Bull-Bear EfficiencyBull-Bear Efficiency
This indicator measures the directional efficiency of price movement across many historical entry points to estimate overall market bias. It is designed as a trend gauge rather than a timing signal.
Concept
For each historical bar (tau) and a chosen lookahead horizon (h), the script evaluates how efficiently price has traveled from that starting point to the endpoint. Efficiency is defined as the net price change divided by the total absolute movement that occurred along the path.
Formula:
E(tau,h) = ( Price - Price ) / ( Sum from i = tau+1 to tau+h of | Price - Price | )
This measures how "straight" the path was from the entry to the current bar:
If price moved steadily upward, the numerator and denominator are nearly equal, and E approaches +1 (efficient bullish trend).
If price moved steadily downward, E approaches -1 (efficient bearish trend).
If price chopped back and forth, the denominator grows faster than the numerator, and E approaches 0 (inefficient movement).
The algorithm computes this efficiency for many past starting points and multiple horizons, optionally normalizing by ATR to account for volatility. The efficiencies are then weighted by recency to emphasize more recent behavior.
From this, the script derives:
Bull = weighted average of positive efficiencies
Bear = weighted average of negative efficiencies (absolute value)
Net = Bull - Bear (net directional efficiency)
Interpretation
Bull, Bear, and Net quantify how coherently the market has been trending.
Bull near 1.0, Bear near 0.0, Net > 0 -> clean upward trends; long positions have been more efficient.
Bear near 1.0, Bull near 0.0, Net < 0 -> clean downward trends; short positions have been more efficient.
Bull and Bear both small or similar -> low-efficiency, range-bound environment.
Net therefore acts as a "trend coherence index" that measures whether price action is directionally organized or noisy.
Practical Use
Trend filter:
Apply trend-following systems only when Net is strongly positive or negative.
Avoid them when Net is near zero.
Regime change detection:
Crossings through zero often correspond to transitions between trending and ranging regimes.
Momentum loss detection:
If price makes new highs but Net or Bull weakens, it suggests trend exhaustion.
Settings Overview
Lookback: Number of historical bars considered as entry points (tau values).
Horizons: List of forward projection lengths (in bars) for measuring efficiency.
Recency Decay (lambda): Exponential weighting that emphasizes recent data.
Normalize by ATR: Adjusts "effort" to account for volatility changes.
Display Options: Toggle Bull, Bear, Net, or Signed Average (S). Customize line colors.
Notes
This indicator does not produce entry or exit signals.
It is a statistical tool that measures how efficiently price has trended over time.
High Net values indicate smooth, coherent trends.
Low or neutral Net values indicate noisy, directionless conditions.
المؤشرات والاستراتيجيات
ParallaxMind™️ MACD-V: Volatility Normalized Momentum Candles🚀 Award-Winning Momentum Indicator that Outperforms the Standard MACD in All Market Conditions
📈 ParallaxMind™️ MACD-V: Volatility Normalized Momentum Colored Bars with Alerts
The MACD-V (Volatility Normalized MACD) was first developed by trader Alex Spiroglou in 2015, published in a 2022 research paper, and awarded the Charles H. Dow Award for outstanding research in technical analysis.
Unlike the standard MACD, which often suffers from noisy false signals and inconsistent readings, the MACD-V introduces volatility normalization. This innovation creates a hybrid momentum tool that solves the five core limitations of the classic MACD — making signals stable across time, universally comparable across markets, and structured within a clear momentum framework.
🔑 Key Features & Benefits
Time-Stable & Cross-Market Comparable: A reading of +100 or -100 has the same meaning across decades and across assets — stocks, forex, commodities, and crypto.
Objective Momentum Framework: Levels at +150, +50, -50, and -150 create universal benchmarks to identify rallying, declining, ranging, and extreme conditions.
Alerting Capability: Built-in alerts notify you the moment momentum shifts — including crossovers, zero-line breaks, and entries into overbought/oversold zones. This ensures you never miss critical setups without constantly watching charts.
Momentum Stage Labels: Clear, automatic labels appear on your chart to define the current state of the market — Rallying, Retracing, Ranging, Declining, Rebounding, or Risk Zones. These labels cut through noise and provide instant clarity about market conditions.
With these features, the MACD-V transforms momentum analysis from subjective art into objective science, delivering cleaner entries, smarter exits, and greater confidence in any market.
ParallaxMind™️ MACD-V: Volatility Normalized Momentum w/Alerts🚀 Award-Winning Momentum Indicator that Outperforms the Standard MACD in All Market Conditions
📈 ParallaxMind™️ MACD-V: Volatility Normalized Momentum with Alerts
The MACD-V (Volatility Normalized MACD) was first developed by trader Alex Spiroglou in 2015, published in a 2022 research paper, and awarded the Charles H. Dow Award for outstanding research in technical analysis.
Unlike the standard MACD, which often suffers from noisy false signals and inconsistent readings, the MACD-V introduces volatility normalization. This innovation creates a hybrid momentum tool that solves the five core limitations of the classic MACD — making signals stable across time, universally comparable across markets, and structured within a clear momentum framework.
🔑 Key Features & Benefits
Time-Stable & Cross-Market Comparable: A reading of +100 or -100 has the same meaning across decades and across assets — stocks, forex, commodities, and crypto.
Objective Momentum Framework: Levels at +150, +50, -50, and -150 create universal benchmarks to identify rallying, declining, ranging, and extreme conditions.
Alerting Capability: Built-in alerts notify you the moment momentum shifts — including crossovers, zero-line breaks, and entries into overbought/oversold zones. This ensures you never miss critical setups without constantly watching charts.
Momentum Stage Labels: Clear, automatic labels appear on your chart to define the current state of the market — Rallying, Retracing, Ranging, Declining, Rebounding, or Risk Zones. These labels cut through noise and provide instant clarity about market conditions.
With these features, the MACD-V transforms momentum analysis from subjective art into objective science, delivering cleaner entries, smarter exits, and greater confidence in any market.
SEIZ - Statistical External & Internal Zones [Pro]Overview
SEIZ (Statistical External & Internal Zones) visualizes how far price typically travels beyond a prior candle’s range (external to previous candles high/low) or within it (internal to previous candles high/low).
It displays percentile thresholds that highlight when movement is statistically common vs. stretched relative to recent structure.
Key Features
• External zones: mark areas where price historically tends to extend beyond the previous range.
Example: a 50th external high percentile is a historically common extension above the prior candle range’s high; a 50th external low percentile is a historically common extension below the prior candle range’s low.
• Internal zones: mark areas where price historically tends to retrace while remaining inside the previous range.
Example: a 50th internal high percentile represents a historically common move that remained within the prior candle range on the high side; similarly for internal low.
• Auto-switching: When "enabled" the indicator will automatically switch to the correct internal or external zones. For example if the indicator is on the daily timeframe it will automatically show external high zones and levels if it has gone above the previous days high. It will then hide/filter out the internal high zones because price is no longer within the previous daily range.
• Multi-time-frame table: summarizes the most significant percentile reached on each enabled timeframe (e.g., 15m → 12h, 1D) with an interval-progress readout. For example if indicator is set to "Daily" it will show the highest level reached within the day under the "High" column, and the lowest level reached in the day under the "Low" column. The "Progress" column shows how much of the timeframe of that row has completed its candle/interval.
• Highly customizable settings:
- "Show Historic": When on will show current interval zones and as many previous intervals as possible
- "Show Intervals 2 Only": When on will show only the current and previous interval zones and levels.
- Choose between drawing lines for levels or zones or both. Customize colors and transparency of zones.
Methodology (transparency)
• SEIZ uses pre-computed, timeframe-specific percentile datasets that quantify typical extensions and retracements observed in historical data.
• The datasets are embedded in the script for deterministic plotting across timeframes; no external connections are used.
• Percentile values reflect empirical frequencies (not assumptions of a normal distribution).
• These levels do not have any prediction power over future price. They are a visual to compare historically where highs and lows most commonly formed for a time period with current price.
How to use
Choose the Timeframe to reference for zones.
Leave Auto external/internal zones filtering ON for regime-aware plotting.
Optional: enable percentile lines (25 / 50 / 75 / 85 / 95) and/or filled zones; adjust opacity and labels to taste.
Set alerts on percentile crosses to be notified when price reaches statistically rare areas.
Treat SEIZ as context; it does not generate entries or exits.
Notes
• Descriptive tool — no prediction or performance claims.
• Percentiles summarize historical behavior and can vary with market conditions.
• Source is protected to safeguard the proprietary construction of percentile datasets.
• Non-standard chart types (e.g., Heikin Ashi, Renko) are for display only.
Credits
Developed by LevelLogic Indicators to help interpret market structure through empirical percentile context.
Bollinger Band ToolkitBollinger Band Toolkit
An advanced, adaptive Bollinger Band system for traders who want more context, precision, and edge.
This indicator expands on the classic Bollinger Bands by combining statistical and volatility-based methods with modern divergence and squeeze detection tools. It helps identify volatility regimes, potential breakouts, and early momentum shifts — all within one clean overlay.
🔹 Core Features
1. Adaptive Bollinger Bands (σ + ATR)
Classic 20-period bands enhanced with an ATR-based volatility adjustment, making them more responsive to true market movement rather than just price variance.
Reduces “overreacting” during chop and avoids bands collapsing too tightly during trends.
2. %B & RSI Divergence Detection
🟢 Green dots: Positive %B divergence — price makes a lower low, but %B doesn’t confirm (bullish).
🔴 Red dots: Negative %B divergence — price makes a higher high, but %B doesn’t confirm (bearish).
✚ Red/green crosses: RSI divergence confirmation — momentum fails to confirm the price’s new extreme.
These signals highlight potential reversal or slowdown zones that are often invisible to the naked eye.
3. Bollinger Band Squeeze (with Volume Filter)
Yellow squares (■) show periods when Bollinger Bands are at their narrowest relative to recent history.
Volume confirmation ensures the squeeze only triggers when both volatility and participation contract.
Often marks the “calm before the storm” — breakout potential zones.
4. Multi-Timeframe Breakout Markers
Optionally displays breakouts from higher or lower timeframes using different colors/symbols.
Lets you see when a higher timeframe band break aligns with your current chart — a strong trend continuation signal.
5. Dual- and Triple-Band Visualization (±1σ, ±2σ, ±3σ)
Optional inner (±1σ) and outer (±3σ) bands provide a layered volatility map:
Price holding between ±1σ → stable range / mean-reverting behavior
Price riding near ±2σ → trending phase, sustained momentum
Price touching or exceeding ±3σ → volatility expansion or exhaustion zone
This triple-band layout visually distinguishes normal movement from statistical extremes, helping you read when the market is balanced, expanding, or approaching its limits.
⚙️ Inputs & Customization
Choose band type (SMA/EMA/SMMA/WMA/VWMA)
Adjust deviation multiplier (σ) and ATR multiplier
Toggle individual features (divergence dots, squeeze markers, inner bands, etc.)
Multi-timeframe and colour controls for advanced users
🧠 How to Use
Watch for squeeze markers followed by a breakout bar beyond ±2σ → volatility expansion signal.
Combine divergence dots with RSI or price structure to anticipate slowdowns or reversals.
Confirm direction using multi-timeframe breakouts and volume expansion.
💬 Why It Works
This toolkit transforms qualitative chart reading (tight bands, hidden divergence) into quantitative, testable conditions — giving you objective insights that can be backtested, coded, or simply trusted in live setups.
Confirmed Breakout Detector v2This indicator automatically:
Detects breakouts above recent resistance (pivot high).
Confirms volume surge (≥ 1.5× average 50-day volume).
Compares RS line vs QQQ to ensure leadership.
Checks candle strength (close in upper half).
Verifies MACD slope ≥ 0 (no bearish divergence).
Plots green triangles under confirmed buys, orange for watch-list breakouts.
Displays an on-chart label (HUD) with real-time confirmation status.
Supports TradingView alerts, so you can set “Confirmed Buy Alert” → Send Email / App Notification.
Institutional AI-Enhanced Market StructureInstitutional AI-Enhanced Market Structure Indicator
COMPREHENSIVE DESCRIPTION
Overview and Purpose
This indicator combines institutional trading concepts (Smart Money Concepts) with a proprietary AI-inspired probability scoring system to identify high-probability trading opportunities. Unlike standard trend-following or support/resistance indicators, this tool integrates multiple institutional order flow concepts and quantifies their confluence through a dynamic scoring algorithm that adapts to market conditions.
The indicator is closed-source because it contains a unique multi-factor probability calculation engine and adaptive parameter optimization system that took extensive development and backtesting to create. The specific weighting, thresholds, and interaction between components represent proprietary intellectual property.
What Makes This Original
1. AI-Inspired Adaptive Probability Scoring System
The core innovation is a dynamic scoring algorithm that evaluates trade setups based on 6 confluence factors:
Market Structure Quality (20 points): Validates Break of Structure (BOS) or Change of Character (CHoCH) using pivot-based swing analysis
Order Flow Strength (15 points): Measures institutional volume participation relative to 20 and 50-period moving averages with standard deviation filtering
Liquidity Engineering (15 points): Detects liquidity sweeps at equal highs/lows (EQL) where retail stop losses cluster
Imbalance Presence (10 points): Identifies unfilled Fair Value Gaps (3-candle imbalances) as institutional entry zones
Market Regime Alignment (10 points): Confirms directional bias through multi-factor regime classification
Volatility Environment (5 points): Penalizes signals during high-volatility "chop" periods
Each factor is weighted based on backtested importance, and the total score (50-100%) must exceed a user-defined threshold before displaying signals. This is NOT a simple indicator mashup—the scoring system dynamically evaluates how these concepts work together in real-time.
2. Dynamic Market Regime Detection
Most indicators use static parameters. This indicator continuously classifies the market into one of four regimes using four calculations:
Trend Strength: EMA(21) vs EMA(50) divergence relative to price
Volatility Ratio: Current price standard deviation vs 50-period average
Volume Regime: Current volume vs 50-period SMA
Average Daily Range: 20-bar high-low range normalized to price
Based on these inputs, the algorithm classifies markets as:
BULL_TREND: Strong upward momentum with above-average volume
BEAR_TREND: Strong downward momentum with above-average volume
RANGING: Low trend strength with contained volatility
VOLATILE: Elevated volatility ratio above 1.5x average
The regime detection then adaptively modifies:
ATR multipliers for stop placement (2.5x in volatile, 1.2x in ranging, 1.8x in trending)
Signal probability requirements (higher in volatile conditions)
Order block decay rates
Fair value gap sensitivity
3. Institutional Order Flow Integration
The indicator detects and tracks institutional footprints through three proprietary methods:
Order Blocks: Unlike simple supply/demand zones, this uses a multi-condition filter:
Volume spike > 2.0 standard deviations above 20-period average
Large candle body > 0.8x ATR
Confirmation of Break of Structure in the same direction
Touch tracking and "tested" status when price revisits
Automatic decay after user-defined bars (prevents chart clutter)
Fair Value Gaps (Imbalances): 3-candle inefficiency detection where:
Bullish FVG: low > high AND close > high (gap between candle 0 and 2)
Bearish FVG: high < low AND close < low
Real-time fill percentage tracking as price revisits the gap
Assumes institutions will defend or fill these imbalances
Liquidity Zones: Detects equal highs/lows where retail stops cluster:
Identifies swing points within user-defined percentage threshold (default 0.3%)
Tracks "sweep" events when price spikes through then reverses (wick through level, close back inside)
Differentiates swept vs unswept liquidity for entry timing
4. Volume-Weighted Dynamic Levels
Instead of simple moving averages or static pivots, support/resistance are calculated using volume-weighted price:
Support = Σ(low × volume ) / Σ(volume ) for i=0 to 19
Resistance = Σ(high × volume ) / Σ(volume ) for i=0 to 19
This gives more weight to price levels with higher institutional participation, creating more reliable stop-loss placement when "Adaptive Stop Loss" is enabled.
5. Multi-Timeframe Confluence
The indicator queries daily timeframe data for higher-timeframe confirmation:
Daily EMA trend direction (21 vs 50)
Daily volume regime (above/below 20-period average)
Daily market regime classification
Signals only trigger when current timeframe setup aligns with daily timeframe bias, filtering out counter-trend noise.
How It Works - Technical Methodology
Market Structure Detection (Smart Money Concepts)
Uses ta.pivothigh() and ta.pivotlow() with user-defined strength (default 5 bars each side)
Stores last 50 swing highs and lows in arrays for historical reference
Break of Structure (BOS): Price closes beyond the most recent swing high (bullish) or swing low (bearish)
Change of Character (CHoCH): Price breaks counter-trend structure (low breaks above previous swing low = potential reversal)
Signal Generation Logic
A valid LONG signal requires ALL of the following:
Setup: Bullish BOS or CHoCH confirmed
Confirmation: Bullish liquidity sweep OR unfilled bullish FVG present
HTF Alignment: Daily timeframe in uptrend with above-average volume
Probability Score: AI scoring system returns ≥65% (user adjustable 50-95%)
Risk:Reward: Calculated stop (ATR-based or adaptive) allows minimum 2:1 R:R (user adjustable)
SHORT signals use inverse logic (bearish structure, bearish sweeps/FVGs, daily downtrend).
Adaptive Risk Management
Stop loss calculation adapts based on:
Current market regime (wider stops in volatile markets)
Volume-weighted support/resistance levels when "Adaptive" enabled
Minimum risk threshold (0.2% of price) to avoid over-tight stops
Take profit targets automatically calculate based on user-defined risk:reward ratio (default 2:1).
How To Use This Indicator
Initial Setup
Market Structure Group:
Start with default Swing Strength (5) for 1H-4H timeframes
Increase to 10-15 for daily timeframes
Decrease to 3 for scalping on 5-15min timeframes
AI Features Group:
Set "Signal Probability Threshold" to 65% for balanced approach
Increase to 75-80% for fewer but higher-quality signals
Lower to 60% in strong trending markets for more entries
Risk Management:
Enable "Adaptive Stop Loss" for dynamic support/resistance-based stops
Set "Minimum Risk:Reward" to 2.0 or higher (institutional standard)
Adjust ATR Length (14) based on timeframe (shorter for intraday)
Reading The Signals
Visual Elements:
Small triangles: Swing highs (red) and lows (green) - market structure pivots
Circles: Break of Structure - lime (bullish) or red (bearish)
Diamonds: Change of Character - cyan (bullish reversal) or orange (bearish reversal)
Boxes: Order blocks (green=bullish, red=bearish, yellow border=tested)
Transparent boxes: Fair Value Gaps (blue=bullish, purple=bearish)
Dashed/solid lines: Liquidity zones (purple=unswept, yellow=swept)
Large arrows: Trade signals with probability % (🔼 LONG / 🔽 SHORT)
Red/Green lines: Stop loss and take profit levels
Statistics Dashboard (top right by default):
Market Regime: Current classification (BULL_TREND, BEAR_TREND, RANGING, VOLATILE)
Volatility Ratio: Current vs average volatility (>1.5 = avoid trading)
Volume Regime: Current vs average volume (>1.2 = strong institutional participation)
Active Order Blocks: Number of untested institutional zones
Unfilled FVGs: Number of imbalances awaiting price return
Liquidity Zones: Unswept equal highs/lows (potential reversal areas)
HTF Alignment: Daily timeframe bias (confirm direction)
Last Signal Prob: Confidence score of most recent signal
Trading Strategy
For LONG Entries:
Wait for bullish BOS or CHoCH marker (circle/diamond below price)
Confirm market regime is BULL_TREND or RANGING (not VOLATILE)
Look for bullish liquidity sweep (yellow line below price) or unfilled bullish FVG (blue box)
When all align, watch for 🔼 LONG signal with probability ≥65%
Enter on signal candle close
Stop loss = red line, Take profit = green line
Monitor FVG fills and order block tests for possible early exit
For SHORT Entries:
Same logic in reverse (bearish structure, BEAR_TREND regime, bearish sweeps/FVGs, 🔽 SHORT signals)
Advanced Usage:
Order Block Confluence: Highest probability entries occur when price retraces to tested order block (yellow border) + FVG overlap
Liquidity Sweep Reversals: Best entries often follow immediate sweep (yellow line) then signal in opposite direction
Regime Filtering: Avoid trading during VOLATILE regime or when volatility ratio >1.5
HTF Confirmation: Only take signals when HTF Alignment matches direction (BULLISH for longs, BEARISH for shorts)
Customization:
Every visual element has individual toggle and color controls in settings:
Hide swing points if chart too cluttered
Disable BOS/CHoCH markers if only using order blocks
Turn off FVGs if focusing on liquidity sweeps
Customize colors to match your chart theme
Reposition dashboard to any corner
Why This Requires Closed-Source Protection
This indicator represents months of development integrating:
Proprietary probability weighting system - The specific point allocation (20/15/15/10/10/5) and interaction logic between factors is based on extensive backtesting across multiple markets and timeframes
Adaptive parameter optimization algorithms - How the indicator modifies ATR multipliers, decay rates, and thresholds based on regime detection uses proprietary mathematical relationships
Volume-weighted level calculations - The specific lookback periods and weighting formulas for dynamic support/resistance are optimized through statistical analysis
Multi-factor regime classification - The exact thresholds for trend strength (0.02), volatility ratio (1.3/1.5), and volume regime (1.0/1.2) are calibrated values
While the underlying concepts (SMC, order blocks, FVGs) are known, the integration methodology, scoring system, and adaptive algorithms are original intellectual property. An open-source version would allow immediate copying of years of development work, defeating the purpose of creating a professional-grade tool.
The detailed description above provides traders with complete transparency on WHAT the indicator does and HOW to use it effectively, without revealing the exact mathematical relationships and thresholds that make it effective.
Disclaimer
This indicator is an analytical tool for identifying potential trading opportunities based on institutional order flow concepts. It does not guarantee profits and should be used alongside proper risk management, fundamental analysis, and personal trading rules. Past performance does not indicate future results. Always use stop losses and never risk more than you can afford to lose.
Previous Day High, Low, and Mid (Extended)This indicator shows the previous sessions high, low, and midpoint with extended lines for the trading session.
CI Volatility UVXY Spike LevelsThis handy script tracks potential spikes for UVXY, VXX, or UVIX, pinpointing exactly where each needs to hit for 20%, 50%, 75%, or 100% gains. Check the handy levels box in the top-right corner for quick reference, plus real-time updates on your current spike progress. Say goodbye to endless manual math.
www.CIVolatility.com
AlphaTrend - Medium Term Trend Probability Indicator on TOTALESWHAT IS ALPHATREND?
AlphaTrend is a consensus-based trend identification system that combines 7 independent trend detection methodologies into a single probability score. Designed for medium-term trading (days to weeks), it aggregates diverse analytical approaches—from volatility-adjusted moving averages to statistical oscillators—to determine directional bias with quantifiable confidence.
Unlike single-indicator systems prone to false signals during consolidation, AlphaTrend requires majority agreement across multiple uncorrelated methods before generating directional signals, significantly reducing whipsaws in choppy markets.
METHODOLOGY - THE 7-INDICATOR VOTING SYSTEM
Each indicator analyzes trend from a mathematically distinct perspective and casts a vote: +1 (bullish), -1 (bearish), or 0 (neutral). The average of all 7 votes creates the final probability score ranging from -1 (strong bearish) to +1 (strong bullish).
1. FLXWRT RMA (VOLATILITY-ADJUSTED BASELINE)
Method: RMA (Running Moving Average) with ATR-based dynamic bands
Calculation:
RMA = Running MA of price over 12 periods
ATR = Average True Range over 20 periods
Long Signal: Price > RMA + ATR
Short Signal: Price < RMA - ATR
Logic: Trend confirmed only when price breaks beyond volatility-adjusted boundaries, not just the moving average itself. This filters noise by requiring momentum sufficient to overcome recent volatility.
Why it works: Standard MA crossovers generate excessive false signals in ranging markets. Adding ATR bands ensures price has genuine directional momentum, not just minor fluctuations.
Settings:
RMA Length (12): Base trend smoothing
ATR Length (20): Volatility measurement period
2. BOOSTED MOVING AVERAGE (MOMENTUM-ENHANCED TREND)
Method: Double EMA with acceleration boost factor
Calculation:
EMA1 = EMA(close, length)
EMA2 = EMA(close, length/2) // Faster EMA
Boosted Value = EMA2 + sensitivity × (EMA2 - EMA1)
Final = EMA smoothing of Boosted Value
Logic: Amplifies the difference between fast and slow EMAs to emphasize trend momentum. The boost factor (1.3) accelerates response to directional moves while subsequent smoothing prevents over-reaction.
Why it works: Traditional MAs lag price action. The boost mechanism projects trend direction forward by amplifying the momentum differential between two EMAs, providing earlier signals without sacrificing reliability.
Settings:
Length (36): Base EMA period
Boost Factor (1.3): Momentum amplification multiplier
Originality: This is a proprietary enhancement to standard double EMA systems. Most indicators simply cross fast/slow EMAs; this one mathematically projects momentum trajectory.
3. HEIKIN ASHI TREND (T3-SMOOTHED CANDLES)
Method: Heikin Ashi candles with T3 exponential smoothing
Calculation:
Heikin Ashi candles = Smoothed OHLC transformation
T3 Smoothing = Triple-exponential smoothing (Tillson T3)
Signal: T3(HA_Open) crosses T3(HA_Close)
Logic: Heikin Ashi candles filter intrabar noise by averaging consecutive bars. T3 smoothing adds additional filtering using Tillson's generalized DEMA algorithm with custom volume factor.
Why it works: Regular candlesticks contain high-frequency noise. Heikin Ashi transformation creates smoother trends, and T3 smoothing eliminates remaining whipsaws while maintaining responsiveness. The T3 algorithm specifically addresses the lag-vs-smoothness tradeoff.
Settings:
T3 Length (13): Smoothing period
T3 Factor (0.3): Volume factor for T3 algorithm
Percent Squeeze (0.2): Sensitivity adjustment
Technical Note: T3 is superior to simple EMA smoothing because it applies the generalized DEMA formula recursively, reducing lag while maintaining smooth output.
4. VIISTOP (ATR-BASED TREND FILTER)
Method: Simple trend detection using price position vs smoothed baseline with ATR confirmation
Calculation:
Baseline = SMA(close, 16)
ATR = ATR(16)
Uptrend: Close > Baseline
Downtrend: Close < Baseline
Logic: The simplest component—pure price position relative to medium-term average. While basic, it provides a "sanity check" against over-optimized indicators.
Why it works: Sometimes the simplest approach is most robust. In strong trends, price consistently stays above/below its moving average. This indicator prevents the system from over-complicating obvious directional moves.
Settings:
Length (16): Baseline period
Multiplier (2.8): ATR scaling (not actively used in vote logic)
Purpose in Ensemble: Provides grounding in basic trend logic. Complex indicators can sometimes generate counterintuitive signals; ViiStop ensures the system stays aligned with fundamental price positioning.
5. NORMALIZED KAMA OSCILLATOR (ADAPTIVE EFFICIENCY-BASED TREND)
Method: Kaufman Adaptive Moving Average normalized to oscillator format
Calculation:
Efficiency Ratio = |Close - Close | / Sum(|Close - Close |, 8)
Smoothing Constant = ER × (Fast SC - Slow SC) + Slow SC
KAMA = Adaptive moving average using dynamic smoothing
Normalized = (KAMA - Lowest) / (Highest - Lowest) - 0.5
Logic: KAMA adjusts its smoothing speed based on market efficiency. In trending markets (high efficiency), it speeds up. In ranging markets (low efficiency), it slows down. Normalization converts absolute values to -0.5/+0.5 oscillator for consistent voting.
Why it works: Fixed-period moving averages perform poorly across varying market conditions. KAMA's adaptive nature makes it effective in both trending and choppy environments by automatically adjusting its responsiveness.
Settings:
Fast Period (9): Maximum responsiveness
Slow Period (21): Minimum responsiveness
ER Period (8): Efficiency calculation window
Normalization Lookback (35): Oscillator scaling period
Mathematical Significance: Kaufman's algorithm is one of the most sophisticated adaptive smoothing methods in technical analysis. The Efficiency Ratio mathematically quantifies trend strength vs noise.
6. LÉVY FLIGHT RSI (HEAVY-TAILED MOMENTUM)
Method: Modified RSI using Lévy distribution weighting for gains/losses
Calculation:
Weighted Gain = (Max(Price Change, 0))^Alpha
Weighted Loss = (-Min(Price Change, 0))^Alpha
RSI = 100 - (100 / (1 + RMA(Gain) / RMA(Loss)))
Centered RSI = RSI - 50
Logic: Standard RSI treats all price changes linearly. Lévy Flight RSI applies power-law weighting (Alpha = 1.5) to emphasize larger moves, modeling heavy-tailed distributions observed in real market data.
Why it works: Market returns exhibit "fat tails"—large moves occur more frequently than normal distribution predicts. Lévy distributions (Alpha between 1-2) better model this behavior. By weighting larger price changes more heavily, this RSI variant becomes more sensitive to genuine momentum shifts while filtering small noise.
Settings:
RSI Length (14): Standard period
Alpha (1.5): Lévy exponent (1=linear, 2=quadratic)
MA Length (12): Final smoothing
Originality: Standard RSI uses unweighted gains/losses. This implementation applies stochastic process theory (Lévy flights) from quantitative finance to create a momentum indicator more aligned with actual market behavior.
Mathematical Background: Lévy flights describe random walks with heavy-tailed step distributions, observed in financial markets, animal foraging patterns, and human mobility. Alpha=1.5 balances between normal distribution (Alpha=2) and Cauchy distribution (Alpha=1).
7. REGULARIZED-MA OSCILLATOR (Z-SCORED TREND DEVIATION)
Method: Moving average converted to z-score oscillator
Calculation:
MA = EMA(close, 19)
Mean = SMA(MA, 30)
Std Dev = Standard Deviation(MA, 30)
Z-Score = (MA - Mean) / Std Dev
Logic: Converts absolute MA values to statistical standard deviations from mean. Positive z-score = MA above its typical range (bullish), negative = below range (bearish).
Why it works: Raw moving averages don't indicate strength—a 50-day MA at $50k vs $60k has no contextual meaning. Z-scoring normalizes this to "how unusual is current MA level?" This makes signals comparable across different price levels and time periods.
Settings:
Length (19): Base MA period
Regularization Length (30): Statistical normalization window
Statistical Significance: Z-scores are standard in quantitative analysis. This indicator asks: "Is the current trend statistically significant or just random noise?"
AGGREGATION METHODOLOGY
Voting System:
Each indicator returns: +1 (bullish), -1 (bearish), or 0 (neutral)
Total Score = Sum of all 7 votes (-7 to +7)
Average Score = Total / 7 (-1.00 to +1.00)
Signal Generation:
Long Signal: Average > 0 (majority bullish)
Short Signal: Average < 0 (majority bearish)
Neutral: Average = 0 (perfect split or all neutral)
Why Equal Weighting:
Each indicator represents a fundamentally different analytical approach:
Volatility-adjusted (RMA, ViiStop)
Momentum-based (Boosted MA, Lévy RSI)
Adaptive smoothing (KAMA)
Statistical (MA Oscillator)
Noise-filtered (Heikin Ashi T3)
Equal weighting ensures no single methodology dominates. This diversification reduces bias and improves robustness across market conditions.
ORIGINALITY - WHY THIS COMBINATION WORKS
Traditional Multi-Indicator Approaches:
Combine similar indicators (multiple MAs, multiple oscillators)
Use arbitrary thresholds for each indicator
Don't normalize signals (hard to compare RSI to MACD)
Often just "if RSI > 70 AND MACD > 0 = buy"
AlphaTrend MTPI Innovations:
Methodological Diversity: Includes volatility-adaptive (RMA), momentum-enhanced (Boosted MA), efficiency-based (KAMA), heavy-tailed statistics (Lévy RSI), and smoothed candles (HA). No redundant indicators.
Binary Voting: Each indicator reduces to simple +1/-1/0 vote, making aggregation transparent and preventing any indicator from overwhelming the consensus.
Medium-Term Optimization: Parameter choices (12-36 period averages) specifically target multi-day to multi-week trends, not scalping or long-term positioning.
Advanced Mathematics: Incorporates Tillson T3, Kaufman Efficiency Ratio, Lévy distributions, and statistical z-scoring—not just basic MAs and RSIs.
No Overfit Risk: With 7 diverse components voting equally, the system can't overfit to any specific market regime. If trending markets favor KAMA, but choppy markets favor Boosted MA, the ensemble stays robust.
Why 7 Indicators, Not 3 or 10:
Fewer than 5: Insufficient diversification, single indicator failures impact results heavily
More than 9: Diminishing returns, redundancy increases, computational load grows
7 provides: Odd number (no ties), sufficient diversity, manageable complexity
VISUAL COMPONENTS
1. Bar Coloring:
Cyan bars: Bullish consensus (average score > 0)
Magenta bars: Bearish consensus (average score < 0)
No color: Neutral (score = 0 or date filter disabled)
2. MTPI Summary Table (Bottom Center):
MTPI Signal: Current directional bias (LONG/SHORT/NEUTRAL)
Average Score: Precise consensus reading (-1.00 to +1.00)
3. Indicator Status Table (Bottom Right):
Shows all 7 individual indicator scores
Score column: +1 (bullish), -1 (bearish), 0 (neutral)
Signal column: Text interpretation of each vote
Color-coded cells: Cyan (long), Magenta (short), Gray (neutral)
HOW TO USE
For Swing Trading (Recommended - Days to Weeks):
Entry Signals:
Strong Long: 5+ indicators bullish (score ≥ 0.71)
Standard Long: 4+ indicators bullish (score ≥ 0.57)
Weak Long: Simple majority (score > 0) — use with caution
Exit Signals:
Hard Stop: Score flips negative (consensus reverses)
Partial Take Profit: Score drops to +0.30 or below (weakening)
Trailing Stop: Use ATR-based stop below entry
Position Sizing:
Strong signals (|score| > 0.7): Full position
Moderate signals (0.4-0.7): 50-75% position
Weak signals (< 0.4): 25-50% or skip
For Trend Confirmation:
Use alongside your primary strategy for confluence
Only take trades when AlphaTrend agrees with your analysis
Avoid counter-trend trades when score is extreme (|score| > 0.7)
Best Timeframes:
4H: Primary timeframe for swing trading
1D: Position trading and major trend identification
1H: Active trading (shorter hold periods)
< 1H: Not recommended (designed for medium-term)
Market Conditions:
Trending markets: System excels (consensus emerges quickly)
Ranging markets: Expect mixed signals (score oscillates near zero)
High volatility: RMA and ViiStop provide stabilization
Low volatility: KAMA and Boosted MA maintain responsiveness
SETTINGS EXPLAINED
General Settings:
Use Date Filter: Enable/disable historical backtesting range
Start Date: When to begin signal generation (default: Jan 1, 2018)
Flxwrt RMA Settings:
RMA Length (12): Base trend smoothing
ATR Length (20): Volatility measurement period
Source: Price input (default: close)
Boosted MA Settings:
Length (36): Base EMA period
Boost Factor (1.3): Momentum amplification
Source: Price input
Heikin Ashi Settings:
Percent Squeeze (0.2): Sensitivity adjustment
T3 Factor (0.3): Tillson volume factor
T3 Length (13): Smoothing period
ViiStop Settings:
Length (16): Baseline period
Multiplier (2.8): ATR scaling
Source: Price input
KAMA Settings:
Fast Period (9): Maximum responsiveness
Slow Period (21): Minimum responsiveness
ER Period (8): Efficiency calculation
Normalization Lookback (35): Oscillator scaling
Levy RSI Settings:
RSI Length (14): Standard period
Alpha (1.5): Lévy exponent (power-law weighting)
MA Length (12): Final smoothing
Source: Price input
MA Oscillator Settings:
Length (19): Base MA period
Regularize Length (30): Z-score normalization window
PERFORMANCE CHARACTERISTICS
Strengths:
✅ Reduced whipsaws vs single indicators
✅ Works across varying market conditions (adaptive components)
✅ Transparent methodology (see every vote)
✅ Customizable to trading style via timeframe selection
✅ No curve-fitting (equal weighting, no optimization)
Limitations:
⚠️ Medium-term focus (not for scalping or very long-term)
⚠️ Lagging by design (consensus requires confirmation)
⚠️ Less effective in violent reversals (momentum carries votes)
⚠️ Requires clean price data (gaps/thin volume can distort)
ALERTS & AUTOMATION
No built-in alerts in current version (visual-only indicator). Users can create custom alerts based on:
Bar color changes (cyan to magenta or vice versa)
Average score crossing above/below thresholds
Specific indicator status changes in the table
BEST PRACTICES
Risk Management:
Never risk more than 1-2% per trade regardless of score
Use stop losses (ATR-based recommended)
Scale positions based on signal strength
Don't average down on losing positions
Combining with Other Analysis:
✅ Support/Resistance levels for entries
✅ Volume confirmation (accumulation/distribution)
✅ Market structure (higher highs/lower lows)
✅ Volatility regimes (adjust position size)
❌ Don't combine with redundant trend indicators (adds no value)
❌ Don't override strong consensus with gut feeling
❌ Don't use on news-driven spikes (wait for stabilization)
Backtesting Notes:
Use "Date Filter" to test specific periods
Forward-test before live deployment
Remember: consensus systems perform best in trending markets, expect reduced edge in ranges
IMPORTANT NOTES
Not a standalone strategy - Use with proper risk management
Requires clean data - Works best on liquid markets with tight spreads
Medium-term by design - Don't expect scalping signals
No magic - No indicator predicts the future; this shows current trend probability
Diversification within - The 7-component ensemble IS the diversification strategy
Not financial advice. This indicator identifies medium-term trend probability based on multi-component consensus. Past performance does not guarantee future results. Always use proper risk management and position sizing.
Previous Day High, Low, and MidThis indicator will draw out levels for the previous sessions highs and lows as well as the middle point between the two. Might not work with indices
SMMA Strategy [SMMA ULTIMATE]SMMA 21/50/200 + RSI — M5/M15 (Rule-marked entries & exits)
Release Notes (EN)
Version: 1.0 (Pine v6 — Indicator)
Date: 14 Oct 2025
Type: Multi-TF overlay indicator with rule-based entry/exit markers and optional runtime alerts
🚀 Summary
A disciplined multi-timeframe scanner for M5 and M15 that highlights rule-driven setups (R1…R4) around SMMA 21/50/200, RSI (buy > 52 / sell < 48), directional VWAP, volume, and ATR activity.
It also simulates ATR-based TP/SL/Break-Even to provide immediate visual feedback and tags each trade idea with the origin rule.
✨ Highlights
• Full MTF stack (M5 & M15) with dedicated series (price, volume, SMMA, ATR, VWAP, RSI) and lookahead_off to avoid repaint.
• 4 modular entry rules (enable/disable independently):
◦ R1: Price crosses the max/min of SMMA(21/50/200) + RSI filter + market OK.
◦ R2: Touch of SMMA21 (pullback) + trend alignment + RSI + market OK.
◦ R3: Three candles impulse + engulfing reversal + RSI + market OK.
◦ R4: SMMA21/SMMA50 cross (structural momentum) + market OK.
• Stackable filters (toggle): Trend (price vs SMMA200), Directional VWAP (price vs VWAP + slope), Volume (Vol > MA×k), ATR activity (ATR > MA(ATR,20)×k).
• RSI thresholds: BUY if RSI > 52, SELL if RSI < 48 (per TF).
• ATR exit simulation: SL = k×ATR, TP = k×ATR, Break-Even armed after ATR gain (return to entry → BE exit).
• Clear rule tags: Entry/exit markers carry R1…R4 for immediate provenance.
• Optional runtime alerts: Human-readable messages on entries and exits, per TF and rule.
🔧 Key Inputs
General
• Price source for display: chart candles / force regular / force Heikin Ashi.
• Lengths: SMMA 21/50/200, RSI (14), ATR (14), Volume MA (20).
• RSI thresholds: Buy > 52, Sell < 48.
Filters (on/off)
• Trend (price vs SMMA200).
• Directional VWAP (price relative to VWAP and VWAP slope).
• ATR activity gate.
• Volume gate (Volume > MA×multiplier).
Rules (on/off)
• Enable R1/R2/R3/R4 individually.
Exit simulation
• Use ATR stops (SL/TP multipliers).
• Break-Even (armed by ATR progress).
Alerts
• Enable runtime alerts to fire alert() at bar close.
🧠 Rule Logic (condensed)
• R1 BUY/SELL: Cross of max/min(SMMA21,50,200) + RSI gate + all selected filters OK.
• R2 BUY/SELL: Touch of SMMA21 + price aligned vs SMMA50/200 + RSI + filters OK.
• R3 BUY/SELL: Three consecutive bars in one direction + engulfing opposite + RSI + filters OK.
• R4 BUY/SELL: SMMA21/SMMA50 crossover + filters OK.
Entry priority per TF: R1 > R4 > R2 > R3.
🔔 Runtime Alerts
When enabled, the script emits close-of-bar alerts with TF and rule tag:
• 🚀 M5/M15 ENTRY LONG (R#)
• 🔻 M5/M15 ENTRY SHORT (R#)
• ✅ M5/M15 EXIT TP (R#)
• ❌ M5/M15 EXIT SL (R#)
• 🟨 M5/M15 EXIT BE (R#)
(You can still build custom UI alerts if you need additional combinations.)
🖼 Visuals
• SMMA 21/50/200 and VWAP (green when price above, red below).
• Plotshape per rule and exit type (TP/SL/BE) with R1…R4 tags on M5 and M15.
• Optional Heikin Ashi for display (core MTF calculations remain consistent).
🔒 Robustness & No-Repaint Notes
• All MTF request.security calls use lookahead_off.
• Pattern logic (three bars, engulfing) is evaluated on bar close.
• ATR/TP/SL/BE are indicator-level simulations using the chart’s H/L/Close (standard intrabar limitations).
⚠️ Limitations & Tips
• This is an indicator, not a strategy: no orders are sent; exits are simulated for visualization.
• Signals are generated on bar close.
• MTF signals synchronize to the chart TF’s close, not intrabar ticks.
ICT AMD Model – Full Engine [Forex.lk] (Phase 1–4)⚙️ ICT AMD Model – Full Engine (Phase 1–4)
By Forex.lk | info@forex.lk
The ICT AMD Model – Full Engine is a
structured market-phase framework developed to help traders recognize the natural rhythm of price delivery.
It maps the evolving cycle of Accumulation → Manipulation → Distribution to highlight when the market is building, faking, or delivering directional intent.
The indicator automatically adapts to your selected timeframes, monitors bias alignment, and presents a clean visual roadmap of market behavior in real time.
With clear on-chart highlights and a compact dashboard, it assists traders in timing entries and exits based on phase context and higher-timeframe direction.
Designed for traders who study market structure, timing, and precision execution within the AMD model.
It’s a practical, research-driven visual aid—simple to interpret, powerful in insight.
Developed by Forex.lk
📩 Contact : info@forex.lk
🌐 www.forex.lk
VWAP + EMA + RSI + MACD Confluence (Options Trader)VWAP
EMAs (9, 21, 50)
RSI
MACD
and clear visual + alert signals for option-style entries (bullish = calls, bearish = puts).
Here’s what it’ll do visually:
✅ Plot EMAs (9, 21, 50)
✅ Plot VWAP
✅ Show background color when confluence aligns for bullish or bearish entries
✅ Add optional alerts (so you can set triggers)
✅ Display RSI + MACD panels for confirmation
Logic:
Bullish (“Call”) signal:
Price > VWAP and > EMA50
EMA9 > EMA21
MACD line > signal line
RSI > 50
Bearish (“Put”) signal:
Price < VWAP and < EMA50
EMA9 < EMA21
MACD line < signal line
RSI < 50
Measured Pattern Move (Bulkowski) [SS]Hey everyone,
This is the Measured Pattern Move using Bulkowski's process for measured move calculation.
What the indicator does:
The indicator has the associated measured move across 20 of the most common and frequent Bulkowski patterns, including:
Double Bottom / Adam Eve Bottom
Double Top / Adam Eve Top
Inverse Head and Shoulders
Bear Flag
Bull Flag
Horn Bottom
Horon Top
Broadening Top
Descending Broadening Wedge
Broadening Bottoms
Broadening Tops
Cup and Handle
Inverted cup and handle
Diamond Bottom
Diamond Top
Falling Wedge
Rising Wedge
Pipe Bottom
Pipe Top
Head and Shoulders
It will calculate the measured move according to the Bulkowski process.
What is the Bulkowski Process?
Each move has an associated continuation percentage, which Bulkowski has studied, analyzed and concluded statistically.
For example, Double tops have a continuation percent of 54%. Bear flags, 47%. These are "constants" that are associated with the pattern.
Bulkowski applies them to the daily, but how I have formulated this, it can be used on all timeframes, and with the constant, it will correctly calculate the measured move of the pattern.
What this indicator DOES NOT DO
This indicator will not identify the pattern for you.
I tried this using Dynamic Time Warping (DTW) using my own pre-trained Bulkowski model in R. I was successfully able to get Pinescript to calculate DTW which was amazing! But applying it to all these patterns actually went over the execution time limit, which is understandable.
As such, you will need to identify the pattern yourself, then use this indicator to hilight the pattern and it will calculate the measured move based on the constant and the pattern range.
Let's look at some examples:
Use examples
Double bottom / adam eve bottom on SPY on the 1-Minute chart
Adam and Eve Double Bottom QQQ 1-Hour Chart
Adam Eve Double Bottom MSFT Daily Chart
Bearish Head and Shoulders Pattern MSFT Daily
You get the point.
How to use the indicator
To use the indicator, identify the pattern of interest to you.
Then, highlight the pattern using the indicator (it will ask you to select start time of the pattern and end time of the pattern). The indicator will then highlight the pattern and calculate the measured move, as seen in the examples above.
Best approaches
To make the most of the indicator, its best to draw out your pattern and wait for an actual break, the point of the break is usually the end of the pattern formation.
From here, you will then apply this indicator to calculate the expected up or down move.
Let me show you an example:
Here we see CME_MINI:ES1! has made an Adam bottom pattern. We know the Eve should be forming soon and it indeed does:
We mark the top of the pattern like so:
Then we use our Measured move indicator to calculate the measured move:
Measured move here for CME_MINI:ES1! is 6,510.
Now let's see....
Voila!
Selecting the Pattern
After you highlight the selected pattern, in the indicator settings, simply select the type of pattern it is, for example "head and shoulders" or "Broadening wedge", etc.
The indicator will then adjust its measurements to the appropriate constant and direction.
Concluding remarks
That is the indicator!
It is helpful for determining the actual projected move of a pattern on breakout.
Remember, it does not find the pattern for you , you are responsible for identifying the pattern. But this will calculate the actual TP of the pattern for you, without you having to do your own calculations.
I hope you find it useful, I actually use this indicator every day, especially on the lower timeframes!
And you will find, the more you use it, the better you get at recognizing significant patterns!
If you are not aware of these patterns, Bulkowski lists all of this information freely accessible on his website. I cannot link it here but you can just Google him and he has graciously made his information public and free!
That's it, I hope you enjoy and safe trades!
Disclaimer
This is not my intellectual property. The pattern calculations come from the work of Thomas Bulkowski and not myself. I simply coded this into an indicator using his publicly accessible information.
You can get more information from Bulkowski's official website about his work and patterns.
MicroX_Trader Psychology Simulatorيحاكي هذا المؤشر مشاعر التفاؤل والخوف لدى المتداول.
It simulates the feelings of optimism and fear in a trader
SP2L Strategy Tool by Rava AcademyRava Academy - SP2L Strategy Tool
This indicator has been designed and developed by Rava Academy to implement the SP2L trading strategy. The primary goal of this tool is to automate the process of identifying potential trade setups based on this specific strategy, helping traders to save valuable time and reduce analytical errors.
Key Features:
Automatic Setup Detection: The indicator automatically scans the chart for conditions that align with the SP2L strategy rules.
Clear Visual Signals: It provides straightforward visual cues on the chart, using arrows to indicate potential setups, which simplifies the decision-making process.
Time-Saving Analysis: This tool is designed to minimize the need for manual and repetitive analysis, allowing traders to focus on other aspects of their trading plan.
Multi-Market Compatibility: It is optimized for use in various financial markets, including Forex and Cryptocurrencies.
How to Use:
Green Arrow (▲): Indicates a potential buy setup according to the strategy's rules. Traders should look for their own confirmation before entering a trade.
Red Arrow (▼): Indicates a potential sell setup according to the strategy's rules. Traders should look for their own confirmation before entering a trade.
IMPORTANT NOTE:
This indicator is a powerful assistive tool, not a standalone "buy/sell" signal generator. For best results, it is essential to combine its signals with your own analysis of market structure, price action, and a robust risk management plan. It should be used to augment, not replace, your trading judgment.
About Rava Academy:
This indicator is a contribution to the trading community from Rava Academy. We specialize in financial market education, building custom trading tools, and converting strategies into intelligent indicators.
For more educational content and trading tools, follow us on Instagram: @RavaFinance
Disclaimer:
Trading in financial markets involves significant risk. This tool is provided for educational and analytical purposes only and should not be considered financial advice. All trading decisions, profits, and losses are the sole responsibility of the user. Past performance is not indicative of future results.
AlphaZ-Score - Bitcoin Market Cycle IndicatorWHAT IS ALPHAZ-SCORE?
AlphaZ-Score is a Bitcoin-specific market cycle indicator that identifies extreme market conditions (tops and bottoms) by aggregating up to 7 independent on-chain and market metrics into a single normalized z-score. Unlike traditional oscillators that analyze only price action, AlphaZ-Score incorporates blockchain fundamentals, investor profitability metrics, and capital flow data to determine where Bitcoin sits within its long-term market cycle.
The output ranges from -3 (extreme oversold/cycle bottom) to +3 (extreme overbought/cycle top), with readings beyond ±2 indicating high-probability reversal zones.
METHODOLOGY - THE 7-COMPONENT SYSTEM
Each component analyzes Bitcoin's market state from a unique perspective, then gets z-scored (statistical normalization) so all metrics can be compared on equal footing. The final score is a weighted average of all enabled indicators.
Default Configuration (3 indicators enabled):
Stablecoin Supply Ratio (SSRO)
MVRV Z-Score
SOPR Z-Score
Optional Advanced Components (4 indicators disabled by default):
Days Higher Streak Valuation (DHSV)
High Probability OB/OS (HPOB)
Risk Index Z-Score
Comprehensive On-chain Z-Score
COMPONENT BREAKDOWN
1. STABLECOIN SUPPLY RATIO OSCILLATOR (SSRO) - ENABLED BY DEFAULT
What it measures: Ratio of Bitcoin market cap to total stablecoin supply (USDT + USDC)
Data sources:
CRYPTOCAP:BTC - Bitcoin market cap
CRYPTOCAP:USDT - Tether market cap
CRYPTOCAP:USDC - USD Coin market cap
Logic:
SSR = BTC Market Cap / (USDT + USDC Supply)
Z-Score = Standardized SSR over 200 periods
Interpretation:
High SSR (positive z-score): Bitcoin overvalued relative to available stablecoin buying power → Overbought
Low SSR (negative z-score): Massive stablecoin reserves relative to BTC value → Potential bottom (dry powder)
Why it works: Stablecoins represent "dry powder" - capital waiting to enter crypto. When stablecoin supply is high relative to BTC value, it signals accumulation potential. When low, it suggests exhausted buying power.
2. MVRV Z-SCORE - ENABLED BY DEFAULT
What it measures: Market Value to Realized Value ratio, z-scored over 520 periods
Data source: INTOTHEBLOCK:BTC_MVRV
Logic:
MVRV = Market Cap / Realized Cap
Z-Score = (MVRV - Mean) / Std Dev
Interpretation:
High MVRV (positive z-score): Average holder in significant profit → Distribution phase
Low MVRV (negative z-score): Average holder near breakeven/loss → Accumulation phase
Why it works: MVRV compares Bitcoin's market price to its "fair value" (realized price = average cost basis of all coins). Extreme deviations historically mark cycle tops (MVRV > 3.5) and bottoms (MVRV < 1.0).
Historical significance:
2017 top: MVRV z-score ~7
2018 bottom: MVRV z-score ~-1.5
2021 top: MVRV z-score ~6
2022 bottom: MVRV z-score ~-1.0
3. SOPR Z-SCORE - ENABLED BY DEFAULT
What it measures: Spent Output Profit Ratio, smoothed and z-scored
Data source: GLASSNODE:BTC_SOPR
Logic:
SOPR = Value of spent outputs / Value at creation
SOPR EMA = 7-period exponential moving average
Z-Score = Standardized SOPR EMA over 180 periods
Interpretation:
SOPR > 1 (positive z-score): Coins being spent at profit → Potential distribution
SOPR < 1 (negative z-score): Coins being spent at loss → Capitulation/bottom
Why it works: SOPR measures aggregate profitability of spent coins. When holders are forced to sell at losses (SOPR < 1), it indicates capitulation and potential bottoms. When everyone sells at profit (SOPR >> 1), it signals euphoria and potential tops.
4. DAYS HIGHER STREAK VALUATION (DHSV) - DISABLED BY DEFAULT
What it measures: Number of historical bars with prices higher than current level
Logic:
For last N bars, count how many had close > current close
Apply streak decay logic based on price threshold
Z-Score result over lookback period
Interpretation:
Few days higher (negative z-score): Price near all-time highs → Potential overbought
Many days higher (positive z-score): Price deep below historical levels → Oversold
Why it works: Measures how "expensive" current price is relative to history. When 90%+ of historical bars are higher, you're near cycle bottoms.
Settings:
Historical Bars (1000): How far back to look
Threshold & Decay: Sensitivity adjustments
5. HIGH PROBABILITY OVERBOUGHT/OVERSOLD (HPOB) - DISABLED BY DEFAULT
What it measures: Volume-weighted price momentum divergence
Logic:
Volume-weighted Hull MA vs Standard Hull MA
Difference normalized by 100-period SMA
Result inverted and scaled to match z-score range
Interpretation:
Positive score: Volume-weighted momentum diverging up → Overbought
Negative score: Volume-weighted momentum diverging down → Oversold
Why it works: When volume-weighted price movement diverges from standard price movement, it reveals institutional vs retail behavior mismatches.
Settings:
SVWHMA Length (50): Volume-weighted smoothing
HMA Length (50): Standard momentum baseline
Smooth Length (50): Final output smoothing
6. RISK INDEX Z-SCORE - DISABLED BY DEFAULT
What it measures: Modified Puell Multiple approach using realized cap
Data sources:
COINMETRICS:BTC_MARKETCAPREAL - Realized market cap
GLASSNODE:BTC_MARKETCAP - Current market cap
Logic:
Delta = Risk Multiplier × Realized Cap - Historical Realized Cap
Risk Index = (Delta / Market Cap × 100) / 24
Z-Score = Standardized Risk Index over 1500 periods
Interpretation:
High risk (positive z-score): Realized cap growth outpacing market cap → Overextended
Low risk (negative z-score): Market cap collapsed relative to realized cap → Undervalued
Why it works: Compares the rate of realized cap change to market cap. Rapid realized cap growth during low market cap periods signals accumulation.
7. COMPREHENSIVE ON-CHAIN Z-SCORE - DISABLED BY DEFAULT
What it measures: Average of three on-chain metrics: NUPL, SOPR, and MVRV
Data sources:
GLASSNODE:BTC_MARKETCAP - Current market cap
COINMETRICS:BTC_MARKETCAPREAL - Realized cap
GLASSNODE:BTC_SOPR - SOPR data
Logic:
NUPL = (Market Cap - Realized Cap) / Market Cap × 100
SOPR Z-Score = (SOPR - Mean) / Std Dev with EMA smoothing
MVRV = Market Cap / Realized Cap
Final Score = Average of all three z-scores
Interpretation:
Combines profitability (NUPL), spending behavior (SOPR), and valuation (MVRV) into single comprehensive on-chain metric.
AGGREGATION METHODOLOGY
Scoring System:
Each enabled indicator produces a z-score (typically -3 to +3 range)
Scores are weighted equally (weight = 1.0 for all)
Final output = Weighted average of all enabled indicators
Why Equal Weighting:
Each metric analyzes fundamentally different aspects of Bitcoin's market state. Equal weighting prevents any single data source from dominating and ensures diversification.
Customization:
Users can enable/disable indicators to:
Simplify analysis (3 core metrics)
Increase complexity (all 7 metrics)
Focus on specific aspects (only on-chain, only market-based, etc.)
INTERPRETATION GUIDE
Z-Score Ranges:
+3.0 and above - EXTREME OVERBOUGHT
Historical cycle tops
Maximum euphoria
High-probability distribution zone
Consider taking profits
+2.0 to +3.0 - OVERBOUGHT
Late bull market phase
Elevated risk
Cautious positioning recommended
-2.0 to +2.0 - NEUTRAL
Normal market conditions
Trend-following strategies appropriate
-2.0 to -3.0 - OVERSOLD
Early accumulation phase
Fear/capitulation stage
Begin DCA strategies
-3.0 and below - EXTREME OVERSOLD
Historical cycle bottoms
Maximum fear
High-probability accumulation zone
Prime buying opportunity
VISUAL COMPONENTS
1. Main Z-Score Line:
Dynamic color gradient based on value
Green shades: Oversold (buying opportunity)
Red shades: Overbought (distribution zone)
White: Neutral
2. Reference Lines:
0: Neutral baseline
±2: Overbought/Oversold thresholds
±3: Extreme zones (highest probability reversals)
3. Background Shading:
Light green: Oversold (-2 to -3)
Bright green: Extreme oversold (< -3)
Light red: Overbought (+2 to +3)
Bright red: Extreme overbought (> +3)
4. Bar Coloring:
Cyan bars: Oversold conditions
Red bars: Overbought conditions
Default: Neutral
5. Summary Table (Top Right):
Market State: Current condition (Extreme OB/OS, Overbought/Oversold, Neutral)
Z-Score Value: Precise numeric reading
HOW TO USE
For Long-Term Investors (DCA Strategy):
Aggressive accumulation: Z-score < -2 (especially < -3)
Regular accumulation: Z-score between -2 and 0
Hold: Z-score between 0 and +2
Take profits: Z-score > +2 (especially > +3)
For Cycle Traders:
Buy zone: Wait for z-score to drop below -2
Hold through: Ignore noise between -2 and +2
Sell zone: Start distributing when z-score exceeds +2
Exit: Complete exit if z-score reaches +3
Risk Management:
Never buy in extreme overbought (>+3) - Historically always preceded major crashes
Scale into positions - Don't go all-in at any single reading
Use with price action - Confirm with support/resistance levels
Best Timeframes:
1D (Daily): Primary timeframe for cycle analysis
1W (Weekly): Macro cycle perspective
Lower timeframes not recommended (designed for long-term cycles)
SETTINGS CONFIGURATION
General Settings:
Toggle each of 7 indicators on/off
Default: 3 indicators enabled (SSRO, MVRV, SOPR)
Advanced: Enable all 7 for maximum sensitivity
Individual Indicator Settings:
Each indicator has dedicated parameter groups:
DHSV: Historical lookback, threshold decay
HPOB: HMA and VWMA lengths, smoothing
SSRO: Z-score calculation period (200)
MVRV: Z-score length (520)
Risk: Multiplier and z-score length
SOPR: EMA smoothing (7), z-score period (180)
On-chain: Separate lengths for NUPL, SOPR, MVRV components
DATA REQUIREMENTS
Required External Data Sources:
Default configuration (3 indicators):
CRYPTOCAP:BTC - Bitcoin market cap
CRYPTOCAP:USDT - Tether supply
CRYPTOCAP:USDC - USD Coin supply
INTOTHEBLOCK:BTC_MVRV - MVRV ratio
GLASSNODE:BTC_SOPR - SOPR data
Optional indicators require:
GLASSNODE:BTC_MARKETCAP - Market cap (on-chain)
COINMETRICS:BTC_MARKETCAPREAL - Realized cap
Additional Glassnode metrics
Important: This indicator requires TradingView data subscriptions for on-chain metrics. Some data sources may not be available on all accounts.
HISTORICAL PERFORMANCE
Major Cycle Tops Identified:
November 2021: Z-score peaked at ~+2.8 before -50% crash
December 2017: Z-score exceeded +3.0 before -84% bear market
April 2013: Z-score hit extreme overbought before correction
Major Cycle Bottoms Identified:
November 2022: Z-score reached -2.5 before +100% rally
December 2018: Z-score dropped to -2.8 before +300% bull run
January 2015: Z-score hit -3.2 before multi-year bull market
Key Insight: Extreme readings (beyond ±2.5) have preceded major market reversals with high accuracy. The indicator is designed for cycle identification, not short-term trading.
ORIGINALITY - WHY THIS IS UNIQUE
Traditional Cycle Indicators:
Use single metrics (MVRV only, SOPR only, etc.)
No normalization - hard to compare metrics
Fixed thresholds that don't adapt to market evolution
Often proprietary black boxes
AlphaZ-Score Advantages:
Multi-Metric Aggregation: Combines on-chain fundamentals, market structure, and capital flows into single score
Statistical Normalization: Z-scoring allows fair comparison of completely different metrics (market cap ratios vs profitability metrics)
Modular Design: Enable only the metrics you trust or have data access to
Transparent Calculations: All formulas visible in open-source code
Bitcoin-Specific Optimization: Tuned specifically for Bitcoin's 4-year halving cycle and on-chain characteristics
Customizable Weighting: Advanced users can modify weights for different market regimes
Visual Clarity: Single line that clearly shows cycle position, unlike juggling multiple indicators
LIMITATIONS
Requires on-chain data subscriptions - Some metrics need premium TradingView data
Lagging indicator - Identifies cycles after they begin, not predictive
Bitcoin-specific - Not designed for altcoins or traditional markets
Long-term focus - Not suitable for day trading or short-term speculation
Data availability - Historical on-chain data only goes back to ~2010
External dependencies - Relies on Glassnode, CoinMetrics data accuracy
ALERTS
No built-in alerts (indicator designed for visual analysis of long-term cycles). Users can create custom alerts based on z-score thresholds.
BEST PRACTICES
✅ Use on daily or weekly timeframe only
✅ Combine with long-term moving averages (200 MA, 200 WMA)
✅ Wait for extreme readings (beyond ±2) before major decisions
✅ Scale positions - don't go all-in at any single reading
✅ Verify on-chain data sources are updating properly
❌ Don't use for short-term trading (minutes/hours)
❌ Don't ignore price action - confirm with chart patterns
❌ Don't expect perfect timing - cycles can extend beyond extremes
❌ Don't trade solely on this indicator - use as confluence
Not financial advice. This indicator identifies market cycles based on historical patterns and on-chain data. Past performance does not guarantee future results. Always use proper risk management and position sizing.
SMA with Background ColorThis is a Simple Moving Average that changes the background color of the chart to green if the moving average is trending up and red if the moving average is trending down. A flat SMA generates no background color.
TOP GAINER V2
The "TOP GAINER" is a custom TradingView indicator designed to identify and trade high-potential momentum stocks, particularly top pre-market gainers with strong hype and volatility. It's tailored for day traders focusing on small-cap, low-float stocks that exhibit explosive price movements, allowing for quick entries and exits to capitalize on short-term pumps. This indicator combines technical signals (MACD, RSI, and EMA) with fundamental filters to spot setups in pre-market and early regular trading hours, ideally on a 5-minute chart for precise timing.
Key Features and How It Works
Scanning for Top Gainers: The indicator targets stocks that are among the day's top pre-market performers. It evaluates criteria like:
Price Range ($2–$20): Focuses on affordable stocks where you can buy a large number of shares with limited capital. Lower-priced stocks often have higher volatility, enabling them to double, triple, or more in a single session due to hype-driven momentum.
Pre-Market Gain (≥20%): Identifies stocks with significant upside from the pre-market open (4:00 AM ET), signaling strong early interest and potential for continuation.
High Volume (≥500,000 shares from pre-market open): Ensures liquidity and confirms genuine hype, as elevated pre-market volume often precedes big moves at market open.
Small Market Cap (<$500M): Prioritizes small-cap companies, which are more prone to rapid price swings from news, catalysts, or retail frenzy compared to large caps.
Low Float (<50M shares): Low-float stocks have fewer shares available for trading, making them susceptible to sharp rallies when demand surges (e.g., from social media buzz or short squeezes).
These criteria are displayed in a real-time table on the chart for quick scanning—green checkmarks (✅) indicate a match, red crosses (❌) show failures, and "N/A" appears if data is unavailable (e.g., for non-stocks).
Entry Signals (Buy Opportunities): Once a stock meets the filters, the indicator watches for bullish momentum during pre-market or at market open:
EMA Exit (default enabled): Sells when price crosses below a 40-period EMA (orange line), signaling a potential trend reversal. STRONGLY RECOMMEND TURNING THIS OFF
MACD Exit (default enabled, now using standard line/signal crossunder): Sells on a bearish MACD crossover for momentum-based exits.
Plots orange (EMA) or red (MACD) downward triangles above the bar for exits.
Built-in alerts notify you of buy and sell signals in real-time.
Why This Strategy?
This indicator is built for "hype trading" on volatile small-caps, where pre-market scanners highlight gappers, and the tool helps time entries post-open (e.g., on 5-min charts) to catch breakouts. Small floats and caps amplify moves— a 20%+ gainer with high volume can surge 50–200% intraday due to supply/demand imbalances. The $2–$20 range keeps it accessible: with $1,000, you could buy 500 shares of a $2 stock, turning a $1 gain into $500 profit. It's not for long-term investing but for scalping or swinging on daily catalysts like earnings, news, or memes.
Usage Tips
This tool streamlines spotting and trading "lotto plays" while providing visual and alert-based discipline for entries/exits.
AlphaBTC - Long Term Trend Probability Indicator on BitcoinWHAT IS ALPHABTC?
AlphaBTC is a consensus-based long-term trend probability indicator designed specifically for Bitcoin and cryptocurrency markets. It combines 9 independent trend detection methodologies into a single probabilistic score ranging from -1 (strong bearish) to +1 (strong bullish). Unlike single-indicator systems that can produce frequent false signals, AlphaBTC requires agreement across multiple analytical frameworks before generating directional signals.
METHODOLOGY - THE 9-INDICATOR CONSENSUS MODEL
Each indicator analyzes trend from a different mathematical perspective, providing a binary vote: +1 (bullish), -1 (bearish), or 0 (neutral). The average of all 9 votes creates the final probability score.
1. AADTREND (Average Absolute Deviation Trend)
Method: Calculates average absolute deviation from a moving average using 7 different MA types (SMA, EMA, HMA, DEMA, TEMA, RMA, FRAMA)
Logic: Price crossovers above/below AAD-adjusted bands signal trend changes
Purpose: Adapts to varying market volatility conditions
2. GAUSSIAN SMOOTH TREND (GST)
Method: Multi-stage smoothing using DEMA → Gaussian Filter → SMMA → Standard Deviation bands
Logic: Long when price > (SMMA + SDmultiplier), Short when price < (SMMA - SDmultiplier)
Purpose: Removes high-frequency noise while preserving trend structure
3. RTI (RELATIVE TREND INDEX)
Method: Percentile-based ranking system comparing current price to historical upper/lower trend boundaries
Logic: Generates 0-100 index score; >80 = bullish, <20 = bearish
Purpose: Identifies price position within statistical distribution
4. HIGHEST-LOWEST DEVIATIONS TREND
Method: Dual moving average system (100/50 periods) with dynamic standard deviation bands
Logic: Compares highest and lowest boundaries from both MAs to determine trend extremes
Purpose: Identifies breakouts from multi-timeframe volatility envelopes
5. 25-75 PERCENTILE SUPERTREND
Method: Modified SuperTrend using 25th and 75th percentile bands instead of simple highs/lows
Logic: ATR-based trailing stop system anchored to percentile boundaries
Purpose: More stable trend following by filtering outlier price spikes
6. TS VOLATILITY-ADJUSTED EWMA
Method: Exponentially Weighted Moving Average with dynamic period adjustment based on ATR
Logic: Speeds up during high volatility, slows during low volatility
Purpose: Adaptive responsiveness to changing market conditions
7. NORMALIZED KAMA OSCILLATOR
Method: Kaufman Adaptive Moving Average normalized to 0-centered oscillator
Logic: Uses Efficiency Ratio to adjust smoothing constant; >0 = bullish, <0 = bearish
Purpose: Adapts to both trending and ranging markets automatically
8. EHLERS MESA ADAPTIVE MOVING AVERAGE (EMAMA)
Method: John Ehlers' MAMA/FAMA system using Hilbert Transform for cycle period detection
Logic: MAMA crossover FAMA = bullish, crossunder = bearish
Purpose: Advanced DSP-based trend detection with phase-based adaptation
9. EMA Z-SCORE
Method: Statistical z-score applied to EMA values over lookback period
Logic: >1.0 standard deviation = bullish, <0.0 = bearish
Purpose: Identifies statistically significant trend deviations
AGGREGATION METHODOLOGY
Scoring System:
Each indicator produces: +1 (bullish), -1 (bearish), or 0 (neutral)
Total score = sum of all 9 indicators (-9 to +9)
Average score = total / 9 (displayed as -1.00 to +1.00)
Signal Interpretation:
+0.50 to +1.00: STRONG BULLISH (majority consensus)
+0.30 to +0.50: MODERATE BULLISH
-0.30 to +0.30: WEAK/NEUTRAL (mixed signals)
-0.50 to -0.30: MODERATE BEARISH
-1.00 to -0.50: STRONG BEARISH (majority consensus)
Bar Coloring:
Cyan bars: Bullish consensus (score > 0)
Magenta bars: Bearish consensus (score < 0)
WHY THIS APPROACH WORKS
Traditional Single-Indicator Problems:
Overfitting to specific market conditions
High false signal rates during consolidation
No mechanism for confidence measurement
AlphaBTC Multi-Consensus Solution:
Diversification: 9 uncorrelated methodologies reduce individual indicator bias
Robustness: Requires majority agreement before signaling (prevents whipsaws)
Adaptability: Mix of momentum, volatility, and statistical indicators captures multiple market regimes
Confidence Measurement: Score magnitude indicates signal strength
Why These 9 Specific Indicators:
AADTrend - Volatility adaptation
GST - Noise filtering
RTI - Statistical positioning
HL Deviations - Multi-timeframe breakouts
Percentile ST - Robust trend following
Volatility EWMA - Dynamic responsiveness
KAMA - Efficiency-based adaptation
EMAMA - Cycle-period awareness
EMA Z-Score - Statistical confirmation
This combination covers:
Trend following (ST, EWMA, KAMA, EMAMA)
Volatility adaptation (AAD, GST, HL Dev, EWMA)
Statistical validation (RTI, Z-Score)
Adaptive smoothing (KAMA, EMAMA, Gaussian)
No single indicator covers all these bases. The ensemble approach creates a more reliable system.
VISUAL COMPONENTS
1. Score Table (Bottom Right):
Shows all 9 individual indicator scores
Color-coded: Green (bullish), Red (bearish), Gray (neutral)
Individual signals visible for transparency
2. Main Score Display (Bottom Center):
LTPI SCORE: The averaged consensus (-1.00 to +1.00)
SIGNAL: Current directional bias (LONG/SHORT)
STRENGTH: Signal confidence (STRONG/MODERATE/WEAK)
3. Bar Coloring:
Visual trend indication directly on price bars
Cyan = bullish consensus
Magenta = bearish consensus
HOW TO USE
For Long-Term Position Trading (Recommended):
Wait for average score to cross above 0 for long entries
Exit when score crosses below 0 or reverses to negative territory
Use STRENGTH indicator - only trade STRONG or MODERATE signals
For Trend Confirmation:
Use AlphaBTC as confluence with your existing strategy
Enter trades only when AlphaBTC agrees with your analysis
Avoid counter-trend trades when consensus is strong (|score| > 0.5)
Risk Management:
STRONG signals (|score| > 0.5): Full position size
MODERATE signals (0.3-0.5): Reduced position size
WEAK signals (< 0.3): Avoid trading or use for exits only
Best Timeframes:
1D chart: Primary trend identification for swing/position trading
4H chart: Intermediate trend for multi-day holds
1H chart: Short-term trend for active trading
Not Recommended:
Scalping (too many indicators create lag)
Timeframes < 1H (designed for longer-term trends)
SETTINGS EXPLAINED
Each of the 9 indicators has customizable parameters in its dedicated settings group:
AadTrend Settings:
Average Length (48): Base period for deviation calculation
AAD Multiplier (1.35): Band width adjustment
Average Type: Choose from 7 different MA types
GST Settings:
DEMA Length (9), Gaussian Length (4), SMMA Length (13)
SD Length (66): Standard deviation lookback
Multipliers for upper/lower bands
RTI Settings:
Trend Length (75): Historical data points for boundary calculation
Sensitivity (88%): Percentile threshold
Long/Short Thresholds (80/20): Entry trigger levels
HL Deviations Settings:
Dual MA system (100/50 periods)
Separate deviation coefficients for upper/lower bands
25-75 Percentile ST Settings:
SuperTrend Length (100)
Multiplier (2.35)
Percentile Length (50)
EWMA Settings:
Length (81), ATR Lookback (14)
Volatility Factor (1.0) for dynamic adjustment
KAMA Settings:
Fast/Slow Periods (50/100)
Efficiency Ratio Period (8)
Normalization Lookback (53)
EMAMA Settings:
Fast/Slow Limits (0.08/0.01) for cycle adaptation
EMA Z-Score Settings:
EMA Length (50)
Lookback Period (25)
Threshold levels for long/short signals
ALERTS
Four alert conditions available:
LTPI Long Signal: When average score crosses above 0
LTPI Short Signal: When average score crosses below 0
LTPI Long: Any bar with bullish consensus
LTPI Short: Any bar with bearish consensus
IMPORTANT NOTES
This is a CONSENSUS indicator - it shows probability, not prediction
Designed for Bitcoin
Best for long-term trend identification (days to weeks, not minutes to hours)
Lagging by design - prioritizes accuracy over speed
Not a standalone strategy - use with proper risk management and position sizing
Requires minimum 100+ bars of historical data for proper indicator calculation
Candle Range Theory Range FinderThe video below will explain how to use the indicator.
In a nutshell, it'll shows range candles after 2 strong closes below a prior day's low or above a prior day's high for a possible range candle to trade a reversal off of.
Red arrows are to be treated as a range where you may want to start to look for longs.
Green arrows show where a range where you may want to look for shorts.
Again, the video will make it clearer.
TOP GAINERS Overview and Purpose
The "All-in-One Scanner" is a custom TradingView indicator designed to identify and trade high-potential momentum stocks, particularly top pre-market gainers with strong hype and volatility. It's tailored for day traders focusing on small-cap, low-float stocks that exhibit explosive price movements, allowing for quick entries and exits to capitalize on short-term pumps. This indicator combines technical signals (MACD, RSI, and EMA) with fundamental filters to spot setups in pre-market and early regular trading hours, ideally on a 5-minute chart for precise timing.
Key Features and How It Works
Scanning for Top Gainers: The indicator targets stocks that are among the day's top pre-market performers. It evaluates criteria like:
Price Range ($2–$20): Focuses on affordable stocks where you can buy a large number of shares with limited capital. Lower-priced stocks often have higher volatility, enabling them to double, triple, or more in a single session due to hype-driven momentum.
Pre-Market Gain (≥20%): Identifies stocks with significant upside from the pre-market open (4:00 AM ET), signaling strong early interest and potential for continuation.
High Volume (≥500,000 shares from pre-market open): Ensures liquidity and confirms genuine hype, as elevated pre-market volume often precedes big moves at market open.
Small Market Cap (<$500M): Prioritizes small-cap companies, which are more prone to rapid price swings from news, catalysts, or retail frenzy compared to large caps.
Low Float (<50M shares): Low-float stocks have fewer shares available for trading, making them susceptible to sharp rallies when demand surges (e.g., from social media buzz or short squeezes).
These criteria are displayed in a real-time table on the chart for quick scanning—green checkmarks (✅) indicate a match, red crosses (❌) show failures, and "N/A" appears if data is unavailable (e.g., for non-stocks).
Exit Signals (Sell Opportunities): To lock in profits and minimize losses, it provides two optional exit methods:
EMA Exit (default enabled): Sells when price crosses below a 40-period EMA (orange line), signaling a potential trend reversal.
MACD Exit (default enabled, now using standard line/signal crossunder): Sells on a bearish MACD crossover for momentum-based exits. I HIGLY RECOMMEND ONLY HAVING THIS ON FOR EXIT
Plots orange (EMA) or red (MACD) downward triangles above the bar for exits.
Built-in alerts notify you of buy and sell signals in real-time.
Why This Strategy?
This indicator is built for "hype trading" on volatile small-caps, where pre-market scanners highlight gappers, and the tool helps time entries post-open (e.g., on 5-min charts) to catch breakouts. Small floats and caps amplify moves— a 20%+ gainer with high volume can surge 50–200% intraday due to supply/demand imbalances. The $2–$20 range keeps it accessible: with $1,000, you could buy 500 shares of a $2 stock, turning a $1 gain into $500 profit. It's not for long-term investing but for scalping or swinging on daily catalysts like earnings, news, or memes.
Usage Tips
Apply to watchlists of pre-market scanners (e.g., via TradingView or external tools).
Best on US stocks with extended hours data enabled.
Customize inputs like MACD lengths or min gain/volume for your risk tolerance.
Backtest on historical data, and always use stop-losses—momentum trades can reverse quickly.
Ad your alarm and wait
