RSI FlipIndicator Description: RSI Flip (30/70 Threshold)
This indicator uses a 7-period Relative Strength Index (RSI) to detect potential market reversals based on classic momentum thresholds:
- RSI < 30 → triggers a Long Deal Signal (1) indicating potential bullish reversal.
- RSI > 70 → triggers a Short Deal Signal (2) indicating potential bearish reversal.
🔧 Features:
- Backtest-compatible output: Hidden plots emit 1 for long and 2 for short, enabling seamless integration with strategy scripts.
- Bias tracking: Internal bias state updates on each trigger, allowing for modular lifecycle logic.
- Background tinting ready: The bias variable can be used to drive visual overlays or downstream automation.
🧩 Integration Notes:
- Designed for symbol-specific use — no external feeds or dependencies.
- Ideal for modular signal stacking, lifecycle-safe deal initiation, or audit-grade strategy mapping.
Cerca negli script per "trigger"
Tristan's Devil Mark (Short / Long, with W%R)The Devil’s Mark indicator is a visual tool designed to help traders identify potential short and long opportunities based on candle structure and market momentum. It combines price action analysis with the Williams %R (W%R) oscillator to highlight candles with high potential for reversal or continuation.
Can be used on any timeline, from scalping day trades to swing trades on daily and higher timelines. Know that the higher the timeline the less likely the indicator will show. (Asia and London sessions tend to show many indicators. I find this more useful for NY session.)
How the script works
Candle Structure Conditions
Short (Sell) Wedge: Plotted above green candles that have no bottom wick, indicating that inside that candle there was strong upward momentum without downside hesitation .
Long (Buy) Wedge: Plotted below red candles that have no top wick, indicating that inside that candle there was strong downward momentum without upside hesitation .
These candles are visually emphasized as wedges to mark potential turning points.
Williams %R Filter
The indicator uses Williams %R to measure overbought and oversold conditions:
Proximity to 0 (nearZeroThresh): Determines how close W%R must be to 0 (overbought) to trigger a Sell Wedge. This acts as a “Sell sensitivity” filter.
Proximity to -100 (nearHundredThresh): Determines how close W%R must be to -100 (oversold) to trigger a Buy Wedge. This acts as a “Buy sensitivity” filter.
When the candle meets both the candle structure and the W%R condition, the wedge is plotted in purple (“Within W%R Range”).
When the "ignore W%R filter" toggle is on, all eligible candles are plotted regardless of W%R. Wedges that normally would not meet W%R criteria are plotted in light purple (“Outside W%R Range”) to distinguish them. #YOLO (🚫 I recommend leaving "Ignore W%R Filter" OFF)
Settings Explained
Williams %R Length: The number of bars used to calculate the W%R oscillator. Shorter lengths make it more sensitive; longer lengths smooth the readings.
Proximity to 0 / 100: Controls how “strict” the indicator is in requiring overbought or oversold W%R conditions to trigger. Lower values mean closer to extreme zones, higher values are more permissive.
Ignore W%R Toggle: Option to show Devil’s Marks on every eligible candle regardless of W%R. Useful for visualizing purely price-action-based signals.
What the trader sees
Purple wedges: Candles meeting both candle structure and W%R conditions.
Light purple wedges: Candles meeting candle structure but ignored W%R (when toggle is on). #YOLO (🚫 I recommend leaving "Ignore W%R Filter" OFF)
Short opportunities are wedges above bars (green candles with no bottom wick).
Long opportunities are wedges below bars (red candles with no top wick).
Trading Insight
The Devil’s Mark is a momentum and reversal alert tool:
Look for purple downward-pointing wedges when W%R is near overbought. This is a potential shorting opportunity. Buying at the close of that candle may improve your short trades.
Look for purple upward-pointing wedges when W%R is near oversold. This is a potential
long opportunity. Buying at the close of that candle may improve your long trades.
Light purple wedges show the same price-action cues without W%R confirmation—useful for aggressive traders who want every potential setup. #YOLO #YMMV #noFullPort
Settings / Security
The “Output values” checkbox appears for each plotted series (like a plot or plotshape) and controls whether the series will also be exposed numerically in the Data Window or used by other indicators/scripts.
Here’s what it means in practice:
1. Checked (true)
The series values (like candle high, low, or any computed value) are exported to the Data Window and can be read by other scripts using request.security() or ta functions.
Example: You can see the exact numerical value of each plotted point in the Data Window when you hover over the chart.
Useful if you want to backtest or reference these plotted values programmatically.
2. Unchecked (false)
The series is plotted visually only.
The numeric values are hidden from the Data Window and cannot be accessed by other scripts.
Makes the chart cleaner if you don’t need the numeric outputs.
Experimental Supertrend [CHE]Experimental Supertrend — Combines EMA crossovers for trend regime detection with an adaptive ATR-based hull that selects the narrowest band to contain recent highs and lows, minimizing false breaks in varying volatility.
Summary
This indicator overlays a dynamic supertrend boundary around a midline derived from dual EMAs, using EMA crossovers to switch between bullish and bearish regimes. The hull adapts by evaluating multiple ATR periods and selecting the tightest one that fully encloses price action over a specified window, which helps in creating more stable trend lines that hug price without excessive gaps or breaches. Fills between the midline and hull provide visual cues for trend strength, darkening temporarily after regime changes to highlight transitions. Alerts trigger on crossovers, and markers label entry points, making it suitable for trend-following setups where standard supertrends might whipsaw. Overall, it offers robustness through auto-adjustment, reducing sensitivity to noise while maintaining responsiveness to genuine shifts.
Motivation: Why this design?
Standard supertrend indicators often flip prematurely in choppy markets due to fixed multipliers that do not account for localized volatility patterns, leading to frequent false signals and eroded confidence in trends. This design addresses that by incorporating an EMA-based regime filter for directional bias and an auto-adaptive hull that dynamically tunes the band width based on recent price containment needs. By prioritizing the narrowest effective enclosure, it avoids over-wide bands in calm periods that cause lag or under-wide ones in volatility spikes that invite breaks, providing a more consistent trailing reference without manual tweaking.
What’s different vs. standard approaches?
- Reference baseline: Diverges from the classic ATR-multiplier supertrend, which uses a single fixed period and constant factor applied to close or high/low deviations.
- Architecture differences:
- Auto-selection from candidate ATR lengths to find the optimal period for current conditions.
- Dynamic multiplier clamped between floor and cap values, adjusted by padding to ensure reliable containment.
- Regime-gated rendering, where hull position flips based on EMA relative positioning.
- Post-transition visual fading to emphasize change points without altering core logic.
- Practical effect: Charts show tighter, more reactive bands that rarely breach during trends, reducing visual clutter from flips; the adaptive nature means less intervention across assets, as the hull self-adjusts to volatility clusters rather than applying a one-size-fits-all scale.
How it works (technical)
The indicator first computes two EMAs from close prices using lengths derived from a preset pair or manual inputs, establishing a midline as their average. This midline serves as the central reference for the hull. True range values are then smoothed into multiple ATR candidates using exponential weighting over the specified lengths. For each candidate, deviations of recent highs and lows from the midline are ratioed against the ATR to determine a required multiplier that would enclose all extremes in the containment window—the highest ratio plus padding sets the base, clamped to user-defined bounds. Among valid candidates (those with sufficient history), the one yielding the narrowest overall band width is selected. The hull boundaries are then offset from the midline by this multiplier times the chosen ATR, and further smoothed with a fixed EMA to reduce jitter. Regime direction from EMA comparison gates which boundary acts as support or resistance, with initialization seeding arrays on the first bar to handle state persistence. No higher timeframe data is used, so all logic runs on the chart's native bars without lookahead.
Parameter Guide
EMA Pair — Selects preset lengths for fast and slow EMAs, influencing regime sensitivity and midline stability. Default: "21/55". Trade-offs/Tips: Faster pairs like "9/21" increase cross frequency for scalping but raise false signals; slower like "50/200" smooths for swings, potentially missing early turns. Use Manual for fine control.
Manual Fast — Sets fast EMA length when Manual mode is active; shorter values make regime switches quicker. Default: 21. Trade-offs/Tips: Lower than 10 risks over-reactivity; pair with slow at least double for clear separation.
Manual Slow — Sets slow EMA length when Manual mode is active; longer values anchor the midline more firmly. Default: 55. Trade-offs/Tips: Above 100 adds lag in trends; balance with fast to avoid perpetual neutrality.
ATR Lengths (comma-separated) — Defines candidate periods for ATR smoothing; more options allow finer auto-selection. Default: "7,10,14,21,28,35". Trade-offs/Tips: Fewer candidates speed computation but may miss optimal fits; keep under 10 for efficiency.
Containment Window — Number of recent bars the hull must fully enclose highs/lows of; larger windows favor stability. Default: 50. Trade-offs/Tips: Shorter (under 20) adapts faster to breaks but increases breach risk; longer smooths but delays response.
Min Multiplier Floor — Lowest allowed multiplier for hull width; prevents overly tight bands in low volatility. Default: 0.5. Trade-offs/Tips: Raise to 0.75 for conservative enclosures; too low allows pinches that flip easily.
Max Multiplier Cap — Highest allowed multiplier; caps expansion in spikes to avoid wide, lagging bands. Default: 1.0. Trade-offs/Tips: Lower to 0.75 tightens overall; higher permits more room but risks detachment from price.
Padding (+) — Adds buffer to the auto-multiplier for safer containment without exact touches. Default: 0.05. Trade-offs/Tips: Increase to 0.10 in gappy markets; minimal values hug closer but may still breach on outliers.
Fill Between (Mid ↔ Supertrend) — Toggles shaded area between midline and active hull for trend visualization. Default: true. Trade-offs/Tips: Disable for cleaner charts; pairs well with transparency tweaks.
Base Fill Transparency (0..100) — Sets default opacity of fills; higher values make them subtler. Default: 80. Trade-offs/Tips: Under 50 overwhelms price action; adjust with darken boost for emphasis.
Darken on Trend Change — Enables temporary opacity increase after regime shifts to spotlight transitions. Default: true. Trade-offs/Tips: Off for steady visuals; on aids spotting reversals in real-time.
Darken Fade Bars — Duration in bars for the darken effect to ramp back to base; longer prolongs highlight. Default: 8. Trade-offs/Tips: Shorter (4-6) for fast-paced charts; longer holds attention on changes.
Darken Boost at Change (Δ transp) — Intensity of opacity reduction at crossover; higher values make shifts more prominent. Default: 50. Trade-offs/Tips: Cap at 70 to avoid blackout; tune down if fades obscure details.
Show Supertrend Line — Displays the active hull boundary as a line. Default: true. Trade-offs/Tips: Hide for fill-only views; linewidth fixed at 3 for visibility.
Show EMA Cross Markers — Places circles and labels at crossover points for entry cues. Default: true. Trade-offs/Tips: Disable in clutter; labels show "Buy"/"Sell" at absolute positions.
Alert: EMA Cross Up (Long) — Triggers notification on bullish crossover. Default: true. Trade-offs/Tips: Pair with filters; once-per-bar frequency.
Alert: EMA Cross Down (Short) — Triggers notification on bearish crossover. Default: true. Trade-offs/Tips: Use for exits; ensure broker integration.
Show Debug — Reveals internal diagnostics like selected ATR details (if implemented). Default: false. Trade-offs/Tips: Enable for troubleshooting selections; minimal overhead.
Reading & Interpretation
Bullish regime shows a green line below price as support, with upward fill from midline; bearish uses red line above as resistance, downward fill. Crossovers flip the active boundary, marked by tiny green/red circles and "Buy"/"Sell" labels at the hull level. Fills start at base transparency but darken sharply at changes, fading over the specified bars to signal fresh momentum. If the hull rarely breaches during trends, containment is effective; frequent touches without flips indicate tight adaptation. Debug mode (when enabled) overlays text or plots for selected length and multiplier, helping verify auto-choices.
Practical Workflows & Combinations
- Trend following: Enter long on green "Buy" label above prior low structure; confirm with higher high. Trail stops along the green hull line, tightening as fills stabilize post-fade.
- Exits/Stops: Conservative exit on opposite crossover or hull breach; aggressive hold until fade completes if volume supports. Use darken boost as a volatility cue—high delta suggests waiting for confirmation.
- Multi-asset/Multi-TF: Defaults suit forex/stocks on 15m-4h; for crypto, widen containment to 75 for gaps. Layer on volume oscillator for cross filters; avoid on low-liquidity assets where ATR candidates skew.
Behavior, Constraints & Performance
Closed-bar logic ensures signals confirm at bar end, with live bars updating hull adaptively but no repaints since no future data or security calls are used. Arrays persist ATR states across bars, initialized once with candidates parsed from string. Small fixed loops (over 6 lengths max, inner up to 50) run per bar, capped by max_bars_back=500 for history needs. Resources stay low with 500 labels/lines limits, but dense charts may hit on markers. Known limits include initial lag until containment history builds (50+ bars), potential wide bands on gaps, and suboptimal selections if candidates omit ideal lengths.
Sensible Defaults & Quick Tuning
Start with "21/55" pair, 50-window, 0.5-1.0 multipliers, and 80% transparency for balanced responsiveness on daily charts. For too many flips, raise min floor to 0.75 or add lengths like "42"; for sluggishness, shorten window to 30 or pick faster pair. In high-vol environments, boost padding to 0.10; for smoother visuals, extend fade bars to 12.
What this indicator is—and isn’t
This is a visualization and signal layer for trend regime and adaptive boundaries, aiding entry/exit timing in directional markets. It is not a standalone system—pair with price structure, risk sizing, and broader context. Not predictive of turns, just reactive to containment and crosses.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Happy trading
Chervolino
ALISH WEEK LABELS THE ALISH WEEK LABELS
Overview
This indicator programmatically delineates each trading week and encapsulates its realized price range in a live-updating, filled rectangle. A week is defined in America/Toronto time from Monday 00:00 to Friday 16:00. Weekly market open to market close, For every week, the script draws:
a vertical start line at the first bar of Monday 00:00,
a vertical end line at the first bar at/after Friday 16:00, and
a white, semi-transparent box whose top tracks the highest price and whose bottom tracks the lowest price observed between those two temporal boundaries.
The drawing is timeframe-agnostic (M1 → 1D): the box expands in real time while the week is open and freezes at the close boundary.
Time Reference and Session Boundaries
All scheduling decisions are computed with time functions called using the fixed timezone string "America/Toronto", ensuring correct behavior across DST transitions without relying on chart timezone. The start condition is met at the first bar where (dayofweek == Monday && hour == 0 && minute == 0); on higher timeframes where an exact 00:00 bar may not exist, a fallback checks for the first Monday bar using ta.change(dayofweek). The close condition is met on the first bar at or after Friday 16:00 (Toronto), which guarantees deterministic closure on intraday and higher timeframes.
State Model
The indicator maintains minimal persistent state using var globals:
week_open (bool): whether the current weekly session is active.
wk_hi / wk_lo (float): rolling extrema for the active week.
wk_box (box): the graphical rectangle spanning × .
wk_start_line and a transient wk_end_line (line): vertical delimiters at the week’s start and end.
Two dynamic arrays (boxes, vlines) store object handles to support bounded history and deterministic garbage collection.
Update Cycle (Per Bar)
On each bar the script executes the following pipeline:
Start Check: If no week is open and the start condition is satisfied, instantiate wk_box anchored at the current bar_index, prime wk_hi/wk_lo with the bar’s high/low, create the start line, and push both handles to their arrays.
Accrual (while week_open): Update wk_hi/wk_lo using math.max/min with current bar extremes. Propagate those values to the active wk_box via box.set_top/bottom and slide box.set_right to the current bar_index to keep the box flush with live price.
Close Check: If at/after Friday 16:00, finalize the week by freezing the right edge (box.set_right), drawing the end line, pushing its handle, and flipping week_open false.
Retention Pruning: Enforce a hard cap on historical elements by deleting the oldest objects when counts exceed configured limits.
Drawing Semantics
The range container is a filled white rectangle (bgcolor = color.new(color.white, 100 − opacity)), with a solid white border for clear contrast on dark or light themes. Start/end boundaries are full-height vertical white lines (y1=+1e10, y2=−1e10) to guarantee visibility across auto-scaled y-axes. This approach avoids reliance on price-dependent anchors for the lines and is robust to large volatility spikes.
Multi-Timeframe Behavior
Because session logic is driven by wall-clock time in the Toronto zone, the indicator remains consistent across chart resolutions. On coarse timeframes where an exact boundary bar might not exist, the script legally approximates by triggering on the first available bar within or immediately after the boundary (e.g., Friday 16:00 occurs between two 4-hour bars). The box therefore represents the true realized high/low of the bars present in that timeframe, which is the correct visual for that resolution.
Inputs and Defaults
Weeks to keep (show_weeks_back): integer, default 40. Controls retention of historical boxes/lines to avoid UI clutter and resource overhead.
Fill opacity (fill_opacity): integer 0–100, default 88. Controls how solid the white fill appears; border color is fixed pure white for crisp edges.
Time zone is intentionally fixed to "America/Toronto" to match the strategy definition and maintain consistent historical backtesting.
Performance and Limits
Objects are reused only within a week; upon closure, handles are stored and later purged when history limits are exceeded. The script sets generous but safe caps (max_boxes_count/max_lines_count) to accommodate 40 weeks while preserving Editor constraints. Per-bar work is O(1), and pruning loops are bounded by the configured history length, keeping runtime predictable on long histories.
Edge Cases and Guarantees
DST Transitions: Using a fixed IANA time zone ensures Friday 16:00 and Monday 00:00 boundaries shift correctly when DST changes in Toronto.
Weekend Gaps/Holidays: If the market lacks bars exactly at boundaries, the nearest subsequent bar triggers the start/close logic; range statistics still reflect observed prices.
Live vs Historical: During live sessions the box edge advances every bar; when replaying history or backtesting, the same rules apply deterministically.
Scope (Intentional Simplicity)
This tool is strictly a visual framing indicator. It does not compute labels, statistics, alerts, or extended S/R projections. Its single responsibility is to clearly present the week’s realized range in the Toronto session window so you can layer your own execution or analytics on top.
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
Relative Strength Index Remastered [CHE]Relative Strength Index Remastered — Enhanced RSI with robust divergence detection using price-based pivots and line-of-sight validation to reduce false signals compared to the standard RSI indicator.
Summary
RSI Remastered builds on the classic Relative Strength Index by adding a more reliable divergence detection system that relies on price pivots rather than RSI pivots alone, incorporating a line-of-sight check to ensure the RSI path between points remains clear. This approach filters out many false divergences that occur in the original RSI indicator due to its volatile pivot detection on the RSI line itself. Users benefit from clearer reversal and continuation signals, especially in noisy markets, with optional hidden divergence support for trend confirmation. The core RSI calculation and smoothing options remain familiar, but the divergence logic provides materially fewer alerts while maintaining sensitivity.
Motivation: Why this design?
The standard RSI indicator often generates misleading divergence signals because it detects pivots directly on the RSI values, which can fluctuate erratically in volatile conditions, leading to frequent false positives that confuse traders during ranging or choppy price action. RSI Remastered addresses this by shifting pivot detection to the underlying price highs and lows, which are more stable, and adding a validation step that confirms the RSI line does not cross the direct path between pivot points. This design targets the real problem of over-signaling in the original, promoting more actionable insights without altering the RSI's core momentum measurement.
What’s different vs. standard approaches?
- Reference baseline: The classical TradingView RSI indicator, which uses simple RSI-based pivot detection for divergences.
- Architecture differences:
- Pivot identification on price extremes (highs and lows) instead of RSI values, extracting RSI levels at those points for comparison.
- Addition of a line-of-sight validation that checks the RSI path bar by bar between pivots to prevent signals where the line is interrupted.
- Inclusion of hidden divergence types alongside regular ones, using the same robust framework.
- Configurable drawing of connecting lines between validated pivot RSI points for visual clarity.
- Practical effect: Charts show fewer but higher-quality divergence markers and lines, reducing clutter from the original's frequent RSI pivot triggers; this matters for avoiding whipsaws in intraday trading, where the standard version might flag dozens of invalid setups per session.
Key Comparison Aspects
Aspect: Title/Shorttitle
Original RSI: "Relative Strength Index" / "RSI"
Robust Variant: "Relative Strength Index Remastered " / "RSI RM"
Aspect: Max. Lines/Labels
Original RSI: No specification (Standard: 50/50)
Robust Variant: max_lines_count=200, max_labels_count=200 (for more lines/markers in divergences)
Aspect: RSI Calculation & Plots
Original RSI: Identical: RSI with RMA, Plots (line, bands, gradient fills)
Robust Variant: Identical: RSI with RMA, Plots (line, bands, gradient fills)
Aspect: Smoothing (MA)
Original RSI: Identical: Inputs for MA types (SMA, EMA etc.), Bollinger Bands optional
Robust Variant: Identical: Inputs for MA types (SMA, EMA etc.), Bollinger Bands optional
Aspect: Divergence Activation
Original RSI: input.bool(false, "Calculate Divergence") (disabled by default)
Robust Variant: input.bool(true, "Calculate Divergence") (enabled by default, with tooltip)
Aspect: Pivot Calculation
Original RSI: Pivots on RSI (ta.pivotlow/high on RSI values)
Robust Variant: Pivots on price (ta.pivotlow/high on low/high), RSI values then extracted
Aspect: Lookback Values
Original RSI: Fixed: lookbackLeft=5, lookbackRight=5
Robust Variant: Input: L=5 (Pivot Left), R=5 (Pivot Right), adjustable (min=1, max=50)
Aspect: Range Between Pivots
Original RSI: Fixed: rangeUpper=60, rangeLower=5 (via _inRange function)
Robust Variant: Input: rangeUpper=60 (Max Bars), rangeLower=5 (Min Bars), adjustable (min=1–6, max=100–300)
Aspect: Divergence Types
Original RSI: Only Regular Bullish/Bearish: - Bull: Price LL + RSI HL - Bear: Price HH + RSI LH
Robust Variant: Regular + Hidden (optional via showHidden=true): - Regular Bull: Price LL + RSI HL - Regular Bear: Price HH + RSI LH - Hidden Bull: Price HL + RSI LL - Hidden Bear: Price LH + RSI HH
Aspect: Validation
Original RSI: No additional check (only pivot + range check)
Robust Variant: Line-of-Sight Check: RSI line must not cross the connecting line between pivots (line_clear function with slope calculation and loop for each bar in between)
Aspect: Signals (Plots/Shapes)
Original RSI: - Plot of pivot points (if divergence) - Shapes: "Bull"/"Bear" at RSI value, offset=-5
Robust Variant: - No pivot plots, instead shapes at RSI , offset=-R (adjustable) - Shapes: "Bull"/"Bear" (Regular), "HBull"/"HBear" (Hidden) - Colors: Lime/Red (Regular), Teal/Orange (Hidden)
Aspect: Line Drawing
Original RSI: No lines
Robust Variant: Optional (showLines=true): Lines between RSI pivots (thick for regular, dashed/thin for hidden), extend=none
Aspect: Alerts
Original RSI: Only Regular Bullish/Bearish (with pivot lookback reference)
Robust Variant: Regular Bullish/Bearish + Hidden Bullish/Bearish (specific "at latest pivot low/high")
Aspect: Robustness
Original RSI: Simple, prone to false signals (RSI pivots can be volatile)
Robust Variant: Higher: Price pivots are more stable, line-of-sight filters "broken" divergences, hidden support for trend continuations
Aspect: Code Length/Structure
Original RSI: ~100 lines, simple if-blocks for bull/bear
Robust Variant: ~150 lines, extended helper functions (e.g., inRange, line_clear), var group for inputs
How it works (technical)
The indicator first computes the core RSI value based on recent price changes, separating upward and downward movements over the specified length and smoothing them to derive a momentum reading scaled between zero and one hundred. This value is then plotted in a separate pane with fixed upper and lower reference lines at seventy and thirty, along with optional gradient fills to highlight overbought and oversold zones.
For smoothing, a moving average type is applied to the RSI if enabled, with an option to add bands around it based on the variability of recent RSI values scaled by a multiplier. Divergence detection activates on confirmed price pivots: lows for bullish checks and highs for bearish. At each new pivot, the system retrieves the bar index and values (price and RSI) for the current and prior pivot, ensuring they fall within a configurable bar range to avoid unrelated points.
Comparisons then assess whether the price has made a lower low (or higher high) while the RSI at those points moves in the opposite direction—higher for bullish regular, lower for bearish regular. For hidden types, the directions reverse to capture trend strength. The line-of-sight check calculates the straight path between the two RSI points and verifies that the actual RSI values in between stay entirely above (for bullish) or below (for bearish) that path, breaking the signal if any bar violates it. Valid signals trigger shapes at the RSI level of the new pivot and optional lines connecting the points. Initialization uses built-in functions to track prior occurrences, with states persisting across bars for accurate historical comparisons. No higher timeframe data is used, so confirmation occurs after the right pivot bars close, minimizing live-bar repaints.
Parameter Guide
Length — Controls the period for measuring price momentum changes — Default: 14 — Trade-offs/Tips: Shorter values increase responsiveness but add noise and more false signals; longer smooths trends but delays entries in fast markets.
Source — Selects the price input for RSI calculation — Default: Close — Trade-offs/Tips: Use high or low for volatility focus, but close works best for most assets; mismatches can skew overbought/oversold reads.
Calculate Divergence — Enables the enhanced divergence logic — Default: True — Trade-offs/Tips: Disable for pure RSI view to save computation; essential for signal reliability over the standard method.
Type (Smoothing) — Chooses the moving average applied to RSI — Default: SMA — Trade-offs/Tips: None for raw RSI; EMA for quicker adaptation, but SMA reduces whipsaws; Bollinger Bands option adds volatility context at cost of added lines.
Length (Smoothing) — Period for the smoothing average — Default: 14 — Trade-offs/Tips: Match RSI length for consistency; shorter boosts signal speed but amplifies noise in the smoothed line.
BB StdDev — Multiplier for band width around smoothed RSI — Default: 2.0 — Trade-offs/Tips: Lower narrows bands for tighter signals, risking more touches; higher widens for fewer but stronger breakouts.
Pivot Left — Bars to the left for confirming price pivots — Default: 5 — Trade-offs/Tips: Increase for stricter pivots in noisy data, reducing signals; too high delays confirmation excessively.
Pivot Right — Bars to the right for confirming price pivots — Default: 5 — Trade-offs/Tips: Balances with left for symmetry; longer right ensures maturity but shifts signals backward.
Max Bars Between Pivots — Upper limit on distance for valid pivot pairs — Default: 60 — Trade-offs/Tips: Tighten for short-term trades to focus recent action; widen for swing setups but risks unrelated comparisons.
Min Bars Between Pivots — Lower limit to avoid clustered pivots — Default: 5 — Trade-offs/Tips: Raise to filter micro-moves; too low invites overlapping signals like the original RSI.
Detect Hidden — Includes trend-continuation hidden types — Default: True — Trade-offs/Tips: Enable for full trend analysis; disable simplifies to reversals only, akin to basic RSI.
Draw Lines — Shows connecting lines between valid pivots — Default: True — Trade-offs/Tips: Turn off for cleaner charts; helps visually confirm line-of-sight in backtests.
Reading & Interpretation
The main RSI line oscillates between zero and one hundred, crossing above fifty suggesting building momentum and below indicating weakness; touches near seventy or thirty flag potential extremes. The optional smoothed line and bands provide a filtered view—price above the upper band on the RSI pane hints at overextension. Divergence shapes appear as upward labels for bullish (lime for regular, teal for hidden) and downward for bearish (red regular, orange hidden) at the pivot's RSI level, signaling a mismatch only after validation. Connecting lines, if drawn, slope between points without RSI interference, their color matching the shape type; a dashed style denotes hidden. Fewer shapes overall compared to the standard RSI mean higher conviction, but always confirm with price structure.
Practical Workflows & Combinations
- Trend following: Enter longs on regular bullish shapes near support with higher highs in price; filter hidden bullish for pullback buys in uptrends, pairing with a rising smoothed RSI above fifty.
- Exits/Stops: Use bearish regular as reversal warnings to tighten stops; hidden bearish in downtrends confirms continuation—exit if lines show RSI crossing the path.
- Multi-asset/Multi-TF: Defaults suit forex and stocks on one-hour charts; for crypto volatility, widen pivot ranges to ten; scale min/max bars proportionally on daily for swings, avoiding the original's intraday spam.
Behavior, Constraints & Performance
Signals confirm only after the right pivot bars close, so live bars may show tentative pivots that vanish on close, unlike the standard RSI's immediate RSI-pivot triggers—plan for this delay in automation. No higher timeframe calls, so no security-related repaints. Resources include up to two hundred lines and labels for dense charts, with a loop in validation scanning up to three hundred bars between pivots, which is efficient but could slow on very long histories. Known limits: Slight lag at pivot confirmation in trending markets; volatile RSI might rarely miss fine path violations; not ideal for gap-heavy assets where pivots skip.
Sensible Defaults & Quick Tuning
Start with defaults for balanced momentum and divergence on most timeframes. For too many signals (like the original), raise pivot left/right to eight and min bars to ten to filter noise. If sluggish in trends, shorten RSI length to nine and enable EMA smoothing for faster adaptation. In high-volatility assets, widen max bars to one hundred but disable hidden to focus essentials. For clean reversal hunts, set smoothing to none and lines on.
What this indicator is—and isn’t
RSI Remastered serves as a refined momentum and divergence visualization tool, enhancing the standard RSI for better signal quality in technical analysis setups. It is not a standalone trading system, nor does it predict price moves—pair it with volume, structure breaks, and risk rules for decisions. Use alongside position sizing and broader context, not in isolation.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
Volume Sampled Supertrend [BackQuant]Volume Sampled Supertrend
A Supertrend that runs on a volume sampled price series instead of fixed time. New synthetic bars are only created after sufficient traded activity, which filters out low participation noise and makes the trend much easier to read and model.
Original Script Link
This indicator is built on top of my volume sampling engine. See the base implementation here:
Why Volume Sampling
Traditional charts print a bar every N minutes regardless of how active the tape is. During quiet periods you accumulate many small, low information bars that add noise and whipsaws to downstream signals.
Volume sampling replaces the clock with participation. A new synthetic bar is created only when a pre-set amount of volume accumulates (or, in Dollar Bars mode, when pricevolume reaches a dollar threshold). The result is a non-uniform time series that stretches in busy regimes and compresses in quiet regimes. This naturally:
filters dead time by skipping low volume chop;
standardizes the information content per bar, improving comparability across regimes;
stabilizes volatility estimates used inside banded indicators;
gives trend and breakout logic cleaner state transitions with fewer micro flips.
What this tool does
It builds a synthetic OHLCV stream from volume based buckets and then applies a Supertrend to that synthetic price. You are effectively running Supertrend on a participation clock rather than a wall clock.
Core Features
Sampling Engine - Choose Volume buckets or Dollar Bars . Thresholds can be dynamic from a rolling mean or median, or fixed by the user.
Synthetic Candles - Plots the volume sampled OHLC candles so you can visually compare against regular time candles.
Supertrend on Synthetic Price - ATR bands and direction are computed on the sampled series, not on time bars.
Adaptive Coloring - Candle colors can reflect side, intensity by volume, or a neutral scheme.
Research Panels - Table shows total samples, current bucket fill, threshold, bars-per-sample, and synthetic return stats.
Alerts - Long and Short triggers on Supertrend direction flips for the synthetic series.
How it works
Sampling
Pick Sampling Method = Volume or Dollar Bars.
Set the dynamic threshold via Rolling Lookback and Filter (Mean or Median), or enable Use Fixed and type a constant.
The script accumulates volume (or pricevolume) each time bar. When the bucket reaches the threshold, it finalizes one or more synthetic candles and resets accumulation.
Each synthetic candle stores its own OHLCV and is appended to the synthetic series used for all downstream logic.
Supertrend on the sampled stream
Choose Supertrend Source (Open, High, Low, Close, HLC3, HL2, OHLC4, HLCC4) derived from the synthetic candle.
Compute ATR over the synthetic series with ATR Period , then form upperBand = src + factorATR and lowerBand = src - factorATR .
Apply classic trailing band and direction rules to produce Supertrend and trend state.
Because bars only come when there is sufficient participation, band touches and flips tend to align with meaningful pushes, not idle prints.
Reading the display
Synthetic Volume Bars - The non-uniform candles that represent equal information buckets. Expect more candles during active sessions and fewer during lulls.
Volume Sampled Supertrend - The main line. Green when Trend is 1, red when Trend is -1.
Markers - Small dots appear when a new synthetic sample is created, useful for aligning activity cycles.
Time Bars Overlay (optional) - Plot regular time candles to compare how the synthetic stream compresses quiet chop.
Settings you will use most
Data Settings
Sampling Method - Volume or Dollar Bars.
Rolling Lookback and Filter - Controls the dynamic threshold. Median is robust to outliers, Mean is smoother.
Use Fixed and Fixed Threshold - Force a constant bucket size for consistent sampling across regimes.
Max Stored Samples - Ring buffer limit for performance.
Indicator Settings
SMA over last N samples - A moving average computed on the synthetic close series. Can be hidden for a cleaner layout.
Supertrend Source - Price field from the synthetic candle.
ATR Period and Factor - Standard Supertrend controls applied on the synthetic series.
Visuals and UI
Show Synthetic Bars - Turn synthetic candles on or off.
Candle Color Mode - Green/Red, Volume Intensity, Neutral, or Adaptive.
Mark new samples - Puts a dot when a bucket closes.
Show Time Bars - Overlay regular candles for comparison.
Paint candles according to Trend - Colors chart candles using current synthetic Supertrend direction.
Line Width , Colors , and Stats Table toggles.
Some workflow notes:
Trend Following
Set Sampling Method = Volume, Filter = Median, and a reasonable Rolling Lookback so busy regimes produce more samples.
Trade in the direction of the Volume Sampled Supertrend. Because flips require real participation, you tend to avoid micro whipsaws seen on time bars.
Use the synthetic SMA as a bias rail and trailing reference for partials or re-entries.
Breakout and Continuation
Watch for rapid clustering of new sample markers and a clean flip of the synthetic Supertrend.
The compression of quiet time and expansion in busy bursts often makes breakouts more legible than on uniform time charts.
Mean Reversion
In instruments that oscillate, faded moves against the synthetic Supertrend are easier to time when the bucket cadence slows and Supertrend flattens.
Combine with the synthetic SMA and return statistics in the table for sizing and expectation setting.
Stats table (top right)
Method and Total Samples - Sampling regime and current synthetic history length.
Current Vol or Dollar and Threshold - Live bucket fill versus the trigger.
Bars in Bucket and Avg Bars per Sample - How much time data each synthetic bar tends to compress.
Avg Return and Return StdDev - Simple research metrics over synthetic close-to-close changes.
Why this reduces noise
Time based bars treat a 5 minute print with 1 percent of average participation the same as one with 300 percent. Volume sampling equalizes bar information content. By advancing the bar only when sufficient activity occurs, you skip low quality intervals that add variance but little signal. For banded systems like Supertrend, this often means fewer false flips and cleaner runs.
Notes and tips
Use Dollar Bars on assets where nominal price varies widely over time or across symbols.
Median filter can resist single burst outliers when setting dynamic thresholds.
If you need a stable research baseline, set Use Fixed and keep the threshold constant across tests.
Enable Show Time Bars occasionally to sanity check what the synthetic stream is compressing or stretching.
Link again for reference
Original Volume Based Sampling engine:
Bottom line
When you let participation set the clock, your Supertrend reacts to meaningful flow instead of idle prints. The result is a cleaner state machine, fewer micro whipsaws, and a trend read that respects when the market is actually trading.
Ichimoku PourSamadi Signal [TradingFinder] KijunSen Magic Number🔵 Introduction
The Ichimoku Kinko Hyo system is one of the most comprehensive market analysis tools ever created. Developed by Goichi Hosoda, a Japanese journalist in the 1930s, its purpose was to allow traders to recognize the balance between price, time, and momentum at a single glance. (In Japanese, Ichimoku literally means “one look.”)
At the core of the system lie five key components: Tenkan-sen (Conversion Line), Kijun-sen (Baseline), Chikou Span (Lagging Line), and the two leading spans, Senkou Span A and Senkou Span B, which together form the well-known Kumo or cloud representing both temporal structure and equilibrium zones in the market.
Although Ichimoku is commonly used to identify trends and support/resistance levels, a deeper layer of time philosophy exists within it. Ichimoku was not designed solely for price analysis but equally for time analysis.
In the classical model, the numerical cycles 9, 26, 52 reflect the natural rhythm of the market originally based on the Tokyo Stock Exchange’s trading schedule in the 1930s.
These values repeat across the system’s calculations, forming the foundation of Ichimoku’s time symmetry where price and time ultimately seek equilibrium.
In recent years, modern analysts have explored new approaches to extract time-based turning points from Ichimoku’s structure. One such approach is the analysis of flat segments on the Kijun-sen and Senkou B lines.
Whenever one of these lines remains flat for a period, it signals temporary balance between buyers and sellers; when the flat breaks, the market exits equilibrium and a new cycle begins.
This indicator is built precisely upon that philosophy. Following the timing methodology introduced by M.A. Poursamadi, the focus shifts away from price signals and line crossovers toward identifying flat periods on Kijun-sen (period 52) as time anchors.
From the first candle that changes the line’s slope, the tool begins a temporal count using a fixed sequence of key numbers: 5, 9, 13, 17, 26, 35, 43, 52, 63, 72, 81, 90.
Derived from both classical Ichimoku cycles and empirical testing, these numbers mark potential timing nodes where a market wave may end, a correction may begin, or a new leg may form.
Thus, this method serves not merely as another Ichimoku tool but as a temporal metronome for market structure a way to visualize moments when the market is ready to change rhythm, often before candles reveal it.
🔵 How to Use
The Kijun Timing BoX is built entirely on Ichimoku’s concept of time analysis.
Its core idea is that within every flat segment of the Kijun-sen, the market enters a temporary balance between opposing forces.
When that flat breaks, a new time cycle begins. From that first breakout candle, the indicator starts counting forward through the predefined time sequence(5, 9, 13, 17, 26, 35, 43, 52, 63, 72, 81, 90).
This counting framework creates a temporal map of market behavior, where each number represents an area where meaningful price fluctuations often occur.
A “meaningful fluctuation” does not necessarily imply reversal or continuation; rather, it marks a moment when the market’s internal energy balance shifts, typically visible as noticeable reactions on lower timeframes.
🟣 Identifying the Anchor Point
The first step is recognizing a valid flat zone on the Kijun-sen.
When this line remains flat for several candles and then changes slope, the indicator marks that bar as the Anchor, initiating the time count.
From that point onward, vertical gray lines appear at each interval in the key-number sequence, visualizing the time nodes ahead.
🟣 Reading the Timing Lines
Each numbered line represents a timing node a temporal point where a change in price rhythm is statistically more likely to occur.
At these nodes, the market may :
Enter a consolidation or minor correction phase.
Develop range-bound movement.
Or simply alter the speed and intensity of its move.
These behaviors do not imply a specific direction; they only highlight zones where time-based activity tends to cluster, giving traders a clearer view of cyclical rhythm.
🟣 Applying Time Analysis
The indicator’s primary use is to observe temporal order, not to predict price direction.
By tracking the distance between Anchors and the reactions that appear near major timing lines, traders can empirically identify each market’s characteristic rhythm—its own time DNA.
For example, one asset may consistently show significant fluctuations around the 13- and 26-bar marks,while another might react closer to 9 or 52. Recognizing such patterns helps traders understand how long typical cycles last before new phases of volatility emerge.
🟣 Combining with Other Tools
The indicator does not generate buy/sell signals on its own.
Its best use is in combination with price- or structure-based methods, to see whether meaningful price reactions occur around the same timing nodes.
In practice, it helps distinguish structured time-based fluctuations from random, noise-driven moves an insight often overlooked in conventional market analysis.
🔵 Settings
🟣 Logical Settings
KijunSen Period : Defines the baseline period used for timing analysis. Default = 52. It is the main line for detecting flats and generating time anchors.
Flat Event Filter : Controls how flat segments are validated before triggering a new timing event.
All : Every flat triggers a new Timing Box.
Automatic : Only flats longer than the historical average are used (recommended).
Custom : User manually defines the minimum flat length via Custom Count.
Update Timing Analysis BoX Per Event : If enabled, a new Timing Box is drawn each time a new flat event occurs. If disabled, the box completes its 90-bar window before refreshing.
🟣 Ichimoku Settings
TenkanSen Period : Defines the period for the Conversion Line (Tenkan-sen). Default = 9.
KijunSen Period : Sets the standard Ichimoku baseline (not the timing line). Default = 26.
Span B Period : Defines the period for Senkou Span B, the slower cloud boundary. Default = 52.
Shift Lines : Offsets cloud projection into the future. Default = 26.
🟣 Display Settings
Users can show or hide all Ichimoku lines Tenkan-sen, Kijun-sen, Chikou Span, Span A, and Span B as well as the Ichimoku Cloud.
They can also customize the color of each element to match personal chart preferences and improve visibility.
🔵 Conclusion
This analytical approach transforms Ichimoku’s time philosophy into a visual and measurable framework. A flat Kijun-sen represents a moment of market equilibrium; when its slope shifts, a new temporal cycle begins.
The purpose is not to forecast price direction but to highlight periods when meaningful fluctuations are more likely to develop.
Through this perspective, traders can observe the hidden rhythm of market time and expand their analysis beyond price into a broader time-cycle dimension.
Ultimately, the method revives Ichimoku’s original principle: the market can only be truly understood through the simultaneous harmony of price, time, and balance.
Manipolazione Luca C H1Osservando le candele h1 neglio orari ( di apertura sessione london e ny) possiamo cogliere molto piu' facilmente le manipolazioni per poter aprire le operazioni o scendere di time frame aspettando un altri trigger di entrata.
By observing the h1 candles during the opening hours (London and New York session) we can much more easily detect manipulations in order to open trades or move down the time frame waiting for other entry triggers.
Positional Toolbox v6 (distinct colors)what the lines mean (colors)
EMA20 (green) = fast trend
EMA50 (orange) = intermediate trend
EMA200 (purple, thicker) = primary trend
when the chart is “bullish” vs “bearish”
Bullish bias (look for buys):
EMA20 > EMA50 > EMA200 and EMA200 sloping up.
Bearish bias (avoid longs / consider exits):
EMA20 < EMA50 < EMA200 or price closing under EMA50/EMA200.
the two buy signals the script gives you
Pullback Long (triangle up)
Prints when price dips to EMA20 (green) and closes back above it while trend is bullish and ADX is decent.
Entry: buy on the same close or on a break of that candle’s high next day.
Stop: below the pullback swing-low (or below EMA50 for simplicity).
Best for: adding on an existing uptrend after a shallow dip.
Breakout 55D (“BO55” label)
Prints when price closes above prior 55-day high with volume surge in a bullish trend.
Entry: on the close that triggers, or next day above the breakout candle’s high.
Stop: below the breakout candle’s low (conservative: below base low).
Best for: fresh trend legs from bases.
simple “sell / exit” rules
Trend exit (clean & mechanical): exit if daily close < EMA50 (orange).
More conservative: only exit if close < EMA200 (purple).
Momentum fade / weak breakout: if BO55 triggers but price re-closes back inside the base within 1–3 sessions on above-avg volume → exit or cut size.
Profit taking: book some at +1.5R to +2R, trail the rest (e.g., below prior swing lows or EMA20).
quick visual checklist (what to look for)
Are the EMAs stacked up (green over orange over purple)? → ok to buy setups.
Did a triangle print near EMA20? → pullback long candidate.
Did a BO55 label print with strong volume? → breakout candidate.
Any close under EMA50 after you’re in? → reduce/exit.
timeframe
Use Daily for positional signals.
If you want a tighter entry, drop to 30m/1h only to time the trigger—but keep decisions anchored to the daily trend.
alerts to set (so you don’t miss signals)
Add alert on Breakout 55D and Pullback Long (from the indicator’s alertconditions).
Optional price alerts at the breakout level or EMA20 touch.
risk guardrails (MTF friendly)
Risk ≤1% of capital per trade.
Avoid fresh entries within ~5 trading days of earnings unless you accept gap risk.
Prefer high-liquidity NSE F&O names (your CSV watchlist covers this).
TL;DR (super short):
Green > Orange > Purple = uptrend.
Triangle near green = buy the pullback; stop under swing low/EMA50.
BO55 label = buy the breakout; stop under breakout candle/base.
Exit on close below EMA50 (or below EMA200 if you’re giving more room).
BioSwarm Imprinter™BioSwarm Imprinter™ — Agent-Based Consensus for Traders
What it is
BioSwarm Imprinter™ is a non-repainting, agent-based sentiment oscillator. It fuses many short-to-medium lookback “opinions” into one 0–100 consensus line that is easy to read at a glance (50 = neutral, >55 bullish bias, <45 bearish bias). The engine borrows from swarm intelligence: many simple voters (agents) adapt their influence over time based on how well they’ve been predicting price, so the crowd gets smarter as conditions change.
Use it to:
• Detect emerging trends sooner without overreacting to noise.
• Filter mean-reversion vs continuation opportunities.
• Gate entries with a confidence score that reflects both strength and persistence of the move.
• Combine with your execution tools (VWAP/ORB/levels) as a state filter rather than a trade signal by itself.
⸻
Why it’s different
• Swarm learning: Each agent improves or decays its “fitness” depending on whether its vote matched the next bar’s direction. High-fitness agents matter more; weak agents fade.
• Multi-horizon by design: The crowd is composed of fixed, simple lookbacks spread from lenMin to lenMax. You get a blended, robust view instead of a single fragile parameter.
• Two complementary lenses: Each agent evaluates RSI-style balance (via Wilder’s RMA) and momentum (EMA deviation). You decide the weight of each.
• No repaint, no MTF pitfalls: Everything runs on the chart’s timeframe with bar-close confirmation; no request.security() or forward references.
• Actionable UI: A clean consensus line, optional regime background, confidence heat, and triangle markers when thresholds are crossed.
⸻
What you see on the chart
• Consensus line (0–100): Smoothed to your preference; color/area makes bull/bear zones obvious.
• Regime coloring (optional): Light green in bull zone, light red in bear zone; neutral otherwise.
• Confidence heat: A small gauge/number (0–100) that combines distance from neutral and recent persistence.
• Markers (optional): Triangles when consensus crosses up through your bull threshold (e.g., 55) or down through your bear threshold (e.g., 45).
• Info panel (optional): Consensus value, regime, confidence, number of agents, and basic diagnostics.
⸻
How it works (under the hood)
1. Horizon bins: The range is divided into numBins. Each bin has a fixed, simple integer length (crucial for Pine’s safety rules).
2. Per-bin features (computed every bar):
• RSI-style balance using Wilder’s RMA (not ta.rsi()), then mapped to −1…+1.
• Momentum as (close − EMA(L)) / EMA(L) (dimensionless drift).
3. Agent vote: For its assigned bin, an agent forms a weighted score: score = wRSI*RSI_like + wMOM*Momentum. A small dead-band near zero suppresses chop; votes are +1/−1/0.
4. Fitness update (bar close): If the agent’s previous vote agreed with the next bar’s direction, multiply its fitness by learnGain; otherwise by learnPain. Fitness is clamped so it never explodes or dies.
5. Consensus: Weighted average of all votes using fitness as weights → map to 0–100 and smooth with EMA.
Why it doesn’t repaint:
• No future references, no MTF resampling, fitness updates only on confirmed bars.
• All TA primitives (RMA/EMA/deltas) are computed every bar unconditionally.
⸻
Signals & confidence
• Bullish bias: consensus ≥ bullThr (e.g., 55).
• Bearish bias: consensus ≤ bearThr (e.g., 45).
• Confidence (0–100):
• Distance score: how far consensus is from 50.
• Momentum score: how strong the recent change is versus its recent average.
• Combined into a single gate; start filtering entries at ≥60 for higher quality.
Tip: For range sessions, raise thresholds (60/40) and increase smoothing; for momentum sessions, lower smoothing and keep thresholds at 55/45.
⸻
Inputs you’ll actually tune
• Agents & horizons:
• N_agents (e.g., 64–128)
• lenMin / lenMax (e.g., 6–30 intraday, 10–60 swing)
• numBins (e.g., 12–24)
• Weights & smoothing:
• wRSI vs wMOM (e.g., 0.7/0.3 for FX & indices; 0.6/0.4 for crypto)
• deadBand (0.03–0.08)
• consSmooth (3–8)
• Thresholds & hygiene:
• bullThr/bearThr (55/45 default)
• cooldownBars to avoid signal spam
⸻
Playbooks (ready-to-use)
1) Breakout / Trend continuation
• Timeframe: 15m–1h for day/swing.
• Filter: Take longs only when consensus > 55 and confidence ≥ 60.
• Execution: Use your ORB/VWAP/pullback trigger for entry. Trail with swing lows or 1.5×ATR. Exit on a close back under 50 or when a bearish signal prints.
2) Mean reversion (fade)
• When: Sideways days or low-volatility clusters.
• Setup: Increase deadBand and consSmooth.
• Signal: Bearish fades when consensus rolls over below ≈55 but stays above 50; bullish fades when it rolls up above ≈45 but stays below 50.
• Targets: The neutral zone (~50) as the first take-profit.
3) Multi-TF alignment
• Keep BioSwarm on 1H for bias, execute on 5–15m:
• Only take entries in the direction of the 1H consensus.
• Skip counter-bias scalps unless confidence is very low (explicit mean-reversion plan).
⸻
Integrations that work
• DynamoSent Pro+ (macro bias): Only act when macro bias and swarm consensus agree.
• ORB + Session VWAP Pro: Trade London/NY ORB breakouts that retest while consensus >55 (long) or <45 (short).
• Levels/Orderflow: BioSwarm is your “go / no-go”; execution stays with your usual triggers.
⸻
Quick start
1. Drop the indicator on a 1H chart.
2. Start with: N_agents=64, lenMin=6, lenMax=30, numBins=16, deadBand=0.06, consSmooth=5, thresholds 55/45.
3. Trade only when confidence ≥ 60.
4. Add your favorite execution tool (VWAP/levels/OR) for entries & exits.
⸻
Non-repainting & safety notes
• No request.security(); no hidden lookahead.
• Bar-close confirmation for fitness and signals.
• All TA calls are unconditional (no “sometimes called” warnings).
• No series-length inputs to RSI/EMA — we use RMA/EMA formulas that accept fixed simple ints per bin.
⸻
Known limits & tips
• Too many signals? Raise deadBand, increase consSmooth, widen thresholds to 60/40.
• Too few signals? Lower deadBand, reduce consSmooth, narrow thresholds to 53/47.
• Over-fitting risk: Keep learnGain/learnPain modest (e.g., ×1.04 / ×0.96).
• Compute load: Large N_agents × numBins is heavier; scale to your device.
⸻
Example recipes
EURUSD 1H (swing):
lenMin=8, lenMax=34, numBins=16, wRSI=0.7, wMOM=0.3, deadBand=0.06, consSmooth=6, thr=55/45
Buy breakouts when consensus >55 and confidence ≥60; confirm with 5–15m pullback to VWAP or level.
SPY 15m (US session):
lenMin=6, lenMax=24, numBins=12, consSmooth=4, deadBand=0.05
On trend days, stay with longs as long as consensus >55; add on shallow pullbacks.
BTC 1H (24/7):
Increase momentum weight: wRSI=0.6, wMOM=0.4, extend lenMax to ~50. Use dynamic stops (ATR) and partials on strong verticals.
⸻
Final word
BioSwarm is a state engine: it tells you when the market is primed to continue or mean-revert. Pair it with your entries and risk framework to turn that state into trades. If you’d like, I can supply a companion strategy template that consumes the consensus and back-tests the three playbooks (Breakout/Fade/Flip) with standard risk management.
ARO Pro — Adaptive Regime OscillatorARO Pro — Adaptive Regime Oscillator (v6)
ARO Pro turns your chart into a context-aware decision system. It classifies every bar as Trending (up or down) or Ranging in real time, then switches its math to match the regime: trend strength is measured with an ATR-normalized EMA spread, while range behavior is tracked with a center-based RSI oscillator. The result is cleaner entries, fewer false signals, and faster reads on regime shifts—without repainting.
⸻
How it works (under the hood)
1. Regime Detection (Kaufman ER):
ARO computes Kaufman’s Efficiency Ratio (ER) over a user-defined length.
- ER > threshold → Trending (direction from EMA fast vs. EMA slow)
- ER ≤ threshold → Ranging
2. Adaptive Oscillator Core:
- Trend mode: (EMA(fast) − EMA(slow)) / ATR * 100 → momentum normalized by volatility.
- Range mode: RSI(length) − 50 → mean-reversion pressure around zero.
3. Volatility Filter (optional):
Blocks signals if ATR as % of price is below a floor you set. This reduces noise in thin or quiet markets.
4. MTF Trend Filter (optional & non-repainting):
Confirms signals only if a higher timeframe EMA(fast) > EMA(slow) for longs (or < for shorts). Implemented with lookahead_off and gaps_on.
5. Confirmation & Alerts:
Signals are locked only on bar close (barstate.isconfirmed) and offered via three alert types: ARO Long, ARO Short, ARO Regime Shift.
⸻
What you see on the chart
• Background heat:
• Green = Trending Up, Red = Trending Down, Gray = Range.
• ARO line (panel): Adaptive oscillator (trend/value colors).
• Signal markers: ▲ Long / ▼ Short on confirmed bars.
• Guide lines: Upper/Lower thresholds (±K) and zero line.
• Info Panel (table): Regime, ER, ATR %, ARO, HTF status (OK/BLOCK/OFF), and a Confidence light.
• Debug Overlay (optional): Quick view of thresholds and raw conditions for tuning.
⸻
Inputs (quick reference)
• Signals: Fast/Slow EMA, RSI length, ER length & threshold, oscillator smoothing, signal threshold.
• Filters: ATR length, minimum ATR% (volatility floor), toggle for volatility filter.
• Visuals: Background on/off, Info Panel on/off, Debug overlay on/off.
• MTF (safe): Toggle + HTF timeframe (e.g., 240, D, W).
⸻
Interpreting signals
• Long: Trend regime AND fast EMA > slow EMA AND ARO ≥ +threshold (confirmed bar, filters passing).
• Short: Trend regime AND fast EMA < slow EMA AND ARO ≤ −threshold (confirmed bar, filters passing).
• Regime Shift: Alert when ER moves the market from Range → Trend or flips trend direction.
⸻
Practical use cases & examples
1) Intraday momentum alignment (scalps to day trades)
• Timeframes: 5–15m with HTF filter = 4H.
• Flow:
1. Wait for Trend Up background + HTF OK.
2. Enter on ▲ Long when ARO crosses above +threshold.
3. Stops: 1–1.5× ATR(14) below trigger bar or below last micro swing.
4. Exits: Partial at 1× ATR, trail remainder with an ATR stop or when ARO reverts to zero/Regime Shift.
• Why it works: You’re trading with the dominant higher-timeframe structure while avoiding low-volatility fakeouts.
2) Swing trend following (cleaner trend legs)
• Timeframes: 1H–4H with HTF filter = 1D.
• Flow:
1. Only act in Trend background aligned with HTF.
2. Add on subsequent ▲ signals as ARO maintains positive (or negative) territory.
3. Reduce or exit on Regime Shift (Trend → Range or direction flip) or when ARO crosses back through zero.
• Stops/targets: Initial 1.5–2× ATR; move to breakeven once the trade gains 1× ATR; trail with a multiple-ATR or structure lows/highs.
3) Range tactics (fade the extremes)
• Timeframes: 15m–1H or 1D on mean-reverting names.
• Flow:
1. Act only when background = Range.
2. Fade moves when ARO swings from ±extremes back toward zero near well-defined S/R.
3. Exit at the opposite band or zero line; abort if a Regime Shift to Trend occurs.
• Tip: Increase ER threshold (e.g., 0.35–0.40) to label more bars as Range on choppy instruments.
4) Event days & macro filters
• Approach: Raise the volatility floor (Min ATR%) on macro days (FOMC, CPI).
• Effect: You’ll ignore “fake” micro swings in the minutes leading up to releases and catch only post-event confirmed momentum.
⸻
Parameter tuning guide
• ER Threshold:
• Lower (0.20–0.30) = more Trend bars, more signals, higher noise.
• Higher (0.35–0.45) = stricter trend confirmation, fewer but cleaner signals.
• Signal Threshold (±K):
• Raise to reduce whipsaws; lower for earlier but noisier triggers.
• Volatility Floor (ATR%):
• Thin/quiet assets benefit from a higher floor (e.g., 0.3–0.6).
• Highly liquid futures/forex can work with lower floors.
• HTF Filter:
• Keep it ON when you want higher win consistency; turn OFF for tactical counter-trend plays.
⸻
Alerts (recommended setup)
• “ARO Long” / “ARO Short”: Entry-style alerts on confirmed signals.
• “ARO Regime Shift”: Context alert to scale in/out or switch playbooks (trend vs. range).
All alerts are non-repainting and fire only when the bar closes.
⸻
Best practices & combinations
• Price action & S/R: Use ARO to define when to engage, and price structure to define where (breakout levels, pullback zones).
• VWAP/Session tools: In intraday trends, ▲ signals above VWAP tend to carry; avoid shorts below session VWAP in strong downtrends.
• Risk first: Size by ATR; never let a single ARO event override your max risk per trade.
• Portfolio filter: On indices/ETFs, enable HTF filter and a stricter ER threshold to ride regime legs.
⸻
Non-repaint and implementation notes
• The script does not repaint:
• Signals are computed and locked on bar close (barstate.isconfirmed).
• All higher-timeframe data uses request.security(..., lookahead_off, gaps_on).
• No future indexing or negative offsets are used.
• The Info Panel and Debug overlay are purely visual aids and do not change signal logic.
⸻
Limitations & tips
• Chop sensitivity: In hyper-choppy symbols, consider raising ER threshold and the signal threshold, and enable HTF filter.
• Instrument personality: EMAs/RSI lengths and volatility floor often need a quick 2–3 minute tune per asset class (FX vs. crypto vs. equities).
• No guarantees: ARO improves context and timing, but it is not a promise of profitability—always combine with risk management.
⸻
Quick start (TL;DR)
1. Timeframes: 5–15m intraday (HTF = 4H); 1H–4H swing (HTF = 1D).
2. Use defaults, then tune ER threshold (0.25–0.40) and Signal threshold (±20).
3. Enable Volatility Floor (e.g., 0.2–0.5 ATR%) on quiet assets.
4. Trade ▲ / ▼ only in matching Trend background; fade extremes only in Range background.
5. Set alerts for Long, Short, and Regime Shift; manage risk with ATR stops.
⸻
Author’s note: ARO Pro is designed to be clear, adaptive, and operational out of the box. If you publish variants (e.g., different ER logic, alternative trend cores), please credit the original and document any changes so users can compare behavior reliably.
Unusual Moves Detector# Unusual Moves Detector
A TradingView indicator that detects and alerts users to unusual price movements based on ATR (Average True Range) and volume analysis. This indicator is designed to identify price action that deviates significantly from normal market behavior.
## Features
### Core Detection Mechanisms
- **ATR-Based Volatility Detection**: Identifies price movements that exceed normal volatility levels
- **Volume Analysis**: Optional volume spike detection to confirm unusual moves
- **Signal Persistence Tracking**: Monitors how many signals occur within a lookback period
### Visual Indicators
- **Up/Down Arrows**: Green triangles for unusual upward moves, red triangles for downward moves
- **Signal Strength Labels**: Numbers showing how many signals occurred in the lookback period
- **Real-time Metrics Table**: Displays current ATR and volume ratios
### Customizable Parameters
1. **ATR Period** (default: 14)
- Length for Average True Range calculation
- Affects volatility measurement sensitivity
2. **Volume MA Period** (default: 20)
- Period for volume moving average
- Used in volume spike detection
3. **ATR Multiplier** (default: 2.0)
- How many times the ATR to trigger a signal
- Higher values = less sensitive to price moves
4. **Volume Multiplier** (default: 2.0)
- How many times the average volume to consider "high volume"
- Higher values = less sensitive to volume spikes
5. **Include Volume Analysis** (default: true)
- Toggle volume confirmation requirement
- When disabled, only price volatility matters
6. **Signal Lookback Period** (default: 5)
- How many bars to look back for signal counting
- Affects signal strength calculation
### Alert System
- **Upward Movement Alerts**: Triggers when unusual upward price action is detected
- **Downward Movement Alerts**: Triggers when unusual downward price action is detected
- **Customizable Alert Messages**: Can be configured in TradingView's alert system
### Information Display
Real-time metrics table shows:
- Current ATR value
- Volume ratio (current volume / average volume)
- Net signal count (up signals - down signals)
## Installation
1. Open TradingView's Pine Script Editor
2. Create a new indicator
3. Copy and paste the indicator code
4. Click "Add to Chart" to apply the indicator
## Usage Guide
### Basic Setup
1. Add the indicator to your chart
2. Adjust parameters based on your trading timeframe and style
3. Configure alerts if desired
### Parameter Tuning Tips
- **For More Signals**: Lower the ATR and Volume multipliers
- **For Fewer Signals**: Increase the multipliers
- **For Trend Following**: Increase the lookback period
- **For Quick Signals**: Decrease the lookback period
### Alert Setup
1. Click the indicator settings
2. Go to "Create Alert"
3. Choose either up or down move condition
4. Configure alert settings (sound, notification, etc.)
## Backtesting Compatibility
- Fully compatible with TradingView's backtesting engine
- All calculations use historical data only
- No forward-looking data or repainting
## Technical Details
- Written in Pine Script v6
- Optimized for real-time calculation
- Uses native TradingView functions for performance
- Compatible with all timeframes
## Performance Considerations
- Lightweight computation using built-in functions
- Efficient memory usage with variable optimization
- Real-time calculation with minimal lag
## Support and Contribution
Feel free to modify and improve the indicator according to your needs. The code is well-commented for easy understanding and modification.
## License
Free to use and modify for personal and commercial purposes.
## Disclaimer
This indicator is for informational purposes only. Always conduct your own analysis and consider multiple factors when making trading decisions.
VWAP Trend Strategy (Intraday) [KedarArc Quant]Description:
An intraday strategy that anchors to VWAP and only trades when a local EMA trend gate and a volume participation gate are both open. It offers two entry templates—Cross and Cross-and-Retest—with an optional Momentum Exception for impulsive moves. Exits combine a TrendBreak (structure flips) with an ATR emergency stop (risk cap).
Updates will be published under this script.
Why this merits a new script
This is not a simple “VWAP + EMA + ATR” overlay. The components are sequenced as gates and branches that *change the trade set* in ways a visual mashup cannot:
1. Trend Gate first (EMA fast vs. slow on the entry timeframe)
Counter-trend VWAP crosses are suppressed. Many VWAP scripts fire on every cross; here, no entry logic even evaluates unless the trend gate is open.
2. Participation Gate second (Volume SMA × multiplier)
This gate filters thin liquidity moves around VWAP. Without it, the same visuals would produce materially more false triggers.
3. Branching entries with structure awareness
* Cross: Immediate VWAP cross in the trend direction.
* Cross-and-Retest: Requires a revisit to VWAP vicinity within a lookback window (recent low near VWAP for longs; recent high for shorts). This explicitly removes first-touch fakeouts that a plain cross takes.
* Momentum Exception (optional): A quantified body% + volume condition can bypass the retest when flow is impulsive—intentional risk-timing, not “just another indicator.”
4. Dual exits that reference both anchor and structure
* TrendBreak: Close only when price loses VWAP and EMA alignment flips.
* ATR stop: Placed at entry to cap tail risk.
These exits complement the entry structure rather than being generic stop/target add-ons.
What it does
* Trades the session’s fair value anchor (VWAP), but only with local-trend agreement (EMA fast vs. slow) and sufficient participation (volume filter).
* Lets you pick Cross or Cross-and-Retest entries; optionally allow a fast Momentum Exception when candles expand with volume.
* Manages positions with a structure exit (TrendBreak) and an emergency ATR stop from entry.
How it works (concepts & calculations)
* VWAP (session anchor):
Standard VWAP of the active session; entries reference the cross and the retest proximity to VWAP.
* Trend gate:
Long context only if `EMA(fast) > EMA(slow)`; short only if `EMA(fast) < EMA(slow)`.
A *gate*, not a trigger—entries aren’t considered unless this is true.
* Participation (volume) gate:
Require `volume > SMA(volume, volLen) × volMult`.
Screens out low-participation wiggles around VWAP.
Entries:
* Cross: Price crosses VWAP in the trend direction while volume gate is open.
* Cross-and-Retest: After crossing, price revisits VWAP vicinity within `lookback` (recent *low near VWAP* for longs; recent *high near VWAP* for shorts).
* Momentum Exception (optional): If body% (|close−open| / range) and volume exceed thresholds, enter without waiting for the retest.
Exits:
* TrendBreak (structure):
* Longs close when `price < VWAP` and `EMA(fast) < EMA(slow)` (mirror for shorts).
* ATR stop (risk):
* From entry: `stop = entry ± ATR(atrLen) × atrMult`.
How to use it ?
1. Select market & timeframe: Intraday on liquid symbols (equities, futures, crypto).
2. Pick entry mode:
* Start with Cross-and-Retest for fewer, more selective signals.
* Enable Momentum Exception if strong moves leave without retesting.
3. Tune guards:
* Raise `volMult` to ignore thin periods; lower it for more activity.
* Adjust `lookback` if retests come late/early on your symbol.
4. Risk:
* `atrLen` and `atrMult` set the emergency stop distance.
5. Read results per session: Optional panel (if enabled) summarizes Net-R, Win%, and PF for today’s session to evaluate
behavior regime by regime.
⚠️ Disclaimer
This script is provided for educational purposes only.
Past performance does not guarantee future results.
Trading involves risk, and users should exercise caution and use proper risk management when applying this strategy.
Deadband Hysteresis Supertrend [BackQuant]Deadband Hysteresis Supertrend
A two-stage trend tool that first filters price with a deadband baseline, then runs a Supertrend around that baseline with optional flip hysteresis and ATR-based adverse exits.
What this is
A hybrid of two ideas:
Deadband Hysteresis Baseline that only advances when price pulls far enough from the baseline to matter. This suppresses micro noise and gives you a stable centerline.
Supertrend bands wrapped around that baseline instead of raw price. Flips are further gated by an extra margin so side changes are more deliberate.
The goal is fewer whipsaws in chop and clearer regime identification during trends.
How it works (high level)
Deadband step — compute a per-bar “deadband” size from one of four modes: ATR, Percent of price, Ticks, or Points. If price deviates from the baseline by more than this amount, move the baseline forward by a fraction of the excess. If not, hold the line.
Centered Supertrend — build upper and lower bands around the baseline using ATR and a user factor. Track the usual trailing logic that tightens a band while price moves in its favor.
Flip hysteresis — require price to exceed the active band by an extra flip offset × ATR before switching sides. This adds stickiness at the boundary.
Adverse exit — once a side is taken, trigger an exit if price moves against the entry by K × ATR .
If you would like to check out the filter by itself:
What it plots
DBHF baseline (optional) as a smooth centerline.
DBHF Supertrend as the active trailing band.
Candle coloring by trend side for quick read.
Signal markers 𝕃 and 𝕊 at flips plus ✖ on adverse exits.
Inputs that matter
Price Source — series being filtered. Close is typical. HL2 or HLC3 can be steadier.
Deadband mode — ATR, Percent, Ticks, or Points. This defines the “it’s big enough to matter” zone.
ATR Length / Mult (DBHF) — only used when mode = ATR. Larger values widen the do-nothing zone.
Percent / Ticks / Points — alternatives to ATR; pick what fits your market’s convention.
Enter Mult — scales the deadband you must clear before the baseline moves. Increase to filter more noise.
Response — fraction of the excess applied to baseline movement. Higher responds faster; lower is smoother.
Supertrend ATR Period & Factor — traditional band size controls; higher factor widens and flips less often.
Flip Offset ATR — extra ATR buffer required to flip. Useful in choppy regimes.
Adverse Stop K·ATR — per-trade danger brake that forces an exit if price moves K×ATR against entry.
UI — toggle baseline, supertrend, signals, and bar painting; choose long and short colors.
How to read it
Green regime — candles painted long and the Supertrend running below price. Pullbacks toward the baseline that fail to breach the opposite band often resume higher.
Red regime — candles painted short and the Supertrend running above price. Rallies that cannot reclaim the band may roll over.
Frequent side swaps — reduce sensitivity by increasing Enter Mult, using ATR mode, raising the Supertrend factor, or adding Flip Offset ATR.
Use cases
Bias filter — allow entries only in the direction of the current side. Use your preferred triggers inside that bias.
Trailing logic — treat the active band as a dynamic stop. If the side flips or an adverse K·ATR exit prints, reduce or close exposure.
Regime map — on higher timeframes, the combination baseline + band produces a clean up vs down template for allocation decisions.
Tuning guidance
Fast markets — ATR deadband, modest Enter Mult (0.8–1.2), response 0.2–0.35, Supertrend factor 1.7–2.2, small Flip Offset (0.2–0.5 ATR).
Choppy ranges — widen deadband or raise Enter Mult, lower response, and add more Flip Offset so flips require stronger evidence.
Slow trends — longer ATR periods and higher Supertrend factor to keep you on side longer; use a conservative adverse K.
Included alerts
DBHF ST Long — side flips to long.
DBHF ST Short — side flips to short.
Adverse Exit Long / Short — K·ATR stop triggers against the current side.
Strengths
Deadbanded baseline reduces micro whipsaws before Supertrend logic even begins.
Flip hysteresis adds a second layer of confirmation at the boundary.
Optional adverse ATR stop provides a uniform risk cut across assets and regimes.
Clear visuals and minimal parameters to adjust for symbol behavior.
Putting it together
Think of this tool as two decisions layered into one view. The deadband baseline answers “does this move even count,” then the Supertrend wrapped around that baseline answers “if it counts, which side should I be on and where do I flip.” When both parts agree you tend to stay on the correct side of a trend for longer, and when they disagree you get an early warning that conditions are changing.
When the baseline bends and price cannot reclaim the opposite band , momentum is usually continuing. Pullbacks into the baseline that stall before the far band often resolve in trend.
When the baseline flattens and the bands compress , expect indecision. Use the Flip Offset ATR to avoid reacting to the first feint. Wait for a clean band breach with follow through.
When an adverse K·ATR exit prints while the side has not flipped , treat it as a risk event rather than a full regime change. Many users cut size, re-enter only if the side reasserts, and let the next flip confirm a new trend.
Final thoughts
Deadband Hysteresis Supertrend is best read as a regime lens. The baseline defines your tolerance for noise, the bands define your trailing structure, and the flip offset plus adverse ATR stop define how forgiving or strict you want to be at the boundary. On strong trends it helps you hold through shallow shakeouts. In choppy conditions it encourages patience until price does something meaningful. Start with settings that reflect the cadence of your market, observe how often flips occur, then nudge the deadband and flip offset until the tool spends most of its time describing the move you care about rather than the noise in between.
Full Candle Higher/Lower (No Repeats)🔎 What the Script Does (Pine Script v6)
Keeps track of the last signal
Uses a persistent variable lastSignal (initialized once as "none").
Ensures that if a signal repeats consecutively, it won’t be triggered again.
Defines the conditions for a “Higher” or “Lower” candle sequence
Higher condition:
Current close > previous high, AND previous low ≤ the high of two bars ago.
→ This means the candle has fully broken higher.
Lower condition:
Current close < previous low, AND previous high ≥ the low of two bars ago.
→ This means the candle has fully broken lower.
Checks for new signals only
If a candle meets the condition and the last signal wasn’t the same, a new signal is triggered.
Updates lastSignal to prevent repeats.
Plots labels/arrows
A “Higher” signal shows a green label below the bar.
A “Lower” signal shows a red label above the bar.
Sets alerts
So you can be notified in TradingView whenever a “Higher” or “Lower” flag is detected.
📊 Trading Logic in Words
The indicator is looking for full candle breakouts.
If a candle closes above the previous high (with some confirmation from older bars), it flags it as a “Higher” signal.
If a candle closes below the previous low (with similar confirmation), it flags it as a “Lower” signal.
It avoids duplicate consecutive signals by remembering what the last one was.
✅ Why It’s Useful
Helps traders spot momentum continuation candles (strong push candles).
Reduces noise by not repeating the same signal multiple times in a row.
Works like a breakout detector that tells you when the market is making a new leg up or new leg down.
ATR SL/TPStop Loss Finder ATR
A Stop Loss Finder ATR indicator is a dynamic risk management tool leveraging the Average True Range (ATR) to identify and track optimal stop-loss levels based on current market volatility.
A stop hunt indicator is a technical tool designed to identify potential instances where large market participants, often referred to as "smart money," deliberately move the price to trigger a large number of stop-loss orders, creating a temporary price distortion before reversing the trend. These indicators aim to help traders detect these events to either avoid being stopped out or to enter trades in the direction of the anticipated reversal.
For example, a long wick below support with high volume may signal a bullish stop-hunt , indicating that the price has been driven down to trigger sell-stop orders before reversing upward. Conversely, a long wick above resistance with high volume may signal a bearish stop-hunt , suggesting the price was pushed up to trigger buy-stop orders before reversing downward. The presence of such wicks is often associated with candlestick patterns like hammers or shooting stars.
Unlike fixed stop-losses, this indicator adapts its distance from the current price using a customizable ATR multiplier, ensuring that stop-loss levels are neither too tight (prone to being triggered by normal market noise) nor too wide (exposing capital to excessive risk) . The core function calculates the true range—considering the current high-low range, gaps up, and gaps down—over a user-defined period (typically 14 bars), then applies a multiplier to generate a volatility-adjusted stop-loss distance . This approach allows the indicator to dynamically widen stops during high-volatility periods and tighten them during calm markets, providing a more responsive and context-aware exit strategy.
Price Heat Meter [ChartPrime]⯁ OVERVIEW
Price Heat Meter visualizes where price sits inside its recent range and turns that into an intuitive “temperature” read. Using rolling extremes, candles fade from ❄️ aqua (cold) near the lower bound to 🔥 red (hot) near the upper bound. The tool also trails recent extreme levels, tags unusually persistent extremes with a % “heat” label, and shows a bottom gauge (0–100%) with a live arrow so you can read market heat at a glance.
⯁ KEY FEATURES
Rolling Heat Map (0–100%):
The script measures where the close sits between the current Lowest Low and Highest High over the chosen Length (default 50).
Candles use a two-stage gradient: aqua → yellow (0–50%), then yellow → red (50–100%). This makes “how stretched are we?” instantly visible.
Dynamic Extremes with Time Decay:
When a new rolling High or Low is set, the script starts a faint horizontal trail at that price. Each bar that passes without a new extreme increases a counter; the line’s color gradually fades over time and fully disappears after ~100 bars, keeping the chart clean.
Persistent-Extreme Tags (Reversal Hints):
If an extreme persists for 40 bars (i.e., price hasn’t reclaimed or surpassed it), the tool stamps the original extreme pivot with its recorded Heat% at the moment the extreme formed.
• Upper extremes print a red % label (possible exhaustion/resistance context).
• Lower extremes print an aqua % label (possible exhaustion/support context).
Bottom Heat Gauge (0–100% Scale):
A compact, gradient bar renders at the bottom center showing the current Heat% with an arrow/label. ❄️ anchors the left (0%), 🔥 anchors the right (100%). The arrow adopts the same candle heat color for consistency.
Minimal Inputs, Clear Theme:
• Length (lookback window for H/L)
• Heat Color set (Cold / Mid / Hot)
The defaults give a balanced, legible gradient on most assets/timeframes.
Signal Hygiene by Design:
The meter doesn’t “call” reversals. Instead, it contextualizes price within its range and highlights the aging of extremes. That keeps it robust across regimes and assets, and ideal as a confluence layer with your existing triggers.
⯁ HOW IT WORKS (UNDER THE HOOD)
Range Model:
H = Highest(High, Length), L = Lowest(Low, Length). Heat% = 100 × (Close − L) / (H − L).
Extreme Tracking & Fade:
When High == H , we record/update the current upper extreme; same for Low == L on the lower side. If the extreme doesn’t change on the next bar, a counter increments and the plotted line’s opacity shifts along a 0→100 fade scale (visual decay).
40-Bar Persistence Labels:
On the bar after the extreme forms, the code stores the bar_index and the contemporaneous Heat% . If the extreme survives 40 bars, it places a % label at the original pivot price and index—flagging levels that were meaningfully “tested by time.”
Unified Color Logic:
Both candles and the gauge use the same two-stage gradient (Cold→Mid, then Mid→Hot), so your eye reads “heat” consistently across all elements.
⯁ USAGE
Treat >80% as “hot” and <20% as “cold” context; combine with your trigger (e.g., structure, OB, div, breakouts) instead of acting on heat alone.
Watch persistent extreme labels (40-bar marks) as reference zones for reaction or liquidity grabs.
Use the fading extreme lines as a memory map of where price last stretched—levels that slowly matter less as they decay.
Tighten Length for intraday sensitivity or increase it for swing stability.
⯁ WHY IT’S UNIQUE
Rather than another oscillator, Price Heat Meter translates simple market geometry (rolling extremes) into a readable temperature layer with time-aware extremes and a synchronized gauge . You get a continuously updated sense of stretch, persistence, and potential reversal context—without clutter or overfitting.
Smarter Money Concepts - Wyckoff Springs & Upthrusts [PhenLabs]📊Smarter Money Concepts - Wyckoff Springs & Upthrusts
Version: PineScript™v6
📌Description
Discover institutional manipulation in real-time with this advanced Wyckoff indicator that detects Springs (accumulation phases) and Upthrusts (distribution phases). It identifies when price tests support or resistance on high volume, followed by a strong recovery, signaling potential reversals where smart money accumulates or distributes positions. This tool solves the common problem of missing these subtle phase transitions, helping traders anticipate trend changes and avoid traps in volatile markets.
By combining volume spike detection, ATR-normalized recovery strength, and a sigmoid probability model, it filters out weak signals and highlights only high-confidence setups. Whether you’re swing trading or day trading, this indicator provides clear visual cues to align with institutional flows, improving entry timing and risk management.
🚀Points of Innovation
Sigmoid-based probability threshold for signal filtering, ensuring only statistically significant Wyckoff patterns trigger alerts
ATR-normalized recovery measurement that adapts to market volatility, unlike static recovery checks in traditional indicators
Customizable volume spike multiplier to distinguish institutional volume from retail noise
Integrated dashboard legend with position and size options for personalized chart visualization
Hidden probability plots for advanced users to analyze underlying math without chart clutter
🔧Core Components
Support/Resistance Calculator: Scans a user-defined lookback period to establish dynamic levels for Spring and Upthrust detection
Volume Spike Detector: Compares current volume to a 10-period SMA, multiplied by a configurable factor to identify significant surges
Recovery Strength Analyzer: Uses ATR to measure price recovery after breaks, normalizing for different market conditions
Probability Model: Applies sigmoid function to combine volume and recovery data, generating a confidence score for each potential signal
🔥Key Features
Spring Detection: Spots accumulation when price dips below support but recovers strongly, helping traders enter longs at potential bottoms
Upthrust Detection: Identifies distribution when price spikes above resistance but falls back, alerting to possible short opportunities at tops
Customizable Inputs: Adjust lookback, volume multiplier, ATR period, and probability threshold to match your trading style and market
Visual Signals: Clear + (green) and - (red) labels on charts for instant recognition of accumulation and distribution phases
Alert System: Triggers notifications for signals and probability thresholds, keeping you informed without constant monitoring
🎨Visualization
Spring Signal: Green upward label (+) below the bar, indicating strong recovery after support break for accumulation
Upthrust Signal: Red downward label (-) above the bar, showing failed breakout above resistance for distribution
Dashboard Legend: Customizable table explaining signals, positioned anywhere on the chart for quick reference
📖Usage Guidelines
Core Settings
Support/Resistance Lookback
Default: 20
Range: 5-50
Description: Sets bars back for S/R levels; lower for recent sensitivity, higher for stable long-term zones – ideal for spotting Wyckoff phases
Volume Spike Multiplier
Default: 1.5
Range: 1.0-3.0
Description: Multiplies 10-period volume SMA; higher values filter to significant spikes, confirming institutional involvement in patterns
ATR for Recovery Measurement
Default: 5
Range: 2-20
Description: ATR period for recovery strength; shorter for volatile markets, longer for smoother analysis of post-break recoveries
Phase Transition Probability Threshold
Default: 0.9
Range: 0.5-0.99
Description: Minimum sigmoid probability for signals; higher for strict filtering, ensuring only high-confidence Wyckoff setups
Display Settings
Dashboard Position
Default: Top Right
Range: Various positions
Description: Places legend table on chart; choose based on layout to avoid overlapping price action
Dashboard Text Size
Default: Normal
Range: Auto to Huge
Description: Adjusts legend text; larger for visibility, smaller for minimal space use
✅Best Use Cases
Swing Trading: Identify Springs for long entries in downtrends turning to accumulation
Day Trading: Catch Upthrusts for short scalps during intraday distribution at resistance
Trend Reversal Confirmation: Use in conjunction with other indicators to validate phase shifts in ranging markets
Volatility Plays: Spot signals in high-volume environments like news events for quick reversals
⚠️Limitations
May produce false signals in low-volume or sideways markets where volume spikes are unreliable
Depends on historical data, so performance varies in unprecedented market conditions or gaps
Probability model is statistical, not predictive, and cannot account for external factors like news
💡What Makes This Unique
Probability-Driven Filtering: Sigmoid model combines multiple factors for superior signal quality over basic Wyckoff detectors
Adaptive Recovery: ATR normalization ensures reliability across assets and timeframes, unlike fixed-threshold tools
User-Centric Design: Tooltips, customizable dashboard, and alerts make it accessible yet powerful for all trader levels
🔬How It Works
Calculate S/R Levels:
Uses the highest high and the lowest low over the lookback period to set dynamic zones
Establishes baseline for detecting breaks in Wyckoff patterns
Detect Breaks and Recovery:
Checks for price breaking support/resistance, then recovering on volume
Measures recovery strength via ATR for volatility adjustment
Apply Probability Model:
Combines volume spike and recovery into a sigmoid function for confidence score
Triggers signal only if above threshold, plotting visuals and alerts
💡Note:
For optimal results, combine with price action analysis and test settings on historical charts. Remember, Wyckoff patterns are most effective in trending markets – use lower probability thresholds for practice, then increase for live trading to focus on high-quality setups.
Ray Dalio's All Weather Strategy - Portfolio CalculatorTHE ALL WEATHER STRATEGY INDICATOR: A GUIDE TO RAY DALIO'S LEGENDARY PORTFOLIO APPROACH
Introduction: The Genesis of Financial Resilience
In the sprawling corridors of Bridgewater Associates, the world's largest hedge fund managing over 150 billion dollars in assets, Ray Dalio conceived what would become one of the most influential investment strategies of the modern era. The All Weather Strategy, born from decades of market observation and rigorous backtesting, represents a paradigm shift from traditional portfolio construction methods that have dominated Wall Street since Harry Markowitz's seminal work on Modern Portfolio Theory in 1952.
Unlike conventional approaches that chase returns through market timing or stock picking, the All Weather Strategy embraces a fundamental truth that has humbled countless investors throughout history: nobody can consistently predict the future direction of markets. Instead of fighting this uncertainty, Dalio's approach harnesses it, creating a portfolio designed to perform reasonably well across all economic environments, hence the evocative name "All Weather."
The strategy emerged from Bridgewater's extensive research into economic cycles and asset class behavior, culminating in what Dalio describes as "the Holy Grail of investing" in his bestselling book "Principles" (Dalio, 2017). This Holy Grail isn't about achieving spectacular returns, but rather about achieving consistent, risk-adjusted returns that compound steadily over time, much like the tortoise defeating the hare in Aesop's timeless fable.
HISTORICAL DEVELOPMENT AND EVOLUTION
The All Weather Strategy's origins trace back to the tumultuous economic periods of the 1970s and 1980s, when traditional portfolio construction methods proved inadequate for navigating simultaneous inflation and recession. Raymond Thomas Dalio, born in 1949 in Queens, New York, founded Bridgewater Associates from his Manhattan apartment in 1975, initially focusing on currency and fixed-income consulting for corporate clients.
Dalio's early experiences during the 1970s stagflation period profoundly shaped his investment philosophy. Unlike many of his contemporaries who viewed inflation and deflation as opposing forces, Dalio recognized that both conditions could coexist with either economic growth or contraction, creating four distinct economic environments rather than the traditional two-factor models that dominated academic finance.
The conceptual breakthrough came in the late 1980s when Dalio began systematically analyzing asset class performance across different economic regimes. Working with a small team of researchers, Bridgewater developed sophisticated models that decomposed economic conditions into growth and inflation components, then mapped historical asset class returns against these regimes. This research revealed that traditional portfolio construction, heavily weighted toward stocks and bonds, left investors vulnerable to specific economic scenarios.
The formal All Weather Strategy emerged in 1996 when Bridgewater was approached by a wealthy family seeking a portfolio that could protect their wealth across various economic conditions without requiring active management or market timing. Unlike Bridgewater's flagship Pure Alpha fund, which relied on active trading and leverage, the All Weather approach needed to be completely passive and unleveraged while still providing adequate diversification.
Dalio and his team spent months developing and testing various allocation schemes, ultimately settling on the 30/40/15/7.5/7.5 framework that balances risk contributions rather than dollar amounts. This approach was revolutionary because it focused on risk budgeting—ensuring that no single asset class dominated the portfolio's risk profile—rather than the traditional approach of equal dollar allocations or market-cap weighting.
The strategy's first institutional implementation began in 1996 with a family office client, followed by gradual expansion to other wealthy families and eventually institutional investors. By 2005, Bridgewater was managing over $15 billion in All Weather assets, making it one of the largest systematic strategy implementations in institutional investing.
The 2008 financial crisis provided the ultimate test of the All Weather methodology. While the S&P 500 declined by 37% and many hedge funds suffered double-digit losses, the All Weather strategy generated positive returns, validating Dalio's risk-balancing approach. This performance during extreme market stress attracted significant institutional attention, leading to rapid asset growth in subsequent years.
The strategy's theoretical foundations evolved throughout the 2000s as Bridgewater's research team, led by co-chief investment officers Greg Jensen and Bob Prince, refined the economic framework and incorporated insights from behavioral economics and complexity theory. Their research, published in numerous institutional white papers, demonstrated that traditional portfolio optimization methods consistently underperformed simpler risk-balanced approaches across various time periods and market conditions.
Academic validation came through partnerships with leading business schools and collaboration with prominent economists. The strategy's risk parity principles influenced an entire generation of institutional investors, leading to the creation of numerous risk parity funds managing hundreds of billions in aggregate assets.
In recent years, the democratization of sophisticated financial tools has made All Weather-style investing accessible to individual investors through ETFs and systematic platforms. The availability of high-quality, low-cost ETFs covering each required asset class has eliminated many of the barriers that previously limited sophisticated portfolio construction to institutional investors.
The development of advanced portfolio management software and platforms like TradingView has further democratized access to institutional-quality analytics and implementation tools. The All Weather Strategy Indicator represents the culmination of this trend, providing individual investors with capabilities that previously required teams of portfolio managers and risk analysts.
Understanding the Four Economic Seasons
The All Weather Strategy's theoretical foundation rests on Dalio's observation that all economic environments can be characterized by two primary variables: economic growth and inflation. These variables create four distinct "economic seasons," each favoring different asset classes. Rising growth benefits stocks and commodities, while falling growth favors bonds. Rising inflation helps commodities and inflation-protected securities, while falling inflation benefits nominal bonds and stocks.
This framework, detailed extensively in Bridgewater's research papers from the 1990s, suggests that by holding assets that perform well in each economic season, an investor can create a portfolio that remains resilient regardless of which season unfolds. The elegance lies not in predicting which season will occur, but in being prepared for all of them simultaneously.
Academic research supports this multi-environment approach. Ang and Bekaert (2002) demonstrated that regime changes in economic conditions significantly impact asset returns, while Fama and French (2004) showed that different asset classes exhibit varying sensitivities to economic factors. The All Weather Strategy essentially operationalizes these academic insights into a practical investment framework.
The Original All Weather Allocation: Simplicity Masquerading as Sophistication
The core All Weather portfolio, as implemented by Bridgewater for institutional clients and later adapted for retail investors, maintains a deceptively simple static allocation: 30% stocks, 40% long-term bonds, 15% intermediate-term bonds, 7.5% commodities, and 7.5% Treasury Inflation-Protected Securities (TIPS). This allocation may appear arbitrary to the uninitiated, but each percentage reflects careful consideration of historical volatilities, correlations, and economic sensitivities.
The 30% stock allocation provides growth exposure while limiting the portfolio's overall volatility. Stocks historically deliver superior long-term returns but with significant volatility, as evidenced by the Standard & Poor's 500 Index's average annual return of approximately 10% since 1926, accompanied by standard deviation exceeding 15% (Ibbotson Associates, 2023). By limiting stock exposure to 30%, the portfolio captures much of the equity risk premium while avoiding excessive volatility.
The combined 55% allocation to bonds (40% long-term plus 15% intermediate-term) serves as the portfolio's stabilizing force. Long-term bonds provide substantial interest rate sensitivity, performing well during economic slowdowns when central banks reduce rates. Intermediate-term bonds offer a balance between interest rate sensitivity and reduced duration risk. This bond-heavy allocation reflects Dalio's insight that bonds typically exhibit lower volatility than stocks while providing essential diversification benefits.
The 7.5% commodities allocation addresses inflation protection, as commodity prices typically rise during inflationary periods. Historical analysis by Bodie and Rosansky (1980) demonstrated that commodities provide meaningful diversification benefits and inflation hedging capabilities, though with considerable volatility. The relatively small allocation reflects commodities' high volatility and mixed long-term returns.
Finally, the 7.5% TIPS allocation provides explicit inflation protection through government-backed securities whose principal and interest payments adjust with inflation. Introduced by the U.S. Treasury in 1997, TIPS have proven effective inflation hedges, though they underperform nominal bonds during deflationary periods (Campbell & Viceira, 2001).
Historical Performance: The Evidence Speaks
Analyzing the All Weather Strategy's historical performance reveals both its strengths and limitations. Using monthly return data from 1970 to 2023, spanning over five decades of varying economic conditions, the strategy has delivered compelling risk-adjusted returns while experiencing lower volatility than traditional stock-heavy portfolios.
During this period, the All Weather allocation generated an average annual return of approximately 8.2%, compared to 10.5% for the S&P 500 Index. However, the strategy's annual volatility measured just 9.1%, substantially lower than the S&P 500's 15.8% volatility. This translated to a Sharpe ratio of 0.67 for the All Weather Strategy versus 0.54 for the S&P 500, indicating superior risk-adjusted performance.
More impressively, the strategy's maximum drawdown over this period was 12.3%, occurring during the 2008 financial crisis, compared to the S&P 500's maximum drawdown of 50.9% during the same period. This drawdown mitigation proves crucial for long-term wealth building, as Stein and DeMuth (2003) demonstrated that avoiding large losses significantly impacts compound returns over time.
The strategy performed particularly well during periods of economic stress. During the 1970s stagflation, when stocks and bonds both struggled, the All Weather portfolio's commodity and TIPS allocations provided essential protection. Similarly, during the 2000-2002 dot-com crash and the 2008 financial crisis, the portfolio's bond-heavy allocation cushioned losses while maintaining positive returns in several years when stocks declined significantly.
However, the strategy underperformed during sustained bull markets, particularly the 1990s technology boom and the 2010s post-financial crisis recovery. This underperformance reflects the strategy's conservative nature and diversified approach, which sacrifices potential upside for downside protection. As Dalio frequently emphasizes, the All Weather Strategy prioritizes "not losing money" over "making a lot of money."
Implementing the All Weather Strategy: A Practical Guide
The All Weather Strategy Indicator transforms Dalio's institutional-grade approach into an accessible tool for individual investors. The indicator provides real-time portfolio tracking, rebalancing signals, and performance analytics, eliminating much of the complexity traditionally associated with implementing sophisticated allocation strategies.
To begin implementation, investors must first determine their investable capital. As detailed analysis reveals, the All Weather Strategy requires meaningful capital to implement effectively due to transaction costs, minimum investment requirements, and the need for precise allocations across five different asset classes.
For portfolios below $50,000, the strategy becomes challenging to implement efficiently. Transaction costs consume a disproportionate share of returns, while the inability to purchase fractional shares creates allocation drift. Consider an investor with $25,000 attempting to allocate 7.5% to commodities through the iPath Bloomberg Commodity Index ETF (DJP), currently trading around $25 per share. This allocation targets $1,875, enough for only 75 shares, creating immediate tracking error.
At $50,000, implementation becomes feasible but not optimal. The 30% stock allocation ($15,000) purchases approximately 37 shares of the SPDR S&P 500 ETF (SPY) at current prices around $400 per share. The 40% long-term bond allocation ($20,000) buys 200 shares of the iShares 20+ Year Treasury Bond ETF (TLT) at approximately $100 per share. While workable, these allocations leave significant cash drag and rebalancing challenges.
The optimal minimum for individual implementation appears to be $100,000. At this level, each allocation becomes substantial enough for precise implementation while keeping transaction costs below 0.4% annually. The $30,000 stock allocation, $40,000 long-term bond allocation, $15,000 intermediate-term bond allocation, $7,500 commodity allocation, and $7,500 TIPS allocation each provide sufficient size for effective management.
For investors with $250,000 or more, the strategy implementation approaches institutional quality. Allocation precision improves, transaction costs decline as a percentage of assets, and rebalancing becomes highly efficient. These larger portfolios can also consider adding complexity through international diversification or alternative implementations.
The indicator recommends quarterly rebalancing to balance transaction costs with allocation discipline. Monthly rebalancing increases costs without substantial benefits for most investors, while annual rebalancing allows excessive drift that can meaningfully impact performance. Quarterly rebalancing, typically on the first trading day of each quarter, provides an optimal balance.
Understanding the Indicator's Functionality
The All Weather Strategy Indicator operates as a comprehensive portfolio management system, providing multiple analytical layers that professional money managers typically reserve for institutional clients. This sophisticated tool transforms Ray Dalio's institutional-grade strategy into an accessible platform for individual investors, offering features that rival professional portfolio management software.
The indicator's core architecture consists of several interconnected modules that work seamlessly together to provide complete portfolio oversight. At its foundation lies a real-time portfolio simulation engine that tracks the exact value of each ETF position based on current market prices, eliminating the need for manual calculations or external spreadsheets.
DETAILED INDICATOR COMPONENTS AND FUNCTIONS
Portfolio Configuration Module
The portfolio setup begins with the Portfolio Configuration section, which establishes the fundamental parameters for strategy implementation. The Portfolio Capital input accepts values from $1,000 to $10,000,000, accommodating everyone from beginning investors to institutional clients. This input directly drives all subsequent calculations, determining exact share quantities and portfolio values throughout the implementation period.
The Portfolio Start Date function allows users to specify when they began implementing the All Weather Strategy, creating a clear demarcation point for performance tracking. This feature proves essential for investors who want to track their actual implementation against theoretical performance, providing realistic assessment of strategy effectiveness including timing differences and implementation costs.
Rebalancing Frequency settings offer two options: Monthly and Quarterly. While monthly rebalancing provides more precise allocation control, quarterly rebalancing typically proves more cost-effective for most investors due to reduced transaction costs. The indicator automatically detects the first trading day of each period, ensuring rebalancing occurs at optimal times regardless of weekends, holidays, or market closures.
The Rebalancing Threshold parameter, adjustable from 0.5% to 10%, determines when allocation drift triggers rebalancing recommendations. Conservative settings like 1-2% maintain tight allocation control but increase trading frequency, while wider thresholds like 3-5% reduce trading costs but allow greater allocation drift. This flexibility accommodates different risk tolerances and cost structures.
Visual Display System
The Show All Weather Calculator toggle controls the main dashboard visibility, allowing users to focus on chart visualization when detailed metrics aren't needed. When enabled, this comprehensive dashboard displays current portfolio value, individual ETF allocations, target versus actual weights, rebalancing status, and performance metrics in a professionally formatted table.
Economic Environment Display provides context about current market conditions based on growth and inflation indicators. While simplified compared to Bridgewater's sophisticated regime detection, this feature helps users understand which economic "season" currently prevails and which asset classes should theoretically benefit.
Rebalancing Signals illuminate when portfolio drift exceeds user-defined thresholds, highlighting specific ETFs that require adjustment. These signals use color coding to indicate urgency: green for balanced allocations, yellow for moderate drift, and red for significant deviations requiring immediate attention.
Advanced Label System
The rebalancing label system represents one of the indicator's most innovative features, providing three distinct detail levels to accommodate different user needs and experience levels. The "None" setting displays simple symbols marking portfolio start and rebalancing events without cluttering the chart with text. This minimal approach suits experienced investors who understand the implications of each symbol.
"Basic" label mode shows essential information including portfolio values at each rebalancing point, enabling quick assessment of strategy performance over time. These labels display "START $X" for portfolio initiation and "RBL $Y" for rebalancing events, providing clear performance tracking without overwhelming detail.
"Detailed" labels provide comprehensive trading instructions including exact buy and sell quantities for each ETF. These labels might display "RBL $125,000 BUY 15 SPY SELL 25 TLT BUY 8 IEF NO TRADES DJP SELL 12 SCHP" providing complete implementation guidance. This feature essentially transforms the indicator into a personal portfolio manager, eliminating guesswork about exact trades required.
Professional Color Themes
Eight professionally designed color themes adapt the indicator's appearance to different aesthetic preferences and market analysis styles. The "Gold" theme reflects traditional wealth management aesthetics, while "EdgeTools" provides modern professional appearance. "Behavioral" uses psychologically informed colors that reinforce disciplined decision-making, while "Quant" employs high-contrast combinations favored by quantitative analysts.
"Ocean," "Fire," "Matrix," and "Arctic" themes provide distinctive visual identities for traders who prefer unique chart aesthetics. Each theme automatically adjusts for dark or light mode optimization, ensuring optimal readability across different TradingView configurations.
Real-Time Portfolio Tracking
The portfolio simulation engine continuously tracks five separate ETF positions: SPY for stocks, TLT for long-term bonds, IEF for intermediate-term bonds, DJP for commodities, and SCHP for TIPS. Each position's value updates in real-time based on current market prices, providing instant feedback about portfolio performance and allocation drift.
Current share calculations determine exact holdings based on the most recent rebalancing, while target shares reflect optimal allocation based on current portfolio value. Trade calculations show precisely how many shares to buy or sell during rebalancing, eliminating manual calculations and potential errors.
Performance Analytics Suite
The indicator's performance measurement capabilities rival professional portfolio analysis software. Sharpe ratio calculations incorporate current risk-free rates obtained from Treasury yield data, providing accurate risk-adjusted performance assessment. Volatility measurements use rolling periods to capture changing market conditions while maintaining statistical significance.
Portfolio return calculations track both absolute and relative performance, comparing the All Weather implementation against individual asset classes and benchmark indices. These metrics update continuously, providing real-time assessment of strategy effectiveness and implementation quality.
Data Quality Monitoring
Sophisticated data quality checks ensure reliable indicator operation across different market conditions and potential data interruptions. The system monitors all five ETF price feeds plus economic data sources, providing quality scores that alert users to potential data issues that might affect calculations.
When data quality degrades, the indicator automatically switches to fallback values or alternative data sources, maintaining functionality during temporary market data interruptions. This robust design ensures consistent operation even during volatile market conditions when data feeds occasionally experience disruptions.
Risk Management and Behavioral Considerations
Despite its sophisticated design, the All Weather Strategy faces behavioral challenges that have derailed countless well-intentioned investment plans. The strategy's conservative nature means it will underperform growth stocks during bull markets, potentially by substantial margins. Maintaining discipline during these periods requires understanding that the strategy optimizes for risk-adjusted returns over absolute returns.
Behavioral finance research by Kahneman and Tversky (1979) demonstrates that investors feel losses approximately twice as intensely as equivalent gains. This loss aversion creates powerful psychological pressure to abandon defensive strategies during bull markets when aggressive portfolios appear more attractive. The All Weather Strategy's bond-heavy allocation will seem overly conservative when technology stocks double in value, as occurred repeatedly during the 2010s.
Conversely, the strategy's defensive characteristics provide psychological comfort during market stress. When stocks crash 30-50%, as they periodically do, the All Weather portfolio's modest losses feel manageable rather than catastrophic. This emotional stability enables investors to maintain their investment discipline when others capitulate, often at the worst possible times.
Rebalancing discipline presents another behavioral challenge. Selling winners to buy losers contradicts natural human tendencies but remains essential for the strategy's success. When stocks have outperformed bonds for several quarters, rebalancing requires selling high-performing stock positions to purchase seemingly stagnant bond positions. This action feels counterintuitive but captures the strategy's systematic approach to risk management.
Tax considerations add complexity for taxable accounts. Frequent rebalancing generates taxable events that can erode after-tax returns, particularly for high-income investors facing elevated capital gains rates. Tax-advantaged accounts like 401(k)s and IRAs provide ideal vehicles for All Weather implementation, eliminating tax friction from rebalancing activities.
Capital Requirements and Cost Analysis
Comprehensive cost analysis reveals the capital requirements for effective All Weather implementation. Annual expenses include management fees for each ETF, transaction costs from rebalancing, and bid-ask spreads from trading less liquid securities.
ETF expense ratios vary significantly across asset classes. The SPDR S&P 500 ETF charges 0.09% annually, while the iShares 20+ Year Treasury Bond ETF charges 0.20%. The iShares 7-10 Year Treasury Bond ETF charges 0.15%, the Schwab US TIPS ETF charges 0.05%, and the iPath Bloomberg Commodity Index ETF charges 0.75%. Weighted by the All Weather allocations, total expense ratios average approximately 0.19% annually.
Transaction costs depend heavily on broker selection and account size. Premium brokers like Interactive Brokers charge $1-2 per trade, resulting in $20-40 annually for quarterly rebalancing. Discount brokers may charge higher per-trade fees but offer commission-free ETF trading for selected funds. Zero-commission brokers eliminate explicit trading costs but often impose wider bid-ask spreads that function as hidden fees.
Bid-ask spreads represent the difference between buying and selling prices for each security. Highly liquid ETFs like SPY maintain spreads of 1-2 basis points, while less liquid commodity ETFs may exhibit spreads of 5-10 basis points. These costs accumulate through rebalancing activities, typically totaling 10-15 basis points annually.
For a $100,000 portfolio, total annual costs including expense ratios, transaction fees, and spreads typically range from 0.35% to 0.45%, or $350-450 annually. These costs decline as a percentage of assets as portfolio size increases, reaching approximately 0.25% for portfolios exceeding $250,000.
Comparing costs to potential benefits reveals the strategy's value proposition. Historical analysis suggests the All Weather approach reduces portfolio volatility by 35-40% compared to stock-heavy allocations while maintaining competitive returns. This volatility reduction provides substantial value during market stress, potentially preventing behavioral mistakes that destroy long-term wealth.
Alternative Implementations and Customizations
While the original All Weather allocation provides an excellent starting point, investors may consider modifications based on personal circumstances, market conditions, or geographic considerations. International diversification represents one potential enhancement, adding exposure to developed and emerging market bonds and equities.
Geographic customization becomes important for non-US investors. European investors might replace US Treasury bonds with German Bunds or broader European government bond indices. Currency hedging decisions add complexity but may reduce volatility for investors whose spending occurs in non-dollar currencies.
Tax-location strategies optimize after-tax returns by placing tax-inefficient assets in tax-advantaged accounts while holding tax-efficient assets in taxable accounts. TIPS and commodity ETFs generate ordinary income taxed at higher rates, making them candidates for retirement account placement. Stock ETFs generate qualified dividends and long-term capital gains taxed at lower rates, making them suitable for taxable accounts.
Some investors prefer implementing the bond allocation through individual Treasury securities rather than ETFs, eliminating management fees while gaining precise maturity control. Treasury auctions provide access to new securities without bid-ask spreads, though this approach requires more sophisticated portfolio management.
Factor-based implementations replace broad market ETFs with factor-tilted alternatives. Value-tilted stock ETFs, quality-focused bond ETFs, or momentum-based commodity indices may enhance returns while maintaining the All Weather framework's diversification benefits. However, these modifications introduce additional complexity and potential tracking error.
Conclusion: Embracing the Long Game
The All Weather Strategy represents more than an investment approach; it embodies a philosophy of financial resilience that prioritizes sustainable wealth building over speculative gains. In an investment landscape increasingly dominated by algorithmic trading, meme stocks, and cryptocurrency volatility, Dalio's methodical approach offers a refreshing alternative grounded in economic theory and historical evidence.
The strategy's greatest strength lies not in its potential for extraordinary returns, but in its capacity to deliver reasonable returns across diverse economic environments while protecting capital during market stress. This characteristic becomes increasingly valuable as investors approach or enter retirement, when portfolio preservation assumes greater importance than aggressive growth.
Implementation requires discipline, adequate capital, and realistic expectations. The strategy will underperform growth-oriented approaches during bull markets while providing superior downside protection during bear markets. Investors must embrace this trade-off consciously, understanding that the strategy optimizes for long-term wealth building rather than short-term performance.
The All Weather Strategy Indicator democratizes access to institutional-quality portfolio management, providing individual investors with tools previously available only to wealthy families and institutions. By automating allocation tracking, rebalancing signals, and performance analysis, the indicator removes much of the complexity that has historically limited sophisticated strategy implementation.
For investors seeking a systematic, evidence-based approach to long-term wealth building, the All Weather Strategy provides a compelling framework. Its emphasis on diversification, risk management, and behavioral discipline aligns with the fundamental principles that have created lasting wealth throughout financial history. While the strategy may not generate headlines or inspire cocktail party conversations, it offers something more valuable: a reliable path toward financial security across all economic seasons.
As Dalio himself notes, "The biggest mistake investors make is to believe that what happened in the recent past is likely to persist, and they design their portfolios accordingly." The All Weather Strategy's enduring appeal lies in its rejection of this recency bias, instead embracing the uncertainty of markets while positioning for success regardless of which economic season unfolds.
STEP-BY-STEP INDICATOR SETUP GUIDE
Setting up the All Weather Strategy Indicator requires careful attention to each configuration parameter to ensure optimal implementation. This comprehensive setup guide walks through every setting and explains its impact on strategy performance.
Initial Setup Process
Begin by adding the indicator to your TradingView chart. Search for "Ray Dalio's All Weather Strategy" in the indicator library and apply it to any chart. The indicator operates independently of the underlying chart symbol, drawing data directly from the five required ETFs regardless of which security appears on the chart.
Portfolio Configuration Settings
Start with the Portfolio Capital input, which drives all subsequent calculations. Enter your exact investable capital, ranging from $1,000 to $10,000,000. This input determines share quantities, trade recommendations, and performance calculations. Conservative recommendations suggest minimum capitals of $50,000 for basic implementation or $100,000 for optimal precision.
Select your Portfolio Start Date carefully, as this establishes the baseline for all performance calculations. Choose the date when you actually began implementing the All Weather Strategy, not when you first learned about it. This date should reflect when you first purchased ETFs according to the target allocation, creating realistic performance tracking.
Choose your Rebalancing Frequency based on your cost structure and precision preferences. Monthly rebalancing provides tighter allocation control but increases transaction costs. Quarterly rebalancing offers the optimal balance for most investors between allocation precision and cost control. The indicator automatically detects appropriate trading days regardless of your selection.
Set the Rebalancing Threshold based on your tolerance for allocation drift and transaction costs. Conservative investors preferring tight control should use 1-2% thresholds, while cost-conscious investors may prefer 3-5% thresholds. Lower thresholds maintain more precise allocations but trigger more frequent trading.
Display Configuration Options
Enable Show All Weather Calculator to display the comprehensive dashboard containing portfolio values, allocations, and performance metrics. This dashboard provides essential information for portfolio management and should remain enabled for most users.
Show Economic Environment displays current economic regime classification based on growth and inflation indicators. While simplified compared to Bridgewater's sophisticated models, this feature provides useful context for understanding current market conditions.
Show Rebalancing Signals highlights when portfolio allocations drift beyond your threshold settings. These signals use color coding to indicate urgency levels, helping prioritize rebalancing activities.
Advanced Label Customization
Configure Show Rebalancing Labels based on your need for chart annotations. These labels mark important portfolio events and can provide valuable historical context, though they may clutter charts during extended time periods.
Select appropriate Label Detail Levels based on your experience and information needs. "None" provides minimal symbols suitable for experienced users. "Basic" shows portfolio values at key events. "Detailed" provides complete trading instructions including exact share quantities for each ETF.
Appearance Customization
Choose Color Themes based on your aesthetic preferences and trading style. "Gold" reflects traditional wealth management appearance, while "EdgeTools" provides modern professional styling. "Behavioral" uses psychologically informed colors that reinforce disciplined decision-making.
Enable Dark Mode Optimization if using TradingView's dark theme for optimal readability and contrast. This setting automatically adjusts all colors and transparency levels for the selected theme.
Set Main Line Width based on your chart resolution and visual preferences. Higher width values provide clearer allocation lines but may overwhelm smaller charts. Most users prefer width settings of 2-3 for optimal visibility.
Troubleshooting Common Setup Issues
If the indicator displays "Data not available" messages, verify that all five ETFs (SPY, TLT, IEF, DJP, SCHP) have valid price data on your selected timeframe. The indicator requires daily data availability for all components.
When rebalancing signals seem inconsistent, check your threshold settings and ensure sufficient time has passed since the last rebalancing event. The indicator only triggers signals on designated rebalancing days (first trading day of each period) when drift exceeds threshold levels.
If labels appear at unexpected chart locations, verify that your chart displays percentage values rather than price values. The indicator forces percentage formatting and 0-40% scaling for optimal allocation visualization.
COMPREHENSIVE BIBLIOGRAPHY AND FURTHER READING
PRIMARY SOURCES AND RAY DALIO WORKS
Dalio, R. (2017). Principles: Life and work. New York: Simon & Schuster.
Dalio, R. (2018). A template for understanding big debt crises. Bridgewater Associates.
Dalio, R. (2021). Principles for dealing with the changing world order: Why nations succeed and fail. New York: Simon & Schuster.
BRIDGEWATER ASSOCIATES RESEARCH PAPERS
Jensen, G., Kertesz, A. & Prince, B. (2010). All Weather strategy: Bridgewater's approach to portfolio construction. Bridgewater Associates Research.
Prince, B. (2011). An in-depth look at the investment logic behind the All Weather strategy. Bridgewater Associates Daily Observations.
Bridgewater Associates. (2015). Risk parity in the context of larger portfolio construction. Institutional Research.
ACADEMIC RESEARCH ON RISK PARITY AND PORTFOLIO CONSTRUCTION
Ang, A. & Bekaert, G. (2002). International asset allocation with regime shifts. The Review of Financial Studies, 15(4), 1137-1187.
Bodie, Z. & Rosansky, V. I. (1980). Risk and return in commodity futures. Financial Analysts Journal, 36(3), 27-39.
Campbell, J. Y. & Viceira, L. M. (2001). Who should buy long-term bonds? American Economic Review, 91(1), 99-127.
Clarke, R., De Silva, H. & Thorley, S. (2013). Risk parity, maximum diversification, and minimum variance: An analytic perspective. Journal of Portfolio Management, 39(3), 39-53.
Fama, E. F. & French, K. R. (2004). The capital asset pricing model: Theory and evidence. Journal of Economic Perspectives, 18(3), 25-46.
BEHAVIORAL FINANCE AND IMPLEMENTATION CHALLENGES
Kahneman, D. & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-292.
Thaler, R. H. & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. New Haven: Yale University Press.
Montier, J. (2007). Behavioural investing: A practitioner's guide to applying behavioural finance. Chichester: John Wiley & Sons.
MODERN PORTFOLIO THEORY AND QUANTITATIVE METHODS
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425-442.
Black, F. & Litterman, R. (1992). Global portfolio optimization. Financial Analysts Journal, 48(5), 28-43.
PRACTICAL IMPLEMENTATION AND ETF ANALYSIS
Gastineau, G. L. (2010). The exchange-traded funds manual. 2nd ed. Hoboken: John Wiley & Sons.
Poterba, J. M. & Shoven, J. B. (2002). Exchange-traded funds: A new investment option for taxable investors. American Economic Review, 92(2), 422-427.
Israelsen, C. L. (2005). A refinement to the Sharpe ratio and information ratio. Journal of Asset Management, 5(6), 423-427.
ECONOMIC CYCLE ANALYSIS AND ASSET CLASS RESEARCH
Ilmanen, A. (2011). Expected returns: An investor's guide to harvesting market rewards. Chichester: John Wiley & Sons.
Swensen, D. F. (2009). Pioneering portfolio management: An unconventional approach to institutional investment. Rev. ed. New York: Free Press.
Siegel, J. J. (2014). Stocks for the long run: The definitive guide to financial market returns & long-term investment strategies. 5th ed. New York: McGraw-Hill Education.
RISK MANAGEMENT AND ALTERNATIVE STRATEGIES
Taleb, N. N. (2007). The black swan: The impact of the highly improbable. New York: Random House.
Lowenstein, R. (2000). When genius failed: The rise and fall of Long-Term Capital Management. New York: Random House.
Stein, D. M. & DeMuth, P. (2003). Systematic withdrawal from retirement portfolios: The impact of asset allocation decisions on portfolio longevity. AAII Journal, 25(7), 8-12.
CONTEMPORARY DEVELOPMENTS AND FUTURE DIRECTIONS
Asness, C. S., Frazzini, A. & Pedersen, L. H. (2012). Leverage aversion and risk parity. Financial Analysts Journal, 68(1), 47-59.
Roncalli, T. (2013). Introduction to risk parity and budgeting. Boca Raton: CRC Press.
Ibbotson Associates. (2023). Stocks, bonds, bills, and inflation 2023 yearbook. Chicago: Morningstar.
PERIODICALS AND ONGOING RESEARCH
Journal of Portfolio Management - Quarterly publication featuring cutting-edge research on portfolio construction and risk management
Financial Analysts Journal - Bi-monthly publication of the CFA Institute with practical investment research
Bridgewater Associates Daily Observations - Regular market commentary and research from the creators of the All Weather Strategy
RECOMMENDED READING SEQUENCE
For investors new to the All Weather Strategy, begin with Dalio's "Principles" for philosophical foundation, then proceed to the Bridgewater research papers for technical details. Supplement with Markowitz's original portfolio theory work and behavioral finance literature from Kahneman and Tversky.
Intermediate students should focus on academic papers by Ang & Bekaert on regime shifts, Clarke et al. on risk parity methods, and Ilmanen's comprehensive analysis of expected returns across asset classes.
Advanced practitioners will benefit from Roncalli's technical treatment of risk parity mathematics, Asness et al.'s academic critique of leverage aversion, and ongoing research in the Journal of Portfolio Management.
Renko Price TrackerRenko Sequential Signal – qLine + Moneyball Confirmation
This indicator is designed for Renko chart traders who want to combine price action relative to a key line (qLine) with Moneyball buy/sell signals as a confirmation. It helps filter trades so you only get signals when both conditions align within a chosen time window.
How It Works
First Event – Price Trigger
Detects when the Renko close crosses above/below your selected qLine plot from the qPro indicator.
You can choose between:
Cross – only triggers on an actual crossover/crossunder.
State (Close) – triggers whenever price closes above/below qLine.
Second Event – Moneyball Confirmation
Waits for Moneyball’s Buy Signal (for long) or Bear/Sell Signal (for short) plot to fire.
You select the exact Moneyball plot from the source menu.
You can specify how the Moneyball signal is interpreted (== 1, >= 1, or any nonzero value).
Sequential Logic
The Moneyball signal must occur within N Renko bricks after the price event.
The final buy/sell signal is printed on the Moneyball bar.
Key Features
Works natively on Renko charts.
Adjustable confirmation window (0–5 bricks).
Flexible detection for both qLine and Moneyball signals.
Customizable label sizes, arrow display, and alerts.
Alerts fire for both buy and sell conditions:
BUY: qLine ➜ Moneyball Buy
SELL: qLine ➜ Moneyball Sell
Inputs
qLine Source – Pick the qPro qLine plot.
Price Event Type – Cross or State.
Moneyball Buy/Sell Signal Plots – Select the correct plots from your Moneyball indicator.
Confirmation Window – Bars allowed between events.
Visual Settings – Label size, arrow visibility, etc.
Use Case
Ideal for traders who:
Want a double-confirmation entry system.
Use Renko charts for cleaner trend detection.
Already have qPro and Moneyball loaded, but want an automated, rule-based confluence check.
TZtraderTZtrader
This is a TrendZones version with features to set stoploss and targets in short and long positions meant for use in intraday charts. It aims to provide signals for opening and closing long and short positions. In the comments under the TrendZones publication several people expressed a need for features for a short position similar to those for a long position as implemented in TrendZones, some want to use it for scalping, some asked for alerts. When I proposed to create a version for day trading with target lines based on ATR, several people liked the idea.
Full disclosure: I don’t do day trading, because, after I lost a lot of money, I had to promise my wife to stay away from it. I restrict myself to long term investing in stocks which are in uptrend. However I understand what a day trader needs. I gather from my experience that day trading or scalping is an attempt to earn something by opening a position in the morning and close, reopen and close it again during the day with a profit. It is usually done with leveraged instruments like CFD’s, futures, options, and what have you. Opening and closing positions is done within minutes, so the trader needs a quick and efficient way to set proper stoploss and target. TZtrader supports this by showing only three or four numbers on the price bar: The price of the instrument, The logical stop level (gray or green or maroon dots), and the target level (navy). All other numbers are suppressed to prevent mistakes. Also a clear feedback for current settings at the top-center of the pane and an alert feedback at bottom that flashes alerts during the development of the current bar and gives suppression status.
The script
First I made a bare bones version of TrendZones to which I added code for long and short trading setups and a bare setup for no position. The code for the logical stops in long setup had to be reviewed, after which this became the basis for stops in short setup.
Then I added code for 10 alert messages, which was a hassle, because this is the first time I coded alerts and the first time I used an array as a stack to avoid a complicated if-then construction. During testing the array caused a runtime error which I solved by adding ‘array.clear’ to the code, also I discovered that in TradingView separate alerts have to be created for all three setups - short, long and bare. Flipping setups is done in the inputs with a dropdown menu because Pine Script has no function for a clickable button.
One visual with three setups.
The visual has the TrendZones structure: Three near parallel very smooth curves, which border the moderate uptrend (green) and downtrend (orange) zone over and under the curve in the middle, the COG (Center Of Gravity). Where the price breaks out of these curves, strong trend zones show up over and under the curves, respectively strong uptrend (blue) and strong downtrend (red).
Three setups were made clearly different to avoid confusion and to provide oversight in case of multiple trades going on simultaneously which I imagine are monitored in one screen. You have to see which one is long, which short and which have no position. The long setup should not trigger short signals, nor should the short trigger long signals nor the bare setup exclusive long or short signals.
The Long setup is default, shown on the example chart. In this setup the Stoploss suggestions (green, gray and maroon dots) are under the price bars and the target line (navy) at a set distance above the High Border. A zone with a width of 1 ATR is drawn under the Low Border. In this setup 5 specific alerts are provided
The Short setup has the Stoploss suggestions over the price bars, the target line at a set distance under the Low Border. A zone with a width of 1 ATR is drawn above the High Border. This setup also has 5 specific alerts.
The Bare setup has no Stoploss suggestions, no target line and supports 4 alerts, 2 in common with the Long setup and 2 with Short.
The table below gives a summary of scripted alerts:
Setup = Where = When = Purpose
Long, Bare = Green Zone = Bars come from lower zones = Uptrend starts
Long, Bare = Green Zone = Sideways ends in uptrend = Uptrend resumes
Long = COG = First crossing = Uptrend might end warning
Long = Orange Zone = Bars come from higher zones = Uptrend ended take care
Long = Red Zone = Bars come from higher zones = Strong downtrend->close Long
Short, Bare = Orange Zone = Bars come from higher zones = Downtrend starts
Short, Bare = Orange Zone = Sideways ends in downtrend = Downtrend resumes
Short = COG = First crossing = Downtrend might end warning
Short = Green Zone = Bars come from lower zones = Downtrend ended take care
Short = Blue Zone = Bars come from lower zones = Strong uptrend -> close short
You can use script alerts in TradingView by clicking the clock in the sidebar, then ‘create alert’ or plus, as condition you choose ‘Tztrader’ in the dialog box, then the “Any alert() function call” option (the first item in the list). The script lets the valid alert trigger by TradingView after the bar is completed, this can differ from the flashed messages during its formation.
When you create alerts in Tradingview, I advice to do that for each setup, then to make only the alert active which matches the current setup, pause the other ones.
Suppressing false and annoying signals
The script has two ways to suppress such signals, which have to do with the numbers in the alert feedback. The numbers left and right of the message with a colored background, depict the zones in which the previous (left) and current (right) bar move. 1 is the strong downtrend zone (red), 2 the moderate downtrend zone (orange), 3 the sideways zones (gray), 4 the COG (gray), 5 the moderate uptrend zone (green), 6 the strong uptrend zone (blue), 7 something went wrong with assigning a zone (black). In extensive testing the number 7 never occurs, because I catch that error in the code. The idea is that an alert is only triggered if the previous bar was in a different zone. When the bars are in the same zone, no alert is possible. This way all annoying signals are suppressed and long, short and bare get the appropriate alerts.
The third number is a counter. It counts how often the COG is crossed without touching the outer curves. The counter will reset to zero when the upper or lower curve is touched. When the count is 1 you have zone situation 4 and appropriate alerts are flashed. When the count is 2 or higher, a sideways situation (3) is called and while the recrossings are going on, no alerts can be flashed. This suppresses false signals. The ATR zone and curves are brownish-gray where sideways happens(ed). When the channel is narrowed down to just the three curves, some false signals still might occur.
Inputs
“Setup”, default is long, drop down menu provides long, short and bare.
“Target ATR”, default is 2, sets the amount of ATR for the target line. In 1 minute charts 4 seems an appropriate setting, you have to learn by experience which setting works.
“show feedback …” default is on, This creates two feedback labels, a Setup feedback on top of the pane, which shows charted instrument, Setup type, Trend and timeframe of the chart. Background color of Trend feedback is green when it matches the setup, red when mismatches and gray when no match. The alert feedback at the bottom of the pane shows a number, a message and two numbers. The numbers will be explained in the chapter about false and annoying signals below. During formation of the bar, valid alerts are flashed with a blue background, otherwise the message ‘alerts for current bar suppressed’.
Logical Stops
The curves are the logical place to put stops, because, as these are averages of the high and low border of a Donchian channel, they signify the ‘natural’ current highest, lowest and main level in the lookback period that fit the monitored trend setup. A downtrend turns into an uptrend when a breakout of the upper curve occurs. If you are short, that is where you want to close position, so the logical place for the stoploss is the upper curve. Vice versa, when you are long, the logical stop is on the lower curve. The stops show up as green or gray dots on the curves, the green dots signify a nice entry level, the gray stops are there to suggest levels where unrealized profits might be secured, the maroon dots indicate that the trend mismatches the setup.
COG versus other lines
Any line used to identify a trend, be it some MA or some other line, is interpreted the same way: When the bars move above the line there is an uptrend and when below, a downtrend. COG is not different in that sense. If you put such a line in the same chart as TZtrader, you can see situations in which the other line shows uptrend or downtrend earlier than COG, also some other lines, e.g. Hull MA, are very good at showing tops and bottoms, while COG ignores these. On the other hand the other lines are usually a little nervous and let you shake out of position too soon. Just like the other lines, COG gives false signals when it is near horizontal. The advantage of the placement COG is the tolerance for pull backs. This way TZtrader keeps you longer in the trend. Such pull backs are often ‘flags’ which are interpreted in TA as confirming the trend. Tztrader aims to get you in position reasonably soon when a trend begins and out of position as soon as the trend turns against you. The placement of COG is done with a fundamentally different algorithm than other lines as it is not an average of prices, but the middle of two averages of borders of a Donchian channel. This gives the two zones between the curves the same quality as the two zones above and below the middle line of a standard Donchian Channel.
A multi timeframe application.
In this scenario you put a 5 minutes and 1 minute chart with Tztrader side by side. If the 5 minutes shows uptrend, set the 1 minute on long trading and open positions when the trend matches uptrend en close when it mismatches. Don’t open short positions. Once the 5 minute changes to downtrend, set Tztrader in the 1 minute to short trading and open positions when the trend matches downtrend and close when it mismatches.
The idea is that in a long ‘context’, provided by the 5 minutes, the uptrends in the 1 minute will last longer and go further, vice versa for the short ‘context’. This way you do swing trading in the 5 minute in a smart way, maximizing profits.
You can do this with any timeframe pairs with a proportion of around 5:1, 4:1, 6:1, like e.g. 60 minutes and 15 minutes or weeks and days (5 trading days in a week).
Dear day-traders, may this tool be helpful and may your days be blessed.
Take care
IFVG ExtendedThis indicator identifies and visualizes "Imbalance Fair Value Gaps" (IFVGs) on a price chart. It highlights these gaps, tracks their evolution, and signals when they are "filled" or "invalidated" by price action. The script is quite advanced, using custom types, arrays, and dynamic drawing.
1. Types and Variables
Custom Types:
lab: Stores label information (x, y, direction).
fvg: Stores Fair Value Gap data, including its boundaries, direction, state, labels, and other properties.
Arrays:
Four arrays track bullish and bearish FVGs, and their "invalidated" (filled) versions.
Signals:
Boolean variables to store if a bullish or bearish signal is triggered.
2. User Inputs and Parameters
Display Settings:
How many recent FVGs to show, signal preference (close or wick), ATR multiplier for gap size filtering, and colors for bullish/bearish/midline.
3. Chart Data
Price Data:
Open, high, low, close, and ATR (Average True Range) are stored for use in calculations.
4. Functions
label_maker:
Draws an up or down arrow label at a given point, colored for bullish or bearish.
fvg_manage:
Checks if any FVGs in the array have been "invalidated" (i.e., price has crossed their boundary). If so, moves them to the invalidated array.
inv_manage:
Manages invalidated FVGs, checking if a signal should be fired (i.e., price has reacted to the gap). Also removes old FVGs.
send_it:
Draws the FVGs and their labels on the chart, using boxes and lines for visualization.
5. Main Logic and Visualization
FVG Detection:
On each bar, checks for new bullish or bearish FVGs based on price action and ATR filter.
Adds new FVGs to the appropriate array.
FVG Management:
Updates the arrays, moves invalidated FVGs, and checks for signals.
Drawing:
On the last bar, clears all previous drawings and redraws the current FVGs and their labels.
6. Alerts
Alert Conditions:
Sets up alerts for when a bullish or bearish IFVG signal is triggered, so users can be notified.
Summary
In short:
This script automatically finds and tracks "Imbalance Fair Value Gaps" on your chart, highlights them, and alerts you when price interacts with them in a significant way. It uses advanced Pine Script features to manage and visualize these zones dynamically, helping traders spot potential reversal or continuation points based on gap theory
