Breadth-Driven Swing StrategyWhat it does
This script trades the S&P 500 purely on market breadth extremes:
• Data source : INDEX:S5TH = % of S&P 500 stocks above their own 200-day SMA (range 0–100).
• Buy when breadth is washed-out.
• Sell when breadth is overheated.
It is long-only by design; shorting and ATR trailing stops have been removed to keep the logic minimal and transparent.
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Signals in plain English
1. Long entry
A. A 200-EMA trough in breadth is printed and the trough value is ≤ 40 %.
or
B. A 5-EMA trough appears, its prominence passes the user threshold, and the lowest breadth reading in the last 20 bars is ≤ 20 %.
(Toggle this secondary trigger on/off with “ Enter also on 5-EMA trough ”.)
2. Exit (close long)
First 200-EMA peak whose breadth value is ≥ 70 %.
3. Risk control
A fixed stop-loss (% of entry price, default 8 %) is attached to every long trade.
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Key parameters (defaults shown)
• Long EMA length 200 • Short EMA length 5
• Peak prominence 0.5 pct-pts • Trough prominence 3 pct-pts
• Peak level 70 % • Trough level 40 % • 5-EMA trough level 20 %
• Fixed stop-loss 8 %
• “Enter also on 5-EMA trough” = true (allows additional entries on extreme momentum reversals)
Feel free to tighten or relax any of these thresholds to match your risk profile or account for different market regimes.
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How to use it
1. Load the script on a daily SPX / SPY chart.
(The price chart drives order execution; the breadth series is pulled internally and does not need to be on the chart.)
2. Verify the breadth feed.
INDEX:S5TH is updated after each session; your broker must provide it.
3. Back-test across several cycles.
Two decades of daily data is recommended to see how the rules behave in bear markets, range markets, and bull trends.
4. Adjust position sizing in the Properties tab.
The default is “100 % of equity”; change it if you prefer smaller allocations or pyramiding caps.
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Why it can help
• Breadth signals often lead price, allowing entries before index-level momentum turns.
• Simple, rule-based exits prevent “waiting for confirmation” paralysis.
• Only one input series—easy to audit, no black-box math.
Trade-offs
• Relies on a single breadth metric; other internals (advance/decline, equal-weight returns, etc.) are ignored.
• May sit in cash during shallow pullbacks that never push breadth ≤ 40 %.
• Signals arrive at the end of the session (breadth is EoD data).
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Disclaimer
This script is provided for educational purposes only and is not financial advice. Markets are risky; test thoroughly and use your own judgment before trading real money.
ストラテジー概要
本スクリプトは S&P500 のマーケットブレッド（内部需給） だけを手がかりに、指数をスイングトレードします。
• ブレッドデータ : INDEX:S5TH
（S&P500 採用銘柄のうち、それぞれの 200 日移動平均線を上回っている銘柄比率。0–100 %）
• 買い : ブレッドが極端に売られたタイミング。
• 売り : ブレッドが過熱状態に達したタイミング。
余計な機能を削り、ロングオンリー & 固定ストップ のシンプル設計にしています。
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シグナルの流れ
1. ロングエントリー
• 条件 A : 200-EMA がトラフを付け、その値が 40 % 以下
• 条件 B : 5-EMA がトラフを付け、
・プロミネンス条件を満たし
・直近 20 本のブレッドス最小値が 20 % 以下
• B 条件は「5-EMA トラフでもエントリー」を ON にすると有効
2. ロング決済
最初に出現した 200-EMA ピーク で、かつ値が 70 % 以上 のバーで手仕舞い。
3. リスク管理
各トレードに 固定ストップ（初期価格から 8 %）を設定。
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主なパラメータ（デフォルト値）
• 長期 EMA 長さ : 200 • 短期 EMA 長さ : 5
• ピーク判定プロミネンス : 0.5 %pt • トラフ判定プロミネンス : 3 %pt
• ピーク水準 : 70 % • トラフ水準 : 40 % • 5-EMA トラフ水準 : 20 %
• 固定ストップ : 8 %
• 「5-EMA トラフでもエントリー」 : ON
相場環境やリスク許容度に合わせて閾値を調整してください。
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使い方
1. 日足の SPX / SPY チャート にスクリプトを適用。
2. ブレッドデータの供給 (INDEX:S5TH) がブローカーで利用可能か確認。
3. 20 年以上の期間でバックテスト し、強気相場・弱気相場・レンジ局面での挙動を確認。
4. 資金配分 は プロパティ → 戦略実行 で調整可能（初期値は「資金の 100 %」）。
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強み
• ブレッドは 価格より先行 することが多く、天底を早期に捉えやすい。
• ルールベースの出口で「もう少し待とう」と迷わずに済む。
• 入力 series は 1 本のみ、ブラックボックス要素なし。
注意点・弱み
• 単一指標に依存。他の内部需給（A/D ライン等）は考慮しない。
• 40 % を割らない浅い押し目では機会損失が起こる。
• ブレッドは終値ベースの更新。ザラ場中の変化は捉えられない。
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免責事項
本スクリプトは 学習目的 で提供しています。投資助言ではありません。
実取引の前に必ず自己責任で十分な検証とリスク管理を行ってください。
Penunjuk Breadth
Market Breadth Peaks & Troughs IndicatorIndicator Overview
Market Breadth (S5TH) visualizes extremes of market strength and weakness by overlaying -
a 200-period EMA (long-term trend)
a 5-period EMA (short-term trend, user-adjustable)
on the percentage of S&P 500 constituents trading above their 200-day SMA (INDEX:S5TH).
Peaks (▼) and troughs (▲) are detected with prominence filters so you can quickly spot overbought and oversold conditions.
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1. Core Logic
Component Description
Breadth series INDEX:S5TH — % of S&P 500 stocks above their 200-SMA
Long EMA 200-EMA to capture the primary trend
Short EMA 5-EMA (default, editable) for short-term swings
Peak detection ta.pivothigh + prominence ⇒ major peaks marked with red ▼
Trough detection (200 EMA) ta.pivotlow + prominence + value < longTroughLvl ⇒ blue ▲
Trough detection (5 EMA) ta.pivotlow + prominence + value < shortTroughLvl ⇒ green ▲
Background shading Pink when 200 EMA slope is down and 5 EMA sits below 200 EMA
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2. Adjustable Parameters (input())
Group Variable Default Purpose
Symbol breadthSym INDEX:S5TH Breadth index
Long EMA longLen 200 Period of long EMA
Short EMA shortLen 5 Period of short EMA
Pivot width (long) pivotLen 20 Bars left/right for 200-EMA peaks/troughs
Pivot width (short) pivotLenS 10 Bars for 5-EMA troughs
Prominence (long) promThresh 0.5 %-pt Depth filter for 200-EMA pivots
Prominence (short) promThreshS 3.0 %-pt Depth filter for 5-EMA pivots
Trough level (long) longTroughLvl 50 % Max value to accept a 200-EMA trough
Trough level (short) shortTroughLvl 30 % Max value to accept a 5-EMA trough
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3. Signal Guide
Marker / Color Meaning Typical reading
Red ▼ Major breadth peak Overbought / possible top
Blue ▲ Deep 200-EMA trough End of mid-term correction
Green ▲ Shallow 5-EMA trough (early) Short-term rebound setup
Pink background Long-term down-trend and short-term weak Risk-off phase
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4. Typical Use Cases
1. Counter-trend timing
• Fade greed: trim longs on red ▼
• Buy fear: scale in on green ▲; add on blue ▲
2. Trend filter
• Avoid new longs while the background is pink; wait for a trough & recovery.
3. Risk management
• Reduce exposure when peaks appear, reload partial size on confirmed troughs.
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5. Notes & Tips
• INDEX:S5TH is sourced from TradingView and may be back-adjusted when index membership changes.
• Fine-tune pivotLen, promThresh, and level thresholds to match current volatility before relying on alerts or automated rules.
• Slope thresholds (±0.10 %-pt) that trigger background shading can also be customized for different market regimes.
Zweig Market Breadth Thrust Indicator+Trigger [LazyBear x rwak]The Breadth Thrust (BT) indicator is a market momentum indicator developed by Dr. Martin Zweig. According to Dr. Zweig, a Breadth Thrust occurs when, during a 10-day period, the Breadth Thrust indicator rises from below 40 percent to above 61.5 percent.
A "Thrust" indicates that the stock market has rapidly changed from an oversold condition to one of strength, but has not yet become overbought. This is very rare and has happened only a few times. Dr. Zweig also points out that most bull markets begin with a Breadth Thrust.
This version of the Breadth Thrust indicator includes a trigger visualized with red circles, making it easier to spot when the indicator crosses the critical 61.5% level, signaling potential bullish momentum.
All parameters are configurable. You can draw BT for NYSE, NASDAQ, AMEX, or based on combined data (i.e., AMEX+NYSE+NASD). There is also a "CUSTOM" mode supported, so you can enter your own ADV/DEC symbols.
Credit: The original Breadth Thrust logic was created by LazyBear, whose public indicators can be found here , and app-store indicators here .
More info:
Definition of Breadth Thrust
A Breadth Thrust Signal
A Rare "Zweig" Buy Signal
Zweig Breadth Thrust: Redux
SuperZweig thrust (<= 30 dias)SuperZweig Thrust (≤ 30-day breadth trigger)
This study tracks the classic Zweig Breadth Thrust pattern, but restricts valid signals to a 30-bar (≈ 30-trading-day) window.
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What it plots
| Plot | Meaning |
|------|---------|
| **Blue line** – `EMA10` | 10-bar exponential moving average of the _breadth ratio_:`advancing issues / (advancing + declining)` |
| **Red h-line 0.35** | Oversold threshold ( < 0.35 ) |
| **Green h-line 0.64** | Overbought threshold ( > 0.64 ) |
| **Red “×”** | The moment EMA10 crosses **down** through 0.35 |
| **Green “●”** | The moment EMA10 crosses **up** through 0.64 |
| **Green “BUY” label** | Complete Super-Zweig thrust: red × followed by green ● **within 30 daily bars** |
Signal logic
1. **Trigger phase** – when EMA10 drops below 0.35
*Script starts a 30-bar countdown.*
2. **Confirmation phase** – if, while the countdown is active, EMA10 rises above 0.64:
*A single “BUY” label is plotted beneath that bar.*
3. **Expiry** – if 30 bars elapse without the 0.64 cross, the cycle resets; no signal is produced.
4. After any valid “BUY” the cycle also resets, so a new signal requires a fresh cross < 0.35.
Inputs
* **EMA length** – default 10.
* **Advancing / Declining symbols** – default `ADVS` / `DECS` (NYSE issues); can be pointed to any Exchange-specific or custom breadth tickers.
Typical use
Apply on a **daily chart** of a broad index (e.g., S&P; 500).
A printed “BUY” indicates a historically rare surge in market breadth often associated with durable rallies. Combine with other risk-management and trend filters before trading.
ADR% Extension Levels from SMA 50I created this indicator inspired by RealSimpleAriel (a swing trader I recommend following on X) who does not buy stocks extended beyond 4 ADR% from the 50 SMA and uses extensions from the 50 SMA at 7-8-9-10-11-12-13 ADR% to take profits with a 20% position trimming.
RealSimpleAriel's strategy (as I understood it):
-> Focuses on leading stocks from leading groups and industries, i.e., those that have grown the most in the last 1-3-6 months (see on Finviz groups and then select sector-industry).
-> Targets stocks with the best technical setup for a breakout, above the 200 SMA in a bear market and above both the 50 SMA and 200 SMA in a bull market, selecting those with growing Earnings and Sales.
-> Buys stocks on breakout with a stop loss set at the day's low of the breakout and ensures they are not extended beyond 4 ADR% from the 50 SMA.
-> 3-5 day momentum burst: After a breakout, takes profits by selling 1/2 or 1/3 of the position after a 3-5 day upward move.
-> 20% trimming on extension from the 50 SMA: At 7 ADR% (ADR% calculated over 20 days) extension from the 50 SMA, takes profits by selling 20% of the remaining position. Continues to trim 20% of the remaining position based on the stock price extension from the 50 SMA, calculated using the 20-period ADR%, thus trimming 20% at 8-9-10-11 ADR% extension from the 50 SMA. Upon reaching 12-13 ADR% extension from the 50 SMA, considers the stock overextended, closes the remaining position, and evaluates a short.
-> Trailing stop with ascending SMA: Uses a chosen SMA (10, 20, or 50) as the definitive stop loss for the position, depending on the stock's movement speed (preferring larger SMAs for slower-moving stocks or for long-term theses). If the stock's closing price falls below the chosen SMA, the entire position is closed.
In summary:
-->Buy a breakout using the day's low of the breakout as the stop loss (this stop loss is the most critical).
--> Do not buy stocks extended beyond 4 ADR% from the 50 SMA.
--> Sell 1/2 or 1/3 of the position after 3-5 days of upward movement.
--> Trim 20% of the position at each 7-8-9-10-11-12-13 ADR% extension from the 50 SMA.
--> Close the entire position if the breakout fails and the day's low of the breakout is reached.
--> Close the entire position if the price, during the rise, falls below a chosen SMA (10, 20, or 50, depending on your preference).
--> Definitively close the position if it reaches 12-13 ADR% extension from the 50 SMA.
I used Grok from X to create this indicator. I am not a programmer, but based on the ADR% I use, it works.
Below is Grok from X's description of the indicator:
Script Description
The script is a custom indicator for TradingView that displays extension levels based on ADR% relative to the 50-period Simple Moving Average (SMA). Below is a detailed description of its features, structure, and behavior:
1. Purpose of the Indicator
Name: "ADR% Extension Levels from SMA 50".
Objective: Draw horizontal blue lines above and below the 50-period SMA, corresponding to specific ADR% multiples (4, 7, 8, 9, 10, 11, 12, 13). These levels represent potential price extension zones based on the average daily percentage volatility.
Overlay: The indicator is overlaid on the price chart (overlay=true), so the lines and SMA appear directly on the price graph.
2. Configurable Inputs
The indicator allows users to customize parameters through TradingView settings:
SMA Length (smaLength):
Default: 50 periods.
Description: Specifies the number of periods for calculating the Simple Moving Average (SMA). The 50-period SMA serves as the reference point for extension levels.
Constraint: Minimum 1 period.
ADR% Length (adrLength):
Default: 20 periods.
Description: Specifies the number of days to calculate the moving average of the daily high/low ratio, used to determine ADR%.
Constraint: Minimum 1 period.
Scale Factor (scaleFactor):
Default: 1.0.
Description: An optional multiplier to adjust the distance of extension levels from the SMA. Useful if levels are too close or too far due to an overly small or large ADR%.
Constraint: Minimum 0.1, increments of 0.1.
Tooltip: "Adjust if levels are too close or far from SMA".
3. Main Calculations
50-period SMA:
Calculated with ta.sma(close, smaLength) using the closing price (close).
Serves as the central line around which extension levels are drawn.
ADR% (Average Daily Range Percentage):
Formula: 100 * (ta.sma(dhigh / dlow, adrLength) - 1).
Details:
dhigh and dlow are the daily high and low prices, obtained via request.security(syminfo.tickerid, "D", high/low) to ensure data is daily-based, regardless of the chart's timeframe.
The dhigh / dlow ratio represents the daily percentage change.
The simple moving average (ta.sma) of this ratio over 20 days (adrLength) is subtracted by 1 and multiplied by 100 to obtain ADR% as a percentage.
The result is multiplied by scaleFactor for manual adjustments.
Extension Levels:
Defined as ADR% multiples: 4, 7, 8, 9, 10, 11, 12, 13.
Stored in an array (levels) for easy iteration.
For each level, prices above and below the SMA are calculated as:
Above: sma50 * (1 + (level * adrPercent / 100))
Below: sma50 * (1 - (level * adrPercent / 100))
These represent price levels corresponding to a percentage change from the SMA equal to level * ADR%.
4. Visualization
Horizontal Blue Lines:
For each level (4, 7, 8, 9, 10, 11, 12, 13 ADR%), two lines are drawn:
One above the SMA (e.g., +4 ADR%).
One below the SMA (e.g., -4 ADR%).
Color: Blue (color.blue).
Style: Solid (style=line.style_solid).
Management:
Each level has dedicated variables for upper and lower lines (e.g., upperLine1, lowerLine1 for 4 ADR%).
Previous lines are deleted with line.delete before drawing new ones to avoid overlaps.
Lines are updated at each bar with line.new(bar_index , level, bar_index, level), covering the range from the previous bar to the current one.
Labels:
Displayed only on the last bar (barstate.islast) to avoid clutter.
For each level, two labels:
Above: E.g., "4 ADR%", positioned above the upper line (style=label.style_label_down).
Below: E.g., "-4 ADR%", positioned below the lower line (style=label.style_label_up).
Color: Blue background, white text.
50-period SMA:
Drawn as a gray line (color.gray) for visual reference.
Diagnostics:
ADR% Plot: ADR% is plotted in the status line (orange, histogram style) to verify the value.
ADR% Label: A label on the last bar near the SMA shows the exact ADR% value (e.g., "ADR%: 2.34%"), with a gray background and white text.
5. Behavior
Dynamic Updating:
Lines update with each new bar to reflect new SMA 50 and ADR% values.
Since ADR% uses daily data ("D"), it remains constant within the same day but changes day-to-day.
Visibility Across All Bars:
Lines are drawn on every bar, not just the last one, ensuring visibility on historical data as well.
Adaptability:
The scaleFactor allows level adjustments if ADR% is too small (e.g., for low-volatility symbols) or too large (e.g., for cryptocurrencies).
Compatibility:
Works on any timeframe since ADR% is calculated from daily data.
Suitable for symbols with varying volatility (e.g., stocks, forex, cryptocurrencies).
6. Intended Use
Technical Analysis: Extension levels represent significant price zones based on average daily volatility. They can be used to:
Identify potential price targets (e.g., take profit at +7 ADR%).
Assess support/resistance zones (e.g., -4 ADR% as support).
Measure price extension relative to the 50 SMA.
Trading: Useful for strategies based on breakouts or mean reversion, where ADR% levels indicate reversal or continuation points.
Debugging: Labels and ADR% plot help verify that values align with the symbol’s volatility.
7. Limitations
Dependence on Daily Data: ADR% is based on daily dhigh/dlow, so it may not reflect intraday volatility on short timeframes (e.g., 1 minute).
Extreme ADR% Values: For low-volatility symbols (e.g., bonds) or high-volatility symbols (e.g., meme stocks), ADR% may require adjustments via scaleFactor.
Graphical Load: Drawing 16 lines (8 upper, 8 lower) on every bar may slow the chart for very long historical periods, though line management is optimized.
ADR% Formula: The formula 100 * (sma(dhigh/dlow, Length) - 1) may produce different values compared to other ADR% definitions (e.g., (high - low) / close * 100), so users should be aware of the context.
8. Visual Example
On a chart of a stock like TSLA (daily timeframe):
The 50 SMA is a gray line tracking the average trend.
Assuming an ADR% of 3%:
At +4 ADR% (12%), a blue line appears at sma50 * 1.12.
At -4 ADR% (-12%), a blue line appears at sma50 * 0.88.
Other lines appear at ±7, ±8, ±9, ±10, ±11, ±12, ±13 ADR%.
On the last bar, labels show "4 ADR%", "-4 ADR%", etc., and a gray label shows "ADR%: 3.00%".
ADR% is visible in the status line as an orange histogram.
9. Code: Technical Structure
Language: Pine Script @version=5.
Inputs: Three configurable parameters (smaLength, adrLength, scaleFactor).
Calculations:
SMA: ta.sma(close, smaLength).
ADR%: 100 * (ta.sma(dhigh / dlow, adrLength) - 1) * scaleFactor.
Levels: sma50 * (1 ± (level * adrPercent / 100)).
Graphics:
Lines: Created with line.new, deleted with line.delete to avoid overlaps.
Labels: Created with label.new only on the last bar.
Plots: plot(sma50) for the SMA, plot(adrPercent) for debugging.
Optimization: Uses dedicated variables for each line (e.g., upperLine1, lowerLine1) for clear management and to respect TradingView’s graphical object limits.
10. Possible Improvements
Option to show lines only on the last bar: Would reduce visual clutter.
Customizable line styles: Allow users to choose color or style (e.g., dashed).
Alert for anomalous ADR%: A message if ADR% is too small or large.
Dynamic levels: Allow users to specify ADR% multiples via input.
Optimization for short timeframes: Adapt ADR% for intraday timeframes.
Conclusion
The script creates a visual indicator that helps traders identify price extension levels based on daily volatility (ADR%) relative to the 50 SMA. It is robust, configurable, and includes debugging tools (ADR% plot and labels) to verify values. The ADR% formula based on dhigh/dlow
Dskyz (DAFE) Aurora Divergence - Dskyz (DAFE) Aurora Divergence Indicator
Advanced Divergence Detection for Traders. Unleash the power of divergence trading with this cutting-edge indicator that combines price and volume analysis to spot high-probability reversal signals.
🧠 What Is It?
The Dskyz (DAFE) Aurora Divergence Indicator is designed to identify bullish and bearish divergences between the price trend and the On Balance Volume (OBV) trend. Divergence occurs when the price of an asset and a technical indicator (in this case, OBV) move in opposite directions, signaling a potential reversal. This indicator uses linear regression slopes to calculate the trends of both price and OBV over a specified lookback period, detecting when these two metrics are diverging. When a divergence is detected, it highlights potential reversal points with visually striking aurora bands, orbs, and labels, making it easy for traders to spot key signals.
⚙️ Inputs & How to Use Them
The indicator is highly customizable, with inputs grouped under "⚡ DAFE Aurora Settings" for clarity. Here’s how each input works:
Lookback Period: Determines how many bars are used to calculate the price and OBV slopes. Higher values detect longer-term trends (e.g., 20 for 1H charts), while lower values are more responsive to short-term movements.
Price Slope Threshold: Sets the minimum slope value for the price to be considered in an uptrend or downtrend. A value of 0 allows all slopes to be considered, while higher values filter for stronger trends.
OBV Slope Threshold: Similar to the price slope threshold but for OBV. Helps filter out weak volume trends.
Aurora Band Width: Adjusts the width of the visual bands that highlight divergence areas. Wider bands make the indicator more visible but may clutter the chart.
Divergence Sensitivity: Scales the strength of the divergence signals. Higher values make the indicator more sensitive to smaller divergences.
Minimum Strength: Filters out weak signals by only showing divergences above this strength level. A default of 0.3 is recommended for beginners.
Signal Cooldown (Bars): Prevents multiple signals from appearing too close together. Default is 5 bars, reducing chart clutter and helping traders focus on significant signals.
These inputs allow traders to fine-tune the indicator to match their trading style and timeframe.
🚀 What Makes It Unique?
This indicator stands out with its innovative features:
Price-Volume Divergence: Combines price trend (slope) and OBV trend for more reliable signals than price-only divergences.
Aurora Bands: Dynamic visual bands that highlight divergence zones, making it easier to spot potential reversals at a glance.
Interactive Dashboard: Displays real-time information on trend direction, volume flow, signal type, strength, and recommended actions (e.g., "Consider Buying" or "Consider Selling").
Signal Cooldown: Ensures only the most significant divergences are shown, reducing noise and improving usability.
Alerts: Built-in alerts for both bullish and bearish divergences, allowing traders to stay informed even when not actively monitoring the chart.
Beginner Guide: Explains the indicator’s visuals (e.g., aqua orbs for bullish signals, fuchsia orbs for bearish signals), making it accessible for new users.
🎯 Why It Works
The indicator’s effectiveness lies in its use of price-volume divergence, a well-established concept in technical analysis. When the price trend and OBV trend diverge, it often signals a potential reversal because the underlying volume support (or lack thereof) is not aligning with the price action. For example:
Bullish Divergence: Occurs when the price is making lower lows, but the OBV is making higher lows, indicating weakening selling pressure and potential upward reversal.
Bearish Divergence: Occurs when the price is making higher highs, but the OBV is making lower highs, suggesting weakening buying pressure and potential downward reversal.
The use of linear regression ensures smooth and accurate trend calculations over the specified lookback period. The divergence strength is then normalized and filtered based on user-defined thresholds, ensuring only high-quality signals are displayed. Additionally, the cooldown period prevents signal overload, allowing traders to focus on the most significant opportunities.
🧬 Indicator Recommendation
Best For: Traders looking to identify potential trend reversals in any market, especially those where volume data is reliable (e.g., stocks, futures, forex).
Timeframes: Suitable for all timeframes. Adjust the lookback period accordingly—smaller values for shorter timeframes (e.g., 1H), larger for longer ones (e.g., 4H or daily).
Pair With: Support and resistance levels, trend lines, other oscillators (e.g., RSI, MACD) for confirmation, and volume profile tools for deeper analysis.
Tips:
Look for divergences at key support/resistance levels for higher-probability setups.
Pay attention to signal strength; higher strength divergences are often more reliable.
Use the dashboard to quickly assess market conditions before entering a trade.
Set up alerts to catch divergences even when not actively watching the chart.
🧾 Credit & Acknowledgement
This indicator builds upon the classic concept of price-volume divergence, enhancing it with modern visualization techniques, advanced filtering, and user-friendly features. It is designed to provide traders with a powerful yet intuitive tool for spotting reversals.
📌 Final Thoughts
The Dskyz (DAFE) Aurora Divergence Indicator is more than just a divergence tool; it’s a comprehensive trading assistant that combines advanced calculations, intuitive visualizations, and actionable insights. Whether you’re a seasoned trader or just starting out, this indicator can help you spot high-probability reversal points with confidence.
Use it with discipline. Use it with clarity. Trade smarter.
**I will continue to release incredible strategies and indicators until I turn this into a brand or until someone offers me a contract.
-Dskyz
Moving average with different timeThis script allowing you to plot up to 6 different types of moving averages (MAs) on the chart, each with customizable parameters such as type, length, source, color, and timeframe. It also allows you to set different timeframes for each moving average.
Key Features:
Multiple Moving Averages: You can add up to 6 different moving averages to your chart.
Each MA can be one of the following types: SMA, EMA, SMMA (RMA), WMA, or VWMA.
Custom Timeframes: Each moving average can be applied to a specific timeframe, giving you flexibility to compare different periods (e.g., a 50-period moving average on the 1-hour chart and a 200-period moving average on the 4-hour chart).
Customizable Inputs:
Type: Choose between SMA, EMA, SMMA, WMA, or VWMA for each MA.
Source: You can select the price data source (e.g., close, open, high, low).
Length: Set the number of periods (length) for each moving average.
Color: Each moving average can be assigned a specific color.
Timeframe: Customize the timeframe for each moving average individually (e.g., MA1 on 15-minute, MA2 on 1-hour).
User Interface:
The script includes a data window display for each moving average, allowing you to control whether to show each MA and configure its settings directly from the settings menu.
Flexible Use:
Toggle individual moving averages on and off with the show checkbox for each MA.
Customize each MA's parameters without affecting others.
Parameters:
MA Type: You can choose between different moving averages (SMA, EMA, etc.).
Source: Price data used for calculating the moving average (e.g., close, open, etc.).
Length: Defines the period (number of bars) for each moving average.
Color: Change the line color for each moving average for better visualization.
Timeframe: Set a different timeframe for each moving average (e.g., 1-day MA vs. 1-week MA).
Example Use Case:
You might use this indicator to track short-term, medium-term, and long-term trends by adding multiple MAs with different lengths and timeframes. For example:
MA1 (20-period) might be an SMA on a 1-hour chart.
MA2 (50-period) might be an EMA on a 4-hour chart.
MA3 (100-period) might be a WMA on a daily chart.
This setup allows you to visually track the market's behavior across different timeframes and better identify trends, crossovers, and other patterns.
How to Customize:
Show/Hide MAs: Enable or disable each moving average from the input menu.
Modify Parameters: Change the MA type, source, length, and color for each individual moving average.
Timeframes: Set different timeframes for each moving average for more detailed analysis.
With this Moving Average Ribbon, you get a versatile and visually rich tool to aid in technical analysis.
[blackcat] L2 Ehlers Autocorrelation Indicator V2OVERVIEW
The Ehlers Autocorrelation Indicator is a technical analysis tool developed by John F. Ehlers that measures the correlation between price data and its lagged versions to identify potential market cycles and reversals.
BACKGROUND
Originally introduced in Ehlers' "Cycle Analytics for Traders" (2013), this indicator leverages autocorrelation principles to detect patterns in market data that deviate from random noise or perfect sine waves.
FEATURES
• Calculates Pearson correlation coefficients for lags from 0 to 60 bars
• Visualizes correlations using colored bars ranging from red (negative correlation) to yellow (positive correlation)
• Provides minimum averaging option through AvgLength input parameter
• Displays sharp reversal signals at price turning points
• Shows variations in bar thickness and count over time
HOW TO USE
Add the indicator to your chart
Adjust the AvgLength input as needed:
• Set to 0 for no averaging
• Increase value for smoother results
Interpret the colored bars:
• Red: Negative correlation
• Yellow: Positive correlation
• Sharp transitions indicate potential reversal points
LIMITATIONS
• Requires sufficient historical data for accurate calculations
• Performance may vary across different market conditions
• Results depend on proper parameter settings
NOTES
• The indicator uses highpass filtering and super smoother filtering techniques
• Color intensity varies based on correlation strength
• Multiple lag periods are displayed simultaneously for comprehensive analysis
THANKS
This implementation is based on Ehlers' original work and has been adapted for TradingView's Pine Script platform.
Average Body RangeThe Average Body Range (ABR) indicator calculates the average size of a candle's real body over a specified period. Unlike the traditional Average Daily Range (ADR), which measures the full range from high to low, the ABR focuses solely on the absolute difference between the open and close of each bar. This provides insight into market momentum and trading activity by reflecting how much price is actually moving from open to close , not just in total.
This indicator is especially useful for identifying:
Periods of strong directional movement (larger body sizes)
Low-volatility or indecisive markets (smaller body sizes)
Changes in trend conviction or momentum
Customization:
Length: Number of bars used to compute the average (default: 14)
Use ABR to enhance your understanding of price behavior and better time entries or exits based on market strength.
FT-RSISummary of the Custom RSI Indicator Script (For Futu Niuniu Platform):
This Pine Script code implements a triple-period RSI indicator with horizontal reference lines (70, 50, 30) for technical analysis on the Futu Niuniu trading platform.
Key Features:
Multi-period RSI Calculation:
Computes three RSI values using 9, 14, and 22-period lengths to capture short-term, standard, and smoothed momentum signals.
Utilizes the Relative Moving Average (RMA) method for RSI calculation (ta.rma function).
Horizontal Reference Bands:
Upper Band (70): Red dotted line (semi-transparent) to identify overbought conditions.
Middle Band (50): Green dotted line as the neutral equilibrium level.
Lower Band (30): Blue dotted line (semi-transparent) to highlight oversold zones.
Visual Customization:
Distinct colors for each RSI line:
RSI (9): Orange (#F79A00)
RSI (14): Green (#49B50D)
RSI (22): Blue (#5188FF)
All lines have a thickness of 2 pixels for clear visibility.
Platform Compatibility:
This script is designed for Futu Niuniu’s charting system, leveraging Pine Script syntax adaptations supported by the platform. The horizontal bands and multi-period RSI logic help traders analyze trend strength and potential reversal points efficiently.
Note: Ensure Futu Niuniu’s scripting environment supports ta.rma and hline functions for proper execution.
OBV & AD Oscillators with Dual Smoothing OptionsOn Balance Volume and Accumulation/Distribution
Overlaid into 1 and then some,
Now it is an oscillator!
3 customizable moving average types
- Ehlers Deviation Scaled Moving Average
- Volatility Dynamic Moving Average
- Simple Moving Average
Each with customizable periods
And with the ability to overlay a second set too
Default Settings have a longer period MA of 377 using Ehlers DSMA to better capture the standard view of OBV and A/D.
An extra overlay of a shorter period using a Volatility DMA uses Average True Range with its own custom settings, seeks to act more as an RSI
Mongoose Yield Spread Dashboard v5 – Labeled, Alerted, ReadableCurveGuard: Mongoose Edition
Track the macro tide before it turns.
This tool visualizes the three most-watched U.S. Treasury yield curve spreads:
2s10s (10Y - 2Y)
5s30s (30Y - 5Y)
3M10Y (10Y - 3M)
Each spread is plotted with dynamic color logic, inversion alerts, and floating labels. Background shading highlights historical inversion zones to help spot macro regime shifts in real time.
✅ Alert-ready
✅ Dark mode optimized
✅ Floating labels
✅ Clean layout for fast macro insight
📌 For educational and informational purposes only.
This script does not provide financial advice or trade recommendations.
DI+/- Cross Strategy with ATR SL and 2% TPDI+/- Cross Strategy with ATR Stop Loss and 2% Take Profit
📝 Script Description for Publishing:
This strategy is based on the directional movement of the market using the Average Directional Index (ADX) components — DI+ and DI- — to generate entry signals, with clearly defined risk and reward targets using ATR-based Stop Loss and Fixed Percentage Take Profit.
🔍 How it works:
Buy Signal: When DI+ crosses above 40, signaling strong bullish momentum.
Sell Signal: When DI- crosses above 40, indicating strong bearish momentum.
Stop Loss: Dynamically calculated using ATR × 1.5, to account for market volatility.
Take Profit: Fixed at 2% above/below the entry price, for consistent reward targeting.
🧠 Why it’s useful:
Combines momentum breakout logic with volatility-based risk management.
Works well on trending assets, especially when combined with higher timeframe filters.
Clean BUY and SELL visual labels make it easy to interpret and backtest.
✅ Tips for Use:
Use on assets with clear trends (e.g., major forex pairs, trending stocks, crypto).
Best on 30m – 4H timeframes, but can be customized.
Consider combining with other filters (e.g., EMA trend direction or Bollinger Bands) for even better accuracy.
Long Short Momentum with Signals
Long and Short momentum
WHEN SHORT MOMENTUM CHANGES 2.0 POINTS and long term changes 5 points on day basis write A for Bullish and B for Bearish on Main Price chart
WHEN SHORT MOMENTUM CHANGES .30 per hour POINTS and long term changes 1 points on 1 hour basis. Put a green dot for Bull and red for bear in short term and for long termRespectively on price chart
PMO + Daily SMA(55)PMO + Daily SMA(55)
This script plots the Price Momentum Oscillator (PMO) using the classic DecisionPoint methodology, along with its signal line and the 55-period Simple Moving Average (SMA) of the daily PMO.
PMO is a smoothed momentum indicator that measures the rate of change and helps identify trend direction and strength. The signal line is an EMA of the PMO, commonly used for crossover signals.
The 55-period SMA of the daily PMO is added as a longer-term trend filter. It remains based on daily data, even when applied to intraday charts, making it useful for aligning lower timeframe trades with higher timeframe momentum.
Ideal for swing and position traders looking to combine short-term momentum with broader trend context.
Nifty Advance/Decline Ratio - First 20 StocksNifty 20 Advance/Decline Ratio Indicator
This Pine Script tracks the Advance/Decline Ratio of the top 20 Nifty stocks (by weightage as of March 31, 2025). It helps gauge the market's strength by comparing the number of advancing vs. declining stocks among major Nifty heavyweights. The script calculates and plots the ratio, with a reference line at 1 (neutral point). This indicator resets daily and provides insights into overall market trends based on the performance of the top Nifty stocks.
Key Features:
Tracks advance/decline movements of top 20 Nifty stocks.
Plots the Advance/Decline Ratio on the chart.
Resets daily for fresh analysis.
Stochastic Order Flow Momentum [ScorsoneEnterprises]This indicator implements a stochastic model of order flow using the Ornstein-Uhlenbeck (OU) process, combined with a Kalman filter to smooth momentum signals. It is designed to capture the dynamic momentum of volume delta, representing the net buying or selling pressure per bar, and highlight potential shifts in market direction. The volume delta data is sourced from TradingView’s built-in functionality:
www.tradingview.com
For a deeper dive into stochastic processes like the Ornstein-Uhlenbeck model in financial contexts, see these research articles: arxiv.org and arxiv.org
The SOFM tool aims to reveal the momentum and acceleration of order flow, modeled as a mean-reverting stochastic process. In markets, order flow often oscillates around a baseline, with bursts of buying or selling pressure that eventually fade—similar to how physical systems return to equilibrium. The OU process captures this behavior, while the Kalman filter refines the signal by filtering noise. Parameters theta (mean reversion rate), mu (mean level), and sigma (volatility) are estimated by minimizing a squared-error objective function using gradient descent, ensuring adaptability to real-time market conditions.
How It Works
The script combines a stochastic model with signal processing. Here’s a breakdown of the key components, including the OU equation and supporting functions.
// Ornstein-Uhlenbeck model for volume delta
ou_model(params, v_t, lkb) =>
theta = clamp(array.get(params, 0), 0.01, 1.0)
mu = clamp(array.get(params, 1), -100.0, 100.0)
sigma = clamp(array.get(params, 2), 0.01, 100.0)
error = 0.0
v_pred = array.new(lkb, 0.0)
array.set(v_pred, 0, array.get(v_t, 0))
for i = 1 to lkb - 1
v_prev = array.get(v_pred, i - 1)
v_curr = array.get(v_t, i)
// Discretized OU: v_t = v_{t-1} + theta * (mu - v_{t-1}) + sigma * noise
v_next = v_prev + theta * (mu - v_prev)
array.set(v_pred, i, v_next)
v_curr_clean = na(v_curr) ? 0 : v_curr
v_pred_clean = na(v_next) ? 0 : v_next
error := error + math.pow(v_curr_clean - v_pred_clean, 2)
error
The ou_model function implements a discretized Ornstein-Uhlenbeck process:
v_t = v_{t-1} + theta (mu - v_{t-1})
The model predicts volume delta (v_t) based on its previous value, adjusted by the mean-reverting term theta (mu - v_{t-1}), with sigma representing the volatility of random shocks (approximated in the Kalman filter).
Parameters Explained
The parameters theta, mu, and sigma represent distinct aspects of order flow dynamics:
Theta:
Definition: The mean reversion rate, controlling how quickly volume delta returns to its mean (mu). Constrained between 0.01 and 1.0 (e.g., clamp(array.get(params, 0), 0.01, 1.0)).
Interpretation: A higher theta indicates faster reversion (short-lived momentum), while a lower theta suggests persistent trends. Initial value is 0.1 in init_params.
In the Code: In ou_model, theta scales the pull toward \mu, influencing the predicted v_t.
Mu:
Definition: The long-term mean of volume delta, representing the equilibrium level of net buying/selling pressure. Constrained between -100.0 and 100.0 (e.g., clamp(array.get(params, 1), -100.0, 100.0)).
Interpretation: A positive mu suggests a bullish bias, while a negative mu indicates bearish pressure. Initial value is 0.0 in init_params.
In the Code: In ou_model, mu is the target level that v_t reverts to over time.
Sigma:
Definition: The volatility of volume delta, capturing the magnitude of random fluctuations. Constrained between 0.01 and 100.0 (e.g., clamp(array.get(params, 2), 0.01, 100.0)).
Interpretation: A higher sigma reflects choppier, noisier order flow, while a lower sigma indicates smoother behavior. Initial value is 0.1 in init_params.
In the Code: In the Kalman filter, sigma contributes to the error term, adjusting the smoothing process.
Summary:
theta: Speed of mean reversion (how fast momentum fades).
mu: Baseline order flow level (bullish or bearish bias).
sigma: Noise level (variability in order flow).
Other Parts of the Script
Clamp
A utility function to constrain parameters, preventing extreme values that could destabilize the model.
ObjectiveFunc
Defines the objective function (sum of squared errors) to minimize during parameter optimization. It compares the OU model’s predicted volume delta to observed data, returning a float to be minimized.
How It Works: Calls ou_model to generate predictions, computes the squared error for each timestep, and sums it. Used in optimization to assess parameter fit.
FiniteDifferenceGradient
Calculates the gradient of the objective function using finite differences. Think of it as finding the "slope" of the error surface for each parameter. It nudges each parameter (theta, mu, sigma) by a small amount (epsilon) and measures the change in error, returning an array of gradients.
Minimize
Performs gradient descent to optimize parameters. It iteratively adjusts theta, mu, and sigma by stepping down the "hill" of the error surface, using the gradients from FiniteDifferenceGradient. Stops when the gradient norm falls below a tolerance (0.001) or after 20 iterations.
Kalman Filter
Smooths the OU-modeled volume delta to extract momentum. It uses the optimized theta, mu, and sigma to predict the next state, then corrects it with observed data via the Kalman gain. The result is a cleaner momentum signal.
Applied
After initializing parameters (theta = 0.1, mu = 0.0, sigma = 0.1), the script optimizes them using volume delta data over the lookback period. The optimized parameters feed into the Kalman filter, producing a smoothed momentum array. The average momentum and its rate of change (acceleration) are calculated, though only momentum is plotted by default.
A rising momentum suggests increasing buying or selling pressure, while a flattening or reversing momentum indicates fading activity. Acceleration (not plotted here) could highlight rapid shifts.
Tool Examples
The SOFM indicator provides a dynamic view of order flow momentum, useful for spotting directional shifts or consolidation.
Low Time Frame Example: On a 5-minute chart of SEED_ALEXDRAYM_SHORTINTEREST2:NQ , a rising momentum above zero with a lookback of 5 might signal building buying pressure, while a drop below zero suggests selling dominance. Crossings of the zero line can mark transitions, though the focus is on trend strength rather than frequent crossovers.
High Time Frame Example: On a daily chart of NYSE:VST , a sustained positive momentum could confirm a bullish trend, while a sharp decline might warn of exhaustion. The mean-reverting nature of the OU process helps filter out noise on longer scales. It doesn’t make the most sense to use this on a high timeframe with what our data is.
Choppy Markets: When momentum oscillates near zero, it signals indecision or low conviction, helping traders avoid whipsaws. Larger deviations from zero suggest stronger directional moves to act on, this is on $STT.
Inputs
Lookback: Users can set the lookback period (default 5) to adjust the sensitivity of the OU model and Kalman filter. Shorter lookbacks react faster but may be noisier; longer lookbacks smooth more but lag slightly.
The user can also specify the timeframe they want the volume delta from. There is a default way to lower and expand the time frame based on the one we are looking at, but users have the flexibility.
No indicator is 100% accurate, and SOFM is no exception. It’s an estimation tool, blending stochastic modeling with signal processing to provide a leading view of order flow momentum. Use it alongside price action, support/resistance, and your own discretion for best results. I encourage comments and constructive criticism.
Trend Confirmation StrategyComprehensive Trend Confirmation System
Indicator Features (Professional Description):
Comprehensive Trend Confirmation System is a versatile indicator meticulously designed to identify and confirm trend-based trading opportunities with exceptional efficiency. By seamlessly integrating analysis from a suite of leading technical tools, it aims to provide superior accuracy and reliability for informed trading decisions.
Key Features:
Intelligent Trend Identification: A robust trend analysis system that considers:
Adjustable Moving Averages: Utilizes three customizable moving average periods (fast, medium, slow) with user-selectable lengths and types (SMA, EMA, WMA, VWMA) to accurately determine the prevailing trend across different timeframes.
In-depth Price Action Analysis: Examines the formation of Higher Highs/Higher Lows (uptrend) and Lower Highs/Lower Lows (downtrend) to validate price direction.
Average Directional Index (ADX) with Adjustable Threshold: Measures the strength of a trend and employs the comparison between +DI and -DI to pinpoint the dominant momentum, featuring a customizable threshold to filter out weak signals.
Multi-Factor Signal Confirmation System: Enhances the reliability of trading signals through verification from four distinct confirmation tools:
Volume Analysis with Average Reference: Assesses whether trading volume supports price movements by comparing it to historical averages.
Relative Strength Index (RSI) with Reference Levels: Measures price momentum and identifies overbought/oversold conditions to confirm trend strength.
Moving Average Convergence Divergence (MACD) Divergence and Crossovers: Detects shifts in momentum and potential trend changes through the relationship between the MACD line and the Signal line.
Stochastic Oscillator with Reference Levels: Measures the current price's position relative to its historical range to evaluate overbought/oversold conditions and potential reversal opportunities.
Intelligent Signal Generation Logic:
Buy Signal: Triggered when a strong uptrend is identified (meeting defined criteria) and confirmed by at least three out of the four confirmation tools.
Sell Signal: Triggered when a strong downtrend is identified (meeting defined criteria) and confirmed by at least three out of the four confirmation tools.
User-Friendly Visualizations:
Moving Averages (MA): Displays three MA lines on the chart with user-configurable colors (default: fast-blue, medium-orange, slow-red) for easy visual trend analysis.
Clear Buy and Sell Signal Symbols: Presents distinct green upward-pointing triangles for buy signals and red downward-pointing triangles for sell signals at the corresponding candlestick.
Dynamic Candlestick Color Coding: Candlesticks are dynamically colored green upon a buy signal and red upon a sell signal for quick identification of trading opportunities.
Highly Customizable Parameters: Users have extensive control over the indicator's parameters, including:
Lengths and types of Moving Averages.
Length and Threshold of the ADX.
Length of the RSI.
Parameters for the MACD (Fast Length, Slow Length, Signal Length).
Parameters for the Stochastic Oscillator (%K Length, %D Length, Smoothing).
Ideal For:
Traders seeking a robust tool to accurately identify and confirm market trends.
Individuals aiming to reduce false signals and enhance the precision of their trading decisions.
Traders employing trend-following strategies in markets with clear directional movement.
Important Note:
While Comprehensive Trend Confirmation System is engineered to improve trading accuracy, no indicator can guarantee 100% profitable trades. Users are advised to utilize this indicator in conjunction with relevant fundamental analysis and sound risk management practices for optimal trading outcomes.
Order Flow Hawkes Process [ScorsoneEnterprises]This indicator is an implementation of the Hawkes Process. This tool is designed to show the excitability of the different sides of volume, it is an estimation of bid and ask size per bar. The code for the volume delta is from www.tradingview.com
Here’s a link to a more sophisticated research article about Hawkes Process than this post arxiv.org
This tool is designed to show how excitable the different sides are. Excitability refers to how likely that side is to get more activity. Alan Hawkes made Hawkes Process for seismology. A big earthquake happens, lots of little ones follow until it returns to normal. Same for financial markets, big orders come in, causing a lot of little orders to come. Alpha, Beta, and Lambda parameters are estimated by minimizing a negative log likelihood function.
How it works
There are a few components to this script, so we’ll go into the equation and then the other functions used in this script.
hawkes_process(params, events, lkb) =>
alpha = clamp(array.get(params, 0), 0.01, 1.0)
beta = clamp(array.get(params, 1), 0.1, 10.0)
lambda_0 = clamp(array.get(params, 2), 0.01, 0.3)
intensity = array.new_float(lkb, 0.0)
events_array = array.new_float(lkb, 0.0)
for i = 0 to lkb - 1
array.set(events_array, i, array.get(events, i))
for i = 0 to lkb - 1
sum_decay = 0.0
current_event = array.get(events_array, i)
for j = 0 to i - 1
time_diff = i - j
past_event = array.get(events_array, j)
decay = math.exp(-beta * time_diff)
past_event_val = na(past_event) ? 0 : past_event
sum_decay := sum_decay + (past_event_val * decay)
array.set(intensity, i, lambda_0 + alpha * sum_decay)
intensity
The parameters alpha, beta, and lambda all represent a different real thing.
Alpha (α):
Definition: Alpha represents the excitation factor or the magnitude of the influence that past events have on the future intensity of the process. In simpler terms, it measures how much each event "excites" or triggers additional events. It is constrained between 0.01 and 1.0 (e.g., clamp(array.get(params, 0), 0.01, 1.0)). A higher alpha means past events have a stronger influence on increasing the intensity (likelihood) of future events. Initial value is set to 0.1 in init_params. In the hawkes_process function, alpha scales the contribution of past events to the current intensity via the term alpha * sum_decay.
Beta (β):
Definition: Beta controls the rate of exponential decay of the influence of past events over time. It determines how quickly the effect of a past event fades away. It is constrained between 0.1 and 10.0 (e.g., clamp(array.get(params, 1), 0.1, 10.0)). A higher beta means the influence of past events decays faster, while a lower beta means the influence lingers longer. Initial value is set to 0.1 in init_params. In the hawkes_process function, beta appears in the decay term math.exp(-beta * time_diff), which reduces the impact of past events as the time difference (time_diff) increases.
Lambda_0 (λ₀):
Definition: Lambda_0 is the baseline intensity of the process, representing the rate at which events occur in the absence of any excitation from past events. It’s the "background" rate of the process. It is constrained between 0.01 and 0.3 .A higher lambda_0 means a higher natural frequency of events, even without the influence of past events. Initial value is set to 0.1 in init_params. In the hawkes_process function, lambda_0 sets the minimum intensity level, to which the excitation term (alpha * sum_decay) is added: lambda_0 + alpha * sum_decay
Alpha (α): Strength of event excitation (how much past events boost future events).
Beta (β): Rate of decay of past event influence (how fast the effect fades).
Lambda_0 (λ₀): Baseline event rate (background intensity without excitation).
Other parts of the script.
Clamp
The clamping function is a simple way to make sure parameters don’t grow or shrink too much.
ObjectiveFunction
This function defines the objective function (negative log-likelihood) to minimize during parameter optimization.It returns a float representing the negative log-likelihood (to be minimized).
How It Works:
Calls hawkes_process to compute the intensity array based on current parameters.Iterates over the lookback period:lambda_t: Intensity at time i.event: Event magnitude at time i.Handles na values by replacing them with 0.Computes log-likelihood: event_clean * math.log(math.max(lambda_t_clean, 0.001)) - lambda_t_clean.Ensures lambda_t_clean is at least 0.001 to avoid log(0).Accumulates into log_likelihood.Returns -log_likelihood (negative because the goal is to minimize, not maximize).
It is used in the optimization process to evaluate how well the parameters fit the observed event data.
Finite Difference Gradient:
This function calculates the gradient of the objective function we spoke about. The gradient is like a directional derivative. Which is like the direction of the rate of change. Which is like the direction of the slope of a hill, we can go up or down a hill. It nudges around the parameter, and calculates the derivative of the parameter. The array of these nudged around parameters is what is returned after they are optimized.
Minimize:
This is the function that actually has the loop and calls the Finite Difference Gradient each time. Here is where the minimizing happens, how we go down the hill. If we are below a tolerance, we are at the bottom of the hill.
Applied
After an initial guess the parameters are optimized with a mix of bid and ask levels to prevent some over-fitting for each side while keeping some efficiency. We initialize two different arrays to store the bid and ask sizes. After we optimize the parameters we clamp them for the calculations. We then get the array of intensities from the Hawkes Process of bid and ask and plot them both. When the bids are greater than the ask it represents a bullish scenario where there are likely to be more buy than sell orders, pushing up price.
Tool examples:
The idea is that when the bid side is more excitable it is more likely to see a bullish reaction, when the ask is we see a bearish reaction.
We see that there are a lot of crossovers, and I picked two specific spots. The idea of this isn’t to spot crossovers but avoid chop. The values are either close together or far apart. When they are far, it is a classification for us to look for our own opportunities in, when they are close, it signals the market can’t pick a direction just yet.
The value works just as well on a higher timeframe as on a lower one. Hawkes Process is an estimate, so there is a leading value aspect of it.
The value works on equities as well, here is NASDAQ:TSLA on a lower time frame with a lookback of 5.
Inputs
Users can enter the lookback value and timeframe.
No tool is perfect, the Hawkes Process value is also not perfect and should not be followed blindly. It is good to use any tool along with discretion and price action.
Multi Oscillator OB/OS Signals v3 - Scope TestIndicator Description: Multi Oscillator OB/OS Signals
Purpose:
The "Multi Oscillator OB/OS Signals" indicator is a TradingView tool designed to help traders identify potential market extremes and momentum shifts by monitoring four popular oscillators simultaneously: RSI, Stochastic RSI, CCI, and MACD. Instead of displaying these oscillators in separate panes, this indicator plots distinct visual symbols directly onto the main price chart whenever specific predefined conditions (typically related to overbought/oversold levels or line crossovers) are met for each oscillator. This provides a consolidated view of potential signals from these different technical tools.
How It Works:
The indicator calculates the values for each of the four oscillators based on user-defined settings (like length periods and price sources) and then checks for specific signal conditions on every bar:
Relative Strength Index (RSI):
It monitors the standard RSI value.
When the RSI crosses above the user-defined Overbought (OB) level (e.g., 70), it plots an "Overbought" symbol (like a downward triangle) above that price bar.
When the RSI crosses below the user-defined Oversold (OS) level (e.g., 30), it plots an "Oversold" symbol (like an upward triangle) below that price bar.
Stochastic RSI:
This works similarly to RSI but is based on the Stochastic calculation applied to the RSI value itself (specifically, the %K line of the Stoch RSI).
When the Stoch RSI's %K line crosses above its Overbought level (e.g., 80), it plots its designated OB symbol (like a downward arrow) above the bar.
When the %K line crosses below its Oversold level (e.g., 20), it plots its OS symbol (like an upward arrow) below the bar.
Commodity Channel Index (CCI):
It tracks the CCI value.
When the CCI crosses above its Overbought level (e.g., +100), it plots its OB symbol (like a square) above the bar.
When the CCI crosses below its Oversold level (e.g., -100), it plots its OS symbol (like a square) below the bar.
Moving Average Convergence Divergence (MACD):
Unlike the others, MACD signals here are not based on fixed OB/OS levels.
It identifies when the main MACD line crosses above its Signal line. This is considered a bullish crossover and is indicated by a specific symbol (like an upward label) plotted below the price bar.
It also identifies when the MACD line crosses below its Signal line. This is a bearish crossover, indicated by a different symbol (like a downward label) plotted above the price bar.
Visualization:
All these signals appear as small, distinct shapes directly on the price chart at the bar where the condition occurred. The shapes, their colors, and their position (above or below the bar) are predefined for each signal type to allow for quick visual identification. Note: In the current version of the underlying code, the size of these shapes is fixed (e.g., tiny) and not user-adjustable via the settings.
Configuration:
Users can access the indicator's settings to customize:
The calculation parameters (Length periods, smoothing, price source) for each individual oscillator (RSI, Stoch RSI, CCI, MACD).
The specific Overbought and Oversold threshold levels for RSI, Stoch RSI, and CCI.
The colors associated with each type of signal (OB, OS, Bullish Cross, Bearish Cross).
(Limitation Note: While settings exist to toggle the visibility of signals for each oscillator individually, due to a technical workaround in the current code, these toggles may not actively prevent the shapes from plotting if the underlying condition is met.)
Alerts:
The indicator itself does not automatically generate pop-up alerts. However, it creates the necessary "Alert Conditions" within TradingView's alert system. This means users can manually set up alerts for any of the specific signals generated by the indicator (e.g., "RSI Overbought Enter," "MACD Bullish Crossover"). When creating an alert, the user selects this indicator, chooses the desired condition from the list provided by the script, and configures the alert actions.
Intended Use:
This indicator aims to provide traders with convenient visual cues for potential over-extension in price (via OB/OS signals) or shifts in momentum (via MACD crossovers) based on multiple standard oscillators. These signals are often used as potential indicators for:
Identifying areas where a trend might be exhausted and prone to a pullback or reversal.
Confirming signals generated by other analysis methods or trading strategies.
Noting shifts in short-term momentum.
Disclaimer: As with any technical indicator, the signals generated should not be taken as direct buy or sell recommendations. They are best used in conjunction with other forms of analysis (price action, trend analysis, volume, fundamental analysis, etc.) and within the framework of a well-defined trading plan that includes risk management. Market conditions can change, and indicator signals can sometimes be false or misleading.
Transient Impact Model [ScorsoneEnterprises]This indicator is an implementation of the Transient Impact Model. This tool is designed to show the strength the current trades have on where price goes before they decay.
Here are links to more sophisticated research articles about Transient Impact Models than this post arxiv.org and arxiv.org
The way this tool is supposed to work in a simple way, is when impact is high price is sensitive to past volume, past trades being placed. When impact is low, it moves in a way that is more independent from past volume. In a more sophisticated system, perhaps transient impact should be calculated for each trade that is placed, not just the total volume of a past bar. I didn't do it to ensure parameters exist and aren’t na, as well as to have more iterations for optimization. Note that the value will change as volume does, as soon as a new candle occurs with no volume, the values could be dramatically different.
How it works
There are a few components to this script, so we’ll go into the equation and then the other functions used in this script.
// Transient Impact Model
transient_impact(params, price_change, lkb) =>
alpha = array.get(params, 0)
beta = array.get(params, 1)
lambda_ = array.get(params, 2)
instantaneous = alpha * volume
transient = 0.0
for t = 1 to lkb - 1
if na(volume )
break
transient := transient + beta * volume * math.exp(-lambda_ * t)
predicted_change = instantaneous + transient
math.pow(price_change - predicted_change, 2)
The parameters alpha, beta, and lambda all represent a different real thing.
Alpha (α):
Represents the instantaneous impact coefficient. It quantifies the immediate effect of the current volume on the price change. In the equation, instantaneous = alpha * volume , alpha scales the current bar's volume (volume ) to determine how much of the price change is due to immediate market impact. A larger alpha suggests that current volume has a stronger instantaneous influence on price.
Beta (β):
Represents the transient impact coefficient.It measures the lingering effect of past volumes on the current price change. In the loop calculating transient, beta * volume * math.exp(-lambda_ * t) shows that beta scales the volume from previous bars (volume ), contributing to a decaying effect over time. A higher beta indicates a stronger influence from past volumes, though this effect diminishes with time due to the exponential decay factor.
Lambda (λ):
Represents the decay rate of the transient impact.It controls how quickly the influence of past volumes fades over time in the transient component. In the term math.exp(-lambda_ * t), lambda determines the rate of exponential decay, where t is the time lag (in bars). A larger lambda means the impact of past volumes decays faster, while a smaller lambda implies a longer-lasting effect.
So in full.
The instantaneous term, alpha * volume , captures the immediate price impact from the current volume.
The transient term, sum of beta * volume * math.exp(-lambda_ * t) over the lookback period, models the cumulative, decaying effect of past volumes.
The total predicted_change combines these two components and is compared to the actual price change to compute an error term, math.pow(price_change - predicted_change, 2), which the script minimizes to optimize alpha, beta, and lambda.
Other parts of the script.
Objective function:
This is a wrapper function with a function to minimize so we get the best alpha, beta, and lambda values. In this case it is the Transient Impact Function, not something like a log-likelihood function, helps with efficiency for a high iteration count.
Finite Difference Gradient:
This function calculates the gradient of the objective function we spoke about. The gradient is like a directional derivative. Which is like the direction of the rate of change. Which is like the direction of the slope of a hill, we can go up or down a hill. It nudges around the parameter, and calculates the derivative of the parameter. The array of these nudged around parameters is what is returned after they are optimized.
Minimize:
This is the function that actually has the loop and calls the Finite Difference Gradient each time. Here is where the minimizing happens, how we go down the hill. If we are below a tolerance, we are at the bottom of the hill.
Applied
After an initial guess, we optimize the parameters and get the transient impact value. This number is huge, so we apply a log to it to make it more readable. From here we need some way to tell if the value is low or high. We shouldn’t use standard deviation because returns are not normally distributed, an IQR is similar and better for non normal data. We store past transient impact values in an array, so that way we can see the 25th and 90th percentiles of the data as a rolling value. If the current transient impact is above the 90th percentile, it is notably high. If below the 25th percentile, notably low. All of these values are plotted so we can use it as a tool.
Tool examples:
The idea around it is that when impact is low, there is room for big money to get size quickly and move prices around.
Here we see the price reacting in the IQR Bands. We see multiple examples where the value above the 90th percentile, the red line, corresponds to continuations in the trend, and below the 25th percentile, the purple line, corresponds to reversals. There is no guarantee these tools will be perfect, that is outlined in these situations, however there is clearly a correlation in this tool and trend.
This tool works on any timeframe, daily as we saw before, or lower like a two minute. The bands don’t represent a direction, like bullish or bearish, we need to determine that by interpreting price action. We see at open and at close there are the highest values for the transient impact. This is to be expected as these are the times with the highest volume of the trading day.
This works on futures as well as equities with the same context. Volume can be attributed to volatility as well. In volatile situations, more volatility comes in, and we can perceive it through the transient impact value.
Inputs
Users can enter the lookback value.
No tool is perfect, the transient impact value is also not perfect and should not be followed blindly. It is good to use any tool along with discretion and price action.
ICT & SMC Multi-Timeframe by [KhedrFX]Transform your trading experience with the ICT & SMC Multi-Timeframe by indicator. This innovative tool is designed for traders who want to harness the power of multi-timeframe analysis, enabling them to make informed trading decisions based on key market insights. By integrating concepts from the Inner Circle Trader (ICT) and Smart Money Concepts (SMC), this indicator provides a comprehensive view of market dynamics, helping you identify potential trading opportunities with precision.
Key Features
- Multi-Timeframe Analysis: Effortlessly switch between various timeframes (5 minutes, 15 minutes, 30 minutes, 1 hour, 4 hours, daily, and weekly) to capture the full spectrum of market movements.
- High and Low Levels: Automatically calculates and displays the highest and lowest price levels over the last 20 bars, highlighting critical support and resistance zones.
- Market Structure Visualization: Identifies the last swing high and swing low, allowing you to recognize current market trends and potential reversal points.
- Order Block Detection: Detects significant order blocks, pinpointing areas of strong buying or selling pressure that can indicate potential market reversals.
- Custom Alerts: Set alerts for when the price crosses above or below identified order block levels, enabling you to act swiftly on trading opportunities.
How to Use the Indicator
1. Add the Indicator to Your Chart
- Open TradingView.
- Click on the "Indicators" button at the top of the screen.
- Search for "ICT & SMC Multi-Timeframe by " in the search bar.
- Click on the indicator to add it to your chart.
2. Select Your Timeframe
- Use the dropdown menu to choose your preferred timeframe (5, 15, 30, 60, 240, D, W) for analysis.
3. Interpret the Signals
- High Level (Green Line): Represents the highest price level over the last 20 bars, acting as a potential resistance level.
- Low Level (Red Line): Represents the lowest price level over the last 20 bars, acting as a potential support level.
- Last Swing High (Blue Cross): Indicates the most recent significant high, useful for identifying potential reversal points.
- Last Swing Low (Orange Cross): Indicates the most recent significant low, providing insight into market structure.
- Order Block High (Purple Line): Marks the upper boundary of a detected order block, suggesting potential selling pressure.
- Order Block Low (Yellow Line): Marks the lower boundary of a detected order block, indicating potential buying pressure.
4. Set Alerts
- Utilize the alert conditions to receive notifications when the price crosses above or below the order block levels, allowing you to stay informed about potential trading opportunities.
5. Implement Risk Management
- Always use proper risk management techniques. Consider setting stop-loss orders based on the identified swing highs and lows or the order block levels to protect your capital.
Conclusion
The ICT & SMC Multi-Timeframe by indicator is an essential tool for traders looking to enhance their market analysis and decision-making process. By leveraging multi-timeframe insights, market structure visualization, and order block detection, you can navigate the complexities of the market with confidence. Start using this powerful indicator today and take your trading to the next level.
⚠️ Trade Responsibly
This tool helps you analyze the market, but it’s not a guarantee of profits. Always do your own research, manage risk, and trade with caution.
Sahid Strategy v2This script identifies potential buy/sell signals using:
Pivot Points - Detects swing highs/lows (price reversals)
Confirmation Filters - Reduces false signals using:
RSI (momentum)
Moving Average (trend direction)
Optional MACD (trend confirmation)
Key Features
Signal Type Trigger Conditions
BUY - Price makes a swing low (pivot)
Copy
- RSI ≤ 30 (oversold)
- Price above trend MA
- MACD bullish (optional) |
| SELL | - Price makes a swing high (pivot)
- RSI ≥ 70 (overbought)
- Price below trend MA
- MACD bearish (optional) |
Visual Signals
Green "BUY" labels below price bars
Red "SELL" labels above price bars
Purple trend line (20-period EMA/SMA)
Orange/blue circles showing raw pivot points
Optional Tools
Debug Table (top-right): Shows real-time:
RSI value
Price vs MA position
MACD status
Alerts - Triggers audible/visual notifications
Customization
Adjust in settings:
Pivot sensitivity (left/right bars)
RSI levels (30/70 by default)
MA type/length (20-period EMA/SMA)
Toggle MACD filter on/off
Best For: Swing trading in trending markets (1H-4H timeframes). Signals appear faster than classic pivot strategies but still require confirmation from other analysis tools.
