Bollinger Bands Heatmap (BBH)The Bollinger Bands Heatmap (BBH) Indicator provides a unique visualization of Bollinger Bands by displaying the full distribution of prices as a heatmap overlaying your price chart. Unlike traditional Bollinger Bands, which plot the mean and standard deviation as lines, BBH illustrates the entire statistical distribution of prices based on a normal distribution model.
This heatmap indicator offers traders a visually appealing way to understand the probabilities associated with different price levels. The lower the weight of a certain level, the more transparent it appears on the heatmap, making it easier to identify key areas of interest at a glance.
Key Features
Dynamic Heatmap: Changes in real-time as new price data comes in.
Fully Customizable: Adjust the scale, offset, alpha, and other parameters to suit your trading style.
Visually Engaging: Uses gradients of colors to distinguish between high and low probabilities.
Settings
Scale
Tooltip: Scale the size of the heatmap.
Purpose: The 'Scale' setting allows you to adjust the dimensions of each heatmap box. A higher value will result in larger boxes and a more generalized view, while a lower value will make the boxes smaller, offering a more detailed look at price distributions.
Values: You can set this from a minimum of 0.125, stepping up by increments of 0.125.
Scale ATR Length
Tooltip: The ATR used to scale the heatmap boxes.
Purpose: This setting is designed to adapt the heatmap to the instrument's volatility. It determines the length of the Average True Range (ATR) used to size the heatmap boxes.
Values: Minimum allowable value is 5. You can increase this to capture more bars in the ATR calculation for greater smoothing.
Offset
Tooltip: Offset mean by ATR.
Purpose: The 'Offset' setting allows you to shift the mean value by a specified ATR. This could be useful for strategies that aim to capitalize on extreme price movements.
Values: The value can be any floating-point number. Positive values shift the mean upward, while negative values shift it downward.
Multiplier
Tooltip: Bollinger Bands Multiplier.
Purpose: The 'Multiplier' setting determines how wide the Bollinger Bands are around the mean. A higher value will result in a wider heatmap, capturing more extreme price movements. A lower value will tighten the heatmap around the mean price.
Values: The minimum is 0, and you can increase this in steps of 0.2.
Length
Tooltip: Length of Simple Moving Average (SMA).
Purpose: This setting specifies the period for the Simple Moving Average that serves as the basis for the Bollinger Bands. A higher value will produce a smoother average, while a lower value will make it more responsive to price changes.
Values: Can be set to any integer value.
Heat Map Alpha
Tooltip: Opacity level of the heatmap.
Purpose: This controls the transparency of the heatmap. A lower value will make the heatmap more transparent, allowing you to see the price action more clearly. A higher value will make the heatmap more opaque, emphasizing the bands.
Values: Ranges from 0 (completely transparent) to 100 (completely opaque).
Color Settings
High Color & Low Color: These settings allow you to customize the gradient colors of the heatmap.
Purpose: Use contrasting colors for better visibility or colors that you prefer. The 'High Color' is used for areas with high density (high probability), while the 'Low Color' is for low-density areas (low probability).
Usage Scenarios for Settings
For Volatile Markets: Increase 'Scale ATR Length' for better smoothing and set a higher 'Multiplier' to capture wider price movements.
For Trend Following: You might want to set a larger 'Length' for the SMA and adjust 'Scale' and 'Offset' to focus on more probable price zones.
These are just recommendations; feel free to experiment with these settings to suit your specific trading requirements.
How To Interpret
The heatmap gives a visual representation of the range within which prices are likely to move. Areas with high density (brighter color) indicate a higher probability of the price being in that range, whereas areas with low density (more transparent) indicate a lower probability.
Bright Areas: Considered high-probability zones where the price is more likely to be.
Transparent Areas: Considered low-probability zones where the price is less likely to be.
Tips For Use
Trend Confirmation: Use the heatmap along with other trend indicators to confirm the strength and direction of a trend.
Volatility: Use the density and spread of the heatmap as an indication of market volatility.
Entry and Exit: High-density areas could be potential support and resistance levels, aiding in entry and exit decisions.
Caution
The Bollinger Bands Heatmap assumes a normal distribution of prices. While this is a standard assumption in statistics, it is crucial to understand that real-world price movements may not always adhere to a normal distribution.
Conclusion
The Bollinger Bands Heatmap Indicator offers traders a fresh perspective on Bollinger Bands by transforming them into a visual, real-time heatmap. With its customizable settings and visually engaging display, BBH can be a useful tool for traders looking to understand price probabilities in a dynamic way.
Feel free to explore its features and adjust the settings to suit your trading strategy. Happy trading!
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Bollinger Bands Liquidity Cloud [ChartPrime]This indicator overlays a heatmap on the price chart, providing a detailed representation of Bollinger bands' profile. It offers insights into the price's behavior relative to these bands. There are two visualization styles to choose from: the Volume Profile and the Z-Score method.
Features
Volume Profile: This method illustrates how the price interacts with the Bollinger bands based on the traded volume.
Z-Score: In this mode, the indicator samples the real distribution of Z-Scores within a specified window and rescales this distribution to the desired sample size. It then maps the distribution as a heatmap by calculating the corresponding price for each Z-Score sample and representing its weight via color and transparency.
Parameters
Length: The period for the simple moving average that forms the base for the Bollinger bands.
Multiplier: The number of standard deviations from the moving average to plot the upper and lower Bollinger bands.
Main:
Style: Choose between "Volume" and "Z-Score" visual styles.
Sample Size: The size of the bin. Affects the granularity of the heatmap.
Window Size: The lookback window for calculating the heatmap. When set to Z-Score, a value of `0` implies using all available data. It's advisable to either use `0` or the highest practical value when using the Z-Score method.
Lookback: The amount of historical data you want the heatmap to represent on the chart.
Smoothing: Implements sinc smoothing to the distribution. It smoothens out the heatmap to provide a clearer visual representation.
Heat Map Alpha: Controls the transparency of the heatmap. A higher value makes it more opaque, while a lower value makes it more transparent.
Weight Score Overlay: A toggle that, when enabled, displays a letter score (`S`, `A`, `B`, `C`, `D`) inside the heatmap boxes, based on the weight of each data point. The scoring system categorizes each weight into one of these letters using the provided percentile ranks and the median.
Color
Color: Color for high values.
Standard Deviation Color: Color to represent the standard deviation on the Bollinger bands.
Text Color: Determines the color of the letter score inside the heatmap boxes. Adjusting this parameter ensures that the score is visible against the heatmap color.
Usage
Once this indicator is applied to your chart, the heatmap will be overlaid on the price chart, providing a visual representation of the price's behavior in relation to the Bollinger bands. The intensity of the heatmap is directly tied to the price action's intensity, defined by your chosen parameters.
When employing the Volume Profile style, a brighter and more intense area on the heatmap indicates a higher trading volume within that specific price range. On the other hand, if you opt for the Z-Score method, the intensity of the heatmap reflects the Z-Score distribution. Here, a stronger intensity is synonymous with a more frequent occurrence of a specific Z-Score.
For those seeking an added layer of granularity, there's the "Weight Score Overlay" feature. When activated, each box in your heatmap will sport a letter score, ranging from `S` to `D`. This score categorizes the weight of each data point, offering a concise breakdown:
- `S`: Data points with a weight of 1.
- `A`: Weights below 1 but greater than or equal to the 75th percentile rank.
- `B`: Weights under the 75th percentile but at or above the median.
- `C`: Weights beneath the median but surpassing the 25th percentile rank.
- `D`: All that fall below the 25th percentile rank.
This scoring feature augments the heatmap's visual data, facilitating a quicker interpretation of the weight distribution across the dataset.
Further Explanations
Volume Profile
A volume profile is a tool used by traders to visualize the amount of trading volume occurring at specific price levels. This kind of profile provides a deep insight into the market's structure and helps traders identify key areas of support and resistance, based on where the most trading activity took place. The concept behind the volume profile is that the amount of volume at each price level can indicate the potential importance of that price.
In this indicator:
- The volume profile mode creates a visual representation by sampling trading volumes across price levels.
- The representation displays the balance between bullish and bearish volumes at each level, which is further differentiated using a color gradient from `low_color` to `high_color`.
- The volume profile becomes more refined with sinc smoothing, helping to produce a smoother distribution of volumes.
Z-Score and Distribution Resampling
Z-Score, in the context of trading, represents the number of standard deviations a data point (e.g., closing price) is from the mean (average). It’s a measure of how unusual or typical a particular data point is in relation to all the data. In simpler terms, a high Z-Score indicates that the data point is far away from the mean, while a low Z-Score suggests it's close to the mean.
The unique feature of this indicator is that it samples the real distribution of z-scores within a window and then resamples this distribution to fit the desired sample size. This process is termed as "resampling in the context of distribution sampling" . Resampling provides a way to reconstruct and potentially simplify the original distribution of z-scores, making it easier for traders to interpret.
In this indicator:
- Each Z-Score corresponds to a price value on the chart.
- The resampled distribution is then used to display the heatmap, with each Z-Score related price level getting a heatmap box. The weight (or importance) of each box is represented as a combination of color and transparency.
How to Interpret the Z-Score Distribution Visualization:
When interpreting the Z-Score distribution through color and alpha in the visualization, it's vital to understand that you're seeing a representation of how unusual or typical certain data points are without directly viewing the numerical Z-Score values. Here's how you can interpret it:
Intensity of Color: This often corresponds to the distance a particular data point is from the mean.
Lighter shades (closer to `low_color`) typically indicate data points that are more extreme, suggesting overbought or oversold conditions. These could signify potential reversals or significant deviations from the norm.
Darker shades (closer to `high_color`) represent data points closer to the mean, suggesting that the price is relatively typical compared to the historical data within the given window.
Alpha (Transparency): The degree of transparency can indicate the significance or confidence of the observed deviation. More opaque boxes might suggest a stronger or more reliable deviation from the mean, implying that the observed behavior is less likely to be a random occurrence.
More transparent boxes could denote less certainty or a weaker deviation, meaning that the observed price behavior might not be as noteworthy.
- Combining Color and Alpha: By observing both the intensity of color and the level of transparency, you get a richer understanding. For example:
- A light, opaque box could suggest a strong, significant deviation from the mean, potentially signaling an overbought or oversold scenario.
- A dark, transparent box might indicate a weak, insignificant deviation, suggesting the price is behaving typically and is close to its average.
Floor and Roof IndicatorThe Floor and Roof indicator is a tool developed to help traders identify potential areas of support and resistance both for trend following and for mean reversal trading decisions.
The indicator plots the "Roof" which is the main level of resistance, and the "Floor" which is the main level of support. These lines are calculated on the "Lenght" parameter and smoothed by the "Smooth" parameter, and they use both the volatility and the main market structure as calculation methods.
Additionally, this indicator plots an area that can be modified by the "Zone width" parameter and two other lines, called "Second floor" and "Second roof" respectively, which are plotted only whenever they are significant to the price current level.
This indicator can be used in several ways:
- In a clear trend, you could wait for a break of the second floor or roof as an indication of a change in the market direction
- As the price goes out of the reversal zones, this can be an indication of a reversal
- In a clear trend, you can wait for the price to bounce on the second floor or roof lines to enter a trade
DBMA - Dual Bollinger Moving AverageThe Dual Bollinger moving average (DBMA) consists of a moving average (MA) & two Bollinger Bands (BB), with the color of the bands representing the level of price compression. In its default settings, it is a 20-day simple moving average with 2 upper Bollinger Bands, having the standard deviation (SD) settings of 0.5 & 1, respectively.
How close the price is to the moving average?
For a pullback trader, the entry point should be close to the moving average, preferably with price compression. How close should it be, is where the bands serve as a guide. The low of the pullback candle should be within the bands, that is, at least within the far band (1 SD of the MA), or even better if it's within the near band (0.5 SD). When the price is outside the bands, it should not be considered favourable for a pullback entry.
For how long has the price been closer to the moving average?
John Carter’s TTM Squeeze indicator looked at the relationship between Bollinger Bands and Keltner's Channels to help identify period of volatility contractions. Bollinger Bands being completely enclosed within the Keltner Channels is indicative of a very low volatility. This is a state of volatility contraction known as squeeze. Using different ATR lengths (1.0, 1.5 and 2.0) for Keltner Channels, we can differentiate between levels of squeeze (High, Mid & Low compression, respectively). Greater the compression, higher the potential for explosive moves.
The squeeze portion of the script is based on LazyBear's script ( Squeeze Momentum Indicator )
The High, Mid & Low compression squeezes are depicted via the color of the bands being red, orange, or yellow, respectively. With the low of the pullback candle within the bands, & the squeeze color changing to red, it should be considered favourable for a pullback entry.
Trailing the price with the lower bands
The lower bands can be used for trailing with the moving average. While trailing, once the price closes below the moving average, the trailing stoploss (TSL) is said to be triggered, & the trade is exited. Here we use the bands to give it some cushion. Let the price close below the 1SD band for labelling the TSL as being triggered to exit the trade. If the price closes below the MA but is still within the bands, the signal is to keep holding the trade.
Extreme Reversal SignalThe Extreme Reversal Signal is designed to signal potential pivot points when the price of an asset becomes extremely overbought or oversold. Extreme conditions typically signal a brief or extensive price reversal, offering valuable entry or exit points. It's important to note that this indicator may produce multiple signals, making it essential to corroborate these signals with other forms of analysis to determine their validity. While the default settings provide valuable insights, it might be beneficial to experiment with different configurations to ensure the indicator's efficacy.
Two primary conditions define extremely overbought and oversold states. The first condition is that the price must deviate by two standard deviations from the 20-day Simple Moving Average (SMA). The second condition is that the 3-day SMA of the 14-day Stochastic Oscillator (STO) derived from the 14-day Relative Strength Index (RSI) is above or below the upper or lower limit.
Oversold states arise when the first condition is met and the 3-day SMA of the 14-day Stochastic RSI falls below the lower limit, suggesting a buy signal. These are visually represented by green triangles below the price bars. Overbought states arise when the first condition is met and the 3-day SMA of the 14-day Stochastic RSI rises above the upper limit, suggesting a sell signal. These are visually represented by red triangles above the price bars. It's also possible to set up automated alerts to get notifications when either of these two conditions is met to avoid missing out.
While this indicator has traditionally identified overbought and oversold conditions in various different assets, past performance does not guarantee future results. Therefore, it is advisable to supplement this indicator with other technical tools. For instance, trend indicators can greatly improve the decision-making process when planning for entries and exit points.
PercentX Trend Follower [Trendoscope]"Trendoscope" was born from our trading journey, where we first delved into the world of trend-following methods. Over time, we discovered the captivating allure of pattern analysis and the exciting challenges it presented, drawing us into exploring new horizons. However, our dedication to trend-following methodologies remains steadfast and continues to be an integral part of our core philosophy.
Here we are, introducing another effective trend-following methodology, employing straightforward yet powerful techniques.
🎲 Concepts
Introducing the innovative PercentX Oscillator , a representation of Bollinger PercentB and Keltner Percent K. This powerful tool offers users the flexibility to customize their PercentK oscillator, including options for the type of moving average and length.
The Oscillator Range is derived dynamically, utilizing two lengths - inner and outer. The inner length initiates the calculation of the oscillator's highest and lowest range, while the outer length is used for further calculations, involving either a moving average or the opposite side of the highest/lowest range, to obtain the oscillator ranges.
Next, the Oscillator Boundaries are derived by applying another round of high/low or moving average calculations on the oscillator range values.
Breakouts occur when the close price crosses above the upper boundary or below the lower boundary, signaling potential trading opportunities.
🎲 How to trade a breakout?
To reduce false signals, we employ a simple yet effective approach. Instead of executing market trades, we use stop orders on both sides at a certain distance from the current close price.
In case of an upper side breakout, a long stop order is placed at 1XATR above the close, and a short stop order is placed at 2XATR below the close. Conversely, for a lower side breakout, a short stop order is placed at 1XATR below the close, and a long stop order is placed at 2XATR above the ATR. As a trend following method, our first inclination is to trade on the side of breakout and not to find the reversals. Hence, higher multiplier is used for the direction opposite to the breakout.
The script provides users with the option to specify ATR multipliers for both sides.
Once a trade is initiated, the opposite side of the trade is converted into a stop-loss order. In the event of a breakout, the script will either place new long and short stop orders (if no existing trade is present) or update the stop-loss orders if a trade is currently running.
As a trend-following strategy, this script does not rely on specific targets or target levels. The objective is to run the trade as long as possible to generate profits. The trade is only stopped when the stop-loss is triggered, which is updated with every breakout to secure potential gains and minimize risks.
🎲 Default trade parameters
Script uses 10% equity per trade and up to 4 pyramid orders. Hence, the maximum invested amount at a time is 40% of the equity. Due to this, the comparison between buy and hold does not show a clear picture for the trade.
Feel free to explore and optimize the parameters further for your favorite symbols.
🎲 Visual representation
The blue line represents the PercentX Oscillator, orange and lime colored lines represent oscillator ranges. And red/green lines represent oscillator boundaries. Oscillator spikes upon breakout are highlighted with color fills.
Reversion Zone IndexThe Reversion Zone Index (RZI) is an indicator that combines the Commodity Channel Index (CCI), Choppiness Index (CI), and Bollinger Bands Percentage (BBPct) to identify mean reversion signals in the market. It is plotted as an Exponential Moving Average (EMA) smoothed oscillator with overbought and oversold zones, and mean reversion signals are represented by red and green arrows.
The three indicators are combined to benefit from their complementary aspects and create a more comprehensive view of mean reversion conditions. Here's a brief overview of each indicator's benefits:
1. Commodity Channel Index (CCI): CCI measures the current price level relative to its average over a specified period. It helps identify overbought and oversold conditions, as well as potential trend retracements. By incorporating CCI, the RZI gains insights into momentum and potential turning points.
2. Choppiness Index (CI): CI quantifies the market's choppiness or trendiness by analyzing the range between the highest high and lowest low over a specific period. It indicates whether the market is in a trending or ranging phase. CI provides valuable information about the market state, which can be useful in mean reversion analysis.
3. Bollinger Bands Percentage (BBPct): BBPct measures the current price's position relative to the Bollinger Bands. It calculates the percentage difference between the current price and the bands, identifying potential overbought or oversold conditions. BBPct helps gauge the market's deviation from its typical behavior and highlights potential reversal opportunities.
The RZI combines the three indicators by taking an average of their values and applying further calculations. It smooths the combined oscillator using an EMA to reduce noise and enhance the visibility of the trends. Smoothing with EMA provides a more responsive representation of the overall trend and helps filter out short-term fluctuations.
The overbought and oversold zones are marked on the chart as reference levels. When the combined oscillator is above the overbought zone or below the oversold zone, it suggests a potential mean reversion signal. Red and green arrows are displayed to visually indicate these mean retracement signals.
The RZI is a valuable tool for identifying mean reversion opportunities in the market. It incorporates multiple indicators, each providing unique insights into different aspects of mean reversion, such as momentum, volatility, and price positioning. Traders can use this indicator to spot potential turning points and time their trades accordingly.
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.
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.
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.
MyVolatilityBandsLibrary "MyVolatilityBands"
Just a lil' library of volatility bands that I use in some scripts
bollingerbands(src, lkbk, mult, basis)
Bollinger Bands
Parameters:
src (float) : float
lkbk (int) : int
mult (float) : float
basis (float)
Returns: Bollinger Bands
donchianchannels(src, lkbk, band_width)
Donchian Channels
Parameters:
src (float) : float
lkbk (int) : int
band_width (float) : float
Returns: Donchian Channels with an outer band of varying thickness adjusted by the band_width input
doublehalfdonchianchannels(src, lkbk, divisor)
Double Half Donchian Channels
Parameters:
src (float) : float
lkbk (int) : int
divisor (float) : float
Returns: two adjustable bases calculated using Donchian Channels calculation that act as a measure of volatility
Bollinger Bands Lab - by InFinitoVariation of the Moving Average Lab that includes Bollinger Bands functionality for any manually created Moving Average. It includes:
- Standard Deviations for any MA
- Fixed Symmetrical Deviations for any MA that remain at a constant % away from the MA
- The same Moving Average creation settings from the Moving Average Lab
"The Moving Average Lab allows to create any possible combination of up to 3 given MAs. It is meant to help you find the perfect MA that fits your style, strategy and market type.
This script allows to average, weight, double and triple multiple types and lengths of Moving Averages
Currently supported MA types are:
SMA
EMA
VWMA
WMA
SMMA (RMA)
HMA
LSMA
DEMA
TEMA
Features:
- Double or Triple any type of Moving Average using the same logic used for calculating DEMAs and TEMAs
- Average 2 or 3 different types and lengths of Moving Average
- Weight each MA manually
- Average up to 3 personalized MAs
- Average different Moving Averages with different length each "
The preview screenshot shows:
- The combination of:
- 200 LSMA - Weight: 1
- 200 HMA - Weight: 2
- 200 VWMA - Weight: 1 - Double
- The regular Bollinger Band setting, 2 standard deviations
- Two fixed symmetrical deviations at 15% and 20% away from the XMA
Bollinger Bands and SMA Channel Buy and Sell
This Indicator is a combination of a standard BB indicator incorporated with a SSL Channel by ErwinBeckers which is Simple Moving average with a length of set at 10 (Default) and calculates the high and low set for the default 10 to form a Channel.
The Settings for the Bollinger Band is the standard settings on a normal Bollinger Band - Length 20, source close and Standard dev 2
The setting for the SMA is length 10 and the high and low calculated or that length to form a channel.
The SMA Channel gives a green line for the Up channel and the Red line for the down Channel.
The basis of the indicator is that the Candle close above the Basis line of the BB and the SMA green line will give a buy indicator
and the same for Sell indicator the candle close below the basis BB and the SMA line Red will give a Sell indicator.
Please note that this indicator is a mix of 2 basic indicators found in Trading view, giving Buy and Sell indicators to make things easier to not look for this visually.
This code will be open source for anyone to use or back test or use it for whatever they want.
This code is for my own personal trading and cannot be relied upon. This indicator cannot be used and cannot guarantee anything, and caution should always be taken when trading. Use this with other indicators to give certanty.
Again use this for Paper Trading only.
I want to thank TradingView for its platform that facilitates development and learning.
TTP VIX SpyTTP VIX Spy is an indicator that uses data from TVC:VIX to better time entries in the market.
The assumption used is that when the VIX is coming down from the top of its range then the risk on assets can move to the upside and when the VIX is is pushing higher there's a high likelihood or risk on assets going down.
This indicator observes the momentum of VIX using MACD. It offers two different signals both for longs and shorts: signal 1 and 2.
Signal 1 is activate when the begging of a new trend for the VIX is confirmed.
Signal 2 is activated when the VIX pulls back from an extreme value.
You can configure the parameters of the internal super trend and the look back for the slope applied to price and RSIs.
The indicator offers the following filter parameters:
- Price RSI slope: it filters signals that have RSI slope pointing in the opposite direction of the signal.
- Counter trend: it filters signals that are not counter trending super trend.
- Wide BBW: it filters signals that happen when there hasn't been high price volatility
- Price slope: it filters signals when the price is not pointing in the direction of the signal (buy: up, sell: down)
- VIX RSI filter: it filters VIX RSI values overextended. MACD can be in the right range, but sometimes RSI contradicts it. By default is OFF since it can cause false negatives.
- Working days only: it filters signals that occur in the weekend.
The colours below the price action show how the VIX momentum is changing. Transitions from red into pink and then green show how the fear is fading which tends to lead to lead to bullish moves, and the opposite when the transitions are from green to red.
Performance and initial thoughts.
I have tried VIX Spy on both BINANCE:BTCUSDT.P and BINANCE:ETHUSDT.P and it seems to offer a decent win ratio. As you can see I had to add many filter to remove bad entries and left toggles available to decide which ones you want to use.
I tried the signal in the 4H, 1H and 15min with mixed results. I tend to incline for the results in the 1H.
VIX signal offers a backtestable stream and alerts both for signals 1 and 2.
Probability Envelopes (PBE)Introduction
In the world of trading, technical analysis is vital for making informed decisions about the future direction of an asset's price. One such tool is the use of indicators, mathematical calculations that can help traders predict market trends. This article delves into an innovative indicator called the Probability Envelopes Indicator, which offers valuable insights into the potential price levels an asset may reach based on historical data. This in-depth look explores the statistical foundations of the indicator, highlighting its key components and benefits.
Section 1: Calculating Price Movements with Log Returns and Percentages
The Probability Envelopes Indicator provides the option to use either log returns or percentage changes when calculating price movements. Each method has its advantages:
Log Returns: These are calculated as the natural logarithm of the ratio of the current price to the previous price. Log returns are considered more stable and less sensitive to extreme price fluctuations.
Percentage Changes: These are calculated as the percentage difference between the current price and the previous price. They are simpler to interpret and easier to understand for most traders.
Section 2: Understanding Mean, Variance, and Standard Deviation
The Probability Envelopes Indicator utilizes various statistical measures to analyze historical price movements:
Mean: This is the average of a set of numbers. In the context of this indicator, it represents the average price movement for bullish (green) and bearish (red) scenarios.
Variance: This measure represents the dispersion of data points in a dataset. A higher variance indicates a greater spread of data points from the mean. Variance is calculated as the average of the squared differences from the mean.
Standard Deviation: This is the square root of the variance. It is a measure of the amount of variation or dispersion in a dataset. In the context of this indicator, standard deviations are used to calculate the width of the bands around the expected mean.
Section 3: Analyzing Historical Price Movements and Probabilities
The Probability Envelopes Indicator examines historical price movements and calculates probabilities based on their frequency:
The indicator first identifies and categorizes price movements into bullish (green) and bearish (red) scenarios.
It then calculates the probability of each price movement occurring by dividing the frequency of the movement by the total number of occurrences in each category (bullish or bearish).
The expected green and red movements are calculated by multiplying the probabilities by their respective price movements and summing the results.
The total expected movement, or weighted average, is calculated by combining the expected green and red movements and dividing by the total number of occurrences.
Section 4: Constructing the Probability Envelopes
The Probability Envelopes Indicator utilizes the calculated statistics to construct its bands:
The expected mean is calculated using the total expected movement and applied to the current open price.
An exponential moving average (EMA) is used to smooth the expected mean, with the smoothing length determining the degree of responsiveness.
The upper and lower bands are calculated by adding and subtracting the mean green and red movements, respectively, along with their standard deviations multiplied by a user-defined multiplier.
Section 5: Benefits of the Probability Envelopes Indicator
The Probability Envelopes Indicator offers numerous advantages to traders:
Enhanced Decision-Making: By providing probability-based estimations of future price levels, the indicator can help traders make more informed decisions and potentially improve their trading strategies.
Versatility: The indicator is applicable to various financial instruments, such as stocks, forex, commodities, and cryptocurrencies, making it a valuable tool for traders in different markets.
Customization: The indicator's parameters, including the use of log returns, multiplier values, and smoothing length, can be adjusted according to the user's preferences and trading style. This flexibility allows traders to fine-tune the Probability Envelopes Indicator to better suit their needs and goals.
Risk Management: The Probability Envelopes Indicator can be used as a component of a risk management strategy by providing insight into potential price movements. By identifying potential areas of support and resistance, traders can set stop-loss and take-profit levels more effectively.
Visualization: The graphical representation of the indicator, with its clear upper and lower bands, makes it easy for traders to quickly assess the market and potential price levels.
Section 6: Integrating the Probability Envelopes Indicator into Your Trading Strategy
When incorporating the Probability Envelopes Indicator into your trading strategy, consider the following tips:
Confirmation Signals: Use the indicator in conjunction with other technical analysis tools, such as trend lines, moving averages, or oscillators, to confirm the strength and direction of the market trend.
Timeframes: Experiment with different timeframes to find the optimal settings for your trading strategy. Keep in mind that shorter timeframes may generate more frequent signals but may also increase the likelihood of false signals.
Risk Management: Always establish a proper risk management strategy that includes setting stop-loss and take-profit levels, as well as managing your position sizes.
Backtesting: Test the Probability Envelopes Indicator on historical data to evaluate its effectiveness and fine-tune its parameters to optimize your trading strategy.
Section 7: Cons and Limitations of the Probability Envelopes Indicator
While the Probability Envelopes Indicator offers several advantages to traders, it is essential to be aware of its potential cons and limitations. Understanding these can help you make better-informed decisions when incorporating the indicator into your trading strategy.
Lagging Nature: The Probability Envelopes Indicator is primarily based on historical data and price movements. As a result, it may be less responsive to real-time changes in market conditions, and the predicted price levels may not always accurately reflect the market's current state. This lagging nature can lead to late entry and exit signals.
False Signals: As with any technical analysis tool, the Probability Envelopes Indicator can generate false signals. These occur when the indicator suggests a potential price movement, but the market does not follow through. It is crucial to use other technical analysis tools to confirm the signals and minimize the impact of false signals on your trading decisions.
Complex Statistical Concepts: The Probability Envelopes Indicator relies on complex statistical concepts and calculations, which may be challenging to grasp for some traders, particularly beginners. This complexity can lead to misunderstandings and misuse of the indicator if not adequately understood.
Overemphasis on Past Data: While historical data can be informative, relying too heavily on past performance to predict future movements can be limiting. Market conditions can change rapidly, and relying solely on past data may not provide an accurate representation of the current market environment.
No Guarantees: The Probability Envelopes Indicator, like all technical analysis tools, cannot guarantee success. It is essential to approach trading with realistic expectations and understand that no indicator or strategy can provide foolproof results.
To overcome these limitations, it is crucial to combine the Probability Envelopes Indicator with other technical analysis tools and utilize a comprehensive risk management strategy. By doing so, you can better understand the market and increase your chances of success in the ever-changing financial markets.
Section 8: Probability Envelopes Indicator vs. Bollinger Bands
Bollinger Bands and the Probability Envelopes Indicator are both technical analysis tools designed to identify potential support and resistance levels, as well as potential trend reversals. However, they differ in their underlying concepts, calculations, and applications. This section will provide a deep dive into the differences between these two indicators and how they can complement each other in a trading strategy.
Underlying Concepts and Calculations:
Bollinger Bands:
Bollinger Bands are based on a simple moving average (SMA) of the price data, with upper and lower bands plotted at a specified number of standard deviations away from the SMA.
The distance between the bands widens during periods of increased price volatility and narrows during periods of low volatility, indicating potential trend reversals or breakouts.
The standard settings for Bollinger Bands typically involve a 20-period SMA and a 2 standard deviation distance for the upper and lower bands.
Probability Envelopes Indicator:
The Probability Envelopes Indicator calculates the expected price movements based on historical data and probabilities, utilizing mean and standard deviation calculations for both upward and downward price movements.
It generates upper and lower bands based on the calculated expected mean movement and the standard deviation of historical price changes, multiplied by a user-defined multiplier.
The Probability Envelopes Indicator also allows users to choose between using log returns or percentage changes for the calculations, adding flexibility to the indicator.
Key Differences:
Calculation Method: Bollinger Bands are based on a simple moving average and standard deviations, while the Probability Envelopes Indicator uses statistical probability calculations derived from historical price changes.
Flexibility: The Probability Envelopes Indicator allows users to choose between log returns or percentage changes and adjust the multiplier, offering more customization options compared to Bollinger Bands.
Risk Management: Bollinger Bands primarily focus on volatility, while the Probability Envelopes Indicator incorporates probability calculations to provide additional insights into potential price movements, which can be helpful for risk management purposes.
Complementary Use:
Using both Bollinger Bands and the Probability Envelopes Indicator in your trading strategy can offer valuable insights into market conditions and potential price levels.
Bollinger Bands can provide insights into market volatility and potential breakouts or trend reversals based on the widening or narrowing of the bands.
The Probability Envelopes Indicator can offer additional information on the expected price movements based on historical data and probabilities, which can be helpful in anticipating potential support and resistance levels.
Combining these two indicators can help traders to better understand market dynamics and increase their chances of identifying profitable trading opportunities.
In conclusion, while both Bollinger Bands and the Probability Envelopes Indicator aim to identify potential support and resistance levels, they differ significantly in their underlying concepts, calculations, and applications. By understanding these differences and incorporating both tools into your trading strategy, you can gain a more comprehensive understanding of the market and make more informed trading decisions.
In conclusion, the Probability Envelopes Indicator is a powerful and versatile technical analysis tool that offers unique insights into expected price movements based on historical data and probability calculations. It provides traders with the ability to identify potential support and resistance levels, as well as potential trend reversals. When compared to Bollinger Bands, the Probability Envelopes Indicator offers more customization options and incorporates probability-based calculations for a different perspective on market dynamics.
Although the Probability Envelopes Indicator has its limitations and potential cons, such as the reliance on historical data and the assumption that past performance is indicative of future results, it remains a valuable addition to any trader's toolkit. By using the Probability Envelopes Indicator in conjunction with other technical analysis tools, such as Bollinger Bands, traders can gain a more comprehensive understanding of the market and make more informed trading decisions.
Ultimately, the success of any trading strategy relies on the ability to interpret and apply multiple indicators effectively. The Probability Envelopes Indicator serves as a unique and valuable tool in this regard, providing traders with a deeper understanding of the market and its potential price movements. By utilizing this indicator in combination with other tools and techniques, traders can increase their chances of success and optimize their trading strategies.
Bollinger Band ribbonThis indicator plots 9 upper and lower lines with increasing length. Lines are 0.618 upper and lower level of Bollinger band.
Trend IndicatorThis indicator has different features:
1. Ichimoku = this indicator can plot Ichimoku calculated both in the common formula and with the volume average, you can choose the calculator method for each line.
2. Channel and Bands = this mode allows the user to choose from channel and band, "channel" shows the Keltner channel, and "band" shows the Bollinger bands. Both the indicators are calculated including the volume in the formula of the average midpoint.
3. Color candle = this function allows the user to see two different colors of candles on the chart, the positive color occurs when both the long-term average and the short team average of price calculated using the volume is above the two averages calculated without the volume. This function is great to analyze the volume pressure, useful to identify trend continuation and exhaustion.
4. Extreme reversal zones = this is a version of the Keltner channels calculated over a high number of candles and with high deviation, to identify the potential zones of reversal.
Note that in the "Ichimoku" indicator, the backline is the T.R.A.M.A. indicator, created and published open source by Lux Algo, which I thank for the script.
Weighted Deviation Bands [Loxx]What are Weighted Deviation Bands?
Variation of the Bollinger bands but it uses linear weighted average and weighted deviation via Mladen Rakic.
What is Weighted Deviation?
This weighted deviation is a sort of all linear weighted deviation. It uses linear weighting in all the steps calculated (which makes it different from the built in deviation in a case when linear weighted ma is used in the ma method). It is more responsive than the standard deviation
Included
Bar coloring
Faytterro Bandswhat is Faytterro Bands?
it is a channel indicator like "Bollinger Bands".
what it does?
creates a channel using standard deviations and means. thus giving users an idea about the expensive and cheap zones. It uses a special weighted moving average different from standard bollinger bands, it also averages not only price but also deviations.
how it does it?
it uses this formulas:
how to use it?
its usage is the same as "bollinger band".
length represents the number of candles to be taken into account, source represents the source of those candles and stdev represents the coefficient of the standard deviation.
you can use it with other indicators:
RSI TREND FILTERRSI TREND Filter on Chart
RSI scaled to fit on chart instead of oscillator, Trend Analysis is easy and Hidden Divergence is revealed using this indicator. This indicator is an aim to reduce confusing RSI Situations. The Oversold and Overbought lines help to determine the price conditions so its easy to avoid Traps.
Oversold and Overbought conditions are marked on Chart to make it useful to confirm a Buy or Sell Signals.
RSI 50 level is plotted with reference to EMA50 and Oversold and Overbought Conditions are calculated accordingly.
Uptrend: RSI Cloud / Candles above RSI 50 Level
Down Trend: RSI Cloud / Candles below RSI 50 Level
Sideways : Candles in the Gray Area above and below RSI 50 Level
Default RSI (14) : is the Candlestick pattern itself
Disclaimer: Use Solely at your own Risk.
Squeeze Range: Bollinger Bands / Keltner Channels [Whvntr]Presenting Squeeze Range: Bollinger Bands / Keltner Channels
TTMSqueeze method is a volatility and momentum indicator introduced by John Carter of Simpler Trading, which capitalizes on the tendency for price to break out strongly after consolidating in a tight trading range.
How did I make this indicator? The Bollinger Bands & Keltner Channels base scripts are from the standard indicators of their class in the Technicals section... I made this indicator first then noticed there were 3 others with a similar concept, but this differs in it's unique features and application of the TTMSqueeze strategy. This indicator plots the True Range of the Keltner Channel (Customizable in 'Bands Style" in the Inputs Menu) the instances the Bollinger Bands are within the range of the Keltner channel (the market just entered a squeeze).
Featuring: customizable Moving Averages
1. Exponential (Default for both BB & KC)
2. Simple
3. RMA (MA used in RSI )
Keltner channels have a multiplier of 2 & 3 on the Chart (3 being the outer).
How do I use this indicator? Once the teal dots are inside the solid red lines this would indicate that TTMperiod of low market volatility (the market is preparing itself for an explosive move up or down). Do some research and study how to use the TTMSqueeze method by John Carter. Disclaimer: not a guarantee of future favorable results.
Strategy Myth-Busting #7 - MACDBB+SSL+VSF - [MYN]This is part of a new series we are calling "Strategy Myth-Busting" where we take open public manual trading strategies and automate them. The goal is to not only validate the authenticity of the claims but to provide an automated version for traders who wish to trade autonomously.
Our seventh one we are automating is the "Magic MACD Indicator: Crazy Accurate Scalping Trading Strategy ( 74% Win Rate )" strategy from "TradeIQ" who claims to have backtested this manually and achieved 427% profit with a 74% winrate over 100 trades in just a 4 months. I was unable to emulate these results consistently accommodating for slippage and commission but even so the results and especially the high win-rate and low markdown is pretty impressive and quite respectable.
This strategy uses a combination of 3 open-source public indicators:
AK MACD BB v 1.00 by Algokid
SSL Hybrid by Mihkel00
Volume Strength Finder by Saravanan_Ragavan
This is considered a trend following Strategy. AK MACD BB is being used as the primary short term trend direction indicator with an interesting approach of using Bollinger Bands to define an upper and lower range and upon the MACD going above the upper Bollinger Bands, it's indicative of an up trend, where as if the MACD is below the lower Bollinger Band, it's indicative of a down trend. To eliminate false signals, SSL Hyrbid is used as a trend confirmation filter, confirming and eliminating false signals from the MACD BB. It does this by validating the price action is above the the EMA and the SSL is positive that is a confirmation of an uptrend. When the price action is below the EMA and the SSL is negative, that is an confirmation of a downtrend. To avoid taking trades during ranged markets, VSF Buyer's Strength is used so the buyers/sellers strength and must be above 50% or the trade will not be inititiated.
Trading Rules
5 min candles but other lower time frames even below 5m work quite well too.
Best results can be found by tweaking these 2 input parameters:
Number Of bars to look back to ensure MACD isn't above/below Zero Line
Number Of bars back to look for SSL pullback
Long Entry when these conditions are true
AK MACD BB BB issues a new continuation long signal. A new green circle must appear on the indicator and these circles should not be touching across the zero level while they were previously red
SSL Hybrid price action closes above the EMA and the line is blue color and then creates a pullback . The pullback is confirmed when the color changes from blue to gray or from blue to red.
VSF Buyers strength above 50% at the time the MACD indicator issues a new long signal.
Short Entry when these conditions are true
AK MACD BB issues a new continuation short signal. A new red circle must appear on the indicator and these circles should not be touching across the zero level while they were previously green
SSL Hybrid price action closes below the EMA and the line is red color then it has to create a pullback . The pullback is confirmed when the color changes from red to gray or from red to blue.
VSF Sellers strength above 50% at the time the MACD indicator issues a new short signal.
Stop Loss at EMA Line with TP Target 1.5x the risk
If you know of or have a strategy you want to see myth-busted or just have an idea for one, please feel free to message me.
Volume Weighted Standard Deviation (VWSD)The Volume Weighted Standard Deviation indicator is a custom technical analysis tool that uses the volume of trading to calculate the standard deviation of a stock's price. This indicator takes the source of data, the length of data, and the deviation as inputs, and calculates the volume weighted standard deviation using the values.
The indicator first calculates the mean price and mean volume by using simple moving average over the given length of data. Then it calculates the squared difference between the mean price and the actual price, multiplied by the volume. This gives a volume-weighted squared difference. The indicator then calculates the square root of the sum of the volume-weighted squared differences divided by the sum of the volumes over the given length of data. This gives the volume weighted standard deviation.
The indicator then plots the standard deviation and deviation as a band around the simple moving average of the source data, providing a clear view of the volatility of the stock.
In summary, the Volume Weighted Standard Deviation indicator is a powerful tool for measuring the volatility of a stock by taking into account the volume of trading. It uses the volume of trading to calculate the standard deviation of a stock's price, giving a more accurate representation of the volatility of the stock. It can be useful for traders to identify entry and exit points and make more informed trading decisions.
