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.
M-oscillator
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.
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
自用事件30M - 优化版V8This is a strategy designed specifically for the 30-minute period of the Ethereum Event Contract, which is suitable for use during the 1-minute cycle to gain insights into the 30-minute period.
Enhanced Holt-Winters RSI [BOSWaves]Enhanced Holt-Winters RSI – Next-Level Momentum Smoothing & Signal Precision
Overview
The Enhanced Holt-Winters RSI transforms the classic Relative Strength Index into a robust, lag-minimized momentum oscillator through Holt-Winters triple exponential smoothing. By modeling the level, trend, and cyclical behavior of the RSI series, this indicator delivers smoother, more responsive signals that highlight overbought/oversold conditions, momentum shifts, and high-conviction trading setups without cluttering the chart with noise.
Unlike traditional RSI, which reacts to historical data and produces frequent whipsaws, the Enhanced Holt-Winters RSI filters transient price fluctuations, enabling traders to detect emerging momentum and potential reversal zones earlier.
Theoretical Foundation
The traditional RSI measures relative strength by comparing average gains and losses, but suffers from:
Lag in trend recognition : Signals often arrive after momentum has shifted.
Noise sensitivity : High-frequency price movements generate unreliable crossovers.
Limited insight into structural market shifts : Standard RSI cannot contextualize cyclical or momentum patterns.
The Enhanced Holt-Winters RSI addresses these limitations by applying triple exponential smoothing directly to the RSI series. This decomposes the series into:
Level (Lₜ) : Represents the smoothed central tendency of RSI.
Trend (Tₜ) : Captures rate-of-change in smoothed momentum.
Seasonal Component (Sₜ) : Models short-term cyclical deviations in momentum.
By incorporating these elements, the oscillator produces smoothed RSI values that react faster to emerging trends while suppressing erratic noise. Its internal forecast is mathematical, influencing the smoothed RSI output and signals, rather than being directly plotted.
How It Works
The Enhanced Holt-Winters RSI builds its signal framework through several layers:
1. Base RSI Calculation
Computes standard RSI over the selected period as the primary momentum input.
2. Triple Exponential Smoothing (Holt-Winters)
The RSI is smoothed recursively to extract underlying momentum structure:
Level, trend, and seasonal components are combined to produce a smoothed RSI.
This internal smoothing reduces lag and enhances signal reliability.
3. Momentum Analysis
Short-term momentum shifts are tracked via a moving average of the smoothed RSI, highlighting acceleration or deceleration in directional strength.
4. Volume Confirmation (Optional)
Buy/sell signals can be filtered through a configurable volume threshold, ensuring only high-conviction moves trigger alerts.
5. Visual Output
Colored Candles : Represent overbought (red), oversold (green), or neutral (yellow) conditions.
Oscillator Panel : Plots the smoothed RSI with dynamic color coding for immediate trend context.
Signals : Triangular markers indicate bullish or bearish setups, with stronger signals flagged in extreme zones.
Interpretation
The Enhanced Holt-Winters RSI provides a multi-dimensional perspective on price action:
Trend Strength : Smoothed RSI slope and color coding reflect the direction and momentum intensity.
Momentum Shifts : Rapid changes in the smoothed RSI indicate emerging strength or weakness.
Overbought/Oversold Zones : Highlight areas where price is stretched relative to recent momentum.
High-Conviction Signals : Combined with volume filtering, markers indicate optimal entries/exits.
Cycle Awareness : Smoothing reveals structural patterns, helping traders avoid reacting to noise.
By combining these elements, traders gain early insight into market structure and momentum without relying on raw, lag-prone RSI data.
Strategy Integration
The Enhanced Holt-Winters RSI can be applied across trading styles:
Trend Following
Enter when RSI is aligned with price momentum and color-coded signals confirm trend direction.
Strong slope in the smoothed RSI signals trend continuation.
Reversal Trading
Look for RSI extremes with momentum shifts and strong signal markers.
Compression in oscillator values often precedes reversal setups.
Breakout Detection
Oscillator flattening in neutral zones followed by directional expansion indicates potential breakout conditions.
Multi-Timeframe Confluence
Higher timeframes provide directional bias; lower timeframes refine entry timing using smoothed RSI dynamics.
Technical Implementation Details
Input Source : Close, open, high, low, or price.
Smoothing : Holt-Winters triple exponential smoothing applied to RSI.
Parameters :
Level (α) : Controls smoothing of RSI.
Trend (β) : Adjusts responsiveness to momentum changes.
Seasonal Length : Defines cycles for short-term adjustments.
Delta Smoothing : Reduces choppiness in smoothed RSI difference.
Outputs :
Smoothed RSI
Colored candles and oscillator panel
Buy/Sell signal markers (with optional strength filtering)
Volume Filtering : Optional threshold to confirm signals.
Optimal Application Parameters
Asset-Specific Guidance:
Forex : Use moderate smoothing (α, β) to capture medium-term momentum swings while filtering minor price noise. Works best when combined with volume or volatility filters.
Equities : Balance responsiveness and smoothness to identify sustained sector momentum or rotational shifts; ideal for capturing clean directional transitions.
Cryptocurrency : Increase smoothing parameters slightly to stabilize RSI during extreme volatility; optional volume confirmation can help filter false signals.
Futures/Indices : Lower smoothing sensitivity emphasizes macro momentum and structural trend durability over short-term fluctuations.
Timeframe Optimization:
Scalping (1-5m) : Use higher sensitivity (lower smoothing factors) to react quickly to micro-momentum reversals.
Intraday (15m-1h) : Balance smoothing and responsiveness for detecting short-term acceleration and exhaustion zones.
Swing (4h-Daily) : Apply moderate smoothing to reveal underlying directional persistence and cyclical reversals.
Position (Daily-Weekly) : Use stronger smoothing to isolate dominant momentum trends and filter temporary pullbacks.
Integration Guidelines
Combine with trend filters (EMAs, SuperSmoother MA, ATR-based tools) for confirmation.
Use volume and signal strength markers to filter low-conviction trades.
Slope, color, and signal alignment can guide entry, stop placement, and scaling.
Disclaimer
The Enhanced Holt-Winters RSI is a technical analysis tool, not a guaranteed profit system. Effectiveness depends on proper settings, market structure, and disciplined risk management. Always backtest before live trading.
Multi IndicatorThis script uses combination of RSI, W %, BB, EMA signals to find movement direction and reversals.
Prakash Balkawade
UOT Gold Pressure IndexGold Pressure Index combines the momentum of the US Dollar Index (DXY) and US 10-Year Treasury Yields into a single, easy-to-read oscillator that helps traders identify high-probability setups in gold markets.
What Does This Indicator Do?
This indicator measures the combined directional pressure from the two primary fundamental drivers of gold prices:
DXY (US Dollar Index) - Gold's primary inverse correlation
US 10-Year Treasury Yields - Alternative to gold for safe-haven flows
When both are rising together, gold typically faces strong selling pressure. When both are falling together, gold typically finds support. The GPI simplifies this analysis into one visual metric.
AO Divergence RCT PRO//@description=This indicator, AO Divergence Pro, is a powerful tool designed to automatically identify and plot both classic and hidden divergences on the Awesome Oscillator (AO). Divergences occur when the price action and the oscillator move in opposite directions, often signaling a potential shift in market momentum.
//
// --- Key Features ---
// 1. Regular (Classic) Divergence Detection: This feature identifies potential trend reversals.
// - A **Bullish Regular Divergence** (labeled 'R') is found when the price makes a lower low, but the AO makes a higher low. This suggests that downward momentum is weakening and a reversal to the upside may be imminent.
// - A **Bearish Regular Divergence** (labeled 'R') is found when the price makes a higher high, but the AO makes a lower high. This suggests that upward momentum is fading and a reversal to the downside may be coming.
//
// 2. Hidden Divergence Detection: This feature identifies potential trend continuations.
// - A **Bullish Hidden Divergence** (labeled 'H') is found when the price makes a higher low, but the AO makes a lower low. This often occurs during a pullback in an uptrend, suggesting the trend is likely to resume.
// - A **Bearish Hidden Divergence** (labeled 'H') is found when the price makes a lower high, but the AO makes a higher high. This often occurs during a rally in a downtrend, suggesting the downtrend is likely to continue.
//
// 3. Full Customization: The indicator allows you to toggle the display of each type of divergence (Bullish/Bearish, Regular/Hidden) independently. You can also adjust the pivot detection sensitivity and the time range between divergences to filter signals according to your trading style.
//
// --- How to Use ---
// 1. **Identify Reversals:** Look for the 'R' labels on the chart. A bullish 'R' in a downtrend is a strong signal to consider a long position. A bearish 'R' in an uptrend is a signal to consider a short position.
// 2. **Confirm Continuations:** Look for the 'H' labels. A bullish 'H' during an uptrend pullback can be a good opportunity to add to your position. A bearish 'H' during a downtrend rally can be a signal to enter a short trade.
// 3. **Filter Signals:** Use the settings panel to control the number of signals. For example, increasing the "Min Bars Between" will show fewer, but potentially more reliable, divergences.
//
// --- Attribution ---
// Created by Carlos Mauricio Vizcarra.
//
// --- Disclaimer ---
// This script is for informational and educational purposes only. It is not financial advice. Past performance is not indicative of future results.
Smart Risk DCA Meter — Adaptive Market Risk EngineThe **Smart Risk DCA Meter** is an adaptive market-risk indicator that helps you invest smarter by scaling your DCA buys based on actual market conditions instead of emotion. It combines momentum, distance from trend, and drawdown factors into a single 0–1 risk score that automatically adjusts to each asset’s volatility — from stable indices like SPX to high-beta assets like BTC. Low readings (green zones) signal opportunity to buy heavier, while high readings (red zones) warn to slow down and protect capital.
AI Bot Regime Feed (v6) — stableThis indicator generates real-time, structured JSON alerts for external trading bots or automation systems.
It combines multiple technical layers to identify market regimes and high-probability buy/sell events, and sends them to any webhook endpoint (e.g., a FastAPI or Zapier listener).
Short-Term Capitulation Oscillator (STCO, Diodato 2019)Description:
This script is a faithful implementation of the Short-Term Capitulation Oscillator (STCO) from Chris Diodato's 2019 CMT paper, "Making The Most Of Panic". It's a tactical breadth and volume oscillator designed to "fish for market bottoms" by identifying short-term investor capitulation.
What It Is
The STCO combines the 10-day moving averages of NYSE up-volume and advancing issues. It measures the ratio of advancing momentum (in both volume and number of issues) relative to the total traded momentum. The result is a raw, un-normalized oscillator that typically ranges from 0 to 200.
How to Interpret
The STCO is a tactical tool for identifying near-term oversold conditions and potential bounces.
Low Readings: Indicate that sellers have likely exhausted themselves in the short term, creating a potential entry point for a bounce. The paper found that readings below 90, 85, and 80 were often followed by strong market performance over the next 5-20 days.
Overbought/Oversold Lines: Use the customizable overbought/oversold lines to define your own capitulation zones and potential entry areas.
Settings
Data Sources: Allows toggling the use of "Unchanged" issues/volume data.
Thresholds: You can set the overbought and oversold levels based on the paper's research or your own testing.
Long-Term Capitulation Oscillator (LTCO, Diodato 2019)Description:
This script is a faithful implementation of the Long-Term Capitulation Oscillator (LTCO) from Chris Diodato's award-winning 2019 CMT paper, "Making The Most Of Panic". It is a strategic, market-wide breadth and volume oscillator designed to identify major, long-term market bottoms.
What It Is
The LTCO combines long-term moving averages (34, 55, 89, 144, and 233-day) of NYSE advancing/declining issues and up/down volume. It uses a unique "average of averages" method to create a responsive yet strategic long-term indicator. This script plots the raw, un-normalized value as described in the paper, which typically oscillates in the 700-1100 range.
How to Interpret
The LTCO is a strategic tool for identifying potentially significant market turning points.
Extremely Low Readings: Suggest that a long-term period of selling has reached a point of exhaustion, potentially marking a major bear market low or a generational buying opportunity. The paper backtested various thresholds, with values below 950, 925, and especially 875 showing historically strong forward returns over the next 6-24 months.
Overbought/Oversold Lines: The script includes customizable overbought/oversold lines to help you visually identify these critical zones.
Settings
Data Sources: Allows toggling the use of "Unchanged" issues/volume data for the calculation.
Thresholds: You can set the overbought and oversold levels to your preference, based on the paper's findings or your own research.
Diodato 'All Stars Align' SignalDescription:
This indicator is an overlay that plots the "All Stars Align" buy signal from Chris Diodato's 2019 CMT paper, "Making The Most Of Panic." It is designed to identify high-conviction, short-term buying opportunities by requiring a confluence of both price-based momentum and market-internal weakness.
What It Is
This script works entirely in the background, calculating three separate indicators: the 14-day Slow Stochastic, the Short-Term Capitulation Oscillator (STCO), and the 3-DMA of % Declining Issues. It then plots a signal directly on the main price chart only when the specific "All Stars Align" conditions are met.
How to Interpret
A green cross (+) appears below a price bar when a high-conviction buy signal is generated. This signal triggers only when two primary conditions are true:
The 14-day Slow Stochastic is in "oversold" territory (e.g., below 20).
AND at least one of the market internal indicators shows a state of panic:
Either the STCO is oversold (e.g., below 140).
Or the 3-DMA % Declines shows a panic spike (e.g., above 65).
This confluence signifies a potential exhaustion of sellers and can mark an opportune moment to look for entries.
Settings
Trigger Thresholds: You can customize the exact levels that define an "oversold" or "panic" state for each of the three underlying indicators.
Data Sources: Allows toggling the use of "Unchanged" data for the background calculations.
Stochastic Settings: You can adjust the parameters for the Slow Stochastic calculation.
Alarm Pack (MA14/21 - MACD - CU-RSI - Pivot PP) - SigmorAlgoA clean alarm/confirmation pack by SigmorAlgo.
4 MAs (14/21/50/100) with selectable type (EMA/SMA/SMMA), CU-RSI (22/66) crosses, MACD confirmations, and optional Daily Pivot PP.
Built for clarity: trend filter (MA50/MA100), real-time alerts, and minimal visuals.
Suggested RSI preset: Fast 22, Slow 66 (balanced). For faster signals try 14/42; for slower 28/84.
Dual RSI TL (AI Trend Mapper) - SigmorAlgoDual RSI TL (AI Trend Mapper) — an intelligent momentum and trendline mapping system built to give traders clarity, structure, and precision.
It merges a dual-layer RSI framework (fast & slow) with automatic RSI trendlines to identify strength, exhaustion, and reversals in real time.
⚙️ Main Features:
• Dual RSI system (fast & slow) with fully adjustable lengths
• Automatic RSI trendline mapping (AI-driven slope detection)
• Real-time crossover and confirmation alerts
• Clean visual markers for entry & exit points
• Compatible with EMA, SMA, and Pivot-based systems
💡 Recommended Settings:
• Default: Fast = 25, Slow = 75 (1:3 ratio) — ideal balance for 15m–1D traders
• Faster reaction: 12/36 or 14/42
• Slower/long-term: 28/84 or 30/90
Whether you trade scalps, intraday setups, or daily swings, Dual RSI TL adapts dynamically to price behavior — giving you a visual edge without noise.
Created by SigmorAlgo — for traders who value clarity over clutter.
OG Indicators - EnhancedA simple effort to combine William's % R, MACD & Stochastic into single script
Wilder's ADX/DIワイルダー氏が作ったトレンドの強弱を計るインジケーターです。証券会社のものは微妙に計算式が違うため、ワイルダー氏のオリジナルの計算式で作りました。
It’s an indicator created by Mr. Wilder to measure the strength of a trend.
Since the calculation formulas used by brokerage firms vary slightly, this version is built using Mr. Wilder’s original formula.
Standard Deviation VolatilityThe Standard Deviation (StDev) measures the volatility or dispersion of price from its historical average. Higher values suggest greater price fluctuation and potentially a trending market. Lower values indicate lower volatility, often found during consolidation or ranging markets.
標準偏差（Standard Deviation）は、価格の過去の平均からの**ばらつき（ボラティリティ）**を測る指標です。値が高いほど価格変動が激しく、トレンド相場であることを示唆します。値が低いほど、レンジ相場または保ち合いであることを示します。
Ultimate RSI (14) TDBurbin's RSI Alerts:
RSI alerts can be used ONLY when you're awaiting a chart to shift it's momentum. Example: You are waiting for a take profit signal and you'd like a push notification when this is triggered.
These are NOT intended to be Buy and Sell signals. Only to get your attention. Pair with other confirmations.
**There are 4 alerts. "RSI Bullish Cross" "RSI Bearish Cross" "RSI Bounce Buy" "RSI Sell".
Both of the Cross alerts can be early. Can be too early. The RSI Bounce Buy and RSI Sell are when the RSI line has crossed back inside the outer bands; from Oversold or Overbought. They are a fairly reliable signal, especially when used with other TA such as support, volume, etc.
Default Overbought is 80, default oversold is 20.
Can be used on multiple timeframes.
This is a modified version of LuxAlgo's Ultimate RSI. This is for education purposes only and personal use by Burbin. Inspired by AA, and dedicated to TD.
LuxAlgo's Description:
The Ultimate RSI indicator is a new oscillator based on the calculation of the Relative Strength Index that aims to put more emphasis on the trend, thus having a less noisy output. Opposite to the regular RSI, this oscillator is designed for a trend trading approach instead of a contrarian one.
🔶 USAGE
While returning the same information as a regular RSI, the Ultimate RSI puts more emphasis on trends, and as such can reach overbought/oversold levels faster as well as staying longer within these areas. This can avoid the common issue of an RSI regularly crossing an overbought or oversold level while the trend makes new higher highs/lower lows.
The Ultimate RSI crossing above the overbought level can be indicative of a strong uptrend (highlighted as a green area), while an Ultimate RSI crossing under the oversold level can be indicative of a strong downtrend (highlighted as a red area).
The Ultimate RSI crossing the 50 midline can also indicate trends, with the oscillator being above indicating an uptrend, else a downtrend. Unlike a regular RSI, the Ultimate RSI will cross the midline level less often, thus generating fewer whipsaw signals.
For even more timely indications users can observe the Ultimate RSI relative to its signal line. An Ultimate RSI above its signal line can indicate it is increasing, while the opposite would indicate it is decreasing.
🔹Smoothing Methods
Users can return more reactive or smoother results depending on the selected smoothing method used for the calculation of the Ultimate RSI. Options include:
Exponential Moving Average (EMA)
Simple Moving Average (SMA)
Wilder's Moving Average (RMA)
Triangular Moving Average (TMA)
These are ranked by the degree of reactivity of each method, with higher ones being more reactive (but less smooth).
Users can also select the smoothing method used by the signal line.
🔶 DETAILS
The RSI returns a normalized exponential average of price changes in the range (0, 100), which can be simply calculated as follows:
ema(d) / ema(|d|) × 50 + 50
🔶 SETTINGS
Length: Calculation period of the indicator
Method: Smoothing method used for the calculation of the indicator.
Source: Input source of the indicator
🔹Signal Line
Smooth: Degree of smoothness of the signal line
Method: Smoothing method used to calculation the signal line.
Ultimate RSI (2) TDBurbin's RSI Alerts:
RSI alerts can be used ONLY when you're awaiting a chart to shift it's momentum. Example: You are waiting for a take profit signal and you'd like a push notification when this is triggered.
These are NOT intended to be Buy and Sell signals. Only to get your attention. Pair with other confirmations.
This is a modified version of LuxAlgo's Ultimate RSI. This is for education purposes only and personal use by Burbin. Inspired by AA, and dedicated to TD.
LuxAlgo's Description:
The Ultimate RSI indicator is a new oscillator based on the calculation of the Relative Strength Index that aims to put more emphasis on the trend, thus having a less noisy output. Opposite to the regular RSI, this oscillator is designed for a trend trading approach instead of a contrarian one.
🔶 USAGE
While returning the same information as a regular RSI, the Ultimate RSI puts more emphasis on trends, and as such can reach overbought/oversold levels faster as well as staying longer within these areas. This can avoid the common issue of an RSI regularly crossing an overbought or oversold level while the trend makes new higher highs/lower lows.
The Ultimate RSI crossing above the overbought level can be indicative of a strong uptrend (highlighted as a green area), while an Ultimate RSI crossing under the oversold level can be indicative of a strong downtrend (highlighted as a red area).
The Ultimate RSI crossing the 50 midline can also indicate trends, with the oscillator being above indicating an uptrend, else a downtrend. Unlike a regular RSI, the Ultimate RSI will cross the midline level less often, thus generating fewer whipsaw signals.
For even more timely indications users can observe the Ultimate RSI relative to its signal line. An Ultimate RSI above its signal line can indicate it is increasing, while the opposite would indicate it is decreasing.
🔹Smoothing Methods
Users can return more reactive or smoother results depending on the selected smoothing method used for the calculation of the Ultimate RSI. Options include:
Exponential Moving Average (EMA)
Simple Moving Average (SMA)
Wilder's Moving Average (RMA)
Triangular Moving Average (TMA)
These are ranked by the degree of reactivity of each method, with higher ones being more reactive (but less smooth).
Users can also select the smoothing method used by the signal line.
🔶 DETAILS
The RSI returns a normalized exponential average of price changes in the range (0, 100), which can be simply calculated as follows:
ema(d) / ema(|d|) × 50 + 50
🔶 SETTINGS
Length: Calculation period of the indicator
Method: Smoothing method used for the calculation of the indicator.
Source: Input source of the indicator
🔹Signal Line
Smooth: Degree of smoothness of the signal line
Method: Smoothing method used to calculation the signal line.
Market Regime (w/ Adaptive Thresholds)Logic Behind This Indicator
This indicator identifies market regimes (trending vs. mean-reverting) using adaptive thresholds that adjust to recent market conditions.
Core Components
1. Regime Score Calculation (0-100 scale)
Starts at 50 (neutral) and adjusts based on two factors:
A. Trend Strength
Compares fast EMA (5) vs. slow EMA (10)
If fast > slow by >1% → +60 points (strong uptrend)
If fast < slow by >1% → -60 points (strong downtrend)
B. RSI Momentum
Uses 7-period RSI smoothed with 3-period EMA
RSI > 70 → +20 points (overbought/trending)
RSI < 30 → -20 points (oversold/mean-reverting)
The score is then smoothed and clamped between 0-100.
2. Adaptive Thresholds
Instead of fixed levels, thresholds adjust to recent market behavior:
Looks back 100 bars to find the min/max regime score
High threshold = 80% of the range (trending regime)
Low threshold = 20% of the range (mean-reverting regime)
This prevents false signals in different volatility environments.
3. Regime Classification
Regime Score Classification Meaning
Above high threshold STRONG TREND Market is trending strongly (follow momentum)
Below low threshold STRONG MEAN REVERSION Market is choppy/oversold (fade moves)
Between thresholds NEUTRAL No clear regime (stay out or wait)
4. Regime Persistence Filter
Requires the regime to hold for a minimum number of bars (default: 1) before confirming
Prevents whipsaws from brief score fluctuations
What It Aims to Detect
When to use trend-following strategies (green = buy breakouts, ride momentum)
When to use mean-reversion strategies (red = buy dips, sell rallies)
When to stay out (gray = unclear conditions, high risk of false signals)
Visual Cues
Green background = Strong trend (momentum strategies work)
Red background = Strong mean reversion (contrarian strategies work)
Table = Shows current regime, color, and score
Alerts = Notifies when regime changes
