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Tìm kiếm tập lệnh với "Candlestick"

特典インジケーター　（ボリンジャーバンド+移動平均線）BTCやSP500向けのチャート解析ツールです。 - ボリンジャーバンド（オレンジ上下線、水色中央線） - EMA5（青線）、EMA25（黄色線）、EMA200（赤線） 使い方のポイント - トレンド判定: EMA200（赤）より上なら上昇基調、下なら下降基調が優勢。 - 短中期の勢い: EMA5（青）とEMA25（黄）のゴールデンクロス／デッドクロスで勢いの変化を確認。 - ボラティリティと逆張り: ボリンジャーバンドの上限／下限タッチは伸びの継続か反転の初動かを、中央線（基準・水色）復帰でフォロー確認。 - 時間軸: 1時間～4時間は短期、日足は中期のトレンド確認に適合。複数時間軸で整合性を取ると精度が上がります。 ツールの解説 ボリンジャーバンド（Bollinger Bands） ボリンジャーバンドは、20期間の単純移動平均（SMA）を中央線とし、その上下に標準偏差×2のバンドを配置します。 - 上限バンド：相場の上振れが過熱している可能性を示すレジスタンスライン - 下限バンド：相場の下振れが過冷却している可能性を示すサポートライン - バンド幅の拡大：ボラティリティ上昇局面を示唆 - バンド幅の収縮：レンジ相場や転換前の低ボラティリティを示唆 --------------------- EMA5（Exponential Moving Average 5） EMA5は直近5本の価格により重み付けされた指数移動平均です。 - 非常に短期的な価格の変化を捉え、エントリーや還流のタイミングに敏感 - EMA25とのクロスオーバーで、短期モメンタムの変化を判断 EMA25（Exponential Moving Average 25） EMA25は中期的なトレンドを表す指数移動平均です。 - EMA5との位置関係でトレンドの強さや方向性を評価 - 価格がEMA25を上回れば短期的な買い優勢、下回れば売り優勢 EMA200（Exponential Moving Average 200） EMA200は長期トレンドの大局を示す指数移動平均です。 - プロのトレーダーにも重要視されるサポート／レジスタンスライン - 価格がEMA200を上回ると長期的に強気、市場全体のセンチメント確認に利用 Chart Analysis Tool for BTC and S&P500 - Bollinger Bands (orange upper/lower lines, light blue middle line) - EMA5 (blue line), EMA25 (yellow line), EMA200 (red line)
Chỉ báo Pine Script®
bởi N-1111
Combined Triggers Dashboard//@version=6 indicator("Combined Triggers Dashboard", overlay=true) // ======================= INPUTS ======================= // Daily Trigger shortDEMA_D = input.int(10, "Daily 10 DEMA") longDEMA_D = input.int(20, "Daily 20 DEMA") volAvgLen_D = input.int(20, "Daily 20-day Avg Volume") volMultiplier_D = input.float(3, "Daily Volume Multiplier") weekDEMAlen_D = input.int(10, "10-Week DEMA Reference for Daily Trigger") // Weekly Trigger shortDEMA_W = input.int(10, "Weekly 10 W DEMA") longDEMA_W = input.int(20, "Weekly 20 W DEMA") volAvgLen_W = input.int(50, "50-day Avg Volume for Weekly Trigger") volMultiplier_W = input.float(3, "Weekly Volume Multiplier") // Original Trigger (example) shortDEMA_O = input.int(10, "Original 10 DEMA") longDEMA_O = input.int(20, "Original 20 DEMA") volAvgLen_O = input.int(20, "Original 20-day Avg Volume") volMultiplier_O = input.float(3, "Original Volume Multiplier") // ======================= FUNCTIONS ======================= f_dema(_src, _len) => ema1 = ta.ema(_src, _len) ema2 = ta.ema(ema1, _len) 2 * ema1 - ema2 // ======================= DAILY TRIGGER ======================= dema10D = f_dema(close, shortDEMA_D) dema20D = f_dema(close, longDEMA_D) dailyVol = volume avgVol20 = ta.sma(dailyVol, volAvgLen_D) volCondition_D = dailyVol > volMultiplier_D * avgVol20 priceCondition_D = close > dema10D demaCondition_D = dema10D > dema20D weeklyClose_D = request.security(syminfo.tickerid, "W", close) dema10W_D = ta.ema(weeklyClose_D, weekDEMAlen_D) * 2 - ta.ema(ta.ema(weeklyClose_D, weekDEMAlen_D), weekDEMAlen_D) trigger_D = priceCondition_D and demaCondition_D and volCondition_D plotshape(trigger_D, title="Daily Trigger", style=shape.triangleup, location=location.abovebar, text="DTRG", textcolor=color.white, color=color.new(#FF4500, 0), size=size.small) alertcondition(trigger_D, title="Daily DEMA Trigger Alert", message="Daily DEMA trigger detected") // ======================= WEEKLY TRIGGER ======================= weeklyClose_W = request.security(syminfo.tickerid, "W", close) dema10W = f_dema(weeklyClose_W, shortDEMA_W) dema20W = f_dema(weeklyClose_W, longDEMA_W) dailyVolW = volume avgVol50 = ta.sma(dailyVolW, volAvgLen_W) volCondition_W = dailyVolW > volMultiplier_W * avgVol50 priceCondition_W = close > dema20W demaCondition_W = dema10W > dema20W trigger_W = priceCondition_W and demaCondition_W and volCondition_W plotshape(trigger_W, title="Weekly Trigger", style=shape.triangledown, location=location.abovebar, text="WTRG", textcolor=color.white, color=color.new(#1E90FF, 0), size=size.small) alertcondition(trigger_W, title="Weekly DEMA Trigger Alert", message="Weekly DEMA trigger detected") // ======================= ORIGINAL TRIGGER ======================= dema10O = f_dema(close, shortDEMA_O) dema20O = f_dema(close, longDEMA_O) dailyVolO = volume avgVolO = ta.sma(dailyVolO, volAvgLen_O) volCondition_O = dailyVolO > volMultiplier_O * avgVolO priceCondition_O = close > dema10O demaCondition_O = dema10O > dema20O trigger_Orig = priceCondition_O and demaCondition_O and volCondition_O plotshape(trigger_Orig, title="Original Trigger", style=shape.labelup, location=location.belowbar, text="TRG", textcolor=color.white, color=color.new(#32CD32, 0), size=size.small) // ======================= COMBINED TABLE ======================= var table dash = table.new(position.top_right, 2, 20, border_width=1) if barstate.islast // --- DAILY TRIGGER (rows 0-4) --- table.cell(dash, 0, 0, "Daily Trigger", text_color=color.white, bgcolor=color.new(#555555, 30)) table.cell(dash, 1, 0, trigger_D ? "YES ✅" : "NO ❌", text_color=color.white, bgcolor=trigger_D ? color.new(#FF4500, 0) : color.new(#555555, 50)) table.cell(dash, 0, 1, "CMP > 10D DEMA", text_color=color.white, bgcolor=color.new(#555555, 30)) table.cell(dash, 1, 1, str.tostring(close, format.price), text_color=color.white, bgcolor=priceCondition_D ? color.new(#32CD32, 0) : color.new(#AAAAAA, 50)) table.cell(dash, 0, 2, "10D > 20D DEMA", text_color=color.white, bgcolor=color.new(#555555, 30)) table.cell(dash, 1, 2, str.tostring(dema10D, format.price) + " > " + str.tostring(dema20D, format.price), text_color=color.white, bgcolor=demaCondition_D ? color.new(#FFFF00, 0) : color.new(#AAAAAA, 50)) table.cell(dash, 0, 3, "Daily Vol > 3x20D Avg", text_color=color.white, bgcolor=color.new(#555555, 30)) table.cell(dash, 1, 3, str.tostring(dailyVol, format.volume) + " / " + str.tostring(avgVol20, format.volume), text_color=color.white, bgcolor=volCondition_D ? color.new(#FF00FF, 0) : color.new(#AAAAAA, 50)) table.cell(dash, 0, 4, "10W DEMA Ref", text_color=color.white, bgcolor=color.new(#555555, 30)) table.cell(dash, 1, 4, str.tostring(dema10W_D, format.price), text_color=color.white, bgcolor=color.new(#00FFFF, 0)) // --- WEEKLY TRIGGER (rows 5-9) --- table.cell(dash, 0, 5, "Weekly Trigger", text_color=color.white, bgcolor=color.new(#555555, 30)) table.cell(dash, 1, 5, trigger_W ? "YES ✅" : "NO ❌", text_color=color.white, bgcolor=trigger_W ? color.new(#1E90FF, 0) : color.new(#555555, 50)) table.cell(dash, 0, 6, "CMP > 20W DEMA", text_color=color.white, bgcolor=color.new(#555555, 30)) table.cell(dash, 1, 6, str.tostring(close, format.price), text_color=color.white, bgcolor=priceCondition_W ? color.new(#32CD32, 0) : color.new(#AAAAAA, 50)) table.cell(dash, 0, 7, "10W > 20W DEMA", text_color=color.white, bgcolor=color.new(#555555, 30)) table.cell(dash, 1, 7, str.tostring(dema10W, format.price) + " > " + str.tostring(dema20W, format.price), text_color=color.white, bgcolor=demaCondition_W ? color.new(#FFFF00, 0) : color.new(#AAAAAA, 50)) table.cell(dash, 0, 8, "Daily Vol > 3x50D Avg", text_color=color.white, bgcolor=color.new(#555555, 30)) table.cell(dash, 1, 8, str.tostring(dailyVolW, format.volume) + " / " + str.tostring(avgVol50, format.volume), text_color=color.white, bgcolor=volCondition_W ? color.new(#FF00FF, 0) : color.new(#AAAAAA, 50)) // --- ORIGINAL TRIGGER (rows 10-14) --- table.cell(dash, 0, 10, "Original Trigger", text_color=color.white, bgcolor=color.new(#555555, 30)) table.cell(dash, 1, 10, trigger_Orig ? "YES ✅" : "NO ❌", text_color=color.white, bgcolor=trigger_Orig ? color.new(#32CD32, 0) : color.new(#555555, 50)) table.cell(dash, 0, 11, "CMP > 10D DEMA", text_color=color.white, bgcolor=color.new(#555555, 30)) table.cell(dash, 1, 11, str.tostring(close, format.price), text_color=color.white, bgcolor=priceCondition_O ? color.new(#32CD32, 0) : color.new(#AAAAAA, 50)) table.cell(dash, 0, 12, "10D > 20D DEMA", text_color=color.white, bgcolor=color.new(#555555, 30)) table.cell(dash, 1, 12, str.tostring(dema10O, format.price) + " > " + str.tostring(dema20O, format.price), text_color=color.white, bgcolor=demaCondition_O ? color.new(#FFFF00, 0) : color.new(#AAAAAA, 50)) table.cell(dash, 0, 13, "Daily Vol > 3x20D Avg", text_color=color.white, bgcolor=color.new(#555555, 30)) table.cell(dash, 1, 13, str.tostring(dailyVolO, format.volume) + " / " + str.tostring(avgVolO, format.volume), text_color=color.white, bgcolor=volCondition_O ? color.new(#FF00FF, 0) : color.new(#AAAAAA, 50))
Chỉ báo Pine Script®
bởi Nekhileshinvestor
Session First 5-Min High/LowHere's a professional description for your indicator: Session First 5-Min High/Low Marker This indicator automatically identifies and marks the high and low price levels established during the first 5 minutes of major trading sessions, helping traders identify key intraday support and resistance zones. Key Features: Tracks three major trading sessions in IST (Indian Standard Time): Asian Session: 5:30 AM - 5:35 AM London Session: 12:30 PM - 12:35 PM New York Session: 5:30 PM - 5:35 PM Draws horizontal lines at the highest and lowest prices reached during each session's opening 5-minute window Color-coded for easy identification (Yellow for Asian, Blue for London, Red for New York) Lines extend across the chart to help track price reactions throughout the day Clean, minimal design with optional labels Best Used For: Identifying key intraday support and resistance levels Session breakout trading strategies Understanding institutional order flow at market opens Works on 1-minute timeframe for precise tracking Customizable Settings: Toggle line extensions on/off Adjust line width (1-5) Change colors for each session Show/hide session labels Perfect for day traders and scalpers who trade around major session openings and want to identify high-probability support/resistance zones established during peak liquidity periods. This description explains what the indicator does, its practical applications, and its key features in a way that's clear for TradingView users.RetryClaude can make mistakes. Please double-check responses.
Chỉ báo Pine Script®
bởi petergangmei
RSI Price Sensitivity v3 [Quant-Stable]The RSI Price Sensitivity v3 indicator measures how efficiently and consistently price responds to RSI movement — revealing when RSI momentum actually matters, and when it’s just noise. It’s designed as a quant-grade analytical tool combining RSI, ADX, volatility, regression, and correlation logic to form a single normalized “sensitivity” score. Core Concept Traditional RSI often moves without price follow-through. This indicator quantifies the strength of the connection between RSI and price, dynamically adapting to volatility and trend context. It blends: 📊 RSI-Price Correlation: Statistical relationship between RSI momentum and price momentum. ⚙️ Efficiency Ratio: Measures how direct and smooth the RSI-price relationship is (noise filtering). 📈 Regression Confidence: Tests whether price moves are statistically aligned with RSI structure. 💡 Momentum Alignment: Checks directional agreement between RSI trend and price trend, weighted by ADX. All components are dynamically normalized and weighted into one composite sensitivity score.
Chỉ báo Pine Script®
bởi WSR1
Volume Bubbles & Liquidity Heatmap 30% + biasLuxAlgo gave us an open script, I just primmed it up with the use of Chat GPT:There is no single magic number (like “delta must be 800”) that will guarantee directional follow-through in every market. But you can make a mathematically rigorous filter that gives you a high-probability test — by normalizing the delta against that market’s typical behavior and requiring multiple confirmations. Below is a compact, actionable algorithm you can implement immediately (in your platform or spreadsheet) plus concrete thresholds and the math behind them. High-IQ rule set (math + trade logic) Use three independent checks. Only take the trade if ALL three pass. 1) Z-score (statistical significance of the delta) Compute rolling mean 𝜇 μ and std dev 𝜎 σ of delta on the same timeframe (e.g. 5m) over a lookback window 𝑊 W (suggest 𝑊 = 50 W=50–200 bars). 𝑍 = delta bar − 𝜇 𝑊 𝜎 𝑊 Z= σ W ​ delta bar ​ −μ W ​ ​ Threshold: require 𝑍 ≥ 2.5 Z≥2.5 (strong) — accept 2.0 for less strict, 3.0 for very rare signals. Why: a Z>=2.5 means this delta is an outlier (~<1% one-sided), not normal noise. 2) Relative Imbalance (strength vs total volume) Compute imbalance ratio: 𝑅 = ∣ delta bar ∣ volume bar R= volume bar ​ ∣delta bar ​ ∣ ​ Threshold: require 𝑅 ≥ 0.25 R≥0.25 (25% of the bar’s volume is one-sided). For scalping you can tighten to 0.30–0.40. Why: a big delta with tiny volume isn’t meaningful; this normalizes to participation. 3) Net follow-through over a confirmation window Look ahead 𝑁 N bars (or check the next bar if you need intrabar speed). Compute cumulative delta and price move: cum_delta 𝑁 = ∑ 𝑖 = 1 𝑁 delta bar + 𝑖 cum_delta N ​ = i=1 ∑ N ​ delta bar+i ​ price_move = close bar + 𝑁 − close bar price_move=close bar+N ​ −close bar ​ Thresholds: require cum_delta 𝑁 cum_delta N ​ has the same sign as the trigger and ∣ cum_delta 𝑁 ∣ ≥ 0.5 × ∣ delta bar ∣ ∣cum_delta N ​ ∣≥0.5×∣delta bar ​ ∣, and price_move price_move exceeds a minimum meaningful tick amount (instrument dependent). For ES / US30 type futures: price move ≥ 5–10 ticks; for forex pairs maybe 10–20 pips? Use ATR 20 20 ​ ×0.05 as a generic minimum. Why: separates immediate absorption (buy delta then sellers soak it) from genuine continuation. Bonus check — Structural context (must be satisfied) Trigger should not occur against a strong structural barrier (VWAP, daily high/low, previous session POC) unless you’re explicitly trading exhaustion/absorption setups. If signal occurs near resistance and price does not clear that resistance within 𝑁 N bars, treat as probable trap. Putting it together — final trade decision Take the long (example): If 𝑍 ≥ 2.5 Z≥2.5 and 𝑅 ≥ 0.25 R≥0.25 and cum_delta_N confirms and no hard resistance above (or you’re willing to trade absorption), then enter. Place stop: under the low of the last 2–3 bars or X ATR (instrument dependent). Initial target: risk:reward 1:1 minimum, scale out at 1.5–2R after confirming further delta. Concrete numeric illustration using your numbers You saw FOL = 456, then sell reaction with ~350 opposite. How to interpret: Suppose your 5-min rolling mean 𝜇 μ = 100 and 𝜎 σ=120 (example): 𝑍 = ( 456 − 100 ) / 120 ≈ 2.97 ⇒ statistically big Z=(456−100)/120≈2.97⇒statistically big So it passes Z. If volume on that bar = 2000 contracts: 𝑅 = 456 / 2000 = 0.228 ⇒ just below 0.25 threshold R=456/2000=0.228⇒just below 0.25 threshold So it fails R (weak participation proportionally), explaining why 456 alone didn’t move price. Seller came back with 350 opposite soon after — check cum_delta_N: cum_delta 𝑛 𝑒 𝑥 𝑡 3 ≈ 456 − 350 = 106  net cum_delta next3 ​ ≈456−350=106 net Net is small relative to the initial spike — not convincing follow-through. Conclusion: despite a big absolute number (456), relative measures and lack of follow-through meant the move failed. That’s exactly why raw numbers alone are unreliable. Advanced refinement (for elite performance) Use rolling median + MAD instead of mean/std if delta distribution is skewed. Scale Z by volume volatility: divide Z by volume bar / volume ‾ volume bar ​ / volume ​ to penalize low-volume bars. Use a signed cumulative delta over micro-windows: compute windowed cumulative delta standardized: 𝑍 cum = ∑ 𝑖 = 0 𝑀 − 1 delta 𝑡 − 𝑖 − 𝑀 𝜇 𝑀 𝜎 Z cum ​ = M ​ σ ∑ i=0 M−1 ​ delta t−i ​ −Mμ ​ and require 𝑍 cum ≥ 3 Z cum ​ ≥3 for very high confidence. Add market-depth / DOM confirmation (if available): require that resting ask size above is less than resting bid size below by a factor (i.e. visible liquidity supports the move). Practical thresholds to start with (tune per instrument) Lookback 𝑊 W: 100 bars (5m → about a trading day) Z threshold: 2.5 Imbalance ratio 𝑅 R: ≥ 0.25 (tighten to 0.30 for scalping) Confirmation window 𝑁 N: next 1–3 bars for intraday scalps; 3–6 bars for short swings Cum delta confirmation: ≥ 0.5 × initial delta and same sign Price move confirmation: ≥ 0.03 × ATR20 (or set absolute ticks per instrument) TL;DR — the single best rule Don’t chase absolute numbers. Use Z-score of delta + delta/volume ratio + real follow-through. If all three align, odds are heavily in your favor. If you want, I can: give you a tiny script (pseudocode or Pine / Python) that computes Z, R, cum_delta_N and flags qualifying bars, or run the calculation on a sample of your recent bars if you paste a small CSV of time, delta, volume, close. Which do you want? (I’ll give code right away.) You said: give me a percentage of difference where we have a high probability ChatGPT said: Thought for 7s Nice — straight to the point. Here’s a concise, practical set of percentage thresholds (and associated confidence estimates) you can use right now to flag a “high probability” delta signal. I’ll give you three tiers (good / strong / elite), explain what each means, show how to compute it, and give a short example using your 456/2000 bar. One-line rule Compute delta percent 𝑃 = ∣ delta ∣ volume × 100 % P= volume ∣delta∣ ​ ×100%. Then require Z-score of delta ≥ 2.5 and P to be at or above the threshold below for the desired confidence tier. Threshold tiers (percent of bar volume) Good (high-probability trade candidate) P ≥ 20–25% Typical success probability (when combined with Z≥2.5 and no hard structural resistance): ~60–70%. Use when you want a steady flow of signals with decent edge. Strong (high confidence) P ≥ 30–35% Typical success probability: ~70–80% (with Z≥2.5 + structural alignment). Fewer signals, higher win-rate. Good default for intraday. Elite (very high probability / low false positives) P ≥ 40–50% Typical success probability: ~80–90% (with Z≥3 and confirmation). Rare signals. Use for meaningful size or when you need maximum certainty. Why percentage matters (short) Absolute delta (456) is meaningless without volume context. A 456 delta on 2,000 volume → P = 456/2000 = 22.8% → sits just below the Good threshold, which matches why it failed: decent but not dominant participation. Additional required checks (don’t skip these) To turn P into a reliable filter, always require: Z-score of the delta ≥ 2.5 (or ≥3 for Elite signals). No immediate structural barrier (VWAP, strong resistance) that would plausibly absorb the order. Follow-through: within next N bars (N = 1–3 for scalps) price moves in the same direction by ≥ 0.03×ATR20 (or instrument-specific tick threshold). If this fails, cut fast. Combine all three and your realized win-rate will approach the probabilities above. Practical quick checklist (to apply each signal) Compute P = |delta|/volume. Is P ≥ 30%? (or your chosen tier) Compute Z = (delta − mean)/std over last 100 bars. Is Z ≥ 2.5? Is price not pinned under a heavy resistance? Enter on next bar close (or intrabar on continuation), stop under last 2-bar low (for longs). If price fails to move ≥ 0.03×ATR20 within 3 bars, exit. Example (your bar) delta = 456, volume = 2000 → P = 22.8% → below Good threshold (25%). Even if Z was high, P failed — explains the failure. You need P ≥ ~30% plus Z≥2.5 to call it a Strong signal. Final notes (instrument & timeframe) These percentages were calibrated for futures/US indices and short intraday bars (1–5m). For FX or low-tick instruments, use absolute ticks + a smaller P threshold (start at 15–20%). Always backtest on the specific instrument/timeframe and tune thresholds. If you want, I’ll generate a tiny script (Python or Pine) that calculates P and Z and flags Good/Strong/Elite signals for your chart history so you can see exact historical hit rates. Which language (Python or Pine)? You said: so if we have 456 g vs 350 r what pct is that in favour of green? ChatGPT said: Perfect — let’s calculate it carefully, digit by digit so we don’t make a slip. We have: Green (buy) volume = 456 Red (sell) volume = 350 We want to know what percentage is in favor of green, relative to the total volume. Step 1. Compute total volume Total = 456 + 350 = 806 Total=456+350=806 Step 2. Compute green’s share \text{Green %} = \frac{456}{806} \times 100 Now do it precisely: 456 ÷ 806 = 0.565506 456÷806=0.565506 (rounded) So: 0.565506 × 100 = 56.55 % 0.565506×100=56.55% Step 3. Compute red’s share (for context) 350 806 = 0.434494 → 43.45 % 806 350 ​ =0.434494→43.45% Step 4. Compute difference (bias toward green) 56.55 % − 43.45 % = 13.10 % 56.55%−43.45%=13.10% ✅ Final answer: Green = 56.6% of total Red = 43.4% of total Bias = +13.1 percentage points in favor of green So that’s only a 13% advantage to buyers — modest pressure, not a dominant imbalance. For a high-probability signal (as we discussed earlier), you generally want ≥30% imbalance (e.g. ~65/35 split or stronger). Original: ''The Volume Bubbles & Liquidity Heatmap indicator is a powerful trading tool designed to give traders a clearer picture of volume dynamics and liquidity distribution across multiple timeframes. By combining dynamic volume bubbles with a liquidity heatmap, this indicator makes it easy to identify areas of price interest, spot market imbalances, and improve decision-making for both scalpers and swing traders. This trading indicator is ideal for volume traders, price action traders, and liquidity-focused traders who need a clean, multi-dimensional view of buyer/seller activity and the zones where market participants are most active. With full customization over bubble display, timeframes, and visual settings, traders can tailor the tool to fit virtually any trading strategy or market.''
Chỉ báo Pine Script®
bởi TRADE_the_ORB
bar count plot only for far lookbackPurpose: TradingView limits the number of text/label objects (≈500), which causes traditional bar-count indicators to stop showing numbers when you scroll far back in history. This plots-only version bypasses that limitation entirely, allowing you to view bar numbers anywhere on the chart, even thousands of bars back. How It Works: Displays each bar’s in-day sequence number (1–78 by default) under the candles. Counts restart automatically at the start of each trading day. Uses a dual-channel “digit plot” system (tens + ones) instead of labels—extremely light on performance and unlimited in lookback. The digits are drawn every N bars (default = 3) to keep the view uncluttered. Key Parameters: Show every Nth bar: Controls how often numbers appear (1 = every bar, 3 = every 3 bars, etc.). Notes: Digits are plotted directly via plotshape()—no labels—so they remain visible even 5 000 + bars back. Alignment may vary slightly depending on chart zoom; this version is intended mainly for deep historical review rather than precise near-term alignment.
Chỉ báo Pine Script®
bởi rinaldo3
Microgaps (plots-only, 4-channel, same-day only)Purpose: This indicator visually highlights 3-bar price gaps on your chart, showing clear visual structure for gap zones without lag or diagonal artifacts. It draws two outer lines (top and bottom of the gap) for every valid 3-bar gap, and optionally a midline when the gap is considered “large.” ⚙️ How it works A bull gap is detected when the current bar’s low is higher than the high from two bars ago (low > high ). A bear gap is detected when the current bar’s high is lower than the low from two bars ago (high < low ). The lines are centered at the middle bar of the 3-bar sequence. Gaps are only drawn within the same trading day to avoid false overnight gaps. To prevent overlapping artifacts, up to four concurrent gap channels can be drawn efficiently using GPU-friendly plot() lines. 🔵 Midline logic The midline (center of the gap) is only displayed when the gap’s vertical size is “large” relative to recent volatility. “Large” means the gap height is greater than a user-defined fraction of the average bar range over the past N bars. Example: if the average 8-bar range = 2 points, and the threshold = 0.3, then only gaps larger than 0.6 points will show the midline. 🧩 Parameters Setting Description Bull Gap Color / Width Style of bullish gaps (top and bottom lines). Bear Gap Color / Width Style of bearish gaps (top and bottom lines). Mid Gap Color / Width Style of the optional midline (shown only when “large”). Large Gap — Lookback (bars) Number of bars used to calculate the average range (default: 8). Large Gap — Size vs Avg Range Fraction of the average range that defines a “large” gap (default: 0.5). Set lower (e.g. 0.3) to show more midlines. 💡 Tips Set threshold lower (0.2–0.4) for more midlines, higher (0.6–1.0) to highlight only extreme gaps. Works best on intraday timeframes (1-min to 30-min). Fully GPU-efficient — can scroll back thousands of bars without lag.
Chỉ báo Pine Script®
bởi rinaldo3