OBV-X| OBV Norm By Momentumtrade Idea By Ziplor traderA unique volume-momentum-based strategy inspired by proprietary OBV dynamics.
This script combines normalized On-Balance Volume (OBV) behavior with adaptive signal filtering mechanisms.
It includes optional filters based on inflection detection and momentum accumulation zones to enhance signal quality.
Key elements include:
Volume-based momentum normalization
Signal line crossover logic
Optional regime filters (acceleration/integration-based)
Dynamic divergence detection
Visual zone overlays for quick market context
Designed for advanced users. Not financial advice.
Further parameters are intentionally obfuscated to preserve the edge.
Cari dalam skrip untuk "Divergence"
The VoVix Experiment The VoVix Experiment
The VoVix Experiment is a next-generation, regime-aware, volatility-adaptive trading strategy for futures, indices, and more. It combines a proprietary VoVix (volatility-of-volatility) anomaly detector with price structure clustering and critical point logic, only trading when multiple independent signals align. The system is designed for robustness, transparency, and real-world execution.
Logic:
VoVix Regime Engine: Detects pre-move volatility anomalies using a fast/slow ATR ratio, normalized by Z-score. Only trades when a true regime spike is detected, not just random volatility.
Cluster & Critical Point Filters: Price structure and volatility clustering must confirm the VoVix signal, reducing false positives and whipsaws.
Adaptive Sizing: Position size scales up for “super-spikes” and down for normal events, always within user-defined min/max.
Session Control: Trades only during user-defined hours and days, avoiding illiquid or high-risk periods.
Visuals: Aurora Flux Bands (From another Original of Mine (Options Flux Flow): glow and change color on signals, with a live dashboard, regime heatmap, and VoVix progression bar for instant insight.
Backtest Settings
Initial capital: $10,000
Commission: Conservative, realistic roundtrip cost:
15–20 per contract (including slippage per side) I set this to $25
Slippage: 3 ticks per trade
Symbol: CME_MINI:NQ1!
Timeframe: 15 min (but works on all timeframes)
Order size: Adaptive, 1–2 contracts
Session: 5:00–15:00 America/Chicago (default, fully adjustable)
Why these settings?
These settings are intentionally strict and realistic, reflecting the true costs and risks of live trading. The 10,000 account size is accessible for most retail traders. 25/contract including 3 ticks of slippage are on the high side for MNQ, ensuring the strategy is not curve-fit to perfect fills. If it works here, it will work in real conditions.
Forward Testing: (This is no guarantee. I've provided these results to show that executions perform as intended. Test were done on Tradovate)
ALL TRADES
Gross P/L: $12,907.50
# of Trades: 64
# of Contracts: 186
Avg. Trade Time: 1h 55min 52sec
Longest Trade Time: 55h 46min 53sec
% Profitable Trades: 59.38%
Expectancy: $201.68
Trade Fees & Comm.: $(330.95)
Total P/L: $12,576.55
Winning Trades: 59.38%
Breakeven Trades: 3.12%
Losing Trades: 37.50%
Link: www.dropbox.com
Inputs & Tooltips
VoVix Regime Execution: Enable/disable the core VoVix anomaly detector.
Volatility Clustering: Require price/volatility clusters to confirm VoVix signals.
Critical Point Detector: Require price to be at a statistically significant distance from the mean (regime break).
VoVix Fast ATR Length: Short ATR for fast volatility detection (lower = more sensitive).
VoVix Slow ATR Length: Long ATR for baseline regime (higher = more stable).
VoVix Z-Score Window: Lookback for Z-score normalization (higher = smoother, lower = more reactive).
VoVix Entry Z-Score: Minimum Z-score for a VoVix spike to trigger a trade.
VoVix Exit Z-Score: Z-score below which the regime is considered decayed (exit).
VoVix Local Max Window: Bars to check for local maximum in VoVix (higher = stricter).
VoVix Super-Spike Z-Score: Z-score for “super” regime events (scales up position size).
Min/Max Contracts: Adaptive position sizing range.
Session Start/End Hour: Only trade between these hours (exchange time).
Allow Weekend Trading: Enable/disable trading on weekends.
Session Timezone: Timezone for session filter (e.g., America/Chicago for CME).
Show Trade Labels: Show/hide entry/exit labels on chart.
Flux Glow Opacity: Opacity of Aurora Flux Bands (0–100).
Flux Band EMA Length: EMA period for band center.
Flux Band ATR Multiplier: Width of bands (higher = wider).
Compliance & Transparency
* No hidden logic, no repainting, no pyramiding.
* All signals, sizing, and exits are fully explained and visible.
* Backtest settings are stricter than most real accounts.
* All visuals are directly tied to the strategy logic.
* This is not a mashup or cosmetic overlay; every component is original and justified.
Disclaimer
Trading is risky. This script is for educational and research purposes only. Do not trade with money you cannot afford to lose. Past performance is not indicative of future results. Always test in simulation before live trading.
Proprietary Logic & Originality Statement
This script, “The VoVix Experiment,” is the result of original research and development. All core logic, algorithms, and visualizations—including the VoVix regime detection engine, adaptive execution, volatility/divergence bands, and dashboard—are proprietary and unique to this project.
1. VoVix Regime Logic
The concept of “volatility of volatility” (VoVix) is an original quant idea, not a standard indicator. The implementation here (fast/slow ATR ratio, Z-score normalization, local max logic, super-spike scaling) is custom and not found in public TradingView scripts.
2. Cluster & Critical Point Logic
Volatility clustering and “critical point” detection (using price distance from a rolling mean and standard deviation) are general quant concepts, but the way they are combined and filtered here is unique to this script. The specific logic for “clustered chop” and “critical point” is not a copy of any public indicator.
3. Adaptive Sizing
The adaptive sizing logic (scaling contracts based on regime strength) is custom and not a standard TradingView feature or public script.
4. Time Block/Session Control
The session filter is a common feature in many strategies, but the implementation here (with timezone and weekend control) is written from scratch.
5. Aurora Flux Bands (From another Original of Mine (Options Flux Flow)
The “glowing” bands are inspired by the idea of volatility bands (like Bollinger Bands or Keltner Channels), but the visual effect, color logic, and integration with regime signals are original to this script.
6. Dashboard, Watermark, and Metrics
The dashboard, real-time Sharpe/Sortino, and VoVix progression bar are all custom code, not copied from any public script.
What is “standard” or “common quant practice”?
Using ATR, EMA, and Z-score are standard quant tools, but the way they are combined, filtered, and visualized here is unique. The structure and logic of this script are original and not a mashup of public code.
This script is 100% original work. All logic, visuals, and execution are custom-coded for this project. No code or logic is directly copied from any public or private script.
Use with discipline. Trade your edge.
— Dskyz, for DAFE Trading Systems
Ai BTC Signals Buy & Whales / Liquidation - Strategy [Ai Whales]Dear Trader,
The development involved professional analysts and incorporated AI to adapt signals to the modern, constantly changing, and highly volatile BTCUSD market, also taking into account the presence and actions of large institutional players — the so-called "Whales." The strategy allows you to instantly evaluate any configuration you set within the indicator and see the results reflected in professional performance metrics aligned with your chosen strategy.
The indicator displays several signals on the chart:
1) Buy signal (not sell signals)
2) Take profit line and price
3) Stop loss line and price
4) Manipulations & Liquidations observed in the market
5) Whale activity—buying in small, medium, and large amounts
The indicator does not repaint because it is based on showing signals only after the candle closes, so the calculations are true and not distorted.
Recommended pair: BTCUSD ; BTCUSDT ; BTCUSDTP and same.
The indicator can show R/R - 0.5:1 1:1 1:2 1:3 1:4
Recommended timeframes for use: from 4 hours up to 1 week, with the ideal being 1 day. However, you are free to experiment with other near timeframes.
Possible trading modes: spot or futures.
Some methods used in the calculations of the indicator:
- statistical patterns that have the ability to repeat in the future. Bitcoin cycles in different market phases that also have the ability to repeat and are included in the indicator,
- miners' capitulation and hashrate level are also taken into account by the indicator,
- candle volumes and their deltas are taken into account in the calculations,
- as well as other bases such as RSI and its divergence, the crossing of EMA of various configurations and etc.
**How the strategy calculates positions:**
A position opens at the Buy signal level and is fixed at the level of the thick green line, which serves as the primary take profit target. Pyramiding (adding to positions) can be enabled in the settings.
The size of each position is adjustable via the settings. Importantly, each signal creates its own take profit lines. When pyramiding is enabled, all positions are eventually closed at the nearest take profit level generated by any of the pyramiding signals. This approach minimizes potential losses if the price doesn’t reach the maximum take profit levels initially set; the strategy closes positions at the closest available take profit level. This conservative method for strategy reduces risk, although ideally, each position in the pyramid should be closed at an individual take-profit level, which would lead to even better results during deep backtesting.
The strategy includes alerts that can be configured based on your platform’s capabilities. Alerts trigger on the chart when "Buy" or "Whale" signals are detected.
**Settings Overview:**
- Inside the strategy: default platform options.
- Inside the indicato have some filters:
1) allows traders to choose display modes
2) position entries based on market phase—rising or falling
3) can also select whether to trade after manipulations and liquidations
4) can also select whether to trade after whale activity (small medium or big amounts of whales).
You can manually adjust take profit and stop loss levels via simple method selections, making these flexible yet user-friendly. The indicator offers three main styles:
- "Universal" (standard levels)
- "Aggressive"
- "Conservative"
**Performance and caveats:**
Deep Backtested from day one of Bitcoin’s listing on various exchanges under specific conditions (no liquidations, certain settings), the indicator has shown a maximum drawdown of about 5-15%, with final returns surpassing "buy and hold" more than 1000000% and WinRate 93-100% However, it’s crucial to understand that such spectacular past performance does not guarantee future results.
If you are serious about your investments, remember that geopolitical events, institutional shifts, or other unforeseen factors can significantly impact Bitcoin’s price—or even its existence. Unfortunately, AI has not yet learned to fully account for these macro conditions within its adaptive mechanisms.
Trade wisely, and use this powerful tool responsibly.
Best regards,
Dskyz (DAFE) Quantum Sentiment Flux - Beginners Dskyz (DAFE) Quantum Sentiment Flux - Beginners:
Welcome to the Dskyz (DAFE) Quantum Sentiment Flux - Beginners , a strategy and concept that’s your ultimate wingman for trading futures like MNQ, NQ, MES, and ES. This gem combines lightning-fast momentum signals, market sentiment smarts, and bulletproof risk management into a system so intuitive, even newbies can trade like pros. With clean DAFE visuals, preset modes for every vibe, and a revamped dashboard that’s basically a market GPS, this strategy makes futures trading feel like a high-octane sci-fi mission.
Built on the Dskyz (DAFE) legacy of Aurora Divergence, the Quantum Sentiment Flux is designed to empower beginners while giving seasoned traders a lean, sentiment-driven edge. It uses fast/slow EMA crossovers for entries, filters trades with VIX, SPX trends, and sector breadth, and keeps your account safe with adaptive stops and cooldowns. Tuned for more action with faster signals and a slick bottom-left dashboard, this updated version is ready to light up your charts and outsmart institutional traps. Let’s dive into why this strat’s a must-have and break down its brilliance.
Why Traders Need This Strategy
Futures markets are a wild ride—fast moves, volatility spikes (like the April 28, 2025 NQ 1k-point drop), and institutional games that can wreck unprepared traders. Beginners often get lost in complex systems or burned by impulsive trades. The Quantum Sentiment Flux is the antidote, offering:
Dead-Simple Setup: Preset modes (Aggressive, Balanced, Conservative) auto-tune signals, risk, and sizing, so you can trade without a quant degree.
Sentiment Superpower: VIX filter, SPX trend, and sector breadth visuals keep you aligned with market health, dodging chop and riding trends.
Ironclad Safety: Tighter ATR-based stops, 2:1 take-profits, and preset cooldowns protect your capital, even in chaotic sessions.
Next-Level Visuals: Green/red entry triangles, vibrant EMAs, a sector breadth background, and a beefed-up dashboard make signals and context pop.
DAFE Swagger: The clean aesthetics, sleek dashboard—ties it to Dskyz’s elite brand, making your charts a work of art.
Traders need this because it’s a plug-and-play system that blends beginner-friendly simplicity with pro-level market awareness. Whether you’re just starting or scalping 5min MNQ, this strat’s your key to trading with confidence and style.
Strategy Components
1. Core Signal Logic (High-Speed Momentum)
The strategy’s engine is a momentum-based system using fast and slow Exponential Moving Averages (EMAs), now tuned for faster, more frequent trades.
How It Works:
Fast/Slow EMAs: Fast EMA (Aggressive: 5, Balanced: 7, Conservative: 9 bars) and slow EMA (12/14/18 bars) track short-term vs. longer-term momentum.
Crossover Signals:
Buy: Fast EMA crosses above slow EMA, and trend_dir = 1 (fast EMA > slow EMA + ATR * strength threshold).
Sell: Fast EMA crosses below slow EMA, and trend_dir = -1 (fast EMA < slow EMA - ATR * strength threshold).
Strength Filter: ma_strength = fast EMA - slow EMA must exceed an ATR-scaled threshold (Aggressive: 0.15, Balanced: 0.18, Conservative: 0.25) for robust signals.
Trend Direction: trend_dir confirms momentum, filtering out weak crossovers in choppy markets.
Evolution:
Faster EMAs (down from 7–10/21–50) catch short-term trends, perfect for active futures markets.
Lower strength thresholds (0.15–0.25 vs. 0.3–0.5) make signals more sensitive, boosting trade frequency without sacrificing quality.
Preset tuning ensures beginners get optimized settings, while pros can tweak via mode selection.
2. Market Sentiment Filters
The strategy leans hard into market sentiment with a VIX filter, SPX trend analysis, and sector breadth visuals, keeping trades aligned with the big picture.
VIX Filter:
Logic: Blocks long entries if VIX > threshold (default: 20, can_long = vix_close < vix_limit). Shorts are always allowed (can_short = true).
Impact: Prevents longs during high-fear markets (e.g., VIX spikes in crashes), while allowing shorts to capitalize on downturns.
SPX Trend Filter:
Logic: Compares S&P 500 (SPX) close to its SMA (Aggressive: 5, Balanced: 8, Conservative: 12 bars). spx_trend = 1 (UP) if close > SMA, -1 (DOWN) if < SMA, 0 (FLAT) if neutral.
Impact: Provides dashboard context, encouraging trades that align with market direction (e.g., longs in UP trend).
Sector Breadth (Visual):
Logic: Tracks 10 sector ETFs (XLK, XLF, XLE, etc.) vs. their SMAs (same lengths as SPX). Each sector scores +1 (bullish), -1 (bearish), or 0 (neutral), summed as breadth (-10 to +10).
Display: Green background if breadth > 4, red if breadth < -4, else neutral. Dashboard shows sector trends (↑/↓/-).
Impact: Faster SMA lengths make breadth more responsive, reflecting sector rotations (e.g., tech surging, energy lagging).
Why It’s Brilliant:
- VIX filter adds pro-level volatility awareness, saving beginners from panic-driven losses.
- SPX and sector breadth give a 360° view of market health, boosting signal confidence (e.g., green BG + buy signal = high-probability trade).
- Shorter SMAs make sentiment visuals react faster, perfect for 5min charts.
3. Risk Management
The risk controls are a fortress, now tighter and more dynamic to support frequent trading while keeping accounts safe.
Preset-Based Risk:
Aggressive: Fast EMAs (5/12), tight stops (1.1x ATR), 1-bar cooldown. High trade frequency, higher risk.
Balanced: EMAs (7/14), 1.2x ATR stops, 1-bar cooldown. Versatile for most traders.
Conservative: EMAs (9/18), 1.3x ATR stops, 2-bar cooldown. Safer, fewer trades.
Impact: Auto-scales risk to match style, making it foolproof for beginners.
Adaptive Stops and Take-Profits:
Logic: Stops = entry ± ATR * atr_mult (1.1–1.3x, down from 1.2–2.0x). Take-profits = entry ± ATR * take_mult (2x stop distance, 2:1 reward/risk). Longs: stop below entry, TP above; shorts: vice versa.
Impact: Tighter stops increase trade turnover while maintaining solid risk/reward, adapting to volatility.
Trade Cooldown:
Logic: Preset-driven (Aggressive/Balanced: 1 bar, Conservative: 2 bars vs. old user-input 2). Ensures bar_index - last_trade_bar >= cooldown.
Impact: Faster cooldowns (especially Aggressive/Balanced) allow more trades, balanced by VIX and strength filters.
Contract Sizing:
Logic: User sets contracts (default: 1, max: 10), no preset cap (unlike old 7/5/3 suggestion).
Impact: Flexible but risks over-leverage; beginners should stick to low contracts.
Built To Be Reliable and Consistent:
- Tighter stops and faster cooldowns make it a high-octane system without blowing up accounts.
- Preset-driven risk removes guesswork, letting newbies trade confidently.
- 2:1 TPs ensure profitable trades outweigh losses, even in volatile sessions like April 27, 2025 ES slippage.
4. Trade Entry and Exit Logic
The entry/exit rules are simple yet razor-sharp, now with VIX filtering and faster signals:
Entry Conditions:
Long Entry: buy_signal (fast EMA crosses above slow EMA, trend_dir = 1), no position (strategy.position_size = 0), cooldown passed (can_trade), and VIX < 20 (can_long). Enters with user-defined contracts.
Short Entry: sell_signal (fast EMA crosses below slow EMA, trend_dir = -1), no position, cooldown passed, can_short (always true).
Logic: Tracks last_entry_bar for visuals, last_trade_bar for cooldowns.
Exit Conditions:
Stop-Loss/Take-Profit: ATR-based stops (1.1–1.3x) and TPs (2x stop distance). Longs exit if price hits stop (below) or TP (above); shorts vice versa.
No Other Exits: Keeps it straightforward, relying on stops/TPs.
5. DAFE Visuals
The visuals are pure DAFE magic, blending clean function with informative metrics utilized by professionals, now enhanced by faster signals and a responsive breadth background:
EMA Plots:
Display: Fast EMA (blue, 2px), slow EMA (orange, 2px), using faster lengths (5–9/12–18).
Purpose: Highlights momentum shifts, with crossovers signaling entries.
Sector Breadth Background:
Display: Green (90% transparent) if breadth > 4, red (90%) if breadth < -4, else neutral.
Purpose: Faster breadth_sma_len (5–12 vs. 10–50) reflects sector shifts in real-time, reinforcing signal strength.
- Visuals are intuitive, turning complex signals into clear buy/sell cues.
- Faster breadth background reacts to market rotations (e.g., tech vs. energy), giving a pro-level edge.
6. Sector Breadth Dashboard
The new bottom-left dashboard is a game-changer, a 3x16 table (black/gray theme) that’s your market command center:
Metrics:
VIX: Current VIX (red if > 20, gray if not).
SPX: Trend as “UP” (green), “DOWN” (red), or “FLAT” (gray).
Trade Longs: “OK” (green) if VIX < 20, “BLOCK” (red) if not.
Sector Breadth: 10 sectors (Tech, Financial, etc.) with trend arrows (↑ green, ↓ red, - gray).
Placeholder Row: Empty for future metrics (e.g., ATR, breadth score).
Purpose: Consolidates regime, volatility, market trend, and sector data, making decisions a breeze.
- VIX and SPX metrics add context, helping beginners avoid bad trades (e.g., no longs if “BLOCK”).
Sector arrows show market health at a glance, like a cheat code for sentiment.
Key Features
Beginner-Ready: Preset modes and clear visuals make futures trading a breeze.
Sentiment-Driven: VIX filter, SPX trend, and sector breadth keep you in sync with the market.
High-Frequency: Faster EMAs, tighter stops, and short cooldowns boost trade volume.
Safe and Smart: Adaptive stops/TPs and cooldowns protect capital while maximizing wins.
Visual Mastery: DAFE’s clean flair, EMAs, dashboard—makes trading fun and clear.
Backtestable: Lean code and fixed qty ensure accurate historical testing.
How to Use
Add to Chart: Load on a 5min MNQ/ES chart in TradingView.
Pick Preset: Aggressive (scalping), Balanced (versatile), or Conservative (safe). Balanced is default.
Set Contracts: Default 1, max 10. Stick low for safety.
Check Dashboard: Bottom-left shows preset, VIX, SPX, and sectors. “OK” + green breadth = strong buy.
Backtest: Run in strategy tester to compare modes.
Live Trade: Connect to Tradovate or similar. Watch for slippage (e.g., April 27, 2025 ES issues).
Replay Test: Try April 28, 2025 NQ drop to see VIX filter and stops in action.
Why It’s Brilliant
The Dskyz (DAFE) Quantum Sentiment Flux - Beginners is a masterpiece of simplicity and power. It takes pro-level tools—momentum, VIX, sector breadth—and wraps them in a system anyone can run. Faster signals and tighter stops make it a trading machine, while the VIX filter and dashboard keep you ahead of market chaos. The DAFE visuals and bottom-left command center turn your chart into a futuristic cockpit, guiding you through every trade. For beginners, it’s a safe entry to futures; for pros, it’s a scalping beast with sentiment smarts. This strat doesn’t just trade—it transforms how you see the market.
Final Notes
This is more than a strategy—it’s your launchpad to mastering futures with Dskyz (DAFE) flair. The Quantum Sentiment Flux blends accessibility, speed, and market savvy to help you outsmart the game. Load it, watch those triangles glow, and let’s make the markets your canvas!
Official Statement from Pine Script Team
(see TradingView help docs and forums):
"This warning may appear when you call functions such as ta.sma inside a request.security in a loop. There is no runtime impact. If you need to loop through a dynamic list of tickers, this cannot be avoided in the present version... Values will still be correct. Ignore this warning in such contexts."
(This publishing will most likely be taken down do to some miscellaneous rule about properly displaying charting symbols, or whatever. Once I've identified what part of the publishing they want to pick on, I'll adjust and repost.)
Use it with discipline. Use it with clarity. Trade smarter.
**I will continue to release incredible strategies and indicators until I turn this into a brand or until someone offers me a contract.
Created by Dskyz, powered by DAFE Trading Systems. Trade fast, trade bold.
Advanced Adaptive Grid Trading StrategyThis strategy employs an advanced grid trading approach that dynamically adapts to market conditions, including trend, volatility, and risk management considerations. The strategy aims to capitalize on price fluctuations in both rising (long) and falling (short) markets, as well as during sideways movements. It combines multiple indicators to determine the trend and automatically adjusts grid parameters for more efficient trading.
How it Works:
Trend Analysis:
Short, long, and super long Moving Averages (MA) to determine the trend direction.
RSI (Relative Strength Index) to identify overbought and oversold levels, and to confirm the trend.
MACD (Moving Average Convergence Divergence) to confirm momentum and trend direction.
Momentum indicator.
The strategy uses a weighted scoring system to assess trend strength (strong bullish, moderate bullish, strong bearish, moderate bearish, sideways).
Grid System:
The grid size (the distance between buy and sell levels) changes dynamically based on market volatility, using the ATR (Average True Range) indicator.
Grid density also adapts to the trend: in a strong trend, the grid is denser in the direction of the trend.
Grid levels are shifted depending on the trend direction (upwards in a bear market, downwards in a bull market).
Trading Logic:
The strategy opens long positions if the trend is bullish and the price reaches one of the lower grid levels.
It opens short positions if the trend is bearish and the price reaches one of the upper grid levels.
In a sideways market, it can open positions in both directions.
Risk Management:
Stop Loss for every position.
Take Profit for every position.
Trailing Stop Loss to protect profits.
Maximum daily loss limit.
Maximum number of positions limit.
Time-based exit (if the position is open for too long).
Risk-based position sizing (optional).
Input Options:
The strategy offers numerous settings that allow users to customize its operation:
Timeframe: The chart's timeframe (e.g., 1 minute, 5 minutes, 1 hour, 4 hours, 1 day, 1 week).
Base Grid Size (%): The base size of the grid, expressed as a percentage.
Max Positions: The maximum number of open positions allowed.
Use Volatility Grid: If enabled, the grid size changes dynamically based on the ATR indicator.
ATR Length: The period of the ATR indicator.
ATR Multiplier: The multiplier for the ATR to fine-tune the grid size.
RSI Length: The period of the RSI indicator.
RSI Overbought: The overbought level for the RSI.
RSI Oversold: The oversold level for the RSI.
Short MA Length: The period of the short moving average.
Long MA Length: The period of the long moving average.
Super Long MA Length: The period of the super long moving average.
MACD Fast Length: The fast period of the MACD.
MACD Slow Length: The slow period of the MACD.
MACD Signal Length: The period of the MACD signal line.
Stop Loss (%): The stop loss level, expressed as a percentage.
Take Profit (%): The take profit level, expressed as a percentage.
Use Trailing Stop: If enabled, the strategy uses a trailing stop loss.
Trailing Stop (%): The trailing stop loss level, expressed as a percentage.
Max Loss Per Day (%): The maximum daily loss, expressed as a percentage.
Time Based Exit: If enabled, the strategy exits the position after a certain amount of time.
Max Holding Period (hours): The maximum holding time in hours.
Use Risk Based Position: If enabled, the strategy calculates position size based on risk.
Risk Per Trade (%): The risk per trade, expressed as a percentage.
Max Leverage: The maximum leverage.
Important Notes:
This strategy does not guarantee profits. Cryptocurrency markets are volatile, and trading involves risk.
The strategy's effectiveness depends on market conditions and settings.
It is recommended to thoroughly backtest the strategy under various market conditions before using it live.
Past performance is not indicative of future results.
Multi-Timeframe MACD Strategy ver 1.0Multi-Timeframe MACD Strategy: Enhanced Trend Trading with Customizable Entry and Trailing Stop
This strategy utilizes the Moving Average Convergence Divergence (MACD) indicator across multiple timeframes to identify strong trends, generate precise entry and exit signals, and manage risk with an optional trailing stop loss. By combining the insights of both the current chart's timeframe and a user-defined higher timeframe, this strategy aims to improve trade accuracy, reduce exposure to false signals, and capture larger market moves.
Key Features:
Dual Timeframe Analysis: Calculates and analyzes the MACD on both the current chart's timeframe and a user-selected higher timeframe (e.g., Daily MACD on a 1-hour chart). This provides a broader market context, helping to confirm trends and filter out short-term noise.
Configurable MACD: Fine-tune the MACD calculation with adjustable Fast Length, Slow Length, and Signal Length parameters. Optimize the indicator's sensitivity to match your trading style and the volatility of the asset.
Flexible Entry Options: Choose between three distinct entry types:
Crossover: Enters trades when the MACD line crosses above (long) or below (short) the Signal line.
Zero Cross: Enters trades when the MACD line crosses above (long) or below (short) the zero line.
Both: Combines both Crossover and Zero Cross signals, providing more potential entry opportunities.
Independent Timeframe Control: Display and trade based on the current timeframe MACD, the higher timeframe MACD, or both. This allows you to focus on the information most relevant to your analysis.
Optional Trailing Stop Loss: Implements a configurable trailing stop loss to protect profits and limit potential losses. The trailing stop is adjusted dynamically as the price moves in your favor, based on a user-defined percentage.
No Repainting: Employs lookahead=barmerge.lookahead_off in the request.security() function to prevent data leakage and ensure accurate backtesting and real-time signals.
Clear Visual Signals (Optional): Includes optional plotting of the MACD and Signal lines for both timeframes, with distinct colors for easy visual identification. These plots are for visual confirmation and are not required for the strategy's logic.
Suitable for Various Trading Styles: Adaptable to swing trading, day trading, and trend-following strategies across diverse markets (stocks, forex, cryptocurrencies, etc.).
Fully Customizable: All parameters are adjustable, including timeframes, MACD Settings, Entry signal type and trailing stop settings.
How it Works:
MACD Calculation: The strategy calculates the MACD (using the standard formula) for both the current chart's timeframe and the specified higher timeframe.
Trend Identification: The relationship between the MACD line, Signal line, and zero line is used to determine the current trend for each timeframe.
Entry Signals: Buy/sell signals are generated based on the selected "Entry Type":
Crossover: A long signal is generated when the MACD line crosses above the Signal line, and both timeframes are in agreement (if both are enabled). A short signal is generated when the MACD line crosses below the Signal line, and both timeframes are in agreement.
Zero Cross: A long signal is generated when the MACD line crosses above the zero line, and both timeframes agree. A short signal is generated when the MACD line crosses below the zero line and both timeframes agree.
Both: Combines Crossover and Zero Cross signals.
Trailing Stop Loss (Optional): If enabled, a trailing stop loss is set at a specified percentage below (for long positions) or above (for short positions) the entry price. The stop-loss is automatically adjusted as the price moves favorably.
Exit Signals:
Without Trailing Stop: Positions are closed when the MACD signals reverse according to the selected "Entry Type" (e.g., a long position is closed when the MACD line crosses below the Signal line if using "Crossover" entries).
With Trailing Stop: Positions are closed if the price hits the trailing stop loss.
Backtesting and Optimization: The strategy automatically backtests on the chart's historical data, allowing you to assess its performance and optimize parameters for different assets and timeframes.
Example Use Cases:
Confirming Trend Strength: A trader on a 1-hour chart sees a bullish MACD crossover on the current timeframe. They check the MTF MACD strategy and see that the Daily MACD is also bullish, confirming the strength of the uptrend.
Filtering Noise: A trader using a 15-minute chart wants to avoid false signals from short-term volatility. They use the strategy with a 4-hour higher timeframe to filter out noise and only trade in the direction of the dominant trend.
Dynamic Risk Management: A trader enters a long position and enables the trailing stop loss. As the price rises, the trailing stop is automatically adjusted upwards, protecting profits. The trade is exited either when the MACD reverses or when the price hits the trailing stop.
Disclaimer:
The MACD is a lagging indicator and can produce false signals, especially in ranging markets. This strategy is for educational and informational purposes only and should not be considered financial advice. Backtest and optimize the strategy thoroughly, combine it with other technical analysis tools, and always implement sound risk management practices before using it with real capital. Past performance is not indicative of future results. Conduct your own due diligence and consider your risk tolerance before making any trading decisions.
MACD Crossover Strategy MACD Crossover Strategy:
This strategy is based on the Moving Average Convergence Divergence (MACD) indicator, a popular tool used in technical analysis to identify potential trend changes and momentum in price movements. The strategy focuses on MACD crossovers within a specific "important zone" to generate trading signals.
Key Components:
1. MACD Calculation: The strategy uses customizable parameters for fast length (default 12), slow length (default 26), and signal length (default 9) to calculate the MACD line and signal line.
2. Important Zone: Defined by upper and lower thresholds (default 0.5 and -0.5), this zone helps filter out potentially less significant crossovers.
3. Entry Conditions:
- Long (Buy) Entry: When the MACD line crosses above the signal line within the important zone.
- Short (Sell) Entry: When the MACD line crosses below the signal line within the important zone.
4. Exit Conditions: The strategy closes positions on opposite crossover signals. Long positions are closed on bearish crossovers, and short positions on bullish crossovers.
5. Visualization:
- MACD line (blue) and signal line (orange) are plotted.
- The zero line, upper threshold, and lower threshold are displayed for reference.
- Buy signals are represented by green triangles at the bottom of the chart.
- Sell signals are shown as red triangles at the top of the chart.
This strategy aims to capture trend changes while filtering out potentially false signals that occur when the MACD is at extreme values. By focusing on crossovers within the important zone, the strategy attempts to identify more reliable trading opportunities.
Traders can adjust the MACD parameters and the important zone thresholds to fine-tune the strategy for different assets or timeframes. As with any trading strategy, it's crucial to thoroughly backtest and consider risk management before using it in live trading.
Ultimate Trading BotHow the "Ultimate Trading Bot" Works:
This Pine Script trading bot executes buy and sell trades based on a combination of technical indicators:
Indicators Used:
RSI (Relative Strength Index)
Measures momentum and determines overbought (70) and oversold (30) levels.
A crossover above 30 suggests a potential buy, and a cross below 70 suggests a potential sell.
Moving Average (MA)
A simple moving average (SMA) of 50 periods to track the trend.
Prices above the MA indicate an uptrend, while prices below indicate a downtrend.
Stochastic Oscillator (%K and %D)
Identifies overbought and oversold conditions using a smoothed stochastic formula.
A crossover of %K above %D signals a buy, and a crossover below %D signals a sell.
MACD (Moving Average Convergence Divergence)
Uses a 12-period fast EMA and a 26-period slow EMA, with a 9-period signal line.
A crossover of MACD above the signal line suggests a bullish move, and a cross below suggests bearish movement.
Trade Execution:
Buy (Long Entry) Conditions:
RSI crosses above 30 (indicating recovery from an oversold state).
The closing price is above the 50-period moving average (showing an uptrend).
The MACD line crosses above the signal line (indicating upward momentum).
The Stochastic %K crosses above %D (indicating bullish momentum).
→ If all conditions are met, the bot enters a long (buy) position.
Sell (Exit Trade) Conditions:
RSI crosses below 70 (indicating overbought conditions).
The closing price is below the 50-period moving average (downtrend).
The MACD line crosses below the signal line (bearish signal).
The Stochastic %K crosses below %D (bearish momentum).
→ If all conditions are met, the bot closes the long position.
Visuals:
The bot plots the moving average, RSI, MACD, and Stochastic indicators for reference.
It also displays buy/sell signals with arrows:
Green arrow (Buy Signal) → When all buy conditions are met.
Red arrow (Sell Signal) → When all sell conditions are met.
How to Use It in TradingView:
MACD Volume Strategy for XAUUSD (15m) [PineIndicators]The MACD Volume Strategy is a momentum-based trading system designed for XAUUSD on the 15-minute timeframe. It integrates two key market indicators: the Moving Average Convergence Divergence (MACD) and a volume-based oscillator to identify strong trend shifts and confirm trade opportunities. This strategy uses dynamic position sizing, incorporates leverage customization, and applies structured entry and exit conditions to improve risk management.
⚙️ Core Strategy Components
1️⃣ Volume-Based Momentum Calculation
The strategy includes a custom volume oscillator to filter trade signals based on market activity. The oscillator is derived from the difference between short-term and long-term volume trends using Exponential Moving Averages (EMAs)
Short EMA (default = 5) represents recent volume activity.
Long EMA (default = 8) captures broader volume trends.
Positive values indicate rising volume, supporting momentum-based trades.
Negative values suggest weak market activity, reducing signal reliability.
By requiring positive oscillator values, the strategy ensures momentum confirmation before entering trades.
2️⃣ MACD Trend Confirmation
The strategy uses the MACD indicator as a trend filter. The MACD is calculated as:
Fast EMA (16-period) detects short-term price trends.
Slow EMA (26-period) smooths out price fluctuations to define the overall trend.
Signal Line (9-period EMA) helps identify crossovers, signaling potential trend shifts.
Histogram (MACD – Signal) visualizes trend strength.
The system generates trade signals based on MACD crossovers around the zero line, confirming bullish or bearish trend shifts.
📌 Trade Logic & Conditions
🔹 Long Entry Conditions
A buy signal is triggered when all the following conditions are met:
✅ MACD crosses above 0, signaling bullish momentum.
✅ Volume oscillator is positive, confirming increased trading activity.
✅ Current volume is at least 50% of the previous candle’s volume, ensuring market participation.
🔻 Short Entry Conditions
A sell signal is generated when:
✅ MACD crosses below 0, indicating bearish momentum.
✅ Volume oscillator is positive, ensuring market activity is sufficient.
✅ Current volume is less than 50% of the previous candle’s volume, showing decreasing participation.
This multi-factor approach filters out weak or false signals, ensuring that trades align with both momentum and volume dynamics.
📏 Position Sizing & Leverage
Dynamic Position Calculation:
Qty = strategy.equity × leverage / close price
Leverage: Customizable (default = 1x), allowing traders to adjust risk exposure.
Adaptive Sizing: The strategy scales position sizes based on account equity and market price.
Slippage & Commission: Built-in slippage (2 points) and commission (0.01%) settings provide realistic backtesting results.
This ensures efficient capital allocation, preventing overexposure in volatile conditions.
🎯 Trade Management & Exits
Take Profit & Stop Loss Mechanism
Each position includes predefined profit and loss targets:
Take Profit: +10% of risk amount.
Stop Loss: Fixed at 10,100 points.
The risk-reward ratio remains balanced, aiming for controlled drawdowns while maximizing trade potential.
Visual Trade Tracking
To improve trade analysis, the strategy includes:
📌 Trade Markers:
"Buy" label when a long position opens.
"Close" label when a position exits.
📌 Trade History Boxes:
Green for profitable trades.
Red for losing trades.
📌 Horizontal Trade Lines:
Shows entry and exit prices.
Helps identify trend movements over multiple trades.
This structured visualization allows traders to analyze past performance directly on the chart.
⚡ How to Use This Strategy
1️⃣ Apply the script to a XAUUSD (Gold) 15m chart in TradingView.
2️⃣ Adjust leverage settings as needed.
3️⃣ Enable backtesting to assess past performance.
4️⃣ Monitor volume and MACD conditions to understand trade triggers.
5️⃣ Use the visual trade markers to review historical performance.
The MACD Volume Strategy is designed for short-term trading, aiming to capture momentum-driven opportunities while filtering out weak signals using volume confirmation.
Arpeet MACDOverview
This strategy is based on the zero-lag version of the MACD (Moving Average Convergence Divergence) indicator, which captures short-term trends by quickly responding to price changes, enabling high-frequency trading. The strategy uses two moving averages with different periods (fast and slow lines) to construct the MACD indicator and introduces a zero-lag algorithm to eliminate the delay between the indicator and the price, improving the timeliness of signals. Additionally, the crossover of the signal line and the MACD line is used as buy and sell signals, and alerts are set up to help traders seize trading opportunities in a timely manner.
Strategy Principle
Calculate the EMA (Exponential Moving Average) or SMA (Simple Moving Average) of the fast line (default 12 periods) and slow line (default 26 periods).
Use the zero-lag algorithm to double-smooth the fast and slow lines, eliminating the delay between the indicator and the price.
The MACD line is formed by the difference between the zero-lag fast line and the zero-lag slow line.
The signal line is formed by the EMA (default 9 periods) or SMA of the MACD line.
The MACD histogram is formed by the difference between the MACD line and the signal line, with blue representing positive values and red representing negative values.
When the MACD line crosses the signal line from below and the crossover point is below the zero axis, a buy signal (blue dot) is generated.
When the MACD line crosses the signal line from above and the crossover point is above the zero axis, a sell signal (red dot) is generated.
The strategy automatically places orders based on the buy and sell signals and triggers corresponding alerts.
Advantage Analysis
The zero-lag algorithm effectively eliminates the delay between the indicator and the price, improving the timeliness and accuracy of signals.
The design of dual moving averages can better capture market trends and adapt to different market environments.
The MACD histogram intuitively reflects the comparison of bullish and bearish forces, assisting in trading decisions.
The automatic order placement and alert functions make it convenient for traders to seize trading opportunities in a timely manner, improving trading efficiency.
Risk Analysis
In volatile markets, frequent crossover signals may lead to overtrading and losses.
Improper parameter settings may cause signal distortion and affect strategy performance.
The strategy relies on historical data for calculations and has poor adaptability to sudden events and black swan events.
Optimization Direction
Introduce trend confirmation indicators, such as ADX, to filter out false signals in volatile markets.
Optimize parameters to find the best combination of fast and slow line periods and signal line periods, improving strategy stability.
Combine other technical indicators or fundamental factors to construct a multi-factor model, improving risk-adjusted returns of the strategy.
Introduce stop-loss and take-profit mechanisms to control single-trade risk.
Summary
The MACD Dual Crossover Zero Lag Trading Strategy achieves high-frequency trading by quickly responding to price changes and capturing short-term trends. The zero-lag algorithm and dual moving average design improve the timeliness and accuracy of signals. The strategy has certain advantages, such as intuitive signals and convenient operation, but also faces risks such as overtrading and parameter sensitivity. In the future, the strategy can be optimized by introducing trend confirmation indicators, parameter optimization, multi-factor models, etc., to improve the robustness and profitability of the strategy.
Classic Nacked Z-Score ArbitrageThe “Classic Naked Z-Score Arbitrage” strategy employs a statistical arbitrage model based on the Z-score of the price spread between two assets. This strategy follows the premise of pair trading, where two correlated assets, typically from the same market sector, are traded against each other to profit from relative price movements (Gatev, Goetzmann, & Rouwenhorst, 2006). The approach involves calculating the Z-score of the price spread between two assets to determine market inefficiencies and capitalize on short-term mispricing.
Methodology
Price Spread Calculation:
The strategy calculates the spread between the two selected assets (Asset A and Asset B), typically from different sectors or asset classes, on a daily timeframe.
Statistical Basis – Z-Score:
The Z-score is used as a measure of how far the current price spread deviates from its historical mean, using the standard deviation for normalization.
Trading Logic:
• Long Position:
A long position is initiated when the Z-score exceeds the predefined threshold (e.g., 2.0), indicating that Asset A is undervalued relative to Asset B. This signals an arbitrage opportunity where the trader buys Asset B and sells Asset A.
• Short Position:
A short position is entered when the Z-score falls below the negative threshold, indicating that Asset A is overvalued relative to Asset B. The strategy involves selling Asset B and buying Asset A.
Theoretical Foundation
This strategy is rooted in mean reversion theory, which posits that asset prices tend to return to their long-term average after temporary deviations. This form of arbitrage is widely used in statistical arbitrage and pair trading techniques, where investors seek to exploit short-term price inefficiencies between two assets that historically maintain a stable price relationship (Avery & Sibley, 2020).
Further, the Z-score is an effective tool for identifying significant deviations from the mean, which can be seen as a signal for the potential reversion of the price spread (Braucher, 2015). By capturing these inefficiencies, traders aim to profit from convergence or divergence between correlated assets.
Practical Application
The strategy aligns with the Financial Algorithmic Trading and Market Liquidity analysis, emphasizing the importance of statistical models and efficient execution (Harris, 2024). By utilizing a simple yet effective risk-reward mechanism based on the Z-score, the strategy contributes to the growing body of research on market liquidity, asset correlation, and algorithmic trading.
The integration of transaction costs and slippage ensures that the strategy accounts for practical trading limitations, helping to refine execution in real market conditions. These factors are vital in modern quantitative finance, where liquidity and execution risk can erode profits (Harris, 2024).
References
• Gatev, E., Goetzmann, W. N., & Rouwenhorst, K. G. (2006). Pairs Trading: Performance of a Relative-Value Arbitrage Rule. The Review of Financial Studies, 19(3), 1317-1343.
• Avery, C., & Sibley, D. (2020). Statistical Arbitrage: The Evolution and Practices of Quantitative Trading. Journal of Quantitative Finance, 18(5), 501-523.
• Braucher, J. (2015). Understanding the Z-Score in Trading. Journal of Financial Markets, 12(4), 225-239.
• Harris, L. (2024). Financial Algorithmic Trading and Market Liquidity: A Comprehensive Analysis. Journal of Financial Engineering, 7(1), 18-34.
Adaptive Trend Flow Strategy with Filters for SPXThe Adaptive Trend Flow Strategy with Filters for SPX is a complete trading algorithm designed to identify traits and offer actionable alerts for the SPX index. This Pine Script approach leverages superior technical signs and user-described parameters to evolve to marketplace conditions and optimize performance.
Key Features and Functionality
Dynamic Trend Detection: Utilizes a dual EMA-based totally adaptive method for fashion calculation.
The script smooths volatility the usage of an EMA filter and adjusts sensitivity through the sensitivity enter. This allows for real-time adaptability to market fluctuations.
Trend Filters for Precision:
SMA Filter: A Simple Moving Average (SMA) guarantees that trades are achieved best while the rate aligns with the shifting average trend, minimizing false indicators.
MACD Filter: The Moving Average Convergence Divergence (MACD) adds some other layer of confirmation with the aid of requiring alignment among the MACD line and its sign line.
Signal Generation:
Long Signals: Triggered when the fashion transitions from bearish to bullish, with all filters confirming the pass.
Short Signals: Triggered while the trend shifts from bullish to bearish, imparting opportunities for final positions.
User Customization:
Adjustable parameters for EMAs, smoothing duration, and sensitivity make certain the strategy can adapt to numerous buying and selling patterns.
Enable or disable filters (SMA or MACD) based totally on particular market conditions or consumer possibilities.
Leverage and Position Sizing: Incorporates a leverage aspect for dynamic position sizing.
Automatically calculates the exchange length based on account fairness and the leverage element, making sure hazard control is in area.
Visual Enhancements: Plots adaptive fashion ranges (foundation, top, decrease) for actual-time insights into marketplace conditions.
Color-coded bars and heritage to visually represent bullish or bearish developments.
Custom labels indicating crossover and crossunder occasions for clean sign visualization.
Alerts and Automation: Configurable alerts for each lengthy and quick indicators, well matched with automated buying and selling structures like plugpine.Com.
JSON-based alert messages consist of account credentials, motion type, and calculated position length for seamless integration.
Backtesting and Realistic Assumptions: Includes practical slippage, commissions, and preliminary capital settings for backtesting accuracy.
Leverages excessive-frequency trade sampling to make certain strong strategy assessment.
How It Works
Trend Calculation: The method derives a principal trend basis with the aid of combining fast and gradual EMAs. It then uses marketplace volatility to calculate adaptive upper and decrease obstacles, creating a dynamic channel.
Filter Integration: SMA and MACD filters work in tandem with the fashion calculation to ensure that handiest excessive-probability signals are accomplished.
Signal Execution: Signals are generated whilst the charge breaches those dynamic tiers and aligns with the fashion and filters, ensuring sturdy change access situations.
How to Use
Setup: Apply the approach to SPX or other well suited indices.
Adjust person inputs, together with ATR length, EMA smoothing, and sensitivity, to align together with your buying and selling possibilities.
Enable or disable the SMA and MACD filters to test unique setups.
Alerts: Configure signals for computerized notifications or direct buying and selling execution through third-celebration systems.
Use the supplied JSON payload to integrate with broking APIs or automation tools.
Optimization:
Experiment with leverage, filter out settings, and sensitivity to find most effective configurations to your hazard tolerance and marketplace situations.
Considerations and Best Practices
Risk Management: Always backtest the method with realistic parameters, together with conservative leverage and commissions.
Market Suitability: While designed for SPX, this method can adapt to other gadgets by means of adjusting key parameters.
Limitations: The method is trend-following and can underperform in enormously risky or ranging markets. Regularly evaluate and modify parameters primarily based on recent market conduct.
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Sunil High-Frequency Strategy with Simple MACD & RSISunil High-Frequency Strategy with Simple MACD & RSI
This high-frequency trading strategy uses a combination of MACD and RSI to identify quick market opportunities. By leveraging these indicators, combined with dynamic risk management using ATR, it aims to capture small but frequent price movements while ensuring tight control over risk.
Key Features:
Indicators Used:
MACD (Moving Average Convergence Divergence): The strategy uses a shorter MACD configuration (Fast Length of 6 and Slow Length of 12) to capture quick price momentum shifts. A MACD crossover above the signal line triggers a buy signal, while a crossover below the signal line triggers a sell signal.
RSI (Relative Strength Index): A shorter RSI length of 7 is used to gauge overbought and oversold market conditions. The strategy looks for RSI confirmation, with a long trade initiated when RSI is below the overbought level (70) and a short trade initiated when RSI is above the oversold level (30).
Risk Management:
Dynamic Stop Loss and Take Profit: The strategy uses ATR (Average True Range) to calculate dynamic stop loss and take profit levels based on market volatility.
Stop Loss is set at 0.5x ATR to limit risk.
Take Profit is set at 1.5x ATR to capture reasonable price moves.
Trailing Stop: As the market moves in the strategy’s favor, the position is protected by a trailing stop set at 0.5x ATR, allowing the strategy to lock in profits as the price moves further.
Entry & Exit Signals:
Long Entry: Triggered when the MACD crosses above the signal line (bullish crossover) and RSI is below the overbought level (70).
Short Entry: Triggered when the MACD crosses below the signal line (bearish crossover) and RSI is above the oversold level (30).
Exit Conditions: The strategy exits long or short positions based on the stop loss, take profit, or trailing stop activation.
Frequent Trades:
This strategy is designed for high-frequency trading, with trade signals occurring frequently as the MACD and RSI indicators react quickly to price movements. It works best on lower timeframes such as 1-minute, 5-minute, or 15-minute charts, but can be adjusted for different timeframes based on the asset’s volatility.
Customizable Parameters:
MACD Settings: Adjust the Fast Length, Slow Length, and Signal Length to tune the MACD’s sensitivity.
RSI Settings: Customize the RSI Length, Overbought, and Oversold levels to better match your trading style.
ATR Settings: Modify the ATR Length and multipliers for Stop Loss, Take Profit, and Trailing Stop to optimize risk management according to market volatility.
Important Notes:
Market Conditions: This strategy is designed to capture smaller, quicker moves in trending markets. It may not perform well during choppy or sideways markets.
Optimizing for Asset Volatility: Adjust the ATR multipliers based on the asset’s volatility to suit the risk-reward profile that fits your trading goals.
Backtesting: It's recommended to backtest the strategy on different assets and timeframes to ensure optimal performance.
Summary:
The Sunil High-Frequency Strategy leverages a simple combination of MACD and RSI with dynamic risk management (using ATR) to trade small but frequent price movements. The strategy ensures tight stop losses and reasonable take profits, with trailing stops to lock in profits as the price moves in favor of the trade. It is ideal for scalping or intraday trading on lower timeframes, aiming for quick entries and exits with controlled risk.
Stronger V4.0 - Optimized Trading Strategy
Name: Stronger V4.0 - Optimized Trading Strategy
Introduction:
Stronger V4.0 is a structured trading strategy designed to identify and act on market breakout and reversal opportunities. By employing advanced filtering tools such as RSI (Relative Strength Index), MACD (Moving Average Convergence Divergence), and Bollinger Bands, this strategy aims to reduce market noise and provide reliable trading signals.
The strategy dynamically adapts to changing market conditions, focusing on delivering high-quality signals rather than frequent ones. This allows traders to approach markets with more confidence and clarity.
How the Strategy Works and Key Features:
How Stronger V4.0 Works:
Stronger V4.0 combines advanced technical indicators and custom logic to identify optimal entry and exit points in the market. By dynamically integrating filters like RSI, MACD, and Bollinger Bands, the strategy adjusts to market conditions and minimizes noise to deliver high-quality signals.
Key Features:
Dynamic Price Analysis:
Tracks price movements within specific periods to detect breakout and reversal opportunities.
Advanced Filtering Mechanisms:
RSI Filter: Avoids trades in overbought/oversold market conditions.
MACD Filter: Confirms market momentum and trend direction.
Bollinger Bands: Adapts thresholds based on market volatility.
Risk Management:
Limits trade risk to sustainable levels to preserve equity.
Encourages consistent growth by maintaining a maximum risk per trade.
Customizable Parameters:
Users can toggle long or short trades and adjust filter settings to match their trading preferences.
Minimalist Display:
Focuses on essential signals only, ensuring a clean and easy-to-read chart layout.
Market Breakout Identification:
One of Stronger V4.0's core functionalities is identifying significant breakout points. These breakout points are calculated based on dynamic price movements and market momentum.
Key moments are highlighted when the price exits a consolidation phase and transitions into a new trend. These points represent strong market opportunities, offering actionable insights for traders.
Using adjustable period settings, the strategy enables traders to tailor the analysis to their preferred timeframe and trading style. By eliminating market noise, Stronger V4.0 helps traders focus on high-probability setups and make informed decisions during volatile conditions.
Why Stronger V4.0 Stands Out:
Adaptive Filters:
Dynamically integrates RSI, MACD, and Bollinger Bands to reduce noise and highlight high-probability setups.
Precision Execution:
Focuses on executing trades at optimal moments, ensuring a balance between sustainability and profitability.
Rigorous Testing:
Extensively backtested under realistic market conditions for consistent performance.
Tailored and Exclusive:
Designed for traders seeking a balance between quality and adaptability.
Risk Disclaimer:
Stronger V4.0 has been backtested under various market conditions; however, past performance does not guarantee future results. The strategy is provided as-is, and traders are encouraged to test it thoroughly and apply appropriate risk management measures. Always trade responsibly.
Supertrend and MACD strategyThe Supertrend and MACD Strategy is a comprehensive trading approach designed to capitalize on market trends by using a combination of the Supertrend indicator, the Exponential Moving Average (EMA), and the Moving Average Convergence Divergence (MACD). This strategy aims to identify optimal entry and exit points for both long and short trades, while incorporating strict risk management rules.
Indicators Used:
Supertrend: This indicator is used to identify the overall trend direction. It provides clear signals for trend reversals, helping traders to enter trades in the direction of the prevailing trend.
200-period EMA: This long-term moving average is used to determine the primary trend direction. The strategy only takes long trades when the price is above the 200 EMA and short trades when the price is below it.
MACD: The MACD is used to gauge the momentum and confirm the signals provided by the Supertrend and EMA. It consists of the MACD line, the signal line, and the histogram.
Entry Conditions:
Long Entry:
The Supertrend indicator shows an uptrend (direction > 0).
The MACD line is above the signal line (macd > signal).
The price is above the 200-period EMA (close > ema200).
Short Entry:
The Supertrend indicator shows a downtrend (direction < 0).
The MACD line is below the signal line (macd < signal).
The price is below the 200-period EMA (close < ema200).
Exit Conditions:
Long Exit:
Exit the long position when the MACD line crosses below the signal line (ta.crossunder(macd, signal)).
Set a stop loss (SL) below the lowest low of the last 10 periods (lowestLow - 1).
Short Exit:
Exit the short position when the MACD line crosses above the signal line (ta.crossover(macd, signal)).
Set a stop loss (SL) above the highest high of the last 10 periods (highestHigh + 1).
Risk Management:
The strategy ensures that no new positions are opened if there is already an open trade, preventing overexposure in the market.
Alerts:
Alerts are set to notify traders when the MACD crosses the signal line, providing timely updates for potential exit points.
Global Index Spread RSI StrategyThis strategy leverages the relative strength index (RSI) to monitor the price spread between a global benchmark index (such as AMEX) and the currently opened asset in the chart window. By calculating the spread between these two, the strategy uses RSI to identify oversold and overbought conditions to trigger buy and sell signals.
Key Components:
Global Benchmark Index: The strategy compares the current asset with a predefined global index (e.g., AMEX) to measure relative performance. The choice of a global benchmark allows the trader to analyze the current asset's movement in the context of broader market trends.
Spread Calculation:
The spread is calculated as the percentage difference between the current asset's closing price and the global benchmark index's closing price:
Spread=Current Asset Close−Global Index CloseGlobal Index Close×100
Spread=Global Index CloseCurrent Asset Close−Global Index Close×100
This metric provides a measure of how the current asset is performing relative to the global index. A positive spread indicates the asset is outperforming the benchmark, while a negative spread signals underperformance.
RSI of the Spread: The RSI is then calculated on the spread values. The RSI is a momentum oscillator that ranges from 0 to 100 and is commonly used to identify overbought or oversold conditions in asset prices. An RSI below 30 is considered oversold, indicating a potential buying opportunity, while an RSI above 70 is overbought, suggesting that the asset may be due for a pullback.
Strategy Logic:
Entry Condition: The strategy enters a long position when the RSI of the spread falls below the oversold threshold (default 30). This suggests that the asset may have been oversold relative to the global benchmark and might be due for a reversal.
Exit Condition: The strategy exits the long position when the RSI of the spread rises above the overbought threshold (default 70), indicating that the asset may have become overbought and a price correction is likely.
Visual Reference:
The RSI of the spread is plotted on the chart for visual reference, making it easier for traders to monitor the relative strength of the asset in relation to the global benchmark.
Overbought and oversold levels are also drawn as horizontal reference lines (70 and 30), along with a neutral level at 50 to show market equilibrium.
Theoretical Basis:
The strategy is built on the mean reversion principle, which suggests that asset prices tend to revert to a long-term average over time. When prices move too far from this mean—either being overbought or oversold—they are likely to correct back toward equilibrium. By using RSI to identify these extremes, the strategy aims to profit from price reversals.
Mean Reversion: According to financial theory, asset prices oscillate around a long-term average, and any extreme deviation (overbought or oversold conditions) presents opportunities for price corrections (Poterba & Summers, 1988).
Momentum Indicators (RSI): The RSI is widely used in technical analysis to measure the momentum of an asset. Its application to the spread between the asset and a global benchmark allows for a more nuanced view of relative performance and potential turning points in the asset's price trajectory.
Practical Application:
This strategy works best in markets where relative strength is a key factor in decision-making, such as in equity indices, commodities, or forex markets. By assessing the performance of the asset relative to a global benchmark and utilizing RSI to identify extremes in price movements, the strategy helps traders to make more informed decisions based on potential mean reversion points.
While the "Global Index Spread RSI Strategy" offers a method for identifying potential price reversals based on relative strength and oversold/overbought conditions, it is important to recognize that no strategy is foolproof. The strategy assumes that the historical relationship between the asset and the global benchmark will hold in the future, but financial markets are subject to a wide array of unpredictable factors that can lead to sudden changes in price behavior.
Risk of False Signals:
The strategy relies heavily on the RSI to trigger buy and sell signals. However, like any momentum-based indicator, RSI can generate false signals, particularly in highly volatile or trending markets. In such conditions, the strategy may enter positions too early or exit too late, leading to potential losses.
Market Context:
The strategy may not account for macroeconomic events, news, or other market forces that could cause sudden shifts in asset prices. External factors, such as geopolitical developments, monetary policy changes, or financial crises, can cause a divergence between the asset and the global benchmark, leading to incorrect conclusions from the strategy.
Overfitting Risk:
As with any strategy that uses historical data to make decisions, there is a risk of overfitting the model to past performance. This could result in a strategy that works well on historical data but performs poorly in live trading conditions due to changes in market dynamics.
Execution Risks:
The strategy does not account for slippage, transaction costs, or liquidity issues, which can impact the execution of trades in real-market conditions. In fast-moving markets, prices may move significantly between order placement and execution, leading to worse-than-expected entry or exit prices.
No Guarantee of Profit:
Past performance is not necessarily indicative of future results. The strategy should be used with caution, and risk management techniques (such as stop losses and position sizing) should always be implemented to protect against significant losses.
Traders should thoroughly test and adapt the strategy in a simulated environment before applying it to live trades, and consider seeking professional advice to ensure that their trading activities align with their risk tolerance and financial goals.
References:
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Ichimoku + RSI + MACD Strategy1. Relative Strength Index (RSI)
Overview:
The Relative Strength Index (RSI) is a momentum oscillator that measures the speed and change of price movements. It ranges from 0 to 100 and is typically used to identify overbought or oversold conditions in a market.
How to Use with Ichimoku:
Long Entry: Look for RSI to be above 30 (indicating it is not oversold) when the price is above the Ichimoku Cloud.
Short Entry: Look for RSI to be below 70 (indicating it is not overbought) when the price is below the Ichimoku Cloud.
2. Moving Average Convergence Divergence (MACD)
Overview:
The MACD is a trend-following momentum indicator that shows the relationship between two moving averages of a security’s price. It consists of the MACD line, signal line, and histogram.
How to Use with Ichimoku:
Long Entry: Enter a long position when the MACD line crosses above the signal line while the price is above the Ichimoku Cloud.
Short Entry: Enter a short position when the MACD line crosses below the signal line while the price is below the Ichimoku Cloud.
Combined Strategy Example
Here’s a brief outline of how to structure a trading strategy using Ichimoku, RSI, and MACD:
Long Entry Conditions:
Price is above the Ichimoku Cloud.
RSI is above 30.
MACD line crosses above the signal line.
Short Entry Conditions:
Price is below the Ichimoku Cloud.
RSI is below 70.
MACD line crosses below the signal line.
Exit Conditions:
Exit long when MACD line crosses below the signal line.
Exit short when MACD line crosses above the signal line.
[ETH] Optimized Trend Strategy - Lorenzo SuperScalpStrategy Title: Optimized Trend Strategy - Lorenzo SuperScalp
Description:
The Optimized Trend Strategy is a comprehensive trading system tailored for Ethereum (ETH) and optimized for the 15-minute timeframe but adaptable to various timeframes. This strategy utilizes a combination of technical indicators—RSI, Bollinger Bands, and MACD—to identify and act on price trends efficiently, providing traders with actionable buy and sell signals based on market conditions.
Key Features:
Multi-Indicator Approach:
RSI (Relative Strength Index): Identifies overbought and oversold conditions to time market entries and exits.
Bollinger Bands: Acts as a dynamic support and resistance level, helping to pinpoint precise entry and exit zones.
MACD (Moving Average Convergence Divergence): Detects momentum changes through bullish and bearish crossovers.
Signal Conditions:
Buy Signal:
RSI is below 45 (indicating an oversold condition).
Price is near or below the lower Bollinger Band.
MACD bullish crossover occurs.
Sell Signal:
RSI is above 55 (indicating an overbought condition).
Price is near or above the upper Bollinger Band.
MACD bearish crossunder occurs.
Trade Execution Logic:
Long Trades: Opened when a buy signal flashes. If there’s an open short position, it is closed before opening a long.
Short Trades: Opened when a sell signal flashes. If there’s an open long position, it is closed before opening a short.
The strategy also ensures a minimum number of bars between consecutive trades to avoid rapid trading in choppy conditions.
Pyramiding Support:
Up to 3 consecutive trades in the same direction are allowed, enabling traders to scale into positions based on strong signals.
Visual Indicators:
RSI Levels: Dotted lines at 45 and 55 for quick reference to oversold and overbought levels.
Buy and Sell Signals: Visual markers on the chart indicate where trades are executed, ensuring clarity on entry and exit points.
Best Used For:
Swing Trading & Scalping: While optimized for the 15-minute timeframe, this strategy works across various timeframes, making it suitable for both short-term scalping and swing trading.
Crypto Trading: Tailored for Ethereum but effective for other cryptocurrencies due to its dynamic indicator setup.
Stochastic RSI OHLC StrategyThe script titled "Stochastic RSI High Low Close Bars" is a versatile trading strategy implemented in Pine Script, designed for TradingView. Here's an overview of its features:
Description
This strategy leverages the Stochastic RSI to determine entry and exit signals in the market, focusing on high, low, and close values of the indicator. It incorporates various trading styles, stop-loss mechanisms, and multi-timeframe analysis to adapt to different market conditions.
Key Features
Stochastic RSI Analysis:
Uses the Stochastic RSI to identify potential entry points for long and short positions.
Tracks high, low, and close values for more granular analysis.
Multiple Trading Styles:
Supports diverse trading styles like Volume Color Swing, RSI Divergence, RSI Pullback, and more.
Allows switching between these styles to suit market dynamics.
Session-Based Trading:
Offers session control, limiting trades to specific hours (e.g., NY sessions).
Can close all positions at the end of the trading day.
Stop-Loss and Take-Profit Mechanisms:
Includes both static and dynamic stop-losses, with options for time-based stops, trailing stops, and momentum-based exits.
Customizable take-profit levels ensure efficient trade management.
Volume Analysis:
Integrates volume indicators to add a bias for trade entries and exits, enhancing signal reliability.
Multi-Timeframe Integration:
Employs multi-timeframe RSI analysis, allowing the strategy to capture broader trends and optimize entries.
This script is designed to provide flexibility and adaptability, making it useful for different trading strategies and market conditions. It is suitable for traders looking to refine their entries and exits with a focus on the Stochastic RSI.
Adaptive MA Scalping StrategyAdaptive MA Scalping Strategy
The Adaptive MA Scalping Strategy is an innovative trading approach that merges the strengths of the Kaufman's Adaptive Moving Average (KAMA) with the Moving Average Convergence Divergence (MACD) histogram. This combination results in a momentum-adaptive moving average that dynamically adjusts to market conditions, providing traders with timely and reliable signals.
How It Works
Kaufman's Adaptive Moving Average (KAMA): Unlike traditional moving averages, KAMA adjusts its sensitivity based on market volatility. It becomes more responsive during trending markets and less sensitive during periods of consolidation, effectively filtering out market noise.
MACD Histogram Integration: The strategy incorporates the MACD histogram, a momentum indicator that measures the difference between a fast and a slow exponential moving average (EMA). By adding the MACD histogram values to the KAMA, the strategy creates a new line—the momentum-adaptive moving average (MOMA)—which captures both trend direction and momentum.
Signal Generation:
Long Entry: The strategy enters a long position when the closing price crosses above the MOMA. This indicates a potential upward momentum shift.
Exit Position: The position is closed when the closing price crosses below the MOMA, signaling a potential decline in momentum.
Cloud Calculation Detail
The MOMA is calculated by adding the MACD histogram value to the KAMA of the price. This addition effectively adjusts the KAMA based on the momentum indicated by the MACD histogram. When momentum is strong, the MACD histogram will have higher values, causing the MOMA to adjust accordingly and provide earlier entry or exit signals.
Performance on Stocks
This strategy has demonstrated excellent performance on stocks when applied to the 1-hour timeframe. Its adaptive nature allows it to respond swiftly to market changes, capturing profitable trends while minimizing the impact of false signals caused by market noise. The combination of KAMA's adaptability and MACD's momentum detection makes it particularly effective in volatile market conditions commonly seen in stock trading.
Key Parameters
KAMA Length (malen): Determines the sensitivity of the KAMA. A length of 100 is used to balance responsiveness with noise reduction.
MACD Fast Length (fast): Sets the period for the fast EMA in the MACD calculation. A value of 24 helps in capturing short-term momentum changes.
MACD Slow Length (slow): Sets the period for the slow EMA in the MACD calculation. A value of 52 smooths out longer-term trends.
MACD Signal Length (signal): Determines the period for the signal line in the MACD calculation. An 18-period signal line is used for timely crossovers.
Advantages of the Strategy
Adaptive to Market Conditions: By adjusting to both volatility and momentum, the strategy remains effective across different market phases.
Enhanced Signal Accuracy: The fusion of KAMA and MACD reduces false signals, improving the accuracy of trade entries and exits.
Simplicity in Execution: With straightforward entry and exit rules based on price crossovers, the strategy is user-friendly for traders at all experience levels
MACD Enhanced Strategy MTF with Stop Loss [LTB]Test strategy for MACD
This strategy, named "MACD Enhanced Strategy MTF with Stop Loss ," is a modified Moving Average Convergence Divergence (MACD) strategy with enhancements such as multi-timeframe (MTF) analysis, custom scoring, and a dynamic stop loss mechanism. Let’s break down how to effectively use it:
Key Elements of the Strategy
MACD Indicator with Modifications:
The strategy uses MACD, a well-known momentum indicator, with customizable parameters:
fastLength, slowLength, and signalLength represent the standard MACD settings.
Instead of relying solely on MACD crossovers, it introduces scoring parameters for histogram direction (histside), indicator direction (indiside), and signal cross (crossscore). This allows for a more nuanced decision-making process when determining buy and sell signals.
Multi-Timeframe Analysis (MTF):
The strategy compares the current timeframe's MACD score with that of a higher timeframe (HTF). It dynamically selects the higher timeframe based on the current timeframe. For example, if the current chart period is 1, it will select 5 as the higher timeframe.
This MTF approach aims to align trades with broader trends, filtering out false signals that could be present when analyzing only a single timeframe.
Scoring System:
A custom scoring system (count() function) is used to evaluate buy and sell signals. This includes calculations based on the direction and momentum of MACD (indi) and the histogram. The score is used to determine the strength of signals.
Positive scores indicate bullish sentiment, while negative scores indicate bearish sentiment.
This scoring mechanism aims to reduce the influence of noise and provide more reliable entries.
Entry Conditions:
Long Condition: When the Result value (a combination of MTF and current MACD analysis) changes and becomes positive, a long entry is triggered.
Short Condition: When the Result changes and becomes negative, a short entry is initiated.
Stop Loss Mechanism:
The countstop() function calculates dynamic stop loss values for both long and short trades. It is based on the Average True Range (ATR) multiplied by a factor (Mult), providing adaptive stop loss levels depending on market volatility.
The stop loss is plotted on the chart to show potential risk levels for open trades, with the line appearing only if shotsl is enabled.
How to Use the Strategy
To properly use the strategy, follow these steps:
Parameter Optimization:
Adjust the input parameters such as fastLength, slowLength, and signalLength to tune the MACD indicator to the specific asset you’re trading. The values provided are typical defaults, but optimizing these values based on backtesting can help improve performance.
Customize the scoring parameters (crossscore, indiside, histside) to balance how much weight you want to put on the direction, histogram, and cross events of the MACD indicator.
Select Appropriate Timeframes:
This strategy employs a multi-timeframe (MTF) approach, so it's important to understand how the higher timeframe (HTF) is selected based on the current timeframe. For instance, if you are trading on a 5-minute chart, the higher timeframe will be 15 minutes, which helps filter out lower timeframe noise.
Ensure you understand the relationship between the timeframe you’re using and the HTF it automatically selects. The strategy’s effectiveness can vary depending on how these timeframes align with the asset’s overall volatility.
Run Backtests:
Always backtest the strategy over historical data to determine its reliability for the asset and timeframes you’re interested in. Note that the MTF approach may require substantial data to capture how different timeframes interact.
Use the backtest results to adjust the scoring parameters or the Stop Loss Factor (Mult) for better risk management.
Stop Loss Usage:
The stop loss is calculated dynamically using ATR, which means that it adjusts with changing volatility. This can be useful to avoid being stopped out too often during periods of increased volatility.
The shotsl parameter can be set to true to visualize the stop loss line on the chart. This helps to monitor the protection level and make better decisions regarding holding or closing a trade manually.
Entry Signals and Trade Execution:
Look for changes in the Result value to determine entry points. For a long position, the Result needs to become positive, and for a short position, it must be negative.
Note that the strategy's entries are more conservative because it waits for the Result to confirm the direction using multiple factors, which helps filter out false breakouts.
Risk Management:
The adaptive stop loss mechanism reduces the risk by basing the stop level on market volatility. However, you must still consider additional risk management practices such as position sizing and profit targets.
Given the scoring mechanism, it might not enter trades frequently, which means using this strategy may result in fewer but potentially more accurate trades. It’s important to be patient and not force trades that don’t align with the calculated results.
Real-Time Monitoring:
Make sure to monitor trades actively. Since the strategy recalculates the score on each bar, real-time changes in the Result value could provide exit opportunities even if the stop loss isn't triggered.
Summary
The "MACD Enhanced Strategy MTF with Stop Loss " is a sophisticated version of the MACD strategy, enhanced with multi-timeframe analysis and adaptive stop loss. Properly using it involves optimizing MACD and scoring parameters, selecting suitable timeframes, and actively managing entries and exits based on a combination of scoring and volatility-based stop losses. Always conduct thorough backtesting before applying it in a live environment to ensure the strategy performs well on the asset you're trading.
Reflected ema Difference (RED) This script, titled "Reflected EMA Difference (RED)," is based on the logic of evaluating the percentage of convergence and divergence between two moving averages, specifically the Hull Moving Averages (HMA), to make price-related decisions. The Hull Moving Average, created by Alan Hull, is used as the foundation of this strategy, offering a faster and more accurate way to analyze market trends. In this script, the concept is employed to measure and reflect price variations.
Script Functionality Overview:
Hull Moving Averages (HMA): The script utilizes two HMAs, one short-term and one long-term. The main idea is to compute the Delta Difference between these two moving averages, which represents how much they are converging or diverging from each other. This difference is key to identifying potential market trend changes.
Reflected HMA Value: Using the Delta Difference between the HMAs, the value of the short-term HMA is reflected, creating a visual reference point that helps traders see the relationship between price and HMAs on the chart.
Percentage Change Index: The second key parameter is the percentage change index. This determines when a trend is reversing, allowing buy or sell orders to be established based on significant changes in the relationship between the HMAs and the price.
Delta Multiplier: The script comes with a default Delta multiplier of 2 for calculating the difference between HMAs, allowing traders to adjust the sensitivity of the analysis based on the time frame being analyzed.
Trend Reversal Signals: When the price crosses the thresholds defined by the percentage change index, buy or sell signals are triggered, based on the detection of a potential trend reversal.
Visual Cues with Boxes: Boxes are drawn on the chart when the HullMA crosses the reflected HMA value, providing a visual aid to identify critical moments where risk should be evaluated.
Alerts for Receiving Signals:
This script allows you to set up buy and sell alerts via TradingView's alert system. These alerts are triggered when trend changes are detected based on the conditions coded in the script. Traders can receive instant notifications, allowing them to make decisions without needing to constantly monitor the chart.
Additional Considerations:
The percentage change parameter is adjustable and should be configured based on the time frame you are trading on. For longer time frames, it's advisable to use a larger percentage change to avoid false signals.
The use of Hull Moving Averages (HMA) provides a faster and more reactive approach to trend evaluation compared to other moving averages, making it a powerful tool for traders seeking quick reversal signals.
This approach combines the power of Hull Moving Averages with an alert system to improve the trader’s response to trend changes.
Spanish
Este script, titulado "Reflected EMA Difference (RED)", está fundamentado en la lógica de evaluar el porcentaje de acercamiento y distancia entre dos medias móviles, específicamente las medias móviles de Hull (HMA), para tomar decisiones sobre el valor del precio. El creador de la media móvil de Hull, Alan Hull, diseñó este indicador para ofrecer una forma más rápida y precisa de analizar tendencias de mercado, y en este script se utiliza su concepto como base para medir y reflejar las variaciones de precio.
Descripción del funcionamiento:
Medias Móviles de Hull (HMA): Se utilizan dos HMAs, una de corto plazo y otra de largo plazo. La idea principal es calcular la diferencia Delta entre estas dos medias móviles, que representa cuánto se están alejando o acercando entre sí. Esta diferencia es clave para identificar cambios potenciales en la tendencia del mercado.
Valor Reflejado de la HMA: Con la diferencia Delta calculada entre las HMAs, se refleja el valor de la HMA corta, creando un punto de referencia visual que ayuda a los traders a observar la relación entre el precio y las HMAs en el gráfico.
Índice de Cambio de Porcentaje: El segundo parámetro clave del script es el índice de cambio porcentual. Este define el momento en que una tendencia está revirtiendo, permitiendo establecer órdenes de compra o venta en función de un cambio significativo en la relación entre las HMAs y el precio.
Multiplicador Delta: El script tiene un multiplicador predeterminado de 2 para el cálculo de la diferencia Delta, lo que permite ajustar la sensibilidad del análisis según la temporalidad del gráfico.
Señales de Reversión de Tendencia: Cuando el precio cruza los límites definidos por el índice de cambio porcentual, se emiten señales para comprar o vender, basadas en la detección de una posible reversión de tendencia.
Visualización con Cajas: Se dibujan cajas en el gráfico cuando el indicador HullMA cruza el valor reflejado de la HMA, ayudando a identificar visualmente los momentos críticos en los que se debe evaluar el riesgo de las operaciones.
Alertas para Recibir Señales:
Este script permite configurar alertas de compra y venta desde el apartado de alertas de TradingView. Estas alertas se activan cuando se detectan cambios de tendencia en función de las condiciones establecidas en el código. El trader puede recibir notificaciones instantáneas, lo que facilita la toma de decisiones sin necesidad de estar constantemente observando el gráfico.
Consideraciones adicionales:
El porcentaje de cambio es un parámetro ajustable y debe configurarse según la temporalidad que se esté operando. En temporalidades más largas, es recomendable usar un porcentaje de cambio mayor para evitar señales falsas.
La utilización de las medias móviles de Hull (HMA) proporciona un enfoque más rápido y reactivo para evaluar tendencias en comparación con otras medias móviles, lo que lo convierte en una herramienta poderosa para traders que buscan señales rápidas de reversión.
Este enfoque combina la potencia de las medias móviles de Hull con un sistema de alertas que mejora la reactividad a cambios de tendencia.
Scalping with Williams %R, MACD, and SMA (1m)Overview:
This trading strategy is designed for scalping in the 1-minute timeframe. It uses a combination of the Williams %R, MACD, and SMA indicators to generate buy and sell signals. It also includes alert functionalities to notify users when trades are executed or closed.
Indicators Used:
Williams %R : A momentum indicator that measures overbought and oversold conditions. The Williams %R values range from -100 to 0.
Length: 140 bars (i.e., 140-period).
MACD (Moving Average Convergence Divergence) : A trend-following momentum indicator that shows the relationship between two moving averages of a security's price.
Fast Length: 24 bars
Slow Length: 52 bars
MACD Length: 9 bars (signal line)
SMA (Simple Moving Average) : A trend-following indicator that smooths out price data to create a trend-following indicator.
Length: 7 bars
Conditions and Logic:
Timeframe Check :
The strategy is designed specifically for the 1-minute timeframe. If the current chart is not on the 1-minute timeframe, a warning label is displayed on the chart instructing the user to switch to the 1-minute timeframe.
Williams %R Conditions :
Buy Condition: The strategy looks for a crossover of Williams %R from below -94 to above -94. This indicates a potential buying opportunity when the market is moving out of an oversold condition.
Sell Condition: The strategy looks for a crossunder of Williams %R from above -6 to below -6. This indicates a potential selling opportunity when the market is moving out of an overbought condition.
Deactivate Buy: If Williams %R crosses above -40, the buy signal is deactivated, suggesting that the buying condition is no longer valid.
Deactivate Sell: If Williams %R crosses below -60, the sell signal is deactivated, suggesting that the selling condition is no longer valid.
MACD Conditions :
MACD Histogram: Used to identify the momentum and the direction of the trend.
Long Entry: The strategy initiates a buy order if the MACD histogram shows a positive bar after a negative bar while a buy condition is active and Williams %R is above -94.
Long Exit: The strategy exits the buy position if the MACD histogram turns negative and is below the previous histogram bar.
Short Entry: The strategy initiates a sell order if the MACD histogram shows a negative bar after a positive bar while a sell condition is active and Williams %R is below -6.
Short Exit: The strategy exits the sell position if the MACD histogram turns positive and is above the previous histogram bar.
Trend Confirmation (Using SMA) :
Bullish Trend: The strategy considers a bullish trend if the current price is above the 7-bar SMA. A buy signal is only considered if this condition is met.
Bearish Trend: The strategy considers a bearish trend if the current price is below the 7-bar SMA. A sell signal is only considered if this condition is met.
Alerts:
Long Entry Alert: An alert is triggered when a buy order is executed.
Long Exit Alert: An alert is triggered when the buy order is closed.
Short Entry Alert: An alert is triggered when a sell order is executed.
Short Exit Alert: An alert is triggered when the sell order is closed.
Summary:
Buy Signal: Activated when Williams %R crosses above -94 and the price is above the 7-bar SMA. A buy order is placed if the MACD histogram shows a positive bar after a negative bar. The buy order is closed when the MACD histogram turns negative and is below the previous histogram bar.
Sell Signal: Activated when Williams %R crosses below -6 and the price is below the 7-bar SMA. A sell order is placed if the MACD histogram shows a negative bar after a positive bar. The sell order is closed when the MACD histogram turns positive and is above the previous histogram bar.
This strategy combines momentum (Williams %R), trend-following (MACD), and trend confirmation (SMA) to identify trading opportunities in the 1-minute timeframe. It is designed for short-term trading or scalping.
