Combine two tickers OHLC bars with selectable sourcesCombines the bars of two chosen tickers, used to fix issues with split history, new ticker names
e.g. MYTIL went to LSE from ATHEX and changed ticker names but with "broken"/ split chart history. With this script you get the full history combined with the two tickers.
An SMA50 was used as an example of how this can be used with others custom indicators inside this script.
Trendanalyse
XAUUSD/SPX Slope with Bollinger Bands %BThe XAUUSD/SPX Slope with Bollinger Bands %B Indicator plots the slope of the XAUUSD (Gold) to SPX (S&P 500) ratio (blue line) using linear regression (default 20 bars) to show Gold’s relative performance against stocks. Bollinger Bands %B (length: 20, StdDev: 2.0) is calculated on the chart’s current symbol (e.g., GLD, BTCUSD) and plotted in yellow, indicating the symbol’s price position within its bands (0 to 1 range). The slope’s zero is aligned with the %B middle band (0.5) for easy comparison. A positive slope (above 0.5) suggests Gold outperforming SPX, while %B above 1 indicates overbought conditions and below 0 indicates oversold for the chart’s symbol. Overbought (1), middle (0.5), and oversold (0) lines, with optional background fills (green for overbought, blue for middle, red for oversold, unchecked by default), aid interpretation. Adjust the slope scale factor for visibility.
Range Grid From Two LevelsRange Grid From Two Levels of Initial Balance (works great with next day levels)
CryptoThunder Storm v1.21CryptoThunder Storm v1.21 — Strategy (non-repainting, HTF-aware)
CryptoThunder Storm is a Pine v6 strategy that trades the cross of two moving-average variants computed on an alternate (higher) timeframe derived from your current chart. It’s built to be non-repainting by evaluating signals only at HTF bar boundaries and by avoiding lookahead. The script can trade LONG, SHORT, BOTH, or be disabled, and it includes a one-click invert Long/Short mode.
How it works
Two MA streams (Open/Close series).
You can choose from multiple MA types (SMA/EMA/DEMA/TEMA/WMA/VWMA/SMMA/Hull/LSMA/ALMA/SSMA/TMA). The script computes:
closeSeries – MA of the (possibly delayed) close
openSeries – MA of the (possibly delayed) open
Alternate Resolution (HTF).
The inputs allow you to multiply your current chart’s timeframe (e.g., on 5m with multiplier 3 → HTF = 15m). Both series are requested via request.security() with lookahead_off.
Non-repainting gating.
Signals are evaluated once per HTF bar (htfClosed gate). This ensures entries/alerts are aligned with HTF boundaries and prevents forward-shifting.
Entry logic.
Long when closeSeriesAlt crosses above openSeriesAlt.
Short when closeSeriesAlt crosses below openSeriesAlt.
Invert mode swaps these actions (a former long signal opens a short, and vice versa).
Orders are processed on bar close (process_orders_on_close=true).
Risk management (optional).
Optional initial TP/SL exits via strategy.exit() (ticks/points). Set 0 to disable.
Visuals.
The script colors bars (optional) and plots the two HTF series with a filled band, plus compact UP/DN/CL markers that match the executed side after inversion/filtering.
Inputs & configuration
Use Alternate Resolution?
Turns the HTF logic on/off. When off, the strategy uses the chart timeframe.
Multiplier for Alternate Resolution
Multiplies the current timeframe to form the HTF (e.g., 3×).
MA Type / Period / Offsets
MA Type — choose from 12 variants.
MA Period — core length.
Offset for LSMA / Sigma for ALMA — MA-specific tuning.
Offset for ALMA — center of mass for ALMA.
Delay Open/Close MA — shifts the source back by n bars for a more conservative (non-peek) calculation. Keep at 0 unless you know you want extra delay.
Show coloured Bars to indicate Trend?
Colors bars relative to HTF band.
What trades should be taken: LONG / SHORT / BOTH / NONE
Filters which sides are actually traded.
Invert Long/Short logic?
Swaps long ↔ short everywhere (orders, markers, JSON alerts).
Backtest window (Number of Bars for Back Testing)
Crude limiter to speed up testing. 0 = test full history.
TP/SL (Initial Stop Loss / Target Profit Points)
Values in ticks/points. 0 disables. They apply to both sides via strategy.exit().
Alert options
Turn on alerts (JSON)
Show alert marks (UP/DOWN/CLOSE)
Send CLOSE alerts (toggle)
The strategy fires alert() internally. Create an alert on “Any alert() function call”.
The payload is a simple JSON string:{ "text":"C98USDT.P UP"}
Messages:
UP — a long entry was executed (or, with Invert on: the inverted long signal that opens a long).
DOWN — a short entry executed.
CLOSE — position closed or flipped.
Tip: If you want to route long/short to different webhooks, parse the text field for UP, DOWN, or CLOSE
Plotting & markers
Band: Fills between the two HTF MA lines.
Bar color (optional): Quick visual trend cue.
Markers:
▲ “UP” below bar when a long executes.
▼ “DN” above bar when a short executes.
✖ “CL” on position close/flip.
These reflect the final executed side, after trade filters and after Invert mode
Best practices & notes
Non-repainting design.
request.security(..., lookahead_off) prevents future data leakage.
Signals are gated to HTF bar boundaries, so you won’t get intra-HTF recalculations.
Strategy orders are processed at bar close.
Choosing the multiplier.
A 2×–4× multiplier often balances responsiveness vs stability (e.g., 5m→15m or 20m). Larger multipliers reduce churn and false signals.
TP/SL units.
Values are in ticks/points of the chart symbol. On crypto, check your instrument’s tick size and adjust accordingly.
Trade filters apply after inversion.
With invertLS = true and tradeType = LONG, only final longs (post-inversion) are allowed.
Strategy vs chart counts.
The Tester reports closed trades; your chart shows entries/markers including the latest open trade. This can explain 8 vs 12 discrepancies over short windows.
Performance.
calc_on_every_tick=false and the backtest limiter keep the script responsive on long histories.
Tips: user on mid-volume crypto pair, 1M chart, best MA is: SMMA, Hull, SSMA, DEMA, TEMA.
This strategy is for research and education. Markets carry risk; past performance doesn’t guarantee future results. Always forward-test on paper and validate your exchange execution, tick size, and fees before deploying live.
Kerzen-Zähler über/unter EMADieses Skript zeigt die Anzahl an Zeitperioden ober/unterhalb eines individuellen EMAs an.
XAUUSD/SPX Slope with MACDThe XAUUSD/SPX Slope with MACD Indicator tracks the slope of the XAUUSD (Gold) to SPX (S&P 500) ratio using linear regression over a customizable lookback period (default 20 bars), plotted as a blue line to show Gold’s relative performance against stocks. A MACD overlay (fast: 12, slow: 26, signal: 9, scaled by 5) is applied to the slope, with a green MACD line, red signal line, and green/red histogram to highlight momentum. A positive slope indicates Gold outperforming SPX, while MACD trends provide additional context. A zero line and background coloring (green for positive slope, red for negative) aid interpretation. Ensure XAUUSD and SPX symbols are supported by your platform.
Usage Notes:
Interpretation:
Slope (Blue): Positive values indicate Gold outperforming SPX; negative values show SPX outperforming.
MACD Components: The green MACD line, red signal line, and histogram (green for positive, red for negative) reflect momentum in the slope. Crossovers or histogram shifts can be analyzed manually for potential trend changes.
Customization: Adjust lookback, macd_fast, macd_slow, or macd_signal to tune sensitivity. The MACD scale is fixed at 5 for clarity.
Symbol Check: Verify "XAUUSD" and "SPX" match your platform’s tickers (e.g., "GOLD" or "SP500").
Multi-Period SMA - flack0xA comprehensive moving average indicator featuring 7 fully customizable SMA periods designed for multi-timeframe trend analysis. Perfect for traders who want to visualize multiple moving average periods simultaneously without cluttering their charts with separate indicators.Key Features:
7 Independent SMAs with default periods: 2, 3, 9, 27, 81, 243, 2187
Individual Customization - Each SMA has its own period, color, line width, and visibility controls
Smart Defaults - Shorter SMAs use thinner lines, longer SMAs use thicker lines for visual hierarchy
Overlay Design - Properly overlays on price data without Y-axis attachment issues
Alert System - Built-in crossover alerts for key SMA levels (9 and 27 period)
XAUUSD/SPX Slope IndicatorThe XAUUSD/SPX Slope Indicator for TradingView calculates the slope of the XAUUSD (Gold) to SPX (S&P 500) ratio using linear regression over a customizable lookback period (default 20 bars). It plots the slope to show whether Gold is outperforming or underperforming the S&P 500, with a positive slope indicating Gold's strength and a negative slope favoring stocks. A zero line and color-coded background (green for positive, red for negative) enhance readability, making it a useful tool for analyzing relative market trends. Ensure your platform supports XAUUSD and SPX symbols.
RSI SMA ColorRSI 14 with SMA 21 Color. A momentum oscillator that measures the speed and change of price movements. It ranges from 0 to 100 and is typically used to identify overbought or oversold conditions in a market. An RSI above 70 may indicate that an asset is overbought, while an RSI below 30 may suggest it is oversold.
CF Cycle Low Projection V2Overview
This indicator helps traders analyze repeating market cycles by detecting significant pivot lows and projecting when the next cycle low may occur. It provides timing context to support decision-making but does not generate direct buy/sell signals.
How it works
Pivot detection : Confirms swing lows using left/right bars. Filters (minimum % move and optional ATR separation) ensure only meaningful lows are counted.
Cycle averaging : Calculates the average interval (and standard deviation) between recent pivot lows.
Projection : Adds the average interval to the last pivot low to forecast the next potential cycle low. If that point lies in the past, the script rolls forward until the projection is in the future.
Timing window : A shaded area around the ETA is drawn, based on either standard deviation or a percentage of the average, showing when a low is statistically more likely to occur.
Visualization:
• Vertical line = projected cycle low
• Shaded box = timing window
• Label = countdown in weeks/days/hours
• HUD = status, ETA, intervals used
How to use
Select your preferred timeframe (works on intraday and higher).
Allow pivots to accumulate; once the HUD shows Status: OK, projections will appear.
Use the ETA line and timing window together with structure, liquidity levels, and support/resistance zones.
Combine with your own strategy and risk management rules.
Notes
Works on any market supported by TradingView (crypto, stocks, forex, indices).
Filters can be adjusted to reduce noise (e.g., increase % move or ATR multiplier).
This tool is designed for cycle timing analysis only. It does not predict exact prices or guarantee outcomes.
Some traders refer to this approach as “camel cycle trading,” but here it is implemented as a pivot-based cycle projection tool.
Ichimoku Fractal Flow### Ichimoku Fractal Flow (IFF)
By Gurjit Singh
Ichimoku Fractal Flow (IFF) distills the Ichimoku system into a single oscillator by merging fractal echoes of price and cloud dynamics into one flow signal. Instead of static Ichimoku lines, it measures the "flow" between Conversion/Base, Span A/B, price echoes, and cloud echoes. The result is a multidimensional oscillator that reveals hidden rhythm, momentum shifts, and trend bias.
#### 📌 Key Features
1. Fourfold Fusion – The oscillator blends:
* Phase: Tenkan vs. Kijun spread (short vs. medium trend).
* Kumo Phase: Span A vs. Span B spread (cloud thickness).
* Echo: Price vs lagged reflection.
* Cloud Echo: Price vs. projected cloud center.
2. Oscillator Output – A unified flow line oscillating around zero.
3. Dual Calculation Modes – Oscillator can be built using:
* High-Low Midpoint (classic Ichimoku-style averaging).
* Wilder’s RMA (smoother, less noisy averaging averaging).
4. Optional Smoothing – EMA or Wilder’s RMA creates a trend line, enabling MACD-style crossovers.
5. Dynamic Coloring – Bullish/Bearish color shifts for quick bias recognition.
6. Fill Styling – Highlighted regions between oscillator & smoothing line.
7. Zero Line Reference – Acts as a structural pivot (bull vs. bear).
#### 🔑 How to Use
1. Add to Chart: Works across all assets and timeframes.
2. Flow Bias (Zero Line):
* Above 0 → Bullish flow 🐂
* Below 0 → Bearish flow 🐻
3. With Signal Line:
* Oscillator above smoothing line → Possible upward trend shift.
* Oscillator below smoothing line → Possible downward trend shift.
4. Strength:
* Wide separation from smoothing = strong trend.
* Flat, tight clustering = indecision/range.
5. Contextual Edge: Combine signals with Ichimoku Cloud analysis for stronger confluence.
#### ⚙️ Inputs & Options
* Conversion Line (Tenkan, default 9)
* Base Line (Kijun, default 26)
* Leading Span B (default 52)
* Lag/Lead Shift (default 26)
* Oscillator Mode: High-Low Midpoint vs Wilder’s RMA
* Use Smoothing (toggle on/off)
* Signal Smoothing: Wilder/EMA option
* Smoothing Length (default 9)
* Bullish/Bearish Colors + Transparency
#### 💡 Tips
* Wilder’s RMA (both oscillator & smoothing) is gentler, reducing whipsaws in sideways markets.
* High-Low Mid captures pure Ichimoku-style ranges, good for structure-based traders.
* EMA reacts faster than RMA; use if you want early momentum signals.
* Zero-line flips act like momentum pivots—watch them near cloud boundaries.
* Signal line crossovers behave like MACD-style triggers.
* Strongest signals appear when oscillator, signal line, and Ichimoku Cloud all align.
👉 In short: Ichimoku Fractal Flow compresses multi-layered Ichimoku system into a single fractal oscillator that detects flow, pivotal shifts, and momentum with clarity—bridging price, cloud, and echoes into one signal. Where the cloud shows structure, IFF reveals the underlying flow. Together, they offer a fractal lens into market rhythm.
Strong tendence detector - Detector de Fuerte TendenciaThis chart shows when an asset is in a strong uptrend or downtrend. The legend on the left indicates if the RSI is above 62 or below 38 on the monthly, weekly, and daily timeframes. A strong uptrend is confirmed when all three timeframes are above 62, while a strong downtrend is confirmed when they are all below 38. Periods of a strong uptrend are highlighted with a green background, and periods of a strong downtrend are highlighted in red.
High Probability Order Blocks [AlgoAlpha]🟠 OVERVIEW
This script detects and visualizes high-probability order blocks by combining a volatility-based z-score trigger with a statistical survival model inspired by Kaplan-Meier estimation. It builds and manages bullish and bearish order blocks dynamically on the chart, displays live survival probabilities per block, and plots optional rejection signals. What makes this tool unique is its use of historical mitigation behavior to estimate and plot how likely each zone is to persist, offering traders a probabilistic perspective on order block strength—something rarely seen in retail indicators.
🟠 CONCEPTS
Order blocks are regions of strong institutional interest, often marked by large imbalances between buying and selling. This script identifies those areas using z-score thresholds on directional distance (up or down candles), detecting statistically significant moves that signal potential smart money footprints. A bullish block is drawn when a strong up-move (zUp > 4) follows a down candle, and vice versa for bearish blocks. Over time, each block is evaluated: if price “mitigates” it (i.e., closes cleanly past the opposite side and confirmed with a 1 bar delay), it’s considered resolved and logged. These resolved blocks then inform a Kaplan-Meier-like survival curve, estimating the likelihood that future blocks of a given age will remain unbroken. The indicator then draws a probability curve for each side (bull/bear), updating it in real time.
🟠 FEATURES
Live label inside each block showing survival probability or “N.E.D.” if insufficient data.
Kaplan-Meier survival curves drawn directly on the chart to show estimated strength decay.
Rejection markers (▲ ▼) if price bounces cleanly off an active order block.
Alerts for zone creation and rejection signals, supporting rule-based trading workflows.
🟠 USAGE
Read the label inside each block for Age | Survival% (or N.E.D. if there aren’t enough samples yet); higher survival % suggests blocks of that age have historically lasted longer.
Use the right-side survival curves to gauge how probability decays with age for bull vs bear blocks, and align entries with the side showing stronger survival at current age.
Treat ▲ (bullish rejection) and ▼ (bearish rejection) as optional confluence when price tests a boundary and fails to break.
Turn on alerts for “Bullish Zone Created,” “Bearish Zone Created,” and rejection signals so you don’t need to watch constantly.
If your chart gets crowded, enable Prevent Overlap ; tune Max Box Age to your timeframe; and adjust KM Training Window / Minimum Samples to trade off responsiveness vs stability.
Tzotchev Trend Measure [EdgeTools]Are you still measuring trend strength with moving averages? Here is a better variant at scientific level:
Tzotchev Trend Measure: A Statistical Approach to Trend Following
The Tzotchev Trend Measure represents a sophisticated advancement in quantitative trend analysis, moving beyond traditional moving average-based indicators toward a statistically rigorous framework for measuring trend strength. This indicator implements the methodology developed by Tzotchev et al. (2015) in their seminal J.P. Morgan research paper "Designing robust trend-following system: Behind the scenes of trend-following," which introduced a probabilistic approach to trend measurement that has since become a cornerstone of institutional trading strategies.
Mathematical Foundation and Statistical Theory
The core innovation of the Tzotchev Trend Measure lies in its transformation of price momentum into a probability-based metric through the application of statistical hypothesis testing principles. The indicator employs the fundamental formula ST = 2 × Φ(√T × r̄T / σ̂T) - 1, where ST represents the trend strength score bounded between -1 and +1, Φ(x) denotes the normal cumulative distribution function, T represents the lookback period in trading days, r̄T is the average logarithmic return over the specified period, and σ̂T represents the estimated daily return volatility.
This formulation transforms what is essentially a t-statistic into a probabilistic trend measure, testing the null hypothesis that the mean return equals zero against the alternative hypothesis of non-zero mean return. The use of logarithmic returns rather than simple returns provides several statistical advantages, including symmetry properties where log(P₁/P₀) = -log(P₀/P₁), additivity characteristics that allow for proper compounding analysis, and improved validity of normal distribution assumptions that underpin the statistical framework.
The implementation utilizes the Abramowitz and Stegun (1964) approximation for the normal cumulative distribution function, achieving accuracy within ±1.5 × 10⁻⁷ for all input values. This approximation employs Horner's method for polynomial evaluation to ensure numerical stability, particularly important when processing large datasets or extreme market conditions.
Comparative Analysis with Traditional Trend Measurement Methods
The Tzotchev Trend Measure demonstrates significant theoretical and empirical advantages over conventional trend analysis techniques. Traditional moving average-based systems, including simple moving averages (SMA), exponential moving averages (EMA), and their derivatives such as MACD, suffer from several fundamental limitations that the Tzotchev methodology addresses systematically.
Moving average systems exhibit inherent lag bias, as documented by Kaufman (2013) in "Trading Systems and Methods," where he demonstrates that moving averages inevitably lag price movements by approximately half their period length. This lag creates delayed signal generation that reduces profitability in trending markets and increases false signal frequency during consolidation periods. In contrast, the Tzotchev measure eliminates lag bias by directly analyzing the statistical properties of return distributions rather than smoothing price levels.
The volatility normalization inherent in the Tzotchev formula addresses a critical weakness in traditional momentum indicators. As shown by Bollinger (2001) in "Bollinger on Bollinger Bands," momentum oscillators like RSI and Stochastic fail to account for changing volatility regimes, leading to inconsistent signal interpretation across different market conditions. The Tzotchev measure's incorporation of return volatility in the denominator ensures that trend strength assessments remain consistent regardless of the underlying volatility environment.
Empirical studies by Hurst, Ooi, and Pedersen (2013) in "Demystifying Managed Futures" demonstrate that traditional trend-following indicators suffer from significant drawdowns during whipsaw markets, with Sharpe ratios frequently below 0.5 during challenging periods. The authors attribute these poor performance characteristics to the binary nature of most trend signals and their inability to quantify signal confidence. The Tzotchev measure addresses this limitation by providing continuous probability-based outputs that allow for more sophisticated risk management and position sizing strategies.
The statistical foundation of the Tzotchev approach provides superior robustness compared to technical indicators that lack theoretical grounding. Fama and French (1988) in "Permanent and Temporary Components of Stock Prices" established that price movements contain both permanent and temporary components, with traditional moving averages unable to distinguish between these elements effectively. The Tzotchev methodology's hypothesis testing framework specifically tests for the presence of permanent trend components while filtering out temporary noise, providing a more theoretically sound approach to trend identification.
Research by Moskowitz, Ooi, and Pedersen (2012) in "Time Series Momentum in the Cross Section of Asset Returns" found that traditional momentum indicators exhibit significant variation in effectiveness across asset classes and time periods. Their study of multiple asset classes over decades revealed that simple price-based momentum measures often fail to capture persistent trends in fixed income and commodity markets. The Tzotchev measure's normalization by volatility and its probabilistic interpretation provide consistent performance across diverse asset classes, as demonstrated in the original J.P. Morgan research.
Comparative performance studies conducted by AQR Capital Management (Asness, Moskowitz, and Pedersen, 2013) in "Value and Momentum Everywhere" show that volatility-adjusted momentum measures significantly outperform traditional price momentum across international equity, bond, commodity, and currency markets. The study documents Sharpe ratio improvements of 0.2 to 0.4 when incorporating volatility normalization, consistent with the theoretical advantages of the Tzotchev approach.
The regime detection capabilities of the Tzotchev measure provide additional advantages over binary trend classification systems. Research by Ang and Bekaert (2002) in "Regime Switches in Interest Rates" demonstrates that financial markets exhibit distinct regime characteristics that traditional indicators fail to capture adequately. The Tzotchev measure's five-tier classification system (Strong Bull, Weak Bull, Neutral, Weak Bear, Strong Bear) provides more nuanced market state identification than simple trend/no-trend binary systems.
Statistical testing by Jegadeesh and Titman (2001) in "Profitability of Momentum Strategies" revealed that traditional momentum indicators suffer from significant parameter instability, with optimal lookback periods varying substantially across market conditions and asset classes. The Tzotchev measure's statistical framework provides more stable parameter selection through its grounding in hypothesis testing theory, reducing the need for frequent parameter optimization that can lead to overfitting.
Advanced Noise Filtering and Market Regime Detection
A significant enhancement over the original Tzotchev methodology is the incorporation of a multi-factor noise filtering system designed to reduce false signals during sideways market conditions. The filtering mechanism employs four distinct approaches: adaptive thresholding based on current market regime strength, volatility-based filtering utilizing ATR percentile analysis, trend strength confirmation through momentum alignment, and a comprehensive multi-factor approach that combines all methodologies.
The adaptive filtering system analyzes market microstructure through price change relative to average true range, calculates volatility percentiles over rolling windows, and assesses trend alignment across multiple timeframes using exponential moving averages of varying periods. This approach addresses one of the primary limitations identified in traditional trend-following systems, namely their tendency to generate excessive false signals during periods of low volatility or sideways price action.
The regime detection component classifies market conditions into five distinct categories: Strong Bull (ST > 0.3), Weak Bull (0.1 < ST ≤ 0.3), Neutral (-0.1 ≤ ST ≤ 0.1), Weak Bear (-0.3 ≤ ST < -0.1), and Strong Bear (ST < -0.3). This classification system provides traders with clear, quantitative definitions of market regimes that can inform position sizing, risk management, and strategy selection decisions.
Professional Implementation and Trading Applications
The indicator incorporates three distinct trading profiles designed to accommodate different investment approaches and risk tolerances. The Conservative profile employs longer lookback periods (63 days), higher signal thresholds (0.2), and reduced filter sensitivity (0.5) to minimize false signals and focus on major trend changes. The Balanced profile utilizes standard academic parameters with moderate settings across all dimensions. The Aggressive profile implements shorter lookback periods (14 days), lower signal thresholds (-0.1), and increased filter sensitivity (1.5) to capture shorter-term trend movements.
Signal generation occurs through threshold crossover analysis, where long signals are generated when the trend measure crosses above the specified threshold and short signals when it crosses below. The implementation includes sophisticated signal confirmation mechanisms that consider trend alignment across multiple timeframes and momentum strength percentiles to reduce the likelihood of false breakouts.
The alert system provides real-time notifications for trend threshold crossovers, strong regime changes, and signal generation events, with configurable frequency controls to prevent notification spam. Alert messages are standardized to ensure consistency across different market conditions and timeframes.
Performance Optimization and Computational Efficiency
The implementation incorporates several performance optimization features designed to handle large datasets efficiently. The maximum bars back parameter allows users to control historical calculation depth, with default settings optimized for most trading applications while providing flexibility for extended historical analysis. The system includes automatic performance monitoring that generates warnings when computational limits are approached.
Error handling mechanisms protect against division by zero conditions, infinite values, and other numerical instabilities that can occur during extreme market conditions. The finite value checking system ensures data integrity throughout the calculation process, with fallback mechanisms that maintain indicator functionality even when encountering corrupted or missing price data.
Timeframe validation provides warnings when the indicator is applied to unsuitable timeframes, as the Tzotchev methodology was specifically designed for daily and higher timeframe analysis. This validation helps prevent misapplication of the indicator in contexts where its statistical assumptions may not hold.
Visual Design and User Interface
The indicator features eight professional color schemes designed for different trading environments and user preferences. The EdgeTools theme provides an institutional blue and steel color palette suitable for professional trading environments. The Gold theme offers warm colors optimized for commodities trading. The Behavioral theme incorporates psychology-based color contrasts that align with behavioral finance principles. The Quant theme provides neutral colors suitable for analytical applications.
Additional specialized themes include Ocean, Fire, Matrix, and Arctic variations, each optimized for specific visual preferences and trading contexts. All color schemes include automatic dark and light mode optimization to ensure optimal readability across different chart backgrounds and trading platforms.
The information table provides real-time display of key metrics including current trend measure value, market regime classification, signal strength, Z-score, average returns, volatility measures, filter threshold levels, and filter effectiveness percentages. This comprehensive dashboard allows traders to monitor all relevant indicator components simultaneously.
Theoretical Implications and Research Context
The Tzotchev Trend Measure addresses several theoretical limitations inherent in traditional technical analysis approaches. Unlike moving average-based systems that rely on price level comparisons, this methodology grounds trend analysis in statistical hypothesis testing, providing a more robust theoretical foundation for trading decisions.
The probabilistic interpretation of trend strength offers significant advantages over binary trend classification systems. Rather than simply indicating whether a trend exists, the measure quantifies the statistical confidence level associated with the trend assessment, allowing for more nuanced risk management and position sizing decisions.
The incorporation of volatility normalization addresses the well-documented problem of volatility clustering in financial time series, ensuring that trend strength assessments remain consistent across different market volatility regimes. This normalization is particularly important for portfolio management applications where consistent risk metrics across different assets and time periods are essential.
Practical Applications and Trading Strategy Integration
The Tzotchev Trend Measure can be effectively integrated into various trading strategies and portfolio management frameworks. For trend-following strategies, the indicator provides clear entry and exit signals with quantified confidence levels. For mean reversion strategies, extreme readings can signal potential turning points. For portfolio allocation, the regime classification system can inform dynamic asset allocation decisions.
The indicator's statistical foundation makes it particularly suitable for quantitative trading strategies where systematic, rules-based approaches are preferred over discretionary decision-making. The standardized output range facilitates easy integration with position sizing algorithms and risk management systems.
Risk management applications benefit from the indicator's ability to quantify trend strength and provide early warning signals of potential trend changes. The multi-timeframe analysis capability allows for the construction of robust risk management frameworks that consider both short-term tactical and long-term strategic market conditions.
Implementation Guide and Parameter Configuration
The practical application of the Tzotchev Trend Measure requires careful parameter configuration to optimize performance for specific trading objectives and market conditions. This section provides comprehensive guidance for parameter selection and indicator customization.
Core Calculation Parameters
The Lookback Period parameter controls the statistical window used for trend calculation and represents the most critical setting for the indicator. Default values range from 14 to 63 trading days, with shorter periods (14-21 days) providing more sensitive trend detection suitable for short-term trading strategies, while longer periods (42-63 days) offer more stable trend identification appropriate for position trading and long-term investment strategies. The parameter directly influences the statistical significance of trend measurements, with longer periods requiring stronger underlying trends to generate significant signals but providing greater reliability in trend identification.
The Price Source parameter determines which price series is used for return calculations. The default close price provides standard trend analysis, while alternative selections such as high-low midpoint ((high + low) / 2) can reduce noise in volatile markets, and volume-weighted average price (VWAP) offers superior trend identification in institutional trading environments where volume concentration matters significantly.
The Signal Threshold parameter establishes the minimum trend strength required for signal generation, with values ranging from -0.5 to 0.5. Conservative threshold settings (0.2 to 0.3) reduce false signals but may miss early trend opportunities, while aggressive settings (-0.1 to 0.1) provide earlier signal generation at the cost of increased false positive rates. The optimal threshold depends on the trader's risk tolerance and the volatility characteristics of the traded instrument.
Trading Profile Configuration
The Trading Profile system provides pre-configured parameter sets optimized for different trading approaches. The Conservative profile employs a 63-day lookback period with a 0.2 signal threshold and 0.5 noise sensitivity, designed for long-term position traders seeking high-probability trend signals with minimal false positives. The Balanced profile uses a 21-day lookback with 0.05 signal threshold and 1.0 noise sensitivity, suitable for swing traders requiring moderate signal frequency with acceptable noise levels. The Aggressive profile implements a 14-day lookback with -0.1 signal threshold and 1.5 noise sensitivity, optimized for day traders and scalpers requiring frequent signal generation despite higher noise levels.
Advanced Noise Filtering System
The noise filtering mechanism addresses the challenge of false signals during sideways market conditions through four distinct methodologies. The Adaptive filter adjusts thresholds based on current trend strength, increasing sensitivity during strong trending periods while raising thresholds during consolidation phases. The Volatility-based filter utilizes Average True Range (ATR) percentile analysis to suppress signals during abnormally volatile conditions that typically generate false trend indications.
The Trend Strength filter requires alignment between multiple momentum indicators before confirming signals, reducing the probability of false breakouts from consolidation patterns. The Multi-factor approach combines all filtering methodologies using weighted scoring to provide the most robust noise reduction while maintaining signal responsiveness during genuine trend initiations.
The Noise Sensitivity parameter controls the aggressiveness of the filtering system, with lower values (0.5-1.0) providing conservative filtering suitable for volatile instruments, while higher values (1.5-2.0) allow more signals through but may increase false positive rates during choppy market conditions.
Visual Customization and Display Options
The Color Scheme parameter offers eight professional visualization options designed for different analytical preferences and market conditions. The EdgeTools scheme provides high contrast visualization optimized for trend strength differentiation, while the Gold scheme offers warm tones suitable for commodity analysis. The Behavioral scheme uses psychological color associations to enhance decision-making speed, and the Quant scheme provides neutral colors appropriate for quantitative analysis environments.
The Ocean, Fire, Matrix, and Arctic schemes offer additional aesthetic options while maintaining analytical functionality. Each scheme includes optimized colors for both light and dark chart backgrounds, ensuring visibility across different trading platform configurations.
The Show Glow Effects parameter enhances plot visibility through multiple layered lines with progressive transparency, particularly useful when analyzing multiple timeframes simultaneously or when working with dense price data that might obscure trend signals.
Performance Optimization Settings
The Maximum Bars Back parameter controls the historical data depth available for calculations, with values ranging from 5,000 to 50,000 bars. Higher values enable analysis of longer-term trend patterns but may impact indicator loading speed on slower systems or when applied to multiple instruments simultaneously. The optimal setting depends on the intended analysis timeframe and available computational resources.
The Calculate on Every Tick parameter determines whether the indicator updates with every price change or only at bar close. Real-time calculation provides immediate signal updates suitable for scalping and day trading strategies, while bar-close calculation reduces computational overhead and eliminates signal flickering during bar formation, preferred for swing trading and position management applications.
Alert System Configuration
The Alert Frequency parameter controls notification generation, with options for all signals, bar close only, or once per bar. High-frequency trading strategies benefit from all signals mode, while position traders typically prefer bar close alerts to avoid premature position entries based on intrabar fluctuations.
The alert system generates four distinct notification types: Long Signal alerts when the trend measure crosses above the positive signal threshold, Short Signal alerts for negative threshold crossings, Bull Regime alerts when entering strong bullish conditions, and Bear Regime alerts for strong bearish regime identification.
Table Display and Information Management
The information table provides real-time statistical metrics including current trend value, regime classification, signal status, and filter effectiveness measurements. The table position can be customized for optimal screen real estate utilization, and individual metrics can be toggled based on analytical requirements.
The Language parameter supports both English and German display options for international users, while maintaining consistent calculation methodology regardless of display language selection.
Risk Management Integration
Effective risk management integration requires coordination between the trend measure signals and position sizing algorithms. Strong trend readings (above 0.5 or below -0.5) support larger position sizes due to higher probability of trend continuation, while neutral readings (between -0.2 and 0.2) suggest reduced position sizes or range-trading strategies.
The regime classification system provides additional risk management context, with Strong Bull and Strong Bear regimes supporting trend-following strategies, while Neutral regimes indicate potential for mean reversion approaches. The filter effectiveness metric helps traders assess current market conditions and adjust strategy parameters accordingly.
Timeframe Considerations and Multi-Timeframe Analysis
The indicator's effectiveness varies across different timeframes, with higher timeframes (daily, weekly) providing more reliable trend identification but slower signal generation, while lower timeframes (hourly, 15-minute) offer faster signals with increased noise levels. Multi-timeframe analysis combining trend alignment across multiple periods significantly improves signal quality and reduces false positive rates.
For optimal results, traders should consider trend alignment between the primary trading timeframe and at least one higher timeframe before entering positions. Divergences between timeframes often signal potential trend reversals or consolidation periods requiring strategy adjustment.
Conclusion
The Tzotchev Trend Measure represents a significant advancement in technical analysis methodology, combining rigorous statistical foundations with practical trading applications. Its implementation of the J.P. Morgan research methodology provides institutional-quality trend analysis capabilities previously available only to sophisticated quantitative trading firms.
The comprehensive parameter configuration options enable customization for diverse trading styles and market conditions, while the advanced noise filtering and regime detection capabilities provide superior signal quality compared to traditional trend-following indicators. Proper parameter selection and understanding of the indicator's statistical foundation are essential for achieving optimal trading results and effective risk management.
References
Abramowitz, M. and Stegun, I.A. (1964). Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. Washington: National Bureau of Standards.
Ang, A. and Bekaert, G. (2002). Regime Switches in Interest Rates. Journal of Business and Economic Statistics, 20(2), 163-182.
Asness, C.S., Moskowitz, T.J., and Pedersen, L.H. (2013). Value and Momentum Everywhere. Journal of Finance, 68(3), 929-985.
Bollinger, J. (2001). Bollinger on Bollinger Bands. New York: McGraw-Hill.
Fama, E.F. and French, K.R. (1988). Permanent and Temporary Components of Stock Prices. Journal of Political Economy, 96(2), 246-273.
Hurst, B., Ooi, Y.H., and Pedersen, L.H. (2013). Demystifying Managed Futures. Journal of Investment Management, 11(3), 42-58.
Jegadeesh, N. and Titman, S. (2001). Profitability of Momentum Strategies: An Evaluation of Alternative Explanations. Journal of Finance, 56(2), 699-720.
Kaufman, P.J. (2013). Trading Systems and Methods. 5th Edition. Hoboken: John Wiley & Sons.
Moskowitz, T.J., Ooi, Y.H., and Pedersen, L.H. (2012). Time Series Momentum. Journal of Financial Economics, 104(2), 228-250.
Tzotchev, D., Lo, A.W., and Hasanhodzic, J. (2015). Designing robust trend-following system: Behind the scenes of trend-following. J.P. Morgan Quantitative Research, Asset Management Division.
Kyoshiro - FVG + Order Blocks📌 Kyoshiro – FVG + Order Blocks
This indicator combines Order Block (OB) detection with an intelligent auto-management system and a clean visual display on the chart.
It is designed to help traders better identify institutional zones where price frequently reacts.
⚙️ Key Features:
✅ Real-time detection of bullish and bearish Order Blocks.
✅ Automatic cleanup: invalidated OBs are removed to keep the chart clean.
✅ Customizable display:
Maximum number of visible OBs (bullish / bearish).
Zone colors, outlines, and midlines.
Line styles (solid, dashed, dotted) and adjustable width.
✅ Choice of mitigation method:
Wick
Close
✅ Built-in alerts:
Formation of bullish or bearish OB.
Mitigation of an existing OB.
🔔 Available Alerts:
Bullish OB Formed → A bullish order block is detected.
Bearish OB Formed → A bearish order block is detected.
Bullish OB Mitigated → A bullish OB has been invalidated.
Bearish OB Mitigated → A bearish OB has been invalidated.
🎯 Use Cases:
Quickly identify key liquidity zones.
Track institutional activity in the market.
Improve entry and exit precision.
Multiple Divergence Scanner (move to candles and merge scales)This indicator detects and visualizes multiple types of RSI-based divergences, including Regular, Hidden, and Dual-source (Multi) Bullish/Bearish signals. Not limited with RSI only. You can add move functions and it will automaticly combine your options.
It offers customizable score filtering, label positioning, and visual styling.
Ideal for traders who seek both technical precision and symbolic clarity in their charts.
You have to drag it to your candles after adding to your chart. Then right click on price->Merge all scales to right/left.
ETFs Sector PerformanceDisplays a table of the Top 8 performing ETFs over a selected period (1M / 2M / 3M / 6M) to quickly identify industry strength.
Pre-Set Universe (39 ETFs)
ITA — iShares U.S. Aerospace & Defense ETF
DBA — Invesco DB Agriculture Fund
BOTZ — Global X Robotics & Artificial Intelligence ETF
JETS — U.S. Global Jets ETF
XLB — Materials Select Sector SPDR Fund
XBI — SPDR S&P Biotech ETF
PKB — Invesco Dynamic Building & Construction ETF
ICLN — iShares Global Clean Energy ETF
SKYY — First Trust Cloud Computing ETF
DBC — Invesco DB Commodity Index Tracking Fund
XLY — Consumer Discretionary Select Sector SPDR Fund
XLP — Consumer Staples Select Sector SPDR Fund
BLOK — Amplify Transformational Data Sharing ETF
KARS — KraneShares Electric Vehicles & Future Mobility ETF
XLE — Energy Select Sector SPDR Fund
ESPO — VanEck Video Gaming and eSports ETF
XLF — Financial Select Sector SPDR Fund
PBJ — Invesco Dynamic Food & Beverage ETF
ITB — iShares U.S. Home Construction ETF
XLI — Industrial Select Sector SPDR Fund
PAVE — Global X U.S. Infrastructure Development ETF
PEJ — Invesco Dynamic Leisure & Entertainment ETF
LIT — Global X Lithium & Battery Tech ETF
IHI — iShares U.S. Medical Devices ETF
XME — SPDR S&P Metals & Mining ETF
FCG — First Trust Natural Gas ETF
URA — Global X Uranium ETF
PPH — VanEck Pharmaceutical ETF
QTUM — Defiance Quantum Computing & Machine Learning ETF
IYR — iShares U.S. Real Estate ETF
XRT — SPDR S&P Retail ETF
SOXX — iShares Semiconductor ETF
BOAT — SonicShares Global Shipping ETF
IGV — iShares Expanded Tech-Software Sector ETF
TAN — Invesco Solar ETF
SLX — VanEck Steel ETF
IYZ — iShares U.S. Telecommunications ETF
IYT — iShares U.S. Transportation ETF
XLU — Utilities Select Sector SPDR Fund
Bollinger Breakout A3 updateBollinger Breakout A3 update from LuxAlgo signal
You can try it with some another signal.
All-In-One MA Stack ScalperWhat is this Indicator?
This tool is an advanced, multi-layered breakout and trend-following indicator designed for lower timeframes. It identifies high-conviction buy and sell signals by combining moving average stacking with a suite of professional-grade filters.
How Does It Work?
A signal is generated only when ALL of the following conditions are met:
Moving Average Stack (5M Chart):
Buy: The close price is above all five moving averages (MAs: 100, 48, 36, 24, 12).
Sell: The close price is below all five MAs.
Volatility Filter (ATR):
Signals only print when the current ATR (14) is at least 80% of its 100-period average, ensuring you only trade in actively moving markets.
Candle Structure Filter:
The current candle must have a real body that is at least 35% of the candle’s total range, filtering out dojis and indecision bars.
Big Candle Filter:
The candle’s total range must be at least 40% of the current ATR, avoiding signals on minor, insignificant moves.
Volume Filter:
The current volume must be at least 80% of its 50-period average, filtering out signals during illiquid or quiet market conditions.
Minimum Distance from All MAs:
Price must be a minimum distance (20% ATR) away from each MA, confirming a clean breakout and avoiding signals in tight MA clusters or ranging markets.
RSI Momentum Filter:
Buy: RSI(14) must be greater than 55.
Sell: RSI(14) must be less than 45.
This ensures trades are only taken in the direction of momentum.
ADX Trend Filter:
ADX(14,14) must be above 20, ensuring signals only print in trending conditions (not in chop/range).
Minimum Bars Between Signals:
Only one signal per direction is allowed every 10 bars to avoid overtrading and signal clustering.
What Does This Achieve?
Reduces noise and false signals common in basic MA cross or stack systems.
Captures only strong, high-momentum, and high-conviction moves.
Helps you avoid chop, range, and news whipsaws by combining multiple market filters.
Perfect for advanced scalpers, intraday trend followers, or as a trade filter for algos/EAs.
How to Use It:
Apply to your 5-minute chart.
Green BUY signals: Only when all bullish conditions align.
Red SELL signals: Only when all bearish conditions align.
Use as a stand-alone system or as a filter for your own entries.
Recommended For:
Scalpers & intraday traders who want only the best opportunities.
EA and bot builders seeking reliable signal logic.
Manual traders seeking confirmation of high-probability breakouts.
Tip:
Adjust any of the filters (e.g., RSI/ADX thresholds, minBars, minDist) to make it more/less selective for your style or market.
Ripping and Dipping (Reversal + Trend Signals)Waits for a series of EMAs to be stacking from fastest to slowest for a user input X bars, then signals trend or reversal trades based on a simple close above/below the high/low of the last bar. Designed to catch quick trend trades once strength is confirmed, and quick reversal trades once trend has overextended.
MS - Crypto RSI-Based Trading StrategyThis is a comprehensive trend-following and momentum-based strategy designed for the cryptocurrency market. It combines multiple leading indicators to filter out market noise and generate high-quality buy and sell signals.
Key Indicators:
Moving Average (MA): To determine the main trend direction.
Relative Strength Index (RSI): To measure momentum and identify overbought/oversold conditions.
Directional Movement Index (DMI): To confirm the strength of the trend.
Volume & ATR: To validate market interest and filter out excessive volatility.
Buy Conditions (All Must Be True):
Price and Trend Alignment: The current price is above the MA50 (with a 5% buffer).
Momentum Confirmation: The RSI is between 50 and 70.
Trend Strength: The +DI is greater than the -DI.
Market Interest: Volume is 1.5 times its moving average.
Low Volatility: The ATR is below its average.
Sell Conditions (Any One Is True):
Trend Reversal: The price drops below the MA50 (with a 5% buffer).
Momentum Loss: The RSI drops below 45.
Trend Weakness: The -DI crosses above the +DI.
Market Fatigue: Volume drops below 50% of its moving average.
High Volatility: The ATR is above its average.
Disclaimer: This is a backtesting tool and not financial advice. Past performance is not an indicator of future results. Always use proper risk management and conduct your own research before trading.
