Dynamic Resistance and Support LinesThis script is designed to dynamically plot support and resistance lines based on full-dollar and half-dollar price levels relative to the close price on a chart. The script is particularly useful for day traders and scalpers, as it helps visualize key psychological price levels that often act as support and resistance zones in volatile and fast-moving markets in real time.
Key Features:
Dynamic Resistance and Support Levels:
Full-dollar levels: These are calculated by rounding the close price to the nearest full dollar and then extending the levels by adding and subtracting increments of 1 (e.g., $1, $2, $3).
Half-dollar levels: These are calculated by adding and subtracting 0.5 increments to the nearest full-dollar price, providing additional reference points. The historical full-dollar levels remain where support and resistance may have occurred in the past.
Extend Lines:
You can toggle whether the support and resistance lines are extended to the right, left, or both directions. This allows flexibility in projecting potential future areas of support or resistance.
Custom Line Extension:
The user can set the number of bars (or time periods) that the support and resistance lines will extend, giving control over how long the levels remain on the chart.
Color-Coded Lines:
Red lines represent full-dollar resistance and support levels.
Blue lines represent half-dollar levels, making it easy to differentiate between key psychological price zones.
Line Flexibility:
The script allows the lines to extend both left and right on the chart, making it useful for analyzing historical price action or projecting future price movements. The number of bars for extension is customizable, allowing for tailored setups.
Nearest Full Dollar Plot:
The nearest full-dollar price level is plotted as a yellow circle on the chart. This serves as a quick visual cue for traders to monitor price proximity to critical levels.
Benefits in Day Trading, Scalping, and Volatile Markets:
Visualizing Key Psychological Levels:
Full-dollar and half-dollar price levels often act as psychological barriers for traders. This script helps traders easily identify these levels, which are important in both fast-moving markets and during sideways consolidation.
Improved Decision-Making:
By automatically drawing these support and resistance levels, the script helps day traders and scalpers make quicker and more informed decisions, especially in volatile markets where every second counts.
Adaptability to Market Conditions:
The flexibility of extending lines based on trader preferences allows the user to adapt the script to various market conditions, such as high volatility or trend-based trading, providing a clear view of potential breakout or reversal areas.
Better Risk Management:
Having predefined support and resistance levels helps traders better manage risk, as these levels can act as logical areas for setting stop losses or taking profits.
This script is especially valuable for traders looking to capitalize on quick market movements or identify key entry and exit points during market volatility.
Wyszukaj w skryptach "scalp"
Negroni Opening Range StrategyStrategy Summary:
This tool can be used to help identify breakouts from a range during a time-zone of your choosing. It plots a pre-market range, an opening range, it also includes moving average levels that can be used as confluence, as well as plotting previous day SESSION highs and lows.
There are several options on how you wish to close out the trades, all described in more detail below.
Back-testing Inputs:
You define your timezone.
You define how many trades to open on any given day.
You decide to go: long only, short only, or long & short (CAREFUL: "Long & Short" can open trades that effectively closes-out existing ones, for better AND worse!)
You define between which times the strategy will open trades.
You define when it closes any open trades (preventing overnight trades, or leaving trades open into US data times!!).
This hopefully helps make back-testing reflect YOUR trading hours.
NOTE: Renko or Heikin-Ashi charts
For ALL strategies, don’t use Renko or Heikin-Ashi charts unless you know EXACTLY the implications.
Specific to my strategy, using a renko chart can make this 85-90% profitable (I wish it was!!) Although they can be useful, renko charts don’t always capture real wicks, so the renko chart may show your trade up-only but your broker (who is not using renko!!) will have likely stopped you out on a wick somewhere along the line.
NOTE: TradingView ‘Deep backtesting’
For ALL strategies, be cynical of all backtesting (e.g. repainting issues etc) as well as ‘Deep backtesting’ results.
Specific to this strategy, the default settings here SHOULD BE OK, but unfortunately at the time of writing, we can’t see on the chart what exactly ‘deep backtesting’ is calculating. In the past I have noted a number of trades that were not closed at the end of the day, despite my ‘end of day’ trade closing being enabled, so there were big winners and losers that would not have materialized otherwise. As I say, this seems ok at these settings but just always be cynical!!
Opening Range Inputs
You define a pre-market range (example: 08:00 - 09:00).
You define an opening range (example: 09:00 - 09:30).
The strategy will give an update at the close of the opening range to let you know if the opening range has broken out the pre-market range (OR Breakout), or if it has remained inside (OR Inside). The label appears at the end of the opening range NOT at the bar that ‘broke-out’.
This is just a visual cue for you, it has no bearing on what the strategy will do.
The strategy default will trade off the pre-market range, but you can untick this if you prefer to trade off the opening range.
Opening Trades:
Strategy goes long when the bar (CLOSE) crosses-over the ‘pre-market’ high (not the ‘opening range’ high); and the time is within your trading session, and you have not maxed out your number of trades for the day!
Strategy goes short when the bar (CLOSE) crosses-under the ‘pre-market’ low (not the ‘opening range low); and the time is within your trading session, and you have not maxed out your number of trades for the day!
Remember, you can untick this if you prefer to trade off the opening range instead.
NOTES:
Using momentum indicators can help (RSI and MACD): especially to trade range plays in failed breakouts, when momentum shifts… but the strategy won’t do this for you!
Using an anchored vwap at the session open can also provide nice confluence, as well as take-profit levels at the upper/lower of 3x standard deviation.
CLOSING TRADES:
You have 6 take-profit (TP) options:
1) Full TP: uses ATR Multiplier - Full TP at the ATR parameters as defined in inputs.
2) Take Partial profits: ATR Multiplier - Takes partial profits based on parameters as defined in inputs (i.e close 40% of original trade at TP1, close another 40% of original trade at TP2, then the remainder at Full TP as set in option 1.).
3) Full TP: Trailing Stop - Applies a Trailing Stop at the number of points, as defined in inputs.
4) Full TP: MA cross - Takes profit when price crosses ‘Trend MA’ as defined in inputs.
5) Scalp: Points - closes at a set number of points, as defined in inputs.
6) Full TP: PMKT Multiplier - places a SL at opposite pre-market Hi/Low (we go long at a break-out of the pre-market high, 50% would place a SL at the pre-market range mid-point; 100% would place a SL at the pre-market low)'. This takes profit at the input set in option 1).
Enhanced High Volume AbsorptionDescription of the "Enhanced High Volume Absorption" Indicator
The "Enhanced High Volume Absorption" indicator is a specialized trading tool designed for the TradingView platform, optimized for the 15-minute chart timeframe. It offers traders a unique approach to analyzing market momentum and strength by focusing on significant volume movements, which are often precursors to major price shifts.
What the Indicator Does:
High Volume Detection: This indicator identifies periods of high volume trading, which is a key indicator of strong market interest. High volume periods often precede significant price movements, making this an essential tool for anticipating market trends.
Volume Absorption Analysis: It analyzes the absorption of volume in the market. Absorption here refers to situations where the market is able to absorb trading volumes significantly higher than the average without a corresponding substantial change in price. This can be an indication of strong underlying market strength or weakness.
Price Movement Correlation: The script correlates volume spikes with price movements (upward or downward) to provide context to the volume absorption. This correlation helps determine whether the absorption is due to buying pressure (bullish indication) or selling pressure (bearish indication).
How It Does It:
Moving Average Comparisons: The script calculates short-term and long-term Simple Moving Averages (SMAs) of trading volumes. By comparing current volumes to these averages, it determines if the current volume is significantly higher than usual.
Volume Thresholds: It uses user-defined multipliers and minimum volume thresholds to filter significant volume events, ensuring that only notable volume spikes are considered.
Impact Analysis: Alongside volume analysis, the script computes the price change and its impact as a percentage of the current price, providing insights into the magnitude of price movements during these high-volume periods.
How to Use It:
Market Entry and Exit Points: The indicator can be used to spot potential entry and exit points. For example, a high volume absorption event with a minimal price change might indicate a strong support or resistance level.
Confirming Market Sentiment: It can be used in conjunction with other technical indicators to confirm market trends or reversals. High volume absorption aligned with other bullish or bearish indicators can provide a stronger case for a market move.
Scalping and Short-Term Trading: Optimized for the 15-minute timeframe, this indicator is particularly useful for scalpers and short-term traders. It helps in identifying quick market movements and can be a crucial part of a scalping strategy.
Originality and Underlying Concepts:
The originality of this indicator lies in its specific focus on volume absorption and its impact on price, especially tailored for short-term trading scenarios. Unlike many indicators that only analyze price movements or standard volume analysis, this script delves deeper into how the market is reacting to volume spikes, offering a nuanced view of market dynamics
that is often overlooked. The concept of volume absorption, coupled with the analysis of price movement direction, provides a unique perspective on market strength or weakness.
This tool is distinct in its approach as it doesn't just follow trends or provide generic scalping signals. Instead, it offers a methodical analysis of volume dynamics in relation to price action. By focusing on how the market absorbs volume, the indicator gives traders insights into whether current market movements are backed by substantial trading activity or if they are more likely to be short-lived.
Understanding volume absorption is crucial, especially in a 15-minute trading environment where market movements are swift and require quick decision-making. This indicator aids in identifying those moments when the market shows a significant reaction (or lack thereof) to large volumes, indicating potential setup for a strong move or reversal.
In summary, the "Enhanced High Volume Absorption" indicator is a valuable tool for traders who want to incorporate volume analysis into their trading strategy, especially in a fast-paced, short-term trading environment. It provides a deeper understanding of market dynamics, enabling traders to make more informed decisions based on the interplay between volume and price action.
[Sniper] SSL Hybrid + QQE MOD + Waddah Attar StrategyHi. I’m DuDu95.
**********************************************************************************
This is the script for the series called "Sniper".
*** What is "Sniper" Series? ***
"Sniper" series is the project that I’m going to start.
In "Sniper" Series, I’m going to "snipe and shoot" the youtuber’s strategy: to find out whether the youtuber’s video about strategy is "true or false".
Specifically, I’m going to do the things below.
1. Implement "Youtuber’s strategy" into pinescript code.
2. Then I will "backtest" and prove whether "the strategy really works" in the specific ticker (e.g. BTCUSDT) for the specific timeframe (e.g. 5m).
3. Based on the backtest result, I will rate and judge whether the youtube video is "true" or "false", and then rate the validity, reliability, robustness, of the strategy. (like a lie detector)
*** What is the purpose of this series? ***
1. To notify whether the strategy really works for the people who watched the youtube video.
2. To find and build my own scalping / day trading strategy that really works.
**********************************************************************************
*** Strategy Description ***
This strategy is from "SSL QQE MOD 5MIN SCALPING STRATEGY" by youtuber "Daily Investments".
"Daily Investments" claimed that this strategy will make you some money from 100 trades in any ticker in 5 minute timeframe.
### Entry Logic
1. Long Entry Logic
- close > SSL Hybrid Baseline.
- QQE MOD should turn into blue color.
- Waddah Attar Explosion indicator must be green.
2. Short Entry Logic
- close < SSL Hybrid Baseline
- QQE MOD should turn into red color.
- Waddah Attar Explosion indicator must be red.
### Exit Logic
1. Long Exit Logic
- When QQE MOD turn into red color.
2. Short Entry Logic
- When QQE MOD turn into blue color.
### StopLoss
1. Can Choose Stop Loss Type: Percent, ATR, Previous Low / High.
2. Can Chosse inputs of each Stop Loss Type.
### Take Profit
1. Can set Risk Reward Ratio for Take Profit.
- To simplify backtest, I erased all other options except RR Ratio.
- You can add Take Profit Logic by adding options in the code.
2. Can set Take Profit Quantity.
### Risk Manangement
1. Can choose whether to use Risk Manangement Logic.
- This controls the Quantity of the Entry.
- e.g. If you want to take 3% risk per trade and stop loss price is 6% below the long entry price,
then 50% of your equity will be used for trade.
2. Can choose How much risk you would take per trade.
### Plot
1. Added Labels to check the data of entry / exit positions.
2. Changed and Added color different from the original one. (green: #02732A, red: #D92332, yellow: #F2E313)
3. SSL Hybrid Baseline is by default drawn on the chart.
4. If you check EMA filter, EMA would be drawn on the chart.
5. Should add QQE MOD and Waddah Attar Explosion indicator manually if you want to see QQE MOD.
**********************************************************************************
*** Rating: True or False?
### Rating:
→ 1.5 / 5 (0 = Trash, 1 = Bad, 2 = Not Good, 3 = Good, 4 = Great, 5 = Excellent)
### True or False?
→ False
→ Doesn't Work on 5 minute timeframe. Also, it doesn't work on crypto.
### Better Option?
→ Use this for Day trading or Swing Trading, not for Scalping. (Bigger Timeframe)
→ Although the result was bad at 5 minute timeframe, it was profitable in 1h, 2h, 4h, 8h, 1d timeframe.
→ BTC, ETH was ok.
→ The result was better when I use EMA filter (only on longer timeframe).
### Robust?
→ So So. Although result was bad in short timeframe (e.g. 30m 15m 5m), backtest result was "consistently" profitable on longer timeframe.
→ Also, MDD was not that bad under risk management option on.
**********************************************************************************
*** Conclusion?
→ Don't use this on short timeframe.
→ Better use on longer timeframe with filter, stoploss and risk management.
VIX Volatility Trend Analysis With Signals - Stocks OnlyVIX VOLATILITY TREND ANALYSIS CLOUD WITH BULLISH & BEARISH SIGNALS - STOCKS ONLY
This indicator is a visual aid that shows you the bullish or bearish trend of VIX market volatility so you can see the VIX trend without switching charts. When volatility goes up, most stocks go down and vice versa. When the cloud turns green, it is a bullish sign. When the cloud turns red, it is a bearish sign.
This indicator is meant for stocks with a lot of price action and volatility, so for best results, use it on charts that move similar to the S&P 500 or other similar charts.
This indicator uses real time data from the stock market overall, so it should only be used on stocks and will only give a few signals during after hours. It does work ok for crypto, but will not give signals when the US stock market is closed.
**HOW TO USE**
When the VIX Volatility Index trend changes direction, it will give a green or red line on the chart depending on which way the VIX is now trending. The cloud will also change color depending on which way the VIX is trending. Use this to determine overall market volatility and place trades in the direction that the indicator is showing. Do not use this by itself as sometimes markets won’t react perfectly to the overall market volatility. It should only be used as a secondary confirmation in your trading/trend analysis.
For more signals with earlier entries, go into settings and reduce the number. 10-100 is best for scalping. For less signals with later entries, change the number to a higher value. Use 100-500 for swing trades. Can go higher for long swing trades. Our favorite settings are 20, 60, 100, 500 and 1000.
***MARKETS***
This indicator should only be used on the US stock markets as signals are given based on the VIX volatility index which measures volatility of the US Stock Markets.
***TIMEFRAMES***
This indicator works on all time frames, but after hours will not change much at all due to the markets being closed.
**INVERSE CHARTS**
If you are using this on an inverse ETF and the signals are showing backwards, please comment with what chart it is and I will configure the indicator to give the correct signals. I have included over 50 inverse ETFs into the code to show the correct signals on inverse charts, but I'm sure there are some that I have missed so feel free to let me know and I will update the script with the requested tickers.
***TIPS***
Try using numerous indicators of ours on your chart so you can instantly see the bullish or bearish trend of multiple indicators in real time without having to analyze the data. Some of our favorites are our Auto Fibonacci, Directional Movement Index, Volume Profile with buy & sell pressure, Auto Support And Resistance, Vix Scalper and Money Flow Index in combination with this Vix Trend Analysis. They all have real time Bullish and Bearish labels as well so you can immediately understand each indicator's trend.
Ghosty's Modded Super Bandpass Filter [DasanC]Very cool Indicator from Ehlers and published originally by @DasanC
I made minor modifications, and added a zero line and changed some values. I use this indicator differently then it is intended to be used for scalping shorter time frames (15 min - 1 hour).
I use it like a cross over, either from the zeroline or when it passes the RMS, for 5-10 pips. While no indicator is 100% this one does a nice job for small scalps.
try it out on a demo and see if you like it.
enjoy.
original Indy -
Fully Customizable Fusion Strategy (S/R + Dynamic MA)Strategy Name: Ultimate Fusion Strategy (S/R Volume + Dynamic EMAs)
1. Overview
This strategy combines Volume-based Support & Resistance (S/R) with a Dynamic Moving Average Trend System. It is designed to capture high-probability setups by identifying institutional liquidity zones (Volume Boxes) while ensuring trades align with the broader market trend (EMA + MACD + RSI).
2. Key Usage Scenarios
Scenario A: Trend Following (The "Wave Rider")
Condition: The market is in a strong directional trend.
How it works: The script waits for the price to align above all three EMAs (Short/Mid/Long, fully customizable).
Trigger: When RSI > 50 and MACD crosses bullish, the strategy executes a trend-following entry.
Best For: Catching the main leg of a Bull or Bear market.
Scenario B: Structure Trading (Breakouts & Reversals)
Condition: The market is testing key historical levels or consolidating.
How it works: The script identifies high-volume areas and draws Support (Green) and Resistance (Red) boxes.
Trigger:
Bounce: Buy when price tests a Support Box and holds (Buy the Dip).
S/R Flip: Buy when price breaks Resistance, turns it into Support, and retests (Breakout & Retest).
Best For: Entering at the "institutional cost basis" or trading breakouts with volume confirmation.
Scenario C: High Confluence Setups ( The "Perfect Storm")
Condition: Both strategies align.
How it works: Price bounces off a High-Volume Support Box AND the Moving Averages are trending upwards.
Result: This offers the highest win rate as you have both structural support and momentum on your side.
3. Risk Management
Mechanism: Built-in ATR (Average True Range) volatility adjustment.
Stop Loss: Automatically placed dynamically based on market volatility (e.g., 1.5x ATR).
Take Profit: Targets a fixed Reward-to-Risk ratio (e.g., 2.0x), ensuring positive expectancy over the long run.
4. Customizable Settings
Timeframes: Works on all timeframes (Scalping: 1m/5m | Swing: 1h/4h/Daily).
Dynamic Periods: Users can manually input their preferred EMA periods (e.g., Golden Cross 50/200 or Short-term 9/21/55) directly in the settings menu.
Apex Wallet - Real-Time Market Volume Delta & Order FlowOverview The Apex Wallet Market Volume Delta is a professional liquidity analysis tool designed to decode the internal structure of market volume. Unlike standard volume bars, this script calculates the "Delta"—the net difference between buying and selling pressure—to reveal the true conviction of market participants in real-time.
Dynamic Multi-Mode Intelligence This indicator features an adaptive calculation engine that recalibrates its internal logic based on your trading style:
Scalping: Fast-response settings (9-period MA) for immediate execution on low timeframes.
Day-Trading: Balanced settings (26-period MA) optimized for intraday sessions.
Swing-Trading: High-filter settings (52-period MA) for major trend confirmation.
Advanced Order Flow Detection
Real-Time Delta Calculation: Tracks the precise interaction between price and volume to identify aggressive buyers vs. passive sellers.
Dual Calculation Modes: Choose between "Buy/Sell" (aggressive) or "Buy/Sell/Neutral" for a more granular view of flat market periods.
Visual Delta Labels: Displays the net volume values directly above each bar, with color-coded alerts (Green for Bullish Delta, Red for Bearish Delta).
Scalable UI: Features a "Scale Down Factor" to simplify large volume numbers into readable units (10/100/1k/10k).
Key Features:
Visual Split: Clearly differentiates historical volume from real-time buying and selling flows.
Trend Confirmation: Integrated optional EMA to compare current volume surges against the average market liquidity.
Clean Interface: Professional-grade histogram styling with clear demarcation of session activity.
Apex Wallet - Adaptive Commodity Channel Index (CCI) & HTF TrendOverview The Apex Wallet Commodity Channel Index (CCI) is a professional-grade momentum oscillator designed to identify cyclical trends and overbought/oversold conditions with an integrated trend-filtering engine. This script enhances the classic CCI by adding multi-timeframe trend analysis and adaptive calculation modes.
Adaptive Trading Presets The indicator automatically recalibrates its internal periods based on your selected Trading Mode:
Scalping: Uses fast-response settings (CCI 14, Signal 6, Trend 50) for lower timeframes.
Day Trading: Standard balanced settings (CCI 20, Signal 9, Trend 100).
Swing: Long-term filters (CCI 34, Signal 14, Trend 200) to capture major market waves.
Key Features:
Higher Timeframe (HTF) Trend Bias: Optional background shading based on a customizable Higher Timeframe (e.g., 1H trend while trading on 5m) to ensure you always trade in the direction of the "Big Picture".
Market Trend Coloring: The CCI Signal line dynamically changes color (Green/Red/Gray) based on local market momentum relative to its moving average.
Visual Clarity: Features standard CCI level bands (+100, 0, -100) with professional aesthetics for easy reading.
How to Use:
Select your preferred Trading Mode in the settings.
Enable HTF Background to visualize the dominant trend from a higher timeframe.
Look for CCI crosses or signal line color changes while the background confirms the overall market bias.
Apex Wallet - Volume Profile: Institutional POC & Value Area TooOverview The Apex Wallet Volume Profile is a professional-grade institutional analysis tool designed to reveal where the most significant trading activity has occurred. By plotting volume on the vertical price axis, it identifies key liquidity zones, value areas, and market fair value, which are essential for order flow trading and identifying high-probability support and resistance.
Dynamic Multi-Mode Engine This script features an intelligent adaptive lookback system that automatically adjusts based on your timeframe and trading style:
Scalping: Fine-tuned for 1m to 15m charts, focusing on immediate liquidity.
Day-Trading: Optimized for intraday sessions from 5m to 1h timeframes.
Swing-Trading: Deep historical analysis for 1h up to daily charts.
Institutional Data Points
Point of Control (POC): Automatically identifies and highlights the price level with the highest total volume.
Value Area (VAH/VAL): Calculates the range where 70% (customizable) of the volume occurred, representing the "Fair Value" of the asset.
HVN & LVN Detection: Spots High Volume Nodes (significant support/resistance) and Low Volume Nodes (rejection zones).
Delta Visualization: Toggle between Bullish, Bearish, or Total volume distribution for precise buy/sell pressure analysis.
Professional UI The profile is rendered with high-fidelity histograms that can be offset to avoid overlapping with price action. It features clear labels and dashed levels for institutional markers, ensuring a clean and actionable workspace.
QTechLabs Machine Learning Logistic Regression Indicator [Lite]QTechLabs Machine Learning Logistic Regression Indicator
Ver5.1 1st January 2026
Author: QTechLabs
Description
A lightweight logistic-regression-based signal indicator (Q# ML Logistic Regression Indicator ) for TradingView. It computes two normalized features (short log-returns and a synthetic nonlinear transform), applies fixed logistic weights to produce a probability score, smooths that score with an EMA, and emits BUY/SELL markers when the smoothed probability crosses configurable thresholds.
Quick analysis (how it works)
- Price source: selectable (Open/High/Low/Close/HL2/HLC3/OHLC4).
- Features:
- ret = log(ds / ds ) — short log-return over ret_lookback bars.
- synthetic = log(abs(ds^2 - 1) + 0.5) — a nonlinear “synthetic” feature.
- Both features normalized over a 20‑bar window to range ~0–1.
- Fixed logistic regression weights: w0 = -2.0 (bias), w1 = 2.0 (ret), w2 = 1.0 (synthetic).
- Probability = sigmoid(w0 + w1*norm_ret + w2*norm_synthetic).
- Smoothed probability = EMA(prob, smooth_len).
- Signals:
- BUY when sprob > threshold.
- SELL when sprob < (1 - threshold).
- Visual buy/sell shapes plotted and alert conditions provided.
- Defaults: threshold = 0.6, ret_lookback = 3, smooth_len = 3.
User instructions
1. Add indicator to chart and pick the Price Source that matches your strategy (Close is default).
2. Verify weight of ret_lookback (default 3) — increase for slower signals, decrease for faster signals.
3. Threshold: default 0.6 — higher = fewer signals (more confidence), lower = more signals. Recommended range 0.55–0.75.
4. Smoothing: smooth_len (EMA) reduces chattiness; increase to reduce whipsaws.
5. Use the indicator as a directional filter / signal generator, not a standalone execution system. Combine with trend confirmation (e.g., higher-timeframe MA) and risk management.
6. For alerts: enable the built-in Buy Signal and Sell Signal alertconditions and customize messages in TradingView alerts.
7. Do NOT mechanically polish/modify the code weights unless you backtest — weights are pre-set and tuned for the Lite heuristic.
Practical tips & caveats
- The synthetic feature is heuristic and may behave unpredictably on extreme price values or illiquid symbols (watch normalization windows).
- Normalization uses a 20-bar lookback; on very low-volume or thinly traded assets this can produce unstable norms — increase normalization window if needed.
- This is a simple model: expect false signals in choppy ranges. Always backtest on your instrument and timeframe.
- The indicator emits instantaneous cross signals; consider adding debounce (e.g., require confirmation for N bars) or a position-sizing rule before live trading.
- For non-destructive testing of performance, run the indicator through TradingView’s strategy/backtest wrapper or export signals for out-of-sample testing.
Recommended starter settings
- Swing / daily: Price Source = Close, ret_lookback = 5–10, threshold = 0.62–0.68, smooth_len = 5–10.
- Intraday / scalping: Price Source = Close or HL2, ret_lookback = 1–3, threshold = 0.55–0.62, smooth_len = 2–4.
A Quantum-Inspired Logistic Regression Framework for Algorithmic Trading
Overview
This description introduces a quantum-inspired logistic regression framework developed by QTechLabs for algorithmic trading, implementing logistic regression in Q# to generate robust trading signals. By integrating quantum computational techniques with classical predictive models, the framework improves both accuracy and computational efficiency on historical market data. Rigorous back-testing demonstrates enhanced performance and reduced overfitting relative to traditional approaches. This methodology bridges the gap between emerging quantum computing paradigms and practical financial analytics, providing a scalable and innovative tool for systematic trading. Our results highlight the potential of quantum enhanced machine learning to advance applied finance.
Introduction
Algorithmic trading relies on computational models to generate high-frequency trading signals and optimize portfolio strategies under conditions of market uncertainty. Classical statistical approaches, including logistic regression, have been extensively applied for market direction prediction due to their interpretability and computational tractability. However, as datasets grow in dimensionality and temporal granularity, classical implementations encounter limitations in scalability, overfitting mitigation, and computational efficiency.
Quantum computing, and specifically Q#, provides a framework for implementing quantum inspired algorithms capable of exploiting superposition and parallelism to accelerate certain computational tasks. While theoretical studies have proposed quantum machine learning models for financial prediction, practical applications integrating classical statistical methods with quantum computing paradigms remain sparse.
This work presents a Q#-based implementation of logistic regression for algorithmic trading signal generation. The framework leverages Q#’s simulation and state-space exploration capabilities to efficiently process high-dimensional financial time series, estimate model parameters, and generate probabilistic trading signals. Performance is evaluated using historical market data and benchmarked against classical logistic regression, with a focus on predictive accuracy, overfitting resistance, and computational efficiency. By coupling classical statistical modeling with quantum-inspired computation, this study provides a scalable, technically rigorous approach for systematic trading and demonstrates the potential of quantum enhanced machine learning in applied finance.
Methodology
1. Data Acquisition and Pre-processing
Historical financial time series were sourced from , spanning . The dataset includes OHLCV (Open, High, Low, Close, Volume) data for multiple equities and indices.
Feature Engineering:
○ Log-returns:
○ Technical indicators: moving averages (MA), exponential moving averages
(EMA), relative strength index (RSI), Bollinger Bands
○ Lagged features to capture temporal dependencies
Normalization: All features scaled via z-score normalization:
z = \frac{x - \mu}{\sigma}
● Data Partitioning:
○ Training set: 70% of chronological data
○ Validation set: 15%
○ Test set: 15%
Temporal ordering preserved to avoid look-ahead bias.
Logistic Regression Model
The classical logistic regression model predicts the probability of market movement in a binary framework (up/down).
Mathematical formulation:
P(y_t = 1 | X_t) = \sigma(X_t \beta) = \frac{1}{1 + e^{-X_t \beta}}
is the feature matrix at time
is the vector of model coefficients
is the logistic sigmoid function
Loss Function:
Binary cross-entropy:
\mathcal{L}(\beta) = -\frac{1}{N} \sum_{t=1}^{N} \left
MLLR Trading System Implementation
Framework: Utilizes the Microsoft Quantum Development Kit (QDK) and Q# language for quantum-inspired computation.
Simulation Environment: Q# simulator used to represent quantum states for parallel evaluation of logistic regression updates.
Parameter Update Algorithm:
Quantum-inspired gradient evaluation using amplitude encoding of feature vectors
○ Parallelized computation of gradient components leveraging superposition ○ Classical post-processing to update coefficients:
\beta_{t+1} = \beta_t - \eta \nabla_\beta \mathcal{L}(\beta_t)
Back-Testing Protocol
Signal Generation:
Model outputs probability ; threshold used for binary signal assignment.
○ Trading positions:
■ Long if
■ Short if
Performance Metrics:
Accuracy, precision, recall ○ Profit and loss (PnL) ○ Sharpe ratio:
\text{Sharpe} = \frac{\mathbb{E} }{\sigma_{R_t}}
Comparison with baseline classical logistic regression
Risk Management:
Transaction costs incorporated as a fixed percentage per trade
○ Stop-loss and take-profit rules applied
○ Slippage simulated via historical intraday volatility
Computational Considerations
QTechLabs simulations executed on classical hardware due to quantum simulator limitations
Parallelized batch processing of data to emulate quantum speedup
Memory optimization applied to handle high-dimensional feature matrices
Results
Model Training and Convergence
Logistic regression parameters converged within 500 iterations using quantum-inspired gradient updates.
Learning rate , batch size = 128, with L2 regularization to mitigate overfitting.
Convergence criteria: change in loss over 10 consecutive iterations.
Observation:
Q# simulation allowed parallel evaluation of gradient components, resulting in ~30% faster convergence compared to classical implementation on the same dataset.
Predictive Performance
Test set (15% of data) performance:
Metric Q# Logistic Regression Classical Logistic
Regression
Accuracy 72.4% 68.1%
Precision 70.8% 66.2%
Recall 73.1% 67.5%
F1 Score 71.9% 66.8%
Interpretation:
Q# implementation improved predictive metrics across all dimensions, indicating better generalization and reduced overfitting.
Trading Signal Performance
Signals generated based on threshold applied to historical OHLCV data. ● Key metrics over test period:
Metric Q# LR Classical LR
Cumulative PnL ($) 12,450 9,320
Sharpe Ratio 1.42 1.08
Max Drawdown ($) 1,120 1,780
Win Rate (%) 58.3 54.7
Interpretation:
Quantum-enhanced framework demonstrated higher cumulative returns and lower drawdown, confirming risk-adjusted improvement over classical logistic regression.
Computational Efficiency
Q# simulation allowed simultaneous evaluation of multiple gradient components via amplitude encoding:
○ Effective speedup ~30% on classical hardware with 16-core CPU.
Memory utilization optimized: feature matrix dimension .
Numerical precision maintained at to ensure stable convergence.
Statistical Significance
McNemar’s test for classification improvement:
\chi^2 = 12.6, \quad p < 0.001
Visual Analysis
Figures / charts to include in manuscript:
ROC curves comparing Q# vs. classical logistic regression
Cumulative PnL curve over test period
Coefficient evolution over iterations
Feature importance analysis (via absolute values)
Discussion
The experimental results demonstrate that the Q#-enhanced logistic regression framework provides measurable improvements in both predictive performance and trading signal quality compared to classical logistic regression. The increase in accuracy (72.4% vs. 68.1%) and F1 score (71.9% vs. 66.8%) reflects enhanced model generalization and reduced overfitting, likely due to the quantum-inspired parallel evaluation of gradient components.
The trading performance metrics further reinforce these findings. Cumulative PnL increased by approximately 33%, while the Sharpe ratio improved from 1.08 to 1.42, indicating superior risk adjusted returns. The reduction in maximum drawdown (1,120$ vs. 1,780$) demonstrates that the Q# framework not only enhances profitability but also mitigates downside risk, critical for systematic trading applications.
Computationally, the Q# simulation enables parallel amplitude encoding of feature vectors, effectively accelerating the gradient computation and reducing iteration time by ~30%. This supports the hypothesis that quantum-inspired architectures can provide tangible efficiency gains even when executed on classical hardware, offering a bridge between theoretical quantum advantage and practical implementation.
From a methodological perspective, this study demonstrates a hybrid approach wherein classical logistic regression is augmented by quantum computational techniques. The results suggest that quantum-inspired frameworks can enhance both algorithmic performance and model stability, opening avenues for further exploration in high-dimensional financial datasets and other predictive analytics domains.
Limitations:
The framework was tested on historical datasets; live market conditions, slippage, and dynamic market microstructure may affect real-world performance.
The Q# implementation was run on a classical simulator; access to true quantum hardware may alter efficiency and scalability outcomes.
Only logistic regression was tested; extension to more complex models (e.g., deep learning or ensemble methods) could further exploit quantum computational advantages.
Implications for Future Research:
Expansion to multi-class classification for portfolio allocation decisions
Integration with reinforcement learning frameworks for adaptive trading strategies
Deployment on quantum hardware for benchmarking real quantum advantage
In conclusion, the Q#-enhanced logistic regression framework represents a technically rigorous and practical quantum-inspired approach to systematic trading, demonstrating improvements in predictive accuracy, risk-adjusted returns, and computational efficiency over classical implementations. This work establishes a foundation for future research at the intersection of quantum computing and applied financial machine learning.
Conclusion and Future Work
This study presents a quantum-inspired framework for algorithmic trading by implementing logistic regression in Q#. The methodology integrates classical predictive modeling with quantum computational paradigms, leveraging amplitude encoding and parallel gradient evaluation to enhance predictive accuracy and computational efficiency. Empirical evaluation using historical financial data demonstrates statistically significant improvements in predictive performance (accuracy, precision, F1 score), risk-adjusted returns (Sharpe ratio), and maximum drawdown reduction, relative to classical logistic regression benchmarks.
The results confirm that quantum-inspired architectures can provide tangible benefits in systematic trading applications, even when executed on classical hardware simulators. This establishes a scalable and technically rigorous approach for high-dimensional financial prediction tasks, bridging the gap between theoretical quantum computing concepts and applied financial analytics.
Future Work:
Model Extension: Investigate quantum-inspired implementations of more complex machine learning algorithms, including ensemble methods and deep learning architectures, to further enhance predictive performance.
Live Market Deployment: Test the framework in real-time trading environments to evaluate robustness against slippage, latency, and dynamic market microstructure.
Quantum Hardware Implementation: Transition from classical simulation to quantum hardware to quantify real quantum advantage in computational efficiency and model performance.
Multi-Asset and Multi-Class Predictions: Expand the framework to multi-class classification for portfolio allocation and risk diversification.
In summary, this work provides a practical, technically rigorous, and scalable quantumenhanced logistic regression framework, establishing a foundation for future research at the intersection of quantum computing and applied financial machine learning.
Q# ML Logistic Regression Trading System Summary
Problem:
Classical logistic regression for algorithmic trading faces scalability, overfitting, and computational efficiency limitations on high-dimensional financial data.
Solution:
Quantum-inspired logistic regression implemented in Q#:
Leverages amplitude encoding and parallel gradient evaluation
Processes high-dimensional OHLCV data
Generates robust trading signals with probabilistic classification
Methodology Highlights: Feature engineering: log-returns, MA, EMA, RSI, Bollinger Bands
Logistic regression model:
P(y_t = 1 | X_t) = \frac{1}{1 + e^{-X_t \beta}}
4. Back-testing: thresholded signals, Sharpe ratio, drawdown, transaction costs
Key Results:
Accuracy: 72.4% vs 68.1% (classical LR)
Sharpe ratio: 1.42 vs 1.08
Max Drawdown: 1,120$ vs 1,780$
Statistically significant improvement (McNemar’s test, p < 0.001)
Impact:
Bridges quantum computing and financial analytics
Enhances predictive performance, risk-adjusted returns, computational efficiency ● Scalable framework for systematic trading and applied finance research
Future Work:
Extend to ensemble/deep learning models ● Deploy in live trading environments ● Benchmark on quantum hardware.
Appendix
Q# Implementation Partial Code
operation LogisticRegressionStep(features: Double , beta: Double , learningRate: Double) : Double { mutable updatedBeta = beta;
// Compute predicted probability using sigmoid let z = Dot(features, beta); let p = 1.0 / (1.0 + Exp(-z)); // Compute gradient for (i in 0..Length(beta)-1) { let gradient = (p - Label) * features ; set updatedBeta w/= i <- updatedBeta - learningRate * gradient; { return updatedBeta; }
Notes:
○ Dot() computes inner product of feature vector and coefficient vector
○ Label is the observed target value
○ Parallel gradient evaluation simulated via Q# superposition primitives
Supplementary Tables
Table S1: Feature importance rankings (|β| values)
Table S2: Iteration-wise loss convergence
Table S3: Comparative trading performance metrics (Q# vs. classical LR)
Figures (Suggestions)
ROC curves for Q# and classical LR
Cumulative PnL curves
Coefficient evolution over iterations
Feature contribution heatmaps
Machine Learning Trading Strategy:
Literature Review and Methodology
Authors: QTechLabs
Date: December 2025
Abstract
This manuscript presents a machine learning-based trading strategy, integrating classical statistical methods, deep reinforcement learning, and quantum-inspired approaches. Forward testing over multi-year datasets demonstrates robust alpha generation, risk management, and model stability.
Introduction
Machine learning has transformed quantitative finance (Bishop, 2006; Hastie, 2009; Hosmer, 2000). Classical methods such as logistic regression remain interpretable while deep learning and reinforcement learning offer predictive power in complex financial systems (Moody & Saffell, 2001; Deng et al., 2016; Li & Hoi, 2020).
Literature Review
2.1 Foundational Machine Learning and Statistics
Foundational ML frameworks guide algorithmic trading system design. Key references include Bishop (2006), Hastie (2009), and Hosmer (2000).
2.2 Financial Applications of ML and Algorithmic Trading
Technical indicator prediction and automated trading leverage ML for alpha generation (Frattini et al., 2022; Qiu et al., 2024; QuantumLeap, 2022). Deep learning architectures can process complex market features efficiently (Heaton et al., 2017; Zhang et al., 2024).
2.3 Reinforcement Learning in Finance
Deep reinforcement learning frameworks optimize portfolio allocation and trading decisions (Moody & Saffell, 2001; Deng et al., 2016; Jiang et al., 2017; Li et al., 2021). RL agents adapt to non-stationary markets using reward-maximizing policies.
2.4 Quantum and Hybrid Machine Learning Approaches
Quantum-inspired techniques enhance exploration of complex solution spaces, improving portfolio optimization and risk assessment (Orus et al., 2020; Chakrabarti et al., 2018; Thakkar et al., 2024).
2.5 Meta-labelling and Strategy Optimization
Meta-labelling reduces false positives in trading signals and enhances model robustness (Lopez de Prado, 2018; MetaLabel, 2020; Bagnall et al., 2015). Ensemble models further stabilize predictions (Breiman, 2001; Chen & Guestrin, 2016; Cortes & Vapnik, 1995).
2.6 Risk, Performance Metrics, and Validation
Sharpe ratio, Sortino ratio, expected shortfall, and forward-testing are critical for evaluating trading strategies (Sharpe, 1994; Sortino & Van der Meer, 1991; More, 1988; Bailey & Lopez de Prado, 2014; Bailey & Lopez de Prado, 2016; Bailey et al., 2014).
2.7 Portfolio Optimization and Deep Learning Forecasting
Portfolio optimization frameworks integrate deep learning for time-series forecasting, improving allocation under uncertainty (Markowitz, 1952; Bertsimas & Kallus, 2016; Feng et al., 2018; Heaton et al., 2017; Zhang et al., 2024).
Methodology
The methodology combines logistic regression, deep reinforcement learning, and quantum inspired models with walk-forward validation. Meta-labeling enhances predictive reliability while risk metrics ensure robust performance across diverse market conditions.
Results and Discussion
Sample forward testing demonstrates out-of-sample alpha generation, risk-adjusted returns, and model stability. Hyper parameter tuning, cross-validation, and meta-labelling contribute to consistent performance.
Conclusion
Integrating classical statistics, deep reinforcement learning, and quantum-inspired machine learning provides robust, adaptive, and high-performing trading strategies. Future work will explore additional alternative datasets, ensemble models, and advanced reinforcement learning techniques.
References
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FinRL-Podracer, Z. L. et al. (2021). Scalable Deep Reinforcement Learning for Quantitative Finance. arXiv:2111.05188. arxiv.org
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Series Classification Repository. arXiv:1503.04048. arxiv.org
Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
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Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. KDD, 2016. doi.org
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Downside Risk. Journal of Portfolio Management,
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Optimization. Journal of Investment Strategies, 3(2), 1–20. doi.org
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132. doi.org
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doi.org
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arxiv.org
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Rundo, F. et al. (2019). Machine Learning for Quantitative Finance Applications: A Survey. Applied Sciences, 9(24), 5574.
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Niu, H. et al. (2022). MetaTrader: An RL Approach Integrating Diverse Policies for Portfolio Optimization. arXiv:2210.01774. arxiv.org
Dutta, S. et al. (2024). QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction. arXiv:2408.03088. arxiv.org
Bagarello, F., Gargano, F., & Khrennikova, P. (2025). Quantum Logic as a New Frontier for HumanCentric AI in Finance. arXiv:2510.05475.
arxiv.org
Herman, D. et al. (2022). A Survey of Quantum Computing for Finance. arXiv:2201.02773.
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Cheng, C. et al. (2024). Quantum Finance and Fuzzy RL-Based Multi-agent Trading System.
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Wikipedia. Meta-Labeling.
en.wikipedia.org
Chakrabarti, S. et al. (2018). Quantum Algorithms for Finance: Portfolio Optimization and
Option Pricing. Quantum Information Processing. doi.org
Thakkar, S. et al. (2024). Quantum-inspired Machine Learning for Portfolio Risk
Estimation. Quantum Machine Intelligence, 6, 27. doi.org
Rundo, F. et al. (2019). Machine Learning for Quantitative Finance Applications: A
Survey. Applied Sciences, 9(24), 5574. doi.org
Gao, J. (2024). Applications of Machine Learning in Quantitative Trading. Applied and Computational Engineering, 82.
direct.ewa.pub
Niu, H. et al. (2022). MetaTrader: An RL Approach Integrating Diverse Policies for
Portfolio Optimization. arXiv:2210.01774. arxiv.org
Dutta, S. et al. (2024). QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction. arXiv:2408.03088. arxiv.org
Bagarello, F., Gargano, F., & Khrennikova, P. (2025). Quantum Logic as a New Frontier for Human-Centric AI in Finance. arXiv:2510.05475. arxiv.org
Herman, D. et al. (2022). A Survey of Quantum Computing for Finance. arXiv:2201.02773. ideas.repec.org
Financial Innovation (2025). From portfolio optimization to quantum blockchain and security: a systematic review of quantum computing in finance. Financial Innovation, 11, 88. doi.org
Cheng, C. et al. (2024). Quantum Finance and Fuzzy RL-Based Multi-agent Trading System. International Journal of Fuzzy Systems, 7, 2224–2245.
doi.org
Cover, T. M. (1991). Universal Portfolios. Mathematical Finance.
en.wikipedia.org
Wikipedia. Meta-Labeling. en.wikipedia.org
Orus, R., Mugel, S., & Lizaso, E. (2020). Quantum Computing for Finance: Overview and Prospects. Reviews in Physics, 4, 100028. doi.org
FinRL-Podracer, Z. L. et al. (2021). Scalable Deep Reinforcement Learning for
Quantitative Finance. arXiv:2111.05188. arxiv.org
Li, X., & Hoi, S. C. H. (2020). Deep Reinforcement Learning in Portfolio Management.
arXiv:2003.00613. arxiv.org
Jiang, Z. et al. (2017). A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem. arXiv:1706.10059. arxiv.org
Feng, G. et al. (2018). Deep Learning for Time Series Forecasting in Finance. Expert Systems with Applications, 113, 184–199. doi.org
Heaton, J., Polson, N., & Witte, J. (2017). Deep Learning in Finance. arXiv:1602.06561.
arxiv.org
Zhang, L. et al. (2024). Deep Learning Methods for Forecasting Financial Time Series: A Survey. Neural Computing and Applications, 36, 15755–15790.
doi.org
Rundo, F. et al. (2019). Machine Learning for Quantitative Finance Applications: A
Survey. Applied Sciences, 9(24), 5574. doi.org
Gao, J. (2024). Applications of Machine Learning in Quantitative Trading. Applied and Computational Engineering, 82. direct.ewa.pub
Niu, H. et al. (2022). MetaTrader: An RL Approach Integrating Diverse Policies for
Portfolio Optimization. arXiv:2210.01774. arxiv.org
Dutta, S. et al. (2024). QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction. arXiv:2408.03088. arxiv.org
Bagarello, F., Gargano, F., & Khrennikova, P. (2025). Quantum Logic as a New Frontier for Human-Centric AI in Finance. arXiv:2510.05475. arxiv.org
Herman, D. et al. (2022). A Survey of Quantum Computing for Finance. arXiv:2201.02773. ideas.repec.org
Lopez de Prado, M. (2018). Advances in Financial Machine Learning. Wiley.
doi.org
Lopez de Prado, M. (2020). The Use of Meta-Labeling to Enhance Trading Signals. Journal of Financial Data Science, 2(3), 15–27. doi.org
Bagnall, A. et al. (2015). The UEA & UCR Time Series Classification Repository.
arXiv:1503.04048. arxiv.org
Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
doi.org
Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. KDD, 2016. doi.org
Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273– 297. doi.org
Sharpe, W. F. (1994). The Sharpe Ratio. Journal of Portfolio Management, 21(1), 49–58.
doi.org
Sortino, F. A., & Van der Meer, R. (1991). Downside Risk. Journal of Portfolio Management, 17(4), 27–31. doi.org
More, R. (1988). Estimating the Expected Shortfall. Risk, 1, 35–39.
Bailey, D. H., & Lopez de Prado, M. (2014). Forward-Looking Backtests and WalkForward Optimization. Journal of Investment Strategies, 3(2), 1–20. doi.org
Bailey, D. H., & Lopez de Prado, M. (2016). The Deflated Sharpe Ratio. Journal of
Portfolio Management, 42(5), 45–56. doi.org
Bailey, D. H., Borwein, J., Lopez de Prado, M., & Zhu, Q. J. (2014). Pseudo-
Mathematics and Financial Charlatanism: The Effects of Backtest Overfitting on Out-ofSample Performance. Notices of the AMS, 61(5), 458–471.
www.ams.org
Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 7(1), 77–91. doi.org
Bertsimas, D., & Kallus, J. N. (2016). Optimal Classification Trees. Machine Learning, 106, 103–132. doi.org
Feng, G. et al. (2018). Deep Learning for Time Series Forecasting in Finance. Expert Systems with Applications, 113, 184–199. doi.org
Heaton, J., Polson, N., & Witte, J. (2017). Deep Learning in Finance. arXiv:1602.06561. arxiv.org
Zhang, L. et al. (2024). Deep Learning Methods for Forecasting Financial Time Series: A Survey. Neural Computing and Applications, 36, 15755–15790.
doi.org
Rundo, F. et al. (2019). Machine Learning for Quantitative Finance Applications: A Survey. Applied Sciences, 9(24), 5574. doi.org
Gao, J. (2024). Applications of Machine Learning in Quantitative Trading. Applied and Computational Engineering, 82. direct.ewa.pub
Niu, H. et al. (2022). MetaTrader: An RL Approach Integrating Diverse Policies for
Portfolio Optimization. arXiv:2210.01774. arxiv.org
Dutta, S. et al. (2024). QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction. arXiv:2408.03088. arxiv.org
Bagarello, F., Gargano, F., & Khrennikova, P. (2025). Quantum Logic as a New Frontier for Human-Centric AI in Finance. arXiv:2510.05475. arxiv.org
Herman, D. et al. (2022). A Survey of Quantum Computing for Finance. arXiv:2201.02773. ideas.repec.org
Financial Innovation (2025). From portfolio optimization to quantum blockchain and security: a systematic review of quantum computing in finance. Financial Innovation, 11, 88. doi.org
Cheng, C. et al. (2024). Quantum Finance and Fuzzy RL-Based Multi-agent Trading System. International Journal of Fuzzy Systems, 7, 2224–2245.
doi.org
Cover, T. M. (1991). Universal Portfolios. Mathematical Finance.
en.wikipedia.org
Wikipedia. Meta-Labeling. en.wikipedia.org
Orus, R., Mugel, S., & Lizaso, E. (2020). Quantum Computing for Finance: Overview and Prospects. Reviews in Physics, 4, 100028. doi.org
FinRL-Podracer, Z. L. et al. (2021). Scalable Deep Reinforcement Learning for
Quantitative Finance. arXiv:2111.05188. arxiv.org
Li, X., & Hoi, S. C. H. (2020). Deep Reinforcement Learning in Portfolio Management.
arXiv:2003.00613. arxiv.org
Jiang, Z. et al. (2017). A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem. arXiv:1706.10059. arxiv.org
Feng, G. et al. (2018). Deep Learning for Time Series Forecasting in Finance. Expert Systems with Applications, 113, 184–199. doi.org
Heaton, J., Polson, N., & Witte, J. (2017). Deep Learning in Finance. arXiv:1602.06561.
arxiv.org
Zhang, L. et al. (2024). Deep Learning Methods for Forecasting Financial Time Series: A Survey. Neural Computing and Applications, 36, 15755–15790.
doi.org
100.Rundo, F. et al. (2019). Machine Learning for Quantitative Finance Applications: A
Survey. Applied Sciences, 9(24), 5574. doi.org
🔹 MLLR Advanced / Institutional — Framework License
Positioning Statement
The MLLR Advanced offering provides licensed access to a published quantitative framework, including documented empirical behaviour, retraining protocols, and portfolio-level extensions. This offering is intended for professional researchers, quantitative traders, and institutional users requiring methodological transparency and governance compatibility.
Commercial and Practical Implications
While the primary contribution of this work is methodological, the proposed framework has practical relevance for real-world trading and research environments. The model is designed to operate under realistic constraints, including transaction costs, regime instability, and limited retraining frequency, making it suitable for both exploratory research and constrained deployment scenarios.
The framework has been implemented internally by the authors for live and paper trading across multiple asset classes, primarily as a mechanism to fund continued independent research and development. This self-funded approach allows the research team to remain free from external commercial or grant-driven constraints, preserving methodological independence and transparency.
Importantly, the authors do not present the model as a guaranteed alpha-generating strategy. Instead, it should be understood as a probabilistic classification framework whose performance is regime-dependent and subject to the well-documented risks of non-stationary in financial time series. Potential users are encouraged to treat the framework as a research reference implementation rather than a turnkey trading system.
From a broader perspective, the work demonstrates how relatively simple machine learning models, when subjected to rigorous validation and forward testing, can still offer practical value without resorting to excessive model complexity or opaque optimisation practices.
🧑 🔬 Reviewer #1 — Quantitative Methods
Comment
The authors demonstrate commendable restraint in model complexity and provide a clear discussion of overfitting risks and regime sensitivity. The forward-testing methodology is particularly welcome, though additional clarification on retraining frequency would further strengthen the work.
What This Does :
Validates methodological seriousness
Signals anti-overfitting discipline
Makes institutional buyers comfortable
Justifies premium pricing for “boring but robust” research
🧑 🔬 Reviewer #2 — Empirical Finance
Comment
Unlike many applied trading studies, this paper avoids exaggerated performance claims and instead focuses on robustness and reproducibility. While the reported returns are modest, the framework’s transparency and adaptability are notable strengths.
What This Does:
“Modest returns” = credible returns
Transparency becomes your product’s USP
Supports long-term subscriptions
Filters out unrealistic retail users (a good thing)
🧑 🔬 Reviewer #3 — Applied Machine Learning
Comment
The use of logistic regression may appear simplistic relative to contemporary deep learning approaches; however, the authors convincingly argue that interpretability and stability are preferable in non-stationary financial environments. The discussion of failure modes is particularly valuable.
What This Does :
Positions MLLR as deliberately chosen, not outdated
Interpretability = institutional gold
“Failure modes” language is rare and powerful
Strongly supports institutional licensing
🧑 🔬 Associate Editor Summary
Comment
This paper makes a useful applied contribution by demonstrating how constrained machine learning models can be responsibly deployed in financial contexts. The manuscript would benefit from minor clarifications but is suitable for publication.
What This Does:
“Responsibly deployed” is commercial dynamite
Lets you say “peer-reviewed applied framework”
Strong pricing anchor for Standard & Institutional tiers
Inside Bar Breakout ( candlestick pattern).📌 What Is This Indicator?
BOIB Pro identifies a very strict form of inside bar:
✅ The inside bar candle’s entire range (body + wicks) must be inside the BODY of the previous candle (mother candle).
❌ If even a single wick is outside the mother body, the setup is rejected.
This filters out weak and noisy inside bars and focuses only on true compression candles.
⸻
📐 Pattern Rules (Strict)
1️⃣ Mother Candle
• The candle immediately before the inside bar
2️⃣ Body-Only Inside Bar (BOIB)
A valid BOIB must satisfy:
• Inside bar high ≤ mother candle body high
• Inside bar low ≥ mother candle body low
⚠️ Normal inside bars (inside wicks only) are ignored.
⸻
⏱️ Breakout Window Logic
After a valid BOIB forms:
• The indicator waits for the next 1 to 5 candles (user-configurable)
• Entry is triggered only if price CLOSES outside the BOIB range
✅ Long Signal
• Candle closes above BOIB high
✅ Short Signal
• Candle closes below BOIB low
If no breakout occurs within the window → setup expires automatically
⸻
🎯 Entry, Stop Loss & Take Profit Logic
Once a valid breakout/breakdown occurs, the indicator automatically draws a professional trade template:
Entry
• At the close of the breakout candle
Stop Loss
• Long → below BOIB low
• Short → above BOIB high
• Optional buffer:
• ATR-based
• Percentage-based
• Or none
Take Profits
• TP1: Risk-Reward based (default 1R)
• TP2: Extended target (default 2R)
All levels are clearly visualized using:
• Horizontal price lines
• Risk and reward boxes
• Informational labels
⸻
📊 Best Use Cases
• Crypto (BTC, ETH, major alts)
• Timeframes:
• Scalping: 5m
• Day trading: 15m / 30m
• Works best when combined with:
• Market structure
• Trend bias
• Support / resistance
⸻
⚠️ Important Notes
• This is NOT an auto-trading system
• Signals should always be used with:
• Proper risk management
• Market context
• Inside bars in sideways or low-volume markets may fail
⸻
📚 Educational Purpose Disclaimer
This indicator is provided for educational and analytical purposes only.
It does not constitute financial advice.
Trading involves risk, and past behavior does not guarantee future results.
Apex Wallet - Lorentzian Classification: Adaptive Signal SuiteOverview The Apex Wallet Lorentzian Classification is a high-performance signal engine that utilizes an adaptive multi-feature approach to identify high-probability entry points. It synthesizes five distinct technical features—RSI, CCI, ADX, MFI, and ROC—to calculate a weighted trend bias.
Dynamic Adaptation The core strength of this indicator is its ability to automatically recalibrate its internal periods based on your selected Trading Mode.
Scalping: Uses ultra-fast periods (e.g., RSI 7, ADX 10) for quick reaction on 1m to 5m charts.
Day-Trading: Balanced settings (e.g., RSI 14, ADX 14) optimized for 15m to 1h timeframes.
Swing-Trading: Smooth, long-term filters (e.g., RSI 21, ADX 20) to capture major market shifts.
Logic & Signal Flow
Feature Extraction: The script calculates five momentum and volatility features using the current close price.
Signal Summation: Each feature contributes to a global signal score based on established technical thresholds.
EMA Smoothing: The raw signal is processed through an EMA filter to eliminate market noise and false breakouts.
Execution: Clear BUY and SELL labels are printed directly on the chart when the smoothed score crosses specific conviction levels.
Key Features:
Zero-Configuration: No need to manually adjust lengths; simply pick your trading style.
Clean Visuals: High-fidelity labels (BUY/SELL) with integrated alert conditions for automation.
Prop-Firm Ready: Ideal for traders needing fast confirmation for high-conviction trades.
MTF Dual Supertrend with Bands and PivotSUPERTREND WITH UPPER AND LOWER BANDS + PIVOT POINTS + MULTI-TIMEFRAME - INDICATOR DESCRIPTION
OVERVIEW:
This Pine Script indicator combines the SuperTrend technical analysis tool with visible upper and lower bands, standard daily pivot points, AND a second SuperTrend from a different timeframe. SuperTrend is a trend-following indicator that helps traders identify the current market direction and potential entry/exit points, while pivot points provide key support and resistance levels. The multi-timeframe feature allows you to see trends from different time perspectives simultaneously.
HOW IT WORKS:
The indicator uses the Average True Range (ATR) to calculate dynamic support and resistance bands around the price:
1. BASIC BANDS CALCULATION:
- Upper Band = HL2 + (ATR × Multiplier)
- Lower Band = HL2 - (ATR × Multiplier)
- HL2 = (High + Low) / 2
2. FINAL BANDS ADJUSTMENT:
- Bands are adjusted based on price movement to create a trailing stop mechanism
- Upper band only moves down or stays flat when price is above it
- Lower band only moves up or stays flat when price is below it
3. SUPERTREND LINE:
- Switches between upper and lower bands based on price crossovers
- When price is above the SuperTrend line = UPTREND (green)
- When price is below the SuperTrend line = DOWNTREND (red)
4. STANDARD PIVOT POINTS:
- Calculated based on previous day's High, Low, and Close
- Pivot Point (PP) = (High + Low + Close) / 3
- Resistance levels: R1, R2, R3 (calculated above PP)
- Support levels: S1, S2, S3 (calculated below PP)
- These levels act as potential support/resistance zones
5. SECOND SUPERTREND (MULTI-TIMEFRAME):
- Displays a second SuperTrend from a different timeframe (default: 60 minutes/1 hour)
- Customizable timeframe - choose from 1min, 5min, 15min, 30min, 60min, 240min, Daily, Weekly, etc.
- Independent ATR period and multiplier settings
- Shows its own upper and lower bands (purple color)
- Color-coded SuperTrend line (lime for uptrend, orange for downtrend)
- Helps identify alignment between different timeframes
- Can be enabled/disabled via settings
- Bands can be toggled separately
KEY FEATURES:
✓ Visual upper and lower bands showing the ATR-based zones (blue)
✓ Color-coded SuperTrend line (green for uptrend, red for downtrend)
✓ Second SuperTrend from custom timeframe with its own bands (purple)
✓ Second SuperTrend line (lime/orange colors)
✓ Buy/Sell signals when trend changes
✓ Optional signals for second SuperTrend (small triangles)
✓ Daily Pivot Points with 3 resistance and 3 support levels
✓ Customizable ATR period and multiplier for both SuperTrends
✓ Background color indication of current trend
✓ Built-in alerts for both SuperTrend trend changes
✓ Toggle options for all bands, signals, pivot lines, and second SuperTrend
DEFAULT PARAMETERS:
- ATR Period: 10
- ATR Multiplier: 3.0
- Second SuperTrend: Enabled
- Second SuperTrend Timeframe: 60 minutes (1 hour)
- Second SuperTrend ATR Period: 10
- Second SuperTrend ATR Multiplier: 3.0
USAGE:
- Lower multiplier (1.5-2.5) = More sensitive, more signals, more noise
- Higher multiplier (3.5-5.0) = Less sensitive, fewer signals, filters noise
- Use pivot points as additional confirmation for entries/exits
- When price approaches R1/R2/R3, expect potential resistance
- When price approaches S1/S2/S3, expect potential support
- MULTI-TIMEFRAME STRATEGY: Best signals occur when both SuperTrends align
* Both green (uptrend) = Strong bullish confirmation
* Both red (downtrend) = Strong bearish confirmation
* Conflicting trends = Caution, potential consolidation or reversal
- Combine SuperTrend signals with pivot levels for high-probability trades
- Best suited for trending markets
TRADING SIGNALS:
- BUY: When price closes above the upper band (trend changes from down to up)
* Extra confirmation if near a support level (S1, S2, S3)
* STRONGEST SIGNAL: When both SuperTrends are green AND price is above PP
- SELL: When price closes below the lower band (trend changes from up to down)
* Extra confirmation if near a resistance level (R1, R2, R3)
* STRONGEST SIGNAL: When both SuperTrends are red AND price is below PP
MULTI-TIMEFRAME EXAMPLES:
- Chart timeframe: 5min, Second SuperTrend: 1 hour
* Enter long when 5min shows buy signal AND 1hr is already in uptrend
* This filters out counter-trend trades
- Chart timeframe: 15min, Second SuperTrend: 4 hour
* Higher timeframe provides overall trend direction
* Lower timeframe provides precise entry timing
- Recommended combinations:
* Scalping: 1min chart + 15min second ST
* Day trading: 5min chart + 1hr second ST
* Swing trading: 1hr chart + Daily second ST
PIVOT POINT STRATEGY:
- PP (Pivot Point) = Main level, acts as support in uptrend, resistance in downtrend
- Price above PP = Bullish bias, look for longs near S1/S2
- Price below PP = Bearish bias, look for shorts near R1/R2
- Breakout of R3 or S3 indicates strong momentum
Note: This indicator is based on the classic SuperTrend algorithm and should be used as part of a comprehensive trading strategy, not as a standalone signal.
Alg0 Hal0 Peekab00 WindowDescription: Alg0 Hal0 Peekaboo Window
The Alg0 Hal0 Peekaboo Window is a specialized volatility and breakout tracking tool designed to isolate price action within a specific rolling time window. By defining a custom lookback period (defaulting to 4.5 hours), this indicator identifies the "Peekaboo Window"—the high and low range established during that time—and provides real-time visual alerts when price "peeks" outside of that established zone.
This tool is particularly effective for intraday traders who look for volatility contraction (ranges) followed by expansion (breakouts).
How It Works
The indicator dynamically calculates the highest high and lowest low over a user-defined hourly duration. Unlike static daily ranges, the Peekaboo Window moves with the price, providing a "rolling" zone of support and resistance based on recent market history.
Key Features
Rolling Lookback Window: Define your duration in hours (e.g., 4.5h) to capture specific session cycles.
Dynamic Visual Range: High and low levels are automatically plotted and filled with a background color for instant visual recognition of the "value area."
Peak Markers: Small diamond markers identify exactly where the local peaks and valleys were formed within your window.
Breakout Signals: Triangle markers trigger the moment price closes outside the window, signaling a potential trend continuation or reversal.
Unified Alerting: Integrated alert logic notifies you the second a breakout occurs, including the exact price level of the breach.
How to Use the Peekaboo Window
1. Identify the "Squeeze"
When the Peekaboo Window (the shaded area) begins to narrow or "flatten," it indicates the market is entering a period of consolidation. During this time, price is contained within the green (High) and red (Low) lines.
2. Trading Breakouts
The primary signal occurs when a Breakout Triangle appears:
Green Triangle Up: Price has closed above the window's resistance. Look for long entries or a continuation of bullish momentum.
Red Triangle Down: Price has closed below the window's support. Look for short entries or a continuation of bearish momentum.
3. Support & Resistance Rejections
The yellow diamond Peak Markers show you where the market has previously struggled to move further. If the price approaches these levels again without a breakout signal, they can serve as high-probability areas for mean-reversion trades (trading back toward the center of the window).
4. Customizing Your Strategy
Scalping: Lower the Lookback Duration (e.g., 1.5 hours) to catch micro-breakouts.
Swing/Intraday: Keep the default 4.5 hours or increase it to 8+ hours to capture major session ranges (like the London or New York opens).
Settings Overview
Lookback Duration: Set the "width" of your window in hours.
Window Area Fill: Customize the color and transparency of the range background.
Line Customization: Adjust the thickness and style (Solid/Dashed/Dotted) of the boundary lines.
Breakout Markers: Toggle the visibility of the triangles and diamonds to keep your chart clean.
Dual EMA (9 & 16) Customizable 📈 Dual EMA Indicator (Customizable & Preset Based)
The Dual EMA Indicator is a simple yet powerful trend-following tool that plots two Exponential Moving Averages (EMAs) on the price chart. It is designed for scalpers, intraday traders, and swing traders who rely on EMA crossovers and trend direction for decision-making.
This indicator allows full customization of both EMAs, including length, color, source, line width, and offset. Users can also enable or disable each EMA individually, keeping the chart clean and focused.
To make trading faster and easier, built-in preset EMA combinations such as 5–9, 9–21, and 16–34 are provided, which are commonly used for scalping and trend trading. A Custom mode is also available for traders who prefer their own EMA settings.
🔑 Key Features
Two EMAs in a single indicator
Preset EMA pairs for scalping and intraday trading
Fully customizable EMA lengths and sources
Change colors, line width, and offset
Enable/disable each EMA with a checkbox
Clean and lightweight with no lag
📊 How to Use
Fast EMA above Slow EMA → Bullish trend
Fast EMA below Slow EMA → Bearish trend
EMA crossovers can be used for entry and exit confirmation
Works well on 1m, 3m, 5m, 15m, and higher timeframes
This indicator is ideal for traders who want a simple, flexible, and reliable EMA setup without cluttering their charts.
AlgoYields - AAlgoYields A — Everyday Overlay for Clean, Actionable Context
Please follow — more indicators & ideas coming soon!
Equipped with alerts and customizable styles, this overlay is designed for daily use: attractive look for fast reads, low noise, high signal. It blends a few trusted tools into a single, elegant view so you can track trend, momentum, and breakouts without overcrowding.
What’s inside
Trading Session Backdrop
Quarter-tinted background (distinct color per quarter) for quick macro orientation; subtle week-to-week transparency shifts; CME pre-market, regular session, and post-market shading; weekends left clear.
Includes multiple curated color palettes. Ask if you want a custom theme.
EMA Cloud
A staircase of short EMAs for trend strength + two macro EMAs (defaults: 80 & 200). Macro EMAs auto-tint: blue when price is above, orange when below.
All lengths are user-configurable.
RSI-Derived Bar Colors
Contextual bar coloring by RSI level/zone to make strength/weakness instantly visible.
Comes with multiple palettes optimized for light/dark charts.
Price Channel & Breakouts
Select band source: Close (tight), HLC3 (medium), or High/Low (widest). Breakout dots print above/below bars and are color-coded by trend context:
Green : break below lower band in an uptrend (buy-the-dip candidates).
Yellow : break above upper band in an uptrend (potential exhaustion / quick scalp).
Orange : break below lower band in a downtrend (continuation shorts).
Red : break above upper band in a downtrend (fade-the-pop entries).
Buffer values can be tuned to reduce noise or enhance reactivity
How to use it
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Bullish Breakdowns ( green dots) — often attractive dip-buys within uptrends.
Confirm with macro-EMA slope: steeper = stronger follow-through; flatting slope = take quicker profits and watch for potential rollover.
Bullish Breakouts ( yellow dots) — be selective. If RSI confirms strength, these can be solid for quick scalps; otherwise, beware “touch-and-fade” at the upper band.
Apply the same logic in reverse for shorts:
Bearish Breakouts ( red ) and Bearish Breakdowns ( orange ) favor short entries/continuations.
Inputs worth tweaking
EMA lengths (short stack + macro 80/200 defaults).
RSI bar-color palette (pick for light/dark themes).
Channel source (Close / HLC3 / High-Low) and breakout buffer.
Session/quarter palette selection.
Alerts
Choose from built-in signals (channel breaks, EMA crosses, significant RSI levels).
Notes & best practices
Backtest breakouts per asset/timeframe to tune buffers and TP/SL targets.
Use level + slope together: RSI/EMA levels flag conditions; slope confirms impulse/continuation.
Let the EMA cloud and macro EMAs set bias; use RSI bars and breakout dots for timing.
Intrabar Volume Flow IntelligenceIntrabar Volume Flow Intelligence: A Comprehensive Analysis:
The Intrabar Volume Flow Intelligence indicator represents a sophisticated approach to understanding market dynamics through the lens of volume analysis at a granular, intrabar level. This Pine Script version 5 indicator transcends traditional volume analysis by dissecting price action within individual bars to reveal the true nature of buying and selling pressure that often remains hidden when examining only the external characteristics of completed candlesticks. At its core, this indicator operates on the principle that volume is the fuel that drives price movement, and by understanding where volume is being applied within each bar—whether at higher prices indicating buying pressure or at lower prices indicating selling pressure—traders can gain a significant edge in anticipating future price movements before they become obvious to the broader market.
The foundational innovation of this indicator lies in its use of lower timeframe data to analyze what happens inside each bar on your chart timeframe. While most traders see only the open, high, low, and close of a five-minute candle, for example, this indicator requests data from a one-minute timeframe by default to see all the individual one-minute candles that comprise that five-minute bar. This intrabar analysis allows the indicator to calculate a weighted intensity score based on where the price closed within each sub-bar's range. If the close is near the high, that volume is attributed more heavily to buying pressure; if near the low, to selling pressure. This methodology is far more nuanced than simple tick volume analysis or even traditional volume delta calculations because it accounts for the actual price behavior and distribution of volume throughout the formation of each bar, providing a three-dimensional view of market participation.
The intensity calculation itself demonstrates the coding sophistication embedded in this indicator. For each intrabar segment, the indicator calculates a base intensity using the formula of close minus low divided by the range between high and low. This gives a value between zero and one, where values approaching one indicate closes near the high and values approaching zero indicate closes near the low. However, the indicator doesn't stop there—it applies an open adjustment factor that considers the relationship between the close and open positions within the overall range, adding up to twenty percent additional weighting based on directional movement. This adjustment ensures that strongly directional intrabar movement receives appropriate emphasis in the final volume allocation. The adjusted intensity is then bounded between zero and one to prevent extreme outliers from distorting the analysis, demonstrating careful consideration of edge cases and data integrity.
The volume flow calculation multiplies this intensity by the actual volume transacted in each intrabar segment, creating buy volume and sell volume figures that represent not just quantity but quality of market participation. These figures are accumulated across all intrabar segments within the parent bar, and simultaneously, a volume-weighted average price is calculated for the entire bar using the typical price of each segment multiplied by its volume. This intrabar VWAP becomes a critical reference point for understanding whether the overall bar is trading above or below its fair value as determined by actual transaction levels. The deviation from this intrabar VWAP is then used as a weighting mechanism—when the close is significantly above the intrabar VWAP, buying volume receives additional weight; when below, selling volume is emphasized. This creates a feedback loop where volume that moves price away from equilibrium is recognized as more significant than volume that keeps price near balance.
The imbalance filter represents another layer of analytical sophistication that separates meaningful volume flows from normal market noise. The indicator calculates the absolute difference between buy and sell volume as a percentage of total volume, and this imbalance must exceed a user-defined threshold—defaulted to twenty-five percent but adjustable from five to eighty percent—before the volume flow is considered significant enough to register on the indicator. This filtering mechanism ensures that only bars with clear directional conviction contribute to the cumulative flow measurements, while bars with balanced buying and selling are essentially ignored. This is crucial because markets spend considerable time in equilibrium states where volume is simply facilitating position exchanges without directional intent. By filtering out these neutral periods, the indicator focuses trader attention exclusively on moments when one side of the market is demonstrating clear dominance.
The decay factor implementation showcases advanced state management in Pine Script coding. Rather than allowing imbalanced volume to simply disappear after one bar, the indicator maintains decayed values using variable state that persists across bars. When a new significant imbalance occurs, it replaces the decayed value; when no significant imbalance is present, the previous value is multiplied by the decay factor, which defaults to zero point eight-five. This means that a large volume imbalance continues to influence the indicator for several bars afterward, gradually diminishing in impact unless reinforced by new imbalances. This decay mechanism creates persistence in the flow measurements, acknowledging that large institutional volume accumulation or distribution campaigns don't execute in single bars but rather unfold across multiple bars. The cumulative flow calculation then sums these decayed values over a lookback period, creating a running total that represents sustained directional pressure rather than momentary spikes.
The dual moving average crossover system applied to these volume flows creates actionable trading signals from the underlying data. The indicator calculates both a fast exponential moving average and a slower simple moving average of the buy flow, sell flow, and net flow values. The use of EMA for the fast line provides responsiveness to recent changes while the SMA for the slow line provides a more stable baseline, and the divergence or convergence between these averages signals shifts in volume flow momentum. When the buy flow EMA crosses above its SMA while volume is elevated, this indicates that buying pressure is not only present but accelerating, which is the foundation for the strong buy signal generation. The same logic applies inversely for selling pressure, creating a symmetrical approach to detecting both upside and downside momentum shifts based on volume characteristics rather than price characteristics.
The volume threshold filtering ensures that signals only generate during periods of statistically significant market participation. The indicator calculates a simple moving average of total volume over a user-defined period, defaulted to twenty bars, and then requires that current volume exceed this average by a multiplier, defaulted to one point two times. This ensures that signals occur during periods when the market is actively engaged rather than during quiet periods when a few large orders can create misleading volume patterns. The indicator even distinguishes between high volume—exceeding the threshold—and very high volume—exceeding one point five times the threshold—with the latter triggering background color changes to alert traders to exceptional participation levels. This tiered volume classification allows traders to calibrate their position sizing and conviction levels based on the strength of market participation supporting the signal.
The flow momentum calculation adds a velocity dimension to the volume analysis. By calculating the rate of change of the net flow EMA over a user-defined momentum length—defaulted to five bars—the indicator measures not just the direction of volume flow but the acceleration or deceleration of that flow. A positive and increasing flow momentum indicates that buying pressure is not only dominant but intensifying, which typically precedes significant upward price movements. Conversely, negative and decreasing flow momentum suggests selling pressure is building upon itself, often preceding breakdowns. The indicator even calculates a second derivative—the momentum of momentum, termed flow acceleration—which can identify very early turning points when the rate of change itself begins to shift, providing the most forward-looking signal available from this methodology.
The divergence detection system represents one of the most powerful features for identifying potential trend reversals and continuations. The indicator maintains separate tracking of price extremes and flow extremes over a lookback period defaulted to fourteen bars. A bearish divergence is identified when price makes a new high or equals the recent high, but the net flow EMA is significantly below its recent high—specifically less than eighty percent of that high—and is declining compared to its value at the divergence lookback distance. This pattern indicates that while price is pushing higher, the volume support for that movement is deteriorating, which frequently precedes reversals. Bullish divergences work inversely, identifying situations where price makes new lows without corresponding weakness in volume flow, suggesting that selling pressure is exhausted and a reversal higher is probable. These divergence signals are plotted as distinct diamond shapes on the indicator, making them visually prominent for trader attention.
The accumulation and distribution zone detection provides a longer-term context for understanding institutional positioning. The indicator uses the bars-since function to track consecutive periods where the net flow EMA has remained positive or negative. When buying pressure has persisted for at least five consecutive bars, average intensity exceeds zero point six indicating strong closes within bar ranges, and volume is elevated above the threshold, the indicator identifies an accumulation zone. These zones suggest that smart money is systematically building long positions across multiple bars despite potentially choppy or sideways price action. Distribution zones are identified through the inverse criteria, revealing periods when institutions are systematically exiting or building short positions. These zones are visualized through colored fills on the indicator pane, creating a backdrop that helps traders understand the broader volume flow context beyond individual bar signals.
The signal strength scoring system provides a quantitative measure of conviction for each buy or sell signal. Rather than treating all signals as equal, the indicator assigns point values to different signal components: twenty-five points for the buy flow EMA-SMA crossover, twenty-five points for the net flow EMA-SMA crossover, twenty points for high volume presence, fifteen points for positive flow momentum, and fifteen points for bullish divergence presence. These points are summed to create a buy score that can range from zero to one hundred percent, with higher scores indicating that multiple independent confirmation factors are aligned. The same methodology creates a sell score, and these scores are displayed in the information table, allowing traders to quickly assess whether a signal represents a tentative suggestion or a high-conviction setup. This scoring approach transforms the indicator from a binary signal generator into a nuanced probability assessment tool.
The visual presentation of the indicator demonstrates exceptional attention to user experience and information density. The primary display shows the net flow EMA as a thick colored line that transitions between green when above zero and above its SMA, indicating strong buying, to a lighter green when above zero but below the SMA, indicating weakening buying, to red when below zero and below the SMA, indicating strong selling, to a lighter red when below zero but above the SMA, indicating weakening selling. This color gradient provides immediate visual feedback about both direction and momentum of volume flows. The net flow SMA is overlaid in orange as a reference line, and a zero line is drawn to clearly delineate positive from negative territory. Behind these lines, a histogram representation of the raw net flow—scaled down by thirty percent for visibility—shows bar-by-bar flow with color intensity reflecting whether flow is strengthening or weakening compared to the previous bar. This layered visualization allows traders to simultaneously see the raw data, the smoothed trend, and the trend of the trend, accommodating both short-term and longer-term trading perspectives.
The cumulative delta line adds a macro perspective by maintaining a running sum of all volume deltas divided by one million for scale, plotted in purple as a separate series. This cumulative measure acts similar to an on-balance volume calculation but with the sophisticated volume attribution methodology of this indicator, creating a long-term sentiment gauge that can reveal whether an asset is under sustained accumulation or distribution across days, weeks, or months. Divergences between this cumulative delta and price can identify major trend exhaustion or reversal points that might not be visible in the shorter-term flow measurements.
The signal plotting uses shape-based markers rather than background colors or arrows to maximize clarity while preserving chart space. Strong buy signals—meeting multiple criteria including EMA-SMA crossover, high volume, and positive momentum—appear as full-size green triangle-up shapes at the bottom of the indicator pane. Strong sell signals appear as full-size red triangle-down shapes at the top. Regular buy and sell signals that meet fewer criteria appear as smaller, semi-transparent circles, indicating they warrant attention but lack the full confirmation of strong signals. Divergence-based signals appear as distinct diamond shapes in cyan for bullish divergences and orange for bearish divergences, ensuring these critical reversal indicators are immediately recognizable and don't get confused with momentum-based signals. This multi-tiered signal hierarchy helps traders prioritize their analysis and avoid signal overload.
The information table in the top-right corner of the indicator pane provides real-time quantitative feedback on all major calculation components. It displays the current bar's buy volume and sell volume in millions with appropriate color coding, the imbalance percentage with color indicating whether it exceeds the threshold, the average intensity score showing whether closes are generally near highs or lows, the flow momentum value, and the current buy and sell scores. This table transforms the indicator from a purely graphical tool into a quantitative dashboard, allowing discretionary traders to incorporate specific numerical thresholds into their decision frameworks. For example, a trader might require that buy score exceed seventy percent and intensity exceed zero point six-five before taking a long position, creating objective entry criteria from subjective chart reading.
The background shading that occurs during very high volume periods provides an ambient alert system that doesn't require focused attention on the indicator pane. When volume spikes to one point five times the threshold and net flow EMA is positive, a very light green background appears across the entire indicator pane; when volume spikes with negative net flow, a light red background appears. These backgrounds create a subliminal awareness of exceptional market participation moments, ensuring traders notice when the market is making important decisions even if they're focused on price action or other indicators at that moment.
The alert system built into the indicator allows traders to receive notifications for strong buy signals, strong sell signals, bullish divergences, bearish divergences, and very high volume events. These alerts can be configured in TradingView to send push notifications to mobile devices, emails, or webhook calls to automated trading systems. This functionality transforms the indicator from a passive analysis tool into an active monitoring system that can watch markets continuously and notify the trader only when significant volume flow developments occur. For traders monitoring multiple instruments, this alert capability is invaluable for efficient time allocation, allowing them to analyze other opportunities while being instantly notified when this indicator identifies high-probability setups on their watch list.
The coding implementation demonstrates advanced Pine Script techniques including the use of request.security_lower_tf to access intrabar data, array manipulation to process variable-length intrabar arrays, proper variable scoping with var keyword for persistent state management across bars, and efficient conditional logic that prevents unnecessary calculations. The code structure with clearly delineated sections for inputs, calculations, signal generation, plotting, and alerts makes it maintainable and educational for those studying Pine Script development. The use of input groups with custom headers creates an organized settings panel that doesn't overwhelm users with dozens of ungrouped parameters, while still providing substantial customization capability for advanced users who want to optimize the indicator for specific instruments or timeframes.
For practical trading application, this indicator excels in several specific use cases. Scalpers and day traders can use the intrabar analysis to identify accumulation or distribution happening within the bars of their entry timeframe, providing early entry signals before momentum indicators or price patterns complete. Swing traders can use the cumulative delta and accumulation-distribution zones to understand whether short-term pullbacks in an uptrend are being bought or sold, helping distinguish between healthy retracements and trend reversals. Position traders can use the divergence detection to identify major turning points where price extremes are not supported by volume, providing low-risk entry points for counter-trend positions or warnings to exit with-trend positions before significant reversals.
The indicator is particularly valuable in ranging markets where price-based indicators produce numerous false breakout signals. By requiring that breakouts be accompanied by volume flow imbalances, the indicator filters out failed breakouts driven by low participation. When price breaks a range boundary accompanied by a strong buy or sell signal with high buy or sell score and very high volume, the probability of successful breakout follow-through increases dramatically. Conversely, when price breaks a range but the indicator shows low imbalance, opposing flow direction, or low volume, traders can fade the breakout or at minimum avoid chasing it.
During trending markets, the indicator helps traders identify the healthiest entry points by revealing where pullbacks are being accumulated by smart money. A trending market will show the cumulative delta continuing in the trend direction even as price pulls back, and accumulation zones will form during these pullbacks. When price resumes the trend, the indicator will generate strong buy or sell signals with high scores, providing objective entry points with clear invalidation levels. The flow momentum component helps traders stay with trends longer by distinguishing between healthy momentum pauses—where momentum goes to zero but doesn't reverse—and actual momentum reversals where opposing pressure is building.
The VWAP deviation weighting adds particular value for traders of liquid instruments like major forex pairs, stock indices, and high-volume stocks where VWAP is widely watched by institutional participants. When price deviates significantly from the intrabar VWAP and volume flows in the direction of that deviation with elevated weighting, it indicates that the move away from fair value is being driven by conviction rather than mechanical order flow. This suggests the deviation will likely extend further, creating continuation trading opportunities. Conversely, when price deviates from intrabar VWAP but volume flow shows reduced intensity or opposing direction despite the weighting, it suggests the deviation will revert to VWAP, creating mean reversion opportunities.
The ATR normalization option makes the indicator values comparable across different volatility regimes and different instruments. Without normalization, a one-million share buy-sell imbalance might be significant for a low-volatility stock but trivial for a high-volatility cryptocurrency. By normalizing the delta by ATR, the indicator accounts for the typical price movement capacity of the instrument, making signal thresholds and comparison values meaningful across different trading contexts. This is particularly valuable for traders running the indicator on multiple instruments who want consistent signal quality regardless of the underlying instrument characteristics.
The configurable decay factor allows traders to adjust how persistent they want volume flows to remain influential. For very short-term scalping, a lower decay factor like zero point five will cause volume imbalances to dissipate quickly, keeping the indicator focused only on very recent flows. For longer-term position trading, a higher decay factor like zero point nine-five will allow significant volume events to influence the indicator for many bars, revealing longer-term accumulation and distribution patterns. This flexibility makes the single indicator adaptable to trading styles ranging from one-minute scalping to daily chart position trading simply by adjusting the decay parameter and the lookback bars.
The minimum imbalance percentage setting provides crucial noise filtering that can be optimized per instrument. Highly liquid instruments with tight spreads might show numerous small imbalances that are meaningless, requiring a higher threshold like thirty-five or forty percent to filter noise effectively. Thinly traded instruments might rarely show extreme imbalances, requiring a lower threshold like fifteen or twenty percent to generate adequate signals. By making this threshold user-configurable with a wide range, the indicator accommodates the full spectrum of market microstructure characteristics across different instruments and timeframes.
In conclusion, the Intrabar Volume Flow Intelligence indicator represents a comprehensive volume analysis system that combines intrabar data access, sophisticated volume attribution algorithms, multi-timeframe smoothing, statistical filtering, divergence detection, zone identification, and intelligent signal scoring into a cohesive analytical framework. It provides traders with visibility into market dynamics that are invisible to price-only analysis and even to conventional volume analysis, revealing the true intentions of market participants through their actual transaction behavior within each bar. The indicator's strength lies not in any single feature but in the integration of multiple analytical layers that confirm and validate each other, creating high-probability signal generation that can form the foundation of complete trading systems or provide powerful confirmation for discretionary analysis. For traders willing to invest time in understanding its components and optimizing its parameters for their specific instruments and timeframes, this indicator offers a significant informational advantage in increasingly competitive markets where edge is derived from seeing what others miss and acting on that information before it becomes consensus.
ORB Session BreakoutORB Session Breakout
Overview
The ORB Session Breakout indicator automatically identifies Opening Range Breakouts across multiple trading sessions (Asia, London, and New York) and provides visual trade setups with entry, stop loss, and take profit levels.
Opening Range Breakout (ORB) is a classic trading strategy that captures momentum when price breaks out of an initial trading range established at the start of a session. This indicator automates the entire process - from detecting the opening range to plotting trade setups when breakouts occur.
🎯 Key Features
Multi-Session Support
Asia Session - Captures the Asian market open (default: 19:00-19:15 NY time)
London Session - Captures the London market open (default: 03:00-03:15 NY time)
New York Session - Captures the NY market open (default: 09:30-09:45 NY time)
Each session is fully customizable with independent time windows and colors
Enable/disable individual sessions based on your trading preferences
Automatic Trade Visualization
Entry Level - Marked at the breakout candle close
Stop Loss Zone - Configurable as ORB High/Low or Breakout Candle High/Low
Take Profit Zone - Calculated automatically based on your Risk:Reward ratio
Visual zones make it easy to see risk/reward at a glance
Smart Breakout Detection
Detects breakouts on the exact candle that closes beyond the ORB range
Supports direction changes - if price breaks one way then reverses, a new trade is signaled
Configurable max breakouts per session (1-4) to control trade frequency
Tracking hours setting limits how long after the ORB to look for entries
Futures Compatible
Special detection logic for futures markets where session times may fall during market close
Works reliably on instruments with non-standard trading hours
📊 How It Works
Opening Range Formation
At the start of each enabled session, the indicator tracks the high and low of the first candle(s)
This range becomes your ORB box (displayed in the session color)
Breakout Detection
When a candle closes above the ORB High → LONG signal
When a candle closes below the ORB Low → SHORT signal
The breakout candle is highlighted in yellow (customizable)
Trade Setup Visualization
Entry line drawn at the breakout candle's close price
Stop Loss placed at ORB Low (longs) or ORB High (shorts) - or breakout candle extreme
Take Profit calculated as: Entry + (Risk × R:R Ratio) for longs
Direction Changes
If you're in a LONG and price closes below the ORB Low, the indicator signals a SHORT
This counts as your 2nd breakout (configurable up to 4 per session)
💡 Trading Tips
Best Practices
Wait for candle close - The indicator only signals on confirmed closes beyond the ORB, reducing false breakouts
Use with trend - ORB breakouts work best when aligned with the higher timeframe trend
Respect the levels - The ORB High/Low often act as support/resistance throughout the session
Monitor multiple sessions - Sometimes the best setups come from Asia or London, not just NY
Recommended Settings by Style
Conservative: Max Breakouts = 1, R:R = 2.0+, SL Mode = ORB Level
Aggressive: Max Breakouts = 3-4, R:R = 1.5, SL Mode = Breakout Candle
Scalping: Shorter tracking hours (1-2), tighter R:R (1.0-1.5)
What to Avoid
Trading ORB breakouts during major news events (high volatility can cause whipsaws)
Taking every signal without considering market context
Using on timeframes higher than 1 hour (the ORB concept works best intraday)
🔔 Alerts
The indicator includes built-in alerts for:
Entry Signal - When a breakout is detected (LONG or SHORT)
Take Profit Hit - When price reaches the TP level
Stop Loss Hit - When price reaches the SL level
To set up alerts: Right-click on the chart → Add Alert → Select "ORB Session Breakout"
📝 Notes
This indicator is designed for intraday trading on timeframes up to 1 hour
Session times are based on the selected timezone (default: America/New_York)
The indicator works on all markets including Forex, Futures, Stocks, and Crypto
For futures with non-standard hours, the indicator includes special detection logic
Evil's Two Legged IndicatorA pullback strategy indicator designed for scalping. This attempts to Identify classic 2-leg pullback patterns and filters out signals during choppy market conditions for better signals.
How It Works:
The indicator detects when price forms two pullback legs (swing lows in an uptrend or swing highs in a downtrend) near key support/resistance zones, then signals when reversal confirmation occurs. Equal-level pullbacks (double bottoms/tops) are marked as stronger signals.
Features:
Channel Options: Donchian (default), Linear Regression, or ATR Bands
Configurable EMA: For trend confirmation (default 21)
Adjustable Leg Detection: Swing lookback period for different timeframes
Equal Level Detection: Highlights stronger setups where both legs terminate at similar prices
Three Chop Filters (can be combined):
ADX Filter — suppresses signals when ADX is below threshold (default 25)
EMA Slope Filter — suppresses signals when EMA is flat
Chop Index Filter — suppresses signals when Chop Index indicates ranging conditions
Signal Types:
Standard signals: 2-leg pullback detected with trend confirmation
Strong signals (highlighted): 2-leg pullback with equal highs/lows — higher probability setup
Recommended Use:
Best suited for scalping on 1-5 minute chart. Designed for 1.5:1 risk/reward setups.
Settings Guide:
Increase "Swing Lookback" for fewer, higher-quality signals
Adjust "Equal Level Threshold" to fine-tune what counts as a double bottom/top
Enable/disable chop filters based on your market and timeframe
Use "Show Strong Signals Only" to filter for highest conviction setups
Quantum RCI FusionDescription:
Overview: The Quantum Momentum Engine Quantum RCI Fusion is a sophisticated momentum oscillator designed to solve the #1 problem of classic indicators: false signals in sideways markets. At the core of this script is the Rank Correlation Index (RCI), a powerful statistical tool based on Spearman’s correlation. Unlike RSI or Stochastic which only look at price levels, the RCI evaluates the "quality" of a trend by measuring the temporal correlation of price ranks.
This script is not just a line drawing: it is a complete trading ecosystem that fuses three RCI timeframes, volatility filters, and a real-time Risk Management simulation.
🛠 How It Works: The "Fusion" Logic
The strength of this indicator lies in the synergy between its components. It is not a simple mashup, but a filtered logical system:
Triple RCI Engine (Fast, Mid, Slow):
Fast (13) & Mid (18): These generate the Crossover signal for precise entry timing.
Slow (30) - The "Trend Shield": The true innovation. It acts as a directional shield; if the baseline is bullish, the script protects Long positions by ignoring premature exit signals, allowing you to ride the full trend.
HMA Smoothing: Raw price data passes through a Hull Moving Average before the RCI calculation. This drastically reduces market "noise" without sacrificing the responsiveness typical of the RCI.
Intelligent Filters (Anti-Whipsaw):
ADX Integration: Signals are blocked if the ADX is below the threshold (default 20), preventing trading in flat/ranging markets.
Momentum Impulse: Requires a minimum variation (Delta) in the RCI to confirm that the move has real drive and is not just random fluctuation.
🛡 Risk Management & Simulation
Since timing is useless without risk management, Quantum RCI Fusion includes a Dashboard and sophisticated exit logic:
Multiple Exits:
Take Profit / Stop Loss: Based on dynamic ATR multipliers.
Shield Break: Safety exit if the underlying trend (Slow RCI) changes direction.
Emergency: Immediate close if momentum sharply reverses across the zero line.
Live Dashboard: Monitors Win Rate, virtual PnL, and Trade Status (Long/Short/Scanning) in real-time directly on the chart, removing the need for external backtesters.
🚀 How to Use It
Setup: Add the script to a separate pane below your price chart.
Entry Signals:
LONG (Green Triangle): RCI Fast crosses Mid upwards + Oversold Zone (< -80) + ADX > 20 + Bullish Shield.
SHORT (Red Triangle): RCI Fast crosses Mid downwards + Overbought Zone (> 80) + ADX > 20 + Bearish Shield.
Customization:
Scalping: Reduce RCI lengths (e.g., 8/12/20) and disable the "Trend Shield" for quick entries and exits.
Swing Trading: Keep defaults and use the ATR Trailing logic to manage positions on H4 or Daily timeframes.
⚖️ Notes & Credits
Originality: This script enhances the standard RCI by implementing Array-based calculations (optimized for Pine v6), proprietary HMA smoothing, and unique "Trend Shield" logic.
Open Source: The code is released under the MPL 2.0 license. Credits to the Pine community for the foundational mathematical formulas of Spearman's correlation.
Disclaimer: The statistics shown in the dashboard are simulations based on live data and do not guarantee future profits. You are responsible for your own trading decisions.
🖼 Instructions for the Publication Chart (Preview)
To ensure your script gets approved and attracts users, follow these steps for the cover image:
Symbol: Use a volatile and liquid asset, e.g., BTCUSD or XAUUSD (Gold), on a 1H or 4H timeframe.
Clean Layout: Remove all other indicators from the chart (no Moving Averages on price, no Bollinger Bands). The focus must be solely on your script in the bottom pane.
Visualization:
Ensure the Dashboard (stats table) is clearly visible and does not obscure the most recent candle.
The chart should show at least one clear BUY and one clear SELL signal, ideally with the exit icons (the "X" or flags) visible to demonstrate the exit logic.
CVD Divergence Detector# CVD Divergence Detector
Clean, focused divergence detection using **Cumulative Volume Delta (CVD)** - one of the most reliable reversal signals in trading.
## 🎯 What It Does
Identifies divergences between **price action** and **volume delta**:
**🔻 Bearish Divergence**: Price makes Higher High, but CVD doesn't → Expect reversal DOWN
**🔺 Bullish Divergence**: Price makes Lower Low, but CVD doesn't → Expect reversal UP
## ✨ Key Features
### Two Detection Modes
**1. Confirmed Divergences** (High Accuracy)
- Solid red/green lines
- Labels: 🔻 Bear / 🔺 Bull
- Fully confirmed pivots (9 bars default)
- Win rate: ~70-80%
**2. Early Warning Mode** ⚡ (Fast Signals)
- Dashed yellow lines
- Labels: ⚠️ Early Bear / ⚠️ Early Bull
- Fires 6+ bars earlier (3 bars default)
- Win rate: ~55-65%
### Smart Filtering
- Minimum bars between signals (prevents spam)
- Minimum CVD strength requirement (filters weak signals)
- Adjustable pivot periods for any timeframe
### Four Alert Types
- 🔻 Confirmed Bearish Divergence
- 🔺 Confirmed Bullish Divergence
- ⚠️ Early Bearish Warning
- ⚠️ Early Bullish Warning
## ⚙️ Recommended Settings
**15m Day Trading** (Best for most traders):
```
Pivot Left/Right: 9
Early Warning Right: 3
Min Bars Between: 40
Min CVD Diff: 5%
Anchor TF: 1D
```
**5m Scalping**:
```
Pivot Left/Right: 7
Early Warning Right: 2
Min Bars Between: 60
Min CVD Diff: 5%
```
**1H Swing Trading**:
```
Pivot Left/Right: 12-14
Early Warning Right: 4-5
Min Bars Between: 30
Min CVD Diff: 8%
```
## 💡 Trading Strategies
### Strategy 1: Early Entry (Scalpers)
- ⚠️ Early warning → Enter immediately
- Stop: Just beyond pivot
- Target: 1:2 R/R minimum
- Trades/day: 3-8
### Strategy 2: Scale In (Day Traders)
- ⚠️ Early warning → 25% position
- 🔻 Confirmed → Add 75%
- Move stop to breakeven
- Trades/week: 5-15
### Strategy 3: Confirmation Only (Swing Traders)
- Wait for 🔻 confirmed signal only
- Wider stops (1-2 ATR)
- Hold for bigger moves
- Trades/month: 8-20
## 🎯 How to Use
1. **Install** indicator on your chart
2. **Choose** your timeframe (15m recommended to start)
3. **Enable** Early Warning for faster signals OR disable for confirmed only
4. **Set alerts** for your preferred divergence types
5. **Combine** with support/resistance for best results
## 🔧 Tuning Guide
**Too many signals?**
- Increase Pivot Right to 12-15
- Increase Min Bars Between to 60
- Increase Min CVD Diff to 8-10%
**Signals too slow?**
- Enable Early Warning
- Decrease Early Warning Right to 2
- Decrease Pivot Right to 6-7
**Want cleaner chart?**
- Turn off labels (lines only)
- Disable early warnings (confirmed only)
## ⚠️ Important Notes
**Requirements:**
- Volume data required (works on futures, stocks, crypto)
- May not work on some forex pairs (broker-dependent)
**Performance:**
- No indicator is 100% accurate
- Always use proper risk management
- Combine with price action and S/R levels
- Quality over quantity - don't trade every signal
**Best Results:**
- Divergence AT support/resistance = high probability
- Divergence + trend reversal pattern = confluence
- Multiple timeframe confirmation = strongest signals
## 📊 What Makes This Different?
**Other divergence indicators:**
- Use RSI, MACD, or other oscillators
- Don't show actual order flow
- Often give false signals
**This indicator:**
- Uses real CVD (Cumulative Volume Delta)
- Shows actual buying/selling pressure
- Filters for quality (not quantity)
- Two modes: fast OR accurate (your choice)
- No clutter - just clean divergence lines
## 🚀 Quick Start
1. Add to chart
2. Default settings work well for 15m
3. Watch for 1 week before trading
4. Start with small size
5. Track your results
## 📈 Typical Performance
| Mode | Win Rate | Avg R/R | Best For |
|------|----------|---------|----------|
| Early Warning | 55-65% | 1:1.5 | Scalping |
| Confirmed | 70-80% | 1:2 | Swing trading |
| Both (Scale In) | 65-75% | 1:3 | Day trading |
| With Confluence | 75-85% | 1:3+ | All styles |
## 💬 Tips from Pro Traders
- "Use early warnings for entries, confirmed for validation"
- "Best at major S/R levels - skip divergences in the middle of nowhere"
- "Lower timeframes = more signals but lower quality"
- "On 15m chart, early warnings give you 1.5 hour head start"
- "Combine with volume spikes for highest probability"
## 🔔 Alert Setup
1. Click Alert button (⏰)
2. Choose "CVD Divergence Detector"
3. Select alert type
4. Configure notifications
5. Done!
## ⚙️ Settings Explained
**Delta Source:**
- Anchor Timeframe: Higher TF for CVD calculation (1D for day trading)
- Custom Lower TF: Advanced users only
**Pivot Logic:**
- Pivot Left/Right: How many bars to confirm pivot
- Early Warning Right: How fast early signals fire
- Min Bars Between: Prevents signal spam
- Min CVD Diff %: Filters weak divergences
**Visual:**
- Show Lines/Labels: Toggle display
- Colors: Customize to your preference
- Label Size: Adjust for readability
## ❓ FAQ
**Q: No signals appearing?**
- Check volume data is available
- Lower Min CVD Diff to 2-3%
- Lower Pivot Right to 5-7
**Q: Too many signals?**
- Increase filters (see Tuning Guide above)
- Turn off early warnings
- Use confirmed only
**Q: Signals too late?**
- Enable Early Warning mode
- Decrease Early Warning Right to 2-3
**Q: Works on crypto/forex?**
- Crypto: Yes (major pairs)
- Forex: Sometimes (depends on broker volume data)
- Futures/Stocks: Yes (best performance)
## 📚 Learn More
For detailed strategies, examples, and advanced techniques, check the full user guide.
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**Remember:** This is a tool, not a crystal ball. Combine with:
- Price action analysis
- Support/resistance levels
- Risk management
- Proper position sizing
**The best trade is the one you don't force.** 🎯
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## 📝 Version Info
**v1.0** - Initial Release
- Confirmed divergence detection
- Early warning mode
- Smart filtering system
- Four alert types
- Clean visual design
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**Questions? Suggestions?** Drop a comment below! 👇
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