💎DrFX Diamond Algo 💎Diamond Algo - Multi-Feature Trading System
Advanced trading system combining Supertrend signals with multiple confirmation filters, risk management tools, and a comprehensive market analysis dashboard.
═══ CORE FEATURES ═══
• Smart Buy/Sell signals using modified Supertrend algorithm
• Multi-timeframe trend analysis (M1 to D1)
• Support & Resistance zone detection
• Risk management with automatic TP/SL levels (1:1, 2:1, 3:1)
• Real-time market dashboard with key metrics
• Multiple trend cloud overlays for visual confirmation
═══ SIGNAL GENERATION ═══
BUY Signal:
• Supertrend bullish crossover
• Price above SMA filter
• Optional smart signals (EMA 200 confirmation)
SELL Signal:
• Supertrend bearish crossunder
• Price below SMA filter
• Optional smart signals (EMA 200 confirmation)
═══ DASHBOARD COMPONENTS ═══
• Multi-timeframe trend status (8 timeframes)
• Current position indicator
• Market state analysis (Trending/Ranging/No trend)
• Volatility percentage
• Institutional activity monitor
• Trading session tracker (NY/London/Tokyo/Sydney)
• Trend pressure indicator
═══ VISUAL OVERLAYS ═══
• Trend Cloud: Long-term trend visualization
• Trend Follower: Adaptive trend line
• Comulus Cloud: Dual ALMA-based trend zones
• Cirrus Cloud: Short-term trend bands
• Smart Trail: Fibonacci-based trailing stop
• Dynamic trend lines with breakout alerts
═══ RISK MANAGEMENT ═══
• Automatic Stop-Loss placement (ATR-based)
• Three Take-Profit levels with Risk:Reward ratios
• Entry price labeling
• Optional distance and decimal customization
• Visual lines connecting entry to targets
═══ INPUT PARAMETERS ═══
Sensitivity (1-20): Controls signal frequency
Smart Signals Only: Filters for high-probability setups
Bar Coloring: Trend-based or gradient coloring
Dashboard Location/Size: Customizable placement
Multiple overlay toggles for clean charts
═══ BEST PRACTICES ═══
• Lower sensitivity (1-5) for swing trading
• Higher sensitivity (10-20) for scalping
• Enable Smart Signals for conservative approach
• Use dashboard to confirm multi-timeframe alignment
• Monitor volatility % before entering trades
═══ ALERT CONDITIONS ═══
• Buy Alert: Triggered on bullish signal
• Sell Alert: Triggered on bearish signal
• Trend line breakout alerts (automated)
═══ VERSION INFO ═══
Pine Script: v5
Max Labels: 500
Repainting: Minimal (uses confirmed bars for signals)
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Indicateurs et stratégies
RSI: chart overlay
This indicator maps RSI thresholds directly onto price. Since the EMA of price aligns with RSI’s 50-line, it draws a volatility-based band around the EMA to reveal levels such as 70 and 30.
By converting RSI values into visible price bands, the overlay lets you see exactly where price would have to move to hit traditional RSI boundaries. These bands adapt in real time to both price movement and market volatility, keeping the classic RSI logic intact while presenting it in the context of price action. This approach helps traders interpret RSI signals without leaving the main chart window.
The calculation uses the same components as the RSI: alternative derivation script: Wilder’s EMA for smoothing, a volatility-based unit for scaling, and a normalization factor. The result is a dynamic band structure on the chart, representing RSI boundary levels in actual price terms.
Key components and calculation breakdown:
Wilder’s EMA
Used as the anchor point for measuring price position.
myEMA = ta.rma(close, Length)
Volatility Unit
Derived from the EMA of absolute close-to-close price changes.
CC_vol = ta.rma(math.abs(close - close ), Length)
Normalization Factor
Scales the volatility unit to align with the RSI formula’s structure.
normalization_factor = 1 / (Length - 1)
Upper and Lower Boundaries
Defines price bands corresponding to selected RSI threshold values.
up_b = myEMA + ((upper - 50) / 50) * (CC_vol / normalization_factor)
down_b = myEMA - ((50 - lower) / 50) * (CC_vol / normalization_factor)
Inputs
RSI length
Upper boundary – RSI level above 50
Lower boundary – RSI level below 50
ON/OFF toggle for 50-point line (EMA of close prices)
ON/OFF toggle for overbought/oversold coloring (use with line chart)
Interpretation:
Each band on the chart represents a chosen RSI level.
When price touches a band, RSI is at that threshold.
The distance between moving average and bands adjusts automatically with volatility and your selected RSI length.
All calculations remain fully consistent with standard RSI values.
Feedback and code suggestions are welcome, especially regarding implementation efficiency and customization.
Jarass regression linesDouble Linear Regression Ultimate + MA Ribbon (DLRC + MA)
The DLRC + MA indicator is an advanced technical analysis tool that combines double linear regression channels with a moving average ribbon (MA Ribbon). Designed for traders who want to simultaneously track trend, volatility, and potential support/resistance levels.
Key Features:
1. Double Linear Regression Channels:
• Inner Channel – shorter period, more sensitive to recent price movements.
• Outer Channel – longer period, reflects the long-term trend.
• Both channels display upper and lower boundaries and a midline.
• Optional logarithmic scale for price adjustment.
• Real-time R² values to assess regression accuracy.
2. MA Ribbon:
• Up to 4 different moving averages simultaneously.
• Supports SMA, EMA, SMMA (RMA), WMA, VWMA.
• Each MA can be individually enabled/disabled, with customizable period, source, and color.
• Helps identify trend direction and dynamic support/resistance levels.
3. Visualization:
• Channels are filled with semi-transparent colors for clarity.
• Midline for quick trend direction assessment.
• Label displays R² values of the channels in real time.
4. Suitable For:
• Short-term and long-term traders seeking a combination of linear regression analysis and classic trend-following tools.
• Useful for identifying overbought/oversold zones and potential trend reversal points.
Summary:
DLRC + MA combines statistical precision of linear regression with intuitive trend visualization via a MA ribbon. It provides quick insight into market direction, volatility, and potential turning points, all in one chart overlay.
Aggregated Scores Oscillator [Alpha Extract]A sophisticated risk-adjusted performance measurement system that combines Omega Ratio and Sortino Ratio methodologies to create a comprehensive market assessment oscillator. Utilizing advanced statistical band calculations with expanding and rolling window analysis, this indicator delivers institutional-grade overbought/oversold detection based on risk-adjusted returns rather than traditional price movements. The system's dual-ratio aggregation approach provides superior signal accuracy by incorporating both upside potential and downside risk metrics with dynamic threshold adaptation for varying market conditions.
🔶 Advanced Statistical Framework
Implements dual statistical methodologies using expanding and rolling window calculations to create adaptive threshold bands that evolve with market conditions. The system calculates cumulative statistics alongside rolling averages to provide both historical context and current market regime sensitivity with configurable window parameters for optimal performance across timeframes.
🔶 Dual Ratio Integration System
Combines Omega Ratio analysis measuring excess returns versus deficit returns with Sortino Ratio calculations focusing on downside deviation for comprehensive risk-adjusted performance assessment. The system applies configurable smoothing to both ratios before aggregation, ensuring stable signal generation while maintaining sensitivity to regime changes.
// Omega Ratio Calculation
Excess_Return = sum((Daily_Return > Target_Return ? Daily_Return - Target_Return : 0), Period)
Deficit_Return = sum((Daily_Return < Target_Return ? Target_Return - Daily_Return : 0), Period)
Omega_Ratio = Deficit_Return ≠ 0 ? (Excess_Return / Deficit_Return) : na
// Sortino Ratio Framework
Downside_Deviation = sqrt(sum((Daily_Return < Target_Return ? (Daily_Return - Target_Return)² : 0), Period) / Period)
Sortino_Ratio = (Mean_Return / Downside_Deviation) * sqrt(Annualization_Factor)
// Aggregated Score
Aggregated_Score = SMA(Omega_Ratio, Omega_SMA) + SMA(Sortino_Ratio, Sortino_SMA)
🔶 Dynamic Band Calculation Engine
Features sophisticated threshold determination using both expanding historical statistics and rolling window analysis to create adaptive overbought/oversold levels. The system incorporates configurable multipliers and sensitivity adjustments to optimize signal timing across varying market volatility conditions with automatic band convergence logic.
🔶 Signal Generation Framework
Generates overbought conditions when aggregated score exceeds adjusted upper threshold and oversold conditions below lower threshold, with neutral zone identification for range-bound markets. The system provides clear binary signal states with background zone highlighting and dynamic oscillator coloring for intuitive market condition assessment.
🔶 Enhanced Visual Architecture
Provides modern dark theme visualization with neon color scheme, dynamic oscillator line coloring based on signal states, and gradient band fills for comprehensive market condition visualization. The system includes zero-line reference, statistical band plots, and background zone highlighting with configurable transparency levels.
snapshot
🔶 Risk-Adjusted Performance Analysis
Utilizes target return parameters for customizable risk assessment baselines, enabling traders to evaluate performance relative to specific return objectives. The system's focus on downside deviation through Sortino analysis provides superior risk-adjusted signals compared to traditional volatility-based oscillators that treat upside and downside movements equally.
🔶 Multi-Timeframe Adaptability
Features configurable calculation periods and rolling windows to optimize performance across various timeframes from intraday to long-term analysis. The system's statistical foundation ensures consistent signal quality regardless of timeframe selection while maintaining sensitivity to market regime changes through adaptive band calculations.
🔶 Performance Optimization Framework
Implements efficient statistical calculations with optimized variable management and configurable smoothing parameters to balance responsiveness with signal stability. The system includes automatic band adjustment mechanisms and rolling window management for consistent performance across extended analysis periods.
This indicator delivers sophisticated risk-adjusted market analysis by combining proven statistical ratios in a unified oscillator framework. Unlike traditional overbought/oversold indicators that rely solely on price movements, the ASO incorporates risk-adjusted performance metrics to identify genuine market extremes based on return quality rather than price volatility alone. The system's adaptive statistical bands and dual-ratio methodology provide institutional-grade signal accuracy suitable for systematic trading approaches across cryptocurrency, forex, and equity markets with comprehensive visual feedback and configurable risk parameters for optimal strategy integration.
Volume Based Sampling [BackQuant]Volume Based Sampling
What this does
This indicator converts the usual time-based stream of candles into an event-based stream of “synthetic” bars that are created only when enough trading activity has occurred . You choose the activity definition:
Volume bars : create a new synthetic bar whenever the cumulative number of shares/contracts traded reaches a threshold.
Dollar bars : create a new synthetic bar whenever the cumulative traded dollar value (price × volume) reaches a threshold.
The script then keeps an internal ledger of these synthetic opens, highs, lows, closes, and volumes, and can display them as candles, plot a moving average calculated over the synthetic closes, mark each time a new sample is formed, and optionally overlay the native time-bars for comparison.
Why event-based sampling matters
Markets do not release information on a clock: activity clusters during news, opens/closes, and liquidity shocks. Event-based bars normalize for that heteroskedastic arrival of information: during active periods you get more bars (finer resolution); during quiet periods you get fewer bars (coarser resolution). Research shows this can reduce microstructure pathologies and produce series that are closer to i.i.d. and more suitable for statistical modeling and ML. In particular:
Volume and dollar bars are a common event-time alternative to time bars in quantitative research and are discussed extensively in Advances in Financial Machine Learning (AFML). These bars aim to homogenize information flow by sampling on traded size or value rather than elapsed seconds.
The Volume Clock perspective models market activity in “volume time,” showing that many intraday phenomena (volatility, liquidity shocks) are better explained when time is measured by traded volume instead of seconds.
Related market microstructure work on flow toxicity and liquidity highlights that the risk dealers face is tied to information intensity of order flow, again arguing for activity-based clocks.
How the indicator works (plain English)
Choose your bucket type
Volume : accumulate volume until it meets a threshold.
Dollar Bars : accumulate close × volume until it meets a dollar threshold.
Pick the threshold rule
Dynamic threshold : by default, the script computes a rolling statistic (mean or median) of recent activity to set the next bucket size. This adapts bar size to changing conditions (e.g., busier sessions produce more frequent synthetic bars).
Fixed threshold : optionally override with a constant target (e.g., exactly 100,000 contracts per synthetic bar, or $5,000,000 per dollar bar).
Build the synthetic bar
While a bucket fills, the script tracks:
o_s: first price of the bucket (synthetic open)
h_s: running maximum price (synthetic high)
l_s: running minimum price (synthetic low)
c_s: last price seen (synthetic close)
v_s: cumulative native volume inside the bucket
d_samples: number of native bars consumed to complete the bucket (a proxy for “how fast” the threshold filled)
Emit a new sample
Once the bucket meets/exceeds the threshold, a new synthetic bar is finalized and stored. If overflow occurs (e.g., a single native bar pushes you past the threshold by a lot), the code will emit multiple synthetic samples to account for the extra activity.
Maintain a rolling history efficiently
A ring buffer can overwrite the oldest samples when you hit your Max Stored Samples cap, keeping memory usage stable.
Compute synthetic-space statistics
The script computes an SMA over the last N synthetic closes and basic descriptors like average bars per synthetic sample, mean and standard deviation of synthetic returns, and more. These are all in event time , not clock time.
Inputs and options you will actually use
Data Settings
Sampling Method : Volume or Dollar Bars.
Rolling Lookback : window used to estimate the dynamic threshold from recent activity.
Filter : Mean or Median for the dynamic threshold. Median is more robust to spikes.
Use Fixed? / Fixed Threshold : override dynamic sizing with a constant target.
Max Stored Samples : cap on synthetic history to keep performance snappy.
Use Ring Buffer : turn on to recycle storage when at capacity.
Indicator Settings
SMA over last N samples : moving average in synthetic space . Because its index is sample count, not minutes, it adapts naturally: more updates in busy regimes, fewer in quiet regimes.
Visuals
Show Synthetic Bars : plot the synthetic OHLC candles.
Candle Color Mode :
Green/Red: directional close vs open
Volume Intensity: opacity scales with synthetic size
Neutral: single color
Adaptive: graded by how large the bucket was relative to threshold
Mark new samples : drop a small marker whenever a new synthetic bar prints.
Comparison & Research
Show Time Bars : overlay the native time-based candles to visually compare how the two sampling schemes differ.
How to read it, step by step
Turn on “Synthetic Bars” and optionally overlay “Time Bars.” You will see that during high-activity bursts, synthetic bars print much faster than time bars.
Watch the synthetic SMA . Crosses in synthetic space can be more meaningful because each update represents a roughly comparable amount of traded information.
Use the “Avg Bars per Sample” in the info table as a regime signal. Falling average bars per sample means activity is clustering, often coincident with higher realized volatility.
Try Dollar Bars when price varies a lot but share count does not; they normalize by dollar risk taken in each sample. Volume Bars are ideal when share count is a better proxy for information flow in your instrument.
Quant finance background and citations
Event time vs. clock time : Easley, López de Prado, and O’Hara advocate measuring intraday phenomena on a volume clock to better align sampling with information arrival. This framing helps explain volatility bursts and liquidity droughts and motivates volume-based bars.
Flow toxicity and dealer risk : The same authors show how adverse selection risk changes with the intensity and informativeness of order flow, further supporting activity-based clocks for modeling and risk management.
AFML framework : In Advances in Financial Machine Learning , event-driven bars such as volume, dollar, and imbalance bars are presented as superior sampling units for many ML tasks, yielding more stationary features and fewer microstructure distortions than fixed time bars. ( Alpaca )
Practical use cases
1) Regime-aware moving averages
The synthetic SMA in event time is not fooled by quiet periods: if nothing of consequence trades, it barely updates. This can make trend filters less sensitive to calendar drift and more sensitive to true participation.
2) Breakout logic on “equal-information” samples
The script exposes simple alerts such as breakout above/below the synthetic SMA . Because each bar approximates a constant amount of activity, breakouts are conditioned on comparable informational mass, not arbitrary time buckets.
3) Volatility-adaptive backtests
If you use synthetic bars as your base data stream, most signal rules become self-paced : entry and exit opportunities accelerate in fast markets and slow down in quiet regimes, which often improves the realism of slippage and fill modeling in research pipelines (pair this indicator with strategy code downstream).
4) Regime diagnostics
Avg Bars per Sample trending down: activity is dense; expect larger realized ranges.
Return StdDev (synthetic) rising: noise or trend acceleration in event time; re-tune risk.
Interpreting the info panel
Method : your sampling choice and current threshold.
Total Samples : how many synthetic bars have been formed.
Current Vol/Dollar : how much of the next bucket is already filled.
Bars in Bucket : native bars consumed so far in the current bucket.
Avg Bars/Sample : lower means higher trading intensity.
Avg Return / Return StdDev : return stats computed over synthetic closes .
Research directions you can build from here
Imbalance and run bars
Extend beyond pure volume or dollar thresholds to imbalance bars that trigger on directional order flow imbalance (e.g., buy volume minus sell volume), as discussed in the AFML ecosystem. These often further homogenize distributional properties used in ML. alpaca.markets
Volume-time indicators
Re-compute classical indicators (RSI, MACD, Bollinger) on the synthetic stream. The premise is that signals are updated by traded information , not seconds, which may stabilize indicator behavior in heteroskedastic regimes.
Liquidity and toxicity overlays
Combine synthetic bars with proxies of flow toxicity to anticipate spread widening or volatility clustering. For instance, tag synthetic bars that surpass multiples of the threshold and test whether subsequent realized volatility is elevated.
Dollar-risk parity sampling for portfolios
Use dollar bars to align samples across assets by notional risk, enabling cleaner cross-asset features and comparability in multi-asset models (e.g., correlation studies, regime clustering). AFML discusses the benefits of event-driven sampling for cross-sectional ML feature engineering.
Microstructure feature set
Compute duration in native bars per synthetic sample , range per sample , and volume multiple of threshold as inputs to state classifiers or regime HMMs . These features are inherently activity-aware and often predictive of short-horizon volatility and trend persistence per the event-time literature. ( Alpaca )
Tips for clean usage
Start with dynamic thresholds using Median over a sensible lookback to avoid outlier distortion, then move to Fixed thresholds when you know your instrument’s typical activity scale.
Compare time bars vs synthetic bars side by side to develop intuition for how your market “breathes” in activity time.
Keep Max Stored Samples reasonable for performance; the ring buffer avoids memory creep while preserving a rolling window of research-grade data.
John Bollinger's Bollinger BandsJapanese below / 日本語説明は下記
This indicator replicates how John Bollinger, the inventor of Bollinger Bands, uses Bollinger Bands, displaying Bollinger Bands, %B and Bandwidth in one indicator with alerts and signals.
Bollinger Bands is created by John Bollinger in 1980s who is an American financial trader and analyst. He introduced %B and Bandwidth 30 years later.
🟦 What's different from other Bollinger Bands indicator?
Unlike the default Bollinger Bands or other custom Bollinger Bands indicators on TradingView, this indicator enables to display three Bollinger Bands tools into a single indicator with signals and alerts capability.
You can plot the classic Bollinger Bands together with either %B or Bandwidth or three tools altogether which requires the specific setting(see below settings).
This makes it easy to quantitatively monitor volatility changes and price position in relation to Bollinger Bands in one place.
🟦 Features:
Plots Bollinger Bands (Upper, Basis, Lower) with fill between bands.
Option to display %B or Bandwidth with Bollinger Bands.
Plots highest and lowest Bandwidth levels over a customizable lookback period.
Adds visual markers when Bandwidth reaches its highest (Bulge) or lowest (Squeeze) value.
Includes ready-to-use alert conditions for Bulge and Squeeze events.
📈Chart
Green triangles and red triangles in the bottom chart mark Bulges and Squeezes respectively.
🟦 Settings:
Length: Number of bars used for Bollinger Band middleline calculation.
Basis MA Type: Choose SMA, EMA, SMMA (RMA), WMA, or VWMA for the midline.
StdDev: Standard deviation multiplier (default = 2.0).
Option: Select "Bandwidth" or "%B" (add the indicator twice if you want to display both).
Period for Squeeze and Bulge: Lookback period for detecting the highest and lowest Bandwidth levels.(default = 125 as specified by John Bollinger )
Style Settings: Colors, line thickness, and transparency can be customized.
📈Chart
The chart below shows an example of three Bollinger Bands tools: Bollinger Band, %B and Bandwidth are in display.
To do this, you need to add this indicator TWICE where you select %B from Option in the first addition of this indicator and Bandwidth from Option in the second addition.
🟦 Usage:
🟠Monitor Volatility:
Watch Bandwidth values to spot volatility contractions (Squeeze) and expansions (Bulge) that often precede strong price moves.
John Bollinger defines Squeeze and Bulge as follows;
Squeeze:
The lowest bandwidth in the past 125 period, where trend is born.
Bulge:
The highest bandwidth in the past 125 period where trend is going to die.
According to John Bollinger, this 125 period can be used in any timeframe.
📈Chart1
Example of Squeeze
You can see uptrends start after squeeze(red triangles)
📈Chart2
Example of Bulge
You can see the trend reversal from downtrend to uptrends at the bulge(green triangles)
📈Chart3
Bulge DOES NOT NECESSARILY mean the beginning of a trend in opposite direction.
For example, you can see a bulge happening in the right side of the chart where green triangles are marked. Nevertheless, uptrend still continues after the bulge.
In this case, the bulge marks the beginning of a consolidation which lead to the continuation of the trend. It means that a phase of the trend highlighted in the light blue box came to an end.
Note: light blue box is not drawn by the indicator.
Like other technical analysis methods or tools, these setups do not guarantee birth of new trends and trend reversals. Traders should be carefully observing these setups along with other factors for making decisions.
🟠Track Price Position:
Use %B to see where price is located in relation to the Bollinger Bands.
If %B is close to 1, the price is near upper band while %B is close to 0, the price is near lower band.
🟠Set Alerts:
Receive alerts when Bandwidth hits highest and lowest values of bandwidth, helping you prepare for potential breakout, ending of trends and trend reversal opportunities.
🟠Combine with Other Tools:
This indicator would work best when combined with price action, trend analysis, or
market environmental analysis.
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このインジケーターはボリンジャーバンドの考案者であるジョン・ボリンジャー氏が提唱するボリンジャーバンドの使い方を再現するために、ボリンジャーバンド、%B、バンドウィズ(Bandwidth) の3つを1つのインジケーターで表示可能にしたものです。シグナルやアラートにも対応しています。
ボリンジャーバンドは1980年代にアメリカ人トレーダー兼アナリストのジョン・ボリンジャー氏によって開発されました。彼はその30年後に%Bとバンドウィズを導入しました。
🟦 他のボリンジャーバンドとの違い
TradingView標準のボリンジャーバンドや他のボリンジャーバンドとは異なり、このインジケーターでは3つのボリンジャーバンドツールを1つのインジケーターで表示し、シグナルやアラート機能も利用できるようになっています。
一般的に知られている通常のボリンジャーバンドに加え、%Bやバンドウィズを組み合わせて表示でき、設定次第では3つすべてを同時にモニターすることも可能です。これにより、価格とボリンジャーバンドの位置関係とボラティリティ変化をひと目で、かつ定量的に把握することができます。
🟦 機能:
ボリンジャーバンド（アッパーバンド・基準線・ロワーバンド）を描画し、バンド間を塗りつぶし表示。
オプションで%Bまたはバンドウィズを追加表示可能。
バンドウィズの最高値・最安値を、任意の期間で検出して表示。
バンドウィズが指定期間の最高値（バルジ※）または最安値（スクイーズ）に達した際にシグナルを表示。
※バルジは一般的にボリンジャーバンドで用いられるエクスパンションとほぼ同じ意味ですが、定義が異なります。(下記参照)
バルジおよびスクイーズ発生時のアラート設定が可能。
📈 チャート例
下記チャートの緑の三角と赤の三角は、それぞれバルジとスクイーズを示しています。
🟦 設定:
Length: ボリンジャーバンドの基準線計算に使う期間。
Basis MA Type: SMA, EMA, SMMA (RMA), WMA, VWMAから選択可能。
StdDev: 標準偏差の乗数（デフォルト2.0）。
Option: 「Bandwidth」または「%B」を選択（両方表示するにはこのインジケーターを2回追加）。
Period for Squeeze and Bulge: Bandwidthの最高値・最安値を検出する期間（デフォルトはジョン・ボリンジャー氏が推奨する125）。
Style Settings: 色、線の太さ、透明度などをカスタマイズ可能。
📈 チャート例
下のチャートは「ボリンジャーバンド」「%B」「バンドウィズ」の3つを同時に表示した例です。
この場合、インジケーターを2回追加し、最初に追加した方ではOptionを「%B」に、次に追加した方では「Bandwidth」を選択します。
🟦 使い方:
🟠 ボラティリティを監視する:
バンドウィズの値を見ることで、価格変動の収縮（スクイーズ）や拡大（バルジ）を確認できます。
これらはしばしば強い値動きの前兆となります。
ジョン・ボリンジャー氏はスクイーズとバルジを次のように定義しています：
スクイーズ: 過去125期間の中で最も低いバンドウィズ→ 新しいトレンドが生まれる場所。
バルジ: 過去125期間の中で最も高いバンドウィズ → トレンドが終わりを迎える場所。
この「125期間」はどのタイムフレームでも利用可能とされています。
📈 チャート1
スクイーズの例
赤い三角のスクイーズの後に上昇トレンドが始まっているのが確認できます。
📈 チャート2
バルジの例
緑の三角のバルジの箇所で下降トレンドから上昇トレンドへの反転が見られます。
📈 チャート3
バルジが必ずしも反転を意味しない例
下記のチャート右側の緑の三角で示されたバルジの後も、上昇トレンドが継続しています。
この場合、バルジは反転ではなく「トレンド一時的な調整（レンジ入り）」を示しており、結果的に上昇トレンドが継続しています。
この場合、バルジは水色のボックスで示されたトレンドのフェーズの終わりを示しています。
※水色のボックスはインジケーターが描画したものではありません。
また、他のテクニカル分析と同様に、これらのセットアップは必ず新しいトレンドの発生やトレンド転換を保証するものではありません。トレーダーは他の要素も考慮し、慎重に意思決定する必要があります。
🟠 価格とボリンジャーバンドの位置関係を確認する:
%Bを利用すれば、価格がバンドのどこに位置しているかを簡単に把握できます。
%Bが1に近ければ価格はアッパーバンド付近、0に近ければロワーバンド付近にあります。
🟠 アラートを設定する:
バンドウィズが一定期間の最高値または最安値に到達した際にアラートを設定することで、ブレイクアウトやトレンド終了、反転の可能性に備えることができます。
🟠 他のツールと組み合わせる:
このインジケーターは、プライスアクション、トレンド分析、環境認識などと組み合わせて活用すると最も効果的です。
RTH Bias by @traderprimezTired of guessing the intraday direction? The RTH Bias indicator provides a powerful, data-driven statistical edge by analyzing the behavior of price after the initial Regular Trading Hours (RTH) range is set.
It meticulously tracks historical outcomes to show you the most probable "story" for the rest of the trading day.
This tool is designed for day traders of US indices, stocks, and other assets most active during the New York session. It moves beyond simple "opening range breakout" strategies by classifying each day into one of six distinct scenarios, giving you a much deeper insight into the day's potential character.
Core Concept
The opening period of the RTH session (e.g., the first one, two, or three hours) is dominated by high volume and institutional activity. The high and low established during this time often act as a critical pivot or springboard for the remainder of the day.
This indicator captures that initial range and then analyzes thousands of historical days to answer the key question: "Once the opening range is set, what happens next?" Does price tend to break out and trend? Does it fake out in one direction and reverse? Or does it stay trapped? The dashboard provides these probabilities at a glance.
Key Features
Choose the range that best fits your trading style and the asset you're trading:
09:30 - 10:30 (Micro): The classic, volatile first hour.
09:30 - 11:30 (Major): A broader range capturing the morning momentum.
09:30 - 12:30 (Macro): The full morning session, often defining the entire day's extremes.
📊 The Statistical Dashboard
This is the heart of the indicator. It provides a complete statistical breakdown of historical price action:
Scenario: The name of the price action profile.
Distribution: A visual bar chart showing the relative frequency of each scenario.
Count: The raw number of times each scenario has occurred over the lookback period.
Contribution: The percentage probability of each scenario occurring.
🎲 The Six Scenarios Explained
The indicator classifies each day's price action into one of these profiles:
↑ High, then ↓ Low (XAMD): A classic "stop hunt high, then sell-off." Price breaks the range high first, luring in buyers, before reversing to take the range low.
↓ Low, then ↑ High (XAMD): A classic "stop hunt low, then rally." Price breaks the range low first, stopping out sellers, before reversing to take the range high.
One-Sided Breakout (AMDX): A strong trend day. Price breaks only one side of the range and continues in that direction without ever violating the other side.
Search & Destroy (S&D): A volatile, choppy day. Price takes one side, reverses to take the other, and then reverses again.
No Breakout (Inside Day): A consolidation day. Price fails to break either the high or the low of the opening range.
🟩 On-Chart Bias Box
A simple visual aid that tracks the session in real-time:
Neutral (Gray): During Session 1, as the range is forming.
Bullish (Green): The Session 1 high has been broken.
Bearish (Red): The Session 1 low has been broken.
Both (Orange): Both the high and low have been broken (XAMD or S&D profile).
🛡️ RTH Guard Logic
This is a crucial feature for accuracy. The indicator locks in the day's scenario at the RTH close (e.g., 4 PM ET). This ensures that post-market (ETH) price action does not corrupt the historical statistics, giving you clean, reliable data based purely on regular trading hours.
🔔 Custom Alerts
Enable the "First Breakout" alert to be notified the moment the opening range is breached, so you don't have to watch the chart all day.
How to Use in Your Trading
This indicator does not give buy/sell signals. It provides a statistical framework to build a high-probability trading hypothesis for the day.
Select Your Range: In the settings, choose the opening range (Micro, Major, or Macro) you want to analyze.
Wait for the Range to Form: Let the neutral box fully form on your chart.
Analyze the Dashboard: Once the range is set, look at the "Contribution" column. Identify the scenario with the highest probability.
Form a Hypothesis: Build your trade idea around the most likely scenario.
Execute and Manage: You would wait for the box to turn red (low is broken). Instead of shorting, you would look for bullish confirmation (e.g., a market structure shift on a lower timeframe) to enter a long position, with the opening range high as a logical target.
Disclaimer: This indicator is a tool for analysis and probability assessment, not a standalone trading system. It should be used in conjunction with your own strategy and risk management. Past performance is not indicative of future results.
Total Points Range by exp3rtsThis indicator measures and displays the true intraday movement of a market by approximating tick-level activity using 1-second data aggregation. Instead of only looking at net candle movement, it sums every price change during a session, giving traders a more accurate picture of market effort and volatility.
Total Points Moved (TPM) – Captures the full distance traveled by price, not just the net gain/loss.
Bullish vs. Bearish Movement – Separates upward and downward moves so you can see who dominated the session.
Custom Sessions – Define your own session start/end times and time zone for precise tracking.
End-of-Session Summary – Automatically plots a label at session completion with totals for TPM, bullish, and bearish movement.
Visual Session Highlighting – Background shading makes it easy to see when the chosen session is active.
This tool is useful for:
Understanding the true effort vs. result of price movement
Comparing volatility across sessions
Identifying whether bulls or bears contributed more to market swings
Supporting order flow and tick-based trading strategies
Natural Gas Intraday Strategy [15m] with Partial Profit & TrailBuy when:
1. Close > EMA 100 and EMA 20 > EMA 100
2. MACD (8,21,5) > Signal and histogram rising
3. RSI > 60
4. ATR > threshold (avoid flat market)
Sell when:
1. Close < EMA 100 and EMA 20 < EMA 100
2. MACD (8,21,5) < Signal and histogram falling
3. RSI < 40
4. ATR > threshold
Exit:
• SL = recent swing ± 0.5 ATR
• TP1 = 1 ATR, trail rest with EMA 20
CCI + MACD Signal MTF (2nd-cross)This custom indicator combines the Commodity Channel Index (CCI) and the MACD to generate trading signals.
Basic signals (dots):
A green dot is plotted when CCI is above +100 and MACD is positive.
A red dot is plotted when CCI is below –100 and MACD is negative.
These dots help visualize momentum alignment between the two indicators.
Second-cross signals (text + alert):
The indicator also tracks cycles of the CCI.
When CCI first moves above +100 and later falls back below +100, this is counted as one completed cycle.
The next time CCI crosses back above +100 (the second cross), if MACD is still positive, a “BUY” label is plotted and a buy alert is triggered.
Conversely, when CCI first moves below –100 and later rises back above –100, that is one completed cycle.
The next time CCI crosses back below –100 (the second cross), if MACD is negative, a “SELL” label is plotted and a sell alert is triggered.
Alerts:
Alerts are only fired on the second-cross events (BUY or SELL), making them rarer but potentially more reliable than the basic dot conditions.
Timeframe flexibility:
Both the CCI and the MACD can be calculated on custom timeframes independently of the chart’s timeframe.
Zero Lag + Momentum Bias StrategyZero Lag + Momentum Bias Strategy (MTF + Strong MBI + R:R + Partial TP + Alerts)
New Candle Alert with Time WindowJust needed a way to specify a time window on a timeframe and get alerts for each new candle
CCI + MACD Signal MTF (2nd-cross)This custom indicator combines the Commodity Channel Index (CCI) and the MACD to generate trading signals.
Basic signals (dots):
A green dot is plotted when CCI is above +100 and MACD is positive.
A red dot is plotted when CCI is below –100 and MACD is negative.
These dots help visualize momentum alignment between the two indicators.
Second-cross signals (text + alert):
The indicator also tracks cycles of the CCI.
When CCI first moves above +100 and later falls back below +100, this is counted as one completed cycle.
The next time CCI crosses back above +100 (the second cross), if MACD is still positive, a “BUY” label is plotted and a buy alert is triggered.
Conversely, when CCI first moves below –100 and later rises back above –100, that is one completed cycle.
The next time CCI crosses back below –100 (the second cross), if MACD is negative, a “SELL” label is plotted and a sell alert is triggered.
Alerts:
Alerts are only fired on the second-cross events (BUY or SELL), making them rarer but potentially more reliable than the basic dot conditions.
Timeframe flexibility:
Both the CCI and the MACD can be calculated on custom timeframes independently of the chart’s timeframe.
Fisher Transform Trend Navigator [QuantAlgo]🟢 Overview
The Fisher Transform Trend Navigator applies a logarithmic transformation to normalize price data into a Gaussian distribution, then combines this with volatility-adaptive thresholds to create a trend detection system. This mathematical approach helps traders identify high-probability trend changes and reversal points while filtering market noise in the ever-changing volatility conditions.
🟢 How It Works
The indicator's foundation begins with price normalization, where recent price action is scaled to a bounded range between -1 and +1:
highestHigh = ta.highest(priceSource, fisherPeriod)
lowestLow = ta.lowest(priceSource, fisherPeriod)
value1 = highestHigh != lowestLow ? 2 * (priceSource - lowestLow) / (highestHigh - lowestLow) - 1 : 0
value1 := math.max(-0.999, math.min(0.999, value1))
This normalized value then passes through the Fisher Transform calculation, which applies a logarithmic function to convert the data into a Gaussian normal distribution that naturally amplifies price extremes and turning points:
fisherTransform = 0.5 * math.log((1 + value1) / (1 - value1))
smoothedFisher = ta.ema(fisherTransform, fisherSmoothing)
The smoothed Fisher signal is then integrated with an exponential moving average to create a hybrid trend line that balances statistical precision with price-following behavior:
baseTrend = ta.ema(close, basePeriod)
fisherAdjustment = smoothedFisher * fisherSensitivity * close
fisherTrend = baseTrend + fisherAdjustment
To filter out false signals and adapt to market conditions, the system calculates dynamic threshold bands using volatility measurements:
dynamicRange = ta.atr(volatilityPeriod)
threshold = dynamicRange * volatilityMultiplier
upperThreshold = fisherTrend + threshold
lowerThreshold = fisherTrend - threshold
When price momentum pushes through these thresholds, the trend line locks onto the new level and maintains direction until the opposite threshold is breached:
if upperThreshold < trendLine
trendLine := upperThreshold
if lowerThreshold > trendLine
trendLine := lowerThreshold
🟢 Signal Interpretation
Bullish Candles (Green): indicate normalized price distribution favoring bulls with sustained buying momentum = Long/Buy opportunities
Bearish Candles (Red): indicate normalized price distribution favoring bears with sustained selling pressure = Short/Sell opportunities
Upper Band Zone: Area above middle level indicating statistically elevated trend strength with potential overbought conditions approaching mean reversion zones
Lower Band Zone: Area below middle level indicating statistically depressed trend strength with potential oversold conditions approaching mean reversion zones
Built-in Alert System: Automated notifications trigger when bullish or bearish states change, allowing you to act on significant developments without constantly monitoring the charts
Candle Coloring: Optional feature applies trend colors to price bars for visual consistency and clarity
Configuration Presets: Three parameter sets available - Default (balanced settings), Scalping (faster response with higher sensitivity), and Swing Trading (slower response with enhanced smoothing)
Color Customization: Four color schemes including Classic, Aqua, Cosmic, and Custom options for personalized chart aesthetics
Portfolio Simulator & BacktesterMulti-asset portfolio simulator with different metrics and ratios, DCA modeling, and rebalancing strategies.
Core Features
Portfolio Construction
Up to 5 assets with customizable weights (must total 100%)
Support for any tradable symbol: stocks, ETFs, crypto, indices, commodities
Real-time validation of allocations
Dollar Cost Averaging
Monthly or Quarterly contributions
Applies to both portfolio and benchmark for fair comparison
Model real-world investing behavior
Rebalancing
Four strategies: None, Monthly, Quarterly, Yearly
Automatic rebalancing to target weights
Transaction cost modeling (customizable fee %)
Key Metrics Table
CAGR: Annualized compound return (S&P 500 avg: ~10%)
Alpha: Excess return vs. benchmark (positive = outperformance)
Sharpe Ratio: Return per unit of risk (>1.0 is good, >2.0 excellent)
Sortino Ratio: Like Sharpe but only penalizes downside (better metric)
Calmar Ratio: CAGR / Max Drawdown (>1.0 good, >2.0 excellent)
Max Drawdown: Largest peak-to-trough decline
Win Rate: % of positive days (doesn't indicate profitability)
Visualization
Dual-chart comparison - Portfolio vs. Benchmark
Dollar or percentage view toggle
Customizable colors and line width
Two tables: Statistics + Asset Allocation
Adjustable table position and text size
🚀 Quick Start Guide
Enter 1-5 ticker symbols (e.g., SPY, QQQ, TLT, GLD, BTCUSD)
Make sure percentage weights total 100%
Choose date range (ensure chart shows full period - zoom out!)
Configure DCA and rebalancing (optional)
Select benchmark (default: SPX)
Analyze results in statistics table
💡 Pro Tips
Chart data matters: Load SPY or your longest-history asset as main chart
If you select an asset that was not available for the selected period, the chart will not show up! E.g. BTCUSD data: Only available from ~2017 onwards.
Transaction fees: 0.1% default (adjust to match your broker)
⚠️ Important Notes
Requires visible chart data (zoom out to show full date range)
Limited by each asset's historical data availability
Transaction fees and costs are modeled, but taxes/slippage are not
Past performance ≠ future results
Use for research and education only, not financial advice
Let me know if you have any suggestions to improve this simulator.
Smart Money Concept v1Smart Money Concept Indicator – Visual Interpretation Guide
What Happens When Liquidity Lines Are Broken
🟩 Green Line Broken (Buy-Side Liquidity Pool Swept)
- Indicates price has dipped below a previous swing low where sell stops are likely placed.
- Market Makers may be triggering these stops to accumulate long positions.
- Often followed by a bullish reversal.
- Trader Actions:
• Look for a bullish candle close after the sweep.
• Confirm with nearby Bullish Order Block or Fair Value Gap.
• Consider entering a Buy trade (SLH entry).
- If price continues falling: Indicates trend continuation and invalidation of the buy-side liquidity zone.
🟥 Red Line Broken (Sell-Side Liquidity Pool Swept)
- Indicates price has moved above a previous swing high where buy stops are likely placed.
- Market Makers may be triggering these stops to accumulate short positions.
- Often followed by a bearish reversal.
- Trader Actions:
• Look for a bearish candle close after the sweep.
• Confirm with nearby Bearish Order Block or Fair Value Gap.
• Consider entering a Sell trade (SLH entry).
- If price continues rising: Indicates trend continuation and invalidation of the sell-side liquidity zone.
Chart-Based Interpretation of Green Line Breaks
In the provided DOGE/USD 15-minute chart image:
- Green lines represent buy-side liquidity zones.
- If these lines are broken:
• It may be a stop hunt before a bullish continuation.
• Or a false Break of Structure (BOS) leading to deeper retracement.
- Confirmation is needed from candle structure and nearby OB/FVG zones.
Is the Pink Zone a Valid Bullish Order Block?
To validate the pink zone as a Bullish OB:
- It should be formed by a strong down-close candle followed by a bullish move.
- Price should have rallied from this zone previously.
- If price is now retesting it and showing bullish reaction, it confirms validity.
- If formed during low volume or price never rallied from it, it may not be valid.
Smart Money Concept - Liquidity Line Breaks Explained
This document explains how traders should interpret the breaking of green (buy-side) and red (sell-side) liquidity lines when using the Smart Money Concept indicator. These lines represent key liquidity pools where stop orders are likely placed.
🟩 Green Line Broken (Buy-Side Liquidity Pool Swept)
When the green line is broken, it indicates:
• - Price has dipped below a previous swing low where sell stops were likely placed.
• - Market Makers have triggered those stops to accumulate long positions.
• - This is often followed by a bullish reversal.
Trader Actions:
• - Look for a bullish candle close after the sweep.
• - Confirm with a nearby Bullish Order Block or Fair Value Gap.
• - Consider entering a Buy trade (SLH entry).
🟥 Red Line Broken (Sell-Side Liquidity Pool Swept)
When the red line is broken, it indicates:
• - Price has moved above a previous swing high where buy stops were likely placed.
• - Market Makers have triggered those stops to accumulate short positions.
• - This is often followed by a bearish reversal.
Trader Actions:
• - Look for a bearish candle close after the sweep.
• - Confirm with a nearby Bearish Order Block or Fair Value Gap.
• - Consider entering a Sell trade (SLH entry).
📌 Additional Notes
• - If price continues beyond the liquidity line without reversal, it may indicate a trend continuation rather than a stop hunt.
• - Always confirm with Higher Time Frame bias, Institutional Order Flow, and price reaction at the zone.
FEI: Futures Entry Identifier📘 FEI: Futures Entry Identifier
FEI is a modular, futures-grade entry engine designed for precision trading across GC1!, MNQ1!, ES1!, and related contracts. It combines manual SVP structure, CHoCH detection, and Colby-style candle strength filters to identify high-probability long and short entries.
🔧 Features
• Manual SVP inputs (VAH, VAL, POC)
• Symbol-aware filters for micro vs standard contracts
• Multi-timeframe signal logic (3m, 5m, 10m, 15m, 30m)
• CHoCH detection with optional engulfing filter (default off)
• FRVP entry zone plotting after CHoCH confirmation
• Candle coloring on CHoCH trigger
• Session-aware logic (ETH default, optional RTH-only)
• Narratable visuals and audit-safe alerts
🧭 How to Use
1. Input VAH, VAL, and POC manually
2. Select signal timeframe (e.g. 3m or 5m)
3. Watch for CHoCH (white candle = structural shift)
4. Entry line plots at top/bottom of recent range
5. Long/short markers appear when SVP + candle strength align
6. Toggle RTH-only mode if needed
🌟 Why It’s Unique
FEI is built for traders who demand clarity, structure, and precision. Every signal is narratable, audit-safe, and resolution-aware—ideal for futures overlays and sniper-grade entries.
Multi-Timeframe Trend Table - EMA Based Trend Analysis📊 Stay Aligned with Higher Timeframe Trends While Scalping
This powerful indicator displays real-time trend direction for 1-hour and 4-hour timeframes in a clean, easy-to-read table format. Perfect for traders who want to align their short-term trades with higher timeframe momentum.
🎯 Key Features
Multi-Timeframe Analysis: Monitor 1H and 4H trends while trading on any timeframe (3min, 5min, 15min, etc.)
EMA-Based Logic: Uses proven EMA 50 and EMA 100 crossover methodology
Visual Clarity: Color-coded table with green (uptrend) and red (downtrend) indicators
Customizable Display: Toggle EMA values and adjust table position
Real-Time Updates: Automatically refreshes with each bar close
Lightweight: Minimal resource usage with efficient data requests
📈 How It Works
The indicator determines trend direction using a simple but effective rule:
UPTREND: Price is above both EMA 50 AND EMA 100
DOWNTREND: Price is below either EMA 50 OR EMA 100
🔧 Settings
Show EMA Values: Display actual EMA 50/100 values in the table
Table Position: Choose from 4 corner positions (Top Right, Top Left, Bottom Right, Bottom Left)
Plot Current EMAs: Optional display of EMA lines on your current chart
💡 Trading Applications
✅ Trend Confirmation: Ensure your trades align with higher timeframe direction
✅ Risk Management: Avoid counter-trend trades in strong directional markets
✅ Entry Timing: Use lower timeframe for entries while respecting higher timeframe bias
✅ Scalping Enhancement: Perfect for 1-5 minute scalping with higher timeframe context
🎨 Visual Design
Clean, professional table design
Intuitive color coding (Green = Up, Red = Down)
Compact size that doesn't obstruct your chart
Clear typography for quick reading
📋 Perfect For
Day traders and scalpers
Swing traders seeking trend confirmation
Multi-timeframe analysis enthusiasts
Traders who want simple, effective trend identification
🚀 Easy Setup
Add to any chart (works on all timeframes)
Customize table position and settings
Start trading with higher timeframe awareness
Watch the table update automatically
No complex configurations needed - just add and trade!
This indicator is designed for educational and informational purposes. Always combine with proper risk management and your own analysis.
RWE (MASTER CƯỜNG BOSS)Tôi là một nhà giao dịch master, tôi muốn chia sẻ đến các bạn những chỉ báo tuyệt vời nhất
Bollinger Breakout MarkersSubtle triangle markers that indicate when price extends out of the Bollinger bands to indicate overbought and oversold conditions
