Current Weekly Open LineThis indicator is an indicator to make your weekly review.
It shows exactly where the last weekly open candle has been, so you don't have to search it manually.
사이클
Current Weekly Open LineVertical line on current weekly open.
To know exactly on every chart where the current weekly opening is, without having to do it manually.
Micro cycle0-Minute Quarter Cycle Indicator (Q90-Final)
This indicator plots vertical lines marking the four quarters (Q1,Q2,Q3,Q4) of a continuous 90-minute cycle.
It is designed for traders who utilize time-based cycles for market analysis and entry/exit timing.
So you can easy identify the cycles off the micro cycles Q1,Q2,Q3 and Q4
Buyside & Sellside Liquidity The Buyside & Sellside Liquidity Indicator is an advanced Smart Money Concepts (SMC) tool that automatically detects and visualizes liquidity zones and liquidity voids (imbalances) directly on the chart.
🟢 Function and meaning:
1. Buyside Liquidity (green):
Highlights price zones above current price where short traders’ stop-loss orders are likely resting.
When price sweeps these areas, it often indicates a liquidity grab or stop hunt.
👉 These zones are labeled with 💵💰 emojis for a clear visual cue where smart money collects liquidity.
2. Sellside Liquidity (red):
Highlights zones below the current price where long traders’ stop-losses are likely placed.
Once breached, these often signal a potential reversal upward.
👉 The 💵💰🪙 emojis make these liquidity targets visually intuitive on the chart.
3. Liquidity Voids (bright areas):
Indicate inefficient price areas, where the market moved too quickly without filling orders.
These zones are often revisited later as the market seeks balance (fair value).
👉 Shown as light shaded boxes with 💰 emojis to emphasize imbalance regions.
💡 Usage:
• Helps spot smart money manipulation and stop hunts.
• Marks potential reversal or breakout zones.
• Great for traders applying SMC, ICT, or Fair Value Gap strategies.
✨ Highlight:
Dollar and money bag emojis (💵💰🪙💸) are integrated directly into chart labels to create a clear and visually engaging representation of liquidity areas.
Fibonacci levels MTF 2WEEK KKKKA Fibonacci arc trading strategy uses circular arcs drawn at Fibonacci retracement levels (38.2%, 50%, 61.8%) to identify potential support and resistance zones, often intersecting with a trend line. This strategy helps traders anticipate price reversals or pullbacks, and it should be used in conjunction with other indicators
WaveTrend RBF What it does
WT-RBF extracts a “wave” of momentum by subtracting a fast Gaussian-weighted smoother from a slow one, then robust-normalizes that wave with a median/MAD proxy to produce a z-score (z). A short EMA of z forms the signal line. Optional dynamic thresholds use the MAD of z itself so overbought/oversold levels adapt to volatility regimes.
How it’s built:
Radial (Gaussian) smoothers
Causal, exponentially-decaying weights over the last radius bars using σ (sigma) to control spread.
fast = rbf_smooth(src, fastR, fastSig)
slow = rbf_smooth(src, slowR, slowSig)
wave = fast − slow (band-pass)
Robust normalization
A two-stage EMA approximates the median; MAD is estimated from EMA of absolute deviations and scaled by 1.4826 to be stdev-comparable.
z = (wave − center) / MAD
Thresholds
Dynamic OB/OS: ±2.5 × MAD(z) (or fixed levels when disabled)
Reading the indicator
Bull Cross: z crosses above sig → momentum turning up.
Bear Cross: z crosses below sig → momentum turning down.
Exits / Bias flips: zero-line crosses (below 0 → exit long bias; above 0 → exit short bias).
Overbought/Oversold: z > +thrOB or z < thrOS. With dynamics on, the bands widen/narrow with recent noise; with dynamics off, static guides at ±2 / ±2.5 are shown.
Core Inputs
Source: Price series to analyze.
Fast Radius / Fast Sigma (defaults 6 / 2.5): Shorter radius/smaller σ = snappier, higher-freq.
Slow Radius / Slow Sigma (defaults 14 / 5.0): Larger radius/σ = smoother, lower-freq baseline.
Normalization
Robust Z-Score Window (default 200): Lookback for median/MAD proxy (stability vs responsiveness).
Small ε for MAD: Floor to avoid division by zero.
Signal & Thresholds
Dynamic Thresholds (MAD-based) (on by default): Adaptive OB/OS; toggle off to use fixed guides.
Visuals
Shade OB/OS Regions: Background highlights when z is beyond thresholds.
Show Zero Line: Midline reference.
(“Plot Cross Markers” input is present for future use.)
Ehlers Even Better Sinewave (EBSW)# EBSW: Ehlers Even Better Sinewave
## Overview and Purpose
The Ehlers Even Better Sinewave (EBSW) indicator, developed by John Ehlers, is an advanced cycle analysis tool. This implementation is based on a common interpretation that uses a cascade of filters: first, a High-Pass Filter (HPF) to detrend price data, followed by a Super Smoother Filter (SSF) to isolate the dominant cycle. The resulting filtered wave is then normalized using an Automatic Gain Control (AGC) mechanism, producing a bounded oscillator that fluctuates between approximately +1 and -1. It aims to provide a clear and responsive measure of market cycles.
## Core Concepts
* **Detrending (High-Pass Filter):** A 1-pole High-Pass Filter removes the longer-term trend component from the price data, allowing the indicator to focus on cyclical movements.
* **Cycle Smoothing (Super Smoother Filter):** Ehlers' Super Smoother Filter is applied to the detrended data to further refine the cycle component, offering effective smoothing with relatively low lag.
* **Wave Generation:** The output of the SSF is averaged over a short period (typically 3 bars) to create the primary "wave".
* **Automatic Gain Control (AGC):** The wave's amplitude is normalized by dividing it by the square root of its recent power (average of squared values). This keeps the oscillator bounded and responsive to changes in volatility.
* **Normalized Oscillator:** The final output is a single sinewave-like oscillator.
## Common Settings and Parameters
| Parameter | Default | Function | When to Adjust |
| ----------- | ------- | --------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------- |
| Source | close | Price data used for calculation. | Typically `close`, but `hlc3` or `ohlc4` can be used for a more comprehensive price representation. |
| HP Length | 40 | Lookback period for the 1-pole High-Pass Filter used for detrending. | Shorter periods make the filter more responsive to shorter cycles; longer periods focus on longer-term cycles. Adjust based on observed cycle characteristics. |
| SSF Length | 10 | Lookback period for the Super Smoother Filter used for smoothing the detrended cycle component. | Shorter periods result in a more responsive (but potentially noisier) wave; longer periods provide more smoothing. |
**Pro Tip:** The `HP Length` and `SSF Length` parameters should be tuned based on the typical cycle lengths observed in the market and the desired responsiveness of the indicator.
## Calculation and Mathematical Foundation
**Simplified explanation:**
1. Remove the trend from the price data using a 1-pole High-Pass Filter.
2. Smooth the detrended data using a Super Smoother Filter to get a clean cycle component.
3. Average the output of the Super Smoother Filter over the last 3 bars to create a "Wave".
4. Calculate the average "Power" of the Super Smoother Filter output over the last 3 bars.
5. Normalize the "Wave" by dividing it by the square root of the "Power" to get the final EBSW value.
**Technical formula (conceptual):**
1. **High-Pass Filter (HPF - 1-pole):**
`angle_hp = 2 * PI / hpLength`
`alpha1_hp = (1 - sin(angle_hp)) / cos(angle_hp)`
`HP = (0.5 * (1 + alpha1_hp) * (src - src )) + alpha1_hp * HP `
2. **Super Smoother Filter (SSF):**
`angle_ssf = sqrt(2) * PI / ssfLength`
`alpha2_ssf = exp(-angle_ssf)`
`beta_ssf = 2 * alpha2_ssf * cos(angle_ssf)`
`c2 = beta_ssf`
`c3 = -alpha2_ssf^2`
`c1 = 1 - c2 - c3`
`Filt = c1 * (HP + HP )/2 + c2*Filt + c3*Filt `
3. **Wave Generation:**
`WaveVal = (Filt + Filt + Filt ) / 3`
4. **Power & Automatic Gain Control (AGC):**
`Pwr = (Filt^2 + Filt ^2 + Filt ^2) / 3`
`EBSW_SineWave = WaveVal / sqrt(Pwr)` (with check for Pwr == 0)
> 🔍 **Technical Note:** The combination of HPF and SSF creates a form of band-pass filter. The AGC mechanism ensures the output remains scaled, typically between -1 and +1, making it behave like a normalized oscillator.
## Interpretation Details
* **Cycle Identification:** The EBSW wave shows the current phase and strength of the dominant market cycle as filtered by the indicator. Peaks suggest cycle tops, and troughs suggest cycle bottoms.
* **Trend Reversals/Momentum Shifts:** When the EBSW wave crosses the zero line, it can indicate a potential shift in the short-term cyclical momentum.
* Crossing up through zero: Potential start of a bullish cyclical phase.
* Crossing down through zero: Potential start of a bearish cyclical phase.
* **Overbought/Oversold Levels:** While normalized, traders often establish subjective or statistically derived overbought/oversold levels (e.g., +0.85 and -0.85, or other values like +0.7, +0.9).
* Reaching above the overbought level and turning down may signal a potential cyclical peak.
* Falling below the oversold level and turning up may signal a potential cyclical trough.
## Limitations and Considerations
* **Parameter Sensitivity:** The indicator's performance depends on tuning `hpLength` and `ssfLength` to prevailing market conditions.
* **Non-Stationary Markets:** In strongly trending markets with weak cyclical components, or in very choppy non-cyclical conditions, the EBSW may produce less reliable signals.
* **Lag:** All filtering introduces some lag. The Super Smoother Filter is designed to minimize this for its degree of smoothing, but lag is still present.
* **Whipsaws:** Rapid oscillations around the zero line can occur in volatile or directionless markets.
* **Requires Confirmation:** Signals from EBSW are often best confirmed with other forms of technical analysis (e.g., price action, volume, other non-correlated indicators).
## References
* Ehlers, J. F. (2002). *Rocket Science for Traders: Digital Signal Processing Applications*. John Wiley & Sons.
* Ehlers, J. F. (2013). *Cycle Analytics for Traders: Advanced Technical Trading Concepts*. John Wiley & Sons.
Ehlers Phasor Analysis (PHASOR)# PHASOR: Phasor Analysis (Ehlers)
## Overview and Purpose
The Phasor Analysis indicator, developed by John Ehlers, represents an advanced cycle analysis tool that identifies the phase of the dominant cycle component in a time series through complex signal processing techniques. This sophisticated indicator uses correlation-based methods to determine the real and imaginary components of the signal, converting them to a continuous phase angle that reveals market cycle progression. Unlike traditional oscillators, the Phasor provides unwrapped phase measurements that accumulate continuously, offering unique insights into market timing and cycle behavior.
## Core Concepts
* **Complex Signal Analysis** — Uses real and imaginary components to determine cycle phase
* **Correlation-Based Detection** — Employs Ehlers' correlation method for robust phase estimation
* **Unwrapped Phase Tracking** — Provides continuous phase accumulation without discontinuities
* **Anti-Regression Logic** — Prevents phase angle from moving backward under specific conditions
Market Applications:
* **Cycle Timing** — Precise identification of cycle peaks and troughs
* **Market Regime Analysis** — Distinguishes between trending and cycling market conditions
* **Turning Point Detection** — Advanced warning system for potential market reversals
## Common Settings and Parameters
| Parameter | Default | Function | When to Adjust |
|-----------|---------|----------|----------------|
| Period | 28 | Fixed cycle period for correlation analysis | Match to expected dominant cycle length |
| Source | Close | Price series for phase calculation | Use typical price or other smoothed series |
| Show Derived Period | false | Display calculated period from phase rate | Enable for adaptive period analysis |
| Show Trend State | false | Display trend/cycle state variable | Enable for regime identification |
## Calculation and Mathematical Foundation
**Technical Formula:**
**Stage 1: Correlation Analysis**
For period $n$ and source $x_t$:
Real component correlation with cosine wave:
$$R = \frac{n \sum x_t \cos\left(\frac{2\pi t}{n}\right) - \sum x_t \sum \cos\left(\frac{2\pi t}{n}\right)}{\sqrt{D_{cos}}}$$
Imaginary component correlation with negative sine wave:
$$I = \frac{n \sum x_t \left(-\sin\left(\frac{2\pi t}{n}\right)\right) - \sum x_t \sum \left(-\sin\left(\frac{2\pi t}{n}\right)\right)}{\sqrt{D_{sin}}}$$
where $D_{cos}$ and $D_{sin}$ are normalization denominators.
**Stage 2: Phase Angle Conversion**
$$\theta_{raw} = \begin{cases}
90° - \arctan\left(\frac{I}{R}\right) \cdot \frac{180°}{\pi} & \text{if } R eq 0 \\
0° & \text{if } R = 0, I > 0 \\
180° & \text{if } R = 0, I \leq 0
\end{cases}$$
**Stage 3: Phase Unwrapping**
$$\theta_{unwrapped}(t) = \theta_{unwrapped}(t-1) + \Delta\theta$$
where $\Delta\theta$ is the normalized phase difference.
**Stage 4: Ehlers' Anti-Regression Condition**
$$\theta_{final}(t) = \begin{cases}
\theta_{final}(t-1) & \text{if regression conditions met} \\
\theta_{unwrapped}(t) & \text{otherwise}
\end{cases}$$
**Derived Calculations:**
Derived Period: $P_{derived} = \frac{360°}{\Delta\theta_{final}}$ (clamped to )
Trend State:
$$S_{trend} = \begin{cases}
1 & \text{if } \Delta\theta \leq 6° \text{ and } |\theta| \geq 90° \\
-1 & \text{if } \Delta\theta \leq 6° \text{ and } |\theta| < 90° \\
0 & \text{if } \Delta\theta > 6°
\end{cases}$$
> 🔍 **Technical Note:** The correlation-based approach provides robust phase estimation even in noisy market conditions, while the unwrapping mechanism ensures continuous phase tracking across cycle boundaries.
## Interpretation Details
* **Phasor Angle (Primary Output):**
- **+90°**: Potential cycle peak region
- **0°**: Mid-cycle ascending phase
- **-90°**: Potential cycle trough region
- **±180°**: Mid-cycle descending phase
* **Phase Progression:**
- Continuous upward movement → Normal cycle progression
- Phase stalling → Potential cycle extension or trend development
- Rapid phase changes → Cycle compression or volatility spike
* **Derived Period Analysis:**
- Period < 10 → High-frequency cycle dominance
- Period 15-40 → Typical swing trading cycles
- Period > 50 → Trending market conditions
* **Trend State Variable:**
- **+1**: Long trend conditions (slow phase change in extreme zones)
- **-1**: Short trend or consolidation (slow phase change in neutral zones)
- **0**: Active cycling (normal phase change rate)
## Applications
* **Cycle-Based Trading:**
- Enter long positions near -90° crossings (cycle troughs)
- Enter short positions near +90° crossings (cycle peaks)
- Exit positions during mid-cycle phases (0°, ±180°)
* **Market Timing:**
- Use phase acceleration for early trend detection
- Monitor derived period for cycle length changes
- Combine with trend state for regime-appropriate strategies
* **Risk Management:**
- Adjust position sizes based on cycle clarity (derived period stability)
- Implement different risk parameters for trending vs. cycling regimes
- Use phase velocity for stop-loss placement timing
## Limitations and Considerations
* **Parameter Sensitivity:**
- Fixed period assumption may not match actual market cycles
- Requires cycle period optimization for different markets and timeframes
- Performance degrades when multiple cycles interfere
* **Computational Complexity:**
- Correlation calculations over full period windows
- Multiple mathematical transformations increase processing requirements
- Real-time implementation requires efficient algorithms
* **Market Conditions:**
- Most effective in markets with clear cyclical behavior
- May provide false signals during strong trending periods
- Requires sufficient historical data for correlation analysis
Complementary Indicators:
* MESA Adaptive Moving Average (cycle-based smoothing)
* Dominant Cycle Period indicators
* Detrended Price Oscillator (cycle identification)
## References
1. Ehlers, J.F. "Cycle Analytics for Traders." Wiley, 2013.
2. Ehlers, J.F. "Cybernetic Analysis for Stocks and Futures." Wiley, 2004.
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).”
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.
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.
Relative Valuation OscillatorThis is a Relative Valuation Oscillator (RVO) this is attempt of replication OTC Valuation - a sophisticated multi-asset comparison indicator designed to measure whether the current asset is overvalued or undervalued relative to up to three reference assets.
Overview
The RVO compares the current chart's asset against reference assets (default: 30-Year Treasury Bonds, Gold, and US Dollar Index) to determine relative strength and valuation extremes. It outputs normalized oscillator values ranging from -100 (undervalued) to +100 (overvalued).
Key Features
Multiple Calculation Methods
The indicator offers 5 different calculation approaches:
Simple Ratio - Normalized ratio deviation from average
Percentage Difference - Percentage change comparison
Ratio Z-Score - Standard deviation-based comparison
Rate of Change Comparison - Momentum differential analysis (default)
Normalized Ratio - Min-max normalized ratio
Configurable Reference Assets
Asset 1: Default ZB (30-Year Treasury Bond Futures) - tracks interest rate sensitivity
Asset 2: Default GC (Gold Futures) - tracks safe-haven and inflation dynamics
Asset 3: Default DXY (US Dollar Index) - tracks currency strength
Each asset can be enabled/disabled independently
Fully customizable symbols
Visual Components
Multiple oscillator lines - One for each active reference asset (color-coded)
Average line - Combined signal from all active assets
Overbought/Oversold zones - Configurable threshold levels (default: ±80)
Zero line - Neutral valuation reference
Background coloring - Visual zones for extreme conditions
Signal line - Optional smoothed average
Entry markers - Long/short signals at key reversals
Signal Generation
Crossover alerts - When crossing overbought/oversold levels
Entry signals - Reversals from extreme zones
Divergence detection - Bullish/bearish divergences between price and oscillator
Zero-line crosses - Trend strength changes
Customization Options
Lookback period (10-500): Controls statistical calculation window
Normalization period (50-1000): Determines scaling sensitivity
Smoothing toggle: Optional EMA/SMA smoothing with adjustable period
Visual customization: Colors, levels, and display options
Information Table
Real-time dashboard showing:
Average oscillator value
Current status (Overvalued/Undervalued/Neutral)
Current asset price
Individual values for each active reference asset
Use Cases
Mean reversion trading - Identify extreme relative valuations for reversal trades
Sector rotation - Compare assets within similar categories
Hedging strategies - Understand correlation dynamics
Multi-asset analysis - Simultaneously compare against bonds, commodities, and currencies
Divergence trading - Spot price/oscillator divergences
Trading Strategy Applications
Long signals: When oscillator crosses above oversold level (asset recovering from undervaluation)
Short signals: When oscillator crosses below overbought level (asset declining from overvaluation)
Confirmation: Use multiple reference assets for stronger signals
Risk management: Avoid trading when all assets show neutral readings
This indicator is particularly useful for traders who want to incorporate inter-market analysis and relative strength concepts into their trading decisions, especially in OTC (Over-The-Counter) and futures markets.
DG Market Structure (Inspired By Deadcat)MS Indicator taken from Deadcat and enhanced a little bit
I added CHoCH and BOS to better tell the story of why price is moving a certain way. Also made a lot more of the values Input based for testing.
I tried to add in retracement values on the MTF chart but I don't think the math is right, maybe someone can figure out the math.
Institutional Zones: Opening & Closing Trend HighlightsDescription / Content:
Track key institutional trading periods on Nifty/Bank Nifty charts with dynamic session zones:
Opening Volatility Zone: 9:15 AM – 9:45 AM IST (Green)
Closing Institutional Zone: 1:30 PM – 3:30 PM IST (Orange)
Both zones are bounded by the day’s high and low to help visualize institutional activity and price behavior.
Key Observations:
Breakout in both closing trend and opening trends often occurs on uptrending days.
Breakdown in both closing range and opening range usually happens on downside trending days.
Price opening above the previous closing trend is often a sign of a strong opening.
This script helps traders identify trend strength, breakout/breakdown zones, and institutional participation during critical market hours.
Disclaimer:
This indicator is for educational and informational purposes only. It is not a financial advice or recommendation to buy or sell any instrument. Always confirm with your own analysis before taking any trade.
Pine Script Features:
Dynamic boxes for opening and closing sessions
Boxes adjust to the day’s high and low
Optional labels at session start
Works on intraday charts (1m, 5m, 15m, etc.)
Usage Tip:
Use this indicator in combination with trend analysis and volume data to spot strong breakout/breakdown opportunities in Nifty and Bank Nifty.
MTF Supertrend Heatmap (D / 4H / 1H / 15m / 5m)MTF Supertrend Heatmap (D / 4H / 1H / 15m / 5m)
A clean dashboard that tells you whether the same Supertrend (ATR Length, Multiplier) is BUY or SELL across five timeframes—all on one chart. Higher-TF values are fetched with request.security() and, when Confirm HTF bar close is ON, they do not repaint after that bar closes.
Optional toggles let you plot the current-TF Supertrend line and show bar-anchored flip markers (BUY/SELL) for each timeframe. Includes alerts for ALL-TF alignment and MAJORITY (≥3/5) agreement. Timeframes and Supertrend parameters are fully configurable. Use the heatmap for quick confirmation, reduce noise by keeping markers off unless needed.
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.
VWAP Balance HeatmapVWAP Balance Heatmap visually highlights where price stands relative to the dynamic equilibrium of bullish and bearish VWAP averages. The indicator builds two running VWAP arrays — one for bullish candles, one for bearish — then plots their averages and the midpoint between them. It fills the space between price and this midpoint, coloring it green when price is above balance and red when below. The result is a smooth heatmap that reveals whether the market is trading in premium or discount zones, helping you see shifts in momentum and balance without clutter or lag.
Custom Date MarkersCustom Date Markers - Pine Script Indicator
This indicator provides a powerful visual tool for technical and pattern analysis by allowing traders to mark up to 10 specific historical dates with customizable vertical lines on any chart. Each date can be assigned its own unique color, making it easy to categorize and distinguish between different types of events or market catalysts.
Primary Use Cases:
The indicator excels at identifying cyclical patterns and recurring market behavior. By marking significant dates such as earnings announcements, Federal Reserve meetings, dividend ex-dates, or seasonal events, traders can quickly visualize whether stocks consistently react in similar ways around these recurring dates. This is particularly valuable for discovering hidden patterns that might not be obvious from price action alone.
Practical Applications:
Earnings Analysis: Mark historical earnings dates to see if a stock tends to rally or sell-off before/after announcements
Macro Events: Identify how assets respond to FOMC meetings, CPI releases, or other economic data
Seasonal Patterns: Track dates that show recurring volatility or directional moves (like tax deadline periods, end-of-quarter re balancing, etc.)
Event Studies: Analyze the impact of company-specific events like product launches, FDA approvals, or leadership changes
Advanced Insights:
What makes this tool particularly interesting is its ability to reveal non-obvious correlations. For example, you might discover that a retail stock consistently experiences volume spikes 2-3 weeks before Black Friday across multiple years, or that certain tech stocks show weakness during specific conference dates. The color-coding feature allows you to layer multiple event types simultaneously—perhaps using red for bearish catalysts and green for bullish ones—creating a visual heat map of historical market reactions.
The indicator's 6-month default spacing (covering 4.5 years) is strategically designed to capture multiple business cycles while maintaining clarity on the chart. This timeframe is long enough to identify genuine patterns rather than coincidences, yet focused enough to remain relevant to current market conditions.
Pro Tip: Combine this indicator with volume analysis or other technical indicators to validate whether the patterns you observe are accompanied by meaningful market participation or if they're statistical noise.
Midnight Lines for Tokyo, London, New Yorkممتاز 👌 إليك **تعريفًا محدثًا وكاملًا للمؤشر باللغتين العربية والإنجليزية**، مع إدراج توضيح دقيق لتعامل المؤشر مع **تغيّر التوقيت الصيفي والشتوي (DST)** في لندن ونيويورك:
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## 🇬🇧 **English Description (with DST behavior)**
**Indicator name:** *Midnight Lines for Tokyo, London, and New York*
**Purpose:**
This indicator automatically draws **vertical lines** on the chart at **midnight (00:00)** for the three major global trading sessions:
* **Tokyo**
* **London**
* **New York**
### 🔹 How it works:
1. The script checks each candle’s time using the built-in TradingView time zone function:
* `"Asia/Tokyo"`
* `"Europe/London"`
* `"America/New_York"`
2. When it detects **00:00** in any of these zones, it draws:
* A **vertical dotted line** that extends from the top to the bottom of the chart.
* A **label** at the top with the session name (e.g., “Tokyo Midnight”).
3. Each session has its own color for clarity:
* **Blue** → Tokyo Midnight
* **Green** → London Midnight
* **Red** → New York Midnight
### 🕒 Automatic Daylight Saving Time (DST) Adjustment:
The indicator automatically adapts to **Daylight Saving Time changes** in both **London** and **New York**:
* When London switches between **GMT and GMT+1**, the midnight line shifts automatically to remain accurate.
* When New York switches between **EST and EDT**, the script also updates accordingly.
* Tokyo does **not** observe DST, so its timing stays constant year-round.
### 🎯 Purpose:
Helps traders visually track the start of each new trading day in the major sessions and analyze:
* Session overlaps (e.g., London–New York overlap)
* Session-based trading strategies
* Price movement behavior at each new day open
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## 🇸🇦 **الوصف بالعربية (مع إدراج تغير التوقيت)**
**اسم المؤشر:** خطوط منتصف الليل لجلسات طوكيو، لندن، ونيويورك
**الهدف:**
يقوم هذا المؤشر تلقائيًا برسم **خطوط عمودية** على الرسم البياني عند **منتصف الليل (00:00)** لكل من الجلسات الثلاث الرئيسية:
* **جلسة طوكيو**
* **جلسة لندن**
* **جلسة نيويورك**
### 🔹 كيفية العمل:
1. يستخدم المؤشر دوال TradingView لحساب الوقت الفعلي لكل مدينة:
* `"Asia/Tokyo"` لطوكيو
* `"Europe/London"` للندن
* `"America/New_York"` لنيويورك
2. عند وصول الساعة إلى **00:00** بتوقيت أي مدينة، يرسم المؤشر:
* **خطًا عموديًا متقطعًا** يمتد من أعلى إلى أسفل الرسم البياني.
* **تسمية (Label)** أعلى الخط باسم الجلسة (مثل “Tokyo Midnight”).
3. كل جلسة لها لون مختلف:
* **أزرق** → منتصف طوكيو
* **أخضر** → منتصف لندن
* **أحمر** → منتصف نيويورك
### 🕒 التعامل مع تغيّر التوقيت الصيفي والشتوي (DST):
يتكيّف المؤشر تلقائيًا مع تغيّر التوقيت في لندن ونيويورك:
* عندما تنتقل لندن بين **التوقيت الشتوي (GMT)** و**التوقيت الصيفي (GMT+1)**، يتحرك الخط تلقائيًا ليبقى في الساعة 00:00 المحلية.
* وعندما تنتقل نيويورك بين **EST** و**EDT**، يتم تعديل الخط كذلك تلقائيًا.
* أما طوكيو فلا تعتمد التوقيت الصيفي، لذا يبقى وقتها ثابتًا دائمًا على الساعة **00:00 JST**.
### 🎯 الفائدة:
يساعد المتداولين على تحديد **بداية كل جلسة تداول رئيسية**، ومراقبة:
* **تداخل الجلسات** مثل لندن ونيويورك
* **تحركات السعر عند بداية اليوم الجديد**
* **استراتيجيات التداول الزمنية حسب الجلسة**
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