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Smart Ichimoku | GainzAlgo
Overview
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:
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:
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.
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
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
Timeframe Notes
This indicator works across all timeframes but behaves differently depending on context:
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!
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!
開源腳本
秉持TradingView一貫精神，這個腳本的創作者將其設為開源，以便交易者檢視並驗證其功能。向作者致敬！您可以免費使用此腳本，但請注意，重新發佈代碼需遵守我們的社群規範。
Get GainzAlgo only at gainzalgo.com
免責聲明
這些資訊和出版物並非旨在提供，也不構成TradingView提供或認可的任何形式的財務、投資、交易或其他類型的建議或推薦。請閱讀使用條款以了解更多資訊。
開源腳本
秉持TradingView一貫精神，這個腳本的創作者將其設為開源，以便交易者檢視並驗證其功能。向作者致敬！您可以免費使用此腳本，但請注意，重新發佈代碼需遵守我們的社群規範。
Get GainzAlgo only at gainzalgo.com
免責聲明
這些資訊和出版物並非旨在提供，也不構成TradingView提供或認可的任何形式的財務、投資、交易或其他類型的建議或推薦。請閱讀使用條款以了解更多資訊。