เริ่มกันเลย

อินดิเคเตอร์และกลยุทธ์

Pivot point moving averagesPivot Point Moving Averages builds moving averages from confirmed pivots, not from every bar. Instead of averaging all highs and lows, this script: Detects swing pivot highs and pivot lows using a configurable Pivot length (pivotLen). Converts these sparse pivot prices into continuous series of: last confirmed pivot low last confirmed pivot high Applies a user-selectable moving average (SMA / EMA / RMA / WMA / VWMA) to each of those pivot series. Plots the two resulting lines and shades the area between them as a pivot value cloud. Because the lines only move when a new pivot is confirmed, they represent structural acceptance rather than raw volatility. Short “noise” moves and stop hunts between pivots have much less impact on these averages. You can also enable an optional second pivot MA cloud: Uses the same Pivot length for structural detection. Has its own MA length and type. Can run on a different timeframe (e.g. D, 240, W). Is projected back onto the current chart so you see local pivot value and higher-timeframe pivot value together. Why it’s useful Traditional MAs: React to every bar. Move on noise, wicks, and stop runs. Don’t distinguish between “meaningful” structure and random fluctuation. This tool uses confirmed pivots, so it is better suited to market structure and phase analysis: Pivot MA low reflects how demand is stepping up (or down) as new swing lows form. Pivot MA high reflects how supply is pressing down (or easing) as new swing highs form. The cloud between them acts as a dynamic, structure-based value area. Typical interpretations: Price inside the pivot cloud → balance / fair value area. Price above the pivot cloud → bullish value expansion. Price below the pivot cloud → bearish value expansion. Cloud compressing → possible energy build-up, transition between phases. Cloud expanding → stronger directional conviction. With the second cloud enabled on a higher timeframe, you can: See whether lower-timeframe structure is building with or against the higher-timeframe pivot value. Use the HTF cloud as a background bias and the LTF cloud for timing and fine-grained context. Notes All pivot-based tools have inherent delay: a pivot is only confirmed after pivotLen bars to the right. On very low timeframes, long pivotLen + long MA lengths will make the lines slower to react. This is intended as a context and structure tool, not a standalone entry signal.
อินดิเคเตอร์ Pine Script®
โดย pc75
A program written by a beginner# TXF Choppy Market Detector (Whipsaw Filter) ## Introduction This project is a technical indicator developed in **Pine Script v5**, specifically optimized for **Taiwan Index Futures (TXF)** intraday trading. The TXF market is known for its frequent periods of low-volatility consolidation following sharp moves, often resulting in "whipsaws" (double-loss scenarios for trend followers). This script utilizes **volatility analysis** and **trend efficiency metrics** to filter out noise and detect potential "Stop Hunting" or "Liquidity Sweep" setups within range-bound markets. ## Methodology & Algorithms The strategy operates on the principle of **Mean Reversion**, combining two core components: ### 1. Market Regime Filter: Choppiness Index (CHOP) We use the Choppiness Index (originally developed by E.W. Dreiss) to determine if the market is trending or consolidating based on **Fractal Dimension** theory. * **Logic**: The index ranges from 0 to 100. Higher values indicate low trend efficiency (consolidation), while lower values indicate strong directional trends. * **Condition**: `CHOP > Threshold` (Default: 50). * **Application**: When this condition is met, the background turns **gray**, signaling a "No-Trade Zone" for trend strategies and activating the Mean Reversion logic. ### 2. Whipsaw Detection: Bollinger Bands Bollinger Bands are used to define the dynamic statistical extremities of price action. * **Logic**: We identify **Fakeouts** (False Breakouts) that occur specifically during the choppy regime identified above. This is often where institutional traders hunt for liquidity (stops) before reversing the price. #### Signal Algorithms (Pseudocode) **A. Bull Trap (Washout High)** A false upside breakout designed to trap long traders. ```pine Condition: 1. Is_Choppy == true (Market is sideways) 2. High > Upper_Bollinger_Band (Price pierces the upper band) 3. Close < Upper_Bollinger_Band (Price fails to hold and closes back inside)
อินดิเคเตอร์ Pine Script®
โดย aammyytseng
AI PRE-MARKET PRO - True/Fake Gap Classification-Version 1.0## **AI PRE-MARKET PRO: QUICK START GUIDE** This indicator classifies market gaps by comparing the **Current Price** to yesterday’s **High (PDH)**, **Low (PDL)**, and **Close (PDC)**. ### **1. GAP CLASSIFICATIONS** * **🔥 TRUE GAPS (High Momentum)** * **True Gap Up:** Price is above PDH. The market is in "Discovery Mode." High probability of trend continuation. * **True Gap Down:** Price is below PDL. Significant bearish sentiment. High probability of further selling. * **⚠️ FAKE GAPS (Mean Reversion)** * **Fake Gap Up:** Above PDC but below PDH. Price is "trapped" in yesterday's value. Often reverts to the Close (PDC). * **Fake Gap Down:** Below PDC but above PDL. Price is "trapped." Often bounces back toward the Close (PDC). ### **2. TRADING STRATEGY CHEAT SHEET** | Scenario | Primary Play | Entry Logic | | --- | --- | --- | | **True Gap Up** | **Continuation** | Wait for a pullback to **PDH**; buy the hold. | | **True Gap Down** | **Continuation** | Wait for a rally to **PDL**; short the rejection. | | **Fake Gap Up** | **Fade/Range** | Short the rejection of **PDH** or **ONH**; target **PDC**. | | **Fake Gap Down** | **Fade/Range** | Buy the bounce at **PDL** or **ONL**; target **PDC**. | ### **3. CRITICAL LEVELS ON YOUR CHART** * **PDH / PDL:** The "Line in the Sand." Breaking these turns a Fake Gap into a True Gap. * **ONH / ONL:** Overnight High/Low. These are your immediate support/resistance targets for the first 30 minutes of trading. * **PDC:** Previous Day Close. The "Magnet." If the market doesn't trend, it usually returns here. ### **4. HOW TO READ THE AI TABLES** * **Left Table:** Shows real-time distance (RT Δ) to key levels and whether they have been hit yet (**Mitigated**). * **Bottom Tables:** Provide a probability-based "Game Plan" and specific execution rules (e.g., "Wait for 15-minute confirmation"). --- **Next Step:** Would you like me to show you how to set up an alert for when the price crosses the **PDH** or **PDL** to catch a True Gap breakout?
อินดิเคเตอร์ Pine Script®
โดย DeLeBlanc
2
Bollinger Bands + MA 50/100/200📊 Bollinger Bands + MA 50 / 100 / 200 Indicator This indicator combines Bollinger Bands with key Moving Averages (50, 100, 200) to help you spot trend direction, volatility, and potential reversal zones in one clean view. 🔹 Bollinger Bands * Customizable length & MA type (SMA, EMA, RMA, WMA, VWMA) * Visualizes market volatility * Upper & lower bands help identify overbought / oversold conditions 🔹 Moving Averages * MA 50 → Short-term trend * MA 100 → Medium-term trend * MA 200 → Long-term trend & major support/resistance * Easy toggle on/off for clean charting 💡 How to use * Price near upper band + strong MA trend → possible continuation * Price near lower band → watch for bounce or breakdown * MA alignment (50 > 100 > 200) → bullish trend * MA cross & BB squeeze → potential breakout incoming ⚠️ Best used with price action & risk management 📌 Works on stocks, crypto, forex, indices
อินดิเคเตอร์ Pine Script®
โดย qwyra
Effort-Result Divergence [Interakktive]The Effort-Result Divergence (ERD) measures whether volume effort is producing proportional price result. It quantifies the classic Wyckoff principle: when price moves easily, momentum is real; when price struggles despite heavy volume, absorption is occurring. Think of ERD as "energy efficiency" for price movement — green means price is gliding, red means price is grinding. █ WHAT IT DOES • Measures volume EFFORT relative to average volume • Measures price RESULT relative to ATR-normalized movement • Computes ERD = Result minus Effort (each scaled 0-100) • Flags statistical divergences via Z-score analysis • Absorption events: high effort, low result (negative ERD) • Vacuum events: low effort, high result (positive ERD) █ WHAT IT DOES NOT DO • NO buy/sell signals • NO entry/exit recommendations • NO alerts (v1 is educational only) • NO performance claims or guarantees This is a context tool for understanding market participation quality. █ HOW IT WORKS The ERD analyzes two dimensions of market activity and compares them. EFFORT (Volume Intensity) Compares current volume to a moving average baseline: Effort Ratio = Volume ÷ SMA(Volume, Length) Effort Score = clamp(100 × Effort Ratio ÷ Effort Cap) High effort means above-average volume participation. Low effort means below-average volume participation. RESULT (Price Efficiency) Measures how much price moved relative to expected volatility: Result Ratio = |Close − Previous Close| ÷ ATR Result Score = clamp(100 × Result Ratio ÷ Result Cap) High result means price moved significantly for the volatility regime. Low result means price barely moved despite market activity. ERD SCORE ERD = Result − Effort • Positive ERD: Result exceeds effort → price moved easily (vacuum/thin liquidity) • Negative ERD: Effort exceeds result → price struggled (absorption/accumulation) • Near zero: Balanced effort-to-result relationship STATISTICAL DIVERGENCE DETECTION Z-score analysis identifies statistically significant extremes: Z = (ERD − Mean) ÷ StdDev • Absorption Event: Z ≤ −threshold (extreme negative ERD) • Vacuum Event: Z ≥ +threshold (extreme positive ERD) █ INTERPRETATION GREEN BARS (Positive ERD) Price moved with relatively little volume effort. This suggests: • Thin liquidity / low resistance • Strong directional interest • Momentum is "real" — not forced RED BARS (Negative ERD) Heavy volume was used but price barely moved. This suggests: • Absorption / accumulation occurring • Large players opposing the move • Inefficiency — someone is working hard for little result THE KEY INSIGHT When you see: • Down moves = high effort (red spikes) • Up moves = low effort (green bars) This means: It's easier for price to go up than down. That is asymmetric strength — classic bullish pressure. The reverse (red on up moves, green on down moves) signals bearish pressure. PRACTICAL RULES Without any other indicators: • Avoid shorting when ERD is mostly green and red spikes appear only on down candles • Be cautious buying when ERD turns red on up candles (signals absorption of buying pressure) • Vacuum events (extreme green) often precede continuation or pause — not violent reversal • Absorption events (extreme red) often precede reversals or range formation █ VOLUME DATA NOTE This indicator uses the volume variable which represents: • Exchange volume on stocks and futures • Tick volume on Forex and CFD instruments Tick volume is a proxy for activity, not actual exchange volume. The indicator remains useful on Forex as relative volume comparisons are still meaningful, but interpretation should account for this limitation. █ INPUTS Core Settings • Volume Average Length: Baseline period for effort calculation (default: 20) • ATR Length: Volatility normalization period (default: 14) • Effort Cap: Volume ratio that maps to 100% effort (default: 3.0) • Result Cap: ATR multiple that maps to 100% result (default: 1.0) Divergence Detection • Z-Score Lookback: Statistical analysis window (default: 100) • Z-Score Threshold: Standard deviations for event flags (default: 2.0) Visual Settings • Show ERD Histogram: Toggle main display • Show Zero Line: Toggle reference line • Show Divergence Markers: Toggle event circles • Show Effort/Result Lines: Display component breakdown █ ORIGINALITY While Wyckoff's effort-versus-result principle is well-established, existing implementations are typically: • Purely visual with no quantification • Pattern-based requiring subjective interpretation • Not statistically normalized for comparison across instruments ERD is original because it: 1. Normalizes both effort and result to 0-100 scales for direct comparison 2. Uses ATR for result normalization (adapts to volatility regime) 3. Applies statistical Z-score for objective divergence detection 4. Provides quantified output suitable for systematic analysis █ DATA WINDOW EXPORTS When enabled, the following values are exported: • Effort (0-100) • Result (0-100) • ERD Score • Z-Score • Absorption Event (1/0) • Vacuum Event (1/0) █ SUITABLE MARKETS Works on: Stocks, Futures, Forex, Crypto Best on: Instruments with reliable volume data (stocks, futures, crypto) Timeframes: All timeframes — interpretation adapts accordingly █ RELATED • Market Efficiency Ratio — measures price path efficiency • Wyckoff Volume Spread Analysis — conceptual foundation █ DISCLAIMER This indicator is for educational purposes only. It does not constitute financial advice. Past performance does not guarantee future results. Always conduct your own analysis before making trading decisions.
อินดิเคเตอร์ Pine Script®
โดย Interakktive
1
QUANT TRADING ENGINE [PointAlgo]Quant Trading Engine is a quantitative market-analysis indicator that combines multiple statistical factors to study trend behavior, mean reversion, volatility, execution efficiency, and market stability. The indicator converts raw price behavior into standardized signals to help evaluate directional bias and risk conditions in a systematic way. This script focuses on factor alignment and regime awareness, not prediction certainty. Design Philosophy Markets move through different regimes such as trending, ranging, volatile expansion, and instability. This indicator attempts to model these regimes by blending: Momentum strength Mean-reversion pressure Volatility risk Trend filtering Execution context (VWAP) Correlation structure Each component is normalized and combined into a single Quant Alpha framework. Factor Construction 1. Momentum Factor Measures directional strength using percentage price change over a rolling window. Standardized using mean and standard deviation. Represents trend continuation pressure. 2. Mean Reversion Factor Measures deviation from a longer moving average. Standardized to identify stretched conditions. Designed to capture counter-trend behavior. Directional Clamping Mean-reversion signals are dynamically restricted: No counter-trend buying during downtrends. No counter-trend selling during uptrends. Allows both sides only in neutral regimes. This prevents conflicting signals in strong trends. 3. Volatility Factor Uses realized volatility derived from price changes. Penalizes environments where volatility deviates significantly from its norm. Acts as a risk adjustment rather than a directional driver. 4. Composite Quant Alpha The final Quant Alpha is a weighted blend of: Momentum Mean reversion (trend-clamped) Volatility risk The composite is standardized into a Z-score, allowing consistent interpretation across instruments and timeframes. Signal Logic Buy signal occurs when Quant Alpha crosses above zero. Sell signal occurs when Quant Alpha crosses below zero. Zero-cross logic is used to represent shifts from negative to positive statistical bias and vice versa. Signals reflect statistical regime change, not trade instructions. Volatility Smile Context Measures price deviation from its statistical distribution. Identifies skewed conditions where upside or downside volatility becomes dominant. Highlights extreme deviations that may imply elevated derivative risk. Exotic Risk Conditions Detects sudden price expansion combined with volatility spikes. Highlights environments where execution and risk become unstable. Visual background cues are used for awareness only. Execution Context (VWAP) Measures price distance from VWAP. Used to assess execution efficiency rather than direction. Helps identify stretched conditions relative to average traded price. Correlation Structure Evaluates short-term return correlations. Detects when price behavior becomes less predictable. Flags structural instability rather than trend direction. Visualization The indicator plots: Quant Alpha (scaled) with directional coloring Volatility smile deviation Price vs VWAP distance Correlation structure Signal markers indicate Quant Alpha zero-cross events and risk conditions. Dashboard A compact dashboard summarizes: Trend filter state Quant Alpha polarity and value Individual factor readings Current action state (Buy / Sell / Wait / Risk) The dashboard provides a real-time snapshot of internal model conditions. Usage Notes Designed for analytical interpretation and research. Best used alongside price action and risk management tools. Factor behavior depends on instrument liquidity and volatility. Not optimized for illiquid or irregular markets. Disclaimer This script is provided for educational and analytical purposes only. It does not provide financial, investment, or trading advice. All outputs should be independently validated before making any trading decisions.
อินดิเคเตอร์ Pine Script®
โดย pointalgo
Seasonality Table: % Move by Day x Month (Open vs Prev Close)Short description A compact seasonality heatmap that shows the average daily open vs previous session close move for each calendar day (1–31) across months (Jan–Dec). What it does This indicator builds a Day × Month table where each cell displays the historical average of: (Open/Close-1) -1 x 100 In other words: how the market typically “opened” relative to the prior day’s close, grouped by day of month and month. How to read it Rows = Day of month (1–31) Columns = Months (Jan–Dec) Cell value = average percentage move (signed format like +0.23% or -0.33%) Heatmap = stronger color intensity indicates larger absolute average moves Today highlight = the current calendar day cell is visually highlighted for fast context Key settings Reference timeframe (Daily): uses daily session data as the source of truth Decimals / Signed formatting: control numeric display Theme controls: fully customizable colors for positive/negative/neutral cells, headers, labels, and text Font sizes: independently adjust header/labels/values Heatmap scaling: set “max abs (%)” to match the volatility of the instrument Notes / limitations The indicator depends on the historical data available on TradingView for the selected symbol and timeframe. This is a statistical visualization tool. It does not predict future returns and does not generate trade signals. Disclaimer This script is for educational and informational purposes only and is not financial advice. Trading involves risk. Always do your own research and use proper risk management.
อินดิเคเตอร์ Pine Script®
โดย Yesid_Correa_Cano
AlphaTrend_TC// This source code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org // author © KivancOzbilgic // developer © KivancOzbilgic // I'm just playing with it.... Jake Ryan //@version=5 indicator('AlphaTrend', shorttitle='AT', overlay=true, format=format.price, precision=2, timeframe='') coeff = input.float(1, 'Multiplier', step=0.1) AP = input(14, 'Common Period') ATR = ta.sma(ta.tr, AP) src = input(close) showsignalsk = input(title='Show Signals?', defval=true) novolumedata = input(title='Change calculation (no volume data)?', defval=false) upT = low - ATR * coeff downT = high + ATR * coeff AlphaTrend = 0.0 AlphaTrend := (novolumedata ? ta.rsi(src, AP) >= 50 : ta.mfi(hlc3, AP) >= 50) ? upT < nz(AlphaTrend ) ? nz(AlphaTrend ) : upT : downT > nz(AlphaTrend ) ? nz(AlphaTrend ) : downT color1 = AlphaTrend > AlphaTrend ? #00E60F : AlphaTrend < AlphaTrend ? #80000B : AlphaTrend > AlphaTrend ? #00E60F : #80000B k1 = plot(AlphaTrend, color=color.new(#0022FC, 0), linewidth=3) k2 = plot(AlphaTrend , color=color.new(#FC0400, 0), linewidth=3) fill(k1, k2, color=color1) buySignalk = ta.crossover(AlphaTrend, AlphaTrend ) sellSignalk = ta.crossunder(AlphaTrend, AlphaTrend ) // Calculate Bollinger Bands around AlphaTrend length = input(20, title="Bollinger Bands Length") mult = input(2.0, title="Bollinger Bands Multiplier") basis = ta.sma(AlphaTrend, length) dev = mult * ta.stdev(AlphaTrend, length) upperBand = basis + dev lowerBand = basis - dev // Plot Bollinger Bands plot(upperBand, color=#2962FF, linewidth=1, title="Upper Bollinger Band") plot(lowerBand, color=#2962FF, linewidth=1, title="Lower Bollinger Band") // Rest of the code remains the same for generating signals and plotting arrows K1 = ta.barssince(buySignalk) K2 = ta.barssince(sellSignalk) O1 = ta.barssince(buySignalk ) O2 = ta.barssince(sellSignalk ) plotshape(buySignalk and showsignalsk and O1 > K2 ? AlphaTrend * 0.9999 : na, title='BUY', text='BUY', location=location.absolute, style=shape.labelup, size=size.tiny, color=color.new(#0022FC, 0), textcolor=color.new(color.white, 0)) plotshape(sellSignalk and showsignalsk and O2 > K1 ? AlphaTrend * 1.0001 : na, title='SELL', text='SELL', location=location.absolute, style=shape.labeldown, size=size.tiny, color=color.new(color.maroon, 0), textcolor=color.new(color.white, 0)) alertcondition(buySignalk and O1 > K2, title='Potential BUY Alarm', message='BUY SIGNAL!') alertcondition(sellSignalk and O2 > K1, title='Potential SELL Alarm', message='SELL SIGNAL!') alertcondition(buySignalk and O1 > K2, title='Confirmed BUY Alarm', message='BUY SIGNAL APPROVED!') alertcondition(sellSignalk and O2 > K1, title='Confirmed SELL Alarm', message='SELL SIGNAL APPROVED!') alertcondition(ta.cross(close, AlphaTrend), title='Price Cross Alert', message='Price - AlphaTrend Crossing!') alertcondition(ta.crossover(low, AlphaTrend), title='Candle CrossOver Alarm', message='LAST BAR is ABOVE ALPHATREND') alertcondition(ta.crossunder(high, AlphaTrend), title='Candle CrossUnder Alarm', message='LAST BAR is BELOW ALPHATREND!') alertcondition(ta.cross(close , AlphaTrend ), title='Price Cross Alert After Bar Close', message='Price - AlphaTrend Crossing!') alertcondition(ta.crossover(low , AlphaTrend ), title='Candle CrossOver Alarm After Bar Close', message='LAST BAR is ABOVE ALPHATREND!') alertcondition(ta.crossunder(high , AlphaTrend ), title='Candle CrossUnder Alarm After Bar Close', message='LAST BAR is BELOW ALPHATREND!') //from AlphaTrend
อินดิเคเตอร์ Pine Script®
โดย tradecraftcap
Refined Liquidity Flow IndicatorRefined Liquidity Flow Indicator - How It Works The Refined Liquidity Flow Indicator is designed to help traders identify the flow of liquidity into and out of the market based on multiple technical factors. It combines price movement, market sentiment, volatility, and volume to give a comprehensive view of market conditions. The indicator gives buy and sell signals by calculating the flow of liquidity based on these factors. Key Components of the Indicator: Liquidity Flow Calculation: The core of the indicator is the liquidity flow calculation, which is based on several factors: Liquidity Flow=(V×ΔP)+(α×ATR)+(β×RSI)+(γ×ΔP) Where: 𝑉 is the volume (the amount of trading activity). ΔP is the price change (the difference between the current and previous closing price). ATR (Average True Range) is used to measure market volatility. RSI (Relative Strength Index) reflects market sentiment. 𝛼 𝛽 𝛾 are adjustable weights (parameters) that allow you to control how much influence each factor has on the liquidity flow calculation. Key Indicators: Volume (V): The amount of trades occurring in the market. A high volume indicates more activity, which is essential for confirming liquidity flow. Price Change (ΔP): The difference between the current price and the previous price, which helps assess the strength and direction of the market move. ATR (Average True Range): A measure of market volatility, indicating how much the price fluctuates over a specified period. A higher ATR suggests greater volatility, which often corresponds with a greater flow of liquidity. RSI (Relative Strength Index): A momentum oscillator that measures whether a market is overbought or oversold. The RSI can help determine whether the market sentiment is bullish or bearish. How to Use the Indicator: Set Up: After adding the Refined Liquidity Flow Indicator to your chart, you can adjust the following settings directly from the indicator's settings panel: α: Weight for volatility (ATR). β: Weight for market sentiment (RSI). γ: Weight for price change. ATR Length: Customize the period for the ATR. RSI Length: Customize the period for the RSI. SMA Length: Customize the period for the Simple Moving Average. Interpreting Signals: Green Signal (Liquidity In): Indicates that liquidity is entering the market. This often signals a potential buy opportunity when the price is moving upwards with strong volume and market sentiment. Red Signal (Liquidity Out): Indicates that liquidity is leaving the market. This typically signals a potential sell opportunity when the price is moving downwards with strong volume and market sentiment. Fine-Tuning for Your Strategy: By adjusting the weights and the lengths of the indicators, you can fine-tune the indicator to match your trading style. For example, if you want to give more weight to price movements, you can increase γ. If you want to focus more on market sentiment, adjust β.
อินดิเคเตอร์ Pine Script®
โดย khbajako1981
Multi Hourly ATP (Average Trade Price)"Multi-timeframe average trade price" analysis combines two concepts: using the Average Trade Price (ATP) as a benchmark and applying a multi-timeframe analysis (MTFA) trading strategy. The benefits stem from using the ATP for position management and MTFA for better-informed trading decisions. Benefits of Averaging the Trade Price Averaging the trade price (using methods like "averaging down" or "averaging up," or the Volume-Weighted Average Price - VWAP) helps investors manage their positions and costs. Better Cost Basis Assessment: The ATP provides a clear benchmark for your overall cost per share, including fees. This helps you understand your true breakeven point and accurately assess whether a position is currently profitable or at a loss. Risk Mitigation: In a falling market, buying more shares at a lower price (averaging down) reduces the average purchase price, which means the stock does not have to recover to its initial price for you to break even or make a profit. Profit Accumulation: In a rising market, buying more shares as the price increases (averaging up or pyramiding) allows you to accumulate more profits if the upward trend continues, increasing your overall position size in a winning trade. Emotional Discipline: By following a predefined averaging strategy, traders can reduce the impact of emotional decisions like panic selling or holding onto losing trades for too long. Managing Volatility: Averaging helps smooth out the impact of short-term price fluctuations on your overall portfolio performance, which is particularly useful in volatile markets.
อินดิเคเตอร์ Pine Script®
โดย akghuf19ag24
1
SMA vs Candle True CloudSMA vs Candle – Trend Cloud Indicator (Brief Note) This indicator compares price (candle source) with a long-period Hull Moving Average (SMA) to identify trend direction, momentum shifts, and regime changes. The SMA, being momentum-sensitive, reacts to changes in price speed, while price itself represents real-time market action. A dynamic two-way cloud is drawn between price and SMA: Green cloud when price is above SMA → bullish dominance and accumulation Red cloud when price is below SMA → bearish control and distribution The width of the cloud reflects the strength of momentum: Narrow cloud → compression / consolidation Expanding cloud → impulse move or trend acceleration This setup is especially effective on short timeframes with long SMA periods, where it filters noise while preserving early trend signals. Overall, the indicator acts as a visual trend-momentum framework, highlighting early warnings, trend confirmation, and exhaustion zones in a single view.
อินดิเคเตอร์ Pine Script®
โดย akghuf19ag24
1
MRX_M7 777//@version=5 indicator("MRX_M7 777 MTF ALERT (jgar)", overlay=true) // === SOZLAMALAR === tfInput = input.timeframe("15", "Qaysi TF") showZone = input.bool(true, "Zonani ko‘rsat / o‘chirish") zoneColor = color.new(color.lime, 75) // === MTF DATA (BITTA QATORDA!) === = request.security(syminfo.tickerid, tfInput, ) // === ENGULF === engulf = mtfHigh > mtfHigh and mtfLow < mtfLow // === ZONA === zoneHigh = mtfHigh zoneLow = mtfLow // === CHARTGA CHIZISH === if engulf and showZone box.new(bar_index - 1, zoneHigh, bar_index, zoneLow, bgcolor = zoneColor, border_color = color.lime) label.new(bar_index, zoneHigh, "ENGULF " + tfInput, style = label.style_label_down, textcolor = color.white, bgcolor = color.lime) // === ALERT === alertcondition(engulf, title="MTF ENGULF", message="ENGULF " + tfInput + " timeframe da sodir bo‘ldi")
อินดิเคเตอร์ Pine Script®
โดย M7_FX
High Volume Breakout DetectorThis indicator is a dedicated volume analysis tool displayed in a separate pane below the price chart. It visually highlights significant volume surges (spikes) by comparing the current bar's volume to a dynamic threshold based on a Simple Moving Average (SMA) of volume. Key Concepts and Methodology: - The core calculation uses a user-configurable Simple Moving Average (default: 20 periods) of historical volume to establish a baseline of "normal" trading activity. - A customizable multiplier (default: 1.50, meaning 150% of the SMA) defines the threshold for a volume spike. When the current bar's volume meets or exceeds this threshold, it is classified as a spike—indicating unusually high participation that often accompanies breakouts, reversals, climaxes, or institutional activity. - Volume bars are plotted as columns and colored based on two factors: - Candle direction: Green shades for bullish candles (close ≥ open), red shades for bearish candles (close < open). - Spike status: Brighter/solid colors for confirmed spikes, muted/translucent colors for normal volume. This candle-matched coloring helps traders quickly assess whether the surge supports buying pressure (green spike on up candle) or selling/distribution (red spike on down candle). - Optional overlays include the volume SMA line (blue) and the dynamic threshold line (orange, plotted as circles for easy distinction). Features and Customization: - Fully adjustable inputs: SMA length, multiplier threshold, colors for up/down/normal/spike bars, and toggles for showing the SMA line, threshold line, or background highlighting on spikes. - Built-in alert condition triggers reliably on volume spikes (≥ selected multiplier of SMA), with a constant message string including ticker, timeframe, volume value, and threshold reference. How to Use: - Add to any chart in a separate pane (overlay=false). - Look for brighter colored volume bars as potential signals of conviction in price moves. For example: - Green spikes on up candles may signal strong accumulation or breakout confirmation. - Red spikes on down candles may indicate distribution or exhaustion selling. - Combine with price action, support/resistance, or trend indicators for confluence. - Ideal for day trading, swing trading, or spotting volume climaxes on stocks, forex, crypto, or futures across any timeframe. The unique combination of candle-direction-matched coloring for spikes, visual threshold plotting, and focused spike highlighting provides clearer, more actionable insight into directional volume pressure compared to standard volume displays.
อินดิเคเตอร์ Pine Script®
โดย hmorganrrt
Amihud Illiquidity Ratio [MarkitTick]💡This indicator implements the Amihud Illiquidity Ratio, a financial metric designed to measure the price impact of trading volume. It assesses the relationship between absolute price returns and the volume required to generate that return, providing traders with insight into the "stress" levels of the market liquidity. Concept and Originality Standard volume indicators often look at volume in isolation. This script differentiates itself by contextualizing volume against price movement. It answers the question: "How much did the price move per unit of volume?" Furthermore, unlike static indicators, this implementation utilizes dynamic percentile zones (Linear Interpolation) to adapt to the changing volatility profile of the specific asset you are viewing. Methodology The calculation proceeds in three distinct steps: 1. Daily Return: The script calculates the absolute percentage change of the closing price relative to the previous close. 2. Raw Ratio: The absolute return is divided by the volume. I have introduced a standard scaling factor (1,000,000) to the calculation. This resolves the issue of the values being astronomically small (displayed as roughly 0) without altering the fundamental logic of the Amihud ratio (Absolute Return / Volume). - High Ratio: Indicates that price is moving significantly on low volume (Illiquid/Thin Order Book). - Low Ratio: Indicates that price requires massive volume to move (Liquid/Deep Order Book). 3. Dynamic Regimes: The script calculates the 75th and 25th percentiles of the ratio over a lookback period. This creates adaptive bands that define "High Stress" and "Liquid" zones relative to recent history. How to Use Traders can use this tool to identify market fragility: - High Stress Zone (Red Background): When the indicator crosses above the 75th percentile, the market is in a High Illiquidity Regime. Price is slipping easily. This is often observed during panic selling or volatile tops where the order book is thin. - Liquid Zone (Green Background): When the indicator drops below the 25th percentile, the market is in a Liquid Regime. The market is absorbing volume well, which is often characteristic of stable trends or accumulation phases. - Dashboard: A visual table on the chart displays the current Amihud Ratio and the active Market Regime (High Stress, Normal, or Liquid). Inputs - Calculation Period: The lookback length for the average illiquidity (Default: 20). - Smoothing Period: The length of the additional moving average to smooth out noise (Default: 5). - Show Quant Dashboard: Toggles the visibility of the on-screen information table. ● How to read this chart • Spike in Illiquidity (Red Zones) Price is moving on "thin air." Expect high volatility or potential reversals. • Low Illiquidity (Green/Stable Zones) The market is deep and liquid. Trends here are more sustainable and reliable. • Divergence Watch for price making new highs while liquidity is drying up—a classic sign of an exhausted trend. Example: ● Chart Overview The chart displays the Amihud Illiquidity indicator applied to a Gold (XAUUSD) 4-hour timeframe. Top Pane: Price action with manual text annotations highlighting market reversals relative to liquidity zones. Bottom Pane: The specific technical indicator defined in the logic. It features a Blue Line (Raw Illiquidity), a Red Line (Signal/Smoothed), and dynamic background coloring (Red and Green vertical strips). ● Deep Visual Analysis • High Stress Regime (Red Zones) Visual Event: In the bottom pane, the background periodically shifts to a translucent red. Technical Logic: This event is triggered when the amihudAvg (the smoothed illiquidity ratio) exceeds the 75th percentile ( hZone ) of the lookback period. Forensic Interpretation: The logic calculates the absolute price change relative to volume. A spike into the red zone indicates that price is moving significantly on relatively lower volume (high price impact). Visually, the chart shows these red zones aligning with local price peaks (volatility expansion), leading to the bearish reversal marked by the red box in the top pane. • Liquid Regime (Green Zones) Visual Event: The background shifts to a translucent green in the bottom pane. Technical Logic: This triggers when the amihudAvg falls below the 25th percentile ( lZone ). Forensic Interpretation: This state represents a period where large volumes are absorbed with minimal price impact (efficiency). On the chart, this green zone corresponds to the consolidation trough (green box, top pane), validating the annotated accumulation phase before the bullish breakout. • Indicator Lines Blue Line: This is the illiquidityRaw value. It represents the raw daily return divided by volume. Red Line: This is the smoothedVal , a Simple Moving Average (SMA) of the raw data, used to filter out noise and define the trend of liquidity stress. ● Anomalies & Critical Data • The Reversal Pivot The transition from the "High Stress" (Red) background to the "Liquid" (Green) background serves as a visual proxy for market regime change. The chart shows that as the Red zones dissipate (volatility contraction), the market enters a Green zone (efficient liquidity), which acted as the precursor to the sustained upward trend on the right side of the chart. ● About Yakov Amihud Yakov Amihud is a leading researcher in market liquidity and asset pricing. • Brief Background Professor of Finance, affiliated with New York University (NYU). Specializes in market microstructure, liquidity, and quantitative finance. His work has had a major impact on both academic research and practical investment models. ● The Amihud (2002) Paper In 2002, he published his influential paper: “Illiquidity and Stock Returns: Cross-Section and Time-Series Effects” . • Key Contributions Introduced the Amihud Illiquidity Measure, a simple yet powerful proxy for market liquidity. Demonstrated that less liquid stocks tend to earn higher expected returns as compensation for liquidity risk. The measure became one of the most widely used liquidity metrics in finance research. ● Why It Matters in Practice Used in quantitative trading models. Applied in portfolio construction and risk management. Helpful as a liquidity filter to avoid assets with excessive price impact. In short: Yakov Amihud established a practical and robust link between liquidity and returns, making his 2002 work a cornerstone in modern financial economics. Disclaimer: All provided scripts and indicators are strictly for educational exploration and must not be interpreted as financial advice or a recommendation to execute trades. I expressly disclaim all liability for any financial losses or damages that may result, directly or indirectly, from the reliance on or application of these tools. Market participation carries inherent risk where past performance never guarantees future returns, leaving all investment decisions and due diligence solely at your own discretion.
อินดิเคเตอร์ Pine Script®
โดย MarkitTick
RSI Distribution [Kodexius]RSI Distribution is a statistics driven visualization companion for the classic RSI oscillator. In addition to plotting RSI itself, it continuously builds a rolling sample of recent RSI values and projects their distribution as a forward drawn histogram, so you can see where RSI has spent most of its time over the selected lookback window. The indicator is designed to add context to oscillator readings. Instead of only treating RSI as a single point estimate that is either “high” or “low”, you can evaluate the current RSI level relative to its own recent history. This makes it easier to recognize when the market is operating inside a familiar regime, and when RSI is pushing into rarer tail conditions that tend to appear during momentum bursts, exhaustion, or volatility expansion. To complement the histogram, the script can optionally overlay a Gaussian curve fitted to the sample mean and standard deviation. It also runs a Jarque Bera normality check, based on skewness and excess kurtosis, and surfaces the result both visually and in a compact dashboard. On the oscillator panel itself, RSI is presented with a clean gradient line and standard overbought and oversold references, with fills that become more visible when RSI meaningfully extends beyond key thresholds. 🔹 Features 1. Distribution Histogram of Recent RSI Values The script stores the last N RSI values in an internal sample and uses that rolling window to compute a frequency distribution across a user selected number of bins. The histogram is drawn into the future by a configurable width in bars, which keeps it readable and prevents it from colliding with the active RSI plot. The result is a compact visual summary of where RSI clusters most often, whether it is spending more time near the center, or shifting toward higher or lower regimes. 2. Gaussian Overlay for Shape Intuition If enabled, a fitted bell curve is drawn on top of the histogram using the sample mean and standard deviation. This overlay is not intended as a direct trading signal. Its purpose is to provide a fast visual comparator between the empirical RSI distribution and a theoretical normal shape. When the histogram diverges strongly from the curve, you can quickly spot skew, heavy tails, or regime changes that often occur when market structure or volatility conditions shift. 3. Jarque Bera Normality Check With Clear PASS/FAIL Feedback The script computes skewness and excess kurtosis from the RSI sample, then forms the Jarque Bera statistic and compares it to a fixed 95% critical value. When the distribution is closer to normal under this test, the status is marked as PASS, otherwise it is marked as FAIL. This result is displayed in the dashboard and can also influence the histogram styling, giving immediate feedback about whether the recent RSI behavior resembles a bell shaped distribution or a more distorted, regime driven profile. Jarque Bera is a goodness of fit test that evaluates whether a dataset looks consistent with a normal distribution by checking two shape properties: skewness (asymmetry) and kurtosis (tail heaviness, expressed here as excess kurtosis where a perfect normal has 0). Under the null hypothesis of normality, skewness should be near 0 and excess kurtosis should be near 0. The test combines deviations in both into a single statistic, which is then compared to a chi square threshold. A PASS in this script means the sample does not show strong evidence against normality at the chosen threshold, while a FAIL means the sample is meaningfully skewed, heavy tailed, or both. In practical trading terms, a FAIL often suggests RSI is behaving in a regime where extremes and asymmetry are more common, which is typical during strong trends, volatility expansions, or one sided market pressure. It is still a statistical diagnostic, not a prediction tool, and results can vary with lookback length and market conditions. 4. Integrated Stats Dashboard A compact table in the top right summarizes key distribution moments and the normality result: Mean, StdDev, Skewness, Kurtosis, and the JB statistic with PASS/FAIL text. Skewness is color coded by sign to quickly distinguish right skew (more time at higher RSI) versus left skew (more time at lower RSI), which can be helpful when diagnosing trend bias and momentum persistence. 5. RSI Visual Quality and Context Zones RSI is plotted with a gradient color scheme and standard overbought and oversold reference lines. The overbought and oversold areas are filled with a smart gradient so visual emphasis increases when RSI meaningfully extends beyond the 70 and 30 regions, improving readability without overwhelming the panel. 🔹 Calculations This section summarizes the main calculations and transformations used internally. 1. RSI Series RSI is computed from the selected source and length using the standard RSI function: rsi_val = ta.rsi(rsi_src, rsi_len) 2. Rolling Sample Collection A float array stores recent RSI values. Each bar appends the newest RSI, and if the array exceeds the configured lookback, the oldest value is removed. Conceptually: rsi_history.push(rsi_val) if rsi_history.size() > lookback rsi_history.shift() This maintains a fixed size window that represents the most recent RSI behavior. 3. Mean, Variance, and Standard Deviation The script computes the sample mean across the array. Variance is computed as sample variance using (n - 1) in the denominator, and standard deviation is the square root of that variance. These values serve both the dashboard display and the Gaussian overlay parameters. 4. Skewness and Excess Kurtosis Skewness is calculated from the standardized third central moment with a small sample correction. Kurtosis is computed as excess kurtosis (kurtosis minus 3), so the normal baseline is 0. These two metrics summarize asymmetry and tail heaviness, which are the core ingredients for the Jarque Bera statistic. 5. Jarque Bera Statistic and Decision Rule Using skewness S and excess kurtosis K, the Jarque Bera statistic is computed as: JB = (n / 6.0) * (S^2 + 0.25 * K^2) Normality is flagged using a fixed critical value: is_normal = JB < 5.991 This produces a simple PASS/FAIL classification suitable for fast chart interpretation. 6. Histogram Binning and Scaling The RSI domain is treated as 0 to 100 and divided into a configurable number of bins. Bin size is: bin_size = 100.0 / bins Each RSI sample maps to a bin index via floor(rsi / bin_size), with clamping to ensure the index stays within valid bounds. The script counts occurrences per bin, tracks the maximum frequency, and normalizes each bar height by freq/max_freq so the histogram remains visually stable and comparable as the window updates. 7. Gaussian Curve Overlay (Optional) The Gaussian overlay uses the normal probability density function with mu as the sample mean and sigma as the sample standard deviation: normal_pdf(x) = (1 / (sigma * sqrt(2*pi))) * exp(-0.5 * ((x - mu)/sigma)^2) For drawing, the script samples x across the histogram width, evaluates the PDF, and normalizes it relative to its peak so the curve fits within the same visual height scale as the histogram.
อินดิเคเตอร์ Pine Script®
โดย Kodexius