PhaseAlgo PremiumPhaseAlgo Indicator includes multiple features such as standard buy and sell signals on the chart, key levels, trend direction & trend candle momentum.
There are two main settings used to determine the sensitivity of the buy and sell signals which are:
- The "Power" setting controllers how reactive the algorithm reacts to the simple trend changes in the market. This allows you to adjust the sensitivity to relatively fit your preferred trading time frame.
- The "Strength" setting controllers how accurate the buy & sells are in correspondence with the most recent swing in the market. This allows you to refine the signals based on the current time frame you are on to give the most accurate entries.
We also provide a trend filter option which allows the user to only see buy and sell signals in the same direction as the colour of the trend.
Our trend direction Indicator:
Use: Helps you trade with the trend, understand when the market is changing trend & allows you to stay out of trade that could be going in the wrong direction
Our trend direction indicator is derived from multiple factors, mainly using price action and a bunch of our own formally created volatility, volume and momentum tools. We also allow the user to show the trend on the candle colors to give a more visually pleasing experience. The settings for the trend direction tool are not customizable as we have created a strong blend using volatility, volume and momentum along with price action which we believe is the best possible for our indicator.
The trend direction can also be displayed on multiple time frames on the top right with our custom made dashboard. The user has the choice to input the time frames they wish to view. This allows the user to see the trend direction on multiple time frames without having to open multiple charts or switch the current time frame on screen.
Key Levels Feature:
Use: This is used to see areas of importance in which price could react from.
This is a unique feature based on price action along with a combination of market structure using a method that builds synthetic support and resistance areas & trend direction to show us market sentiment. We have created a unique system which incorporates price action and the sensitivity can be adjusted by the user to determine how strong the price action needs to be in order to show the key levels.
Reversal Areas Feature:
Use: The user can see areas where the market is likely to reverse.
This is a blend of past price data, support and resistance & trend momentum to determine where the most likely area is to reverse based on calculations to project these areas forward.
Use the link below to get access to this indicator
Volatility
MTF Fusion - High Volume Expansion Channel [TradingIndicators]Exceptionally high volume and rapid price expansion are key markers of powerful moves, especially when they occur during a breakout or breakdown. The High Volume Expansion Channel (HVEC) uses our multi-timeframe fusion and price compression/expansion algorithms to look for high volume and rapid expansion from multiple higher timeframes at once. It uses this info to determine a high volume and expansion 'grade', and then encodes this result into a colored channel. This channel coloring varies in intensity based on how exceptionally high volume is and how rapidly price is expanding in either direction.
What is MTF Fusion?
Multi-Timeframe (MTF) Fusion is the process of combining calculations from multiple timeframes higher than the chart's into one 'fused' value or indicator. It is based on the idea that integrating data from higher timeframes can help us to better identify short-term trading opportunities within the context of long-term market trends.
How does it work?
Let's use the context of this indicator, which calculates a 'high volume and expansion grade' (let's call it HVEG), as an example to explain how MTF Fusion works and how you can perform it yourself.
Step 1: Selecting Higher Timeframes
The first step is to determine the appropriate higher timeframes to use for the fusion calculation. These timeframes should typically be chosen based on their ability to provide meaningful data and action which actively affect the price action of the smaller timeframe you're focused on. For example, if you are trading the 5 minute chart, you might select the 15 minute, 30 minute, and hourly timeframe as the higher timeframes you want to fuse in order to give you a more holistic view of the trends and action affecting you on the 5 minute. In this indicator, four higher timeframes are automatically selected depending on the timeframe of the chart it is applied to.
Step 2: Gathering Data and Calculations
Once the higher timeframes are identified, the next step is to calculate the data from these higher timeframes that will be used to calculate your fused values. In this indicator, for example, the HVEG value is calculated by determining the HVEG for all four higher timeframes.
Step 3: Fusing the Values From Higher Timeframes
The next step is to actually combine the values from these higher timeframes to obtain your 'fused' indicator values. The simplest approach to this is to simply average them. If you have calculated the HVEG value from three higher timeframes, you can, for example, calculate your 'multi-timeframe fused HVEG' as (HigherTF_HVEG_1 + HigherTF_HVEG_2 + HigherTF_HVEG_3) / 3.0.
Step 4: Visualization and Interpretation
Once the calculations are complete, the resulting fused indicator values are plotted on the chart. These values reflect the fusion of data from the multiple higher timeframes, giving a broader perspective on the market's behavior and potentially valuable insights without the need to manually consider values from each higher timeframe yourself.
What makes this script unique? Why is it closed source?
While the process described above is fairly unique and sounds simple, the truly important key lies in determining which higher timeframes to fuse together, and how to weight their values when calculating the fused end result in such a way that best leverages their relationship for useful TA.
This MTF Fusion indicator employs a smart, adaptive algorithm which automatically selects appropriate higher timeframes to use in fusion calculations depending on the timeframe of the chart it is applied to. It also uses a dynamic algorithm to adjust and weight the high volume and price expansion grade calculations depending on each higher timeframe's relationship to the chart timeframe. These algorithms are based on extensive testing and are the reason behind this script's closed source status.
Included Features
MTF Fusion high volume and expansion coloring
MTF Fusion ATR-based channel for visual effect
Channel width customization and explanatory labels
Pre-built color stylings
Options
Show Channel Lines: Show/hide the upper and lower lines of the channel
Fill Channel: Fill the channel with coloring depicting the current degree of high volume and rapid price expansion
Channel Width Multiplier: Sets the width of the ATR-based channel
Explanatory Labels: Show/hide explanatory labels describing the visuals
Lookback: Select how you want the degree of high volume expansion to be calculated (longer = long-term high volume and expansion, shorter = short-term high volume and expansion)
Pre-Built Color Styles: Use a pre-built color styling (uncheck to use your own colors)
Manual Color Styles: When pre-built color styles are disabled, use these color inputs to define your own
Volatility Capture RSI-Bollinger - Strategy [presentTrading]- Introduction and how it is different
The 'Volatility Capture RSI-Bollinger - Strategy ' is a trading strategy that combines the concepts of Bollinger Bands (BB), Relative Strength Index (RSI), and Simple Moving Average (SMA) to generate trading signals. The uniqueness of this strategy is it calculates which is a dynamic level between the upper and lower Bollinger Bands based on the closing price. This unique feature allows the strategy to adapt to market volatility and price movements.
The market in Crypto and Stock are highly volatile, making them suitable for a strategy that uses Bollinger Bands. The RSI can help identify overbought or oversold conditions in this often speculative market.
BTCUSD 4hr chart
(700.hk) 3hr chart
Remember, the effectiveness of a trading strategy also depends on other factors such as the timeframe used, the specific settings of the indicators, and the overall market conditions. It's always recommended to backtest and paper trade a strategy before using it in live trading.
- Strategy, How it Works
Dynamic Bollinger Band: The strategy works by first calculating the upper and lower Bollinger Bands based on the user-defined length and multiplier. It then uses the Bollinger Bands and the closing price to dynamically adjust the presentBollingBand value. In the end, it generates a long signal when the price crosses over the present Bolling Band and a short signal when the price crosses under the present Bolling Band.
RSI: If the user has chosen to use RSI for signals, the strategy also calculates the RSI and its SMA, and uses these to generate additional long and short signals. The RSI-based signals are only used if the 'Use RSI for signals' option is set to true.
The strategy then checks the chosen trading direction and enters a long or short position accordingly. If the trading direction is set to 'Both', the strategy can enter both long and short positions.
Finally, the strategy exits a position when the close price crosses under the present Bolling Band for a long position, or crosses over the present Bolling Band for a short position.
- Trade direction
The strategy also includes a trade direction parameter, allowing the user to choose whether to enter long trades, short trades, or both. This makes the strategy adaptable to different market conditions and trading styles.
- Usage
1. Set the input parameters as per your trading preferences. You can choose the price source, the length of the moving average, the multiplier for the ATR, whether to use RSI for signals, the RSI and SMA periods, the bought and sold range levels, and the trading direction.
2. The strategy will then generate buy and sell signals based on these parameters. You can use these signals to enter and exit trades.
- Default settings
1. Source: hlc3
2. Length: 50
3. Multiplier: 2.7183
4. Use RSI for signals: True
5. RSI Period: 10
6. SMA Period: 5
7. Bought Range Level: 55
8. Sold Range Level: 50
9. Trade Direction: Both
- Strategy's default Properties
1. Default Quantity Type: 'strategy.percent_of_equity'
2. commission_value= 0.1, commission_type=strategy.commission.percent, slippage= 1: These parameters set the commission and slippage for the strategy. The commission is set to 0.1% of the trade value, and the slippage (the difference between the expected price of a trade and the price at which the trade is executed) is set to 1.
3. default_qty_type = strategy.percent_of_equity, default_qty_value = 15: These parameters set the default quantity for trades. The default_qty_type is set to strategy.percent_of_equity, which means that the size of each trade will be a percentage of the account equity. The default_qty_value is set to 15, which means that each trade will be 15% of the account equity.
4. initial_capital= 10000: This parameter sets the initial capital for the strategy to $10,000.
PresentTrend - Strategy [presentTrading]- Introduction and how it is different
The PresentTrend strategy is a unique custom trend-following strategy. This combination allows the strategy to take advantage of both short-term and long-term market trends, making it suitable for various market conditions.
BTCUSDT 4hr chart
(700.hk) 3D chart
- Strategy, How it Works
RSI or MFI: The first part uses a custom indicator based on either the Relative Strength Index (RSI) or the Money Flow Index (MFI). The indicator calculates a PresentTrend value, which generates buy and sell signals based on its crossover and crossunder, indicating potential trend reversals.
ATR: The second part is a popular trend-following indicator that uses the Average True Range (ATR).
The strategy enters a long position when all buy signals from both strategies are true, and a short position when all sell signals are true. This ensures trades are entered when both short-term and long-term trends align, potentially increasing the strategy's reliability.
- Trade direction
The strategy also includes a trade direction parameter, allowing the user to choose whether to enter long trades, short trades, or both. This makes the strategy adaptable to different market conditions and trading styles.
- Usage
1. Set the input parameters for the custom trend-following strategy.
2. Choose whether to use the RSI or MFI for the custom strategy.
3. Choose the trade direction: long, short, or both.
4. The strategy will generate buy and sell signals based on the conditions of both strategies.
5. Enter a trade when a buy or sell signal is generated, depending on the chosen trade direction.
Please note that this strategy is meant to be a tool to aid in your trading decisions and not a standalone trading system. Always use proper risk management and make sure to test the strategy thoroughly before using it in live trading.
- Default settings
1. Source: 'hlc3', a balanced price level for calculations.
2. Length: 14, a common setting for many technical indicators.
3. Multiplier: 1.618 (the golden ratio), used in calculating the upper and lower thresholds.
4. RSI or MFI: Set to use MFI by default, both are momentum indicators.
5. Trade Direction: 'Both', allowing for both long and short trades.
The default settings are designed to provide a balanced approach to trend detection. However, these can be adjusted based on the user's preferences and the specific characteristics of the market being traded.
- Strategy's default Properties
1. Default Quantity Type: 'strategy.percent_of_equity'
2. commission_value= 0.1, commission_type=strategy.commission.percent, slippage= 1: These parameters set the commission and slippage for the strategy. The commission is set to 0.1% of the trade value, and the slippage (the difference between the expected price of a trade and the price at which the trade is executed) is set to 1.
3. default_qty_type = strategy.percent_of_equity, default_qty_value = 10: These parameters set the default quantity for trades. The default_qty_type is set to strategy.percent_of_equity, which means that the size of each trade will be a percentage of the account equity. The default_qty_value is set to 10, which means that each trade will be 10% of the account equity.
4. initial_capital= 10000: This parameter sets the initial capital for the strategy to $10,000.
Bollinger Bands Modified (Stormer)This strategy is based and shown by trader and investor Alexandre Wolwacz "Stormer".
Overview
The strategy uses two indicators Bollinger Bands and EMA (optional for EMA).
Calculates Bollinger Bands, EMA, highest high, and lowest low values based on the input parameters, evaluating the conditions to determine potential long and short entry signals.
The conditions include checks for crossovers and crossunders of the price with the upper and lower Bollinger Bands, as well as the position of the price relative to the EMA.
The script also incorporates the option to add an inside bar pattern check for additional information.
Entry Position
Long Position:
Price cross over the superior band of bollinger bands.
The EMA is used to add support for trend analysis, it is an optional input, when used, it checks if price is above EMA.
Short Position:
Price cross under the inferior band of bollinger bands.
The EMA is used to add support for trend analysis, it is an optional input, when used, it checks if price is under EMA.
Risk Management
Stop Loss:
The stop loss is calculated based on the input highest high (for short position) and lowest low (for long position).
It gets the length based on the input from the last candles to set which is the highest high and which is the lowest low.
Take Profit:
According to the author, the profit target should be at least 1:1.6 the risk, so to have the strategy mathematically positive.
The profit target is configured input, can be increased or decreased.
It calculates the take profit based on the price of the stop loss with the profit target input.
StdDev ChannelsThis script draws two sets of standard deviation channels on the price chart, providing a nuanced view of price volatility over different lengths.
The script starts by declaring a set of user-defined inputs allowing traders to customize the tool according to their individual requirements. The price input sets the source of the price data, defaulting to the closing price but customizable to use open, high, or low prices. The deviations parameter defines the width of the channels, with larger numbers resulting in wider channels. The length and length2 inputs represent the number of periods (in bars) that the script considers when calculating the regression line and standard deviation. Traders can also personalize the visual aspects of the indicator on the chart using the color, linewidth, and linestyle parameters.
Calculation of Standard Deviation:
The core of this script lies in calculating the regression line and standard deviation. This is where the InertiaAll function comes into play. This function calculates the linear regression line, which serves as the middle line of each channel. The function takes in two parameters: y (price data) and n (length for calculation). It returns an array containing the values for the regression line (InertiaTS), counter variable (x), slope of the line (a), and y-intercept (b). The standard deviation is then calculated using the built-in function ta.stdev, which measures the amount of variation or dispersion from the average.
After the calculation, the script proceeds to draw the channels. It creates two sets of lines (upper, middle, and lower) for each channel. These lines are initialized at the lowest price point on the chart (low). The coordinates for these lines get updated in the last section of the script, which runs only on the last bar on the chart (if barstate.islast). The functions line.set_xy1 and line.set_xy2 are used to adjust the starting and ending points for each line, forming the channels.
If the "full range" toggle is enabled, the script uses the maximum number of bars available on the chart to calculate the regression and standard deviation. This can give a broader perspective of the price's volatility over the entire available data range.
A Basic Strategy
The channels generated by this script may inform your trading decisions. If the price hits the upper line of a channel, it could suggest an 'overbought' condition indicating a potential selling opportunity. Conversely, if the price hits the lower line, it might signal an 'oversold' condition, suggesting a buying opportunity. The second channel, calculated over a different length, may serve to confirm these signals or identify longer-term trends.
Stop loss and position size calculator (ATR)Calculates and plots Stop Loss and Position Size, based on desired ATR factor.
Displays Stop Loss Price, SL Distance Percent and Position Size overlayed in the chart and plots a stop loss line.
Additionally Stop Loss, ATR, SL Distance and Position Size are also displayed in the Data Window.
Available settings:
Trade - long / short
ATR timeframe - Allows to use ATR based on different timeframe than the current chart.
ATR factor - Stop Loss will be calculated by multiplying ATR with this factor.
Show last - Amount of previous stop loss lines to plot
Line Offset - Positions previous sl calculation under/above current price.
Calculation Offset - Displays calculation based on price action of previous bar(s).
Label Display Distance - Defines position of label relative to current bar.
Risk - Amount you are going to risk if stop loss is hit.
[UPRIGHT Trading] Academy of Forex - Scalp Strategy█ OVERVIEW
This is a collaboration of efforts of The Academy of Forex and UPRIGHT Trading .
The Academy of Forex - Scalp Strategy Indicator is a clean & reimagined lower indicator. To enable optimization & potential automation, we re-coded & optimized it at UPRIGHT Trading.
It is based on the one presented on its YT channel.
The idea is for it to be an easy to use - simple indicator - that works not just for confirmation, but also entering and exiting quickly (scalping).
█ CONCEPTS
The idea is that %B (derived from BBs) is able to pick up some pretty significant moves. With that as one of the bases the Inverse Fisher Transform (Ribbon) acts to show some of the movement of the asset highlighting when it at extremes. The RSI highlights are there as another confirmation to help normalize the sometimes too frequent movement of %B.
As expected the indicator often acts as a reversal indicator, but with the optimizations of logic it's able to pick up more than just the reversals and works as a pretty decent Buy/Sell Algorithm.
█ CALCULATIONS
Calculations used included, but not limited to:
- %B - Quantifies the price as a percentage to the Upper and Lower Band of the Popular Bollinger Bands, which were named after their inventor John Bollinger in 1980.
- Inverse Fisher Transform RSI - is a variation of the IFT, created by John Ehlers, the idea is for the IFT to convert Gaussian normal distribution and to take it a step further the RSI version is to just use overbought and oversold placements. This indicator is meant to highlight when price has moved to an extreme and in this process helps to spot turning points.
- Relative Strength Index (RSI) - As most of you know already the RSI is a technical analysis tool invented by J. Welles Wilder, that oscillates and is used to measure the momentum of price changes. It normalizes to index 0-100 with overbought and oversold defined by the user, but often around 70 as overbought and 30 as oversold.
- Pivot/Swing Points - Implemented to show recent Higher-Highs or Lower-Lows, Pivot points are included in the indicator for structure tracking.
- Moving Averages - Moving averages help to get an idea of when price is moving near the norm or outside to extremes.
█ FEATURES
Indicator Features:
-2 Buy/Sell Signals.
-U Signals (UPRIGHT optimized).
-Exit Reminders.
-Alerts allowing Automation of Scalp Strategy.
-H/L Swings.
-Color Customization.
-Clean Mode.
-Inverse Fisher Transform Ribbon.
-RSI Bullish/Bearish Highlights.
-Options for More Signals (including: Oversold/Overbought Circles, %B Bull/Bear Squares and Triangles, and IFT Highlights).
Showing some of the signals close up.
Should look like this:
Enjoy!
Sincerely,
Mike
Advanced Volatility-Adjusted Momentum IndexAdvanced Volatility-Adjusted Momentum Index (AVAMI)
The AVAMI is a powerful and versatile trading index which enhances the traditional momentum readings by introducing a volatility adjustment. This results in a more nuanced interpretation of market momentum, considering not only the rate of price changes but also the inherent volatility of the asset.
Settings and Parameters:
Momentum Length: This parameter sets the number of periods used to calculate the momentum, which is essentially the rate of change of the asset's price. A shorter length value means the momentum calculation will be more sensitive to recent price changes. Conversely, a longer length will yield a smoother and more stabilized momentum value, thereby reducing the impact of short-term price fluctuations.
Volatility Length: This parameter is responsible for determining the number of periods to be considered in the calculation of standard deviation of returns, which acts as the volatility measure. A shorter length will result in a more reactive volatility measure, while a longer length will produce a more stable, but less sensitive measure of volatility.
Smoothing Length: This parameter sets the number of periods used to apply a moving average smoothing to the AVAMI and its signal line. The purpose of this is to minimize the impact of volatile periods and to make the indicator's lines smoother and easier to interpret.
Lookback Period for Scaling: This is the number of periods used when rescaling the AVAMI values. The rescaling process is necessary to ensure that the AVAMI values remain within a consistent and interpretable range over time.
Overbought and Oversold Levels: These levels are thresholds at which the asset is considered overbought (potentially overvalued) or oversold (potentially undervalued), respectively. For instance, if the AVAMI exceeds the overbought level, traders may consider it as a possible selling opportunity, anticipating a price correction. Conversely, if the AVAMI falls below the oversold level, it could be seen as a buying opportunity, with the expectation of a price bounce.
Mid Level: This level represents the middle ground between the overbought and oversold levels. Crossing the mid-level line from below can be perceived as an increasing bullish momentum, and vice versa.
Show Divergences and Hidden Divergences: These checkboxes give traders the option to display regular and hidden divergences between the AVAMI and the asset's price. Divergences are crucial market structures that often signal potential price reversals.
Index Logic:
The AVAMI index begins with the calculation of a simple rate of change momentum indicator. This raw momentum is then adjusted by the standard deviation of log returns, which acts as a measure of market volatility. This adjustment process ensures that the resulting momentum index encapsulates not only the speed of price changes but also the market's volatility context.
The raw AVAMI is then smoothed using a moving average, and a signal line is generated as an exponential moving average (EMA) of this smoothed AVAMI. This signal line serves as a trigger for potential trading signals when crossed by the AVAMI.
The script also includes an algorithm to identify 'fractals', which are distinct price patterns that often act as potential market reversal points. These fractals are utilized to spot both regular and hidden divergences between the asset's price and the AVAMI.
Application and Strategy Concepts:
The AVAMI is a versatile tool that can be integrated into various trading strategies. Traders can utilize the overbought and oversold levels to identify potential reversal points. The AVAMI crossing the mid-level line can signify a change in market momentum. Additionally, the identification of regular and hidden divergences can serve as potential trading signals:
Regular Divergence: This happens when the asset's price records a new high/low, but the AVAMI fails to follow suit, suggesting a possible trend reversal. For instance, if the asset's price forms a higher high but the AVAMI forms a lower high, it's a regular bearish divergence, indicating potential price downturn.
Hidden Divergence: This is observed when the price forms a lower high/higher low, but the AVAMI forms a higher high/lower low, suggesting the continuation of the prevailing trend. For example, if the price forms a lower low during a downtrend, but the AVAMI forms a higher low, it's a hidden bullish divergence, signaling the potential continuation of the downtrend.
As with any trading tool, the AVAMI should not be used in isolation but in conjunction with other technical analysis tools and within the context of a well-defined trading plan.
Average Range PercentageIt is indicator for average percent range (range from high to low of stock/index price) of N days,
This will help to find high percentage moving stock/index for intraday.
Spinn: SuperStopAdaptive Trailing Stop-Loss Indicator
This indicator will be beneficial for traders who have already opened a position and are looking to maximize their profits but are uncertain about the optimal time to exit. It provides clear and adaptive stop-loss levels based on market data, especially in highly volatile markets. It offers the ability to close trades automatically (through the use of web-hooks).
The algorithm is based on using the Average True Range (ATR) to set stop-loss levels. The scaling factor allows you to adjust the optimal distance from the stop-loss line to the price line.
A unique feature of this indicator is that the user can set the target timeframe (Target TF). This means that instead of just using the current chart's timeframe, you can set a multiplier or choose the target timeframe manually. This offers the ability to analyze volatility across different timeframes, which can be valuable for various trading strategies.
The timeframe multiplier is a highlight of this indicator. When switching the current timeframe, there is no need to manually change the target timeframe - this is very convenient.
The ability for automatic alerts when the price touches or crosses stop-loss levels is included.
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Индикатор адаптивных плавающих Стоп-лоссов
Индикатор будет полезен для трейдеров, которые уже открыли сделку и хотят максимизировать свою прибыль, но не уверены в оптимальном моменте для выхода. Он предоставляет четкие и адаптивные уровни стоп-лоссов, основанные на рыночных данных, особенно при высокой волатильности. Дает возможность закрывать сделки в автоматическом режиме (через использование веб-хуков).
Алгоритм основан на использовании среднего истинного диапазона (ATR) для определения уровней стоп-лоссов. Коэффициент масштабирования дает возможность настроить оптимальное расстояние от линии стоп-лосса до линии цены.
Особенность индикатора в том, что пользователь может настроить целевой таймфрейм (Target TF). Это значит, что вместо того чтобы просто использовать текущий таймфрейм графика, можно установить множитель или выбрать целевой таймфрейм вручную. Это дает возможность анализировать волатильность на разных временных рамках, что может быть полезно для различных торговых стратегий.
Множитель таймфрейма - это фишка данного индикатора. При переключении текущего таймфрейма не придется вручную менять целевой таймфрейм - это очень удобно.
Предусмотрена возможность автоматических оповещений при касании или пересечении уровней стоп-лоссов.
Multi Kernel Regression [ChartPrime]The "Multi Kernel Regression" is a versatile trading indicator that provides graphical interpretations of market trends by using different kernel regression methods. It's beneficial because it smoothes out price data, creating a clearer picture of price movements, and can be tailored according to the user's preference with various options.
What makes this indicator uniquely versatile is the 'Kernel Select' feature, which allows you to choose from a variety of regression kernel types, such as Gaussian, Logistic, Cosine, and many more. In fact, you have 17 options in total, making this an adaptable tool for diverse market contexts.
The bandwidth input parameter directly affects the smoothness of the regression line. While a lower value will make the line more sensitive to price changes by sticking closely to the actual prices, a higher value will smooth out the line even further by placing more emphasis on distant prices.
It's worth noting that the indicator's 'Repaint' function, which re-estimates work according to the most recent data, is not a deficiency or a flaw. Instead, it’s a crucial part of its functionality, updating the regression line with the most recent data, ensuring the indicator measurements remain as accurate as possible. We have however included a non-repaint feature that provides fixed calculations, creating a steady line that does not change once it has been plotted, for a different perspective on market trends.
This indicator also allows you to customize the line color, style, and width, allowing you to seamlessly integrate it into your existing chart setup. With labels indicating potential market turn points, you can stay on top of significant price movements.
Repaint : Enabling this allows the estimator to repaint to maintain accuracy as new data comes in.
Kernel Select : This option allows you to select from an array of kernel types such as Triangular, Gaussian, Logistic, etc. Each kernel has a unique weight function which influences how the regression line is calculated.
Bandwidth : This input, a scalar value, controls the regression line's sensitivity towards the price changes. A lower value makes the regression line more sensitive (closer to price) and higher value makes it smoother.
Source : Here you denote which price the indicator should consider for calculation. Traditionally, this is set as the close price.
Deviation : Adjust this to change the distance of the channel from the regression line. Higher values widen the channel, lower values make it smaller.
Line Style : This provides options to adjust the visual style of the regression lines. Options include Solid, Dotted, and Dashed.
Labels : Enabling this introduces markers at points where the market direction switches. Adjust the label size to suit your preference.
Colors : Customize color schemes for bullish and bearish trends along with the text color to match your chart setup.
Kernel regression, the technique behind the Multi Kernel Regression Indicator, has a rich history rooted in the world of statistical analysis and machine learning.
The origins of kernel regression are linked to the work of Emanuel Parzen in the 1960s. He was a pioneer in the development of nonparametric statistics, a domain where kernel regression plays a critical role. Although originally developed for the field of probability, these methods quickly found application in various other scientific disciplines, notably in econometrics and finance.
Kernel regression became really popular in the 1980s and 1990s along with the rise of other nonparametric techniques, like local regression and spline smoothing. It was during this time that kernel regression methods were extensively studied and widely applied in the fields of machine learning and data science.
What makes the kernel regression ideal for various statistical tasks, including financial market analysis, is its flexibility. Unlike linear regression, which assumes a specific functional form for the relationship between the independent and dependent variables, kernel regression makes no such assumptions. It creates a smooth curve fit to the data, which makes it extremely useful in capturing complex relationships in data.
In the context of stock market analysis, kernel regression techniques came into use in the late 20th century as computational power improved and these techniques could be more easily applied. Since then, they have played a fundamental role in financial market modeling, market prediction, and the development of trading indicators, like the Multi Kernel Regression Indicator.
Today, the use of kernel regression has solidified its place in the world of trading and market analysis, being widely recognized as one of the most effective methods for capturing and visualizing market trends.
The Multi Kernel Regression Indicator is built upon kernel regression, a versatile statistical method pioneered by Emanuel Parzen in the 1960s and subsequently refined for financial market analysis. It provides a robust and flexible approach to capturing complex market data relationships.
This indicator is more than just a charting tool; it reflects the power of computational trading methods, combining statistical robustness with visual versatility. It's an invaluable asset for traders, capturing and interpreting complex market trends while integrating seamlessly into diverse trading scenarios.
In summary, the Multi Kernel Regression Indicator stands as a testament to kernel regression's historic legacy, modern computational power, and contemporary trading insight.
Webby's Tight IndicatorWebby's Tight Indicator is used to measure a securities volatility relative to itself over time. This is achieved by taking the average of three short term ATR's (average true range) and creating a ratio versus three longer term ATR's.
Mike Webster recently stated he is using the 3,5,8 for the short term ATR's and the 55,89,144 for the long term ATR's. All of the ATR lengths are part of the Fibonacci sequence.
The ratio of the ATR's is then calculated and plotted as a histogram with 0 representing the ATR's being equal. As a stocks short term ATR contracts the histogram will rise above 0 meaning volatility in the short term is contracting relative to long term volatility. On the other hand if the short ATR's are expanding versus the long term ATR's the histogram will fall below 0 and turn red, signifying short term volatility is greater than long term volatility.
The easy visualization of this indicator allows you to quickly see when a stock is in a tight range and could be ready for a potential breakout to the long side or breakdown to the short side.
In this example we see tight price action with a blue histogram followed by volatility to the upside coinciding with a breakout.
In this example we see volatility expanding as a stock continues to fall.
To help differentiate between trending contraction or expansion and just short term blips 5-day exponential moving average of the ratio is also plotted on the histogram and dynamically changes colors as it rises and falls.
Indicator options include:
Change histogram colors
Choose ema line width
Price Exhaustion IndicatorThe Price Exhaustion Indicator (PE) is a powerful tool designed to identify trends weakening and strengthening in the financial markets. It combines the concepts of Average True Range (ATR), Moving Average Convergence Divergence (MACD), and Stochastic Oscillator to provide a comprehensive assessment of trend exhaustion levels. By analyzing these multiple indicators together, traders and investors can gain valuable insights into potential price reversals and long-term market highs and lows.
The aim of combining the ATR, MACD, and Stochastic Oscillator, is to provide a comprehensive analysis of trend exhaustion. The ATR component helps assess the volatility and range of price movements, while the MACD offers insights into the convergence and divergence of moving averages. The Stochastic Oscillator measures the current price in relation to its range, providing further confirmation of trend exhaustion. The exhaustion value is derived by combining the MACD, ATR, and Stochastic Oscillator. The MACD value is divided by the ATR value, and then multiplied by the Stochastic Oscillator value. This calculation results in a single exhaustion value that reflects the combined influence of these three indicators.
Application
The Price Exhaustion Indicator utilizes a unique visual representation by incorporating a gradient color scheme. The exhaustion line dynamically changes color, ranging from white when close to the midline (40) to shades of purple as it approaches points of exhaustion (overbought at 100 and oversold at -20). As the exhaustion line approaches the color purple, this represents extreme market conditions and zones of weakened trends where reversals may occur. This color gradient serves as a visual cue, allowing users to quickly gauge the strength or weakness of the prevailing trend.
To further enhance its usability, the Price Exhaustion Indicator also includes circle plots that signify potential points of trend reversion. These plots appear when the exhaustion lines cross or enter the overbought and oversold zones. Red circle plots indicate potential short entry points, suggesting a weakening trend and the possibility of a downward price reversal. Conversely, green circle plots represent potential long entry points, indicating a strengthening trend and the potential for an upward price reversal.
Traders and investors can leverage the Price Exhaustion Indicator in various ways. It can be utilized as a trend-following tool, or a mean reversion tool. When the exhaustion line approaches the overbought or oversold zones, it suggests a weakening trend and the possibility of a price reversal, helping identify potential market tops and bottoms. This can guide traders in timing their entries or exits in anticipation of a trend shift.
Utility
The Price Exhaustion Indicator is particularly useful for long-term market analysis, as it focuses on identifying long-term market highs and lows. By capturing the gradual weakening or strengthening of a trend, it assists investors in making informed decisions about portfolio allocation, trend continuation, or potential reversals.
In summary, the Price Exhaustion Indicator is a comprehensive and visually intuitive tool that combines ATR, MACD, and Stochastic Oscillator to identify trend exhaustion levels. By utilizing a gradient color scheme and circle plots, it offers traders and investors valuable insights into potential trend reversals and long-term market highs and lows. Its unique features make it a valuable addition to any trader's toolkit, providing a deeper understanding of market dynamics and assisting in decision-making processes. Please note that future performance of any trading strategy is fundamentally unknowable, and past results do not guarantee future performance.
Kalman Filtered ROC & Stochastic with MA SmoothingThe "Smooth ROC & Stochastic with Kalman Filter" indicator is a trend following tool designed to identify trends in the price movement. It combines the Rate of Change (ROC) and Stochastic indicators into a single oscillator, the combination of ROC and Stochastic indicators aims to offer complementary information: ROC measures the speed of price change, while Stochastic identifies overbought and oversold conditions, allowing for a more robust assessment of market trends and potential reversals. The indicator plots green "B" labels to indicate buy signals and blue "S" labels to represent sell signals. Additionally, it displays a white line that reflects the overall trend for buy signals and a blue line for sell signals. The aim of the indicator is to incorporate Kalman and Moving Average (MA) smoothing techniques to reduce noise and enhance the clarity of the signals.
Rationale for using Kalman Filter:
The Kalman Filter is chosen as a smoothing tool in the indicator because it effectively reduces noise and fluctuations. The Kalman Filter is a mathematical algorithm used for estimating and predicting the state of a system based on noisy and incomplete measurements. It combines information from previous states and current measurements to generate an optimal estimate of the true state, while simultaneously minimizing the effects of noise and uncertainty. In the context of the indicator, the Kalman Filter is applied to smooth the input data, which is the source for the Rate of Change (ROC) calculation. By considering the previous smoothed state and the difference between the current measurement and the predicted value, the Kalman Filter dynamically adjusts its estimation to reduce the impact of outliers.
Calculation:
The indicator utilizes a combination of the ROC and the Stochastic indicator. The ROC is smoothed using a Kalman Filter (credit to © Loxx: ), which helps eliminate unwanted fluctuations and improve the signal quality. The Stochastic indicator is calculated with customizable parameters for %K length, %K smoothing, and %D smoothing. The smoothed ROC and Stochastic values are then averaged using the formula ((roc + d) / 2) to create the blended oscillator. MA smoothing is applied to the combined oscillator aiming to further reduce fluctuations and enhance trend visibility. Traders are free to choose their own preferred MA type from 'EMA', 'DEMA', 'TEMA', 'WMA', 'VWMA', 'SMA', 'SMMA', 'HMA', 'LSMA', and 'PEMA' (credit to: © traderharikrishna for this code: ).
Application:
The indicator's buy signals (represented by green "B" labels) indicate potential entry points for buying assets, suggesting a bullish trend. The white line visually represents the trend, helping traders identify and follow the upward momentum. Conversely, the sell signals (blue "S" labels) highlight possible exit points or opportunities for short selling, indicating a bearish trend. The blue line illustrates the bearish movement, aiding in the identification of downward momentum.
The "Smoothed ROC & Stochastic" indicator offers traders a comprehensive view of market trends by combining two powerful oscillators. By incorporating the ROC and Stochastic indicators into a single oscillator, it provides a more holistic perspective on the market's momentum. The use of a Kalman Filter for smoothing helps reduce noise and enhance the accuracy of the signals. Additionally, the indicator allows customization of the smoothing technique through various moving average types. Traders can also utilize the overbought and oversold zones for additional analysis, providing insights into potential market reversals or extreme price conditions. Please note that future performance of any trading strategy is fundamentally unknowable, and past results do not guarantee future performance.
[Rygel] Dual time frame Bollinger Bands with signals and alertsThis indicator displays two Bollinger Bands coming from two different time frames, chart's current one and a higher one.
It analyzes these two Bollinger Bands data and combines them with RSI, MFI and MACD divergences and SuperTrend to identify areas of opportunity where price is the most likely to be at a local top or bottom.
It uses probabilistic data, the Bollinger Bands, to identify convergence areas where the price is statistically overbought or oversold simultaneously at two different time frames, it then looks for signs of a trend exhaustion, using RSI, MFI and MACD divergences, and finally it looks for an early confirmation of a trend reversal, using SuperTrend data with aggressive settings.
This indicator does not produce buy and sell signals. You won't get a buy for every sell or a sell for every buy. In a bearish trend, you may get multiple consecutive bullish signals and in a bullish trend multiple bearish signals.
It is meant to help you to identify and to alert you about areas of opportunity where you could, for instance, consider taking some profits or opening a trade.
It is meant to support your investment or trading decisions, not to induce them.
SIGNALS
This indicator generated multiple types of signals. Diamonds are better than squares. Colored ones are better than grey ones.
Green square: a bullish signal confirmed by a regular divergence
Red square: a bearish signal confirmed by a regular divergence
Blue square: a bullish signal confirmed by a hidden divergence (disabled by default as these signals are less reliable)
Orange square: a bearish signal confirmed by a hidden divergence (disabled by default as these signals are less reliable)
Diamonds: same as the square signals but the signal is forming a divergence with a previous one. Diamond signals are always stronger (i.e. more reliable) than square signals.
Grey signals: same as the previous ones but for weaker signals. These signals appear when price in the current time frame is overbought or oversold but only close to be at the higher timeframe. (disabled by default as these signals are less reliable)
When a weak signal follows a strong one and creates a MACD divergence with it, it will be considered as a strong signal and displayed as a colored signal, even when weak signals are disabled.
When a strong signal follows a weak one, forming a MACD divergence, it will be shown as a diamond signal, even when weak signals are disabled.
Most reliable signals are green and red diamonds.
SETTINGS
Bollinger Bands
Source: the source used to calculate the Bollinger Bands ("close" by default)
Length: the moving-average length of the Bollinger Bands (20 by default)
You will most likely have no need to change these settings. If you're wondering what they actually do, you should most likely not touch them.
Main channel standard deviation: the standard deviation used to calculate the classical Bollinger Bands channel. (2.0 by default)
Outer bands standard deviation: additional channels outside the main one, using a larger standard deviation. (3.0 by default)
Theoretically, with a 1.0 standard deviation, around 68% of the price action should be contained within the Bollinger Bands.
With a 2.0 standard deviation, around 95%.
With a 3.0 standard deviation, around 99.7%.
With a 4.0 standard deviation, around 99.99%.
But as security prices returns have no actual statistical distribution, these probabilities don't strictly apply to Bollinger Bands. According to Wikipedia, studies have found that with a 2.0 standard deviation, only about 88% (85–90%) of the price data remain with the Bollinger Bands, instead of the theoretical 95%.
The higher you set the values, the less signals you'll get.
You should most likely keep the main channel standard deviation between 2 and 3 and add between +0.5 and +1 for the outer bands.
Most commonly used value for Bollinger Bands is 2.0.
Current time frame
Show current time frame Bollinger Bands: these are the Bollinger Bands you're used to. (enabled by default)
Show current time frame outer bands: add two additional bands outside the main channel using a larger standard deviation. (enabled by default)
Higher time frame
Show higher time frame Bollinger Bands: display secondary Bollinger Bands from a higher time frame. Time frames are configured in the below "Time frames" section. (enabled by default)
Show higher time frame outer bands: add two additional bands outside the main channel using a larger standard deviation (enabled by default)
Overbought and oversold
Show oversold and overbought background: add a background to the higher time Bollinger Bands whose color depends on the dual time frame Bollinger Bands oversold / overbought status. (enabled by default)
Asset is considered overbought/oversold when its price is outside of the Bollinger Bands' main channel.
Asset is considered strongly overbought/oversold when its price is outside of the Bollinger Bands' outer bands.
Dark red: both time frame are overbought (outside the main channel)
Red: one time frame is strongly overbought (outside the outer bands) and the other one is overbought (outside the main channel)
Bright red: both time frame are strongly overbought (outside the outer bands)
Dark green: both time frame are oversold (outside the main channel)
Green: one time frame is strongly oversold (outside the outer bands) and the other one is oversold (outside the main channel)
Bright green: both time frame are strongly oversold (outside the outer bands)
Signals
Show signals: display signals when an area of opportunity is detected. Read the introduction and the Signals section for more information. (enabled by default)
Show weak signals: display signals although at the higher time frame price is not yet overbought or oversold but close to be (disabled by default)
Divergences
Use MACD for divergences (enabled by default)
Use MFI for divergences (enabled by default)
Use RSI for divergences (enabled by default)
At least one source of divergences must be enabled for signals to work.
Enable hidden divergences: signals don't use hidden divergences by default as they generate more false positives than regular divergences. You can enable them to get more signals, it can be especially useful at high time frames (like weekly, monthly, etc.) where signals are rarer. (disabled by default)
Show divergences: draw MACD, MFI and RSI divergences on the chart. (disabled by default)
Green: regular bullish divergence
Red: regular bearish divergence
Blue: hidden bullish divergence
Orange: hidden bearish divergence
Confirmation
Confirmation speed: a faster confirmation speed will generate more false positive signals, a slower one will produce delayed but more reliable signals.
Fastest: don't wait for a SuperTrend confirmation, only wait for a divergence confirmation. Lot of false positives.
Fast: wait for a fast SuperTrend confirmation (SuperTrend factor = 1).
Medium: wait for a slower but more reliable SuperTrend confirmation (SuperTrend factor = 2). Fewer false positives but more lagging signals.
Slow: wait for an even slower but very reliable SuperTrend confirmation (SuperTrend factor = 3). Very few false positives but very late signals.
Time frames
You can define the higher time frames you wish to use here.
Default values try to adhere to a x6 to x8 ratio, x4 to x12 at maximum.
Some pairs are more significant than others, like 4 hour + daily, daily + weekly and weekly + monthly.
1 second: 10 seconds
5 seconds: 30 seconds
10 seconds: 1 minute
15 seconds: 2 minutes
30 seconds: 3 minutes
1 minute: 10 minutes
2 minutes: 15 minutes
3-4 minutes: 30 minutes
5-9 minutes: 45 minutes
10-11 minutes: 1 hour
12-14 minutes: 1 hour
15-29 minutes: 2 hours
30-44 minutes: 4 hours
45-59 minutes: 6 hours
1 hour: 8 hours
2 hours: 12 hours
3 hours: 1 day
4-5 hours: 1 day
6-7 hours: 2 days
8-11 hours: 3 days
12-23 hours: 4 days
1 day: 1 week
2 days: 2 weeks
3 days: 3 weeks
4 days: 1 month
5 days: 1 month
6 days: 1 month
1 week: 1 month
2 weeks: 2 months
3 weeks: 3 months
1 month: 6 months
2 months: 9 months
3 months: 12 months
4 months: 15 months
5 months: 21 months
6 months: 24 months
Time frames use the TradingView units:
s = seconds
h = hours
D = days
W = weeks
M = months
no unit = minutes
Time frame strings follow these rules:
They are composed of the multiplier and the time frame unit, e.g., “1S”, “30” (30 minutes), “1D” (one day), “3M” (three months).
The unit is represented by a single letter, with no letter used for minutes: “S” for seconds, “D” for days, “W” for weeks and “M” for months.
When no multiplier is used, 1 is assumed: “S” is equivalent to “1S”, “D” to “1D, etc. If only “1” is used, it is interpreted as “1min”, since no unit letter identifier is used for minutes.
There is no “hour” unit; “1H” is not valid. The correct format for one hour is “60” (remember no unit letter is specified for minutes).
The valid multipliers vary for each time frame unit:
- For seconds, only the discrete 1, 5, 10, 15 and 30 multipliers are valid.
- For minutes, 1 to 1440.
- For days, 1 to 365.
- For weeks, 1 to 52.
- For months, 1 to 12.
Styles
You can configure the appearance of the Bollinger Bands, the overbought / oversold background, the divergences and the signals here.
Advanced - MACD
Settings used for the MACD divergences. You most likely won't need to change these values, especially if you need them to be explained.
Advanced - MFI
Settings used for the MACD divergences. You most likely won't need to change these values, especially if you need them to be explained.
Advanced - RSI
Settings used for the MACD divergences. You most likely won't need to change these values, especially if you need them to be explained.
Advanced - SuperTrend
Settings used for the MACD divergences. You most likely won't need to change these values, especially if you need them to be explained.
ALERTS
Any signal: a bullish or bearish signal has been detected.
Bullish signal: a bullish signal has been detected.
Bullish signal with divergence: a bullish signal forming a divergence with a previous bullish signal has been detected.
Bearish signal: a bearish signal has been detected.
Bearish signal with divergence: a bearish signal forming a divergence with a previous bearish signal has been detected.
Overbought/oversold = asset price is outside of the Bollinger Bands' main channel.
Strongly overbought/oversold = asset price is outside of the Bollinger Bands' outer bands.
Current time frame - Entering overbought: asset is now overbought at the current time frame.
Current time frame - Exiting overbought: asset is not overbought anymore at the current time frame.
Current time frame - Entering strongly overbought: asset is now strongly overbought at the current time frame.
Current time frame - Exiting strongly overbought: asset is not strongly overbought anymore at the current time frame.
Current time frame - Entering oversold: asset is now oversold at the current time frame.
Current time frame - Exiting oversold: asset is not oversold anymore at the current time frame.
Current time frame - Entering strongly oversold: asset is now strongly oversold at the current time frame.
Current time frame - Exiting strongly oversold: asset is not strongly oversold anymore at the current time frame.
Higher time frame - Entering overbought: asset is now overbought at the higher time frame.
Higher time frame - Exiting overbought: asset is not overbought anymore at the higher time frame.
Higher time frame - Entering strongly overbought: asset is now strongly overbought at the higher time frame.
Higher time frame - Exiting strongly overbought: asset is not strongly overbought anymore at the higher time frame.
Higher time frame - Entering oversold: asset is now oversold at the higher time frame.
Higher time frame - Exiting oversold: asset is not oversold anymore at the higher time frame.
Higher time frame - Entering strongly oversold: asset is now strongly oversold at the higher time frame.
Higher time frame - Exiting strongly oversold: asset is not strongly oversold anymore at the higher time frame.
Dual time frame - Entering overbought: asset is now overbought at current and higher time frames.
Dual time frame - Exiting overbought: asset is not overbought anymore at current and higher time frames.
Dual time frame - Entering oversold: asset is now oversold at current and higher time frames.
Dual time frame - Exiting oversold: asset is not oversold anymore at current and higher time frames.
Dual time frame - Entering strongly overbought: asset is now strongly overbought at current and higher time frames.
Dual time frame - Exiting strongly overbought: asset is not strongly overbought anymore at current and higher time frames.
Dual time frame - Entering strongly oversold: asset is now strongly oversold at current and higher time frames.
Dual time frame - Exiting strongly oversold: asset is not strongly oversold anymore at current and higher time frames.
ABOUT THE HIGHER TIME FRAME BOLLINGER BANDS
Using a classical higher time frame Bollinger Bands would produce lagging data. For instance, if we are using a weekly BB at the daily time frame, we'll have to wait up to 7 days for the weekly bar to close to get the actual final weekly BB values. Instead, this indicator generates real time higher time frame Bollinger Bands by multiplying the moving average length of the Bollinger Bands by the higher time frame / current time frame ratio. For instance, a weekly BB in the daily time frame will use a x7 ratio (i.e. a 20 * 7 = 140 days MA BB).
It produces slightly different but very similar bands that are as meaningful and can be used in real time at lower time frames.
Alternatives would have been to wait up to seven days for signals to be finalized, which would have render them meaningless. Or to use previous week data, which would have made the signal inaccurrate.
To sum up, weekly Bollinger Bands use a 20 weeks moving average updated one time a week. In the daily time frame, this indicator also use a 20 weeks (140 days) moving average but updated daily instead of weekly.
A comparison between a traditional higher time frame Bollinger Bands vs the ones used by this indicator:
Blue and orange lines are the actual weekly BBs, grey ones are the daily updated ones.
ABOUT THE DIVERGENCES
This indicator uses the same divergences algorithm as my other indicators:
- RSI with divergences
- MACD with divergences
- Trend Reversal Indicator
You'll find more information about this algorithm on my RSI page.
