Directional Indicator Crossovers v1[JopAlgo]Directional Indicator Crossovers v1 — the classic DMI, made clearer and easier to act on
We'd like to introduce you to a more relaxed, streamlined version of DI. While it may not seem like it at first glance, we've taken the D+/D- method as a starting point and developed our own version of this indicator: two lines, a smooth green/red field indicating who's in control, and clear crossover alerts for a flip. We deliberately chose the step line representation because it closely matches the candlestick patterns on the chart. Designed to help you react faster—without clutter.
What you’ll see
+DI (green) and −DI (red) using classic Wilder smoothing.
A soft control zone between the lines: green when +DI dominates, red when −DI dominates.
Crossover alerts (no labels, no background flooding)—just the turning points.
Why this helps
Instant bias: the shaded field tells you who’s in control without reading values.
Cleaner execution: minimal visuals keep focus on the handoff (+DI↔−DI) and your price levels.
Actionable by design: built-in alerts fire right at the flip to route into your workflow.
How to read it
Bias: Green zone → buyers lead. Red zone → sellers lead.
Trigger: Consider entries on the DI crossover that aligns with your higher-timeframe context (trend, S/R, OB).
Patience in chop: If flips are frequent in tight ranges, wait for sustained zone dominance or confirm on a higher TF.
Exit/flip: Opposite crossover or a clear loss of dominance.
Settings that matter
DI Length (default 14): Higher = calmer, fewer flips. Lower = faster, more signals.
Visuals: Keep the control zone on for quick reads; hide crossover marks if you prefer pure lines.
Alerts: Enable bullish and bearish DI cross alerts; connect to notifications or webhooks as needed.
Starter presets
Intraday (15m–1H): DI Length 12–14 for quicker handoffs.
Swing (4H–1D): DI Length 14–20 for cleaner signals.
Choppy assets: Nudge length higher to dampen noise.
Where it shines (and limits)
Best: Liquid markets (crypto majors, indices, large caps) where handoffs matter.
Works elsewhere: Still useful on slower pairs; extend length for stability.
Limit: Frequent flips in low-range sessions—pair with HTF bias or structure.
Alerts included
Bullish DI Crossover: +DI crosses above −DI.
Bearish DI Crossover: −DI crosses above +DI.
Attribution & License
Built on the Directional Movement Index concept by J. Welles Wilder Jr. (1978).
Independent Pine v6 implementation (not derived from TradingView’s built-in source).
Released as Open Source (MPL-2.0)—please keep the license header intact.
Disclaimer
For educational purposes only; not financial advice. Trading involves risk. Test first, use clear levels, and manage risk. This project is independent and not affiliated with or endorsed by TradingView.
Educational
Lorentzian Harmonic Flow - Temporal Market Dynamic Lorentzian Harmonic Flow - Temporal Market Dynamic (⚡LHF)
By: DskyzInvestments
What this is
LHF Pro is a research‑grade analytical instrument that models market time as a compressible medium , extracts directional flow in curved time using heavy‑tailed kernels, and consults a history‑based memory bank for context before synthesizing a final, bounded probabilistic score . It is not a mashup; each subsystem is mathematically coupled to a single clock (time dilation via gamma) and a single lens (Lorentzian heavy‑tailed weighting). This script is dense in logic (and therefore heavy) because it prioritizes rigor, interpretability, and visual clarity.
Intended use
Education and research. This tool expresses state recognition and regime context—not guarantees. It does not place orders. It is fully functional as published and contains no placeholders. Nothing herein is financial advice.
Why this is original and useful
Curved time: Markets do not move at a constant pace. LHF Pro computes a Lorentz‑style gamma (γ) from relative speed so its analytical windows contract when the tape accelerates and relax when it slows.
Heavy‑tailed lens: Lorentzian kernels weight information with fat tails to respect rare but consequential extremes (unlike Gaussian decay).
Memory of regimes: A K‑nearest‑neighbors engine works in a multi‑feature space using Lorentz kernels per dimension and exponential age fade , returning a memory bias (directional expectation) and assurance (confidence mass).
One ecosystem: Squeeze, TCI, flow, acceleration, and memory live on the same clock and blend into a single final_score —visualized and documented on the dashboard.
Cognitive map: A 2D heat map projects memory resonance by age and flow regime, making “where the past is speaking” visible.
Shadow portfolio metaphor: Neighbor outcomes act like tiny hypothetical positions whose weighted average forms an educational pressure gauge (no execution, purely didactic).
Mathematical framework (full transparency)
1) Returns, volatility, and speed‑of‑market
Log return: rₜ = ln(closeₜ / closeₜ₋₁)
Realized vol: rv = stdev(r, vol_len); vol‑of‑vol: burst = |rv − rv |
Speed‑of‑market (analog to c): c = c_multiplier × (EMA(rv) + 0.5 × EMA(burst) + ε)
2) Trend velocity and Lorentz gamma (time dilation)
Trend velocity: v = |close − close | / (vel_len × ATR)
Relative speed: v_rel = v / c
Gamma: γ = 1 / √(1 − v_rel²), stabilized by caps (e.g., ≤10)
Interpretation: γ > 1 compresses market time → use shorter effective windows.
3) Adaptive temporal scale
Adaptive length: L = base_len / γ^power (bounded for safety)
Harmonic horizons: Lₛ = L × short_ratio, Lₘ = L × mid_ratio, Lₗ = L × long_ratio
4) Lorentzian smoothing and Harmonic Flow
Kernel weight per lag i: wᵢ = 1 / (1 + (d/γ)²), d = i/L
Horizon baselines: lw_h = Σ wᵢ·price / Σ wᵢ
Z‑deviation: z_h = (close − lw_h)/ATR
Harmonic Flow (HFL): HFL = (w_short·zₛ + w_mid·zₘ + w_long·zₗ) / (w_short + w_mid + w_long)
5) Flow kinematics
Velocity: HFL_vel = HFL − HFL
Acceleration (curvature): HFL_acc = HFL − 2·HFL + HFL
6) Squeeze and temporal compression
Bollinger width vs Keltner width using L
Squeeze: BB_width < KC_width × squeeze_mult
Temporal Compression Index: TCI = base_len / L; TCI > 1 ⇒ compressed time
7) Entropy (regime complexity)
Shannon‑inspired proxy on |log returns| with numerical safeguards and smoothing. Higher entropy → more chaotic regime.
8) Memory bank and Lorentzian k‑NN
Feature vector (5D):
Outcomes stored: forward returns at H5, H13, H34
Per‑dimension similarity: k(Δ) = 1 / (1 + Δ²), weighted by user’s feature weights
Age fading: weight_age = mem_fade^age_bars
Neighbor score: sᵢ = similarityᵢ × weight_ageᵢ
Memory bias: mem_bias = Σ sᵢ·outcomeᵢ / Σ sᵢ
Assurance: mem_assurance = Σ sᵢ (confidence mass)
Normalization: mem_bias normalized by ATR and clamped into band
Shadow portfolio metaphor: neighbors behave like micro‑positions; their weighted net forward return becomes a continuous, adaptive expectation.
9) Blended score and breakout proxy
Blend factor: α_mem = 0.45 + 0.15 × (γ − 1)
Final score: final_score = (1−α_mem)·tanh(HFL / (flow_thr·1.5)) + α_mem·tanh(mem_bias_norm)
Breakout probability (bounded): energy = cap(TCI−1) + |HFL_acc|×k + cap(γ−1)×k + cap(mem_assurance)×k; breakout_prob = sigmoid(energy). Caps avoid runaway “100%” readings.
Inputs — every control, purpose, mechanics, and tuning
🔮 Lorentz Core
Auto‑Adapt (Vol/Entropy): On = L responds to γ and entropy (breathes with regime), Off = static testing.
Base Length: Calm‑market anchor horizon. Lower (21–28) for fast tapes; higher (55–89+) for slow.
Velocity Window (vel_len): Bars used in v. Shorter = more reactive γ; longer = steadier.
Volatility Window (vol_len): Bars used for rv/burst (c). Shorter = more sensitive c.
Speed‑of‑Market Multiplier (c_multiplier): Raises/lowers c. Lower values → easier γ spikes (more adaptation). Aim for strong trends to peak around γ ≈ 2–4.
Gamma Compression Power: Exponent of γ in L. <1 softens; >1 amplifies adaptation swings.
Max Kernel Span: Upper bound on smoothing loop (quality vs CPU).
🎼 Harmonic Flow
Short/Mid/Long Horizon Ratios: Partition L into fast/medium/slow views. Smaller short_ratio → faster reaction; larger long_ratio → sturdier bias.
Weights (w_short/w_mid/w_long): Governs HFL blend. Higher w_short → nimble; higher w_long → stable.
📈 Signals
Squeeze Strictness: Threshold for BB1 = compressed (coiled spring); <1 = dilated.
v/c: Relative speed; near 1 denotes extreme pacing. Diagnostic only.
Entropy: Regime complexity; high entropy suggests caution, smaller size, or waiting for order to return.
HFL: Curved‑time directional flow; sign and magnitude are the instantaneous bias.
HFL_acc: Curvature; spikes often accompany regime ignition post‑squeeze.
Mem Bias: Directional expectation from historical analogs (ATR‑normalized, bounded). Aligns or conflicts with HFL.
Assurance: Confidence mass from neighbors; higher → more reliable memory bias.
Squeeze: ON/RELEASE/OFF from BB
RSI VWAP v1 [JopAlgo]RSI VWAP v1.1 made stronger by volume-aware!
We know there's nothing new and the original RSI already does an excellent job. We're just working on small, practical improvements – here's our take: The same basic idea, clearer display, and a single, specially developed rolling line: a VWAP of the RSI that incorporates volume (participation) into the calculation.
Do you prefer the pure classic?
You can still use Wilder or Cutler engines –
but the star here is the VW-RSI + rolling line.
This RSI also offers the possibility of illustrating a possible
POC (Point of Control - or the HAL or VAL) level.
However, the indicator does NOT plot any of these levels itself.
We have included an illustration in the chart for this!
We hope this version makes your decision-making easier.
What you’ll see
The RSI line with a 50 midline and optional bands: either static 70/30 or adaptive μ±k·σ of the Rolling Line.
One smoothing concept only: the Rolling Line (light blue) = VWAP of RSI.
Shadow shading between RSI and the Rolling Line (green when RSI > line, red when RSI < line).
A lighter tint only on the parts of that shadow that sit above the upper band or below the lower band (quick overbought/oversold context).
Simple divergence lines drawn from RSI pivots (green for regular bullish, red for regular bearish). No labels, no buy/sell text—kept deliberately clean.
What’s new, and why it helps
VW-RSI engine (default):
RSI can be computed from volume-weighted up/down moves, so momentum reflects how much traded when price moved—not just the direction.
Rolling Line (VWAP of RSI) with pure VWAP adaptation:
Low volume: blends toward a faster VWAP so early, thin starts aren’t missed.
Volume spikes: blends toward a slower VWAP so a single heavy bar doesn’t whip the curve.
You can reveal the Base Rolling (pre-adaptation) line to see exactly how much adaptation is happening.
Adaptive bands (optional):
Instead of fixed 70/30, use mean ± k·stdev of the Rolling Line over a lookback. Levels breathe with the market—useful in strong trends where static bounds stay pinned.
Minimal, readable panel:
One smoothing, one story. The shadow tells you who’s in control; the lighter highlight shows stretch beyond your lines.
How to read it (fast)
Bias: RSI above 50 (and a rising Rolling Line) → bullish bias; below 50 → bearish bias.
Trigger: RSI crossing the Rolling Line with the bias (e.g., above 50 and crossing up).
Stretch: Near/above the upper band, avoid chasing; near/below the lower band, avoid panic—prefer a cross back through the line.
Divergence lines: Use as context, not as standalone signals. They often help you wait for the next cross or avoid late entries into exhaustion.
Settings that actually matter
RSI Engine: VW-RSI (default), Wilder, or Cutler.
Rolling Line Length: the VWAP length on RSI (higher = calmer, lower = earlier).
Adaptive behavior (pure VWAP):
Speed-up on Low Volume → blends toward fast VWAP (factor of your length).
Dampen Spikes (volume z-score) → blends toward slow VWAP.
Fast/Slow Factors → how far those fast/slow variants sit from the base length.
Bands: choose Static 70/30 or Adaptive μ±k·σ (set the lookback and k).
Visuals: show/hide Base Rolling (ref), main shadow, and highlight beyond bands.
Signal gating: optional “ignore first bars” per day/session if you dislike open noise.
Starter presets
Scalp (1–5m): RSI 9–12, Rolling 12–18, FastFactor ~0.5, SlowFactor ~2.0, Adaptive on.
Intraday (15m–1H): RSI 10–14, Rolling 18–26, Bands k = 1.0–1.4.
Swing (4H–1D): RSI 14–20, Rolling 26–40, Bands k = 1.2–1.8, Adaptive on.
Where it shines (and limits)
Best: liquid markets where volume structure matters (majors, indices, large caps).
Works elsewhere: even with imperfect volume, the shadow + bands remain useful.
Limits: very thin/illiquid assets reduce the benefit of volume-weighting—lengthen settings if needed.
Attribution & License
Based on the concept and baseline implementation of the “Relative Strength Index” by TradingView (Pine v6 built-in).
Released as Open-source (MPL-2.0). Please keep the license header and attribution intact.
Disclaimer
For educational purposes only; not financial advice. Markets carry risk. Test first, use clear levels, and manage risk. This project is independent and not affiliated with or endorsed by TradingView.
☸Gap Detector [NHP]🔶This is a Pine Script code for a “Gap Detector” study in TradingView. The script scans for gaps in the price chart and labels them as either ‘🟢Bull gap’ or ‘🔴Bear gap’. Here’s a brief explanation of the code:
🔶Length and Width are user inputs that define the number of bars to look back and the width of the lines drawn, respectively.
➡️Gap_start and gap_end are variables that store the start and end of a gap.
➡️Gap_bull and gap_bear are boolean variables that indicate whether a bull or bear gap has been detected.
🔶Inf_gap and sup_gap are variables that store the lower and upper bounds of a gap.
The script then iterates over the specified length of bars. If a gap is detected (a high price that is lower than the previous bar’s low price for a bull gap, or a low price that is higher than the previous bar’s high price for a bear gap), it calculates the size of the gap and draws lines and labels on the chart if the gap is larger than 5 pips. ( pips meaning percentage in point)
🔶All content provided is for informational & educational purposes only. Past performance does not guarantee future results.
Seasonality Heatmap [QuantAlgo]🟢 Overview
The Seasonality Heatmap analyzes years of historical data to reveal which months and weekdays have consistently produced gains or losses, displaying results through color-coded tables with statistical metrics like consistency scores (1-10 rating) and positive occurrence rates. By calculating average returns for each calendar month and day-of-week combination, it identifies recognizable seasonal patterns (such as which months or weekdays tend to rally versus decline) and synthesizes this into actionable buy low/sell high timing possibilities for strategic entries and exits. This helps traders and investors spot high-probability seasonal windows where assets have historically shown strength or weakness, enabling them to align positions with recurring bull and bear market patterns.
🟢 How It Works
1. Monthly Heatmap
How % Return is Calculated:
The indicator fetches monthly closing prices (or Open/High/Low based on user selection) and calculates the percentage change from the previous month:
(Current Month Price - Previous Month Price) / Previous Month Price × 100
Each cell in the heatmap represents one month's return in a specific year, creating a multi-year historical view
Colors indicate performance intensity: greener/brighter shades for higher positive returns, redder/brighter shades for larger negative returns
What Averages Mean:
The "Avg %" row displays the arithmetic mean of all historical returns for each calendar month (e.g., averaging all Januaries together, all Februaries together, etc.)
This metric identifies historically recurring patterns by showing which months have tended to rise or fall on average
Positive averages indicate months that have typically trended upward; negative averages indicate historically weaker months
Example: If April shows +18.56% average, it means April has averaged a 18.56% gain across all years analyzed
What Months Up % Mean:
Shows the percentage of historical occurrences where that month had a positive return (closed higher than the previous month)
Calculated as:
(Number of Months with Positive Returns / Total Months) × 100
Values above 50% indicate the month has been positive more often than negative; below 50% indicates more frequent negative months
Example: If October shows "64%", then 64% of all historical Octobers had positive returns
What Consistency Score Means:
A 1-10 rating that measures how predictable and stable a month's returns have been
Calculated using the coefficient of variation (standard deviation / mean) - lower variation = higher consistency
High scores (8-10, green): The month has shown relatively stable behavior with similar outcomes year-to-year
Medium scores (5-7, gray): Moderate consistency with some variability
Low scores (1-4, red): High variability with unpredictable behavior across different years
Example: A consistency score of 8/10 indicates the month has exhibited recognizable patterns with relatively low deviation
What Best Means:
Shows the highest percentage return achieved for that specific month, along with the year it occurred
Reveals the maximum observed upside and identifies outlier years with exceptional performance
Useful for understanding the range of possible outcomes beyond the average
Example: "Best: 2016: +131.90%" means the strongest January in the dataset was in 2016 with an 131.90% gain
What Worst Means:
Shows the most negative percentage return for that specific month, along with the year it occurred
Reveals maximum observed downside and helps understand the range of historical outcomes
Important for risk assessment even in months with positive averages
Example: "Worst: 2022: -26.86%" means the weakest January in the dataset was in 2022 with a 26.86% loss
2. Day-of-Week Heatmap
How % Return is Calculated:
Calculates the percentage change from the previous day's close to the current day's price (based on user's price source selection)
Returns are aggregated by day of the week within each calendar month (e.g., all Mondays in January, all Tuesdays in January, etc.)
Each cell shows the average performance for that specific day-month combination across all historical data
Formula:
(Current Day Price - Previous Day Close) / Previous Day Close × 100
What Averages Mean:
The "Avg %" row at the bottom aggregates all months together to show the overall average return for each weekday
Identifies broad weekly patterns across the entire dataset
Calculated by summing all daily returns for that weekday across all months and dividing by total observations
Example: If Monday shows +0.04%, Mondays have averaged a 0.04% change across all months in the dataset
What Days Up % Mean:
Shows the percentage of historical occurrences where that weekday had a positive return
Calculated as:
(Number of Positive Days / Total Days Observed) × 100
Values above 50% indicate the day has been positive more often than negative; below 50% indicates more frequent negative days
Example: If Fridays show "54%", then 54% of all Fridays in the dataset had positive returns
What Consistency Score Means:
A 1-10 rating measuring how stable that weekday's performance has been across different months
Based on the coefficient of variation of daily returns for that weekday across all 12 months
High scores (8-10, green): The weekday has shown relatively consistent behavior month-to-month
Medium scores (5-7, gray): Moderate consistency with some month-to-month variation
Low scores (1-4, red): High variability across months, with behavior differing significantly by calendar month
Example: A consistency score of 7/10 for Wednesdays means they have performed with moderate consistency throughout the year
What Best Means:
Shows which calendar month had the strongest average performance for that specific weekday
Identifies favorable day-month combinations based on historical data
Format shows the month abbreviation and the average return achieved
Example: "Best: Oct: +0.20%" means Mondays averaged +0.20% during October months in the dataset
What Worst Means:
Shows which calendar month had the weakest average performance for that specific weekday
Identifies historically challenging day-month combinations
Useful for understanding which month-weekday pairings have shown weaker performance
Example: "Worst: Sep: -0.35%" means Tuesdays averaged -0.35% during September months in the dataset
3. Optimal Timing Table/Summary Table
→ Best Month to BUY: Identifies the month with the lowest average return (most negative or least positive historically), representing periods where prices have historically been relatively lower
Based on the observation that buying during historically weaker months may position for subsequent recovery
Shows the month name, its average return, and color-coded performance
Example: If May shows -0.86% as "Best Month to BUY", it means May has historically averaged -0.86% in the analyzed period
→ Best Month to SELL: Identifies the month with the highest average return (most positive historically), representing periods where prices have historically been relatively higher
Based on historical strength patterns in that month
Example: If July shows +1.42% as "Best Month to SELL", it means July has historically averaged +1.42% gains
→ 2nd Best Month to BUY: The second-lowest performing month based on average returns
Provides an alternative timing option based on historical patterns
Offers flexibility for staged entries or when the primary month doesn't align with strategy
Example: Identifies the next-most favorable historical buying period
→ 2nd Best Month to SELL: The second-highest performing month based on average returns
Provides an alternative exit timing based on historical data
Useful for staged profit-taking or multiple exit opportunities
Identifies the secondary historical strength period
Note: The same logic applies to "Best Day to BUY/SELL" and "2nd Best Day to BUY/SELL" rows, which identify weekdays based on average daily performance across all months. Days with lowest averages are marked as buying opportunities (historically weaker days), while days with highest averages are marked for selling (historically stronger days).
🟢 Examples
Example 1: NVIDIA NASDAQ:NVDA - Strong May Pattern with High Consistency
Analyzing NVIDIA from 2015 onwards, the Monthly Heatmap reveals May averaging +15.84% with 82% of months being positive and a consistency score of 8/10 (green). December shows -1.69% average with only 40% of months positive and a low 1/10 consistency score (red). The Optimal Timing table identifies December as "Best Month to BUY" and May as "Best Month to SELL." A trader recognizes this high-probability May strength pattern and considers entering positions in late December when prices have historically been weaker, then taking profits in May when the seasonal tailwind typically peaks. The high consistency score in May (8/10) provides additional confidence that this pattern has been relatively stable year-over-year.
Example 2: Crypto Market Cap CRYPTOCAP:TOTALES - October Rally Pattern
An investor examining total crypto market capitalization notices September averaging -2.42% with 45% of months positive and 5/10 consistency, while October shows a dramatic shift with +16.69% average, 90% of months positive, and an exceptional 9/10 consistency score (blue). The Day-of-Week heatmap reveals Mondays averaging +0.40% with 54% positive days and 9/10 consistency (blue), while Thursdays show only +0.08% with 1/10 consistency (yellow). The investor uses this multi-layered analysis to develop a strategy: enter crypto positions on Thursdays during late September (combining the historically weak month with the less consistent weekday), then hold through October's historically strong period, considering exits on Mondays when intraweek strength has been most consistent.
Example 3: Solana BINANCE:SOLUSDT - Extreme January Seasonality
A cryptocurrency trader analyzing Solana observes an extraordinary January pattern: +59.57% average return with 60% of months positive and 8/10 consistency (teal), while May shows -9.75% average with only 33% of months positive and 6/10 consistency. August also displays strength at +59.50% average with 7/10 consistency. The Optimal Timing table confirms May as "Best Month to BUY" and January as "Best Month to SELL." The Day-of-Week data shows Sundays averaging +0.77% with 8/10 consistency (teal). The trader develops a seasonal rotation strategy: accumulate SOL positions during May weakness, hold through the historically strong January period (which has shown this extreme pattern with reasonable consistency), and specifically target Sunday exits when the weekday data shows the most recognizable strength pattern.
Smart HA Bias v1 [JopAlgo]Smart HA Bias v1 (SHABV1)
What are we showing you here!?
Why is this indicator 'Smart'?
Because we've built it on Heikin-Ashi as a trend/bias tool that automatically adjusts to timeframes and volatility, provides you with transferable thresholds for all assets, and manages trades with a regime-aware trail – so you spend less time adjusting controls and more time trading.
What is SHABV1?
The SHABV1 creates smoothed Heikin-Ashi candlesticks and measures body strength (closing-opening) relative to current volatility—this isn't anything new, though!—but this strength is normalized to a cross-asset scale we call HABO (Heikin-Ashi Body Oscillator). We then apply an adaptive smoother that accelerates in clear trends and decelerates in choppy trends. If HABO holds a few bars above an automatic threshold, the regime (bull/bear market) is switched. A yellow regime trail runs below the price in bull markets and above the price in bear markets and serves as a guide for exits.
Why this is “SMART” (vs a conventional smoothed HA)
Most HA indicators just smooth candles and color them. SHABV1 adds four edges:
Volatility-neutral strength (HABO):
HA body is divided by ATR and scaled → “+12” on BTC 4H ≈ “+12” on EURUSD 4H. Your thresholds travel across assets/TFs.
Adaptive smoothing (KAMA):
Auto reacts to market efficiency (clean trends vs chop). Less noise without delaying the real move.
Hysteresis + confirmation:
Regime changes require holding beyond ±threshold for N bars → far fewer one-bar fakes.
Regime-aware trail:
A one-way ratchet based on smoothed HA bands. In a bull it only steps up; in a bear it only steps down. Great as a soft stop/exit.
The “Auto” features (and why you can’t tweak them)
We’ve moved the “invisible knobs” into the background so you don’t babysit settings that barely show on chart:
Auto ATR length → HABO scale:
Chooses a sensible ATR window by timeframe, then fine-tunes by recent volatility. Keeps HABO comparable across symbols.
Auto KAMA (adaptive smoothing):
Sets the slow side of the adaptive filter by a noise score. Faster in trends, slower in chop—hands-free.
Auto threshold (±HABO):
Derives a dynamic threshold from the distribution of HABO on your chart, then adjusts by your Sensitivity. No more guessing “8 vs 12 vs 15”.
Auto smoothing split:
One HA Smooth slider controls both smoothing layers under the hood at a calibrated ratio.
These are not adjustable because they’re designed to keep behavior consistent and portable. The two knobs you do control have immediate, visible impact: HA Smooth (structure tightness) and Sensitivity (earlier/stricter flips).
What you can control
HA Smooth — lower = tighter/faster, higher = calmer/slower.
Sensitivity — >1.0 earlier flips, <1.0 stricter.
Confirm Bars — 1–3 (default 2). Higher = fewer whipsaws.
HTF Bias — turn Lightweight OFF and set an HTF (e.g., trade 4H, bias 1D) for top-down alignment.
Visuals — Bias Fill, Regime Trail (ON by default), Status Panel.
How to read it
Color / Regime: green = bull bias, red = bear bias (confirmed by hysteresis).
Yellow line: the Regime Trail (bull = rising floor; bear = falling ceiling).
Status Panel (top-right): Regime | HABO | Slope.
HABO > 0 = bullish pressure; < 0 = bearish.
Slope UP/DOWN = oscillator momentum direction (helps spot weakening).
Simple trading playbook
Pick TF (15m/1H/4H/1D).
(Optional) Enable HTF bias: Lightweight OFF → set HTF to the next TF up. Only take trades with HTF alignment.
Entries:
Safer: enter on confirmed regime (color flip after holding beyond threshold).
Earlier (experienced): zero-cross “early flip” only with extra confluence (VWAP/OBV/SR).
Management:
Let winners run while price respects the Trail.
Consider reduce/exit on close through the Trail or Weakening (slope turns against regime).
Tuning:
Too many flips? Raise Sensitivity↓ (<1.0) or Confirm Bars to 3.
Too late? Sensitivity↑ (>1.0) or lower HA Smooth slightly.
Quick presets (starting points)
15m: Smooth 12, Sens 1.05, Confirm 2, HTF=1H (optional)
1H: Smooth 12, Sens 1.00, Confirm 2, HTF=4H
4H: Smooth 14, Sens 1.00, Confirm 2, HTF=1D
1D: Smooth 16, Sens 0.95, Confirm 2–3, HTF=1W
Why traders like SHABV1
Portable logic: HABO + Auto threshold means fewer re-tunes switching BTC ↔ FX ↔ indices.
Chop-aware: Adaptive smoothing + hysteresis trims false flips.
Clear exits: Regime Trail turns structure into a mechanical trailing rule.
Non-repaint: HTF imports are handled with anti-lookahead; trail ratchets one way.
Notes:
This is a bias/management tool, not financial advice. Use with your own risk model (hard stop at structure or % risk).
Volume v4 (Dollar Value) by Koenigsegg📊 Volume v3 (Dollar Value) by Koenigsegg
🎯 Purpose:
Volume v3 (Dollar Value) by Koenigsegg transforms traditional raw-unit volume into dollar-denominated volume, revealing how much money actually flows through each candle.
Instead of measuring how many coins or contracts were traded, this version calculates the total traded value = volume × average price (hlc3), allowing traders to visually assess capital intensity and market participation within each move.
⚙️ Core Features
- Converts raw volume into USD-based traded value for each candle.
- Color-coded bars show bullish (green/teal) vs. bearish (red) activity.
- Built-in SMA and SMMA overlays highlight sustained shifts in value flow.
- Designed for visual clarity to support momentum, exhaustion, and divergence studies.
📖 How to Read It
Rising Dollar Volume — indicates growing market participation and strong capital flow, often aligning with impulsive waves in trend direction.
Falling Dollar Volume — signals waning interest or reduced participation, potentially hinting at correction or exhaustion phases.
Comparing Legs — when price makes new highs/lows but dollar volume weakens, it can reveal divergences between price movement and actual capital commitment.
SMA / SMMA Lines — use them to identify longer-term accumulation or depletion of market activity, separating short bursts from sustained inflows or outflows.
The goal is to visualize the strength of market moves in terms of capital energy, not just tick activity. This distinction helps traders interpret whether a trend is being driven by genuine money flow or low-liquidity drift.
⚠️ Disclaimer
This script is provided for research and educational purposes only.
It does not constitute financial advice, investment recommendations, or trading signals.
Always conduct your own analysis and manage your own risk when trading live markets.
The author accepts no liability for financial losses incurred from use of this tool.
🧠 Credits
Developed and published by Koenigsegg.
Written in Pine Script® v6, fully compliant with TradingView’s House Rules for Pine Scripts.
Licensed under the Mozilla Public License 2.0.
Intrinsic Value AnalyzerThe Intrinsic Value Analyzer is an all-in-one valuation tool that automatically calculates the fair value of a stock using industry-standard valuation techniques. It estimates intrinsic value through Discounted Cash Flow (DCF), Enterprise Value to Revenue (EV/REV), Enterprise Value to EBITDA (EV/EBITDA), and Price to Earnings (P/EPS). The model features adjustable parameters and a built-in alert system that notifies investors in real time when valuation multiples reach predefined thresholds. It also includes a comprehensive, color-coded table that compares the company’s historical average growth rates, valuation multiples, and financial ratios with the most recent values, helping investors quickly assess how current values align with historical averages.
The model calculates the historical Compounded Annual Growth Rates (CAGR) and average valuation multiples over the selected Lookback Period. It then projects Revenue, Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA), Earnings per Share (EPS), and Free Cash Flow (FCF) for the selected Forecast Period and discounts their future values back to the present using the Weighted Average Cost of Capital (WACC) or the Cost of Equity. By default, the model automatically applies the historical averages displayed in the table as the growth forecasts and target multiples. These assumptions can be modified in the menu by entering custom REV-G, EBITDA-G, EPS-G, and FCF-G growth forecasts, as well as EV/REV, EV/EBITDA, and P/EPS target multiples. When new input values are entered, the model recalculates the fair value in real time, allowing users to see how changes in these assumptions affect the company’s fair value.
DCF = (Sum of (FCF × (1 + FCF-G) ^ t ÷ (1 + WACC) ^ t) for each year t until Forecast Period + ((FCF × (1 + FCF-G) ^ Forecast Period × (1 + LT Growth)) ÷ ((WACC - LT Growth) × (1 + WACC) ^ Forecast Period)) + Cash - Debt - Preferred Equity - Minority Interest) ÷ Shares Outstanding
EV/REV = ((Revenue × (1 + REV-G) ^ Forecast Period × EV/REV Target) ÷ (1 + WACC) ^ Forecast Period + Cash - Debt - Preferred Equity - Minority Interest) ÷ Shares Outstanding
EV/EBITDA = ((EBITDA × (1 + EBITDA-G) ^ Forecast Period × EV/EBITDA Target) ÷ (1 + WACC) ^ Forecast Period + Cash - Debt - Preferred Equity - Minority Interest) ÷ Shares Outstanding
P/EPS = (EPS × (1 + EPS-G) ^ Forecast Period × P/EPS Target) ÷ (1 + Cost of Equity) ^ Forecast Period
The discounted one-year average analyst price target (1Y PT) is also displayed alongside the valuation labels to provide an overview of consensus estimates. For the DCF model, the terminal long-term FCF growth rate (LT Growth) is based on the selected country to reflect expected long-term nominal GDP growth and can be modified in the menu. For metrics involving FCF, users can choose between reported FCF, calculated as Cash From Operations (CFO) - Capital Expenditures (CAPEX), or standardized FCF, calculated as Earnings Before Interest and Taxes (EBIT) × (1 - Average Tax Rate) + Depreciation and Amortization - Change in Net Working Capital - CAPEX. Historical average values displayed in the left column of the table are based on Fiscal Year (FY) data, while the latest values in the right column use the most recent Trailing Twelve Month (TTM) or Fiscal Quarter (FQ) data. The indicator displays color-coded price labels for each fair value estimate, showing the percentage upside or downside from the current price. Green indicates undervaluation, while red indicates overvaluation. The table follows a separate color logic:
REV-G, EBITDA-G, EPS-G, FCF-G = Green indicates positive annual growth when the CAGR is positive. Red indicates negative annual growth when the CAGR is negative.
EV/REV = Green indicates undervaluation when EV/REV ÷ REV-G is below 1. Red indicates overvaluation when EV/REV ÷ REV-G is above 2. Gray indicates fair value.
EV/EBITDA = Green indicates undervaluation when EV/EBITDA ÷ EBITDA-G is below 1. Red indicates overvaluation when EV/EBITDA ÷ EBITDA-G is above 2. Gray indicates fair value.
P/EPS = Green indicates undervaluation when P/EPS ÷ EPS-G is below 1. Red indicates overvaluation when P/EPS ÷ EPS-G is above 2. Gray indicates fair value.
EBITDA% = Green indicates profitable operations when the EBITDA margin is positive. Red indicates unprofitable operations when the EBITDA margin is negative.
FCF% = Green indicates strong cash conversion when FCF/EBITDA > 50%. Red indicates unsustainable FCF when FCF/EBITDA is negative. Gray indicates normal cash conversion.
ROIC = Green indicates value creation when ROIC > WACC. Red indicates value destruction when ROIC is negative. Gray indicates positive but insufficient returns.
ND/EBITDA = Green indicates low leverage when ND/EBITDA is below 1. Red indicates high leverage when ND/EBITDA is above 3. Gray indicates moderate leverage.
YIELD = Green indicates positive shareholder return when Shareholder Yield > 1%. Red indicates negative shareholder return when Shareholder Yield < -1%.
The Return on Invested Capital (ROIC) is calculated as EBIT × (1 - Average Tax Rate) ÷ (Average Debt + Average Equity - Average Cash). Shareholder Yield (YIELD) is calculated as the CAGR of Dividend Yield - Change in Shares Outstanding. The Weighted Average Cost of Capital (WACC) is displayed at the top left of the table and is derived from the current Market Cap (MC), Debt, Cost of Equity, and Cost of Debt. The Cost of Equity is calculated using the Equity Beta, Index Return, and Risk-Free Rate, which are based on the selected country. The Equity Beta (β) is calculated as the 5-year Blume-adjusted beta between the weekly logarithmic returns of the underlying stock and the selected country’s stock market index. For accurate calculations, it is recommended to use the stock ticker listed on the primary exchange corresponding to the company’s main index.
Cost of Debt = (Interest Expense on Debt ÷ Average Debt) × (1 - Average Tax Rate)
Cost of Equity = Risk-Free Rate + Equity Beta (β) × (Index Return - Risk-Free Rate)
WACC = (MC ÷ (MC + Debt)) × Cost of Equity + (Debt ÷ (MC + Debt)) × Cost of Debt
This indicator works best for operationally stable and profitable companies that are primarily valued based on fundamentals rather than speculative growth, such as those in the industrial, consumer, technology, and healthcare sectors. It is less suitable for early-stage, unprofitable, or highly cyclical companies, including energy, real estate, and financial institutions, as these often have irregular cash flows or distorted balance sheets. It is also worth noting that TradingView’s financial data provider, FactSet, standardizes financial data from official company filings to align with a consistent accounting framework. While this improves comparability across companies, industries, and countries, it may also result in differences from officially reported figures.
In summary, the Intrinsic Value Analyzer is a comprehensive valuation tool designed to help long-term investors estimate a company’s fair value while comparing historical averages with the latest values. Fair value estimates are driven by growth forecasts, target multiples, and discount rates, and should always be interpreted within the context of the underlying assumptions. By default, the model applies historical averages and current discount rates, which may not accurately reflect future conditions. Investors are therefore encouraged to adjust inputs in the menu to better understand how changes in these key assumptions influence the company’s fair value.
EMA TrendVerseEMA TrendVerse analyzes the relationship between four exponential moving averages to give you a complete picture of market trends. The intuitive color system immediately shows you which way the wind is blowing . The dashboard provides real-time updates on trend direction and strength.
Key Features:
• Four EMA System: Track trends across multiple timeframes with customizable EMA periods (10, 30, 233, 360 by default)
• Smart Dashboard: Choose from three sizes (Small, Medium, Large) to fit your trading style and screen space
• Visual Trend Analysis: Beautiful gradient fills between EMAs create instant visual recognition of trend strength
• Color-Coded Candles: Optional candle coloring based on overall trend direction for quick market assessment
• Professional Themes: Dark, Light, Blue, and Green themes to match your trading environment
• Real-time Momentum: Track percentage momentum between EMA pairs for precise entry and exit timing
• Trend Scoring: Simple 0-3 scoring system tells you at a glance how many timeframes are bullish
India VIX Based Nifty/BankNifty Range Calculator (Auto Fetch)VIX-Based Expected Daily Range (Auto Volatility Forecast)
Created by: Harshiv Symposium
📖 Purpose
This indicator automatically fetches the India VIX value and calculates the expected daily price range for major Indian indices such as Nifty and BankNifty.
It helps traders understand how much the market is likely to move today based on current volatility conditions.
Designed for educational and analytical awareness, not for signals or profit-making systems.
⚙️ Core Logic
Expected Daily Move (Range) = (India VIX × Current Index Price) ÷ Multiplier
- Multiplier for Nifty: 1000
- Multiplier for BankNifty: 700
This calculation projects the 1-standard-deviation (≈ 68% probability) and 2-standard-deviation (≈ 95% probability) movement zones for the day.
📊 Example
If India VIX = 15 and Nifty = 25,000:
Expected Move ≈ (15 × 25,000) ÷ 1000 = 375 points
Hence,
- 68% Range: 24,625 – 25,375
- 95% Range: 24,250 – 25,750
This gives traders a realistic idea of daily volatility boundaries.
🧭 Key Features
✅ Auto-Fetch India VIX
No need for manual input — automatically pulls live data from NSE:INDIAVIX.
✅ Dynamic Range Visualization
Plots upper/lower boundaries for 1σ and 2σ probability zones with shaded expected-move area.
✅ Dashboard Panel
Displays:
- Current VIX
- Expected Move (in points and %)
- Upper and Lower Ranges
✅ Smart Alerts
Alerts when price crosses upper or lower volatility range — potential breakout signal.
🎯 How It Helps
Intraday Traders:
Know the likely daily movement (e.g., ±220 pts on Nifty) and plan realistic targets or stops.
Options Traders:
Quickly assess whether it’s a seller-friendly (low VIX, small range) or buyer-friendly (high VIX, large range) session.
Risk Managers:
Use volatility context for stop-loss width and position sizing.
Breakout Traders:
If price breaks beyond the 2σ range → indicates potential volatility expansion.
💡 Interpretation Guide
Condition Market Behavior Strategy Insight
VIX ↓ ( < 14 ) Calm / Range-bound Option Selling Edge
VIX ↑ ( > 20 ) Volatile Sessions Option Buying Edge
Price within Range Stable Market Mean Reversion Setups
Price breaks Range Volatility Expansion Breakout Trades
⚠️ Disclaimer
This indicator is for educational and awareness purposes only.
It does not generate buy/sell signals or guarantee returns.
Always apply your own analysis and risk management.
VWAP-EMA OscillatorThis oscillator calculates the difference between Monthly VWAP and EMA(100) to identify potential trend changes and price positioning.
Key Features:
• Monthly VWAP calculation that resets at the start of each month
• EMA with fixed 100-period (customizable source)
• Oscillator value = Monthly VWAP - EMA(100)
• Color-coded visualization (green for positive, red for negative)
• Filled area between oscillator and zero line
• Customizable alert thresholds
• Information table showing current values and signals
How to Use:
• Positive values (above zero) indicate VWAP is above EMA - potential bullish signal
• Negative values (below zero) indicate VWAP is below EMA - potential bearish signal
• Zero line crossovers can signal trend changes
• Customizable threshold levels for additional entry/exit signals
Settings:
• EMA Period: 100 (fixed for current timeframe)
• VWAP Source: HLC3 (customizable)
• EMA Source: Close (customizable)
• Alert Threshold: 0.5 (customizable)
Sivaji BTST Trend FinderThis script will mark up triangles where supports a bullish and down triangles for bearish ones.
XAUUSD Watchdog — FVG + BOS (Lite, v6)smart-money structure and FVG alert tool for XAUUSD with auto 1 : R targets.
Adarsh Volume ThresholdThis is one if it's kinds Indicator which Combines multiple indicators to confirm Trend and Signals without a mess on chart.
It will give you a visual idea to take best decision for Stop Loss and Target before executing your order.
Enter your capital/Amount for trading and decide max risk you can take, it will calculate your Qty, SL point and Target Point.
VP Table will keep updating you the min Amount traded on candle as per your input min. threshold.
Pro Trading System - Student Edition
---
### **PRO TRADING SYSTEM - STUDENT EDITION**
🔓 **FREE EDUCATIONAL TOOL FOR STUDENTS**
🎯 **PROFESSIONAL TRADING FRAMEWORK:**
* Advanced Multi-Timeframe Trend Analysis
* Professional EMA Matrix with Smart Visibility Control
* Gann Theory Based Session Levels
* Real-time Multi-Timeframe Trend Dashboard
* Institutional Grade Price Action Analysis
📊 **INTELLIGENT EMA MATRIX:**
* **CORE EMAs:** 5M-21 (Red) & 5M-50 (Green) - Always Visible
* **EXTENDED MATRIX:** 3M-200 to 1W-200 (Optional)
* Smart Persistent Labels - Visible Even When Lines Are Hidden
* Complete Color Customization
* Individual EMA Group Visibility Control
🔄 **MULTI-TIMEFRAME TREND DASHBOARD:**
* **POSITIONAL TRADING** - Daily Chart Analysis
* **MEDIUM SWING** - 4-Hour Trend Direction
* **SHORT SWING** - 1-Hour Market Momentum
* **INTRADAY** - 15-Minute Trading Signals
* **SCALPING** - **5-Minute High-Probability Entries
* Instant Visual Color Coding (Green = Bullish, Red = Bearish)
* Quick Trend Identification Across All Timeframes
* Space-Optimized Mobile & Desktop Design
📈 **GANN-BASED SESSION LEVELS:**
* 12 Strategic BUY Side Levels (Above Session Open)
* 12 Calculated SELL Side Levels (Below Session Open)
* Full Color & Transparency Customization
* Automatic Daily Level Regeneration
⚙️ **PROFESSIONAL CUSTOMIZATION:**
* Complete EMA Color & Visibility Control
* Session Levels Color Schemes
* Line Width & Style Adjustments
* Label Display Management
* Professional Trading Workspace Setup
📱 **UNIVERSAL COMPATIBILITY:**
* Fully Optimized for Mobile Trading
* Flawless Desktop Performance
* Clean, Uncluttered Visual Interface
* Zero Repainting - Real-time Calculations Only
🎓 **EDUCATIONAL PURPOSE:**
* 100% FREE Student Learning Tool
* Professional Trading Concept Implementation
* **Multi-Timeframe Analysis & Scalping Strategy Training**
* Risk Management Principles
* Market Structure Education
🔧 **TECHNICAL SPECIFICATIONS:**
* Built on Pine Script v5
* Maximum Drawing Objects: 500
* Overlay Chart Display
* Real-time Price Calculations
* Lightweight & Efficient
⚠️ **IMPORTANT DISCLAIMER:**
This is a **FREE educational tool** designed for student learning purposes only. It is **not** a paid indicator or financial advice tool. The addition of the 5-minute scalping framework is for educational demonstration of high-frequency timeframes and carries significant risk. Always practice proper risk management and paper trading before live markets. The developers are not responsible for any trading decisions made using this tool.
MTP Pro — Fixed (v6)Forecast price by using some statistic
this project is not finished yet and all of my project are unfinished please refrain from using them.
do visit my project and once it is finish i will update a new one.
i focus mainly on scalping
iFVG Ultimate+ | DodgysDDOVERVIEW
iFVG Ultimate+ | DodgysDD is a professional-grade visualization framework that automates the identification and management of Inversion Fair Value Gaps (IFVGs)
It is designed for analysts and educators studying institutional price behavior, liquidity dynamics, and displacement-based imbalances.
This indicator does not provide trading signals or forecasts.
All logic serves educational and analytical purposes only.
A Fair Value Gap (FVG) appears when strong directional displacement prevents candle bodies from overlapping.When a liquidity sweep occurs and price later closes through that gap, the imbalance is considered inverted. This often marks a shift in order-flow.
iFVG Ultimate+ tracks these transitions using a rule-based sequence:
Liquidity Sweep – Price sweeps a previous swing high or low.
Displacement – Body-to-body gap forms as price accelerates away.
Inversion – Full candle body closes through the gap after raid.
Validation and Tracking – Confirmed inversions are stored and managed until completion or invalidation.
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PURPOSE AND SCOPE
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The framework serves as a research tool to document and analyze IFVG behavior within liquidity and session contexts.
It is commonly used to:
-Record and journal IFVG formations for back-testing and model study.
-Assess how often gaps complete or invalidate after sweeps.
-Evaluate session-based patterns (London, Asia, New York).
-Overlay HTF PD Arrays to observe inter-timeframe delivery.
-Receive custom alerts to your phone
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LOGIC STRUCTURE
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iFVG Ultimate+ runs a five-stage validation process to ensure sequential, non-repainting behavior.
Liquidity Framework:
• Detects swing highs and lows on aligned timeframes (automatic or manual selection).
• Logs session highs/lows for Asia (20:00–00:00 NY) and London (02:00–05:00 NY).
• Includes data wicks around 08:30 NY for event reference.
FVG Detection and Displacement Filter:
• Identifies body-based imbalances using ATR-scaled sensitivity modes (Sensitive / Normal / Strict).
• Supports “Single” or “Series” modes to merge adjacent gaps.
• Excludes weak displacements using minimum ATR thresholds.
Inversion Validation:
• Confirms only when a complete candle body closes through a qualifying FVG within a user-defined window (6 or 15 bars).
• Duplicate detections are ignored; mitigation states are recorded.
HTF Context Integration:
• Maps higher-time-frame PD Arrays and tracks their delivery status.
• Labels active zones (e.g. “H4 PDA”) and updates on HTF close.
Model Lifecycle and Limits:
• Plots the inversion line and derives educational limit levels: Break-Even and Stop-Loss.
• Tracks until opposing liquidity is swept (model complete) or an invalidation event occurs.
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COMPONENTS AND VISUALS
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-IFVG Line — Marks confirmed inversion at close.
-Break-Even / Stop-Loss Lines — Calculated retrospectively for journal grading.
-Session High/Low Markers — London and Asia reference levels.
-Data Wicks — 8:30 NY “DATA.H/L” labels for event volatility.
-SMTs — Compares current symbol to correlated instrument for divergence confirmation.
-Checklist Panel — Tracks liquidity, momentum, HTF delivery, and SMT conditions.
-Setup Grade Display — Computes qualitative score (A+ to C) based on met conditions.
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INPUT CATEGORIES
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General — Detection mode, ATR strictness, bias filter, long/short window.
Liquidity — Automatic or manual timeframe alignment, session visuals.
FVG — Color themes, label sizes, inversion color change, HTF inclusion.
Entry / Limits — Enable or hide Entry, Break-Even, and Stop-Loss levels.
Alerts — Individual toggles for IFVG formation, session sweeps, multi-TF inversions, and invalidations.
Display — Info Box, relationship table, and grade styling.
All alerts output plain text messages only and do not execute orders.
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ALERT FRAMEWORK
-----------------------------------------------------------------------------------------------
When enabled, alerts may notify for:
-Potential inversion detected.
-Confirmed IFVG formation.
-Liquidity sweeps (high/low or session).
-Multi-time-frame inversion.
-Invalidation or close warning.
-Alerts serve as educational markers only, not trade triggers.
The user will have the ability to create custom messages for each of these alert events.
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USAGE GUIDELINES
-----------------------------------------------------------------------------------------------
iFVG Ultimate+ is suited for review and documentation of displacement-based price behavior.
Recommended educational workflows:
-Annotate IFVG events and review delivery into PD Arrays.
-Analyze frequency by session or timeframe.
-Assess how often IFVGs complete versus invalidate.
-Teach ICT-style liquidity mechanics in mentorship or training contexts.
-The indicator works across forex, futures, and crypto markets.
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OPERATIONAL NOTES AND LIMITATIONS
-----------------------------------------------------------------------------------------------
-HTF calculations finalize on bar close (no look-ahead).
-ATR filter strength affects small-gap visibility.
-Session windows use New York time.
-Break-Even and Stop-Loss lines are visual aids only.
-Performance depends on chart density and bar count.
-No strategy module or backtest engine is included.
-----------------------------------------------------------------------------------------------
ORIGINALITY AND PROTECTION
-----------------------------------------------------------------------------------------------
iFVG Ultimate+ | DodgysDD integrates multiple independent systems into a single engine:
-PD Array context alignment with liquidity tracking.
-Dynamic session detection and macro data integration.
-Sequential IFVG validation pipeline with grade assignment.
-Multi-time-frame SMT confirmation module.
-Structured alerts and mitigation tracking.
The logic is entirely original, written in Pine v6, and protected as invite-only to preserve methodology integrity.
-----------------------------------------------------------------------------------------------
ATTRIBUTION
-----------------------------------------------------------------------------------------------
Core concepts such as Fair Value Gaps, Liquidity Sweeps, PD Arrays, and SMT Divergence are publicly taught within ICT-style market education. This implementation was designed and engineered by TakingProphets as iFVG Ultimate+ | DodgysDD, authored for TradingView publication by TakingProphets.
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TERMS AND DISCLAIMER
-----------------------------------------------------------------------------------------------
This indicator is for educational and informational use only. It does not provide financial advice or predictive output. Historical patterns do not guarantee future results. All users remain responsible for their own decisions.Use of this script implies agreement with TradingView’s Vendor Requirements and Terms of Use.
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ACCESS INSTRUCTIONS
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Access is managed through TradingView’s invite-only framework. Users request access via private message to TakingProphets or access link
Лунный Индикатор с RSI и Импульсными АлертамиЭтот индикатор для TradingView сочетает астрономические данные о фазах Луны с техническим анализом (RSI) для выявления потенциальных импульсных движений на рынках, таких как S&P 500 (SPY) и Bitcoin (BTCUSD). Он основан на исторических корреляциях: повышенная доходность и импульсы вверх вокруг новой Луны, а также рост волатильности и риски падений около полнолуния. Индикатор помогает трейдерам получать алерты о возможных торговых возможностях, фильтруя сигналы через RSI для большей точности.
Ключевые функции:
Расчёт фазы Луны: Использует формулу Julian Day для определения текущей фазы (0% — новая Луна, 50% — полнолуние, 100% — следующая новая). Референс: новая Луна 6 января 2000 г. (JD ≈ 2451550.2597). Длина цикла — 29.530588 дней.
Визуализация:
Линия "Фаза Луны (%)" (синяя) отображает фазу в процентах.
Метки на графике: 🌑 Новая (0-3.3% или >96.7%), 🌓 1-я четверть (21.7-28.3%), 🌕 Полная (46.7-53.3%), 🌗 Последняя четверть (71.7-78.3%).
Линия RSI (оранжевая) для мониторинга перекупленности/перепроданности (период настраивается, по умолчанию 14).
Сигналы и алерты (на основе исторических паттернов):
Новая Луна: Алерты о потенциале импульса вверх, если RSI < 50 (не перекуплено). Исторически: выше доходность для S&P 500 и BTC (bullish реверсы). Фон — зелёный.
Полнолуние: Алерты о рисках волатильности/падения, если RSI > 50 (перекуплено). Исторически: sharp moves для BTC, ниже доходность для S&P. Фон — красный.
Алерты срабатывают на переходах фаз (с помощью ta.change), чтобы избежать повторений.
Настройки:
Период RSI: Изменяйте в input для адаптации под таймфрейм (рекомендуется D1 или H4 для лунных циклов).
Как использовать:
Добавьте индикатор на график SPY или BTCUSD.
Настройте алерты в TradingView для уведомлений.
Тестируйте на исторических данных: проверьте корреляции с реальными движениями цен.
Важно: Это не финансовая рекомендация. Рынки непредсказуемы; комбинируйте с другими инструментами. Точность фазы ±1 день; для идеальной — обновите ref_jd по актуальным данным.
Источник данных: Основан на астрономических формулах и исследованиях корреляций Луны с рынками (например, из JSTOR, SSRN). Разработано для образовательных целей.
Description of the Indicator: Lunar Indicator with RSI and Impulse Alerts
Overview:
This TradingView indicator combines astronomical data on lunar phases with technical analysis (RSI) to identify potential impulse movements in markets like S&P 500 (SPY) and Bitcoin (BTCUSD). It is based on historical correlations: higher returns and upward impulses around the new moon, as well as increased volatility and downside risks near the full moon. The indicator helps traders receive alerts about possible trading opportunities, filtering signals through RSI for greater accuracy.
Key Features:
Lunar Phase Calculation: Uses the Julian Day formula to determine the current phase (0% — new moon, 50% — full moon, 100% — next new moon). Reference: new moon on January 6, 2000 (JD ≈ 2451550.2597). Cycle length — 29.530588 days.
Visualization:
"Moon Phase (%)" line (blue) displays the phase in percentages.
Chart labels: 🌑 New (0-3.3% or >96.7%), 🌓 First Quarter (21.7-28.3%), 🌕 Full (46.7-53.3%), 🌗 Last Quarter (71.7-78.3%).
RSI line (orange) for monitoring overbought/oversold conditions (period adjustable, default 14).
Signals and Alerts (Based on Historical Patterns):
New Moon: Alerts for potential upward impulse if RSI < 50 (not overbought). Historically: higher returns for S&P 500 and BTC (bullish reversals). Background — green.
Full Moon: Alerts for volatility/downside risks if RSI > 50 (overbought). Historically: sharp moves for BTC, lower returns for S&P. Background — red.
Alerts trigger on phase transitions (using ta.change) to avoid repetitions.
Settings:
RSI Period: Adjust in input to adapt to timeframe (recommended D1 or H4 for lunar cycles).
How to Use:
Add the indicator to a SPY or BTCUSD chart.
Set up alerts in TradingView for notifications.
Test on historical data: check correlations with real price movements.
Important: This is not financial advice. Markets are unpredictable; combine with other tools. Phase accuracy ±1 day; for perfection — update ref_jd based on current data.
Data Source: Based on astronomical formulas and studies on lunar correlations with markets (e.g., from JSTOR, SSRN). Developed for educational purposes.
Position Size Calculator + ADR % CheckCalculates position size to buy based on account size and current price/LOD
Khosro XAUUSD Strategy [TradingFinder] Trading Room Hunter Setup🔵 Introduction
The Trading Room Hunter (TRH) strategy is an analytical model based on the Smart Money Concept, developed by Khosro, an Iranian international trader based in Dubai. This approach is built upon a deep understanding of liquidity engineering, market structure shifts, and institutional order flow. Its core objective is to identify the so-called TRH Zone, the area where market liquidity gets trapped and institutional investors begin accumulating positions. Unlike traditional indicator-based methods, the TRH Zone focuses purely on price behavior and supply & demand dynamics to pinpoint the most precise reversal zones in the market.
Within Smart Money logic, every impulsive move in price results from the displacement or absorption of liquidity in a specific range. In the TRH model, the last pivot preceding the impulsive move (Origin Pivot) is defined as the Distal Line, and the Break Candle, which disrupts the market structure, forms the Proximal Line. The area between these two points defines the Trading Room Hunter Zone, a reaction zone where price, after creating a displacement or Break of Structure (BoS), often returns to fill an imbalance and provide a precision entry opportunity.
In essence, the TRH Zone is the region where smart money seeks re-entry after a liquidity sweep and a confirmed CHoCH or BoS. It frequently lies between supply/demand boundaries and fair value gaps (FVGs), forming one of the strongest decision-making frameworks within modern price-action theory. Due to its structural accuracy, the TRH setup can also function as a Set & Forget Setup, where the trader defines the zone, places a limit order, and lets the market naturally react, eliminating emotional decision-making and allowing for automated execution aligned with institutional logic.
🔵 How to Use
In the TRH strategy, entries are taken based on price returning to the area between the last impulsive pivot and the break candle. This range (the TRH Zone) represents the region where liquidity from the previous move remains concentrated. Before continuing its main direction, price often revisits this zone to fill imbalances or mitigate unfilled orders. The logic is simple: every explosive move originates from a point where large orders were executed, and TRH precisely highlights that institutional footprint.
🟣 Bullish Setup
When the market breaks a structural high after a strong bearish leg, liquidity shifts from sellers to buyers. The last bearish candle before the breakout marks the origin of the bullish move, and the zone between that candle and the break candle becomes the smart-money entry area. As price revisits this zone and signs of exhaustion in selling pressure appear, that’s the optimal point for a long position. Stop-loss is placed slightly below the origin pivot, and targets are set at the next supply zone or upper liquidity pool.
🟣 Bearish Setup
Conversely, when the market breaks a structural low after a sharp bullish leg, liquidity transitions from buyers to sellers. The last bullish candle before the drop is identified as the origin pivot, while the bearish break candle defines the lower boundary of the zone. The range between these two points forms the TRH Supply Zone, where late buyers are trapped and fresh institutional selling begins. As price retraces into this zone, short entries can be placed near the upper boundary, with stops above the pivot and targets toward the next liquidity pool below.
Because of its structural precision and clearly defined reaction behavior, TRH is one of the most effective Set & Forget setups in Smart Money trading. Simply mark the zone, place your order, and let the market do the rest.
🔵Setting
🟣 Spike Filter | Movement
Minimum Spike Bars : Defines the minimum number of consecutive candles required for a valid spike.
Movement Power : Enables or disables the momentum-based spike filter.
Movement Power Level : Sets the strength threshold; higher values filter out weaker moves and only detect strong spikes.
Pivot Period : Defines the lookback range used to detect swing highs and swing lows in market structure. A higher value smooths out smaller fluctuations and focuses on major pivots, while a lower value increases sensitivity and identifies minor turning points more frequently.
🟣 Position Management
Stop-Loss Threshold : Enables or disables the stop-loss threshold feature.
Stop-Loss Threshold Value : Defines the value of the stop-loss threshold for risk management.
Risk-Reward Ratio : Sets the desired risk-to-reward ratio (e.g., 1:1 or 1:2).
Wide Zone Filter : Filters out zones that exceed a defined width threshold, preventing detection of overly broad TRH areas.
🟣 Display Settings
Display Mode : Chooses between Setup (showing setups) or Signal (showing trade signals).
Show Entry Levels : Displays entry levels on the chart (buy/sell zones) when enabled
Only Display the Last Position : Displays only the most recent position on the chart when enabled.
Setup Width Drawing : Adjusts the visual width of the setup drawings on the chart for better visibility.
🔵 Conclusion
The TRH strategy is a precise structural model of liquidity flow that identifies zones where smart money is most likely to enter and where price is most likely to react. By combining the Origin Pivot and Break Candle, TRH isolates the key areas that drive institutional order flow. Without relying on indicators, it focuses purely on price structure, making it highly effective for both reactive entries and Set & Forget setups.
Ultimately, TRH creates a balance between market structure and liquidity flow, enabling traders to identify institutional decision zones on the chart with minimal risk and maximum clarity
885 ~ damonhmm
zoning script used alongside time fib and a few other concepts
NOT a buy/sell indi
use your brain!
Dons Futures Leverage Calculator🎯 Don's Futures Leverage Calculator - Professional Risk Management Tool
Transform your futures trading with precise leverage calculations and visual risk management!
📊 Key Features:
Real-time Leverage Analysis - Calculate exact position sizes with up to 125x leverage
Visual Trading Lines - Clear entry, take profit, and stop loss levels on your chart
Profit/Loss Zones - Color-coded areas showing potential outcomes
Risk-Reward Calculator - Instant RR ratio analysis with "IDEAL" recommendations
Professional Table Display - Complete risk metrics in organized format
Smart Alerts - Get notified when price hits your key levels
🚀 What This Indicator Does:
This advanced calculator helps futures traders manage leverage and risk with precision. Input your account balance, desired leverage, and trade setup - the indicator instantly calculates:
✅ Position Size - Exact coin quantity based on your leverage ✅ Margin Requirements - Initial margin and percentage of balance used
✅ Profit Potential - Dollar amounts and ROE percentages for take profit ✅ Loss Exposure - Maximum loss with stop loss protection ✅ Risk-Reward Ratio - Automatically flags trades with RR ≥ 2.0 as "IDEAL"
📈 Visual Elements:
Blue Entry Line - Your planned entry point (thick, prominent)
Green Take Profit Line - Target profit level with percentage gains
Red Stop Loss Line - Risk management boundary
Profit Zone - Green fill showing potential gains area
Loss Zone - Red fill showing maximum risk area
Real-time P&L Tracker - Live triangles showing current position performance
⚙️ Easy Setup:
Set your account balance and leverage (1x to 125x)
Choose LONG or SHORT direction
Input entry price, take profit, and stop loss
Set maximum risk percentage (default 2%)
Watch the visual analysis appear on your chart!
🔔 Professional Alerts:
Entry level hit notification
Take profit achievement alert
Stop loss trigger warning
💡 Perfect For:
Futures traders using high leverage
Risk management analysis
Position sizing calculations
Trade planning and visualization
Educational purposes for understanding leverage impact
⚠️ Risk Disclaimer:
Futures trading involves substantial risk. This tool is for educational and planning purposes. Always trade responsibly and never risk more than you can afford to lose.
The Eligible Asset Power Table -> PROFABIGHI_CAPITAL🌟 Overview
The Eligible Asset Power Table is a multi-asset screening dashboard that assesses cryptocurrency portfolios across multiple momentum, risk, and relative metrics to generate eligibility scores. It combines RSI variants, rate of change layers, Sharpe ratios, momentum RSI, price delta analysis, and beta exposure to rank assets, helping traders identify high-conviction opportunities through dual-mode scoring and visual tables.
⚙️ General Settings
- Evaluation mode toggle between aggressive averaging for nuanced scoring or conservative consensus requiring all conditions met for full eligibility
- Number of assets to screen, adjustable up to the platform's data fetch limits for focused or comprehensive portfolio reviews
- Count of top-ranked assets for a dedicated summary table, customizable to highlight leading candidates without overwhelming the display
📊 Indicator Parameters - RSI#1
- Source price selection for RSI input, allowing adaptation to closes, highs, or other series for tailored momentum sensitivity
- Period length for the shorter RSI variant, tuning detection of near-term overbought or oversold zones
- Primary smoothing moving average type, from basic to advanced options like hull or variable index for refined signal clarity
- Length for the first smoothing layer, balancing lag and responsiveness in volatile conditions
- Secondary smoothing moving average type, enabling dual-layer processing or crossover comparisons for added confirmation
- Length for the second smoothing layer, providing deeper trend stability
- Toggle for second MA comparison mode, shifting from fixed thresholds to dynamic relative movements
- Volatility lookback for adaptive smoothing when using variable index methods, responding to market dispersion
📊 Indicator Parameters - RSI#2
- Period length for the medium-term RSI variant, capturing sustained momentum beyond immediate fluctuations
- Primary smoothing moving average type applied to the longer RSI, consistent with RSI#1 for uniform processing
- Length for the first RSI#2 smoothing, optimizing for intermediate trend persistence
- Secondary smoothing moving average type, supporting layered refinement or bullish/bearish crossovers
- Length for the second RSI#2 smoothing, enhancing equilibrium zone reliability
- Toggle for second MA comparison, favoring motion-based conditions over static levels
- Volatility lookback specific to RSI#2's adaptive smoothing, scaling to regime changes
📈 Indicator Parameters - ROC#1
- Period length for the shortest rate of change, emphasizing immediate price acceleration for quick trend shifts
📈 Indicator Parameters - ROC#2
- Period length for the medium rate of change, evaluating ongoing momentum to validate directional strength
📈 Indicator Parameters - ROC#3
- Period length for the longest rate of change, assessing extended trends to confirm multi-period alignment
⚡ Indicator Parameters - Sharpe Ratio
- Lookback period for return averaging and volatility measurement, defining the window for efficiency snapshots
- Smoothing period on raw ratios, applying exponential decay to highlight persistent risk-reward patterns
- Buy threshold for positive conditions, establishing the minimum efficiency level for asset qualification
- Sell threshold for negative flags in averaging mode, pinpointing low-efficiency underperformers
🎯 Momentum RSI Parameters
- Momentum input period, deriving price changes to feed into RSI for velocity-driven insights
- RSI period on the momentum series, normalizing changes into overbought/oversold signals
- Primary smoothing moving average type on momentum RSI, selectable for signal purification
- Length for the first momentum RSI smoothing, aligning lag with trading horizons
- Secondary smoothing moving average type, facilitating comparative layers for crossover setups
- Length for the second momentum RSI smoothing, adding baseline robustness
- Toggle for second MA comparison, prioritizing relative dynamics over absolute readings
- Volatility lookback for momentum RSI's adaptive smoothing, attuning to velocity dispersion
📊 Indicator Parameters - Price Delta RSI
- Condition type between raw delta or RSI-transformed, choosing direct flow analysis or oscillator normalization
- Period for price delta calculation, quantifying net changes to reveal underlying pressure
- RSI period applied to delta, smoothing flow into bounded momentum readings
- Primary smoothing type for delta RSI, from averages to hull for delta refinement
- Length for the first delta RSI smoothing, tuning to flow trends in ranging or trending phases
- Secondary smoothing type, enabling dual-MA for enhanced crossover precision
- Length for the second delta RSI smoothing, bolstering signal stability
- Toggle for second MA comparison on delta RSI, emphasizing motion over static hurdles
- Volatility lookback for delta RSI's adaptive smoothing, adapting to flow-specific variability
📉 Beta Parameters
- Benchmark symbol choice, such as market indices, to gauge asset sensitivity against broader movements
- Lookback period for beta estimation, ensuring sufficient data for covariance while staying current
💼 Assets Configuration
- Sequential symbol inputs for up to 39 assets, supporting diverse crypto pairs across exchanges
- Conditional data loading by count, activating fetches only for selected instruments to conserve resources
- Prefix handling for clean display, stripping exchange details for ticker focus
- Broad compatibility for majors, mid-caps, or niche tokens, enabling sector or theme-based scans
🔧 Helper Functions
- Versatile moving average applicator, supporting multiple types including exponential variants and volatility-adjusted for flexible smoothing
- Sharpe ratio engine, annualizing mean returns over volatility with optional decay for trend focus
- Beta regressor, pulling benchmark data to compute relative risk via return covariances
- Layered ROC calculators, delivering percentage changes across horizons for momentum stacking
📊 Indicator Calculations
- Dual RSI systems with multi-MA options, scoring on optimal ranges or crossover confirmations for balanced strength
- Triple ROC cascade from acute to chronic, binary scoring on positivity to align short, medium, and long trends
- Sharpe derivation prioritizing excess efficiency, with thresholds gating high-reward low-risk assets
- Momentum RSI fusing velocity into RSI, smoothed for level or motion-based bullish filters
- Price delta probing net flow, raw or RSI-normalized, with adaptive layers for directional purity
- Beta isolating systematic exposure, supplementing scores with market-context relativity
🎯 Asset Metrics Calculation
- Parallel per-asset pipeline fetching and computing metrics, unpacking into arrays for centralized handling
- Binary condition evaluation per indicator: thresholds, crossovers, or signs, feeding into mode-specific aggregation
- Eight-metric blend: RSIs, ROCs, Sharpe, momentum RSI, delta, averaged aggressively or unanimous conservatively
- Beta as orthogonal factor, weighting top ranks without score dilution
- Neutral na defaults, preserving evaluations amid data gaps
📦 Data Storage Arrays
- Fixed-size arrays for names, metrics, conditions, and flags, indexed by asset order for efficient access
- Segregated storage for raw values, binaries, and composites, supporting sorting and retrieval
- Scalable to max assets, minimizing overhead in partial loads
📊 Data Retrieval for All Assets
- Conditional security pulls aligned to chart timeframe, ensuring consistent multi-symbol data
- Metrics function per instrument, distributing results to arrays for unified processing
- Benchmark-embedded beta calls, avoiding extra fetches
- Last-bar gating for snapshot accuracy, sidestepping repaints
📊 Main Table for All Assets
- Centered wide-format table listing assets leftward with metric columns spanning conditions and beta
- Headers labeling RSI layers, ROC trio, Sharpe, momentum, delta, beta, and final score for metric traceability
- Green/red cell text for met/failed conditions, white neutrals, with score highlighting above midpoint
- Ticker truncation for compactness, transparent overlay for pane integration
🏆 Top Assets Table
- Left-aligned rankings by score-beta composite, descending to prioritize leaders
- Compact four-column view: ticker, score, beta, Sharpe with threshold colors for efficiency triage
- Temporary key sorting, row clearing for updates, limited to user count
- Visual cues mirroring main table, flagging high-Sharpe standouts
✅ Key Takeaways
- Fuses diverse metrics into probabilistic or strict eligibility for alpha hunting across assets
- Dual modes adapt screening from flexible averages to rigorous confluences
- Custom periods and smoothings tune to timeframes, from scalps to positions
- Dual tables balance detail with digestible summaries for rapid insights
- Beta adds relativity, elevating resilient picks in correlated markets
- Efficient array handling scales screening without lag, quota-aware
