YBL – PAC PREMIUM COMPACT MEDIUM (6 filas, 1 col. derecha)
📑 Document Structure:
Cover Page → YBL logo + Indicator title.
General Description → What the panel is and its purpose.
Row-by-Row Explanation (6 modules):
Volume with Delta
Power vs USD
NY Session
Climax
Trend / Momentum
Correlation
Visual Example → How to interpret values when green, red, or neutral.
Practical Tips → Quick trading rules (e.g., “if strong Δ + Climax rejection = watch for reversal”).
⚡ Now the same question for you:
Do you prefer the PDF in a technical style (with formulas and detailed calculations), or in a practical style (quick guide for traders, with examples and short phrases)?
Indicadores e estratégias
Daily SMA200 Distance – Percentile Zones PROIndicator Description — Weekly/Daily SMA200 Distance – Percentile Zones
The SMA200 Distance – Percentile Zones indicator measures the percentage distance between the price and its 200-period Simple Moving Average (SMA200), and classifies it into historical percentile zones.
This tool helps traders and investors understand the market context of an asset relative to its long-term trend:
Cheap Zone (< P25): price at historically low levels compared to SMA200.
Value Zone (P25–P50): neutral range, where price trades around its long-term average.
Acceptable Zone (P50–P65): moderately high levels, still reasonable within an uptrend.
Not Recommended Zone (P65–P76): overextended territory, with increasing correction risk.
Very Expensive Zone (≥ P76): extreme levels, historically linked to overvaluation and potential market tops.
Percentiles are calculated dynamically from the entire historical dataset (since the SMA200 becomes available), providing a robust and objective statistical framework for decision-making.
✅ In summary:
This indicator works as a quantitative valuation map — showing whether the asset is cheap, fairly valued, acceptable, risky, or very expensive relative to its historical behavior against the SMA200.
RED: MomentumRED: Momentum Panel
This indicator is designed to track the balance of buying and selling pressure in the market and highlight key momentum phases.
It simplifies complex conditions into clear momentum states, helping traders quickly understand whether the market is in a strong zone or transitioning.
- Top zones → when selling pressure reaches extreme levels.
- Bottom zones → when buying pressure reaches extreme levels.
- Momentum Bearish → when momentum shifts down after a strong top.
- Momentum Bullish → when momentum shifts up after a strong bottom.
The panel uses a scoring system in the background to filter noise and show only the dominant side (Buy vs Sell).
Horizontal thresholds make it easy to spot when the market enters or exits extreme conditions.
This tool is not meant to give signals by itself but to provide an intuitive view of where momentum stands right now, top, bottom, bullish, or bearish, at a glance.
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.
Zone Cluster Confluence ProWhat it does
Zone Cluster Confluence Pro automatically finds price “zones” via equal-frequency clustering of HLC3 values and wraps each cluster center with an ATR-based band. Zones are color-coded by a 0–100 Strength % and can optionally highlight confluence with a higher timeframe (HTF) right on your chart.
Key features
• Adaptive Depth by Volatility (ATR regime): zone width scales down in calm markets and widens in volatile regimes.
• Strength % scoring with color mapping (Strong / Work / Mid / Weak). The score blends:
• number of touches (with tolerance),
• dwell time inside the zone (penalized),
• confirmed breakouts (penalized),
• average overshoot beyond the band (penalized),
• recency bonus,
• optional volume-boosted touches (volume > SMA × multiplier).
• HTF Confluence Overlay: computes zones on a higher TF (multiplier of the source TF or a specific TF) and highlights the intersection of LTF zones with the nearest HTF zone (white fill).
• Presets per TF: Aggressive / Stable / Anti-pierce profiles with hand-tuned params for 15/30/60/120/240m; or run fully Manual.
• Clean visuals: centers, borders, filled bands; strength labels with auto-contrast text.
How it works (high level)
• Clustering method: choose K-median or K-means (median/mean of equal-frequency buckets) to place zone centers.
• Zone width = ATR × Depth; Depth becomes Adaptive when the ATR regime deviates from its long SMA.
• Strength % is computed over a lookback window using the components listed above; touches can earn an extra bonus on elevated volume.
Inputs (most useful)
• Source TF: inherit from chart or pick a specific TF.
• Zones (k): 2–5 clusters.
• Presets: Aggressive / Stable / Anti-pierce, or Manual control of Candles Back, ATR length, Depth.
• Adaptive Depth: on/off, regime thresholds & multipliers.
• Strength %: profile (Conservative/Neutral/Optimistic), lookback, breakout/overshoot/touch tolerance.
• Volume boost: SMA length, spike multiplier, weight.
• HTF Confluence: on/off, TF multiplier, HTF preset/method/params, and whether HTF k mirrors LTF k.
Reading the chart
• Zone fills are colored by Strength %:
• 80–100 Strong, 60–80 Work, 40–60 Mid, <40 Weak.
• White fills mark LTF×HTF intersections (confluence areas).
• Strength labels (Z1…Z5) show the current score; label background matches the strength color.
Tips
• Use Stable for most markets, Aggressive for fast intraday, Anti-pierce to reduce whipsaw.
• Turn on HTF confluence to filter LTF zones down to areas aligned with the larger trend structure.
• If you scalp, keep volume boost on; for thin markets consider lowering the spike multiplier.
Notes
• No lookahead is used for HTF data (request.security with lookahead_off).
• Zones update as new bars arrive and as the lookback window rolls; this is not a fixed S/R drawing tool.
• Works on any symbol/timeframe; parameter tuning is encouraged.
Access
This script is Invite-Only.
Disclaimer
For educational purposes only. Not financial advice. Trading involves risk.
Excess Painter — Pin + Engulf + Outside ReversalPurpose:
A clean bar-painter that highlights three high-quality reversal/exhaustion bars only when they’re big enough vs ATR. It helps you see “excess” at important levels without clutter. Optional arrows appear only on the 15-minute chart; all timeframes still paint candle colors.
What it detects
Pin / Excess Tail — Long wick relative to ATR, close near the opposite edge, and a strong wick:body ratio.
Engulfing — Current body engulfs the prior body in the opposite direction, with a minimum body size vs ATR.
Outside Reversal — Range takes out both prior high & low and closes in the reversal direction with a minimum body vs ATR.
Priority (if multiple fire on the same bar): Engulfing → Outside → Pin.
If both bull & bear somehow qualify, the script chooses the side with the larger wick multiple to avoid mixed signals.
Why it’s different
ATR-based size floors (by timeframe): rejects “tiny” bars. You can use a single global floor or TF-specific floors for intraday / Daily / Weekly / Monthly.
MTF-aware: ATR and thresholds adapt to the chart timeframe (5m, 15m, 1H, D, W, M).
Session control: Optional RTH-only filter and “ignore first N bars” on intraday to skip the opening scramble.
Live or confirmed: Choose to paint during the live bar or only after the bar closes.
Cooldown: Prevents rapid back-to-back signals.
Best use
Treat the paint as a heads-up, not an entry by itself. Combine with your process:
Key locations (LIS / VAH / VAL / prior highs/lows)
Context (RVOL, VWAP, higher-timeframe structure)
Entry confirmation (e.g., excess + reclaim at the level)
Settings (quick guide)
Size floors: Use TF-specific size floors (on by default) + per-TF range≥ATR floors.
Raise floors for fewer, stronger signals; lower for more.
Pin: min wick ≥ ATR, close near edge, Wick:Body ratio.
Engulfing / Outside: min body ≥ ATR filters toy bodies.
RTH & Open: RTH only, Ignore first N RTH bars (intraday only).
Arrows: Show arrows (drawn only on 15m).
Live vs close: Paint on live bar.
Cooldown: space out signals.
Supported timeframes
5m, 15m, 1H, Daily, Weekly, Monthly.
(Arrows draw only on 15m; candle colors paint on all.)
Tuning tips
Too many signals? Raise per-TF size floors; increase min body ≥ ATR; tighten Pin’s wick thresholds; increase Cooldown.
Too few? Lower size floors a notch; relax body/min-wick thresholds; allow live painting.
Disclaimer: This tool is for educational/informational use. Not investment advice. Always test and confirm with your own risk management and trade plan.
Cascades & Sloped Lines (RU) • v6How it works
• The base trendline is built from the last two confirmed pivot lows (uptrend) and/or pivot highs (downtrend).
• “Cascades” are a set of parallel lines above and below the base line, spaced equally: either ATR × multiplier or a fixed percentage of price.
• Lines are automatically rebuilt when a new confirmed pivot appears. To avoid overloading the chart, old lines are removed.
Useful settings
• Increase Pivot Left/Right if you want “larger” swing points.
• Switch the step mode to Percent if you want a fixed distance.
• Adjust Lines Above/Below to get a “dense” or “sparse” cascade.
• Colors and thickness — match them to your style.
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.
NY Internal & External Liquidity v2 kingNY Liquidity Sweeps
See where New York hunts.
AM & PM session boxes.
Buyside / Sellside liquidity lines.
Internal vs. External sweeps.
⚡ Catch the traps. Follow the grabs. Trade where the money hides.
Pivot Up & Down range - Máximos y Mínimos de RangoUps and downs from range 3 to 10 candles. Highs are marked with a red arrow and lows with a green one.
Sigmax - AI Sniper v1.0💵 Sigmax – Signal Maximizer Indicator 💵
Sigmax (AI Sniper v1.0) is an advanced trading indicator powered by AI, designed to optimize entry and exit points. It combines multiple signal models and strategies, giving traders flexibility in different market conditions.
🔑 Key Features:
AI Swing Signal – Mid-term Buy/Sell signals with customizable TP/SL for risk management.
AI Sniper Signal – High-accuracy short-term signals, ideal for scalping and fast trades.
AI Reversal Signal – Detects potential trend reversals to secure exits or capture turning points.
AI Order Block Signal – Identifies accumulation/distribution order block zones for strategic entries.
AI Trend Line Signal – Automatically draws and alerts based on trendline setups.
AI Quantitative Signal – Uses RSI + quantitative filters to detect short-term breakouts/reversals.
AI Miracle Signal – A hybrid signal combining multiple algorithms, suited for volatile markets.
Risk Signal – Alerts when signals carry higher risk.
📊 Trend & Money Management:
Magic Trend & Miracle Trend: AI-powered trend detection for reliable market direction.
Support & Resistance Zones: Auto-detects strong and weak levels with customizable sensitivity.
Trend Channel: Automatically plots market channels to track price movements.
Scalp & Swing TP/SL: Multiple Take Profit & Stop Loss levels (based on % or distance) for capital protection.
⚙️ Flexible Settings:
Adjustable AI signal length, RSI, and quantitative thresholds.
Enable/disable individual signal types.
Customizable colors for uptrend, downtrend, and S/R zones.
Risk allocation by percentage for effective money management.
Planète_finance Day Trading 5m - Signal Achat/Vente EMA
Keep the original logic (EMA 8 & EMA 15 crossover on 5m).
Filter with the EMA200 trend on 1h (to stay in the direction of the market).
Add a simple relative volume filter to avoid false signals.
Display BUY when bullish conditions are met and SELL when bearish conditions are met.
No over-complexity (RSI, cooldown, etc.) → that can be added later if you want to refine.
BIST/TL_Volume_ScreenBIST/TL Volume Screening Tool
This script is designed to help traders analyze and screen BIST stocks based on volume conditions and moving average comparisons.
Custom Group Selection:
Choose from predefined BIST groups (BIST-1 to BIST-15) or create your own custom watchlist (“MY LIST”).
Flexible Screening Options:
Screen stocks by higher or lower volume relative to moving averages.
Adjustable screening types: Higher_Vol, Higher_Volx2, Higher_Volx3, Lower_Vol, Lower_Volx2, Lower_Volx3.
Define both the number of bars to check and the minimum conditions required.
Moving Averages:
Daily and weekly volume moving averages are plotted.
Adjustable MA lengths for flexibility:
Real-Time Volume Extrapolation
Estimates end-of-day volume during live sessions based on intraday patterns, allowing traders to anticipate final daily volume.
Pocket Pivot Detection:
Highlights potential pocket pivot signals when the stock trades near its 10-day or 50-day moving averages with strong volume.
Full Customization:
Adjustable bar width, proximity conditions, and pocket pivot length.
Ability to screen multiple stocks (up to 40 in the custom list)
How to Use:
Select the group of BIST stocks you want to analyze.
Set your preferred timeframe (Daily, Weekly, or Monthly).
Define your screening conditions for volume.
Review signals on the chart:
Histogram bars for volume.
Red line for moving average.
Green diamonds marking pocket pivot opportunities
Lanxang V6 – Trend FollowingLanxang V6 – Trend Following
The Lanxang V6 is a clean and simple trend-following tool that helps traders stay aligned with the market’s direction and catch key momentum shifts.
🔑 Features
- Trend Direction – The system colors moving averages and chart areas to make bullish and bearish trends easy to spot at a glance.
- Clear Buy/Sell Tags – When the market shifts direction, the indicator plots Buy or Sell tags directly on the chart for quick confirmation.
- Pullback Highlights – Bars are marked to signal potential continuation setups during trending conditions.
- Custom Visuals – Traders can adjust tag size, padding, and colors to match their chart style.
- Alerts – Real-time alerts for Buy/Sell signals keep you notified of trend changes without watching the screen all the time.
📈 How to Use
- Follow the Trend: Use the trend color as your main directional bias (green for bullish, red for bearish).
- Entry Signals: Take Buy/Sell tags as confirmation points when the trend shifts.
- Pullback Opportunities: Highlighted bars may indicate continuation trades within the existing trend.
- Risk Management: Always confirm with your own analysis and manage risk properly.
⚠️ Disclaimer: This tool is for educational purposes only and does not guarantee results. Always test on demo before applying to live trading.
Lao Version below:
Lanxang V6 ແມ່ນເຄື່ອງມື ຕິດຕາມແນວໂນ້ມ ທີ່ອອກແບບມາໃຫ້ຊ່ວຍນັກລົງທຶນມອງເຫັນທິດທາງຂອງຕະຫຼາດ ແລະ ຈັບໂອກາດໃນການເຄື່ອນໄຫວສໍາຄັນໄດ້ຊັດເຈນຂຶ້ນ.
🔑 ຄຸນນະສົມບັດ
- ການກໍານົດແນວໂນ້ມ – ລະບົບຈະສະແດງສີເສັ້ນ Moving Average ແລະ ພື້ນຫຼັງໃນການຊັດເຈນທັນທີ (ຂຽວ = ແນວໂນ້ມຂຶ້ນ, ແດງ = ແນວໂນ້ມລົງ).
- ສັນຍານ Buy/Sell ຊັດເຈນ – ເມື່ອຕະຫຼາດປ່ຽນທິດທາງ ໂຕຊີ້ Buy ຫຼື Sell ຈະປາກົດໃນກາຟ.
- ການເນັ້ນແທ່ງ Pullback – ກ່ອນຈະໄປຕໍ່ແນວໂນ້ມ ບາງແທ່ງຈະຖືກເນັ້ນເພື່ອໃຫ້ເຫັນໂອກາດໃນການເຂົ້າ.
- ການປັບແຕ່ງຮູບແບບ – ປັບຂະໜາດ ແລະ ສີຂອງສັນຍານໄດ້ຕາມຄວາມຕ້ອງການ.
- Alert ແບບ Real-time – ຮັບແຈ້ງເຕືອນທັນທີເມື່ອມີສັນຍານ Buy/Sell.
📈 ວິທີໃຊ້
- ຕິດຕາມແນວໂນ້ມ: ໃຊ້ສີຂອງເສັ້ນເພື່ອກໍານົດທິດທາງ (ຂຽວ = ຂຶ້ນ, ແດງ = ລົງ).
- ສັນຍານເຂົ້າ: ຕິດຕາມສັນຍານ Buy/Sell ທີ່ປາກົດໃນກາຟ.
- ໂອກາດ Pullback: ແທ່ງທີ່ເນັ້ນອາດຈະບອກໂອກາດໃນການເຂົ້າຕໍ່ຕາມແນວໂນ້ມ.
- ຈັດການຄວາມສ່ຽງ: ຢ່າລືມກວດສອບກັບການວິເຄາະຂອງຕົນເອງ ແລະ ຈັດການຄວາມສ່ຽງໃຫ້ດີ.
⚠️ ຄໍາເຕືອນ: ເຄື່ອງມືນີ້ເປັນໄວ້ໃຊ້ເພື່ອການສຶກສາ ແລະ ບໍ່ຮັບປະກັນຜົນກໍາໄລ. ກ່ອນນໍາໃຊ້ໃນບັນຊີຈິງ ຄວນທົດສອບໃນ Demo ກ່ອນ.
Chanlun clmacd MACDThe commonly used MACD version in China has default parameters of 12, 26, 9. It is slightly different from the built-in MACD on the official TradingView website but generally similar. This MACD version is tailored to the usage habits of domestic users and is mainly designed to be used in conjunction with my Chanlun Theory indicators.
国内常用的macd版本，默认参数12,26,9，跟tradingview官网自带的有些不同，总体差不多，适合国内用户习惯的版本的macd,主要是配套我这边缠论指标使用
VIX Trend Indicator
VIX with Histogram Colors
VIX means Market Fear
Rising VIX means raising Fear in Market > Time to Sell Options
Supporting Indicator Stochastic NCO from Over Bought Zone
RSI NCO from RSI < 60
Falling VIX Means falling Fear in Markets > Time to Buy Options
Supporting Indicator Stochastic PCO from Over SOLD Zone
RSI PCO from RSI > 40
PİRAMİT & ELMAS FORMASYONU
The Pyramid Diamond Formation indicator is a special pattern in technical analysis that aims to interpret price movements within a geometric structure. Its name comes from the fact that prices both create pyramid-like stepwise upward or downward trends and form diamond-shaped symmetrical consolidations at peak or bottom regions.
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.
SMA ProjectionWhat it does
Draws a linear projection of a Simple Moving Average (SMA) 20 bars into the future using the SMA’s recent slope. Optionally shows a tiny momentum flag (just a number) positioned 0.75× ATR below the SMA on the last bar. No future data is read; everything updates on the current bar only.
How it works
SMA: Standard SMA on your chosen source and length.
Projection (fixed 20 bars): Uses a linear extrapolation from the last SMA value with slope
slope = (ma - ma ) / slopeLen
Momentum magnitude (optional): A signed number where >0 = up-slope, <0 = down-slope, ~0 = flat. Units are selectable: price/bar, %/bar, or ATR/bar (default). The flag is rendered small and colored teal (pos) / red (neg) / gray (flat).
Key features
Fixed 20-bar projection (no input—keeps it simple and comparable).
Tiny numeric momentum flag (off by default) placed well below the line (0.75× ATR).
Unit choices for momentum: price/bar, %/bar, ATR/bar.
Deadband option to zero-out tiny slopes.
Non-repainting projection: drawn only on the last bar; updates each candle.
Inputs (summary)
SMA length and Source
Slope lookback (for magnitude)
Show momentum flag (default: Off)
Magnitude units: price/bar, %/bar, ATR/bar (default)
Deadband and Decimals for display control
Tips
For smoother projections, increase slope lookback; for responsiveness, decrease it.
Use ATR/bar or %/bar if you want momentum values that are more comparable across symbols and timeframes.
The projection is indicative, not predictive—combine with structure, volume, and risk management.
Notes & limits
The “future” line is just a linear extrapolation from recent behavior; regime shifts will break linearity.
The momentum flag text is intentionally minimal to avoid chart clutter.
Works on any timeframe; the projection distance is always 20 bars on that timeframe.
Tags: SMA, moving average, projection, slope, momentum, ATR, extrapolation, non-repainting, trading tools
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.
RSI with KAMA and Custom Buy/Sell SignalsUses Kaufman MA on the RSI to generate signals when crossing user thresholds
