Mulakan

Kitaran

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.
Penunjuk Pine Script®
oleh morris220768
4
完整三重濾網指標｜發哥版本adam Triple Moving Average Indicator ✅ This version fixes:- barColor and bgColor are explicitly set to the color type during initialization to avoid NA conflicts- Replaced multi-line ternary operators with if/else statements to prevent "end of line without line continuation"- Manual ADX calculation is fully compatible with Pine v5I can help you take the next step: add RSI overbought/oversold arrows + background transparency to show trend strength, making the chart more intuitive.Do you want me to add it?✅ This version fixes:- barColor and bgColor are explicitly set to the color type during initialization to avoid NA conflicts- Replaced multi-line ternary operators with if/else statements to prevent "end of line without line continuation"- Manual ADX calculation is fully compatible with Pine v5I can help you take the next step: add RSI overbought/oversold arrows + background transparency to show trend strength, making the chart more intuitive.Do you want me to add it?
Penunjuk Pine Script®
oleh Adam418
adam Triple Moving Average Indicator✅ This version fixes:- barColor and bgColor are explicitly set to the color type during initialization to avoid NA conflicts- Replaced multi-line ternary operators with if/else statements to prevent "end of line without line continuation"- Manual ADX calculation is fully compatible with Pine v5I can help you take the next step: add RSI overbought/oversold arrows + background transparency to show trend strength, making the chart more intuitive.Do you want me to add it?
Penunjuk Pine Script®
oleh Adam418
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.)
Penunjuk Pine Script®
oleh Cmo22
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.
Penunjuk Pine Script®
oleh mihakralj
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.
Penunjuk Pine Script®
oleh mihakralj
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).”
Penunjuk Pine Script®
oleh mihakralj
Sensational Profits v6 — Stealth by Dr. CurryNOT DONE External Swing Length / Internal Swing Length: sensitivity of swing points. Max Active Ideas: caps how many idea structures stay on the chart. Risk & Targets TP1 Risk Multiple: 1.0 = 1R. (TP1 = EP ± 1×risk) Show Supply & Demand Zones: toggle AOI boxes (optional). Stop Loss Method Internal Swing / ATR Buffer / Fixed Ticks / Percent + Add ATR on IS buffer. Visuals & Stealth Use Stealth Names: ON by default. To disable, set password to Twista@26 and uncheck. Show Signal Names: show/hide the textual names (Momentum Burst, etc). Show EP/SL/TP Name Tags: show/hide tiny “EP/SL/TP” tags at levels. Panel: shows running stats (Ideas/Wins/Losses/Win-Rate). Entry Prompts Show BUY/SELL Entry Labels: prints green “BUY” or red “SELL” right next to the signal candle. Show Vertical Trigger Lines: the solid entry marker line. Trigger Line Width, Entry Label Offset (×ATR), Entry Label Size for look & spacing.
Penunjuk Pine Script®
oleh claudecurry23
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.
Penunjuk Pine Script®
oleh dragos_cocos_dev
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.
Penunjuk Pine Script®
oleh dunnikarp
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.
Penunjuk Pine Script®
oleh dragos_cocos_dev
RBD + SMA/EMA/ORB + Buy/Sell ComboWhat is SMA (Simple Moving Average)? The Simple Moving Average (SMA) smooths out price data by calculating the average closing price over a specific number of periods. It helps identify trend direction and potential reversals. 📊 SMA 21 and SMA 50 Explained: SMA Description Use SMA 21 Short-term moving average (last 21 candles) Shows short-term trend and momentum SMA 50 Medium-term moving average (last 50 candles) Shows medium-term trend and key support/resistance levels ⚙️ How to Use Them Together: Bullish Signal (Buy) 🟢 When SMA 21 crosses above SMA 50, it’s called a Golden Cross → trend turning up. Indicates potential buy or long opportunity. Bearish Signal (Sell) 🔴 When SMA 21 crosses below SMA 50, it’s called a Death Cross → trend turning down. Indicates potential sell or short opportunity. Trend Confirmation: Price above both SMAs → uptrend. Price below both SMAs → downtrend. Support/Resistance: During uptrends, SMA 21 often acts as dynamic support. During downtrends, SMA 50 can act as resistance. ⏱ Example (for 10-min Nifty chart): If SMA 21 > SMA 50 and price trades above both → look for buy on dips. If SMA 21 < SMA 50 and price stays below → look for sell on rise setups.
Penunjuk Pine Script®
oleh yuvarajvep
DOGE_TRYING_SCALP_V093dont use this this is for my fri he entire purpose of this indicator is to automate the difficult part of the strategy—finding the perfect two-candle setup. It makes trading the system simple, visual, and mechanical. The Three Key Visuals on Your Chart The indicator gives you three pieces of information. Understanding them is the key to using it effectively. The Yellow Candle (The "Setup Candle") What it is: This is the "Rejection Wick Candle." It's the first candle in the two-part pattern. What it means: "Get Ready." A potential trade setup is forming, but it is NOT a signal to enter yet. It tells you that the market tried to push in one direction and failed. Your Action: Do nothing. Simply pay close attention to the next candle that is forming. The Signal Triangle (The "Entry Trigger") What it is: A green "LONG" triangle below the candle or a red "SHORT" triangle above the candle. What it means: "GO." This is your confirmation. It only appears after the candle following the yellow one has closed and confirmed the direction of the trade. Your Action: This is your signal to enter the trade immediately at the market price. The Stop Loss Line (The "Safety Net") What it is: A solid green or red line that appears at the same time as the Signal Triangle. What it means: This is the exact price where your initial Stop Loss should be placed. The indicator calculates it for you automatically based on the rules. Your Action: After entering the trade, place your Stop Loss order at this price level. Step-by-Step Guide to Trading a LONG (Buy) Signal Let's walk through a live example. Step 1: The Setup Appears You are watching the 15-minute chart. The price has been dropping. Suddenly, a candle with a long lower wick closes and the indicator colors it YELLOW. What this tells you: The sellers tried to push the price down, but buyers stepped in and rejected the lower prices. This is a potential bottom. Your Action: Do nothing yet. You are now waiting for confirmation. Step 2: The Confirmation and Entry Trigger You wait for the next 15-minute candle to complete. It closes as a green (bullish) candle. The moment it closes, three things appear instantly: A green "LONG" triangle appears below that confirmation candle. A solid green line appears at the low of the previous yellow candle. The background of the two-candle pattern is shaded. What this tells you: The rejection has been confirmed by bullish momentum. The system's rules for entry have been met. Your Action: Enter a BUY (Long) trade immediately. Place your Stop Loss at the level of the solid green line. Step 3: Manage the Trade The indicator has done its job of getting you into a high-probability trade with a defined risk. Now, you manage the trade manually according to the strategy's rules (trailing your stop loss under the low of each new candle that makes a higher high). Step-by-Step Guide to Trading a SHORT (Sell) Signal Now, let's look at the opposite scenario. Step 1: The Setup Appears You are watching the 15-minute chart. The price has been rising. A candle with a long upper wick closes and the indicator colors it YELLOW. What this tells you: The buyers tried to push the price up, but sellers took control and rejected the higher prices. This is a potential top. Your Action: Wait for confirmation. Step 2: The Confirmation and Entry Trigger You wait for the next 15-minute candle to complete. It closes as a red (bearish) candle. The moment it closes, you will see: A red "SHORT" triangle appear above that confirmation candle. A solid red line appear at the high of the previous yellow candle. The background of the pattern will be shaded. What this tells you: The rejection has been confirmed by bearish momentum. It's time to sell. Your Action: Enter a SELL (Short) trade immediately. Place your Stop Loss at the level of the solid red line. Step 3: Manage the Trade Just like before, your entry and initial risk are set. Your job now is to manage the trade by trailing your stop loss above the high of each new candle that makes a lower low. Summary of the Workflow Check H1 Trend (Optional but Recommended): Look at the 1-Hour chart to know if you should be favoring Buys or Sells. Wait for Yellow: On the M15 chart, wait patiently for the indicator to color a candle yellow. Wait for the Triangle: Wait for the next candle to close. If a green or red triangle appears, the setup is confirmed. Execute: Enter your trade and immediately set your stop loss at the line the indicator provides. Manage: Manage the rest of the trade manually.
Penunjuk Pine Script®
oleh imthetradingdoge