RBLR - GSK Vizag AP IndiaThis indicator identifies the Opening Range High (ORH) and Low (ORL) based on the first 15 minutes of the Indian equity market session (9:15 AM to 9:30 AM IST). It draws horizontal lines extending these levels until market close (3:30 PM IST) and generates visual signals for price breakouts above ORH or below ORL, as well as reversals back into the range.
Key features:
- **Range Calculation**: Captures the high and low during the opening period using real-time bar data.
- **Line Extension**: Lines are dynamically extended bar-by-bar within the session for clear visualization.
- **Signals**:
- Green triangle up: Crossover above ORH (potential bullish breakout).
- Red triangle down: Crossunder below ORL (potential bearish breakout).
- Yellow labels: Reversals from breakout levels back into the range.
- **Labels**: "RAM BAAN" marks the ORH (inspired by a precise arrow from the Ramayana), and "LAKSHMAN REKHA" marks the ORL (inspired by a protective boundary line from the same epic).
- **Customization**: Toggle signals on/off and select line styles (Dotted, Dashed, Solid, or Smoothed, with transparency for Smoothed).
The state-tracking logic prevents redundant signals by monitoring if price remains outside the range after a breakout. This helps users observe range-bound behavior or directional moves without built-in alerts. This indicator is particularly useful for day trading on longer intraday timeframes (e.g., 15-minute charts) to identify session-wide trends and avoid noise in shorter frames. For best results, apply on intraday timeframes on NSE/BSE symbols. Note that lines and labels are limited to the script's max counts to avoid performance issues on long histories.
**Disclaimer**: This indicator is for educational and informational purposes only and does not constitute financial, investment, or trading advice. Trading in financial markets involves significant risk of loss and is not suitable for all investors. Past performance is not indicative of future results. Users should conduct their own research, consider their financial situation, and consult with qualified professionals before making any investment decisions. The author and TradingView assume no liability for any losses incurred from its use.
トレンド分析
SMC by ASHY-JAYASHY-JAY "Smart Money" refers to funds under the control of institutional investors, central banks, funds, market makers, and other financial entities. Ordinary people recognize investments made by those who have a deep understanding of market performance and possess information typically inaccessible to regular investors as "Smart Money".
Consequently, when market movements often diverge from expectations, traders identify the footprints of smart money. For example, when a classic pattern forms in the market, traders take short positions. However, the market might move upward instead. They attribute this contradiction to smart money and seek to capitalize on such inconsistencies in their trades.
The "Smart Money Concept" (SMC) is one of the primary styles of technical analysis that falls under the subset of "Price Action". Price action encompasses various subcategories, with one of the most significant being "Supply and Demand", in which SMC is categorized.
The SMC method aims to identify trading opportunities by emphasizing the impact of large traders (Smart Money) on the market, offering specific patterns, techniques, and trading strategies.
🟣Key Terms of Smart Money Concept (SMC)
• Market Structure (Trend)
• Change of Character (ChoCh)
• Break of Structure (BoS)
• Order Blocks (Supply and Demand)
• Imbalance (IMB)
• Inefficiency (IFC)
• Fair Value Gap (FVG)
• Liquidity
• Premium and Discount
Rainbow Moving Averages (v5 safe)Rainbow Moving Averages — plots multiple moving averages of different lengths in a rainbow colour scheme to visualise market trend strength and direction. The spread and alignment of the lines help identify trend changes and momentum shifts.
TrendFlowTrendFlow is a visual trend analysis tool that helps identify changes in market direction and momentum.
It uses three exponential moving averages (EMA 21, EMA 50, and EMA 200) to define short-, medium-, and long-term trend structure.
A dynamic color fill highlights the relationship between the 21 and 50 EMAs:
When EMA 21 is above EMA 50, a green fill indicates upward momentum.
When EMA 21 is below EMA 50, a red fill shows downward momentum.
The EMA 200 line adapts its color based on its position relative to the shorter EMAs:
Red when above both EMAs (strong upward structure)
Green when below both EMAs (strong downward structure)
Gray when between them (neutral or consolidation phase)
This setup provides a clear visual framework for observing trend flow and directional bias over multiple timeframes.
Developed by The Volume Hub Fintech and Strategy Development
Kalman VWAP Filter [BackQuant]Kalman VWAP Filter
A precision-engineered price estimator that fuses Kalman filtering with the Volume-Weighted Average Price (VWAP) to create a smooth, adaptive representation of fair value. This hybrid model intelligently balances responsiveness and stability, tracking trend shifts with minimal noise while maintaining a statistically grounded link to volume distribution.
If you would like to see my original Kalman Filter, please find it here:
Concept overview
The Kalman VWAP Filter is built on two core ideas from quantitative finance and control theory:
Kalman filtering — a recursive Bayesian estimator used to infer the true underlying state of a noisy system (in this case, fair price).
VWAP anchoring — a dynamic reference that weights price by traded volume, representing where the majority of transactions have occurred.
By merging these concepts, the filter produces a line that behaves like a "smart moving average": smooth when noise is high, fast when markets trend, and self-adjusting based on both market structure and user-defined noise parameters.
How it works
Measurement blend : Combines the chosen Price Source (e.g., close or hlc3) with either a Session VWAP or a Rolling VWAP baseline. The VWAP Weight input controls how much the filter trusts traded volume versus price movement.
Kalman recursion : Each bar updates an internal "state estimate" using the Kalman gain, which determines how much to trust new observations vs. the prior state.
Noise parameters :
Process Noise controls agility — higher values make the filter more responsive but also more volatile.
Measurement Noise controls smoothness — higher values make it steadier but slower to adapt.
Filter order (N) : Defines how many parallel state estimates are used. Larger orders yield smoother output by layering multiple one-dimensional Kalman passes.
Final output : A refined price trajectory that captures VWAP-adjusted fair value while dynamically adjusting to real-time volatility and order flow.
Why this matters
Most smoothing techniques (EMA, SMA, Hull) trade off lag for smoothness. Kalman filtering, however, adaptively rebalances that tradeoff each bar using probabilistic weighting, allowing it to follow market state changes more efficiently. Anchoring it to VWAP integrates microstructure context — capturing where liquidity truly lies rather than only where price moves.
Use cases
Trend tracking : Color-coded candle painting highlights shifts in slope direction, revealing early trend transitions.
Fair value mapping : The line represents a continuously updated equilibrium price between raw price action and VWAP flow.
Adaptive moving average replacement : Outperforms static MAs in variable volatility regimes by self-adjusting smoothness.
Execution & reversion logic : When price diverges from the Kalman VWAP, it may indicate short-term imbalance or overextension relative to volume-adjusted fair value.
Cross-signal framework : Use with standard VWAP or other filters to identify convergence or divergence between liquidity-weighted and state-estimated prices.
Parameter guidance
Process Noise : 0.01–0.05 for swing traders, 0.1–0.2 for intraday scalping.
Measurement Noise : 2–5 for normal use, 8+ for very smooth tracking.
VWAP Weight : 0.2–0.4 balances both price and VWAP influence; 1.0 locks output directly to VWAP dynamics.
Filter Order (N) : 3–5 for reactive short-term filters; 8–10 for smoother institutional-style baselines.
Interpretation
When price > Kalman VWAP and slope is positive → bullish pressure; buyers dominate above fair value.
When price < Kalman VWAP and slope is negative → bearish pressure; sellers dominate below fair value.
Convergence of price and Kalman VWAP often signals equilibrium; strong divergence suggests imbalance.
Crosses between Kalman VWAP and the base VWAP can hint at shifts in short-term vs. long-term liquidity control.
Summary
The Kalman VWAP Filter blends statistical estimation with market microstructure awareness, offering a refined alternative to static smoothing indicators. It adapts in real time to volatility and order flow, helping traders visualize balance, transition, and momentum through a lens of probabilistic fair value rather than simple price averaging.
EMA HeatmapEMA Heatmap — Indicator Description
The EMA Order Heatmap is a visual trend-structure tool designed to show whether the market is currently trending bullish, trending bearish, or moving through a neutral consolidation phase. It evaluates the alignment of multiple exponential moving averages (EMAs) at three different structural layers: short-term daily, medium-term daily, and weekly macro trend. This creates a quick and intuitive picture of how well price movement is organized across timeframes.
Each layer of the heatmap is scored from bearish to bullish based on how the EMAs are stacked relative to each other. When EMAs are in a fully bullish configuration, the row displays a bright green or lime color. Fully bearish alignment is shown in red. Yellow tones appear when the EMAs are mixed or compressing, indicating uncertainty, trend exhaustion, or a change in market character. The three rows combined offer a concise view of whether strength or weakness is isolated to one timeframe or broad across the market.
This indicator is best used as a trend filter before making trading decisions. Traders may find more consistent setups when the majority of the heatmap supports the direction of their trade. Green-dominant conditions suggest a trending bullish environment where long trades can be favored. Red-dominant conditions indicate bearish momentum and stronger potential for short opportunities. When yellow becomes more prominent, the market may be transitioning, ranging, or gearing up for a breakout, making timing more challenging and risk higher.
• Helps quickly identify directional bias
• Highlights when trends strengthen, weaken, or turn
• Provides insight into whether momentum is supported by higher timeframes
• Encourages traders to avoid fighting market structure
It is important to recognize the limitations. EMAs are lagging indicators, so the heatmap may confirm a trend after the initial move is underway, especially during fast reversals. In sideways or low-volume environments, the structure can shift frequently, reducing clarity. This tool does not generate entry or exit signals on its own and should be paired with price action, momentum studies, or support and resistance analysis for precise trade execution.
The EMA Order Heatmap offers a clean and reliable way to stay aligned with the broader market environment and avoid lower-quality trades in indecisive conditions. It supports more disciplined decision-making by helping traders focus on setups that match the prevailing structural trend.
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.
NASDAQ Trading System with PivotsThis TradingView indicator, designed for the 30-minute NASDAQ (^IXIC) chart, guides QQQ options trading using a trend-following strategy. It plots a 20-period SMA (blue) and a 100-period SMA (red), with an optional 250-period SMA (orange) inspired by rauItrades' NASDAQ SMA outfit. A bullish crossover (20 SMA > 100 SMA) triggers a green "BUY" triangle below the bar, signaling a potential long position in QQQ, while a bearish crossunder (20 SMA < 100 SMA) shows a red "SELL" triangle above, indicating a short or exit. The background colors green (bullish) or red (bearish) for trend bias. Orange circles (recent highs) and purple circles (recent lows) mark support/resistance levels using 5-bar pivot points.
S&P Trading System with PivotsThe S&P Trading System with Pivots is a TradingView indicator designed for the 30-minute SPX chart to guide SPY options trading. It uses a trend-following strategy with:
10 SMA and 50 SMA: Plots a 10-period (blue) and 50-period (red) Simple Moving Average. A bullish crossover (10 SMA > 50 SMA) signals a potential buy (green triangle below bar), while a bearish crossunder (10 SMA < 50 SMA) signals a sell or exit (red triangle above bar).
Trend Bias: Colors the background green (bullish) or red (bearish) based on SMA positions.
Pivot Points: Marks recent highs (orange circles) and lows (purple circles) as potential resistance and support levels, using a 5-bar lookback period.
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.
Buy And Hold Performance Screener - [JTCAPITAL]Buy And Hold Performance Screener – is a script designed to track and display multi-asset “buy and hold” performance curves and performance statistics over defined timeframes for selected symbols. It doesn’t attempt to time entries or exits; rather, it shows what would happen if one simply bought the asset at the defined start date and held it.
The indicator works by calculating in the following steps:
Start Date Definition
The script begins by reading an input for the start date. This defines the bar from which the equity curves begin.
Symbol Definitions & Close Price Retrieval
The script allows the user to specify up to ten tickers. For each ticker it uses request.security() on the “1D” timeframe to retrieve the daily close price of that symbol.
Plot Enable Inputs
For each ticker there is an input boolean controlling whether the equity curve for that ticker should be plotted.
Asset Name Cleaning
The helper function clean_name(string asset) => … takes the asset string (e.g., “CRYPTO:SOLUSD”) and manipulates it (via string splitting and replacements) to derive a cleaned short name (e.g., “SOL”). This name is used for visuals (labels, table headers).
Equity Curve Calculation (“HODL”)
The helper function f_HODL(closez) defines a variable equity that assumes a starting equity of 1 unit at the start date and then multiplies by the ratio of each bar’s close to the prior bar’s close: i.e. daily compounding of returns.
Performance Metrics Calculation
The helper function f_performance(closez) calculates, for each symbol’s close series, the percentage change of the current close relative to its close 30 days ago, 90 days ago, 180 days ago, 1 year ago (365 days), 2 years ago (730 days) and 3 years ago (1095 days).
Equity Curve Plots
For each ticker, if the corresponding plot input is true, the script assigns a plotted variable equal to the equity curve value. Its then drawing each selected equity curve on the chart, each in a distinct color.
Table Construction
If the plottable input is true, the script constructs a table and populates it with rows and column corresponding to the assigned tickers and the set 6 timeframes used for display.
Buy and Sell Conditions:
Since this is strictly a “buy-and-hold” performance screener, there are no explicit buy or sell signals generated or plotted. The script assumes: buy at the defined start_date, hold continuously to present. There are no filters, no exit logic, no take-profit or stop-loss. The benefit of this approach is to provide a clean benchmark of how selected assets would have performed if one simply adopted a passive “buy & hold” approach from a given start date.
Features and Parameters:
start_date (input.time) : Defines the date from which performance and equity curves begin.
ticker1 … ticker10 (input.symbol) : User-selectable asset symbols to include in the screener.
plot1 … plot10 (input.bool) : Boolean flags to enable/disable plotting of each asset’s equity curve.
plottable (input.bool) : Flag to enable/disable drawing the performance table.
Colored plotting + Labels for identifying each asset curve on the chart.
Specifications:
Here is a detailed breakdown of every calculation/variable/function used in the script and what each part means:
start_date
This is defined via input.time(timestamp("1 Jan 2025"), title = "Start Date"). It allows the user to pick a specific calendar date from which the equity curves and performance calculations will start.
ticker1 … ticker10
These inputs allow the user to select up to ten different assets (symbols) to monitor. The script uses each of these to fetch daily close prices.
plot1 … plot10
Boolean inputs controlling which of the ten asset equity curves are plotted. If plotX is true, the equity curve for ticker X will be visible; otherwise it will be not plotted. This gives the user flexibility to include or exclude specific assets on the chart.
Returns the cleaned asset short name.
This provides friendly text labels like “BTC”, “ETH”, “SOL”, etc., instead of full symbol codes.
The choice of distinct colours for each asset helps differentiate curves visually when multiple assets are overlaid.
Colour definitions
Variables color1…color10 are explicitly defined via color.rgb(r,g,b) to give each asset a unique colour (e.g., red, orange, yellow, green, cyan, blue, purple, pink, etc.).
What are the benefits of combining these calculations?
By computing equity curves for multiple assets from the same start date and overlaying them, you can visualise comparative performance of different assets under a uniform “buy & hold” assumption.
The performance table adds multi-horizon returns (30 D, 90 D, 180 D, 1 Y, 2 Y, 3 Y) which helps the user see both short-term and longer-term performance without having to manually compute returns.
The use of daily close data via request.security(..., "1D") removes dependency on the chart’s timeframe, thereby standardising the comparison across assets.
The equity curve and table together provide both visual (curve) and numerical (table) summaries of performance, making it easier to spot trends, divergences, and cross-asset comparisons at a glance.
Because it uses compounding (equity := equity * (closez / closez )), the curves reflect the real growth of a 1-unit investment held over time, rather than only simple returns.
The labelling of curves and the color-coding make the multi-asset overlay easier to interpret.
Using a clean start date ensures that all curves begin at the same point (1 unit at start_date), making relative performance intuitive.
Because of this, the script is useful as a benchmarking tool: rather than trying to pick entries or exit points, you can simply compare “what if I had held these assets since Jan 1 2025” (or your chosen date), and see which assets out-/under-performed in that period. It helps an investor or trader evaluate the long-term benefits of passive vs. active management, or of allocation decisions.
Please note:
The script assumes continuous daily data and does not account for dividends, fees, slippage, or tax implications.
It does not attempt to optimise timing or provide trading signals.
Returns prior to the start date are ignored (equity only begins once time >= start_date).
For newly listed assets with fewer than 365 or 730 or 1095 days of history, the longer-horizon returns may return na or misleading values.
Because it uses request.security() without specifying lookahead, and on “1D” timeframe, it complies with standard usage but you should verify there is no look-ahead bias in your particular setup.
ENJOY!
Strong PivotsThis finds pivots based on your inputs (number of candles back and forward that are above or below the range of the potential pivot points) and then optionally changes the color to help you visually identify the pivot. You can also specify pivots as strong pivots if they reverse in 1 time segment beyond a certain percentage (wick % of full candle range).
For example, if the pivot is at a high point but has a green body candle and a wick > 35% of the candle, it will change the body color to red to help visually understand that the candle can be considered a strong part of the downtrend, regardless of it closing green. This will help your mind interpret the top pivot candle as part of the potential trend reversal for the following candles and could even be used as part of your strategy ruleset.
FDF — EMAs+VWAP with setup & entry (stable scale)the 9 and 21, vwap - and support an restianst, marking each entry when it pulling in our out to the 21. used 90% of the candle over the 21
Reactive Curvature Smoother Moving Average IndicatorSummary in one paragraph
RCS MA is a reactive curvature smoother for any liquid instrument on intraday through swing timeframes. It helps you act only when context strengthens by adapting its window length with a normalized path energy score and by smoothing with robust residual weights over a quadratic fit, then optionally blending a capped one step forecast. Add it to a clean chart and watch the single colored line. Shapes can shift while a bar forms and settle on close. For conservative use, judge on bar close.
Scope and intent
• Markets: major FX pairs, index futures, large cap equities, liquid crypto
• Timeframes: one minute to daily
• Purpose: reduce lag in trends while resisting chop and outliers
• Limits: indicator only, no orders
Originality and usefulness
• Novelty: adaptive window selection by minimizing normalized path energy with directionality bias, plus Huber weighted residuals and curvature aware penalty, finished with a mintick capped forecast blend
• Failure modes addressed: whipsaws from fixed length MAs and outlier spikes that pull means
• Testable: Inputs expose all components and optional diagnostics show chosen length, directionality, and energy
• Portable yardstick: forecast cap uses mintick to stay symbol aware
Method overview in plain language
Base measures
• Range span of the tested window and a path energy defined as the sum of squared price increments, normalized by span
Components
Adaptive window chooser: scans L between Min and Max using an energy over trend score and picks the lowest score
Robust smoother: fits a quadratic to the last L bars, computes residuals, applies Huber weights and an exponential residual penalty scaled down when curvature is high
Forecast blend: projects one step ahead from the quadratic, caps displacement by a multiple of mintick, blends by user weight
Fusion rule
• Final line equals robust mean plus optional capped forecast blend
Signal rule
• Visual bias only: color turns lime when close is above the line, red otherwise
What you will see on the chart
• One colored line that tightens in trends and relaxes in chop
• Optional debug overlays for core value, chosen L, directionality, and energy
• Optional last bar label with L, directionality, and energy
• Reminder: drawings can move intrabar and settle on close
Inputs with guidance
Setup
• Source: price series to smooth
Logic
• Min window l_min. Typical 5 to 21. Higher increases stability, adds lag
• Max window l_max. Typical 40 to 128. Higher reduces noise, adds lag ceiling
• Length step grid_step. Typical 1 to 8. Smaller is finer and heavier
• Trend bias trend_bias. Typical 0.50 to 0.80. Higher favors trend persistence
• Residual penalty lambda_base. Typical 0.8 to 2.0. Higher downweights large residuals more
• Huber threshold huber_k. Typical 1.5 to 3.0. Higher admits more outliers
• Curvature guard curv_guard. Typical 0.3 to 1.0. Higher reduces influence when curve is tight
• Forecast blend lead_blend. 0 disables. Typical 0.10 to 0.40
• Forecast cap lead_limit. Typical 1 to 5 minticks
• Show chosen L and metrics show_debug. Diagnostics toggle
Optional: enable diagnostics to see length, direction, and energy
Realism and responsible publication
• No performance claims. Past results never guarantee future outcomes
• Shapes can move while bars are open and settle on close
• Use on standard candles for analysis and combine with your own risk process
Honest limitations and failure modes
• Very quiet regimes can reduce energy contrast, length selection may hover near the bounds
• Gap heavy symbols can disrupt quadratic fit on the window edges
• Excessive forecast blend may look anticipatory; use low values and the cap
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.
