Volume Surge by MashrabThe "Volume Surge" indicator is like a simple market health checkup. It looks at how much of an asset (like a stock or crypto) is being traded right now and compares it to the recent past. Think of it as a way to quickly see if interest in that asset is suddenly spiking, fading, or staying the same.
The indicator shows this information in an easy-to-read table right on your chart.
How it works:
The indicator keeps track of two main things for you:
Current Volume: The total trading volume over the last "N" days (or whatever time period you choose).
Previous Volume: The total trading volume over the period right before that
Then, it gives you a summary:
The "Ratio" tells you how many times bigger or smaller the current volume is.
The "Percent Change" shows the percentage jump in volume.
How to use it:
This indicator helps you see when something interesting might be happening. Here are a few ways traders use it:
Confirm Breakouts: If a stock breaks above a key price level and the indicator shows a huge volume surge, it’s a stronger signal that the move is real and not a false alarm.
Spot Reversals: If a stock has been trending up but the volume starts to drop off, it could mean the trend is losing steam. A sudden, massive volume surge on a down day might indicate panic selling, which can sometimes happen right before the price turns around.
Check Trend Strength: A healthy trend usually has increasing volume going in the same direction. For example, if a stock is in an uptrend, you want to see lots of volume on the days it goes up.
This indicator isn't a crystal ball, but it's a great tool for understanding the "who" and "how much" behind a price move. It helps you see when a price change is backed by a lot of market activity, which often makes the move more trustworthy.
指標和策略
ChartWise Pro🎯 What is ChartWise Pro?
ChartWise Pro is a comprehensive TradingView Pine Script indicator (Version 6) that combines multiple technical analysis tools into one powerful trading system. It helps traders identify high-probability entry and exit points through automated signal detection.
🔧 Core Components:
1. Moving Average Crossover System 📈
Fast MA (Default: 9 periods) - Blue line
Slow MA (Default: 50 periods) - Orange line
Fully customizable periods (1-200)
What it detects:
✅ Golden Cross = Fast MA crosses ABOVE Slow MA = BUY Signal (Green arrow below price)
❌ Death Cross = Fast MA crosses BELOW Slow MA = SELL Signal (Red arrow above price)
2. Support & Resistance Levels 🎯
Auto-detects pivot points using 5-bar lookback
Dynamic S/R lines that update in real-time
Dashed lines for easy visual identification
Color-coded:
🟢 Green = Support levels
🔴 Red = Resistance levels
3. Built-in Alert System 🔔
Audio notifications when signals trigger
Once per bar alert frequency (no spam)
Can be disabled via settings
NY, Asia & London Session Lines + NY First HourEUR/USD last session OHLC Asia + London and first hour NY. defaults to last session if market closed. publishing to save for my self, nothing groundbreaking
Dow Jones Trading System with PivotsThis TradingView indicator, tailored for the 30-minute Dow Jones (^DJI) chart, supports DIA options trading with a trend-following approach. It features a 30-period SMA (blue) and a 60-period SMA (red), with an optional 90-period SMA (orange) drawn from rauItrades' Dow SMA outfit. A bullish crossover (30 SMA > 60 SMA) displays a green "BUY" triangle below the bar for potential DIA longs, while a bearish crossunder (30 SMA < 60 SMA) shows a red "SELL" triangle above for shorts or exits. The background turns green (bullish) or red (bearish) to indicate trend bias. Pivot points highlight recent highs (orange circles) and lows (purple circles) for support/resistance, using a 5-bar lookback. Alerts notify for crossovers.
Triple EMA (5, 8, 13) + Confirmed Alerts with SoundThis indicator uses three Exponential Moving Averages (EMA 5, 8, and 13) to generate buy and sell signals when the EMAs are properly aligned and not touching. Signals are confirmed on candle close and can trigger customizable sound alerts directly from the TradingView alert panel.
DAX Zonen Ergänzungen (Pro Signale + EMAs mit Filter RSI MACD)📊 DAX Zones Enhancements (Pro Signals + EMA with RSI & MACD Filter)
Description:
This indicator enhances DAX trading analysis by combining dynamic support/resistance zones with professional-level signal filters. It automatically detects potential buy and sell zones and confirms them using EMA trends, RSI conditions, and MACD momentum.
Key features:
🔹 Visual display of DAX high- and low-price zones
🔹 EMA-based trend confirmation
🔹 RSI and MACD filters to reduce false signals
🔹 Customizable alerts when price interacts with key zones
🔹 Works on multiple timeframes
Ideal for traders who want a clean, rule-based approach to identifying high-probability entries and exits on the DAX index.
FCBI Brake PressureBrake Pressure (FCBI − USIRYY)
Concept
The Brake Pressure indicator quantifies whether the bond market is braking or releasing liquidity relative to real yields (USIRYY).
It is derived from the Financial-Conditions Brake Index (FCBI) and expresses the balance between long-term yield pressure and real-rate dynamics.
Formula
Brake Pressure = FCBI − USIRYY
where FCBI = (US10Y) − (USINTR) − (CPI YoY)
Purpose
While FCBI measures the intensity of financial-condition pressure, Brake Pressure shows when that brake is being applied or released.
It captures the turning point of liquidity transmission in the financial system.
How to Read
Brake Pressure < 0 (orange) → Brake engaged → financial conditions tighter than real-rate baseline; liquidity constrained.
Brake Pressure ≈ 0 → Neutral zone → transition phase between tightening and easing.
Brake Pressure > 0 (teal) → Brake released → financial conditions looser than real-rate baseline; liquidity flows freely → late-cycle setup before recession.
Zero-Cross Logic
Cross ↑ above 0 → FCBI > USIRYY → brake released → liquidity acceleration → typically 6–18 months before recession.
Cross ↓ below 0 → FCBI < USIRYY → brake re-engaged → tightening resumes.
Historical Behavior
Each major U.S. recession (2001, 2008, 2020) was preceded by a Brake Pressure cross above zero after a negative phase, signaling that long yields had stopped resisting Fed cuts and liquidity was expanding.
Practical Use
• Identify late-cycle turning points and liquidity inflection phases.
• Combine with FCBI for a complete macro transmission picture.
• Watch for sustained positive readings as early macro-recession warnings.
Current Example (Oct 2025)
FCBI ≈ −3.1, USIRYY ≈ +3.0 → Brake Pressure ≈ −6.1 → Brake still engaged. When this crosses above 0, it signals that liquidity is free flowing and the recession countdown has begun.
Summary
FCBI shows how tight the brake is. Brake Pressure shows when the brake releases.
When Brake Pressure > 0, the system has entered the liquidity-expansion phase that historically precedes a U.S. recession.
10 EMA10 ema + color change
35
70
140
420
840
1400
2100
2940
3150
4725
I created this script for use in different chart layouts. I modified it to use the colors and EMA numbers I'm currently using.
TTM Squeeze Pro - IntradayTTM Squeeze Pro – Intraday (AI MTF Edition)
Design Rationale
This indicator is built to help traders identify when markets are consolidating, when volatility is building (squeeze), and when a breakout or trend is starting — all across multiple timeframes.
The design combines three powerful ideas:
Volatility Compression & Expansion (TTM Squeeze Logic):
By comparing Bollinger Bands (BB) and Keltner Channels (KC), the indicator detects when volatility contracts (BB inside KC). These moments often precede explosive moves. White dots on the BB basis line mark these “squeeze” periods.
Trend Strength & Direction (ADX System):
The ADX (Average Directional Index) measures how strong a trend is.
ADX rising above the threshold → trending market.
ADX falling below the threshold → consolidation.
The system classifies each bar as Trending Up, Trending Down, Consolidating, or Neutral, depending on ADX and momentum direction.
Multi-Timeframe (MTF) Alignment:
The same logic is applied to several timeframes (1m, 3m, 5m, 15m, 30m, 1h).
A compact table at the top-right shows each timeframe’s trend and squeeze strength.
This helps traders see whether short-term and higher timeframes are aligned, improving trade confidence and timing.
The AI Enhancer automatically adjusts all parameters (ADX, BB, KC lengths, and thresholds) depending on the current chart timeframe, keeping signals consistent between scalping and swing trading setups.
Trend and squeeze strengths are normalized on a 1–9 scale, giving users a quick numerical sense of trend power and squeeze intensity. The design emphasizes clarity, speed, and adaptability — critical for intraday trading decisions.
How to Use
Identify a Squeeze Setup:
Look for white dots on the chart — this marks low volatility and potential energy buildup.
Wait for Breakout Confirmation:
When the white dots disappear, volatility expands.
Check the MTF table — if multiple timeframes show green (uptrend) or red (downtrend) in the “TR” column, momentum is aligning.
Enter the Trade:
Go long if breakout happens above BB basis and most timeframes show green.
Go short if breakout happens below BB basis and most timeframes show red.
Exit or Manage Position:
When new white dots appear → volatility contracting again → consider exiting or tightening stops.
If MTF colors become mixed → trend losing strength.
In Summary
The TTM Squeeze Pro – Intraday AI MTF Indicator blends volatility analysis, trend strength, momentum, and multi-timeframe alignment into one adaptive tool.
Its design aims to simplify complex market behavior into a visual, data-backed format — enabling traders to catch high-probability breakout trends early and avoid false moves during low-volatility phases.
st 47Усредненный Ишимоку (Custom: 9/48/96) [V6]st47 — Volume in Clouds
This indicator is a custom Ichimoku Cloud modification that dynamically reacts to market volume.
The color intensity of the Kumo (cloud) changes depending on the current trading volume — brighter clouds indicate stronger activity, while dimmer ones reflect low participation.
Key Features:
• Based on the Ichimoku Cloud system (8/48/96 settings)
• Volume-sensitive cloud visualization
• Works on any timeframe and pair
• Supports multi-ticker averaging (BTCUSDT, BTCUSDT.P, etc.)
• Displays additional volume histogram below the chart
Purpose:
Helps visualize both trend structure and the strength behind it by combining Ichimoku logic with real-time volume dynamics.
FDF — EMAs+VWAP with setup & entry (stable scale) - Final 9
21
vwap
entry system
90% candle
tend
This will help you find the perfect entry off the 9 and 21 using the vwap for confluence. We have a strick 90% candle or wick off the 21
We have wick on the entry side more than 30% of the candle
Ehlers Autocorrelation Periodogram (EACP)# EACP: Ehlers Autocorrelation Periodogram
## Overview and Purpose
Developed by John F. Ehlers (Technical Analysis of Stocks & Commodities, Sep 2016), the Ehlers Autocorrelation Periodogram (EACP) estimates the dominant market cycle by projecting normalized autocorrelation coefficients onto Fourier basis functions. The indicator blends a roofing filter (high-pass + Super Smoother) with a compact periodogram, yielding low-latency dominant cycle detection suitable for adaptive trading systems. Compared with Hilbert-based methods, the autocorrelation approach resists aliasing and maintains stability in noisy price data.
EACP answers a central question in cycle analysis: “What period currently dominates the market?” It prioritizes spectral power concentration, enabling downstream tools (adaptive moving averages, oscillators) to adjust responsively without the lag present in sliding-window techniques.
## Core Concepts
* **Roofing Filter:** High-pass plus Super Smoother combination removes low-frequency drift while limiting aliasing.
* **Pearson Autocorrelation:** Computes normalized lag correlation to remove amplitude bias.
* **Fourier Projection:** Sums cosine and sine terms of autocorrelation to approximate spectral energy.
* **Gain Normalization:** Automatic gain control prevents stale peaks from dominating power estimates.
* **Warmup Compensation:** Exponential correction guarantees valid output from the very first bar.
## Implementation Notes
**This is not a strict implementation of the TASC September 2016 specification.** It is a more advanced evolution combining the core 2016 concept with techniques Ehlers introduced later. The fundamental Wiener-Khinchin theorem (power spectral density = Fourier transform of autocorrelation) is correctly implemented, but key implementation details differ:
### Differences from Original 2016 TASC Article
1. **Dominant Cycle Calculation:**
- **2016 TASC:** Uses peak-finding to identify the period with maximum power
- **This Implementation:** Uses Center of Gravity (COG) weighted average over bins where power ≥ 0.5
- **Rationale:** COG provides smoother transitions and reduces susceptibility to noise spikes
2. **Roofing Filter:**
- **2016 TASC:** Simple first-order high-pass filter
- **This Implementation:** Canonical 2-pole high-pass with √2 factor followed by Super Smoother bandpass
- **Formula:** `hp := (1-α/2)²·(p-2p +p ) + 2(1-α)·hp - (1-α)²·hp `
- **Rationale:** Evolved filtering provides better attenuation and phase characteristics
3. **Normalized Power Reporting:**
- **2016 TASC:** Reports peak power across all periods
- **This Implementation:** Reports power specifically at the dominant period
- **Rationale:** Provides more meaningful correlation between dominant cycle strength and normalized power
4. **Automatic Gain Control (AGC):**
- Uses decay factor `K = 10^(-0.15/diff)` where `diff = maxPeriod - minPeriod`
- Ensures K < 1 for proper exponential decay of historical peaks
- Prevents stale peaks from dominating current power estimates
### Performance Characteristics
- **Complexity:** O(N²) where N = (maxPeriod - minPeriod)
- **Implementation:** Uses `var` arrays with native PineScript historical operator ` `
- **Warmup:** Exponential compensation (§2 pattern) ensures valid output from bar 1
### Related Implementations
This refined approach aligns with:
- TradingView TASC 2025.02 implementation by blackcat1402
- Modern Ehlers cycle analysis techniques post-2016
- Evolved filtering methods from *Cycle Analytics for Traders*
The code is mathematically sound and production-ready, representing a refined version of the autocorrelation periodogram concept rather than a literal translation of the 2016 article.
## Common Settings and Parameters
| Parameter | Default | Function | When to Adjust |
|-----------|---------|----------|---------------|
| Min Period | 8 | Lower bound of candidate cycles | Increase to ignore microstructure noise; decrease for scalping. |
| Max Period | 48 | Upper bound of candidate cycles | Increase for swing analysis; decrease for intraday focus. |
| Autocorrelation Length | 3 | Averaging window for Pearson correlation | Set to 0 to match lag, or enlarge for smoother spectra. |
| Enhance Resolution | true | Cubic emphasis to highlight peaks | Disable when a flatter spectrum is desired for diagnostics. |
**Pro Tip:** Keep `(maxPeriod - minPeriod)` ≤ 64 to control $O(n^2)$ inner loops and maintain responsiveness on lower timeframes.
## Calculation and Mathematical Foundation
**Explanation:**
1. Apply roofing filter to `source` using coefficients $\alpha_1$, $a_1$, $b_1$, $c_1$, $c_2$, $c_3$.
2. For each lag $L$ compute Pearson correlation $r_L$ over window $M$ (default $L$).
3. For each period $p$, project onto Fourier basis:
$C_p=\sum_{n=2}^{N} r_n \cos\left(\frac{2\pi n}{p}\right)$ and $S_p=\sum_{n=2}^{N} r_n \sin\left(\frac{2\pi n}{p}\right)$.
4. Power $P_p=C_p^2+S_p^2$, smoothed then normalized via adaptive peak tracking.
5. Dominant cycle $D=\frac{\sum p\,\tilde P_p}{\sum \tilde P_p}$ over bins where $\tilde P_p≥0.5$, warmup-compensated.
**Technical formula:**
```
Step 1: hp_t = ((1-α₁)/2)(src_t - src_{t-1}) + α₁ hp_{t-1}
Step 2: filt_t = c₁(hp_t + hp_{t-1})/2 + c₂ filt_{t-1} + c₃ filt_{t-2}
Step 3: r_L = (M Σxy - Σx Σy) / √
Step 4: P_p = (Σ_{n=2}^{N} r_n cos(2πn/p))² + (Σ_{n=2}^{N} r_n sin(2πn/p))²
Step 5: D = Σ_{p∈Ω} p · ĤP_p / Σ_{p∈Ω} ĤP_p with warmup compensation
```
> 🔍 **Technical Note:** Warmup uses $c = 1 / (1 - (1 - \alpha)^{k})$ to scale early-cycle estimates, preventing low values during initial bars.
## Interpretation Details
- **Primary Dominant Cycle:**
- High $D$ (e.g., > 30) implies slow regime; adaptive MAs should lengthen.
- Low $D$ (e.g., < 15) signals rapid oscillations; shorten lookback windows.
- **Normalized Power:**
- Values > 0.8 indicate strong cycle confidence; consider cyclical strategies.
- Values < 0.3 warn of flat spectra; favor trend or volatility approaches.
- **Regime Shifts:**
- Rapid drop in $D$ alongside rising power often precedes volatility expansion.
- Divergence between $D$ and price swings may highlight upcoming breakouts.
## Limitations and Considerations
- **Spectral Leakage:** Limited lag range can smear peaks during abrupt volatility shifts.
- **O(n²) Segment:** Although constrained (≤ 60 loops), wide period spans increase computation.
- **Stationarity Assumption:** Autocorrelation presumes quasi-stationary cycles; regime changes reduce accuracy.
- **Latency in Noise:** Even with roofing, extremely noisy assets may require higher `avgLength`.
- **Downtrend Bias:** Negative trends may clip high-pass output; ensure preprocessing retains signal.
## References
* Ehlers, J. F. (2016). “Past Market Cycles.” *Technical Analysis of Stocks & Commodities*, 34(9), 52-55.
* Thinkorswim Learning Center. “Ehlers Autocorrelation Periodogram.”
* Fab MacCallini. “autocorrPeriodogram.R.” GitHub repository.
* QuantStrat TradeR Blog. “Autocorrelation Periodogram for Adaptive Lookbacks.”
* TradingView Script by blackcat1402. “Ehlers Autocorrelation Periodogram (Updated).”
Sonic R+EMA PYTAGOYou must determine the supply and demand zone as ema34, ema89, ema200, ema610. Then open the long position or the short position with SL and TP.
Ehlers Ultrasmooth Filter (USF)# USF: Ultrasmooth Filter
## Overview and Purpose
The Ultrasmooth Filter (USF) is an advanced signal processing tool that represents the pinnacle of noise reduction technology for financial time series. Developed by John Ehlers, this filter implements a complex algorithm that provides exceptional smoothing capabilities while minimizing the lag typically associated with heavy filtering. USF builds upon the Super Smooth Filter (SSF) with enhanced noise suppression characteristics, making it particularly valuable for identifying clear trends in extremely noisy market conditions where even traditional smoothing techniques struggle to produce clean signals.
## Core Concepts
* **Maximum noise suppression:** Provides the highest level of noise reduction among Ehlers' filter designs
* **Optimized coefficient structure:** Uses carefully designed mathematical relationships to achieve superior filtering performance
* **Market application:** Particularly effective for long-term trend identification and minimizing false signals in highly volatile market conditions
The core innovation of USF is its second-order filter structure with optimized coefficients that create an exceptionally smooth frequency response. By careful mathematical design, USF achieves near-optimal noise suppression characteristics while minimizing the lag and waveform distortion that typically accompany such heavy filtering. This makes it especially valuable for identifying major market trends amid significant short-term volatility.
## Common Settings and Parameters
| Parameter | Default | Function | When to Adjust |
|-----------|---------|----------|---------------|
| Length | 20 | Controls the cutoff period | Increase for smoother signals, decrease for more responsiveness |
| Source | close | Price data used for calculation | Consider using hlc3 for a more balanced price representation |
**Pro Tip:** USF is ideal for defining major market trends - try using it with a length of 40-60 on daily charts to identify dominant market direction and ignoring shorter-term noise completely.
## Calculation and Mathematical Foundation
**Simplified explanation:**
The Ultrasmooth Filter creates an extremely clean price representation by combining current and past price data with previous filter outputs using precisely calculated mathematical relationships. This creates a highly effective "averaging" process that removes virtually all market noise while still maintaining the essential trend information.
**Technical formula:**
USF = (1-c1)X + (2c1-c2)X₁ - (c1+c3)X₂ + c2×USF₁ + c3×USF₂
Where coefficients are calculated as:
- a1 = exp(-1.414π/length)
- b1 = 2a1 × cos(1.414 × 180/length)
- c1 = (1 + c2 - c3)/4
- c2 = b1
- c3 = -a1²
> 🔍 **Technical Note:** The filter combines both feed-forward (X terms) and feedback (USF terms) components in a second-order structure, creating a response with exceptional roll-off characteristics and minimal passband ripple.
## Interpretation Details
The Ultrasmooth Filter can be used in various trading strategies:
* **Major trend identification:** The direction of USF indicates the dominant market trend with minimal noise interference
* **Signal generation:** Crossovers between price and USF generate high-reliability trade signals with minimal false positives
* **Support/resistance levels:** USF can act as strong dynamic support during uptrends and resistance during downtrends
* **Market regime identification:** The slope of USF helps identify whether markets are in trending or consolidation phases
* **Multiple timeframe analysis:** Using USF across different chart timeframes creates a cohesive picture of nested trend structures
## Limitations and Considerations
* **Significant lag:** The extreme smoothing comes with increased lag compared to lighter filters
* **Initialization period:** Requires more bars than simpler filters to stabilize at the start of data
* **Less suitable for short-term trading:** Generally too slow-responding for short-term strategies
* **Parameter sensitivity:** Performance depends on appropriate length selection for the timeframe
* **Complementary tools:** Best used alongside faster-responding indicators for timing signals
## References
* Ehlers, J.F. "Cycle Analytics for Traders," Wiley, 2013
* Ehlers, J.F. "Rocket Science for Traders," Wiley, 2001
NY Midnight High/Low Arrows (Auto-Show)🇺🇸 English Explanation
This indicator automatically marks the daily high and low of the New York session.
It draws arrows (▼▲) at the highest and lowest prices after New York midnight (00:00),
and can optionally display small horizontal dotted lines at those levels.
It helps traders identify daily liquidity zones and key turning points in price action.
🇸🇦 الشرح بالعربية
هذا المؤشر يحدد القمة والقاع اليومية لجلسة نيويورك بشكل تلقائي.
يرسم أسهماً (▼▲) عند أعلى وأدنى سعر بعد منتصف الليل بتوقيت نيويورك (00:00)،
ويمكنه أيضًا عرض خطوط أفقية منقطة صغيرة عند تلك المستويات.
يساعد المتداول في معرفة مناطق السيولة اليومية ونقاط الانعكاس المهمة في حركة السعر.
COT Index Indicator 1) One‑liner
My version of the OTC COT Index indicator: a 0–120 oscillator built from CFTC COT data that shows where Commercial, Noncommercial, and Nonreportable net positions sit relative to recent extremes.
2) Short paragraph
This is my version of the OTC COT Index indicator. It converts CFTC Commitments of Traders (COT) net positions into a normalized 0–120 oscillator for each trader group—Commercials, Noncommercials, and Nonreportables—so you can quickly see when positioning is near recent highs or lows. Data comes from TradingView’s official COT library and supports both “Futures Only” and “Futures and Options” reports.
3) Compact bullets
What: My version of the OTC COT Index indicator
Why: Quickly spot when trader groups are near positioning extremes
Data: CFTC COT via TradingView/LibraryCOT/2; Futures Only or Futures & Options
How: Index = 120 × (Current − Min) ÷ (Max − Min) over a configurable lookback
Plots: Commercials (blue), Noncommercials (orange), Nonreportables (red)
Lines: Overbought, Midline, Oversold, optional 0/100, upper/lower bounds
Note: Values are relative to the chosen window; not trading advice
4) Publication‑ready (sections)
Overview
My version of the OTC COT Index indicator. It turns CFTC COT positioning into a 0–120 oscillator per trader group (Commercials, Noncommercials, Nonreportables) to highlight relative extremes.
Data source
CFTC Commitments of Traders via TradingView’s official library (TradingView/LibraryCOT/2).
Supports “Futures Only” and “Futures and Options.”
Method
Net positions = Longs − Shorts.
Index = 120 × (Current Net − Min(Net, Lookback)) ÷ (Max(Net, Lookback) − Min(Net, Lookback)).
Inputs
Weeks Look Back (normalization window)
Weeks Look Back for Historical Hi/Los (longer reference)
Report Type selection
Visuals
Three indexes by trader group, plus reference levels (OB/OS, Midline, optional 0/100).
Notes
Some symbols map to specific CFTC codes for reliability.
If no relevant COT data exists for the symbol, the script reports it clearly.
If you want this adapted to a specific platform’s character limits (e.g., TradingView’s publish dialog), tell me the target length and I’ll trim it to fit.
MarketMonkey-Indicator-Set-1 - GMMA open 🧠 MarketMonkey-Indicator-Set-1 — GMMA Open
GMMA (Guppy Multiple Moving Average) Toolkit for Trend Clarity & Timing
The MarketMonkey GMMA Open indicators brings a clean, high-performance visual of trend strength and direction using multiple exponential moving averages (EMAs) across short- and long-term time frames.
Designed for traders who want to see momentum shifts and market transitions as they happen, this version overlays directly on the price chart for quick and confident reads.
🔍 How It Works
* Short-term EMAs (3–15) track trader sentiment and momentum.
* Long-term EMAs (30–60) show investor trend commitment.
* The indicator dynamically colors the long-term EMAs:
* 🔵 Blue : Upward momentum
* 🔴 Red : Downward momentum
When the short-term group expands above the long-term group, it signals strength and potential continuation. Tightening or compression may warn of pauses or reversals.
💡 Features
* 12 adjustable EMA periods (customize your GMMA spacing)
* Automatic color shifts for trend clarity
* Live price flag for easy reference
* Compact ticker/date display in the top-right corner
* Minimalist, overlay-based design — no clutter, just clarity
📈 Best Used For
* Spotting early trend changes
* Confirming continuation or breakout setups
* Identifying compression zones before reversals
* Overlaying on ASX, S&P, FX, Gold, or Crypto charts
🔔 Part of the MarketMonkey Indicator Set series — tools built for real-world trend recognition and momentum trading.
F & W SMC Alerthis script is a custom TradingView indicator designed to combine elements of a trend‑following VWAP approach (inspired by the “Fabio” strategy) with a smart‑money‑concepts framework (inspired by Waqar Asim). Here’s what it does:
* **Directional bias:** It calculates a 15‑minute VWAP and compares the current 15‑minute close to it. When price is above the 15‑minute VWAP, the script assumes a long bias; when below, a short bias. This reflects the trend‑following aspect of the Fabio strategy.
* **Liquidity sweeps:** Using recent pivot highs and lows on the current timeframe, it identifies when price takes out a recent high (for potential longs) or low (for potential shorts). This represents a “liquidity sweep” — a fake breakout that collects stops and signals a possible reversal or continuation.
* **Break of structure (BOS):** After a sweep, the script confirms that price is breaking away from the swept level (i.e., higher than recent highs for longs or lower than recent lows for shorts). This BOS confirmation helps avoid false signals.
* **Entry filters:** For a long setup, the bias must be long, there must be a liquidity sweep followed by a BOS, and price must reclaim the current‑timeframe VWAP. For a short setup, the opposite conditions apply (short bias, sweep + BOS to the downside, and price rejecting the VWAP).
* **Alerts and plot:** It provides two alert conditions (“Fabio‑Waqar Long Setup” and “Fabio‑Waqar Short Setup”) that you can attach to notifications. It also plots the intraday VWAP on your chart for visual reference.
In short, this script watches for a confluence of trend direction, liquidity sweeps, structural shifts, and VWAP reclaim/rejection, and then notifies you when those conditions align. You can use it as an alerting tool to identify high‑probability setups based on these combined strategies.
Fabio + Waqar SMC AlertThis script is a custom TradingView indicator designed to combine elements of a trend‑following VWAP approach (inspired by the “Fabio” strategy) with a smart‑money‑concepts framework (inspired by Waqar Asim). Here’s what it does:
* **Directional bias:** It calculates a 15‑minute VWAP and compares the current 15‑minute close to it. When price is above the 15‑minute VWAP, the script assumes a long bias; when below, a short bias. This reflects the trend‑following aspect of the Fabio strategy.
* **Liquidity sweeps:** Using recent pivot highs and lows on the current timeframe, it identifies when price takes out a recent high (for potential longs) or low (for potential shorts). This represents a “liquidity sweep” — a fake breakout that collects stops and signals a possible reversal or continuation.
* **Break of structure (BOS):** After a sweep, the script confirms that price is breaking away from the swept level (i.e., higher than recent highs for longs or lower than recent lows for shorts). This BOS confirmation helps avoid false signals.
* **Entry filters:** For a long setup, the bias must be long, there must be a liquidity sweep followed by a BOS, and price must reclaim the current‑timeframe VWAP. For a short setup, the opposite conditions apply (short bias, sweep + BOS to the downside, and price rejecting the VWAP).
* **Alerts and plot:** It provides two alert conditions (“Fabio‑Waqar Long Setup” and “Fabio‑Waqar Short Setup”) that you can attach to notifications. It also plots the intraday VWAP on your chart for visual reference.
In short, this script watches for a confluence of trend direction, liquidity sweeps, structural shifts, and VWAP reclaim/rejection, and then notifies you when those conditions align. You can use it as an alerting tool to identify high‑probability setups based on these combined strategies.
Bitcoin Fair Price Calculator [bitcoinfairprice.com]1. Purpose of the scriptLong-term Bitcoin valuation based on historical time (days since Genesis block)
Fair Price = theoretically “fair” price according to power law.
Bottom Price = lower support (historically ~58% below Fair Price).
Daily display as on the website – without external access.
Buy/sell signals in case of strong overvaluation/undervaluation.
2. Mathematical model (original formula)pinescript
Bottom Price = Fair Price × 0.42
→ Corresponds historically to ~58% below Fair Price.
Days since Genesis block are calculated automatically per bar.
3. What is displayed in the chart?
Fair Price Average power law line (“fair price”) Blue
Bottom Price Lower support (“floor”) Green
Power Law Corridor Filled area between 0.1× and 2.5× Fair Price Light blue (transparent)
Table (top right) Daily values as on the website Black with white text
Label (for >20% deviation) Shows current prices + percentage Red (overvalued) / Green (undervalued)
4. Recommended use Timeframe
Recommendation Weekly / Monthly Best long-term signals
Daily Good balance
Log scale Be sure to activate! (Right-click on Y-axis → “Logarithmic scale”)
9. Strategy tips (based on the model)
Price near bottom --> Buy / accumulate
Price > 2.5× fair price --> Sell part of position / caution
Price between fair & bottom --> Strong buy zone
Deviation < -20% --> HODL signal
Translated with DeepL.com (free version)
