Multi-Timeframe Candle Color Dashboard (ฺBy WutTrader)// This description should be added to the script's information section on TradingView.
//
// === คู่มือการใช้งาน: Multi-Timeframe Candle Color Dashboard ===
//
// **ภาพรวม:**
// อินดิเคเตอร์นี้แสดงสีของแท่งเทียนจากหลายไทม์เฟรม (M1, M5, M15, M30, H1, H4, D1) บนหน้าจอเดียว
// เพื่อช่วยให้คุณเห็นภาพรวมของแนวโน้มในแต่ละช่วงเวลาได้อย่างรวดเร็ว
//
// - **🟢 สีเขียว:** แสดงว่าแท่งเทียนเป็นขาขึ้น (Bullish) ตามจำนวนแท่งที่กำหนด
// - **🔴 สีแดง:** แสดงว่าแท่งเทียนเป็นขาลง (Bearish) ตามจำนวนแท่งที่กำหนด
// - **⚪ สีเทา:** แสดงว่ายังไม่มีทิศทางที่ชัดเจน
//
// **วิธีการใช้งาน:**
// 1. **การดูสัญญาณ:** ใช้ Dashboard เพื่อยืนยันว่าหลายไทม์เฟรมมีแนวโน้มไปในทิศทางเดียวกัน
// - **ตัวอย่าง:** หากคุณกำลังดูชาร์ต M5 แล้วพบว่า M15, M30 และ H1 เป็นสีเขียวทั้งหมด
// แสดงว่ามีแนวโน้มขาขึ้นที่แข็งแกร่งในภาพรวม ซึ่งอาจเป็นจังหวะที่ดีสำหรับการเข้าซื้อ
// 2. **การตั้งค่า:** คุณสามารถปรับแต่งการแสดงผลได้ในเมนู "Settings"
// - **Global Settings:** เลือกเปิด/ปิดการแสดงผลของแต่ละไทม์เฟรมที่คุณต้องการ
// - **Dashboard Style:** เลือกว่าจะให้ Dashboard แสดงผลเป็นแนวตั้ง (Vertical) หรือแนวนอน (Horizontal)
// - **Color Settings:** ปรับสีสำหรับแนวโน้มขาขึ้น (Bullish) และขาลง (Bearish) ได้ตามใจชอบ
//
// **การตั้งค่าการแจ้งเตือน (Alert):**
// อินดิเคเตอร์นี้รองรับการแจ้งเตือนเมื่อทุกไทม์เฟรมที่คุณเปิดใช้งานเป็นสีเขียวทั้งหมด
// 1. ไปที่เมนู "Alert" (รูปกระดิ่ง) ที่ด้านบนของ TradingView
// 2. ตั้งค่า "Condition" เป็นชื่ออินดิเคเตอร์นี้: `Multi-Timeframe Candle Color Dashboard`
// 3. ตั้งค่า "Condition" เป็น `Bullish Alert`
// 4. ตั้งค่า "Frequency" เป็น `Once Per Bar Close`
//
// === User Manual: Multi-Timeframe Candle Color Dashboard ===
//
// **Overview:**
// This indicator displays the candle color from multiple timeframes (M1, M5, M15, M30, H1, H4, D1) on a single screen,
// helping you to quickly see the trend direction across different time periods.
//
// - **🟢 Green:** Indicates that candles are bullish for the specified number of lookback bars.
// - **🔴 Red:** Indicates that candles are bearish for the specified number of lookback bars.
// - **⚪ Gray:** Indicates a neutral or undefined trend.
//
// **How to Use:**
// 1. **Signal Confirmation:** Use the dashboard to confirm that multiple timeframes are moving in the same direction.
// - **Example:** If you are on an M5 chart and see that the M15, M30, and H1 timeframes are all green,
// it suggests a strong overall bullish momentum, which could be a good entry signal.
// 2. **Settings:** You can customize the display in the "Settings" menu.
// - **Global Settings:** Select which timeframes you want to show or hide.
// - **Dashboard Style:** Choose between a vertical or horizontal layout for the dashboard.
// - **Color Settings:** Adjust the colors for bullish and bearish trends to your preference.
//
// **Setting up an Alert:**
// This indicator supports an alert when all enabled timeframes turn completely green.
// 1. Go to the "Alert" menu (bell icon) at the top of TradingView.
// 2. Set the "Condition" to the name of this indicator: `Multi-Timeframe Candle Color Dashboard`.
// 3. Set the "Condition" to `Bullish Alert`.
// 4. Set the "Frequency" to `Once Per Bar Close`.
Analisi trend
Chanpreet Moving AveragesChanpreet Moving Averages
by Chanpreet Singh
This script plots up to four customizable moving averages (SMA, EMA, SMMA/RMA, WMA, VWMA).
You can adjust:
Moving average type
Source (close, open, hl2, etc.)
Length
Color
An optional input lets you select a higher or custom timeframe for the moving averages (e.g., daily MA on a 1-hour chart). If left empty, the script calculates them on the current chart timeframe, so the lines scale and move naturally when zooming or panning.
This tool is designed for educational and visualization purposes, helping traders see trend direction and potential areas of dynamic support/resistance.
⚠️ Disclaimer: This script does not provide financial advice or trading signals. Use it at your own risk. Always do your own research before making trading decisions.
10-Crypto Normalized IndexOverview
This indicator builds a custom index for up to 10 cryptocurrencies and plots their combined trend as a single line. Each coin is normalized to 100 at a user-selected base date (or at its first available bar), then averaged (equally or by your custom weights). The result lets you see the market direction of your basket at a glance.
How it works
For each symbol, the script finds a base price (first bar ≥ the chosen base date; or the first bar in history if base-date normalization is off).
It converts the current price to a normalized value: price / base × 100.
It then computes a weighted average of those normalized values to form the index.
A dotted baseline at 100 marks the starting point; values above/below 100 represent % performance vs. the base.
Key inputs
Symbols (10 max): Default set: BTC, ETH, SOL, POL, OKB, BNB, SUI, LINK, 1INCH, TRX (USDT pairs). You can change exchange/quote (keep all the same quote, e.g., all USDT).
Weights: Toggle equal weights or enter custom weights. Custom weights are auto-normalized internally, so they don’t need to sum to 1.
Base date: Year/Month/Day (default: 2025-06-01). Turning normalization off uses each symbol’s first available bar as its base.
Smoothing: Optional SMA to reduce noise.
Show baseline: Toggle the horizontal line at 100.
Interpretation
Index > 100 and rising → your basket is up since the base date.
Index < 100 and falling → down since the base date.
Use shorter timeframes for intraday sentiment, higher timeframes for swing/trend context.
Default basket & weights (editable)
Order: BTC, ETH, SOL, POL, OKB, BNB, SUI, LINK, 1INCH, TRX.
Default custom weight factors: 30, 30, 20, 10, 10, 5, 5, 5, 5, 5 (auto-normalized).
Base date: 2025-06-01.
Smart Money Flow Index (SMFI) - This tool is useful for comparing price action with underlying money flow and spotting where smart money may be entering or exiting the market.
Hazel nut BB Strategy, volume base- lite versionHazel nut BB Strategy, volume base — lite version
Having knowledge and information in financial markets is only useful when a trader operates with a well-defined trading strategy. Trading strategies assist in capital management, profit-taking, and reducing potential losses.
This strategy is built upon the core principle of supply and demand dynamics. Alongside this foundation, one of the widely used technical tools — the Bollinger Bands — is employed to structure a framework for profit management and risk control.
In this strategy, the interaction of these tools is explained in detail. A key point to note is that for calculating buy and sell volumes, a lower timeframe function is used. When applied with a tick-level resolution, this provides the most precise measurement of buyer/seller flows. However, this comes with a limitation of reduced historical depth. Users should be aware of this trade-off: if precise tick-level data is required, shorter timeframes should be considered to extend historical coverage .
The strategy offers multiple configuration options. Nevertheless, it should be treated strictly as a supportive tool rather than a standalone trading system. Decisions must integrate personal analysis and other instruments. For example, in highly volatile assets with narrow ranges, it is recommended to adjust profit-taking and stop-loss percentages to smaller values.
◉ Volume Settings
• Buyer and seller volume (up/down volume) are requested from a lower timeframe, with an option to override the automatic resolution.
• A global lookback period is applied to calculate moving averages and cumulative sums of buy/sell/delta volumes.
• Ratios of buyers/sellers to total volume are derived both on the current bar and across the lookback window.
◉ Bollinger Band
• Bands are computed using configurable moving averages (SMA, EMA, RMA, WMA, VWMA).
• Inputs allow control of length, standard deviation multiplier, and offset.
• The basis, upper, and lower bands are plotted, with a shaded background between them.
◉ Progress & Proximity
• Relative position of the price to the Bollinger basis is expressed as percentages (qPlus/qMinus).
• “Near band” conditions are triggered when price progress toward the upper or lower band exceeds a user-defined threshold (%).
• A signed score (sScore) represents how far the close has moved above or below the basis relative to band width.
◉ Info Table
• Optional compact table summarizing:
• - Upper/lower band margins
• - Buyer/seller volumes with moving averages
• - Delta and cumulative delta
• - Buyer/seller ratios per bar and across the window
• - Money flow values (buy/sell/delta × price) for bar-level and summed periods
• The table is neutral-colored and resizable for different chart layouts.
◉ Zone Event Gate
• Tracks entry into and exit from “near band” zones.
• Arming logic: a side is armed when price enters a band proximity zone.
• Trigger logic: on exit, a trade event is generated if cumulative buyer or seller volume dominates over a configurable window.
◉ Trading Logic
• Orders are placed only on zone-exit events, conditional on volume dominance.
• Position sizing is defined as a fixed percentage of strategy equity.
• Long entries occur when leaving the lower zone with buyer dominance; short entries occur when leaving the upper zone with seller dominance.
◉ Exit Rules
• Open positions are managed by a strict priority sequence:
• 1. Stop-loss (% of entry price)
• 2. Take-profit (% of entry price)
• 3. Opposite-side event (zone exit with dominance in the other direction)
• Stop-loss and take-profit levels are configurable
◉ Notes
• This lite version is intended to demonstrate the interaction of Bollinger Bands and volume-based dominance logic.
• It provides a framework to observe how price reacts at band boundaries under varying buy/sell pressure, and how zone exits can be systematically converted into entry/exit signals.
When configuring this strategy, it is essential to carefully review the settings within the Strategy Tester. Ensure that the chosen parameters and historical data options are correctly aligned with the intended use. Accurate back testing depends on applying proper configurations for historical reference. The figure below illustrates sample result and configuration type.
Multi-Timeframe Candle Color Dashboard (Closed Bars Only) V.2Pine Script
// This description should be added to the script's information section on TradingView.
//
// === คู่มือการใช้งาน: Multi-Timeframe Candle Color Dashboard ===
//
// **ภาพรวม:**
// อินดิเคเตอร์นี้แสดงสีของแท่งเทียนจากหลายไทม์เฟรม (M1, M5, M15, M30, H1, H4, D1) บนหน้าจอเดียว
// เพื่อช่วยให้คุณเห็นภาพรวมของแนวโน้มในแต่ละช่วงเวลาได้อย่างรวดเร็ว
//
// - **🟢 สีเขียว:** แสดงว่าแท่งเทียนเป็นขาขึ้น (Bullish) ตามจำนวนแท่งที่กำหนด
// - **🔴 สีแดง:** แสดงว่าแท่งเทียนเป็นขาลง (Bearish) ตามจำนวนแท่งที่กำหนด
// - **⚪ สีเทา:** แสดงว่ายังไม่มีทิศทางที่ชัดเจน
//
// **วิธีการใช้งาน:**
// 1. **การดูสัญญาณ:** ใช้ Dashboard เพื่อยืนยันว่าหลายไทม์เฟรมมีแนวโน้มไปในทิศทางเดียวกัน
// - **ตัวอย่าง:** หากคุณกำลังดูชาร์ต M5 แล้วพบว่า M15, M30 และ H1 เป็นสีเขียวทั้งหมด
// แสดงว่ามีแนวโน้มขาขึ้นที่แข็งแกร่งในภาพรวม ซึ่งอาจเป็นจังหวะที่ดีสำหรับการเข้าซื้อ
// 2. **การตั้งค่า:** คุณสามารถปรับแต่งการแสดงผลได้ในเมนู "Settings"
// - **Global Settings:** เลือกเปิด/ปิดการแสดงผลของแต่ละไทม์เฟรมที่คุณต้องการ
// - **Dashboard Style:** เลือกว่าจะให้ Dashboard แสดงผลเป็นแนวตั้ง (Vertical) หรือแนวนอน (Horizontal)
// - **Color Settings:** ปรับสีสำหรับแนวโน้มขาขึ้น (Bullish) และขาลง (Bearish) ได้ตามใจชอบ
//
// **การตั้งค่าการแจ้งเตือน (Alert):**
// อินดิเคเตอร์นี้รองรับการแจ้งเตือนเมื่อทุกไทม์เฟรมที่คุณเปิดใช้งานเป็นสีเขียวทั้งหมด
// 1. ไปที่เมนู "Alert" (รูปกระดิ่ง) ที่ด้านบนของ TradingView
// 2. ตั้งค่า "Condition" เป็นชื่ออินดิเคเตอร์นี้: `Multi-Timeframe Candle Color Dashboard`
// 3. ตั้งค่า "Condition" เป็น `Bullish Alert`
// 4. ตั้งค่า "Frequency" เป็น `Once Per Bar Close`
//
// === User Manual: Multi-Timeframe Candle Color Dashboard ===
//
// **Overview:**
// This indicator displays the candle color from multiple timeframes (M1, M5, M15, M30, H1, H4, D1) on a single screen,
// helping you to quickly see the trend direction across different time periods.
//
// - **🟢 Green:** Indicates that candles are bullish for the specified number of lookback bars.
// - **🔴 Red:** Indicates that candles are bearish for the specified number of lookback bars.
// - **⚪ Gray:** Indicates a neutral or undefined trend.
//
// **How to Use:**
// 1. **Signal Confirmation:** Use the dashboard to confirm that multiple timeframes are moving in the same direction.
// - **Example:** If you are on an M5 chart and see that the M15, M30, and H1 timeframes are all green,
// it suggests a strong overall bullish momentum, which could be a good entry signal.
// 2. **Settings:** You can customize the display in the "Settings" menu.
// - **Global Settings:** Select which timeframes you want to show or hide.
// - **Dashboard Style:** Choose between a vertical or horizontal layout for the dashboard.
// - **Color Settings:** Adjust the colors for bullish and bearish trends to your preference.
//
// **Setting up an Alert:**
// This indicator supports an alert when all enabled timeframes turn completely green.
// 1. Go to the "Alert" menu (bell icon) at the top of TradingView.
// 2. Set the "Condition" to the name of this indicator: `Multi-Timeframe Candle Color Dashboard`.
// 3. Set the "Condition" to `Bullish Alert`.
// 4. Set the "Frequency" to `Once Per Bar Close`.
63-Day Sector Relative Strength vs NIFTYThis script calculates and displays the 63-day returns of major NSE sectoral indices and their relative strength versus the NIFTY 50.
It,
Covered Indices: CNXIT, CNXAUTO, CNXFMCG, CNXPHARMA, CNXENERGY, CNXMETAL, CNXPSUBANK, CNXINFRA, CNXREALTY, CNXFINANCE, CNXMEDIA, BANKNIFTY, CNXCONSUMPTION, CNXCOMMODITIES
How to use this: Quickly identify which sectors are outperforming or underperforming relative to the NIFTY over the past 63 trading sessions (approx. 3 months).
High Volume Candle Zones (Neutral)Contact the publisher to get full content into using this indicator to its full potential. Will help identify key areas in the market. Can be used on all time frames for confluence
Structure Strategycreated to spot key area needed to take valid trades in most market conditions. use beside RSI MACD
The Maker StrategyDESCRIPTION
The Maker Strategy is a trend-following system built around exponential moving averages (EMAs). By analyzing the alignment of multiple EMAs, the strategy identifies strong bullish or bearish momentum and generates precise entry signals. This method is designed to capture sustained trends while filtering out sideways or noisy market conditions.
USER INPUTS :
• EMA 1 Length (Default: 30)
• EMA 2 Length (Default: 35)
• EMA 3 Length (Default: 40)
• EMA 4 Length (Default: 45)
• EMA 5 Length (Default: 50)
• EMA 6 Length (Default: 60)
LONG CONDITION :
A long signal is triggered when all EMAs are perfectly aligned in ascending order:
EMA1 > EMA2 > EMA3 > EMA4 > EMA5 > EMA6
SHORT CONDITION :
A short signal is triggered when all EMAs are perfectly aligned in descending order:
EMA1 < EMA2 < EMA3 < EMA4 < EMA5 < EMA6
WHY IT IS UNIQUE:
Unlike traditional EMA crossover systems that rely on just 2 or 3 moving averages, The Maker Strategy uses 6 EMAs in sequence. This ensures that trades are only taken when there is clear and strong market momentum. The approach minimizes false signals in ranging markets and focuses on capturing trends with higher probability setups.
HOW USER CAN BENEFIT FROM IT :
• Clear entry alerts for both long and short positions.
• Visual confirmation through candle coloring and EMA band fills.
• Works on multiple timeframes and instruments (stocks, forex, crypto, indices).
• Helps traders stay on the right side of the trend while avoiding whipsaws.
• A simple yet effective tool for those who want a disciplined, rules-based strategy.
Adaptive Log Trend ChannelOne-line Summary / 一句话简介
EN: Adaptive log-scale trend channel using Pearson-optimized regression and deviation bands.
中文：基于皮尔逊优化回归的自适应对数趋势通道，带标准差波动带。
Full Description / 完整介绍
What it does / 功能
EN: This indicator fits a log-linear regression to price and builds a trend channel with ±k·σ deviation bands. It automatically selects the period with the highest Pearson correlation (R), ensuring the channel best matches the dominant market trend.
中文：该指标通过价格的对数线性回归拟合趋势，并在中线上下绘制 ±k·σ 偏差通道。它会自动选择皮尔逊相关系数 (R) 最高的周期，从而保证通道与主要趋势最贴合。
Why it’s useful / 适用价值
EN:
Naturally fits assets with multiplicative growth (crypto, tech stocks).
Adapts dynamically to different market regimes.
Provides CAGR estimates on Daily/Weekly charts for trend strength evaluation.
中文：
自然适用于呈现乘法增长的资产（如加密货币与科技股）。
可动态适应不同的市场阶段。
在日线/周线图上提供 趋势年化收益率 (CAGR)，帮助评估趋势强度。
How it works / 工作原理
EN:
Computes log(price) → regression slope & intercept.
Draws a midline (log regression projection).
Upper & lower bands = ±k·σ in log space.
Info panel shows: Auto-Selected Period, Trend Strength (or Pearson’s R), and CAGR.
中文：
对价格取对数 → 计算回归斜率与截距。
绘制 中线（对数回归投影）。
上下轨 = 对数空间中的 ±k·σ。
信息面板显示：自动选择周期、趋势强度（或皮尔逊 R 值）、以及 CAGR 年化收益率。
Key Settings / 主要参数
EN:
Long-Term Mode: Uses extended periods (300–1200).
Deviation Multiplier (k): Controls channel width (default 2.0).
Styles: Colors, line type, and extension.
Panel Options: Toggle auto-period, Pearson’s R, and CAGR.
中文：
长期模式：采用更长周期 (300–1200)。
偏差倍数 (k)：控制通道宽度（默认 2.0）。
样式：可设置颜色、线型、延长方式。
信息面板：可开关自动周期、皮尔逊 R、CAGR。
Notes / 注意事项
EN:
CAGR is only available on Daily/Weekly timeframes.
Regression-based tools may repaint as new bars form; treat it as context, not signals.
中文：
CAGR 仅在日线与周线周期可用。
回归类指标在新K线形成时可能重绘，仅用于趋势参考而非交易信号。
B-Xtrender @Puppytherapy +vova13I have recreated a trend following indicator published in IFTA Journal by Bharat Jhunjhunwala. It is mainly to be traded on big timeframes.
For anyone looking into the indicators please have in the source below where logic behing the indicator is nicely explained.
The only thing I added is a T3 moving average with momentum shift signals for earlier signals in some cases.
Target Trend+vova13this indicator shows trend plus alerts
The Target Trend indicator is a trend-following tool designed to assist traders in capturing directional moves while managing entry, stop loss, and profit targets visually on the chart. Using adaptive SMA bands as the core trend detection method, this indicator dynamically identifies shifts in trend direction and provides structured exit points through customizable target levels.
Highlight 10-11 AM NY//@version=5
indicator("Highlight 10-11 AM NY", overlay=true)
// Inputs for flexibility
startHour = input.int(10, "Start Hour (NY time)")
endHour = input.int(11, "End Hour (NY time)")
// Check if the current bar is within the session (uses chart time zone)
inSession = (hour(time, syminfo.timezone) >= startHour) and (hour(time, syminfo.timezone) < endHour)
// Highlight background
bgcolor(inSession ? color.new(color.yellow, 85) : na)
さくらんぼーい//@version=6
indicator("さくらんぼーい", overlay=true, max_labels_count=500, max_lines_count=500)
//==================== Inputs ====================//
// ---- Anchor (shared) ----
grpA = "Anchor (shared)"
anchorMode = input.string("Time", "Anchor Mode", options= , group=grpA)
anchorTime = input.time(timestamp("2025-06-24T20:31:00"), "Anchor Time (exchange)", group=grpA)
anchorBarsAgo = input.int(100, "Anchor Bars Ago", minval=1, group=grpA)
anchorPriceMode = input.string("Close", "Anchor Price", options= , group=grpA)
anchorPriceManual = input.float(0.0, "Manual Anchor Price (0=auto)", step=0.0001, group=grpA)
// ---- Light-Cone ----
grpLC = "Light-Cone ATR"
atrLen = input.int(14, "ATR Length", minval=1, group=grpLC)
atrTF = input.timeframe("", "ATR Timeframe (blank=same)", group=grpLC)
projBars = input.int(60, "Projection Horizon (bars)", minval=1, group=grpLC)
coneMode = input.string("Diffusive √n", "Cone Growth Mode", options= , group=grpLC)
mult = input.float(1.0, "ATR Multiplier (σ-ish)", step=0.1, minval=0.0, group=grpLC)
wickMode = input.string("Close", "Height uses", options= , group=grpLC)
coneFillCol= input.color(color.new(color.teal, 90), "Cone Fill", group=grpLC)
coneLineCol= input.color(color.new(color.aqua, 40), "Cone Edge", group=grpLC)
// ---- Light-Cone guide lines ----
grpFan = "Light-Cone Guides (0.5c / 1.5c)"
showFan = input.bool(true, "Show 0.5c & 1.5c guide lines", group=grpFan)
fan05Color = input.color(color.new(color.aqua, 75), "0.5c line", group=grpFan)
fan15Color = input.color(color.new(color.aqua, 60), "1.5c line", group=grpFan)
fanWidth = input.int(1, "Guide line width", minval=1, maxval=3, group=grpFan)
// ---- √n Stripes ----
grpZ = "Convergence (√n stripes)"
stepBars = input.int(20, "Base Step (bars)", minval=1, group=grpZ)
maxOrderM = input.int(8, "Max Order M (m²)", minval=1, maxval=50, group=grpZ)
halfWindow = input.int(2, "Stripe Half-Width (bars)", minval=0, group=grpZ)
stripeColor = input.color(color.new(color.fuchsia, 86), "Stripe Color", group=grpZ)
showCenters = input.bool(false, "Draw Stripe Center Lines", group=grpZ)
// ---- Fractal ----
grpF = "Fractal (Pivot) Detector"
leftP = input.int(2, "Left bars (L)", minval=1, group=grpF)
rightP = input.int(2, "Right bars (R)", minval=1, group=grpF)
hitColHi = input.color(color.new(color.lime, 0), "Pivot High Mark", group=grpF)
hitColLo = input.color(color.new(color.red, 0), "Pivot Low Mark", group=grpF)
// ---- Display / Limits ----
grpO = "Display / Limits"
showHitTable = input.bool(true, "Show m² Hit Table", group=grpO)
limitScreen = input.bool(true, "Reduce drawing near screen", group=grpO)
screenPastBars = input.int(5000, "Screen past window (bars)", minval=100, group=grpO)
futureLimitBars= input.int(500, "FUTURE draw limit (TV max 500)", minval=0, maxval=500, group=grpO)
// ---- Bias Panel ----
grpB = "Bias Panel"
showBiasPanel = input.bool(true, "Show Bias Panel", group=grpB)
biasTf = input.timeframe("15", "HTF timeframe", group=grpB)
emaFast = input.int(20, "HTF EMA fast", minval=1, group=grpB)
emaSlow = input.int(50, "HTF EMA slow", minval=2, group=grpB)
zThresh = input.float(0.30, "Z threshold (±)", step=0.05, group=grpB)
railLookback = input.int(20, "Rail lookback bars", minval=5, group=grpB)
railPct = input.float(0.40, "Rail touch ratio (0–1)", minval=0.1, maxval=0.9, step=0.05, group=grpB)
// ---- Positions (NEW) ----
panelCorner = input.string("Top-Right", "Bias Panel Position",
options= , group=grpB)
hitsCorner = input.string("Top-Left", "Hits Table Position",
options= , group=grpO)
// ---- Helpers: table positions ----
f_pos(s) =>
p = position.top_left
if s == "Top-Right"
p := position.top_right
else if s == "Bottom-Left"
p := position.bottom_left
else if s == "Bottom-Right"
p := position.bottom_right
p
//==================== Anchor Resolve ====================//
var int anchorBar = na
var float anchorPrice = na
var label anchorLbl = na
// 初バー保護付きタイム検索
f_find_anchor_bar_by_time(_t) =>
ta.valuewhen(nz(time , time ) < _t and time >= _t, bar_index, 0)
if anchorMode == "Time"
anchorBar := f_find_anchor_bar_by_time(anchorTime)
else
// バッファ下限保護
anchorBar := math.max(0, bar_index - anchorBarsAgo)
// アンカー価格（安全取得）
f_price_at_anchor(_bar) =>
float _v = na
if anchorPriceMode == "Close"
_v := ta.valuewhen(bar_index == _bar, close, 0)
else if anchorPriceMode == "High"
_v := ta.valuewhen(bar_index == _bar, high, 0)
else if anchorPriceMode == "Low"
_v := ta.valuewhen(bar_index == _bar, low, 0)
else if anchorPriceMode == "Open"
_v := ta.valuewhen(bar_index == _bar, open, 0)
_v
float autoPrice = na
if not na(anchorBar) and anchorBar <= bar_index
autoPrice := f_price_at_anchor(anchorBar)
anchorPrice := (anchorPriceMode == "Manual" and anchorPriceManual != 0.0) ? anchorPriceManual : autoPrice
bool anchorOK = not na(anchorBar) and anchorBar >= 0 and anchorBar <= bar_index + futureLimitBars and not na(anchorPrice)
// ラベル
if anchorOK
if na(anchorLbl)
anchorLbl := label.new(anchorBar, anchorPrice, "Anchor", xloc=xloc.bar_index, yloc=yloc.price, style=label.style_label_down, textcolor=color.black, color=color.yellow, size=size.tiny)
else
label.set_x(anchorLbl, anchorBar), label.set_y(anchorLbl, anchorPrice)
//==================== Light-Cone (ATR-based) ====================//
float atrSame = ta.atr(atrLen)
float atrOther = request.security(syminfo.tickerid, atrTF, ta.atr(atrLen), gaps=barmerge.gaps_off, lookahead=barmerge.lookahead_off)
float baseATR = (na(atrTF) or atrTF == "") ? atrSame : atrOther
float c_main = mult * baseATR
int horizon = math.min(projBars, futureLimitBars)
var line upL = na
var line dnL = na
var line up05 = na
var line dn05 = na
var line up15 = na
var line dn15 = na
var linefill coneFill = na
f_growth(_n) =>
coneMode == "Linear n" ? _n : math.sqrt(_n)
if anchorOK
if not na(coneFill)
linefill.delete(coneFill)
if not na(upL)
line.delete(upL)
if not na(dnL)
line.delete(dnL)
if not na(up05)
line.delete(up05)
if not na(dn05)
line.delete(dn05)
if not na(up15)
line.delete(up15)
if not na(dn15)
line.delete(dn15)
int x1 = anchorBar + horizon
float dyPer= (wickMode == "Wick (High/Low)") ? c_main * 0.5 : c_main
float grow = f_growth(horizon)
float upY = anchorPrice + dyPer * grow
float dnY = anchorPrice - dyPer * grow
float upY05 = anchorPrice + (dyPer * 0.5) * grow
float dnY05 = anchorPrice - (dyPer * 0.5) * grow
float upY15 = anchorPrice + (dyPer * 1.5) * grow
float dnY15 = anchorPrice - (dyPer * 1.5) * grow
upL := line.new(anchorBar, anchorPrice, x1, upY, xloc=xloc.bar_index, extend=extend.none, color=coneLineCol, width=2)
dnL := line.new(anchorBar, anchorPrice, x1, dnY, xloc=xloc.bar_index, extend=extend.none, color=coneLineCol, width=2)
coneFill := linefill.new(upL, dnL, color=coneFillCol)
if showFan
up05 := line.new(anchorBar, anchorPrice, x1, upY05, xloc=xloc.bar_index, extend=extend.none, color=fan05Color, width=fanWidth)
dn05 := line.new(anchorBar, anchorPrice, x1, dnY05, xloc=xloc.bar_index, extend=extend.none, color=fan05Color, width=fanWidth)
up15 := line.new(anchorBar, anchorPrice, x1, upY15, xloc=xloc.bar_index, extend=extend.none, color=fan15Color, width=fanWidth)
dn15 := line.new(anchorBar, anchorPrice, x1, dnY15, xloc=xloc.bar_index, extend=extend.none, color=fan15Color, width=fanWidth)
//==================== √n Stripes ====================//
var array centers = array.new_int()
array.clear(centers)
if not na(anchorBar)
for m = 1 to maxOrderM
array.push(centers, anchorBar + stepBars * m * m)
f_in_any_stripe(_bi) =>
sz = array.size(centers)
if sz == 0
false
else
bool hit = false
for i = 0 to sz - 1
int c0 = array.get(centers, i)
if _bi >= c0 - halfWindow and _bi <= c0 + halfWindow
hit := true
hit
// どの m² ストライプか（なければ na）
f_stripe_index(_bi) =>
int mFound = na
if array.size(centers) > 0
for m = 1 to maxOrderM
int cCenter = array.get(centers, m - 1)
if _bi >= cCenter - halfWindow and _bi <= cCenter + halfWindow
mFound := m
mFound
bgcolor(f_in_any_stripe(bar_index) ? stripeColor : na)
if showCenters and array.size(centers) > 0
for i = 0 to array.size(centers) - 1
int c0 = array.get(centers, i)
bool withinFutureLimit = c0 <= bar_index + futureLimitBars
bool nearScreen = not limitScreen or (c0 >= bar_index - screenPastBars and c0 <= bar_index + futureLimitBars)
if withinFutureLimit and nearScreen
line.new(c0, high, c0, low, xloc=xloc.bar_index, extend=extend.both, color=color.new(color.white, 70), width=1)
//==================== Fractal Detection ====================//
isPH = not na(ta.pivothigh(high, leftP, rightP))
isPL = not na(ta.pivotlow (low , leftP, rightP))
int pivotCenter = bar_index - rightP
hitPH = isPH and f_in_any_stripe(pivotCenter)
hitPL = isPL and f_in_any_stripe(pivotCenter)
//==================== m² Hit Table (robust) ====================//
var table tbHits = na
var hitsHi = array.new_int()
var hitsLo = array.new_int()
f_sync_len_int(_arr, _n, _fill) =>
while array.size(_arr) < _n
array.push(_arr, _fill)
while array.size(_arr) > _n
array.pop(_arr)
_arr
f_safe_get_int(_arr, _idx) =>
(_idx >= 0 and _idx < array.size(_arr)) ? array.get(_arr, _idx) : 0
// 毎バー長さ同期
f_sync_len_int(hitsHi, maxOrderM, 0)
f_sync_len_int(hitsLo, maxOrderM, 0)
// 集計
if (hitPH or hitPL) and array.size(centers) > 0
int nC = array.size(centers)
int nM = math.min(maxOrderM, nC)
for m = 1 to nM
int c0 = array.get(centers, m - 1)
if pivotCenter >= c0 - halfWindow and pivotCenter <= c0 + halfWindow
if hitPH
array.set(hitsHi, m - 1, f_safe_get_int(hitsHi, m - 1) + 1)
if hitPL
array.set(hitsLo, m - 1, f_safe_get_int(hitsLo, m - 1) + 1)
// 表示
if showHitTable and barstate.islast
if na(tbHits)
tbHits := table.new(f_pos(hitsCorner), 3, maxOrderM + 1, border_width=1)
table.cell(tbHits, 0, 0, "m²", bgcolor=color.new(color.blue, 20), text_color=color.white)
table.cell(tbHits, 1, 0, "PH▲", bgcolor=color.new(color.green,10), text_color=color.white)
table.cell(tbHits, 2, 0, "PL▼", bgcolor=color.new(color.red, 10), text_color=color.white)
int rows = array.size(hitsHi)
int nRow = math.min(maxOrderM, rows)
for m = 1 to nRow
table.cell(tbHits, 0, m, str.tostring(m) + "²")
table.cell(tbHits, 1, m, str.tostring(f_safe_get_int(hitsHi, m - 1)))
table.cell(tbHits, 2, m, str.tostring(f_safe_get_int(hitsLo, m - 1)))
//==================== Bias Components ====================//
// 1) HTF trend
bool htfBull = request.security(syminfo.tickerid, biasTf, ta.ema(close, emaFast) > ta.ema(close, emaSlow), gaps=barmerge.gaps_off)
bool htfBear = request.security(syminfo.tickerid, biasTf, ta.ema(close, emaFast) < ta.ema(close, emaSlow), gaps=barmerge.gaps_off)
int scHTF = htfBull ? 1 : (htfBear ? -1 : 0)
string stHTF = htfBull ? "Bull" : (htfBear ? "Bear" : "—")
// 2) Cone Z
float z = 0.0
if anchorOK
int barsFromAnchor = math.max(1, bar_index - anchorBar)
float dyPerNow = (wickMode == "Wick (High/Low)") ? (mult * baseATR * 0.5) : (mult * baseATR)
float growNow = f_growth(math.min(barsFromAnchor, horizon))
float denom = dyPerNow * growNow
z := denom != 0 ? (close - anchorPrice) / denom : 0.0
int scZ = z > zThresh ? 1 : (z < -zThresh ? -1 : 0)
string stZ = anchorOK ? ("z=" + str.tostring(z, "#.00")) : "no anchor"
// 3) Rail-hug（0.5c〜1.0c帯を High/Low の“タッチ”で判定）
// ← 初期バー安全：参照本数を bar_index にクリップ
int effLookback = math.min(railLookback, bar_index) // bar_index 本目までは 0..bar_index しか参照不可
int upTouch = 0, dnTouch = 0
if anchorOK and effLookback > 0
for i = 0 to effLookback - 1
int barsFA = math.max(1, (bar_index - i) - anchorBar)
float growI = f_growth(math.min(barsFA, horizon))
float dyPerI = (wickMode == "Wick (High/Low)") ? (mult * baseATR * 0.5) : (mult * baseATR)
float up05I = anchorPrice + dyPerI * 0.5 * growI
float up10I = anchorPrice + dyPerI * 1.0 * growI
float dn05I = anchorPrice - dyPerI * 0.5 * growI
float dn10I = anchorPrice - dyPerI * 1.0 * growI
upTouch += (high >= up05I and low <= up10I) ? 1 : 0
dnTouch += (low <= dn05I and high >= dn10I) ? 1 : 0
float upRatio = effLookback > 0 ? (upTouch * 1.0) / effLookback : 0.0
float dnRatio = effLookback > 0 ? (dnTouch * 1.0) / effLookback : 0.0
bool railUp = upRatio > railPct
bool railDn = dnRatio > railPct
int scRail = railUp ? 1 : (railDn ? -1 : 0)
string stRail = anchorOK ? (railUp ? ("Up " + str.tostring(upRatio*100, "#") + "%") : (railDn ? ("Dn " + str.tostring(dnRatio*100, "#") + "%") : "—")) : "no anchor"
// 4) Stripe entry → 最初のフラクタル（帯を出た後確定も拾う）
var bool stripeIn = false
var int stripeIdxActive = na
var int stripeEnterBar = na
var int stripeLastStart = na
var int stripeLastEnd = na
var int stripeLastIdx = na
var string firstFrac = "" // "PL" or "PH" or ""
int nowIdx = f_stripe_index(bar_index)
bool nowInStripe = not na(nowIdx)
if nowInStripe and not stripeIn
stripeIn := true
stripeIdxActive := nowIdx
stripeEnterBar := bar_index
firstFrac := ""
else if not nowInStripe and stripeIn
stripeIn := false
stripeLastStart := stripeEnterBar
stripeLastEnd := bar_index - 1
stripeLastIdx := stripeIdxActive
if (isPH or isPL)
int pc = pivotCenter
int pcIdx = f_stripe_index(pc)
bool inActive = stripeIn and not na(stripeIdxActive) and pcIdx == stripeIdxActive and pc >= nz(stripeEnterBar, pc)
bool inClosed = (not stripeIn) and not na(stripeLastIdx) and pcIdx == stripeLastIdx and pc >= nz(stripeLastStart, pc) and pc <= nz(stripeLastEnd, pc)
if firstFrac == "" and (inActive or inClosed)
firstFrac := isPL ? "PL" : (isPH ? "PH" : "")
int scFrac = firstFrac == "PL" ? 1 : (firstFrac == "PH" ? -1 : 0)
string stFrac = firstFrac == "" ? "—" : ("1st " + firstFrac + (not stripeIn and not na(stripeLastIdx) ? " @m=" + str.tostring(stripeLastIdx) : (not na(stripeIdxActive) ? " @m=" + str.tostring(stripeIdxActive) : "")))
// 5) Structure break（簡易）
var float lastPH = na
var float lastPL = na
float ph = ta.pivothigh(high, 2, 2)
float pl = ta.pivotlow (low , 2, 2)
if not na(ph)
lastPH := ph
if not na(pl)
lastPL := pl
bool bullBreak = not na(lastPH) and close > lastPH
bool bearBreak = not na(lastPL) and close < lastPL
int scStruct = bullBreak ? 1 : (bearBreak ? -1 : 0)
string stStruct = bullBreak ? "Break ↑" : (bearBreak ? "Break ↓" : "—")
// ---- Total ----
int biasScore = scHTF + scZ + scRail + scFrac + scStruct
string biasDir = biasScore >= 3 ? "UP" : (biasScore <= -3 ? "DOWN" : "NEUTRAL")
//==================== Bias Panel (table, corner selectable) ====================//
var table tbBias = na
color colG = color.new(color.green, 10)
color colR = color.new(color.red, 10)
color colN = color.new(color.gray, 70)
f_col(v) =>
v > 0 ? colG : (v < 0 ? colR : colN)
if showBiasPanel and barstate.islast
if na(tbBias)
tbBias := table.new(f_pos(panelCorner), 3, 8, border_width=1)
table.cell(tbBias, 0, 0, "Bias Panel", text_color=color.white, bgcolor=color.new(color.blue, 20), text_halign=text.align_center)
table.merge_cells(tbBias, 0, 0, 2, 0)
table.cell(tbBias, 0, 1, "Card", text_halign=text.align_center)
table.cell(tbBias, 1, 1, "State", text_halign=text.align_center)
table.cell(tbBias, 2, 1, "±1", text_halign=text.align_center)
// rows
table.cell(tbBias, 0, 2, "HTF ("+biasTf+")")
table.cell(tbBias, 1, 2, stHTF)
table.cell(tbBias, 2, 2, str.tostring(scHTF), bgcolor=f_col(scHTF), text_halign=text.align_center)
table.cell(tbBias, 0, 3, "Cone z")
table.cell(tbBias, 1, 3, stZ)
table.cell(tbBias, 2, 3, str.tostring(scZ), bgcolor=f_col(scZ), text_halign=text.align_center)
table.cell(tbBias, 0, 4, "Rail 0.5c")
table.cell(tbBias, 1, 4, stRail)
table.cell(tbBias, 2, 4, str.tostring(scRail), bgcolor=f_col(scRail), text_halign=text.align_center)
table.cell(tbBias, 0, 5, "Fractal")
table.cell(tbBias, 1, 5, stFrac)
table.cell(tbBias, 2, 5, str.tostring(scFrac), bgcolor=f_col(scFrac), text_halign=text.align_center)
table.cell(tbBias, 0, 6, "Structure")
table.cell(tbBias, 1, 6, stStruct)
table.cell(tbBias, 2, 6, str.tostring(scStruct), bgcolor=f_col(scStruct), text_halign=text.align_center)
color totCol = biasScore>=3?colG:(biasScore<=-3?colR:colN)
table.cell(tbBias, 0, 7, "TOTAL", bgcolor=color.new(color.black, 0), text_color=color.white, text_halign=text.align_center)
table.cell(tbBias, 1, 7, biasDir, bgcolor=totCol, text_color=color.white, text_halign=text.align_center)
table.cell(tbBias, 2, 7, str.tostring(biasScore), bgcolor=totCol, text_color=color.white, text_halign=text.align_center)
//==================== Alerts ====================//
alertcondition(biasScore >= 3, "Bias UP", "Bias score >= +3")
alertcondition(biasScore <= -3, "Bias DOWN", "Bias score <= -3")
alertcondition(hitPH, "Fractal High in Convergence", "Pivot High detected inside √n convergence stripe.")
alertcondition(hitPL, "Fractal Low in Convergence", "Pivot Low detected inside √n convergence stripe.")
Information Flow Analysis[b🔄 Information Flow Analysis: Systematic Multi-Component Market Analysis Framework
SYSTEM OVERVIEW AND ANALYTICAL FOUNDATION
The Information Flow Kernel - Hybrid combines established technical analysis methods into a unified analytical framework. This indicator systematically processes three distinct data streams - directional price momentum, volume-weighted pressure dynamics, and intrabar development patterns - integrating them through weighted mathematical fusion to produce statistically normalized market flow measurements.
COMPREHENSIVE MATHEMATICAL FRAMEWORK
Component 1: Directional Flow Analysis
The directional component analyzes price momentum through three mathematical vectors:
Price Vector: p = C - O (intrabar directional bias)
Momentum Vector: m = C_t - C_{t-1} (bar-to-bar velocity)
Acceleration Vector: a = m_t - m_{t-1} (momentum rate of change)
Directional Signal Integration:
S_d = \text{sgn}(p) \cdot |p| + \text{sgn}(m) \cdot |m| \cdot 0.6 + \text{sgn}(a) \cdot |a| \cdot 0.3
The signum function preserves directional information while absolute values provide magnitude weighting. Coefficients create a hierarchy emphasizing intrabar movement (100%), momentum (60%), and acceleration (30%).
Final Directional Output: K_1 = S_d \cdot w_d where w_d is the directional weight parameter.
Component 2: Volume-Weighted Pressure Analysis
Volume Normalization: r_v = \frac{V_t}{\overline{V_n}} where \overline{V_n} represents the n-period simple moving average of volume.
Base Pressure Calculation: P_{base} = \Delta C \cdot r_v \cdot w_v where \Delta C = C_t - C_{t-1} and w_v is the velocity weighting factor.
Volume Confirmation Function:
f(r_v) = \begin{cases}
1.4 & \text{if } r_v > 1.2 \
0.7 & \text{if } r_v < 0.8 \
1.0 & \text{otherwise}
\end{cases}
Final Pressure Output: K_2 = P_{base} \cdot f(r_v)
Component 3: Intrabar Development Analysis
Bar Position Calculation: B = \frac{C - L}{H - L} when H - L > 0 , else B = 0.5
Development Signal Function:
S_{dev} = \begin{cases}
2(B - 0.5) & \text{if } B > 0.6 \text{ or } B < 0.4 \
0 & \text{if } 0.4 \leq B \leq 0.6
\end{cases}
Final Development Output: K_3 = S_{dev} \cdot 0.4
Master Integration and Statistical Normalization
Weighted Component Fusion: F_{raw} = 0.5K_1 + 0.35K_2 + 0.15K_3
Sensitivity Scaling: F_{master} = F_{raw} \cdot s where s is the sensitivity parameter.
Statistical Normalization Process:
Rolling Mean: \mu_F = \frac{1}{n}\sum_{i=0}^{n-1} F_{master,t-i}
Rolling Standard Deviation: \sigma_F = \sqrt{\frac{1}{n}\sum_{i=0}^{n-1} (F_{master,t-i} - \mu_F)^2}
Z-Score Computation: z = \frac{F_{master} - \mu_F}{\sigma_F}
Boundary Enforcement: z_{bounded} = \max(-3, \min(3, z))
Final Normalization: N = \frac{z_{bounded}}{3}
Flow Metrics Calculation:
Intensity: I = |z|
Strength Percentage: S = \min(100, I \times 33.33)
Extreme Detection: \text{Extreme} = I > 2.0
DETAILED INPUT PARAMETER SPECIFICATIONS
Sensitivity (0.1 - 3.0, Default: 1.0)
Global amplification multiplier applied to the master flow calculation. Functions as: F_{master} = F_{raw} \cdot s
Low Settings (0.1 - 0.5): Enhanced precision for subtle market movements. Optimal for low-volatility environments, scalping strategies, and early detection of minor directional shifts. Increases responsiveness but may amplify noise.
Moderate Settings (0.6 - 1.2): Balanced sensitivity for standard market conditions across multiple timeframes.
High Settings (1.3 - 3.0): Reduced sensitivity to minor fluctuations while emphasizing significant flow changes. Ideal for high-volatility assets, trending markets, and longer timeframes.
Directional Weighting (0.1 - 1.0, Default: 0.7)
Controls emphasis on price direction versus volume and positioning factors. Applied as: K_{1,weighted} = K_1 \times w_d
Lower Values (0.1 - 0.4): Reduces directional bias, favoring volume-confirmed moves. Optimal for ranging markets where momentum may generate false signals.
Higher Values (0.7 - 1.0): Amplifies directional signals from price vectors and acceleration. Ideal for trending conditions where directional momentum drives price action.
Velocity Weighting (0.1 - 1.0, Default: 0.6)
Scales volume-confirmed price change impact. Applied in: P_{base} = \Delta C \times r_v \times w_v
Lower Values (0.1 - 0.4): Dampens volume spike influence, focusing on sustained pressure patterns. Suitable for illiquid assets or news-sensitive markets.
Higher Values (0.8 - 1.0): Amplifies high-volume directional moves. Optimal for liquid markets where volume provides reliable confirmation.
Volume Length (3 - 20, Default: 5)
Defines lookback period for volume averaging: \overline{V_n} = \frac{1}{n}\sum_{i=0}^{n-1} V_{t-i}
Short Periods (3 - 7): Responsive to recent volume shifts, excellent for intraday analysis.
Long Periods (13 - 20): Smoother averaging, better for swing trading and higher timeframes.
DASHBOARD SYSTEM
Primary Flow Gauge
Bilaterally symmetric visualization displaying normalized flow direction and intensity:
Segment Calculation: n_{active} = \lfloor |N| \times 15 \rfloor
Left Fill: Bearish flow when N < -0.01
Right Fill: Bullish flow when N > 0.01
Neutral Display: Empty segments when |N| \leq 0.01
Visual Style Options:
Matrix: Digital blocks (▰/▱) for quantitative precision
Wave: Progressive patterns (▁▂▃▄▅▆▇█) showing flow buildup
Dots: LED-style indicators (●/○) with intensity scaling
Blocks: Modern squares (■/□) for professional appearance
Pulse: Progressive markers (⎯ to █) emphasizing intensity buildup
Flow Intensity Visualization
30-segment horizontal bar graph with mathematical fill logic:
Segment Fill: For i \in : filled if \frac{i}{29} \leq \frac{S}{100}
Color Coding System:
Orange (S > 66%): High intensity, strong directional conviction
Cyan (33% ≤ S ≤ 66%): Moderate intensity, developing bias
White (S < 33%): Low intensity, neutral conditions
Extreme Detection Indicators
Circular markers flanking the gauge with state-dependent illumination:
Activation: I > 2.0 \land |N| > 0.3
Bright Yellow: Active extreme conditions
Dim Yellow: Normal conditions
Metrics Display
Balance Value: Raw master flow output ( F_{master} ) showing absolute directional pressure
Z-Score Value: Statistical deviation ( z_{bounded} ) indicating historical context
Dynamic Narrative System
Context-sensitive interpretation based on mathematical thresholds:
Extreme Flow: I > 2.0 \land |N| > 0.6
Moderate Flow: 0.3 < |N| \leq 0.6
High Volatility: S > 50 \land |N| \leq 0.3
Neutral State: S \leq 50 \land |N| \leq 0.3
ALERT SYSTEM SPECIFICATIONS
Mathematical Trigger Conditions:
Extreme Bullish: I > 2.0 \land N > 0.6
Extreme Bearish: I > 2.0 \land N < -0.6
High Intensity: S > 80
Bullish Shift: N_t > 0.3 \land N_{t-1} \leq 0.3
Bearish Shift: N_t < -0.3 \land N_{t-1} \geq -0.3
TECHNICAL IMPLEMENTATION AND PERFORMANCE
Computational Architecture
The system employs efficient calculation methods minimizing processing overhead:
Single-pass mathematical operations for all components
Conditional visual rendering (executed only on final bar)
Optimized array operations using direct calculations
Real-Time Processing
The indicator updates continuously during bar formation, providing immediate feedback on changing market conditions. Statistical normalization ensures consistent interpretation across varying market regimes.
Market Applicability
Optimal performance in liquid markets with consistent volume patterns. May require parameter adjustment for:
Low-volume or after-hours sessions
News-driven market conditions
Highly volatile cryptocurrency markets
Ranging versus trending market environments
PRACTICAL APPLICATION FRAMEWORK
Market State Classification
This indicator functions as a comprehensive market condition assessment tool providing:
Trend Analysis: High intensity readings ( S > 66% ) with sustained directional bias indicate strong trending conditions suitable for momentum strategies.
Reversal Detection: Extreme readings ( I > 2.0 ) at key technical levels may signal potential trend exhaustion or reversal points.
Range Identification: Low intensity with neutral flow ( S < 33%, |N| < 0.3 ) suggests ranging market conditions suitable for mean reversion strategies.
Volatility Assessment: High intensity without clear directional bias indicates elevated volatility with conflicting pressures.
Integration with Trading Systems
The normalized output range facilitates integration with automated trading systems and position sizing algorithms. The statistical basis provides consistent interpretation across different market conditions and asset classes.
LIMITATIONS AND CONSIDERATIONS
This indicator combines established technical analysis methods and processes historical data without predicting future price movements. The system performs optimally in liquid markets with consistent volume patterns and may produce false signals in thin trading conditions or during news-driven market events. This indicator is provided for educational and analytical purposes only and does not constitute financial advice. Users should combine this analysis with proper risk management, position sizing, and additional confirmation methods before making any trading decisions. Past performance does not guarantee future results.
Note: The term "kernel" in this context refers to modular calculation components rather than mathematical kernel functions in the formal computational sense.
As quantitative analyst Ralph Vince noted: "The essence of successful trading lies not in predicting market direction, but in the systematic processing of market information and the disciplined management of probability distributions."
— Dskyz, Trade with insight. Trade with anticipation.
Trend Strength Index Long Strategy📈 Trend Strength Index Long Strategy
This strategy combines the Trend Strength Index (TSI) with a Volume-Weighted Moving Average (VWMA) to identify high-probability long entries based on trend momentum and price confirmation.
📊 TSI Calculation : Measures correlation between price and time (bar index) over a user-defined period. Strong TSI values indicate trend momentum.
📏 VWMA Filter : Confirms bullish bias when price is above the VWMA.
🚀 Entry Condition : Long position is triggered when TSI crosses above -0.65 and price is above VWMA.
🔒 Exit Condition : Position is closed when TSI crosses above 0.65.
🎨 Visuals : Gradient fills highlight bullish and bearish zones. VWMA is plotted for trend context.
🧮 TSI Length: Adjustable (default 14)
📐 VWMA Length: Adjustable (default 55)
💸 Commission: 0.1% per trade
📊 Position Size: 75% of equity
⚙️ Slippage: 10 ticks
✅ Best used in trending markets with steady momentum.
⚠️ Avoid in choppy or range-bound conditions.
Levels, EMS, Volume etc.This is a comprehensive trading indicator that combines three main analysis tools:
EMA System — a set of 12 exponential moving averages with color trend indication.
Consolidation — detects and highlights price accumulation zones with breakout alerts.
S/R Levels — identifies key support and resistance levels across different timeframes, shows their strength, and displays them in a table.
Additionally, it includes a volume histogram and a notification system for important chart events. All these features help traders analyze trends and identify potential entry and exit points.
Это комплексный торговый индикатор, который объединяет три основных инструмента анализа:
EMA-система — набор из 12 экспоненциальных скользящих средних с цветовой индикацией тренда.
Консолидация — определяет и выделяет зоны накопления цены с оповещениями о пробоях.
Уровни S/R — находит ключевые уровни поддержки и сопротивления на разных таймфреймах, показывает их силу и выводит в таблицу.
Дополнительно включает гистограмму объёмов и систему уведомлений о важных событиях на графике. Всё это помогает трейдерам анализировать тренды, определять потенциальные точки входа и выхода.
Chartlense Dashboard (Data, Trend & Levels)Chartlense Dashboard (Data, Trend & Levels)
Overview
This dashboard is designed to solve two common problems for traders: chart clutter and the manual drawing of support and resistance levels . It consolidates critical data from multiple indicators into a clean table overlay and automatically plots the most relevant S&R levels based on recent price action. The primary goal is to provide a clear, at-a-glance overview of the market's structure and data.
It offers both a vertical and horizontal layout to fit any trader's workspace.
Key Concepts & Calculations Explained
This indicator is more than a simple collection of values; it synthesizes data to provide unique insights. Here’s a conceptual look at how its core components work:
Automatic Support & Resistance (Pivot-Based):
The dashed support (green) and resistance (red) lines are not manually drawn. They are dynamically calculated based on the most recent confirmed pivot highs and pivot lows . A pivot is a foundational concept in technical analysis that identifies potential turning points in price action.
How it works: A pivot high is a candle whose `high` is higher than a specific number of candles to its left and right (the "Pivot Lookback" is set to 5 by default in the settings). A pivot low is the inverse. By automatically identifying these confirmed structural points, the script visualizes the most relevant levels of potential supply and demand on the chart.
Relative Volume (RVOL):
This value in the table is not the standard volume. It measures the current bar's volume against its recent average (specifically, `current volume / 10-period simple moving average of volume`).
Interpretation: A reading above 2.0 (indicated by green text) suggests that the current volume is more than double the recent average. This technique is used to identify significant volume spikes, which can add conviction to breakouts or signal potential market climaxes.
Consolidated Data for Context:
Other values displayed in the table, such as the EMAs (9, 20, 200) , Bollinger Bands (20, 2) , RSI (14) , MACD (12, 26, 9) , and VWAP (on intraday charts), use their standard industry calculations. They are included to provide a complete contextual picture without needing to load each indicator separately, saving valuable chart space.
How to Use This in Your Trading
This dashboard is designed as a tool for confluence and context , not as a standalone signal generator. Here are some ways to integrate it into your analysis workflow:
As a Trend Filter: Before considering a trade, quickly glance at the EMAs and the MACD values in the table. A price above the key EMAs and a positive MACD can serve as a quick confirmation that you are aligned with the dominant trend.
To Validate Breakouts: When the price is approaching a key Resistance level (red pivot line), watch the RVOL value . A reading above 2.0 on the breakout candle adds significant confirmation that the move is backed by strong interest. The same logic applies to breakdowns below a support level.
To Spot Potential Reversals: Confluence is key. For example, if the price is testing a Support level (green pivot line) AND the RSI in the table is approaching oversold levels (e.g., near 30), it can signal a higher probability reversal setup.
About This Indicator
This indicator was developed by the team at ChartLense to help traders declutter their charts and focus on the data that matters. We believe in making complex analysis more accessible and organized. We hope this free tool is a valuable addition to your trading process.
影武者Ⅲ (safe fixed)minnkofusuki kaizou torendline jidou
Minkowski space is a real vector space equipped with a non-degenerate symmetric bilinear form. It is named after the German mathematician Hermann Minkowski. The framework was used by Albert Einstein in formulating the theory of special relativity. In this specific setting, since it represents spacetime that combines space and time, it is also referred to as Minkowski spacetime in the context of physics.
Distribution DaysThis script marks Distribution Days according to the Investors Business Daily method -- a significant decline on higher volume:
(1.) Price has declined > 0.2% from the prior day's close
(2.) Trading volume is greater than the prior day's volume
