AG Pro Supertrend Pullback Quality [AGPro Series]AG Pro Supertrend Pullback Quality
OVERVIEW
AG Pro Supertrend Pullback Quality is an overlay indicator built to evaluate the quality of pullbacks that occur inside an already established Supertrend direction.
This script is not designed as a simple Supertrend flip tool. Instead of focusing on every direction change, it studies whether a pullback into the active Supertrend structure is orderly, controlled, and potentially supportive of trend continuation. The goal is to help users distinguish between clean retracement behavior and weaker pullbacks that may reflect noise, instability, or reduced trend quality.
The indicator combines Supertrend context with a structured quality model. When price interacts with the active trend line and surrounding touch zone, the script evaluates that event using several internal dimensions such as pullback depth, line acceptance, rejection behavior, recovery efficiency, and local noise conditions. The result is summarized into a class-based readout so the chart remains visual and practical.
Because of this design, the script is better understood as a continuation-quality classifier than as a traditional signal generator. It does not attempt to predict every reversal, and it does not assume that all pullbacks within trend are equal. Its main purpose is to organize pullback structure into a clearer analytical framework.
UNIQUE EDGE
The core difference of this script is its emphasis on pullback quality rather than trend flips.
Many Supertrend-based tools are centered around directional transitions. That approach can be useful, but it also tends to compress several different market behaviors into a single yes/no event. In practice, not every trend pullback carries the same structural character. Some are shallow and noisy, some are too deep, and some interact with the trend line in a more orderly way before continuation attempts develop.
AG Pro Supertrend Pullback Quality focuses on that middle layer. It studies what happens after a trend is already active and asks a more specific question: is the current pullback behaving like a controlled retracement, or is it showing weaker continuation quality?
This creates a more workflow-oriented reading model. Instead of using Supertrend only as a directional switch, the script uses it as a live structural reference and grades the quality of pullback interaction around that reference. That makes the tool different from standard flip scripts, entry-only markers, and pure trend state displays.
METHODOLOGY
The script begins with the native Supertrend framework to define active directional context. Once a bullish or bearish state is established, a touch zone is formed around the relevant Supertrend line using ATR-based spacing. This zone is not meant to represent a guaranteed support or resistance region. It is a structured interaction area used to evaluate how price behaves during retracement.
From there, the script evaluates pullback quality through several components:
1) Trend State and Age
A newly flipped trend often behaves differently from a more established one. For that reason, the script includes a stabilization concept and tracks how mature the current trend leg is before weighting pullback quality.
2) Pullback Depth
The retracement is measured relative to ATR and recent price structure. Pullbacks that are too shallow may carry limited informational value, while pullbacks that are too deep may indicate reduced continuation quality. The script scores depth inside a preferred operating range rather than treating all pullbacks equally.
3) Acceptance Relative to the Supertrend Line
A pullback is not evaluated only by whether price touches the zone. The script also checks whether price remains positioned in a way that supports the active trend state. This helps separate cleaner acceptance behavior from weaker interaction.
4) Rejection Character
When price reaches the pullback area, candle behavior matters. The script looks at rejection-style characteristics within the touch and recovery window to estimate whether price is responding constructively to the active trend reference.
5) Recovery Efficiency
After contact with the zone, the script measures whether the market recovers with enough directional efficiency. Fast and orderly recovery behavior is treated differently from hesitant or weak re-expansion.
6) Noise Filter
Frequent directional churn and inefficient travel can reduce the usefulness of pullback classification. The script includes a noise component so that structurally weaker environments do not receive the same quality treatment as cleaner trend conditions.
These components are combined into a final score, which is then mapped into a simple class output. This makes the visual output easier to read without hiding the fact that pullback quality is multi-factor by nature.
QUALITY CLASSES
The final result is summarized into four broad classes:
A-Class
Represents the strongest pullback quality readings among the currently evaluated conditions.
B-Class
Represents constructive pullback quality, but with less strength than the highest tier.
C-Class
Represents acceptable but weaker pullback structure.
Weak
Represents pullback conditions that do not meet the stronger quality profile.
These classes are meant to organize chart behavior, not to forecast a required outcome. They should be interpreted within broader market context, timeframe behavior, and the user’s own process.
CHART ELEMENTS
The script includes several visual layers:
- Supertrend line for directional context
- ATR-based touch zone around the active Supertrend reference
- Pullback quality labels for qualified events
- Compact information panel showing trend state, depth, acceptance, rejection, recovery, noise, and final status
The visual design is intentionally restrained so that price remains readable. The goal is to keep the chart informative without turning the overlay into a dense signal map.
SIGNALS AND ALERTS
The script can generate alerts for the following event types:
- Bullish Pullback Quality Ready
- Bearish Pullback Quality Ready
- Bullish Pullback Confirmed
- Bearish Pullback Confirmed
- Bullish Pullback Invalidated
- Bearish Pullback Invalidated
In general terms, Ready events indicate that the recent pullback has achieved the minimum quality conditions defined by the model. Confirmed events require stronger follow-through logic. Invalidated events help flag cases where a previously qualified pullback context is no longer aligned with the prior state.
These alerts are designed as structured workflow checkpoints rather than standalone instructions.
KEY INPUTS
ATR Length
Controls the ATR foundation used by the Supertrend and zone logic.
Supertrend Factor
Adjusts the spacing and sensitivity of the Supertrend calculation.
Pullback Lookback
Defines the local structure window used during pullback evaluation.
Stabilization Bars After Flip
Helps reduce the weight of very early post-flip behavior.
Recovery Window
Defines how long the script should monitor post-touch recovery behavior.
Touch Zone Size and Max Line Penetration
Control how the script defines valid interaction around the Supertrend line.
Depth Range Inputs
Allow users to define what the script should consider a more optimal pullback depth range.
Ready and Confirmed Score Thresholds
Allow the strictness of class qualification and alert generation to be tuned.
LIMITATIONS AND TRANSPARENCY
This script is an analytical classification tool. It does not know future price action, and it does not guarantee continuation after a qualified pullback.
Like other trend-following frameworks, Supertrend-based structure can become less reliable during choppy or highly unstable market phases. The inclusion of a noise filter helps address that issue, but it does not remove it.
The quality model is also sensitive to volatility, timeframe selection, and the interaction between ATR-based spacing and local structure. A pullback that appears constructive on one timeframe may not behave the same way on another. Users should test settings carefully and interpret results in context.
Labels and classes summarize a model output. They are not a substitute for broader chart reading, risk planning, liquidity awareness, or execution discipline.
RISK DISCLOSURE
This script is provided for technical analysis and chart study only. It does not provide financial, investment, legal, or tax advice.
Trading and investing involve risk. Past behavior, structural classification, and indicator output do not guarantee future results. Users are responsible for their own decisions, testing process, and risk management framework.
Indicator
SuperTrend Cluster (Zeiierman)█ Overview
SuperTrend Cluster (Zeiierman) is a weighted multi-SuperTrend regime indicator that combines 5 differently configured SuperTrend models into a single consensus view. Instead of relying on a single ATR length and a single factor, the script builds a cluster of fast, medium, and slow SuperTrend members, measures how strongly they agree, and then plots the dominant bullish or bearish regime as a weighted trend line. The result is a cleaner trend map built from internal SuperTrend agreement rather than a single trend line.
█ How It Works
For each bar, the script does 3 main things:
⚪ Build 5 SuperTrend members
Each member first smooths the source with the selected moving average type and length, then applies its own SuperTrend calculation using the chosen ATR Length and Factor.
That gives 5 outputs:
5 direction states
5 active SuperTrend lines
⚪ Measure weighted agreement
Each member contributes its assigned weight to either the bullish side or the bearish side.
The script sums:
Bullish cluster weight
Bearish cluster weight
Those are converted into:
Bull Cluster Score
Bear Cluster Score
Cluster Strength
So the final regime is based on weighted internal agreement rather than on a single member.
⚪ Filter through the Base SuperTrend
Even if the cluster is bullish or bearish enough, the selected Base SuperTrend must also align with that side before the final regime flips.
That means:
A bullish cluster only becomes active if the base member is bullish
A bearish cluster only becomes active if the base member is bearish
This keeps the final output more structured and avoids cluster flips that are not confirmed by the chosen anchor member.
█ How to Use
Use this for trend trading with SuperTrend.
Bull Cluster → look for longs.
Bear Cluster → look for shorts.
The % shows how strong the trend is:
Higher % = stronger agreement
Low % = weak/mixed market
Use the weighted SuperTrend line as your guide:
Above it → bullish bias
Below it → bearish bias
Pullbacks to the line = potential entries
Best trades happen when direction and score are both strong.
█ How to Read the Cluster Scores
Bull Cluster Score
Measures how much of the total weighted cluster is currently bullish.
Bear Cluster Score
Measures how much of the total weighted cluster is currently bearish.
█ Settings
Consensus Threshold — minimum weighted agreement required before the bullish or bearish cluster becomes valid.
Base SuperTrend Index — selects which of the 5 members acts as the base reference for flip markers, label placement, and final alignment.
ATR Length — controls how fast or slow the volatility band reacts.
Factor — controls how far the SuperTrend line sits from the price.
Smoothing — selects the moving average used before the SuperTrend is calculated.
Length — controls how much smoothing is applied.
Weight — controls that member’s influence inside the final weighted cluster.
Key effect:
Lower ATR Length + lower Factor = faster, more reactive member
Higher ATR Length + higher Factor = slower, more stable member
Higher Weight = greater impact on cluster direction
Higher Consensus Threshold = fewer but stronger regime flips
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Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Indicator
StealthTrail SuperTrend ML Pro [WillyAlgoTrader]🤖 StealthTrail SuperTrend ML Pro is an overlay indicator that builds on the adaptive SuperTrend core from StealthTrail and adds three layers of intelligence: an instrument profiling engine that classifies the market into Trending, Ranging, or Volatile regimes and auto-tunes all SuperTrend parameters accordingly; a 13-feature machine learning scoring system that evaluates every candidate signal on momentum, trend, volatility, structure, volume, HTF alignment, divergence, session quality, and regime context — producing a 0–100 confidence score; and a self-learning mechanism that tracks signal outcomes over time and dynamically adjusts the confidence gate to optimize signal quality. The result is a SuperTrend that configures itself, scores its own signals, and learns from its results.
🧩 WHY THESE COMPONENTS WORK TOGETHER
A standard SuperTrend has fixed parameters that work well in one market regime and fail in another. Manually tuning ATR length, multiplier, and filters for each instrument and timeframe is time-consuming and becomes outdated when conditions change.
This indicator solves the problem through a three-layer intelligence stack:
Layer 1 — Regime classification → Auto-tuning: The instrument profiler continuously measures efficiency ratio (trend strength), autocorrelation (serial dependence), volatility clustering, and normalized volatility — classifying the market into TRENDING, RANGING, or VOLATILE. Each regime produces different optimal SuperTrend parameters. The auto-tuner interpolates ATR length, multiplier, cushion, cooldown, and RSI threshold using regime-weighted blending — so the SuperTrend self-configures for current conditions.
Layer 2 — ML signal scoring: Even with auto-tuned parameters, not every SuperTrend flip is a good trade. The 13-feature ML engine evaluates each flip against momentum, volume, trend efficiency, volatility shock, band distance, MACD, price structure, regime confidence, MTF alignment, ADX strength, RSI divergence, volume profile zone, and session quality. Each feature is normalized to 0–100, weighted, passed through a sigmoid function, and combined into a single confidence score. Signals below the confidence gate are rejected — they pass the classic SuperTrend logic but fail the multi-dimensional quality check.
Layer 3 — Self-learning gate: The ML confidence gate itself adapts over time. The system tracks each signal's outcome (win/loss evaluated after N bars using the ATR at entry for fair comparison). When the win rate exceeds 70%, the gate lowers (allowing more signals). When it drops below 50%, the gate raises (becoming stricter). A decay factor prevents old signals from dominating. This creates a feedback loop: the indicator learns which confidence level produces profitable signals on this specific instrument and timeframe.
Without auto-tuning, the SuperTrend uses static parameters. Without ML scoring, good and bad flips are treated equally. Without self-learning, the confidence gate is a fixed guess. Each layer eliminates a specific category of bad signals that the previous layer can't catch.
🔍 WHAT MAKES IT ORIGINAL
1️⃣ Instrument profiling and regime classification.
The profiler computes four metrics over a configurable lookback (default 100 bars):
— 📐 Efficiency Ratio : ER = |close − close | / sum(|close − close |, N). Measures net directional movement vs total path. ER → 1.0 = pure trend, ER → 0.0 = pure chop.
— 🔄 Autocorrelation : correlation between return and return over a sliding window. High positive autocorrelation = trending persistence. Near-zero = random. Negative = mean-reverting.
— 📏 Volatility Clustering : ratio of short-term ATR (20 bars) to long-term ATR (lookback). Values > 1.0 indicate a volatility spike (breakout or crash). Values < 1.0 indicate compression.
— 📈 Normalized Volatility : ATR / close × 100. Measures absolute volatility as percentage of price — allows cross-instrument comparison.
These are smoothed with EMA and combined into three regime scores:
— trendScore = ER × regimeSensitivity
— rangeScore = (1 − ER) × (1 − |autocorrelation|) × regimeSensitivity
— volatScore = clamp(volClustering − 1, 0, 2) × regimeSensitivity
The highest score determines the regime: TRENDING, RANGING, or VOLATILE. Regime confidence = highest_score / total_scores × 100%.
2️⃣ Regime-weighted auto-tuning (6 parameters).
Each SuperTrend parameter is computed as a weighted blend of regime-optimal values:
effectiveParam = wT × trendOptimal + wR × rangeOptimal + wV × volatileOptimal
Where wT, wR, wV are the normalized regime weights. The parameters and their regime-optimal ranges:
— ATR Length: Trending=10, Ranging=16, Volatile=21 (further scaled by normalized volatility)
— Base Multiplier: Trending=2.0, Ranging=3.2, Volatile=3.8 (scaled by normalized vol)
— Flip Cushion: Trending=0.05, Ranging=0.25, Volatile=0.15
— Signal Cooldown: Trending=2, Ranging=5, Volatile=3
— RSI Threshold: Trending=40, Ranging=52, Volatile=45
— RSI Length: derived as ATR Length × 0.9
— Adaptive Smoothing: ATR Length × 4.0
This means in a trending regime, the SuperTrend uses shorter ATR, lower multiplier (tighter bands), minimal cushion, and permissive RSI — catching trends early. In a ranging regime: longer ATR, wider multiplier, large cushion, strict RSI — avoiding chop. In volatile conditions: intermediate settings with wider bands to accommodate spikes.
3️⃣ 13-feature ML scoring engine.
Each feature is normalized to 0–100, multiplied by its weight, summed, normalized by total weight, and passed through a sigmoid function to produce the final 0–100 confidence score.
Features and their weights:
— 💪 F1: Momentum (RSI alignment with trend) — w=0.15
— 📈 F2: Volume Surge (volume / SMA ratio) — w=0.08
— 📐 F3: Trend Efficiency (ER × 100) — w=0.15
— ⚡ F4: Volatility Shock (inverted vol clustering) — w=−0.08 (negative = penalizes vol spikes)
— 📏 F5: Band Distance (sigmoid of close-to-band ATR distance) — w=0.10
— 📊 F6: MACD (normalized histogram vs ATR) — w=0.08
— 🏗️ F7: Price Structure (HH/HL for bull, LL/LH for bear over 10 bars) — w=0.08
— 🧠 F8: Regime Confidence (% confidence in current regime) — w=0.04
— 🌐 F9: MTF Confluence (aligned with HTF = 100, not = 0) — w=0.12
— 💪 F10: ADX Strength (ADX × 2.5, capped at 100) — w=0.10
— 🔀 F11: RSI Divergence (aligned div = 100, counter div = 10) — w=0.08
— 📊 F12: Volume Profile Zone (proximity to highest-volume bar) — w=0.06
— 🕐 F13: Session Quality (hour-based scoring for optimal trading sessions) — w=0.04
The raw weighted sum is normalized, then passed through: mlScore = 100 / (1 + exp(−0.08 × (normalizedScore − 50))). This sigmoid compresses extreme values and centers the output around 50.
All 13 weights plus the bias term are user-configurable — you can adjust how much each factor contributes. Negative weights penalize a factor (e.g., W4 = −0.08 means high volatility shock reduces the score).
4️⃣ Self-learning confidence gate.
The system maintains 5 pending signal slots. Each stores the entry price, direction, bar index, and ATR at entry. After the evaluation horizon (default 15 bars), the outcome is assessed:
win = (close − entryPrice) × direction > 0.5 × entryATR (profitable by at least half an ATR, using the ATR at entry time — not current ATR — for fair comparison)
A decay factor (0.98) is applied to historical totals before adding new results, preventing stale data from dominating. When total tracked signals ≥ 8:
— Win rate > 70% → gate decreases by 1.5 (more permissive)
— Win rate < 50% → gate increases by 1.5 (more restrictive)
— Gate is clamped to
The dashboard shows the current gate value, whether it's auto-adjusted, and the tracked win rate with sample size.
5️⃣ Multi-timeframe confluence with auto-HTF selection.
The indicator automatically selects the appropriate higher timeframe based on your current TF: 1M→15M, 5M→30M, 15M→1H, 1H→4H, 4H→D, D→W. Or you can manually specify the HTF.
Three strictness levels:
— Loose: MTF misalignment adds negative ML score but doesn't block
— Moderate: MTF misalignment reduces signal quality
— Strict: MTF misalignment hard-blocks the signal entirely
HTF trend is determined by EMA(20) vs EMA(50) on the higher timeframe.
6️⃣ Adaptive SuperTrend core (from StealthTrail).
The band calculation uses pre-computed interpolated ATR values (ATR bank at 9 fixed periods with lerp interpolation) for smooth adaptation to any auto-tuned ATR length. Same mechanics as StealthTrail: adaptive multiplier (ATR / SMA ratio), band ratcheting, flip cushion, and cooldown — but now all parameters are regime-driven.
7️⃣ Three-mode trailing TP/SL system.
Three SL modes: ATR (entry ± mult × ATR), Band (SuperTrend band as SL), Fixed % (percentage from entry).
Three trailing modes:
— ATR : trail at fixed ATR distance from price — simple, consistent
— Band : use the SuperTrend band itself as trailing stop — structurally anchored
— ATR → Band (hybrid) : start with tight ATR trail, switch to band when it catches up — best of both
The trail only ratchets in the profit direction (never moves away from price). TP1/TP2/TP3 hit markers (✓) appear on the chart. On TP3 or SL hit, the trade closes and lines are removed.
8️⃣ RSI divergence detection.
Scans for bullish divergence (price lower low, RSI higher low, RSI < 40) and bearish divergence (price higher high, RSI lower high, RSI > 60). Divergences aligned with the signal direction boost the ML score (F11 = 100). Counter-divergences penalize it (F11 = 10). Optional visualization as dots on the chart.
9️⃣ Volume profile zone proximity.
Tracks the highest-volume bar in the last 50 bars. Proximity to this bar's price level is scored: within 1.5× ATR = 100 (near institutional activity), further = proportionally lower. Signals near high-volume zones tend to have more follow-through.
🔟 Session quality scoring.
For intraday timeframes, each hour receives a quality score based on typical institutional activity: London/NY overlap (13–17 UTC) = 100, European session (8–12) = 80, US afternoon (18–20) = 70, Asian session (0–7) = 30. An optional kill zone filter suppresses all signals during specified hours.
⚙️ HOW IT WORKS — CALCULATION FLOW
Step 1 — Instrument profiling: ER, autocorrelation, vol clustering, normalized vol → EMA-smoothed → regime classification (TRENDING / RANGING / VOLATILE) with confidence %.
Step 2 — Auto-tuning: Regime weights blend optimal parameters for ATR length, multiplier, cushion, cooldown, RSI threshold/length.
Step 3 — SuperTrend calculation: Interpolated ATR from pre-computed bank → adaptive multiplier (ATR/SMA ratio) → upper/lower bands → ratcheting → flip detection with cushion + cooldown.
Step 4 — Classic filters: Momentum (RSI), volume, session, MTF hard-block (if strict).
Step 5 — ML feature extraction: 13 features normalized to 0–100 from current market state.
Step 6 — ML scoring: Weighted sum → weight normalization → sigmoid → 0–100 confidence score.
Step 7 — Signal decision: Classic filters pass AND (ML disabled OR mlScore ≥ gate) → confirmed signal.
Step 8 — Self-learning: Signal stored in pending slot → evaluated after horizon bars → win rate updated → gate adjusted.
Step 9 — TP/SL placement: SL from band/ATR/fixed% → TPs as ATR multiples → trailing stop ratchets per mode.
📖 HOW TO USE
🎯 Quick start:
1. Add the indicator — Auto-Tune is ON by default, parameters self-configure
2. The SuperTrend band and Long/Short labels appear
3. Dashboard shows: trend, strength, regime, ML score, gate, win rate, TP/SL status
4. Enable ML Filter for quality scoring (off by default — enable after reviewing signal quality)
5. Self-Learning auto-adjusts the gate over time
👁️ Reading the chart:
— 🟢 Green band + "Long" label = confirmed bullish signal
— 🔴 Red band + "Short" label = confirmed bearish signal
— ⚫ Gray dot = classic filter blocked the flip
— 🟡 Yellow triangle = ML rejected the signal (score below gate)
— 🟢 Green dot below bar = bullish RSI divergence
— 🔴 Red dot above bar = bearish RSI divergence
— 📈 Regime badge = current market classification (📈 TRENDING / 📊 RANGING / ⚡ VOLATILE)
— 🟢 Green dashed = TP1/TP2/TP3, 🔴 Red dashed = SL, 🔵 Blue dotted = entry
— "TP1 ✓" / "SL ✗" labels = outcome markers
📊 Dashboard sections:
— Main: trend, signal, strength, ADX, HTF alignment
— 🤖 ML Engine: ML score, confidence gate (fixed or auto), win rate with sample size
— 🎯 Position: status (LONG/SHORT/FLAT), entry, SL (with trail mode icon), TP levels (✓ for hit), R:R ratio
— 🧠 Regime: classification + confidence %
🔧 Tuning guide:
— Start simple: Auto-Tune ON, ML OFF — let the regime engine handle parameters
— Add ML: Enable ML Filter after 50+ signals to see which score level produces winners
— Enable Self-Learning: after 100+ bars of ML being active — let the gate auto-calibrate
— Adjust weights: if you know your market (e.g., volume is unreliable on forex → set W2 to 0)
— Strict MTF: for higher-timeframe alignment — reduces signals, increases quality
— Trail Mode: Band for trend-following, ATR for scalping, ATR→Band for hybrid
⚙️ KEY SETTINGS REFERENCE
⚙️ Main:
— Auto-Tune (default On): regime-driven parameter self-configuration
— ATR Length / Base Multiplier : manual overrides when auto-tune is off
🧠 Adaptive Engine:
— Profiling Lookback (default 100): bars for regime classification
— Regime Sensitivity (default 1.0): scaling factor for regime scores
📐 Multi-Timeframe:
— MTF Confluence (default On): Auto/Manual HTF selection
— Strictness (default Moderate): Loose / Moderate / Strict
🤖 ML Signal Filter:
— Enable ML (default Off): activate 13-feature scoring
— Confidence Gate (default 21): minimum ML score to pass
— Self-Learning (default On): auto-adjust gate from tracked outcomes
— Evaluation Horizon (default 15 bars): outcome assessment window
— 13 individual weights + bias : fully configurable feature importance
🔍 Filters:
— Flip Cushion, Cooldown, RSI Momentum, Volume, Session Kill Zone
🎯 TP/SL:
— SL Mode (default Band): ATR / Band / Fixed %
— Trail Mode (default Band): ATR / Band / ATR → Band
— TP Levels (1–3): ATR multipliers (default 1.5 / 2.5 / 4.0)
🔔 Alerts
— 🟢 LONG / 🔴 SHORT — ticker, price, TF, band, ML score, ADX, HTF, SL, TP1, regime
— 🎯 TP1 / TP2 / TP3 HIT — trade progress
— 🛑 SL HIT — stopped out
All support plain text and JSON webhook format. Bar-close confirmed.
⚠️ IMPORTANT NOTES
— 🚫 No repainting. All signals require barstate.isconfirmed. The confirmed trend direction is stored separately from the real-time calculation — signals only fire on closed bars. HTF data uses lookahead_off. A warmup period (max of profiling lookback and 55 bars) prevents signals during insufficient data.
— 🤖 The ML scoring is not machine learning in the neural network sense . It's a weighted linear model with sigmoid activation — a logistic regression analog. The 13 features are hand-crafted from market microstructure, and the weights are configurable by the user. There is no gradient descent, backpropagation, or training phase. The "self-learning" adjusts only the confidence gate threshold, not the feature weights.
— 📐 Auto-tuning produces different parameters on every bar as the regime shifts. The ATR length and multiplier change continuously — this is by design. If you prefer fixed parameters, disable Auto-Tune.
— ⚖️ The self-learning gate requires at least 8 tracked signals before it begins adjusting. With the decay factor (0.98), the effective sample is weighted toward recent signals. The gate moves slowly (±1.5 per adjustment) and is clamped to .
— 📊 Win rate evaluation uses ATR at entry time , not current ATR. A signal is a "win" if price moves > 0.5× entry ATR in the signal direction within the evaluation horizon. This prevents volatile periods from inflating win counts.
— 🔄 The pre-computed ATR bank (9 fixed periods with lerp interpolation) is a performance optimization that allows smooth ATR adaptation to any auto-tuned length without calling ta.atr() dynamically — which Pine Script doesn't support with variable-length arguments.
— 📏 Volume Profile Zone tracks the single highest-volume bar in the last 50 bars, not a full volume profile. It decays after 50 bars with no new volume peak.
— 🕐 Session scoring uses UTC-based hours. On daily or higher timeframes, session quality defaults to 50 (neutral) as intraday session distinctions don't apply.
— 🛠️ This is a signal scoring and analysis tool , not an automated trading bot. It classifies regimes, scores signals, tracks outcomes, and manages TP/SL — trade decisions remain yours.
— 🌐 Works on all markets and timeframes. Volume features auto-adapt to instruments without volume data. Indicator
StealthTrail SuperTrend [WillyAlgoTrader]📡 StealthTrail SuperTrend is an overlay indicator that takes the classic SuperTrend concept and adds five layers of noise reduction: an adaptive multiplier that scales bands to current volatility, a flip cushion that requires price to push beyond the band by a configurable ATR distance before confirming a trend change, a cooldown timer that enforces a minimum gap between flips, an RSI momentum filter that blocks trend changes without directional confirmation, and a composite trend strength score (0–100) that quantifies how committed the current trend is. The result is a SuperTrend that stays in trends longer, flips less often in chop, and gives you a real-time confidence reading on every trend leg.
🧩 WHY THESE COMPONENTS WORK TOGETHER
The standard SuperTrend has one well-known problem: whipsaws. In ranging or choppy markets, price repeatedly crosses the band boundary, triggering rapid bullish→bearish→bullish flips that produce losing signals. The root causes are:
1. Fixed multiplier — the band width doesn't adapt when volatility contracts or expands, so the same multiplier is too tight in high-vol and too wide in low-vol
2. Instant flip — a single close beyond the band triggers a trend change, even if it's a minor wick-driven overshoot
3. No recovery time — the indicator can flip back on the very next bar, creating same-bar or next-bar whipsaws
4. No momentum context — the flip happens regardless of whether momentum actually supports the new direction
StealthTrail addresses each root cause with a dedicated mechanism:
Adaptive multiplier → solves #1 : bands automatically widen when ATR is above its average (high volatility) and tighten when below (low volatility), keeping the band distance proportional to current market conditions.
Flip cushion → solves #2 : price must close beyond the band by an additional cushion (configurable fraction of ATR) before the flip is accepted, filtering out marginal crossovers.
Cooldown timer → solves #3 : after a flip, the indicator ignores further flip attempts for N bars (default 3), preventing rapid back-and-forth in chop.
Momentum filter → solves #4 : a bullish flip requires RSI above a threshold, a bearish flip requires RSI below the symmetric threshold, confirming that momentum actually supports the direction change.
Trend strength scoring → provides context : combines price distance from band + RSI alignment into a 0–100 score, so you know whether the current trend is strong, fading, or about to flip.
These five mechanisms work as a pipeline: the adaptive multiplier sets the right band width → the cushion prevents marginal crossovers → the cooldown prevents rapid reversals → the momentum filter confirms directional support → and the strength score tells you how committed the trend is. Removing any one layer reintroduces the specific whipsaw pattern it was designed to prevent.
🔍 WHAT MAKES IT ORIGINAL
1️⃣ Adaptive volatility multiplier.
Instead of using a fixed ATR multiplier (classic SuperTrend), StealthTrail scales the multiplier dynamically:
adaptiveMult = baseMultiplier × (currentATR / SMA(ATR, adaptationPeriod))
Where currentATR = ta.atr(atrLength), and SMA(ATR) is the volatility baseline over the adaptation smoothing period (default 55 bars). The ratio currentATR / averageATR measures whether volatility is currently above (ratio > 1.0) or below (ratio < 1.0) its recent norm.
The adaptive multiplier is clamped between configurable min (default 1.0) and max (default 5.0) to prevent extreme values. When volatility spikes (e.g., news event), the multiplier increases → bands widen → the trend doesn't flip on a volatility-driven spike. When volatility compresses (consolidation), the multiplier decreases → bands tighten → the indicator catches the breakout earlier.
This can be toggled off for classic fixed-multiplier behavior.
2️⃣ Flip cushion — requires extra ATR distance beyond the band.
A standard SuperTrend flips the moment close crosses the band. StealthTrail requires:
Bullish flip: close > upperBand + cushion × ATR
Bearish flip: close < lowerBand − cushion × ATR
The cushion (default 0.15× ATR) creates a dead zone around the band boundary. Price must push meaningfully beyond the band — not just touch it — before a flip is accepted. This single mechanism eliminates a large portion of marginal crossover whipsaws where price briefly clips the band before returning.
Setting the cushion to 0.0 restores classic SuperTrend behavior (flip exactly at the band).
3️⃣ Signal cooldown timer.
After every confirmed trend flip, a counter starts. No new flip can occur for N bars (default 3). This prevents the most destructive whipsaw pattern: a bearish flip immediately followed by a bullish flip on the next bar (or vice versa), which generates two consecutive losing signals.
The cooldown is tracked as barsSinceFlip, incremented each bar, and checked before any flip is processed. Only when barsSinceFlip ≥ cooldownInput can a new flip occur. This is independent of the cushion — both must pass.
4️⃣ RSI momentum filter.
On every raw trend flip (band crossed + cushion passed + cooldown passed), an additional RSI check is applied:
Bullish flip requires: RSI(rsiLength) ≥ rsiThreshold (default 45)
Bearish flip requires: RSI(rsiLength) ≤ (100 − rsiThreshold) = 55
This is deliberately asymmetric around the center by design (threshold 45, not 50): a bullish flip only needs RSI ≥ 45 (slightly below center), meaning it passes unless momentum is actively bearish. A bearish flip only needs RSI ≤ 55. This permissive threshold blocks the worst counter-momentum flips without being so strict that it delays legitimate trend changes.
When the filter blocks a flip, the trend direction does not change and the band continues ratcheting in the original direction. An optional "Show Filtered Flips" setting displays a muted dot where a flip was blocked — useful for understanding filter behavior.
5️⃣ Composite trend strength score (0–100).
On every bar, a strength score is calculated from two components:
Distance score (0–50): How far price is from the SuperTrend band, measured in ATR units:
distScore = min(|close − band| / ATR × 20, 50)
A large distance means price is well away from the band — the trend has room before a potential flip.
Momentum score (0–50): How well RSI aligns with the current trend direction:
For bullish: momScore = min(max(RSI − 50, 0), 50)
For bearish: momScore = min(max(50 − RSI, 0), 50)
RSI reading that confirms the trend direction contributes up to 50 points.
Total: strength = round(min(distScore + momScore, 100))
Classification: Strong ≥ 70, Medium ≥ 40, Weak < 40. Displayed in the dashboard and included in alert messages. This gives you a real-time confidence reading: a Strong reading means price is far from the band with confirming momentum — the trend is well-established. A Weak reading means price is near the band or momentum is fading — a flip may be imminent.
6️⃣ Filtered flip visualization.
When "Show Filtered Flips" is enabled, a muted gray dot appears on the band at every point where a raw flip occurred but was blocked by a filter (momentum or volume). This is a transparency feature: instead of silently suppressing signals, the indicator shows you exactly where it intervened and what it filtered out. You can evaluate whether the filter saved you from a bad signal or delayed a good one.
7️⃣ Gradient fill between price and band.
The space between close and the SuperTrend band is filled with a directional gradient: bullish trends fade from green (near band) to transparent (near price), bearish trends fade from red to transparent. The gradient uses fill() with top/bottom color mapping — the visual intensity naturally increases as the band is further from price, creating an intuitive "strength of trend" visual without needing to read the dashboard.
⚙️ HOW IT WORKS — CALCULATION FLOW
Step 1 — ATR calculation: ta.atr(atrLength) with a high-low fallback for early bars where ATR is unavailable.
Step 2 — Adaptive multiplier: If adaptive mode is on: multiplier = baseMultiplier × (ATR / SMA(ATR, adaptSmoothing)), clamped to . If off: multiplier = baseMultiplier (fixed).
Step 3 — Band calculation: upperBand = hl2 + multiplier × ATR. lowerBand = hl2 − multiplier × ATR. Bands are calculated from hl2 (midpoint) for symmetry.
Step 4 — Band ratcheting: In an uptrend, the lower band can only rise — it is set to max(current_lowerBand, previous_band). In a downtrend, the upper band can only fall — min(current_upperBand, previous_band). This ratcheting prevents the band from retreating during a trend, which would prematurely trigger a flip.
Step 5 — Flip detection: In an uptrend: if close < band − cushion × ATR AND barsSinceFlip ≥ cooldown → raw bearish flip. In a downtrend: if close > band + cushion × ATR AND barsSinceFlip ≥ cooldown → raw bullish flip. On flip, the band resets to the opposite band value and barsSinceFlip resets to 0.
Step 6 — Filter gate: If RSI momentum filter is on: bullish flip requires RSI ≥ threshold, bearish requires RSI ≤ (100 − threshold). If volume filter is on: volume must exceed SMA(volume, 20) × multiplier. Both must pass for the flip to become a confirmed signal. If either fails, the flip is recorded as "filtered" (visualizable) but the trend direction reverts — no signal is emitted.
Step 7 — Signal emission: Confirmed flip + barstate.isconfirmed + warmup check → Buy (▲) or Sell (▼) label appears. Strength score is calculated and updated.
📖 HOW TO USE
🎯 Quick start:
1. Add the indicator — the adaptive SuperTrend band appears immediately
2. Green band = bullish trend, red band = bearish trend
3. ▲ label below bar = confirmed buy (trend flipped bullish through all filters)
4. ▼ label above bar = confirmed sell (trend flipped bearish)
5. Check dashboard for strength score — Strong trends are more reliable
👁️ Reading the chart:
— 🟢 Green step-line = SuperTrend band in bullish mode (following lower band)
— 🔴 Red step-line = SuperTrend band in bearish mode (following upper band)
— 🟢 Large green dot on band = confirmed bullish flip
— 🔴 Large red dot on band = confirmed bearish flip
— ⚫ Gray dot on band (optional) = raw flip that was blocked by a filter
— 🟩🟥 Gradient fill = visual trend strength (denser = further from band = stronger trend)
— Background tint (optional) = overall trend direction shading
📊 Dashboard fields:
— Trend: current direction (Bullish / Bearish)
— Signal: last confirmed signal with bars elapsed
— Strength: 0–100 score with classification (Strong / Medium / Weak)
— Multiplier: current adaptive multiplier value (e.g., 2.83×)
— RSI: current RSI reading
— Timeframe and version
🔧 Tuning guide:
— Too many whipsaws: increase Flip Cushion (0.2–0.3), increase Cooldown (4–6), enable Momentum Filter
— Signals too late: decrease Flip Cushion (0.05–0.10), decrease Cooldown (1–2), lower Base Multiplier
— Bands too wide in low-vol: decrease Min Adaptive Mult (0.8), decrease Adaptation Smoothing (30–40)
— Bands too tight in high-vol: increase Max Adaptive Mult (5.0–6.0), increase Base Multiplier
— Ranging/choppy market: enable Momentum Filter, increase Cooldown, increase Cushion — all three work together to suppress chop signals
— Strong trending market: decrease Cushion (0.05), decrease Cooldown (1), disable Momentum Filter — let the indicator react faster to trend continuation
⚙️ KEY SETTINGS REFERENCE
⚙️ Main:
— ATR Length (default 13): volatility measurement period
— Base Multiplier (default 2.5): base ATR multiplier for band width
— Adaptive Multiplier (default On): scale bands by current/average ATR ratio
— Adaptation Smoothing (default 55): SMA lookback for ATR baseline
🔍 Filters:
— Flip Cushion (default 0.15× ATR): extra distance beyond band required for flip
— Signal Cooldown (default 3 bars): minimum gap between flips
— Momentum Filter (default On): RSI confirmation for trend changes
— RSI Length (default 13) / RSI Threshold (default 45): momentum filter parameters
— Volume Filter (default Off): above-average volume confirmation
🔧 Advanced:
— Min Adaptive Mult (default 1.0): floor for adaptive scaling
— Max Adaptive Mult (default 5.0): ceiling for adaptive scaling
🎨 Visual:
— Band line, gradient fill, filtered flip dots, trend background (all toggleable)
— Auto / Dark / Light theme
🔔 Alerts
— 🟢 BUY — ticker, price, band level, timeframe, strength score
— 🔴 SELL — same fields
Both support plain text and JSON webhook format. Bar-close confirmed, filter-gated, cooldown-enforced.
⚠️ IMPORTANT NOTES
— 🚫 No repainting. All signals require barstate.isconfirmed. Band ratcheting is deterministic — the band value on a closed bar never changes retroactively. A warmup period (max of ATR length, adaptation smoothing, and 50 bars) prevents signals during insufficient data.
— 📐 This is not a standard SuperTrend. The adaptive multiplier, flip cushion, cooldown, and momentum filter fundamentally change the signal generation logic. A classic SuperTrend flip would occur significantly earlier (or later, depending on volatility) than a StealthTrail flip — they are not interchangeable.
— ⚖️ The flip cushion and cooldown work independently . Both must pass for a flip to occur. In chop, the cooldown may block a flip even after the cushion is satisfied, or the cushion may prevent a flip even after the cooldown expires. This dual-gate design is intentional.
— 📊 Trend strength is a real-time reading , not a prediction. A strength of 85 means price is far from the band with confirming momentum right now — it does not predict how long the trend will continue.
— 🔄 "Filtered Flips" (gray dots) show where a trend change would have occurred in a classic SuperTrend but was blocked by the momentum or volume filter. This transparency helps you understand the filter's impact and tune the threshold accordingly.
— 📈 Volume filter auto-disables on instruments without volume data (many forex pairs). On these instruments, only the momentum filter, cushion, and cooldown provide noise reduction.
— 🛠️ This is a trend-following signal tool , not an automated trading bot. It identifies trend direction, scores trend strength, and generates filtered signals — trade decisions remain yours.
— 🌐 Works on all markets and timeframes. The adaptive multiplier self-calibrates to any instrument's volatility profile. Indicator
SIDD EMA RSI Supertrend Signal Table🔥 SIDD EMA RSI SuperTrend Multi-Timeframe Signal Table
**SIDD EMA RSI SuperTrend Signal Table** is a **clean, powerful multi-timeframe trend confirmation dashboard** designed for traders who want **clarity, confluence, and speed** — all in one glance.
This indicator **does NOT repaint** and uses **industry-standard trend logic** combining **EMA structure, RSI momentum, and SuperTrend direction** across **6 different timeframes**.
---
## 🧠 Core Logic Behind the Indicator
This script works on **three independent trend engines**, displayed together in a compact table:
### ✅ 1️⃣ EMA Trend (Structure Based)
* Uses **EMA 50 vs EMA 200**
* **Bullish** → EMA 50 above EMA 200
* **Bearish** → EMA 50 below EMA 200
* Captures **primary market structure**
### ✅ 2️⃣ RSI Trend (Momentum Based)
* RSI Length: **14**
* **Bullish** → RSI > **55**
* **Bearish** → RSI ≤ **55**
* Helps confirm **trend strength & momentum**
### ✅ 3️⃣ SuperTrend (Price Action Based)
* ATR Length: **10**
* Factor: **3.0**
* Clearly defines **trend direction & trailing bias**
* Excellent for **entry & exit alignment**
---
## ⏱️ Multi-Timeframe Coverage
The table analyzes trends across **6 configurable timeframes**:
* Intraday → **5m, 15m, 1H**
* Swing → **4H, Daily**
* Positional → **Weekly**
Each timeframe shows:
* 📈 EMA Trend
* 📊 RSI Trend
* 🔁 SuperTrend Direction
Color-coded for instant readability:
* 🟢 Bullish
* 🔴 Bearish
* ⚪ Neutral
---
## 🎯 How to Use This Indicator
✔ **Trend Trading**
Trade only when **EMA + RSI + SuperTrend align** across higher & lower timeframes.
✔ **Intraday Confirmation**
Use higher TF (1H / 4H) bias and take entries on lower TF.
✔ **Avoid Chop & False Signals**
If signals are mixed → market is likely **sideways or risky**.
✔ **Swing & Positional Trades**
Daily + Weekly alignment gives **high-probability setups**.
---
## ⚙️ Customization Options
* Adjustable **timeframes**
* Table **position** (Top/Bottom – Left/Right)
* Table **size** (Extra Small / Small / Normal)
* Custom **colors, borders & text**
* Optimized for **minimal chart clutter**
---
## ⚠️ Disclaimer
This indicator is a **trend confirmation & decision-support tool**.
Always combine with **price action, support/resistance, and proper risk management**.
Indicator
Hash Supertrend [Hash Capital Research]Hash Supertrend Strategy by Hash Capital Research
Overview
Hash Supertrend is a professional-grade trend-following strategy that combines the proven Supertrend indicator with institutional visual design and flexible time filtering.
The strategy uses ATR-based volatility bands to identify trend direction and executes position reversals when the trend flips.This implementation features a distinctive fluorescent color system with customizable glow effects, making trend changes immediately visible while maintaining the clean, professional aesthetic expected in quantitative trading environments.
Entry Signals:
Long Entry: Price crosses above the Supertrend line (trend flips bullish)
Short Entry: Price crosses below the Supertrend line (trend flips bearish)
Controls the lookback period for volatility calculation
Lower values (7-10): More sensitive to price changes, generates more signals
Higher values (12-14): Smoother response, fewer signals but potentially delayed entries
Recommended range: 7-14 depending on market volatility
Factor (Default: 3.0)
Restricts trading to specific hours
Useful for avoiding low-liquidity sessions, overnight gaps, or known choppy periods
When disabled, strategy trades 24/7
Start Hour (Default: 9) & Start Minute (Default: 30)
Define when the trading session begins
Uses exchange timezone in 24-hour format
Example: 9:30 = 9:30 AM
End Hour (Default: 16) & End Minute (Default: 0)
Controls the vibrancy of the fluorescent color system
1-3: Subtle, muted colors
4-6: Balanced, moderate saturation
7-10: Bright, highly saturated fluorescent appearance
Affects both the Supertrend line and trend zones
Glow Effect (Default: On)
Adds luminous halo around the Supertrend line
Creates a multi-layered visual with depth
Particularly effective during strong trends
Glow Intensity (Default: 5.0)
Displays tiny fluorescent dots at entry points
Green dot below bar: Long entry
Red dot above bar: Short entry
Provides clear visual confirmation of executed trades
Show Trend Zone (Default: On)
Strong trending markets (2020-style bull runs, sustained bear markets)
Markets with clear directional bias
Instruments with consistent volatility patterns
Timeframes: 15m to Daily (optimal on 1H-4H)
Challenging Conditions:
Choppy, range-bound markets
Low volatility consolidation periods
Highly news-driven instruments with frequent gaps
Very low timeframes (1m-5m) prone to noise
Recommended AssetsCryptocurrency:
Strategy
SuperTrend Optimizer Remastered[CHE] SuperTrend Optimizer Remastered — Grid-ranked SuperTrend with additive or multiplicative scoring
Summary
This indicator evaluates a fixed grid of one hundred and two SuperTrend parameter pairs and ranks them by a simple flip-to-flip return model. It auto-selects the currently best-scoring combination and renders its SuperTrend in real time, with optional gradient coloring for faster visual parsing. The original concept is by KioseffTrading Thanks a lot for it.
For years I wanted to shorten the roughly two thousand three hundred seventy-one lines; I have now reduced the core to about three hundred eighty lines without triggering script errors. The simplification is generalizable to other indicators. A multiplicative return mode was added alongside the existing additive aggregation, enabling different rankings and often more realistic compounding behavior.
Motivation: Why this design?
SuperTrend is sensitive to its factor and period. Picking a single pair statically can underperform across regimes. This design sweeps a compact parameter grid around user-defined lower bounds, measures flip-to-flip outcomes, and promotes the combination with the strongest cumulative return. The approach keeps the visual footprint familiar while removing manual trial-and-error. The multiplicative mode captures compounding effects; the additive mode remains available for linear aggregation.
Originally (by KioseffTrading)
Very long script (~2,371 lines), monolithic structure.
SuperTrend optimization with additive (cumulative percentage-sum) scoring only.
Heavier use of repetitive code; limited modularity and fewer UI conveniences.
No explicit multiplicative compounding option; rankings did not reflect sequence-sensitive equity growth.
Now (remastered by CHE)
Compact core (~380 lines) with the same functional intent, no compile errors.
Adds multiplicative (compounding) scoring alongside additive, changing rankings to reflect real equity paths and penalize drawdown sequences.
Fixed 34×3 grid sweep, live ranking, gradient-based bar/wick/line visuals, top-table display, and an optional override plot.
Cleaner arrays/state handling, last-bar table updates, and reusable simplification pattern that can be applied to other indicators.
What’s different vs. standard approaches?
Baseline: A single SuperTrend with hand-picked inputs.
Architecture differences:
Fixed grid of thirty-four factor offsets across three ATR offsets.
Per-combination flip-to-flip backtest with additive or multiplicative aggregation.
Live ranking with optional “Best” or “Worst” table output.
Gradient bar, wick, and line coloring driven by consecutive trend counts.
Optional override plot to force a specific SuperTrend independent of ranking.
Practical effect: Charts show the currently best-scoring SuperTrend, not a static choice, plus an on-chart table of top performers for transparency.
How it works (technical)
For each parameter pair, the script computes SuperTrend value and direction. It monitors direction transitions and treats a change from up to down as a long entry and the reverse as an exit, measuring the move between entry and exit using close prices. Results are aggregated per pair either by summing percentage changes or by compounding return factors and then converting to percent for comparison. On the last bar, open trades are included as unrealized contributions to ranking. The best combination’s line is plotted, with separate styling for up and down regimes. Consecutive regime counts are normalized within a rolling window and mapped to gradients for bars, wicks, and lines. A two-column table reports the best or worst performers, with an optional row describing the parameter sweep.
Parameter Guide
Factor (Lower Bound) — Starting SuperTrend factor; the grid adds offsets between zero and three point three. Default three point zero. Higher raises distance to price and reduces flips.
ATR Period (Lower Bound) — Starting ATR length; the grid adds zero, one, and two. Default ten. Longer reduces noise at the cost of responsiveness.
Best vs Worst — Ranks by top or bottom cumulative return. Default Best. Use Worst for stress tests.
Calculation Mode — Additive sums percents; Multiplicative compounds returns. Multiplicative is closer to equity growth and can change the leaderboard.
Show in Table — “Top Three” or “All”. Fewer rows keep charts clean.
Show “Parameters Tested” Label — Displays the effective sweep ranges for auditability.
Plot Override SuperTrend — If enabled, the override factor and ATR are plotted instead of the ranked winner.
Override Factor / ATR Period — Values used when override is on.
Light Mode (for Table) — Adjusts table colors for bright charts.
Gradient/Coloring controls — Toggles for gradient bars and wick coloring, window length for normalization, gamma for contrast, and transparency settings. Use these to emphasize or tone down visual intensity.
Table Position and Text Size — Places the table and sets typography.
Reading & Interpretation
The auto SuperTrend plots one line for up regimes and one for down regimes. Color intensity reflects consecutive trend persistence within the chosen window. A small square at the bottom encodes the same gradient as a compact status channel. Optional wick coloring uses the same gradient for maximum contrast. The performance table lists parameter pairs and their cumulative return under the chosen aggregation; positive values are tinted with the up color, negative with the down color. “Long” labels mark flips that open a long in the simplified model.
Practical Workflows & Combinations
Trend following: Use the auto line as your primary bias. Enter on flips aligned with structure such as higher highs and higher lows. Filter with higher-timeframe trend or volatility contraction.
Exits/Stops: Consider conservative exits when color intensity fades or when the opposite line is approached. Aggressive traders can trail near the plotted line.
Override mode: When you want stability across instruments, enable override and standardize factor and ATR; keep the table visible for sanity checks.
Multi-asset/Multi-TF: Defaults travel well on liquid instruments and intraday to daily timeframes. Heavier assets may prefer larger lower bounds or multiplicative mode.
Behavior, Constraints & Performance
Repaint/confirmation: Signals are based on SuperTrend direction; confirmation is best assessed on closed bars to avoid mid-bar oscillation. No higher-timeframe requests are used.
Resources: One hundred and two SuperTrend evaluations per bar, arrays for state, and a last-bar table render. This is efficient for the grid size but avoid stacking many instances.
Known limits: The flip model ignores costs, slippage, and short exposure. Rapid whipsaws can degrade both aggregation modes. Gradients are cosmetic and do not change logic.
Sensible Defaults & Quick Tuning
Start with the provided lower bounds and “Top Three” table.
Too many flips → raise the lower bound factor or period.
Too sluggish → lower the bounds or switch to additive mode.
Rankings feel unstable → prefer multiplicative mode and extend the normalization window.
Visuals too strong → increase gradient transparency or disable wick coloring.
What this indicator is—and isn’t
This is a parameter-sweep and visualization layer for SuperTrend selection. It is not a complete trading system, not predictive, and does not include position sizing, transaction costs, or risk management. Combine with market structure, higher-timeframe context, and explicit risk controls.
Attribution and refactor note: The original work is by KioseffTrading. The script has been refactored from approximately two thousand three hundred seventy-one lines to about three hundred eighty core lines, retaining behavior without compiler errors. The general simplification pattern is reusable for other indicators.
Metadata
Name/Tag: SuperTrend Optimizer Remastered
Pine version: v6
Overlay or separate pane: true (overlay)
Core idea/principle: Grid-based SuperTrend selection by cumulative flip returns with additive or multiplicative aggregation.
Primary outputs/signals: Auto-selected SuperTrend up and down lines, optional override lines, gradient bar and wick colors, “Long” labels, performance table.
Inputs with defaults: See Parameter Guide above.
Metrics/functions used: SuperTrend, ATR, arrays, barstate checks, windowed normalization, gamma-based contrast adjustment, table API, gradient utilities.
Special techniques: Fixed grid sweep, compounding vs linear aggregation, last-bar UI updates, gradient encoding of persistence.
Performance/constraints: One hundred and two SuperTrend calls, arrays of length one hundred and two, label budget, last-bar table updates, no higher-timeframe requests.
Recommended use-cases/workflows: Trend bias selection, quick parameter audits, override standardization across assets.
Compatibility/assets/timeframes: Standard OHLC charts across intraday to daily; liquid instruments recommended.
Limitations/risks: Costs and slippage omitted; mid-bar instability possible; not suitable for synthetic chart types.
Debug/diagnostics: Ranking table, optional tested-range label; internal counters for consecutive trends.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
Indicator
Multipower Entry SecretMultipower Entry Secret indicator is designed to be the ultimate trading companion for traders of all skill levels—especially those who struggle with decision-making due to unclear or overwhelming signals. Unlike conventional trading systems cluttered with too many lines and confusing alerts, this indicator provides a clear, adaptive, and actionable guide for market entries and exits.
Key Points:
Clear Buy/Sell/Wait Signals:
The script dynamically analyzes price action, candle patterns, volume, trend strength, and higher time frame context. This means it gives you “Buy,” “Sell,” or “Wait” signals based on real, meaningful market information—filtering out the noise and weak trades.
Multi-Timeframe Adaptive Analysis:
It synchronizes signals between higher and current timeframes, ensuring you get the most reliable direction—reducing the risk of getting caught in fake moves or sudden reversals.
Automatic Support, Resistance & Liquidity Zones:
Key levels like support, resistance, and liquidity zones are auto-detected and displayed directly on the chart, helping you make precise decisions without manual drawing.
Real-Time Dashboard:
All relevant information, such as trend strength, market intent, volume sentiment, and the reason behind each signal, is neatly summarized in a dashboard—making monitoring effortless and intuitive.
Customizable & Beginner-Friendly:
Whether you’re a newcomer wanting straightforward guidance or a professional needing advanced customization, the indicator offers flexible options to adjust analysis depth, timeframes, sensitivity, and more.
Visual & Clutter-Free:
The design ensures that your chart remains clean and readable, showing only the most important information. This minimizes mental overload and allows for instant decision-making.
Who Will Benefit?
Beginners who want to learn trading logic, avoid common traps, and see the exact reason behind every signal.
Advanced traders who require adaptive multi-timeframe analytics, fast execution, and stress-free monitoring.
Anyone who wants to save screen time, reduce analysis paralysis, and have more confidence in every trade they take.
1. No Indicator Clutter
Intent:
Many traders get confused by charts filled with too many indicators and signals. This often leads to hesitation, missed trades, or taking random, risky trades.
In this Indicator:
You get a clean and clutter-free chart. Only the most important buy/sell/wait signals and relevant support/resistance/liquidity levels are shown. These update automatically, removing the “overload” and keeping your focus sharp, so your decision-making is faster and stress-free.
2. Exact Entry Guide
Intent:
Traders often struggle with entry timing, leading to FOMO (fear of missing out) or getting trapped in sudden market reversals.
In this Indicator:
The system uses powerful adaptive logic to filter out weak signals and only highlight the strongest market moves. This not only prevents you from entering late or on noise, but also helps avoid losses from false breakouts or whipsaws. You get actionable suggestions—when to enter, when to hold back—so your entries are high-conviction and disciplined.
3. HTF+LTF Logic: Multitimeframe Sync Analysis
Intent:
Most losing trades happen when you act only on the short-term chart, ignoring the bigger market trend.
In this Indicator:
Signals are based on both the current chart timeframe (LTF) and a higher (HTF, like hourly/daily) timeframe. The indicator synchronizes trend direction, momentum, and structure across both levels, quickly adapting to show you when both are aligned. This filtering results in “only trade with the bigger trend”—dramatically increasing your win rate and market confidence.
4. Auto Support/Resistance & Liquidity Zones
Intent:
Drawing support/resistance and liquidity zones manually is time-consuming and error-prone, especially for beginners.
In this Indicator:
The system automatically identifies and plots the most crucial support/resistance levels and liquidity zones on your chart. This is based on adaptive, real-time price and volume analysis. These zones highlight where major institutional activity, trap setups, or real breakouts/reversals are most likely, removing guesswork and giving you a clear reference for entries, exits, and stop placements.
5. Clear Action/Direction
Intent:
Traders need certainty—what does the market want right now? Most indicators are vague.
In this Indicator:
Your dashboard always displays in plain words (like “BUY”, “SELL”, or “WAIT”) what action makes sense in the current market phase. Whether it’s a bull trap, volume spike, wick reversal, or exhaustion—it’s interpreted and explained clearly. No more confusion—just direct, real-time advice.
6. For Everyone (Beginner to Pro)
Intent:
Most advanced indicators are overwhelming for new traders; simple ones lack depth for professionals.
In this Indicator:
It is simple enough for a beginner—just add it to the chart and instantly see what action to consider. At the same time, it includes advanced adaptive analysis, multi-timeframe logic, and customizable settings so professional traders can fine-tune it for their strategies.
7. Ideal Usage and User Benefits
Instant Decision Support:
Whenever you’re unsure about a trade, just look at the indicator’s suggestion for clarity.
Entry Learning:
Beginners get real-time “practice” by not only seeing signals, but also the reason behind them—improving your chart reading and market understanding.
Screen Time & Stress Reduction:
Clear, relevant information only; no noise, less fatigue, faster decisions.
Makes Trading Confident & Simple:
The smart dashboard splits actionable levels (HTF, LTF, action) so you never miss a move, avoid traps, and stay aligned with high-probability trades.
8. Advanced Input Settings (Smart Customization)
Explained with Examples:
Enable Wick Analysis:
Finds candles with strong upper/lower wicks (signs of rejection/buying/selling force), alerting you to hidden reversals and protecting from FOMO entries.
Enable Absorption:
Detects when heavy order flow from one side is “absorbed” by the other (shows where institutional buyers/sellers are likely active, helps spot fake breakouts).
Enable Unusual Breakout:
Highlights real breakouts—large volatility plus high volume—so you catch genuine moves and avoid random spikes.
Enable Range/Expansion:
Smartly flags sudden range expansions—when the market goes from quiet to volatile—so you can act at the start of real trends.
Trend Bar Lookback:
Adjusts how many bars/candles are used in trend calculations. Short (fast trades, more signals), long (more reliability, fewer whipsaws).
Bull/Bear Bars for Strong Trend Min:
Sets how many candles in a row must support a trend before calling it “strong”—prevents flipping signals, keeps you disciplined.
Volume MA Length:
Lets you adjust how many bars back volume is averaged—fine-tune for your asset and trading style for best volume signals.
Swing Lookback Bars:
Set how many bars to use for swing high/low detection—short (quick swing levels), long (stronger support/resistance).
HTF (Bias Window):
Decide which higher timeframe the indicator should use for big-picture market mood. Adjustable for any style (scalp, swing, position).
Adaptive Lookback (HTF):
Choose how much HTF history is used for detecting major extremes/zones. Quick adjust for more/less sensitivity.
Show Support/Resistance, Liquidity Zones, Trendlines:
Toggle them on/off instantly per your needs—keeps your chart relevant and tailored.
9. Live Dashboard Sections Explained
Intent HTF:
Shows if the bigger timeframe currently has a Bullish, Bearish, or Neutral (“Chop”) intent, based on strict volume/price body calculations. Instant clarity—no more guessing on trend bias.
HTF Bias:
Clear message about which side (buy/sell/sideways) controls the market on the higher timeframe, so you always trade with the “big money.”
Chart Action:
The central action for the current bar—Whether to Buy, Sell, or Wait—calculated from all indicator logic, not just one rule.
TrendScore Long/Short:
See how many candles in your chosen window were bullish or bearish, at a glance. Instantly gauge market momentum.
Reason (WHY):
Every time a signal appears, the “reason” cell tells you the primary logic (breakout, wick, strong trend, etc.) behind it. Full transparency and learning—never trade blindly.
Strong Trend:
Shows if the market is currently in a powerful trend or not—helping you avoid choppy, risky entries.
HTF Vol/Body:
Displays current higher timeframe volume and candle body %—helping spot when big players are active for higher probability trades.
Volume Sentiment:
A real-time analysis of market psychology (strong bullish/bearish, neutral)—making your decision-making much more confident.
10. Smart and User-Friendly Design
Multi-timeframe Adaptive:
All calculations can now be drawn from your choice of higher or current timeframe, ensuring signals are filtered by larger market context.
Flexible Table Position:
You can set the live dashboard/summary anywhere on the chart for best visibility.
Refined Zone Visualization:
Liquidity and order blocks are visually highlighted, auto-tuning for your settings and always cleaning up to stay clutter-free.
Multi-Lingual & Beginner Accessible:
With Hindi and simple English support, descriptions and settings are accessible for a wide audience—anyone can start using powerful trading logic with zero language barrier.
Efficient Labels & Clear Reasoning:
Signal labels and reasons are shown/removed dynamically so your chart stays informative, not messy.
Every detail of this indicator is designed to make trading both simpler and smarter—helping you avoid the common pitfalls, learn real price action, stay in sync with the market’s true mood, and act with discipline for higher consistency and confidence.
This indicator makes professional-grade market analysis accessible to everyone. It’s your trusted assistant for making smarter, faster, and more profitable trading decisions—providing not just signals, but also the “why” behind every action. With auto-adaptive logic, clear visuals, and strong focus on real trading needs, it lets you focus on capturing the moves that matter—every single time. Indicator
PowerTrend Pro Strategy – Gold OptimizedTired of false signals on Gold?
PowerTrend Pro combines VWAP, Supertrend, RSI, and smart MA filters with trailing stops & break-even logic to deliver high-probability trades on XAUUSD.
PowerTrend Pro Strategy is a professional-grade trading system designed to capture high-probability swing and intraday opportunities on XAUUSD (Gold) and other volatile markets.
🔑 Core Features
VWAP Anchoring – institutional fair value reference to filter trades.
Supertrend (ATR-based) – adaptive trend filter tuned for Gold’s volatility.
Multi-Timeframe RSI – confirms momentum alignment across intraday and higher timeframe.
EMA + SMA Combo – ensures trades follow strong directional bias, reducing false signals.
Dynamic Risk Management
Adjustable Take Profit / Stop Loss (%)
Trailing Stop that locks in profits on extended moves
Break-Even Logic (stop loss moves to entry once price is in profit)
⚡ Gold-Tuned Presets
XAUUSD 1H → tighter TP/SL & faster entries for active intraday trading.
XAUUSD 4H → wider ATR filter & trailing stops to capture bigger swings.
Generic Mode → works on Forex, Indices, and Crypto (fully customizable).
🎯 Why It Works
Gold is notoriously volatile — quick spikes wipe out weak strategies. PowerTrend Pro solves this by combining:
✅ Institutional bias (VWAP)
✅ Adaptive trend filter (Supertrend)
✅ Momentum confirmation (RSI MTF)
✅ Robust trend structure (EMA + SMA)
✅ Smart exits (TP, SL, trailing & breakeven)
This multi-layer confirmation makes entries stronger and keeps risk under control.
🛠️ Usage
Add the strategy to your chart.
Choose a preset (XAUUSD 1H, 4H, or Generic).
Run Strategy Tester for performance metrics.
Optimize TP/SL and ATR values for your broker & market conditions.
🔥 Pro Tip: Combine this strategy with a session filter (London/NY overlap) or volume confirmation to boost accuracy in Gold. Strategy
Indicator
Dynamic Trailing (Zeiierman)█ Overview
The Dynamic Trailing (Zeiierman) indicator enhances the traditional SuperTrend approach by providing a more nuanced, adaptable tool for trend analysis and market volatility assessment. It combines techniques to identify dynamic support and resistance levels, trend directions, and market volatility. By integrating the Average True Range (ATR) with a unique multiplier system and smoothing mechanisms, this indicator offers a nuanced approach to trend-following strategies, making it a valuable asset for traders looking to leverage SuperTrend methodologies with additional insights into market dynamics.
█ How It Works
At its core, this indicator builds on the traditional SuperTrend formula by utilizing a modified ATR calculation to define the deviation for dynamic support and resistance levels. These levels are dynamically adjusted based on market volatility. The innovation lies in the addition of the Hull Moving Average (HMA) and the Triple Exponential Moving Average (TEMA) for an enhanced smoothing effect, making the indicator's trend signals more reliable and less prone to market noise. The trend direction is determined by comparing the closing price with the dynamic levels, facilitating clear bullish or bearish signals.
The indicator incorporates a 'Supertrend' function, which uses the dynamic levels and the price’s position relative to them to determine the trend direction. This determination is visualized through color-coded lines and a cloud zone, which expands or contracts based on the ATR and a user-defined width setting, illustrating the market's volatility and trend strength.
ATR Calculation: Utilizes the Average True Range (ATR) to measure market volatility. The ATR is a cornerstone of this indicator, helping to dynamically adjust the support and resistance levels according to the market’s changing conditions.
Supertrend Calculation: Implements a supertrend formula that combines the ATR with user-defined multipliers to plot potential trend directions. This feature helps in identifying whether the market is in an uptrend or downtrend, offering visual cues for potential reversals.
TEMA Calculation: Employs the Triple Exponential Moving Average (TEMA) through a Hull Moving Average (HMA) calculation to smooth out price data. This smoothing process helps in reducing market noise and makes the trend direction clearer.
Dynamic Support and Resistance: Calculates dynamic support and resistance levels by applying a deviation (derived from the ATR and user-defined multiplier) to the smoothed price data. These levels adapt to market conditions, providing areas where price might experience support or resistance.
Trend and Cloud Calculation: Determines the overall trend direction and plots a 'Cloud' zone around it, which adjusts in width based on the ATR and a user-defined cloud width setting. This cloud acts as a visual buffer, indicating the strength and stability of the current trend.
█ How to Use
Trend Identification: The primary function of this indicator is to help traders quickly identify the prevailing market trend. A change in the color of the dynamic trailing line or its position relative to the price can signal potential trend reversals.
Dynamic Support and Resistance: Unlike static levels, the dynamic levels adjust with market conditions, providing current areas where the price might experience support or resistance.
Dynamic Support
Dynamic Resistance
█ Settings
Mult (Multiplier): Adjusts the multiplier for the ATR calculation, affecting the deviation distance for support and resistance levels. Higher values decrease sensitivity and vice versa.
Len (Length): Sets the period for the HMA in the TEMA calculation, influencing the indicator's responsiveness to price changes.
Smoothness: Determines the smoothness of the dynamic support and resistance lines by setting the SMA length. Higher values result in smoother lines.
Cloud Width : Modifies the width of the cloud, providing a visual representation of market volatility.
Color Settings (upcol and dncol): Allows users to customize the colors of the indicator's lines and cloud, aiding in visual trend identification.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Indicator
Machine Learning: SuperTrend Strategy TP/SL [YinYangAlgorithms]The SuperTrend is a very useful Indicator to display when trends have shifted based on the Average True Range (ATR). Its underlying ideology is to calculate the ATR using a fixed length and then multiply it by a factor to calculate the SuperTrend +/-. When the close crosses the SuperTrend it changes direction.
This Strategy features the Traditional SuperTrend Calculations with Machine Learning (ML) and Take Profit / Stop Loss applied to it. Using ML on the SuperTrend allows for the ability to sort data from previous SuperTrend calculations. We can filter the data so only previous SuperTrends that follow the same direction and are within the distance bounds of our k-Nearest Neighbour (KNN) will be added and then averaged. This average can either be achieved using a Mean or with an Exponential calculation which puts added weight on the initial source. Take Profits and Stop Losses are then added to the ML SuperTrend so it may capitalize on Momentum changes meanwhile remaining in the Trend during consolidation.
By applying Machine Learning logic and adding a Take Profit and Stop Loss to the Traditional SuperTrend, we may enhance its underlying calculations with potential to withhold the trend better. The main purpose of this Strategy is to minimize losses and false trend changes while maximizing gains. This may be achieved by quick reversals of trends where strategic small losses are taken before a large trend occurs with hopes of potentially occurring large gain. Due to this logic, the Win/Loss ratio of this Strategy may be quite poor as it may take many small marginal losses where there is consolidation. However, it may also take large gains and capitalize on strong momentum movements.
Tutorial:
In this example above, we can get an idea of what the default settings may achieve when there is momentum. It focuses on attempting to hit the Trailing Take Profit which moves in accord with the SuperTrend just with a multiplier added. When momentum occurs it helps push the SuperTrend within it, which on its own may act as a smaller Trailing Take Profit of its own accord.
We’ve highlighted some key points from the last example to better emphasize how it works. As you can see, the White Circle is where profit was taken from the ML SuperTrend simply from it attempting to switch to a Bullish (Buy) Trend. However, that was rejected almost immediately and we went back to our Bearish (Sell) Trend that ended up resulting in our Take Profit being hit (Yellow Circle). This Strategy aims to not only capitalize on the small profits from SuperTrend to SuperTrend but to also capitalize when the Momentum is so strong that the price moves X% away from the SuperTrend and is able to hit the Take Profit location. This Take Profit addition to this Strategy is crucial as momentum may change state shortly after such drastic price movements; and if we were to simply wait for it to come back to the SuperTrend, we may lose out on lots of potential profit.
If you refer to the Yellow Circle in this example, you’ll notice what was talked about in the Summary/Overview above. During periods of consolidation when there is little momentum and price movement and we don’t have any Stop Loss activated, you may see ‘Signal Flashing’. Signal Flashing is when there are Buy and Sell signals that keep switching back and forth. During this time you may be taking small losses. This is a normal part of this Strategy. When a signal has finally been confirmed by Momentum, is when this Strategy shines and may produce the profit you desire.
You may be wondering, what causes these jagged like patterns in the SuperTrend? It's due to the ML logic, and it may be a little confusing, but essentially what is happening is the Fast Moving SuperTrend and the Slow Moving SuperTrend are creating KNN Min and Max distances that are extreme due to (usually) parabolic movement. This causes fewer values to be added to and averaged within the ML and causes less smooth and more exponential drastic movements. This is completely normal, and one of the perks of using k-Nearest Neighbor for ML calculations. If you don’t know, the Min and Max Distance allowed is derived from the most recent(0 index of data array) to KNN Length. So only SuperTrend values that exhibit distances within these Min/Max will be allowed into the average.
Since the KNN ML logic can cause these exponential movements in the SuperTrend, they likewise affect its Take Profit. The Take Profit may benefit from this movement like displayed in the example above which helped it claim profit before then exhibiting upwards movement.
By default our Stop Loss Multiplier is kept quite low at 0.0000025. Keeping it low may help to reduce some Signal Flashing while not taking extra losses more so than not using it at all. However, if we increase it even more to say 0.005 like is shown in the example above. It can really help the trend keep momentum. Please note, although previous results don’t imply future results, at 0.0000025 Stop Loss we are currently exhibiting 69.27% profit while at 0.005 Stop Loss we are exhibiting 33.54% profit. This just goes to show that although there may be less Signal Flashing, it may not result in more profit.
We will conclude our Tutorial here. Hopefully this has given you some insight as to how Machine Learning, combined with Trailing Take Profit and Stop Loss may have positive effects on the SuperTrend when turned into a Strategy.
Settings:
SuperTrend:
ATR Length: ATR Length used to create the Original Supertrend.
Factor: Multiplier used to create the Original Supertrend.
Stop Loss Multiplier: 0 = Don't use Stop Loss. Stop loss can be useful for helping to prevent false signals but also may result in more loss when hit and less profit when switching trends.
Take Profit Multiplier: Take Profits can be useful within the Supertrend Strategy to stop the price reverting all the way to the Stop Loss once it's been profitable.
Machine Learning:
Only Factor Same Trend Direction: Very useful for ensuring that data used in KNN is not manipulated by different SuperTrend Directional data. Please note, it doesn't affect KNN Exponential.
Rationalized Source Type: Should we Rationalize only a specific source, All or None?
Machine Learning Type: Are we using a Simple ML Average, KNN Mean Average, KNN Exponential Average or None?
Machine Learning Smoothing Type: How should we smooth our Fast and Slow ML Datas to be used in our KNN Distance calculation? SMA, EMA or VWMA?
KNN Distance Type: We need to check if distance is within the KNN Min/Max distance, which distance checks are we using.
Machine Learning Length: How far back is our Machine Learning going to keep data for.
k-Nearest Neighbour (KNN) Length: How many k-Nearest Neighbours will we account for?
Fast ML Data Length: What is our Fast ML Length?? This is used with our Slow Length to create our KNN Distance.
Slow ML Data Length: What is our Slow ML Length?? This is used with our Fast Length to create our KNN Distance.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING! Strategy
Volume SuperTrend AI (Expo)█ Overview
The Volume SuperTrend AI is an advanced technical indicator used to predict trends in price movements by utilizing a combination of traditional SuperTrend calculation and AI techniques, particularly the k-nearest neighbors (KNN) algorithm.
The Volume SuperTrend AI is designed to provide traders with insights into potential market trends, using both volume-weighted moving averages (VWMA) and the k-nearest neighbors (KNN) algorithm. By combining these approaches, the indicator aims to offer more precise predictions of price trends, offering bullish and bearish signals.
█ How It Works
Volume Analysis: By utilizing volume-weighted moving averages (VWMA), the Volume SuperTrend AI emphasizes the importance of trading volume in the trend direction, allowing it to respond more accurately to market dynamics.
Artificial Intelligence Integration - k-Nearest Neighbors (k-NN) Algorithm: The k-NN algorithm is employed to intelligently examine historical data points, measuring distances between current parameters and previous data. The nearest neighbors are utilized to create predictive modeling, thus adapting to intricate market patterns.
█ How to use
Trend Identification
The Volume SuperTrend AI indicator considers not only price movement but also trading volume, introducing an extra dimension to trend analysis. By integrating volume data, the indicator offers a more nuanced and robust understanding of market trends. When trends are supported by high trading volumes, they tend to be more stable and reliable. In practice, a green line displayed beneath the price typically suggests an upward trend, reflecting a bullish market sentiment. Conversely, a red line positioned above the price signals a downward trend, indicative of bearish conditions.
Trend Continuation signals
The AI algorithm is the fundamental component in the coloring of the Volume SuperTrend. This integration serves as a means of predicting the trend while preserving the inherent characteristics of the SuperTrend. By maintaining these essential features, the AI-enhanced Volume SuperTrend allows traders to more accurately identify and capitalize on trend continuation signals.
TrailingStop
The Volume SuperTrend AI indicator serves as a dynamic trailing stop loss, adjusting with both price movement and trading volume. This approach protects profits while allowing the trade room to grow, taking into account volume for a more nuanced response to market changes.
█ Settings
AI Settings:
Neighbors (k):
This setting controls the number of nearest neighbors to consider in the k-Nearest Neighbors (k-NN) algorithm. By adjusting this parameter, you can directly influence the sensitivity of the model to local fluctuations in the data. A lower value of k may lead to predictions that closely follow short-term trends but may be prone to noise. A higher value of k can provide more stable predictions, considering the broader context of market trends, but might lag in responsiveness.
Data (n):
This setting refers to the number of data points to consider in the model. It allows the user to define the size of the dataset that will be analyzed. A larger value of n may provide more comprehensive insights by considering a wider historical context but can increase computational complexity. A smaller value of n focuses on more recent data, possibly providing quicker insights but might overlook longer-term trends.
AI Trend Settings:
Price Trend & Prediction Trend:
These settings allow you to adjust the lengths of the weighted moving averages that are used to calculate both the price trend and the prediction trend. Shorter lengths make the trends more responsive to recent price changes, capturing quick market movements. Longer lengths smooth out the trends, filtering out noise, and highlighting more persistent market directions.
AI Trend Signals:
This toggle option enables or disables the trend signals generated by the AI. Activating this function may assist traders in identifying key trend shifts and opportunities for entry or exit. Disabling it may be preferred when focusing on other aspects of the analysis.
Super Trend Settings:
Length:
This setting determines the length of the SuperTrend, affecting how it reacts to price changes. A shorter length will produce a more sensitive SuperTrend, reacting quickly to price fluctuations. A longer length will create a smoother SuperTrend, reducing false alarms but potentially lagging behind real market changes.
Factor:
This parameter is the multiplier for the Average True Range (ATR) in SuperTrend calculation. By adjusting the factor, you can control the distance of the SuperTrend from the price. A higher factor makes the SuperTrend further from the price, giving more room for price movement but possibly missing shorter-term signals. A lower factor brings the SuperTrend closer to the price, making it more reactive but possibly more prone to false signals.
Moving Average Source:
This setting lets you choose the type of moving average used for the SuperTrend calculation, such as Simple Moving Average (SMA), Exponential Moving Average (EMA), etc.
Different types of moving averages provide various characteristics to the SuperTrend, enabling customization to align with individual trading strategies and market conditions.
-----------------
Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Indicator
SuperTrend AI (Clustering) [LuxAlgo]The SuperTrend AI indicator is a novel take on bridging the gap between the K-means clustering machine learning method & technical indicators. In this case, we apply K-Means clustering to the famous SuperTrend indicator.
🔶 USAGE
Users can interpret the SuperTrend AI trailing stop similarly to the regular SuperTrend indicator. Using higher minimum/maximum factors will return longer-term signals.
The displayed performance metrics displayed on each signal allow for a deeper interpretation of the indicator. Whereas higher values could indicate a higher potential for the market to be heading in the direction of the trend when compared to signals with lower values such as 1 or 0 potentially indicating retracements.
In the image above, we can notice more clear examples of the performance metrics on signals indicating trends, however, these performance metrics cannot perform or predict every signal reliably.
We can see in the image above that the trailing stop and its adaptive moving average can also act as support & resistance. Using higher values of the performance memory setting allows users to obtain a longer-term adaptive moving average of the returned trailing stop.
🔶 DETAILS
🔹 K-Means Clustering
When observing data points within a specific space, we can sometimes observe that some are closer to each other, forming groups, or "Clusters". At first sight, identifying those clusters and finding their associated data points can seem easy but doing so mathematically can be more challenging. This is where cluster analysis comes into play, where we seek to group data points into various clusters such that data points within one cluster are closer to each other. This is a common branch of AI/machine learning.
Various methods exist to find clusters within data, with the one used in this script being K-Means Clustering , a simple iterative unsupervised clustering method that finds a user-set amount of clusters.
A naive form of the K-Means algorithm would perform the following steps in order to find K clusters:
(1) Determine the amount (K) of clusters to detect.
(2) Initiate our K centroids (cluster centers) with random values.
(3) Loop over the data points, and determine which is the closest centroid from each data point, then associate that data point with the centroid.
(4) Update centroids by taking the average of the data points associated with a specific centroid.
Repeat steps 3 to 4 until convergence, that is until the centroids no longer change.
To explain how K-Means works graphically let's take the example of a one-dimensional dataset (which is the dimension used in our script) with two apparent clusters:
This is of course a simple scenario, as K will generally be higher, as well the amount of data points. Do note that this method can be very sensitive to the initialization of the centroids, this is why it is generally run multiple times, keeping the run returning the best centroids.
🔹 Adaptive SuperTrend Factor Using K-Means
The proposed indicator rationale is based on the following hypothesis:
Given multiple instances of an indicator using different settings, the optimal setting choice at time t is given by the best-performing instance with setting s(t) .
Performing the calculation of the indicator using the best setting at time t would return an indicator whose characteristics adapt based on its performance. However, what if the setting of the best-performing instance and second best-performing instance of the indicator have a high degree of disparity without a high difference in performance?
Even though this specific case is rare its however not uncommon to see that performance can be similar for a group of specific settings (this could be observed in a parameter optimization heatmap), then filtering out desirable settings to only use the best-performing one can seem too strict. We can as such reformulate our first hypothesis:
Given multiple instances of an indicator using different settings, an optimal setting choice at time t is given by the average of the best-performing instances with settings s(t) .
Finding this group of best-performing instances could be done using the previously described K-Means clustering method, assuming three groups of interest (K = 3) defined as worst performing, average performing, and best performing.
We first obtain an analog of performance P(t, factor) described as:
P(t, factor) = P(t-1, factor) + α * (∆C(t) × S(t-1, factor) - P(t-1, factor))
where 1 > α > 0, which is the performance memory determining the degree to which older inputs affect the current output. C(t) is the closing price, and S(t, factor) is the SuperTrend signal generating function with multiplicative factor factor .
We run this performance function for multiple factor settings and perform K-Means clustering on the multiple obtained performances to obtain the best-performing cluster. We initiate our centroids using quartiles of the obtained performances for faster centroids convergence.
The average of the factors associated with the best-performing cluster is then used to obtain the final factor setting, which is used to compute the final SuperTrend output.
Do note that we give the liberty for the user to get the final factor from the best, average, or worst cluster for experimental purposes.
🔶 SETTINGS
ATR Length: ATR period used for the calculation of the SuperTrends.
Factor Range: Determine the minimum and maximum factor values for the calculation of the SuperTrends.
Step: Increments of the factor range.
Performance Memory: Determine the degree to which older inputs affect the current output, with higher values returning longer-term performance measurements.
From Cluster: Determine which cluster is used to obtain the final factor.
🔹 Optimization
This group of settings affects the runtime performances of the script.
Maximum Iteration Steps: Maximum number of iterations allowed for finding centroids. Excessively low values can return a better script load time but poor clustering.
Historical Bars Calculation: Calculation window of the script (in bars).
Indicator
Pivot Point SuperTrend Strategy +TrendFilterIn the dynamic world of financial markets, traders are always on the lookout for innovative strategies to identify trends and make timely trades. The "Pivot Point SuperTrend strategy +TrendFilter" has emerged as an intriguing approach, combining two popular indicators - Pivot Points and SuperTrend, while introducing an additional trend filter for added precision. This strategy draws inspiration from Lonesome TheBlue's "Pivot Point SuperTrend" script, aiming to provide traders with a reliable tool for trend following while minimizing false signals.
The Core Concept:
The strategy's foundation lies in the fusion of Pivot Points and SuperTrend indicators, and the addition of a robust trend filter. It begins by calculating Pivot Highs and Lows over a specified period, serving as crucial reference points for trend analysis. Through a weighted average calculation, these Pivot Points create a center line, refining the overall indicator.
Next, based on the center line and the Average True Range (ATR) with a user-defined Factor, upper and lower bands are generated. These bands adapt to market volatility, adding flexibility to the strategy. The heart of the "Pivot Point SuperTrend" strategy lies in accurately identifying the prevailing trend, with the indicator smoothly transitioning between bullish and bearish signals as the price interacts with the SuperTrend bands.
The additional trend filter introduced into the strategy further enhances its capabilities. This filter is based on a moving average, providing a dynamic assessment of the trend's strength and direction. By combining this trend filter with the original Pivot Point SuperTrend signals, the strategy aims to make more informed and reliable trading decisions.
Advantages of "Pivot Point SuperTrend" with Trend Filter:
1. Enhanced Precision: The incorporation of a trend filter improves the strategy's accuracy by confirming the overall trend direction before generating signals.
2. Trend Continuation: The integration of Pivot Points and SuperTrend, along with the trend filter, aims to prolong trades during strong market trends, potentially maximizing profit opportunities.
3. Reduced Whipsaws: The strategy's weighted average calculation, coupled with the trend filter, helps minimize false signals and reduces whipsaws during uncertain or sideways market conditions.
4. Support and Resistance Insights: The strategy continues to provide additional support and resistance levels based on the Pivot Points, offering valuable contextual information to traders.
Strategy
Volume-Weighted Supertrend Strategy [wbburgin]This is a script that can be used as a strategy or a standalone indicator.
The Volume-Weighted Supertrend is a supertrend based on a rolling VWAP, instead of a normal price source. The strategy has two components - a supertrend based off of this VWAP (shown on the chart) and a supertrend from volume itself (not plotted on the chart directly). The supertrend from volume is an example of my "Supertrend Any Source" indicator, where a custom ATR is created from non-OHLC data; this is available as both a separate public script and also in my "wbburgin_utils" library for you to use in your own script creation.
The supertrend from volume acts as a confirmation filter for the VWAP-supertrend shown on-chart. If the volume supertrend is trending up and the VWAP-based supertrend is also trending up, a buy signal is generated. Likewise, if the volume supertrend is trending down and the VWAP-supertrend is trending down, a sell signal is generated. The colors are based off of whether both supertrends are trending up or down: green for both up, blue for only price up, orange for only price down, and red for both down.
The settings enable you to change the volume length and the ATR length separately, as well as the multiplier and the source for the price supertrend. If you load the indicator for the first time and see no entries and exits, this is because "Show Strategy Entries and Exits" is disabled in the settings. This is if you plan on using the strategy as an indicator and don't want to be bothered by the entry and exit symbols on the chart. Additionally, for those who like clean charts (like me), you can turn all the labels off in the settings, as well as the highlighting.
My default strategy settings for the strategy results shown below are as follows: 5% equity per trade, 5 degrees of pyramiding, commissions of 0.08% per trade. This strategy doesn't come with stops yet, so please be aware of that before using it to trade - I highly suggest you create your own stops based off of your R/R ratio and personal risk tolerance. Additionally, it works best on trending assets (b/c of the supertrends) with high volume. This might mean it does not work as well on lower timeframes. Strategy
Trend hunter strategy - buy & sellThe indicator combines multiple technical indicators and conditions to generate buy and sell signals.
Here's how the indicator works and how to use it:
Strategy Selection:
The indicator provides a dropdown menu to choose the type of strategy. The available options are "Pullback" and "Simple."
Supertrend Settings:
The Supertrend indicator is used to identify the trend direction.
The indicator takes two input parameters:
ATR Length: Specifies the length of the Average True Range (ATR) used in the Supertrend calculation. The default value is 10.
Factor: Specifies the factor used in the Supertrend calculation. The default value is 3.0.
EMA Settings:
The indicator also includes an Exponential Moving Average (EMA) condition.
You can enable or disable the EMA condition using the "Ema Condition On/Off" checkbox.
If enabled, the indicator calculates an EMA based on the close price.
You can specify the length of the EMA using the "Ema Length" input parameter. The default value is 200.
RSI Settings:
The Relative Strength Index (RSI) indicator is used to generate additional conditions.
You can enable or disable the RSI condition using the "Rsi Condition On/Off" checkbox.
If enabled, the indicator calculates the RSI based on the close price.
You can specify the length of the RSI using the "Rsi Length" input parameter. The default value is 14.
Additionally, you can set the overbought and oversold levels for the RSI using the "RSI BUY Level" and "RSI SELL Level" input parameters, respectively. The default value for both is 50.
Final Conditions:
The indicator combines the Supertrend, EMA, and RSI conditions to generate buy and sell signals.
The specific conditions depend on the chosen strategy:
For the "Simple" strategy, the buy condition is when the Supertrend is in an up trend, not in a previous long position, the RSI is above the overbought level, and the close price is above the EMA.
For the "Pullback" strategy, the buy condition is when there is a cross under of the previous low with the Supertrend, the Supertrend is in an up trend, the RSI is above the overbought level, and the close price is above the EMA.
The sell conditions are the opposite of the respective buy conditions.
Backtest Period:
You can specify the start and end dates for the backtesting using the "Start calculations from" and "End calculations" inputs, respectively. The default start date is "2005-01-01" and the default end date is "2045-03-01." (this is work in progress) Still working on the table part, it is a bit tricky.
Trade Direction:
You can choose the trade direction using the "Trade Direction" input parameter. The available options are "Long," "Short," and "Both."
Depending on the selected trade direction, the indicator will generate signals accordingly.
Visual Display:
The indicator plots the Supertrend line on the price chart.
Buy signals are shown as green labels below the price bars.
Sell signals are shown as red labels above the price bars.
Adjust the input parameters according to your preferences, and then apply the indicator to a chart to see the generated signals. Please note that this indicator should be used for educational purposes only and should be thoroughly tested before using it for real trading. Indicator
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SuperBollingerTrend (Expo)█ Overview
The SuperBollingerTrend indicator is a combination of two popular technical analysis tools, Bollinger Bands, and SuperTrend. By fusing these two indicators, SuperBollingerTrend aims to provide traders with a more comprehensive view of the market, accounting for both volatility and trend direction. By combining trend identification with volatility analysis, the SuperBollingerTrend indicator provides traders with valuable insights into potential trend changes. It recognizes that high volatility levels often accompany stronger price momentum, which can result in the formation of new trends or the continuation of existing ones.
█ How Volatility Impacts Trends
Volatility can impact trends by expanding or contracting them, triggering trend reversals, leading to breakouts, and influencing risk management decisions. Traders need to analyze and monitor volatility levels in conjunction with trend analysis to gain a comprehensive understanding of market dynamics.
█ How to use
Trend Reversals: High volatility can result in more dramatic price fluctuations, which may lead to sharp trend reversals. For example, a sudden increase in volatility can cause a bullish trend to transition into a bearish one, or vice versa, as traders react to significant price swings.
Volatility Breakouts: Volatility can trigger breakouts in trends. Breakouts occur when the price breaks through a significant support or resistance level, indicating a potential shift in the trend. Higher volatility levels can increase the likelihood of breakouts, as they indicate stronger market momentum and increased buying or selling pressure. This indicator triggers when the volatility increases, and if the price is near a key level when the indicator alerts, it might trigger a great trend.
█ Features
Peak Signal Move
The indicator calculates the peak price move for each ZigZag and displays it under each signal. This highlights how much the market moved between the signals.
Average ZigZag Move
All price moves between two signals are stored, and the average or the median is calculated and displayed in a table. This gives traders a great idea of how much the market moves on average between two signals.
Take Profit
The Take Profit line is placed at the average or the median price move and gives traders a great idea of what they can expect in average profit from the latest signals.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
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Strategy
Simple SuperTrend Strategy for BTCUSD 4HHello guys!, If you are a swing trader and you are looking for a simple trend strategy, you should check this one. Based in the supertrend indicator, this strategy will help you to catch big movements in BTCUSD 4H and avoid losses as much as possible in consolidated situations of the market
This strategy was designed for BTCUSD in 4H timeframe
Backtesting context: 2020-01-02 to 2023-01-05 (The strategy has also worked in previous years)
Trade conditions:
Rules are actually simple, the most important thing is the risk and position management of this strategy
For long:
Once Supertrend changes from a downtrend to a uptrend, you enter into a long position. The stop loss will be defined by the atr stop loss
The first profit will be of 0.75 risk/reward ratio where half position will be closed. When this happens, you move the stop loss to break even.
Now, just will be there two situations:
Once Supertrend changes from a uptrend to a downtrend, you close the other half of the initial long position.
If price goes againts the position, the position will be closed due to breakeven.
For short:
Once Supertrend changes from a uptrend to a downtrend, you enter into a short position. The stop loss will be defined by the atr stop loss
The first profit will be of 0.75 risk/reward ratio where half position will be closed. When this happens, you move the stop loss to break even.
Like in the long position, just will be there two situations:
Once Supertrend changes from a downtrend to a uptrend, you close the other half of the initial short position.
If price goes againts the position, the position will be closed due to breakeven.
Risk management
For calculate the amount of the position you will use just a small percent of your initial capital for the strategy and you will use the atr stop loss for this.
Example: You have 1000 usd and you just want to risk 2,5% of your account, there is a long signal at price of 20,000 usd. The stop loss price from atr stop loss is 19,000. You calculate the distance in percent between 20,000 and 19,000. In this case, that distance would be of 5,0%. Then, you calculate your position by this way: (initial or current capital * risk per trade of your account) / (stop loss distance).
Using these values on the formula: (1000*2,5%)/(5,0%) = 500usd. It means, you have to use 500 usd for risking 2.5% of your account.
We will use this risk management for apply compound interest.
Script functions
Inside of settings, you will find some utilities for display atr stop loss, supertrend or positions.
You will find the settings for risk management at the end of the script if you want to change something. But rebember, do not change values from indicators, the idea is to not over optimize the strategy.
If you want to change the initial capital for backtest the strategy, go to properties, and also enter the commisions of your exchange and slippage for more realistic results.
Signals meanings:
L for long position. CL for close long position.
S for short position. CS for close short position.
Tp for take profit (it also appears when the position is closed due to stop loss, this due to the script uses two kind of positions)
Exit due to break even or due to stop loss
Some things to consider
USE UNDER YOUR OWN RISK. PAST RESULTS DO NOT REPRESENT THE FUTURE.
DEPENDING OF % ACCOUNT RISK PER TRADE, YOU COULD REQUIRE LEVERAGE FOR OPEN SOME POSITIONS, SO PLEASE, BE CAREFULL AND USE CORRECTLY THE RISK MANAGEMENT
The amount of trades closed in the backtest are not exactly the real ones. If you want to know the real ones, go to settings and change % of trade for first take profit to 100 for getting the real ones. In the backtest, the real amount of opened trades was of 194.
Indicators used:
Supertrend
Atr stop loss by garethyeo
This is the fist strategy that I publish in pulsewire, I will be glad with you for any suggestion, support or advice for future scripts. Do not doubt in make any question you have and if you liked this content, leave a boost. I plan to bring more strategies and useful content for you!
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