Sequential Pattern Strength [QuantAlgo]🟢 Overview
The Sequential Pattern Strength indicator measures the power and sustainability of consecutive price movements by tracking unbroken sequences of up or down closes. It incorporates sequence quality assessment, price extension analysis, and automatic exhaustion detection to help traders identify when strong trends are losing momentum and approaching potential reversal or continuation points.
🟢 How It Works
The indicator's key insight lies in its sequential pattern tracking system, where pattern strength is measured by analyzing consecutive price movements and their sustainability:
if close > close
upSequence := upSequence + 1
downSequence := 0
else if close < close
downSequence := downSequence + 1
upSequence := 0
The system calculates sequence quality by measuring how "perfect" the consecutive moves are:
perfectMoves = math.max(upSequence, downSequence)
totalMoves = math.abs(bar_index - ta.valuewhen(upSequence == 1 or downSequence == 1, bar_index, 0))
sequenceQuality = totalMoves > 0 ? perfectMoves / totalMoves : 1.0
First, it tracks price extension from the sequence starting point:
priceExtension = (close - sequenceStartPrice) / sequenceStartPrice * 100
Then, pattern exhaustion is identified when sequences become overextended:
isExhausted = math.abs(currentSequence) >= maxSequence or
math.abs(priceExtension) > resetThreshold * math.abs(currentSequence)
Finally, the pattern strength combines sequence length, quality, and price movement with momentum enhancement:
patternStrength = currentSequence * sequenceQuality * (1 + math.abs(priceExtension) / 10)
enhancedSignal = patternStrength + momentum * 10
signal = ta.ema(enhancedSignal, smooth)
This creates a sequence-based momentum indicator that combines consecutive movement analysis with pattern sustainability assessment, providing traders with both directional signals and exhaustion insights for entry/exit timing.
🟢 Signal Interpretation
Positive Values (Above Zero): Sequential pattern strength indicating bullish momentum with consecutive upward price movements and sustained buying pressure = Long/Buy opportunities
Negative Values (Below Zero): Sequential pattern strength indicating bearish momentum with consecutive downward price movements and sustained selling pressure = Short/Sell opportunities
Zero Line Crosses: Pattern transitions between bullish and bearish regimes, indicating potential trend changes or momentum shifts when sequences break
Upper Threshold Zone: Area above maximum sequence threshold (2x maxSequence) indicating extremely strong bullish patterns approaching exhaustion levels
Lower Threshold Zone: Area below negative threshold (-2x maxSequence) indicating extremely strong bearish patterns approaching exhaustion levels
Indicators
Moving Average Adaptive RSI [BackQuant]Moving Average Adaptive RSI
What this is
A momentum oscillator that reshapes classic RSI into a zero-centered column plot and makes it adaptive. It builds RSI from two parts:
• A sensitivity window that scans several recent bars to capture the strongest up and down impulses.
• A selectable moving average that smooths those impulses before computing RSI.
The output ranges roughly from −100 to +100 with 0 as the midline, with optional extra smoothing and built-in divergence detection.
How it works
Impulse extraction
• For each bar the script inspects the last rsi_sen bars and collects upward and downward price changes versus the current price.
• It keeps the maximum upward change and maximum downward change from that window, emphasizing true bursts over single-bar noise.
MA-based averaging
• The up and down impulse series are averaged with your chosen MA over rsi_len bars.
• Supported MA types: SMA, EMA, DEMA, WMA, HMA, SMMA (RMA), TEMA.
Zero-centered RSI transform
• RS = UpMA ÷ DownMA, then mapped to a symmetric scale: 100 − 200 ÷ (1 + RS) .
• Above 0 implies positive momentum bias. Below 0 implies negative momentum bias.
Optional extra smoothing
• A second smoothing pass can be applied to the final oscillator using smoothing_len and smooth_type . Toggle with “Use Extra Smoothing”.
Visual encoding
• The oscillator is drawn as columns around the zero line with a gradient that intensifies toward extremes.
• Static bands mark 80 to 100 and −80 to −100 for extreme conditions.
Key inputs and what they change
• Price Source : input series for momentum.
• Calculation Period (rsi_len) : primary averaging window on up and down components. Higher = smoother, slower.
• Sensitivity (rsi_sen) : how many recent bars are scanned to find max impulses. Higher = more responsive to bursts.
• Calculation Type (ma_type) : MA family that shapes the core behavior. HMA or DEMA is faster, SMA or SMMA is slower.
• Smoothing Type and Length : optional second pass to calm noise on the final output.
• UI toggles : show or hide the oscillator, candle painting, and extreme bands.
Reading the oscillator
• Midline cross up (0) : momentum bias turning positive.
• Midline cross down (0) : momentum bias turning negative.
• Positive territory :
– 0 to 40: constructive but not stretched.
– 40 to 80: strong momentum, continuation more likely.
– Above 80: extreme risk of mean reversion grows.
• Negative territory : mirror the same levels for the downside.
Divergence detection
The script plots four divergence types using pivot highs and lows on both price and the oscillator. Lookbacks are set by lbL and lbR .
• Regular bullish : price lower low, oscillator higher low. Possible downside exhaustion.
• Hidden bullish : price higher low, oscillator lower low. Bias to trend continuation up.
• Regular bearish : price higher high, oscillator lower high. Possible upside exhaustion.
• Hidden bearish : price lower high, oscillator higher high. Bias to trend continuation down.
Labels: ℝ for regular, ℍ for hidden. Green for bullish, red for bearish.
Candle coloring
• Optional bar painting: green when the oscillator is above 0, red when below 0. This is for visual scanning only.
Strengths
• Adaptive sensitivity via a rolling impulse window that responds to genuine bursts.
• Configurable MA core so you can match responsiveness to the instrument.
• Zero-centered scale for simple regime reads with 0 as a clear bias line.
• Built-in regular and hidden divergence mapping.
• Flexible across symbols and timeframes once tuned.
Limitations and cautions
• Trends can remain extended. Treat extremes as context rather than automatic reversal signals.
• Divergence quality depends on pivot lookbacks. Short lookbacks give more signals with more noise. Long lookbacks reduce noise but add lag.
• Double smoothing can delay zero-line transitions. Balance smoothness and timeliness.
Practical usage ideas
• Regime filter : only take long setups from your separate method when the oscillator is above 0, shorts when below 0.
• Pullback confirmation : in uptrends, look for dips that hold above 0 or turn up from 0 to 40. Reverse for downtrends.
• Divergence as a heads-up : wait for a zero-line cross or a price trigger before acting on divergence.
• Sensitivity tuning : start with rsi_sen 2 to 5 on faster timeframes, increase slightly on slower charts.
Alerts
• MA-A RSI Long : oscillator crosses above 0.
• MA-A RSI Short : oscillator crosses below 0.
Use these as bias or timing aids, not standalone trade commands.
Settings quick reference
• Calculation : Price Source, Calculation Type, Calculation Period, Sensitivity.
• Smoothing : Smoothing Type, Smoothing Length, Use Extra Smoothing.
• UI : Show Oscillator, Paint Candles, Show Static High and Low Levels.
• Divergences : Pivot Lookback Left and Right, Div Signal Length, Show Detected Divergences.
Final thoughts
This tool reframes RSI by extracting strong short-term impulses and averaging them with a moving-average model of your choice, then presenting a zero-centered output for clear regime reads. Pair it with your structure, risk and execution process, and tune sensitivity and smoothing to the market you trade.
Machine Learning BBPct [BackQuant]Machine Learning BBPct
What this is (in one line)
A Bollinger Band %B oscillator enhanced with a simplified K-Nearest Neighbors (KNN) pattern matcher. The model compares today’s context (volatility, momentum, volume, and position inside the bands) to similar situations in recent history and blends that historical consensus back into the raw %B to reduce noise and improve context awareness. It is informational and diagnostic—designed to describe market state, not to sell a trading system.
Background: %B in plain terms
Bollinger %B measures where price sits inside its dynamic envelope: 0 at the lower band, 1 at the upper band, ~ 0.5 near the basis (the moving average). Readings toward 1 indicate pressure near the envelope’s upper edge (often strength or stretch), while readings toward 0 indicate pressure near the lower edge (often weakness or stretch). Because bands adapt to volatility, %B is naturally comparable across regimes.
Why add (simplified) KNN?
Classic %B is reactive and can be whippy in fast regimes. The simplified KNN layer builds a “nearest-neighbor memory” of recent market states and asks: “When the market looked like this before, where did %B tend to be next bar?” It then blends that estimate with the current %B. Key ideas:
• Feature vector . Each bar is summarized by up to five normalized features:
– %B itself (normalized)
– Band width (volatility proxy)
– Price momentum (ROC)
– Volume momentum (ROC of volume)
– Price position within the bands
• Distance metric . Euclidean distance ranks the most similar recent bars.
• Prediction . Average the neighbors’ prior %B (lagged to avoid lookahead), inverse-weighted by distance.
• Blend . Linearly combine raw %B and KNN-predicted %B with a configurable weight; optional filtering then adapts to confidence.
This remains “simplified” KNN: no training/validation split, no KD-trees, no scaling beyond windowed min-max, and no probabilistic calibration.
How the script is organized (by input groups)
1) BBPct Settings
• Price Source – Which price to evaluate (%B is computed from this).
• Calculation Period – Lookback for SMA basis and standard deviation.
• Multiplier – Standard deviation width (e.g., 2.0).
• Apply Smoothing / Type / Length – Optional smoothing of the %B stream before ML (EMA, RMA, DEMA, TEMA, LINREG, HMA, etc.). Turning this off gives you the raw %B.
2) Thresholds
• Overbought/Oversold – Default 0.8 / 0.2 (inside ).
• Extreme OB/OS – Stricter zones (e.g., 0.95 / 0.05) to flag stretch conditions.
3) KNN Machine Learning
• Enable KNN – Switch between pure %B and hybrid.
• K (neighbors) – How many historical analogs to blend (default 8).
• Historical Period – Size of the search window for neighbors.
• ML Weight – Blend between raw %B and KNN estimate.
• Number of Features – Use 2–5 features; higher counts add context but raise the risk of overfitting in short windows.
4) Filtering
• Method – None, Adaptive, Kalman-style (first-order),
or Hull smoothing.
• Strength – How aggressively to smooth. “Adaptive” uses model confidence to modulate its alpha: higher confidence → stronger reliance on the ML estimate.
5) Performance Tracking
• Win-rate Period – Simple running score of past signal outcomes based on target/stop/time-out logic (informational, not a robust backtest).
• Early Entry Lookback – Horizon for forecasting a potential threshold cross.
• Profit Target / Stop Loss – Used only by the internal win-rate heuristic.
6) Self-Optimization
• Enable Self-Optimization – Lightweight, rolling comparison of a few canned settings (K = 8/14/21 via simple rules on %B extremes).
• Optimization Window & Stability Threshold – Governs how quickly preferred K changes and how sensitive the overfitting alarm is.
• Adaptive Thresholds – Adjust the OB/OS lines with volatility regime (ATR ratio), widening in calm markets and tightening in turbulent ones (bounded 0.7–0.9 and 0.1–0.3).
7) UI Settings
• Show Table / Zones / ML Prediction / Early Signals – Toggle informational overlays.
• Signal Line Width, Candle Painting, Colors – Visual preferences.
Step-by-step logic
A) Compute %B
Basis = SMA(source, len); dev = stdev(source, len) × multiplier; Upper/Lower = Basis ± dev.
%B = (price − Lower) / (Upper − Lower). Optional smoothing yields standardBB .
B) Build the feature vector
All features are min-max normalized over the KNN window so distances are in comparable units. Features include normalized %B, normalized band width, normalized price ROC, normalized volume ROC, and normalized position within bands. You can limit to the first N features (2–5).
C) Find nearest neighbors
For each bar inside the lookback window, compute the Euclidean distance between current features and that bar’s features. Sort by distance, keep the top K .
D) Predict and blend
Use inverse-distance weights (with a strong cap for near-zero distances) to average neighbors’ prior %B (lagged by one bar). This becomes the KNN estimate. Blend it with raw %B via the ML weight. A variance of neighbor %B around the prediction becomes an uncertainty proxy ; combined with a stability score (how long parameters remain unchanged), it forms mlConfidence ∈ . The Adaptive filter optionally transforms that confidence into a smoothing coefficient.
E) Adaptive thresholds
Volatility regime (ATR(14) divided by its 50-bar SMA) nudges OB/OS thresholds wider or narrower within fixed bounds. The aim: comparable extremeness across regimes.
F) Early entry heuristic
A tiny two-step slope/acceleration probe extrapolates finalBB forward a few bars. If it is on track to cross OB/OS soon (and slope/acceleration agree), it flags an EARLY_BUY/SELL candidate with an internal confidence score. This is explicitly a heuristic—use as an attention cue, not a signal by itself.
G) Informational win-rate
The script keeps a rolling array of trade outcomes derived from signal transitions + rudimentary exits (target/stop/time). The percentage shown is a rough diagnostic , not a validated backtest.
Outputs and visual language
• ML Bollinger %B (finalBB) – The main line after KNN blending and optional filtering.
• Gradient fill – Greenish tones above 0.5, reddish below, with intensity following distance from the midline.
• Adaptive zones – Overbought/oversold and extreme bands; shaded backgrounds appear at extremes.
• ML Prediction (dots) – The KNN estimate plotted as faint circles; becomes bright white when confidence > 0.7.
• Early arrows – Optional small triangles for approaching OB/OS.
• Candle painting – Light green above the midline, light red below (optional).
• Info panel – Current value, signal classification, ML confidence, optimized K, stability, volatility regime, adaptive thresholds, overfitting flag, early-entry status, and total signals processed.
Signal classification (informational)
The indicator does not fire trade commands; it labels state:
• STRONG_BUY / STRONG_SELL – finalBB beyond extreme OS/OB thresholds.
• BUY / SELL – finalBB beyond adaptive OS/OB.
• EARLY_BUY / EARLY_SELL – forecast suggests a near-term cross with decent internal confidence.
• NEUTRAL – between adaptive bands.
Alerts (what you can automate)
• Entering adaptive OB/OS and extreme OB/OS.
• Midline cross (0.5).
• Overfitting detected (frequent parameter flipping).
• Early signals when early confidence > 0.7.
These are purely descriptive triggers around the indicator’s state.
Practical interpretation
• Mean-reversion context – In range markets, adaptive OS/OB with ML smoothing can reduce whipsaws relative to raw %B.
• Trend context – In persistent trends, the KNN blend can keep finalBB nearer the mid/upper region during healthy pullbacks if history supports similar contexts.
• Regime awareness – Watch the volatility regime and adaptive thresholds. If thresholds compress (high vol), “OB/OS” comes sooner; if thresholds widen (calm), it takes more stretch to flag.
• Confidence as a weight – High mlConfidence implies neighbors agree; you may rely more on the ML curve. Low confidence argues for de-emphasizing ML and leaning on raw %B or other tools.
• Stability score – Rising stability indicates consistent parameter selection and fewer flips; dropping stability hints at a shifting backdrop.
Methodological notes
• Normalization uses rolling min-max over the KNN window. This is simple and scale-agnostic but sensitive to outliers; the distance metric will reflect that.
• Distance is unweighted Euclidean. If you raise featureCount, you increase dimensionality; consider keeping K larger and lookback ample to avoid sparse-neighbor artifacts.
• Lag handling intentionally uses neighbors’ previous %B for prediction to avoid lookahead bias.
• Self-optimization is deliberately modest: it only compares a few canned K/threshold choices using simple “did an extreme anticipate movement?” scoring, then enforces a stability regime and an overfitting guard. It is not a grid search or GA.
• Kalman option is a first-order recursive filter (fixed gain), not a full state-space estimator.
• Hull option derives a dynamic length from 1/strength; it is a convenience smoothing alternative.
Limitations and cautions
• Non-stationarity – Nearest neighbors from the recent window may not represent the future under structural breaks (policy shifts, liquidity shocks).
• Curse of dimensionality – Adding features without sufficient lookback can make genuine neighbors rare.
• Overfitting risk – The script includes a crude overfitting detector (frequent parameter flips) and will fall back to defaults when triggered, but this is only a guardrail.
• Win-rate display – The internal score is illustrative; it does not constitute a tradable backtest.
• Latency vs. smoothness – Smoothing and ML blending reduce noise but add lag; tune to your timeframe and objectives.
Tuning guide
• Short-term scalping – Lower len (10–14), slightly lower multiplier (1.8–2.0), small K (5–8), featureCount 3–4, Adaptive filter ON, moderate strength.
• Swing trading – len (20–30), multiplier ~2.0, K (8–14), featureCount 4–5, Adaptive thresholds ON, filter modest.
• Strong trends – Consider higher adaptive_upper/lower bounds (or let volatility regime do it), keep ML weight moderate so raw %B still reflects surges.
• Chop – Higher ML weight and stronger Adaptive filtering; accept lag in exchange for fewer false extremes.
How to use it responsibly
Treat this as a state descriptor and context filter. Pair it with your execution signals (structure breaks, volume footprints, higher-timeframe bias) and risk management. If mlConfidence is low or stability is falling, lean less on the ML line and more on raw %B or external confirmation.
Summary
Machine Learning BBPct augments a familiar oscillator with a transparent, simplified KNN memory of recent conditions. By blending neighbors’ behavior into %B and adapting thresholds to volatility regime—while exposing confidence, stability, and a plain early-entry heuristic—it provides an informational, probability-minded view of stretch and reversion that you can interpret alongside your own process.
TCP | Market Session | Session Analyzer📌 TCP | Market Session Indicator | Crypto Version
A powerful, real-time market session visualization tool tailored for crypto traders. Track the heartbeat of Asia, Europe, and US trading hours directly on your chart with live session boxes, behavioral analysis, liquidity grab detection, and countdown timers. Know when the action starts, how the market behaves, and where the traps lie.
🔰 Introduction:
Trade the Right Hours with the Right Tools
Time matters in trading. Most significant moves happen during key sessions—and knowing when and how each session unfolds can give you a sharp edge. The TCP Market Session Indicator, developed by Trade City Pro (TCP), puts professional session tracking and behavioral insights at your fingertips.
Whether you're a scalper or swing trader, this indicator gives you the timing context to enter and exit trades with greater confidence and clarity.
🕒 Core Features
• Live Session Boxes :
Highlight active ranges during Asia, Europe, and US sessions with dynamic high/low updates.
• Session Start/End Labels :
Know exactly when each session begins and ends plotted clearly on your chart with context.
• Session Behavior Analysis :
At the end of each session, the indicator classifies the price action as:
- Trend Up
- Trend Down
- Consolidation
- Manipulation
• Liquidity Grab Detection: Automatically detects possible stop hunts (fake breakouts) and marks them on the chart with precision filters (volume, ATR, reversal).
• Session Countdown Table: A live dashboard showing:
- Current active session
- Time left in session
- Upcoming session and how many minutes until it starts
- Utility time converter (e.g. 90 min = 01:30)
• Vertical Session Lines: Visualize past and upcoming session boundaries with customizable history and future range.
• Multi-Day Support: Draw session ranges for previous, current, and future days for better backtesting and forecasting.
⚙️ Settings Panel
Customize everything to fit your trading style and schedule:
• Session Time Settings:
Set the opening and closing time for each session manually using UTC-based minute inputs.
→ For example, enter Asia Start: 0, Asia End: 480 for 00:00–08:00 UTC.
This gives full flexibility to adjust session hours to match your preferred market behavior.
• Enable or Disable Elements:
Toggle the visibility of each session (Asia, Europe, US), as well as:
- Session Boxes
- Countdown Table
- Session Lines
- Liquidity Grab Labels
• Timezone Selection:
Choose between using UTC or your chart’s local timezone for session calculations.
• Customization Options:
Select number of past and future days to draw session data
Adjust vertical line transparency
Fine-tune label offset and spacing for clean layout
📊 Smart Session Boxes
Each session box tracks high, low, open, and close in real time, providing visual clarity on market structure. Once a session ends, the box closes, and the behavior type is saved and labeled ideal for spotting patterns across sessions.
• Asia: Green Box
• Europe: Orange Box
• US: Blue Box
💡 Why Use This Tool?
• Perfect Timing: Don’t get chopped in low-liquidity hours. Focus on sessions where volume and volatility align.
• Pattern Recognition: Study how price behaves session-to-session to build better strategies.
• Trap Detection: Spot manipulation moves (liquidity grabs) early and avoid common retail pitfalls.
• Macro Session Mapping: Use as a foundational layer to align trades with market structure and news cycles.
🔍 Example Use Case
You're watching BTC at 12:45 UTC. The indicator tells you:
The Asia session just ended (label shows “Asia Session End: Trend Up”)
Europe session starts in 15 minutes
A liquidity grab just triggered at the previous high—label confirmed
Now you know who’s active, what the market just did, and what’s about to start—all in one glance.
✅ Why Traders Trust It
• Visual & Intuitive: Fully chart-based, no clutter, no guessing
• Crypto-Focused: Designed specifically for 24/7 crypto markets (not outdated forex models)
• Non-Repainting: All labels and boxes stay as printed—no tricks
• Reliable: Tested across multiple exchanges, pairs, and timeframes
🧩 Built by Trade City Pro (TCP)
The TCP Market Session Indicator is part of a suite of professional tools used by over 150,000 traders. It’s coded in Pine Script v6 for full compatibility with TradingView’s latest capabilities.
🔗 Resources
• Tutorial: Learn how to analyze sessions like a pro in our TradingView guide:
"TradeCityPro Academy: Session Mapping & Liquidity Traps"
• More Tools: Explore our full library of indicators on
Cumulative Volume Delta (SB-1) 2.0
📈 Cumulative Volume Delta (CVD) — Stair-Step + Threshold Alerts
🔍 Overview
This Cumulative Volume Delta (CVD) tool visualizes aggressive buying and selling pressure in the market by plotting candlestick-style bars based on volume delta. It helps traders understand which side — buyers or sellers — is exerting more control on lower timeframes and highlights momentum shifts through stair-step patterns and delta threshold breaks. Resets to zero at EOD
Ideal for futures traders, scalpers, and intraday strategists looking for orderflow-based confirmation.
🧠 What Is CVD?
CVD (Cumulative Volume Delta) measures the difference between market buys and sells over a specific timeframe. When the delta is rising, it suggests buyers are being more aggressive. Falling delta suggests seller dominance.
This script aggregates volume delta from a lower timeframe and plots it in a higher timeframe context, allowing you to track microstructure shifts within larger candles.
📊 Features
✅ CVD Candlesticks
Each bar represents volume delta as an OHLC-style candle using:
Open: Delta at the start of the bar
High/Low: Peak delta range
Close: Final delta value at bar close
Teal candles = Net buying pressure
Red candles = Net selling pressure
✅ Threshold Levels (Key Visual Zones)
The script includes horizontal dashed lines at:
+5,000 and +10,000 → Signify strong buying pressure
-5,000 and -10,000 → Signify strong selling pressure
0 line → Neutrality line (no net pressure)
These levels act as volume-based support/resistance zones and breakout confirmation tools. For example:
A CVD cross above +5,000 shows buyers taking control
A CVD cross above +10,000 implies strong bullish momentum
A CVD cross below -5,000 or -10,000 signals intense selling pressure
📈 Stair-Step Pattern Detection
Detects two specific volume-based continuation setups:
Bullish Stair-Step: Both the high and low of the CVD candle are higher than the previous candle
Bearish Stair-Step: Both the high and low of the CVD candle are lower than the previous candle
These patterns often appear during trending moves and serve as confirmation of strength or continuation.
Visual markers:
🟢 Green triangles below bars = Bullish stair-step
🔴 Red triangles above bars = Bearish stair-step
🔔 Alert Conditions
Get real-time alerts when:
Bullish Stair-Step is detected
Bearish Stair-Step is detected
CVD crosses above +5,000
CVD crosses below -5,000
📢 Alerts only trigger on crossover, not every time CVD remains above or below. This avoids repetitive notifications.
⚙️ Inputs & Customization
Anchor Timeframe: The higher timeframe to which CVD data is applied (default: 1D)
Lower Timeframe: The timeframe used to calculate the CVD delta (default: 5 minutes)
Optional Override: Use custom timeframe toggle to force your own micro timeframe
📌 How to Use This CVD Indicator (Step-by-Step Guide)
✅ 1. Confirm Bias Using the Zero Line
The zero line (0 CVD) represents neutral pressure — neither buyers nor sellers are dominating.
Use it as your first filter:
🔼 If CVD is above 0 and rising → Buyer control
🔽 If CVD is below 0 and falling → Seller control
🧠 Tip: CVD rising while price is consolidating may signal hidden buyer interest.
✅ 2. Watch for Crosses of Key Levels: +5,000 and +10,000
These levels act as momentum thresholds:
Level Signal Type What It Means
+5,000 Buyer breakout Buyers are starting to dominate
+10,000 Strong bull bias Strong institutional or algorithmic buying flow
-5,000 Seller breakout Sellers are taking control
-10,000 Strong bear bias Heavy selling pressure is entering the market
Wait for CVD to cross above +5K or below -5K to confirm the active side.
Use these crossovers as entry triggers, breakout confirmations, or trade filters.
🔔 Alerts fire only when the level is first crossed, not every bar above/below.
✅ 3. Use Stair-Step Patterns for Continuation Confirmation
The indicator shows stair-step patterns using triangle signals:
🟢 Green triangle below bar = Bullish stair-step
Suggests a higher high and higher low in delta → buyers stepping up
🔴 Red triangle above bar = Bearish stair-step
Suggests lower highs and lower lows in delta → selling pressure building
Use stair-step signals:
To confirm a continuation of trend
As an entry or add-on signal
Especially after a threshold breakout
🧠 Example: If CVD breaks above +5K and forms bullish stairs → confirms strong trend, ideal for momentum entries.
✅ 4. Combine with Price Action or Structure
CVD works best when used with price, not in isolation. For example:
📉 Price makes a new low but CVD doesn’t → potential bullish divergence
📈 CVD surges while price lags → buyers are absorbing, breakout likely
Use it with:
VWAP
Orderblocks
Liquidity sweeps
Break of market structure/MSS/BOS
✅ 5.
Set Anchor Timeframe = Daily
Set Lower Timeframe = 5 minutes (default)
This lets you:
See intraday flow inside daily bars
Confirm whether a daily candle is being built on net buying or selling
🧠 You’re essentially seeing intra-bar aggression within a bigger time structure.
🧭 Example Trading Setup
Bullish Scenario:
CVD is rising and above 0
CVD crosses above +5,000 → alert fires
Green stair-step appears
Price breaks local resistance or liquidity sweep completes
✅ Consider long entry with structure and CVD alignment
🎯 Place stops below last stair-step or structural low
📌 Final Notes
This tool does not repaint and is designed to work in real-time across all futures, crypto, and equity instruments that support volume data. If your symbol does not provide volume, the script will notify you.
Use it in confluence with VWAP, liquidity zones, or structure breaks for high-confidence trades.
4 Anchored VWAPs This indicator shows 4 periods of Anchored VWAPs according to specific dates the user chose.
Time-Price Velocity [QuantAlgo]🟢 Overview
The Time-Price Velocity indicator uses advanced velocity-based analysis to measure the rate of price change normalized against typical market movement, creating a dynamic momentum oscillator that identifies market acceleration patterns and momentum shifts. Unlike traditional momentum indicators that focus solely on price change magnitude, this indicator incorporates time-weighted displacement calculations and ATR normalization to create a sophisticated velocity measurement system that adapts to varying market volatility conditions.
This indicator displays a velocity signal line that oscillates around zero, with positive values indicating upward price velocity and negative values indicating downward price velocity. The signal incorporates acceleration background columns and statistical normalization to help traders identify momentum shifts and potential reversal or continuation opportunities across different timeframes and asset classes.
🟢 How It Works
The indicator's key insight lies in its time-price velocity calculation system, where velocity is measured using the fundamental physics formula:
velocity = priceChange / timeWeight
The system normalizes this raw velocity against typical price movement using Average True Range (ATR) to create market-adjusted readings:
normalizedVelocity = typicalMove > 0 ? velocity / typicalMove : 0
where "typicalMove = ta.atr(lookback)" provides the baseline for normal price movement over the specified lookback period.
The Time-Price Velocity indicator calculation combines multiple sophisticated components. First, it calculates acceleration as the change in velocity over time:
acceleration = normalizedVelocity - normalizedVelocity
Then, the signal generation applies EMA smoothing to reduce noise while preserving responsiveness:
signal = ta.ema(normalizedVelocity, smooth)
This creates a velocity-based momentum indicator that combines price displacement analysis with statistical normalization, providing traders with both directional signals and acceleration insights for enhanced market timing.
🟢 How to Use
1. Signal Interpretation and Threshold Zones
Positive Values (Above Zero): Time-price velocity indicating bullish momentum with upward price displacement relative to normalized baseline
Negative Values (Below Zero): Time-price velocity indicating bearish momentum with downward price displacement relative to normalized baseline
Zero Line Crosses: Velocity transitions between bullish and bearish regimes, indicating potential trend changes or momentum shifts
Upper Threshold Zone: Area above positive threshold (default 1.0) indicating strong bullish velocity and potential reversal point
Lower Threshold Zone: Area below negative threshold (default -1.0) indicating strong bearish velocity and potential reversal point
2. Acceleration Analysis and Visual Features
Acceleration Columns: Background histogram showing velocity acceleration (the rate of change of velocity), with green columns indicating accelerating velocity and red columns indicating decelerating velocity. The interpretation depends on trend context: red columns in downtrends indicate strengthening bearish momentum, while red columns in uptrends indicate weakening bullish momentum
Acceleration Column Height: The height of each column represents the magnitude of acceleration, with taller columns indicating stronger acceleration or deceleration forces
Bar Coloring: Optional price bar coloring matches velocity direction for immediate visual trend confirmation
Info Table: Real-time display of current velocity and acceleration values with trend arrows and change indicators
3. Additional Features:
Confirmed vs Live Data: Toggle between confirmed (closed) bar analysis for stable signals or current bar inclusion for real-time updates
Multi-timeframe Adaptability: Velocity normalization ensures consistent readings across different chart timeframes and asset volatilities
Alert System: Built-in alerts for threshold crossovers and direction changes
🟢 Examples with Preconfigured Settings
Default : Balanced configuration suitable for most timeframes and general trading applications, providing optimal balance between sensitivity and noise filtering for medium-term analysis.
Scalping : High sensitivity setup with shorter lookback period and reduced smoothing for ultra-short-term trades on 1-15 minute charts, optimized for capturing rapid momentum shifts and frequent trading opportunities.
Swing Trading : Extended lookback period with enhanced smoothing and higher threshold for multi-day positions, designed to filter market noise while capturing significant momentum moves on 1-4 hour and daily timeframes.
ATR Dynamic Stop (Table + Plot + ATR %)📊 This script displays dynamic stop levels based on ATR, designed for active traders.
Features:
- Shows long and short stop levels (price ± ATR × multiplier).
- Displays values as a floating table on the top-right corner.
- Optional plot lines directly on the chart.
- Option to calculate based on realtime price or last close.
- Displays the ATR value both in price units and as a percentage of the selected price.
- Fully customizable table: text size, text color, background color.
Inputs:
- ATR Multiplier and Length.
- Show/hide stop lines on the chart.
- Select price source (realtime or last close).
- Table appearance options.
Ideal for:
- Traders who want a clear visual stop guide.
- Combining volatility with risk management.
Logarithmic Moving Average (LMA) [QuantAlgo]🟢 Overview
The Logarithmic Moving Average (LMA) uses advanced logarithmic weighting to create a dynamic trend-following indicator that prioritizes recent price action while maintaining statistical significance. Unlike traditional moving averages that use linear or exponential weights, this indicator employs logarithmic decay functions to create a more sophisticated price averaging system that adapts to market volatility and momentum conditions.
The indicator displays a smoothed signal line that oscillates around zero, with positive values indicating bullish momentum and negative values indicating bearish momentum. The signal incorporates trend quality assessment, momentum confirmation, and multiple filtering mechanisms to help traders and investors identify trend continuation and reversal opportunities across different timeframes and asset classes.
🟢 How It Works
The indicator's core innovation lies in its logarithmic weighting system, where weights are calculated using the formula: w = 1.0 / math.pow(math.log(i + steepness), 2) The steepness parameter controls how aggressively recent data is prioritized over historical data, creating a dynamic weight decay that can be fine-tuned for different trading styles. This logarithmic approach provides more nuanced weight distribution compared to exponential moving averages, offering better responsiveness while maintaining stability.
The LMA calculation combines multiple sophisticated components. First, it calculates the logarithmic weighted average of closing prices. Then it measures the slope of this average over a 10-period lookback: lmaSlope = (lma - lma ) / lma * 100 The system also incorporates trend quality assessment using R-squared correlation analysis of log-transformed prices, measuring how well the price data fits a linear trend model over the specified period.
The final signal generation uses the formula: signal = lmaSlope * (0.5 + rSquared * 0.5) which combines the LMA slope with trend quality weighting. When momentum confirmation is enabled, the indicator calculates annualized log-return momentum and applies a multiplier when the momentum direction aligns with the signal direction, strengthening confirmed signals while filtering out weak or counter-trend movements.
🟢 How to Use
1. Signal Interpretation and Threshold Zones
Positive Values (Above Zero): LMA slope indicating bullish momentum with upward price trajectory relative to logarithmic baseline
Negative Values (Below Zero): LMA slope indicating bearish momentum with downward price trajectory relative to logarithmic baseline
Zero Line Crosses: Signal transitions between bullish and bearish regimes, indicating potential trend changes
Long Entry Threshold Zone: Area above positive threshold (default 0.5) indicating confirmed bullish signals suitable for long positions
Short Entry Threshold Zone: Area below negative threshold (default -0.5) indicating confirmed bearish signals suitable for short positions
Extreme Values: Signals exceeding ±1.0 represent strong momentum conditions with higher probability of continuation
2. Momentum Confirmation and Visual Analysis
Signal Color Intensity: Gradient coloring shows signal strength, with brighter colors indicating stronger momentum
Bar Coloring: Optional price bar coloring matches signal direction for quick visual trend identification
Position Labels: Real-time position classification (Bullish/Bearish/Neutral) displayed on the latest bar
Momentum Weight Factor: When short-term log-return momentum aligns with LMA signal direction, the signal receives additional weight confirmation
Trend Quality Component: R-squared values weight the signal strength, with higher correlation indicating more reliable trend conditions
3. Examples: Preconfigured Settings
Default: Universally applicable configuration balanced for medium-term investing and general trading across multiple timeframes and asset classes.
Scalping: Highly responsive setup with shorter period and higher steepness for ultra-short-term trades on 1-15 minute charts, optimized for quick momentum shifts.
Swing Trading: Extended period with moderate steepness and increased smoothing for multi-day positions, designed to filter noise while capturing larger price swings on 1-4 hour and daily charts.
Trend Following: Maximum smoothing with lower steepness for established trend identification, generating fewer but more reliable signals optimal for daily and weekly timeframes.
Mean Reversion: Shorter period with high steepness for counter-trend strategies, more sensitive to extreme moves and reversal opportunities in ranging market conditions.
Strategy Builder With IndicatorsThis strategy script is designed for traders who enjoy building systems using multiple indicators.
Please note: This script does not include any built-in indicators. Instead, it works by referencing the plot outputs of the indicators you’ve already added to your chart.
For example, if you add a MACD and an ATR indicator to your chart, you can assign their plot values as inputs in the settings panel of this strategy.
• MACD as a trigger
• ATR as a filter
How Filters Work
Filters check whether certain conditions are met before a trade can be opened. For instance, if you set a filter like ATR > 30, then no trade will be executed unless that condition is true — even if the trigger fires.
All filters are linked, meaning every active filter must be satisfied for a trade to occur.
How Triggers Work
Triggers are what actually fire a trade signal — such as a moving average crossover or RSI breaking above a specific level. Unlike filters, triggers are independent. Only one active trigger needs to be true for the trade to execute.
Thanks to its modular structure, this strategy can be used with any indicator of your choice.
⸻
Risk Management Features
In the settings, you’ll find flexible options for:
• Stop Loss (SL)
• Trailing Stop Loss (TSL)
• Multi Take-Profit (TP)
These features enhance trade safety and let you tailor your risk management.
SL types available:
• Tick-based SL
• Percent-based SL
• ATR-based SL
Once you select your preferred SL type, you can fine-tune its distance using the offset field.
Trailing SL allows your stop to follow price as it moves in your favor — helping to lock in profits.
Multi-TP lets you take profits at two different levels, helping you secure gains while leaving room for extended moves.
Breakeven option is also available to automatically move your SL to entry after reaching a profit threshold.
⸻
How to Build a Solid Strategy
Let’s break down a good setup into three key components:
1. Trend Filter
Avoid trading against the trend — that’s like swimming against the current.
Use a filter like:
• Supertrend
• Momentum indicators
• Candlestick bias, etc.
Example: In this case, I used Supertrend and filtered for trades only if the price is above the uptrend line.
2. Trigger Condition
Once we confirm the trend is on our side, we need a trigger to execute at the right moment. This can be:
• RSI cross
• Candlestick patterns
• Trendline breaks
• Moving average crossovers, etc.
Example: I used RSI crossing above 50 as the entry trigger.
3. Risk Management
Even in the right trend at the right time — anything can happen. That’s why you should always define Stop Loss and Take Profit levels.
⸻
And there you have it! Your strategy is ready to backtest, refine, and deploy with alerts for live trading.
Questions or suggestions? Feel free to reach out
TCP | Money Management indicator | Crypto Version📌 TCP | Money Management Indicator | Crypto Version
A robust, multi-target risk and capital management indicator tailored for crypto traders. Whether you're trading spot, perpetual futures, or leverage tokens, this tool empowers you with precise control over risk, reward, and position sizing—directly on your chart. Eliminate guesswork and trade with confidence.
🔰 Introduction: Master Your Capital, Master Your Trades
Poor money management is the number one reason traders lose their accounts, even with solid strategies. The TCP Money Management Indicator, built by Trade City Pro (TCP), solves this problem by providing a structured, rule-based approach to capital allocation.
Want to dive deeper into the concept of money management? Check out our comprehensive tutorial on TradingView, " TradeCityPro Academy: Money Management ", to understand the principles that power this indicator and transform your trading mindset.
This indicator equips you to:
• Calculate optimal position sizes based on your capital, risk percentage, and leverage
• Set up to 5 customizable take-profit targets with partial close percentages
• Access real-time metrics like Risk-to-Reward (R/R), USD profit, and margin usage
• Trade with discipline, avoiding emotional or inconsistent decisions
💸 Money Management Formula
The indicator uses a professional capital allocation model:
Position Size = (Capital × Risk %) ÷ (Stop Loss % × Leverage)
From this, it calculates:
• Total risk amount in USD
• Optimal position size for your trade
• Margin required for each take-profit target
• Adjusted R/R for each target, accounting for partial position closures
🛠 How to Use
Enter Trade Parameters: Input your capital, risk %, leverage, entry price, and stop-loss price.
Set Take-Profit Targets: Enable 1 to 5 take-profit levels and specify the percentage of the position to close at each.
Real-Time Calculations: The indicator automatically computes:
• R/R ratio for each target
• Profit in USD for each partial close
• Margin used per target (in % and USD)
Visualize Your Trade:
• Price levels for entry, stop-loss, and take-profits are plotted on the chart.
• A dynamic info panel on the left side displays all key metrics.
🔄 Dynamic Adjustments: As each take-profit target is hit and a portion of the position is closed, the indicator recalculates the remaining position size, expected profit, R/R, and margin for subsequent targets. This ensures accuracy and reflects real-world trade behavior.
📊 Table Overview
The left-side panel provides a clear snapshot:
• Trade Setup: Capital, entry price, stop-loss, risk amount, and position size
• Per Target: Percentage closed, R/R, profit in USD, and margin used
• Summary: Total expected profit across all targets
⚙️ Settings Panel
• Total Capital ($): Your account size for the trade
• Risk per Trade (%): The percentage of capital you’re willing to risk
• Leverage: The leverage applied to the trade
• Entry/Stop-Loss Prices: Define your trade’s risk zone
• Take-Profit Targets (1–5): Set price levels and percentage to close at each
🔍 Use Case Example
Imagine you have $1,000 capital, risking 1%, using 10x leverage:
• Entry: $100 | Stop-Loss: $95
• TP1: $110 (close 50%) | TP2: $115 (close 50%)
The indicator calculates the exact position size, profit at each target, and margin allocation in real time, with all metrics displayed on the chart.
✅ Why Traders Love It
• Precision: No more manual calculations or guesswork
• Versatility: Works on all crypto pairs (BTC, ETH, altcoins, etc.)
• Flexibility: Perfect for scalping, swing trading, or futures strategies
• Universal: Compatible with all timeframes
• Transparency: Fully manual, with clear and reliable outputs
🧩 Built by Trade City Pro (TCP)
Developed by TCP, a trusted name in trading tools, used by over 150,000 traders worldwide. This indicator is coded in Pine Script v5, ensuring compatibility with TradingView’s platform.
🧾 Final Notes
• No Auto-Trading: This is a manual tool for disciplined traders
• No Repainting: All calculations are accurate and non-repainting
• Tested: Rigorously validated across major crypto pairs
• Publish-Ready: Built for seamless use on TradingView
🔗 Resources
• Money Management Tutorial: Learn the fundamentals of capital management with our detailed guide: TradeCityPro Academy: Money Management
• TradingView Profile: Explore more tools by TCP on TradingView
SBC ProtfoSBC Portfo PNL Indicator
Description
The SBC Portfo PNL Indicator is a user-friendly tool designed for Hebrew-speaking traders to track the Profit and Loss (PNL) of their stock portfolios on TradingView charts. It supports up to 5 distinct portfolios, each capable of holding an unlimited number of stocks with unlimited buy commands, allowing real-time monitoring of portfolio performance.
Key Features
- Multi-Portfolio Support: Track up to 5 separate portfolios for different trading strategies or accounts.
- Unlimited Stock Entries: Add unlimited stocks and buy commands per portfolio.
- Detailed Buy Commands: Input for each stock:
- Stock Ticker (e.g., AAPL, TSLA).
- Buy Price (e.g., 150.25).
- Buy Amount (e.g., 10).
- Hebrew-Friendly Interface: Intuitive settings dialog with clear instructions in Hebrew.
- Customizable PNL Tracking: Visualize PNL on charts with real-time updates based on market data.
How to Use
1. Add the Indicator:
- Go to the Indicators menu in TradingView and add the "SBC Portfo" PNL Indicator.
2. Configure Portfolios:
- Open the indicator’s settings dialog.
- For each portfolio (up to 5), enter data in the provided input fields using this format:
PortfolioName:StockTicker:BuyPricexBuyAmount;StockTicker:BuyPricexBuyAmount
Example:
Portfolio1:AAPL:150.25x10;TSLA:266.72x5
- This represents a portfolio named "Portfolio1" with:
- 10 shares of AAPL bought at $150.25.
- 5 shares of TSLA bought at $266.72.
- Repeat for additional portfolios (e.g., Portfolio2, Portfolio3).
- Add multiple buy commands for the same stock if needed (e.g., AAPL:160.50x20).
3. Apply Settings:
- Save settings to display PNL based on current market prices.
4. Monitor PNL:
- View PNL for each portfolio on the chart via tables, labels, or graphical overlays (based on settings).
Input Format
Enter portfolio data manually in the settings dialog, one input field per portfolio:
PortfolioName:StockTicker:BuyPricexBuyAmount;StockTicker:BuyPricexBuyAmount
- PortfolioName: Unique name (e.g., Portfolio1, Growth).
- StockTicker: Stock symbol (e.g., AAPL).
- BuyPrice: Purchase price per share (e.g., 150.25).
- BuyAmount: Number of shares (e.g., 10).
- Use
: to separate portfolio name, ticker, and buy data
x to separate price and amount
; for multiple stocks in the portfolio
Example:
- Portfolio 1: GrowthPortfolio:AAPL:150.25x10;TSLA:266.72x5
- Portfolio 2: DividendPortfolio:KO:55.20x50;PG:145.30x30
Notes
- Hebrew Support: Settings and labels are optimized for Hebrew users.
- Manual Input: Enter portfolio data manually in the settings dialog using the correct format.
- Compatibility: Works with any stock ticker supported by TradingView.
תיאור אינדיקטור SBC Portfo PNL הוא כלי ידידותי למשתמש שתוכנן במיוחד עבור סוחרים דוברי עברית למעקב אחר רווח והפסד (PNL) של תיקי המניות שלהם ישירות בגרפים של TradingView. הוא תומך בעד 5 תיקים נפרדים, כאשר כל תיק יכול להכיל מספר בלתי מוגבל של מניות עם פקודות קנייה בלתי מוגבלות, ומאפשר מעקב בזמן אמת אחר ביצועי התיק.
תכונות עיקריות
- תמיכה בריבוי תיקים: מעקב אחר עד 5 תיקים נפרדים עבור אסטרטגיות מסחר או חשבונות שונים.
- רישום מניות ללא הגבלה: הוספת מספר בלתי מוגבל של מניות ופקודות קנייה לכל תיק.
- פקודות קנייה מפורטות: הזנת נתונים עבור כל מניה:
- סימול המניה (למשל, AAPL, TSLA).
- מחיר קנייה (למשל, 150.25).
- כמות קנייה (למשל, 10).
- ממשק ידידותי לעברית: חלונית הגדרות אינטואיטיבית עם הוראות ברורות בעברית.
- מעקב PNL הניתן להתאמה: הצגת רווח והפסד בגרפים עם עדכונים בזמן אמת בהתבסס על נתוני השוק.
כיצד להשתמש
1. הוספת האינדיקטור:
- נווט לתפריט האינדיקטורים ב-TradingView והוסף את "SBC Portfo PNL Indicator".
2. הגדרת תיקים:
- פתח את חלונית ההגדרות של האינדיקטור.
- עבור כל תיק (עד 5), הזן נתונים בשדות המסופקים בפורמט הבא:
PortfolioName:StockTicker:BuyPricexBuyAmount;StockTicker:BuyPricexBuyAmount
לדוגמה:
Portfolio1:AAPL:150.25x10;TSLA:266.72x5
שורה זו מייצגת תיק בשם "Portfolio1" עם:
- 10 מניות של AAPL שנקנו ב-$150.25.
- 5 מניות של TSLA שנקנו ב-$266.72.
- חזור על התהליך עבור תיקים נוספים (למשל, Portfolio2, Portfolio3).
- ניתן להוסיף פקודות קנייה מרובות לאותה מניה לפי הצורך (למשל, AAPL:160.50x20).
3. החלת ההגדרות:
- שמור את ההגדרות להצגת ה-PNL בהתבסס על מחירי השוק הנוכחיים.
4. מעקב אחר PNL:
- צפה ב-PNL עבור כל תיק בגרף באמצעות טבלאות, תוויות או שכבות גרפיות (בהתאם להגדרות).
פורמט קלט הזן נתוני תיק ידנית בחלונית ההגדרות, שדה קלט אחד לכל תיק: PortfolioName:StockTicker:BuyPricexBuyAmount;StockTicker:BuyPricexBuyAmount
PortfolioName: שם ייחודי (למשל, Portfolio1, Growth).
StockTicker: סימול המניה (למשל, AAPL).
BuyPrice: מחיר רכישה למניה (למשל, 150.25).
BuyAmount: מספר המניות (למשל, 10).
השתמש ב-
: להפרדה בין שם התיק, סימול ונתוני קנייה
x להפרדה בין מחיר וכמות
; להפרדה בין מניות מרובות
דוגמה:
- תיק 1: GrowthPortfolio:AAPL:150.25x10;TSLA:266.72x5
- תיק 2: DividendPortfolio:KO:55.20x50;PG:145.30x30
Release Notes
Version 1.1 includes:
- Calculations for extended hours (Pre-Market & After-Hours).
- Option to display portfolio summary data for stocks not in the portfolio (enable via settings checkbox).
- Table background for better visibility; click to bring table to the front.
- Updated text strings (names, titles, tooltips).
הערות
תמיכה בעברית: ההגדרות והתוויות מותאמות למשתמשים דוברי עברית.
הזנה ידנית: הזן נתוני תיק ידנית בחלונית ההגדרות תוך שימוש בפורמט הנכון.
תאימות: עובד עם כל סימול מניה הנתמך על ידי TradingView.
גרסה 1.1 מכילה:
1. חישובים כוללים שעות מסחר מורחבות (Pre-Market ו-After-Hours).
2. אפשרות להציג נתוני תיק כוללים עבור מניות שאינן בתיק (הפעל באמצעות תיבת סימון בהגדרות).
3. צבע רקע לטבלה לשיפור הנראות; לחיצה על הטבלה מביאה אותה לחזית.
4. תיקון נוסחים (שמות, כותרות, וטולטיפים).
Major Session Highs/LowsThis indicator creates horizontal lines at major session high/lows (US, London, and Asian). The script updates the lines automatically, on session close.
For instance, when viewing during the US session, after the London overlap, horizontal lines will be displayed at the following levels.
The high/low of the most recent London session.
The high/low of the most recent Asian session.
The high/low of the last full US session, i.e. the session of the day prior.
When the current US session closes, the US levels automatically update.
Zweig Market Breadth Thrust Indicator+Trigger [LazyBear x rwak]The Breadth Thrust (BT) indicator is a market momentum indicator developed by Dr. Martin Zweig. According to Dr. Zweig, a Breadth Thrust occurs when, during a 10-day period, the Breadth Thrust indicator rises from below 40 percent to above 61.5 percent.
A "Thrust" indicates that the stock market has rapidly changed from an oversold condition to one of strength, but has not yet become overbought. This is very rare and has happened only a few times. Dr. Zweig also points out that most bull markets begin with a Breadth Thrust.
This version of the Breadth Thrust indicator includes a trigger visualized with red circles, making it easier to spot when the indicator crosses the critical 61.5% level, signaling potential bullish momentum.
All parameters are configurable. You can draw BT for NYSE, NASDAQ, AMEX, or based on combined data (i.e., AMEX+NYSE+NASD). There is also a "CUSTOM" mode supported, so you can enter your own ADV/DEC symbols.
Credit: The original Breadth Thrust logic was created by LazyBear, whose public indicators can be found here , and app-store indicators here .
More info:
Definition of Breadth Thrust
A Breadth Thrust Signal
A Rare "Zweig" Buy Signal
Zweig Breadth Thrust: Redux
QuantumSync Pulse [ w.aritas ]QuantumSync Pulse (QSP) is an advanced technical indicator crafted for traders seeking a dynamic and adaptable tool to analyze diverse market conditions. By integrating momentum, mean reversion, and regime detection with quantum-inspired calculations and entropy analysis, QSP offers a powerful histogram that reflects trend strength and market uncertainty. With multi-timeframe synchronization, adaptive filtering, and customizable visualization, it’s a versatile addition to any trading strategy.
Key Features
Hybrid Signals: Combines momentum and mean reversion, dynamically weighted by market regime.
Quantum Tunneling: Enhances responsiveness in volatile markets using volatility-adjusted calculations.
3-State Entropy: Assesses market uncertainty across up, down, and neutral states.
Regime Detection: Adapts signal weights with Hurst exponent and volatility ROC.
Multi-Timeframe Alignment: Syncs with higher timeframe trends for context.
Customizable Histogram: Displays trend strength with ADX-based visuals and flexible styling.
How to Use and Interpret
Histogram Interpretation
Positive (Above Zero): Bullish momentum; color intensity shows trend strength.
Negative (Below Zero): Bearish momentum; gradients indicate weakness.
Overlaps: Alignment of final_z (signal) and ohlc4 (price) histograms highlights key price levels or turning points.
Regime Visualization
Green Background: Trending market; prioritize momentum signals.
Red Background: Mean-reverting market; focus on reversion signals.
Blue Background: Neutral state; balance both signal types.
Trading Signals
Buy: Histogram crosses above zero or shows positive divergence between histograms.
Sell: Histogram crosses below zero or exhibits negative divergence.
Confirmation: Match signals with regime background—green for trends, red for ranges.
Customization
Tweak Momentum Length, Entropy Lookback, and Hurst Exponent Lookback for sensitivity.
Adjust color themes and transparency to suit your charts.
Tips for Optimal Use
Timeframes: Use higher timeframes (1h, 4h) for trend context and lower (5m, 15m) for entries.
Pairing: Combine with RSI, MACD, or volume indicators for confirmation.
Backtesting: Test settings on historical data for asset-specific optimization.
Overlaps: Watch for histogram overlaps to identify support, resistance, or reversals.
Simulated Performance
Trending Markets: Histogram stays above/below zero, with overlaps at retracements for entries.
Range-Bound Markets: Oscillates around zero; overlaps signal reversals in red regimes.
Volatile Markets: Quantum tunneling ensures quick reactions, with filters reducing noise.
Elevate your trading with QuantumSync Pulse—a sophisticated tool that adapts to the market’s rhythm and your unique style.
[Stop!Loss] ADR Signal ADR Signal - a technical indicator located in a separate window, which displays by default the 80%-level , as well as the 100%-level of the average daily range (ADR) for the last 10 days and compares it with the current intraday range. The indicator helps not only with the use of a mathematical-statistical method to identify a potential reversal at the moment during intraday trading, but can also serves as an effective assistant in risk management.
👉 Basic mechanics of the indicator
Firstly, this indicator tracks the performance of the standard ATR indicator on the daily chart, in other words, ADR (Average Daily Range).
Important ❗️The ATR (Average True Range) indicator was created by J. Welles Wilder Jr. He first introduced ATR in his book "New Concepts in Technical Trading Systems", published in 1978. Wilder developed this indicator to measure market volatility to help traders estimate the range of price movements. This indicator is built into TradingView, more details can be found by link: www.tradingview.com
Like ATR , ADR calculates the average true range for a specified period. In this case, the distance in points from the maximum of each day to its minimum is calculated, after which the arithmetic mean is calculated - this is ADR .
👉 Visualization
ADR Signal is located in a separate window on the chart and has 3 levels:
1) "ADR level" (green line) - the same parameter, the calculations of which are briefly described above. There is 100%-level of ATR on the daily chart (ADR).
2) "Current level" (red line) - this is the current price passage within the day, calculated in points. At the start of a new day, this parameter is reset. Therefore, in the indicator window, this line has sharp drops at the start of a new trading day: "A new trading day - the instrument's power reserve is renewed again".
3) "Signal level" (blue line) - this is an individually customized value that demonstrates a certain part of the ADR parameter.
👉 Inputs
1) - is responsible for the ATR indicator period, the value of which will always be calculated on the daily chart. The default value is "10", that is, ATR is calculated for the last 10 days (not including the current one).
2) - signal level (in %). The default value is "0.8", that is, 80%-level of the ADR parameter (set earlier) is calculated.
👉 Style
1) - by default, this level is colored "blue".
2) - by default, this level is colored "red".
3) - by default, this level is colored "green".
👉 How to use this indicator
Important❗️ The two methods of the use of the ADR Signal indicator described below will be most effective when trading intraday (which is highlighted quite well below), so it is more logical to use the indicator information on time periods H1 and below.
1) Identifying potential reversals during intraday trading:
The ADR Signal indicator can be used as a potential individual reversal strategy.
Important ❗️It should be noted that using it in it without additional confirming analysis tools will be a rather aggressive trading approach. Therefore, it is best to support the entry point in particular with other methods.
In this case, the crossing of the red line (the number of points passed within the current day, that is, from the minimum of the current day to its maximum) and the blue line (color of the Signal level based on the default settings), indicates that the trading instrument has passed 80% (based on the default settings for the "Signal level") of its average distance from the maximum to the minimum over the past 10 days (based on the default settings for the "ADR Length"). Such a situation in the context of the mathematical-statistical approach indicates a probable reversal, since the "power reserve" of this instrument is mostly exhausted, so one can expect with a higher probability, at least, a price stop and possibly a reversal. In case of crossing of the red line and the green one (ADR level), it says again that based on the mathematical-statistical approach, this trading instrument has completely exhausted its intraday "power reserve". In this situation, a stop or reversal of the price will be even more likely.
Of course, using the "Signal level" parameter, one can filter out even more reliable situations for potential price reversals within a day, namely, by specifying, for example, 1.5 in the field of this parameter. Under such conditions, in the case of crossing the red and blue lines (based on the default style settings), to say that the trading instrument has passed 150% of its average distance over the last 10 days (based on the default style settings "ADR length"). In this case, the probability of a stop or reversal of the price increases even more.
2) Use in risk management:
In terms of risk management, this indicator is more applicable to open trades. For example, if one had an open Buy-position (especially if it is an intraday trade) and the price has raised significantly during the day, then the crossing of the red line with the blue line , and especially the red line with the green line , may indicate that the price will most likely stop growing, since the "power reserve" is almost or completely exhausted for this instrument within the current day. In this case, one can, at a minimum, move the trade to breakeven or even partially fix the profit.
We will continue to discuss the methods of using this indicator and strategies based on it here. And we are always waiting for your reactions and feedback on this topic 💬.
Thank you for your support 🚀
Frozen Bias Zones – Sentiment Lock-insOverview
The Frozen Bias Zones indicator visualizes market sentiment lock-ins using a combination of RSI, MACD, and OBV. It creates "bias zones" that indicate whether the market is in a sustained bullish or bearish phase. These zones are then highlighted on the chart, helping traders spot when the market is locked in a bias. The script also detects breakout events from these zones and marks them with clear labels for easier decision-making.
Features
Multi-Indicator Sentiment Analysis: Combines RSI, MACD, and OBV to detect synchronized bullish or bearish sentiment.
Frozen Bias Zones: Identifies and visually represents zones where the market has remained in a particular sentiment (bullish or bearish) for a defined period.
Breakout Alerts: Displays labels to indicate when the price breaks out of the established bias zone.
Customizable Inputs: Adjust the zone duration, RSI, MACD, and breakout label visibility.
Input Parameters
Bias Duration (biasLength)
The minimum number of candles the market must stay in a specific sentiment to consider it a "Frozen Bias Zone".
Default: 5 candles.
RSI Period (rsiPeriod)
Period for the Relative Strength Index (RSI) calculation.
Default: 14 periods.
MACD Settings
MACD Fast (macdFast): The fast-moving average period for the MACD calculation.
Default: 12.
MACD Slow (macdSlow): The slow-moving average period for the MACD calculation.
Default: 26.
MACD Signal (macdSig): The signal line period for MACD.
Default: 9.
Show Break Label (showBreakLabel)
Toggle to show labels when the price breaks out of the bias zone.
Default: True (shows label).
Bias Zone Colors
Bullish Bias Color (bullColor): The color for bullish zones (light green).
Bearish Bias Color (bearColor): The color for bearish zones (light red).
How It Works
This indicator analyzes three key market metrics to determine whether the market is in a bullish or bearish phase:
RSI (Relative Strength Index)
Measures the speed and change of price movements. RSI > 50 indicates a bullish phase, while RSI < 50 indicates a bearish phase.
MACD (Moving Average Convergence Divergence)
Measures the relationship between two moving averages of the price. A positive MACD histogram indicates bullish momentum, while a negative histogram indicates bearish momentum.
OBV (On-Balance Volume)
Uses volume flow to determine if a trend is likely to continue. A rising OBV indicates bullish accumulation, while a falling OBV indicates bearish distribution.
Bias Zone Detection
The market sentiment is considered bullish if all three indicators (RSI, MACD, and OBV) are bullish, and bearish if all three indicators are bearish.
Bullish Zone: A zone is created when the market sentiment remains bullish for the duration of the specified biasLength.
Bearish Zone: A zone is created when the market sentiment remains bearish for the duration of the specified biasLength.
These bias zones are visually represented on the chart as colored boxes (green for bullish, red for bearish).
Breakout Detection
The script automatically detects when the market exits a bias zone. If the price moves outside the bounds of the established zone (either up or down), the script will display one of the following labels:
Bias Break (Up): Indicates that the price has broken upwards out of the zone (with a green label).
Bias Break (Down): Indicates that the price has broken downwards out of the zone (with a red label).
These labels help traders easily identify potential breakout points.
Example Use Case
Bullish Market Conditions: If the RSI is above 50, the MACD histogram is positive, and OBV is increasing, the script will highlight a green bias zone. Traders can watch for potential bullish breakouts or trend continuation after the zone ends.
Bearish Market Conditions: If the RSI is below 50, the MACD histogram is negative, and OBV is decreasing, the script will highlight a red bias zone. Traders can look for potential bearish breakouts when the zone ends.
Conclusion
The Frozen Bias Zones indicator is a powerful tool for traders looking to visualize prolonged market sentiment, whether bullish or bearish. By combining RSI, MACD, and OBV, it helps traders spot when the market is "locked in" to a bias. The breakout labels make it easier to take action when the price moves outside of the established zone, potentially signaling the start of a new trend.
Instructions
To use this script:
Add the Frozen Bias Zones indicator to your TradingView chart.
Adjust the input parameters to suit your trading strategy.
Observe the colored bias zones on your chart, along with breakout labels, to make informed decisions on trend continuation or reversal.
RSI Forecast [Titans_Invest]RSI Forecast
Introducing one of the most impressive RSI indicators ever created – arguably the best on TradingView, and potentially the best in the world.
RSI Forecast is a visionary evolution of the classic RSI, merging powerful customization with groundbreaking predictive capabilities. While preserving the core principles of traditional RSI, it takes analysis to the next level by allowing users to anticipate potential future RSI movements.
Real-Time RSI Forecasting:
For the first time ever, an RSI indicator integrates linear regression using the least squares method to accurately forecast the future behavior of the RSI. This innovation empowers traders to stay one step ahead of the market with forward-looking insight.
Highly Customizable:
Easily adapt the indicator to your personal trading style. Fine-tune a variety of parameters to generate signals perfectly aligned with your strategy.
Innovative, Unique, and Powerful:
This is the world’s first RSI Forecast to apply this predictive approach using least squares linear regression. A truly elite-level tool designed for traders who want a real edge in the market.
⯁ SCIENTIFIC BASIS LINEAR REGRESSION
Linear Regression is a fundamental method of statistics and machine learning, used to model the relationship between a dependent variable y and one or more independent variables 𝑥.
The general formula for a simple linear regression is given by:
y = β₀ + β₁x + ε
Where:
y = is the predicted variable (e.g. future value of RSI)
x = is the explanatory variable (e.g. time or bar index)
β0 = is the intercept (value of 𝑦 when 𝑥 = 0)
𝛽1 = is the slope of the line (rate of change)
ε = is the random error term
The goal is to estimate the coefficients 𝛽0 and 𝛽1 so as to minimize the sum of the squared errors — the so-called Random Error Method Least Squares.
⯁ LEAST SQUARES ESTIMATION
To minimize the error between predicted and observed values, we use the following formulas:
β₁ = /
β₀ = ȳ - β₁x̄
Where:
∑ = sum
x̄ = mean of x
ȳ = mean of y
x_i, y_i = individual values of the variables.
Where:
x_i and y_i are the means of the independent and dependent variables, respectively.
i ranges from 1 to n, the number of observations.
These equations guarantee the best linear unbiased estimator, according to the Gauss-Markov theorem, assuming homoscedasticity and linearity.
⯁ LINEAR REGRESSION IN MACHINE LEARNING
Linear regression is one of the cornerstones of supervised learning. Its simplicity and ability to generate accurate quantitative predictions make it essential in AI systems, predictive algorithms, time series analysis, and automated trading strategies.
By applying this model to the RSI, you are literally putting artificial intelligence at the heart of a classic indicator, bringing a new dimension to technical analysis.
⯁ VISUAL INTERPRETATION
Imagine an RSI time series like this:
Time →
RSI →
The regression line will smooth these values and extend them n periods into the future, creating a predicted trajectory based on the historical moment. This line becomes the predicted RSI, which can be crossed with the actual RSI to generate more intelligent signals.
⯁ SUMMARY OF SCIENTIFIC CONCEPTS USED
Linear Regression Models the relationship between variables using a straight line.
Least Squares Minimizes the sum of squared errors between prediction and reality.
Time Series Forecasting Estimates future values based on historical data.
Supervised Learning Trains models to predict outputs from known inputs.
Statistical Smoothing Reduces noise and reveals underlying trends.
⯁ WHY THIS INDICATOR IS REVOLUTIONARY
Scientifically-based: Based on statistical theory and mathematical inference.
Unprecedented: First public RSI with least squares predictive modeling.
Intelligent: Built with machine learning logic.
Practical: Generates forward-thinking signals.
Customizable: Flexible for any trading strategy.
⯁ CONCLUSION
By combining RSI with linear regression, this indicator allows a trader to predict market momentum, not just follow it.
RSI Forecast is not just an indicator — it is a scientific breakthrough in technical analysis technology.
⯁ Example of simple linear regression, which has one independent variable:
⯁ In linear regression, observations ( red ) are considered to be the result of random deviations ( green ) from an underlying relationship ( blue ) between a dependent variable ( y ) and an independent variable ( x ).
⯁ Visualizing heteroscedasticity in a scatterplot against 100 random fitted values using Matlab:
⯁ The data sets in the Anscombe's quartet are designed to have approximately the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but are graphically very different. This illustrates the pitfalls of relying solely on a fitted model to understand the relationship between variables.
⯁ The result of fitting a set of data points with a quadratic function:
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🥇 This is the world’s first RSI indicator with: Linear Regression for Forecasting 🥇_______________________________________________________________________
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🔮 Linear Regression: PineScript Technical Parameters 🔮
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Forecast Types:
• Flat: Assumes prices will remain the same.
• Linreg: Makes a 'Linear Regression' forecast for n periods.
Technical Information:
ta.linreg (built-in function)
Linear regression curve. A line that best fits the specified prices over a user-defined time period. It is calculated using the least squares method. The result of this function is calculated using the formula: linreg = intercept + slope * (length - 1 - offset), where intercept and slope are the values calculated using the least squares method on the source series.
Syntax:
• Function: ta.linreg()
Parameters:
• source: Source price series.
• length: Number of bars (period).
• offset: Offset.
• return: Linear regression curve.
This function has been cleverly applied to the RSI, making it capable of projecting future values based on past statistical trends.
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⯁ WHAT IS THE RSI❓
The Relative Strength Index (RSI) is a technical analysis indicator developed by J. Welles Wilder. It measures the magnitude of recent price movements to evaluate overbought or oversold conditions in a market. The RSI is an oscillator that ranges from 0 to 100 and is commonly used to identify potential reversal points, as well as the strength of a trend.
⯁ HOW TO USE THE RSI❓
The RSI is calculated based on average gains and losses over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and includes three main zones:
• Overbought: When the RSI is above 70, indicating that the asset may be overbought.
• Oversold: When the RSI is below 30, indicating that the asset may be oversold.
• Neutral Zone: Between 30 and 70, where there is no clear signal of overbought or oversold conditions.
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⯁ ENTRY CONDITIONS
The conditions below are fully flexible and allow for complete customization of the signal.
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🔹 CONDITIONS TO BUY 📈
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• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
📈 RSI Conditions:
🔹 RSI > Upper
🔹 RSI < Upper
🔹 RSI > Lower
🔹 RSI < Lower
🔹 RSI > Middle
🔹 RSI < Middle
🔹 RSI > MA
🔹 RSI < MA
📈 MA Conditions:
🔹 MA > Upper
🔹 MA < Upper
🔹 MA > Lower
🔹 MA < Lower
📈 Crossovers:
🔹 RSI (Crossover) Upper
🔹 RSI (Crossunder) Upper
🔹 RSI (Crossover) Lower
🔹 RSI (Crossunder) Lower
🔹 RSI (Crossover) Middle
🔹 RSI (Crossunder) Middle
🔹 RSI (Crossover) MA
🔹 RSI (Crossunder) MA
🔹 MA (Crossover) Upper
🔹 MA (Crossunder) Upper
🔹 MA (Crossover) Lower
🔹 MA (Crossunder) Lower
📈 RSI Divergences:
🔹 RSI Divergence Bull
🔹 RSI Divergence Bear
📈 RSI Forecast:
🔮 RSI (Crossover) MA Forecast
🔮 RSI (Crossunder) MA Forecast
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🔸 CONDITIONS TO SELL 📉
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• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
📉 RSI Conditions:
🔸 RSI > Upper
🔸 RSI < Upper
🔸 RSI > Lower
🔸 RSI < Lower
🔸 RSI > Middle
🔸 RSI < Middle
🔸 RSI > MA
🔸 RSI < MA
📉 MA Conditions:
🔸 MA > Upper
🔸 MA < Upper
🔸 MA > Lower
🔸 MA < Lower
📉 Crossovers:
🔸 RSI (Crossover) Upper
🔸 RSI (Crossunder) Upper
🔸 RSI (Crossover) Lower
🔸 RSI (Crossunder) Lower
🔸 RSI (Crossover) Middle
🔸 RSI (Crossunder) Middle
🔸 RSI (Crossover) MA
🔸 RSI (Crossunder) MA
🔸 MA (Crossover) Upper
🔸 MA (Crossunder) Upper
🔸 MA (Crossover) Lower
🔸 MA (Crossunder) Lower
📉 RSI Divergences:
🔸 RSI Divergence Bull
🔸 RSI Divergence Bear
📉 RSI Forecast:
🔮 RSI (Crossover) MA Forecast
🔮 RSI (Crossunder) MA Forecast
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🤖 AUTOMATION 🤖
• You can automate the BUY and SELL signals of this indicator.
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⯁ UNIQUE FEATURES
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Linear Regression: (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
Linear Regression (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
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📜 SCRIPT : RSI Forecast
🎴 Art by : @Titans_Invest & @DiFlip
👨💻 Dev by : @Titans_Invest & @DiFlip
🎑 Titans Invest — The Wizards Without Gloves 🧤
✨ Enjoy!
______________________________________________________
o Mission 🗺
• Inspire Traders to manifest Magic in the Market.
o Vision 𐓏
• To elevate collective Energy 𐓷𐓏
EMA 9/21/50 + VWAP + MACD + RSI Pro [v6]Overview:
A powerful multi-indicator tool combining Exponential Moving Averages (EMA 9, 21, 50), Volume-Weighted Average Price (VWAP), Moving Average Convergence Divergence (MACD), and Relative Strength Index (RSI) into a single, easy-to-read system. Designed for traders who want a clean, all-in-one dashboard for trend analysis, momentum confirmation, and overbought/oversold conditions.
Key Features:
1. Triple EMA System (9, 21, 50)
Identifies short-term and medium-term trends.
Bullish Signal: EMA 9 > EMA 21 > EMA 50 (Green Highlight)
Bearish Signal: EMA 9 < EMA 21 < EMA 50 (Red Highlight)
Helps confirm trend direction and potential reversals.
2. VWAP (Volume-Weighted Average Price)
Tracks intraday fair value price based on volume.
Bullish: Price above VWAP (Green)
Bearish: Price below VWAP (Red)
3. MACD (Standard 12, 26, 9 Settings)
Shows momentum shifts.
Bullish: MACD line > Signal line (Green)
Bearish: MACD line < Signal line (Red)
Histogram confirms strength of momentum.
4. RSI (14-Period Default)
Identifies overbought (>70) and oversold (<30) conditions.
Red: Overbought (Potential Reversal)
Green: Oversold (Potential Bounce)
5. Signal Dashboard (Top-Right Table)
Real-time summary of all indicators in one place.
Color-coded for quick interpretation (Green = Bullish, Red = Bearish).
How to Use This Indicator?
✅ Trend Confirmation:
Trade in the direction of EMA alignment (9 > 21 > 50 for uptrends).
Use VWAP as dynamic support/resistance.
✅ Momentum Entries:
Look for MACD crossovers while RSI is not extreme.
Avoid buying when RSI > 70 or selling when RSI < 30 (unless strong trend).
✅ Mean Reversion:
Fade extreme RSI readings (overbought/oversold) when price is at key levels.
Who Is This For?
✔ Swing Traders – EMA + MACD combo for trend-following.
✔ Day Traders – VWAP + EMA for intraday bias.
✔ RSI Traders – Clear overbought/oversold signals.
Settings Customization:
Adjust EMA lengths, RSI periods, and MACD settings as needed.
Toggle VWAP visibility on/off.
Why Use This Script?
📌 All-in-One: No need for multiple indicators cluttering your chart.
📌 Visual Clarity: Color-coded signals for quick decision-making.
📌 Flexible: Works on any timeframe (1M, 5M, 1H, Daily, etc.).
Install now and enhance your trading strategy with a professional-grade multi-indicator system!
Not a financial advice. Use at your own discretion and always apply risk management
RSI Full [Titans_Invest]RSI Full
One of the most complete RSI indicators on the market.
While maintaining the classic RSI foundation, our indicator integrates multiple entry conditions to generate more accurate buy and sell signals.
All conditions are fully configurable, allowing complete customization to fit your trading strategy.
⯁ WHAT IS THE RSI❓
The Relative Strength Index (RSI) is a technical analysis indicator developed by J. Welles Wilder. It measures the magnitude of recent price movements to evaluate overbought or oversold conditions in a market. The RSI is an oscillator that ranges from 0 to 100 and is commonly used to identify potential reversal points, as well as the strength of a trend.
⯁ HOW TO USE THE RSI❓
The RSI is calculated based on average gains and losses over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and includes three main zones:
Overbought: When the RSI is above 70, indicating that the asset may be overbought.
Oversold: When the RSI is below 30, indicating that the asset may be oversold.
Neutral Zone: Between 30 and 70, where there is no clear signal of overbought or oversold conditions.
⯁ ENTRY CONDITIONS
The conditions below are fully flexible and allow for complete customization of the signal.
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🔹 CONDITIONS TO BUY 📈
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• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND/OR .
📈 RSI Conditions:
🔹 RSI > Upper
🔹 RSI < Upper
🔹 RSI > Lower
🔹 RSI < Lower
🔹 RSI > Middle
🔹 RSI < Middle
🔹 RSI > MA
🔹 RSI < MA
📈 MA Conditions:
🔹 MA > Upper
🔹 MA < Upper
🔹 MA > Lower
🔹 MA < Lower
📈 Crossovers:
🔹 RSI (Crossover) Upper
🔹 RSI (Crossunder) Upper
🔹 RSI (Crossover) Lower
🔹 RSI (Crossunder) Lower
🔹 RSI (Crossover) Middle
🔹 RSI (Crossunder) Middle
🔹 RSI (Crossover) MA
🔹 RSI (Crossunder) MA
🔹 MA (Crossover) Upper
🔹 MA (Crossunder) Upper
🔹 MA (Crossover) Lower
🔹 MA (Crossunder) Lower
📈 RSI Divergences:
🔹 RSI Divergence Bull
🔹 RSI Divergence Bear
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🔸 CONDITIONS TO SELL 📉
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• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND/OR .
📉 RSI Conditions:
🔸 RSI > Upper
🔸 RSI < Upper
🔸 RSI > Lower
🔸 RSI < Lower
🔸 RSI > Middle
🔸 RSI < Middle
🔸 RSI > MA
🔸 RSI < MA
📉 MA Conditions:
🔸 MA > Upper
🔸 MA < Upper
🔸 MA > Lower
🔸 MA < Lower
📉 Crossovers:
🔸 RSI (Crossover) Upper
🔸 RSI (Crossunder) Upper
🔸 RSI (Crossover) Lower
🔸 RSI (Crossunder) Lower
🔸 RSI (Crossover) Middle
🔸 RSI (Crossunder) Middle
🔸 RSI (Crossover) MA
🔸 RSI (Crossunder) MA
🔸 MA (Crossover) Upper
🔸 MA (Crossunder) Upper
🔸 MA (Crossover) Lower
🔸 MA (Crossunder) Lower
📉 RSI Divergences:
🔸 RSI Divergence Bull
🔸 RSI Divergence Bear
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🤖 AUTOMATION 🤖
• You can automate the BUY and SELL signals of this indicator.
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⯁ UNIQUE FEATURES
______________________________________________________
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
______________________________________________________
📜 SCRIPT : RSI Full
🎴 Art by : @Titans_Invest & @DiFlip
👨💻 Dev by : @Titans_Invest & @DiFlip
🎑 Titans Invest — The Wizards Without Gloves 🧤
✨ Enjoy the Spell!
______________________________________________________
o Mission 🗺
• Inspire Traders to manifest Magic in the Market.
o Vision 𐓏
• To elevate collective Energy 𐓷𐓏
StatPivot- Dynamic Range Analyzer - indicator [PresentTrading]Hello everyone! In the following few open scripts, I would like to share various statistical tools that benefit trading. For this time, it is a powerful indicator called StatPivot- Dynamic Range Analyzer that brings a whole new dimension to your technical analysis toolkit.
This tool goes beyond traditional pivot point analysis by providing comprehensive statistical insights about price movements, helping you identify high-probability trading opportunities based on historical data patterns rather than subjective interpretations. Whether you're a day trader, swing trader, or position trader, StatPivot's real-time percentile rankings give you a statistical edge in understanding exactly where current price action stands within historical contexts.
Welcome to share your opinions! Looking forward to sharing the next tool soon!
█ Introduction and How it is Different
StatPivot is an advanced technical analysis tool that revolutionizes retracement analysis. Unlike traditional pivot indicators that only show static support/resistance levels, StatPivot delivers dynamic statistical insights based on historical pivot patterns.
Its key innovation is real-time percentile calculation - while conventional tools require new pivot formations before updating (often too late for trading decisions), StatPivot continuously analyzes where current price stands within historical retracement distributions.
Furthermore, StatPivot provides comprehensive statistical metrics including mean, median, standard deviation, and percentile distributions of price movements, giving traders a probabilistic edge by revealing which price levels represent statistically significant zones for potential reversals or continuations. By transforming raw price data into statistical insights, StatPivot helps traders move beyond subjective price analysis to evidence-based decision making.
█ Strategy, How it Works: Detailed Explanation
🔶 Pivot Point Detection and Analysis
The core of StatPivot's functionality begins with identifying significant pivot points in the price structure. Using the parameters left and right, the indicator locates pivot highs and lows by examining a specified number of bars to the left and right of each potential pivot point:
Copyp_low = ta.pivotlow(low, left, right)
p_high = ta.pivothigh(high, left, right)
For a point to qualify as a pivot low, it must have left higher lows to its left and right higher lows to its right. Similarly, a pivot high must have left lower highs to its left and right lower highs to its right. This approach ensures that only significant turning points are recognized.
🔶 Percentage Change Calculation
Once pivot points are identified, StatPivot calculates the percentage changes between consecutive pivot points:
For drops (when a pivot low is lower than the previous pivot low):
CopydropPercent = (previous_pivot_low - current_pivot_low) / previous_pivot_low * 100
For rises (when a pivot high is higher than the previous pivot high):
CopyrisePercent = (current_pivot_high - previous_pivot_high) / previous_pivot_high * 100
These calculations quantify the magnitude of each market swing, allowing for statistical analysis of historical price movements.
🔶 Statistical Distribution Analysis
StatPivot computes comprehensive statistics on the historical distribution of drops and rises:
Average (Mean): The arithmetic mean of all recorded percentage changes
CopyavgDrop = array.avg(dropValues)
Median: The middle value when all percentage changes are arranged in order
CopymedianDrop = array.median(dropValues)
Standard Deviation: Measures the dispersion of percentage changes from the average
CopystdDevDrop = array.stdev(dropValues)
Percentiles (25th, 75th): Values below which 25% and 75% of observations fall
Copyq1 = array.get(sorted, math.floor(cnt * 0.25))
q3 = array.get(sorted, math.floor(cnt * 0.75))
VaR95: The maximum expected percentage drop with 95% confidence
Copyvar95D = array.get(sortedD, math.floor(nD * 0.95))
Coefficient of Variation (CV): Measures relative variability
CopycvD = stdDevDrop / avgDrop
These statistics provide a comprehensive view of market behavior, enabling traders to understand the typical ranges and extreme moves.
🔶 Real-time Percentile Ranking
StatPivot's most innovative feature is its real-time percentile calculation. For each current price, it calculates:
The percentage drop from the latest pivot high:
CopycurrentDropPct = (latestPivotHigh - close) / latestPivotHigh * 100
The percentage rise from the latest pivot low:
CopycurrentRisePct = (close - latestPivotLow) / latestPivotLow * 100
The percentile ranks of these values within the historical distribution:
CopyrealtimeDropRank = (count of historical drops <= currentDropPct) / total drops * 100
This calculation reveals exactly where the current price movement stands in relation to all historical movements, providing crucial context for decision-making.
🔶 Cluster Analysis
To identify the most common retracement zones, StatPivot performs a cluster analysis by dividing the range of historical drops into five equal intervals:
CopyrangeSize = maxVal - minVal
For each interval boundary:
Copyboundaries = minVal + rangeSize * i / 5
By counting the number of observations in each interval, the indicator identifies the most frequently occurring retracement zones, which often serve as significant support or resistance areas.
🔶 Expected Price Targets
Using the statistical data, StatPivot calculates expected price targets:
CopytargetBuyPrice = close * (1 - avgDrop / 100)
targetSellPrice = close * (1 + avgRise / 100)
These targets represent statistically probable price levels for potential entries and exits based on the average historical behavior of the market.
█ Trade Direction
StatPivot functions as an analytical tool rather than a direct trading signal generator, providing statistical insights that can be applied to various trading strategies. However, the data it generates can be interpreted for different trade directions:
For Long Trades:
Entry considerations: Look for price drops that reach the 70-80th percentile range in the historical distribution, suggesting a statistically significant retracement
Target setting: Use the Expected Sell price or consider the average rise percentage as a reasonable target
Risk management: Set stop losses below recent pivot lows or at a distance related to the statistical volatility (standard deviation)
For Short Trades:
Entry considerations: Look for price rises that reach the 70-80th percentile range, indicating an unusual extension
Target setting: Use the Expected Buy price or average drop percentage as a target
Risk management: Set stop losses above recent pivot highs or based on statistical measures of volatility
For Range Trading:
Use the most common drop and rise clusters to identify probable reversal zones
Trade bounces between these statistically significant levels
For Trend Following:
Confirm trend strength by analyzing consecutive higher pivot lows (uptrend) or lower pivot highs (downtrend)
Use lower percentile retracements (20-30th percentile) as entry opportunities in established trends
█ Usage
StatPivot offers multiple ways to integrate its statistical insights into your trading workflow:
Statistical Table Analysis: Review the comprehensive statistics displayed in the data table to understand the market's behavior. Pay particular attention to:
Average drop and rise percentages to set reasonable expectations
Standard deviation to gauge volatility
VaR95 for risk assessment
Real-time Percentile Monitoring: Watch the real-time percentile display to see where the current price movement stands within the historical distribution. This can help identify:
Extreme movements (90th+ percentile) that might indicate reversal opportunities
Typical retracements (40-60th percentile) that might continue further
Shallow pullbacks (10-30th percentile) that might represent continuation opportunities in trends
Support and Resistance Identification: Utilize the plotted pivot points as key support and resistance levels, especially when they align with statistically significant percentile ranges.
Target Price Setting: Use the expected buy and sell prices calculated from historical averages as initial targets for your trades.
Risk Management: Apply the statistical measurements like standard deviation and VaR95 to set appropriate stop loss levels that account for the market's historical volatility.
Pattern Recognition: Over time, learn to recognize when certain percentile levels consistently lead to reversals or continuations in your specific market, and develop personalized strategies based on these observations.
█ Default Settings
The default settings of StatPivot have been carefully calibrated to provide reliable statistical analysis across a variety of markets and timeframes, but understanding their effects allows for optimal customization:
Left Bars (30) and Right Bars (30): These parameters determine how pivot points are identified. With both set to 30 by default:
A pivot low must be the lowest point among 30 bars to its left and 30 bars to its right
A pivot high must be the highest point among 30 bars to its left and 30 bars to its right
Effect on performance: Larger values create fewer but more significant pivot points, reducing noise but potentially missing important market structures. Smaller values generate more pivot points, capturing more nuanced movements but potentially including noise.
Table Position (Top Right): Determines where the statistical data table appears on the chart.
Effect on performance: No impact on analytical performance, purely a visual preference.
Show Distribution Histogram (False): Controls whether the distribution histogram of drop percentages is displayed.
Effect on performance: Enabling this provides visual insight into the distribution of retracements but can clutter the chart.
Show Real-time Percentile (True): Toggles the display of real-time percentile rankings.
Effect on performance: A critical setting that enables the dynamic analysis of current price movements. Disabling this removes one of the key advantages of the indicator.
Real-time Percentile Display Mode (Label): Chooses between label display or indicator line for percentile rankings.
Effect on performance: Labels provide precise information at the current price point, while indicator lines show the evolution of percentile rankings over time.
Advanced Considerations for Settings Optimization:
Timeframe Adjustment: Higher timeframes generally benefit from larger Left/Right values to identify truly significant pivots, while lower timeframes may require smaller values to capture shorter-term swings.
Volatility-Based Tuning: In highly volatile markets, consider increasing the Left/Right values to filter out noise. In less volatile conditions, lower values can help identify more potential entry and exit points.
Market-Specific Optimization: Different markets (forex, stocks, commodities) display different retracement patterns. Monitor the statistics table to see if your market typically shows larger or smaller retracements than the current settings are optimized for.
Trading Style Alignment: Adjust the settings to match your trading timeframe. Day traders might prefer settings that identify shorter-term pivots (smaller Left/Right values), while swing traders benefit from more significant pivots (larger Left/Right values).
By understanding how these settings affect the analysis and customizing them to your specific market and trading style, you can maximize the effectiveness of StatPivot as a powerful statistical tool for identifying high-probability trading opportunities.
Acceleration Bands HTF
This version gives you the ability to see the indicator from the HIGHER timeframes when you are on the timeframes. Please note that this is not the original formula, but a factored one that I found effective for identifying market trends. Thanks to @capissimo who provided the base open-code.
Acceleration Bands are designed to capture potential price breakouts or reversals in an asset. They are calculated based on a stock's price movements over a specified period, typically using the high, low, and closing prices. The idea is to identify moments when the price is accelerating (hence the name) beyond its normal range, which might indicate the beginning of a new trend.
Calculation
Acceleration Bands consist of three lines:
Upper Band (AB Upper): This is calculated by adding a certain percentage of the simple moving average (SMA) to the highest high over a given period.
Middle Band: This is typically the SMA of the stock's price.
Lower Band (AB Lower): This is calculated by subtracting the same percentage of the SMA from the lowest low over a given period.
Mathematically :
AB Upper = SMA + (Highest High * Percentage)
AB Lower = SMA - (Lowest Low * Percentage)
OR
Upper Band = SMA x (1 + (High - Low) / SMA)
Lower Band = SMA x (1 - (High - Low) / SMA)
Interpretation
The bands are used to identify periods when the price of a security is accelerating or decelerating:
Breakout Above Upper Band: This is usually considered a bullish signal, suggesting that the price is accelerating upwards and a new uptrend may be starting.
Breakdown Below Lower Band: This is usually considered a bearish signal, suggesting that the price is accelerating downwards and a new downtrend may be starting.
Reversal Between Bands: When the price re-enters the region between the bands after breaking out, it can be seen as a potential reversal signal.
Trading Strategy
Entry Signals:
Buy when the price breaks above the upper band.
Sell or short when the price breaks below the lower band.
Exit Signals:
Close a long position when the price falls back into the area between the bands.
Close a short position when the price rises back into the area between the bands.
Advantages
Helps capture early trends.
Can be used across various time frames and assets.
Provides clear entry and exit signals.
Stock ETF Tracker 2.0The Stock Sector ETF tracker with Indicators is a versatile tool designed to track the performance of sector-specific ETFs relative to the current asset. It automatically identifies the sector of the underlying symbol and displays the corresponding ETF’s price action alongside key technical indicators. This helps traders analyze sector trends and correlations in real time.
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Key Features
Automatic Sector Detection:
Fetches the sector of the current asset (e.g., "Technology" for AAPL).
Maps the sector to a user-defined ETF (default: SPDR sector ETFs) .
Technical Indicators:
Simple Moving Average (SMA): Tracks the ETF’s trend.
Bollinger Bands: Highlights volatility and potential reversals.
Donchian High (52-Week High): Identifies long-term resistance levels.
SPY Regime Filter: Red background color if SP500 is below 200 day SMA.
Customizable Inputs:
Adjust indicator parameters (length, visibility).
Override default ETFs for specific sectors.
Informative Table:
Displays the current sector and ETF symbol in the bottom-right corner.
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Input Settings
SMA Settings
SMA Length: Period for calculating the Simple Moving Average (default: 200).
Show SMA: Toggle visibility of the SMA line.
Bollinger Bands Settings
BB Length: Period for Bollinger Bands calculation (default: 20).
BB Multiplier: Standard deviation multiplier (default: 2.0).
Show Bollinger Bands: Toggle visibility of the bands.
Donchian High (52-Week High)
Daily High Length: Days used to calculate the high (default: 252, approx. 1 year).
Show High: Toggle visibility of the 52-week high line.
Sector Selections
Customize ETFs for each sector (e.g., replace XLU with another utilities ETF).
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Example Use Cases
Trend Analysis: Compare a stock’s price action to its sector ETF’s SMA for trend confirmation.
Volatility Signals: Use Bollinger Bands to spot ETF price squeezes or breakouts.
Sector Strength: Monitor if the ETF is approaching its 52-week high to gauge sector momentum.
Enjoy tracking sector trends with ease! 🚀
