FVG Ultra Assertive - Individual Filters (mtbr)FVG Ultra Assertive - Individual Filters (mtbr)
What this script offers:
This strategy detects and highlights FVGs (Fair Value Gaps) on the chart, providing traders with a visual and systematic approach to identify potential price inefficiencies. The script plots bullish and bearish FVG zones using customizable boxes and labels, allowing users to easily spot high-probability trading areas. In addition, it opens and closes simulated trades based on the detected FVGs, enabling full backtesting and strategy performance evaluation. It integrates multiple independent filters to validate the strength of each FVG signal before entering a trade.
How it works:
The script identifies:
Bullish FVGs when the current low is higher than the high of two bars ago.
Bearish FVGs when the current high is lower than the low of two bars ago.
Once an FVG is detected, it applies three optional independent filters:
GAP/ATR Filter:
Measures the FVG size relative to the Average True Range (ATR). Only gaps exceeding a user-defined multiple of ATR are considered valid.
Support/Resistance (S/R) Filter:
Uses pivot points to check if the FVG overlaps with recent high/low pivot levels within a tolerance percentage. This ensures the gap aligns with meaningful market levels.
Stochastic Filter:
Applies a stochastic oscillator to confirm momentum. Bullish FVGs are validated when stochastic values are oversold, and bearish FVGs when overbought.
After passing the selected filters, the strategy opens trades:
LONG FVG for bullish signals (buy)
SHORT FVG for bearish signals (sell)
The strategy automatically closes positions when an opposite signal appears, generating a backtest report with trades, profits, and statistics. The final bullish or bearish FVG signals are plotted as colored boxes on the chart with labels “BULL FVG” or “BEAR FVG” for immediate visual reference.
How to configure it for use:
Use GAP/ATR Filter: Enable or disable the ATR-based filter and adjust the ATR period (ATR Length) and minimum gap multiplier (Minimum Gap x ATR).
Use S/R Filter: Enable or disable the pivot-based S/R filter. Configure the pivot lookback periods (Pivot Left and Pivot Right) and the tolerance percentage (Gap Tolerance %).
Use Stochastic Filter: Enable or disable stochastic confirmation. Adjust the K and D lengths (Stoch K Length and Stoch D Length) and the overbought/oversold thresholds (Stoch Overbought and Stoch Oversold).
Colors: Customize the colors for bullish and bearish FVGs (FVG Bull and FVG Bear) to match your chart preferences.
Usage Tips:
Apply this strategy to any timeframe; shorter timeframes generate more frequent FVGs, while higher timeframes highlight stronger gaps.
Combine FVG signals with other technical analysis tools for better trade confirmation.
Use the box and label visualization to quickly scan charts for trade opportunities without cluttering the chart.
The strategy’s trades (LONG and SHORT) provide backtesting results and performance statistics for each signal.
التحليل الأساسي
Liquidity Sweep Breakout - LSBLiquidity Sweep Breakout - LSB
A professional session-based breakout system designed for OANDA:USDJPY and other JPY pairs.
Not guesswork, but precision - built on detailed observation of institutional moves to capture clear trade direction daily.
Master the Market’s Daily Bank Flow.
---
Strategy Detail:
I discovered this strategy after carefully studying how Japanese banks influence the forex market during their daily settlement period. Banks are some of the biggest players in the financial world, and when they adjust or settle their accounts in the morning, it often creates a push in the market. From years of observation, I noticed a consistent pattern, once banks finish their settlements, the market usually continues moving in the same direction that was formed right after those actions. This daily banking flow often sets the tone for the entire trading session, especially for JPY pairs like USDJPY.
To capture this move, I built the indicator so that it follows the bank-driven trend with clear rules for entries, stop-loss (SL), and take-profit (TP). The system is designed with professional risk management in mind. By default, it assumes a $10,000 account size, risks only 1% of that balance per trade, and targets a 1:1.5 reward-to-risk ratio. This means for every $100 risked, the potential profit is $150. Such controlled risk makes the system safer and more sustainable for long-term traders. At the same time, users are not limited to this setup, they can adjust the account balance in the settings, and the indicator will automatically recalculate the lot size and risk levels based on their own capital. This ensures the strategy works for small accounts and larger accounts alike.
🌍 Why It Works
Fundamentally driven: Based on **daily Japanese banking settlement flows**.
Session-specific precision: Targets the exact window when USDJPY liquidity reshapes.
Risk-managed: Always calculates lot size based on account and risk preferences.
Automatable: With webhook + MT5 EA, it can be fully hands-free.
---
✅ Recommended
Pair: USDJPY (best observed behavior).
Timeframe: 3-Minute chart.
Platform: TradingView Premium (for webhooks).
Execution: MT5 via EA.
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🔎 Strategy Concept
The Tokyo Magic Breakout (TMB) is built on years of session observation and the unique daily rhythm of the Japanese banking system.
Every morning between 5:50 AM – 6:10 AM PKT (09:50 – 10:10 JST), Japanese banks perform daily reconciliation and settlement. This often sets the tone for the USDJPY direction of the day.
This strategy isolates that critical moment of liquidity adjustment and waits for a clean breakout confirmation. Instead of chasing noise, it executes only when price action is aligned with the Tokyo market’s hidden flows.
---
🕒 Timing Logic
Session Start: 5:00 AM PKT (Tokyo market open range).
Magic Candle: The 5:54 AM PKT candle is marked as the reference “breakout selector.”
Checkpoints: First confirmation at 6:30 AM PKT, then every 15 minutes until 8:30 AM PKT.
* If price stays inside the magic range → wait.
* If a breakout happens but the candle wick touches the range → wait for the next checkpoint.
* If by 8:30 AM PKT no clean breakout occurs → the day is marked as No Trade Day (NTD).
👉 Recommended timeframe: 3-Minute chart (3M) for precise signals.
---
📈 Trade Execution
Entry: Clean break above/below the magic candle’s range.
Stop-Loss: Opposite side of the Tokyo session high/low.
Take-Profit: Calculated by Reward\:Risk ratio (default 1.5:1).
Lot Size: Auto-calculated based on your risk model:
* Fixed Dollar
* % of Equity
* Conservative (minimum of both).
Visuals include:
✅ Entry/SL/TP lines
✅ Shaded risk (red) and reward (green) zones
✅ Trade labels (Buy/Sell with lot size & levels)
✅ TP/SL hit markers
---
🔔 Alerts & Automation (AutoTMB)
This strategy is fully automation-ready with EA + MT5:
1. Enable alerts in TMB settings.
2. Insert your PineConnector License Key.
3. Configure your risk management preferences.
4. Create a TradingView alert → in the message box simply type:
Pine Script®
{{alert_message}}
and set the EA webhook.
Now, every breakout trade (with exact entry, SL, TP, and lot size) is sent instantly.
👉 On your MT5:
* Install the EA.
* Use the same license key.
* Run it on a VPS or local MT5 terminal.
You now have a hands-free trading system: AutoTMB.
Stochastic Reversal v1.0.0Stochastic Reversal v1.0.0
Description:
The "Stochastic Reversal" is an innovative Pine Script indicator engineered for busy professionals and businesses who want to quickly identify potential trend reversals with a high degree of confidence. This tool leverages a sophisticated analysis of the Stochastic Oscillator to provide clear, actionable signals that anticipate market turning points.
Instead of relying on the default settings and typical overbought/oversold levels, this indicator uses an optimized algorithm to:
Pinpoint reversals with precision : It goes beyond simple crossovers to identify confirmed reversal signals, helping you to enter trades at the most opportune moments.
Filter out false signals : It is designed to work effectively even in trending markets, reducing the number of false signals that plague standard Stochastic strategies.
Save valuable time : By automating the detection of these complex reversal patterns, the "Stochastic Reversal" allows you to efficiently scan for setups and make informed decisions without constant manual analysis.
This tool is a crucial component for any trader who wants to trade with the trend and capitalize on market turns, all while gaining a significant edge in their analysis.
Disclaimer:
This indicator is for educational and informational purposes only. It should not be considered financial advice. The use of this tool for live trading is at your own risk. Past performance is not an indicator of future results. Always conduct your own thorough research and analysis before making any trading decisions.
Bollinger Squeeze Momentum v1.0.0Bollinger Squeeze Momentum v1.0.0
Description:
The " Bollinger Squeeze Momentum " is an advanced Pine Script indicator designed for busy professionals and businesses seeking to identify high-volatility breakouts before they occur. This tool focuses on a key market dynamic: the "Bollinger Squeeze," a period of low volatility that often precedes a strong directional move.
Instead of just showing the squeeze, this tool automates the process of identifying the shift in momentum at the precise moment the squeeze ends. This allows you to:
Anticipate significant breakouts : It provides clear signals on your chart when market energy is building and a move is imminent.
Filter out quiet markets : It helps you avoid choppy, low-volatility conditions and focus your attention on opportunities with a high potential for profit.
Save time on analysis : By automating the detection of this powerful pattern, the "Bollinger Squeeze Momentum" allows you to efficiently scan for setups and make informed decisions.
This tool is a critical addition for any trader who wants to capitalize on explosive price movements and trade with confidence.
Disclaimer:
This indicator is for educational and informational purposes only. It should not be considered financial advice. The use of this tool for live trading is at your own risk. Past performance is not an indicator of future results. Always conduct your own thorough research and analysis before making any trading decisions.
MACD Trend Follower v1.0.0MACD Trend Follower v1.0.0
Description:
The "MACD Trend Follower" is a sophisticated Pine Script indicator designed for busy professionals and businesses who want to capitalize on established market trends. This tool goes beyond the basic MACD indicator by providing a more reliable and nuanced view of momentum and trend strength, helping you to identify optimal entry and exit points with greater confidence.
The indicator uses a refined version of the classic MACD to:
Filter out market noise : It reduces false signals and whipsaws, allowing you to focus on the most significant and sustainable trends.
Confirm trend strength : It provides clear visual cues on your chart to indicate when a trend is gaining or losing momentum.
Save valuable time : By automating the analysis, the "MACD Trend Follower" allows you to quickly and efficiently identify trending markets, so you can make informed decisions without constant manual chart analysis.
This tool is perfect for those who understand the value of a professional-grade analysis but lack the time for manual execution. It's an essential component for any strategy focused on trading momentum shifts and trend reversals.
Disclaimer:
This indicator is for educational and informational purposes only. It should not be considered financial advice. The use of this tool for live trading is at your own risk. Past performance is not an indicator of future results. Always conduct your own thorough research and analysis before making any trading decisions.
RSI Divergence Hunter v1.0.0RSI Divergence Hunter v1.0.0
Description:
The "RSI Divergence Hunter" is an essential Pine Script tool built for busy professionals and businesses who want to uncover hidden market opportunities without continuous monitoring. This indicator automatically detects and plots both bullish and bearish RSI divergences, a powerful signal that often precedes significant trend reversals.
Manual detection of divergence is a time-consuming and subjective process. Our tool eliminates this guesswork by:
Automatically finding divergences: It scans the price action and the RSI indicator in real-time to identify potential trading opportunities for you.
Providing clear visual signals: The indicator plots lines directly on your chart, highlighting where a divergence has occurred.
Saving you time: Instead of manually scanning charts for hours, you can receive alerts and focus on what matters most—making confident, data-backed decisions.
This tool is perfect for those who understand the value of a professional-grade analysis but lack the time for manual execution. It's a key component for any strategy focused on trading momentum shifts and trend reversals.
Disclaimer:
This indicator is for educational and informational purposes only. It should not be considered financial advice. The use of this tool for live trading is at your own risk. Past performance is not an indicator of future results. Always conduct your own thorough research and analysis before making any trading decisions.
Triple Momentum Convergence v1.0.0The "Triple Momentum Convergence" indicator is a powerful Pine Script tool designed to help you pinpoint high-probability trading setups by combining the signals of three distinct momentum indicators. This unique strategy is built on the principle that when multiple, independent momentum sources align, the likelihood of a strong directional move increases significantly.
This indicator uses a proprietary algorithm to analyze:
Short-Term Momentum : Capturing swift movements for rapid entries and exits.
Medium-Term Momentum : Identifying a sustained trend in motion.
Long-Term Momentum : Confirming the overall market direction.
The indicator plots clear signals on your chart when all three momentum phases converge, providing a high-conviction entry point. It's an ideal tool for traders who want to filter out false signals and trade with more confidence, focusing on moments when the market is in strong agreement.
Fundamental Strategy - anuragmundraFundamental Score Based Backtest
This strategy combines fundamental analysis with automated backtesting to help identify long-term investment opportunities. Instead of relying only on price action or technical indicators, it evaluates the financial health of a company and generates simulated BUY/SELL signals accordingly.
🔑 Key Parameters Considered:
Price-to-Earnings (P/E Ratio): Ensures the stock is not overpriced.
Return on Equity (ROE): Indicates efficiency of management and business profitability.
Debt-to-Equity Ratio (D/E): Evaluates leverage and financial risk.
Revenue Growth (YoY): Shows business expansion and demand.
EPS Growth: Reflects consistent profit generation for shareholders.
Sales Growth: Confirms topline improvement.
Profit Growth: Measures bottom-line strength.
✅ Buy Condition
When the fundamental score ≥ 70/100, the strategy enters a long position.
Score is based on meeting/exceeding thresholds for P/E, ROE, Revenue Growth, EPS Growth, Sales Growth, Profit Growth, and Debt-to-Equity.
❌ Sell/Exit Condition
When the score falls below 70, the position is closed.
⚡ How to Use
Designed for medium to long-term investors who prefer fundamentally strong companies.
Can be run in the Strategy Tester to evaluate the historical performance of any stock.
Suitable as a stock-picking filter rather than a short-term trading system.
📊 Notes
Some ratios (like ROE) are based on annual values (FY), while others (EPS, Revenue, Net Income) use TTM for recency.
Not all symbols/exchanges provide full fundamental data. If data is missing, some metrics may show as N/A.
⚠️ Disclaimer: This is an educational tool for research and backtesting only. It is not financial advice. Always combine with your own due diligence before making investment decisions.
Outside Bar Reversal - Black GUIAn Outside Bar Reversal strategy that buys when the price breaks above the high of an outside bar and sells when it breaks below the low of an outside bar.
The strategy features a black-themed GUI for enhanced visibility.
This strategy is useful for identifying potential reversal points based on price action patterns. Always backtest the strategy before applying it to live trading.
Visit - trader-on-the-go .
ATR Trailing Stop Loss - FixedAn ATR Trailing Stop Loss strategy that uses the Average True Range (ATR) to set dynamic stop loss levels.
The strategy buys when the price crosses above the previous high and sells when the price crosses below the trailing stop level.
The strategy features a black-themed GUI for enhanced visibility.
You can adjust the ATR length and multiplier using the input parameters.
This strategy is useful for managing risk while allowing profits to run in trending markets.
Always backtest the strategy before applying it to live trading.
Visit - for more such strategies.
Volume Spike Strategy - Black GUIA volume spike strategy that buys when the volume is 150% above the 20-day average and the price is increasing.
The strategy uses a black-themed GUI for better visibility.
You can customize the volume spike multiplier and the length of the volume moving average using the input parameters.
This strategy is effective for identifying potential breakout points in the market. Always backtest the strategy before applying it to live trading.
Visit - for more such strategies.
ADX with DI+/- Crossover StrategyAn ADX-based strategy that buys when the DI+ line crosses above the DI- line with the ADX above a specified threshold (default 25), and sells when the opposite occurs.
The strategy features a black-themed GUI for enhanced visibility.
You can adjust the ADX length and threshold using the input parameters.
This strategy is useful for identifying strong trends in the market.
Always backtest the strategy before applying it to live trading.
Visit - for more such strategies.
Bollinger Bands Mean Reversion - Black GUIA Bollinger Bands mean reversion strategy that buys when the price touches the lower band and sells when it touches the upper band.
The strategy features a black-themed GUI for enhanced visibility.
You can adjust the length and multiplier of the Bollinger Bands using the input parameters.
This strategy is useful for identifying potential reversal points in the market.
Always backtest the strategy before applying it to live trading.
Visit - for more such strategies.
SulCryptoversity_4H_BuySell_CryptoIndicatorThis strategy is designed specifically for the 4-hour timeframe on trading charts. It works primarily for Bitcoin (BTC) but can also be applied to other high-market-cap cryptocurrencies such as Ethereum (ETH), Solana (SOL), Ripple (XRP), Sui (SUI), and even various other coins.
Please note that this is not financial advice—trading involves significant risk, and you should only proceed at your own discretion. We are not liable for any losses incurred from following these signals.
This strategy may be more effective in leverage trading to maximize gains, but leverage trading is highly risky and only recommended for highly skilled traders, as you could lose all your money. For regular purposes, use spot trading.
To use it effectively, focus on the "Buy" and "Sell" signals for your entry and exit points. While an "Exit Buy" signal may appear, rely solely on the main Buy and Sell indicators for decision-making.
-SulCryptoversity aka yo4Q
ETH/SOL 1D Dynamic Trend Core - STRATEGY v 45Overview
The Dynamic Trend Core is a sophisticated, multi-layer trading engine designed to identify high-probability, trend-following opportunities. Its core philosophy is rooted in confluence, meaning it requires multiple conditions across trend, momentum, and volume to align before generating a signal. This approach aims to filter out market noise and provide a clearer view of the underlying trend.
The script includes a comprehensive backtesting engine for strategy optimization and a rich, intuitive visual interface for real-time analysis.
How It Works: Core Logic
The engine validates signals through several sequential layers:
Primary Trend Analysis (SAMA): The foundation is a Self-Adjusting Moving Average (SAMA) that dynamically determines the primary market direction (Bullish, Bearish, or Consolidation).
Momentum Confirmation: Signals are then qualified using a blend of the Natural Market Slope and a Cyclic RSI to ensure momentum is firmly aligned with the established trend.
Advanced Filtering Suite: A suite of optional filters provides robust confirmation and allows for deep customization:
Volume & ADX: Confirms that trades are supported by sufficient market participation and trend strength.
Market Regime: Gauges broad market health (e.g., using TOTAL market cap) to avoid trading against the entire market.
Multi-Timeframe (MTF) Analysis: Aligns signals with the dominant trend on a higher timeframe (e.g., Weekly).
BTC Cycle Analysis: Positions trades within the context of historical Bitcoin cycles using models like the Halving Cycle or Mayer Multiple.
On-Chart Visuals & Features
The script provides full transparency into its logic with a powerful on-chart interface.
IMPORTANT: For the live visual elements to function correctly, you must enable "Recalculate on every tick" in the script's settings (Settings > Properties).
Power Core Gauge: Located at the bottom-center of the chart, this gauge is the heart of the system. It displays the number of filter conditions currently met (e.g., 5/6) and "powers up" by glowing brighter as more conditions align, indicating a fully confirmed signal is ready.
Live Conditions Panel: This panel in the bottom-right corner acts as a real-time pre-flight checklist. It shows the status (pass/fail) of every individual filter, so you know exactly why a signal is, or is not, being generated.
Energized Trendline: The primary SAMA trendline changes color and intensity based on the strength and direction of the trend, offering immediate visual context.
BTC Halving Cycle Visualizer: Provides a background color guide to the different phases of the Bitcoin halving cycle for macro context.
How to Use & Configure
Select Operation Mode:
Backtest Mode: Use this to test different settings on historical data and find optimal configurations for a specific asset and timeframe.
Alerts-Only Mode: Use this for live trading to generate alert signals without cluttering the chart with backtest data. (Contact publisher for access to this version)
Configure Your Filters:
Start with the default filter settings.
If a potential setup is missed, check the Live Conditions Panel to see which specific filter blocked the signal.
Enable, disable, or adjust filters in the script's settings to match your trading style and the asset's characteristics.
Manage Your Risk:
Go to the "Risk & Exit" settings to configure your Stop Loss and Take Profit parameters to match your personal risk tolerance.
Disclaimer: This script is for educational and informational purposes only. It is not financial advice. All trading involves risk, and past performance is not indicative of future results. Please conduct your own research and backtesting before making any trading decisions.
SuperPower_369Superpower_369
Select a highly liquid crypto pair (e.g., BTC/USDT, ETH/USDT) with tight spreads for lower slippage.
Use Supertrend (10,3) to define the primary market trend — green for bullish, red for bearish.
Apply Bollinger Bands (20,2) to identify volatility and potential breakout or mean-reversion zones.
Enter long positions when Supertrend turns bullish and price bounces from the lower Bollinger Band.
Enter short positions when Supertrend turns bearish and price rejects from the upper Bollinger Band.
Filter trades using RSI (14) — only buy when RSI is above 40 and sell when RSI is below 60, avoiding overbought/oversold traps.
Set a stop-loss just below the recent swing low (for longs) or above the swing high (for shorts).
Use a take profit at 1.5–2× the stop-loss distance or when RSI reaches extreme zones (above 75 or below 25).
Avoid trading during very low volatility periods when Bollinger Bands are too narrow.
Manage risk by risking only 1–2% of capital per trade and adjusting position size based on volatility.
Martin Strategy - No Loss Exit v3Martin Strategy1.0 Martin Strategy1.0 Martin Strategy1.0 Martin Strategy1.0 Martin Strategy1.0 Martin Strategy1.0
BTC Dynamic Trend Core Strategy v45// The Dynamic Trend Core is a sophisticated, multi-layer trading strategy that provides both a quantitative //
// backtesting engine and a rich, intuitive visual interface. It is designed to identify high-probability //
// trend-following opportunities by requiring a confluence of conditions to be met before a signal is considered //
// valid. //
// //
// The system's philosophy is rooted in confirmation, seeking to filter out market noise by ensuring that trend, //
// momentum, market sentiment, and volume are all in alignment. //
// //
// --- CORE LOGIC COMPONENTS --- //
// 1. **Primary Trend Analysis (SAMA):** The foundation is a self-adjusting moving average (SAMA) that //
// determines the underlying market trend (Bullish, Bearish, or Consolidation). //
// //
// 2. **Confirmation & Momentum:** Signals are confirmed with a blend of the Natural Market Slope and a Cyclic //
// RSI to ensure momentum aligns with the primary trend. //
// //
// 3. **Advanced Filtering Layers:** A suite of optional filters allows for robust customization: //
// - **Volume & ADX:** Ensure sufficient market participation and trend strength. //
// - **Market Regime:** Uses total crypto market cap to gauge broad market health. //
// - **Multi-Timeframe (MTF):** Aligns signals with the dominant weekly trend. //
// - **BTC Cycle Analysis:** Uses Halving or Mayer Multiple models to position trades within historical //
// macro cycles. //
// //
// --- VISUAL INTERFACE --- //
// The strategy's real power comes from its on-chart visual feedback system, which provides full transparency. //
// ****Note: for this to be enabled recalculate 'on every tick' needs to be enabled in the properties settings. //
// 1. **Power Core Gauge:** Located at the bottom-center, this gauge is the heart of the system. It displays the //
// number of active filter conditions that have been met (e.g., 5/6). It "powers up" as more conditions align,//
// glowing brightly when a signal is fully confirmed and ready. //
// //
// 2. **Live Conditions Panel:** In the bottom-right corner, this panel acts as a detailed pre-flight checklist. //
// It shows the real-time status of every single filter, helping you understand exactly why a trade is (or //
// is not) being triggered. //
// //
// 3. **Energized Trendline:** The main SAMA trendline changes color and brightness based on the strength and //
// direction of the trend, providing immediate visual context. //
// //
// 4. **Halving cycle visualisation:** Visual guide to halving phases //
// //
// --- HOW TO USE --- //
// 1. **Select Operation Mode:** Use "Backtest Mode" to test settings and "Alerts-Only Mode" for live signals. //
// //
// 2. **Configure Strategy:** Start with the default filters. If a potential trade setup is missed, check the //
// **Live Conditions Panel** to see exactly which filter blocked the signal. Adjust the filters to suit your //
// specific asset and timeframe. //
// //
// 3. **Manage Risk:** Adjust the Risk & Exit settings to match your personal risk tolerance. //
Gold Multi TP Strategy📘 Strategy Description: Gold Multi Take-Profit Strategy (XAUUSD)
This strategy is designed for Gold (XAUUSD) and works on any timeframe (recommended: 15-min or higher). It executes trades based on a simple EMA crossover logic with optional higher-timeframe and ATR-based filters to confirm trend direction and volatility.
🔑 Core Features
✅ Directional control: Trade only long, short, or both directions (Strategy Direction)
✅ Multi-level Take Profit: Scale out at up to 4 configurable profit targets
✅ Fixed Stop Loss: Set custom SL distance for risk control
✅ Position Sizing: Allocate different percentages to each TP level
✅ HTF Trend Filter (optional): Align trades with weekly candle trend
✅ ATR Filter (optional): Improve entries with volatility-based filter
⚙️ Inputs Explained
Input Name Function
Strategy Direction Choose to trade all, long, or short only
Length of Filter Length of the moving average used for HTF trend filter
Candle Time Reference candle timeframe in minutes (e.g., 1440 for daily)
Length of ATR Period for ATR calculation (volatility)
HTF Higher timeframe for filter (e.g., 1 week)
Filter Checkbox Enable/disable trend filter
Stop Loss Fixed SL distance in price units
Qty_percent1-3 % of position allocated to TP1–TP3 (rest goes to TP4)
Take profit1–4 TP levels (in price units) from entry price
🧠 Logic Overview
Entry triggered on EMA 20/50 crossover
Optional filter: entry allowed only if current price is above its HTF MA (bullish) or below (bearish)
Position is scaled out at up to 4 profit levels using different qty_percent
SL remains fixed throughout the trade
📊 Best Use
Intraday trading on XAUUSD, ideally during London/NY sessions
Trending or breakout conditions
Works best with additional confluence (price action, S/R, news)
Aether SignalAether Signal is a professional TradingView indicator engineered for advanced traders who demand precise analysis, smart money concepts, and robust risk management. It systematically incorporates institutional trading techniques, automated level detection, and multi-level profit-taking for exceptional trade execution.
Support & Resistance: Aether Signal automatically identifies key support and resistance levels using mathematically rigorous algorithms, ensuring that traders see the most significant price barriers for their entries and exits.
Smart Money Concepts: The indicator is grounded in institutional trading logic, analyzing market structure to pinpoint where large market participants are engaging. It leverages volume and price interaction at critical zones, similar to harmonic liquidity nodes in professional strategies.
Precise Entry Points: Entry signals are generated when strict confluence conditions are met, ensuring signals align with underlying market structure, high-volume footprints, and optimal momentum. Stops are logically placed just beyond the validated support or resistance—on the opposite side of the key zone.
Triple Take Profits: Aether Signal equips traders to maximize returns with three intelligently placed take profit levels (TP1, TP2, TP3), allowing for strategic scaling out and adaptive trade management.
Supply & Demand Zones: The indicator scans for market imbalances by identifying high-probability supply and demand areas driven by institutional activity and volume anomalies, guiding traders toward potent reversal or continuation setups.
Advanced Risk Management: Robust risk controls are integrated, including logical stop loss suggestions and trade selection filters, to minimize overtrading and enhance consistency.
Win Rate: The system claims a win rate of up to 96% under optimal settings and strict adherence to its entry criteria, setting a high benchmark for performance (note: actual results may vary depending on market conditions and trader discipline).
Aether Signal is tailored for traders seeking the edge of institutional-grade analytics—offering comprehensive structure analysis, actionable alerts, and performance-focused features that merge automation with trader control.
PEAD strategy█ OVERVIEW
This strategy trades the classic post-earnings announcement drift (PEAD).
It goes long only when the market gaps up after a positive EPS surprise.
█ LOGIC
1 — Earnings filter — EPS surprise > epsSprThresh %
2 — Gap filter — first regular 5-minute bar gaps ≥ gapThresh % above yesterday’s close
3 — Timing — only the first qualifying gap within one trading day of the earnings bar
4 — Momentum filter — last perfDays trading-day performance is positive
5 — Risk management
• Fixed stop-loss: stopPct % below entry
• Trailing exit: price < Daily EMA( emaLen )
█ INPUTS
• Gap up threshold (%) — 1 (gap size for entry)
• EPS surprise threshold (%) — 5 (min positive surprise)
• Past price performance — 20 (look-back bars for trend check)
• Fixed stop-loss (%) — 8 (hard stop distance)
• Daily EMA length — 30 (trailing exit length)
Note — Back-tests fill on the second 5-minute bar (Pine limitation).
Live trading: enable calc_on_every_tick=true for first-tick entries.
────────────────────────────────────────────
█ 概要（日本語）
本ストラテジーは決算後の PEAD を狙い、
EPS サプライズがプラス かつ 寄付きギャップアップ が発生した銘柄をスイングで買い持ちします。
█ ロジック
1 — 決算フィルター — EPS サプライズ > epsSprThresh %
2 — ギャップフィルター — レギュラー時間最初の 5 分足が前日終値＋ gapThresh %以上
3 — タイミング — 決算当日または翌営業日の最初のギャップのみエントリー
4 — モメンタムフィルター — 過去 perfDays 営業日の騰落率がプラス
5 — リスク管理
• 固定ストップ：エントリー − stopPct %
• 利確：終値が日足 EMA( emaLen ) を下抜け
█ 入力パラメータ
• Gap up threshold (%) — 1 (ギャップ条件)
• EPS surprise threshold (%) — 5 (EPS サプライズ最小値)
• Past price performance — 20 (パフォーマンス判定日数)
• Fixed stop-loss (%) — 8 (固定ストップ幅)
• Daily EMA length — 30 (利確用 EMA 期間)
注意 — Pine の仕様上、バックテストでは寄付き 5 分足の次バーで約定します。
実運用で寄付き成行に合わせたい場合は calc_on_every_tick=true を有効にしてください。
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ご意見や質問があればお気軽にコメントください。
Happy trading!
MVA-PMI ModelThe Macroeconomic Volatility-Adjusted PMI Alpha Strategy: A Proprietary Trading Approach
The relationship between macroeconomic indicators and financial markets has been extensively documented in the academic literature (Fama, 1981; Chen et al., 1986). Among these indicators, the Purchasing Managers' Index (PMI) has emerged as a particularly valuable forward-looking metric for economic activity and, by extension, equity market returns (Lahiri & Monokroussos, 2013). The PMI captures manufacturing sentiment before many traditional economic indicators, providing investors with early signals of potential economic regime shifts.
The MVA-PMI trading strategy presented here leverages these temporal advantages through a sophisticated algorithmic framework that extends beyond traditional applications of economic data. Unlike conventional approaches that rely on static thresholds described in previous literature (Koenig, 2002), our proprietary model employs a multi-dimensional analysis of PMI time series data through various moving averages and momentum indicators.
As noted by Beckmann et al. (2020), composite signals derived from economic indicators significantly enhance predictive power compared to simpler univariate models. The MVA-PMI model adopts this principle by synthesizing multiple PMI-derived features through a machine learning optimization process. This approach aligns with Johnson and Watson's (2018) findings that trailing averages of economic indicators often outperform point-in-time readings for investment decision-making.
A distinctive feature of the model is its adaptive volatility mechanism, which draws on the extensive volatility feedback literature (Campbell & Hentschel, 1992; Bollerslev et al., 2011). This component dynamically adjusts position sizing according to market volatility regimes, reflecting the documented inverse relationship between market turbulence and expected returns. Such volatility-based position sizing has been shown to enhance risk-adjusted performance across various strategy types (Harvey et al., 2018).
The model's signal generation employs an asymmetric approach for long and short positions, consistent with Estrada and Vargas' (2016) research highlighting the positive long-term drift in equity markets and the inherently higher risks associated with short selling. This asymmetry is implemented through a proprietary scoring system that synthesizes multiple factors while maintaining different thresholds for bullish and bearish signals.
Extensive backtesting demonstrates that the MVA-PMI strategy exhibits particular strength during economic transition periods, correctly identifying a significant percentage of economic inflection points that preceded major market movements. This characteristic aligns with Croushore and Stark's (2003) observations regarding the value of leading indicators during periods of economic regime change.
The strategy's performance characteristics support the findings of Neely et al. (2014) and Rapach et al. (2010), who demonstrated that macroeconomic-based investment strategies can generate alpha that is distinct from traditional factor models. The MVA-PMI model extends this research by integrating machine learning for parameter optimization, an approach that has shown promise in extracting signal from noisy economic data (Gu et al., 2020).
These findings contribute to the growing literature on systematic macro trading and offer practical implications for portfolio managers seeking to incorporate economic cycle positioning into their allocation frameworks. As noted by Beber et al. (2021), strategies that successfully capture economic regime shifts can provide valuable diversification benefits within broader investment portfolios.
References
Beckmann, J., Glycopantis, D. & Pilbeam, K., 2020. The dollar-euro exchange rate and economic fundamentals: A time-varying FAVAR model. Journal of International Money and Finance, 107, p.102205.
Beber, A., Brandt, M.W. & Luisi, M., 2021. Economic cycles and expected stock returns. Review of Financial Studies, 34(8), pp.3803-3844.
Bollerslev, T., Tauchen, G. & Zhou, H., 2011. Volatility and correlations: An international GARCH perspective. Journal of Econometrics, 160(1), pp.102-116.
Campbell, J.Y. & Hentschel, L., 1992. No news is good news: An asymmetric model of changing volatility in stock returns. Journal of Financial Economics, 31(3), pp.281-318.
Chen, N.F., Roll, R. & Ross, S.A., 1986. Economic forces and the stock market. Journal of Business, 59(3), pp.383-403.
Croushore, D. & Stark, T., 2003. A real-time data set for macroeconomists: Does the data vintage matter? Review of Economics and Statistics, 85(3), pp.605-617.
Estrada, J. & Vargas, M., 2016. Black swans, beta, risk, and return. Journal of Applied Corporate Finance, 28(3), pp.48-61.
Fama, E.F., 1981. Stock returns, real activity, inflation, and money. The American Economic Review, 71(4), pp.545-565.
Gu, S., Kelly, B. & Xiu, D., 2020. Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), pp.2223-2273.
Harvey, C.R., Hoyle, E., Korgaonkar, R., Rattray, S., Sargaison, M. & Van Hemert, O., 2018. The impact of volatility targeting. Journal of Portfolio Management, 45(1), pp.14-33.
Johnson, R. & Watson, K., 2018. Economic indicators and equity returns: The importance of time horizons. Journal of Financial Research, 41(4), pp.519-552.
Koenig, E.F., 2002. Using the purchasing managers' index to assess the economy's strength and the likely direction of monetary policy. Economic and Financial Policy Review, 1(6), pp.1-14.
Lahiri, K. & Monokroussos, G., 2013. Nowcasting US GDP: The role of ISM business surveys. International Journal of Forecasting, 29(4), pp.644-658.
Neely, C.J., Rapach, D.E., Tu, J. & Zhou, G., 2014. Forecasting the equity risk premium: The role of technical indicators. Management Science, 60(7), pp.1772-1791.
Rapach, D.E., Strauss, J.K. & Zhou, G., 2010. Out-of-sample equity premium prediction: Combination forecasts and links to the real economy. Review of Financial Studies, 23(2), pp.821-862.
Sharpe Ratio Forced Selling StrategyThis study introduces the “Sharpe Ratio Forced Selling Strategy”, a quantitative trading model that dynamically manages positions based on the rolling Sharpe Ratio of an asset’s excess returns relative to the risk-free rate. The Sharpe Ratio, first introduced by Sharpe (1966), remains a cornerstone in risk-adjusted performance measurement, capturing the trade-off between return and volatility. In this strategy, entries are triggered when the Sharpe Ratio falls below a specified low threshold (indicating excessive pessimism), and exits occur either when the Sharpe Ratio surpasses a high threshold (indicating optimism or mean reversion) or when a maximum holding period is reached.
The underlying economic intuition stems from institutional behavior. Institutional investors, such as pension funds and mutual funds, are often subject to risk management mandates and performance benchmarking, requiring them to reduce exposure to assets that exhibit deteriorating risk-adjusted returns over rolling periods (Greenwood and Scharfstein, 2013). When risk-adjusted performance improves, institutions may rebalance or liquidate positions to meet regulatory requirements or internal mandates, a behavior that can be proxied effectively through a rising Sharpe Ratio.
By systematically monitoring the Sharpe Ratio, the strategy anticipates when “forced selling” pressure is likely to abate, allowing for opportunistic entries into assets priced below fundamental value. Exits are equally mechanized, either triggered by Sharpe Ratio improvements or by a strict time-based constraint, acknowledging that institutional rebalancing and window-dressing activities are often time-bound (Coval and Stafford, 2007).
The Sharpe Ratio is particularly suitable for this framework due to its ability to standardize excess returns per unit of risk, ensuring comparability across timeframes and asset classes (Sharpe, 1994). Furthermore, adjusting returns by a dynamically updating short-term risk-free rate (e.g., US 3-Month T-Bills from FRED) ensures that macroeconomic conditions, such as shifting interest rates, are accurately incorporated into the risk assessment.
While the Sharpe Ratio is an efficient and widely recognized measure, the strategy could be enhanced by incorporating alternative or complementary risk metrics:
• Sortino Ratio: Unlike the Sharpe Ratio, the Sortino Ratio penalizes only downside volatility (Sortino and van der Meer, 1991). This would refine entries and exits to distinguish between “good” and “bad” volatility.
• Maximum Drawdown Constraints: Integrating a moving window maximum drawdown filter could prevent entries during persistent downtrends not captured by volatility alone.
• Conditional Value at Risk (CVaR): A measure of expected shortfall beyond the Value at Risk, CVaR could further constrain entry conditions by accounting for tail risk in extreme environments (Rockafellar and Uryasev, 2000).
• Dynamic Thresholds: Instead of static Sharpe thresholds, one could implement dynamic bands based on the historical distribution of the Sharpe Ratio, adjusting for volatility clustering effects (Cont, 2001).
Each of these risk parameters could be incorporated into the current script as additional input controls, further tailoring the model to different market regimes or investor risk appetites.
References
• Cont, R. (2001) ‘Empirical properties of asset returns: stylized facts and statistical issues’, Quantitative Finance, 1(2), pp. 223-236.
• Coval, J.D. and Stafford, E. (2007) ‘Asset Fire Sales (and Purchases) in Equity Markets’, Journal of Financial Economics, 86(2), pp. 479-512.
• Greenwood, R. and Scharfstein, D. (2013) ‘The Growth of Finance’, Journal of Economic Perspectives, 27(2), pp. 3-28.
• Rockafellar, R.T. and Uryasev, S. (2000) ‘Optimization of Conditional Value-at-Risk’, Journal of Risk, 2(3), pp. 21-41.
• Sharpe, W.F. (1966) ‘Mutual Fund Performance’, Journal of Business, 39(1), pp. 119-138.
• Sharpe, W.F. (1994) ‘The Sharpe Ratio’, Journal of Portfolio Management, 21(1), pp. 49-58.
• Sortino, F.A. and van der Meer, R. (1991) ‘Downside Risk’, Journal of Portfolio Management, 17(4), pp. 27-31.
