Indicator
Order Flow Volume Delta, CVD, Absorption & Divergence [LunqFX]Price shows you WHERE the market went. Order flow shows you WHO pushed it there — buyers or sellers — and whether they had real volume behind the move. This Order Flow indicator reads the volume delta on every candle (the balance of buying volume vs selling volume), builds it into a cumulative volume delta (CVD) trend, and automatically marks the two order-flow events that lead price: absorption and delta divergence. Everything is drawn on your chart as clean delta candles, order-flow support/resistance levels and a live buying-pressure dashboard.
❶ THE CONCEPTS (so it's clear)
▸ VOLUME DELTA — the difference between buying volume and selling volume inside a bar. Positive delta = buyers were more aggressive, negative = sellers. It is the core of all order-flow analysis.
▸ CVD (CUMULATIVE VOLUME DELTA) — delta added up over time. A rising CVD means buyers are steadily accumulating; a falling CVD means distribution by sellers. CVD is how you see the real trend of order flow, not just price.
▸ ABSORPTION — a bar with heavy volume but almost no price movement. It means a large player (smart money / institutional) is absorbing every market order at that level. Absorption very often appears right before a reversal.
▸ DELTA DIVERGENCE — price makes a new high but CVD does not (or a new low but CVD does not). The move has no real volume behind it — a trap / exhaustion signal that warns a reversal is likely.
❷ WHAT YOU SEE ON THE CHART
▸ Delta candles — sky-blue when buyers won the bar, coral when sellers won; the brighter the candle, the more one-sided the flow. You read buying and selling pressure at a glance.
▸ Order-flow levels — every absorption (gold) and divergence (blue / coral) is projected to the right as a support/resistance level with its exact price. These are the levels where big volume actually traded, so price reacts to them again.
▸ Live dashboard — who is in control (buyers vs sellers) from the CVD, the CVD value, the current bar's buy/sell pressure split, and the latest signal.
❸ HOW TO TRADE IT — STEP BY STEP
1 — Read the BIAS. The panel shows ▲ BUYERS or ▼ SELLERS IN CONTROL from the CVD. Trade with the side that controls order flow, not against it.
2 — Watch for DELTA DIVERGENCE against the move. Price higher high while CVD makes a lower high = buyers are exhausted → look for shorts. Price lower low while CVD makes a higher low = sellers are exhausted → look for longs. This is the highest-value order-flow reversal signal.
3 — Use ABSORPTION as a reversal cue. When heavy volume fails to move price, the move is being absorbed; watch for the turn and use that gold level as your invalidation line.
4 — Trade the reaction at order-flow levels. Old absorption and divergence levels act as support and resistance — enter when the delta flips back in your favour as price returns to a level.
5 — Confirm with Bar pressure. The panel's ▲/▼ % buy shows the live buy/sell split — take the trade when it agrees with your setup and the bias.
❹ HOW IT WORKS (fully transparent)
Each bar's volume is split by where price closed in its range: buy-volume = volume × (close − low) ÷ range, sell-volume = volume × (high − close) ÷ range, and delta = buy − sell. This is a transparent, range-based volume-delta estimate — it needs no tick or bid/ask feed, so it runs on any symbol. CVD is the running sum of that delta (session-anchored on intraday charts, fully cumulative on daily and higher, handled automatically). Absorption is flagged when volume rises above its average by your chosen multiple while the candle body stays smaller than a fraction of ATR. Divergence compares each confirmed swing pivot in price with the CVD value at that pivot. Every reading comes from closed bars and confirmed pivots — no repainting, no lookahead.
Order flow is strongest on markets with true exchange volume — crypto, stocks, futures and indices — and on intraday timeframes (1m–4h), where buying and selling pressure is most meaningful. On forex, volume is broker tick-volume, so treat the delta as an approximation of order flow rather than exact.
SETTINGS — CVD reset (Session / Week / None), absorption sensitivity, divergence swing length, number of order-flow levels + glow, delta candles on/off, dashboard position.
ALERTS — bullish delta divergence, bearish delta divergence, absorption, and CVD crossing zero (buyers / sellers taking control).
This indicator is an educational market-analysis tool, not financial advice. The volume delta shown here is a transparent estimate from price and volume, not exchange-audited bid/ask order flow, and past behaviour does not guarantee future results. Always confirm with your own analysis and manage your risk.
Indicator
Bitcoin Almanac [WillyAlgoTrader]₿ Bitcoin Almanac is an overlay indicator that maps the entire Bitcoin macro landscape on one chart: a fixed-length cycle time model (bull/bear phases projected from a single anchor date), two hyperbolic curves fitted through historical cycle lows and cycle highs in log-price space, Fibonacci grids stretched between every macro pivot, halving markers, accumulation and distribution zones, and a hypothetical price path for the next bull leg — all summarized in a live dashboard with projected turn dates, curve prices, and historical correction depths.
The core insight: Bitcoin's completed cycles show a remarkably stable time rhythm (roughly 1064 days up, 364 days down) and a decelerating growth pattern that a hyperbola in log10(price) captures with surprisingly small error. Neither observation is a law of nature — but when the time model and the price curves are combined on one chart, they produce concrete, falsifiable reference points: a projected top date with a curve price, a projected bottom date with a curve price, and buy/sell zones derived from both. The indicator makes the whole framework explicit, configurable, and honest about its assumptions.
Everything is driven by dates and user-defined pivots — not by real-time price action — so nothing repaints: the lines you see today are the lines you saw yesterday.
🧩 WHY THESE COMPONENTS WORK TOGETHER
A cycle-date model alone answers WHEN but not AT WHAT PRICE. A curve through historical lows answers WHERE support migrates but not WHEN price will meet it. Fibonacci retracements answer WHERE pullbacks tend to end, but only if you know which macro leg to anchor them to. Used separately, each tool leaves you guessing at the missing dimension.
Bitcoin Almanac chains them into one pipeline:
Cycle time model (anchor + phase lengths) → projected turn dates → hyperbolic lows/highs curves → curve price AT each projected date → Fibonacci grids between macro pivots → 0.786–0.836 accumulation zones bounded by cycle end dates → ±% distribution zones around each high → replayed bull-path projection between the two curve endpoints → dashboard synthesis
The time model supplies the X-coordinate of every future event. The two hyperbolas supply the Y-coordinate: the lows curve is evaluated exactly at the projected bottom date, the highs curve exactly at the projected top date — the "◎ cycle × curve" labels mark these intersections with date and price. The Fibonacci grids are then anchored to the same pivots the curves are built from, so the 0.786–0.836 buy zone of the current leg stretches in time precisely to the model's next cycle-bottom date. Finally, the projection module takes the two curve × date intersections as endpoints and fills the path between them by replaying the shape of the previous bull phase in log space.
No single component can do this: the intersection of an independent time model with an independent price model is what turns two vague trajectories into specific, checkable coordinates.
🔍 WHAT MAKES IT ORIGINAL
1️⃣ Fixed-rhythm cycle engine — pure time math, zero price input.
The phase of any bar is computed directly from calendar time:
daysSince = (barTime − anchor) / 86 400 000
phasePos = daysSince mod (bullDays + bearDays)
isBull = phasePos < bullDays
Defaults: anchor = 07 Nov 2022, bullDays = 1064, bearDays = 364 (each ≈ the average of the three completed BTC cycles). A true mathematical modulo (always ≥ 0) phases bars BEFORE the anchor correctly, so past cycles line up too. With defaults this reproduces the well-known projected dates: top ≈ 06 Oct 2025, bottom ≈ 05 Oct 2026. An alternative anchor (21 Nov 2022 — the actual lowest trade of the cycle) is documented right in the input tooltip and shifts the bottom to 19 Oct 2026.
Why this matters: because the phase depends only on time, the bull/bear background, turn lines and flip alerts are deterministic and cannot repaint — the model's claims are fully falsifiable in advance.
2️⃣ Hyperbolic curve through cycle lows — exact geometry or least-squares fit, with the error printed on the chart.
The lows model is log10(price) = a + b / (c − t), where t is days from the first pivot. The hyperbola has a vertical asymptote in the past and a horizontal asymptote in the future — it encodes decelerating growth, which a straight log-regression line cannot.
— With exactly 3 enabled lows (default: Aug 2015 ≈ $169, Dec 2018 ≈ $3 122, Nov 2022 ≈ $15 476) the three parameters are solved exactly — the curve passes through the pivots by construction. This is geometry, not statistics, and the label says so: "exact through 3 lows".
— With 4+ enabled lows (three optional early-history slots: 2011, 2013, 2015 are provided) the indicator switches to a least-squares fit: a coarse log-spaced search over 250 candidate asymptote positions, followed by a 200-step linear refinement around the best candidate. The curve label then reports the number of points and the RMSE in log10 units — the fit quality is never hidden.
A fit is accepted only if b > 0 and the asymptote c lies before the earliest pivot — degenerate solutions are rejected and the curve simply doesn't draw.
3️⃣ Second independent hyperbola through cycle highs.
The same model is fitted to cycle tops (defaults: Nov 2013 ≈ $1 238, Dec 2017 ≈ $19 700, Nov 2021 ≈ $69 000, Oct 2025 ≈ $126 200 — four points, so LS fit with visible RMSE). A fifth, disabled slot exists only if you want to force the fit through your own future target; you never need it for the projection, because the future top is marked automatically at the crossing of the highs curve with the projected top date.
Why two curves: lows and highs decelerate at different rates. Fitting them independently (instead of offsetting one curve) lets the model express a narrowing channel without assuming its shape.
4️⃣ "Cycle × curve" intersection labels — the model's testable predictions.
At the projected bottom date the lows curve is evaluated: ◎ label with date ≈ price. At the projected top date the highs curve is evaluated: ◎ label with date ≈ price. These two points are the indicator's headline output — a date AND a price for each future turn, derived from two independent models. Both curves extend beyond their intersection as dashed lines (lows: default 10 years, highs: 5 years) to show the long-term trajectory.
5️⃣ Macro Fibonacci grids with a time-bounded 0.786–0.836 accumulation zone.
All enabled lows and highs are merged chronologically; every leg between two pivots of opposite type receives a grid (low→high = bull grid, high→low = bear grid; same-type neighbours are skipped). Levels are fully user-defined (default: 0, 0.236, 0.382, 0.5, 0.618, 0.786, 0.836, 0.886, 1; values > 1 add extensions).
Two non-standard options:
— Log-scale interpolation : level price = 10^(log10(pA) + f × (log10(pB) − log10(pA))) — matches a fib tool drawn on a log chart. Off by default (arithmetic levels for a linear chart).
— Reverse mode (default ON): ratio 0 sits at the END of the leg, so on a bull leg 0.618 is the classic retracement below the high.
The 0.786–0.836 zone of each bull leg is highlighted as a "BUY ZONE" box — and here is the original part: the box stretches in time from the leg start to the end date of the cycle the leg belongs to (the next projected cycle bottom). Depth from the fib model, deadline from the time model — the zone is a rectangle in (price × time), not just a price band.
6️⃣ Distribution zones tied to the cycle skeleton.
Every enabled high gets a "SELL ZONE" box spanning high ± sellPct (default 5% → from high × 0.95 to high × 1.05). The time span runs from the LATER of (a) the latest enabled low before that high or (b) the model's cycle bottom immediately preceding it — so a projected 2029 high starts its zone at the projected Oct-2026 bottom, not at a 2022 pivot. If no enabled high exists in the upcoming cycle, a projected sell zone is created automatically at the highs-curve × projected-date crossing (duplicate-guarded within half a cycle).
7️⃣ Bull-path projection — fractal replay of the previous bull, rescaled in log space.
The path for the NEXT bull phase (with defaults: 05 Oct 2026 → 03 Sep 2029) is drawn between two model-derived endpoints: start = lows curve at the projected bottom, end = highs curve at the following projected top. Two shapes:
— Replay last bull (default): the log-price trajectory of the previous bull phase is recorded bar by bar (confirmed bars only, thinned to ≤ 400 samples for memory safety on intraday timeframes), then linearly rescaled: y(progress) = yStart + (ref(progress) − ref(0)) × (yEnd − yStart) / (ref(1) − ref(0)). The result keeps the timing character of the last cycle — early acceleration, mid-cycle chop, late blow-off — mapped onto the new endpoints.
— Log-linear : a straight line on a log chart between the endpoints. The replay mode automatically falls back to log-linear if the reference phase covers less than 90% of the bull duration.
The path is explicitly labeled "◇ PROJECTED PATH (hypothetical)" — it is a scenario generator, not a forecast.
8️⃣ Corrections table — every macro drawdown since 2014, including the unfinished one.
The dashboard lists every completed high → following-low leg between enabled pivots since 2014 as a % drop (e.g. 2017–2018: −84%). If the latest pivot is a high with no low after it, the current correction is projected : measured from that high to the lows-curve price at the next model bottom, and marked "(proj.)" in accent color. You always see how the ongoing decline compares with history.
9️⃣ Full-transparency dashboard with a phase gauge and next-leg scenario PNL.
Four toggleable sections: Cycle (phase, day X / Y, ██████░░░░ progress gauge), Projection (top and bottom dates with days-left counters and curve prices, plus the hypothetical PNL of the next bull leg: (topCurve / bottomCurve − 1) × 100% and the × multiple), Corrections (see 8️⃣), Ranges (nearest upcoming buy range = the 0.786–0.836 zone prices; nearest sell range = the ±% zone around the next projected high). The footer states the calibration chart (INDEX:BTCUSD · W · linear scale, with a live ✓ when you're on it), a timeframe warning, and the sample-size caveat "⚠ Sample size: 3 cycles" — the model's biggest limitation is printed on the chart itself.
🔟 Efficient, tick-stable rendering.
All drawings (500+ polyline points, fib grids, boxes, dashboard) are anchored to bar-open times and first-bar curve fits — nothing changes within a bar. A redraw gate rebuilds them once per new bar instead of on every real-time tick, and both curve fits run exactly once on the first bar. The chart stays responsive even with all modules enabled.
📐 HOW IT WORKS — CALCULATION FLOW
Step 1 — Parse pivots: on the first bar, all enabled lows and highs are converted to (days-from-first-pivot, log10(price)) pairs.
Step 2 — Fit the curves: each set is fitted to log10(price) = a + b/(c − t) — exact solve for 3 points, two-stage least-squares search for 4+. Fit validity is checked (b > 0, asymptote before the data).
Step 3 — Phase every bar: calendar-time modulo against the anchor determines bull/bear phase, day-in-phase, and the timestamps of the current, next bottom and next top.
Step 4 — Evaluate intersections: the lows curve at the projected bottom date and the highs curve at the projected top date become the model's price targets.
Step 5 — Record the reference bull: during the anchor cycle's bull phase, confirmed closes are stored as (progress, log10 price) — the shape later replayed by the projection.
Step 6 — Draw (once per bar): turn lines and labels for N past and M future cycles, both hyperbolas with dashed extensions, fib grids per alternating pivot leg, buy/sell zone boxes, halving lines (2012 / 2016 / 2020 / 2024 solid, Apr 2028 dashed "(est.)"), the projected path, the dashboard and the watermark.
Step 7 — Alert: on confirmed bars, phase flips and the pre-turn countdown fire alert() calls in text or JSON format.
📖 HOW TO USE
🎯 Quick start:
1. Open the INDEX:BTCUSD chart, Weekly timeframe, regular (linear) price scale — the model is calibrated there, and the dashboard shows a ✓ when the symbol and timeframe match (the scale must be checked manually — Pine cannot detect it).
2. Add the indicator. The green/red background immediately shows the model's current phase; the dashboard shows the day count and progress gauge.
3. Find the two ◎ labels — the projected bottom (orange, lows curve) and the projected top (red, highs curve). These are the model's date + price coordinates for the next turns.
4. Check the yellow boxes: BUY ZONE (0.786–0.836 of the current bull leg, extended to the cycle end date) and SELL ZONE (±5% around each high).
5. Create ONE alert with condition "Any alert() function call" to receive flips and the pre-turn countdown.
👁️ Reading the chart:
— 🟢 Green background = model bull phase; 🔴 red = bear phase
— Solid green verticals = cycle bottoms; dashed red verticals = cycle tops; future turns are labeled ★ PROJECTED and drawn brighter
— 🟠 Orange curve = hyperbola through cycle lows (solid to the projected bottom, then dashed extension)
— 🔴 Red curve = hyperbola through cycle highs (solid to the projected top, then dashed extension)
— Small circles = the exact pivots each curve is built from
— ◎ labels = cycle × curve intersections with date, ≈ price, and fit info (point count + RMSE, or "exact through 3")
— Fib grids between macro pivots: solid edges (0 / 1), dashed 0.5, dotted intermediate levels, price + ratio labels on the right
— 🟡 Yellow boxes = BUY ZONE (0.786–0.836, time-bounded by the cycle end) and SELL ZONE (±% around highs; "(proj.)" = auto-generated at the projected top)
— ⛏ Grey verticals = halvings; the 2028 line is dashed and marked "(est.)"
— 🔵 Blue dashed path = hypothetical next-bull trajectory with its ◇ end label
📊 Dashboard fields:
— Phase / Phase day / Progress : current model phase, day within it, and a 10-segment gauge
— Proj. top / Proj. bottom : projected turn dates, days remaining, and the curve price at each date
— Next leg PNL : hypothetical bottom→top move of the next bull leg in % and as a × multiple — a scenario, not a forecast
— Corrections : every completed macro drawdown since 2014 (high → following low, %), plus the unfinished one projected to the curve bottom and marked (proj.)
— Next buy range / Next sell range : the price boundaries of the nearest upcoming accumulation and distribution zones
— Footer: recommended chart check, timeframe warning, sample-size caveat, version
🔧 Tuning guide:
— Curve doesn't draw: fewer than 3 pivots enabled, or the fit was rejected as degenerate — enable at least 3 lows (or highs) with sensible dates/prices.
— You disagree with a pivot price: every pivot is an editable date + price input — correct it and both the curve and the fib grids rebuild instantly.
— Want dates matching the actual price low: switch the anchor to 21 Nov 2022 (documented in the tooltip); the projected bottom moves to 19 Oct 2026.
— Fib levels look wrong on a log chart: enable "Log-scale levels" (keep it OFF on the recommended linear chart).
— Chart feels crowded: disable individual modules (grids, zones, halvings, projection) or dashboard sections — every block has its own switch.
— Curious about 2030+: enable "Show 2nd projected cycle" for one more bottom/top pair (~Sep 2030 / ~Aug 2033) — off by default because those dates carry double model uncertainty.
💡 Trading ideas:
— Accumulation planning : scale into the 0.786–0.836 BUY ZONE while the model is in its bear phase; the zone's right edge tells you the model's deadline.
— Distribution planning : scale out inside the ±5% SELL ZONE as the projected top date approaches; the pre-alert (default 30 days) gives you a heads-up.
— Scenario testing : move pivots, change phase lengths, or force High #5 to your own target and watch how the whole framework (curves, zones, PNL) responds — the model is a sandbox, not an oracle.
⚙️ KEY SETTINGS
⚙️ Cycle Model:
— Anchor — cycle bottom (default 07 Nov 2022): date all phases are projected from; alternative 21 Nov 2022 documented in the tooltip
— Bull phase length (default 1064 days) / Bear phase length (default 364 days): ≈ averages of the 3 completed cycles
— Cycles to draw back (default 3) / forward (default 1): how many turn lines and labels are drawn
— Show 2nd projected cycle (default off): one extra bottom/top pair with doubled uncertainty
🎨 Visual Settings:
— Theme (Auto / Dark / Light): Auto detects from the chart background; all text colors adapt
— Phase background , Cycle turn lines , Turn labels , Watermark : independent toggles with color inputs
📊 Dashboard:
— Position (4 corners), font size (Small–Huge; dividers render one step smaller), and per-section switches: Cycle / Projection / Corrections / Ranges
📈 Hyperbola — Lows:
— 3 main cycle lows (2015 / 2018 / 2022, on by default) + 3 optional early-history lows (2011 / 2013 / 2015) — each is a checkbox + date + price
— Dashed extension (default 10 years), curve color, anchor-point markers
📉 Hyperbola — Highs:
— 4 cycle highs (2013 / 2017 / 2021 / 2025, on by default) + a spare projected slot (off), extension (default 5 years), curve color
🔢 Fibonacci Grids:
— Bull grids (default on) / Bear grids (default off) with separate colors
— Levels (default "0, 0.236, 0.382, 0.5, 0.618, 0.786, 0.836, 0.886, 1"; values > 1 = extensions)
— Highlight 0.786–0.836 zone (default on), Log-scale levels (default off), Reverse (default on), level labels
⛏ Halvings: lines + labels toggles, color
🟡 Sell Zones: toggle, color, Zone size % from high (default 5%)
🔮 Price Projection: toggle, Path shape (Replay last bull / Log-linear), color
🔔 Alerts: master switch, Webhook JSON Format (default off), Pre-alert days (default 30)
🔔 ALERTS
— 🟢 CYCLE FLIP → BULL — model bottom date reached; payload: ticker, timeframe, price, next projected top date
— 🔴 CYCLE FLIP → BEAR — model top date reached; payload: ticker, timeframe, price, next projected bottom date
— ⏳ CYCLE TURN APPROACHING — fires once, N days (default 30) before the next projected turn; payload: turn type (TOP/BOTTOM), days left, date
All alerts fire on confirmed bars only (once per bar close) and support both human-readable text and JSON webhook payloads for bot integration. Create a single alert with condition "Any alert() function call".
⚠️ IMPORTANT NOTES
— 🚫 No repainting. The cycle phase is pure calendar-time math against a fixed anchor; the curves are fitted once from user-defined historical pivots; the reference bull shape is recorded from confirmed bars only; all alerts use bar-close frequency. Nothing in the model reads unconfirmed real-time data, so no line, zone or label moves after the fact.
— 📐 Sample size: 3 completed cycles. Every statistical claim in this model rests on three observations. The hyperbolic fits are geometry/regression over 3–6 points. Treat all projected dates and prices as reference scenarios with wide error bars — the dashboard says "Rhythm ≠ law" for a reason.
— 📏 Calibrated chart: INDEX:BTCUSD, Weekly, regular (linear) price scale. Exchange charts start later and distort early-history fits. Keep the fib "Log-scale levels" input OFF on a linear chart. The dashboard's ✓ confirms symbol and timeframe; the scale must be checked manually.
— ⚖️ Scope: this is a macro-cycle framework for Bitcoin. It produces no intraday entry signals, no stop placement, and no position sizing. The projected path is explicitly hypothetical.
— 🛠️ This is a cycle-analysis and scenario-visualization tool, not an automated trading bot. It provides projected turn dates, curve-based price references, and accumulation/distribution zones — trade decisions remain yours.
— 🌐 The script runs on any symbol and timeframe, but the model is designed for Bitcoin on Daily/Weekly charts — a dashboard warning appears on intraday timeframes.
Bitcoin Almanac · v1.5.2 Indicator
Indicator
RSI Volume LadderA long-only pyramiding strategy that scales into corrections using RSI oversold conditions confirmed by above-average volume. Each subsequent entry requires a meaningful price drop from the previous fill, with an asymmetric exit structure: Take Profit anchored to the average entry, Stop Loss anchored to the lowest fill. Built for traders who want to systematically dollar-cost into pullbacks within an uptrend, with clearly defined risk per pyramid stack.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
ORIGINALITY — WHAT MAKES THIS DIFFERENT
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Five specific mechanics that, in this combination, distinguish this script from other averaging-down systems published on PulseWire:
- ASYMMETRIC EXIT ANCHORING
Take Profit is calculated from the average entry price of the pyramid — so winners run from the averaged-down cost basis. Stop Loss is calculated from the LOWEST entry price among all open positions — so the absolute worst-case loss is bounded by your deepest fill, not your average. This asymmetry is the key to making pyramiding viable: averaging works for you on the upside without inflating downside risk.
- PRICE-DROP GATE FOR EACH PYRAMID LEG
Every subsequent entry requires both a fresh RSI + volume signal AND a configurable minimum price drop from the previous fill (default 1.5%). This prevents stacking three positions within a tight range during a slow grind down — pyramiding only triggers on meaningful pullbacks, forcing real averaging instead of cosmetic averaging.
- ASYMMETRIC COOLDOWN ON EXIT
After a Stop Loss, the strategy waits N bars (default 3) before allowing new entries — blocks the "catch the falling knife" pattern where RSI stays oversold for many bars during a cascade. After a Take Profit, cooldown is configurable separately and defaults to 0, because a successful exit signals continued mean-reversion behavior worth participating in.
- ENTRY-TIMING SAFETY
Exit detection runs at the top of the bar evaluation, BEFORE the entry check. This means a Stop Loss hit on a bar cannot trigger a new entry on the same bar — even if RSI is still deeply oversold and volume is elevated. The cooldown counter is set before the entry logic ever sees the bar, eliminating a subtle but realistic execution problem in pyramiding systems.
- VISUAL EXIT CLASSIFICATION
When the position closes, the script automatically classifies the exit as TP or SL by measuring distance from actual fill price to each pre-set level, and renders a fuchsia or red cross at the exit price. No look-ahead, no estimation — the classification uses the closed trade's recorded exit price.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
CREDITS & FOUNDATIONS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
This script builds on established methodologies with original Pine v5 implementation of every layer:
- RSI — Wilder, 1978 (standard implementation via ta.rsi)
- Volume confirmation — classical technical analysis
- Pyramiding / scaling-in — standard portfolio management technique (Tharp, Elder)
- Dollar-cost averaging principles — long-standing investment methodology
ALL CODE in this script was written from scratch. No code has been copied from other public or private scripts. The five mechanics listed under ORIGINALITY above are original combinations and implementations.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
HOW THE STRATEGY WORKS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
ENTRY CONDITIONS (all must be true):
- RSI below the configurable oversold threshold (default 30)
- Volume above SMA × configurable multiplier (default 1.5× of SMA20)
- For pyramiding: price must be at least X% below the last fill (default 1.5%)
- Cooldown not active from the previous exit
- Open positions count below the pyramiding cap (default 3)
PYRAMID BUILDING:
Up to 3 long positions can be opened. Each new entry independently re-checks all conditions — the RSI + volume signal must reappear, AND the price drop gate must be satisfied. Position size per entry is fixed (default 5% of equity), so a full three-position stack uses approximately 15% of equity.
EXIT LOGIC:
Both legs run as an OCO (one-cancels-other) bracket:
Take Profit = average entry × (1 + tpPercent / 100), default +4%
Stop Loss = lowest entry × (1 − slPercent / 100), default −3%
All positions close together when either level is hit. The TP/SL levels update on every bar as the pyramid grows — average shifts on each new fill, lowest fill anchors deeper if a new low is hit.
COOLDOWN MECHANISM:
On exit, the strategy records the bar index and exit type. While bar_index − lastExitBar ≤ requiredCooldown , new entries are blocked. The required cooldown differs by exit type (default 3 bars after SL, 0 bars after TP), reflecting that an SL exit often happens during continuing weakness, while a TP exit signals healthy mean reversion.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
VISUALIZATION
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
ENTRY ARROWS — blue triangles below each fill, labeled Long_1 / Long_2 / Long_3 with the order price
AVERAGE ENTRY LINE — blue, updates as the pyramid grows (staircase pattern)
TAKE PROFIT LINE — fuchsia, updates with the average
STOP LOSS LINE — red, anchored to the lowest fill (steps down only if a deeper entry occurs)
EXIT CROSS — fuchsia at TP hit, red at SL hit, placed at the actual exit price
The line style is plot.style_linebr , which connects within an open position and breaks cleanly between positions — making each pyramid cycle visually self-contained on the chart.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
HOW TO USE IT
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
This is a complete entry + exit strategy, but it is NOT a turn-it-on-and-walk-away system. Use it as a framework to test and adapt to your specific instrument:
- Load on a liquid asset with mean-reverting behavior in an uptrend. The strategy was developed on crypto majors (BTC, ETH) and major equity indices.
Start with the default parameters and run the Strategy Tester on at least 6 months of history.
- Pay attention to:
• Net Profit and Max Drawdown ratio
• Average loss vs. average win
• Whether losing streaks cluster during specific market regimes
Tune to your instrument's volatility:
• Higher-volatility assets (alt-coins, small caps) → increase dropPercent (2-3%), increase slPercent (4-6%)
• Lower-volatility assets (BTC, indices) → decrease dropPercent (1-1.5%), tighter slPercent (2-3%)
• Higher timeframes (1h, 4h) → wider TP/SL to match bar range
- Consider adding an external trend filter for your live use. The strategy will pyramid into any oversold reading regardless of higher-timeframe context. Adding an EMA200 filter (only enter if price > EMA200) materially changes the risk profile on instruments prone to extended downtrends.
- Treat the strategy output as a structured framework for your dip-buying process, not as a guaranteed system. Combine with your own market analysis, position sizing, and broader risk management.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
INPUTS WORTH KNOWING
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
RSI Length (default 14) and Oversold threshold (default 30) — standard RSI configuration. Lower threshold = fewer, deeper-oversold entries.
Volume SMA length (default 20) and Multiplier (default 1.5×) — volume confirmation strength. Higher multiplier = only enter on standout volume spikes, fewer entries.
Min drop between pyramid entries (default 1.5%) — prevents tight clustering. Higher = waits for deeper pullbacks before adding.
Max pyramid positions (default 3) — caps the stack. Note: pyramiding=3 is also set in the strategy() header.
Take Profit % from average (default 4%) — anchored to running average entry price.
Stop Loss % from lowest fill (default 3%) — anchored to the deepest entry, shared across all open positions.
Cooldown after SL (default 3 bars) — blocks the falling-knife pattern after a stop-out.
Cooldown after TP (default 0 bars) — re-enter immediately after a successful exit.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
REALISTIC EXPECTATIONS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Pyramiding into corrections is a well-known approach with well-known limitations. The strategy works best when:
The underlying asset has a structural uptrend
Volatility produces regular pullbacks of meaningful depth
The market is not in a sustained bear regime
What this strategy provides:
A disciplined framework for scaling into pullbacks instead of single-shot entries
Asymmetric exit anchoring that lets winners run from the averaged cost basis
Built-in protection against catching the falling knife via cooldown
Transparent visualization of each pyramid leg, exit, and risk level
What it does NOT provide:
Protection from prolonged bear trends (averaging down into a structural decline is the dominant failure mode of this entire strategy family)
A guaranteed profitable system
A signal generator for assets that don't mean-revert
A replacement for your own market analysis, position sizing, or higher-timeframe context
Treat the output as ONE structured input in your trading process. The biggest risk to your account using a pyramiding strategy is mis-applying it to a downtrending asset — always confirm regime alignment before going live.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
TECHNICAL NOTES & DISCLOSURES
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
NO REPAINT . Uses process_orders_on_close=true — all orders execute on confirmed bar close. No request.security() calls, no lookahead_on, no barmerge.lookahead_on.
REALISTIC EXECUTION . Default commission 0.05% per trade (typical crypto exchange taker fee). Position sizing in % of equity, not fixed contracts. Initial capital 10,000.
DATA USED : only standard Pine inputs — open, high, low, close, volume. Indicators: RSI, SMA of volume. No external feeds, no security() calls, no synthetic data.
PERFORMANCE : max_labels_count=500. Pyramiding capped at 3. No unbounded growth of internal objects.
ASSETS : developed on crypto majors and tested across 5m, 15m timeframes. Works on equities and forex but parameters were tuned on crypto and may need adjustment for assets with different intraday behavior.
EDUCATIONAL AND ANALYTICAL TOOL . Intended for traders who understand pyramiding mechanics and the inherent risk of averaging-down strategies, and who want a structured framework to test on their own instruments.
OPEN SOURCE . Read the code, learn from it, fork it. Feedback and bug reports welcome in the comments.
Check my profile for other published scripts. Strategy
Fenix Wyckoff Squeeze BTC 30mFenix Wyckoff Squeeze — BTC 30m
A systematic trend-following strategy for the 30-minute timeframe, combining four independent signal layers into a single high-confidence entry condition. Default parameters are optimized for BTCUSDC on Binance, but all inputs are fully adjustable — the strategy can be adapted to any symbol or timeframe by tuning the parameters to match that market's characteristics.
Core Philosophy
Most trend-following strategies fail not because the trend concept is wrong, but because they enter too early, in low-quality conditions, or against the dominant momentum. This strategy addresses all three problems simultaneously by requiring four independent conditions to align before any trade is taken. The result is a low-frequency, high-selectivity system — the majority of 30-minute bars produce no signal at all. When a signal does appear, it reflects a convergence of volatility breakout, trend alignment, momentum direction, and oscillator confirmation.
Signal Layer 1 — Squeeze Momentum (Volatility Breakout)
Bollinger Bands (BB) measure price volatility relative to a moving average. Keltner Channels (KC) measure volatility using ATR. When BB contracts inside KC, the market enters a squeeze — a low-volatility compression phase where energy builds. When BB expands back outside KC after a squeeze period, a momentum breakout is detected.
The strategy only considers entries when this breakout occurs following a confirmed squeeze. This filters out random noise and focuses entries on genuine expansions from compressed conditions — a concept rooted in Wyckoff's accumulation and distribution phases, where periods of low volatility precede significant directional moves.
The lookback parameter controls how many bars back the strategy checks for a prior squeeze. A longer lookback means the breakout must follow a longer compression period, producing fewer but higher-quality signals.
Signal Layer 2 — Higher Timeframe EMA Trend Filter
A 2-hour Exponential Moving Average is used as a macro trend filter. Long trades are only allowed when price is above the 2h EMA. Short trades are only allowed when price is below. This single filter eliminates a large proportion of counter-trend entries that would otherwise reduce the strategy's edge.
The EMA length parameter controls how responsive or smooth the trend filter is. Longer values produce a smoother, slower-reacting filter that keeps you aligned with major trends. Shorter values react faster but may produce more whipsaws during trend transitions.
For other timeframes or symbols, this MTF EMA timeframe can be adjusted. The principle is to use a timeframe approximately 4× higher than your entry timeframe.
Signal Layer 3 — HMA Slope (Momentum Acceleration)
The Hull Moving Average is used to confirm that momentum is actively accelerating in the trade direction at the moment of entry. A rising HMA slope confirms long bias. A declining slope confirms short bias.
HMA was chosen over standard MAs because it significantly reduces lag while maintaining smoothness — it reacts faster to momentum shifts than EMA or WMA of equivalent length, making it more effective at confirming the direction of a breakout in real time.
Signal Layer 4 — Phoenix TCI + MoneyFlow + RSI
Three independent momentum oscillators must all agree on direction simultaneously.
The Phoenix TCI (Typical Close Index) is a smoothed momentum oscillator calculated from the typical price (HLC3) relative to its own deviation range, then further smoothed with an EMA. It measures normalized price momentum with reduced noise compared to raw price change.
MoneyFlow is a volume-weighted directional pressure indicator. It compares the sum of bullish volume (bars where price moved up) against bearish volume (bars where price moved down) over a rolling window. When MoneyFlow is rising, institutional buying pressure is dominant. When falling, selling pressure dominates.
RSI is used as a classic momentum confirmation filter. Here it is applied to HLC3 rather than close to reduce sensitivity to wicks and produce a smoother signal.
All three must simultaneously agree on direction — bullish for longs, bearish for shorts. This triple confirmation dramatically reduces false signals compared to using any single oscillator alone.
A secondary LSMA (Least Squares Moving Average) is applied to the composite Phoenix value as an additional trend confirmation. The LSMA slope must also agree with the trade direction.
Exit Logic
Three independent exit mechanisms are active simultaneously. Whichever triggers first closes the position.
The ATR Trailing Stop is set at entry as a multiple of ATR below price for longs, above price for shorts. It moves with price as the trade progresses — locking in profits while giving the trade room to breathe. The stop only moves in the profitable direction and never moves against the trade.
The ATR Take Profit is a fixed multiple of ATR set as a hard profit target at the moment of entry. When price reaches this level, the position closes regardless of other conditions.
The Maximum Hold Bars exit closes the trade after a maximum number of bars regardless of P&L. This prevents positions from being held indefinitely in slow, directionless markets where the original signal has likely expired.
A fourth optional exit is the Phoenix momentum reversal — when the Phoenix oscillator crosses below its signal line (for longs) or above (for shorts), it signals momentum exhaustion and the position closes.
Regime Filter (ADX)
An optional ADX-based regime filter prevents entries during sideways, low-trend markets. When ADX falls below the threshold, the market is considered to be in a ranging phase and no new entries are allowed. This filter is most effective on lower timeframes where choppy, directionless price action is more frequent.
ADX threshold of 18 is the default — values below 18 indicate weak trend, values above indicate a trending environment. This can be adjusted based on the symbol and timeframe.
Volatility-Adaptive ATR (Optional)
An optional volatility scaling mode adjusts the ATR stop and take profit multipliers based on the current volatility percentile. In low-volatility environments, the multipliers are scaled down slightly. In high-volatility environments, they are scaled up. This keeps the strategy's risk-reward profile consistent across different market conditions. Disabled by default — enable with caution and verify on your target symbol before live use.
Position Sizing
Position size is calculated dynamically at each entry using ATR-based risk management. The formula ensures that the distance from entry to stop loss always represents the same fixed percentage of equity, regardless of current volatility. This keeps actual dollar risk per trade approximately constant.
Default risk per trade is 2% of equity. This can be adjusted via the Risk per trade input. The position size is capped at 2× equity to prevent excessive leverage in extreme low-volatility conditions.
Backtest Conditions
Commission: 0.1% per trade (standard Binance Futures taker fee). Slippage: 2 ticks. process_orders_on_close: true — signals are only acted upon at confirmed bar close, eliminating intra-bar look-ahead bias. Lookahead is explicitly set to OFF in all security calls.
Default parameters were tested on BTCUSDC 30m data from December 2018 to March 2026 — covering 8+ years and 507 completed trades across all major market regimes: the 2019 accumulation phase, the 2020–2021 bull run, the 2022 bear market, the 2023 sideways consolidation, and the 2024–2025 recovery.
Adapting to Other Symbols and Timeframes
The default parameters are specifically optimized for BTCUSDC on the 30-minute timeframe. Applying them directly to other symbols or timeframes without adjustment will likely produce suboptimal results, as each market has different volatility characteristics, trend behaviors, and momentum profiles.
To adapt the strategy to a different symbol or timeframe, the following parameters are most important to re-tune: BB length and multipliers, KC multiplier, ATR length and multipliers, Phoenix N1/N2/N3 lengths, HMA length, EMA MTF length, and the squeeze lookback period. The regime filter ADX threshold should also be calibrated to the target market's typical trend strength.
A systematic approach using parameter optimization (grid search or Bayesian optimization) across a representative historical dataset is recommended over manual trial-and-error.
Why the Win Rate Is Low
This is a trend-following system, not a mean-reversion system. It accepts a low win rate (approximately 28% on default settings) in exchange for a high reward-to-risk ratio. The average winning trade is significantly larger than the average losing trade. The mathematical edge comes from this asymmetry, not from being right more often than wrong. This is a normal and expected characteristic of breakout and momentum strategies.
Disclaimer
Past performance does not guarantee future results. Cryptocurrency trading involves significant risk of loss. Only trade with capital you can afford to lose entirely. This script is provided for educational and research purposes only and does not constitute financial advice. You are solely responsible for your trading decisions. Strategy
BTC Round Levels For 500/1000BTC Round Levels 500 / 1000 – Psychological Price Structure Tool
BTC Round Levels automatically plots key psychological price levels based on 500 and 1000 increments. These levels represent areas where liquidity, order flow, and trader attention naturally concentrate.
Financial markets are driven by human behavior and algorithmic systems built around that behavior. Round numbers such as 60,000, 60,500, or 61,000 are not random — they act as psychological anchors. Because traders consistently place stop losses, take profits, breakout entries, and limit orders around these clean numbers, they become areas of repeated reaction.
This indicator systematically displays those levels around the current price, helping traders maintain structural awareness without manually drawing lines.
Why Round Levels Matter
Psychological Anchoring
Market participants naturally gravitate toward clean numbers instead of irregular prices. This creates clustering of orders.
Liquidity Concentration
Large players often target areas where retail orders accumulate. Round levels frequently act as liquidity pools.
Volatility Expansion
Breaks of major 1000 levels often trigger stop cascades and momentum moves.
Repeated Market Reactions
These levels often act as support, resistance, rejection zones, or breakout points.
What This Indicator Provides
Automatic plotting of 500 increment levels
Clear distinction of major 1000 increment levels
Adjustable number of levels above and below current price
Optional price labels
Clean and minimal structure
Use Cases
Intraday trading
Futures trading
Breakout strategies
Liquidity-based trading
Structure-based execution
Risk-to-reward planning
This tool helps maintain market structure clarity in volatile conditions, particularly in BTC where psychological levels frequently influence price behavior. Indicator
SuperTrend AI Adaptive - Strategy [BTC]+2,091% returns. 1.94 profit factor. 28% max drawdown.
Buy and hold returned ~785% over the same period with 75%+ drawdowns. This strategy returned 2,091% with less than a third of the drawdown. Consistent upward equity curve through bull markets, bear markets, and sideways chop.
This is the strategy version of SuperTrend AI . Same regime-adaptive engine, same AI scoring, now with full entries, exits, and risk management built in.
◈ How It Works
The strategy detects market regime shifts (trending, volatile, ranging) and adapts the SuperTrend multiplier automatically. Every trend flip is scored 0-100 by a 5-factor AI engine. Only high-scoring flips become trade entries.
The 5 scoring factors:
Volume Surge: was there conviction behind the flip?
Displacement: how far did price break through the band?
Trend Alignment: does the EMA agree with the direction?
Regime Quality: trending regimes score highest, ranging get penalized
Band Distance: how far did price travel to reach the flip point?
Low-scoring flips are skipped entirely. This is the main edge. Standard SuperTrend enters on every flip. This strategy is selective.
◈ Regime Adaptation
TRENDING regime: multiplier stays at base. Normal conditions, normal entries.
VOLATILE regime: multiplier widens automatically. Prevents noise-driven entries. Band turns amber on chart.
RANGING regime: multiplier tightens slightly. Entries are blocked by default because SuperTrend gets chopped in ranges.
The regime filter alone eliminates most of the losing trades that kill standard SuperTrend strategies.
◈ Risk Management
Three stop loss modes:
ATR-based (default): dynamic stop that adjusts to current volatility
Percent: fixed percentage stop
SuperTrend: exit only on trend flip
Take profit modes:
Risk:Reward ratio (default 2.5:1): TP based on SL distance
Percent: fixed percentage target
None: hold until stop or flip
Optional trailing stop for locking in profits on extended trends. All parameters are adjustable.
◈ Why It Beats Buy and Hold
Buy and hold works in hindsight. In real time, you sit through 50-75% drawdowns hoping for recovery. This strategy:
Shorts during bear markets instead of bleeding. The 2022 and early 2026 bear legs were profitable, not just survivable.
Stays flat during ranging markets. No entries when conditions are bad.
Compounds gains from both directions. Longs in uptrends, shorts in downtrends.
The equity curve tells the story. Consistent staircase up with controlled pullbacks vs the rollercoaster of buy and hold.
◈ Default Settings (optimized for BTCUSDT 4H)
SuperTrend: ATR 10, Base Multiplier 3.0
Regime: Lookback 40, ADX 14, Threshold 20
AI Engine: Trend EMA 50, Volume MA 20, Min Score 65
Risk: SL Mode ATR, SL ATR Mult 6.0, TP Mode RR 2.5:1
Filters: EMA Trend Filter on, Skip Ranging on, Volume Filter on, Cooldown 5
Position: 80% of equity per trade
Commission: 0.06% (Binance futures level), 2 ticks slippage
◈ Adapting to Other Assets
These defaults are tuned for BTCUSDT 4H. For other assets, adjust:
Other crypto (ETH, SOL) 4H: Same settings, may need Min Score 60
Forex 1H to 4H: Lower position size to 20-30%, tighten SL to 2.5-3.0 ATR, trend following works less well on forex
Indices 1H: SL ATR 3.0-4.0, position size 30-50%
SuperTrend strategies work best on assets that trend. Crypto on higher timeframes trends the hardest.
◈ Backtest Notes
Period: Jan 2015 to Feb 2026 (10+ years, includes multiple bull and bear cycles)
Initial capital: $10,000 USDT
Commission: 0.06% per trade (realistic for Binance futures)
Slippage: 2 ticks
Position sizing: 80% of equity (compounding)
No pyramiding. One position at a time.
Signals are non-repainting. Entries on confirmed bar close only.
Returns are compounded. The 2,091% figure reflects reinvesting profits at 80% equity per trade. Without compounding, the raw edge is captured by the profit factor (1.94) and win rate (46% at 2.5:1 RR).
◈ Key Metrics
Total P&L: +2,091%
Profit Factor: 1.94
Win Rate: 46.10% (71 of 154 trades)
Max Drawdown: 28.16%
Average trade count: roughly 15 per year
◈ Features
✓ Regime-adaptive SuperTrend with automatic multiplier adjustment
✓ AI signal scoring filters out low-quality trend flips
✓ Three SL modes (ATR, Percent, SuperTrend flip)
✓ Three TP modes (Risk:Reward, Percent, None)
✓ Optional trailing stop
✓ EMA trend filter, regime filter, volume filter
✓ Realistic commission and slippage included
✓ Dashboard showing trend, regime, position status, and signal score
✓ Non-repainting entries on confirmed bar close
✓ 100% original code
◈ Companion Indicator
This strategy is built on the SuperTrend AI indicator. Use the indicator for live chart analysis and the strategy for backtesting and validation. Both available free on my profile.
◈ Disclaimer
Past backtest performance does not guarantee future results. All backtests have inherent limitations including look-ahead bias in parameter selection. These settings were optimized on the full sample period. Always forward-test before risking real capital. Use proper position sizing and risk management. This is not financial advice.
Happy trading. Strategy
Asset Liquidity Meter by Funded RelayAsset Liquidity Meter by Funded Relay
This indicator estimates the liquidity of any asset by calculating the volume traded per unit of price movement (volume / (high - low)).
Higher values generally indicate better liquidity (more volume in a smaller price range → easier to enter/exit positions with less slippage).
Lower values suggest thinner liquidity (higher risk of price impact and volatility).
The indicator displays:
• Histogram: raw liquidity per bar (green = above SMA, red = below SMA)
• SMA line: smoothed liquidity trend
• Real-time info table in the top-right corner
• Built-in alert conditions
How to Use – Step by Step
1. Adding the Indicator
- Open any chart on PulseWire
- Click the "Indicators" button at the top
- Search for "Asset Liquidity Meter v6" (or find it in Community Scripts / My Scripts)
- Click to add it to the chart
- It will appear in a separate pane below the price chart
2. Customizing Settings
Double-click the indicator name in the pane (or right-click → Settings):
• SMA Length (default: 14)
- Controls the smoothing period of the liquidity trend line
- Smaller values (5–10) → more responsive, good for intraday/scalping
- Larger values (20–50) → smoother trend, better for swing/position trading
• Epsilon (default: 0.00000001)
- Tiny value that prevents division-by-zero errors on flat bars (high = low)
- Almost never needs to be changed
• Colors
- High Liquidity Color: histogram bars when liquidity > SMA
- Low Liquidity Color: histogram bars when liquidity < SMA
- SMA Line Color: color of the smoothed trend line
• Show Alert Conditions in Menu
- Keep enabled (true) to see the built-in alert options when creating alerts
3. Reading & Interpreting the Indicator
• Histogram Bars (Raw Liquidity)
- Height = amount of volume per unit of price range
- Tall bars = high liquidity (market is "thick")
- Short bars = low liquidity (market is "thin")
- Green = current liquidity is stronger than the average (SMA)
- Red = current liquidity is weaker than the average
• Blue SMA Line
- Shows the average liquidity over the selected period
- Rising line → liquidity improving (more participants, easier trading)
- Falling line → liquidity decreasing (thinner market, caution advised)
• Info Table (top-right corner)
- Displays current raw liquidity, SMA value, and status ("High Liquidity" / "Low Liquidity")
- Updates in real-time on the last bar
• Zero Line (dotted gray)
- Visual reference — everything above zero is positive liquidity
4. Practical Trading Applications
• High Liquidity Zones (green bars + rising SMA)
- Favorable conditions for entering or scaling into positions
- Lower expected slippage
- Better for large orders
• Low Liquidity Zones (red bars + falling SMA)
- Higher risk of slippage and exaggerated price moves
- Consider smaller position sizes or waiting for better conditions
- Common during session opens/closes, holidays, or low-volume periods
• Crossovers
- Liquidity crossing above SMA → potential increase in market participation
- Liquidity crossing below SMA → potential drying up of interest
5. Setting Up Alerts
1. Right-click on the chart → "Add Alert"
2. In "Condition", select "Asset Liquidity Meter v6"
3. Choose one of the available alert conditions:
- Liquidity ↑ Crosses Above SMA
- Liquidity ↓ Crosses Below SMA
- Very High Liquidity (2× SMA)
- Very Low Liquidity (<30% SMA)
4. Set frequency (Once Per Bar Close is usually best)
5. Configure notification (email, popup, sound, webhook, etc.)
6. Create the alert
6. Tips for Best Results
• Works on all markets: stocks, forex, crypto, futures, indices
• Best on timeframes with meaningful volume data (5 min and higher usually give clearest signals)
• Compare liquidity across different assets or timeframes using multiple charts
• Combine with support/resistance, volume profile or order flow tools for confirmation
• Not a standalone signal — use in context with your overall strategy
Limitations & Notes
• This is an estimation based on OHLCV data — it does not show real order book depth
• Results vary significantly between centralized exchanges, brokers and instruments
• Zero-volume bars will show zero liquidity (expected behavior)
Enjoy safer and more informed trading!
Questions or suggestions? Feel free to comment below. Indicator
Lakshmi - Low Volatility Range Breakout (LVRB)⚡️ Overview
The Low Volatility Range Breakout (LVRB) indicator is designed to identify consolidation phases characterized by suppressed volatility and generate actionable signals when price breaks out of these ranges. The underlying premise is rooted in the market principle that periods of low volatility often precede significant directional moves—volatility contraction leads to expansion.
Important Note on Optimization: The default parameter settings of this indicator have been specifically optimized for BTCUSDT on the 2-hour (2H) timeframe. While the indicator can be applied to other instruments and timeframes, users are encouraged to adjust the parameters accordingly to suit different trading conditions and asset characteristics.
This indicator automates the detection of "quiet" accumulation/distribution zones and provides clear visual cues and alerts when a breakout occurs.
⚡️ How to Use
1. Add the indicator to your chart. Default settings are optimized for BTCUSDT 2H.
2. Wait for a gray box to appear—this indicates a qualified low-volatility range is forming.
3. Monitor for breakout signals:
• LONG (green triangle below bar): Price broke above the range. Consider entering a long position.
• SHORT (red triangle above bar): Price broke below the range. Consider entering a short position.
4. Set alerts using "LVRB LONG" or "LVRB SHORT" to receive notifications on confirmed breakouts.
5. Adjust parameters as needed for different instruments or timeframes.
Tip: Combine with volume analysis or trend filters for higher-probability setups.
⚡️ How It Works
1. Low Volatility Bar Detection
A bar is classified as "low volatility" when it meets the following criteria:
• True Range (TR) is at or below the average TR (Simple Moving Average) multiplied by a user-defined threshold.
• (Optional) Candle Body is at or below the average body size multiplied by a separate threshold.
This dual-filter approach helps isolate bars that exhibit genuine compression in both range and directional commitment.
2. Range Box Formation
When consecutive low-volatility bars are detected, the indicator begins constructing a consolidation box:
• The box expands to encompass the high and low of qualifying bars.
• A minimum number of bars and a minimum fraction of low-volatility bars are required for the box to become "qualified" (active).
• A configurable tolerance allows for a limited number of consecutive non-low-vol bars within the sequence, accommodating minor noise without invalidating the range.
• If the box height exceeds a maximum threshold (defined as a multiple of the base ATR at sequence start), the range is invalidated.
3. Breakout Detection
Once a qualified range is established, the indicator monitors for breakouts:
• Wick Mode: Requires both a wick pierce beyond the range boundary AND a close outside the range.
• Close Mode: Requires only a close beyond the range boundary.
• (Optional) Breakout Body Filter: The breakout candle's body must exceed a multiple of the average body size at range formation.
• (Optional) Candle Direction Filter: Bullish breakouts require a green candle; bearish breakouts require a red candle.
Signals are displayed in real-time and confirmed upon bar close.
⚡️ Inputs & Parameters
• Volatility Window: Lookback period for calculating average TR and average body size.
• TR Multiplier: A bar's TR must be ≤ avgTR × this value to qualify as low-vol.
• Body Multiplier: A bar's body must be ≤ avgBody × this value (if body filter is enabled).
• Use Body Filter: Toggle the body size filter on/off.
• Min Bars in Box: Minimum number of bars required for a range to become qualified.
• Min Low-Vol Fraction: Minimum proportion of bars in the sequence that must be low-vol.
• Allowed Consecutive Non-Low-Vol Bars: Tolerance for consecutive bars that do not meet low-vol criteria.
• Max Box Height: Maximum allowed range height as a multiple of the base ATR.
• Breakout Mode: Choose between "Wick" (pierce + close) or "Close" (close only).
• Breakout Body Multiplier: Require breakout candle body ≥ avgBody × this value (1.0 = OFF).
• Require Candle Direction: Enforce green candle for LONG, red candle for SHORT.
⚡️ Visual Features
• Consolidation Boxes: Displayed in neutral (gray) color during formation. Upon a confirmed breakout, the box is colored green for bullish breakouts or red for bearish breakouts.
• Breakout Signals:
• LONG: Green upward triangle displayed below the price bar with "LONG" label.
• SHORT: Red downward triangle displayed above the price bar with "SHORT" label.
• Range Levels: Optional horizontal plots for the active range's high and low.
• Invalidated Boxes: Optionally retained in neutral (gray) color or deleted from the chart.
• Full Customization: Colors, transparency, and border width are all adjustable.
⚡️ Alerts
Two alert conditions are available:
• LVRB LONG: Triggered on a confirmed bullish breakout (bar close).
• LVRB SHORT: Triggered on a confirmed bearish breakout (bar close).
⚡️ Use Cases
• Breakout Trading: Enter positions when price escapes a well-defined low-volatility range.
• Volatility Expansion Plays: Anticipate increased volatility following periods of compression.
• Filtering Choppy Markets: Avoid trading during extended consolidation; wait for confirmed breakouts.
• Multi-Timeframe Analysis: Use on higher timeframes to identify major consolidation zones.
⚡️ Notes
• Best used in conjunction with volume analysis, trend context, or support/resistance levels for confirmation.
• Performance varies across instruments and timeframes; backtesting and parameter optimization are recommended.
⚡️ Credits
Developed by Lakshmi. Inspired by volatility contraction principles and range breakout methodologies.
⚡️ Disclaimer
This indicator is provided for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a guarantee of profits. Trading financial instruments involves substantial risk, and you may lose more than your initial investment. Past performance, whether indicated by backtesting or historical analysis, does not guarantee future results. The use of this indicator does not ensure or promise any profits or protection against losses. Users are solely responsible for their own trading decisions and should conduct their own research and/or consult with a qualified financial advisor before making any investment decisions. By using this indicator, you acknowledge and accept that you bear full responsibility for any trading outcomes. Indicator
Kalman Exponential SuperTrendThe Kalman Exponential SuperTrend is a new, smoother & superior version of the famous "SuperTrend". Using Kalman smoothing, a concept from the EMA (Exponential Moving Average), this script leverages the best out of each and combines it into a single indicator.
How does it work?
First, we need to calculate the Kalman smoothed source. This is a kind of complex calculation, so you need to study it if you want to know how it works precisely. It smooths the source of the SuperTrend, which helps us smooth the SuperTrend.
Then, we calculate "a" where:
n = user defined ATR length
a = 2/(n+1)
Now we calculate the ATR over "n" period. Classical calculation, nothing changed here.
Now we calculate the SuperTrend using the Kalman smoothed source & ATR where:
kalman = kalman smoothed source
ATR = Average True Range
m = Factor chosen by user.
Upper Band = kalman + ATR * m
Lower Band = kalman - ATR * m
Now we just smooth it a bit further using the "a" and a concept from the EMA.
u1 = Upper Band a bar ago
l1 = Lower Band a bar ago
u = Upper Band
l = Lower Band
Upper = u1 * (1-a) + u * a
Lower = l1 * (1-a) + u * a
When the classical (not Kalman) source crosses above the Upper, it indicates an uptrend. When it crosses below the Lower, it indicates a downtrend.
Methodology & Concepts
When I took a look at the classical SuperTrend => It was just far too slow, and if I made it faster it was noisy as hell. So I decided I would try to make up for it.
I tried the gaussian, bilateral filter, but then I tried kalman and that worked the best, so I added it. Now it was still too noisy and unconsistent, so I revisited my knowledge of concepts and picked the one from the EMA, and it kinda solved it.
In the core of the indicator, all it does is combine them in a really simple way, but if you go more deeply you see how it fits the puzzlé really well.
It is not about trying out random things´=> but about seeking what it is missing and trying to lessen its bad side.
That is the entire point of this indicator => Offer a unique approach to the SuperTrend type, that lessen the bad sides of it.
I also added different plotting types, this is so everyone can find their favorite
Enjoy Gs!
Thanks @BackQuant for making a open source Kalman code <3 Indicator
ADX Forecast Colorful [DiFlip]ADX Forecast Colorful
Introducing one of the most advanced ADX indicators available — a fully customizable analytical tool that integrates forward-looking forecasting capabilities. ADX Forecast Colorful is a scientific evolution of the classic ADX, designed to anticipate future trend strength using linear regression. Instead of merely reacting to historical data, this indicator projects the future behavior of the ADX, giving traders a strategic edge in trend analysis.
⯁ Real-Time ADX Forecasting
For the first time, a public ADX indicator incorporates linear regression (least squares method) to forecast the future behavior of ADX. This breakthrough approach enables traders to anticipate trend strength changes based on historical momentum. By applying linear regression to the ADX, the indicator plots a projected trendline n periods ahead — helping users make more accurate and timely trading decisions.
⯁ Highly Customizable
The indicator adapts seamlessly to any trading style. It offers a total of 26 long entry conditions and 26 short entry conditions, making it one of the most configurable ADX tools on PulseWire. Each condition is fully adjustable, enabling the creation of statistical, quantitative, and automated strategies. You maintain full control over the signals to align perfectly with your system.
⯁ Innovative and Science-Based
This is the first public ADX indicator to apply least-squares predictive modeling to ADX dynamics. Technically, it embeds machine learning logic into a traditional trend-strength indicator. Using linear regression as a predictive engine adds powerful statistical rigor to the ADX, turning it into an intelligent, forward-looking signal generator.
⯁ Scientific Foundation: Linear Regression
Linear regression is a fundamental method in statistics and machine learning used to model the relationship between a dependent variable y and one or more independent variables x. The basic formula for simple linear regression is:
y = β₀ + β₁x + ε
Where:
y = predicted value (e.g., future ADX)
x = explanatory variable (e.g., bar index or time)
β₀ = intercept
β₁ = slope (rate of change)
ε = random error term
The goal is to estimate β₀ and β₁ by minimizing the sum of squared errors. This is achieved using the least squares method, ensuring the best linear fit to historical data. Once the coefficients are calculated, the model extends the regression line forward, generating the ADX projection based on recent trends.
⯁ Least Squares Estimation
To minimize the error, the regression coefficients are calculated as:
β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²)
β₀ = ȳ - β₁x̄
Where:
Σ = summation
x̄ and ȳ = means of x and y
i ranges from 1 to n (number of data points)
These formulas provide the best linear unbiased estimator under Gauss-Markov conditions — assuming constant variance and linearity.
⯁ Linear Regression in Machine Learning
Linear regression is a foundational algorithm in supervised learning. Its power in producing quantitative predictions makes it essential in AI systems, predictive analytics, time-series forecasting, and automated trading. Applying it to the ADX essentially places an intelligent forecasting engine inside a classic trend tool.
⯁ Visual Interpretation
Imagine an ADX time series like this:
Time →
ADX →
The regression line smooths these values and projects them n periods forward, creating a predictive trajectory. This forecasted ADX line can intersect with the actual ADX, offering smarter buy and sell signals.
⯁ Summary of Scientific Concepts
Linear Regression: Models variable relationships with a straight line.
Least Squares: Minimizes prediction errors for best fit.
Time-Series Forecasting: Predicts future values using historical data.
Supervised Learning: Trains models to predict outcomes from inputs.
Statistical Smoothing: Reduces noise and highlights underlying trends.
⯁ Why This Indicator Is Revolutionary
Scientifically grounded: Based on rigorous statistical theory.
Unprecedented: First public ADX using least-squares forecast modeling.
Smart: Uses machine learning logic.
Forward-Looking: Generates predictive, not just reactive, signals.
Customizable: Flexible for any strategy or timeframe.
⯁ Conclusion
By merging ADX and linear regression, this indicator enables traders to predict market momentum rather than merely follow it. ADX Forecast Colorful is not just another indicator — it’s a scientific leap forward in technical analysis. With 26 fully configurable entry conditions and smart forecasting, this open-source tool is built for creating cutting-edge quantitative strategies.
⯁ Example of simple linear regression with one independent variable
This example demonstrates how a basic linear regression works when there is only one independent variable influencing the dependent variable. This type of model is used to identify a direct relationship between two variables.
⯁ In linear regression, observations (red) are considered the result of random deviations (green) from an underlying relationship (blue) between a dependent variable (y) and an independent variable (x)
This concept illustrates that sampled data points rarely align perfectly with the true trend line. Instead, each observed point represents the combination of the true underlying relationship and a random error component.
⯁ Visualizing heteroscedasticity in a scatterplot with 100 random fitted values using Matlab
Heteroscedasticity occurs when the variance of the errors is not constant across the range of fitted values. This visualization highlights how the spread of data can change unpredictably, which is an important factor in evaluating the validity of regression models.
⯁ The datasets in Anscombe’s quartet were designed to have nearly the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but look very different when plotted
This classic example shows that summary statistics alone can be misleading. Even with identical numerical metrics, the datasets display completely different patterns, emphasizing the importance of visual inspection when interpreting a model.
⯁ Result of fitting a set of data points with a quadratic function
This example illustrates how a second-degree polynomial model can better fit certain datasets that do not follow a linear trend. The resulting curve reflects the true shape of the data more accurately than a straight line.
⯁ What is the ADX?
The Average Directional Index (ADX) is a technical analysis indicator developed by J. Welles Wilder. It measures the strength of a trend in a market, regardless of whether the trend is up or down.
The ADX is an integral part of the Directional Movement System, which also includes the Plus Directional Indicator (+DI) and the Minus Directional Indicator (-DI). By combining these components, the ADX provides a comprehensive view of market trend strength.
⯁ How to use the ADX?
The ADX is calculated based on the moving average of the price range expansion over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and has three main zones:
Strong Trend: When the ADX is above 25, indicating a strong trend.
Weak Trend: When the ADX is below 20, indicating a weak or non-existent trend.
Neutral Zone: Between 20 and 25, where the trend strength is unclear.
⯁ Entry Conditions
Each condition below is fully configurable and can be combined to build precise trading logic.
📈 BUY
🅰️ Signal Validity: The signal will remain valid for X bars .
🅰️ Signal Sequence: Configurable as AND or OR .
🅰️ +DI > -DI
🅰️ +DI < -DI
🅰️ +DI > ADX
🅰️ +DI < ADX
🅰️ -DI > ADX
🅰️ -DI < ADX
🅰️ ADX > Threshold
🅰️ ADX < Threshold
🅰️ +DI > Threshold
🅰️ +DI < Threshold
🅰️ -DI > Threshold
🅰️ -DI < Threshold
🅰️ +DI (Crossover) -DI
🅰️ +DI (Crossunder) -DI
🅰️ +DI (Crossover) ADX
🅰️ +DI (Crossunder) ADX
🅰️ +DI (Crossover) Threshold
🅰️ +DI (Crossunder) Threshold
🅰️ -DI (Crossover) ADX
🅰️ -DI (Crossunder) ADX
🅰️ -DI (Crossover) Threshold
🅰️ -DI (Crossunder) Threshold
🔮 +DI (Crossover) -DI Forecast
🔮 +DI (Crossunder) -DI Forecast
🔮 ADX (Crossover) +DI Forecast
🔮 ADX (Crossunder) +DI Forecast
📉 SELL
🅰️ Signal Validity: The signal will remain valid for X bars .
🅰️ Signal Sequence: Configurable as AND or OR .
🅰️ +DI > -DI
🅰️ +DI < -DI
🅰️ +DI > ADX
🅰️ +DI < ADX
🅰️ -DI > ADX
🅰️ -DI < ADX
🅰️ ADX > Threshold
🅰️ ADX < Threshold
🅰️ +DI > Threshold
🅰️ +DI < Threshold
🅰️ -DI > Threshold
🅰️ -DI < Threshold
🅰️ +DI (Crossover) -DI
🅰️ +DI (Crossunder) -DI
🅰️ +DI (Crossover) ADX
🅰️ +DI (Crossunder) ADX
🅰️ +DI (Crossover) Threshold
🅰️ +DI (Crossunder) Threshold
🅰️ -DI (Crossover) ADX
🅰️ -DI (Crossunder) ADX
🅰️ -DI (Crossover) Threshold
🅰️ -DI (Crossunder) Threshold
🔮 +DI (Crossover) -DI Forecast
🔮 +DI (Crossunder) -DI Forecast
🔮 ADX (Crossover) +DI Forecast
🔮 ADX (Crossunder) +DI Forecast
🤖 Automation
All BUY and SELL conditions are compatible with PulseWire alerts, making them ideal for fully or semi-automated systems.
⯁ Unique Features
Linear Regression: (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
Linear Regression (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
Indicator
Trend Line Methods (TLM)Trend Line Methods (TLM)
Overview
Trend Line Methods (TLM) is a visual study designed to help traders explore trend structure using two complementary, auto-drawn trend channels. The script focuses on how price interacts with rising or falling boundaries over time. It does not generate trade signals or manage risk; its purpose is to support discretionary chart analysis.
Method 1 – Pivot Span Trendline
The Pivot Span Trendline method builds a dynamic channel from major swing points detected by pivot highs and pivot lows.
• The script tracks a configurable number of recent pivot highs and lows.
• From the oldest and most recent stored pivot highs, it draws an upper trend line.
• From the oldest and most recent stored pivot lows, it draws a lower trend line.
• An optional filled area can be drawn between the two lines to highlight the active trend span.
As new pivots form, the lines are recalculated so that the channel evolves with market structure. This method is useful for visualising how price respects a trend corridor defined directly by swing points.
Method 2 – 5-Point Straight Channel
The 5-Point Straight Channel method approximates a straight trend channel using five key points extracted from a fixed lookback window.
Within the selected window:
• The window is divided into five segments of similar length.
• In each segment, the highest high is used as a representative high point.
• In each segment, the lowest low is used as a representative low point.
• A straight regression-style line is fitted through the five high points to form the upper boundary.
• A second straight line is fitted through the five low points to form the lower boundary.
The result is a pair of straight lines that describe the overall directional channel of price over the chosen window. Compared to Method 1, this approach is less focused on the very latest swings and more on the broader slope of the market.
Inputs & Menus
Pivot Span Trendline group (Method 1)
• Enable Pivot Span Trendline – Turns Method 1 on or off.
• High trend line color / Low trend line color – Colors of the upper and lower trend lines.
• Fill color between trend lines – Base color used to shade the area between the two lines. Transparency is controlled internally.
• Trend line thickness – Line width for both high and low trend lines.
• Trend line style – Line style (solid, dashed, or dotted).
• Pivot Left / Pivot Right – Number of bars to the left and right used to confirm pivot highs and lows. Larger values produce fewer but more significant swing points.
• Pivot Count – How many historical pivot points are kept for constructing the trend lines.
• Lookback Length – Number of bars used to keep pivots in range and to extend the trend lines across the chart.
5-Point Straight Channel group (Method 2)
• Enable 5-Point Straight Channel – Turns Method 2 on or off.
• High channel line color / Low channel line color – Colors of the upper and lower channel lines.
• Channel line thickness – Line width for both channel lines.
• Channel line style – Line style (solid, dashed, or dotted).
• Channel Length (bars) – Lookback window used to divide price into five segments and build the straight high/low channel.
Using Both Methods Together
Both methods are designed to visualise the same underlying idea: price tends to move inside rising or falling channels. Method 1 emphasises the most recent swing structure via pivot points, while Method 2 summarises the broader channel over a fixed window.
When the Pivot Span Trendline corridor and the 5-Point Straight Channel boundaries align or intersect, they can highlight zones where multiple ways of drawing trend lines point to similar support or resistance areas. Traders can use these confluence zones as a visual reference when planning their own entries, exits, or risk levels, according to their personal trading plan.
Notes
• This script is meant as an educational and analytical tool for studying trend lines and channels.
• It does not generate trading signals and does not replace independent analysis or risk management.
• The behaviour of both methods is timeframe- and symbol-agnostic; they will adapt to whichever chart you apply them to.
Indicator
Strategy
Market Extreme Zones IndexThe Market Extreme Zones Index is a new mean reversion (valuation) tool focused on catching long term oversold/overbought zones. Combining an enhanced RSI with a smoothed Z-score this indicator allows traders to find oppurtunities during highly oversold/overbought zones.
I will separate the explanation into the following parts:
1. How does it work?
2. Methodologies & Concepts
3. Use cases
How does it work?
The indicator attempts to catch highly unprobable events in either direction to capture reversal points over the long term. This is done by calculating the Z-Score of an enhanced RSI.
First we need to calculate the Enhanced RSI:
For this we need to calculate 2 additional lengths:
Length1 = user defined length
Length2 = Length1/2
Length3 = √Length
Now we need to calculate 3 different RSIs:
1st RSI => uses classic user defined source and classic user defined length.
2nd RSI => uses classic user defined source and Length 2.
3rd RSI => uses RSI 2 as source and Length 2
Now calculate the divergence:
RSI_base => 2nd RSI * 3 - 1st RSI - 3rd RSI
After this we need to calculate the median of the RSI_base over √Length and make a divergence of these 2:
RSI => RSI_base*2 - median
All that remains now is the Z-score calculations:
We need:
Average RSI value
Standard Deviation = a measure of how dispersed or spread out a set of data values are from their average
Z-score = (Current Value - Average Value) / Standard Deviation
After this we just smooth the Z-score with a Weighted Moving average with √Length
Methodology & Concepts
Mean Reversion Methodology:
The methodology behind mean reversion is the theory that asset prices will eventually return to their long-term average after deviating significantly, driven by the belief that extreme moves are temporary.
Z-Score Methodology:
A Z-score, or standard score, is a statistical measure that indicates how many standard deviations a data point is from the mean of a dataset. A positive z-score means the value is above the mean, a negative score means it's below, and a score of zero means the value is equal to the mean.
You might already be able to see where I am going with this:
Z-Score could be used for the extreme moves to capture reversal points.
By applying it to the RSI rather than the Price, we get a more accurate measurement that allow us to get a banger indicator.
Use Cases
Capturing reversal points
Trend Direction
- while the main use it for mean reversion, the values can indicate whether we are in an uptrend or a downtrend.
Advantages:
Visualization:
The indicator has many plots to ensure users can easily see what the indicator signals, such as highlighting extreme conditions with background colors.
Versatility:
This indicator works across multiple assets, including the S&P500 and more, so it is not only for crypto.
Final note:
No indicator alone is perfect.
Backtests are not indicative of future performance.
Hope you enjoy Gs!
Good luck! Indicator
Indicator
MACD Forecast [Titans_Invest]MACD Forecast — The Future of MACD in Trading
The MACD has always been one of the most powerful tools in technical analysis.
But what if you could see where it’s going, instead of just reacting to what has already happened?
Introducing MACD Forecast — the natural evolution of the MACD Full , now taken to the next level. It’s the world’s first MACD designed not only to analyze the present but also to predict the future behavior of momentum.
By combining the classic MACD structure with projections powered by Linear Regression, this indicator gives traders an anticipatory, predictive view, redefining what’s possible in technical analysis.
Forget lagging indicators.
This is the smartest, most advanced, and most accurate MACD ever created.
🍟 WHY MACD FORECAST IS REVOLUTIONARY
Unlike the traditional MACD, which only reflects current and past price dynamics, the MACD Forecast uses regression-based projection models to anticipate where the MACD line, signal line, and histogram are heading.
This means traders can:
• See MACD crossovers before they happen.
• Spot trend reversals earlier than most.
• Gain an unprecedented timing advantage in both discretionary and automated trading.
In other words: this indicator lets you trade ahead of time.
🔮 FORECAST ENGINE — POWERED BY LINEAR REGRESSION
At its core, the MACD Forecast integrates Linear Regression (ta.linreg) to project the MACD’s future behavior with exceptional accuracy.
Projection Modes:
• Flat Projection: Assumes trend continuity at the current level.
• LinReg Projection: Applies linear regression across N periods to mathematically forecast momentum shifts.
This dual system offers both a conservative and adaptive view of market direction.
📐 ACCURACY WITH FULL CUSTOMIZATION
Just like the MACD Full, this new version comes with 20 customizable buy-entry conditions and 20 sell-entry conditions — now enhanced with forecast-based rules that anticipate crossovers and trend reversals.
You’re not just reacting — you’re strategizing ahead of time.
⯁ HOW TO USE MACD FORECAST❓
The MACD Forecast is built on the same foundation as the classic MACD, but with predictive capabilities.
Step 1 — Spot Predicted Crossovers:
Watch for forecasted bullish or bearish crossovers. These signals anticipate when the MACD line will cross the signal line in the future, letting you prepare trades before the move.
Step 2 — Confirm with Histogram Projection:
Use the projected histogram to validate momentum direction. A rising histogram signals strengthening bullish momentum, while a falling projection points to weakening or bearish conditions.
Step 3 — Combine with Multi-Timeframe Analysis:
Use forecasts across multiple timeframes to confirm signal strength (e.g., a 1h forecast aligned with a 4h forecast).
Step 4 — Set Entry Conditions & Automation:
Customize your buy/sell rules with the 20 forecast-based conditions and enable automation for bots or alerts.
Step 5 — Trade Ahead of the Market:
By preparing for future momentum shifts instead of reacting to the past, you’ll always stay one step ahead of lagging traders.
🤖 BUILT FOR AUTOMATION AND BOTS 🤖
Whether for manual trading, quantitative strategies, or advanced algorithms, the MACD Forecast was designed to integrate seamlessly with automated systems.
With predictive logic at its core, your strategies can finally react to what’s coming, not just what already happened.
🥇 WHY THIS INDICATOR IS UNIQUE 🥇
• World’s first MACD with Linear Regression Forecasting
• Predictive Crossovers (before they appear on the chart)
• Maximum flexibility with Long & Short combinations — 20+ fully configurable conditions for tailor-made strategies
• Fully automatable for quantitative systems and advanced bots
This isn’t just an update.
It’s the final evolution of the MACD.
______________________________________________________
🔹 CONDITIONS TO BUY 📈
______________________________________________________
• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
🔹 MACD > Signal Smoothing
🔹 MACD < Signal Smoothing
🔹 Histogram > 0
🔹 Histogram < 0
🔹 Histogram Positive
🔹 Histogram Negative
🔹 MACD > 0
🔹 MACD < 0
🔹 Signal > 0
🔹 Signal < 0
🔹 MACD > Histogram
🔹 MACD < Histogram
🔹 Signal > Histogram
🔹 Signal < Histogram
🔹 MACD (Crossover) Signal
🔹 MACD (Crossunder) Signal
🔹 MACD (Crossover) 0
🔹 MACD (Crossunder) 0
🔹 Signal (Crossover) 0
🔹 Signal (Crossunder) 0
🔮 MACD (Crossover) Signal Forecast
🔮 MACD (Crossunder) Signal Forecast
______________________________________________________
______________________________________________________
🔸 CONDITIONS TO SELL 📉
______________________________________________________
• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
🔸 MACD > Signal Smoothing
🔸 MACD < Signal Smoothing
🔸 Histogram > 0
🔸 Histogram < 0
🔸 Histogram Positive
🔸 Histogram Negative
🔸 MACD > 0
🔸 MACD < 0
🔸 Signal > 0
🔸 Signal < 0
🔸 MACD > Histogram
🔸 MACD < Histogram
🔸 Signal > Histogram
🔸 Signal < Histogram
🔸 MACD (Crossover) Signal
🔸 MACD (Crossunder) Signal
🔸 MACD (Crossover) 0
🔸 MACD (Crossunder) 0
🔸 Signal (Crossover) 0
🔸 Signal (Crossunder) 0
🔮 MACD (Crossover) Signal Forecast
🔮 MACD (Crossunder) Signal Forecast
______________________________________________________
______________________________________________________
🔮 Linear Regression Function 🔮
______________________________________________________
• Our indicator includes MACD forecasts powered by linear regression.
Forecast Types:
• Flat: Assumes prices will stay the same.
• Linreg: Makes a 'Linear Regression' forecast for n periods.
Technical Information:
• Function: ta.linreg()
Parameters:
• source: Source price series.
• length: Number of bars (period).
• offset : Offset.
• return: Linear regression curve.
______________________________________________________
______________________________________________________
⯁ UNIQUE FEATURES
______________________________________________________
Linear Regression: (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
Linear Regression (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
______________________________________________________
📜 SCRIPT : MACD Forecast
🎴 Art by : @Titans_Invest & @DiFlip
👨💻 Dev by : @Titans_Invest & @DiFlip
🎑 Titans Invest — The Wizards Without Gloves 🧤
✨ Enjoy!
______________________________________________________
o Mission 🗺
• Inspire Traders to manifest Magic in the Market.
o Vision 𐓏
• To elevate collective Energy 𐓷𐓏
🎗️ In memory of João Guilherme — your light will live on forever. Indicator
Indicator
Mutanabby_AI | ATR+ | Trend-Following StrategyThis document presents the Mutanabby_AI | ATR+ Pine Script strategy, a systematic approach designed for trend identification and risk-managed position entry in financial markets. The strategy is engineered for long-only positions and integrates volatility-adjusted components to enhance signal robustness and trade management.
Strategic Design and Methodological Basis
The Mutanabby_AI | ATR+ strategy is constructed upon a foundation of established technical analysis principles, with a focus on objective signal generation and realistic trade execution.
Heikin Ashi for Trend Filtering: The core price data is processed via Heikin Ashi (HA) methodology to mitigate transient market noise and accentuate underlying trend direction. The script offers three distinct HA calculation modes, allowing for comparative analysis and validation:
Manual Calculation: Provides a transparent and deterministic computation of HA values.
ticker.heikinashi(): Utilizes PulseWire's built-in function, employing confirmed historical bars to prevent repainting artifacts.
Regular Candles: Allows for direct comparison with standard OHLC price action.
This multi-methodological approach to trend smoothing is critical for robust signal generation.
Adaptive ATR Trailing Stop: A key component is the Average True Range (ATR)-based trailing stop. ATR serves as a dynamic measure of market volatility. The strategy incorporates user-defined parameters (
Key Value and ATR Period) to calibrate the sensitivity of this trailing stop, enabling adaptation to varying market volatility regimes. This mechanism is designed to provide a dynamic exit point, preserving capital and locking in gains as a trend progresses.
EMA Crossover for Signal Generation: Entry and exit signals are derived from the interaction between the Heikin Ashi derived price source and an Exponential Moving Average (EMA). A crossover event between these two components is utilized to objectively identify shifts in momentum, signaling potential long entry or exit points.
Rigorous Stop Loss Implementation: A critical feature for risk mitigation, the strategy includes an optional stop loss. This stop loss can be configured as a percentage or fixed point deviation from the entry price. Importantly, stop loss execution is based on real market prices, not the synthetic Heikin Ashi values. This design choice ensures that risk management is grounded in actual market liquidity and price levels, providing a more accurate representation of potential drawdowns during backtesting and live operation.
Backtesting Protocol: The strategy is configured for realistic backtesting, employing fill_orders_on_standard_ohlc=true to simulate order execution at standard OHLC prices. A configurable Date Filter is included to define specific historical periods for performance evaluation.
Data Visualization and Metrics: The script provides on-chart visual overlays for buy/sell signals, the ATR trailing stop, and the stop loss level. An integrated information table displays real-time strategy parameters, current position status, trend direction, and key price levels, facilitating immediate quantitative assessment.
Applicability
The Mutanabby_AI | ATR+ strategy is particularly suited for:
Cryptocurrency Markets: The inherent volatility of assets such as #Bitcoin and #Ethereum makes the ATR-based trailing stop a relevant tool for dynamic risk management.
Systematic Trend Following: Individuals employing systematic methodologies for trend capture will find the objective signal generation and rule-based execution aligned with their approach.
Pine Script Developers and Quants: The transparent code structure and emphasis on realistic backtesting provide a valuable framework for further analysis, modification, and integration into broader quantitative models.
Automated Trading Systems: The clear, deterministic entry and exit conditions facilitate integration into automated trading environments.
Implementation and Evaluation
To evaluate the Mutanabby_AI | ATR+ strategy, apply the script to your chosen chart on PulseWire. Adjust the input parameters (Key Value, ATR Period, Heikin Ashi Method, Stop Loss Settings) to observe performance across various asset classes and timeframes. Comprehensive backtesting is recommended to assess the strategy's historical performance characteristics, including profitability, drawdown, and risk-adjusted returns.
I'd love to hear your thoughts, feedback, and any optimizations you discover! Drop a comment below, give it a like if you find it useful, and share your results.
Strategy
Indicator
RSI-Adaptive T3 + Squeeze Momentum Strategy✅ Strategy Guide: RSI-Adaptive T3 + Squeeze Momentum Strategy
📌 Overview
The RSI-Adaptive T3 + Squeeze Momentum Strategy is a dynamic trend-following strategy based on an RSI-responsive T3 moving average and Squeeze Momentum detection .
It adapts in real-time to market volatility to enhance entry precision and optimize risk.
⚠️ This strategy is provided for educational and research purposes only.
Past performance does not guarantee future results.
🎯 Strategy Objectives
The main objective of this strategy is to catch the early phase of a trend and generate consistent entry signals.
Designed to be intuitive and accessible for traders from beginner to advanced levels.
✨ Key Features
RSI-Responsive T3: T3 length dynamically adjusts according to RSI values for adaptive trend detection
Squeeze Momentum: Combines Bollinger Bands and Keltner Channels to identify trend buildup phases
Visual Triggers: Entry signals are generated from T3 crossovers and momentum strength after squeeze release
📊 Trading Rules
Long Entry:
When T3 crosses upward, momentum is positive, and the squeeze has just been released.
Short Entry:
When T3 crosses downward, momentum is negative, and the squeeze has just been released.
Exit (Reversal):
When the opposite condition to the entry is triggered, the position is reversed.
💰 Risk Management Parameters
Pair & Timeframe: BTC/USD (30-minute chart)
Capital (simulated): $30,00
Order size: `$100` per trade (realistic, low-risk sizing)
Commission: 0.02%
Slippage: 2 pips
Risk per Trade: 5%
Number of Trades (backtest period): 181
📊 Performance Overview
Symbol: BTC/USD
Timeframe: 30-minute chart
Date Range: January 1, 2024 – July 3, 2025
Win Rate: 47.8%
Profit Factor: 2.01
Net Profit: 173.16 (units not specified)
Max Drawdown: 5.77% or 24.91 (0.79%)
⚙️ Indicator Parameters
Indicator Name: RSI-Adaptive T3 + Squeeze Momentum
RSI Length: 14
T3 Min Length: 5
T3 Max Length: 50
T3 Volume Factor: 0.7
BB Length: 27 (Multiplier: 2.0)
KC Length: 20 (Multiplier: 1.5, TrueRange enabled)
🖼 Visual Support
T3 slope direction, squeeze status, and momentum bars are visually plotted on the chart,
providing high clarity for quick trend analysis and execution.
🔧 Strategy Improvements & Uniqueness
Inspired by the RSI Adaptive T3 by ChartPrime and Squeeze Momentum Indicator by LazyBear ,
this strategy fuses both into a hybrid trend-reversal and momentum breakout detection system .
Compared to traditional trend-following methods, it excels at capturing early trend signals with greater sensitivity .
✅ Summary
The RSI-Adaptive T3 + Squeeze Momentum Strategy combines momentum detection with volatility-responsive risk management.
With a strong balance between visual clarity and practicality, it serves as a powerful tool for traders seeking high repeatability.
⚠️ This strategy is based on historical data and does not guarantee future profits.
Always use appropriate risk management when applying it. Strategy
MACD Full [Titans_Invest]MACD Full — A Smarter, More Flexible MACD.
Looking for a MACD with real customization power?
We present one of the most complete public MACD indicators available on PulseWire.
It maintains the classic MACD structure but is enhanced with 20 fully customizable long entry conditions and 20 short entry conditions , giving you precise control over your strategy.
Plus, it’s fully automation-ready, making it ideal for quantitative systems and algorithmic trading.
Whether you're a discretionary trader or a bot developer, this tool is built to seamlessly adapt to your style.
⯁ WHAT IS THE MACD❓
The Moving Average Convergence Divergence (MACD) is a technical analysis indicator developed by Gerald Appel. It measures the relationship between two moving averages of a security’s price to identify changes in momentum, direction, and strength of a trend. The MACD is composed of three components: the MACD line, the signal line, and the histogram.
⯁ HOW TO USE THE MACD❓
The MACD is calculated by subtracting the 26-period Exponential Moving Average (EMA) from the 12-period EMA. A 9-period EMA of the MACD line, called the signal line, is then plotted on top of the MACD line. The MACD histogram represents the difference between the MACD line and the signal line.
Here are the primary signals generated by the MACD:
Bullish Crossover: When the MACD line crosses above the signal line, indicating a potential buy signal.
Bearish Crossover: When the MACD line crosses below the signal line, indicating a potential sell signal.
Divergence: When the price of the security diverges from the MACD, suggesting a potential reversal.
Overbought/Oversold Conditions: Indicated by the MACD line moving far away from the signal line, though this is less common than in oscillators like the RSI.
⯁ ENTRY CONDITIONS
The conditions below are fully flexible and allow for complete customization of the signal.
______________________________________________________
🔹 CONDITIONS TO BUY 📈
______________________________________________________
• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
🔹 MACD > Signal Smoothing
🔹 MACD < Signal Smoothing
🔹 Histogram > 0
🔹 Histogram < 0
🔹 Histogram Positive
🔹 Histogram Negative
🔹 MACD > 0
🔹 MACD < 0
🔹 Signal > 0
🔹 Signal < 0
🔹 MACD > Histogram
🔹 MACD < Histogram
🔹 Signal > Histogram
🔹 Signal < Histogram
🔹 MACD (Crossover) Signal
🔹 MACD (Crossunder) Signal
🔹 MACD (Crossover) 0
🔹 MACD (Crossunder) 0
🔹 Signal (Crossover) 0
🔹 Signal (Crossunder) 0
______________________________________________________
______________________________________________________
🔸 CONDITIONS TO SELL 📉
______________________________________________________
• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
🔸 MACD > Signal Smoothing
🔸 MACD < Signal Smoothing
🔸 Histogram > 0
🔸 Histogram < 0
🔸 Histogram Positive
🔸 Histogram Negative
🔸 MACD > 0
🔸 MACD < 0
🔸 Signal > 0
🔸 Signal < 0
🔸 MACD > Histogram
🔸 MACD < Histogram
🔸 Signal > Histogram
🔸 Signal < Histogram
🔸 MACD (Crossover) Signal
🔸 MACD (Crossunder) Signal
🔸 MACD (Crossover) 0
🔸 MACD (Crossunder) 0
🔸 Signal (Crossover) 0
🔸 Signal (Crossunder) 0
______________________________________________________
______________________________________________________
🤖 AUTOMATION 🤖
• You can automate the BUY and SELL signals of this indicator.
______________________________________________________
______________________________________________________
⯁ UNIQUE FEATURES
______________________________________________________
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
______________________________________________________
📜 SCRIPT : MACD Full
🎴 Art by : @Titans_Invest & @DiFlip
👨💻 Dev by : @Titans_Invest & @DiFlip
🎑 Titans Invest — The Wizards Without Gloves 🧤
✨ Enjoy!
______________________________________________________
o Mission 🗺
• Inspire Traders to manifest Magic in the Market.
o Vision 𐓏
• To elevate collective Energy 𐓷𐓏 Indicator
Indicator
