Smart Ichimoku | GainzAlgoOverview
Most Ichimoku indicators give you the same signal everyone else gets, a raw cloud cross with no filter, no context, and no target. This indicator rethinks the system from the ground up by combining a smoothed Ichimoku cloud with an inline logistic regression classifier that scores every cloud break in real time, then projects statistically-derived price targets the moment a confirmed signal fires.
The result is a cleaner, higher conviction version of one of the most respected trend frameworks in technical analysis.
The Foundation: Why Smooth the Ichimoku?
Traditional Ichimoku uses simple high-low midpoints (Donchian midlines) for its Tenkan, Kijun, and Senkou components. This makes the cloud visually choppy and prone to false crosses on noisy, volatile instruments like crypto or high-beta equities.
This indicator replaces all three components with Hull Moving Averages (HMA), which are designed to be simultaneously smooth and responsive, reducing lag without the whipsaw of standard smoothing. The cloud body itself becomes cleaner, the baseline is less noisy, and the cross events that trigger signals are more structurally meaningful.
All default periods match classic Ichimoku settings (9 / 26 / 52 / 26 displacement) so the logic stays true to the original system, it's just rendered with better math underneath.
The Signal: Logistic Regression Cloud Break Classifier
Here's where this indicator separates itself. A cloud cross alone is not a signal, it's a candidate. What actually matters is whether the market conditions at the moment of the cross are consistent with a real, sustained breakout or breakdown.
The classifier answers that question with a probability score.
How it works
At the exact bar where price exits the cloud body, four normalized features are computed and fed into a logistic regression model:
1. RSI (centered at 50, scaled by 25)
Measures momentum. On a bearish break, is RSI already extended to the downside? On a bullish break, is it pointing up? RSI near 50 adds little conviction; RSI at 30 on a bear break adds a lot.
2. Stochastic Oscillator (centered at 50, scaled by 25)
Short-term overbought/oversold confirmation. Works similarly to RSI but captures faster-cycle momentum, giving the model a second read on the same question.
3. Z-Score (price vs 20-bar mean, normalized by standard deviation)
Measures how statistically extended price is relative to recent history. A cloud break accompanied by a Z-Score of -2 is much more meaningful than one at Z = -0.2. This feature effectively asks: "Is this break happening from an already-stretched position?"
4. Cloud Break Depth (normalized by ATR)
How far did price close through the cloud boundary, relative to recent volatility? A close that barely clips the edge is very different from one that punches through by a full ATR. This is the most direct measure of breakout conviction.
The Math
Each feature is multiplied by a weight and summed into a single score (z). That score is passed through the sigmoid function:
P = 1 / (1 + e^(-z))
This compresses the output to a probability between 0 and 1. If the probability clears the threshold (default 0.60), the break is confirmed and a signal fires. Below threshold, the cross is rejected — instead of being ignored, it's labeled with a risk tier so you can see exactly how close (or far) it came to confirming.
The probability score is displayed as a small percentage label directly on the signal bar so you always know how strong the classifier rated that particular break.
Self-Calibrating Weights — No Manual Tuning
Unlike a typical multi-feature model, none of the four weights are set by hand. Each one is derived automatically from that feature's own rolling correlation with next-bar returns, recalculated continuously over a user-set lookback window (the "Self-Calibration Window," default 100 bars).
In practice this means: if RSI has been a genuinely useful predictor of direction on this instrument and timeframe recently, its weight rises on its own. If Z-Score has been mostly noise in the current regime, its weight shrinks toward zero — automatically, without anyone touching a slider.
This was a deliberate design choice. Letting people hand-tune regression weights invites a lot of well-intentioned guesswork that usually overfits to a handful of recent candles. By having the model score its own features based on demonstrated, rolling predictive power, the classifier adapts to changing market conditions instead of running on opinions baked in at setup time.
Rejected Crosses: Risk-Tiered Labels
Not every cloud cross clears the threshold, and that's the point. Rather than silently discarding rejected crosses, this indicator labels every one of them with a risk tier so you know exactly what the model saw and how close it came to confirming:
Low Risk: Probability fell just short of the threshold (within 10 points below). A near-miss — the break had real conviction behind it, it simply didn't clear the bar.
Moderate Risk: Probability landed meaningfully below threshold (10–25 points). A weaker break with mixed signals underneath it.
High Risk: Probability came in far below threshold (25+ points). A break with little to no underlying conviction — most consistent with chop or noise.
Each label shows its tier and the actual probability (e.g. "Low Risk ▼ 54%"), so nothing is a black box. A cluster of Low/Moderate Risk labels in one zone often signals a contested area that's likely to resolve into a real breakout once it's worked through — useful context even though no trade signal fired. These labels can be toggled off entirely in settings if you'd rather only see confirmed signals.
The Targets: Mean, Median, Mode
Once a confirmed break fires, three dashed horizontal target lines project from the signal bar. These are not arbitrary multiples, they are derived from the actual statistical distribution of bar-to-bar price moves over the lookback window.
Mean (Yellow): The average absolute bar move over the lookback period, scaled by the target multiplier. This is the "expected" target under normal conditions.
Median (Cyan): The 50th percentile of historical moves. Because move distributions are right-skewed (a few large moves pull the mean up), the median is typically more conservative than the mean and often a more realistic first target.
Mode (Hot Pink): The most frequently occurring move size, derived by bucketing historical moves into ATR-width bins and finding the most populated bin. This represents what the market most commonly does — not what it averages, not the middle value, but the single most likely outcome based on observed frequency.
Together, the three targets give you a realistic range rather than a single arbitrary level — grounded in what this instrument has actually done over the recent past. Bull and bear target sets are tracked independently, so a new bearish break won't erase an active bullish target set still in play, and vice versa.
The Target Multiplier (default 3×) scales all three targets proportionally. Lower it for tighter, shorter-term targets; raise it for swing trades or higher-volatility instruments.
Reading the Chart
Green triangle (▲) below bar: Confirmed bullish cloud break. Price has exited the top of the cloud with sufficient classifier probability. Three upward target lines appear.
Pink triangle (▼) above bar: Confirmed bearish cloud break. Price has exited the bottom of the cloud with sufficient classifier probability. Three downward target lines appear.
Percentage label: The LR probability score for that break (e.g. "73%"). Higher is stronger.
Risk-tiered label (amber/orange/red): A cloud cross that was rejected, with its tier and probability shown.
Yellow dashed line: Mean target
Cyan dashed line: Median target
Hot pink dashed line: Mode target (thicker, as it represents the highest-frequency outcome)
Settings Guide
Smooth Ichimoku
Tenkan / Kijun / Senkou Period: Standard Ichimoku periods. Default 9/26/52 follows the classic system. Shorter periods = more sensitive, more signals. Longer = slower, fewer but stronger signals.
Displacement: How far forward the cloud is projected. Default 26.
Break Classifier
Self-Calibration Window: How many past bars the model uses to learn each feature's weight from its recent correlation with price moves. Shorter windows adapt faster to regime changes but can be noisier; longer windows are more stable but slower to react. Default 100.
Break Probability Threshold: The minimum probability required to confirm a signal. Default 0.60. Raise toward 0.75+ for fewer, higher-conviction signals. Lower toward 0.50 to see more cloud breaks confirmed (effectively turns the filter off at 0.50).
Targets
Lookback (bars): How many bars of historical move data to use for the distribution calculation. Default 60. Longer lookback = more stable targets based on longer-term behavior. Shorter = more reactive to recent volatility.
Target Multiplier: Scales all three target lines proportionally from the signal close. Default 3×. Adjust based on your timeframe and typical holding period.
Risk Labels
Show Risk Labels on Rejected Crosses: Toggles the Low/Moderate/High Risk labels on rejected cloud crosses. Off by default for a cleaner chart; turn on to see every cross the model evaluated, not just the confirmed ones.
How to Use It
As a trend confirmation tool: Use the cloud direction (cyan dominant = bullish structure, pink dominant = bearish) as your bias filter, and only trade signals that align with the cloud color. Bull signals below a cyan cloud, bear signals above a pink cloud.
As a breakout entry trigger: Wait for price to consolidate inside or near the cloud, then take the confirmed break as an entry signal. The probability label tells you how much conviction the model has at that moment.
Using rejected crosses as context: A string of Low Risk labels in a zone suggests the cloud is being tested seriously without quite breaking — often a precursor to a real move once the level finally gives.
For target setting: Use the median as a conservative first target, the mean as a mid-range objective, and the mode as a guide to where the most "normal" move tends to land. The hot pink mode line is often the most useful for setting realistic profit expectations.
For alerts — Four alert conditions are built in: "Confirmed Bull Break," "Confirmed Bear Break," "Rejected Bull Cross," and "Rejected Bear Cross." Set them on your preferred timeframe and let the classifier notify you rather than watching the chart.
Timeframe Notes
This indicator works across all timeframes but behaves differently depending on context:
1H–4H: Good balance of signal frequency and reliability. Recommended starting point.
Daily: Fewer signals, higher structural significance. Best for swing traders.
15m and below: More signals, more noise. Consider raising the threshold to 0.65–0.70 and reducing the lookback to 30. Watch the risk-tiered labels here in particular — they're most useful for filtering chop on fast timeframes.
Example on the Daily with SPY ETF:
Example on the 4 Hour with BTCUSD;
Example on the 15 Minute with QQQ:
A Note on the Model
The logistic regression here is not trained on historical data in the machine learning sense, and it no longer relies on manually-set weights either. Each feature's weight is derived from its own rolling correlation with subsequent price action, recalculated continuously. Think of it less as a black-box ML model and more as a structured, self-adjusting way to combine four momentum and positioning indicators into a single probability score, similar to our Directional Logistic Oscillator.
The advantage over a traditional multi-condition filter (RSI < 40 AND stoch < 30 AND...) is that the sigmoid function produces a continuous probability rather than a binary pass/fail, which means the model degrades gracefully, a break with three strong features and one neutral one still scores well, rather than getting blocked by an arbitrary threshold on the weak feature. And because every rejected cross is shown with its tier and score rather than discarded silently, nothing the model does is hidden from you.
We hope you enjoy! Indicator
Directional Logistic Oscillator | GainzAlgoOverview
The Directional Logistic Oscillator (DLO) is a momentum-based indicator designed to measure directional market strength and identify potential trend reversals or mean-reversion opportunities. It builds on the classic Directional Movement Index (DMI) by transforming its components (+DI, -DI, and ADX) into probabilistic signals using logistic functions, then combining them into a bounded oscillator that oscillates between approximately -1 and +1.
Unlike traditional oscillators like RSI or MACD, DLO emphasizes directional probability by estimating the likelihood of bullish or bearish dominance while factoring in overall trend strength (via ADX). This makes it particularly useful for:
Spotting overbought/oversold conditions in ranging markets.
Confirming trend shifts in trending markets.
Generating reversal signals based on oscillator cycles.
The oscillator is plotted as histogram bars (columns) for visual clarity, with color-coding to highlight strength and direction. Positive values indicate bullish momentum, negative values bearish, and crossings of key levels can signal trading opportunities.
How It Works
At its core, DLO processes DMI data through a logistic transformation to create "probabilities" of directional movement:
1. DMI Calculation : Uses the standard DMI with a user-defined length (default 14) to compute +DI (upward movement), -DI (downward movement), and ADX (trend strength).
2. Logistic Probability : Each DMI component is normalized against its long-term mean and passed through a logistic (sigmoid) function. This creates smooth probabilities between 0 and 1.
The logistic function is defined as:
logistic_prob(series, mean_lb, slope, smooth_len) =>
mean = ta.sma(series, mean_lb)
z = (series - mean) * slope
prob_raw = 1.0 / (1.0 + math.exp(-z))
ta.ema(prob_raw, smooth_len)
This step makes the indicator adaptive to market conditions, with the "slope" controlling how sharply it reacts to deviations from the mean.
3. Net Directional Strength : Bullish minus bearish probability, scaled by ADX probability and a user-defined multiplier, then bounded using a hyperbolic tangent (tanh) function to keep values between -1 and +1.
net_dir = prob_plus - prob_minus
strength_raw = net_dir * prob_adx * osc_scale
strength_bound = tanh(strength_raw)
Tanh ensures smooth, bounded output without clipping extremes unnaturally.
4. Smoothing and Signals: The raw strength is smoothed with EMA, then further processed into SMA and EMA lines for signal generation. Percentile-based thresholds (adaptive over a lookback period) detect extreme zones for mean-reversion signals.
The result is a visually intuitive oscillator: Green bars for bullish, red for bearish, with varying intensity based on momentum.
Inputs
DLO offers customizable settings grouped for ease of use. Defaults are tuned for balanced performance on daily charts.
DMI Settings
DI Length (default: 14): Controls DMI sensitivity. Shorter lengths react faster to price changes but add noise; longer lengths smooth signals for trends.
Mean Lookback (default: 360): The period for calculating the long-term average of DMI components. Higher values provide a more stable baseline, reducing false signals from short-term volatility. Lower values make the indicator more responsive but noisier
Difference between high and low mean lookback period, lower length can pick up on new trends faster but at the cost of increased noise.
Logistic Probability Settings
LR Slope (higher = steeper) (default: 0.18): Adjusts the steepness of the logistic curve. Lower values create gradual transitions (smoother oscillator); higher values make sharp shifts, emphasizing extremes.
Probability Smoothing (EMA) (default: 3): Short EMA to reduce noise in probabilities. Keep low (1-5) for responsiveness; higher for smoothness.
Oscillator Settings
Oscillator Scale (pre-tanh) (default: 2.5): Multiplies net strength before bounding. Higher values increase sensitivity and amplitude (larger swings); lower values compress the range for subtler signals.
Comparison of 4 different settings for Oscillator scale, showing that as the scale parameter increases, the oscillator output becomes more pronounced, exhibiting higher amplitude compression toward the bounds and spending more time saturated near the extreme values of +1 and −1.
Oscillator Smoothing Length (default: 7): Period for SMA/EMA smoothing of the final oscillator. Longer = smoother, fewer signals; shorter = more reactive.
Color & Display Settings
Buy Color / Sell Color: Customize colors for bullish/bearish visuals.
Plot Reversion Signals (default: true): Shows arrows for cycle reversals (local highs/lows).
Plot Mean-Reversion Signals (default: true): Arrows for crossings from extreme percentile zones.
Plot Oscillator MA (default: false): Overlays an SMA on the oscillator for additional confirmation.
Allow Intrabar Updating (default: true): Enables real-time updates within incomplete bars (may cause minor repainting).
Visuals
Oscillator Histogram: Columns colored green (bullish) or red (bearish), with lighter shades for weaker momentum. Crosses above/below zero signal momentum shifts.
Horizontal Lines: Zero (neutral), +0.5 (strong bullish), -0.5 (strong bearish).
Background Highlights: Subtle green/red shading when in strong zones.
Bar Colors: Mirrors oscillator direction on the price chart.
Color-coded trend regimes: green/teal highlight strong and weak uptrends, red/purple mark strong and weak downtrends, while the oscillator histogram confirms direction and strength through its polarity and amplitude.
Signals
Mean-Reversion (MR) Signals : Triangles (▲/▼) when the smoothed oscillator crosses up from low percentiles (oversold) or down from high percentiles (overbought). These are adaptive, using historical data for dynamic extremes.
Buy: Oscillator crosses above lower threshold (e.g., 10th/5th percentile).
Sell: Crosses below upper threshold (e.g., 90th/95th percentile).
Reversion Signals : Arrows (⬆/⬇) at local turning points in the oscillator cycle, indicating potential reversals.
Zero-Line Crosses : Basic bullish/bearish momentum changes.
Usage Tips
Trend Confirmation: Use in trending markets—persistent positive/negative values confirm up/down trends. Pair with moving averages for entries.
Mean-Reversion: In sideways markets, trade MR signals from extremes. Combine with support/resistance.
Divergences: Look for price making new highs/lows while DLO doesn't for reversal setup.
Alerts
MR Buy/Sell: Extreme zone crosses (percentile-based).
Reversion Up/Down: Cycle turning points.
Osc Bullish/Bearish Cross: Zero-line crosses.
Limitations
Like all oscillators, DLO can lag in strong trends or produce false signals in choppy markets, use with confirmation.
Percentile thresholds adapt over time but may vary by asset volatility.
Not a standalone system; always combine with risk management.
Indicator
Machine Learning: Multiple Logistic Regression
Multiple Logistic Regression Indicator
The Logistic Regression Indicator for PulseWire is a versatile tool that employs multiple logistic regression based on various technical indicators to generate potential buy and sell signals. By utilizing key indicators such as RSI, CCI, DMI, Aroon, EMA, and SuperTrend, the indicator aims to provide a systematic approach to decision-making in financial markets.
How It Works:
Technical Indicators:
The script uses multiple technical indicators such as RSI, CCI, DMI, Aroon, EMA, and SuperTrend as input variables for the logistic regression model.
These indicators are normalized to create categorical variables, providing a consistent scale for the model.
Logistic Regression:
The logistic regression function is applied to the normalized input variables (x1 to x6) with user-defined coefficients (b0 to b6).
The logistic regression model predicts the probability of a binary outcome, with values closer to 1 indicating a bullish signal and values closer to 0 indicating a bearish signal.
Loss Function (Cross-Entropy Loss):
The cross-entropy loss function is calculated to quantify the difference between the predicted probability and the actual outcome.
The goal is to minimize this loss, which essentially measures the model's accuracy.
// Error Function (cross-entropy loss)
loss(y, p) =>
-y * math.log(p) - (1 - y) * math.log(1 - p)
// y - depended variable
// p - multiple logistic regression
Gradient Descent:
Gradient descent is an optimization algorithm used to minimize the loss function by adjusting the weights of the logistic regression model.
The script iteratively updates the weights (b1 to b6) based on the negative gradient of the loss function with respect to each weight.
// Adjusting model weights using gradient descent
b1 -= lr * (p + loss) * x1
b2 -= lr * (p + loss) * x2
b3 -= lr * (p + loss) * x3
b4 -= lr * (p + loss) * x4
b5 -= lr * (p + loss) * x5
b6 -= lr * (p + loss) * x6
// lr - learning rate or step of learning
// p - multiple logistic regression
// x_n - variables
Learning Rate:
The learning rate (lr) determines the step size in the weight adjustment process. It prevents the algorithm from overshooting the minimum of the loss function.
Users can set the learning rate to control the speed and stability of the optimization process.
Visualization:
The script visualizes the output of the logistic regression model by coloring the SMA.
Arrows are plotted at crossover and crossunder points, indicating potential buy and sell signals.
Lables are showing logistic regression values from 1 to 0 above and below bars
Table Display:
A table is displayed on the chart, providing real-time information about the input variables, their values, and the learned coefficients.
This allows traders to monitor the model's interpretation of the technical indicators and observe how the coefficients change over time.
How to Use:
Parameter Adjustment:
Users can adjust the length of technical indicators (rsi_length, cci_length, etc.) and the Z score length based on their preference and market characteristics.
Set the initial values for the regression coefficients (b0 to b6) and the learning rate (lr) according to your trading strategy.
Signal Interpretation:
Buy signals are indicated by an upward arrow (▲), and sell signals are indicated by a downward arrow (▼).
The color-coded SMA provides a visual representation of the logistic regression output by color.
Table Information:
Monitor the table for real-time information on the input variables, their values, and the learned coefficients.
Keep an eye on the learning rate to ensure a balance between model adjustment speed and stability.
Backtesting and Validation:
Before using the script in live trading, conduct thorough backtesting to evaluate its performance under different market conditions.
Validate the model against historical data to ensure its reliability.
Indicator
Logistic RSI, STOCH, ROC, AO, ... by DGTExperimental attemt of applying Logistic Map Equation for some of widly used indicators.
With this study "Awesome Oscillator (AO)", "Rate of Change (ROC)", "Relative Strength Index (RSI)", "Stochastic (STOCH)" and a custom interpretation of Logistic Map Equation is presented
Calculations with Logistic Map Equation makes sense when the calculated results are iterated many times within the same equation.
Here is the Logistic Map Equation : Xn+1 = r * Xn * (1 - Xn)
Where, the value of r is the key for this equation which changes amazingly the behaviour of the Logistic Map.
The value we have asigned for r is less then 1 and greater than 0 ( 0 < r < 1) and in this case the iterations performed with the maximum number of output series allowed by Pine is quite enough for our purpose and thanks to arrays we can easiliy store them for further processing
What we have as output:
Each iteration result is then plotted (excluding plotting the first iteration), as circles or line based on user preference
Values above and below zero level (0) are coloured differently to emphasis bull and bear power
Finally Standard Deviation of Array's Elements is ploted as line. Users may choose to display this line only
So where it comes the indicators "Awesome Oscillator (AO)", "Rate of Change (ROC)", "Relative Strength Index (RSI)", "Stochastic (STOCH)".
Those are the indicators whose values are assigned to our key varaiable in the Logistic Map equation forulma which is r
Further details regarding Logistic Map can found under the description of “Logistic EMA w/ Signals by DGT” study
Disclaimer:
Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely
The script is for informational and educational purposes only. Use of the script does not constitute professional and/or financial advice. You alone have the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd PulseWire user liable for any possible claim for damages arising from any decision you make based on use of the script
Indicator
Logistic EMA w/ Signals by DGTLogistic Map Equation - The logistic map connects fluid convection, neuron firing, the Mandelbrot set and so much more.
This study is an attempt to apply Logistic Map Equation in Trading
Logistic Map Equation
Xn+1 = r * Xn * (1 - Xn)
Where,
r - growth rate
Xn - percentage of theoretical maximum of measured event (from 0 to 1)
(1 - Xn) - represents constraints of the environment, presents the idea of negative feedback
For trading the measured event will be the price of the instrument (price is commonly reffered as source in mathematicall forumlations),
hence
r - growth rate can be expressed as => change(source, length) / source, expressing r in such manner mades the equation dynamic with regards to the growth rate
Xn - percentage of theoretical maximum of the price for given duration can be expressed as => source / highest(length)
Putting pieces together we are ready to plot
Printed alone does not seem to provide much useful visualization for trading, in fact not easy to interpret especially when the market is an uptrend
What it has numerically,
Provides a ratio, where sudden changes are much more reflected thanks to negative feedback nature of the logistic equation.
As we know moving average indicators are lagging and the logistic map may fit here to reduce the lag
With this study you will find application of Logistic Map Equation with combination of Exponential Moving Average (EMA)
Logistic EMA (LEMA) and LEMA COLORS
one line with user defined periods of length, where the colors of the line will change automatically depending where the value is compared to 50-100-200 moving average
Multiple LEMAs : optional – three fixed lenght of 50-100-200 period lines
LEMA Signals
Various signals are added by using LEMA and applying some common market approaches. Use with caution and with conjunction of other indicators
Thanks to @allanster for the idea
A fascinating YouTube video explaining the logistic map - “This equation will change how you see the world (the logistic map)”
Disclaimer:
Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely
The script is for informational and educational purposes only. Use of the script does not constitute professional and/or financial advice. You alone have the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd PulseWire user liable for any possible claim for damages arising from any decision you make based on use of the script
Indicator
Volatility Bands by DGTVolatility represents how large an asset's prices swing around the mean price, the degree of variation of a trading price over time, and is commonly measured with beta (β) coefficients, standard deviations (σ) of returns where tools such as Average True Range, Bollinger Bands, Keltner Channel, Squeeze Indicator, etc presents volatility concept
Volatility often refers to the amount of uncertainty or risk related to the size of changes in a security's value. The higher the volatility, the riskier the security - the price of the security can change dramatically over a short time period in either direction. A lower volatility - security's value does not fluctuate dramatically, and tends to be more steady
This study, Volatility Bands , attempts to present a way to measure and visualize volatility , using standard deviations (σ) and average true range indicator, and aims to point out areas that might indicate potential trading opportunities
I will try to explain the usage with examples,
same setup with different option selected
as you may observe from the examples different setting may have advantages and disadvantages over one another, it is recommended to verify a trading setup with different available options.
Additionally, It is recommended to use this indicator in conjunction with other technical indicators, or verify using chart/candle patterns. Below is an usage example using in conjunction with other indicator, in the given example “Neglected Volume by DGT” is selected
Similarities and Differences
Bollinger Bands depicts two standard deviations above and below a simple moving average, and Keltner Channel depicts two times average true range (ATR) above and below an exponential moving average
Volatility Bands study combines the approach of both Bollinger Bands and Keltner Channel, with different settings and different visualization
Default settings are one standard deviations and one time average true range (ATR) above and below 13 period exponential moving average. Setting can be adjusted by users but let me remind all testes are performed with the default settings.
Mathematically expressed as
Upper band area between “ema + stdev” and “ema + atr”
Lower band area between “ema – stdev” and “ema – atr”
A different display is added with the inspiration I get from one of the @quantgym ‘s study, many thanks @quantgym 😉
When difference band display is selected the study will reflect the area between “ema + stdev – atr” and “ema – stdev + atr”. As shown in the examples above
Note: standard deviation calculation can be adjusted based on price action or its moving average.
Other differentiation between BB and KC is with V-BANDS mostly we look for trade opportunities when price action move out of the bands and in most cases we assume market is consolidating when the price action is within the bands
The other indicator that presents similarities to Volatility Bands is Squeeze Indicator, which measures the relationship between Bollinger Bands and Keltner's Channels to help identify consolidations and signal when prices are likely to break out. Mainly Volatility Bands is different version of Squeeze indicator, in fact the purpose is almost same but visualization is completely different. Additionally Volatility Bands Offers trading opportunities whereas Squeeze indicator only presents market states unless a momentum indicator is adapted to Squeeze indicator.
Disclaimer:
Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely
The script is for informational and educational purposes only. Use of the script does not constitute professional and/or financial advice. You alone have the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd PulseWire user liable for any possible claim for damages arising from any decision you make based on use of the script
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