Previous Candle High/Low (Global Rays)Previous Candle High/Low (Global Rays, Corrected)
This indicator tracks the high and low of the most recently closed candle and projects them forward as global horizontal rays.
Features:
✅ Automatically updates the levels once a candle fully closes.
✅ Draws persistent lines at the previous candle’s high (green) and low (red), extending them into the future.
✅ Highlights real-time breakouts:
✅ Includes built-in alert conditions for both breakout events.
How to Use:
Use the levels as reference points for breakout trades, liquidity sweeps, or stop hunts.
Alerts can help you catch moves without needing to constantly watch the chart.
Works on any timeframe and symbol.
Tìm kiếm tập lệnh với "liquidity"
US100 Liquidity Precision StrategyScalping strategy 5-10 point sl / 17 points tp
Automatic BE
Consistent money over time
Ludvig Indicator PROThe Ludvig Indicator is designed to identify high-probability breakout setups by combining trend, volume, volatility, and relative strength filters. It helps you enter stocks (or ETFs/crypto) when institutional money is likely flowing in, while avoiding false breakouts and weak trends.
🔑 Core Features
Zero-Lag EMA (ZLEMA)
Faster, less lagging trend detection compared to traditional EMAs.
Used as the basis for dynamic ATR bands.
ATR Volatility Bands
Adaptive bands based on the Average True Range (ATR).
Define the zone where price must close outside to confirm trend strength.
Breakout Confirmation
Requires price to close above recent highs (lookback configurable).
Ensures signals are “true breakouts,” not just noise around moving averages.
Volume Filter (Relative Volume)
Validates breakouts with significantly higher volume than average.
Prevents low-liquidity signals from triggering.
Trend Strength (ADX)
Built-in ADX calculation ensures only strong, trending moves are considered.
Default filter: ADX ≥ 18 (configurable).
Relative Strength vs. Benchmark
Compares the asset’s momentum against a benchmark (default: SPY).
Only signals when the asset is outperforming the benchmark.
Useful for sector rotation and picking leaders instead of laggards.
Alerts & Signals
Breakout entries are marked with small green triangles.
Built-in alerts for automated notifications (TradingView alerts).
High Timeframe Candle Overlay (Configurable)HTF Candle Overlay — Read Higher Timeframe on Lower Timeframe Charts
What it does
This indicator draws each selected Higher-Timeframe (HTF) candle directly on your lower-timeframe (LTF) chart. It shows a translucent range box (HTF high–low) and an inner body box (HTF open–close), so you can track how the bigger candle is forming while you analyze lower-timeframe structure, liquidity sweeps, and intrabar reactions.
Why it’s helpful
• See where the current HTF candle opened, where price sits inside its body, and how far wicks extend—without leaving your LTF chart.
• Combine HTF context (e.g., 1H/4H) with LTF execution (e.g., 1m–15m) to spot confluence, S/R flips, and failed breaks faster.
• The overlay is locked to the price scale and anchored by bar index, so it pans/zooms exactly with your chart (no drifting while dragging).
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How it works (under the hood)
• Fetches HTF OHLC via request.security.
• When a new HTF bar starts, the previous HTF boxes are frozen at the true close.
• The current HTF bar updates intrabar (so you see live formation) and is clamped to the correct span.
• Horizontal anchoring uses bar index, and a hidden price plot binds the script to the main price scale for stable zoom/pan behavior.
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Inputs
• High Timeframe (HTF): Default 1H (set any TF you like).
• Show High–Low Box: On/off.
• Show Body Box (Open–Close): On/off.
• Opacity for range/body boxes.
• Bull/Bear Colors and Outline + Width.
• Max HTF Candles to Keep: Auto-deletes older boxes to maintain performance.
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Usage tips
• Popular combos: view 1H or 4H candles while trading 1–15m charts.
• Turn off the range box if you only want a clean HTF body overlay.
• Pair with your session/structure tools; this indicator is visual context only (no signals or alerts).
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Notes & limitations
• Non-repainting for closed HTF bars: once an HTF candle closes, its boxes are fixed. The current/in-progress HTF bar updates until it closes (expected live behavior).
• Data alignment depends on your symbol’s feed and session settings. Heikin Ashi/renko/etc. may not match classic OHLC.
• Heavy history + many boxes can affect performance; reduce “Max HTF Candles to Keep” if needed.
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Disclaimer
This script is for education and charting visualization only. It does not provide financial advice, trade signals, or performance guarantees. Always do your own research and manage risk.
StdDev Supply/Demand Zone RefinerThis indicator uses standard deviation bands to identify statistically significant price extremes, then validates these levels through volume analysis and market structure. It employs a proprietary "Zone Refinement" technique that dynamically adjusts zones based on price interaction and volume concentration, creating increasingly precise support/resistance areas.
Key Features:
Statistical Extremes Detection: Identifies when price reaches 2+ standard deviations from mean
Volume-Weighted Zone Creation: Only creates zones at extremes with abnormal volume
Dynamic Zone Refinement: Automatically tightens zones based on touch points and volume nodes
Point of Control (POC) Identification: Finds the exact price with maximum volume within each zone
Volume Profile Visualization: Shows horizontal volume distribution to identify key liquidity levels
Multi-Factor Validation: Combines volume imbalance, zone strength, and touch count metrics
Unlike traditional support/resistance indicators that use arbitrary levels, this system:
Self-adjusts based on market volatility (standard deviation)
Refines zones through machine-learning-like feedback from price touches
Weights by volume to show where real money was positioned
Tracks zone decay - older, untested zones automatically fade
Smart Money SignalsSmart Money Signals – Market Flow & Structure Visualizer
Overview
Smart Money Signals is a precision trading tool designed for traders who want to see market structure and momentum flow in real time. By detecting pivots, momentum imbalances, and dynamic support/resistance levels, the indicator transforms raw price action into a clear visual narrative of where capital is entering and exiting the market.
Instead of lagging averages or cluttered signals, Smart Money Signals highlights the moments that matter most—where bullish and bearish flows are confirmed, where support or resistance breaks, and where momentum zones show the true battleground between buyers and sellers. Its adaptive design makes it equally effective for scalpers seeking sharp entries, swing traders tracking reversals, and longer-term traders looking for confirmation of bias.
How It Works
The engine behind Smart Money Signals relies on swing detection and a configurable sensitivity filter. By monitoring directional momentum across recent bars, the system identifies bullish pivots (where downside exhaustion flips into strength) and bearish pivots (where upward thrust collapses into weakness).
When price confirms a pivot, the indicator draws flow lines to mark the breakout and labels them as either continuation or reversal events, depending on existing market bias. Momentum zones are automatically plotted, highlighting the critical areas where buyers defended price or sellers pressed it lower.
Dynamic support and resistance levels extend forward in time, updating live as price develops. These zones change color when broken, visually signaling whether structure has held or failed. Gradient background shading further emphasizes moments of extreme momentum, such as overbought or oversold surges, so that traders instantly see when market pressure intensifies.
Signals and Market Flows
Smart Money Signals provides visual cues that are both intuitive and actionable:
📈 Bullish Flow Signals appear when price breaks above a confirmed pivot, signaling continuation or reversal into strength.
📉 Bearish Flow Signals appear when price breaks below a confirmed pivot, indicating continuation or reversal into weakness.
Momentum Zones highlight the defended areas between pivots, giving traders a visual map of where structure is strongest.
Dynamic Support & Resistance lines extend across the chart, shifting from defense to failure when broken, ensuring that the most relevant levels are always visible.
Break Signals mark the exact bar where key levels give way, confirming structural violations in real time.
By filtering out noise and focusing on meaningful flow events, the system helps traders avoid overreaction and focus only on high-probability structural shifts.
Strategy Integration
Smart Money Signals is versatile across trading styles:
Trend Continuation : Enter in the direction of flow signals, using dynamic zones as both confirmation and stop-loss placement.
Reversal Trading : Watch for pivots tagged as reversal points, where market bias flips and new structure is created.
Momentum Zone Entries : Use the automatically drawn zones to identify low-risk entries on pullbacks or retests.
Bias Alignment : The integrated dashboard reveals the current market bias—bullish, bearish, or neutral—helping traders stay aligned with the dominant flow.
Stop-losses can be positioned beyond the dynamic zone on the opposite side, while take-profits may be guided by the width of zones or momentum-driven extensions. On higher timeframes, the indicator provides context for macro structure, while lower timeframes allow for tactical entry refinement.
Advanced Techniques
Traders seeking deeper precision can combine Smart Money Signals with volume or order flow tools to validate pivots and zone defenses. Monitoring the sequence of bullish and bearish flows helps identify trend maturity, while analyzing the success rate of pivots in the analytics panel builds a data-driven approach to confidence in signals.
Adjusting swing period and sensitivity allows the indicator to adapt to different market conditions, from volatile crypto pairs to steady forex majors. The flexible visual themes—Cyber, Ocean, Sunset, Matrix—ensure readability across setups, while gradient shading keeps the chart intuitive even under fast-moving conditions.
Why Use Smart Money Signals
Markets are driven by liquidity, momentum, and structure. Smart Money Signals uncovers these forces by translating price action into a clear visual map of flow. It shows:
Where structure was built.
Where it was defended.
Where it was broken.
And where momentum is likely to carry next.
By combining flow detection, dynamic zones, and a live analytics dashboard, the indicator provides traders with a complete framework for reading price action in real time.
Whether you trade crypto, forex, or indices, Smart Money Signals adapts seamlessly to any asset class, giving you clarity, precision, and confidence to execute without second-guessing.
CVD Absorption + Confirmation [Orderflow & Volume]This indicator detects bullish and bearish absorption setups by combining Cumulative Volume Delta (CVD) with price action, candlestick, and volume confirmations.
🔹 What is Absorption?
Absorption happens when aggressive buyers/sellers push CVD to new highs or lows, but price fails to follow through.
Bearish absorption: CVD makes a higher high, but price does not.
Bullish absorption: CVD makes a lower low, but price does not.
This often signals that limit orders are absorbing aggressive market orders, creating potential reversal points.
🔹 Confirmation Patterns
Absorption signals are only shown if they are validated by one of the following patterns:
Engulfing candle with low volume → reversal faces little resistance.
Engulfing candle with high volume → strong aggressive participation.
Pin bar with high volume → absorption visible in the wick.
CVD flattening / slope reversal → shift in aggressive order flow.
🔹 Signals
✅ Bullish absorption confirmed → Green label below the bar.
❌ Bearish absorption confirmed → Red label above the bar.
Each label represents a potential reversal setup after orderflow absorption is validated.
🔹 Alerts
Built-in alerts are included for both bullish and bearish confirmations, so you can track setups in real-time without watching the chart 24/7.
📌 How to Use:
Best applied at key levels (supply/demand, VWAP, OR, liquidity zones).
Look for confluence with your trading strategy before taking entries.
Works on all markets and timeframes where volume is reliable.
Market Sessions [odnac]
This indicator highlights the three main global market sessions (USA, Europe, Asia) and their overlaps directly on the chart.
It helps traders quickly identify active trading periods and potential high-liquidity overlaps.
Features:
Customizable start and end times for each session
Optional daily dividers with weekday labels
Session markers displayed as circles above the candles
Overlap sessions displayed in distinct colors
Adjustable opacity for better chart visibility
Option to hide weekends
Sessions included:
USA Market Session (default 13:30–20:00 UTC)
Europe Market Session (default 07:00–16:00 UTC)
Asia Market Session (default 00:00–09:00 UTC)
Overlaps: USA + Europe, USA + Asia, Europe + Asia
This tool is designed for intraday timeframes (1m–60m) and can be useful for scalping, day trading, or session-based strategies.
Bullish Breakaway Dual Session-Publish-Consolidated FVG
Inspired by the FVG Concept:
This indicator is built on the Fair Value Gap (FVG) concept, with a focus on Consolidated FVG. Unlike traditional FVGs, this version only works within a defined session (e.g., ETH 18:00–17:00 or RTH 09:30–16:00).
Bullish consolidated FVG & Bullish breakaway candle
Begins when a new intraday low is printed. After that, the indicator searches for the 1st bullish breakaway candle, which must have its low above the high of the intraday low candle. Any candles in between are part of the consolidated FVG zone. Once the 1st breakaway forms, the indicator will shades the candle’s range (high to low). Then it will use this candle as an anchor to search for the 2nd, 3rd, etc. breakaways until the session ends.
Session Reset: Occurs at session close.
Repaint Behavior:
If a new intraday (or intra-session) low forms, earlier breakaway patterns are wiped, and the system restarts from the new low.
Counter:
A session-based counter at the top of the chart displays how many bullish consolidated FVGs have formed.
Settings
• Session Setup:
Choose ETH, RTH, or custom session. The indicator is designed for CME futures in New York timezone, but can be adjusted for other markets.
If nothing appears on your chart, check if you loaded it during an inactive session (e.g., weekend/Friday night).
• Max Zones to Show:
Default = 3 (recommended). You can increase, but 3 zones are usually most useful.
• Timeframe:
Best on 1m, 5m, or 15m. (If session range is big, try higher time frame)
Usage
1. Avoid Trading in Wrong Direction
• No bullish breakaway = No long trade.
• Prevents the temptation to countertrade in strong downtrends.
2. Catch the Trend Reversal
• When a bullish breakaway appears after an intraday low, it signals a potential reversal.
• You will need adjust position sizing, watch out liquidity hunt, and place stop loss.
• Best entries of your preferred choices: (this is your own trading edge)
Retest
Breakout
Engulf
MA cross over
Whatever your favorite approach
• Reversal signal is the strongest when price stays within/above the breakaway candle’s
range. Weak if it breaks below.
3. Higher Timeframe Confirmation
• 1m can give false reversals if new lows keep forming.
• 5m often provides cleaner signals and avoids premature reversals.
Failed Trade Example:
This indicator will repaint if a new intraday session low is updated. So it is possible to have a failed trade. Here is an example from the same session in 1m chart. However, if you enter the trade later at another bullish breakaway candle signal. The loss can be mitigated by the profit.
Therefore you should use smaller position size for your 1st trade. You should also considering using 5m chart to avoid 1m bull trap. In this example, if you use 5m chart, you can totally avoid this failed trade.
If you enter the trade, you will see the intraday low is stop loss hunted. You can also see the 1st bullish breakaway candle is super weak. There are a lot of candles below the breakaway candle low, so it is very possible to fail.
In the next chart, you can see the failed traded get stop loss hunted. However you can enter another trade with huge profit to win back the loss from the 1st trade if you follow the rule.
Summary
This indicator offers 3 main advantages:
1. Prevents wrong-direction trades.
2. Confirms trend entry after reversal signals.
3. Filters false positives using higher timeframes.
How to sharp your edge:
1. ⏳Extreme patience⏳: Do not guess the bottom during a downtrend before a confirmed bullish breakaway candle. If you get caught, have the courage to cut loss. This is literally the most important usage of this indicator. Again, this is the most important rule of this indicator and actually the hardest rule to follow.
2. 🛎Better Entry🛎: After a confirmed bullish breakaway, you will always have a good opportunity to enter the trade using established trading technique. Your edge will come from the position size, draw down, stop loss placement, risk/reward ratio.
3. ✂Cut loss fast✂: If you enter a trade according to the rule, but you are still not making profit for a period of time, and the price is below the low of the breakaway candle. It is very likely you may hit stop loss soon (intraday session low). It won't be a bad idea to cut loss before stop loss hit.
4. 🔂Reentry with confidence after stop loss🔂: a stop loss will not invalidate the indicator. If you see a second chance to reenter, you should still follow the trade guide and rule.
5. 🕔Time frame matter🕔: try 1m, 3m, 5m, 10m, 15m time frame. Over time, you should know what time frame work best for you and the market. Higher time frame will reduce the noise of false positive trade, but it comes with a higher stop loss placement and less max profit, however it may come with a lower draw down. Time frame will matter depending on the range of the session. If the session range is small (<0.5%), lower time frame is good. If session range is big (>1%), 5m time frame is better. Remember to wait for candle to close, if you use higher time frame.
Last Mention:
The indicator is only used for bullish side trading.
Opening Range BreakoutOpen Range Breakout (ORB) – Trading Strategy Documentation
Definition:
The Open Range Breakout (ORB) is a short-term trading strategy that identifies the price range established during the initial period of market opening (typically the first 15 to 60 minutes) and uses the high and low of that range as key reference levels for potential breakout entries.
Components:
Open Range High: The highest price traded during the defined opening period.
Open Range Low: The lowest price traded during the same period.
Breakout Trigger: A price move above the Open Range High or below the Open Range Low, signaling potential continuation momentum.
How It Works:
Define the Opening Period: Select a time window (e.g., 30 minutes) at market open to establish the initial range.
Identify Range Boundaries: Record the high and low prices during this period.
Monitor for Breakout: Watch for price to break and close above the Open Range High (bullish breakout) or below the Open Range Low (bearish breakout).
Enter Trade: Enter long on a confirmed break above the Open Range High, or short on a break below the Open Range Low. Entry may be triggered on a retest of the broken level or with volume confirmation.
Set Stop-Loss and Target:
Stop-loss: Placed just inside the open range (e.g., below the high for long, above the low for short).
Profit target: Based on volatility (e.g., ATR multiple) or support/resistance levels.
Key Assumptions:
Early price action reflects initial market sentiment.
A breakout from this range indicates strong directional momentum likely to continue.
Best Conditions:
High liquidity markets (e.g., major indices, large-cap stocks).
Volatile or news-driven trading sessions.
Used primarily in intraday trading.
Limitations:
Prone to false breakouts during low-volume or choppy markets.
Requires strict risk management due to reliance on timing and confirmation.
Conclusion:
The ORB strategy capitalizes on early market momentum by trading breakouts from the initial price range. Its effectiveness depends on precise range definition, timely execution, and disciplined risk control.
Market Structure: HH/HL/LH/LL (v6, simple)What it does
Labels swing High/Low and classifies structure as HH / HL / LH / LL after confirmation.
Uses confirmed fractals (pivothigh/pivotlow) → no repaint after confirmation (there is a right-bar confirmation delay).
Optional swing connectors (lines), optional plain H/L when structure label is not applicable.
Plots last confirmed High/Low levels as reference.
Alerts when a new HH/HL/LH/LL is formed.
How it works
Swings are detected with ta.pivothigh() / ta.pivotlow() using user-defined left and right.
A pivot is confirmed only after right bars on the right—this is the only delay. Once confirmed, the label does not repaint.
Inputs
Left bars & Right bars – fractal sensitivity.
Connect swings with lines – draw lines between consecutive swings.
Show bullish (HH/HL) / Show bearish (LH/LL) – filter what to display.
Show plain H/L – draw H/L when classification is not HH/HL/LH/LL yet.
Recommended settings
1H–4H: left=2, right=2 (responsive).
1D+: left=3, right=3 (cleaner swing map).
Alerts provided
HH formed – new Higher High confirmed.
HL formed – new Higher Low confirmed.
LH formed – new Lower High confirmed.
LL formed – new Lower Low confirmed.
Use them to automate structure tracking or feed your strategy rules.
Tips
Trend up: a sequence of HH + HL; Trend down: LH + LL.
Combine with VWAP/EMA, liquidity zones, or volume/CVD to avoid chasing late signals.
The script is intentionally simple and lightweight; BOS/CHoCH can be added in a future update.
Limitations / Notes
Because the tool relies on confirmed pivots, signals are delayed by right bars.
This is not financial advice and not a buy/sell system on its own.
Changelog
v1.0 – Initial public release (Pine v6). Structure labels, swing connectors, last levels, and alert set.
Keywords
market structure, hh hl lh ll, swing, fractal, pivothigh, pivotlow, trend, structure labels, price action
VWAP For Loop [BackQuant]VWAP For Loop
What this tool does—in one sentence
A volume-weighted trend gauge that anchors VWAP to a calendar period (day/week/month/quarter/year) and then scores the persistence of that VWAP trend with a simple for-loop “breadth” count; the result is a clean, threshold-driven oscillator plus an optional VWAP overlay and alerts.
Plain-English overview
Instead of judging raw price alone, this indicator focuses on anchored VWAP —the market’s average price paid during your chosen institutional period. It then asks a simple question across a configurable set of lookback steps: “Is the current anchored VWAP higher than it was i bars ago—or lower?” Each “yes” adds +1, each “no” adds −1. Summing those answers creates a score that reflects how consistently the volume-weighted trend has been rising or falling. Extreme positive scores imply persistent, broad strength; deeply negative scores imply persistent weakness. Crossing predefined thresholds produces objective long/short events and color-coded context.
Under the hood
• Anchoring — VWAP using hlc3 × volume resets exactly when the selected period rolls:
Day → session change, Week → new week, Month → new month, Quarter/Year → calendar quarter/year.
• For-loop scoring — For lag steps i = , compare today’s VWAP to VWAP .
– If VWAP > VWAP , add +1.
– Else, add −1.
The final score ∈ , where N = (end − start + 1). With defaults (1→45), N = 45.
• Signal logic (stateful)
– Long when score > upper (e.g., > 40 with N = 45 → VWAP higher than ~89% of checked lags).
– Short on crossunder of lower (e.g., dropping below −10).
– A compact state variable ( out ) holds the current regime: +1 (long), −1 (short), otherwise unchanged. This “stickiness” avoids constant flipping between bars without sufficient evidence.
Why VWAP + a breadth score?
• VWAP aggregates both price and volume—where participants actually traded.
• The breadth-style count rewards consistency of the anchored trend, not one-off spikes.
• Thresholds give you binary structure when you need it (alerts, automation), without complex math.
What you’ll see on the chart
• Sub-pane oscillator — The for-loop score line, colored by regime (long/short/neutral).
• Main-pane VWAP (optional) — Even though the indicator runs off-chart, the anchored VWAP can be overlaid on price (toggle visibility and whether it inherits trend colors).
• Threshold guides — Horizontal lines for the long/short bands (toggle).
• Cosmetics — Optional candle painting and background shading by regime; adjustable line width and colors.
Input map (quick reference)
• VWAP Anchor Period — Day, Week, Month, Quarter, Year.
• Calculation Start/End — The for-loop lag window . With 1→45, you evaluate 45 comparisons.
• Long/Short Thresholds — Default upper=40, lower=−10 (asymmetric by design; see below).
• UI/Style — Show thresholds, paint candles, background color, line width, VWAP visibility and coloring, custom long/short colors.
Interpreting the score
• Near +N — Current anchored VWAP is above most historical VWAP checkpoints in the window → entrenched strength.
• Near −N — Current anchored VWAP is below most checkpoints → entrenched weakness.
• Between — Mixed, choppy, or transitioning regimes; use thresholds to avoid reacting to noise.
Why the asymmetric default thresholds?
• Long = score > upper (40) — Demands unusually broad upside persistence before declaring “long regime.”
• Short = crossunder lower (−10) — Triggers only on downward momentum events (a fresh breach), not merely being below −10. This combination tends to:
– Capture sustained uptrends only when they’re very strong.
– Flag downside turns as they occur, rather than waiting for an extreme negative breadth.
Tuning guide
Choose an anchor that matches your horizon
– Intraday scalps : Day anchor on intraday charts.
– Swing/position : Month or Quarter anchor on 1h/4h/D charts to capture institutional cycles.
Pick the for-loop window
– Larger N (bigger end) = stronger evidence requirement, smoother oscillator.
– Smaller N = faster, more reactive score.
Set achievable thresholds
– Ensure upper ≤ N and lower ≥ −N ; if N=30, an upper of 40 can never trigger.
– Symmetric setups (e.g., +20/−20) are fine if you want balanced behavior.
Match visuals to intent
– Enabling VWAP coloring lets you see regime directly on price.
– Background shading is useful for discretionary reading; turn it off for cleaner automation displays.
Playbook examples
• Trend confirmation with disciplined entries — On Month anchor, N=45, upper=38–42: when the long regime engages, use pullbacks toward anchored VWAP on the main pane for entries, with stops just beyond VWAP or a recent swing.
• Downside transition detection — Keep lower around −8…−12 and watch for crossunders; combine with price losing anchored VWAP to validate risk-off.
• Intraday bias filter — Day anchor on a 5–15m chart, N=20–30, upper ~ 16–20, lower ~ −6…−10. Only take longs while score is positive and above a midline you define (e.g., 0), and shorts only after a genuine crossunder.
Behavior around resets (important)
Anchored VWAP is hard-reset each period. Immediately after a reset, the series can be young and comparisons to pre-reset values may span two periods. If you prefer within-period evaluation only, choose end small enough not to bridge typical period length on your timeframe, or accept that the breadth test intentionally spans regimes.
Alerts included
• VWAP FL Long — Fires when the long condition is true (score > upper and not in short).
• VWAP FL Short — Fires on crossunder of the lower threshold (event-driven).
Messages include {{ticker}} and {{interval}} placeholders for routing.
Strengths
• Simple, transparent math — Easy to reason about and validate.
• Volume-aware by construction — Decisions reference VWAP, not just price.
• Robust to single-bar noise — Needs many lags to agree before flipping state (by design, via thresholds and the stateful output).
Limitations & cautions
• Threshold feasibility — If N < upper or |lower| > N, signals will never trigger; always cross-check N.
• Path dependence — The state variable persists until a new event; if you want frequent re-evaluation, lower thresholds or reduce N.
• Regime changes — Calendar resets can produce early ambiguity; expect a few bars for the breadth to mature.
• VWAP sensitivity to volume spikes — Large prints can tilt VWAP abruptly; that behavior is intentional in VWAP-based logic.
Suggested starting profiles
• Intraday trend bias : Anchor=Day, N=25 (1→25), upper=18–20, lower=−8, paint candles ON.
• Swing bias : Anchor=Month, N=45 (1→45), upper=38–42, lower=−10, VWAP coloring ON, background OFF.
• Balanced reactivity : Anchor=Week, N=30 (1→30), upper=20–22, lower=−10…−12, symmetric if desired.
Implementation notes
• The indicator runs in a separate pane (oscillator), but VWAP itself is drawn on price using forced overlay so you can see interactions (touches, reclaim/loss).
• HLC3 is used for VWAP price; that’s a common choice to dampen wick noise while still reflecting intrabar range.
• For-loop cap is kept modest (≤50) for performance and clarity.
How to use this responsibly
Treat the oscillator as a bias and persistence meter . Combine it with your entry framework (structure breaks, liquidity zones, higher-timeframe context) and risk controls. The design emphasizes clarity over complexity—its edge is in how strictly it demands agreement before declaring a regime, not in predicting specific turns.
Summary
VWAP For Loop distills the question “How broadly is the anchored, volume-weighted trend advancing or retreating?” into a single, thresholded score you can read at a glance, alert on, and color through your chart. With careful anchoring and thresholds sized to your window length, it becomes a pragmatic bias filter for both systematic and discretionary workflows.
Imbalance RSI Divergence Strategy# Imbalance RSI Divergence Strategy - User Guide
## What is This Strategy?
This strategy identifies **imbalance** zones in the market and combines them with **RSI divergence** to generate trading signals. It aims to capitalize on price gaps left by institutional investors and large volume movements.
### Main Settings
- **RSI Period (14)**: Period used for RSI calculation. Lower values = more sensitive, higher values = more stable signals.
- **ATR Period (10)**: Period for volatility measurement using Average True Range.
- **ATR Stop Loss Multiplier (2.0)**: How many ATR units to use for stop loss calculation.
- **Risk:Reward Ratio (4.0)**: Risk-reward ratio. 2.0 = 2 units of reward for 1 unit of risk.
- **Use RSI Divergence Filter (true)**: Enables/disables the RSI divergence filter.
### Imbalance Filters
- **Minimum Imbalance Size (ATR) (0.3)**: Minimum imbalance size in ATR units to filter out small imbalances.
- **Enable Lookback Limit (false)**: Activates historical lookback limitations.
- **Maximum Lookback Bars (300)**: Maximum number of bars to look back.
### Visual Settings
- **Show Imbalance Size**: Displays imbalance size in ATR units.
- **Show RSI Divergence Lines**: Shows/hides divergence lines.
- **Divergence Line Colors**: Colors for bullish/bearish divergence lines.
### Volatility-Based Adjustments
- **Low volatility markets**:
- Minimum Imbalance Size: 0.2-0.4 ATR
- ATR Stop Loss Multiplier: 1.5-2.0
- **High volatility markets**:
- Minimum Imbalance Size: 0.5-1.0 ATR
- ATR Stop Loss Multiplier: 2.5-3.5
### Risk Tolerance
- **Conservative approach**:
- Risk:Reward Ratio: 2.0-3.0
- RSI Divergence Filter: Enabled
- Minimum Imbalance Size: Higher (0.5+ ATR)
- **Aggressive approach**:
- Risk:Reward Ratio: 4.0-6.0
- Minimum Imbalance Size: Lower (0.2-0.3 ATR)
###Market Conditions
- **Trending markets**: Higher RSI Period (21-28)
- **Sideways markets**: Lower RSI Period (10-14)
- **Volatile markets**: Higher ATR Multiplier
## Recommended Testing Procedure
1. **Start with default settings** and backtest on 3-6 months of historical data
2. **Adjust RSI Period** to see which value produces better results
3. **Optimize ATR Multiplier** for stop loss levels
4. **Test different Risk:Reward ratios** comparatively
5. **Fine-tune Minimum Imbalance Size** to improve signal quality
## Important Considerations
- **False positive signals**: Imbalances may be less reliable during low volatility periods
- **Market openings**: First hours often produce more imbalances but can be riskier
- **News events**: Consider disabling strategy during major news releases
- **Backtesting**: Test across different market conditions (trending, sideways, volatile)
## Recommended Settings for Beginners
**Safe settings for new users:**
- RSI Period: 14
- ATR Period: 14
- ATR Stop Loss Multiplier: 2.5
- Risk:Reward Ratio: 3.0
- Minimum Imbalance Size: 0.5 ATR
- RSI Divergence Filter: Enabled
## Advanced Tips
### Signal Quality Improvement
- **Combine with market structure**: Look for imbalances near key support/resistance levels
- **Volume confirmation**: Higher volume during imbalance formation increases reliability
- **Multiple timeframe analysis**: Confirm signals on higher timeframes
### Risk Management
- **Position sizing**: Never risk more than 1-2% of account per trade
- **Maximum drawdown**: Set overall stop loss for the strategy
- **Market hours**: Consider avoiding low liquidity periods
### Performance Monitoring
- **Win rate**: Track percentage of profitable trades
- **Average R:R**: Monitor actual risk-reward achieved vs. target
- **Maximum consecutive losses**: Set alerts for strategy review
This strategy works best when combined with proper risk management and market analysis. Always backtest thoroughly before using real money and adjust parameters based on your specific market and trading style.
samc's - Keltner OscillatorThe KELTNER CHANNEL is a widely used technical indicator developed in the 60's by Chester W. Keltner who described it in his 1960 book How To Make Money in Commodities.
so i took the logic, simplified the code and made into an oscillator.
to add a flavor of modern times you can choose among 10 different colorways themes in the settings. (so traders can adjust it for dark or light charts)
Although the initial idea was developed for stocks and commodities, I've carefully back tested this as an oscillator across FX MAJORS , MINORS and high liquidity stocks for the use case of scalping and Medium term trade ideas.
now, this indicator works successfully over all time frames, custom time frames and all assets.
This script builds on the same approach as my earlier session tool — keeping things clean, visual, and easy to read.
I intend to publish more of my work as i develop them from Beta ideas into stable scripts, and i welcome feedback.
XMR Divergences vs KrakenSUMMARY
This script finds the percentage difference between Kraken, and multiple other exchanges, for the price of XMRUSD, and then runs a variable length moving average of those differences. Optionally, you can multiply by the reported volume of the exchange in question. Skip to "USAGE" at the bottom for a quick view of the settings. But I recommend reading DETAILED DESCRIPTION as well.
PURPOSE
The purpose of this script is to get a look into the relative funds flows of XMR between Kraken and the other exchanges. So long as an exchange withdraws are open: 1) Negative divergences indicate XMR outflows from the exchange under consideration, 2) Postive divergences indicate XMR inflows from Kraken to the exchange.
This appears to be moderately correlated with price movements in Monero (but not always). There is also the theory that positive accumulation is a leading indication of a growing probability of postive price action in the general crypto market, and negative accumulation is a leading indicator of an upcoming peak. In other words, exchanges like to accumulate Monero quietly during calm downtimes, and they like to manage its price from gaining too much attention (pump) during broad market positivity.
BACKGROUND
It's well known among XMR traders that most exchanges are operating on a heavy fractional reserve basis as regards Monero. The past 2 years have seen regular and repeated withdraw freezes, sometimes for weeks/months at a time. Occasionally, liquidity stress tests have been performed, with predictable results - none of these exchanges are able to continue supporting withdraws.
Kraken is the only exchange of meaningful volume that has never frozen withdraws for more than an hour or so. Thus, we theorize that Kraken is operating with all, or most of the XMR they claim to have.
Furthermore, we have seen in the past, large price negative price divergences of these fractional reserve exchanges relative to Kraken. As the social outcry grew stronger for this malfeasance, these exchanges have gone to greater lengths to hide their price divergences.
On minute-by-minute ; hour-by-hour basis, typically, a look with the naked eye would show oscillation around the zero point. But when you average it out, especially on lower timeframes (like the 1 and 5 min candles), you can very clearly see that when withdraws are shut down, these exchanges simultaneously diverge their prices downwards as well.
DETAILED DESCRIPTION
The ideal view of price divergence would compare second-by-second prices, and then run something like a rolling 4-hr or 1-day SMA to average out the overall divergences. However, due to limitations of TradingView, this is impractical/impossible for actual usage/viewing. As a result, a balance must be struck, when selecting the combination of the candle period, and the SMA lookback length.
I find that 5min candles, with a 48-period lookback (that equates to a rolling 4-hour SMA), offers the best view of recent and historical price divergence activity. This of course means that we're only sampling price divergences once every 5 minutes, but it still provides a decent look at what's happening. If this script gets popular, I wouldn't be surprised if these exchanges start timing their candle closes to mask their misdeeds, but that's of course speculative on my part.
The other important factor here, *IS TO MULTIPLY BY VOLUME*. Some of these no-volume exchanges have large price divergences. But if they're not doing any real volume, then it doesn't really have any real market impact. Thus, I recommend keeping the "Make volume adjustment" option on.
If that ends up happening, we'll have to infer that by comparing the difference in close prices, vs the difference in the highest or lowest intra-candle prices (wicks). Typically a divergence should have all 3 showing similar results.
Notes regarding "Sum_of_All": This only makes sense when multiplying by volume. So only check this if you also made the volume adjustment. Generally I believe that *Binance* sets the tone. However, we have seen numerous occasions where Binance diverges down, and the others diverge up. I believe this is a social influence tactic, since most people look at Binance price. Meanwhile, they're trying to accumulate some small amount on the other exchanges to minimize their overall loss. This of course assumes collusion by these exchanges, which is a high likely hood, seeing as how in May 2021, they all diverged together simultaneously (among other evidence).
USAGE
I recommend using your browser zoom, to see data beyond 1 month in the past.
Lookback - The number of candles over which to conduct a moving average. On 5-min candles for example, here's how the math works out:
12 - Equates to a 1 hr MA
24 - 2 hrs
48 - 4 hrs (default)
288 - 1 day
2880 - 10 days
Make Volume Adjustment - Recommend that you usually keep this on.
Line Widths - Set to preference
Show_Close_Price? - You can compute the difference at candle close. Or you can check the other boxes to compare the highest/lowest prices for intra candle prices (wicks).
Show Sum_of_All? - You can sum all of the differences, which only makes sense if you're making the volume adjustement. Default is off. Below, you can also choose which exchanges to include in the sum.
This works best on lower timeframes, like the 1m, 5, and 15m charts. I personally use 5m, with 48 or 96 length lookback. You get a better view of the real time price divergences that way.
Machine Learning BBPct [BackQuant]Machine Learning BBPct
What this is (in one line)
A Bollinger Band %B oscillator enhanced with a simplified K-Nearest Neighbors (KNN) pattern matcher. The model compares today’s context (volatility, momentum, volume, and position inside the bands) to similar situations in recent history and blends that historical consensus back into the raw %B to reduce noise and improve context awareness. It is informational and diagnostic—designed to describe market state, not to sell a trading system.
Background: %B in plain terms
Bollinger %B measures where price sits inside its dynamic envelope: 0 at the lower band, 1 at the upper band, ~ 0.5 near the basis (the moving average). Readings toward 1 indicate pressure near the envelope’s upper edge (often strength or stretch), while readings toward 0 indicate pressure near the lower edge (often weakness or stretch). Because bands adapt to volatility, %B is naturally comparable across regimes.
Why add (simplified) KNN?
Classic %B is reactive and can be whippy in fast regimes. The simplified KNN layer builds a “nearest-neighbor memory” of recent market states and asks: “When the market looked like this before, where did %B tend to be next bar?” It then blends that estimate with the current %B. Key ideas:
• Feature vector . Each bar is summarized by up to five normalized features:
– %B itself (normalized)
– Band width (volatility proxy)
– Price momentum (ROC)
– Volume momentum (ROC of volume)
– Price position within the bands
• Distance metric . Euclidean distance ranks the most similar recent bars.
• Prediction . Average the neighbors’ prior %B (lagged to avoid lookahead), inverse-weighted by distance.
• Blend . Linearly combine raw %B and KNN-predicted %B with a configurable weight; optional filtering then adapts to confidence.
This remains “simplified” KNN: no training/validation split, no KD-trees, no scaling beyond windowed min-max, and no probabilistic calibration.
How the script is organized (by input groups)
1) BBPct Settings
• Price Source – Which price to evaluate (%B is computed from this).
• Calculation Period – Lookback for SMA basis and standard deviation.
• Multiplier – Standard deviation width (e.g., 2.0).
• Apply Smoothing / Type / Length – Optional smoothing of the %B stream before ML (EMA, RMA, DEMA, TEMA, LINREG, HMA, etc.). Turning this off gives you the raw %B.
2) Thresholds
• Overbought/Oversold – Default 0.8 / 0.2 (inside ).
• Extreme OB/OS – Stricter zones (e.g., 0.95 / 0.05) to flag stretch conditions.
3) KNN Machine Learning
• Enable KNN – Switch between pure %B and hybrid.
• K (neighbors) – How many historical analogs to blend (default 8).
• Historical Period – Size of the search window for neighbors.
• ML Weight – Blend between raw %B and KNN estimate.
• Number of Features – Use 2–5 features; higher counts add context but raise the risk of overfitting in short windows.
4) Filtering
• Method – None, Adaptive, Kalman-style (first-order),
or Hull smoothing.
• Strength – How aggressively to smooth. “Adaptive” uses model confidence to modulate its alpha: higher confidence → stronger reliance on the ML estimate.
5) Performance Tracking
• Win-rate Period – Simple running score of past signal outcomes based on target/stop/time-out logic (informational, not a robust backtest).
• Early Entry Lookback – Horizon for forecasting a potential threshold cross.
• Profit Target / Stop Loss – Used only by the internal win-rate heuristic.
6) Self-Optimization
• Enable Self-Optimization – Lightweight, rolling comparison of a few canned settings (K = 8/14/21 via simple rules on %B extremes).
• Optimization Window & Stability Threshold – Governs how quickly preferred K changes and how sensitive the overfitting alarm is.
• Adaptive Thresholds – Adjust the OB/OS lines with volatility regime (ATR ratio), widening in calm markets and tightening in turbulent ones (bounded 0.7–0.9 and 0.1–0.3).
7) UI Settings
• Show Table / Zones / ML Prediction / Early Signals – Toggle informational overlays.
• Signal Line Width, Candle Painting, Colors – Visual preferences.
Step-by-step logic
A) Compute %B
Basis = SMA(source, len); dev = stdev(source, len) × multiplier; Upper/Lower = Basis ± dev.
%B = (price − Lower) / (Upper − Lower). Optional smoothing yields standardBB .
B) Build the feature vector
All features are min-max normalized over the KNN window so distances are in comparable units. Features include normalized %B, normalized band width, normalized price ROC, normalized volume ROC, and normalized position within bands. You can limit to the first N features (2–5).
C) Find nearest neighbors
For each bar inside the lookback window, compute the Euclidean distance between current features and that bar’s features. Sort by distance, keep the top K .
D) Predict and blend
Use inverse-distance weights (with a strong cap for near-zero distances) to average neighbors’ prior %B (lagged by one bar). This becomes the KNN estimate. Blend it with raw %B via the ML weight. A variance of neighbor %B around the prediction becomes an uncertainty proxy ; combined with a stability score (how long parameters remain unchanged), it forms mlConfidence ∈ . The Adaptive filter optionally transforms that confidence into a smoothing coefficient.
E) Adaptive thresholds
Volatility regime (ATR(14) divided by its 50-bar SMA) nudges OB/OS thresholds wider or narrower within fixed bounds. The aim: comparable extremeness across regimes.
F) Early entry heuristic
A tiny two-step slope/acceleration probe extrapolates finalBB forward a few bars. If it is on track to cross OB/OS soon (and slope/acceleration agree), it flags an EARLY_BUY/SELL candidate with an internal confidence score. This is explicitly a heuristic—use as an attention cue, not a signal by itself.
G) Informational win-rate
The script keeps a rolling array of trade outcomes derived from signal transitions + rudimentary exits (target/stop/time). The percentage shown is a rough diagnostic , not a validated backtest.
Outputs and visual language
• ML Bollinger %B (finalBB) – The main line after KNN blending and optional filtering.
• Gradient fill – Greenish tones above 0.5, reddish below, with intensity following distance from the midline.
• Adaptive zones – Overbought/oversold and extreme bands; shaded backgrounds appear at extremes.
• ML Prediction (dots) – The KNN estimate plotted as faint circles; becomes bright white when confidence > 0.7.
• Early arrows – Optional small triangles for approaching OB/OS.
• Candle painting – Light green above the midline, light red below (optional).
• Info panel – Current value, signal classification, ML confidence, optimized K, stability, volatility regime, adaptive thresholds, overfitting flag, early-entry status, and total signals processed.
Signal classification (informational)
The indicator does not fire trade commands; it labels state:
• STRONG_BUY / STRONG_SELL – finalBB beyond extreme OS/OB thresholds.
• BUY / SELL – finalBB beyond adaptive OS/OB.
• EARLY_BUY / EARLY_SELL – forecast suggests a near-term cross with decent internal confidence.
• NEUTRAL – between adaptive bands.
Alerts (what you can automate)
• Entering adaptive OB/OS and extreme OB/OS.
• Midline cross (0.5).
• Overfitting detected (frequent parameter flipping).
• Early signals when early confidence > 0.7.
These are purely descriptive triggers around the indicator’s state.
Practical interpretation
• Mean-reversion context – In range markets, adaptive OS/OB with ML smoothing can reduce whipsaws relative to raw %B.
• Trend context – In persistent trends, the KNN blend can keep finalBB nearer the mid/upper region during healthy pullbacks if history supports similar contexts.
• Regime awareness – Watch the volatility regime and adaptive thresholds. If thresholds compress (high vol), “OB/OS” comes sooner; if thresholds widen (calm), it takes more stretch to flag.
• Confidence as a weight – High mlConfidence implies neighbors agree; you may rely more on the ML curve. Low confidence argues for de-emphasizing ML and leaning on raw %B or other tools.
• Stability score – Rising stability indicates consistent parameter selection and fewer flips; dropping stability hints at a shifting backdrop.
Methodological notes
• Normalization uses rolling min-max over the KNN window. This is simple and scale-agnostic but sensitive to outliers; the distance metric will reflect that.
• Distance is unweighted Euclidean. If you raise featureCount, you increase dimensionality; consider keeping K larger and lookback ample to avoid sparse-neighbor artifacts.
• Lag handling intentionally uses neighbors’ previous %B for prediction to avoid lookahead bias.
• Self-optimization is deliberately modest: it only compares a few canned K/threshold choices using simple “did an extreme anticipate movement?” scoring, then enforces a stability regime and an overfitting guard. It is not a grid search or GA.
• Kalman option is a first-order recursive filter (fixed gain), not a full state-space estimator.
• Hull option derives a dynamic length from 1/strength; it is a convenience smoothing alternative.
Limitations and cautions
• Non-stationarity – Nearest neighbors from the recent window may not represent the future under structural breaks (policy shifts, liquidity shocks).
• Curse of dimensionality – Adding features without sufficient lookback can make genuine neighbors rare.
• Overfitting risk – The script includes a crude overfitting detector (frequent parameter flips) and will fall back to defaults when triggered, but this is only a guardrail.
• Win-rate display – The internal score is illustrative; it does not constitute a tradable backtest.
• Latency vs. smoothness – Smoothing and ML blending reduce noise but add lag; tune to your timeframe and objectives.
Tuning guide
• Short-term scalping – Lower len (10–14), slightly lower multiplier (1.8–2.0), small K (5–8), featureCount 3–4, Adaptive filter ON, moderate strength.
• Swing trading – len (20–30), multiplier ~2.0, K (8–14), featureCount 4–5, Adaptive thresholds ON, filter modest.
• Strong trends – Consider higher adaptive_upper/lower bounds (or let volatility regime do it), keep ML weight moderate so raw %B still reflects surges.
• Chop – Higher ML weight and stronger Adaptive filtering; accept lag in exchange for fewer false extremes.
How to use it responsibly
Treat this as a state descriptor and context filter. Pair it with your execution signals (structure breaks, volume footprints, higher-timeframe bias) and risk management. If mlConfidence is low or stability is falling, lean less on the ML line and more on raw %B or external confirmation.
Summary
Machine Learning BBPct augments a familiar oscillator with a transparent, simplified KNN memory of recent conditions. By blending neighbors’ behavior into %B and adapting thresholds to volatility regime—while exposing confidence, stability, and a plain early-entry heuristic—it provides an informational, probability-minded view of stretch and reversion that you can interpret alongside your own process.
Volume Imbalance Analyzer - 70% & 80% Version1.01Here’s a clean “definition” you can drop into your docs. It explains **what** the indicator is, **what it helps with**, and **how** to use it—plain and practical.
# Definition
**Volume Imbalance Analyzer (70% & 80%)** flags bars where estimated buy vs. sell volume is heavily one-sided. It colors those bars, adds labels (B70/B80 or S70/S80), and can alert you in real time. The goal is to quickly spot spots of **aggressive participation** (buyers or sellers) that often act as magnets for a **retest** or as **exhaustion/continuation** areas.
# What it helps you do
* **Find high-energy bars** where one side dominates (potential turning or continuation points).
* **Plan retests:** Track when price comes back into the imbalance candle’s range (common entry/take-profit logic).
* **Filter trades:** Only act when the market shows unusual pressure (≥70% or ≥80%).
* **Add context to setups:** Combine with S/R, FVGs, or trend tools to time entries with less guesswork.
* **Alert-driven workflow:** Get notified the moment extreme pressure prints.
# How it helps (workflow)
1. **Scan for signals:**
* **B80/B70** = strong buying; **S80/S70** = strong selling.
* 80% is “extreme” and overrides 70%.
2. **Mark the zone:** The imbalance candle’s **high–low** defines a zone. Many traders wait for a **retest** into that range.
3. **Decide intent:**
* After **B80/B70**, look for pullbacks to buy (or fades if you see exhaustion).
* After **S80/S70**, look for rallies to sell (or fades if exhaustion).
4. **Confirm with context:** Check trend, key levels, liquidity, session timing, ATR/volatility.
5. **Manage risk:** Place stops beyond the zone; size trades so a failed retest doesn’t ruin the day.
# How it works (under the hood, briefly)
The script **estimates buy/sell volume** from each candle’s body, wicks, and total volume, then computes an **imbalance %**. If the % crosses **70%** or **80%** (scaled by a Sensitivity setting), it paints the bar, drops a label, and optionally fires an alert. It also stores the imbalance candle’s range so you can watch for a **retest**.
# Reading the signals (quick guide)
* **B80**: Extreme buyer pressure → watch for pullback buys or exhaustion shorts, depending on context.
* **B70**: Strong buyer pressure → mild continuation bias.
* **S80**: Extreme seller pressure → watch for rally sells or exhaustion longs.
* **S70**: Strong seller pressure → higher reversal probability noted in the table (informational).
# Configuration tips
* **Sensitivity**: Higher = more bars qualify (more signals).
* **Label distance**: Scales with ATR so labels don’t overlap candles.
* **Colors/opacity**: Separate for 70% vs 80% and buyer vs seller.
* **Alerts**: Enable to catch signals live without staring at the screen.
# Notes & limits
* Uses **estimation** (not true bid/ask) on most symbols; treat as a **context tool**, not a stand-alone system.
* The optional stats table’s “expected outcomes” are **informational**, not live probabilities.
* Works on any timeframe; results improve when combined with structure and risk controls.
BTC Power Law Valuation BandsBTC Power Law Rainbow
A long-term valuation framework for Bitcoin based on Power Law growth — designed to help identify macro accumulation and distribution zones, aligned with long-term investor behavior.
🔍 What Is a Power Law?
A Power Law is a mathematical relationship where one quantity varies as a power of another. In this model:
Price ≈ a × (Time)^b
It captures the non-linear, exponentially slowing growth of Bitcoin over time. Rather than using linear or cyclical models, this approach aligns with how complex systems, such as networks or monetary adoption curves, often grow — rapidly at first, and then more slowly, but persistently.
🧠 Why Power Law for BTC?
Bitcoin:
Has finite supply and increasing adoption.
Operates as a monetary network , where Metcalfe’s Law and power laws naturally emerge.
Exhibits exponential growth over logarithmic time when viewed on a log-log chart .
This makes it uniquely well-suited for power law modeling.
🌈 How to Use the Valuation Bands
The central white line represents the modeled fair value according to the power law.
Colored bands represent deviations from the model in logarithmic space, acting as macro zones:
🔵 Lower Bands: Deep value / Accumulation zones.
🟡 Mid Bands: Fair value.
🔴 Upper Bands: Euphoria / Risk of macro tops.
📐 Smart Money Concepts (SMC) Alignment
Accumulation: Occurs when price consolidates near lower bands — often aligning with institutional positioning.
Markup: As price re-enters or ascends the bands, we often see breakout behavior and trend expansion.
Distribution: When price extends above upper bands, potential for exit liquidity creation and distribution events.
Reversion: Historically, price mean-reverts toward the model — rarely staying outside the bands for long.
This makes the model useful for:
Cycle timing
Long-term DCA strategy zones
Identifying value dislocations
Filtering short-term noise
⚠️ Disclaimer
This tool is for educational and informational purposes only . It is not financial advice. The power law model is a non-predictive, mathematical framework and does not guarantee future price movements .
Always use additional tools, risk management, and your own judgment before making trading or investment decisions.
Mutanabby_AI | ATR+ | Trend-Following StrategyThis document presents the Mutanabby_AI | ATR+ Pine Script strategy, a systematic approach designed for trend identification and risk-managed position entry in financial markets. The strategy is engineered for long-only positions and integrates volatility-adjusted components to enhance signal robustness and trade management.
Strategic Design and Methodological Basis
The Mutanabby_AI | ATR+ strategy is constructed upon a foundation of established technical analysis principles, with a focus on objective signal generation and realistic trade execution.
Heikin Ashi for Trend Filtering: The core price data is processed via Heikin Ashi (HA) methodology to mitigate transient market noise and accentuate underlying trend direction. The script offers three distinct HA calculation modes, allowing for comparative analysis and validation:
Manual Calculation: Provides a transparent and deterministic computation of HA values.
ticker.heikinashi(): Utilizes TradingView's built-in function, employing confirmed historical bars to prevent repainting artifacts.
Regular Candles: Allows for direct comparison with standard OHLC price action.
This multi-methodological approach to trend smoothing is critical for robust signal generation.
Adaptive ATR Trailing Stop: A key component is the Average True Range (ATR)-based trailing stop. ATR serves as a dynamic measure of market volatility. The strategy incorporates user-defined parameters (
Key Value and ATR Period) to calibrate the sensitivity of this trailing stop, enabling adaptation to varying market volatility regimes. This mechanism is designed to provide a dynamic exit point, preserving capital and locking in gains as a trend progresses.
EMA Crossover for Signal Generation: Entry and exit signals are derived from the interaction between the Heikin Ashi derived price source and an Exponential Moving Average (EMA). A crossover event between these two components is utilized to objectively identify shifts in momentum, signaling potential long entry or exit points.
Rigorous Stop Loss Implementation: A critical feature for risk mitigation, the strategy includes an optional stop loss. This stop loss can be configured as a percentage or fixed point deviation from the entry price. Importantly, stop loss execution is based on real market prices, not the synthetic Heikin Ashi values. This design choice ensures that risk management is grounded in actual market liquidity and price levels, providing a more accurate representation of potential drawdowns during backtesting and live operation.
Backtesting Protocol: The strategy is configured for realistic backtesting, employing fill_orders_on_standard_ohlc=true to simulate order execution at standard OHLC prices. A configurable Date Filter is included to define specific historical periods for performance evaluation.
Data Visualization and Metrics: The script provides on-chart visual overlays for buy/sell signals, the ATR trailing stop, and the stop loss level. An integrated information table displays real-time strategy parameters, current position status, trend direction, and key price levels, facilitating immediate quantitative assessment.
Applicability
The Mutanabby_AI | ATR+ strategy is particularly suited for:
Cryptocurrency Markets: The inherent volatility of assets such as #Bitcoin and #Ethereum makes the ATR-based trailing stop a relevant tool for dynamic risk management.
Systematic Trend Following: Individuals employing systematic methodologies for trend capture will find the objective signal generation and rule-based execution aligned with their approach.
Pine Script Developers and Quants: The transparent code structure and emphasis on realistic backtesting provide a valuable framework for further analysis, modification, and integration into broader quantitative models.
Automated Trading Systems: The clear, deterministic entry and exit conditions facilitate integration into automated trading environments.
Implementation and Evaluation
To evaluate the Mutanabby_AI | ATR+ strategy, apply the script to your chosen chart on TradingView. Adjust the input parameters (Key Value, ATR Period, Heikin Ashi Method, Stop Loss Settings) to observe performance across various asset classes and timeframes. Comprehensive backtesting is recommended to assess the strategy's historical performance characteristics, including profitability, drawdown, and risk-adjusted returns.
I'd love to hear your thoughts, feedback, and any optimizations you discover! Drop a comment below, give it a like if you find it useful, and share your results.
VWAP Suite {Phanchai}VWAP Suite {Phanchai}
Compact, readable, TradingView-friendly.
What is VWAP?
The Volume Weighted Average Price (VWAP) is the average price of a period weighted by traded volume. It’s used as a fair-value reference (mean) and resets at the start of each new period.
Included VWAP Modes
Session — resets each trading day (current session).
Week / Month / Quarter / Year — current calendar periods.
Anchored Week / Month / Quarter / Year — starts at the beginning of the previous completed period.
Rolling 7D / 30D / 90D — rolling windows: today + last 6/29/89 daily sessions.
Important
This suite does not generate buy/sell signals. It provides structure and confluence; decisions remain yours.
Use Cases
Identify fair-value zones / mean-reversion areas.
Plan TP / SL around periodic VWAPs.
Define DCA levels (e.g., anchored to prior week/month).
Gauge trend bias via VWAP slope and reactions.
How to Use
Inputs → VWAP 1..5: Choose the period per slot (Session, Anchored, Rolling, etc.) and toggle Show .
Sources: Select the price source for all VWAPs (default: HLC3).
Global: Line offset (bars) shifts plots visually (does not affect calculations).
Style tab: Adjust per-line colors, thickness, and line style.
Alerts
Price crosses a VWAP (per slot).
VWAP slope turns UP or DOWN (per slot).
Tips & Notes
Volume required: Poor/absent volume (e.g., some FX tickers) can degrade accuracy.
Anchored modes: Start at the prior period’s open; values appear only after that timestamp.
Rolling modes: Use completed daily sessions (including today).
Clutter control: If labels crowd, increase Line offset or hide unneeded slots.
Confluence: Combine with market structure, liquidity zones, or momentum filters for stronger context.
Built for clear VWAP workflows. Trade safe!
MACROFLOW 200 — Bias & Triggersstephtradez model
MACROFLOW 200 — at a glance (the elevator pitch)
Trade direction = Macro Bias + 1H 200 EMA filter + DXY confirm.
Locations = 1H supply/demand zones.
Triggers (15m): (T1) Retest rejection, (T2) Liquidity sweep + BOS/CHOCH, (T3) Momentum break + shallow pullback.
Stops: structure‑based beyond zone with ATR buffer.
Targets: 2R base, scale at 1.5R, trail to next HTF zone.
Sessions: 7–10 pm ET and 9:30–10:30 am ET.
Risk: tight, prop‑friendly max 1% per session
Value Matrix – Previous Day VAValue Matrix – Previous Day Volume Profile Indicator
Description:
The Value Matrix – Previous Day VA indicator plots the previous trading session’s Volume Profile key levels directly on your chart, providing clear reference points for intraday trading. This indicator calculates the Value Area High (VAH), Value Area Low (VAL), and Point of Control (POC) from the prior session and projects them across the current trading day, helping traders identify potential support, resistance, and high-volume zones.
Features:
Calculates previous day VAH, VAL, and POC based on a user-defined session (default 09:30–16:00).
Uses Volume Profile bins for precise distribution calculation.
Fully customizable line colors for VAH, VAL, and POC.
Lines extend across the current session for easy intraday reference.
Works on any timeframe, optimized for 1-minute charts for precision.
Optional toggles to show/hide VAH, VAL, and POC individually.
Inputs:
Session Time: Define the trading session for which the volume profile is calculated.
Profile Bins: Number of price intervals used to divide the session range.
Value Area %: Percentage of volume to include in the value area (default 70%).
Show POC / VAH & VAL: Toggle visibility of each level.
Line Colors: Customize VAH, VAL, and POC colors.
Use Cases:
Identify previous session support and resistance levels for intraday trading.
Gauge areas of high liquidity and potential market reaction zones.
Combine with other indicators or price action strategies for improved entries and exits.
Recommended Timeframe:
Works on all timeframes; best used on 1-minute or 5-minute charts for precise intraday analysis.
QFisher-R™ [ParadoxAlgo]QFISHER-R™ (Regime-Aware Fisher Transform)
A research/education tool that helps visualize potential momentum exhaustion and probable inflection zones using a quantitative, non-repainting Fisher framework with regime filters and multi-timeframe (MTF) confirmation.
What it does
Converts normalized price movement into a stabilized Fisher domain to highlight potential turning points.
Uses adaptive smoothing, robust (MAD/quantile) thresholds, and optional MTF alignment to contextualize extremes.
Provides a Reversal Probability Score (0–100) to summarize signal confluence (extreme, slope, cross, divergence, regime, and MTF checks).
Key features
Non-repainting logic (bar-close confirmation; security() with no lookahead).
Dynamic exhaustion bands (data-driven thresholds vs fixed ±2).
Adaptive smoothing (efficiency-ratio based).
Optional divergence tags on structurally valid pivots.
MTF confirmation (same logic computed on a higher timeframe).
Compact visuals with subtle plotting to reduce chart clutter.
Inputs (high level)
Source (e.g., HLC3 / Close / HA).
Core lookback, fast/slow range blend, and ER length.
Band sensitivity (robust thresholding).
MTF timeframe(s) and agreement requirement.
Toggle divergence & intrabar previews (default off).
Signals & Alerts
Turn Candidate (Up/Down) when multiple conditions align.
Trade-Grade Turn when score ≥ threshold and MTF agrees.
Divergence Confirmed when structural criteria are met.
Alerts are generated on confirmed bar close by default. Optional “preview” mode is available for experimentation.
How to use
Start on your preferred timeframe; optionally enable an HTF (e.g., 4×) for confirmation.
Look for RPS clusters near the exhaustion bands, slope inflections, and (optionally) divergences.
Combine with your own risk management, liquidity, and trend context.
Paper test first and calibrate thresholds to your instrument and timeframe.
Notes & limitations
This is not a buy/sell signal generator and does not predict future returns.
Readings can remain extreme during strong trends; use HTF context and your own filters.
Parameters are intentionally conservative by default; adjust carefully.
Compliance / Disclaimer
Educational & research tool only. Not financial advice. No recommendation to buy/sell any security or derivative.
Past performance, backtests, or examples (if any) are not indicative of future results.
Trading involves risk; you are responsible for your own decisions and risk management.
Built upon the Fisher Transform concept (Ehlers); all modifications, smoothing, regime logic, scoring, and visualization are original work by Paradox Algo.
