CryptoSmart Momentum Engine1. Core Concept
The "CryptoSmart Momentum Engine" is an advanced, regime-filtered momentum dashboard. It is designed to solve the single biggest problem with oscillators: false signals during sideways, "chop" markets.
This indicator is not just a MACD and a Stochastic; it's a complete system that combines three elements in one panel:
Momentum (MACD): Measures the primary momentum and trend direction.
Overbought/Oversold (Stochastic): Measures short-term price exhaustion.
Volatility (Bollinger Band Width): Acts as a "Regime Filter" to determine if the market is in a volatile "Trend Mode" or a quiet "Chop Mode".
The indicator's primary function is to visually disable itself—turning the background gray and hiding all signals—during low-volatility chop, forcing you to trade only when the market has enough volatility to be predictable.
2. Key Features
Hybrid Oscillator: A normalized MACD (line, signal, and histogram) and a full Stochastic oscillator (%K and %D) are plotted in a single 0-100 pane.
Volatility "Regime" Filter: Using a Bollinger Band Width (BBW) filter, the indicator automatically detects the difference between a high-volatility Trend Mode (signal-on) and a low-volatility Chop Mode (signal-off).
Filtered Arrow Signals: The primary buy/sell signals (arrows) are only permitted to appear when the indicator is in "Trend Mode." All signals are automatically hidden during "Chop Mode" to prevent you from trading in sideways markets.
Centralized Histogram: The MACD histogram (the difference between the MACD and Signal lines) is visually centered at the 50-line, allowing it to oscillate in the middle of the 0-100 range.
Comprehensive Visuals: The indicator is a full dashboard with fill-shading for the MACD/Signal shadow, the Stochastic K/D spread, and the OB/OS background zones.
3. How It Works: The "Engine" Logic
The indicator's logic is based on its three main components:
Part 1: The Oscillators (MACD & Stochastic)
Normalized MACD: To make the MACD and Stochastic compatible, the MACD's value is "normalized" (mathematically compressed) to fit on the same 0-100 scale as the Stochastic.
Histogram: The histogram is calculated as MACD - Signal but is visually plotted relative to the 50-line (the new "zero").
Stochastic: This is a standard %K and %D oscillator used to identify short-term overbought/oversold levels.
Part 2: The Volatility Filter (The "Brain") This is the most important feature. The indicator calculates the Bollinger Band Width (BBW) and a moving average of that width (bbw_ma).
if bbw > bbw_ma → "Trend Mode" (Current volatility is expanding and higher than average).
if bbw <= bbw_ma → "Chop Mode" (Current volatility is contracting and lower than average).
Part 3: The Filtered Output (The "Result") The indicator's entire visual display is controlled by the Volatility Filter (use_vol_filter):
When in "Chop Mode" (Low Volatility):
All background color fills (for both MACD and Stochastic OB/OS zones) are disabled and replaced by the single, neutral chop_bg_color (Gray).
All arrow signals (longSignal and shortSignal) are disabled.
Meaning: The indicator is visually telling you: "Do not trade. The market is sideways and signals are unreliable."
When in "Trend Mode" (High Volatility):
The indicator "turns on."
The background colors for MACD and Stochastic OB/OS zones become visible.
The arrow signals are enabled and will appear if their conditions are met.
4. How to Read & Use It (Strategy)
The Gray Background (Chop Mode) is the most important signal. It means "Do nothing. Ignore all oscillator crosses. Wait for volatility to return."
The Colored Background (Trend Mode) means the indicator is "armed." You can now look for its signals.
Signal Definitions:
Buy Signal (Up Arrow ⬆️):
The Volatility Filter must be in "Trend Mode" (background is not gray).
The normalized MACD line (blue) crosses UP over the Oversold Level (20).
Interpretation: This is a "recovering from panic" signal. Momentum is returning to the market after an extreme oversold condition, and the market has enough volatility to trend.
Sell Signal (Down Arrow ⬇️):
The Volatility Filter must be in "Trend Mode".
The normalized MACD line (blue) crosses DOWN below the Overbought Level (80).
Interpretation: This is an "exiting from euphoria" signal. Momentum is failing after an extreme overbought condition, and the market has enough volatility to trend downwards.
Secondary Confirmation:
Histogram: Use the histogram (centered at 50) to gauge the speed of momentum. A growing histogram confirms the strength of your signal. A shrinking (diverging) histogram warns that the move is losing strength.
Stochastic: Use the %K and %D lines for faster, shorter-term confirmation or to identify additional entries/exits within the larger trend defined by the MACD and the Volatility Filter.
오실레이터
Range Oascilator + LessDivergences + MACD+StochRSIRange Oscillator + EMA Filter
Calculates a custom oscillator based on the highest high and lowest low over a chosen period.
Generates BUY signals when the oscillator crosses up from the oversold zone and price is above the EMA.
Generates SELL signals when the oscillator crosses down from the overbought zone and price is below the EMA.
MACD (3‑10‑16 EMA Settings)
Uses fast EMA = 3, slow EMA = 10, signal EMA = 16.
Detects bullish and bearish crossovers.
These crossovers only trigger a single unified buy/sell signal if they coincide with Stochastic RSI being in oversold (for buy) or overbought (for sell) zones.
Stochastic RSI
Standard calculation with %K and %D smoothing.
Defines oversold (<20) and overbought (>80) zones.
Used both for divergence detection and as a filter for MACD signals.
Divergence Detection
RSI Divergence: Price makes a lower low but RSI makes a higher low (bullish), or price makes a higher high but RSI makes a lower high (bearish).
MACD Histogram Divergence: Price makes a lower low but MACD histogram makes a higher low (bullish), or price makes a higher high but MACD histogram makes a lower high (bearish).
Stochastic RSI Divergence: Similar logic applied to %K line.
Divergences are flagged only once per pivot to avoid repetitive signals.
Visuals
EMA plotted on chart.
BUY/SELL signals shown as triangles above/below bars.
Divergences shown as labels (e.g., “RSI BullDiv”, “MACD BearDiv”).
Unified MACD+Stoch RSI signals shown in distinct colors (lime for buy, orange for sell).
Trapper Absolute PriceActionThe Trapper Absolute PriceAction (TAPA) indicator is a custom, momentum-based oscillator designed to help traders visually read shifts in bullish and bearish price strength — with no reliance on volume or external data.
TAPA calculates and smooths both bullish and bearish momentum using multiple methods (RSI, Stochastic, or ADX) and compares their relative strength in real time. The result is a clean dual-line oscillator with color-coded histograms that highlight which side of the market currently has control.
It was built to give traders a sniper-level precision tool for detecting early momentum shifts before they appear clearly on price charts, allowing confirmation or invalidation of setups faster than with lagging indicators.
How It Works
Momentum Strength Calculation
The script measures directional price movement across the chosen mode (RSI, Stochastic, or ADX).
These values are smoothed twice using a selectable moving average type (WMA, EMA, SMA, ALMA, HMA, etc.).
Bullish & Bearish Curves
The green line represents smoothed bullish momentum (SmthBulls).
The orange/red line represents smoothed bearish momentum (SmthBears).
Histogram Strength Visualization
The distance between the two curves forms a color-coded histogram.
Green/Lime bars indicate growing bullish control, while Orange/Red bars show bearish dominance.
A gray neutral zone reflects indecision or range-bound conditions.
Signal Triggers
BUY 🐂 appears when the green line crosses up through the orange — signaling a bullish momentum flip.
SELL 🐻 appears when the green line crosses down through the orange — signaling bearish control.
Alerts can be enabled directly in TradingView through the BUY (🐂) or SELL (🐻) alert conditions for automated notifications or integrations.
How to Use
1. Confirm Early Momentum Shifts
When a crossover appears, check that the histogram color supports the move (green shades for bullish, red/orange for bearish).
Avoid signals when both lines are tangled and the histogram alternates gray, that usually indicates consolidation or low volatility.
2. Validate with Higher-Timeframe Structure
TAPA is most powerful when aligned with trend structure from higher timeframes.
Example: A bullish crossover on the 1-hour timeframe, while the daily TAPA shows the green line already rising, can confirm momentum alignment before entry.
3. Combine with Support/Resistance
Mark your key support and resistance zones (manual or using your “Trapper S&R PRO” indicator).
Look for a TAPA bullish crossover occurring at a major support zone, that’s often the start of a reversal move.
4. Multi-Mode Analysis
Experiment with “Indicator Method” in the inputs:
RSI Mode - smoother and responsive for swing trading.
Stochastic Mode - better for short-term entries and exits.
ADX Mode - captures trending momentum on strong breakouts.
Examples
Bullish Example:
Price forms a higher low on the chart while TAPA’s green line crosses up through orange with a lime/green histogram. That’s a strong early signal that momentum is reversing before price confirms on structure.
Bearish Example:
Price rallies into resistance, then TAPA shows a red histogram and a bearish cross (green dropping under orange). That’s typically a high-probability short signal once structure breaks.
What Makes TAPA Different
No Volume Dependency: Focuses purely on price behavior, not volume spikes or anomalies.
Multi-Mode Engine: Switch between RSI, Stochastic, or ADX-style momentum math instantly.
Customizable Visuals: Editable histogram color layers (weak/strong bull/bear, neutral) and line color control.
Sniper Labeling System: Clean, minimal BUY/SELL cues at each verified crossover.
Alert-Ready: Built-in conditions allow for TradingView alerts, webhooks, or bot automation.
Modernized Core: Rebuilt in Pine v6 with optimized performance and compliance to TradingView standards.
TAPA is designed to filter out the noise and show what truly drives a move — the shift in control between buyers and sellers.
Best Pairing Indicators
To get maximum clarity and confluence:
Trapper Support & Resistance PRO
Helps identify key zones where momentum flips from TAPA have the most impact. A bullish crossover at a defined support level often marks an early trend reversal.
Trapper Volume Trigger
While TAPA doesn’t use volume internally, pairing it with a volume-based trigger confirms that momentum shifts have institutional participation.
Simple Moving Averages (5, 20, or 50)
Overlay short and mid-term SMAs on your chart to confirm directional bias. A bullish TAPA cross that aligns with SMA-5 crossing above SMA-20 increases reliability.
Disclaimer
This indicator is provided for educational and analytical purposes only.
It does not constitute financial advice or a recommendation to buy or sell any security.
Always conduct your own due diligence and practice proper risk management before trading any strategy.
© 2025 RAMS-offthecharts | “Read • Analyze • Mark • Snipe.”
TAPA is part of the RAMS ecosystem of tactical market tools, designed for traders who focus on precision, discipline, and momentum awareness.
Range Oscillator Strategy + Stoch Confirm🔹 Short summary
This is a free, educational long-only strategy built on top of the public “Range Oscillator” by Zeiierman (used under CC BY-NC-SA 4.0), combined with a Stochastic timing filter, an EMA-based exit filter and an optional risk-management layer (SL/TP and R-multiple exits). It is NOT financial advice and it is NOT a magic money machine. It’s a structured framework to study how range-expansion + momentum + trend slope can be combined into one rule-based system, often with intentionally RARE trades.
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0. Legal / risk disclaimer
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• This script is FREE and public. I do not charge any fee for it.
• It is for EDUCATIONAL PURPOSES ONLY.
• It is NOT financial advice and does NOT guarantee profits.
• Backtest results can be very different from live results.
• Markets change over time; past performance is NOT indicative of future performance.
• You are fully responsible for your own trades and risk.
Please DO NOT use this script with money you cannot afford to lose. Always start in a demo / paper trading environment and make sure you understand what the logic does before you risk any capital.
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1. About default settings and risk (very important)
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The script is configured with the following defaults in the `strategy()` declaration:
• `initial_capital = 10000`
→ This is only an EXAMPLE account size.
• `default_qty_type = strategy.percent_of_equity`
• `default_qty_value = 100`
→ This means 100% of equity per trade in the default properties.
→ This is AGGRESSIVE and should be treated as a STRESS TEST of the logic, not as a realistic way to trade.
TradingView’s House Rules recommend risking only a small part of equity per trade (often 1–2%, max 5–10% in most cases). To align with these recommendations and to get more realistic backtest results, I STRONGLY RECOMMEND you to:
1. Open **Strategy Settings → Properties**.
2. Set:
• Order size: **Percent of equity**
• Order size (percent): e.g. **1–2%** per trade
3. Make sure **commission** and **slippage** match your own broker conditions.
• By default this script uses `commission_value = 0.1` (0.1%) and `slippage = 3`, which are reasonable example values for many crypto markets.
If you choose to run the strategy with 100% of equity per trade, please treat it ONLY as a stress-test of the logic. It is NOT a sustainable risk model for live trading.
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2. What this strategy tries to do (conceptual overview)
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This is a LONG-ONLY strategy designed to explore the combination of:
1. **Range Oscillator (Zeiierman-based)**
- Measures how far price has moved away from an adaptive mean.
- Uses an ATR-based range to normalize deviation.
- High positive oscillator values indicate strong price expansion away from the mean in a bullish direction.
2. **Stochastic as a timing filter**
- A classic Stochastic (%K and %D) is used.
- The logic requires %K to be below a user-defined level and then crossing above %D.
- This is intended to catch moments when momentum turns up again, rather than chasing every extreme.
3. **EMA Exit Filter (trend slope)**
- An EMA with configurable length (default 70) is calculated.
- The slope of the EMA is monitored: when the slope turns negative while in a long position, and the filter is enabled, it triggers an exit condition.
- This acts as a trend-protection exit: if the medium-term trend starts to weaken, the strategy exits even if the oscillator has not yet fully reverted.
4. **Optional risk-management layer**
- Percentage-based Stop Loss and Take Profit (SL/TP).
- Risk/Reward (R-multiple) exit based on the distance from entry to SL.
- Implemented as OCO orders that work *on top* of the logical exits.
The goal is not to create a “holy grail” system but to serve as a transparent, configurable framework for studying how these concepts behave together on different markets and timeframes.
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3. Components and how they work together
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(1) Range Oscillator (based on “Range Oscillator (Zeiierman)”)
• The script computes a weighted mean price and then measures how far price deviates from that mean.
• Deviation is normalized by an ATR-based range and expressed as an oscillator.
• When the oscillator is above the **entry threshold** (default 100), it signals a strong move away from the mean in the bullish direction.
• When it later drops below the **exit threshold** (default 30), it can trigger an exit (if enabled).
(2) Stochastic confirmation
• Classic Stochastic (%K and %D) is calculated.
• An entry requires:
- %K to be below a user-defined “Cross Level”, and
- then %K to cross above %D.
• This is a momentum confirmation: the strategy tries to enter when momentum turns up from a pullback rather than at any random point.
(3) EMA Exit Filter
• The EMA length is configurable via `emaLength` (default 70).
• The script monitors the EMA slope: it computes the relative change between the current EMA and the previous EMA.
• If the slope turns negative while the strategy holds a long position and the filter is enabled, it triggers an exit condition.
• This is meant to help protect profits or cut losses when the medium-term trend starts to roll over, even if the oscillator conditions are not (yet) signalling exit.
(4) Risk management (optional)
• Stop Loss (SL) and Take Profit (TP):
- Defined as percentages relative to average entry price.
- Both are disabled by default, but you can enable them in the Inputs.
• Risk/Reward Exit:
- Uses the distance from entry to SL to project a profit target at a configurable R-multiple.
- Also optional and disabled by default.
These exits are implemented as `strategy.exit()` OCO orders and can close trades independently of oscillator/EMA conditions if hit first.
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4. Entry & Exit logic (high level)
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A) Time filter
• You can choose a **Start Year** in the Inputs.
• Only candles between the selected start date and 31 Dec 2069 are used for backtesting (`timeCondition`).
• This prevents accidental use of tiny cherry-picked windows and makes tests more honest.
B) Entry condition (long-only)
A long entry is allowed when ALL the following are true:
1. `timeCondition` is true (inside the backtest window).
2. If `useOscEntry` is true:
- Range Oscillator value must be above `entryLevel`.
3. If `useStochEntry` is true:
- Stochastic condition (`stochCondition`) must be true:
- %K < `crossLevel`, then %K crosses above %D.
If these filters agree, the strategy calls `strategy.entry("Long", strategy.long)`.
C) Exit condition (logical exits)
A position can be closed when:
1. `timeCondition` is true AND a long position is open, AND
2. At least one of the following is true:
- If `useOscExit` is true: Oscillator is below `exitLevel`.
- If `useMagicExit` (EMA Exit Filter) is true: EMA slope is negative (`isDown = true`).
In that case, `strategy.close("Long")` is called.
D) Risk-management exits
While a position is open:
• If SL or TP is enabled:
- `strategy.exit("Long Risk", ...)` places an OCO stop/limit order based on the SL/TP percentages.
• If Risk/Reward exit is enabled:
- `strategy.exit("RR Exit", ...)` places an OCO order using a projected R-multiple (`rrMult`) of the SL distance.
These risk-based exits can trigger before the logical oscillator/EMA exits if price hits those levels.
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5. Recommended backtest configuration (to avoid misleading results)
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To align with TradingView House Rules and avoid misleading backtests:
1. **Initial capital**
- 10 000 (or any value you personally want to work with).
2. **Order size**
- Type: **Percent of equity**
- Size: **1–2%** per trade is a reasonable starting point.
- Avoid risking more than 5–10% per trade if you want results that could be sustainable in practice.
3. **Commission & slippage**
- Commission: around 0.1% if that matches your broker.
- Slippage: a few ticks (e.g. 3) to account for real fills.
4. **Timeframe & markets**
- Volatile symbols (e.g. crypto like BTCUSDT, or major indices).
- Timeframes: 1H / 4H / **1D (Daily)** are typical starting points.
- I strongly recommend trying the strategy on **different timeframes**, for example 1D, to see how the behaviour changes between intraday and higher timeframes.
5. **No “caution warning”**
- Make sure your chosen symbol + timeframe + settings do not trigger TradingView’s caution messages.
- If you see warnings (e.g. “too few trades”), adjust timeframe/symbol or the backtest period.
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5a. About low trade count and rare signals
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This strategy is intentionally designed to trade RARELY:
• It is **long-only**.
• It uses strict filters (Range Oscillator threshold + Stochastic confirmation + optional EMA Exit Filter).
• On higher timeframes (especially **1D / Daily**) this can result in a **low total number of trades**, sometimes WELL BELOW 100 trades over the whole backtest.
TradingView’s House Rules mention 100+ trades as a guideline for more robust statistics. In this specific case:
• The **low trade count is a conscious design choice**, not an attempt to cherry-pick a tiny, ultra-profitable window.
• The goal is to study a **small number of high-conviction long entries** on higher timeframes, not to generate frequent intraday signals.
• Because of the low trade count, results should NOT be interpreted as statistically strong or “proven” – they are only one sample of how this logic would have behaved on past data.
Please keep this in mind when you look at the equity curve and performance metrics. A beautiful curve with only a handful of trades is still just a small sample.
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6. How to use this strategy (step-by-step)
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1. Add the script to your chart.
2. Open the **Inputs** tab:
- Set the backtest start year.
- Decide whether to use Oscillator-based entry/exit, Stochastic confirmation, and EMA Exit Filter.
- Optionally enable SL, TP, and Risk/Reward exits.
3. Open the **Properties** tab:
- Set a realistic account size if you want.
- Set order size to a realistic % of equity (e.g. 1–2%).
- Confirm that commission and slippage are realistic for your broker.
4. Run the backtest:
- Look at Net Profit, Max Drawdown, number of trades, and equity curve.
- Remember that a low trade count means the statistics are not very strong.
5. Experiment:
- Tweak thresholds (`entryLevel`, `exitLevel`), Stochastic settings, EMA length, and risk params.
- See how the metrics and trade frequency change.
6. Forward-test:
- Before using any idea in live trading, forward-test on a demo account and observe behaviour in real time.
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7. Originality and usefulness (why this is more than a mashup)
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This script is not intended to be a random visual mashup of indicators. It is designed as a coherent, testable strategy with clear roles for each component:
• Range Oscillator:
- Handles mean vs. range-expansion states via an adaptive, ATR-normalized metric.
• Stochastic:
- Acts as a timing filter to avoid entering purely on extremes and instead waits for momentum to turn.
• EMA Exit Filter:
- Trend-slope-based safety net to exit when the medium-term direction changes against the position.
• Risk module:
- Provides practical, rule-based exits: SL, TP, and R-multiple exit, which are useful for structuring risk even if you modify the core logic.
It aims to give traders a ready-made **framework to study and modify**, not a black box or “signals” product.
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8. Limitations and good practices
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• No single strategy works on all markets or in all regimes.
• This script is long-only; it does not short the market.
• Performance can degrade when market structure changes.
• Overfitting (curve fitting) is a real risk if you endlessly tweak parameters to maximise historical profit.
Good practices:
- Test on multiple symbols and timeframes.
- Focus on stability and drawdown, not only on how high the profit line goes.
- View this as a learning tool and a basis for your own research.
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9. Licensing and credits
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• Core oscillator idea & base code:
- “Range Oscillator (Zeiierman)”
- © Zeiierman, licensed under CC BY-NC-SA 4.0.
• Strategy logic, Stochastic confirmation, EMA Exit Filter, and risk-management layer:
- Modifications by jokiniemi.
Please respect both the original license and TradingView House Rules if you fork or republish any part of this script.
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10. No payments / no vendor pitch
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• This script is completely FREE to use on TradingView.
• There is no paid subscription, no external payment link, and no private signals group attached to it.
• If you have questions, please use TradingView’s comment system or private messages instead of expecting financial advice.
Use this script as a tool to learn, experiment, and build your own understanding of markets.
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11. Example backtest settings used in screenshots
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To avoid any confusion about how the results shown in screenshots were produced, here is one concrete example configuration:
• Symbol: BTCUSDT (or similar major BTC pair)
• Timeframe: 1D (Daily)
• Backtest period: from 2018 to the most recent data
• Initial capital: 10 000
• Order size type: Percent of equity
• Order size: 2% per trade
• Commission: 0.1%
• Slippage: 3 ticks
• Risk settings: Stop Loss and Take Profit disabled by default, Risk/Reward exit disabled by default
• Filters: Range Oscillator entry/exit enabled, Stochastic confirmation enabled, EMA Exit Filter enabled
If you change any of these settings (symbol, timeframe, risk per trade, commission, slippage, filters, etc.), your results will look different. Please always adapt the configuration to your own risk tolerance, market, and trading style.
Swing Trade BUY/SELL + SCORING +COLOUR FIXBUY/SELL labels now appear with a score (1–3) next to them.
Color coding visually distinguishes signal strength:
BUY → 1 yellow, 2 light green, 3 dark green
SELL → 1 orange, 2 red, 3 burgundy
This allows you to instantly see the signal strength both numerically and visually.
Swing Trade AL/SAT + Güç Derecesi_huğurlu
Weak signal → MACD crossover only.
Moderate signal → MACD crossover + RSI confirmation.
Strong signal → MACD crossover + RSI + Stoch RSI confirmation.
BUY/SELL labels appear on the chart in different colors and sizes.
This way, you can instantly see which signal is more reliable.
Zayıf sinyal → sadece MACD kesişim var
Orta sinyal → MACD kesişim + RSI teyidi.
Güçlü sinyal → MACD kesişim + RSI + Stoch RSI teyidi.
Stochastic RSI - WT Confluence Signal Detectors (TraderDemircan)Description
What This Indicator Does:
This indicator combines two powerful momentum oscillators—WaveTrend and Stochastic RSI—to identify high-probability trading signals through confluence. Instead of relying on a single indicator that may generate false signals, this tool only triggers buy/sell alerts when both oscillators simultaneously confirm extreme market conditions and trend reversals. This confluence approach significantly reduces noise and helps traders focus on the most reliable setups.
Key Features:
Dual-Oscillator Confluence: Generates signals only when both WaveTrend crossovers and Stochastic RSI extreme levels align
Normalized Scale Display: Both oscillators are plotted on a unified -100 to +100 scale for easy visual comparison
Visual Signal Confirmation: Clear intersection points marked with colored circles, plus optional candle coloring at crossover moments
Customizable Thresholds: Adjust overbought/oversold levels for both oscillators to match your trading style and asset volatility
Clean Visual Presentation: Optional area fill showing WaveTrend momentum difference, making divergences easier to spot
How It Works:
The indicator operates on a confluence principle where multiple conditions must align:
For BUY Signals (Green):
WaveTrend 1 crosses above WaveTrend 2 (bullish crossover)
WaveTrend is in oversold territory (below -53 or -60)
Stochastic RSI K-line is below 20 (oversold)
For SELL Signals (Red):
WaveTrend 1 crosses below WaveTrend 2 (bearish crossover)
WaveTrend is in overbought territory (above 53 or 60)
Stochastic RSI K-line is above 80 (overbought)
WaveTrend Component:
Uses the hlc3 price (average of high, low, close) to calculate a channel index that identifies market momentum waves. The two WaveTrend lines (WT1 and WT2) act similarly to MACD, where crossovers indicate momentum shifts. The oscillator ranges from approximately -100 to +100, with extreme values suggesting potential reversals.
Stochastic RSI Component:
Applies stochastic calculations to RSI values rather than raw price, creating a more sensitive momentum indicator. Values above 80 indicate overbought conditions (potential selling opportunity), while values below 20 indicate oversold conditions (potential buying opportunity). The indicator includes both K-line (faster) and D-line (slower, smoothed) for additional confirmation.
Normalization Technology:
To enable direct visual comparison, the Stochastic RSI (normally 0-100 scale) is normalized to match WaveTrend's -100 to +100 scale. This allows traders to see both oscillators' movements in relation to the same reference levels, making divergences and convergences more apparent.
How to Use:
For Trend Traders:
Wait for confluence signals in the direction of the larger trend
Use buy signals in uptrends as entry points during pullbacks
Use sell signals in downtrends as entry points during bounces
For Reversal Traders:
Focus on confluence signals at major support/resistance levels
Look for divergences between price and oscillators before confluence signals
Consider stronger signals when both oscillators reach extreme levels (WT beyond ±60, Stoch beyond 20/80)
For Scalpers:
Lower the WaveTrend Channel Length (default 10) to 5-7 for more frequent signals
Tighten overbought/oversold thresholds slightly (e.g., WT: ±50, Stoch: 30/70)
Use on lower timeframes (5m, 15m) with strict stop losses
Settings Guide:
WaveTrend Parameters:
Channel Length (10): Controls sensitivity. Lower = more signals but more noise. Higher = fewer but more reliable signals
Average Length (21): Smoothing period for WT2. Higher values reduce whipsaws
Overbought Levels (60/53): Two-tier system. Breaching 60 indicates strong overbought, 53 is moderate
Oversold Levels (-60/-53): Mirror of overbought levels for downside extremes
Stochastic RSI Parameters:
K-Smooth (3): Smoothing for the K-line. Higher = smoother but delayed
D-Smooth (3): Additional smoothing for the D-line signal
RSI Period (14): Standard RSI calculation period
Stoch Period (14): Stochastic calculation lookback
Oversold (20) / Overbought (80): Classic thresholds for extreme conditions
Visual Options:
Show WT Difference Area: Displays the momentum difference between WT1 and WT2 as a blue shaded area
Show WT Intersection Points: Marks crossover points with colored circles (red for bearish, green for bullish)
Color Candles at Intersection: Changes candle colors at crossover moments (blue for bearish, yellow for bullish)
Show Stoch Over Signals: Displays when Stochastic RSI breaches extreme levels
What Makes This Original:
While WaveTrend and Stochastic RSI are established indicators, this script's originality lies in:
Confluence Logic: The specific combination requiring simultaneous confirmation from both oscillators in extreme zones, not just simple crossovers
Normalization Approach: Displaying both oscillators on the same -100 to +100 scale for direct visual comparison, which is not standard
Multi-Tier Overbought/Oversold: Using two levels (60/53) instead of one, allowing for nuanced signal strength assessment
Integrated Visual System: Combining area fills, intersection markers, and candle coloring in a coordinated display that shows momentum flow at a glance
Important Considerations:
This is a momentum-based oscillator system, which performs best in ranging or trending markets with clear swings
In strong trending markets, the oscillator may remain in extreme zones for extended periods (remain overbought during strong uptrends, oversold during strong downtrends)
Confluence signals are intentionally rare to maintain quality—expect fewer signals than with single-indicator systems
Always combine with price action analysis, support/resistance levels, and proper risk management
Not recommended for extremely low volatility or thin markets where oscillators may produce erratic readings
Best Timeframes:
Intraday: 15m, 1H (with tighter parameters)
Swing Trading: 4H, Daily (with default parameters)
Position Trading: Daily, Weekly (with extended Channel Length 15-20)
Typical Use Cases:
Identifying exhaustion points in trending markets
Timing entries during pullbacks in established trends
Spotting potential reversal zones at key price levels
Filtering out weak momentum signals during consolidation
RADAR Oscillator (Regime Adaptive Directional Analysis)RADAR (Regime Adaptive Directional Analysis)
This script is available by invitation only.
What is it?
The RADAR Oscillator is a multi-layered decision support oscillator designed to filter market noise and detect high-probability trend resumptions. It combines multiple analytical engines that analyze different aspects of the market (Structure, Momentum, Trend Strength, Rhythm) to eliminate the weaknesses of a single indicator. Final buy/sell signals are generated only when a consensus is reached between these engines.
This is not a "strategy," but a signal-generating oscillator. Therefore, it does not provide backtest results (profit/loss, drawdown, etc.) as seen in TradingView's strategy tester. Its purpose is to add clarity and accuracy to the investor's decision-making process.
What Does It Promise, and What Does It Not Promise?
• What Does It Promise:
o Clarity and Noise Filtering: Aims to significantly reduce misleading signals in sideways and unstable markets.
o High-Probability Setup Detection: Thanks to its multiple confirmation mechanism, it generates signals only during strong and distinct market conditions.
o Adaptation to Market Conditions: It offers the ability to automatically adjust the analysis method based on the market's current "regime" (trend or sideways).
• What It Doesn't Promise:
o Guaranteed Profit: No financial instrument can guarantee future profits. RADAR is a probability-enhancing tool, not a magic formula.
o Automatic Wealth: Successful use requires proper risk management, market experience, and user discipline.
o Backtest Results: Because it is an oscillator, it does not provide historical performance metrics. Its value should be measured by its effectiveness in real-time market analysis.
Which Well-Known Indicators Are Used For What Purpose?
While RADAR creates a unique decision-making mechanism, it utilizes the fundamental building blocks of technical analysis. However, these indicators are never used directly to generate signals; instead, they serve as data sources and filters for our unique algorithm.
• ADX and DMI: Used to measure the strength and directional dominance of a trend. RADAR uses this data as a filter to confirm only the existence of a sufficiently strong trend.
• Moving Averages (EMA and SMA): Used as primary inputs to smooth price data and determine overall direction. Their outputs are processed in the consensus engine along with other filters.
• ATR (Average True Range): Does not directly generate signals, but measures market volatility. This data forms the basis of the oscillator's dynamic volatility smoothing engine, helping to adjust risk to market conditions.
Original Methodology and Proprietary Logic
This algorithm is not based on any open-source strategy code. The author's unique methodology combines multi-filter consensus, adaptive thresholding, statistical noise filtering, and market structure-based execution logic. Specifically, the oscillator's ability to analyze market characteristics (trending or sideways) and automatically adjust filtering multipliers accordingly forms the basis of its trading value. This combination is the author's original work, and preserving the source code is preferred.
What Problems Does It Solve?
Problem 1: Misleading Signals and Market Noise
o RADAR Solution: Consensus-Based Decision Mechanism. RADAR never relies on a single signal. No signal is generated unless the different analytical engines agree on the same direction. This filters out market noise, ensuring only high-probability signals are processed.
Problem 2: Static Analysis and Changing Market Conditions
o RADAR Solution: Adaptive Regime Shifting. The Oscillator actively analyzes whether the market is in "Trend Mode" or "Sideways Mode" using its proprietary market character analysis engine. It adapts to conditions like a chameleon, automatically adjusting signal generation rules and filter sensitivity according to the current regime.
Problem 3: Fixed Parameters and Declining Performance
o RADAR Solution: Full Adaptation Principle. To reduce reliance on fixed settings, it dynamically adjusts analysis speed and filter sensitivity based on the market's natural rhythm and volatility.
Automation Ready: Customizable Webhook Alerts
RADAR is more than just a visual analysis tool; it's designed to work seamlessly with full automation systems.
The oscillator generates alert messages in fully configurable JSON format for buy (long) and sell (short) signals. This feature allows you to easily connect RADAR signals to popular automation platforms like 3Commas, PineConnector, Tickeron, or your own custom bots. This allows you to execute your strategy 24/7 without manual intervention.
Why Released "By Invitation Only"?
• Protecting Proprietary Intellectual Property: RADAR is the product of hundreds of hours of research and development. Its consensus logic, regime detection, and engine integration are unique. Opening the source code would instantly destroy this intellectual property and competitive advantage.
• Maintaining Performance Integrity: Uncontrolled distribution can lead to misuse or theft and resale of signals by malicious actors. The invitation model protects the integrity of the oscillator.
• Business Model and Support: RADAR is a premium analysis tool. Access by invitation reflects its value and compensates the developer for ongoing maintenance, support, and future improvements.
____________________________
This indicator is for educational purposes only. Past performance does not guarantee future results. Always practice appropriate risk management and protect your capital.
DAO - Demand Advanced Oscillator# DAO - Demand Advanced Oscillator
## 📊 Overview
DAO (Demand Advanced Oscillator) is a powerful momentum oscillator that measures buying and selling pressure by analyzing consecutive high-low relationships. It helps identify market extremes, divergences, and potential trend reversals.
**Values range from 0 to 1:**
- **Above 0.70** = Overbought (potential reversal down)
- **Below 0.30** = Oversold (potential reversal up)
- **0.30 - 0.70** = Neutral zone
---
## ✨ Key Features
✅ **Automatic Divergence Detection**
- Bullish divergences (price lower low + DAO higher low)
- Bearish divergences (price higher high + DAO lower high)
- Visual lines connecting divergence points
✅ **Multi-Timeframe Analysis**
- View higher timeframe DAO on current chart
- Perfect for trend alignment strategies
✅ **Signal Line (EMA)**
- Customizable EMA for trend confirmation
- Crossover signals for momentum shifts
✅ **Real-Time Statistics Dashboard**
- Current DAO value
- Market status (Overbought/Oversold/Neutral)
- Trend direction indicator
✅ **Complete Alert System**
- Overbought/Oversold signals
- Bullish/Bearish divergences
- Signal line crosses
- Level crosses
✅ **Fully Customizable**
- Adjustable periods and levels
- Customizable colors and zones
- Toggle features on/off
---
## 📈 Trading Signals
### 1. Divergences (Most Powerful)
**Bullish Divergence:**
- Price makes lower low
- DAO makes higher low
- Signal: Strong reversal up likely
**Bearish Divergence:**
- Price makes higher high
- DAO makes lower high
- Signal: Strong reversal down likely
### 2. Overbought/Oversold
**Overbought (>0.70):**
- Market may be overextended
- Consider taking profits or looking for shorts
- Can remain overbought in strong trends
**Oversold (<0.30):**
- Market may be oversold
- Consider buying opportunities
- Can remain oversold in strong downtrends
### 3. Signal Line Crossovers
**Bullish Cross:**
- DAO crosses above signal line
- Momentum turning positive
**Bearish Cross:**
- DAO crosses below signal line
- Momentum turning negative
### 4. Level Crosses
**Cross Above 0.30:** Exiting oversold zone (potential uptrend)
**Cross Below 0.70:** Exiting overbought zone (potential downtrend)
---
## ⚙️ Default Settings
📊 Oscillator Period: 14
Number of bars for calculation
📈 Signal Line Period: 9
EMA period for signal line
🔴 Overbought Level: 0.70
Upper threshold
🟢 Oversold Level: 0.30
Lower threshold
🎯 Divergence Detection: ON
Auto divergence identification
⏰ Multi-Timeframe: OFF
Higher TF overlay (optional)
All parameters are fully customizable!
---
## 🔔 Alerts
Six pre-configured alerts available:
1. DAO Overbought
2. DAO Oversold
3. DAO Bullish Divergence
4. DAO Bearish Divergence
5. DAO Signal Cross Up
6. DAO Signal Cross Down
**Setup:** Right-click indicator → Add Alert → Choose condition
---
## 💡 How to Use
### Best Practices:
✅ Focus on divergences (strongest signals)
✅ Combine with support/resistance levels
✅ Use multiple timeframes for confirmation
✅ Wait for price action confirmation
✅ Practice proper risk management
### Avoid:
❌ Trading on indicator alone
❌ Fighting strong trends
❌ Ignoring market context
❌ Overtrading
### Recommended Settings by Trading Style:
**Day Trading:** Period 7-10, All alerts ON
**Swing Trading:** Period 14-21, Divergence alerts
**Scalping:** Period 5-7, Signal crosses
**Position Trading:** Period 21-30, Weekly/Daily TF
---
## 🌍 Markets & Timeframes
**Works on all markets:**
- Forex (all pairs)
- Stocks (all exchanges)
- Cryptocurrencies
- Commodities
- Indices
- Futures
**Works on all timeframes:** 1m to Monthly
---
## 📊 How It Works
DAO calculates the ratio of buying pressure to total market pressure:
1. **Calculate Buying Pressure (DemandMax):**
- If current high > previous high: DemandMax = difference
- Otherwise: DemandMax = 0
2. **Calculate Selling Pressure (DemandMin):**
- If previous low > current low: DemandMin = difference
- Otherwise: DemandMin = 0
3. **Apply Smoothing:**
- Calculate SMA of DemandMax over N periods
- Calculate SMA of DemandMin over N periods
4. **Final Formula:**
```
DAO = SMA(DemandMax) / (SMA(DemandMax) + SMA(DemandMin))
```
This produces a normalized value (0-1) representing market demand strength.
---
## 🎯 Trading Strategies
### Strategy 1: Divergence Trading
- Wait for divergence label
- Confirm at support/resistance
- Enter on confirming candle
- Stop loss beyond recent swing
- Target: opposite level or 0.50
### Strategy 2: Overbought/Oversold
- Best for ranging markets
- Wait for extreme readings
- Enter on reversal from extremes
- Target: middle line (0.50)
### Strategy 3: Trend Following
- Identify trend direction first
- Use DAO to time entries in trend direction only
- Enter on pullbacks to oversold (uptrend) or overbought (downtrend)
- Trade with the trend
### Strategy 4: Multi-Timeframe
- Enable MTF feature
- Trade only when both timeframes align
- Higher TF = trend direction
- Lower TF = precise entry
---
## 📂 Category
**Primary:** Oscillators
**Secondary:** Statistics, Volatility, Momentum
---
## 🏷️ Tags
dao, oscillator, momentum, overbought-oversold, divergence, reversal, demand-indicator, price-exhaustion, statistics, volatility, forex, stocks, crypto, multi-timeframe, technical-analysis
---
## ⚠️ Disclaimer
**This indicator is for educational purposes only.** It does not constitute financial advice. Trading involves substantial risk of loss. Always conduct your own research, use proper risk management, and consult with financial professionals before making trading decisions. Past performance does not guarantee future results.
---
## 📄 License
Open source - Free to use for personal trading, modify as needed, and share with attribution.
---
**Version:** 1.0
**Status:** Production Ready ✅
**Pine Script:** v5
**Trademark-Free:** 100% Safe to Publish
---
*Made with 💙 for traders worldwide*
RSI OB & MACD Point Down or Crossed - PSPine script screener indicator for RSI OB and MACD point down or cross.
Hidden Bullish Divergence - B166erThis script will paint a line on the chart when hidden bullish divergence is occurring.
RSI + ADX + ATR Strength GaugeThis indicator combines Relative Strength Index (RSI), Average Directional Index (ADX), and Average True Range (ATR) into a unified strength gauge that identifies high-quality trending conditions while filtering out choppy, low-volatility markets.
RSI measures momentum and overbought/oversold conditions.
ADX confirms trend strength (not direction), highlighting when price movement has strong follow-through.
ATR captures volatility expansion, filtering out flat, low-range candles where fake signals occur.
When the components diverge or show contraction, the gauge warns of market chop, suggesting it’s better to avoid entries or reduce position size.
Purpose:
To keep you out of sideways markets and confirm entries only when momentum, trend strength, and volatility all agree — reducing whipsaws and improving trade quality.
Tweak to your liking.
Relative Strength HSIWe add the relative strength indicator. We try to maximize the alpha,
when there is price divergence, we should notice.
NQ Gamma LevelsNQ Gamma Levels - Dynamic Options Flow Visualization
This indicator displays gamma exposure levels from QQQ options data, automatically scaled to NQ/MNQ futures prices. Simply copy gamma data from your dashboard and paste it into the indicator to see key support and resistance levels based on dealer positioning.
Features:
- Automatic QQQ to NQ price conversion using live 1-minute ratios
- Visual strength indicators - thicker/longer lines show stronger gamma concentrations
- Customizable colors for positive and negative gamma levels
- Dotted reference lines extending across the chart for easy price tracking
- Updates every minute to prevent chart clutter and jumping levels
- Filters to show only significant levels above your threshold
- Strongest positive and negative levels are automatically highlighted
The solid colored lines represent gamma strength - longer lines indicate higher concentration at that price level. Dotted lines provide continuous reference points across your chart. Green levels typically act as support (dealers long gamma), while red levels often act as resistance (dealers short gamma).
Best used on 1-5 minute timeframes for intraday trading. Paste fresh data from your options flow dashboard whenever you want updated levels.
Trendlines with Breaks Oscillator [LuxAlgo]The Trendlines with Breaks Oscillator is an oscillator based on the Trendlines with Breaks indicator, and tracks the maximum distance on price from bullish and bearish trendline breakouts.
The oscillator features divergences and trendline breakout detection.
🔶 USAGE
This tool is based on our Trendlines with Breaks indicator, which detects bullish and bearish trendlines and highlights the breaks on the chart. Now, we bring you this tool as an oscillator.
The oscillator calculates the maximum distance between the price and the break of each trendline, for both bullish and bearish cases, then calculates the delta between both.
When the oscillator is above 0, the market is in an uptrend; when it is below 0, it is in a downtrend. An ascending slope indicates positive momentum, and a descending slope indicates negative momentum.
Trendline breaks are displayed as green and red dots on the oscillator. A green dot corresponds to a bullish break of a descending trendline, and a red dot corresponds to a bearish break of an ascending trendline.
The oscillator calculation depends on two parameters from the settings panel: short and long alpha length. These parameters are used to calculate a synthetic EMA with a variable alpha for both bullish and bearish breaks. The final result is the difference between the two averages.
As shown in the image, using the same trend detection parameters but different alphas can produce very different results. The larger the alphas, the smoother the oscillator becomes, detecting bigger trends but making it less reactive.
This tool features the same trendline detection system as the Trendlines with Breaks indicator, which is based on three main parameters: swing length, slope, and calculation method.
As we can see in the image above, the data collected for the oscillator calculation will be different when using different parameters. A larger length detects larger trends. A larger slope or a different calculation method also impacts the final result.
🔹 Signal Line
The signal line is a smoothed version of the oscillator; traders can choose the smoothing method and length used from the settings panel.
In the image, the signal line crossings are displayed as vertical lines. As we can see, the market usually corrects downward after a bearish crossing and corrects upward after a bullish crossing.
Traders can choose among 10 different smoothing methods for the signal line. In the image, we can see how different methods and lengths give different outputs.
🔹 Divergences
The tool features a divergence detector that helps traders understand the strength behind price movements. Traders can adjust the detection length from the settings panel.
As shown in the image, a bearish divergence occurs when the price prints higher highs, but the momentum on the histogram prints lower highs. A bullish divergence occurs when the price prints lower lows, but the histogram prints higher lows.
By adjusting the length of the divergence detector, traders can filter out smaller divergences, allowing the tool to only detect more significant ones.
The image above depicts divergences detected with different lengths; the larger the length, the bigger the divergences are detected.
🔶 SETTINGS
🔹 Trendlines
Swing Detection Lookback: The size of the market structure used for trendline detection.
Slope: Slope steepness, a value of 0 gives horizontal levels, values larger than 1 give a steeper slope
Slope Calculation Method: Choose how the slope is calculated
🔹 Oscillator
Short Alpha Length: Synthetic EMA short period
Long Alpha Length: Synthetic EMA long period
Smoothing Signal: Choose the smoothing method and period
Divergences: Enable or disable divergences and select the detection length.
🔹 Style
Bullish: Select bullish color.
Bearish: Select bearish color.
Screener (MC) [AlgoAlpha]🟠 OVERVIEW
This script is a multi-symbol scanner that works as a companion to the "Momentum Concepts" indicator. It provides a comprehensive dashboard view, allowing traders to monitor the momentum signals of up to 18 different assets in real-time from a single chart. The main purpose is to offer a bird's-eye view of the market, helping you quickly identify assets with strong momentum confluence or potential reversal opportunities without having to switch between different charts.
The screener displays the status of all key components from the Momentum Concepts indicator, including the Fast Oscillator, Scalper's Momentum, Momentum Impulse Oscillator, and Hidden Liquidity Flow, organizing them into a clear and easy-to-read table.
🟠 CONCEPTS
The core of this screener is built upon the analytical framework of the "Momentum Concepts" indicator, which evaluates market momentum across multiple layers: short-term, medium-term, and long-term. This screener applies those complex, proprietary calculations to each symbol in your watchlist and visualizes the current state of each component.
Each column in the table represents a specific aspect of momentum analysis:
Fast Oscillator Columns: These columns reflect the short-term momentum. They show the immediate trend direction, whether the asset is in an overbought or oversold condition, and flag high-probability events like divergences, reversals, or diminishing momentum.
Scalper's Momentum Column: This column gives insight into medium-term momentum. It distinguishes between strong, sustained moves and weakening, corrective moves, which is useful for gauging the health of a trend.
Momentum Impulse Column: This column represents the dominant, long-term trend bias. It helps you understand the underlying market regime (bullish, bearish, or consolidating) to align your trades with the bigger picture.
Hidden Liquidity Flow Column: This column provides a unique view into the market's underlying liquidity dynamics. It signals whether there is net buying or selling pressure and uses special coloring to highlight periods of unusually high liquidity activity, which often precedes volatile price movements.
By combining these perspectives, the screener justifies its utility by enabling traders to make more informed decisions based on multi-layered signal confluence.
🟠 FEATURES
This screener organizes momentum data into several key columns. Here is a breakdown of each column and its possible values:
Asset: Displays the symbol for the asset being analyzed in that row.
Fast Oscillator Trend: Shows the immediate, short-term momentum direction.
▲: Indicates a bullish short-term trend.
▼: Indicates a bearish short-term trend.
–: Indicates a neutral or transitional state.
Fast Oscillator Valuation: Measures whether the asset is in a short-term overbought or oversold state.
OB: Signals an "Overbought" condition, often associated with bullish exhaustion.
OS: Signals an "Oversold" condition, often associated with bearish exhaustion.
Neutral: The asset is trading in a neutral zone, neither overbought nor oversold.
Scalper's Momentum: Assesses the strength and direction of medium-term momentum.
Strong▲: Strong bullish momentum.
Weak▲: Bullish momentum exists but is weakening or corrective.
Strong▼: Strong bearish momentum.
Weak▼: Bearish momentum exists but is weakening or corrective.
–: Neutral or no clear medium-term momentum.
Momentum Impulse: Identifies the dominant, long-term trend bias. A colored background indicates that the momentum is in a strong "impulse" phase.
▲: Indicates a bullish long-term bias.
▼: Indicates a bearish long-term bias.
0: Indicates a neutral or ranging market condition.
Hidden Liquidity Flow: Tracks underlying buying and selling pressure. The background color highlights periods of unusual liquidity activity.
▲: Positive liquidity flow, suggesting net buying pressure.
▼: Negative liquidity flow, suggesting net selling pressure.
–: Neutral liquidity flow.
Dim. Momentum: Provides an early warning that short-term momentum is beginning to fade.
● (Bullish Color): Bullish momentum is weakening.
● (Bearish Color): Bearish momentum is weakening.
–: No diminishing momentum detected.
Divergence: Flags classic or hidden divergences between price and the Fast Oscillator.
Div▲: A bullish divergence has been detected.
Div▼: A bearish divergence has been detected.
–: No active divergence signal.
Reversal: Signals a potential reversal when the Fast Oscillator crosses its trend line from an overbought or oversold zone.
Rev▲: A bullish reversal signal has occurred.
Rev▼: A bearish reversal signal has occurred.
–: No active reversal signal.
🟠 USAGE
The primary function of this screener is to quickly identify trading opportunities and filter setups based on momentum confluence across your watchlist.
1. Setup and Configuration:
Add the indicator to your chart.
Go into the script settings and populate the "Watchlist" group with the symbols you wish to monitor.
Adjust the settings for the various momentum components (Fast Oscillator, Scalper's Momentum, etc.) to align with your trading strategy. These settings will be universally applied to all symbols in the screener.
2. Interpreting the Columns for Trading Decisions:
Momentum Impulse & Hidden Liquidity Flow: Use these columns to establish a directional bias. A bullish "▲" in both columns on an asset suggests a strong underlying uptrend with supportive buying pressure, making it a good candidate for long positions.
Scalper's Momentum: Use this for entry timing and trend health. A "Strong▲" reading can confirm the strength of an uptrend, while a shift to "Weak▲" might suggest it's time to tighten stops or look for an exit.
Fast Oscillator Trend & Valuation: These are best for precise entry triggers. For a "buy the dip" strategy in an uptrend, you could wait for the Fast Oscillator to show "OS" (Oversold) and then enter when the "Trend" column flips back to "▲".
Dim. Momentum: This is an excellent take-profit signal. If you are in a long position and a bullish-colored "●" appears, it's a warning that the upward move is losing steam, and you might consider closing your trade.
Divergence & Reversal: These columns are for identifying potential turning points. A "Div▲" or "Rev▲" signal is a strong alert that a downtrend might be ending, making the asset a prime candidate to watch for a long entry.
3. Finding High-Probability Setups:
Trend Confluence: Look for assets where multiple components show alignment. For example, an ideal long setup might show a bullish "Momentum Impulse" (▲), a "Strong▲" reading in "Scalper's Momentum," and a bullish trend in the "Fast Oscillator." This indicates that the long-term, medium-term, and short-term momentums are all in agreement.
Reversal and Exhaustion: Use the "Divergence" and "Reversal" columns to spot potential turning points. A "Div▲" signal appearing in an asset that is in an oversold "Fast Oscillator Valuation" zone can be a strong indication of an upcoming bounce.
유료 스크립트
Relational RSI - Trend IdentifierThis indicator analyzes the relationship between Price and RSI. It doesn't just show you the current RSI value; it compares the current Price-to-RSI relationship against thousands of historical examples to see if the market is behaving "normally."
The core idea is to identify when this historical relationship "decays" or breaks.
Positive (Green): Price is higher than it "should be" for the current RSI level, based on history. This is a sign of bullish strength or over-exuberance.
Negative (Red): Price is lower than it "should be" for the current RSI level. This is a sign of bearish weakness or being oversold.
Zero Line: Price is exactly where history suggests it should be for the current RSI. This is the "normal" or equilibrium state.
Think of it as an "expectations" indicator. Is the price over-performing or under-performing relative to its typical momentum signature?
How to Read the Indicator
1. The Main Oscillator (Relational Decay)
This is the central line that moves above and below zero.
Rising (Bullish Decay): When the line moves up, it means bulls are in control, pushing price higher than the RSI momentum would normally suggest.
Falling (Bearish Decay): When the line moves down, it means bears are in control, suppressing price lower than the RSI momentum would normally suggest.
Extreme Readings (> 2.0 or < -2.0): These are the dotted/dashed lines. Reaching these zones means the market is in an "extreme" state of deviation—either extremely over-extended (top) or extremely oversold (bottom) relative to its own history.
2. Background Color (Relationship Strength)
The background color tells you how reliable the indicator's main signal is right now.
Blue Background: High strength. The historical Price-RSI relationship is stable and consistent. The oscillator's readings are reliable.
Orange Background: Low strength. The historical relationship is weak, volatile, or inconsistent. The oscillator's readings are less reliable—the market is choppy or "out of character."
3. Diamonds (Extreme Reversal Signals)
These diamonds appear at potential exhaustion points.
Aqua Diamond (at bottom): An "Extreme Bullish Reversal." This appears when the indicator was at an extremely negative (bearish) level and has just started to turn up. It's a potential bottoming signal.
Fuchsia Diamond (at top): An "Extreme Bearish Reversal." This appears when the indicator was at an extremely positive (bullish) level and has just started to turn down. It's a potential topping signal.
4. The Info Table (Top Right)
This table provides a snapshot of the current state:
RSI/Price: Your current values.
Expected Price: The price the indicator "expects" to see based on the current RSI and historical data. This is the most important number.
Relational Decay: The main oscillator's value. It's essentially the difference between the Current Price and the Expected Price, normalized.
State: A simple text description (e.g., "Stable," "Strong Bullish Decay").
Matches Found: How many historical data points the script found to make its calculation.
Strength: The "Relationship Strength" (background color) as a percentage.
Key User Inputs
RSI Period (14): The lookback for the standard RSI calculation.
Historical Lookback (500): How many past bars the indicator should analyze to build its "normal" model. A larger number gives it more historical context.
RSI Similarity Threshold (3.0): How close the current RSI must be to a historical RSI to be considered a "match."
Normalization Method (Z-Score): The statistical method used to scale the output. Z-Score is standard and robust. "Percentile" and "Raw" are other options for different ways of viewing the deviation.
Squeeze Momentum Early In and Out CandlesJohn Carter presented some candles called "Early In and Out Candles". Although I couldn't imitate the exact candles and warnings I create better indications and bars in my opinion.
When the Candles are above Donchian MA then we have a bullish Momentum.
When the Candles are bellow Donchian MA then we have bearish momentum.
This indicator works best to get an WARNING to enter and close EARLY positions.
Bullish:
When the candles are Light Blue then we have early warning to enter.
When the candles are Dark Blue then we have early warning to close the position.
Bearish:
When the candles are Red then we have early warning to enter.
When the candles are Yellow then we have early warning to close the position.
IMPORTANT NOTES:
Always combine it with the Squeeze Pro indicator.
Suggested Donchian MA: 5 (You can adjust it).
Don't let candles only to be your closing indication once again there are EARLY WARNINGS therefore can move your stop loses to maximize your profits when you are exiting.
I tested my self and I found that is the best strategy when we get Dark Blue candle in the Bullish move I move my stop loss little bit bellow the candle.
Therefore here we go we have early warnings for In and Out.
Thank you and Good Luck.
Algorithm Predator - ML-liteAlgorithm Predator - ML-lite
This indicator combines four specialized trading agents with an adaptive multi-armed bandit selection system to identify high-probability trade setups. It is designed for swing and intraday traders who want systematic signal generation based on institutional order flow patterns , momentum exhaustion , liquidity dynamics , and statistical mean reversion .
Core Architecture
Why These Components Are Combined:
The script addresses a fundamental challenge in algorithmic trading: no single detection method works consistently across all market conditions. By deploying four independent agents and using reinforcement learning algorithms to select or blend their outputs, the system adapts to changing market regimes without manual intervention.
The Four Trading Agents
1. Spoofing Detector Agent 🎭
Detects iceberg orders through persistent volume at similar price levels over 5 bars
Identifies spoofing patterns via asymmetric wick analysis (wicks exceeding 60% of bar range with volume >1.8× average)
Monitors order clustering using simplified Hawkes process intensity tracking (exponential decay model)
Signal Logic: Contrarian—fades false breakouts caused by institutional manipulation
Best Markets: Consolidations, institutional trading windows, low-liquidity hours
2. Exhaustion Detector Agent ⚡
Calculates RSI divergence between price movement and momentum indicator over 5-bar window
Detects VWAP exhaustion (price at 2σ bands with declining volume)
Uses VPIN reversals (volume-based toxic flow dissipation) to identify momentum failure
Signal Logic: Counter-trend—enters when momentum extreme shows weakness
Best Markets: Trending markets reaching climax points, over-extended moves
3. Liquidity Void Detector Agent 💧
Measures Bollinger Band squeeze (width <60% of 50-period average)
Identifies stop hunts via 20-bar high/low penetration with immediate reversal and volume spike
Detects hidden liquidity absorption (volume >2× average with range <0.3× ATR)
Signal Logic: Breakout anticipation—enters after liquidity grab but before main move
Best Markets: Range-bound pre-breakout, volatility compression zones
4. Mean Reversion Agent 📊
Calculates price z-scores relative to 50-period SMA and standard deviation (triggers at ±2σ)
Implements Ornstein-Uhlenbeck process scoring (mean-reverting stochastic model)
Uses entropy analysis to detect algorithmic trading patterns (low entropy <0.25 = high predictability)
Signal Logic: Statistical reversion—enters when price deviates significantly from statistical equilibrium
Best Markets: Range-bound, low-volatility, algorithmically-dominated instruments
Adaptive Selection: Multi-Armed Bandit System
The script implements four reinforcement learning algorithms to dynamically select or blend agents based on performance:
Thompson Sampling (Default - Recommended):
Uses Bayesian inference with beta distributions (tracks alpha/beta parameters per agent)
Balances exploration (trying underused agents) vs. exploitation (using proven winners)
Each agent's win/loss history informs its selection probability
Lite Approximation: Uses pseudo-random sampling from price/volume noise instead of true random number generation
UCB1 (Upper Confidence Bound):
Calculates confidence intervals using: average_reward + sqrt(2 × ln(total_pulls) / agent_pulls)
Deterministic algorithm favoring agents with high uncertainty (potential upside)
More conservative than Thompson Sampling
Epsilon-Greedy:
Exploits best-performing agent (1-ε)% of the time
Explores randomly ε% of the time (default 10%, configurable 1-50%)
Simple, transparent, easily tuned via epsilon parameter
Gradient Bandit:
Uses softmax probability distribution over agent preference weights
Updates weights via gradient ascent based on rewards
Best for Blend mode where all agents contribute
Selection Modes:
Switch Mode: Uses only the selected agent's signal (clean, decisive)
Blend Mode: Combines all agents using exponentially weighted confidence scores controlled by temperature parameter (smooth, diversified)
Lock Agent Feature:
Optional manual override to force one specific agent
Useful after identifying which agent dominates your specific instrument
Only applies in Switch mode
Four choices: Spoofing Detector, Exhaustion Detector, Liquidity Void, Mean Reversion
Memory System
Dual-Layer Architecture:
Short-Term Memory: Stores last 20 trade outcomes per agent (configurable 10-50)
Long-Term Memory: Stores episode averages when short-term reaches transfer threshold (configurable 5-20 bars)
Memory Boost Mechanism: Recent performance modulates agent scores by up to ±20%
Episode Transfer: When an agent accumulates sufficient results, averages are condensed into long-term storage
Persistence: Manual restoration of learned parameters via input fields (alpha, beta, weights, microstructure thresholds)
How Memory Works:
Agent generates signal → outcome tracked after 8 bars (performance horizon)
Result stored in short-term memory (win = 1.0, loss = 0.0)
Short-term average influences agent's future scores (positive feedback loop)
After threshold met (default 10 results), episode averaged into long-term storage
Long-term patterns (weighted 30%) + short-term patterns (weighted 70%) = total memory boost
Market Microstructure Analysis
These advanced metrics quantify institutional order flow dynamics:
Order Flow Toxicity (Simplified VPIN):
Measures buy/sell volume imbalance over 20 bars: |buy_vol - sell_vol| / (buy_vol + sell_vol)
Detects informed trading activity (institutional players with non-public information)
Values >0.4 indicate "toxic flow" (informed traders active)
Lite Approximation: Uses simple open/close heuristic instead of tick-by-tick trade classification
Price Impact Analysis (Simplified Kyle's Lambda):
Measures market impact efficiency: |price_change_10| / sqrt(volume_sum_10)
Low values = large orders with minimal price impact ( stealth accumulation )
High values = retail-dominated moves with high slippage
Lite Approximation: Uses simplified denominator instead of regression-based signed order flow
Market Randomness (Entropy Analysis):
Counts unique price changes over 20 bars / 20
Measures market predictability
High entropy (>0.6) = human-driven, chaotic price action
Low entropy (<0.25) = algorithmic trading dominance (predictable patterns)
Lite Approximation: Simple ratio instead of true Shannon entropy H(X) = -Σ p(x)·log₂(p(x))
Order Clustering (Simplified Hawkes Process):
Tracks self-exciting event intensity (coordinated order activity)
Decays at 0.9× per bar, spikes +1.0 when volume >1.5× average
High intensity (>0.7) indicates clustering (potential spoofing/accumulation)
Lite Approximation: Simple exponential decay instead of full λ(t) = μ + Σ α·exp(-β(t-tᵢ)) with MLE
Signal Generation Process
Multi-Stage Validation:
Stage 1: Agent Scoring
Each agent calculates internal score based on its detection criteria
Scores must exceed agent-specific threshold (adjusted by sensitivity multiplier)
Agent outputs: Signal direction (+1/-1/0) and Confidence level (0.0-1.0)
Stage 2: Memory Boost
Agent scores multiplied by memory boost factor (0.8-1.2 based on recent performance)
Successful agents get amplified, failing agents get dampened
Stage 3: Bandit Selection/Blending
If Adaptive Mode ON:
Switch: Bandit selects single best agent, uses only its signal
Blend: All agents combined using softmax-weighted confidence scores
If Adaptive Mode OFF:
Traditional consensus voting with confidence-squared weighting
Signal fires when consensus exceeds threshold (default 70%)
Stage 4: Confirmation Filter
Raw signal must repeat for consecutive bars (default 3, configurable 2-4)
Minimum confidence threshold: 0.25 (25%) enforced regardless of mode
Trend alignment check: Long signals require trend_score ≥ -2, Short signals require trend_score ≤ 2
Stage 5: Cooldown Enforcement
Minimum bars between signals (default 10, configurable 5-15)
Prevents over-trading during choppy conditions
Stage 6: Performance Tracking
After 8 bars (performance horizon), signal outcome evaluated
Win = price moved in signal direction, Loss = price moved against
Results fed back into memory and bandit statistics
Trading Modes (Presets)
Pre-configured parameter sets:
Conservative: 85% consensus, 4 confirmations, 15-bar cooldown
Expected: 60-70% win rate, 3-8 signals/week
Best for: Swing trading, capital preservation, beginners
Balanced: 70% consensus, 3 confirmations, 10-bar cooldown
Expected: 55-65% win rate, 8-15 signals/week
Best for: Day trading, most traders, general use
Aggressive: 60% consensus, 2 confirmations, 5-bar cooldown
Expected: 50-58% win rate, 15-30 signals/week
Best for: Scalping, high-frequency trading, active management
Elite: 75% consensus, 3 confirmations, 12-bar cooldown
Expected: 58-68% win rate, 5-12 signals/week
Best for: Selective trading, high-conviction setups
Adaptive: 65% consensus, 2 confirmations, 8-bar cooldown
Expected: Varies based on learning
Best for: Experienced users leveraging bandit system
How to Use
1. Initial Setup (5 Minutes):
Select Trading Mode matching your style (start with Balanced)
Enable Adaptive Learning (recommended for automatic agent selection)
Choose Thompson Sampling algorithm (best all-around performance)
Keep Microstructure Metrics enabled for liquid instruments (>100k daily volume)
2. Agent Tuning (Optional):
Adjust Agent Sensitivity multipliers (0.5-2.0):
<0.8 = Highly selective (fewer signals, higher quality)
0.9-1.2 = Balanced (recommended starting point)
1.3 = Aggressive (more signals, lower individual quality)
Monitor dashboard for 20-30 signals to identify dominant agent
If one agent consistently outperforms, consider using Lock Agent feature
3. Bandit Configuration (Advanced):
Blend Temperature (0.1-2.0):
0.3 = Sharp decisions (best agent dominates)
0.5 = Balanced (default)
1.0+ = Smooth (equal weighting, democratic)
Memory Decay (0.8-0.99):
0.90 = Fast adaptation (volatile markets)
0.95 = Balanced (most instruments)
0.97+ = Long memory (stable trends)
4. Signal Interpretation:
Green triangle (▲): Long signal confirmed
Red triangle (▼): Short signal confirmed
Dashboard shows:
Active agent (highlighted row with ► marker)
Win rate per agent (green >60%, yellow 40-60%, red <40%)
Confidence bars (█████ = maximum confidence)
Memory size (short-term buffer count)
Colored zones display:
Entry level (current close)
Stop-loss (1.5× ATR)
Take-profit 1 (2.0× ATR)
Take-profit 2 (3.5× ATR)
5. Risk Management:
Never risk >1-2% per signal (use ATR-based stops)
Signals are entry triggers, not complete strategies
Combine with your own market context analysis
Consider fundamental catalysts and news events
Use "Confirming" status to prepare entries (not to enter early)
6. Memory Persistence (Optional):
After 50-100 trades, check Memory Export Panel
Record displayed alpha/beta/weight values for each agent
Record VPIN and Kyle threshold values
Enable "Restore From Memory" and input saved values to continue learning
Useful when switching timeframes or restarting indicator
Visual Components
On-Chart Elements:
Spectral Layers: EMA8 ± 0.5 ATR bands (dynamic support/resistance, colored by trend)
Energy Radiance: Multi-layer glow boxes at signal points (intensity scales with confidence, configurable 1-5 layers)
Probability Cones: Projected price paths with uncertainty wedges (15-bar projection, width = confidence × ATR)
Connection Lines: Links sequential signals (solid = same direction continuation, dotted = reversal)
Kill Zones: Risk/reward boxes showing entry, stop-loss, and dual take-profit targets
Signal Markers: Triangle up/down at validated entry points
Dashboard (Configurable Position & Size):
Regime Indicator: 4-level trend classification (Strong Bull/Bear, Weak Bull/Bear)
Mode Status: Shows active system (Adaptive Blend, Locked Agent, or Consensus)
Agent Performance Table: Real-time win%, confidence, and memory stats
Order Flow Metrics: Toxicity and impact indicators (when microstructure enabled)
Signal Status: Current state (Long/Short/Confirming/Waiting) with confirmation progress
Memory Panel (Configurable Position & Size):
Live Parameter Export: Alpha, beta, and weight values per agent
Adaptive Thresholds: Current VPIN sensitivity and Kyle threshold
Save Reminder: Visual indicator if parameters should be recorded
What Makes This Original
This script's originality lies in three key innovations:
1. Genuine Meta-Learning Framework:
Unlike traditional indicator mashups that simply display multiple signals, this implements authentic reinforcement learning (multi-armed bandits) to learn which detection method works best in current conditions. The Thompson Sampling implementation with beta distribution tracking (alpha for successes, beta for failures) is statistically rigorous and adapts continuously. This is not post-hoc optimization—it's real-time learning.
2. Episodic Memory Architecture with Transfer Learning:
The dual-layer memory system mimics human learning patterns:
Short-term memory captures recent performance (recency bias)
Long-term memory preserves historical patterns (experience)
Automatic transfer mechanism consolidates knowledge
Memory boost creates positive feedback loops (successful strategies become stronger)
This architecture allows the system to adapt without retraining , unlike static ML models that require batch updates.
3. Institutional Microstructure Integration:
Combines retail-focused technical analysis (RSI, Bollinger Bands, VWAP) with institutional-grade microstructure metrics (VPIN, Kyle's Lambda, Hawkes processes) typically found in academic finance literature and professional trading systems, not standard retail platforms. While simplified for Pine Script constraints, these metrics provide insight into informed vs. uninformed trading , a dimension entirely absent from traditional technical analysis.
Mashup Justification:
The four agents are combined specifically for risk diversification across failure modes:
Spoofing Detector: Prevents false breakout losses from manipulation
Exhaustion Detector: Prevents chasing extended trends into reversals
Liquidity Void: Exploits volatility compression (different regime than trending)
Mean Reversion: Provides mathematical anchoring when patterns fail
The bandit system ensures the optimal tool is automatically selected for each market situation, rather than requiring manual interpretation of conflicting signals.
Why "ML-lite"? Simplifications and Approximations
This is the "lite" version due to necessary simplifications for Pine Script execution:
1. Simplified VPIN Calculation:
Academic Implementation: True VPIN uses volume bucketing (fixed-volume bars) and tick-by-tick buy/sell classification via Lee-Ready algorithm or exchange-provided trade direction flags
This Implementation: 20-bar rolling window with simple open/close heuristic (close > open = buy volume)
Impact: May misclassify volume during ranging/choppy markets; works best in directional moves
2. Pseudo-Random Sampling:
Academic Implementation: Thompson Sampling requires true random number generation from beta distributions using inverse transform sampling or acceptance-rejection methods
This Implementation: Deterministic pseudo-randomness derived from price and volume decimal digits: (close × 100 - floor(close × 100)) + (volume % 100) / 100
Impact: Not cryptographically random; may have subtle biases in specific price ranges; provides sufficient variation for agent selection
3. Hawkes Process Approximation:
Academic Implementation: Full Hawkes process uses maximum likelihood estimation with exponential kernels: λ(t) = μ + Σ α·exp(-β(t-tᵢ)) fitted via iterative optimization
This Implementation: Simple exponential decay (0.9 multiplier) with binary event triggers (volume spike = event)
Impact: Captures self-exciting property but lacks parameter optimization; fixed decay rate may not suit all instruments
4. Kyle's Lambda Simplification:
Academic Implementation: Estimated via regression of price impact on signed order flow over multiple time intervals: Δp = λ × Δv + ε
This Implementation: Simplified ratio: price_change / sqrt(volume_sum) without proper signed order flow or regression
Impact: Provides directional indicator of impact but not true market depth measurement; no statistical confidence intervals
5. Entropy Calculation:
Academic Implementation: True Shannon entropy requires probability distribution: H(X) = -Σ p(x)·log₂(p(x)) where p(x) is probability of each price change magnitude
This Implementation: Simple ratio of unique price changes to total observations (variety measure)
Impact: Measures diversity but not true information entropy with probability weighting; less sensitive to distribution shape
6. Memory System Constraints:
Full ML Implementation: Neural networks with backpropagation, experience replay buffers (storing state-action-reward tuples), gradient descent optimization, and eligibility traces
This Implementation: Fixed-size array queues with simple averaging; no gradient-based learning, no state representation beyond raw scores
Impact: Cannot learn complex non-linear patterns; limited to linear performance tracking
7. Limited Feature Engineering:
Advanced Implementation: Dozens of engineered features, polynomial interactions (x², x³), dimensionality reduction (PCA, autoencoders), feature selection algorithms
This Implementation: Raw agent scores and basic market metrics (RSI, ATR, volume ratio); minimal transformation
Impact: May miss subtle cross-feature interactions; relies on agent-level intelligence rather than feature combinations
8. Single-Instrument Data:
Full Implementation: Multi-asset correlation analysis (sector ETFs, currency pairs, volatility indices like VIX), lead-lag relationships, risk-on/risk-off regimes
This Implementation: Only OHLCV data from displayed instrument
Impact: Cannot incorporate broader market context; vulnerable to correlated moves across assets
9. Fixed Performance Horizon:
Full Implementation: Adaptive horizon based on trade duration, volatility regime, or profit target achievement
This Implementation: Fixed 8-bar evaluation window
Impact: May evaluate too early in slow markets or too late in fast markets; one-size-fits-all approach
Performance Impact Summary:
These simplifications make the script:
✅ Faster: Executes in milliseconds vs. seconds (or minutes) for full academic implementations
✅ More Accessible: Runs on any TradingView plan without external data feeds, APIs, or compute servers
✅ More Transparent: All calculations visible in Pine Script (no black-box compiled models)
✅ Lower Resource Usage: <500 bars lookback, minimal memory footprint
⚠️ Less Precise: Approximations may reduce statistical edge by 5-15% vs. academic implementations
⚠️ Limited Scope: Cannot capture tick-level dynamics, multi-order-book interactions, or cross-asset flows
⚠️ Fixed Parameters: Some thresholds hardcoded rather than dynamically optimized
When to Upgrade to Full Implementation:
Consider professional Python/C++ versions with institutional data feeds if:
Trading with >$100K capital where precision differences materially impact returns
Operating in microsecond-competitive environments (HFT, market making)
Requiring regulatory-grade audit trails and reproducibility
Backtesting with tick-level precision for strategy validation
Need true real-time adaptation with neural network-based learning
For retail swing/day trading and position management, these approximations provide sufficient signal quality while maintaining usability, transparency, and accessibility. The core logic—multi-agent detection with adaptive selection—remains intact.
Technical Notes
All calculations use standard Pine Script built-in functions ( ta.ema, ta.atr, ta.rsi, ta.bb, ta.sma, ta.stdev, ta.vwap )
VPIN and Kyle's Lambda use simplified formulas optimized for OHLCV data (see "Lite" section above)
Thompson Sampling uses pseudo-random noise from price/volume decimal digits for beta distribution sampling
No repainting: All calculations use confirmed bar data (no forward-looking)
Maximum lookback: 500 bars (set via max_bars_back parameter)
Performance evaluation: 8-bar forward-looking window for reward calculation (clearly disclosed)
Confidence threshold: Minimum 0.25 (25%) enforced on all signals
Memory arrays: Dynamic sizing with FIFO queue management
Limitations and Disclaimers
Not Predictive: This indicator identifies patterns in historical data. It cannot predict future price movements with certainty.
Requires Human Judgment: Signals are entry triggers, not complete trading strategies. Must be confirmed with your own analysis, risk management rules, and market context.
Learning Period Required: The adaptive system requires 50-100 bars minimum to build statistically meaningful performance data for bandit algorithms.
Overfitting Risk: Restoring memory parameters from one market regime to a drastically different regime (e.g., low volatility to high volatility) may cause poor initial performance until system re-adapts.
Approximation Limitations: Simplified calculations (see "Lite" section) may underperform academic implementations by 5-15% in highly efficient markets.
No Guarantee of Profit: Past performance, whether backtested or live-traded, does not guarantee future performance. All trading involves risk of loss.
Forward-Looking Bias: Performance evaluation uses 8-bar forward window—this creates slight look-ahead for learning (though not for signals). Real-time performance may differ from indicator's internal statistics.
Single-Instrument Limitation: Does not account for correlations with related assets or broader market regime changes.
Recommended Settings
Timeframe: 15-minute to 4-hour charts (sufficient volatility for ATR-based stops; adequate bar volume for learning)
Assets: Liquid instruments with >100k daily volume (forex majors, large-cap stocks, BTC/ETH, major indices)
Not Recommended: Illiquid small-caps, penny stocks, low-volume altcoins (microstructure metrics unreliable)
Complementary Tools: Volume profile, order book depth, market breadth indicators, fundamental catalysts
Position Sizing: Risk no more than 1-2% of capital per signal using ATR-based stop-loss
Signal Filtering: Consider external confluence (support/resistance, trendlines, round numbers, session opens)
Start With: Balanced mode, Thompson Sampling, Blend mode, default agent sensitivities (1.0)
After 30+ Signals: Review agent win rates, consider increasing sensitivity of top performers or locking to dominant agent
Alert Configuration
The script includes built-in alert conditions:
Long Signal: Fires when validated long entry confirmed
Short Signal: Fires when validated short entry confirmed
Alerts fire once per bar (after confirmation requirements met)
Set alert to "Once Per Bar Close" for reliability
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Stochastic + Bollinger Bands Multi-Timeframe StrategyThis strategy fuses the Stochastic Oscillator from the 4-hour timeframe with Bollinger Bands from the 1-hour timeframe, operating on a 10-hour chart to capture a unique volatility rhythm and temporal alignment discovered through observational alpha.
By blending momentum confirmation from the higher timeframe with short-term volatility extremes, the strategy leverages what some traders refer to as “rotating volatility” — a phenomenon where multi-timeframe oscillations sync to reveal hidden trade opportunities.
🧠 Strategy Logic
✅ Long Entry Condition:
Stochastic on the 4H timeframe:
%K crosses above %D
Both %K and %D are below 20 (oversold zone)
Bollinger Bands on the 1H timeframe:
Price crosses above the lower Bollinger Band, indicating a potential reversal
→ A long trade is opened when both momentum recovery and volatility reversion align.
✅ Long Exit Condition:
Stochastic on the 4H:
%K crosses below %D
Both %K and %D are above 80 (overbought zone)
Bollinger Bands on the 1H:
Price reaches or exceeds the upper Bollinger Band, suggesting exhaustion
→ The long trade is closed when either signal suggests a potential reversal or overextension.
🧬 Temporal Structure & Alpha
This strategy is deployed on a 10-hour chart — a non-standard timeframe that may align more effectively with multi-timeframe mean reversion dynamics.
This subtle adjustment exploits what some traders identify as “temporal drift” — the desynchronization of volatility across timeframes that creates hidden rhythm in price action.
→ For example, Stochastic on 4H (lookback 17) and Bollinger Bands on 1H (lookback 20) may periodically sync around 10H intervals, offering unique alpha windows.
📊 Indicator Components
🔹 Stochastic Oscillator (4H, Length 17)
Detects momentum reversals using %K and %D crossovers
Helps define overbought/oversold zones from a mid-term view
🔹 Bollinger Bands (1H, Length 20, ±2 StdDev)
Measures price volatility using standard deviation around a moving average
Entry occurs near lower band (support), exits near upper band (resistance)
🔹 Multi-Timeframe Logic
Uses request.security() to safely reference 4H and 1H indicators from a 10H chart
Avoids repainting by using closed higher-timeframe candles only
📈 Visualization
A plot selector input allows toggling between:
Stochastic Plot (%K & %D, with overbought/oversold levels)
Bollinger Bands Plot (Upper, Basis, Lower from 1H data)
This helps users visually confirm entry/exit triggers in real time.
🛠 Customization
Fully configurable Stochastic and BB settings
Timeframes are independently adjustable
Strategy settings like position sizing, slippage, and commission are editable
⚠️ Disclaimer
This strategy is intended for educational and informational purposes only.
It does not constitute financial advice or a recommendation to buy or sell any asset.
Market conditions vary, and past performance does not guarantee future results.
Always test any trading strategy in a simulated environment and consult a licensed financial advisor before making real-world investment decisions.
Liquidity Absorption OscillatorDescription:
The Liquidity Absorption Oscillator (LAO) is a sophisticated momentum indicator that measures how efficiently price moves relative to trading range while confirming momentum with volume-based liquidity flows. By combining price efficiency analysis with volume velocity, the LAO provides earlier and more reliable signals than traditional price-only oscillators, helping traders identify high-probability trend initiations and reversals.
🔍 Core Technology & Innovation:
Tri-Component Signal Processing:
Price Efficiency Ratio (PER): Measures how "cleanly" price moves by comparing net displacement to total trading range over the lookback period. High PER indicates trending markets with directional conviction.
Volume Velocity Ratio (VVR): Combines price momentum with volume confirmation, normalized by ATR to ensure consistent behavior across different instruments and volatility regimes.
Adaptive Smoothing: Dynamically adjusts responsiveness based on market conditions - becoming more stable during noisy periods and more responsive in clean trends.
Multi-Layer Signal Detection:
Confirmed Crossovers: Traditional zero-line crosses filtered by efficiency thresholds
Early Momentum Signals: Detects momentum shifts BEFORE zero-line crosses for optimal entry timing
Smart Divergence Detection: Identifies hidden bullish/bearish divergences with built-in quality filters
🎯 Trading Signals & Interpretation:
🟢 BULLISH SIGNALS:
Strong Buy: LAO crosses above zero line with medium/high efficiency (PER)
Early Buy: Momentum accelerates while LAO is still negative (anticipates reversal)
Divergence Buy: Price makes lower low while LAO forms higher low
🔴 BEARISH SIGNALS:
Strong Sell: LAO crosses below zero line with medium/high efficiency
Early Sell: Momentum decelerates while LAO is still positive (anticipates top)
Divergence Sell: Price makes higher high while LAO forms lower high
⚪ SIGNAL QUALITY FILTERING:
Automatic signal suppression during low-efficiency (choppy) market conditions
Configurable PER threshold ensures only high-quality signals are considered
📊 Visual Features:
Clean Oscillator Display: Smooth line plot with gradient fills above/below zero line
Multiple Coloring Options: Choose between no coloring, trend-based, or slope-based bar coloring
Professional Styling: Inspired by institutional-grade indicator design with subtle visual cues
Non-Repainting Logic: All signals confirmed on bar close for reliable backtesting
⚙️ Input Parameters:
Core Settings:
Lookback Period: Base period for efficiency and velocity calculations (default: 24)
Base Smooth Period: Starting point for adaptive smoothing (default: 8)
Min Efficiency for Signals: PER threshold for signal validation (default: 35)
Divergence Lookback: Bars to search for divergence patterns (default: 5)
UI Options:
Bar Coloring: Choose visual style (None, Trend, Slope)
🔔 Alert Conditions:
Buy/Sell Signal: Traditional zero-line crosses with quality filtering
Early Buy/Early Sell: Momentum-based signals before traditional crosses
All alerts use confirmed, non-repainting logic
Market Profile Dominance Analyzer# Market Profile Dominance Analyzer
## 📊 OVERVIEW
**Market Profile Dominance Analyzer** is an advanced multi-factor indicator that combines Market Profile methodology with composite dominance scoring to identify buyer and seller strength across higher timeframes. Unlike traditional volume profile indicators that only show volume distribution, or simple buyer/seller indicators that only compare candle colors, this script integrates six distinct analytical components into a unified dominance measurement system.
This indicator helps traders understand **WHO controls the market** by analyzing price position relative to Market Profile key levels (POC, Value Area) combined with volume distribution, momentum, and trend characteristics.
## 🎯 WHAT MAKES THIS ORIGINAL
### **Hybrid Analytical Approach**
This indicator uniquely combines two separate methodologies that are typically analyzed independently:
1. **Market Profile Analysis** - Calculates Point of Control (POC) and Value Area (VA) using volume distribution across price channels on higher timeframes
2. **Multi-Factor Dominance Scoring** - Weights six independent factors to produce a composite dominance index
### **Six-Factor Composite Analysis**
The dominance score integrates:
- Price position relative to POC (equilibrium assessment)
- Price position relative to Value Area boundaries (acceptance/rejection zones)
- Volume imbalance within Value Area (institutional bias detection)
- Price momentum (directional strength)
- Volume trend comparison (participation analysis)
- Normalized Value Area position (precise location within fair value zone)
### **Adaptive Higher Timeframe Integration**
The script features an intelligent auto-selection system that automatically chooses appropriate higher timeframes based on the current chart period, ensuring optimal Market Profile structure regardless of the trading timeframe being analyzed.
## 💡 HOW IT WORKS
### **Market Profile Construction**
The indicator builds a Market Profile structure on a higher timeframe by:
1. **Session Identification** - Detects new higher timeframe sessions using `request.security()` to ensure accurate period boundaries
2. **Data Accumulation** - Stores high, low, and volume data for all bars within the current higher timeframe session
3. **Channel Distribution** - Divides the session's price range into configurable channels (default: 20 rows)
4. **Volume Mapping** - Distributes each bar's volume proportionally across all price channels it touched
### **Key Level Calculation**
**Point of Control (POC)**
- Identifies the price channel with the highest accumulated volume
- Represents the price level where the most trading activity occurred
- Serves as a magnetic level where price often returns
**Value Area (VA)**
- Starts at POC and expands both upward and downward
- Includes channels until reaching the specified percentage of total volume (default: 70%)
- Expansion algorithm compares adjacent volumes and prioritizes the direction with higher activity
- Defines the "fair value" zone where most market participants agreed to trade
### **Dominance Score Formula**
```
Dominance Score = (price_vs_poc × 10) +
(price_vs_va × 5) +
(volume_imbalance × 0.5) +
(price_momentum × 100) +
(volume_trend × 5) +
(va_position × 15)
```
**Component Breakdown:**
- **price_vs_poc**: +1 if above POC, -1 if below (shows which side of equilibrium)
- **price_vs_va**: +2 if above VAH, -2 if below VAL, 0 if inside VA
- **volume_imbalance**: Percentage difference between upper and lower VA volumes
- **price_momentum**: 5-period SMA of price change (directional acceleration)
- **volume_trend**: Compares 5-period vs 20-period volume averages
- **va_position**: Normalized position within Value Area (-1 to +1)
The composite score is then smoothed using EMA with configurable sensitivity to reduce noise while maintaining responsiveness.
### **Market State Determination**
- **BUYERS Dominant**: Smooth dominance > +10 (bullish control)
- **SELLERS Dominant**: Smooth dominance < -10 (bearish control)
- **NEUTRAL**: Between -10 and +10 (balanced market)
## 📈 HOW TO USE THIS INDICATOR
### **Trend Identification**
- **Green background** indicates buyers are in control - look for long opportunities
- **Red background** indicates sellers are in control - look for short opportunities
- **Gray background** indicates neutral market - consider range-bound strategies
### **Signal Interpretation**
**Buy Signals** (green triangle) appear when:
- Dominance crosses above -10 from oversold conditions
- Previous state was not already bullish
- Suggests shift from seller to buyer control
**Sell Signals** (red triangle) appear when:
- Dominance crosses below +10 from overbought conditions
- Previous state was not already bearish
- Suggests shift from buyer to seller control
### **Value Area Context**
Monitor the information table (top-right) to understand market structure:
- **Price vs POC**: Shows if trading above/below equilibrium
- **Volume Imbalance**: Positive values favor buyers, negative favors sellers
- **Market State**: Current dominant force (BUYERS/SELLERS/NEUTRAL)
### **Multi-Timeframe Strategy**
The auto-timeframe feature analyzes higher timeframe structure:
- On 1-minute charts → analyzes 2-hour structure
- On 5-minute charts → analyzes Daily structure
- On 15-minute charts → analyzes Weekly structure
- On Daily charts → analyzes Yearly structure
This higher timeframe context helps avoid counter-trend trades against the dominant force.
### **Confluence Trading**
Strongest signals occur when multiple factors align:
1. Price above VAH + positive volume imbalance + buyers dominant = Strong bullish setup
2. Price below VAL + negative volume imbalance + sellers dominant = Strong bearish setup
3. Price at POC + neutral state = Potential breakout/breakdown pivot
## ⚙️ INPUT PARAMETERS
- **Higher Time Frame**: Select specific HTF or use 'Auto' for intelligent selection
- **Value Area %**: Percentage of volume contained in VA (default: 70%)
- **Show Buy/Sell Signals**: Toggle signal triangles visibility
- **Show Dominance Histogram**: Toggle histogram display
- **Signal Sensitivity**: EMA period for dominance smoothing (1-20, default: 5)
- **Number of Channels**: Market Profile resolution (10-50, default: 20)
- **Color Settings**: Customize buyer, seller, and neutral colors
## 🎨 VISUAL ELEMENTS
- **Histogram**: Shows smoothed dominance score (green = buyers, red = sellers)
- **Zero Line**: Neutral equilibrium reference
- **Overbought/Oversold Lines**: ±50 levels marking extreme dominance
- **Background Color**: Highlights current market state
- **Information Table**: Displays key metrics (state, dominance, POC relationship, volume imbalance, timeframe, bars in session, total volume)
- **Signal Shapes**: Triangle markers for buy/sell signals
## 🔔 ALERTS
The indicator includes three alert conditions:
1. **Buyers Dominate** - Fires on buy signal crossovers
2. **Sellers Dominate** - Fires on sell signal crossovers
3. **Dominance Shift** - Fires when dominance crosses zero line
## 📊 BEST PRACTICES
### **Timeframe Selection**
- **Scalping (1-5min)**: Focus on 2H-4H dominance shifts
- **Day Trading (15-60min)**: Monitor Daily and Weekly structure
- **Swing Trading (4H-Daily)**: Track Weekly and Monthly dominance
### **Confirmation Strategies**
1. **Trend Following**: Enter in direction of dominance above/below ±20
2. **Reversal Trading**: Fade extreme readings beyond ±50 when diverging with price
3. **Breakout Trading**: Look for dominance expansion beyond ±30 with increasing volume
### **Risk Management**
- Avoid trading during NEUTRAL states (dominance between -10 and +10)
- Use POC levels as logical stop-loss placement
- Consider VAH/VAL as profit targets for mean reversion
## ⚠️ LIMITATIONS & WARNINGS
**Data Requirements**
- Requires sufficient historical data on current chart (minimum 100 bars recommended)
- Lower timeframes may show fewer bars per HTF session initially
- More accurate results after several complete HTF sessions have formed
**Not a Standalone System**
- This indicator analyzes market structure and participant control
- Should be combined with price action, support/resistance, and risk management
- Does not guarantee profitable trades - past dominance does not predict future results
**Repainting Characteristics**
- Higher timeframe levels (POC, VAH, VAL) update as new bars form within the session
- Dominance score recalculates with each new bar
- Historical signals remain fixed, but current session data is developing
**Volume Limitations**
- Uses exchange-provided volume data which varies by instrument type
- Forex and some CFDs use tick volume (not actual transaction volume)
- Most accurate on instruments with reliable volume data (stocks, futures, crypto)
## 🔍 TECHNICAL NOTES
**Performance Optimization**
- Uses `max_bars_back=5000` for extended historical analysis
- Efficient array management prevents memory issues
- Automatic cleanup of session data on new period
**Calculation Method**
- Market Profile uses actual volume distribution, not TPO (Time Price Opportunity)
- Value Area expansion follows traditional Market Profile auction theory
- All calculations occur on the chart's current symbol and timeframe
## 📚 EDUCATIONAL VALUE
This indicator helps traders understand:
- How institutional traders use Market Profile to identify fair value
- The relationship between price, volume, and market acceptance
- Multi-factor analysis techniques for assessing market conditions
- The importance of higher timeframe structure in trade planning
## 🎓 RECOMMENDED READING
To better understand the concepts behind this indicator:
- "Mind Over Markets" by James Dalton (Market Profile foundations)
- "Markets in Profile" by James Dalton (Value Area analysis)
- Volume Profile analysis in institutional trading
## 💬 USAGE TERMS
This indicator is provided as an educational and analytical tool. It does not constitute financial advice, investment recommendations, or trading signals. Users are responsible for their own trading decisions and should conduct their own research and due diligence.
Trading involves substantial risk of loss. Past performance does not guarantee future results. Always use proper risk management and never risk more than you can afford to lose.
