HODL Waves ApproximationHODL Waves HODL Waves göstergesi, Bitcoin'in belirli sürelerde tutulma oranlarına dayanır. Bu gösterge, Bitcoin’in hangi yaş aralıklarında tutulduğunu gösterir. Pine Script’te doğrudan HODL Waves verisi sağlanmadığı için, bu göstergenin tam olarak hesaplanması için on-chain verilere ihtiyacınız vardır. Ancak, buna benzer bir görselleştirme yapmak için belirli zaman dilimlerini kullanabilirsiniz.
市場センチメントを測るインジケーター
Trend Neural Grid-Aynet Core Concept
Imagine a trading indicator that:
Learns from the market by dynamically analyzing multiple timeframes and inputs.
Visualizes the market as a neural grid that predicts probable price paths.
Adapts dynamically based on volatility, trend strength, and sentiment.
This isn’t just a tool—it’s an intelligent system that gives traders a real-time edge.
Features
Neural Grid:
Price zones are visualized as interconnected nodes forming a grid.
Each node represents a price level, with size and color indicating probability and strength.
Dynamic Prediction Paths:
Uses momentum, volatility, and volume to predict future price paths visually.
Multi-Timeframe Sentiment:
Combines sentiment data across 3 timeframes to gauge market psychology.
Adaptive Volatility Zones:
Expands and contracts based on current market volatility.
Real-Time Alerts:
Alerts fire when the system detects high-probability breakouts or reversals.
Key Features in Detail
Neural Grid Nodes:
Visualized as circles along key price levels.
Node size and transparency represent proximity and strength.
Prediction Path:
Plots a series of future price levels using momentum as the driver.
Appears as a smooth wave, allowing you to see the possible trajectory of the market.
Multi-Timeframe Sentiment:
Combines short, mid, and long-term RSI to create a unified sentiment score.
Dynamic Background:
Green, red, or yellow background based on sentiment thresholds:
Green: Bullish sentiment.
Red: Bearish sentiment.
Yellow: Neutral.
Breakout Alerts:
Alerts trigger when the probability of a breakout (based on grid node activity) exceeds the threshold.
Why Is This Groundbreaking?
Multi-Dimensional Visualization:
Combines dynamic grids, predictive paths, and sentiment layers into a cohesive framework.
Predictive and Adaptive:
Tracks not only where price has been but also where it’s likely to go.
Integrated AI-Like Sentiment:
Uses a neural-inspired grid and multi-timeframe sentiment for a holistic market view.
Scalable Complexity:
Can be extended with additional layers, such as fractals, machine learning probabilities, or trader-specific configurations.
This is unlike any traditional indicator. Let me know if you'd like even more enhancements, such as fractal grids, volatility clustering, or machine learning integration!
Trend Odyssey - AynetConcept: The Market Odyssey isn’t just an indicator—it’s an immersive experience. It visualizes price action as a journey through:
Dynamic constellations: Price points connect into predictive paths.
Orbital signals: Momentum is visualized as gravitational orbits—showing whether the market is stable or about to collapse.
Time flow rivers: Candlesticks are replaced by flowing ribbons, illustrating momentum waves in a completely new visual format.
Features of Market Odyssey
Constellations:
Key price points are linked dynamically like stars, with predictive paths lighting up based on momentum and volume.
Orbital Signals:
Indicators like RSI or MACD are reimagined as planets orbiting a center.
Stable orbits indicate consolidation, while erratic paths hint at impending breakouts.
Time Flow Rivers:
Candlesticks transform into a wave ribbon, showing the flow of price action and time.
How It Works
Time Flow Ribbons:
Instead of traditional candlesticks, a ribbon flows dynamically across time.
The ribbon represents the smoothened price with "ripples" to indicate shifts in momentum.
Orbital Momentum:
Momentum oscillates like a planet orbiting a center (the price ribbon).
Orbital deviation increases with volatility and breakout potential.
Constellations:
High-volume spikes are visualized as constellation stars.
Lines connect significant price points, creating a predictive constellation pattern.
Immersive Colors:
The background color changes dynamically based on bullish or bearish momentum.
Why Is This Revolutionary?
Replaces Traditional Visuals: No more boring lines, boxes, or basic indicators. Instead, the chart becomes a living piece of art.
Gamification of Data: Feel like you're navigating the galaxy of price action instead of reading static plots.
Actionable Insights in Real Time: Every visual component (ribbons, orbits, constellations) provides meaningful and intuitive data.
Let me know if you'd like more advanced layers, such as multi-timeframe constellations or AI-predicted orbits! 🚀
Market BioRhythms - AynetConcept:
The Market BioRhythms indicator models the market as a living ecosystem, where price movements, volatility, and momentum are represented as interacting species:
Predators: Represent price reversals, dominant momentum.
Prey: Represent continuation trends and minor pullbacks.
Ecosystem Zones: Represent support, resistance, and breakout probabilities.
This isn’t just a tool—it’s market art.
Key Features:
Life-Like Behavior Tracking:
Price “eats” momentum, volatility “hunts” trends. Visualize the push-pull dynamics of the market.
Evolution Zones:
The chart dynamically colors areas where price action transitions from:
Expansion (Growth): High breakout probability.
Consolidation (Stability): Market calm zones.
Reversal (Collapse): Extreme conditions.
Predator-Prey Relationships:
Uses RSI, volatility, and momentum cycles to show how “life forms” interact:
Predator: Bears or bulls dominating the market.
Prey: Weak hands or corrections.
Hunting Alerts:
Alerts trigger when predators (e.g., reversals) move into prey zones (e.g., continuation trends).
Explanation:
1. Ecosystem Zones:
Green Heatmap (Expansion): High probability of breakout continuation.
Blue Heatmap (Consolidation): Stable zones with low activity.
Red Heatmap (Reversal): Market predators (e.g., trend reversals).
2. Predator/Prey Alerts:
Prey Zones: Trigger when momentum aligns with ATR but no predator activity (RSI in neutral range).
Predator Alerts: Trigger when RSI indicates extreme overbought/oversold conditions, signaling a reversal.
3. Dynamic Risk Zones:
Green and red dashed lines show the risk boundaries based on ATR.
4. Momentum Wave:
Overlayed purple momentum wave helps visualize market velocity.
Why Is It Unique?
Market as a Living System:
Simulates market dynamics visually, offering a story-like interaction with price action.
Behavioral Insights:
Tracks breakout, reversal, and consolidation phases as interactions between predators and prey.
Multi-Dimensional Visualization:
Combines heatmaps, dynamic lines, and momentum waves for intuitive market reading.
This is no longer just an indicator—it’s a living experience. Let me know if you'd like even more features, such as adaptive AI for learning patterns or multi-timeframe integration. 🚀
Commodity Channel Index PLTPLT//@version=5
indicator(title="Commodity Channel Index", shorttitle="CCI", format=format.price, precision=2, timeframe="", timeframe_gaps=true)
length = input.int(25, minval=1)
src = input(hlc3, title="Source")
ma = ta.sma(src, length)
cci = (src - ma) / (0.015 * ta.dev(src, length))
plot(cci, "CCI", color=#2962FF)
band1 = hline(90, "Upper Band", color=#787B86, linestyle=hline.style_dashed)
hline(0, "Middle Band", color=color.new(#787B86, 50))
band0 = hline(-90, "Lower Band", color=#787B86, linestyle=hline.style_dashed)
fill(band1, band0, color=color.rgb(33, 150, 243, 90), title="Background")
// Smoothing MA inputs
GRP = "Moving Average"
TT_BB = "Only applies when 'SMA + Bollinger Bands' is selected. Determines the distance between the SMA and the bands."
maTypeInput = input.string("SMA", "Type", options = , group = GRP, display = display.data_window)
maLengthInput = input.int(14, "Length", group = GRP, display = display.data_window)
bbMultInput = input.float(2.0, "BB StdDev", minval = 0.001, maxval = 50, step = 0.5, tooltip = TT_BB, group = GRP, display = display.data_window)
var enableMA = maTypeInput != "None"
var isBB = maTypeInput == "SMA + Bollinger Bands"
// Smoothing MA Calculation
ma(source, length, MAtype) =>
switch MAtype
"SMA" => ta.sma(source, length)
"SMA + Bollinger Bands" => ta.sma(source, length)
"EMA" => ta.ema(source, length)
"SMMA (RMA)" => ta.rma(source, length)
"WMA" => ta.wma(source, length)
"VWMA" => ta.vwma(source, length)
// Smoothing MA plots
smoothingMA = enableMA ? ma(cci, maLengthInput, maTypeInput) : na
smoothingStDev = isBB ? ta.stdev(cci, maLengthInput) * bbMultInput : na
plot(smoothingMA, "CCI-based MA", color=color.yellow, display = enableMA ? display.all : display.none)
bbUpperBand = plot(smoothingMA + smoothingStDev, title = "Upper Bollinger Band", color=color.green, display = isBB ? display.all : display.none)
bbLowerBand = plot(smoothingMA - smoothingStDev, title = "Lower Bollinger Band", color=color.green, display = isBB ? display.all : display.none)
fill(bbUpperBand, bbLowerBand, color= isBB ? color.new(color.green, 90) : na, title="Bollinger Bands Background Fill", display = isBB ? display.all : display.none)
Fractal Navigator -AynetConcept: Market Fractal Navigator 🚀
Imagine viewing the market as a living, breathing fractal that:
Dynamically unfolds patterns within patterns across price, time, and sentiment.
Incorporates advanced mathematics (e.g., chaos theory, fractals, Fourier transforms) to provide multi-dimensional insights.
Leverages adaptive visualizations—patterns evolve, colors shift, and zones expand/contract based on real-time market behavior.
Provides context-sensitive insights (alerts and visuals adapt depending on trend, volume, and volatility).
Core Features
Fractal Pattern Tracker:
Detects fractal structures dynamically on price movements.
Marks major patterns (e.g., tops, bottoms, triangles) and evolving structures.
Time-Weighted Heatmap:
Visualizes price density over time.
Areas of high trading activity glow brighter; low-activity zones fade into the background.
Fourier Momentum Spectrum:
Analyzes momentum frequencies using Fourier transforms to reveal cyclical behavior.
Visually shows the dominant momentum cycles as "waves."
Chaos Zones:
Highlights areas where market behavior is unstable, signaling caution.
How It Works
1. Fractal Patterns
What It Does:
Dynamically identifies fractal highs and lows.
Marks them with labels (⬆️ for tops, ⬇️ for bottoms).
What It Means:
Helps traders visualize turning points in market structure.
2. Time-Weighted Heatmap
What It Does:
Calculates areas of high/low trading activity using a normalized volume-weighted density.
The background changes dynamically: brighter zones = more trades.
What It Means:
Guides traders to areas of importance or zones to avoid.
3. Fourier Momentum Spectrum
What It Does:
Uses Fourier analysis to measure dominant momentum cycles in the market.
Plots the momentum spectrum dynamically as waves.
What It Means:
Helps traders identify recurring cycles and time their entries better.
4. Chaos Zones
What It Does:
Flags areas of extreme price behavior (large candles, volatility spikes).
Adds ⚠️ Chaos Zone labels for immediate caution.
What It Means:
Warns traders of unstable zones where risk is higher.
Why Is This Different?
Multi-Dimensional Visualization:
Combines fractals, heatmaps, momentum spectrums, and chaos zones into one cohesive indicator.
Mathematical Depth:
Incorporates Fourier transforms and normalized density metrics for deeper insights.
Actionable Clarity:
Fractal markers and chaos warnings give direct, easy-to-understand signals.
Art Meets Analysis:
Visually transforms the chart into something engaging and interactive, not just a flat analysis tool.
Next Steps
If this concept resonates with you, we can:
Expand fractal behavior with multi-timeframe overlays.
Add custom zones for identified patterns like triangles or head-and-shoulders.
Include machine learning elements to predict next likely fractal zones.
Let’s take this further—your feedback is the fuel for innovation!
RSI Support//@version=5
indicator("RSI, SMA, EMA, BB, Support/Resistance with Buy/Sell Signals and DXY", overlay=true)
// パラメータ設定
rsiPeriod = input(14, title="RSI期間")
smaPeriod = input(50, title="SMA期間")
emaPeriod = input(50, title="EMA期間")
bbPeriod = input(20, title="BB期間")
bbStdDev = input(2.0, title="標準偏差")
// 指標計算
rsi = ta.rsi(close, rsiPeriod)
sma = ta.sma(close, smaPeriod)
ema = ta.ema(close, emaPeriod)
basis = ta.sma(close, bbPeriod)
dev = bbStdDev * ta.stdev(close, bbPeriod)
// ボリンジャーバンド
upperBand = na(basis) ? na : basis + dev
lowerBand = na(basis) ? na : basis - dev
// サポート/レジスタンスライン
highestHigh = ta.highest(high, 50)
lowestLow = ta.lowest(low, 50)
// DXYデータを取得
dxy = request.security("DXY", timeframe.period, close)
// DXYと金の逆相関を示すためのプロット
plot(dxy * -1, color=color.gray, title="DXY (逆相関)", linewidth=1)
// 売買シグナルのロジック
var float buySignal = na
var float sellSignal = na
if rsi < 30
buySignal := 1
else
buySignal := na
if rsi > 70
sellSignal := 1
else
sellSignal := na
// チャートにプロット
plot(rsi, color=color.blue, title="RSI")
hline(70, "Overbought", color=color.red)
hline(30, "Oversold", color=color.green)
plot(sma, color=color.orange, title="SMA")
plot(ema, color=color.purple, title="EMA")
plot(upperBand, color=color.green, title="Upper BB")
plot(lowerBand, color=color.red, title="Lower BB")
plot(highestHigh, color=color.red, title="Resistance", linewidth=2, style=plot.style_stepline)
plot(lowestLow, color=color.green, title="Support", linewidth=2, style=plot.style_stepline)
// 売買シグナルを表示
plotshape(not na(buySignal), style=shape.labelup, location=location.belowbar, color=color.green, size=size.small, text="Buy")
plotshape(not na(sellSignal), style=shape.labeldown, location=location.abovebar, color=color.red, size=size.small, text="Sell")
Market DNA Decoder -AynetDetailed Explanation
1. Market DNA Visualization
Price action is converted into "strands" using fractals.
Lines connect fractal highs and lows, forming a DNA-like pattern on the chart.
Provides a visual summary of price behavior.
2. Fractal Momentum Zones
Fractals highlight minor reversals within larger trends.
Calculates momentum using fractals to signal trend continuation or reversal probabilities.
3. Volume Heatmap
Highlights high-volume areas on the chart with orange zones.
Identifies accumulation (buying) and distribution (selling) zones.
4. Phase-Cycle Detection
Classifies the market into three phases:
Expansion: Strong trend with increased volatility.
Consolidation: Low volatility and sideways movement.
Reversal: Market at a potential turning point.
Uses rolling range and thresholds for precise phase detection.
5. Alerts and Visualization
Alerts trigger on significant fractals during expansion or reversal phases.
Phase background colors indicate the current market state:
Green = Expansion.
Blue = Consolidation.
Red = Reversal.
Why Is It Innovative?
Unique Visualization:
DNA strands and heatmaps add a new perspective to price analysis.
Advanced Market Insights:
Combines fractals, volume, and phase detection for a holistic approach.
Actionable Signals:
Alerts traders to opportunities with clear buy/sell signals based on multiple factors.
This concept can be expanded further — let me know if you’d like additional features, such as machine learning for predictive capabilities or custom time-frame integration! 🚀
Specific Time Candlesbelirli saat aralığında belirli saat aralığında belirli saat aralığında belirli saat aralığında belirli saat aralığında belirli saat aralığında belirli saat aralığında belirli saat aralığında belirli saat aralığında belirli saat aralığında belirli saat aralığında
A+BЭто один из самых популярных инструментов технического анализа, используемый для сглаживания ценовых данных и выявления трендов на рынке. Он рассчитывается как среднее значение цен за определённый период, что позволяет трейдерам лучше понимать направление движения цены.
Galagtic Radar Grid - AYNETFeatures:
Concentric Circles:
Drawn using points (•) placed around a center.
The number of circles and their spacing are customizable.
Radial Lines:
Straight lines radiate outward from the center.
You can customize the number of lines (e.g., 12 for 30° intervals).
Highlight Marker:
An orange marker is placed at a specific angle (customizable) on the outermost circle.
Key Customization Inputs:
Circle Count: Number of concentric circles.
Circle Spacing: Distance between circles.
Line Count: Number of radial lines.
Highlight Angle: Position of the orange marker in degrees.
Colors: Customize grid and marker colors.
Core Logic:
Circles and radial lines are calculated using trigonometric functions (math.cos and math.sin).
The x-coordinates are tied to bar_index (integer), ensuring compatibility with TradingView's requirements.
This script is ideal for creating a visual radar-like grid on TradingView charts. Let me know if you'd like further enhancements! 😊
Wick Trend Analysis - AYNETScientific Explanation
1. Wick Trend Lines
Upper Wick Trend Line: The upper_wick_trend is calculated as the Simple Moving Average (SMA) of the upper wick lengths over the user-defined period (trend_length).
pinescript
Kodu kopyala
float upper_wick_trend = ta.sma(upper_wick_length, trend_length)
Lower Wick Trend Line: The lower_wick_trend is similarly calculated for the lower wick lengths.
pinescript
Kodu kopyala
float lower_wick_trend = ta.sma(lower_wick_length, trend_length)
2. Filling Between Lines
fill Function: The fill function colors the area between two plotted lines (plot_upper and plot_lower) based on a defined condition.
pinescript
Kodu kopyala
fill(plot_upper, plot_lower, color=fill_color, title="Wick Trend Area")
Condition for Coloring: The color is determined based on whether the upper wick trend is greater or less than the lower wick trend:
Green Fill: Indicates that the upper wick trend is dominant (i.e., upper_wick_trend > lower_wick_trend).
Red Fill: Indicates that the lower wick trend is dominant (i.e., upper_wick_trend <= lower_wick_trend).
Visualization Features
Trend Lines:
Upper wick trend is plotted as a green line.
Lower wick trend is plotted as a red line.
Filled Area:
The area between the two trend lines is filled:
Green when the upper wick trend is dominant.
Red when the lower wick trend is dominant.
Dynamic Adjustments:
The user can adjust the trend_length to change the sensitivity of the SMA calculations.
Applications
Sentiment Analysis:
Green Fill (Upper Trend Dominance): Indicates stronger rejection at higher prices, suggesting bearish sentiment.
Red Fill (Lower Trend Dominance): Indicates stronger rejection at lower prices, suggesting bullish sentiment.
Signal Generation:
Transitions in the fill color (from green to red or vice versa) can serve as potential trade signals.
Volatility Assessment:
Wider gaps between the trend lines indicate higher market volatility, while narrower gaps suggest lower volatility.
Enhancements
1. Trend Strength Filtering
Add thresholds to filter out minor trends or insignificant wick activity:
pinescript
Kodu kopyala
bool significant_upper_wick = upper_wick_length > 10 // Minimum length for upper wick
bool significant_lower_wick = lower_wick_length > 10
2. Alerts for Trend Changes
Trigger alerts when the dominance of the trend changes:
pinescript
Kodu kopyala
alertcondition(upper_wick_trend > lower_wick_trend, title="Upper Wick Dominance", message="Upper wick trend is now dominant.")
alertcondition(lower_wick_trend > upper_wick_trend, title="Lower Wick Dominance", message="Lower wick trend is now dominant.")
3. Combined Wick Analysis
Incorporate total wick activity (upper + lower wicks) for holistic analysis:
pinescript
Kodu kopyala
float total_wick_trend = ta.sma(upper_wick_length + lower_wick_length, trend_length)
Conclusion
This script provides a robust visualization of wick trends with dynamic color filling to indicate trend dominance. By observing the relative strength of upper and lower wick trends, traders can assess market sentiment, detect potential reversals, and gauge volatility. This method can be further enhanced with additional filters, alerts, and composite indicators to refine trading strategies.
15-Minute EMA Crossover with RSIhi
this is strategy is based on very good testing data with accuracy 70-80% win rate.
Export Data//@version=5
indicator("Export Data", shorttitle="Export", overlay=true)
// 타임스탬프 생성
datetime = str.format("{0}-{1:02}-{2:02} {3:02}:{4:02}",
year, month, dayofmonth, hour, minute)
// 데이터 포맷 설정 (CSV 형식)
csv_line = str.format("{0},{1},{2},{3},{4},{5}",
datetime, open, high, low, close, volume)
// 라벨로 데이터 출력 (가시화)
if bar_index % 10 == 0 // 10개 단위 출력
label.new(bar_index, high, text=csv_line, style=label.style_none, textcolor=color.white, size=size.small)
// Plot을 사용해 확인용 출력 (차트 시각화)
plot(close, color=color.blue, linewidth=1, title="Export Close Values")
sarina1mix of nadaraya watson indicator and EMA indicator
it's better to trade when nadaraya crosses EMA
you can change the numbers for the best setting leave a message
Ichimoku rút gọn - ScalpĐây là chỉ báo phân tích xu hướng cũng như điểm vào lệnh tiềm năng!
Giúp cho trader có đánh giá tốt nhất về xu hướng của thị trường cũng như các điểm đảo chiều tiềm năng!
DX-trade//@version=5
indicator('DX-trade', shorttitle='DX-trade', overlay=true)
// إعداد معلمات المؤشر
rsiPeriod = input(14, title='RSI Period')
upperLevel = input(70, title='Upper RSI Level')
lowerLevel = input(30, title='Lower RSI Level')
takeProfitPips = input(300, title='Take Profit (points)')
stopLossPips = input(300, title='Stop Loss (points)')
secondTargetMultiplier = input(1.5, title='Second Target Multiplier') // المضاعف للهدف الثاني
thirdTargetMultiplier = input(2, title='Third Target Multiplier') // المضاعف للهدف الثالث
// حساب مؤشر RSI
rsi = ta.rsi(close, rsiPeriod)
// تعريف شروط الكسر
buySignal = ta.crossover(rsi, upperLevel)
sellSignal = ta.crossunder(rsi, lowerLevel)
// عدد الأعمدة التي يمتد خلالها الخط
extendBars = 20 // تقليل مدى امتداد الخط أفقيًا إلى 20 عمود (يمكنك التعديل هنا)
// حساب أهداف متعددة
buyTP1 = close + takeProfitPips * syminfo.pointvalue
buyTP2 = close + takeProfitPips * syminfo.pointvalue * secondTargetMultiplier
buyTP3 = close + takeProfitPips * syminfo.pointvalue * thirdTargetMultiplier
sellTP1 = close - takeProfitPips * syminfo.pointvalue
sellTP2 = close - takeProfitPips * syminfo.pointvalue * secondTargetMultiplier
sellTP3 = close - takeProfitPips * syminfo.pointvalue * thirdTargetMultiplier
// رسم خطوط الأهداف ووقف الخسارة
if (buySignal)
// صفقات الشراء
line.new(bar_index, buyTP1, bar_index + extendBars, buyTP1, color=color.green, width=1, style=line.style_solid)
line.new(bar_index, buyTP2, bar_index + extendBars, buyTP2, color=color.green, width=1, style=line.style_solid)
line.new(bar_index, buyTP3, bar_index + extendBars, buyTP3, color=color.green, width=1, style=line.style_solid)
line.new(bar_index, close - stopLossPips * syminfo.pointvalue, bar_index + extendBars, close - stopLossPips * syminfo.pointvalue, color=color.red, width=1, style=line.style_solid)
// إضافة نصوص TP و SL عند الطرف الآخر
label.new(bar_index + extendBars, buyTP1, text="TP1", style=label.style_label_down, color=color.new(color.green, 0), textcolor=color.white)
label.new(bar_index + extendBars, buyTP2, text="TP2", style=label.style_label_down, color=color.new(color.green, 0), textcolor=color.white)
label.new(bar_index + extendBars, buyTP3, text="TP3", style=label.style_label_down, color=color.new(color.green, 0), textcolor=color.white)
label.new(bar_index + extendBars, close - stopLossPips * syminfo.pointvalue, text="SL", style=label.style_label_up, color=color.new(color.red, 0), textcolor=color.white)
// رسم مربعات شفافة مع عكس الألوان
box.new(bar_index, close - stopLossPips * syminfo.pointvalue, bar_index + extendBars, close, border_color=color.red, bgcolor=color.new(color.red, 90)) // عكس الألوان إلى الأحمر
box.new(bar_index, close, bar_index + extendBars, buyTP1, border_color=color.green, bgcolor=color.new(color.green, 90)) // عكس الألوان إلى الأخضر
box.new(bar_index, buyTP1, bar_index + extendBars, buyTP2, border_color=color.green, bgcolor=color.new(color.green, 90)) // عكس الألوان إلى الأخضر
box.new(bar_index, buyTP2, bar_index + extendBars, buyTP3, border_color=color.green, bgcolor=color.new(color.green, 90)) // عكس الألوان إلى الأخضر
if (sellSignal)
// صفقات البيع
line.new(bar_index, sellTP1, bar_index + extendBars, sellTP1, color=color.green, width=1, style=line.style_solid)
line.new(bar_index, sellTP2, bar_index + extendBars, sellTP2, color=color.green, width=1, style=line.style_solid)
line.new(bar_index, sellTP3, bar_index + extendBars, sellTP3, color=color.green, width=1, style=line.style_solid)
line.new(bar_index, close + stopLossPips * syminfo.pointvalue, bar_index + extendBars, close + stopLossPips * syminfo.pointvalue, color=color.red, width=1, style=line.style_solid)
// إضافة نصوص TP و SL عند الطرف الآخر
label.new(bar_index + extendBars, sellTP1, text="TP1", style=label.style_label_down, color=color.new(color.green, 0), textcolor=color.white)
label.new(bar_index + extendBars, sellTP2, text="TP2", style=label.style_label_down, color=color.new(color.green, 0), textcolor=color.white)
label.new(bar_index + extendBars, sellTP3, text="TP3", style=label.style_label_down, color=color.new(color.green, 0), textcolor=color.white)
label.new(bar_index + extendBars, close + stopLossPips * syminfo.pointvalue, text="SL", style=label.style_label_up, color=color.new(color.red, 0), textcolor=color.white)
// رسم مربعات شفافة
box.new(bar_index, close + stopLossPips * syminfo.pointvalue, bar_index + extendBars, close, border_color=color.red, bgcolor=color.new(color.red, 90))
box.new(bar_index, close, bar_index + extendBars, sellTP1, border_color=color.green, bgcolor=color.new(color.green, 90))
box.new(bar_index, sellTP1, bar_index + extendBars, sellTP2, border_color=color.green, bgcolor=color.new(color.green, 90))
box.new(bar_index, sellTP2, bar_index + extendBars, sellTP3, border_color=color.green, bgcolor=color.new(color.green, 90))
// رسم إشارات الدخول على الرسم البياني
plotshape(series=buySignal, location=location.belowbar, color=color.new(color.green, 0), style=shape.labelup, title='Buy Signal')
plotshape(series=sellSignal, location=location.abovebar, color=color.new(color.red, 0), style=shape.labeldown, title='Sell Signal')
// رسم مستوى RSI
hline(upperLevel, 'Upper RSI Level', color=color.red)
hline(lowerLevel, 'Lower RSI Level', color=color.red)
plot(rsi, title='RSI', color=color.new(color.orange, 0), linewidth=2)
Trend & Volume Dynamics Indicator (Color identifying the Trend)Benefits
1. Trend Identification:
o The script calculates a 20-period Weighted Moving Average (WMA) of the closing prices. This helps in smoothing out price data to identify the underlying trend.
o The color of the WMA line changes based on the price position relative to the WMA:
Green: When the current price is above the WMA, indicating a potential uptrend.
Red: When the current price is below the WMA, indicating a potential downtrend.
Blue: When the price is exactly at the WMA, indicating no clear trend.
2. Volume Dynamics:
o The script also plots the volume with a color-coding mechanism:
Green: When the current volume is higher than the previous period's volume, indicating increasing trading activity.
Red: When the current volume is lower than the previous period's volume, indicating decreasing trading activity.
o The volume bars are plotted with 90% transparency, making them less visually dominant but still informative.
Usage
• Overlay: The indicator is set to overlay=true, meaning it will be plotted directly on the price chart, allowing users to see the WMA and volume dynamics in the context of the price movements.
• WMA Length: The length of the WMA is set to 20 periods, which is a common setting for short to medium-term trend analysis.
• Visual Cues: The color changes in both the WMA and volume bars provide immediate visual cues about the trend and volume dynamics, helping traders make quicker decisions.
Detailed Explanation of the Script
1. Indicator Declaration:
o Declares the indicator with a descriptive name and specifies that it should be overlaid on the price chart.
2. WMA Calculation:
o Defines the length of the WMA and calculates it using the closing prices.
3. Plotting the WMA:
o Plots the WMA with full brightness (0 transparency).
4. Color-Coding the WMA:
o Changes the color of the WMA line based on the price's position relative to the WMA.
5. Volume Indicator:
o Plots the volume bars with color-coding based on the volume change from the previous period and with 90% transparency.
Conclusion
One of the most best combinations of Volume and Moving average and works on any given timeframe and charts
Time Change Indicator-AYNETDetailed Scientific Explanation of the Time Change Indicator Code
This Pine Script code implements a financial indicator designed to measure and visualize the percentage change in the closing price of an asset over a specified timeframe. It uses historical data to calculate changes and displays them as a histogram for intuitive analysis. Below is a comprehensive scientific breakdown of the code:
1. User Inputs
The script begins by defining user-configurable parameters, enabling flexibility in analysis:
timeframe: The user selects the timeframe for measuring price changes (e.g., 1 hour, 1 day). This determines the granularity of the analysis.
positive_color and negative_color: Users choose the colors for positive and negative changes, enhancing visual interpretation.
2. Data Retrieval
The script employs request.security to fetch closing price data (close) for the specified timeframe. This function ensures that the indicator adapts to different timeframes, providing consistent results regardless of the chart's base timeframe.
Current Closing Price (current_close):
current_close
=
request.security(syminfo.tickerid, timeframe, close)
current_close=request.security(syminfo.tickerid, timeframe, close)
Retrieves the closing price for the defined timeframe.
Previous Closing Price (prev_close): The script uses a variable (prev_close) to store the previous closing price. This variable is updated dynamically as new data is processed.
3. Price Change Calculation
The script calculates both the absolute and percentage change in closing price:
Absolute Price Change (price_change):
price_change
=
current_close
−
prev_close
price_change=current_close−prev_close
Measures the difference between the current and previous closing prices.
Percentage Change (percent_change):
percent_change
=
price_change
prev_close
×
100
percent_change=
prev_close
price_change
×100
Normalizes the change relative to the previous closing price, making it easier to compare changes across different assets or timeframes.
4. Conditional Logic for Visualization
The script uses a conditional statement to determine the color of each histogram bar:
Positive Change: If price_change > 0, the bar is assigned the user-defined positive_color.
Negative Change: If price_change < 0, the bar is assigned the negative_color.
This differentiation provides a clear visual cue for understanding price movement direction.
5. Visualization
The script visualizes the percentage change using a histogram and enhances the chart with dynamic labels:
Histogram (plot.style_histogram):
Each bar represents the percentage change for a given timeframe.
Bars above the zero line indicate positive changes, while bars below the zero line indicate negative changes.
Zero Line (hline(0)): A reference line at zero provides a baseline for interpreting changes.
Dynamic Labels (label.new):
Each bar is annotated with its exact percentage change value.
The label's position and color correspond to the bar, improving clarity.
6. Algorithmic Flow
Data Fetching: Retrieve the current and previous closing prices for the specified timeframe.
Change Calculation: Compute the absolute and percentage changes between the two prices.
Bar Coloring: Determine the color of the histogram bar based on the change's direction.
Plotting: Visualize the changes as a histogram and add labels for precise data representation.
7. Applications
This indicator has several practical applications in financial analysis:
Volatility Analysis: By visualizing percentage changes, traders can assess the volatility of an asset over specific timeframes.
Trend Identification: Positive and negative bars highlight periods of upward or downward momentum.
Cross-Asset Comparison: Normalized percentage changes enable the comparison of price movements across different assets, regardless of their nominal values.
Market Sentiment: Persistent positive or negative changes may indicate prevailing bullish or bearish sentiment.
8. Scientific Relevance
This script applies fundamental principles of data visualization and time-series analysis:
Statistical Normalization: Percentage change provides a scale-invariant metric for comparing price movements.
Dynamic Data Processing: By updating the prev_close variable with real-time data, the script adapts to new market conditions.
Visual Communication: The use of color and labels improves the interpretability of quantitative data.
Conclusion
This indicator combines advanced Pine Script functions with robust financial analysis techniques to create an effective tool for evaluating price changes. It is highly adaptable, providing users with the ability to tailor the analysis to their specific needs. If additional features, such as smoothing or multi-timeframe analysis, are required, the code can be further extended.
Torus Visualization-Secret Geometry-AYNETExplanation:
Outer and Inner Circles:
The script draws two main circles: the outer boundary and the inner boundary of the Torus.
Bands Between Circles:
Additional concentric circles are drawn to create the illusion of a Torus structure.
Customizable Inputs:
You can control the outer radius, inner radius, number of segments for smoother circles, and the number of bands to improve visualization.
Parameters:
center_x and center_y define the center of the Torus on the chart.
outer_radius and inner_radius control the size of the Torus.
segments define the resolution of the circles (more segments = smoother appearance).
Visualization:
The Torus appears as a series of concentric circles, giving a 2D approximation of the 3D structure.
This script can be visualized on any chart, and the Torus will adjust its position based on the specified center and radius values.
Meme Coin Buy Signal Indicator by asharThis custom TradingView indicator is specifically designed for meme coins, using technical analysis indicators to identify optimal buy signals. It combines short-term moving averages, volume spikes, and Bitcoin trend alignment to pinpoint potential entry points during high-momentum periods.
Indicator Components:
Moving Averages (MA): A 5-period fast MA and a 13-period slow MA highlight short-term price momentum. Buy signals are generated when the fast MA crosses above the slow MA, indicating potential upward momentum.
Volume Spike Detection: The indicator detects high-volume periods using a multiplier. If the current volume exceeds the 10-period average volume by the set multiplier (default: 2.0), it indicates increased buying interest, which is crucial for meme coins.
Bitcoin Trend Alignment: The trend of Bitcoin, a market-wide sentiment indicator, is gauged with a 20-day moving average. Buy signals are validated only when Bitcoin is also in an uptrend, providing additional bullish confirmation for meme coins.
Buy Signal Criteria: A buy signal is triggered when:
The fast MA crosses above the slow MA.
Volume is above the average by the set multiplier.
The price is above the slow MA.
Bitcoin is trending up based on the 20-day moving average.
This indicator is ideal for meme coin traders looking to time entries with momentum-driven trends, aligning volume and trend indicators for a more comprehensive approach to high-risk assets.
Old Price OscillatorThe Old Price Oscillator (OPO) is a momentum indicator widely used by traders and analysts to gauge the direction and strength of price trends. It works by calculating the difference between two moving averages—a shorter-term moving average and a longer-term moving average—of a security’s price. This difference is plotted as an oscillating line, helping traders visualize the momentum and determine when price reversals or continuations might occur. Typically, when the oscillator value is positive, the price is trending upwards, suggesting potential buy signals; conversely, when the oscillator turns negative, it indicates downward momentum, which could signal a potential sell.
The OPO is similar to other oscillators, like the Moving Average Convergence Divergence (MACD), in that it uses moving averages to smooth out price fluctuations and clarify trends. Traders often customize the length of the short- and long-term moving averages to better suit specific assets or market conditions. Generally, this indicator is especially useful in markets that exhibit clear trends. However, it may generate false signals during sideways or highly volatile periods, so many traders combine the OPO with other technical indicators or filters to improve accuracy.
Multi-Timeframe Moving Averages by Skyito"Hope everyone likes this and finds it useful! This multi-timeframe moving average indicator provides a comprehensive view of moving averages from various timeframes directly on one chart. It’s designed to help traders analyze market trends and levels more effectively without constantly switching between charts.
Script Explanation: This indicator supports a range of moving average types, including SMA, EMA, HMA, WMA, VWMA, RMA, SSMA, and DEMA, allowing for flexibility in analysis. Each moving average is fully customizable by length and type for each timeframe, giving you control over how trends are represented.
The indicator includes timeframes such as 15 minutes, 1 hour, 4 hours, 6 hours, 8 hours, 12 hours, 1 day, 3 days, 5 days, 1 week, 3 weeks, and 1 month. Each moving average is displayed as a line with a small dashed extension, showing a label that contains the moving average’s timeframe, type, and current price level. The dark blue labels are slightly enlarged to enhance readability on the chart, making it easier to track important levels at a glance.
Use Case: This tool is ideal for traders looking to stay aware of trend levels across multiple timeframes on one chart. Adjusting the moving averages’ lengths and types enables customization for any strategy, while the label information provides an immediate understanding of the timeframe and trend context.
Enjoy the streamlined view and the added insights from multi-timeframe analysis!"
