (R-Multiples) 1.5 / 2.5 Default reward zones
RSI Divergence Enabled Adds secondary reversal confirmation
Volume Climax Enabled Detects high-volume exhaustion
Vol SMA Length 21 Volume baseline calculation
Climax ≥ k × SMA 7 Strength multiplier for volume spikes
EMA Length 200 Trend bias reference
Bias Both Allows both long and short setups
Dual EMA Bias Enabled Uses fast (21) vs slow (100) bias tracking
Min Distance from EMA Bias 2.55% Filter to avoid signals too close to MAs
Reclaim Buffer After Sweep 0.22% Ensures valid break-and-reclaim setups
Max Bars for Retest 1 Tight retest condition
Momentum Thrust Confirm Enabled Ensures volume and price thrust
Body ≥ ATR -6 Controls candle thrust sizing
TR SMA Length 20 Measures dynamic volatility
Body ≥ k × TR-SMA -4.4 Confirms structure-based rejection
Opposite-Signal Exit Enabled Auto-clears opposite signals
Opposite Signal Window 5 bars Short-term conflict filter
Swing Lookback (SFP) 2 Finds recent liquidity highs/lows
Cooldown Bars After Signal 8 Prevents over-triggering
🟢 Inputs are fully adjustable, so traders can optimize for:
Scalping (lower EMA, smaller swing lookback)
Swing trading (higher EMA, larger retest window)
Aggressive vs conservative confirmations
🧭 Recommended Use
Works best on 5m–15m timeframes
Pair with VWAP or EMA cloud overlays for directional context
Use Trend Guard to align only with higher-timeframe trend
Ideal for indices, forex majors, and large-cap stocks
🚀 Highlights
✅ Smart confluence-based reversal detection
✅ Built-in retest and rejection logic
✅ Dual EMA and volume climax filters
✅ Customizable momentum thrust confirmation
✅ Optimized for scalpers and intraday swing traders
🧱 Suggested Layout
Chart type: Candlestick
Timeframe: 5m or 15m
Overlay: VWAP / EMA Cloud / ORB Zone
Optional filters: ATR Bands, Volume Profile (VPVR), Session Boxes
⚠️ Disclaimer
The Reversal Nexus Pro indicator is provided for educational and informational purposes only. It is not financial advice and should not be interpreted as a recommendation to buy, sell, or trade any financial instrument.
Trading involves significant risk and may not be suitable for all investors. Past performance does not guarantee future results. Always perform your own analysis and use proper risk management before placing any trades.
The author of this script is not responsible for any financial losses or decisions made based on the use of this tool.
By using this indicator, you acknowledge that you understand these terms and accept full responsibility for your own trading results.
© 2025. All rights reserved. Redistribution or resale of this indicator, in full or in part, is strictly prohibited without the author’s written consent.
ORB + Lq-💰-Enhanced-R6 [J-Algo]# ORB-Enhanced-R6 with Key Liquidity
## 🎯 **Professional Opening Range Breakout + Institutional Liquidity Analysis**
Transform your trading with this comprehensive indicator that combines **Opening Range Breakouts** with **Key Liquidity Levels** - bridging the gap between retail and institutional trading concepts.
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## 🚀 **Key Features**
### **📊 Opening Range Breakout (ORB)**
- **Customizable time periods** - 15min default, or set custom session times
- **Dynamic fill colors** - Session comparison or breakout direction
- **Professional labeling** - Clear ORH/ORL markers with tooltips
- **Historical data toggle** - Clean charts or full history view
### **🌍 Multi-Session Analysis**
- **Tokyo, London, New York, Sydney** sessions with individual colors
- **Session high/low tracking** - Identify key intraday levels
- **Timezone flexibility** - Supports all major trading timezones
- **Clean visual separation** - Each session clearly distinguished
### **💧 Dual-Timeframe Key Liquidity**
- **HTF Liquidity (4H default)** - Major institutional levels where big money sits
- **LTF Liquidity (1H default)** - Precision entry levels for optimal timing
- **Smart mitigation** - Levels disappear when broken (optional: keep visible)
- **Timeframe labels** - Clear "4H", "1H" identification at line ends
### **📈 Advanced Confirmation Tools**
- **EMA Integration** - Trend bias with customizable length
- **Multi-Timeframe Analysis** - Higher timeframe trend confirmation
- **Volume Spike Detection** - Identify high-conviction moves
- **Smart Alerts** - Quality-scored breakouts (High Quality vs Standard)
---
## 🎨 **Visual Excellence**
### **Professional Design**
- **Color-coded sessions** - Easy visual distinction
- **Consistent labeling** - ORB style text for all levels
- **Flexible extensions** - Short, Current, or Max line extensions
- **Clean interface** - Show/hide any component
### **Customization Options**
- **Individual session colors** - Personalize your chart appearance
- **Line thickness control** - Adjust visual prominence
- **Text size options** - Tiny to Huge sizing
- **Transparency settings** - Perfect visual balance
---
## ⚡ **Smart Alert System**
### **Quality-Scored Breakouts**
- **HIGH QUALITY** alerts when volume + MTF bias confirm
- **STANDARD** alerts for regular breakouts
- **Detailed information** - Price levels, volume strength, MTF bias
- **Once-per-bar frequency** - No spam, only actionable signals
---
## 🎯 **Trading Applications**
### **Scalping Strategy**
1. **LTF liquidity sweep** + **ORB breakout** = Precision entry
2. **Volume confirmation** = High-conviction trade
3. **Session awareness** = Optimal timing
### **Swing Trading**
1. **HTF liquidity levels** = Major support/resistance
2. **ORB direction** + **MTF bias** = Trend confirmation
3. **Session breaks** = Momentum continuation
### **Institutional Approach**
1. **HTF liquidity** = Where institutions position
2. **LTF liquidity** = Where retail stops cluster
3. **ORB + Liquidity confluence** = Highest probability setups
---
## ⚙️ **Configuration Guide**
### **Quick Setup**
1. **Enable Key Liquidity** - Turn on HTF (4H) for major levels
2. **Optional LTF** - Add 1H levels for precision (can be toggled off)
3. **Session Selection** - Choose relevant trading sessions
4. **ORB Timeframe** - Default 15min or customize
### **Advanced Setup**
- **MTF Analysis** - Enable for trend bias confirmation
- **Volume Analysis** - Add conviction to breakouts
- **Alert Configuration** - Set up quality-scored notifications
- **Visual Customization** - Colors, thickness, extensions
---
## 📊 **Best Timeframes**
### **Recommended Usage**
- **Chart Timeframe**: 5min - 15min
- **HTF Liquidity**: 4H - 1D
- **LTF Liquidity**: 1H - 4H
- **ORB Period**: 15min - 1H
---
## 🛠️ **Technical Excellence**
- **Pine Script v6** - Latest TradingView technology
- **Optimized Performance** - Efficient array management
- **Error Handling** - Robust code prevents crashes
- **Memory Management** - Display limits prevent overload
---
## 💡 **Why This Indicator?**
### **Combines Best of Both Worlds**
- **Retail Strategy** - ORB breakouts for clear signals
- **Institutional Context** - Liquidity levels for market structure
- **Professional Execution** - Clean, reliable, customizable
### **Complete Trading Solution**
- **Entry Signals** - ORB breakouts with confluence
- **Context Levels** - Key liquidity for S/R
- **Risk Management** - Clear invalidation levels
- **Timing Tools** - Session and volume awareness
---
## 🎖️ **Perfect For**
✅ **Forex Traders** - Session-based with liquidity context
✅ **Crypto Traders** - 24/7 ORB with institutional levels
✅ **Scalpers** - Precision entries with LTF liquidity
✅ **Swing Traders** - HTF context with ORB momentum
✅ **Professional Traders** - Institutional-grade analysis
---
## 🔥 **Get Started**
Add this indicator to your chart and experience the power of combining **Opening Range Breakouts** with **Key Liquidity Analysis**. Transform your trading from guesswork to precision with institutional-level market structure awareness.
**Happy Trading! 📈**
---
## 📝 **Disclaimer**
This indicator is for educational and informational purposes only. Past performance does not guarantee future results. Always use proper risk management and never risk more than you can afford to lose.
Hybrid Trend Line-J-AlgoOverview
The Hybrid Trend Line-J-Algo is an advanced multi-layered trendline detection system that identifies market trends across three distinct timeframes simultaneously. This indicator combines confirmed, developing, and real-time trend analysis to provide traders with a comprehensive view of market structure and momentum shifts.
Key Features
[✅ Three-Tier Trend Detection System:
Confirmed Trendlines (⚡T💧/⚡T🩸) - High-probability, established trends with 20-period confirmation
Developing Trendlines (⚡D💧/⚡D🩸) - Emerging trends with 8-period detection for early entries
Real-Time Trendlines (⚡R💧/⚡R🩸) - Immediate trend identification with minimal lag (10-period lookback)
✅ Visual Channel System:
Gradient-filled channels between trendlines and parallel support/resistance zones
Adjustable channel padding for volatility-based spacing
Color-coded bullish (blue/teal/lime) and bearish (gray/red/orange) trends
✅ Customizable Display:
Toggle each trendline type independently
Adjustable detection lengths for all three systems
Custom colors and label sizes
Optional gradient fills or solid colors
✅ Smart Trendline Management:
Automatic trendline extension to current price
Pivot-based detection for accurate swing points
Dynamic slope calculations
Labeled indicators for easy trend identification
How It Works
Confirmed Trendlines use pivot highs/lows with a 20-bar lookback to identify well-established trends. These represent the most reliable trend structure and are ideal for position trading and trend confirmation.
Developing Trendlines employ an 8-bar detection period to catch trends as they form. These provide earlier signals than confirmed lines, making them suitable for swing trading and anticipating trend continuations.
Real-Time Trendlines track the most recent price action with minimal lag, connecting recent highs and lows to identify immediate momentum shifts. Perfect for intraday trading and quick reversals.
Best Use Cases
📈 Trend Following - Align trades with confirmed trendlines for high-probability setups
📉 Early Entry Detection - Use developing trendlines to enter before the crowd
⚡ Scalping & Day Trading - Real-time trendlines provide instant trend direction
🎯 Multi-Timeframe Analysis - View all three trend layers simultaneously for confluence
Settings Guide
Confirmed Trend Lines:
Detection Length: 20 (default) - Higher = fewer, stronger signals
Colors: Customizable bullish/bearish
Developing Trend Lines:
Detection Length: 8 (default) - Lower = more responsive
Dashed style for visual distinction
Real-Time Trend Lines:
Lookback: 10 (default) - Minimal lag for immediate feedback
Dotted style for differentiation
Visual Settings:
Gradient Fills: Toggle smooth color transitions
Channel Padding: Adjust spacing (2.0 default)
Label Size: Choose from Tiny to Huge
Trading Tips
💡 Look for confluence when multiple trendline types align in the same direction
💡 Watch for breaks of confirmed trendlines as potential reversal signals
💡 Use developing trendlines to anticipate confirmed trend formations
💡 Combine with volume and momentum indicators for enhanced accuracy
💡 Respect the channel boundaries as dynamic support/resistance zones
Unique Advantages
✨ No Repainting - All trendlines are based on confirmed pivots and historical data
✨ Clean Visual Design - Emoji labels and gradient fills for intuitive interpretation
✨ Fully Customizable - Adapt to any trading style or timeframe
✨ Multiple Confirmation Levels - Reduces false signals through multi-tier analysis
✨ Beginner Friendly - Clear visual cues with labeled trend indicators
Disclaimer
This indicator is for educational and informational purposes only. It does not constitute financial advice. Always conduct your own analysis and risk management before making trading decisions.
Version: 6
Type: Overlay Indicator
Max Lines/Labels: 500
Perfect for: Trend traders, swing traders, day traders, and multi-timeframe analysts
