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AMD Po3 with Live Edge Stats [WillyAlgoTrader]📊 AMD Po3 with Live Edge Stats is a free overlay indicator that detects the complete Power of Three cycle — Accumulation → Manipulation → Distribution — using a strict finite-state machine, validates every liquidity sweep by its return (not its depth), projects a Fibonacci-extension target from the full manipulation leg, and then does something most pattern indicators never do: it tracks its own results bar by bar and shows you the honest walk-forward statistics right on the chart. The core insight: in the AMD model, a "smart money" cycle starts with a quiet accumulation range, continues with a manipulation (a fake breakout that sweeps resting stop-losses beyond the range boundary), and resolves with a distribution — the real move in the opposite direction. The single detail that separates a manipulation from a genuine breakout is the return : manipulated price snaps back inside the range quickly, a real breakout does not. This indicator is built entirely around that distinction. New to the concept? Here is the whole idea in plain language. Markets often pause in a sideways box (accumulation). Below and above that box sit thousands of stop-loss orders. Sometimes price pokes out of the box just far enough to trigger those stops (manipulation), then reverses hard and travels through the box to the other side (distribution). This indicator finds the box, waits for the poke, demands proof that it was a fake-out (the return), and only then marks the expected move with an Entry, a Stop, and a Target — while a built-in scoreboard counts how often the pattern actually reaches that target on your symbol. Works on all markets — crypto, forex, stocks, indices, futures — and all timeframes (tuned by default for 15M). Completely free. 🧩 WHY THESE COMPONENTS WORK TOGETHER A range detector alone draws boxes but cannot tell a fake-out from a breakout. A sweep detector alone fires on every wick beyond a level with no context of where the range came from. A fib tool alone needs you to pick the anchors by hand. And none of them ever tell you whether the pattern has been working lately. This indicator chains them into one pipeline where each stage feeds the next: Compression detection → Range anchoring with impulse-tail trim → Boundary breach → Return-based sweep validation → Trade-at-manipulation reference model (Entry / Stop) → Fib-extension target from the manipulation leg → Outcome tracking → Walk-forward statistics dashboard Compression finds ranges that are statistically quiet, not just visually flat. Tail trimming keeps the leftovers of the previous impulse out of the boundaries, so the box measures real accumulation. The breach arms a candidate sweep; the return deadline decides whether it was a manipulation or a breakout. Only a confirmed return opens the reference trade — and because the sweep extreme is known at that exact bar, the stop and the fib target are fully determined with zero lookahead. Finally, every closed cycle feeds the statistics engine, so the dashboard always answers the only question that matters: does this pattern currently have an edge on this chart? Remove any link and the chain breaks: without compression the boxes are arbitrary; without the return rule every breakout poisons the signal set; without the reference model there is nothing to measure; without the statistics you are trading a story instead of data. 🔍 WHAT MAKES IT ORIGINAL 1️⃣ The full AMD cycle as a strict finite-state machine. Most Po3 scripts paint boxes and arrows independently. Here the cycle is a formal FSM with phases IDLE → ACCUMULATION → SWEEP? → DISTRIBUTION, where every transition fires only on a confirmed bar close. A cycle cannot skip a stage, cannot exist twice, and every cycle ends in exactly one outcome: TARGET, STOP, TIMEOUT, EXPIRED, FAILED or BREAKOUT. This is what makes honest statistics possible — each detected cycle is one countable experiment. 2️⃣ Statistical compression detection with impulse-tail trimming. A range is anchored only when the market is measurably quiet: — Donchian(20) width must sit in the bottom N-th percentile (default 25) of its own distribution over a 200-bar window: percentrank(high20 − low20, 200) ≤ 25 — Minimum range width filter (default 0.15% of price) rejects micro-ranges where stop and target would drown in the spread — Impulse-tail trim (default 15%): while removing the oldest bar of the anchoring window shrinks the range width by more than 15%, that bar is dropped — so the tail of a preceding impulse leg never contaminates the boundaries. The window can never shrink below the minimum range maturity — Boundaries come from confirmed pivots (default) or absolute extremes, and may "breathe" within a tolerance (default 10% of width) without resetting the range 3️⃣ Sweep validation by RETURN, not by depth. A breach beyond the boundary (plus tolerance) does not make a sweep. The close must come back inside the range within a hard deadline (default 6 bars). If it does — manipulation confirmed. If it does not — the move is labeled BREAKOUT , no trade is ever opened, and the cycle is excluded from trade statistics (it is counted separately as pattern diagnostics). Optional strictness filters: sweep depth capped at a True Range percentile, required equal highs/lows (min 2 confirmed pivots within 0.1× width over 96 bars) beyond the boundary, and London / New York killzone session windows. 4️⃣ Trade-at-manipulation reference model — everything is fixed on one bar. The reference trade opens on the very bar where the manipulation confirms (the M/D bar). All three levels are fully determined by data already printed: — Entry = the close of that bar — Stop = beyond the FULL sweep excursion (including that bar's wick) ± a buffer of 0.4 × ATR-anchor — ATR-anchor is ATR(14) taken from the bar BEFORE the range started — this cures a subtle circularity where volatility measured inside the compression would shrink the buffer exactly when it matters most — riskR = |entry − stop|, floored at one tick, so R math can never divide by zero No waiting for extra confirmation candles, no repainting, no lookahead anywhere. 5️⃣ Fibonacci-extension target of the manipulation leg. The target is not a fixed multiple of the range width — it scales with the manipulation itself. A fib grid is anchored from the sweep extreme (level 0) to the OPPOSITE accumulation boundary (level 1); the target sits at the extension level (default 1.5): — Short: target = rangeLow − (fibExt − 1) × (sweepExtreme − rangeLow) — Long: target = rangeHigh + (fibExt − 1) × (rangeHigh − sweepExtreme) A deeper stop-hunt means a longer measured leg and a farther target — target and stop scale from the same extreme, which keeps the R:R profile consistent across cycles. Common values: 1.272, 1.5, 1.618, 2.0. Important: this level is the expected reach of the distribution , a structural projection — not a take-profit ladder. 6️⃣ Honest, conservative walk-forward statistics. Every closed cycle is scored in R against the reference model, in strict chronological order, with three deliberately pessimistic rules: — If one bar touches BOTH the target and the stop, the cycle counts as a STOP (−1R) and increments a separate "ambiguous" counter — intrabar order is unknowable, so the model refuses to guess in its own favor — Percentages and Average R stay hidden until a minimum sample (default 10 cycles) is collected — a "78% win rate" built on 4 trades is noise dressed as insight — TIMEOUT cycles (neither level within 64 bars) close at the actual R from the final close and are included in Average R, not silently dropped 7️⃣ Period statistics engine with a form strip. The Stats section can be switched between 24 Hours, 30 Days and All-Time. Period figures come from a rolling timestamped buffer (31-day horizon); All-Time reads the full-history counters. A target-rate gauge (▰▰▰▰▱▱▱▱) and a form strip of the last 10 cycles (▰ = target, ▱ = stop, ◌ = timeout) let you read the recent behavior of the pattern in one glance. 8️⃣ Re-arm — the double-sweep scenario. When the optional higher-timeframe bias filter (HTF close vs EMA50, fetched repaint-free from the last CLOSED HTF bar) rejects the projected direction, the range is not discarded: it re-arms and waits for a sweep of the opposite side, with a fresh time budget and a block on repeating the same side. Double-sweep cycles are tracked with their own Average R so you can see whether the second sweep carries a different edge. 9️⃣ Pattern diagnostics separated from trade results. BREAKOUT (sweep never returned) and FAILED (returned, but the HTF filter rejected it with no re-arm left) are counted outside the trade statistics — no trade was opened, so there is nothing to score. The Failed / Breakout row answers a different question than the Target rate: not "does the trade work?" but "how often does this market actually play the manipulation game at all?" ⚙️ HOW IT WORKS — CALCULATION FLOW Step 1 — Warm-up: the indicator waits for the statistics window (default 200 bars) before detecting anything. Step 2 — Compression: on each confirmed bar in IDLE, the Donchian width percentile and minimum-width checks decide whether a range is born; the impulse tail is trimmed and boundaries are anchored (pivot or absolute mode). A 10-bar cooldown separates cycles. Step 3 — Accumulation: the range ages, boundaries may breathe within tolerance. A breach of both boundaries on one bar (news spike) resets the cycle; exceeding the max age (default 96 bars) expires it; a breach before minimum maturity (default 12 bars) is an early break, not a sweep. Step 4 — Sweep candidate: a qualifying breach starts the return countdown. The excursion extreme keeps updating with every wick. Step 5 — Resolution: close back inside within the deadline → manipulation confirmed → the M and D labels print, the reference trade opens (Entry / Stop / Target drawn, DIST alert fires). No return in time → BREAKOUT. HTF filter rejection → re-arm or FAILED. Step 6 — Outcome: from the next bar, each confirmed bar is checked against target and stop (conservative ambiguity rule) until TARGET, STOP or TIMEOUT closes the cycle, prints the outcome label with its R value, fires the outcome alert and updates every statistic. 📖 HOW TO USE 🎯 Quick start: 1. Add the indicator to a 15M chart (any symbol; other timeframes work too) 2. Let it load history — the dashboard starts collecting cycles immediately 3. Watch the phases: blue box = accumulation, orange box = sweep in progress, M = manipulation confirmed, D ▲/▼ = distribution direction with Entry / Stop / Target lines 4. Do nothing until the Stats section reaches the minimum sample (default 10 cycles) 5. Then read Target rate and Avg R — that is the measured edge of the pattern on THIS symbol and timeframe 👁️ Reading the chart: — 🔵 Dashed blue box = accumulation range (boundaries + fill) — 🟠 Orange box = manipulation excursion beyond the boundary; solid when the sweep is confirmed — M label = manipulation confirmed at the sweep extreme (M ↻ = re-armed, M ✗ = failed) — D ▲ / D ▼ = distribution direction, below the bar for longs, above for shorts; hover it for a tooltip with sweep side, all levels and R:R — 📏 Level lines: subtle dotted Entry, solid Stop, dashed Target — each with a price label and % distance from entry — 🟢 Target line turns solid teal with "Target ✓" when reached; it dims gray on timeout — 🏷️ Outcome labels: ✓ +xR (target), ✗ −1R (stop), AMB ✗ (ambiguous bar), ◌ xR (timeout), plus EXPIRED / NEWS BAR / BREAKOUT markers — Older cycles fade to outlines (or hide) — configurable history depth 📊 Dashboard fields: — Header : current phase, colored green/red during a live distribution — Market : Phase (+ bars in it), Compression percentile with a tightness gauge, active Range boundaries, HTF Bias, timeframe (with a ⚠ hint when off 15M) — Cycle : Long/Short @ Entry, Stop, Target, R:R, Risk %, Age — or a one-line status when no cycle is live — Stats : Cycles (with double-sweep count), Target / Stop / Timeout with percentages, Target rate gauge, Avg R, Form strip, Failed / Breakout diagnostics — all switchable between 24H / 30D / All-Time — Every row has a hover tooltip explaining exactly what it measures 🔧 Tuning guide: — Too few cycles: raise the compression percentile (25 → 35) or lower minimum range width — Too many breakouts: your market resolves ranges by trending — raise "Max bars until return" (6 → 8-12) to admit slower manipulations, or enable the sweep depth cap to reject wide breaches early — Targets rarely reached: lower the fib extension (1.5 → 1.272); targets too easy — raise it (1.618 / 2.0) — Stopped by wick retests: increase the stop buffer (0.4 → 0.6 × ATR-anchor) — Only trade with the trend: enable HTF bias — rejected directions will re-arm for the opposite sweep ⚙️ KEY SETTINGS ⚙️ Range (Accumulation): — Min range maturity (default 12 bars): breaches before this = reset, not sweep — Max range age (default 96): time budget before EXPIRED — Compression threshold (default 25): width percentile ceiling — Distribution window (default 200): statistical lookback for percentiles — Boundary tolerance (default 0.10), Min range width % (default 0.15), Boundary method (pivot / absolute), Impulse-tail trim (default 15%) 🌀 Manipulation (Sweep): — Max bars until return (default 6): the manipulation deadline — Require liquidity beyond boundary (off): EQH/EQL confirmation — Soft depth cap (100 = off): True Range percentile ceiling for the excursion — Allow re-arm (on): double-sweep second chance under the HTF filter 🎯 Reference Model & Stats: — Stop buffer (default 0.4 × ATR-anchor), Fib extension target (default 1.5) — Level lines / price labels / % distance toggles, line styles per level — Distribution timeout (default 64 bars) 🔍 Filters: Killzones (London / New York sessions, any timezone), HTF bias (EMA50, any higher timeframe, validated at load) 🎨 Visual: Auto / Dark / Light theme, per-layer toggles (boxes, event labels, killzone highlight, watermark), event and level label font sizes, history depth and style, Min sample for % and Avg R (default 10) 📊 Dashboard: position, font size, per-section toggles (Market / Cycle / Stats), stats period (24 Hours / 30 Days / All-Time) 🔔 ALERTS — 🌀 SWEEP — boundary swept: side, ticker, TF, price, time, cycle id — 🟢/🔴 DIST — manipulation confirmed and the reference trade opened: direction, sweep side, Entry, Stop, Target, R:R — 🎯/🛑/⏱ OUTCOME — cycle closed: result (target / stop / ambiguous / timeout), R value, reference entry All alerts fire once per bar close, support plain text and JSON webhook payloads, and are wired via a single "Any alert() function call" condition. A master switch disables everything at once. ⚠️ IMPORTANT NOTES — 🚫 No repainting. Every FSM transition, signal and outcome is evaluated strictly on confirmed bar closes (barstate.isconfirmed). Higher-timeframe data uses the last CLOSED HTF bar via the + lookahead idiom. Alerts fire once per bar close. Boundaries use confirmed pivots with equal left/right lookback — delayed confirmation, not repainting of future values. — 📐 The statistics measure a reference model , not your execution: fills at bar close, no commissions, no slippage, no position sizing. Real results will differ. Past performance does not guarantee future results. — 📐 The ambiguity rule is intentionally pessimistic: same-bar target+stop counts as a stop. Your live numbers can only be equal or better than this assumption, never worse because of it. — ⚖️ Statistics reset when the chart reloads and depend on the loaded history depth. The period stats buffer covers a rolling 31 days. — ⚖️ This is a cycle DETECTOR with an embedded measurement model — it does not manage positions, trail stops or scale out. The target is a structural projection, not trade advice. — 🛠️ This is an analysis tool, not an automated trading bot. It detects AMD cycles, projects levels and reports honest statistics — trade decisions remain yours. — 🌐 Works on all markets and timeframes; defaults are tuned for 15M charts (the dashboard reminds you when you are on a different timeframe).

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của WillyAlgoTrader

Setup Scanner [GBB]SETUP SCANNER Most scanners tell you a setup fired and stop there. This one attaches a full trade plan to every signal: ATR stop, three R-multiple targets, an expiry and then tracks the outcome and keeps score per setup. New strategies can also be easily added to the scanner, so you do not have 20 different chart tabs open anymore at the same time. How does it work? On every confirmed bar the script evaluates six independent setups. Each one reports a state in the panel: off, dormant, forming, LONG or SHORT. When a setup fires, a simulated trade opens at the close of the signal bar with: - a stop at ATR(14) × 1.2 (configurable) - TP1 / TP2 / TP3 at 1R / 2R / 3R - an expiry after 60 bars The trade is drawn as a risk box, a reward box, an entry line and a three-rung target ladder. As targets fill, their rungs promote from dashed to solid, so you can read how far a trade got at a glance. When it resolves, the whole drawing fades and gets an outcome tag: stop, TP3, or expired. The panel counts, per setup, how many trades it opened and what share of them reached TP1. Included Strategies in v1.0: 1. VWAP RECLAIM Price must spend at least N consecutive closes (default 6) on one side of session VWAP, then close back across it. Volume filter applies. Forming state triggers when the run is mature and price is drifting back within 0.4 ATR of VWAP. 2. EMA PULLBACK (9/21) Trend is defined by EMA21 vs EMA50 with EMA50 sloping in the same direction over three bars. Price must have touched EMA21 within the last N bars (default 3), then close back beyond EMA9 on a directional bar that takes out the previous bar's extreme. Volume filter applies. 3. BREAK AND RETEST A confirmed swing pivot (length 5 either side) is broken on a close. Within the retest window (default 20 bars), price returns to within 0.3 ATR of the broken level and closes back beyond it on a directional bar. Pivots that form while a break is still live are queued rather than overwriting the level — the retest gets to finish before the reference moves. No volume filter. 4. LIQUIDITY SWEEP REVERSAL A wick takes out the highest high or lowest low of the last 20 bars, the body closes back inside, and the rejection wick is larger than the opposite wick. Volume filter applies. 5. RSI DIVERGENCE Consecutive confirmed pivots with a lower price low against a higher RSI low (or the inverse for shorts), within a maximum pivot gap of 60 bars. This confirms five bars after the pivot by construction. It is late, on purpose, and it has no forming state. 6. OPENING RANGE BREAK The range is built from the session open for N minutes (default 15) in your chosen session and timezone, then the first close beyond either edge fires. One break per session, in either direction. The range levels plot once the range closes. For 24h crypto, set the timezone to UTC and pick your own anchor session. TRADE COUNTING Entry is the close of the signal bar. Risk is one ATR unit × the stop multiple, and the ladder is measured in multiples of that risk. Exit accounting is deliberately conservative. If a bar touches both the stop and a target, the stop wins — intrabar sequence is unknowable from OHLC, so the outcome is on purpose counted as pessimistic. A trade that reaches TP1 or TP2 marks the rung and keeps running, only TP3 or the stop or the expiry closes it. Three controls decide which signals become trades: - Max concurrent trades (default 1) - Block opposite-direction entries (default on) - Re-entry cooldown in bars (default 0) With the default of one concurrent trade, setups are evaluated in a fixed order — VWAP, EMA, break and retest, sweep, divergence, opening range — so when two fire on the same bar, the earlier one in that order takes the slot. Turn on "Mark blocked signals too" if you want to see the ones the limit swallowed. The cooldown defaults to 0, which keeps every signal. That is why stops sometimes cluster back to back on an impulse bar. Raising it changes the record, so reset your counts when you change it. READING THE SCOREBOARD — AND WHAT IT IS NOT The "fired" column counts trades the engine actually opened, not raw setup fires. Signals blocked by the concurrency limit, the direction lock or the cooldown are not counted. The "→TP1" column is trades that reached TP1 divided by trades fired. Reached, not captured: a trade that touches TP1 and later stops out still counts in the numerator. Read it as "how often does this setup get moving in my favour", not as a win rate and definitely not as an expectancy. IMPORTANT This is not a backtest. Counts accumulate forward over the bars loaded on your chart, they reset on every settings change and chart reload, and they include no commission, no spread, no slippage and no partial fills. DEFAULTS AND TUNING The defaults are not optimised. They are round numbers chosen to be readable and to avoid fitting a parameter set to whatever symbol happened to be on the chart during development. The ATR stop, the R ladder, the pivot length and the setup-specific windows are all exposed so you can adapt them to your instrument and timeframe, but every change invalidates the counts already on the panel. If you tune, tune on one instrument at a time, and treat any improvement that does not survive on data you did not tune on as noise. DISPLAY - Trade zones can be turned off entirely if you only want the panel. - Signal labels come in Full, Short, Arrow only, or Off. - Closed trades fade by an adjustable amount; filled targets always fade less than unfilled ones, so a resolved trade still shows how far it ran. - Keep last N drawings caps the chart clutter without affecting the counts. - Panel position and text size are configurable; colours default to a dark-chart palette and should be dimmed for white backgrounds. ALERTS Every trade the engine opens fires an alert() call carrying the setup name, direction and price, once per bar close. Two alertcondition entries are also available for any long setup and any short setup. Note that those fire on the raw signal, whether or not the engine had room to take it. LIMITATIONS - Signals evaluate on confirmed bars only. Nothing is drawn or counted on an unclosed bar. - Break and retest and divergence depend on pivots, which confirm five bars after the fact. - The VWAP setup requires volume data and stays dormant on symbols that have none. The volume filter also passes automatically where volume is unavailable. - The opening range setup depends on your session and timezone inputs being correct for the instrument. - Designed for intraday timeframes. It will run on higher timeframes but the trade model and expiry are not calibrated for them.

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của GoodBadBitcoin

Fibonacci Trend Continuation Signals [AlgoAlpha]🟠 OVERVIEW Fibonacci Trend Continuation Signals maps Fibonacci retracement levels inside an adaptive trend structure. It combines a smoothed price midline, volatility-based outer bands, and Fibonacci ratios to show where price is trading within the current bullish or bearish trend range. The trend changes only when price moves beyond a volatility-adjusted outer band. Once a direction is active, the script projects 0.236, 0.382, 0.500, 0.618, and 0.786 levels between the active outer band and the midline. This creates a moving Fibonacci framework that adjusts as price and volatility change. Continuation signals appear when price closes back through an enabled Fibonacci level in the direction of the active trend. This lets traders use retracements within an established trend instead of treating each Fibonacci level as a fixed reversal point. 🟠 CONCEPTS Trend Midline — An exponential moving average of closing price. It forms the central reference for the trend structure and the endpoint of the Fibonacci range. Volatility Bands — Outer boundaries placed above and below the midline using a smoothed measure of the high-to-low price range. Price crossing an outer band changes the active trend direction. Fibonacci Trend Range — The distance between the active outer band and the midline. In bullish trends, levels are measured upward from the lower band. In bearish trends, they are measured downward from the upper band. Fibonacci Levels — The 0.236, 0.382, 0.500, 0.618, and 0.786 ratios divide the active trend range into retracement zones that move with the underlying trend structure. Continuation Signal — A bullish signal occurs when price closes upward through an enabled Fibonacci level during a bullish trend. A bearish signal occurs when price closes downward through an enabled Fibonacci level during a bearish trend. 🟠 FEATURES Adaptive Fibonacci Profile — Displays five configurable Fibonacci levels between the active volatility band and trend midline. Trend Continuation Signals — Shows bullish and bearish markers when price closes through an enabled Fibonacci level in the direction of the active trend. Current Level Labels — Shows the current price value of each enabled Fibonacci level at the latest bar. Trend Change Markers — Marks the Fibonacci structure when a new bullish or bearish trend begins. 🟠 HOW TO USE Identify the active trend structure — A bullish structure projects Fibonacci levels from the lower band toward the midline, while a bearish structure projects them from the upper band toward the midline. Watch price during a retracement — Use the displayed Fibonacci zones to see how far price has moved through the active trend range. Look for continuation signals — An upward triangle shows that price crossed above an enabled Fibonacci level during a bullish trend. A downward triangle shows the equivalent bearish close below a level. Compare signals with price structure — Use nearby swing points, support, resistance, or your existing confirmation method before acting on a continuation signal. Adjust Midline Length, Pivot Length, and Band Width to control how quickly the trend framework responds to price and how wide its outer boundaries are. Enable or disable individual Fibonacci levels to keep only the retracement levels relevant to your method. 🟠 CONCLUSION Fibonacci Trend Continuation Signals combines volatility-based trend detection with adaptive Fibonacci retracement levels and directional continuation signals. It gives traders a moving reference for measuring pullbacks and identifying closes that resume movement in the active trend direction.

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của AlgoAlpha

HTF Auction Candle (Zeiierman)█ Overview HTF Auction Candle (Zeiierman) is a multi-timeframe auction profiling indicator that reconstructs the currently forming Higher Timeframe candle and analyzes the lower-timeframe activity developing inside it. Rather than viewing the Higher Timeframe candle only as a single OHLC structure, the indicator breaks its full high-to-low range into individual price cells. It estimates how buying and selling activity is distributed across those levels. Battle Bubbles provide an additional view of the auction by showing which side is winning across broader price segments and the relative strength of each battle. █ HTF Auction Structure The center of the indicator displays the reconstructed Higher Timeframe candle. ⚪ Buy and sell activity is displayed on opposite sides of the candle: • Sell activity extends to the left. • Buy activity extends to the right. The width of each profile section represents the estimated amount of activity concentrated at that price level. Wider areas therefore highlight prices where greater participation occurred during the developing Higher Timeframe auction. ⚪ When Delta Dominance is enabled, each price cell also compares estimated buying and selling activity. • Positive Delta extends to the right. • Negative Delta extends to the left. • Larger Delta cells represent stronger directional imbalance. ⚪ Battle Bubbles summarize buyer-versus-seller control across 20 equal sections of the Higher Timeframe range. • Green bubbles indicate a buyer win. • Red bubbles indicate a seller win. • Larger bubbles represent stronger battles with greater participation. Together, the Volume Wings, Delta Dominance, and Battle Bubbles provide different views of participation, imbalance, and directional control inside the developing Higher Timeframe candle. █ How It Works ⚪ Higher Timeframe Reconstruction The indicator reconstructs the selected Higher Timeframe candle using its live open, high, low, and current close. The high and low are also linked back to the chart bars where those extremes first formed. ⚪ Lower Timeframe Sampling The internal auction is built from Lower Timeframe candles, using their open, high, low, close, and volume. The selected Lower Timeframe must remain below the Higher Timeframe and cannot exceed the chart timeframe. ⚪ Buy and Sell Volume Estimation Each Lower Timeframe candle’s volume is divided into estimated buy and sell activity using its close position, candle direction, and wick structure. buyVolume = volume × buyShare sellVolume = volume - buyVolume A stronger bullish structure receives a larger estimated buy share, while a stronger bearish structure receives a larger sell share. This is an estimation model and does not use true bid and ask transaction data. ⚪ Price Cell Distribution The Higher Timeframe range is divided into Price Cells, with each Lower Timeframe candle contributing activity only to the cells touched by its range. Buy and sell volume is weighted toward separate directional areas, while Cell Concentration controls how tightly that activity is distributed. ⚪ Volume Wings Estimated sell activity forms the left profile and buy activity forms the right profile. Wider sections indicate greater participation at that price level. ⚪ Delta Dominance Delta measures the difference between estimated buy and sell activity inside each Price Cell. delta = buyVolume - sellVolume • Positive Delta indicates stronger estimated buying. • Negative Delta indicates stronger estimated selling. • Wider Delta areas represent stronger imbalance. ⚪ Battle Bubbles The Higher Timeframe range is divided into 20 equal Battle segments. Each segment combines estimated volume Delta with directional Lower Timeframe win consistency to determine buyer or seller control. • Green bubbles indicate buyer control. • Red bubbles indicate seller control. • Larger bubbles represent stronger battles with greater participation. Bubble size is normalized against the strongest Battle segment in the current Higher Timeframe candle. ⚪ Higher Timeframe Delta The indicator also calculates estimated Delta across the entire Higher Timeframe candle. Delta % = 100 × (Buy Volume - Sell Volume) / Total Volume Positive values indicate overall buying dominance, while negative values indicate selling dominance. █ How to Use ⚪ Analyze the Developing Higher Timeframe Candle Use the reconstructed candle to monitor a Higher Timeframe auction without leaving the current chart timeframe. Instead of waiting for the Higher Timeframe candle to close, traders can observe how its structure and internal participation are developing in realtime. This can be useful when monitoring larger timeframe candles from lower execution timeframes. ⚪ Identify High-Participation Areas Wide sections of the Volume Wings show price levels where more estimated activity has accumulated. These areas can highlight important zones of acceptance, consolidation, support, resistance, or repeated participation within the current Higher Timeframe candle. Narrow profile areas show prices where relatively less activity occurred. ⚪ Use the Control Price The Control Price identifies the price cell with the highest combined estimated activity. Traders can use it as a reference for where the current Higher Timeframe auction has concentrated the greatest participation. Price holding around the Control Price can suggest continued acceptance, while movement away from it can help highlight changes in the developing auction. ⚪ Read Delta Across the Range Delta Dominance shows which side is stronger at individual price levels. • Positive Delta highlights areas of stronger estimated buying activity. • Negative Delta highlights areas of stronger estimated selling activity. • Large Delta cells highlight stronger directional imbalance. This can help reveal whether buying or selling pressure is concentrated near specific parts of the Higher Timeframe candle. For example, strong positive Delta near the upper portion of the range can show aggressive bullish participation, while strong negative Delta near the highs can indicate selling pressure developing into higher prices. ⚪ Read the Battle Bubbles Battle Bubbles provide a simplified view of which side is winning across different parts of the Higher Timeframe range. Clusters of larger buyer or seller bubbles can highlight areas where directional control is especially strong, while smaller bubbles indicate weaker or less significant battles. They can be used alongside the Volume Wings and Delta Dominance to distinguish broad directional control from the more detailed activity occurring inside individual price cells. █ Settings Higher Timeframe: Selects the Higher Timeframe candle used for the live auction. It must be greater than the chart timeframe. Auto LTF: Automatically selects a suitable Lower Timeframe used to build the internal auction. Manual LTF: Selects the Lower Timeframe manually when Auto LTF is disabled. It must remain below the Higher Timeframe and no higher than the chart timeframe. Price Cells: Controls how many price levels divide the Higher Timeframe range. More cells provide finer profile and Delta resolution. Cell Concentration: Controls how tightly estimated buy and sell activity is distributed around each Lower Timeframe candle's directional activity centers. LTF Sample Capacity: Sets the maximum number of Lower Timeframe samples retained before older samples are compressed to maintain performance. ----------------- Disclaimer The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.

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của Zeiierman

3D Market Profile [BOSWaves]3D Market Profile - TPO Time Price Distribution with Polyline 3D Extrusion, Value Area Construction, and Initial Balance Mapping Overview 3D Market Profile is a Time Price Opportunity profile system that constructs a TPO distribution from recent chart history and renders it as a three-dimensional extruded structure using polyline geometry, where row width reflects the number of TPO period prints at each price level, density-based gradient coloring communicates participation concentration, and perspective faces on the top and side edges of each row provide spatial depth that standard two-dimensional profiles cannot convey. Instead of displaying a flat histogram alongside price, this system aggregates chart bars into complete TPO periods at the configured timeframe, assigns sequential alphabet letters to each period, and accumulates per-row letter counts across the full profile window. The resulting distribution drives a full market profile analytical framework including Point of Control identification, Value Area expansion from the POC outward, and Initial Balance range derivation from the opening TPO periods. All structural levels are rendered as extending reference lines and labeled price readouts that project beyond the profile face. This creates a market profile visualization that combines the structural depth of conventional TPO analysis with a three-dimensional rendering system that makes profile shape and density immediately readable across varying chart zoom levels. The extruded top and side faces follow the profile contour row by row, the rear glass plane frames the distribution within a perspective bounding box, and the complete wireframe connects front and rear geometry with depth-offset corner lines that reinforce the spatial orientation of the profile structure. Price is therefore evaluated against a fully formed TPO distribution anchored to recent chart history, with POC, Value Area, and Initial Balance levels providing the standard market profile reference framework in a spatially rich visual presentation. Conceptual Framework 3D Market Profile is founded on the principle that TPO market profile analysis becomes significantly more visually accessible when the distribution is rendered with three-dimensional spatial depth, allowing traders to read profile shape, density concentration, and structural levels at a glance without the visual ambiguity that flat two-dimensional histogram representations produce at different zoom levels and screen resolutions. Traditional market profile implementations render uniform flat row boxes where the only visual differentiation between high and low density rows is bar width, which becomes difficult to assess comparatively when profiles contain many rows or are viewed on smaller screens. This framework adds spatial depth through extruded top and side faces that follow the profile contour, creating a volumetric bar chart where density is communicated through both width and three-dimensional geometry simultaneously, and gradient coloring that progresses from the configured low-density to high-density colors adds a third visual dimension that reinforces the density reading. Three core principles guide the design: TPO distribution should be constructed from complete timeframe periods rather than individual bars, ensuring each period contributes exactly one letter print to each price row it trades through regardless of how many chart bars compose that period. The three-dimensional extrusion should follow the profile contour row by row rather than applying a uniform rectangular extrusion, preserving the profile's structural shape in the depth geometry and maintaining the analytical significance of the distribution's outline. Standard market profile reference levels including POC, Value Area High, Value Area Low, and Initial Balance should be derived from the TPO distribution using conventional methodology and rendered as structural references that extend beyond the profile face for ongoing price interaction monitoring. This shifts market profile visualization from flat histogram reading into a spatially rich three-dimensional distribution analysis where density, shape, and structural levels are simultaneously communicated through geometry, color, and depth. Theoretical Foundation The indicator combines chart bar aggregation into complete TPO periods at the configured timeframe, sequential alphabetical letter assignment per period, per-row letter count accumulation across the full profile window, density-normalized gradient coloring with power transformation, POC identification through maximum count with proximity tiebreaking, outward value area expansion from POC with alternating upper and lower priority, Initial Balance derivation from the opening period count, and polyline-based 3D geometry construction using depth-offset coordinates for top faces, side end caps, and rear glass plane. Period aggregation groups chart bars by their TPO clock value, merging consecutive bars sharing the same period timestamp into a single period high-low range. Each completed period receives the next sequential alphabet letter from A through Z then a through z, repeating modularly for profiles with more periods than the alphabet. Row assignment maps each period's full high-low range to the corresponding row bins, contributing one print to every row from the period low bin to the period high bin inclusive. The POC selection resolves ties between equal-count rows by choosing the one closest to the mid-range price. Value area expansion adds rows alternately from the high and low sides of the POC based on which adjacent row has the greater count, continuing until the accumulated count reaches the target percentage of total prints. Four internal systems operate in tandem: Period Aggregation Engine : Groups chart bars by TPO clock timestamp into complete periods, accumulating per-period high-low ranges and assigning sequential letter identifiers that are used for both letter rendering and Initial Balance period counting. TPO Distribution Builder : Accumulates per-row letter strings and print counts from the period high-low ranges, producing the row width and text data that drives all subsequent profile rendering and level calculations. 3D Geometry Engine : Constructs polyline polygon arrays for the extruded top faces following each row's width contour, side end cap faces at the outer edge of each row, and the rear glass plane bounding box, using configurable depth bar and depth row offsets to establish perspective coordinates. Level and Label System : Derives POC, VAH, VAL, and IB level prices from the distribution, renders dual glow and core lines extending across the profile and beyond with configurable tail length, and places price readout labels at the right edge of the level lines. This design ensures the 3D geometry follows the actual distribution contour while all standard market profile reference levels are derived from the same underlying TPO count data that drives the visual rendering. How It Works 3D Market Profile evaluates price through a sequence of aggregation and profile-building processes: Profile Range Calculation : The highest high and lowest low across the configured bar count establish the full profile range, which is divided into the configured number of rows to produce the uniform row height used for all price-to-row mapping. TPO Period Aggregation : Chart bars are grouped by their TPO timeframe clock value. Consecutive bars sharing the same period timestamp are merged into a single period with the accumulated high and low. Each completed period boundary triggers storage of the completed period data. Letter Assignment and Row Filling : Each period is assigned the next sequential alphabet letter. For each period, the low and high row bins are calculated and the period letter is appended to the row text string for every row from low to high inclusive, with the row print count incremented accordingly. Initial Balance Derivation : The first N periods as configured by the IB Periods setting contribute their highs and lows to the Initial Balance range, producing the IBH and IBL reference levels that mark the range established during the opening portion of the profile window. POC Identification : The row with the maximum print count is identified as the POC. When multiple rows share the maximum count the row closest to the profile mid-range price is selected, providing a consistent tie-breaking rule that favors centrally located levels. Value Area Construction : Starting from the POC row, adjacent rows are added one at a time by selecting the higher-count neighbor on each iteration until the accumulated count reaches the VA percentage target of total prints. Profile Geometry Calculation : The profile start position is determined by the placement setting, with Right of Price positioning the profile in forward chart space at the configured right offset and On Range anchoring it to the beginning of the analyzed window. Row widths are calculated as print count multiplied by cell width. 3D Top Face Construction : For each occupied row, a quadrilateral polyline polygon is constructed connecting the row's front top-left, front top-right, depth-offset rear top-right, and depth-offset rear top-left coordinates, producing the extruded top surface that follows the profile contour. 3D Side Cap Construction : For each occupied row, a quadrilateral polyline polygon connects the front right edge top, front right edge bottom, depth-offset rear right bottom, and depth-offset rear right top, producing the side end cap that closes the extruded row geometry. Front Profile Rendering : Box objects are created for each occupied row with density-gradient fill coloring, POC and single print override coloring, and border styling that distinguishes the POC row, single print rows, Value Area rows, and standard rows. TPO Letter Rendering : Individual label objects are placed at the center x and mid-price y position of each letter's cell within the profile, with color distinguishing POC, single print, and standard rows. Level and Label Rendering : POC lines are rendered as dual glow and core polylines. VAH and VAL render as dotted lines. IBH and IBL render as dashed lines. All extend from the configured tail length left of the profile to the right edge of the depth geometry. Price labels are placed at the right extent of each line. Together, these elements form a complete three-dimensional market profile rendering where the TPO distribution drives both the analytical reference framework and the spatial geometry of the extruded visualization. Interpretation 3D Market Profile should be interpreted as a conventional TPO market profile with three-dimensional spatial depth encoding applied to the distribution geometry: Profile Width : The horizontal extent of each row reflects its total TPO print count relative to the maximum, with wider rows indicating more time spent at that price level and narrower rows indicating less acceptance. Density Gradient Coloring : Row fill progresses from the configured low-density color at minimum print count to the high-density color at maximum print count, with a power-transformed gradient that spreads color differentiation across the density range. Point of Control Row (POC) : The widest row rendered in the POC color identifies the price level with the greatest time-at-price concentration across the profile window, representing the most accepted price during the analyzed period. Value Area : Rows between VAH and VAL rendered with full opacity represent the price range containing the configured percentage of total TPO prints, identifying the zone of concentrated acceptance around the POC. Non-Value Area Rows : Rows outside the Value Area are rendered with additional transparency and darkened coloring, visually receding from the Value Area rows to communicate their reduced relative acceptance. Single Print Rows : Rows with exactly one TPO period print are highlighted in the high-density color with a distinct border, marking price levels visited by only one period that price may return to complete the auction. TPO Letters : Sequential alphabet letters within each row identify which periods traded through that price level, with the letter sequence providing a chronological audit trail of price movement across the profile window. 3D Top Faces : Extruded top surfaces following the profile contour row by row provide depth cues that make the profile shape readable as a three-dimensional structure, with the contour step pattern revealing the distribution's outline in spatial form. 3D Side Caps : End cap faces at the outer edge of each row close the extruded geometry and reinforce the row width differences through the depth dimension, with darker coloring distinguishing the side geometry from the front profile. 3D Frame : The rear glass plane and wireframe perspective box orient the profile within three-dimensional chart space, connecting front and rear geometry with corner depth lines that establish the spatial extent of the distribution structure. POC Level : Dual glow and core lines extending from the profile mark the POC price for ongoing reference, with the glow providing visual prominence that makes the POC immediately identifiable against background price action. VAH and VAL Lines : Dotted lines at the Value Area boundaries mark the upper and lower edges of the high-acceptance zone for ongoing price interaction monitoring. IBH and IBL Lines : Dashed lines at the Initial Balance high and low mark the range established during the opening TPO periods, providing the conventional IB reference framework for range extension and balance monitoring. Profile shape, POC location, Value Area extent, single print density, and IB range collectively provide more structural context than any element in isolation. Signal Logic & Visual Cues 3D Market Profile does not generate discrete entry or exit signals but provides continuous structural reference through the standard market profile analytical framework: POC Magnetic Reference : The POC level identifies the price where the greatest time-based acceptance occurred within the window, frequently acting as a mean reversion magnet when price extends to the Value Area boundaries. Value Area Boundary Interaction : Price moving outside the Value Area enters statistically less accepted price territory, providing context for potential reversion trades back toward the Value Area or range extension trades when acceptance establishes beyond the boundary. Initial Balance range interactions provide the conventional IB framework where price accepting above IBH or below IBL signals potential range extension, while price remaining within the IB range suggests balanced auction conditions. Strategy Integration 3D Market Profile fits within conventional market profile and auction theory-based analytical approaches: POC Reversion Framework : Use the POC line as a dynamic mean reversion reference when price has extended to or beyond the Value Area boundaries, with the POC representing the price where the distribution's greatest acceptance occurred and where reversion activity frequently concentrates. Value Area Boundary Trading : Monitor price behavior at VAH and VAL for acceptance or rejection signals. Price accepting above VAH or below VAL by closing multiple bars beyond the boundary suggests genuine range extension. Price rejecting at the boundaries and returning inside the Value Area suggests reversion continuation. Initial Balance Range Extension : Use IBH and IBL as directional range extension references. Price establishing acceptance above IBH with sustained closes indicates potential bullish range extension. Price accepting below IBL indicates potential bearish extension. Single Print Targeting : Treat single print rows as potential return targets for incomplete auction areas, monitoring for price to revisit and fill these levels with additional TPO participation in subsequent sessions. Profile Placement Selection : Use Right of Price placement to keep the profile in forward chart space as a live reference for current and upcoming price interaction. Use On Range placement to anchor the profile to the historical window for post-session analysis and retrospective structural assessment. Timeframe and Length Calibration : Match the TPO timeframe to the trading session type being analyzed. Thirty-minute TPOs with standard length produce a conventional daily profile. Shorter timeframes produce intraday micro-profiles. Longer timeframes produce multi-day macro profiles that reveal larger structural distribution patterns. Technical Implementation Details Period Aggregation : TPO clock-based grouping of chart bars into complete periods with high-low range accumulation and sequential letter assignment Distribution Builder : Per-row letter string accumulation and print count tracking across the full period set POC Selection : Maximum count identification with mid-range proximity tiebreaking for consistent level placement Value Area Engine : Outward expansion from POC with alternating upper and lower priority based on adjacent row count comparison 3D Geometry : Polyline quadrilateral construction for top faces and side caps using depth bar and depth row offset coordinates Front Profile : Box objects with density gradient fill, POC override, single print override, and Value Area transparency differentiation Level System : Dual glow and core POC lines, dotted VA lines, dashed IB lines, and price readout labels at configurable tail length Performance Profile : Full object cleanup and rebuild on last bar with label count cap enforcement and configurable profile length maximum Optimal Application Parameters Timeframe Guidance: 1 - 5 min : Intraday micro-profile analysis with shorter length and faster TPO timeframe for session-level distribution tracking within a single trading day 15 - 60 min : Session profile construction with standard thirty-minute TPO timeframe and balanced row count for conventional daily market profile analysis 4H - Daily : Multi-session macro-profile analysis with longer length and higher TPO timeframe for structural distribution patterns spanning multiple sessions Suggested Baseline Configuration: Length : 180 Rows : 24 Value Area % : 70 Placement : Right of Price TPO Timeframe : 30 Show TPO Letters : Enabled Initial Balance Periods : 2 3D Depth (Bars) : 6 3D Height (Rows) : 0.42 Show 3D Frame : Enabled Show POC / VA : Enabled Show Initial Balance : Enabled Show Level Labels : Enabled These suggested parameters should be used as a baseline; their effectiveness depends on the instrument's session structure, typical daily range, and preferred profile granularity, so fine-tuning is expected for optimal performance. Parameter Calibration Notes Use the following adjustments to refine behavior without altering the core logic: Too few or too many rows : Adjust Rows to increase or decrease vertical price resolution, calibrating row height to the instrument's typical daily range so rows represent meaningful price increments rather than noise-level or excessively large bands. Letters too cramped or too sparse : Adjust TPO Cell Width to control the horizontal space allocated to each letter, increasing for wider, more readable letter display or decreasing to fit more prints within the same profile width. Profile too narrow or too wide : The profile width scales automatically with the maximum row print count multiplied by cell width. Adjust Length to include more or fewer bars in the profile window, and adjust Cell Width to scale the resulting profile width. 3D extrusion too deep or shallow : Adjust 3D Depth (Bars) to control horizontal perspective extent and 3D Height (Rows) to control vertical depth, calibrating the spatial effect to the chart's aspect ratio and current zoom level. Profile overlapping price action : Use Right of Price placement with a larger Right Offset value to push the profile further from current price, or switch to On Range placement to anchor it to the beginning of the analyzed window instead. Initial Balance range too narrow or wide : Adjust Initial Balance Periods to include more or fewer opening TPO periods in the IB calculation. Two periods represents the conventional first trading hour using thirty-minute TPOs; increase for a wider opening range definition. TPO timeframe mismatch : Match the TPO Timeframe to the session type being analyzed. Using a thirty-minute timeframe on a daily chart produces very few periods and minimal distribution differentiation; use a timeframe appropriate to the chart timeframe and session length. Adjustments should be incremental and evaluated across multiple session types rather than isolated market conditions. Performance Characteristics High Effectiveness: Session-structured markets where a defined trading window produces a clear distribution with identifiable POC and Value Area that serve as reliable structural references across subsequent sessions Instruments with consistent daily range where row height calibrates to meaningful price increments and the distribution produces a recognizable bell-curve or skewed profile shape Market profile-based trading approaches where POC, Value Area, and Initial Balance provide the primary structural reference framework for directional bias and level interaction analysis Three-dimensional visualization workflows where the extruded profile geometry provides immediate spatial readability that improves structural assessment speed relative to flat two-dimensional profiles Reduced Effectiveness: Markets with highly variable daily ranges where a fixed row count produces inconsistent row heights across different sessions, reducing the comparability of distribution shapes between profile windows Instruments without clear session structure where the TPO period aggregation produces few complete periods within the profile length, resulting in sparse distributions with limited analytical differentiation Very short profile lengths where insufficient bars produce too few TPO periods to create a statistically meaningful distribution with identifiable POC and Value Area levels Extremely compressed consolidation environments where price trades within a narrow range and all rows receive similar print counts, producing a flat distribution without the structural differentiation that makes POC and Value Area meaningful Markets with frequent large gaps where the profile range is dominated by gap space rather than traded price, producing distributions with many empty rows that reduce the analytical value of the concentration levels Integration Guidelines Confluence : Combine with BOSWaves structural tools, order flow analysis, or momentum indicators to validate POC and Value Area interactions with broader analytical context before acting on market profile reference levels POC Migration Tracking : Monitor whether successive profile POC levels are migrating higher, lower, or remaining stable as a secondary directional bias indicator. Consistently rising POC levels across multiple sessions suggest sustained upward value migration. Value Area Overlap Assessment : Compare Value Area ranges across successive profiles to assess whether price is finding acceptance in overlapping value zones, indicating market balance, or developing non-overlapping value areas, indicating directional value migration. Single Print Completion Monitoring : Track single print rows as potential return targets in subsequent sessions. The market profile principle that incomplete auctions tend to be revisited makes single prints valuable anticipatory reference levels for future price behavior. IB Range Context : Use the Initial Balance range as a daily bias anchor. Strong directional moves that establish acceptance well outside the IB range early in the session carry greater continuation probability than marginal IB extensions that quickly revert inside. Disclaimer 3D Market Profile is a professional-grade TPO market profile visualization and auction theory analysis tool. It uses timeframe-based period aggregation with standard market profile methodology but does not predict future price movements. Results depend on market conditions, instrument session structure, parameter selection, and disciplined execution. BOSWaves recommends deploying this indicator within a broader analytical framework that incorporates order flow context, structural analysis, and comprehensive risk management.

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TriPack Rule Scanner TriPack Rule Scanner A table-based market scanner for up to 120 symbols on one chart. It is an indicator , not a strategy: it does not place orders, run a portfolio backtest, or report net profit. TradingView allows a limited number of unique request.security() calls per script (40 on most plans). This script packs three symbols into one formula ticker , then unpacks close (and uses open / high / low inside rules). Oscillators are calculated inside each request on the scan timeframe you choose, not on the chart's bar size. That keeps RSI and the other metrics aligned with Daily (or whatever TF you set) even if the chart is 5-minute. What you get Up to 120 user-picked symbols (defaults can be changed; any exchange TradingView supports). One metric column: RSI, StochRSI, Bollinger %B, ROC, PPO, distance from SMA, or CMO. Up to eight scan rules. Each rule is Buy or Sell, compares two fields or a field vs a number, and can use greater/less, equality, or cross up / cross down. Buy rules combine with AND or OR. Sell rules combine separately with AND or OR. Table signals: BUY, SELL, BOTH. A star (*) means the signal is new on this scan bar. Banner counts and optional alerts when enough new BUY or SELL names appear on a new scan-timeframe bar. The chart's own symbol is marked in the table if it is in the list. How to use it Set Timeframe (Daily is a sensible default for a swing scan). Load or edit the 40 packs. In each pack, put the highest-priced name in Expensive, then Mid, then Cheap. Wrong order can collide when values are unpacked. Turn on the rules you want. If no Buy rule is On, nothing will show BUY. Same for Sell. Use Show rows = New to keep only fresh signals, or Buy / Sell to hide the rest. Create a TradingView alert on this script if you want notifications. The script does not send alerts by itself until you create one. This tool does not tell you what to trade. It lists names that match rules you defined. Past matches do not imply future results. You are responsible for risk, costs, and whether a setup fits your account. What to watch (important) Pack order. Expensive / Mid / Cheap are digit slots in one float, not labels of quality. Mix a very cheap name into the Expensive slot (or the reverse) and unpacking can fail or look like "Invalid". Close is the reliable series. Oscillators use unpacked close. High and low of a spread / formula ticker are approximate. Prefer Close, RSI, StochRSI, %B, ROC, PPO, SMA distance, and CMO in rules. Treat High/Low rules as rough. No volume. Spread volume is not three real volumes. There is no MFI, VWAP, or volume filter. AND vs OR. AND on several Buy rules will show few rows. That is expected. OR will show many. New vs still true. RSI still below 30 is BUY without a star. BUY* is the bar it became true. Alerts follow new counts on a new scan-timeframe bar, not every chart tick. Plan limits. One copy of the script uses the 40-request budget. Adding the same script twice does not share settings; each copy has its own universe. Memory / load. Heavy oscillator + rule work on 120 names can hitch on a weak machine or a very low chart timeframe. If TradingView reports a memory limit, reduce rules or stay on Daily. Invalid rows. Delisted, halted, or badly packed names show Invalid on Close only. They are not signals. Not a backtest. There is no commission, slippage, or equity curve. Do not treat the table as proof of a trading system. Publication chart. If you publish, use a clean chart: this script only, table readable, no extra indicators unless the text says they are required. Disclaimer Information is for education and visual scanning only. It is not financial, investment, or trading advice. Securities and other instruments can result in loss of capital. TradingView and the author are not responsible for decisions you make with this script. Scripts, ideas, and third-party content on the platform are provided as-is.

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của only_fibonacci

RSI Probability Matrix [ChartPrime]RSI Probability Matrix 🔶 OVERVIEW Traders frequently rely on rigid, fixed overbought and oversold thresholds for the Relative Strength Index, ignoring how historical price action actually responds at different momentum levels. The RSI Probability Matrix introduces a statistical learning engine to your charts. It tracks and measures real-time trade outcomes across 10-point RSI brackets, turning historical momentum reactions into actionable win-rate probabilities. Instead of guessing whether an overbought or oversold signal holds weight, this indicator dynamically evaluates success and failure rates using an ATR-based target and stop-loss matrix. 🔶 HOW IT WORKS The indicator executes its statistical and tracking workflow through a multi-stage architecture: Dynamic Momentum Engine: Calculates a standard RSI and smoothed signal line pair, backed by an Average True Range (ATR) volatility measurement to size structural targets and stops. Real-Time Trade Tracking Array: Maintains live arrays spanning all 101 RSI index values (0 to 100), recording historical win and loss outcomes whenever buy or sell crossover triggers are hit. ATR-Based Risk Framework: When an oversold buy or overbought sell signal triggers, the engine projects dynamic target and stop-loss levels based on a custom multiplier of the current ATR value. Probability Matrix Dashboard: Aggregates the tracked data into 10-point bracket intervals, calculating real-time win percentages for both buy and sell directions across the entire chart history. 🔶 KEY FEATURES Statistical Matrix Table: A clean, configurable dashboard displaying detailed trade counts and directional win probabilities broken down across structured RSI bands. Automated Signal & Outcome Markers: Pins clear entry badges onto the chart along with success (✅) or failure (❌) markers when trades hit their target or stop parameters. Gradient Color Customization: Fluidly shifts RSI line colors across customizable bullish and bearish palettes depending on prevailing momentum zones. Flexible Target Management: Full control over ATR lookback periods, multiplier thresholds, and overbought/oversold trigger boundaries to fit your strategy. 🔶 TRADING APPLICATIONS Probability-Weighted Entries: Before taking a trade at a specific RSI level, check the dashboard matrix to see the historical win percentage for that exact momentum bracket. Only take setups backed by favorable statistical odds. Objective Risk-to-Reward Execution: Utilize the automated ATR target and stop lines to enforce disciplined trade management, allowing the probability engine to accurately record wins and losses. Momentum Exhaustion Filtering: Combine overbought or oversold crossovers with the matrix summary totals to identify which RSI zones hold the strongest historical defense from institutional participants. 🔶 SETTINGS Indicator Settings (RSI Length / Signal / OB-OS Levels): Controls the core lookback periods and the boundary thresholds required to trigger buy and sell signals. Target & Stop Settings (ATR Length / Multiplier): Adjusts the volatility distance used to calculate structural profit targets and stop-loss zones. Dashboard Settings (Visibility / Position / Size): Configures the placement and layout of the real-time statistical probability matrix table on your workspace. 🔶 CONCLUSION The RSI Probability Matrix removes the guesswork from momentum trading. By dynamically tracking and grading historical trade success across customized RSI bands, it gives you a data-driven edge built entirely on real market feedback.

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của ChartPrime

HTF Log-Regression Volume Profile [BigBeluga]🔵 OVERVIEW The HTF Log-Regression Volume Profile is an advanced technical indicator created by BigBeluga to map higher-timeframe logarithmic regression channels combined with an integrated volume profile distribution. Traditional volume profiles are often fixed to standard horizontal ranges or static session boxes that fail to capture curving price trends and logarithmic growth dynamics. In order to provide a solution to this problem, this indicator calculates adaptive higher-timeframe sessions using logarithmic regression curves and standard deviation boundaries, plotting internal volume histogram bins and Point of Control (POC) lines directly onto the chart. The indicator aims to visualize institutional volume distribution, equilibrium pricing, and structural value areas across multi-bar sessions. The core element of its calculation involves evaluating logarithmic regression slope and intercept metrics alongside session standard deviations defined as: = f_log_regression(close, htf_length) float deviation = ta.stdev(close, htf_length) where slope and intercept establish the curving regression baseline, and deviation dictates the channel width for the volume profile bounds. Higher values of bin counts and channel width multipliers allow the indicator to filter out minor market noise and isolate major high-volume structural nodes. 🔵 FEATURES The system utilizes a multi-layered matrix structure to provide actionable market intelligence: 1 — Higher-Timeframe Adaptive Engine Auto HTF Resolution: Automatically adapts higher-timeframe sessions based on the chart's current resolution or allows manual session customization. Session Tracking & History: Maintains active session boundaries while archiving historical finished sessions up to a user-defined limit. 2 — Logarithmic Regression Channels & Projections Regression Bounds: Plots session high, low, and equilibrium midlines curved along a logarithmic regression formula via math.exp(intercept + slope * htf_length) . Level Extensions: Projects session boundaries and midlines forward into future bars using configurable extension limits. 3 — Integrated Volume Profile & POC Tracking Horizontal Volume Bins: Projects dynamic volume profile histograms inside the regression channel using custom polylines and color-coded delta metrics. Point of Control (POC): Highlights the highest-volume price node within the active session via a dedicated POC line and provides an interactive statistics dashboard box. 🔵 HOW TO USE Apart from the basic visualization of regression channels, this tool can also act in alternative ways to support decision-making: Identify Value Areas via Volume Profile: Observe the horizontal volume bins and the Point of Control (POC) line to spot where the heaviest volume concentration occurred during the higher-timeframe session. Trade Channel Rejections: Use the upper and lower logarithmic regression boundaries and their forward extensions as dynamic support and resistance zones. Monitor Buying and Selling Pressure: Check the integrated statistics label displaying buy volume, sell volume, and net delta percentage to gauge order flow dominance within the active session. 🔵 NOTES Why this implementation is unique: It merges logarithmic regression channels with an internal volume profile distribution across higher-timeframe sessions. The dynamic polyline volume profile engine automatically adapts bin widths to curving regression slopes. The script is fully optimized for Pine Script version 6, utilizing advanced user-defined types, custom arrays, and polyline rendering for maximum performance.

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của BigBeluga

Gaussian Filter Trend [QuantAlgo]🟢 Overview The Gaussian Filter Trend passes price through a multi-pole Gaussian filter and holds the result inside an adaptive volatility deadband, producing a stepped trend path that advances only once a move has cleared the band. That band is sized by an Efficiency Ratio, tightening when price travels directionally and widening through chop, so the line tracks sustained moves and sits still through noise. Around that path, a star field orbits at two volatility-scaled radii that fade with distance, echoing the decay of the filter's own weighting and making the current trend distinctly recognizable at a glance on any instrument or timeframe. 🟢 How It Works The indicator's core methodology combines two mechanisms: a cascaded Gaussian filter that smooths the source series, and an efficiency-driven deadband that governs when that smoothed value is permitted to move the trend line. First, the selected source is passed through one to four cascaded single-pole stages. A beta term derived from the filter length and the pole count sets the smoothing coefficient. Because pole count enters that calculation directly, adding poles rescales the filter response rather than layering more averaging onto the same curve: beta = (1 - math.cos(2 * math.pi / length)) / (math.pow(1.414, 2.0 / poleCount) - 1) alpha = -beta + math.sqrt(beta * beta + 2 * beta) Next, efficiency is measured by comparing net directional movement against the total distance traveled over the efficiency window. The ratio moves toward one when travel is more directional and toward zero when price covers ground without net progress. It is then smoothed, so the deadband width is less likely to shift sharply from one bar to the next: efficiency_ratio = path_length == 0 ? 0.0 : net_move / path_length smoothed_efficiency = ta.ema(efficiency_ratio, efficiency_smooth) The smoothed reading blends between a wider chop multiplier and a tighter trend multiplier, and that result scales Average True Range into the deadband width. Higher readings pull the envelope in, so the line can follow a move more closely. Lower readings push it out, which is intended to reduce flips in conditions where they are more likely. Disabling Adaptive Width bypasses the blend and applies a single fixed multiplier: width_multiplier = adaptive_width ? chop_multiplier + (trend_multiplier - chop_multiplier) * smoothed_efficiency : fixed_multiplier trend_width = ta.atr(atr_length) * width_multiplier Finally, the trend line carries its previous value forward and steps only when the envelope has moved past it. It drops when the upper band falls below the current level and rises when the lower band climbs above it, producing a stepped path rather than a continuous curve: if upper_band < trend_line trend_line := upper_band if lower_band > trend_line trend_line := lower_band A persistent direction state records the last step and carries it through flat segments, so the line color, star field, bar coloring and alerts all read from the same value rather than diverging while the line is stationary. The star field orbits that path at a distance scaled to recent average bar range, spreading as ranges expand and drawing in as they compress, so the trend and the volatility it is being measured against are visible in one read. 🟢 Signal Interpretation ▶ Bullish Trend (Long/Buy): When the lower band climbs above the trend line, the line steps higher and the indicator enters bullish state. The trend line and star field switch to the bullish color. This condition identifies potential long or buy opportunities and remains active until the upper band falls below the line and confirms a bearish step. ▶ Bearish Trend (Short/Sell): When the upper band falls below the trend line, the line steps lower and the indicator enters bearish state. The visual elements switch to the bearish color. This condition identifies potential short or sell opportunities and holds until the lower band climbs above the line and confirms a bullish step. ▶ Flat Path (Hold): When price stays inside the deadband, neither band displaces the line and it holds level. Color does not change, so the prior state is carried rather than reconfirmed. Extended flat runs indicate the efficiency reading has widened the band against choppier conditions, and the state resolves only when one side of the envelope clears the line. 🟢 Features ▶ Preconfigured Presets: Three parameter sets cover a range of trading styles and timeframes. "Default" uses four poles over a fourteen bar window for a balanced configuration aimed at swing trading on 1-hour and daily charts. "Fast Response" shortens the filter length and drops to two poles for a tighter path on 5-minute to 1-hour charts, which may suit intraday work at the cost of more frequent steps in choppier conditions. "Smooth Trend" lengthens the filter and widens the chop multiplier for a steadier baseline on daily and weekly charts, aimed at position trading. Selecting any preset other than Default overrides every Gaussian Filter and Trend Width input beneath it. ▶ Built-in Alerts: Three alert conditions support automated monitoring of trend transitions. "Bullish Trend Signal" fires on the bar the direction state flips to bullish. "Bearish Trend Signal" fires on the bar it flips to bearish. "Any Trend Change" triggers on either transition for traders who want a single unified alert regardless of direction. All alerts include the exchange, ticker, and timeframe in the message for immediate context. ▶ Visual Customization: Six color presets, Custom, Classic, Aqua, Cosmic, Cyber, and Neon, provide coordinated bullish and bearish color pairings suited to different chart themes and personal preferences. Selecting Custom exposes independent color pickers for both states, alongside an adjustable neutral color used during the initial warmup before the first directional step. Line width is configurable from one for a minimal look up to eight for a heavier path, and the star field toggles separately from the line so either element can be displayed on its own. Optional bar coloring and background shading tint the candles and chart field with the active trend color at configurable transparency levels, reflecting the current state without reading the line directly.

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của QuantAlgo

Ticker Tag [theUltimator5]This indicator is a compact, dynamic, over-engineered... tag. It was designed to show a bunch of information about the chart at a glance rather than actually having to look at the chart for yourself. The tag is designed to be as visually pleasing as possible ( by my standards, at least ) while providing the maximum amount of information that can be mentally absorbed with two seconds after glancing at the tag. If you don't care about the chart information as much, you can customize it in the settings to turn it into a logo-style tag that can be horizontally and vertically adjusted relative to the chart with your own custom text. At its center is the current ticker symbol, surrounded by directional corner brackets and accompanied by the company name, an optional company-specific tagline, the current price, daily percentage change, and a configurable five-segment strength meter. By default, the center of the tag displays the chart's ticker symbol. The ticker can be replaced with custom Logo Text through the indicator settings if a different abbreviation or label is preferred. The company-name line can be independently enabled or disabled (disabled in image above) Ticker Tag contains a large internal library of company-specific market-themed taglines. When a supported ticker is detected, an appropriate phrase is automatically displayed beneath the company name. The tagline is displayed in an italicized style to visually separate it from the company name. Automatic taglines can be disabled independently from the company-name display, and users can enter their own 'Bottom Tagline Override', which is A custom tagline takes precedence over the automatically mapped phrase. If no predefined tagline exists for a ticker, the indicator simply omits that line rather than inserting a generic fallback. Above the central ticker, the indicator normally displays the current price using the symbol's native minimum tick formatting. The price is dynamically colored according to the current day's performance: Positive day: Positive Color (Green default) Negative day: Negative Color (Red default) Consolidating/low-directionality condition: White (special condition) A custom top text can also replace the live price entirely. When custom top text is used, it uses its own configurable color rather than the market-state coloring applied to the live price. The lower portion of the tag displays the live percentage change from the previous daily close. The calculation is: Current Price / Previous Daily Close − 1 and is displayed as a percentage. The percentage is colored using the selected Positive and Negative colors so the current day's direction can be identified at a glance. This daily calculation is performed from daily-timeframe data even when the indicator is being viewed on an intraday chart. Four brackets frame the central ticker and act as a simple visual representation of the current day's direction. The brackets use the Positive Color when the current price is above the previous daily close and the Negative Color when price is below it. When a previous daily close is unavailable, the current daily open is used as the directional reference. These brackets are separate from the strength meter and therefore provide a quick daily directional cue regardless of which signal is selected for the meter. Below the main ticker symbol is a five-segment strength meter. This is designed to give a very quick price action indication for the current timeframe The meter converts the selected market signal into a normalized 0–100 strength score and progressively fills from left to right. The default color progression moves from weak to strong: Red → Orange → Yellow → Lime → Green Unfilled segments remain dimmed. Each segment's color can be customized independently. The meter is intentionally progressive. If the fourth segment is illuminated, for example, the first three segments are illuminated as well. Selectable Strength Signals The strength meter can be driven by any of six different calculations: 1) Combined Score 2) RSI 3) MACD 4) Bollinger Bands 5) Stochastic 6) ATR A small letter beside the meter identifies the selected source: C - Combined Score R - RSI M - MACD B - Bollinger Bands S - Stochastic A - ATR This makes it possible to change the meter's interpretation without losing track of the active calculation. Several of the meter's underlying signals naturally operate on very different numerical scales. To make them comparable, Ticker Tag normalizes them into a common 0–100 framework. For unbounded signals, the indicator evaluates the signal relative to its own historical mean and standard deviation: Normalized Score = 50 + 15 × Z-Score The result is then constrained between 0 and 100. Under this system: 50 represents approximately neutral or historically average behavior. Values progressively above 50 represent increasingly strong positive conditions. Values progressively below 50 represent increasingly weak or negative conditions. The normalization lookback is user configurable and defaults to 252 bars (1-year in daily timeframe) Because this strength meter operates on the current chart timeframe, changing the chart timeframe also changes the context being measured. The default meter mode is Combined Score. The combined score combines five different measurements into an equal-weighted composite: 1) RSI - A standard 14-period Relative Strength Index is calculated and then statistically normalized relative to its own history. 2) MACD - The difference between the standard MACD line and signal line using : 12 / 26 / 9 settings is normalized relative to its historical distribution. 3) Bollinger Bands - A 20-period Bollinger Band with a two-standard-deviation envelope is used to determine where price sits within the band structure. That position is then normalized relative to its historical behavior. 4) Stochastic - A 14-period Stochastic calculation with a three-period smoothing component contributes a direct 0–100 momentum measurement. 5) ATR Directional Volatility - ATR is combined with directional movement information rather than being treated as pure volatility alone. The indicator considers the difference between +DI and −DI and scales it according to the instrument's ATR as a percentage of price relative to its historical ATR behavior. This produces a directional-volatility measurement intended to distinguish volatility associated with bullish directional pressure from volatility associated with bearish directional pressure. The five normalized components are then equally weighted The result drives the five-segment meter. The Combined Score is intended to provide a broader view of current market strength than any single momentum calculation alone. Consolidation Detection Ticker Tag can also visually identify periods where directional trend strength becomes weak. When white/Bold Price on Low ADX is enabled (on by default), the live price changes to bold white text when the indicator's consolidation criteria are satisfied. The condition evaluates multiple conditions that all need to result in true: 1) ADX below the user-defined low threshold 2) +DI below 25 3) −DI below 25 4) relatively little separation between the directional components This state is intended to visually distinguish low-directionality or consolidating conditions from ordinary bullish and bearish price movement. It affects only the default live-price display. If custom Top Text is entered, that text retains its selected custom color. Positioning Ticker Tag is positioned beyond the most recent chart bar rather than directly on top of historical candles. The horizontal position begins one bar beyond the last chart bar and then applies the user-defined **Offset from Right Edge**. The default horizontal offset is 30 bars. Vertical positioning is volatility aware. Instead of using a fixed percentage of price, the vertical offset is measured in multiples of ATR: Tag Position = Current Price + Vertical Offset × ATR A value of: 0 positions the tag near current price. A positive value moves it above current price. A negative value moves it below current price. Because the offset scales with ATR, its placement adapts more naturally across instruments with very different prices and volatility characteristics. The ATR length used for positioning is independently configurable. In summary (if you read this far), the Ticker Tag is a compact, dynamic, over-engineered... tag.

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của TheUltimator5

Previous Day, Week & Month Levels [ITA]🟠 OVERVIEW Previous Day, Week & Month Levels plots the high and low of each completed higher timeframe period and keeps them on the chart until price trades through them. Once a level is taken, it fades to grey instead of disappearing, so the chart separates liquidity that is still resting from liquidity that has already been collected. The indicator covers daily, weekly and monthly periods independently, with optional midpoints for each range. This lets an intraday trader run daily levels alone, or stack all three groups to see how short-term and higher timeframe references line up. 🟠 CONCEPTS * Previous Level - The high or low of the last completed daily, weekly or monthly candle, pulled from the higher timeframe regardless of the chart timeframe in use. * Untaken Level - A level that price has not traded through since its period closed. Drawn at full opacity because the orders resting behind it are still there. * Taken Level - A level that price has traded through during the current period. Recolored grey to show the liquidity has been collected and the level has lost its role as a target. * Level Reset - Each group tracks its own taken state and resets when a new period begins. Daily flags clear every session while weekly and monthly flags run on their own cycles. * Midpoint - The 50% level of a previous range, marking the equilibrium of that period rather than its extremes. 🟠 FEATURES * Multi-Period Levels - Plots previous day, week and month highs and lows, each group toggleable on its own. * Taken Level Fading - Automatically recolors any level that price trades through, leaving untouched levels highlighted. * Optional Midpoints - Adds the 50% level of each enabled range for equilibrium reference. * Level Labels - Tags each line with its name on the right edge of the chart, with configurable size. * Level Alerts - Fires when price trades above a previous high or below a previous low. 🟠 HOW TO USE * Run daily levels alone for intraday work, or enable weekly and monthly for a broader structural view. * Treat bright levels as unfinished business and faded levels as history. What stays highlighted is where liquidity has not yet been taken. * Watch for clusters where a daily level sits close to a weekly one. A single move that clears both tends to produce a sharper reaction than clearing either alone. * Use midpoints as a filter. Price rotating around the midpoint of the previous day often points to balance rather than direction. * Adjust Extend Right if the levels project too far past the current candle or stop short of it. 🟠 CONCLUSION Previous Day, Week & Month Levels combines multi-period reference levels with automatic tracking of which levels have already been traded through. Instead of showing every level identically, it separates active liquidity from collected liquidity, giving traders a clear view of which reference points are still relevant to the current session.

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của itamardrori_

Market Rotor - Phase-Space Rotation Geometry [FibonacciFlux]Third in a series of published experiments. The first script asked whether an observed rotation survives a null. The second asked whether the circulation between price and flow is just the price increment leaking into the flow axis. This one publishes the full phase-space geometry those two were carved out of, and then applies the same treatment to its own headline verdict. The verdict does not survive. Against a null that breaks only the thing being tested, the orbital-rotation state is not distinguishable from noise on BTCUSDT and is worse than noise on ETHUSDT. The numbers are below and the indicator ships with the state still labelled, because a label you can check is more useful than one quietly removed. 🔶 USAGE 🔹 What is in the pane Two dimensionless series share one scale by design. C is the signed rotation consistency - net turning divided by total turning over a window, bounded to . S* is a spiral index, tanh-compressed to the same range, positive when the trajectory winds outward. The bands behind them are the regime label; the dots along the top are phase slips. Angular velocity ω is off by default, and that is a units decision rather than a judgement. C and S* are dimensionless; ω is rad/bar and reaches ‖ω‖ of 1.92 on BTCUSDT 1H against 0.597 for C and 0.768 for S*. Sharing an axis, it sets the scale and flattens the two series the pane exists to show. Turn it on and expect the pane to rescale. 🔹 Where the numbers come from Every figure below comes from a reimplementation of the core outside Pine, cross-checked against the live script on the same bar, and run over 3856 plotted bars of BINANCE:BTCUSDT and BINANCE:ETHUSDT 1H covering 3 March to 17 August 2026. The null draws use a seeded generator, so they reproduce. Warm-up discards the first 144 bars of each 4000-bar series. 🔹 The background will look like a barcode, and that is the result These are per-bar labels drawn as bands, not regimes. Over 3856 bars at the defaults the label changes on 34.5% of BTCUSDT 1H bars, with a median unbroken run of 1 bar and a 90th percentile of 6. On ETHUSDT it is 38.0%, again a median run of 1 bar. Nothing here smooths that into looking more stable, because smoothing it would be the lie. The practical reading is blunt: on most bars this tells you nothing. Phase noise alone takes 59.2% of BTCUSDT bars, and the label changes on the next bar about one time in three - the median unbroken run is a single bar because short runs are common, not because most bars are isolated. Use it to look at a stretch of history, not to make a decision on the current bar. BTCUSDT 1H at the shipped defaults, seven weeks. Three things to look at. The background is stripes rather than blocks, which is the 34.5% figure made visible. The C line - green above zero, pink below - stays well inside the two faint threshold lines at ±0.45 for almost the entire span; the handful of touches are some of the 8 episodes counted below. And the amber S* area is far wider than C, which is why the pane scales the way it does and why ω, wider still, is off by default. What the bars get called, BTCUSDT / ETHUSDT: phase noise 59.2% / 51.7%, outward spiral 15.7% / 18.3%, inward spiral 13.6% / 16.8%, core 8.7% / 11.3%, phase slip 1.2% / 1.7%, orbital rotation 1.5% / 0.1%. Those last two are lower than the raw |C| rates below, because the cascade tests slip, core and spiral before it ever tests |C|. 🔹 The orbital-rotation verdict, and the null that kills it The obvious null is to shuffle the second axis. That is the wrong null, and it is the mistake this script shipped with. Shuffling destroys y's autocorrelation as well as its alignment with x - lag-1 goes from 0.545 to 0.034 - and a y that jumps every bar inflates the total turning in the denominator, pushing C toward zero for reasons that have nothing to do with the two axes. A circular shift of y keeps the autocorrelation at 0.543 and breaks only the alignment, which is the thing under test. Rate of the verdict against the p50 of each null over 20 draws, and p = the share of shift-null draws that match or beat the real rate. 3856 bars, both symbols: BTCUSDT ETHUSDT W real shuffle shift p real shuffle shift p 24 6.52% 2.63% 7.68% 0.76 7.09% 3.14% 8.69% 0.90 48 1.84% 0.16% 1.12% 0.29 0.29% 0.13% 1.63% 1.00 96 0.03% 0.00% 0.00% 0.24 0.00% 0.00% 0.00% 1.00 Read the shuffle and shift columns side by side. Against the shuffle the verdict looks like it separates at every window. Against the shift it separates at none: on BTCUSDT the real rate sits inside the bulk of its own null, and on ETHUSDT it sits below every draw. At the window this script originally shipped with, W = 24, the real rate is below the null median on both symbols. The window was moved from 24 to 48 in an earlier cycle precisely because that drove the shuffle null to zero. What it actually did was widen a gap against a null that had been crippled. It stays at 48 as the more conservative reading, not because it separates anything. And 71 and 11 are not 71 and 11 independent trials. Adjacent bars share 47 of their 48 turning angles. Counted as contiguous episodes, both symbols enter the state exactly 8 times ; on BTCUSDT a single episode of 30 bars is 42% of every firing bar. The gap between the two symbols is how long one episode lasted. 🔹 Warm-up, alerts and cost Nothing plots for 3 × the longest window, which now includes the consistency window - 144 bars at the defaults. Four alerts fire on entry into the phase-slip, outward-spiral and orbital-rotation states and on a change of rotation direction; they name the trigger and deliberately say nothing about what price should do next. Four request.* calls sit at global scope and run every bar whatever the settings say; what is conditional is the timeframe each asks for, so the lower-timeframe pull only requests a real lower timeframe when the order-flow axis is selected. That is not the same as free, and the cost has not been measured. Two chart tabs did go unresponsive on a two-core machine during this work, one of them here during a symbol change - but the order-flow axis was afterwards exercised on 1H without incident and gave genuinely different values from the default. Read those as unexplained, not as a warning about a setting. On the order-flow axis there is one silent substitution to know about: past the intrabar history limit, or on a plan that cannot serve the chosen lower timeframe, y falls back to the CLV proxy with no visible break, so the plotted history can mix two different second axes. 🔶 DETAILS 🔹 The construction Both axes are z-scored over a rolling window and passed through a 2×2 whitening Σ^(-1/2), so a merely correlated pair does not draw a tilted ellipse and read as rotation. Turning per bar is the signed angle between successive position vectors, taken from the cross and dot products rather than by differencing φ, and spans inside a central dead zone count as zero turning because the angle there is noise. C is the ratio of the two rolling sums, gated so a window carrying less than π of total turning reports nothing at all. 🔹 The noise floor, measured honestly median |C| real 0.133 y shuffled 0.097 y circularly shifted 0.123 both null figures are the p50 across 20 draws, not a single draw Break only the alignment and the level barely moves - and at the window this script originally shipped with, W = 24, it does not move at all: real 0.169 against a shift null of 0.167. Nearly everything you see is the random walk of a partial sum over 48 angles. The earlier "about seventy per cent" reading came from the shuffle column, and was therefore too generous to the indicator. 🔹 Which knobs are load-bearing One. The lag on the second axis: with no lag, y is close to the derivative of x, the point circles for that reason alone, and median signed consistency comes back at -0.283 against -0.036 at the shipped lag of one bar. Two that are not: moving the dead zone from 0 to 1.0 moves median |C| from 0.144 to 0.128, and turning whitening off moves it from 0.133 to 0.138. Defensible hygiene, neither load-bearing. 🔹 What is not established Incremental alpha, none of it - no IC against existing signals, no out-of-sample improvement, no PBO or DSR. This is an observer, and on the evidence above it is an observer of something it has not shown to be there. The experimental signal arrows are off by default and stay in only as a comparison: shuffling the second axis barely dented their performance, which points at the plain |x| threshold rather than at anything rotational. 🔶 SETTINGS 🔹 Axes and normalisation Second axis y - five options: deviation × momentum (default), deviation × order flow from lower-timeframe volume delta, deviation × order flow via a CLV × volume proxy, return × volatility change, and deviation × open-interest change. Momentum is close to the derivative of x, so read the default as the control rather than as the best proxy. Every number in this description is measured on that default axis, deliberately - it is the weakest case, and a statistic that cannot beat its null there has not earned a test on a better axis. The order-flow axis was not re-measured. Lag on the second axis - default 1. The one setting that decides whether the measurement means anything. Equilibrium EMA 34, z-score length 40, 2×2 whitening on, z clip 4.0, optional anchored normalisation. 🔹 Windows Consistency window for C - default 48. Read the window sweep above before changing it - no setting of it separates the verdict from the shift null. Total-turning gate - default π. Below it, C and S* report na rather than a ratio of two small numbers. It is a safeguard that never bound on the data here: total turning had a median of 27.7 rad and a minimum of 12.6 against a gate of 3.14, so it was open on all 3856 bars of both symbols. Expect no gaps from it. Central dead zone 0.30, winding window 32, loop-area window 24, warm-up multiplier 3. 🔹 Regimes and display Thresholds for orbital rotation, spiral, core radius and phase-slip hazard. C, S*, the regime background and the slip markers are on by default; ω, winding number W, loop area A, the Kuramoto multi-timeframe readout and the experimental arrows are off. The Kuramoto readout R carries the same units caveat as ω: it lives on and is positive only, so switching it on rescales the pane. It is drawn beneath C so it cannot hide it. Its two higher timeframes now default to 4H and 12H - they were 1H and 4H, and on the shipped 1H chart that made two of the three phases identical and put a floor of 1/3 under R. Keep both above your chart timeframe. Radius, raw S*, phase-slip hazard, total turning, raw loop area, both axis positions and the regime number are in the Data Window. Open source under the Mozilla Public License 2.0. Replications and refutations welcome - particularly a symbol, timeframe or axis pair on which the orbital-rotation state does separate from a shift null. I did not find one.

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của FibonacciFlux

Market Rotor - Increment Contamination Test [FibonacciFlux]Second in a series of published experiments: take a correction that is widely assumed to work, implement it honestly, and find out what it actually does. This one tests the most common way of accidentally manufacturing rotation - putting the same-bar price increment into the second axis and then reading the resulting circulation as structure. It detects that contamination reliably, though not precisely - the spread is measured below. It cannot do the converse, and the boundary where it starts getting the converse wrong is measured below. 🔶 USAGE 🔹 What this is for Any indicator that claims rotation, circulation or lead-lag between price and a flow-like series is reading the antisymmetric part of a lag-1 covariance. Load your two axes here first. If the reading comes back near 1, the circulation that indicator is showing you is the increment leaking into its own second axis, and there is nothing else to interpret. 🔹 Reading the shading Red - the ratio sits within 0.25 of 1. An increment-contamination component accounts for the observed circulation. Grey - anything else. Not classified. This is not evidence that the rotation is genuine. BTCUSDT 1H at the defaults. The band is red across effectively the whole span, and that is the finding rather than a rendering quirk: on this data the circulation between price and flow is contamination-shaped nearly all of the time. Note also how much of the pane the amber R_art area occupies relative to the pale R_obs line running through it - the artifact term is routinely larger in magnitude than the thing it is decomposing, which is why the ratio sits above 1 in about two thirds of readings. There is no green state, for the same reason as in the first script: this test is one-directional. It can find contamination; it cannot certify its absence. 🔹 Reading the plots The amber filled area is R_art, the circulation Identity I alone accounts for; the pale line inside it is R_obs, the observed circulation; green is R_res, what is left. The verdict band is red rather than amber precisely so that it is not the same colour as the series it is a verdict about. The ratio is a signed share, not a fraction. R_res is defined as R_obs - R_art, so the additive identity cannot fail and proves nothing by itself; what justifies the split is that LAMBDA is linear in y, so writing y = beta*dx + e gives R_art and R_res exactly as the circulation contributed by each part. Nothing bounds the ratio to , and on real data it usually is not there: on BTCUSDT 1H it exceeds 1 in 66% of readings on the default axis, 79% on the order-flow axis. Read "near 1" as "contamination accounts for it", never as a percentage of it. The Data Window carries the ratio, beta, corr(y, dx), LAMBDA, T_K and both axes. 🔹 Choosing the second axis CLV x volume is the default, and deliberately not the best proxy. Order flow from lower-timeframe volume delta is the better second axis. The original reason for not defaulting to it was that a chart tab hung twice while it was selected, once on a daily chart and once on the 1H default, both on a two-core machine. That attribution has since weakened and this paragraph is the correction. Both of those tabs stopped responding during automated testing, not during ordinary use. The same symptom later appeared twice more with no lower-timeframe request involved at all, and the axis has since been selected and exercised on a 1H chart without incident. Nothing here is attributable to the request. The default stays on CLV as the conservative choice rather than as a fix for a diagnosed problem - select order flow deliberately, not nervously. What the default does buy you is real, and it is a cost rather than a hazard: request.* must sit at global scope, so the call always runs, but the timeframe it asks for is now conditional. On any axis other than order flow it is pointed at the chart's own timeframe and returns one intrabar per bar instead of sixty. Volume z-score on its own is the useful control: it carries almost no increment at all (beta +0.016, corr +0.009), and its ratio consequently degenerates - a non-verdict, not a clean bill of health. Momentum is a control, not a choice - it sets y to a difference of return EMAs, which is nearly dx itself. Watch beta when you switch to it, not the ratio: measured on one bar of BTCUSDT 1H the ratio barely moved (1.119 to 1.084) because the default is already saturated near 1, while beta on that same bar jumped from 0.838 to 1.270. The identity is firing; the ratio has no room left to show it. The four axes disagree, and that is information rather than a defect. On one bar of BTCUSDT 1H: order flow 1.119, CLV x volume 0.965, momentum 1.084 - and volume z-score -2.104, which is not a verdict at all but the signature of a near-zero denominator, discussed below. 🔹 Warm-up and repainting Nothing plots until 2 x W + z-length + detrend-length bars have elapsed: 574 at the defaults, about 24 days of 1H data. Two things that will bite you. First, when R_obs is near zero the ratio is meaningless and still prints to five decimals - on the volume z-score axis this was measured at R_obs 0.013 giving a ratio of -2.10 with beta 0.07, that is, no contamination at all and a nonsense ratio. Read LAMBDA first: if there is no circulation, decomposing it means nothing. Second, if you switch to the order-flow axis, set the lower timeframe explicitly before going above 4H - on a daily chart the warm-up needs roughly 14,000 intrabars. And one silent substitution. Where the lower-timeframe request returns nothing - beyond the intrabar history limit, or on a plan that cannot serve the chosen lower timeframe - y falls back to CLV with no visible break. The plotted history can therefore be a mixture of two different second axes with an invisible switchover, and every Data Window statistic inherits it. Watch corr(y, dx) and beta for a step change if that matters to you. 🔶 DETAILS 🔹 The identity being tested R_obs = 2 * LAMBDA, LAMBDA = 0.5 * mean_W if y carries the same-bar increment dx with coefficient beta then Q(w) = -beta * S_xx(w) * sin(w) appears with no rotation present at all beta = cov_W(dx, y) / var_W(dx) R_art = 2 * beta * 0.5 * mean_W In discrete time x_s(y_{s+1}-y_s) - y_s(x_{s+1}-x_s) collapses exactly to x_s*y_{s+1} - y_s*x_{s+1}, so the circulation needs no angles, no atan2, no origin estimate and no lag operator. An earlier version of this work used atan2 and every one of those was required - each a free parameter able to manufacture the result. Call it circulation, not a rotation rate: mean is mean , an areal velocity, and it equals a rotation rate only if the squared radius and the angular velocity are uncorrelated. 🔹 What it detects, and what it does not Reimplemented outside Pine and swept over synthetic series whose truth is known by construction, 5 seeds, W = 250: y = beta*dx + noise, no rotation at all - the ratio has median 0.94 to 0.97 with a 10th-to-90th percentile spread of roughly 0.77 to 1.17. Centred on 1, but not precise, and independent of the sign of beta. a clean 2-D rotation with no dx in y at all - the ratio reads 0.46 to 0.83, rising with rotation speed. The test therefore cannot certify that a rotation is genuine. The false positive depends on persistence at least as much as on speed. The rotation speed at which a clean, uncontaminated rotation starts reading above 0.75 and is reported as contamination, by the AR persistence rho of the rotating system: rho 0.90 never, up to 1.5 rad/bar rho 0.95 0.50 - 0.70 rad/bar (~9-13 bar cycle) rho 0.98 0.25 - 0.40 rad/bar (~16-25 bar cycle) rho 0.995 0.12 - 0.16 rad/bar (~39-52 bar cycle) W and the noise level did not move that boundary; persistence moved it by more than 5x. The sign of beta is deliberately not used for anything. Across more than 50 configurations it tracks the sign of whatever coupling exists, flipping with the direction of a genuine rotation as readily as with the sign of the contamination. Related structural fact, worth checking rather than trusting: LXD is non-positive by construction, so the sign of R_art is fixed by the sign of beta. Measured across synthetic rotation in both directions, synthetic contamination and real data, LXD was positive in 0.0% of readings. 🔹 On real data Two structurally different flow proxies give the same verdict on 1H crypto, which is the finding worth taking away: ratio beta corr(y,dx) CLV x volume BTCUSDT 1.082 +0.49 +0.30 ETHUSDT 1.085 +0.58 +0.36 taker flow BTCUSDT 1.090 +0.79 +0.47 ETHUSDT 1.052 +0.65 +0.39 volume z BTCUSDT 0.664 +0.016 +0.009 <- the control My own earlier work on this data, which this script reimplements, reported 108.3% with corr +0.377 on its own flow proxy, and the live script on lower-timeframe volume delta reads 1.119 with corr +0.533. Four different constructions of the second axis, one answer: the circulation people read between price and flow is the increment leaking into the flow axis. 🔹 Implementation and reproducibility Covariances are computed from their definition because Pine has no ta.cov. y is never lagged: an earlier cycle established that lagging it manufactures rotation and reverses the sign of the true antisymmetric lag covariance. The x pipeline was verified against an independent reimplementation - identical to five decimal places on the same bar. The synthetic scenarios use a seeded generator, so every number above is reproducible. 🔶 SETTINGS 🔹 Axes Second axis y - CLV x volume (default), order flow from lower-timeframe volume delta, volume z, or the momentum control. Equilibrium EMA - detrends x (default 34). Z-score length - standardises both axes (default 40). Lower timeframe for volume delta - blank picks one from the chart timeframe. 🔹 Windows Rolling window W - LAMBDA, beta and T_K are all estimated over it (default 250, minimum 100; shorter windows make the verdict band fragment into bar-to-bar stripes). Warm-up multiplier - bars withheld before plotting, as a multiple of W (default 2). 🔹 Display R_obs, R_art, R_res - the three series. T_K residual torque - off by default; a volume-weighted detailed-balance statistic that did not survive its own null. Shade background when contamination is identified - the red band. T_K display scale - default 30, because T_K is far smaller than the three circulation series and draws as a flat line on zero at 1; 30 is simply what made it readable here. Open source under the Mozilla Public License 2.0. Replications and refutations welcome, particularly a second-axis construction that stays uncontaminated under a test I have not thought of.

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của FibonacciFlux

Market Rotor - Rotation Artifact Null Test [FibonacciFlux]Market Rotor is the first of a series of published experiments: take a correction that is widely assumed to work, implement it honestly, and find out what it actually does. This one tests the standard artifact correction for cross-sectional rotation - the claim that you can estimate each asset's own autocorrelation, subtract the rotation that autocorrelation implies, and treat whatever is left as genuine lead-lag. Implemented and measured, it does not do that. The script is published so the test can be repeated and the result attacked. 🔶 USAGE 🔹 What this is for Before you build on, or believe, a rotation or lead-lag indicator, load your basket here. If an artifact component is identified, an artifact component is present in the rotation this basket produces at this window - which is reason to check whether the indicator you were about to trust is reading the same thing. If nothing is resolved, you have learned that this particular correction cannot tell you either way - which is worth knowing, because the correction is usually assumed to work rather than checked. 🔹 Reading the shading The background has two states and neither of them is an all-clear: Amber - R-squared above zero. An artifact component was identified inside the rotation you are looking at. Grey - R-squared at or below zero. Nothing was resolved. This is not evidence that the rotation is genuine. Two months of BINANCE:BTCUSDT 1H at the defaults. The two states alternate throughout - there is no long stretch where the correction cleanly resolves anything. There is deliberately no green state anywhere in this script. A reassuring colour would be the most misleading thing it could draw, because the test is structurally incapable of certifying that an observed rotation is real. 🔹 Reading the plots Three Frobenius norms share the pane. Grey is the observed rotation, the amber filled area is the artifact estimate, green is the residual. These are matrix norms, not components: they do not sum, and the residual can and often does exceed the observed norm. When it does, subtracting the artifact estimate has added variance rather than removed it - see the noise floor below. The Data Window carries the quantities that actually matter: R-squared, the residual share, the direction agreement cos(Ω, Ω_art), the mean off-diagonal of G0, the four estimated AR(1) coefficients, the effective sample count, and the legacy statistic from the first version of this script, kept only for comparison. 🔹 Choosing the symbol set The artifact term is proportional to contemporaneous co-movement. If your four assets barely co-move, or their bars do not align - mixing a stock with 24/7 crypto is the common case - the artifact estimate collapses toward zero for reasons that have nothing to do with lead-lag, and the test silently becomes vacuous. Watch the mean off-diagonal of G0 in the Data Window: if it is small, the reading means nothing. The same script on NASDAQ:AAPL daily with the three Binance defaults left in place - the first thing most readers will try. Nothing warns you. On the bar shown, the mean off-diagonal of G0 falls from 0.87 to 0.34, the artifact estimate collapses with it to 0.016 against an observed norm of 0.081, cos(Ω, Ω_art) reads -0.85, and the residual ends up larger than the observed rotation it came from. Note what the old v1 statistic does here: 0.20, which under that version's bands was its most reassuring possible reading. The tool's least trustworthy configuration was the one it praised. Requests use gaps_on, so a symbol with a missing bar yields na and that bar is skipped rather than having a stale return carried into the estimate. 🔹 Warm-up and repainting Nothing plots until the window is full and W + z-score-length bars have elapsed: 500 bars at the defaults, roughly three weeks of 1H data, but around two years on a daily chart. If the pane is empty, that is why. The accumulator only advances on confirmed bars, so historical values do not repaint. 🔶 DETAILS 🔹 The identity being tested Let each asset follow its own AR(1) with coefficient phi_i, and let the true cross-asset lead/lag be identically zero: r = phi_i * r + e G1 = E [ r * r ] = phi_j * G0 G0 is symmetric, therefore Om = 0.5 * (phi_j - phi_i) * G0 Exact in population. The proposal it suggests - estimate phi from each asset's own autocorrelation, form the implied artifact, subtract it, call the remainder genuine lead-lag - is what this script implements so that it can be tested rather than assumed. 🔹 Where the numbers come from The core was reimplemented outside Pine and agrees with this script to five decimal places on the same bar; every figure below comes from that reimplementation. Figures labelled synthetic are generated with a seeded generator and known ground truth, so they reproduce exactly. Real-data figures name the symbol, timeframe and window they were taken on, and the two single-bar readings are marked as such. 🔹 Result 1: the correction is not identified phi_i estimated from asset i's own autocorrelation cannot distinguish "asset i is autocorrelated" from "asset i is led by asset j", because the second necessarily produces the first. On synthetic data where a genuine lead-lag is injected, the artifact model fits better , not worse: R-squared rises to +0.40 and cos(Ω, Ω_art) to +0.74 at W=250. The correction absorbs precisely the thing it is meant to leave behind. 🔹 Result 2: the subtraction is noise-dominated Ω is estimated from the off-diagonal of G1; the artifact term from the diagonal of G1 plus G0. Under the null these estimate the same population quantity. If their errors are of comparable size and not strongly correlated, Var(Ω_res) = Var(Ω) + Var(Ω_art) and the residual share tends to sqrt(2) = 1.414 when noise dominates. Both of those are assumptions rather than results - the two estimators are built from the same return pairs over the same window, and the null identity ties them together - so read the measured spread as the test of them, not as a confirmation of the algebra. On synthetic all-artifact data it lands between 1.10 and 1.88, straddling that value, and the finding that survives either way is that the residual is larger than the quantity it was subtracted from. On BINANCE:BTCUSDT 1H with the default basket at W=250, the median R-squared is about -0.7 - near the noise floor of -1. 🔹 What the first version of this script got wrong Version 1 shaded on ‖Ω_art‖ / ‖Ω‖. That statistic compares magnitudes only and is not a fraction-explained, because the artifact and residual matrices are not orthogonal. It read 0.96 - "almost entirely artifact" - on data whose true explained share was 0.41, while the residual line sat at 77% of the observed line directly beside it. It also produced its most reassuring readings on baskets that barely co-move. Both statistics are exposed in the Data Window now - R-squared and the v1 ratio side by side - so the discrepancy can be inspected rather than taken on trust. 🔹 Implementation and reproducibility N is fixed at four because the external series are requested at global scope. G0 and G1 are uncentred second moments, not covariances; centring them inside the window was tested and moved R-squared by less than 0.02, so the extra accumulators are not carried. Rolling sums of the sixteen products are maintained by hand in a circular buffer, because built-in series functions keep per-call-site state and return wrong values inside loops. The core was independently reimplemented outside Pine and agrees with this script to five decimal places on the same bar; the synthetic scenarios use a seeded generator so the numbers above are reproducible. 🔶 SETTINGS 🔹 Symbols Symbol 2, Symbol 3, Symbol 4 - the three series joined to the chart symbol, which is always the first. Defaults are ETHUSDT, SOLUSDT and XRPUSDT on Binance. 🔹 Windows Rolling window W - length of the moment window, default 250, minimum 100, maximum 1000. Below about 100 the estimates are pure noise and the shading strobes, which is why the minimum is not lower. Return z-score length - lookback used to standardise each return series, default 250. 🔹 Display Show ‖Ω‖, ‖Ω_art‖, ‖Ω_res‖ - the three norms. Shade background when an artifact component is identified - the amber/grey band. Show residual signal for the chart symbol - off by default, and drawn for inspection only. It is signed and lives on a different scale to the three norms. Result 2 above is the reason not to trade it: this residual is noise-dominated and routinely larger than the observed norm it came from. Signal display scale - default 0.2. Raising it flattens everything else in the pane. Open source under the Mozilla Public License 2.0. Replications and refutations are welcome, particularly from anyone who can construct an estimator of the artifact term that stays identified when a genuine lead-lag is present.

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của FibonacciFlux

Breadth Topping Syndrome This indicator plots a normalized breadth deterioration score in a separate pane and fires a discrete topping signal when several independent NYSE internal breadth conditions assemble while the market is still rising. The thesis is that major tops are a syndrome, not a single event: internal bifurcation, weakening participation and fading leadership tend to appear together near distributive peaks, and their conjunction inside an uptrend carries more information than any one of them alone. █ OVERVIEW Breadth Topping Syndrome (BTS) condenses four warning conditions into one framework: - C1: a Miekka style divergence, where NYSE new 52 week highs and new 52 week lows are simultaneously elevated as a percentage of advances plus declines. - C2: Norman Fosback's High Low Logic Index at a high percentile of its own trailing history. - C3: weak S&P 500 participation (percentage of constituents above their 200 day moving average) while the trend reference index is in an uptrend. - C4: a weighted deterioration score built from the same normalized components exceeding a threshold. When a configurable minimum number of these conditions has been observed within a short synchronization span and the trend gate is up, a trigger fires and opens a signal window. Inside the window the syndrome is Active only while the McClellan Oscillator is negative. Repeated triggers within a trailing lookback are counted as a cluster. █ HISTORY / BACKGROUND The components have documented lineages. The simultaneous new highs and new lows divergence condition follows James R. Miekka's Hindenburg Omen specification (1995), itself derived from work by Martin Zweig and Norman Fosback: both extremes elevated at once, measured against advances plus declines, valid only in an uptrend, with the McClellan Oscillator acting as an activation gate inside a fixed window rather than as a co equal trigger. The High Low Logic Index is Fosback's, published in 1976: the minimum of new highs and new lows relative to total issues isolates the disagreement component of breadth. Percentage of stocks above the 200 day moving average is a standard participation measure. The McClellan Oscillator is the 19/39 period EMA differential of net advances, per Sherman and Marian McClellan. The syndrome architecture that combines them is novel and is described in full below. The conceptual basis is that each component captures a different failure mode of an advance, so requiring several to appear near simultaneously filters the false positives that any single measure produces on its own. █ HOW IT WORKS The script requests seven external series at the chart timeframe: NYSE advancing issues, declining issues, new 52 week highs, new 52 week lows, a trend reference index, and a primary plus fallback participation symbol. All requests use ignore_invalid_symbol, and a data integrity gate suppresses every signal when any core feed returns no value. On each bar the script computes: - The Miekka ratios: new highs and new lows each divided by advances plus declines, times 100. C1 is true when both meet the threshold. - The High Low Logic Index: the minimum of new highs and new lows divided by advances plus declines, times 100, smoothed with an EMA, then converted to a percentile rank over the normalization lookback. C2 is true when the percentile meets the warning level. - The participation percentile: the participation series (gap filled with its last valid value so feed gaps do not distort the distribution) percentile ranked over the same lookback. C3 is true when the percentile is at or below the warning level while the trend reference index is above its close a configurable number of bars ago. This is the divergence conjunction: price rising, participation weak relative to its own recent history. - The leadership share: new highs divided by new highs plus new lows, times 100, percentile ranked and inverted. - The deterioration score: the weighted average of the HLLI percentile, the inverted participation percentile and the inverted leadership percentile, scaled 0 to 100. If both participation symbols fail to resolve, the score reweights automatically over the two remaining components. C4 is true when the score meets its threshold. Each condition contributes to the syndrome if it was true on any bar within the synchronization span. When the count of contributing conditions reaches the required minimum while the uptrend gate is true, a trigger fires on the first such bar (edge triggered, so a persisting syndrome does not retrigger). The trigger opens a signal window measured in trading days. Within the window, the syndrome is Active while the McClellan Oscillator, computed as the fast EMA minus the slow EMA of net advances, is below zero, and deactivates when it turns positive without closing the window. The cluster count is the number of triggers within the trailing cluster lookback. █ HOW TO USE The script is designed for the 1D timeframe. The breadth feeds are daily series, the window and cluster inputs are specified in trading days, and the Miekka and McClellan parameters are daily conventions, so daily resolution matches the granularity of the logic. - Blue score line: current breadth deterioration, 0 to 100, against a dashed threshold line and a dotted midline at 50. The line turns orange above the threshold and red while the syndrome is Active. Gray indicates missing core data. - Faint purple line: the HLLI percentile, shown separately because it is the slowest moving and most historically studied component. - Red triangle at the top of the pane: a syndrome trigger fired on that bar. - Small maroon diamond: the Miekka condition alone was true on that bar without a full trigger, useful for tracking the classical signal inside the broader framework. - Maroon background: syndrome Active (inside a signal window with the McClellan Oscillator negative). Orange background: window open but the oscillator is positive, so the syndrome is temporarily deactivated and will reactivate if the oscillator turns negative before the window expires. - Status table (top right): overall state, each condition's current value and contribution, the syndrome count, the oscillator value, bars remaining in the window, and the cluster count. A single trigger is a caution flag. Two or more triggers within the cluster lookback have historically been the more serious configuration for divergence based breadth signals, and the script exposes a dedicated alert for that case. Four alerts are provided: trigger fired, syndrome turned Active, clustered trigger, and score crossing above its threshold. █ SETTINGS - Conditions Required (N of 4): syndrome count needed to trigger. Default 3. - Condition Sync Span: bars within which a condition still counts toward the syndrome. Default 5. - Signal Window: trading days a trigger keeps the window open. Default 30. - Cluster Lookback: trailing trading days over which triggers are counted. Default 60. - C1 Miekka NH/NL Threshold: minimum percent of advances plus declines for both new highs and new lows. Default 2.8. - C2 HLLI Warning Percentile: percentile of the smoothed HLLI that flags bifurcation. Default 90. - C3 Participation Warning Percentile: participation percentile at or below which weakness is flagged in an uptrend. Default 25. - C4 Deterioration Score Threshold: score level that flags composite weakness. Default 75. - Uptrend Lookback: bars over which the trend reference must have risen. Default 50. - HLLI EMA Length: smoothing applied to the raw HLLI ratio. Default 50. - Percentile Rank Lookback: window for all percentile ranks. Default 252. - Score weights for the bifurcation, participation and leadership components. Default 33.3 each. - MCO Fast EMA and Slow EMA: McClellan Oscillator periods. Defaults 19 and 39. - Data Symbols: all seven feeds are exposed as string inputs and can be substituted. - Show Status Table: toggles the table. Default on. █ WHAT MAKES IT ORIGINAL The individual components are public domain methods. What this script does differently is the combination architecture. First, every component is percentile ranked against its own trailing distribution before use, so the warning levels adapt to the prevailing breadth regime instead of relying on fixed absolute thresholds calibrated to a decades old NYSE universe. Second, the conditions are fused through an N of M syndrome count with a synchronization span, not a same bar AND, which acknowledges that breadth deterioration components rarely align to the exact day. Third, the trigger inherits the two phase Miekka mechanism but generalizes it: the syndrome, not a single divergence, opens the window, and the McClellan Oscillator gates activation inside it. Fourth, trigger clustering is quantified directly on the chart rather than left to visual inspection. This conjunction of adaptive normalization, tolerant multi condition assembly, windowed gating and cluster counting does not correspond to any single published method and is the substance of the script. █ NOTES / LIMITATIONS - The breadth feeds have limited historical depth. No signals can exist before the feeds begin, and because every percentile rank requires the full normalization lookback (default 252 bars), the first year of available feed history produces unreliable ranks and should be disregarded. - The logic is designed for daily resolution. On other timeframes the external series return whatever the feeds report at that resolution, and the day denominated windows lose their intended meaning. - All values on the developing realtime bar update until the bar closes. Signals should be evaluated on closed bars. The script uses same timeframe requests with lookahead off and does not reference future data. - If neither participation symbol resolves, condition C3 can never contribute. With the default requirement of 3 of 4, all three remaining conditions must then assemble, which makes triggers strictly rarer. The table marks participation as N/A in that state. - Data is pulled from fixed external symbols regardless of the chart symbol. The chart symbol only determines the bar grid, so the indicator belongs on a US equity index chart at 1D. - Breadth divergence signals of this family have a documented false positive history. This tool flags conditions that have accompanied past tops. It is a risk assessment input, not a standalone trading signal, and no claim is made about future results.

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Earnings Overview & Valuation Toolkit Earnings Overview & Valuation Toolkit Understanding how corporate earnings impact a stock’s price is one of the most critical challenges for stock traders and investors. The Earnings Overview & Valuation Toolkit is a comprehensive, institutional-grade analysis suite designed to combine macro fundamental valuation, historical announcement reaction patterns, and forward-looking volatility forecasting into a single, unified indicator. By utilizing the latest Pine Script® v6 force_overlay capabilities, this toolkit runs in a separate sub-panel to display clean percentage-based metrics (like earnings surprises and price reactions), while simultaneously overlaying price-based valuation bands, volatility cones, and a statistical dashboard directly onto the main price chart. Key Features & Components Dynamic P/E Valuation Bands (Main Chart Overlay) Instead of static valuation lines, this component tracks the company's Trailing Twelve Month (TTM) EPS and projects historical valuation multiples onto the chart. It calculates the 10th percentile (Undervalued), 50th percentile (Median/Fair Value), and 90th percentile (Overvalued) P/E ratios over a rolling lookback window (default: 500 bars). The zones are color-coded in soft green and red to help you immediately identify when a stock like NASDAQ:AAPL or NASDAQ:NVDA is entering a historical discount or premium zone. Earnings Surprise & Price Reaction Tracker (Sub-Pane) This panel analyzes analyst bias and the subsequent market response. Earnings Surprise Column : Plots the percentage beat (green) or miss (red) compared to consensus analyst estimates. Reaction Dot : Calculates the percentage price change over a configurable window (default: 5 bars) post-earnings. A negative offset is applied so that this dot aligns directly above or below the earnings column on the day of the release, helping you visually correlate the surprise with the subsequent momentum. Catalyst Countdown & Volatility Predictor (Main Chart Overlay) To protect you from binary earnings day risk, this tool pulls the upcoming expected earnings date ( earnings.future_time ) and projects a dynamic Volatility Cone directly onto the chart starting from the last candle. The boundaries of the cone represent the expected trading range on the release day, calculated using the absolute average return from the last 8 earnings releases. Unified Stats Dashboard (Main Chart Table) A clean, real-time table anchored to the top-right of your main chart that consolidates: Trailing Twelve Months (TTM) EPS & Latest Reported EPS. Current P/E multiple relative to price. Dynamic Valuation Status ("Undervalued", "Fair Value", "Overvalued"). Average Earnings Day Move % and the Volatility Multiplier compared to standard daily ATR (e.g. "2.5x normal daily volatility"). Consensus Analyst Estimates for the next quarter (EPS and formatted Revenue). Next Earnings Release Date and countdown (days/hours). How to Use this Toolkit Profitably The Post-Earnings Announcement Drift (PEAD) Strategy Scan for stocks that have reported earnings in the last 1 to 5 days. Look for a positive surprise (green column) paired with a positive price reaction (green dot). If the stock is currently trading below its orange Median PE Band , this confirms institutional buying momentum with a margin of safety. Hold for 2 to 4 weeks, placing your stop-loss below the low of the earnings day candle. The Pre-Earnings Volatility Ride Scan for stocks with an upcoming earnings countdown of 7 to 10 days. Verify they have a strong track record of beating earnings (high 8-quarter average surprise) and a high Volatility Multiplier (e.g. >1.5x normal ATR). Buy the Call/Put option contract expiring the week after earnings to capture rising Implied Volatility (IV) and price run-up. Crucial Rule: Sell to close the contract the afternoon before the release to capture the maximum IV peak and avoid the overnight gap and IV crush. The Macro Value Rebound For long-term investors, monitor your watchlist for high-quality, profitable companies whose stock prices fall to or below the green Undervalued Band . Wait for a technical reversal or a positive earnings reaction dot to confirm a floor, and buy shares or long-dated LEAP options to ride the reversion back to the orange Median and red Overvalued bands. Indicator Inputs & Customization The toolkit is designed to be highly customizable to fit your trading style: Reaction Window (Bars) : Change the length of time used to measure the post-earnings price impact. Max Surprise Clamp % : Caps extreme surprise % columns to keep the sub-pane scale readable. P/E Lookback (Bars) : Adjust the length of history used to compute valuation percentiles. Show Volatility Cone : Toggle the dynamic future projection lines and box on/off. Color Customization : Easily match the bands, fills, and columns to your light or dark chart theme. Disclaimer: Corporate earnings data is subject to reporting schedules and analyst revisions. Always practice proper risk management and size your option plays according to your account size. Past performance does not guarantee future results.

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của CapitalCodersLLC

Black-Litterman Allocator [BackQuant]# Black-Litterman Allocator IMPORTANT: Concept / Educational Implementation Black-Litterman Allocator is a research and educational concept that implements a practical version of the Black-Litterman portfolio-allocation framework inside TradingView and Pine Script. It is intended to demonstrate how equilibrium priors, covariance estimates, subjective investor views, view confidence, mean-variance optimization, portfolio constraints, volatility targeting and portfolio backtesting can be combined into one visual allocation model. It should not be interpreted as an institutional-grade portfolio optimizer, automated investment product, portfolio recommendation, or guarantee that the resulting allocation is optimal. The outputs depend heavily on: The selected asset universe. The chart timeframe. The covariance lookback. The quality and synchronization of TradingView price data. The chosen prior-weight scheme. Risk-aversion assumptions. The investor views entered by the user. The confidence attached to those views. Portfolio constraints. Volatility-target settings. Transaction-cost assumptions. The optional regime filter. The default universe and default views are examples for demonstrating the framework. They are not investment recommendations. The script is best treated as a portfolio-allocation laboratory : a way to study how changing assumptions about equilibrium, risk, correlations and expected returns can propagate through a Black-Litterman-style allocation process. Overview Black-Litterman Allocator is a 15-asset cross-asset portfolio model that starts with a neutral portfolio prior, reverse-engineers the expected returns implied by that prior, optionally incorporates up to five investor views, solves for a new posterior allocation, applies portfolio constraints and volatility targeting, and then simulates the resulting portfolio through time. The model follows a broad sequence: Collect return history for the selected 15-asset universe. Estimate an annualized covariance matrix. Stabilize that matrix using diagonal covariance shrinkage. Construct a prior portfolio. Estimate the market risk-aversion parameter. Reverse-optimize the prior into implied equilibrium returns. Convert investor views into the Black-Litterman P, Q and uncertainty structure. Blend the prior with those views to obtain posterior expected returns. Optionally calculate posterior covariance. Solve a mean-variance portfolio from the posterior. Apply availability, short-selling, gross exposure and position-size constraints. Target a desired portfolio volatility. Apply additional leverage and gross-exposure caps. Rebalance periodically. Track the resulting equity curve and portfolio statistics. The script also provides detailed visualizations showing: Prior versus final active weights. Equilibrium versus posterior expected returns. The impact of individual views. Current gross and net exposure. Portfolio volatility and scaling. Turnover. Portfolio equity versus a benchmark. Drawdown and daily returns. A broad set of performance and risk statistics. Why Black-Litterman exists Traditional mean-variance optimization has an important practical weakness. The optimizer is extremely sensitive to expected-return estimates. Suppose several assets have similar volatility and correlation characteristics, but one asset is assigned an expected return only slightly higher than the others. A mathematical optimizer can interpret that small difference very aggressively and allocate an unrealistic amount of capital to that asset. Small estimation errors in expected returns can therefore produce very large changes in portfolio weights. This is one reason unconstrained mean-variance portfolios often produce allocations that appear unstable or unintuitive. The Black-Litterman framework was developed by Fischer Black and Robert Litterman as a way of approaching the problem from the opposite direction. Instead of beginning with a set of independently estimated expected returns, the framework begins with an equilibrium portfolio and asks: What expected returns would make this portfolio mathematically optimal? Those implied returns become the prior. Investor views are then introduced as controlled deviations from that equilibrium rather than replacing the equilibrium assumptions entirely. This creates a useful distinction: Prior = what the portfolio implies before the investor expresses a view. Views = where the investor believes equilibrium is wrong. Posterior = the combined result after balancing both sources of information. That is the central idea behind this indicator. Important distinction: the prior in this script In textbook Black-Litterman, the equilibrium portfolio is often represented using market-capitalization weights. This script is intentionally more flexible. It provides three different prior schemes: Equal Weight. Inverse Volatility. Manual Weights. For that reason, the word equilibrium should be interpreted carefully. If Equal Weight or Inverse Volatility is selected, the prior is a user-selected equilibrium proxy , not necessarily the true global market portfolio. If Manual Weights is selected and the user enters representative market-cap or benchmark weights, the prior can be made closer to the traditional Black-Litterman interpretation. This flexibility is intentional because TradingView users may want to study Black-Litterman mechanics without first sourcing a complete set of institutional market-cap weights. Asset universe The allocator supports fifteen simultaneously selected assets. The default universe is designed as a broad cross-asset example containing: Cryptocurrency. US equities. International equities. Precious metals. Energy. The US dollar. Long-duration Treasury exposure. The default list includes assets such as Bitcoin, Ethereum, Solana, major equity indices, gold, silver, oil, DXY and TLT. Every symbol can be replaced by the user. This allows the framework to be adapted to: Global macro portfolios. Equity-sector portfolios. Cryptocurrency portfolios. ETF portfolios. Multi-asset portfolios. However, all assets should represent actual price series . Market-capitalization series, synthetic quantities or unrelated non-price data should not be inserted as if they were tradable asset prices, because the resulting returns would contaminate the covariance matrix and portfolio calculations. Data availability protection A multi-asset allocator has a specific problem when some assets have shorter histories than others. Suppose fourteen assets have ten years of data but the fifteenth asset was only listed six months ago. If missing values are simply converted into zeros, the new asset may appear to have: Almost no volatility. Artificially stable returns. Artificial correlations. This is especially dangerous when using inverse-volatility weighting, because an asset with incorrectly measured near-zero volatility could receive a very large prior allocation. The script protects against this by maintaining a separate data-availability state for every asset. An asset is only admitted into the active universe once it has accumulated at least one complete covariance lookback of valid price history. Until then: Its active mask remains disabled. It receives no prior weight. It receives no optimized weight. Views referencing it are ignored. The allocation table displays it as having no usable data. This makes the universe dynamic. A newly listed asset can eventually become active once enough genuine history has accumulated. Return calculations The allocator uses two forms of return data for different purposes. Log returns Log returns are used for covariance estimation: Log Return = ln(Price / Previous Price) These are stored in a rolling history matrix. Simple returns Simple returns are used when compounding the simulated portfolio: Simple Return = Price / Previous Price - 1 This distinction is deliberate. Log returns are convenient for statistical covariance calculations, while simple returns are appropriate for directly multiplying portfolio wealth through time. Rolling return-history matrix The script maintains a rolling matrix containing return history for all fifteen assets. Each row represents a historical bar and each column represents one asset. Once the requested covariance lookback has been collected, the matrix acts as the input for the covariance engine. Rather than recalculating years of historical data from scratch on every bar, the script operates the history as a rolling buffer. The full Black-Litterman calculation is also performed only on rebalance events rather than continuously. This is important because: Covariance estimation is computationally expensive. Matrix multiplication is expensive. Matrix inversion is expensive. TradingView imposes execution limits. The indicator therefore approximates how a real asset-allocation process is normally operated: weights remain relatively stable between scheduled portfolio reviews and are recomputed at discrete intervals. Covariance matrix The covariance matrix is one of the central inputs to the entire model. For N assets, covariance produces an N × N matrix. The diagonal contains the variance of each asset. The off-diagonal entries contain covariance between pairs of assets. Conceptually: Positive covariance means two assets tend to move in the same direction. Negative covariance means they tend to move in opposing directions. Covariance near zero suggests weaker linear co-movement. The portfolio does not consider the risk of each asset independently. Instead, portfolio risk depends on: Individual asset volatility. Portfolio weights. The covariance relationships between every pair of assets. This is why diversification cannot be measured simply by counting positions. Ten highly correlated assets may behave more like one large risk exposure than ten independent exposures. Covariance Lookback The Covariance Lookback controls how many bars are used to estimate the covariance matrix. Shorter windows: Adapt more quickly. Reflect recent correlation changes. Contain fewer observations. Produce noisier covariance estimates. Longer windows: Provide more observations. Create more statistically stable estimates. Adapt more slowly when correlations change. This parameter is particularly important when the number of assets is large relative to the number of observations. With fifteen assets, an extremely short covariance window can create a poorly conditioned or nearly singular matrix. That can make matrix inversion unstable and produce extreme portfolio weights. Annualization The covariance matrix is annualized using the Trading Days per Year input. The script supports: 252 days. 365 days. 252 is generally appropriate for traditional financial markets operating primarily on weekdays. 365 may be more appropriate for a crypto-only daily portfolio. Mixed universes require judgement because crypto trades continuously while many traditional markets do not. The annualization setting affects: Covariance. Volatility. Return statistics. Risk-aversion estimates. It should therefore be selected consistently with the universe and timeframe being studied. Covariance shrinkage Raw sample covariance matrices can be noisy. This is particularly problematic when: The lookback is short. There are many assets. Several assets are highly correlated. Market relationships change rapidly. The script applies a simple fixed-coefficient shrinkage toward a diagonal covariance target. The diagonal variances are retained. The off-diagonal covariance terms are multiplied by: 1 - Shrinkage Therefore: Shrinkage = 0 leaves the sample covariance relationships largely unchanged. Shrinkage = 1 removes the off-diagonal covariance terms and effectively treats the assets as uncorrelated for optimization purposes. Intermediate values partially reduce estimated correlations. This is best described as Ledoit-Wolf-style diagonal shrinkage , not as a full automatic Ledoit-Wolf estimator. A true Ledoit-Wolf implementation estimates an optimal shrinkage intensity statistically. Here, the user directly controls the shrinkage coefficient. Why shrinkage can help Portfolio optimization involves matrix inversion. If covariance estimates are noisy, the inverse matrix can amplify those errors dramatically. Shrinkage intentionally sacrifices some estimated correlation detail in exchange for greater numerical stability. A moderate amount of shrinkage can therefore: Reduce unstable allocations. Reduce sensitivity to short-term correlation noise. Improve matrix conditioning. Too much shrinkage can also remove genuine diversification information. The parameter is a bias-versus-variance trade-off. Safe matrix inversion Black-Litterman requires several matrix inversions. Matrices can become singular or nearly singular when: Assets are highly correlated. Lookbacks are too short. Data is incomplete. The script checks whether the matrix is square and sufficiently non-singular before using a standard inverse. When necessary, it falls back to a pseudo-inverse. This does not magically make poor data reliable, but it prevents a singular matrix from immediately destroying the calculation. A pseudo-inverse should still be interpreted cautiously because the underlying portfolio problem may be poorly conditioned. Prior portfolio Before Black-Litterman can estimate equilibrium returns, it requires a prior portfolio. Three schemes are provided. Equal Weight Every active asset receives an equal allocation: Weight = 1 / Number of Active Assets This is the simplest prior. It expresses no preference based on: Market capitalization. Volatility. Expected return. Its strength is simplicity. Its weakness is that it assumes every asset deserves the same capital allocation regardless of risk. Inverse Volatility Inverse Volatility gives greater prior weight to assets with lower historical volatility. Conceptually: Raw Weight ∝ 1 / Volatility The weights are then normalized. This produces a risk-oriented prior rather than a capital-oriented prior. Lower-volatility assets receive more weight. Higher-volatility assets receive less. This can be useful for diversified macro portfolios, but it has an important implication: the quietest asset may dominate the prior. For example, a bond or currency exposure may receive much more prior weight than cryptocurrency simply because its realized volatility is lower. This is not a bug. It is the direct consequence of using inverse volatility as the prior definition. Manual Weights Manual mode allows the user to enter fifteen raw numbers corresponding to the fifteen selected assets. The entries are normalized automatically. This means the values do not need to sum to 100. The user can enter: Percentages. Market capitalizations. Benchmark weights. Relative notional values. Only their proportions matter. If the intention is to approximate traditional Black-Litterman market equilibrium, Manual Weights can be used to supply actual or approximate market-cap weights. Reverse optimization Once the prior weights are known, the model derives the returns that would make those weights consistent with mean-variance equilibrium. The implied equilibrium excess-return vector is: Pi = Delta × Sigma × Wprior where: Pi = implied equilibrium excess returns. Delta = risk-aversion coefficient. Sigma = covariance matrix. Wprior = prior portfolio weights. This is called reverse optimization . Normal portfolio optimization asks: Given expected returns, what weights should I own? Reverse optimization asks: Given the portfolio weights, what expected returns would justify owning them? That reversal is one of the key ideas behind Black-Litterman. Why implied returns matter Expected returns are difficult to estimate directly. Historical averages are noisy. Forecast models disagree. Small errors can create enormous portfolio changes. Black-Litterman instead begins from a portfolio that the user considers a reasonable neutral starting point. The model then backs out the expected returns consistent with that portfolio. These implied returns become the equilibrium prior against which investor opinions are expressed. Risk aversion: Delta Delta controls the relationship between expected return and risk. Higher Delta means: Greater assumed aversion to risk. A larger equilibrium return requirement for a given covariance structure and prior. Lower Delta implies less risk aversion. The script provides: Auto (Implied). Manual. Manual Delta Manual mode allows the user to directly select the risk-aversion coefficient. This is useful when: A stable assumption is preferred. The user is reproducing an external Black-Litterman study. The portfolio prior is known but a particular Delta is desired. Auto Delta Auto mode estimates Delta from the current prior portfolio. The script estimates: Prior portfolio variance. An annualized return estimate over the covariance horizon. The selected risk-free rate. It then forms an implied risk-aversion estimate from excess return relative to variance. The value is constrained to a practical range to prevent extreme estimates from destabilizing the optimizer. This Auto mode is a practical implementation choice for the concept. It should not be interpreted as a uniquely correct market risk-aversion estimate. Tau: uncertainty in the prior Tau is one of the most important Black-Litterman parameters. It scales uncertainty in the equilibrium prior. Conceptually: Prior Uncertainty = Tau × Sigma A smaller Tau implies stronger confidence in the equilibrium-return prior. A larger Tau gives the model more freedom to move away from the prior when investor views are introduced. In practical terms: Smaller Tau Makes the prior harder to move. Reduces the effect of views. Larger Tau Increases prior uncertainty. Allows views to exert more influence. Tau should not be interpreted in isolation. Its effect interacts with: The covariance matrix. View confidence. View direction. The number of views. Investor views The script supports up to five simultaneous investor views. Each view contains: A view type. Asset A. Optional Asset B. Expected return Q. Confidence. Each view can be: Off. Absolute. Relative. The expected-return input is interpreted as an annualized expected return or annualized relative return . Absolute views An absolute view expresses an opinion about one asset. For example: “Asset A will return 10% annually.” In matrix notation, the corresponding row of the P matrix contains: +1 for Asset A. 0 for all other assets. Q then contains: 0.10 for a 10% annual view. Relative views A relative view expresses one asset relative to another. For example: “Asset A will outperform Asset B by 5% annually.” The corresponding P row contains: +1 for Asset A. -1 for Asset B. 0 elsewhere. Q becomes: 0.05 This does not necessarily mean Asset A itself must return +5%. It means: Expected Return A - Expected Return B = 5% Relative views are one of the most useful features of Black-Litterman because investors are often more confident about relative relationships than exact absolute returns. It may be easier to hold the view: “Gold will outperform equities.” than: “Gold will return exactly 12.4%.” P matrix The P matrix describes which assets each investor view references. Each row corresponds to one active view. Each column corresponds to one of the fifteen assets. An absolute view creates one non-zero exposure. A relative view creates a long-versus-short pair. P therefore translates a verbal market opinion into portfolio mathematics. Q vector Q contains the expected return associated with each view. For absolute views: Q = expected annual asset return. For relative views: Q = expected annual outperformance of A relative to B. The relationship: P × Returns = Q defines what the investor believes. View confidence Black-Litterman does not require every opinion to be treated as equally reliable. Each view therefore receives a confidence value. Confidence controls its uncertainty. The basic principle is: Low confidence = large view uncertainty. High confidence = small view uncertainty. The script converts intuitive percentage confidence into an Omega uncertainty term using a confidence mapping related to the Idzorek-style approach to expressing subjective confidence. User-specified confidence was developed precisely to make the otherwise difficult view-uncertainty input more interpretable. Omega Omega represents uncertainty in the views. For each active view, the script first measures the variance of the corresponding view portfolio using: P × TauSigma × P' It then scales that variance according to confidence: Omega = ((1 - Confidence) / Confidence) × View Variance This has intuitive behaviour. High confidence If confidence approaches 100%: (1 - c) / c approaches zero. Omega becomes small. The view receives substantial influence. Low confidence If confidence approaches zero: (1 - c) / c becomes very large. Omega becomes large. The view has little effect. The script bounds confidence away from exactly zero and one for numerical stability. Why confidence matters Suppose two investors both believe Bitcoin will outperform gold by 10%. Investor A has 90% confidence. Investor B has 20% confidence. Their view Q is identical. But their portfolio allocations should not necessarily be identical. The confidence parameter allows the same directional opinion to produce very different posterior tilts. This is one of the most useful parts of Black-Litterman. It separates: What you believe. How strongly you believe it. View disagreement: Q - PΠ The Views table displays: Q - PΠ This measures how far the investor view differs from the equilibrium prior. Suppose equilibrium already implies that Asset A will outperform Asset B by 8%. If the user enters a relative view of 9%, the disagreement is only 1%. The posterior may therefore change only slightly. If the user instead enters 20%, the disagreement with equilibrium is much larger. The same confidence level will then produce a much larger posterior adjustment. This quantity is extremely useful because it shows that the impact of a view depends not only on the view itself, but on how different it is from what the prior already expects. Posterior expected returns Once P, Q and Omega have been constructed, the script calculates the Black-Litterman posterior expected-return vector. Conceptually: Posterior = Prior + Confidence-Weighted Adjustment The full adjustment depends on: Tau. Sigma. P. Q. Omega. The disagreement Q - PΠ. The model therefore does not simply overwrite the expected return of the named asset. The adjustment can propagate across the entire asset universe through covariance relationships. This is a fundamental feature of Black-Litterman. If two assets are strongly related, a view about one may alter the posterior expectation of the other even if that second asset was not explicitly named. Why views propagate Suppose the user enters a strong bullish view on one equity index. If several other equity indices are highly correlated with it, the covariance matrix tells the model that those assets are economically related. The posterior adjustment therefore does not exist in isolation. This means: Views influence related assets. Portfolio effects depend on covariance. The same view can produce different tilts under different correlation regimes. That behaviour is intentional. No active views If no usable views are active: Posterior expected returns remain equal to the equilibrium prior returns. The allocation is then driven by: The prior. Covariance. Risk aversion. Portfolio constraints. Volatility targeting. This makes the script useful even without discretionary views. It can be used to study how the prior portfolio behaves under the optimization and risk-management layers by itself. Posterior covariance The script can optionally include the Black-Litterman posterior covariance adjustment. Investor views introduce uncertainty about expected returns. The posterior covariance calculation incorporates additional uncertainty associated with combining the prior and the views. When enabled, the optimizer uses this adjusted covariance matrix. When disabled, optimization uses the original covariance estimate. The practical effect is usually more subtle than changing the expected-return vector, but it can affect: Position sizes. Diversification. Volatility estimates. View-driven tilts. Portfolio optimization After calculating posterior expected returns, the script solves a mean-variance allocation. The unconstrained portfolio is conceptually: w* = (Delta × SigmaPosterior)^-1 × PiPosterior This converts posterior return expectations and covariance into portfolio weights. If: There are no views. The prior and covariance are internally consistent. No constraints alter the result. the solution tends toward the prior portfolio. Views create deviations away from that starting point. Why unconstrained weights can be extreme Mean-variance optimization can produce very large positive or negative positions. This happens because matrix inversion magnifies differences between: Expected returns. Volatility. Correlations. If two assets are highly correlated but have slightly different expected returns, the optimizer may create a large long position in one and a large short position in the other. Mathematically this can be valid. Practically it may be unusable. The script therefore applies several layers of portfolio constraints after the raw solution. Data mask Assets without sufficient price history receive zero weight regardless of what the raw optimizer produces. This prevents incomplete covariance columns from entering the live portfolio. Long-only mode When Allow Short Weights is disabled: All negative optimizer weights are clipped to zero. The remaining positive positions are then normalized. This converts the portfolio into a long-only allocation. The result is no longer the exact unconstrained analytical Black-Litterman solution. That is expected. Real portfolios frequently require constraints that alter the theoretical optimum. Short-enabled mode When shorting is enabled, negative posterior weights are permitted. This allows: Long-short portfolios. Relative-value expressions. Negative allocations to assets receiving sufficiently weak posterior expectations. Gross exposure becomes especially important in this mode because a portfolio can have low net exposure while still carrying substantial absolute risk. For example: +150% long. -50% short. = 100% net exposure. = 200% gross exposure. Gross Exposure The Gross Exposure input controls the target sum of absolute portfolio weights before volatility targeting. Gross exposure is: Gross = Sum of |Weight| This differs from net exposure: Net = Sum of Weight For long-only portfolios, gross and net are normally similar. For long-short portfolios, they can differ significantly. Volatility targeting After the portfolio has been normalized, the script estimates total portfolio volatility using: Portfolio Variance = w' × Sigma × w Portfolio Volatility = sqrt(Portfolio Variance) This is a full covariance-aware portfolio volatility calculation. It does not simply average asset volatility. The model then calculates a volatility scaling factor: Volatility Scale = Target Volatility / Estimated Portfolio Volatility subject to minimum and maximum limits. If estimated portfolio volatility is below target: Exposure can increase. If estimated volatility is above target: Exposure is reduced. Why portfolio volatility matters Suppose two assets each have 20% volatility. A 50/50 portfolio does not necessarily have 20% volatility. If the assets are weakly correlated, portfolio volatility may be much lower. If they are highly correlated, it may remain close to 20%. Using: sqrt(w'Σw) allows the volatility target to account for diversification. Target Volatility Target Volatility defines the desired annualized risk level of the portfolio before later hard caps are considered. Examples might conceptually include: A lower target for a defensive multi-asset portfolio. A higher target for a crypto-focused portfolio. The setting is not automatically appropriate simply because the portfolio reaches it. A volatility target does not account for: Tail risk. Liquidity. Gap risk. Regime changes. Nonlinear derivatives. It is one risk-control dimension. Maximum volatility-target leverage A very low-volatility portfolio can theoretically require enormous leverage to reach a high volatility target. The Max Vol-Target Leverage setting prevents this. For example, if the mathematical scaling factor is 6× but the maximum leverage is 3×: The model uses no more than 3×. This protects against explosive leverage during unusually quiet covariance estimates. Maximum weight per asset After volatility targeting, every individual position is subjected to a hard position-size cap. This ordering is important. If the position cap were applied before leverage scaling, the volatility scaler could simply increase the capped position again. Applying the cap afterward ensures the final position magnitude cannot exceed the selected maximum. For example: Max Weight = 30% means no individual position can remain above 30% after the volatility scaling stage. Maximum gross exposure after volatility targeting After individual caps are applied, the portfolio is also checked against a maximum total gross exposure. If gross exposure exceeds that maximum, every position is scaled downward proportionally. This provides a second portfolio-level safeguard. The result is a hierarchy: Generate raw Black-Litterman weights. Apply long/short rules. Normalize initial gross exposure. Apply volatility targeting. Cap individual positions. Cap final gross exposure. Why the target may not be reached The volatility target is not guaranteed to be achieved exactly. Suppose the model wants to increase portfolio exposure enough to reach 15% volatility. If doing so would violate: Maximum leverage. Maximum asset weight. Maximum gross exposure. the constraints take priority. The resulting portfolio may therefore have volatility below the requested target. This is intentional. Risk limits are allowed to override the target. Rebalancing The complete optimizer does not run on every bar. The user selects a Rebalance Every N Bars interval. For a daily chart: Approximately 21 bars corresponds roughly to one trading month. Longer rebalance intervals: Reduce turnover. Reduce computation. Allow allocations to persist longer. Shorter intervals: React faster to new covariance and view conditions. Increase turnover. Increase computational load. The covariance matrix and Black-Litterman solve run only on rebalance events. Forced rebalances Two events can trigger a solve outside the normal schedule: The regime filter changes from CASH back to ACTIVE. The number of assets with sufficient history changes. This prevents the portfolio from waiting many bars before responding to a material change in state. Regime filter The script includes an optional regime filter based on the chart symbol. The filter compares: A fast EMA. A slow EMA. When the fast EMA is above the slow EMA: Regime = ACTIVE When the fast EMA is not above the slow EMA: Regime = CASH This filter applies to the chart symbol , not individually to the fifteen assets. That distinction is important. If the indicator is placed on SPX, the regime filter reflects SPX. If it is placed on Bitcoin, it reflects Bitcoin. The regime state therefore acts as a global risk-on/risk-off switch for the entire portfolio. CASH regime When the regime filter turns off: The live asset weights are flattened to zero. The strategy stops compounding asset returns while the regime remains inactive. When the filter turns ACTIVE again: A new Black-Litterman solve is forced immediately. The user should therefore choose the chart symbol intentionally if the regime filter is enabled. Regime filter limitation A single chart-symbol EMA regime is an intentionally simple overlay on a much more sophisticated cross-asset model. It should not be confused with a multi-asset economic-regime model. It answers only: Is the fast trend of the chart symbol above its slower trend? The regime layer can have a very large impact on historical results. Backtests with and without it are therefore testing materially different systems. Transaction costs The script calculates turnover on each committed rebalance: Turnover = Sum of |New Weight - Previous Weight| The selected transaction-fee rate is then applied to that turnover. This is more realistic than assuming rebalancing is free. However, the cost model remains simplified. It does not separately model: Bid-ask spread. Slippage. Market impact. Short borrow fees. Financing costs. Taxes. Different fee schedules by asset. The fee input should therefore be treated as an approximate portfolio-level trading-cost assumption. Important backtest implementation note The current implementation charges transaction fees when a new active portfolio is committed during a rebalance. The transition that flattens the portfolio when the regime filter enters CASH is not separately charged an explicit turnover fee in the current code. Therefore, backtests using the regime filter may slightly understate transaction costs associated with risk-off exits. This is one reason the script should be treated as a concept rather than a production execution simulator. No-lookahead portfolio return handling The portfolio return for the current bar is calculated using the weights that were already active before the current rebalance solve. Only after that return has been calculated does a new set of weights become active. This prevents the optimizer from using newly calculated current-bar weights to capture a return that occurred before those weights could have existed. This ordering is essential for a meaningful historical simulation. Prior versus posterior weight chart One of the main visual components is the paired horizontal weight chart. Each asset receives two bars: Prior weight. Final active portfolio weight. The prior represents the selected equilibrium starting allocation. The active portfolio reflects the portfolio after: Views. Optimization. Short constraints. Gross normalization. Volatility targeting. Position caps. Final gross caps. Therefore, the visible gap between the bars represents more than the mathematical Black-Litterman posterior alone. It represents the complete practical allocation change from prior to final active book . If the regime filter is currently in CASH, the live active weights may be zero. This distinction is important when interpreting the chart. Allocation table The Allocation Table shows each of the fifteen assets with: Prior Weight. Post Weight. Delta Weight. Equilibrium Expected Return. Posterior Expected Return. Prior Weight The allocation before investor views and final portfolio construction. Post Weight The current active portfolio weight after the complete optimization and risk-control process. Delta Weight The difference between the active weight and prior weight. Positive values indicate the asset has been increased relative to the prior. Negative values indicate it has been reduced. Equilibrium E The implied return derived through reverse optimization. Posterior E The expected return after the active investor views have been incorporated. Comparing equilibrium and posterior expected return is often more informative than looking only at weights. A return expectation can change substantially while the final weight changes only modestly because: The asset is highly volatile. It is highly correlated with another holding. The maximum-weight constraint binds. Portfolio volatility limits exposure. Views table The Views Table shows each active view and includes: View description. Q. Confidence. Omega. Q - PΠ. This allows the user to inspect not only what the view says, but how strongly it conflicts with equilibrium and how uncertain it is. Two views with identical Q values may have very different portfolio effects if: Confidence differs. Covariance differs. Equilibrium expectations differ. Current Book table The Current Book table provides a compact summary of the active portfolio. It includes: ACTIVE or CASH regime. Prior scheme. Number of active views. Number of rebalances. Gross exposure. Net exposure. Number of live assets. Turnover. Risk-aversion Delta. Tau. Estimated portfolio volatility. Volatility scaling factor. This table is useful for diagnosing why the allocator currently looks the way it does. For example: Large view changes but small weights may be explained by a tight volatility target or maximum-weight constraint. Large gross but low net may indicate significant long-short exposure. Few live assets means part of the universe has not yet accumulated sufficient historical data. Equity curve The script maintains a simulated portfolio equity curve beginning from the selected Initial Capital. Initial Capital affects only the scale of the equity curve. It does not affect: Weights. Sharpe ratio. Volatility. Portfolio optimization. The equity curve compounds the historical portfolio returns generated by the active weights. The line changes colour according to whether equity increased or decreased from the previous bar. Benchmark Buy & Hold A benchmark equity curve can be displayed beside the portfolio. Both curves begin from the same nominal capital. The benchmark is also used in: Beta. Alpha. The benchmark can be changed independently from the fifteen-asset universe. For meaningful interpretation, the benchmark should be relevant to the portfolio being studied. A broad global macro portfolio compared only with SPX is answering a different question from an equity portfolio compared with SPX. Daily returns The script can optionally plot the portfolio’s per-bar percentage return. This is useful for visually inspecting: Return clustering. Large gains. Large losses. Regime-filter cash periods. Because it shares the pane with the equity curve, it is generally best viewed separately. Rolling drawdown Drawdown is measured relative to the previous portfolio-equity peak: Drawdown = (Current Equity - Peak Equity) / Peak Equity The result is negative while the portfolio remains below its historical high. The visual fill becomes stronger as drawdown deepens. The Max DD for Scaling input affects only the visual intensity scale. It does not limit portfolio losses or modify the allocation. Performance metrics The metrics table includes a broad range of return and risk statistics. Net Profit Percentage change in portfolio equity from initial capital. Maximum Drawdown Largest historical peak-to-trough decline in the simulated portfolio. Win Rate Percentage of non-zero portfolio-return bars that were positive. Flat CASH bars are excluded from the win/loss count. This prevents periods where the portfolio is deliberately inactive from automatically being classified as losing periods. Annual Mean Return Arithmetic average per-bar portfolio return multiplied by the selected annualization factor. This is not identical to CAGR. Annual Standard Deviation Per-bar return standard deviation scaled by the square root of the annualization factor. Variance Square of annualized standard deviation. Sharpe Ratio Measures annualized excess mean return relative to total return volatility using the selected risk-free rate. Sortino Ratio Measures return relative to downside-return variability rather than total volatility. Omega Ratio Compares the aggregate positive portfolio returns with the magnitude of aggregate negative portfolio returns. Gain-to-Pain Compares net return with the aggregate magnitude of negative returns. CAGR Compound annual growth rate based on beginning equity, ending equity and elapsed calendar time. Calmar Ratio CAGR divided by absolute maximum drawdown. Beta Measures covariance of portfolio returns with benchmark returns relative to benchmark variance. Alpha Estimates annualized portfolio return in excess of the return implied by its benchmark Beta and selected risk-free rate. Skewness Measures asymmetry of the historical portfolio-return distribution. Positive skew indicates a longer or heavier positive tail. Negative skew indicates a more pronounced negative tail. VaR 95th Percentile The implementation reports the fifth percentile of historical portfolio returns. It can be interpreted as the lower-tail return threshold associated with approximately the worst 5% of observations. It is displayed as a return value rather than converting the loss into a positive number. Conditional VaR Conditional VaR averages the returns in the lowest 5% tail. This provides information about the average severity of outcomes beyond the VaR threshold. Historical VaR and Conditional VaR rely entirely on the observed backtest sample. They should not be interpreted as guarantees about future tail losses. Risk-free rate The selected Risk-Free Rate influences: Sharpe. Alpha. Auto risk-aversion estimation. Changing it therefore affects both reported performance statistics and potentially the portfolio itself when Auto Delta is enabled. Understanding prior versus posterior The most important conceptual visualization in the script is the difference between the prior and posterior state. Suppose the prior allocation is: Asset A: 20% Asset B: 20% Asset C: 20% Asset D: 20% Asset E: 20% Now suppose the investor enters: Asset A will outperform Asset B by 8%, with high confidence. Black-Litterman does not simply add 8% weight to A and remove 8% from B. Instead, the model asks: What did equilibrium already imply about A versus B? How uncertain is the prior? How confident is the investor? What is the covariance of the A-minus-B view? How are A and B related to the rest of the portfolio? The resulting posterior return adjustment then passes through the optimizer. The final weights are subsequently modified by the portfolio constraints. This explains why Black-Litterman allocations can behave very differently from manually applying arbitrary portfolio tilts. Example: low-confidence relative view Suppose equilibrium implies: Expected A return = 8% Expected B return = 7% The equilibrium difference is 1%. The investor believes: A will outperform B by 5% but assigns only 20% confidence. The view disagrees with equilibrium, but Omega is relatively large because confidence is low. The posterior therefore moves toward the investor view without fully accepting it. Example: high-confidence relative view Using the same equilibrium assumptions, suppose confidence is increased to 90%. Omega becomes much smaller. The investor view therefore carries much greater influence. The posterior A-minus-B expected-return spread moves much closer toward the stated view. The final weights may then shift significantly, subject to risk and portfolio constraints. Example: view already priced into equilibrium Suppose the user believes A will outperform B by 5%. But the equilibrium prior already implies approximately 5%. Then: Q - PΠ ≈ 0 There is little disagreement to resolve. Even a high-confidence view may produce only a small posterior adjustment. This is an important property of the model. Black-Litterman does not reward the user simply for entering a strong opinion. The opinion must differ from equilibrium before it meaningfully changes the posterior. Absolute versus relative confidence Absolute views generally require greater confidence in the expected return level itself. Relative views can be easier to interpret because the user only needs an opinion about the spread between two assets. For example: “Equities will return 14%.” is a stronger forecasting statement than: “Equities will outperform bonds by 4%.” Neither is inherently superior. The model supports both because portfolio managers frequently express views in both forms. Why the model is useful conceptually The value of Black-Litterman is not that it discovers the future. It provides a disciplined method for converting beliefs into portfolio changes. Without a framework, an investor may say: “I like gold.” “I am bearish equities.” “Bitcoin should outperform bonds.” but those statements do not specify: How much the portfolio should change. How volatility should affect the position. How correlated assets should respond. How conviction should change the allocation. Black-Litterman forces those opinions into a structured portfolio context. That is what this indicator is intended to demonstrate. Important implementation difference from institutional Black-Litterman The script implements the core Black-Litterman mechanics, but several choices are intentionally simplified for TradingView. These include: A fixed maximum universe of fifteen assets. Up to five investor views. User-selected fixed covariance shrinkage rather than automatically estimated shrinkage intensity. Equal-weight and inverse-volatility priors in addition to manual market-style priors. A simplified Auto Delta estimate. Discrete bar-based rebalancing. Simplified transaction costs. A single chart-symbol regime filter. Historical covariance from TradingView price data. These choices make the model practical and interpretable inside Pine Script. They also mean that results should not be compared directly with a production institutional implementation without understanding the differences. Mixed-market data considerations Cross-asset portfolios introduce data-alignment problems. Cryptocurrency trades continuously. Equities, commodities and bonds have market sessions and holidays. Different TradingView symbols may also come from different exchanges or data providers. The covariance matrix assumes the return observations are meaningfully aligned. Users should therefore be careful with: Intraday mixed-asset universes. Assets from incompatible sessions. Symbols with limited historical coverage. Synthetic or non-tradable price series. Daily or broader timeframes are generally easier to interpret for a macro allocation concept. Backtest limitations Historical simulation is useful for understanding behaviour, but this should not be treated as proof of future performance. The backtest does not model every real-world implementation issue. Examples include: Bid-ask spreads. Market impact. Execution latency. Portfolio financing. Borrow availability. Short borrow costs. Taxes. Different trading sessions. Rebalancing at exact executable prices. Changes in instrument availability. Survivorship effects in a manually selected universe. The model also uses historical covariance as an estimate of future covariance. Correlations can change abruptly during stress periods. The most diversified-looking portfolio based on historical data can become much more concentrated in risk when formerly independent assets begin moving together. No automatic investment views The script does not create investor views for the user. Q and confidence are deliberately manual. This is important because Black-Litterman is a framework for combining beliefs with equilibrium. It does not tell the investor what those beliefs should be. Views could theoretically come from: Macro analysis. Valuation models. Momentum models. Fundamental research. Quantitative forecasts. Discretionary judgement. The quality of the posterior cannot exceed the quality of the assumptions provided to it. Parameter interaction Black-Litterman parameters should not be tuned independently. Several important interactions exist. Tau + Confidence Both influence how aggressively views move the posterior. Higher prior uncertainty combined with high view confidence can create strong posterior changes. Covariance Lookback + Shrinkage A short noisy covariance window may require more shrinkage for stability. A long sample may tolerate less. Target Volatility + Leverage Caps A high volatility target may have little effect if maximum leverage or gross exposure is restrictive. Views + Max Weight A strong posterior preference for one asset may never appear fully in the active portfolio if the asset cap is binding. Shorts + Gross Exposure Allowing shorts can materially increase gross exposure even when net exposure looks conservative. Rebalance Frequency + Fees Frequent optimization allows faster adaptation but increases turnover and assumed trading cost. Prior selection The choice of prior is not cosmetic. It changes the equilibrium return vector itself. The same investor views can therefore produce different posterior portfolios depending on whether the starting prior is: Equal Weight. Inverse Volatility. Market-like Manual Weights. Users studying the framework should therefore treat prior construction as one of the primary model assumptions. Suggested research workflow A useful way to study the indicator is: Begin with no investor views. Choose a prior. Observe the implied equilibrium returns. Inspect the covariance-driven allocation. Add one low-confidence relative view. Observe Q - PΠ. Compare equilibrium and posterior returns. Increase confidence gradually. Observe how the posterior and weights respond. Add a second view. Experiment with Tau. Enable and disable posterior covariance. Compare long-only and short-enabled portfolios. Change the volatility target. Observe when position or gross caps become binding. This is generally more informative than immediately entering five aggressive views and trying to interpret the final result. Example research questions The allocator can be used to study questions such as: How much does a 70% confidence view move the portfolio compared with 30% confidence? How does inverse-volatility equilibrium differ from equal-weight equilibrium? How does covariance shrinkage change portfolio concentration? How do relative views propagate into assets not explicitly named? How much does volatility targeting alter the raw optimizer? How often do hard position caps bind? How different are equilibrium expected returns from posterior expected returns? How much turnover is generated by monthly versus weekly rebalancing? How does a regime filter alter drawdown and opportunity cost? These are the types of questions the concept is designed to explore. Input guide Initial Capital Controls the starting dollar value of the simulated equity curve. It does not change portfolio weights. Trading Days/Year Controls annualization. Use a value consistent with the universe being studied. Target Volatility Sets the desired annualized portfolio-volatility target before hard leverage and weight constraints. Transaction Fees Approximate fee charged per unit of rebalance turnover. Rebalance Every N Bars Controls how frequently the full covariance and Black-Litterman solve occurs. Allow Short Weights Allows negative optimized weights. Max Weight per Asset Hard cap on individual position magnitude after volatility targeting. Gross Exposure Target absolute exposure before volatility scaling. Max Gross After Vol Target Final portfolio-level ceiling on gross exposure. Max Vol-Target Leverage Maximum scaling multiplier permitted by volatility targeting. Covariance Lookback Historical window used for covariance estimation and minimum data availability. Covariance Shrinkage Reduces off-diagonal covariance estimates toward zero. Tau Controls uncertainty in the equilibrium prior. Use Posterior Covariance Allows view uncertainty to modify the covariance matrix used by the optimizer. Risk Aversion Selects automatically estimated or manually specified Delta. Prior Weight Scheme Selects Equal Weight, Inverse Volatility or Manual Weights. Investor Views Supports up to five annualized absolute or relative return views. Confidence Controls the uncertainty assigned to each view. Start Date Defines the beginning of simulated portfolio equity. Historical data before the date may still be used to warm up covariance estimates. Risk-Free Rate Used in portfolio statistics and Auto Delta estimation. Benchmark Used for the buy-and-hold comparison, Alpha and Beta. Regime Filter Optional chart-symbol fast/slow EMA filter that moves the portfolio between ACTIVE and CASH. Prior vs Posterior visualization Displays the difference between the selected prior allocation and current final portfolio weights. Strengths Implements the central Black-Litterman prior-and-views framework directly in Pine. Supports both absolute and relative investor views. Allows confidence to directly control view uncertainty. Uses a complete cross-asset covariance matrix. Includes diagonal covariance shrinkage. Supports dynamic asset-data availability. Provides equal-weight, inverse-volatility and manual priors. Supports long-only and long-short allocation. Uses covariance-aware portfolio volatility targeting. Includes individual and portfolio-level exposure constraints. Accounts for rebalance turnover fees. Provides extensive allocation, view and portfolio diagnostics. Includes a visual prior-versus-final-weight comparison. Includes portfolio equity, benchmark and risk statistics. Limitations This is a concept and educational implementation, not an institutional portfolio-management system. Historical covariance is only an estimate of future relationships. The 15-asset universe is fixed in size. A maximum of five views can be entered. The prior is only a true market-equilibrium proxy if the selected weights appropriately represent one. Equal Weight and Inverse Volatility are practical prior substitutes rather than literal global market-cap equilibrium. The shrinkage coefficient is user-selected rather than statistically estimated. Auto Delta is a practical approximation. Portfolio optimization remains sensitive to inputs. Poor views can produce poor posterior estimates. High-confidence incorrect views can materially damage the portfolio. Volatility targeting does not protect against all forms of risk. Historical volatility can underestimate future crisis volatility. Hard constraints mean the final portfolio may differ substantially from the analytical unconstrained Black-Litterman optimum. The final volatility target may not be reached when position, leverage or gross limits bind. The regime filter is based only on the chart symbol. The backtest uses simplified transaction costs. Regime-driven exits to CASH are not separately charged an explicit turnover fee in the current implementation. Mixed-market TradingView data can contain differing sessions and histories. Backtested performance does not establish future performance. Historical and theoretical context The Black-Litterman framework was developed to address practical problems encountered when applying mean-variance optimization to global portfolios. Its central contribution is not simply another optimization equation. It is a different way of constructing expected returns. Instead of requiring the investor to estimate every asset’s return independently, equilibrium returns provide a coherent starting point. Investor views then alter only the parts of that equilibrium where the investor has an opinion. This structure can be summarized as: Start neutral. Reverse-engineer equilibrium. State where you disagree. State how strongly you disagree. Let covariance propagate those beliefs. Re-optimize the portfolio. The original Black-Litterman work emphasized equilibrium as a neutral starting point and allowed investor opinions about absolute or relative performance to tilt that equilibrium according to confidence. Later work on user-specified confidence made the view-uncertainty problem easier to interpret by expressing conviction in intuitive percentage terms rather than requiring users to manually specify an abstract uncertainty covariance for every view. This indicator takes those principles and translates them into a practical TradingView research environment. Summary Black-Litterman Allocator is an experimental portfolio-allocation framework designed to demonstrate how equilibrium, investor beliefs and portfolio risk can be combined inside TradingView. The model begins with fifteen selectable assets and estimates their annualized covariance structure using historical log returns. A user-controlled shrinkage process reduces noisy cross-asset covariance estimates, while assets without sufficient historical data are excluded until a complete covariance window becomes available. The user then selects an Equal Weight, Inverse Volatility or Manual prior portfolio. That prior is reverse-optimized into implied equilibrium expected returns: Pi = Delta × Sigma × Prior Weights Up to five absolute or relative investor views can then be introduced. Each view specifies: What the investor expects. Which assets the view applies to. How confident the investor is. Confidence is translated into view uncertainty, allowing weak opinions to create small tilts and high-confidence opinions to exert greater influence. The Black-Litterman posterior combines those views with equilibrium while accounting for covariance relationships across the entire portfolio. The resulting posterior expected returns are converted into an optimized allocation, after which the script applies: Data-availability rules. Optional long-only constraints. Gross-exposure normalization. Portfolio volatility targeting. Maximum leverage. Maximum position sizes. Maximum gross exposure. The portfolio is then rebalanced through time, transaction costs are approximated, an optional chart-level regime filter can move the book into CASH, and the resulting historical equity curve is compared with a selectable benchmark. Extensive tables show: Prior and final weights. Equilibrium and posterior returns. View confidence and uncertainty. View disagreement with equilibrium. Gross and net exposure. Portfolio volatility. Turnover. Performance and risk statistics. The purpose of the script is not to claim that Black-Litterman can identify the optimal future portfolio. Its purpose is to make the framework tangible. It provides a way to explore how a neutral portfolio can be translated into implied expected returns, how subjective beliefs can be incorporated without completely discarding that prior, how confidence changes the strength of those beliefs, how covariance spreads their effects across the portfolio, and how practical constraints can transform a theoretical posterior into a more realistic active allocation. Treat the indicator as a concept, a research tool, and a visual implementation of portfolio-allocation theory rather than as an automated investment recommendation.

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của BackQuant

Robust Regression Residual Bands [Pineify]Robust Regression Residual Bands Overview This overlay fits a rolling line while bounding influence from unusual closes. It shows a robust center, two MAD shells, confirmed extremes, and an optional dashboard. It is context, not a forecast. Problem Definition Least-squares channels and standard deviation magnify large errors. One gap, wick, or bad print can rotate the line and widen its bands, changing both the reference and the meaning of “far.” Short windows add noise; long ones preserve distortion. The invariant is that ordinary observations define the path, extremes stay visible, and their influence remains bounded. Design Rationale Regression stays because slope and residual distance answer different questions. Finite Huber-style refits replace unrestricted influence: residuals inside a threshold keep full weight; those outside receive progressively less. Hard deletion was rejected because values switch abruptly at a cutoff. Final scale uses median absolute deviation (MAD) times 1.4826. MAD resists isolated extremes but is less efficient for Gaussian errors. Two passes balance refreshed weights with bounded workload; users may select one to three. Key Features Rolling regression with bounded influence refits. Two shells sized from final residual MAD. Center color for normalized slope. Confirmed outer-entry diamonds. Optional bar color and dashboard for residual z, slope/MAD, scale, window, and passes. How It Works Each bar loads a chronological rolling window and fits an equal-weight line. It finds every residual, their median, and median absolute distance from that median. MAD times 1.4826 becomes robust scale; minimum tick prevents zero division. Each distance is compared with clipping threshold times scale. Inside values keep weight 1. Outside values receive threshold divided by distance, smoothly capping influence. The line is refitted for the selected passes; residual median and MAD are then recomputed. Displayed center is the newest fit plus median residual. Bands equal center plus or minus selected MAD multiples. Residual z divides current distance by scale; slope divided by scale controls color. A full window without missing data is required. Open-bar values can change; markers and alerts require confirmation. How Multiple Indicators Work Together These are causal stages, not unrelated indicators. Regression supplies direction but needs clipping to limit leverage. Clipping needs scale, and MAD prevents the same extreme from dominating it. Final residual measures price against the stabilized path; normalized slope separates direction from dispersion. Without refitting, bands inherit a tilted center; without scale, distance is not comparable. All visuals expose one model. Trading Ideas and Insights A confirmed outer entry means the close is unusual relative to current path and scale; it does not imply reversal. Alignment with strong slope can describe expansion, while repeated extremes with flattening slope can motivate a balance review. Alternating center crosses expose noise. Compare states with structure, liquidity, events, and risk controls. The script provides no entries, stops, sizing, or expected returns. Unique Aspects The contribution couples bounded influence refits with a median-centered MAD field. Common channels let an extreme affect slope and width through squared error. Here distance sets a smooth influence cap, the line is rebuilt, and final residuals size the tunnel. Median residual shifts the newest fit instead of assuming zero arithmetic mean. Center is primary, shells encode distance, and amber diamonds encode confirmed entries—not probability. How to Use Add it to a standard chart and wait for a full window. Choose a window matching the horizon and review several regimes. Read center color as normalized direction and bands as robust distance. Use the dashboard to compare raw and scale-relative movement. Alert on confirmed outer entry or center crossing, then apply independent context and risk rules. Secondary layers can be disabled without changing the model. Customization Short windows adapt faster and vary more; long ones smooth more and retain old regimes. Extra refit passes can limit leverage further but cost computation and may underweight a true break. Lower clipping resists extremes sooner; higher clipping approaches ordinary regression. MAD multiples set tunnel thresholds, with a minimum shell gap enforced. Visual layers and palette are independent. Defaults are not universal optima. Assumptions and Limitations The model assumes a useful local line and comparable source data. Curves, breaks, gaps, rolls, illiquidity, adjusted history, and non-standard charts weaken it. Robust weights bound influence but cannot label an extreme as error or regime change. Results lag, parameters matter, and small MAD makes flat markets sensitive to the tick floor. Open-bar values may change. Confirmed alerts still depend on feed and settings. Missing data restarts warm-up. The script has no volume, order flow, higher-timeframe request, future value, pivot, or simulation. It estimates neither reversal probability nor fair value, execution, risk, or profit. Outer distance is deviation, not proof of return. Conclusion Bounded refits stabilize rolling path, MAD stabilizes scale, and the tunnel exposes both. Treat distance and direction as lagging context, not a forecast; use independent confirmation.

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CQ_(9)_Phase Counter + Adaptive Projection ================================================================================ CQ_(9)_Phase Counter + Adaptive Projection User Manual & Function Reference Pine Script v6 | TradingView | Author: CQuevedo345 / Based on Julien Exe poblic code ================================================================================ -------------------------------------------------------------------------------- 1. OVERVIEW -------------------------------------------------------------------------------- This indicator is a ZigZag-based "phase counter" for price swings. It performs four jobs at once, all built on the same underlying ZigZag pivot detection: 1. Draws a confirmed ZigZag on the chart (bullish/bearish colored legs). 2. Draws the currently forming ("live") leg separately, updating every bar. 3. Projects two forward legs from the live price using the average length/slope of historical bullish and bearish legs (Adaptive Projection). 4. Numbers every pivot 1-5 in a repeating cycle, anchored permanently to a specific pivot/time you choose, so the same physical pivot always shows the same number every time you reload the chart, refresh, or come back later (Leg Numbering / Phase Counter). The indicator is overlay = true (draws directly on the price chart), and raises Pine's drawing-object ceilings to max_lines_count = 500 and max_labels_count = 500 so long pivot histories don't get silently truncated. -------------------------------------------------------------------------------- 2. VISUAL ELEMENTS ON THE CHART -------------------------------------------------------------------------------- - CONFIRMED ZIGZAG LEGS: Solid/Dashed/Dotted lines connecting each confirmed pivot to the next. Colored per-leg: green-ish (Bullish Leg) if the leg fell from a high to a low... actually colored by comparing the leg's start/end price (see "Confirmed Style" settings) — bullish color when the earlier point is higher than the later one is NOT the rule; the rule used is: y1 > y2 -> Bullish color, else Bearish color, matching how each leg's own start (y1) and end (y2) prices compare. - LIVE LEG: A separate line (style set by "Last Leg Style", default Dashed) connecting the most recent confirmed pivot to the current bar's close. This redraws every bar as price moves, showing the swing that hasn't reversed yet. - ADAPTIVE PROJECTION: Up to two dotted (by default) forward-projected line segments extending from the current live price into the future. Segment 1 completes the current leg out to the historical average length for its direction; Segment 2 projects the following opposite-direction leg using that direction's average length and slope. Purely illustrative/statistical — not a prediction guarantee. - LEG NUMBER LABELS: A circled/styled digit (1-5, style configurable) placed at each pivot (or at that leg's midpoint, depending on settings), showing where that pivot falls in the repeating 1-5 counting cycle. - ANCHOR MARKER (⚓): A small anchor-emoji label marking the exact pivot that was chosen as the permanent numbering origin, plus the leg number and timeframe it was anchored at (e.g. "⚓2 4h"). Only shown if enabled. -------------------------------------------------------------------------------- 3. SETTINGS REFERENCE -------------------------------------------------------------------------------- --- GROUP: "🌀 Phases (ZigZag)" --- Period (p1) Integer, default 45, range 10-100. Lookback window used to detect ZigZag pivots. A pivot high/low is confirmed when it is the highest high / lowest low over this many bars. Larger values = fewer, larger swings picked up; smaller values = more, smaller swings. Bullish Leg / Bearish Leg (bull_color / bear_color) Colors for confirmed ZigZag legs, based on whether the leg's start price is above (bearish-looking down-leg colored "Bearish") or below ("Bullish") its end price. Confirmed Style (s) Solid / Dashed / Dotted. Line style used for CONFIRMED ZigZag legs only. Width (w) Integer, default 6, range 1-8. Line width for confirmed ZigZag legs (also reused for the live leg). Last Leg Style (last_leg_style) Solid / Dashed / Dotted, default Dashed. Line style used ONLY for the live (currently forming, unconfirmed) leg, so it's visually distinct from confirmed legs even though it shares the same colors and width. --- GROUP: "📈 Adaptive Projection" --- Show Projection (show_projection) Boolean, default true. Master toggle for the two forward-projected line segments. Bullish Projection / Bearish Projection (proj_bull_color / proj_bear_color) Colors for the projected segments, based on the direction of that specific projected segment (not the live leg's direction). Style (proj_style) Solid / Dashed / Dotted, default Dotted. Line style for both projection segments. Width (proj_width) Integer, default 1, range 1-5. Line width for both projection segments. --- GROUP: "🔢 Leg Numbering" --- Anchor Time (1h) / Anchor Pivot's Leg # (1h) Anchor Time (4h) / Anchor Pivot's Leg # (4h) Anchor Time (1d) / Anchor Pivot's Leg # (1d) Anchor Time (1w) / Anchor Pivot's Leg # (1w) Anchor Time (1m) / Anchor Pivot's Leg # (1m) Each of the 5 supported chart timeframes (1 hour, 4 hour, 1 day, 1 week, 1 month) gets its OWN independent pair of settings: - Anchor Time: a specific date/time. The script finds whichever CONFIRMED pivot's time is closest to this timestamp and treats that exact pivot as the permanent numbering origin for that timeframe. - Anchor Pivot's Leg #: which number (1-5) that specific pivot should be labeled with. Every other pivot counts forward/backward from it, wrapping 5 back to 1 (and 1 back to 5 going the other direction). Because these are saved per-timeframe, switching timeframes never overwrites another timeframe's calibration. Defaults: 1h/1d/1w/1m = "2024-01-01 00:00", leg #1 (generic placeholders — recalibrate these to a pivot you can actually verify on your chart). 4h = "2026-08-01 11:00 -0500", leg #2 (a working example calibration). Anchor Timeframe Selection (anchor_tf_mode) Manual / Automatic, default Manual. - Manual: you explicitly pick which of the 5 saved anchor setups is "active" via the Anchor Timeframe (Manual) picker below, regardless of what timeframe the chart is actually showing. The counter only actually draws when the chart's real timeframe happens to match your pick — otherwise it stays silent. - Automatic: the script detects the chart's ACTUAL current timeframe itself (via timeframe.isintraday/isdaily/isweekly/ismonthly and timeframe.multiplier) and automatically uses that timeframe's saved anchor/leg#, with no manual switching needed. Flip between 1h, 4h, 1d, 1w, and 1m charts and the same indicator instance keeps working, each using its own calibration. If the chart is on a timeframe outside those 5 presets (e.g. 15m, 2h, 12h), there is no saved anchor for it, so the counter silently displays nothing on that timeframe — same behavior as an unmatched Manual pick. Anchor Timeframe (Manual) (anchor_tf_manual) 1h / 4h / 1d / 1w / 1m, default 1h. Only used when Anchor Timeframe Selection above is set to Manual. Mark Anchor Pivot (show_anchor_marker) Boolean, default true. Draws the ⚓ marker described in Section 2 on the pivot that was snapped to as the anchor, so you can visually confirm the right point was picked. Numbering Style (char_style) One of: ①②③④⑤ / ❶❷❸❹❺ / Ⅰ Ⅱ Ⅲ Ⅳ Ⅴ / 1️⃣2️⃣3️⃣4️⃣5️⃣ / ₁ ₂ ₃ ₄ ₅ / ⑴ ⑵ ⑶ ⑷ ⑸. Cosmetic choice of glyph set used to render the numbers 1 through 5. Label Size (size_choice) Tiny / Small / Normal / Large / Huge, default Small. Size of the leg number labels. Auto-Color by Leg Direction (auto_dir_color) Boolean, default true. - When ON: each label's background/text color is picked from the Bullish/Bearish Label Color settings below, based on whether the LEG THAT PIVOT STARTS rose or fell in price (background shown faded per "Label Background Fade %"). - When OFF: every label instead uses the flat Custom Label Background Color / Custom Label Text Color below, ignoring direction entirely. Bullish Label Color / Bearish Label Color (label_bull_color / label_bear_color) Colors used per-leg-direction when Auto-Color is ON. Custom Label Background Color / Custom Label Text Color (manual_bg_color / manual_txt_color) Flat colors used for all labels when Auto-Color is OFF. Label Background Fade % (bg_fade_pct) Integer, default 90, range 0-100. Transparency applied to the label background color (0 = fully opaque, 100 = fully transparent). Text color is unaffected — it's shown at full opacity. Label Position (label_pos) Pivot / Leg Midpoint, default Pivot. - Pivot: label sits directly on the pivot point itself. - Leg Midpoint: label sits halfway (averaging both time and price) along the leg that pivot STARTS — i.e. the leg running forward to the next numbered pivot. For the single newest numbered pivot (whose forward leg hasn't confirmed/frozen yet), the "next pivot" used for this averaging is the LIVE leg's current endpoint (current bar's time/close) rather than a confirmed pivot — so that one label keeps smoothly walking toward the eventual midpoint as the leg extends, instead of jumping abruptly the moment the leg finally confirms. -------------------------------------------------------------------------------- 4. HOW THE 1-5 NUMBERING CYCLE WORKS -------------------------------------------------------------------------------- - Every confirmed pivot is assigned a number from 1 to 5, cycling continuously (…3,4,5,1,2,3,4,5,1…) in BOTH directions from a single anchor pivot you calibrate once. - The anchor pivot is identified by finding whichever CONFIRMED pivot's timestamp is closest to your chosen "Anchor Time" for the active timeframe. Only confirmed pivots are eligible — the still-forming, constantly-moving newest pivot is deliberately excluded from this search, so the anchor never attaches itself to a point that later shifts position. - Once found, that pivot's number is fixed to whatever you set in "Anchor Pivot's Leg #". Every pivot AFTER it counts forward with wraparound (5 -> 1); every pivot BEFORE it counts backward with the same wraparound (1 -> 5) applied in reverse. - This entire numbering pass happens ONCE per chart load ("seeding"), the first time there are at least 2 confirmed pivots available. After that, each NEW pivot that confirms (i.e. the live leg finally reverses and freezes) is appended to the sequence by simply continuing the same forward wraparound from the last assigned number — it does not need to re-search for the anchor again. - Because the anchor is a fixed timestamp saved in your settings (not a "most recent N pivots" scheme), the same physical pivot gets the same number every single time the script runs — after a refresh, a new session, or reopening the chart weeks later. -------------------------------------------------------------------------------- 5. TYPICAL SETUP WORKFLOW -------------------------------------------------------------------------------- 1. Add the indicator to a chart on one of the 5 supported timeframes (1h, 4h, 1d, 1w, or 1m). 2. Decide Manual or Automatic under "Anchor Timeframe Selection". Automatic is recommended if you plan to flip between several of the 5 supported timeframes on the same chart/instance. 3. Identify a specific pivot on your chart you can clearly see and verify (e.g. a well-defined recent swing high/low). 4. Set that timeframe's "Anchor Time" input to that pivot's timestamp, and set "Anchor Pivot's Leg #" to whatever number you want that specific pivot to represent (1 through 5) — this is your calibration choice, not something the script derives on its own. 5. Enable "Mark Anchor Pivot" to visually confirm the ⚓ marker landed on the pivot you intended. 6. Repeat steps 3-4 for any other timeframes you plan to use. 7. Adjust cosmetic settings (numbering style, label size, colors, label position) to taste. -------------------------------------------------------------------------------- 6. LIMITS & BEHAVIOR NOTES -------------------------------------------------------------------------------- - Hard ceiling of 500 lines and 500 labels total (Pine Script platform limit, set via max_lines_count / max_labels_count). With very long pivot histories on low timeframes, the oldest labels/lines may eventually stop being drawable — no trimming logic recycles them. - All drawings use xloc.bar_time (absolute timestamps) rather than xloc.bar_index, specifically so that pivots far back in chart history don't trigger Pine's "x-value too far from current bar" restriction. - The leg-numbering pass and the anchor marker only render on the last bar (barstate.islast) — cosmetic labels are not drawn bar-by-bar historically, only re-drawn fresh each time the last bar updates. - If the chart's real timeframe does not match the active anchor timeframe (manual mismatch, or Automatic on an unsupported timeframe), the counter intentionally shows nothing rather than displaying numbers that don't correspond to the pivots you're actually viewing. - The projection segments extrapolate from historical AVERAGE leg length/slope; they are not forecasts and carry no statistical guarantee of accuracy. ================================================================================ FUNCTION REFERENCE ================================================================================ -------------------------------------------------------------------------------- f_drawLeg(arr, x1, y1, x2, y2, col, lw, sty) -------------------------------------------------------------------------------- Purpose: Draws a single ZigZag/live-leg line segment and registers it in a tracking array for later cleanup (delete-before-redraw pattern). Parameters: arr - array, the registry this new line gets pushed into. x1,y1 - start point (x1 = a UNIX-ms timestamp, since xloc.bar_time is used; y1 = price). x2,y2 - end point (same coordinate system as x1,y1). col - line color. lw - line width. sty - style string: "Solid", "Dashed", or "Dotted". Returns: Nothing directly; the created line.new() reference is appended to `arr`. Notes: Always uses xloc = xloc.bar_time to avoid Pine's bar-index distance restriction on historical drawings. -------------------------------------------------------------------------------- add_to_zigzag(p, v, b) -------------------------------------------------------------------------------- Purpose: Inserts a brand-new pivot (a fresh reversal) at the FRONT of the pivot array, storing it as a pair. Parameters: p - array, the pivot array (`z`). v - float, the pivot's price. b - float/int, the pivot's time (UNIX ms). Returns: Nothing (mutates `p` in place). After the two unshifts, p = price, p = time for the new pivot. -------------------------------------------------------------------------------- update_zigzag(p, v, b, d) -------------------------------------------------------------------------------- Purpose: Updates the CURRENTLY FORMING pivot (front of the array) in place as a swing continues to extend, rather than creating a new pivot entry. Parameters: p - array, the pivot array (`z`). v - float, candidate new extreme price. b - float/int, candidate new time. d - int, current direction (1 = up/bullish, -1 = down/bearish). Behavior: - If `p` is empty, delegates to add_to_zigzag() to seed the first pivot. - Otherwise, only overwrites p /p if the new price is a more extreme high (d==1) or more extreme low (d==-1) than what's currently stored — i.e. the swing is still extending in the same direction. Returns: 0. (dummy numeric return; the real effect is the in-place mutation.) -------------------------------------------------------------------------------- f_tfMatches(sel) -------------------------------------------------------------------------------- Purpose: Checks whether the CHART'S ACTUAL current timeframe matches one of the 5 supported preset buckets. Parameters: sel - string, one of "1h", "4h", "1d", "1w", "1m" (the timeframe being tested against). Returns: bool - true if the chart's real timeframe (via timeframe.isintraday / isdaily / isweekly / ismonthly plus timeframe.multiplier) matches `sel`; false otherwise (including for any `sel` value outside the 5 presets). Notes: Compares structurally (intraday/daily/weekly/monthly + multiplier) rather than string-matching timeframe.period, since raw period strings ("60" vs "1H", etc.) vary across data feeds/script versions. -------------------------------------------------------------------------------- f_detectTf() -------------------------------------------------------------------------------- Purpose: The reverse of f_tfMatches() — with no preset given, works out on its own which (if any) of the 5 supported buckets the chart's actual current timeframe falls into. Powers "Automatic" anchor timeframe mode. Parameters: None (reads timeframe.isintraday/isdaily/isweekly/ismonthly and timeframe.multiplier directly). Returns: string - one of "1h", "4h", "1d", "1w", "1m" if the chart's timeframe matches a supported preset, otherwise "none". Notes: Returning "none" is intentional: downstream, f_tfMatches("none") falls through every case to its default `false`, so the counter cleanly stays silent on unsupported timeframes with no special-case handling required elsewhere in the script. -------------------------------------------------------------------------------- f_labelSize(sel) -------------------------------------------------------------------------------- Purpose: Maps the "Label Size" dropdown's string value to the corresponding Pine `size.*` constant. Parameters: sel - string, one of "Tiny", "Small", "Normal", "Large", "Huge". Returns: A `size.*` built-in constant (size.tiny / size.small / size.normal / size.large / size.huge). -------------------------------------------------------------------------------- f_legChar(num, style) -------------------------------------------------------------------------------- Purpose: Converts a leg number (1-5) into the display glyph for the user's chosen numbering style. Parameters: num - int, 1 through 5. style - string, one of the 6 supported style option strings (e.g. "①②③④⑤"). Returns: string - the single glyph representing `num` in the chosen style (e.g. f_legChar(3, "①②③④⑤") returns "③"). Notes: Internally builds all 6 possible 5-element glyph arrays every call and picks the matching one; simple and correct at this scale, not optimized for call frequency (only invoked inside the once-per-last-bar label redraw loop, so this is not a performance concern in practice). ================================================================================ END OF DOCUMENT ================================================================================

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của CQuevedo345

Square Bar/Calendar Count Verticals (Gann)█ OVERVIEW Square Bar/Calendar Count Verticals plots vertical time lines on the chart at squared counts measured from user-selected pivot anchors, in two independent units: trading bars and calendar days. The thesis, drawn from W.D. Gann's time-counting methods, is that elapsed time from a significant price extreme reaching a perfect square (81, 100, 121, 144, 289, 361) marks a date of elevated probability for a trend pause, inflection, or termination, and that the strongest of these dates occur where a bar-count square and a calendar-day square coincide. █ HISTORY / BACKGROUND Counting time in squares from major highs and lows originates with W.D. Gann, who treated time and price as interchangeable quantities and used the squares of small integers as recurring measures of trend duration. Constance Brown's published work on Gann analysis demonstrates the method in modern markets: bar counts of 9², 10², 11², and 12² projected from a swing extreme, a 17² calendar-day count from a significant low, repeats of the 144 count monitored from key pivots, and the square of 19 tracked as a separate helix cycle. This indicator implements that specific working set as a chart tool. The conceptual basis is that these verticals are time factors only. They carry no directional information. Their value is realized when a squared count expires while price is simultaneously at a level identified by independent price-based methods. █ HOW IT WORKS 1 — The user supplies a comma-separated list of roots (default 9,10,11,12,17,19). At the first bar the script parses the list and computes the square of each root. 2 — Each of up to three anchors is a timestamp selected on the chart. The anchor is resolved to a bar by containment: the anchor bar is the first bar whose closing time exceeds the anchor timestamp. This makes resolution independent of exchange timezone and safe when the timestamp falls on a weekend or holiday. 3 — For every anchor and every square s, the script computes two targets: - a trading-bar target at anchor bar index + s (bar unit follows the chart timeframe), and - a calendar-day target at anchor time + s days (independent of timeframe). With inclusive counting enabled (default) the anchor bar or anchor day is counted as 1, so targets land at s minus 1 units after the anchor. 4 — When the root 12 is present and the repeat setting exceeds 1, additional verticals are drawn at 288, 432, and further multiples of 144, in both units, up to the chosen repeat count. 5 — On the last bar the script draws the verticals once: solid lines in bar-index coordinates for bar squares (projected up to roughly 490 bars into the future) and dashed lines in time coordinates for calendar squares (projectable to any future date). Each line carries a label naming the root, the square, and the unit. The anchor itself is marked with a heavier line and a date label. 6 — A status table reports, per active anchor, the elapsed count in both units and the next upcoming square in each unit, with the bars remaining and the calendar date. 7 — Three alert conditions fire on the bar that completes a bar-count square, the bar that contains a calendar-day square date, and the bar on which both occur together. █ HOW TO USE The verticals are appointments in time, not signals. The intended workflow: - Anchor each slot on a significant swing extreme. Significance is an analytical judgment; the script deliberately performs no automatic pivot detection. - Validate an anchor by inspecting the verticals already in the past. If historical squared counts from that anchor align with real pivots, the anchor is worth keeping. If they align with nothing, move or disable it. - When price approaches an upcoming vertical, consult independent price analysis. A squared count expiring while price sits at a level derived from other methods is the condition of interest. A squared count expiring in open space warns at most of a pause or stall. - The highest-weight event this tool can flag is the third alert: a bar square and a calendar square completing on the same bar. Bar counts are timeframe-relative by design: the same anchor produces different bar-square dates on daily and weekly charts, and both are legitimate counts on their own timeframe. Calendar-day counts are identical on every timeframe. The tool is designed for daily and weekly swing analysis, where Gann's counts were applied; on intraday charts the calendar counts remain valid but bar counts become session-dependent. Visual elements: solid vertical lines are trading-bar squares, dashed vertical lines are calendar-day squares, the heavy line with a date label is the anchor, labels above price name bar counts, labels below price name calendar counts, and the top-right table summarizes elapsed and upcoming counts. █ SETTINGS - Square roots: comma-separated integer roots to square. Default 9,10,11,12,17,19. - Inclusive count: anchor bar or day counts as 1 when enabled. Default on. - Trading-bar squares: show or hide bar-count verticals. Default on. - Calendar-day squares: show or hide calendar-count verticals. Default on. - 144-cycle repeats: number of 144 multiples to project; 1 disables repeats. Default 3. - Anchor 1, 2, 3: enable flag, pivot timestamp (Anchor 1 prompts for a chart click on load), and line color per anchor. Defaults: Anchor 1 enabled, Anchors 2 and 3 disabled. - Status table: show or hide the summary table. Default on. - Label size: tiny, small, or normal. Default small. █ WHAT MAKES IT ORIGINAL Most Gann-count scripts plot a single count series in a single unit. This implementation differs in three specific ways. First, it runs trading-bar and calendar-day counts in parallel from the same anchor and explicitly detects their coincidence, which is the confluence condition emphasized in the source methodology rather than an afterthought. Second, anchor resolution uses closing-time containment instead of naive date equality, so anchors survive timezone differences, weekends, and holidays without silently shifting by one bar. Third, projection is handled correctly per unit: calendar squares are drawn in time coordinates and can therefore mark dates arbitrarily far in the future, while bar squares are drawn in bar coordinates and are projected only within the platform's future-bar range, keeping both unit systems accurate to their own definition. █ NOTES / LIMITATIONS - Drawings are created once per script load on the last bar. As new realtime bars form, elapsed counts in the table and newly reachable verticals refresh only when the script recalculates, for example after a settings change or chart reload. - Bar-count verticals can be projected at most about 490 bars beyond the current bar, a platform ceiling on future bar-index coordinates. Calendar-day verticals have no such ceiling. - The script draws up to 500 lines and 500 labels. Many roots combined with three anchors, both units, and repeats can reach this ceiling, at which point the oldest objects are removed. - If an anchor timestamp predates the symbol's available history, the anchor resolves to the first available bar and every count measures from there, which is unlikely to be the intended pivot. - Bar counts depend on the chart timeframe and on the symbol's session definition. Symbols with irregular sessions or many holidays will show bar squares and calendar squares diverging substantially, which is expected behavior, not an error. - The indicator plots time factors only. It produces no directional forecast, and no claim is made about the outcome of price at any vertical.

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Composite Valuation Standard Score █ OVERVIEW Composite Valuation Standard Score (CVSS) plots a single 0 to 100 line that measures how expensive the broad US equity market is against its own entire recorded history, by combining up to six valuation ratios through point-in-time statistics. The thesis: one valuation metric can mislead in isolation, but the average anchored z-score of several independent lenses (earnings, cyclically adjusted earnings, book value, sales, output, replacement cost) gives a robust reading of how uniformly stretched or depressed valuations are, without using any future data at any bar. █ HISTORY / BACKGROUND Averaging the historical percentile of many valuation ratios into one composite is a long-standing practice in institutional market research. The individual components carry their own lineage: the cyclically adjusted price to earnings ratio was developed by Robert Shiller, the market capitalization to GDP ratio is widely associated with Warren Buffett, and the ratio of corporate equity value to corporate net worth descends from James Tobin's Q. The specific construction used here, an expanding winsorized z-score per component with a minimum-history admission gate and a composite-level percentile mapping, is a novel method built for this script. Its conceptual basis is that every observation should be judged only against the history that existed when it printed, and that a metric making new all-time highs should keep conveying magnitude instead of freezing at the top of a percentile scale. █ HOW IT WORKS All series are sampled once per calendar month through request.security at the 1M timeframe with lookahead off. Two of the six components are ratios computed from a numerator and denominator symbol: Market Cap / GDP (a total market index divided by nominal GDP) and the Q Ratio proxy (nonfinancial corporate equities at market value divided by nonfinancial corporate net worth). Because every series is immediately transformed to ranks and z-scores, absolute units and level calibration are irrelevant; only the shape of each series matters. The algorithm, step by step: 1. On each new monthly bar, each enabled component's value is inserted into that component's sorted history array. The arrays only ever grow; nothing is discarded. 2. A component becomes "live" once its array holds at least the minimum-history gate (default 120 monthly observations). Before that it accumulates data but does not contribute, which prevents thin early samples from producing meaningless statistics. 3. Each live component's current value is converted to an anchored z-score against the expanding mean and standard deviation of its own array, then winsorized by clamping to plus or minus 3 (adjustable). 4. The composite z is the equal-weight average of all live winsorized z-scores, computed whenever at least the minimum number of components (default 2) is live. 5. The composite z is itself inserted into an expanding array and converted to its own expanding percentile rank. That rank is the 0 to 100 headline line. 6. Separately, each live component's expanding percentile rank is compared with the extreme threshold (default 90). The share of live components above the threshold is plotted as the extremes-breadth columns. An optional Excess CAPE Yield series (100 divided by CAPE, minus the 10-year Treasury yield) can be plotted and is always available in the table when enabled. █ HOW TO USE Apply the script on a Monthly chart of a symbol with deep monthly history. The chart symbol only supplies the time axis; the valuation data comes from the configured feeds. Charting the trailing P/E series itself, or a long-history index, exposes the full record back to the late 19th century. On a short-history chart symbol the statistics rank against a short window and the reading is not comparable. Reading the pane is simple: • The teal line is the market's expensiveness rank from 0 to 100. A reading of 96 means the current composite valuation is richer than 96 percent of everything that came before it. A reading of 5 means cheaper than 95 percent of prior history. • Above the dotted 90 line with a red background: valuations are in their most expensive historical decile. Below the dotted 10 line with a green background: cheapest decile. • The orange columns show agreement. At 100, every live metric is simultaneously in its own extreme zone; at 0, none is. High teal with low orange means the composite is stretched but the stretch is concentrated in few metrics. • The table in the top right shows each component's status (off, gated with progress, or live), its current percentile, and its z-score, plus the composite row and the Excess CAPE Yield row. This is a slow macro positioning gauge, not a timing signal. Elevated readings can persist for years. Its practical use is context: sizing long-term risk, framing regime, and flagging when many independent valuation lenses agree at an extreme. █ SETTINGS • Components group: six on/off toggles, each with editable symbol fields. Trailing P/E (default on), Shiller CAPE (default on), Price / Book (default on), Price / Sales (default on), Market Cap / GDP with numerator and denominator symbols (default on), Q Ratio proxy with numerator and denominator symbols (default on). • Minimum-history gate: monthly observations a component needs before it contributes. Default 120. • Winsorize z at +/-: clamp magnitude for component z-scores. Default 3. • Minimum live components: fewest live components required for the composite to plot. Default 2. • Extreme threshold (percentile): level defining the expensive zone for the background, and the per-component extreme used by the breadth columns. Default 90. • Cheap threshold (percentile): level defining the cheap zone for the background. Default 10. • Plot Excess CAPE Yield: adds the ECY series in percent to the pane and status line. Default off. Its 10-year yield symbol is editable. • Show component table: toggles the status table. Default on. █ WHAT MAKES IT ORIGINAL Published valuation scripts overwhelmingly track a single ratio, and existing multi-series composites in other domains rank each input over a fixed rolling window or against full-sample statistics. This script differs in four specific, verifiable ways. First, every statistic is point-in-time: each bar is ranked and scored only against observations that existed at that bar, so no early reading benefits from data that had not yet occurred. Second, the primary transform is a winsorized anchored z-score rather than a percentile, so a component that breaks above all prior history continues to register increasing magnitude up to the clamp instead of pinning at 100 and going silent. Third, a minimum-history admission gate handles the unequal start dates of the underlying feeds explicitly: short-history components accumulate until they are statistically meaningful, and the effective composition of the composite changes transparently over time, disclosed live in the table. Fourth, the extremes-breadth columns quantify cross-metric agreement, separating a composite driven by one distorted ratio from one where independent valuation lenses are stretched simultaneously. █ NOTES / LIMITATIONS • Sample depth is bounded by the chart symbol's bar history, because expanding statistics can only accumulate on bars that exist on the chart. Use a deep-history monthly chart. • The effective component set varies by era. Only the two earnings-based series reach the 19th century; book value and sales feeds begin near 2000, and the market cap and Q feeds clear the gate later still. Early readings are a two-component composite. The table always shows which components are live. • Components whose feeds return no data stay gated and are excluded; the composite requires the configured minimum of live components or it plots na. • The value on the developing monthly bar updates until that bar closes. On timeframes below monthly the current month's reading evolves intraperiod. No lookahead is used and completed bars do not repaint from the script's side. • The underlying economic feeds are revised at the source. National accounts and flow of funds series can be restated historically, which changes past values of the affected components when the data provider updates them. • Quarterly feeds repeat their value across the months of a quarter, which mildly smooths the expanding distributions. • This indicator describes valuation rank relative to history. It makes no claim about future returns or the timing of any reversal.

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Gold Fibonacci Multiple Grid █ OVERVIEW Gold Fibonacci Multiple Grid draws, on the price chart, a static grid of horizontal levels at Fibonacci-number multiples of a fixed base price, 35 currency units per ounce by default. The thesis under examination is an observation from technical analysis literature that gold has tended to reverse near prices equal to Fibonacci numbers times the 35 dollar official price fixed in 1934. The script renders the grid, identifies the nearest levels above and below the current price, and flags proximity; it does not assert that the observation is valid. █ HISTORY / BACKGROUND The Gold Reserve Act of 1934 fixed the official US dollar price of gold at 35 dollars per troy ounce. That price held through the two-tier market of 1968 and ended with the suspension of dollar convertibility in 1971, after which gold floated. Because 35 dollars was the last administratively fixed price, some technicians, including Robert Prechter, have used it as a permanent anchor and observed that subsequent multi-year turning points in gold have clustered near Fibonacci-number multiples of it, for example 55 times 35 equals 1925, 89 times 35 equals 3115, and 144 times 35 equals 5040. The observation is two sided: certain multiples have coincided with major highs while others have coincided with major lows, so each multiple carries a role, not just a price. Whether that clustering exceeds what random level placement would produce is an open empirical question. This script is a visualization of the hypothesis, built so the reader can inspect the historical record directly. █ HOW IT WORKS • On the first bar, the script generates the sequence of distinct Fibonacci numbers, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987 and onward, up to the configured count. • Each level equals the base price times one Fibonacci number. The level prices are constants that never move or depend on price history. To keep the price scale usable, only levels inside the visible price range expanded by the range margin are drawn as horizontal lines extended across the whole chart. The possible turning multiples are exempt from the margin when the always-draw option is on, or automatically when the chart is zoomed out so the visible window spans at least the configured number of years; zooming back in hides them again. The script recalculates when the chart is scrolled or zoomed, so the drawn set follows the current view; levels outside the band remain part of every calculation. • On every bar, the script computes the percent distance from the closing price to each level, records the nearest level, the nearest level above and the nearest level below, and detects whether the close crossed any level relative to the prior close. • When the distance to the nearest level is at or below the proximity threshold, that level's line and label switch to the proximity color and gain width, and an alert condition becomes true on the first bar of the approach. A second alert condition fires on any bar whose close crossed a level. Every other drawn level is colored by its assigned role: three user-editable lists designate each multiple as a high target, a low target or a possible future turning level, multiples on no list use the neutral color, possible turning levels draw dashed, and labels append the assigned role. • An optional table shows the nearest level above and below with their multiples and percent distances, the nearest possible turning level at or above the current price, and the distance to the nearest level. The exact distance is also exposed in the data window. █ HOW TO USE In plain terms, the script draws a ladder of fixed price rungs. Every rung is the old 35 dollar gold price multiplied by a Fibonacci number, so the rungs get further apart as price rises, roughly 62 percent apart, matching the way gold's swings have grown with its price. The claim being examined is that gold tends to stall or turn near these rungs. Scroll back through history and judge for yourself how often turns landed near a rung and how often they ignored the grid entirely. The table always tells you the next rung overhead, the next rung underneath, and how far away the closest one is. When price gets within the threshold of a rung, that rung lights up. The grid is designed for charts quoted in US dollars per troy ounce of gold: spot gold, gold futures, or a gold index. The levels are timeframe independent; daily and weekly charts are the practical choices because the observation concerns multi-year turning points. Treat a highlighted level as a location of interest for confluence with independent analysis, not as a prediction of reversal. A level is one price; nothing in the script measures whether price will respect it. █ SETTINGS • Base price, currency units per ounce. Default 35. Every level is this value times a Fibonacci number. Changing it repurposes the grid for any anchored-multiple study. • Number of Fibonacci multiples. Default 15, which spans 35 to 34545 at the default base. Maximum 20. • Proximity threshold, percent of level. Default 2. Price within this percent of a level counts as at the level. • Range margin, percent. Default 20. A level beyond the visible price extremes is drawn only when it lies within this percent of them, so the price scale stays close to the price data on arithmetic charts and a level appears overhead or underneath as price approaches it. Raising the margin draws more of the grid at the cost of scale headroom. Undrawn levels still participate in every calculation and in the table. • High target multiples, comma separated. Default 21, 55, 144. Levels at these multiples always draw in the high target color. • Low target multiples, comma separated. Default 1, 3, 8, 34, 89. Levels at these multiples always draw in the low target color. Multiples on no list draw in the neutral color. • Possible turning multiples, comma separated. Default 233, 377, 610. Unreached multiples treated as possible future turning levels, drawn dashed in the possible turning color. • Always draw possible turning levels. Default off. Forces the possible turning multiples on screen regardless of zoom, which expands the price axis on an arithmetic scale; a logarithmic scale is recommended while enabled. When off, the nearest possible turning level is still reported in the table. • Auto-show possible levels when visible span exceeds, years. Default 10. When the visible window spans at least this many years, the possible turning multiples draw even though they lie beyond the range margin, and they hide again when the chart is zoomed back in. • Show level labels. Default on. Each label states the multiple, the base, the resulting price and the assigned role. • Show nearest-level table. Default on. • High target color, Low target color, Possible turning color, Unassigned level color and Proximity color. Defaults red, green, blue, gray and orange. The table rows for the nearest level above and below inherit the role color of that level; the proximity color overrides the nearest level when price is within the threshold. █ WHAT MAKES IT ORIGINAL Fibonacci tools on this platform are almost always relative: retracements, extensions and fans measured between two user-selected swing points, which move whenever the anchors change. This grid is absolute. Every level is a constant derived from one historically fixed price and the integer Fibonacci sequence, so the entire level set is defined before any chart data is read, identical on every symbol and timeframe, and immune to anchor-selection bias. The script adds mechanical proximity detection, crossing detection and nearest-level reporting on top of the static grid, which turns a qualitative literature observation into an inspectable, falsifiable chart object. █ NOTES / LIMITATIONS • The levels are meaningful only on series quoted in US dollars per troy ounce. On gold ETFs, gold miners, or gold quoted in other currencies, the default grid does not correspond to the underlying observation; the base price would need to be redefined. • The level prices are static constants and there are no lookahead or higher-timeframe requests. Which levels are drawn depends on the visible price range, so the drawn subset changes as the chart is scrolled or zoomed, and the script recalculates on each change of the visible range. Calculations, alerts and the table always use the full level set and are unaffected by the view. • Proximity and crossing calculations use closing prices. On the developing bar they update until the close and do not change afterward. • Labels are positioned a few bars past the last bar and reposition as new bars print. The table renders on the last bar only. • The script draws at most 20 lines and 20 labels, far below platform object limits. • Level spacing follows the Fibonacci sequence, so consecutive levels are roughly 62 percent apart at scale. Between rungs the script is silent by design. • The default role assignments restate a published observation about which multiples coincided with historical highs and lows. The script does not verify the assignments, and both lists are editable. • The script visualizes a hypothesis. It performs no statistical test of whether reversals near these levels occur more often than chance would produce.

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Index Peak Dispersion █ OVERVIEW Index Peak Dispersion plots, in a separate pane, two normalized series computed across a configurable universe of up to twelve equity indexes: the calendar-day dispersion of their all-time-high dates, expressed as a percent of a topping window, and the share of indexes that printed a fresh all-time high within a short recent window. The thesis is that healthy advances register all-time highs across indexes nearly simultaneously, while major distributive tops fragment, spreading index peak dates across weeks or months. █ HISTORY / BACKGROUND The concept descends from the non-confirmation principle of Dow Theory as developed by Charles Dow, William Hamilton and Robert Rhea, in which a new high in one average unaccompanied by a new high in another warns that the trend is losing sponsorship. Classic non-confirmation is measured in the price domain: one index fails to exceed its prior peak while another does. Market historians and technicians, including Robert Prechter, have documented a related phenomenon in the time domain: at major tops, the final all-time highs of the major indexes scatter across the calendar rather than clustering. At the 2000 top, the Dow Industrials peaked in January, the S&P 500 and NASDAQ Composite in March, and the NYSE Composite in September. At the 2007 top, the Dow Jones Composite peaked in July while the Dow Industrials and S&P 500 peaked in October. This script converts that qualitative observation into a mechanical, reproducible statistic. █ HOW IT WORKS The script performs the following steps on each bar: • For each of up to twelve enabled symbols, one same-timeframe request.security() call evaluates a function inside the requested symbol's context. The function maintains a running maximum of closing prices over the symbol's loaded history and records the timestamp of the bar on which that maximum was last exceeded. This running maximum is point-in-time by construction: no future data enters the calculation, and lookahead is off. • On the chart symbol, each recorded timestamp is converted to an age in calendar days: current bar time minus the all-time-high time, divided by the number of milliseconds in a day. • Each enabled index with data is classified. An age at or below the fresh window makes it Fresh. An age at or below the topping window makes it part of the in-window set. An age beyond the topping window makes it Stale. • When the in-window set contains at least the minimum required count of indexes, the dispersion span equals the maximum in-window age minus the minimum in-window age, in calendar days. The plotted dispersion value is that span divided by the topping window length, times 100. When the in-window count is below the minimum, the dispersion plot returns na. • The participation value equals the count of Fresh indexes divided by the count of enabled indexes with data, times 100, plotted as columns. • The fractured top condition is true when the dispersion value is at or above the warning threshold while at least one index is Fresh. The pane background is shaded on those bars, and an alert fires on the first bar of each new occurrence. A second alert fires when every enabled index with data is simultaneously Fresh, which marks a synchronized advance, the opposite condition. • On the last bar, an optional table lists each index with its all-time-high date, age in days and classification, plus summary counts and the raw span in days. █ HOW TO USE The script is designed for the 1D timeframe. The running all-time high is intended to operate on daily closes, and both windows are specified in calendar days, so daily resolution matches the granularity of the logic. In plain terms, the blue columns answer one question: how many of the enabled indexes hit a record high this week? The red line answers another: how spread out in time are everyone's record highs? In a strong market, the indexes peak together, so the columns are tall and the line stays low. At major tops, the market tends to fall apart in slow motion: one index peaks, then months later another, and by the time the last index prints its final record, several others stopped making records long ago. Each new high is carried by fewer indexes, so the columns thin out while the line climbs. The shaded background marks the combination of both: the market is still printing record highs, but the set of indexes confirming them has been shrinking for months. That is the structure documented at the 2000 and 2007 tops. The same combination also appears during rotation phases that resolve higher, so treat it as a statement that conditions resemble past major tops, not as an instruction to act. Read the two plotted series together. Low dispersion with high participation describes a synchronized advance in which the enabled indexes are registering highs together. Rising dispersion while some indexes continue to print fresh highs describes fragmentation: leadership is narrowing and earlier leaders have stopped confirming. The shaded background marks bars on which the dispersion value is at or above the dashed threshold line while at least one fresh high exists, the specific combination in which fragmentation is present at a live high rather than in an established downtrend. The table gives the attribution behind the numbers: which indexes are Fresh, which remain inside the topping window, and which have gone Stale, along with each all-time-high date. Stale entries are non-confirmations older than the topping window and are deliberately excluded from the span so that a single long-dormant index does not saturate the statistic. The condition is a warning context, not a timing trigger. It identifies an environment consistent with historical distributive tops. It does not predict the date or the existence of a decline. █ SETTINGS • Index universe, twelve slots, each with an enable checkbox and a symbol field. Defaults: DJI, DJT, DJU, DJA, SPX, NDX, IXIC, NYA, RUT, SOX, MID, SPXEW. All twelve are enabled by default. Any slot can be repointed to another symbol or disabled. • Fresh high window, calendar days. Default 7. An index whose all-time high printed within this many days counts as Fresh. • Topping window, calendar days. Default 378. An index whose all-time high printed within this many days participates in the dispersion span. Older highs are classified Stale. • Dispersion warning threshold, percent of topping window. Default 25. The dashed reference line and the threshold for the fractured top condition. • Minimum in-window index count for a valid span. Default 4. Below this count the dispersion plot returns na, which prevents a span computed from too few indexes. • Show status table. Default on. • Table position. Default Top right. █ WHAT MAKES IT ORIGINAL Breadth and non-confirmation tools on this platform generally measure the price domain: divergences between an index and an internal line, counts of components above a moving average, or new-high and new-low tallies within one exchange universe. This script instead measures the time domain across whole indexes. It reduces the peak-date scatter of a user-defined index universe to a single bounded statistic, the in-window span of all-time-high ages, and pairs it with a participation series so that fragmentation is only flagged while a high is live. The classification into Fresh, in-window and Stale, with the Stale exclusion and the minimum-count validity gate, is what allows the scatter of a historical topping process to be plotted as one continuous, comparable series across eras. █ NOTES / LIMITATIONS • The running all-time high is computed only over the bars loaded for each requested symbol. Symbols with short available history, and the early portion of any chart, understate the true age of the all-time high. Treat the plot as reliable only after all enabled symbols have substantial loaded history. • The logic is designed for the 1D timeframe. On intraday charts the running maximum operates on intraday closes and the calendar-day windows lose their intended granularity. On weekly or monthly charts a fresh window shorter than one bar cannot register. • All request.security() calls run on the chart timeframe with lookahead off. Values on the developing bar update until the bar closes and do not repaint afterward. • The script issues twelve security calls. A symbol slot that fails to resolve or returns no data is excluded from every count and appears in the table as No data. • Ages and spans are measured in calendar days, not trading days, so weekends and holidays are included in the counts. • The warning threshold is expressed as a percent of the topping window. Changing the topping window changes the day-equivalent of the same percent threshold. • The dispersion plot returns na whenever fewer than the minimum required indexes have an all-time high inside the topping window. • The status table renders on the last bar only.

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