ETH/SOL 1D Dynamic Trend Core - Indicator v46🚀 Dynamic Trend Core
The Dynamic Trend Core is a sophisticated, multi-layer trading engine designed to identify high-probability, trend-following opportunities. It offers both a quantitative backtesting engine and a rich, intuitive visual interface.
Its core philosophy is simple: confirmation. The system seeks to filter out market noise by requiring a confluence of conditions—trend, momentum, price action, and volume—to be in alignment before a signal is considered valid.
⚙️ Core Logic Components
Primary Trend Analysis (SAMA): The foundation is a Self-Adjusting Moving Average (SAMA) that determines the underlying market trend (Bullish, Bearish, or Consolidation).
Confirmation & Momentum: Signals are confirmed with a blend of the Natural Market Slope and a Cyclic RSI to ensure momentum aligns with the primary trend.
Advanced Filtering Layers: A suite of optional filters allows for robust customization:
Volume & ADX: Ensure sufficient market participation and trend strength.
Market Regime: Uses the total crypto market cap to gauge broad market health.
Multi-Timeframe (MTF): Aligns signals with the dominant weekly trend.
BTC Cycle Analysis: Uses Halving or Mayer Multiple models to position trades within historical macro cycles.
Delta Zones: An additional filter to confirm signals with recent buy or sell pressure detected in candle wicks.
📊 The On-Chart Command Center
The strategy's real power comes from its on-chart visual feedback system, which provides full transparency into the engine's decision-making process.
Note: For the dashboard to update in real-time, you must enable "Recalculate on every tick" in the script's settings.
Power Core Gauge: Located at the bottom-center, this gauge is the heart of the system. It displays the number of active filter conditions met (e.g., 6/7) and "powers up" by glowing brightly as a signal becomes fully confirmed.
Live Conditions Panel: In the bottom-right corner, this panel acts as a detailed pre-flight checklist. It shows the real-time status of every single filter, helping you understand exactly why a trade is (or is not) being triggered.
Energized Trendline: The main SAMA trendline changes color and brightness based on the strength and direction of the trend, providing immediate visual context.
Halving Cycle Visualization: An optional visual guide to the phases of the Bitcoin halving cycle.
Delta Zone Pressure Boxes: A visual guide that draws boxes around candles exhibiting significant buying or selling pressure.
🛠️ How to Use
Operation Mode: "Alerts-Only Mode" for generating live signals.
Configure Strategy: Start with the default filters. If a potential trade setup is missed, check the Live Conditions Panel to see exactly which filter blocked the signal. Adjust the filters to suit your specific asset and timeframe.
Manage Risk: Adjust the Risk & Exit settings to match your personal risk tolerance.
考夫曼自適應移動平均線(KAMA)
BTC Dynamic Trend Core - Indicator v46🚀 Dynamic Trend Core
The Dynamic Trend Core is a sophisticated, multi-layer trading engine designed to identify high-probability, trend-following opportunities. It offers both a quantitative backtesting engine and a rich, intuitive visual interface.
Its core philosophy is simple: confirmation. The system seeks to filter out market noise by requiring a confluence of conditions—trend, momentum, price action, and volume—to be in alignment before a signal is considered valid.
⚙️ Core Logic Components
Primary Trend Analysis (SAMA): The foundation is a Self-Adjusting Moving Average (SAMA) that determines the underlying market trend (Bullish, Bearish, or Consolidation).
Confirmation & Momentum: Signals are confirmed with a blend of the Natural Market Slope and a Cyclic RSI to ensure momentum aligns with the primary trend.
Advanced Filtering Layers: A suite of optional filters allows for robust customization:
Volume & ADX: Ensure sufficient market participation and trend strength.
Market Regime: Uses the total crypto market cap to gauge broad market health.
Multi-Timeframe (MTF): Aligns signals with the dominant weekly trend.
BTC Cycle Analysis: Uses Halving or Mayer Multiple models to position trades within historical macro cycles.
Delta Zones: An additional filter to confirm signals with recent buy or sell pressure detected in candle wicks.
📊 The On-Chart Command Center
The strategy's real power comes from its on-chart visual feedback system, which provides full transparency into the engine's decision-making process.
Note: For the dashboard to update in real-time, you must enable "Recalculate on every tick" in the script's settings.
Power Core Gauge: Located at the bottom-center, this gauge is the heart of the system. It displays the number of active filter conditions met (e.g., 6/7) and "powers up" by glowing brightly as a signal becomes fully confirmed.
Live Conditions Panel: In the bottom-right corner, this panel acts as a detailed pre-flight checklist. It shows the real-time status of every single filter, helping you understand exactly why a trade is (or is not) being triggered.
Energized Trendline: The main SAMA trendline changes color and brightness based on the strength and direction of the trend, providing immediate visual context.
Halving Cycle Visualization: An optional visual guide to the phases of the Bitcoin halving cycle.
Delta Zone Pressure Boxes: A visual guide that draws boxes around candles exhibiting significant buying or selling pressure.
🛠️ How to Use
Indicator version of BTC DTC Strategy: "Alerts-Only Mode" for generating live signals.
Configure Strategy: Start with the default filters. If a potential trade setup is missed, check the Live Conditions Panel to see exactly which filter blocked the signal. Adjust the filters to suit your specific asset and timeframe.
Manage Risk: Adjust the Risk & Exit settings to match your personal risk tolerance.
KAMA Trend Flip - SightLing LabsBuckle up, traders—this open-source KAMA Trend Flip indicator is your ticket to sniping trend reversals with a Kaufman Adaptive Moving Average (KAMA) that’s sharper than a Wall Street shark’s tooth. No voodoo, no fluff—just raw, volatility-adaptive math that dances with the market’s rhythm. It zips through trending rockets and chills in choppy waters, slashing false signals like a samurai. Not laggy like the others - this thing is the real deal!
Core Mechanics:
• Efficiency Ratio (ER): Reads the market’s pulse (0-1). High ER = turbo-charged MA, low ER = smooth operator.
• Adaptive Smoothing: Mixes fast (default power 2) and slow (default 30) constants to match market mood swings.
• Trend Signals: KAMA climbs = blue uptrend (bulls run wild). KAMA dips = yellow downtrend (bears take over). Flat = gray snooze-fest.
• Alerts: Instant pings on flips—“Trend Flip Up” for long plays, “Down” for shorts. Plug into bots for set-and-forget domination.
Why It Crushes:
• Smokes static MAs in volatile arenas (crypto, stocks, you name it). Backtests show 20-30% fewer fakeouts than SMA50.
• Visual Pop: Overlays price with bold blue/yellow signals. Slap it on BTC 1D to see trends light up like Times Square.
• Tweakable: Dial ER length (default 50) to your timeframe. Short for scalps, long for swing trades.
Example Settings in Action:
• 10s Chart (Hyper-Scalping): Set Source: Close, ER Length: 100, Fast Power: 1, Slow Power: 6. Catches micro-trends in crypto like a heat-seeking missile. Blue/yellow flips scream entry/exit on fast moves.
• 2m Chart (Quick Trades): Set Source: Close, ER Length: 14, Fast Power: 1, Slow Power: 6. Perfect for rapid trend shifts in stocks or forex. Signals align with momentum bursts—check historical flips for proof.
Deployment:
• Drop it on any chart. Backtest settings to match your asset’s volatility—tweak until it sings.
• Pair with RSI or volume spikes for killer confirmation. Pro move: Enter on flip + volume pop, exit on reverse.
• Strategy-Ready: Slap long/short logic on alerts to build a lean, mean trading machine.
Open source from SightLing Labs—grab it, hack it, profit from it. Share your tweaks in the comments and let’s outsmart the market together. Trade hard, win big!
AMA-ST Sup/Res V 2.0AMA-ST Sup/Res v2.0 (AI + SuperTrend + Breakout System)
AMA-ST Sup/Res v2.0 is a powerful all-in-one indicator that blends together:
✅ Adaptive Moving Average (AMA) for dynamic trend smoothing
✅ SuperTrend (with VWMA/MAs/ATR options) for robust trend detection
✅ AI-Inspired KNN Filtering to reduce noise & false signals
✅ Breakout Arrow Signals (Buyer/Seller strength) with RSI & time-session filters
✅ Multi-Timeframe Trend Table to confirm higher timeframe alignment
This makes it a complete toolkit for detecting trend shifts, clean entries, and confluence zones.
🔎 Key Features
🔹 AI-Enhanced SuperTrend
Uses K-Nearest Neighbors (KNN) logic to classify current trend direction.
Weighted moving averages (price + SuperTrend) feed the algorithm.
Helps smooth out false flips and confirms bullish vs. bearish bias.
🔹 Adaptive Moving Average (AMA)
Adjusts sensitivity based on volatility.
Turns green when adaptive trend is rising and red when falling.
Useful for trailing, re-entry, and staying in strong moves.
🔹 Breakout Arrows (Buyer/Seller Strength)
Up arrow = Bullish Breakout (price > previous high + RSI confirmation).
Down arrow = Bearish Breakout (price < previous low + RSI confirmation).
Optional RSI filter to avoid weak signals.
Time filter lets you restrict signals to specific trading sessions.
🔹 Multi-Timeframe Trend Table
Shows trend status (Bull / Bear / Neutral) across 5 higher timeframes.
Displays sync status ✓/✗ with current timeframe for confluence.
Helps avoid taking trades against the higher-timeframe trend.
🔹 Visual Tools
Cloud edges to highlight breakout structures.
Colored SuperTrend line & fills for quick trend visualization.
Adaptive arrows for easy entry spotting.
⚙️ How to Use
1️⃣ Setup
Apply AMA-ST Sup/Res v2.0 to your chart.
Adjust SuperTrend length & ATR factor for desired sensitivity.
Set RSI levels (default 50/50) if you want to filter weak signals.
Enable/disable AI Trend Signals depending on your style.
2️⃣ Entry Rules
BUY when:
A blue up arrow prints,
SuperTrend flips green,
RSI is above the set buy threshold,
Multi-timeframe table shows at least 2 higher TFs in BULL sync.
SELL when:
A pink down arrow prints,
SuperTrend flips red,
RSI is below the set sell threshold,
Multi-timeframe table shows higher TFs in BEAR sync.
3️⃣ Exits & Risk Management
Use AMA line (green/red) as a dynamic trailing stop.
Alternatively, use SuperTrend flips as exit signals.
Avoid trading when the MTF table shows mixed/neutral trends.
💡 Tips for Best Results
Combine with price action (support/resistance zones).
Use on 15m+ timeframes for reliability, but scalpers can use 1m/5m with stricter filters.
For swing trading, watch confluence between 1H / 4H / Daily trends.
If signals feel too frequent, increase SuperTrend ATR length/factor.
If you want earlier signals, lower AMA length and reduce ATR factor.
⚠️ Disclaimer
This tool is for educational purposes only. It does not guarantee profits. Always combine with your own analysis and apply proper risk management before trading live.
ETH/SOL 1D Dynamic Trend Core - STRATEGY v 45Overview
The Dynamic Trend Core is a sophisticated, multi-layer trading engine designed to identify high-probability, trend-following opportunities. Its core philosophy is rooted in confluence, meaning it requires multiple conditions across trend, momentum, and volume to align before generating a signal. This approach aims to filter out market noise and provide a clearer view of the underlying trend.
The script includes a comprehensive backtesting engine for strategy optimization and a rich, intuitive visual interface for real-time analysis.
How It Works: Core Logic
The engine validates signals through several sequential layers:
Primary Trend Analysis (SAMA): The foundation is a Self-Adjusting Moving Average (SAMA) that dynamically determines the primary market direction (Bullish, Bearish, or Consolidation).
Momentum Confirmation: Signals are then qualified using a blend of the Natural Market Slope and a Cyclic RSI to ensure momentum is firmly aligned with the established trend.
Advanced Filtering Suite: A suite of optional filters provides robust confirmation and allows for deep customization:
Volume & ADX: Confirms that trades are supported by sufficient market participation and trend strength.
Market Regime: Gauges broad market health (e.g., using TOTAL market cap) to avoid trading against the entire market.
Multi-Timeframe (MTF) Analysis: Aligns signals with the dominant trend on a higher timeframe (e.g., Weekly).
BTC Cycle Analysis: Positions trades within the context of historical Bitcoin cycles using models like the Halving Cycle or Mayer Multiple.
On-Chart Visuals & Features
The script provides full transparency into its logic with a powerful on-chart interface.
IMPORTANT: For the live visual elements to function correctly, you must enable "Recalculate on every tick" in the script's settings (Settings > Properties).
Power Core Gauge: Located at the bottom-center of the chart, this gauge is the heart of the system. It displays the number of filter conditions currently met (e.g., 5/6) and "powers up" by glowing brighter as more conditions align, indicating a fully confirmed signal is ready.
Live Conditions Panel: This panel in the bottom-right corner acts as a real-time pre-flight checklist. It shows the status (pass/fail) of every individual filter, so you know exactly why a signal is, or is not, being generated.
Energized Trendline: The primary SAMA trendline changes color and intensity based on the strength and direction of the trend, offering immediate visual context.
BTC Halving Cycle Visualizer: Provides a background color guide to the different phases of the Bitcoin halving cycle for macro context.
How to Use & Configure
Select Operation Mode:
Backtest Mode: Use this to test different settings on historical data and find optimal configurations for a specific asset and timeframe.
Alerts-Only Mode: Use this for live trading to generate alert signals without cluttering the chart with backtest data. (Contact publisher for access to this version)
Configure Your Filters:
Start with the default filter settings.
If a potential setup is missed, check the Live Conditions Panel to see which specific filter blocked the signal.
Enable, disable, or adjust filters in the script's settings to match your trading style and the asset's characteristics.
Manage Your Risk:
Go to the "Risk & Exit" settings to configure your Stop Loss and Take Profit parameters to match your personal risk tolerance.
Disclaimer: This script is for educational and informational purposes only. It is not financial advice. All trading involves risk, and past performance is not indicative of future results. Please conduct your own research and backtesting before making any trading decisions.
BTC Dynamic Trend Core Strategy v45// The Dynamic Trend Core is a sophisticated, multi-layer trading strategy that provides both a quantitative //
// backtesting engine and a rich, intuitive visual interface. It is designed to identify high-probability //
// trend-following opportunities by requiring a confluence of conditions to be met before a signal is considered //
// valid. //
// //
// The system's philosophy is rooted in confirmation, seeking to filter out market noise by ensuring that trend, //
// momentum, market sentiment, and volume are all in alignment. //
// //
// --- CORE LOGIC COMPONENTS --- //
// 1. **Primary Trend Analysis (SAMA):** The foundation is a self-adjusting moving average (SAMA) that //
// determines the underlying market trend (Bullish, Bearish, or Consolidation). //
// //
// 2. **Confirmation & Momentum:** Signals are confirmed with a blend of the Natural Market Slope and a Cyclic //
// RSI to ensure momentum aligns with the primary trend. //
// //
// 3. **Advanced Filtering Layers:** A suite of optional filters allows for robust customization: //
// - **Volume & ADX:** Ensure sufficient market participation and trend strength. //
// - **Market Regime:** Uses total crypto market cap to gauge broad market health. //
// - **Multi-Timeframe (MTF):** Aligns signals with the dominant weekly trend. //
// - **BTC Cycle Analysis:** Uses Halving or Mayer Multiple models to position trades within historical //
// macro cycles. //
// //
// --- VISUAL INTERFACE --- //
// The strategy's real power comes from its on-chart visual feedback system, which provides full transparency. //
// ****Note: for this to be enabled recalculate 'on every tick' needs to be enabled in the properties settings. //
// 1. **Power Core Gauge:** Located at the bottom-center, this gauge is the heart of the system. It displays the //
// number of active filter conditions that have been met (e.g., 5/6). It "powers up" as more conditions align,//
// glowing brightly when a signal is fully confirmed and ready. //
// //
// 2. **Live Conditions Panel:** In the bottom-right corner, this panel acts as a detailed pre-flight checklist. //
// It shows the real-time status of every single filter, helping you understand exactly why a trade is (or //
// is not) being triggered. //
// //
// 3. **Energized Trendline:** The main SAMA trendline changes color and brightness based on the strength and //
// direction of the trend, providing immediate visual context. //
// //
// 4. **Halving cycle visualisation:** Visual guide to halving phases //
// //
// --- HOW TO USE --- //
// 1. **Select Operation Mode:** Use "Backtest Mode" to test settings and "Alerts-Only Mode" for live signals. //
// //
// 2. **Configure Strategy:** Start with the default filters. If a potential trade setup is missed, check the //
// **Live Conditions Panel** to see exactly which filter blocked the signal. Adjust the filters to suit your //
// specific asset and timeframe. //
// //
// 3. **Manage Risk:** Adjust the Risk & Exit settings to match your personal risk tolerance. //
Kaufman Trend Strength Signal█ Overview
Kaufman Trend Strength Signal is an advanced trend detection tool that decomposes price action into its underlying directional trend and localized oscillation using a vector-based Kalman Filter.
By integrating adaptive smoothing and dynamic weighting via a weighted moving average (WMA), this indicator provides real-time insight into both trend direction and trend strength — something standard moving averages often fail to capture.
The core model assumes that observed price consists of two components:
(1) a directional trend, and
(2) localized noise or oscillation.
Using a two-step Predict & Update cycle, the filter continuously refines its trend estimate as new market data becomes available.
█ How It Works
This indicator employs a Kalman Filter model that separates the trend from short-term fluctuations in a price series.
Predict & Update Cycle : With each new bar, the filter predicts the price state and updates that prediction using the latest observed price, producing a smooth but adaptive trend line.
Trend Strength Normalization : Internally, the oscillator component is normalized against recent values (N periods) to calculate a trend strength score between -100 and +100.
(Note: The oscillator is not plotted on the chart but is used for signal generation.)
Filtered MA Line : The trend component is plotted as a smooth Kalman Filter-based moving average (MA) line on the main chart.
Threshold Cross Signals : When the internal trend strength crosses a user-defined threshold (default: ±60), visual entry arrows are displayed to signal momentum shifts.
█ Key Features
Adaptive Trend Estimation : Real-time filtering that adjusts dynamically to market changes.
Visual Buy/Sell Signals : Entry arrows appear when the trend strength crosses above or below the configured threshold.
Built-in Range Filter : The MA line turns blue when trend strength is weak (|value| < 10), helping you filter out choppy, sideways conditions.
█ How to Use
Trend Detection :
• Green MA = bullish trend
• Red MA = bearish trend
• Blue MA = no trend / ranging market
Entry Signals :
• Green triangle = trend strength crossed above +Threshold → potential bullish entry
• Red triangle = trend strength crossed below -Threshold → potential bearish entry
█ Settings
Entry Threshold : Level at which the trend strength triggers entry signals (default: 60)
Process Noise 1 & 2 : Control the filter’s responsiveness to recent price action. Higher = more reactive; lower = smoother.
Measurement Noise : Sets how much the filter "trusts" price data. High = smoother MA, low = faster response but more noise.
Trend Lookback (N2) : Number of bars used to normalize trend strength. Lower = more sensitive; higher = more stable.
Trend Smoothness (R2) : WMA smoothing applied to the trend strength calculation.
█ Visual Guide
Green MA Line → Bullish trend
Red MA Line → Bearish trend
Blue MA Line → Sideways/range
Green Triangle → Entry signal (trend strengthening)
Red Triangle → Entry signal (trend weakening)
█ Best Practices
In high-volatility conditions, increase Measurement Noise to reduce false signals.
Combine with other indicators (e.g., RSI, MACD, EMA) for confirmation and filtering.
Adjust "Entry Threshold" and noise settings depending on your timeframe and trading style.
❗ Disclaimer
This script is provided for educational purposes only and should not be considered financial advice or a recommendation to buy/sell any asset.
Trading involves risk. Past performance does not guarantee future results.
Always perform your own analysis and use proper risk management when trading.
Kaufman Trend Strategy# ✅ Kaufman Trend Strategy – Full Description (Script Publishing Version)
**Kaufman Trend Strategy** is a dynamic trend-following strategy based on Kaufman Filter theory.
It detects real-time trend momentum, reduces noise, and aims to enhance entry accuracy while optimizing risk.
⚠️ _For educational and research purposes only. Past performance does not guarantee future results._
---
## 🎯 Strategy Objective
- Smooth price noise using Kaufman Filter smoothing
- Detect the strength and direction of trends with a normalized oscillator
- Manage profits using multi-stage take-profits and adaptive ATR stop-loss logic
---
## ✨ Key Features
- **Kaufman Filter Trend Detection**
Extracts directional signal using a state space model.
- **Multi-Stage Profit-Taking**
Automatically takes partial profits based on color changes and zero-cross events.
- **ATR-Based Volatility Stops**
Stops adjust based on swing highs/lows and current market volatility.
---
## 📊 Entry & Exit Logic
**Long Entry**
- `trend_strength ≥ 60`
- Green trend signal
- Price above the Kaufman average
**Short Entry**
- `trend_strength ≤ -60`
- Red trend signal
- Price below the Kaufman average
**Exit (Long/Short)**
- Blue trend color → TP1 (50%)
- Oscillator crosses 0 → TP2 (25%)
- Trend weakens → Final exit (25%)
- ATR + swing-based stop loss
---
## 💰 Risk Management
- Initial capital: `$3,000`
- Order size: `$100` per trade (realistic, low-risk sizing)
- Commission: `0.002%`
- Slippage: `2 ticks`
- Pyramiding: `1` max position
- Estimated risk/trade: `~0.1–0.5%` of equity
> ⚠️ _No trade risks more than 5% of equity. This strategy follows TradingView script publishing rules._
---
## ⚙️ Default Parameters
- **1st Take Profit**: 50%
- **2nd Take Profit**: 25%
- **Final Exit**: 25%
- **ATR Period**: 14
- **Swing Lookback**: 10
- **Entry Threshold**: ±60
- **Exit Threshold**: ±40
---
## 📅 Backtest Summary
- **Symbol**: USD/JPY
- **Timeframe**: 1H
- **Date Range**: Jan 3, 2022 – Jun 4, 2025
- **Trades**: 924
- **Win Rate**: 41.67%
- **Profit Factor**: 1.108
- **Net Profit**: +$1,659.29 (+54.56%)
- **Max Drawdown**: -$1,419.73 (-31.87%)
---
## ✅ Summary
This strategy uses Kaufman filtering to detect market direction with reduced lag and increased smoothness.
It’s built with visual clarity and strong trade management, making it practical for both beginners and advanced users.
---
## 📌 Disclaimer
This script is for educational and informational purposes only and should not be considered financial advice.
Use with proper risk controls and always test in a demo environment before live trading.
Trailing Monster StrategyTrailing Monster Strategy
This is an experimental trend-following strategy that incorporates a custom adaptive moving average (PKAMA), RSI-based momentum filtering, and dynamic trailing stop-loss logic. It is designed for educational and research purposes only, and may require further optimization or risk management considerations prior to live deployment.
Strategy Logic
The strategy attempts to participate in sustained price trends by combining:
- A Power Kaufman Adaptive Moving Average (PKAMA) for dynamic trend detection,
- RSI and Simple Moving Average (SMA) filters for market condition confirmation,
- A delayed trailing stop-loss to manage exits once a trade is in profit.
Entry Conditions
Long Entry:
- RSI exceeds the overbought threshold (default: 70),
- Price is trading above the 200-period SMA,
- PKAMA slope is positive (indicating upward momentum),
- A minimum number of bars have passed since the last entry.
Short Entry:
- RSI falls below the oversold threshold (default: 30),
- Price is trading below the 200-period SMA,
- PKAMA slope is negative (indicating downward momentum),
-A minimum number of bars have passed since the last entry.
Exit Conditions
- A trailing stop-loss is applied once the position has been open for a user-defined number of bars.
- The trailing distance is calculated as a fixed percentage of the average entry price.
Technical Notes
This script implements a custom version of the Power Kaufman Adaptive Moving Average (PKAMA), conceptually inspired by alexgrover’s public implementation on TradingView .
Unlike traditional moving averages, PKAMA dynamically adjusts its responsiveness based on recent market volatility, allowing it to better capture trend changes in fast-moving assets like altcoins.
Disclaimer
This strategy is provided for educational purposes only.
It is not financial advice, and no guarantee of profitability is implied.
Always conduct thorough backtesting and forward testing before using any strategy in a live environment.
Adjust inputs based on your individual risk tolerance, asset class, and trading style.
Feedback is encouraged. You are welcome to fork and modify this script to suit your own preferences and market approach.
Litecoin Trailing-Stop StrategyAltcoins Trailing-Stop Strategy
This strategy is based on a momentum breakout approach using PKAMA (Powered Kaufman Adaptive Moving Average) as a trend filter, and a delayed trailing stop mechanism to manage risk effectively.
It has been designed and fine-tuned Altcoins, which historically shows consistent volatility patterns and clean trend structures, especially on intraday timeframes like 15m and 30m.
Strategy Logic:
Entry Conditions:
Long when PKAMA indicates an upward move
Short when PKAMA detects a downward trend
Minimum spacing of 30 bars between trades to avoid overtrading
Trailing Stop:
Activated only after a customizable delay (delayBars)
User can set trailing stop % and delay independently
Helps avoid premature exits due to short-term volatility
Customizable Parameters:
This strategy uses a custom implementation of PKAMA (Powered Kaufman Adaptive Moving Average), inspired by the work of alexgrover
PKAMA is a volatility-aware moving average that adjusts dynamically to market conditions, making it ideal for altcoins where trend strength and direction change frequently.
This script is for educational and experimental purposes only. It is not financial advice. Please test thoroughly before using it in live conditions, and always adapt parameters to your specific asset and time frame.
Feedback is welcome! Feel free to clone and adapt it for your own trading style.
Fourier Transformed & Kalman Filtered EMA Crossover [Mattes]The Fourier Transformed & Kalman Filtered EMA Crossover (FTKF EMAC) is a trend-following indicator that leverages Fourier Transform approximation, Kalman Filtration, and two Exponential Moving Averages (EMAs) of different lengths to provide accurate and smooth market trend signals. By combining these three components, it captures the underlying market cycles, reduces noise, and produces actionable insights, making it suitable for detecting both emerging trends and confirming existing ones.
TECHNICALITIES:
>>> The Fourier Transform approximation is designed to identify dominant cyclical patterns in price action by focusing on key frequencies, while filtering out noise and less significant movements. It emphasizes the most meaningful price cycles, enabling the indicator to isolate important trends while ignoring minor fluctuations. This cyclical awareness adds an extra layer of depth to trend detection, allowing the EMAs to work with a cleaner and more reliable data set.
>>> The Kalman Filter adds dynamic noise reduction, adjusting its predictions of future price trends based on past and current data. As new price data comes in, the filter recalibrates itself to ensure that the price action remains smooth and devoid of erratic movements. This real-time adjustment is key to minimizing lag while avoiding false signals, which ensures that the EMAs react to more accurate and stable market data. The Kalman Filter’s ability to smooth price data without losing sensitivity to trend changes complements the Fourier approximation, ensuring a high level of precision in volatile and stable market environments.
>>> The EMA Crossover involves using two EMAs: a shorter EMA that reacts quickly to price movements and a longer EMA that responds more slowly. The shorter EMA is responsible for capturing immediate market shifts, detecting potential bullish or bearish trends. The longer EMA smooths out price fluctuations and provides trend confirmation, working with the shorter EMA to ensure the signals are reliable. When the shorter EMA crosses above the longer EMA, it indicates a bullish trend, likewise when it goes below the longer EMA, it signals a bearish trend. This setup provides a clear way to track market direction, with color-coded signals (green for bullish, red for bearish) for visual clarity. The flexibility of adjusting the EMA periods allows traders to fine-tune the indicator to their preferred timeframe and strategy, making it adaptable to different market conditions.
|-> A key technical aspect is that the first EMA should always be shorter than the second one. If the first EMA is longer than the second, the tool’s effectiveness is compromised because the faster EMA is designed to signal long conditions, while the longer one is made for signaling a bearish trend. Reversing their roles would lead to delayed or confused signals, reducing the indicator’s ability to detect trend shifts early and making it less efficient in volatile markets. This is the only key weakness of the indicator, failure to submit to this rule will result in confusion.
>>> These components work together like a clock to create a comprehensive and effective trend-following system. The Fourier approximation highlights key cyclical movements, the Kalman Filter refines these movements by removing noise, and the EMAs interpret the filtered data to generate actionable trend signals. Each component enhances the next, ensuring that the final output is both responsive and reliable, with minimal false signals or lag. creating an indicator using widespread concepts which haven't been combined before.
Summary
This indicator combines Fourier Transform approximation, Kalman Filtration, and two EMAs of different lengths to deliver accurate and timely trend-following signals. The Fourier approximation identifies dominant market cycles, while the Kalman Filter dynamically removes noise and refines the price data in real time. The two EMAs then use this filtered data to generate buy and sell signals based on their crossovers. The shorter EMA reacts quickly to price changes, while the longer EMA provides smoother trend confirmation. The components work in synergy to capture trends with minimal false signals or lag, ensuring traders can act promptly on market shifts. Customizable EMA periods make the tool adaptable to different market conditions, enhancing its versatility for various trading strategies.
To use the indicator, traders should adjust the EMA lengths based on their timeframe and strategy, ensuring that the shorter EMA remains shorter than the longer EMA to preserve the tool’s responsiveness. The color-coded signals offer visual clarity, making it easy to identify potential entry and exit points. This confluence of Fourier, Kalman, and EMA methodologies provides a smooth, highly effective trend-following tool that excels in both trending and ranging markets.
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
KAMA Cloud STIndicator:
Description:
The KAMA Cloud indicator is a sophisticated trading tool designed to provide traders with insights into market trends and their intensity. This indicator is built on the Kaufman Adaptive Moving Average (KAMA), which dynamically adjusts its sensitivity to filter out market noise and respond to significant price movements. The KAMA Cloud leverages multiple KAMAs to gauge trend direction and strength, offering a visual representation that is easy to interpret.
How It Works:
The KAMA Cloud uses twenty different KAMA calculations, each set to a distinct lookback period ranging from 5 to 100. These KAMAs are calculated using the average of the open, high, low, and close prices (OHLC4), ensuring a balanced view of price action. The relative positioning of these KAMAs helps determine the direction of the market trend and its momentum.
By measuring the cumulative relative distance between these KAMAs, the indicator effectively assesses the overall trend strength, akin to how the Average True Range (ATR) measures market volatility. This cumulative measure helps in identifying the trend’s robustness and potential sustainability.
The visualization component of the KAMA Cloud is particularly insightful. It plots a 'cloud' formed between the base KAMA (set at a 100-period lookback) and an adjusted KAMA that incorporates the cumulative relative distance scaled up. This cloud changes color based on the trend direction — green for upward trends and red for downward trends, providing a clear, visual representation of market conditions.
How the Strategy Works:
The KAMA Cloud ST strategy employs multiple KAMA calculations with varying lengths to capture the nuances of market trends. It measures the relative distances between these KAMAs to determine the trend's direction and strength, much like the original indicator. The strategy enhances decision-making by plotting a 'cloud' formed between the base KAMA (set to a 100-period lookback) and an adjusted KAMA that scales according to the cumulative relative distance of all KAMAs.
Key Components of the Strategy:
Multiple KAMA Layers: The strategy calculates KAMAs for periods ranging from 5 to 100 to analyze short to long-term market trends.
Dynamic Cloud: The cloud visually represents the trend’s strength and direction, updating in real-time as the market evolves.
Signal Generation: Trade signals are generated based on the orientation of the cloud relative to a smoothed version of the upper KAMA boundary. Long positions are initiated when the market trend is upward, and the current cloud value is above its smoothed average. Conversely, positions are closed when the trend reverses, indicated by the cloud falling below the smoothed average.
Suggested Usage:
Market: Stocks, not cryptocurrency
Timeframe: 1 Hour
Indicator:
KAMA CloudDescription:
The KAMA Cloud indicator is a sophisticated trading tool designed to provide traders with insights into market trends and their intensity. This indicator is built on the Kaufman Adaptive Moving Average (KAMA), which dynamically adjusts its sensitivity to filter out market noise and respond to significant price movements. The KAMA Cloud leverages multiple KAMAs to gauge trend direction and strength, offering a visual representation that is easy to interpret.
How It Works:
The KAMA Cloud uses twenty different KAMA calculations, each set to a distinct lookback period ranging from 5 to 100. These KAMAs are calculated using the average of the open, high, low, and close prices (OHLC4), ensuring a balanced view of price action. The relative positioning of these KAMAs helps determine the direction of the market trend and its momentum.
By measuring the cumulative relative distance between these KAMAs, the indicator effectively assesses the overall trend strength, akin to how the Average True Range (ATR) measures market volatility. This cumulative measure helps in identifying the trend’s robustness and potential sustainability.
The visualization component of the KAMA Cloud is particularly insightful. It plots a 'cloud' formed between the base KAMA (set at a 100-period lookback) and an adjusted KAMA that incorporates the cumulative relative distance scaled up. This cloud changes color based on the trend direction — green for upward trends and red for downward trends, providing a clear, visual representation of market conditions.
Benefits:
Dynamic Sensitivity: By adapting to the market's volatility, KAMA provides more reliable signals than traditional moving averages.
Trend Clarity: The color-coded cloud visually enhances the perception of the trend’s direction and strength, making it easier for traders to decide on their trading strategy.
Versatility: Suitable for various asset classes, including stocks, forex, commodities, and cryptocurrencies, across different timeframes.
Decision Support: Helps traders understand not just the direction but the strength of trends, aiding in more informed decision-making regarding entries, exits, and risk management.
Usage:
The KAMA Cloud is ideal for traders who need a robust trend-following tool that adjusts according to market dynamics. It can be used as a standalone indicator or in conjunction with other technical analysis tools to enhance trading strategies. Look for the cloud’s color shifts as potential signals for trend reversals or continuations, and consider the cloud’s thickness as an indication of trend strength.
Whether you are a day trader, swing trader, or long-term investor, the KAMA Cloud offers a unique approach to understanding market trends, helping you navigate the complexities of various market conditions with confidence.
No Lag SupertrendNo Lag Supertrend indicator improves upon the original supertrend by incorporating calculation methods that enhance responsiveness and accuracy. Traditional supertrend indicators often suffer from lag, which can delay signals and affect trading decisions. No Lag Supertrend addresses this issue through the use of KAMA (Kaufman’s Adaptive Moving Average) and Hull ATR (Average True Range) calculations.
Goals of No Lag Supertrend:
- Lag reduction: one of the main issues with traditional supertrend indicators is their lag, which can result in delayed entry and exit signals. By integrating KAMA and Hull ATR, the no lag supertrend minimizes this delay, providing more timely signals.
- Market Noise Filtering: The combined use of KAMA and Hull ATR effectively filters out market noise, ensuring that signals are based on significant price movements rather than minor fluctuations.
- Consistency Across Different Market Conditions: The adaptive nature of KAMA and the smooth responsiveness of Hull ATR ensure that the No Lag Supertrend performs consistently across various market conditions, from trending to volatile markets.
Credits: This code is based on the TradingView supertrend but improved the ATR calculations.
Kaufman Adaptive Moving Average (KAMA) Strategy [TradeDots]"The Kaufman Adaptive Moving Average (KAMA) Strategy" is a trend-following system that leverages the adaptive qualities of the Kaufman Adaptive Moving Average (KAMA). This strategy is distinguished by its ability to adjust dynamically to market volatility, enhancing trading accuracy by minimizing the effects of false and delayed signals often associated with the Simple Moving Average (SMA).
HOW IT WORKS
This strategy is centered around use of the Kaufman Adaptive Moving Average (KAMA) indicator, which refines the principles of the Exponential Moving Average (EMA) with a superior smoothing technique.
KAMA distinguishes itself by its responsiveness to changes in market prices through an "Efficiency Ratio (ER)." This ratio is computed by dividing the recent absolute net price change by the cumulative sum of the absolute price changes over a specified period. The resulting ER value ranges between 0 and 1, where 0 indicates high market noise and 1 reflects stronger market momentum.
Using ER, we could get the smoothing constant (SC) for the moving average derived using the following formula:
fastest = 2/(fastma_length + 1)
slowest = 2/(slowma_length + 1)
SC = math.pow((ER * (fastest-slowest) + slowest), 2)
The KAMA line is then calculated by applying the SC to the difference between the current price and the previous KAMA.
APPLICATION
For entering long positions, this strategy initializes when there is a sequence of 10 consecutive rising KAMA lines. Conversely, a sequence of 10 consecutive falling KAMA lines triggers sell orders for long positions. The same logic applies inversely for short positions.
DEFAULT SETUP
Commission: 0.01%
Initial Capital: $10,000
Equity per Trade: 80%
Users are advised to adjust and personalize this trading strategy to better match their individual trading preferences and style.
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.
Adaptive Fisher [BackQuant]Adaptive Fisher
What is it at its core:
Custom Kaufman Adaptive Moving Average Smoothed Price Data, Fisher Transformation.
Why did we choose to make an Adaptive Fisher ?
The Adaptive Fisher Transformation Indicator is an advanced technical tool designed to signal potential turning points in market prices by transforming asset price data into a nearly Gaussian normal distribution. This transformation, initially conceptualized by John F. Ehlers, aims to make extreme price behavior, which could indicate potential market reversals, more identifiable. Unlike the standard distribution of asset prices, the Gaussian normal distribution provides a clearer framework for identifying price extremes and trends.
With that being considered there are key things to take into consideration:
As the transformation seeks to normalize price data, it's crucial to remember that asset prices inherently do not follow a normal distribution. Thus, traders should use this tool in conjunction with other analyses to confirm potential trading signals. The effectiveness can vary across different assets and market conditions, underscoring the importance of customization and adaptation to specific trading strategies. As the same for all tools, all must be backtested. Past performance is not a guarantee for future results.
Now for the Key Features
Normalization of Prices: The Adaptive Fisher Transformation normalizes price data, enhancing the visibility of turning points. This normalization is critical for identifying moments when the price movement is statistically significant, thereby aiding in decision-making.
Adaptivity through Kaufman's Adaptive Moving Average (KAMA): Unlike traditional indicators, this version employs KAMA to dynamically adjust to market volatility. By doing so, it smoothens the price data more effectively, providing signals that are more responsive to current market conditions.
Divergence Detection: It includes the capability to detect divergences between the indicator and price movement, a powerful signal of potential trend reversals. Traders can specify the length over which divergences are calculated, allowing for customization based on their trading strategy.
Visual Enhancements: The indicator features color gradients to delineate strength levels and extreme values, improving readability and the quick assessment of market conditions.
Customizable Smoothing Mechanism: To accommodate different assets and timeframes, the indicator includes an option to select from various moving averages for smoothing, with an Exponential Moving Average (EMA) recommended for its effectiveness.
Application and Interpretation:
Traders can utilise this tool to identify potential reversal points by looking for extreme values in the transformed price data. Changes in the direction of the indicator can also signal shifts in market trends.
The inclusion of a normalized Relative Strength Index (RSI) provides additional confluence, aiding traders in recognizing overbought and oversold conditions through color-coded background hues in the chart.
Alert conditions are programmed for various scenarios, including trend shifts, Fisher Transform crossings over the midline, and both regular and hidden divergences, enabling traders to react promptly to potential market movements.
Empirical Soundness
Mathematical Foundation in Gaussian Distribution: At its core, the Fisher Transformation's application to financial markets is based on transforming prices to conform more closely to a Gaussian normal distribution, which is a fundamental concept in statistics. This transformation aims to make the identification of price extremes more reliable. Empirical studies have shown that while raw financial data may not follow a normal distribution, the application of transformations can facilitate the identification of critical turning points in market data (Ehlers, John F., "Cybernetic Analysis for Stocks and Futures", Wiley & Sons, 2004).
Adaptivity through KAMA: The use of Kaufman's Adaptive Moving Average introduces a dynamic element to the indicator, allowing it to adjust to market volatility automatically. This adaptivity is particularly relevant in today's financial markets, where volatility patterns can shift rapidly due to economic news, geopolitical events, and changes in market sentiment. The empirical strength of KAMA lies in its foundational logic, designed to account for market noise and smoothing price data more effectively than traditional moving averages (Kaufman, Perry J., "Trading Systems and Methods", Wiley & Sons, 2013).
Innovative Divergence Detection Mechanism: Divergence detection adds an empirical layer to the Adaptive Fisher Transformation by highlighting discrepancies between price action and the indicator's performance. This feature is grounded in the principle that divergences can often precede reversals, providing early warning signs of potential shifts in market direction. The ability to customize the calculation length for divergences enables the indicator to be fine-tuned to the characteristics of specific assets or market conditions, enhancing its practical application.
User Inputs Explained:
Calculation Source (price): This input determines the base price used for calculations, typically the closing price (close). Traders can adjust this to open, high, low, or another average, tailoring the indicator to focus on specific aspects of price action.
Fisher Lookback (ftPeriod): Defines the period over which the Fisher Transform is calculated. A shorter period makes the indicator more sensitive to price movements, while a longer period smoothens the output, reducing sensitivity.
Make Fisher Adaptive (adapt): A boolean input that enables the adaptation feature of the Fisher Transform using KAMA. When set to true, it dynamically adjusts the Fisher Transform according to market volatility, enhancing its responsiveness to recent price changes.
Adaptive Period (length), Fast Length (fast), Slow Length (slow): These inputs configure the KAMA calculation, affecting its sensitivity to price movements. The length determines the lookback period for volatility calculation, while fast and slow set the speed of adjustment to market conditions.
Smooth Fisher (smooth): Allows for additional smoothing of the Fisher Transform output to reduce noise. This is particularly useful in highly volatile markets or when the indicator is too reactive to price changes.
Smoothing Type (modeSwitch) and Smooth Period (smoothlen): Determine the method and period for smoothing. Options include various moving averages (EMA, SMA, etc.), providing flexibility in how the smoothing is applied.
Show Fisher, Show Fisher Moving Average, Moving Average Period (malen): These inputs control the visibility of the Fisher Transform and its moving average on the chart, as well as the period of the moving average. This helps in identifying trends and the direction of the market.
Show Detected Trend Shifts (trendshift): Enables the highlighting of moments when the indicator suggests a potential shift in market trend, providing early signals for traders.
Show Fisher Strength levels (showextreme): Displays predefined levels indicating extreme values of the Fisher Transform, which could suggest overbought or oversold conditions.
Show Confluence RSI (showrsi), RSI Period (rsiPeriod): These inputs add a normalized Relative Strength Index to the chart for additional analysis, offering a secondary measure of market conditions.
Show Overbought and Oversold Signals: When enabled, the background color changes to highlight overbought or oversold conditions based on the RSI, aiding in visual identification of potential trading opportunities.
Use Case of Midline Crossover Fisher:
Midline Crossover Fisher: The Fisher Transform's midline crossover is a critical signal for traders. A crossover above the midline indicates a bullish market sentiment, suggesting that it might be a good time to consider entering a long position. Conversely, a crossover below the midline suggests bearish sentiment, potentially signaling an opportunity to go short. This is based on the principle that the Fisher Transform makes turning points more evident, and crossing the midline reflects a change in momentum.
Overbought and Oversold Hues:
RSI Overbought and Oversold Background Color: The background color feature for RSI OB (overbought) and OS (oversold) conditions enhances visual cues for market extremes. When the RSI exceeds upper thresholds (Above 70), indicating overbought conditions, the background will turn to warn traders of potential price reversals. Similarly, when the RSI falls below lower thresholds (Below 30), suggesting oversold conditions, green can highlight potential opportunities for buying.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
This is using the Midline Crossover:
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
TASC 2024.01 Gap Momentum System█ OVERVIEW
TASC's January 2024 edition of Traders' Tips features an article titled “Gap Momentum” by Perry J. Kaufman. The article discusses how a trader might create a momentum strategy based on opening gap data. This script implements the Gap Momentum system presented therein.
█ CONCEPTS
In the article, Perry J. Kaufman introduces Gap Momentum as a cumulative series constructed in the same way as On-Balance Volume (OBV) , but using gap openings (today’s open minus yesterday’s close).
To smoothen the resulting time series (i.e., obtain the " signal line "), the author applies a simple moving average . Subsequently, he proposes the following two trading rules for a long-only trading system:
• Enter a long position when the signal line is moving higher.
• Exit when the signal line is moving lower.
█ CALCULATIONS
The calculation of Gap Momentum involves the following steps:
1. Calculate the ratio of the sum of positive gaps over the past N days to the sum of negative gaps (absolute values) over the same time period.
2. Add the resulting gap ratio to the cumulative time series. This time series is the Gap Momentum.
3. Keep moving forward, as in an N-day moving average.
Kaufman Efficiency Ratio (KER)The Kaufman Efficiency Ratio (also known as the Efficiency Ratio or ER) is a technical indicator used in technical analysis to measure the efficiency of a financial instrument's price movement. It was developed by Perry J. Kaufman and is designed to help traders and analysts identify the trendiness or choppiness of a market.
The Kaufman Efficiency Ratio is calculated using the following formula:
ER = (Change in Price over N periods) / (Sum of the absolute price changes over N periods)
Here's how the formula works:
"Change in Price over N periods" is the net price change over a specified number of periods (usually days or bars). It's calculated by subtracting the closing price of N periods ago from the current closing price.
"Sum of the absolute price changes over N periods" is the sum of the absolute values of price changes (i.e., ignoring the direction) over the same N periods.
The resulting Efficiency Ratio (ER) value will fall within the range of 0 to 1, with 1 indicating a perfectly trending market and 0 indicating a perfectly choppy or range-bound market. In other words, the closer the ER is to 1, the stronger and more efficient the trend is perceived to be.
Volume-Weighted Kaufman's Adaptive Moving AverageThe Volume-Weighted Kaufman's Adaptive Moving Average (VW-KAMA) is a technical indicator that combines the Volume-Weighted Moving Average (VWMA) and the Kaufman's Adaptive Moving Average (KAMA) to create a more responsive and adaptable moving average.
Advantages:
Volume-Weighted: It takes into account the volume of trades, giving more weight to periods with higher trading volume, which can help filter out periods of low activity.
Adaptive: The indicator adjusts its smoothing constant based on market conditions, becoming more sensitive in trending markets and less sensitive in choppy or sideways markets.
Versatility: VW-KAMA can be used for various purposes, including trend identification, trend following, and determining potential reversal points and act as dynamic support and resistance level.
twisted SMA strategy [4h] Hello
I would like to introduce a very simple strategy that uses a combination of 3 simple moving averages ( SMA 4 , SMA 9 , SMA 18 )
this is a classic combination showing the most probable trend directions
Crosses were marked on the basis of the color of the candles (bulish cross - blue / bearish cross - maroon)
ma 100 was used to determine the main trend, which is one of the most popular 4-hour candles
We define main trend while price crosses SMA100 ( for bullish trend I use green candle color )
The long position strategy was created in combination of 3 moving averages with Kaufman's adaptive moving average by alexgrover
The strategy is very accurate and is easy to use indicators
the strategy uses only Buy (Long) signals in a combination of crossovers of the SMA 4, SMA 9, SMA 18 and the Kaufman Adaptive Moving Average.
As a signal to close a long position, only the opposite signal of the intersection of 3 different moving averages is used
the current strategy is recommended for higher time zones (4h +) due to the strength of the closing candles, which translates into signal strength
works fascinatingly well for long-term bullish market assets (for example 4h Apple, Tesla charts)
Enjoy and trade safe ;)
Seer's HutThis is a strategy based on Exponential Moving Averages or Volume Weighted Moving Averages against Adaptive fib resistance / support level and profit percentage which can be definetly defined by user and targeting small profits(profits will be raised by leverages).
In this strategy, there are predefined values which are collected one by one with statistical background and backtests. This gives an advantage to see which ratios are working better for each symbol. Also this statistics are re-evaluated monthly and if there is a need they are going to be changed with the help of libraries. Also IT IS RECOMMENDED TO USE IN DAILY INTERVAL GRAPHICS!!!!
When we deep dive to strategy, it is based on profit percentages. it is similar to the MOST system. MOST only changes the way with default value of %2. But this hardcoded strategy is not working well with each Symbol.
So this is the point where DC and ADR Statistics are involved.
For Ex. while BTC is suits well with %2, it does not do wonders for RSR or RUNE which is 4-5% for each.
There is 3 options for setting the statistical usage of this indicator.
1. Auto calculated based on 1000 days of ADR and DC
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2. Using Library where statistical values are stored.
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3. User-defined values used. Yeah you read it right. Fully on-demand changes are supported. Which gives freedom to users for setup their own Adaptive FIB and Profit Percentages.
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Based on this 3 options, TP and SL points are calculated on bar closures. Strategy Orders are also shown / raised with the closures.
Ok, system calculates these values but how to read / use them. what is this strategy based on ?
This strategy is mostly looking for minimizing the LOSS in case of any stop. So because of this, in each TP, system gives order signal to close half of the remaining open position.
There are 7 type of orders
OL : Open Long (Close Short and Open Long if in position)
CL 50 : Close Long - %50 of Open Position
CL 100 : Close Long - Close all position
OS : Open Short (Close Long and Open Short if in position)
CL 50 : Close Short - %50 of Open Position
CL 100 : Close Short - Close all position
TP5 : Highest TP reached. Close all position.
Script checks cross of EMA / VWMA and adFib to decide open a position. In reversal / crosses, adFib line had been set to defined Fib. Percentage (FP) level.
For creating the TP points, Profit Percentage (PP) parameter had been used which I briefly introduce at the beginning with the options.
One important topic about this strategy, it is not stacking / pyramiding the positions. Which means, it always calculate one way position. For example we are in the long position after OL signal.
We reached TP values and take profits. Later on due to FP crossing EMA, OS order signal given. This means you have to close all long position and open short position.
But beware. These calculated points are based on given values or calculated regarding to average ADR / DC ratings. For supporting strategy, several methods also had been included in the options.
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These are:
1. MA plotting (Optional 4 EMA, 1WMA) - checking for Golden and Death Cross
2. Bollinger Bands (Length 25 and Multiplier 2.5 set as default. Used in correlation with TEMA)
3. Kama 2 / Kama 5 - Crossing speaks of Trend way
4. TEMA (TEMA 50, VWMA 25 calculations and plotting. Used for TEMA 50 / VWMA 25 / SMA 25 cross checks for weakening or strengthening trend analysis)
5. ATR plotting
6. Chandelier Exit plotting (Widely used for calculating Stop levels in market)
7. PSAR (Widely used for indicating trend reversal)
Also for the ease of use, if the users does not want to plot any values on the graph and just want to see the values there is couple of tables also included.
1. EMA info
2. KAMA info
3. Order info
4. TP/SL info
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Some important notes:
1. To minimize the stop just after the order opening candle in volatile grounds, system prevents to raise new order signals if there is a signal already raised in last 4 candle.
2. if system reach and give close order in one of the TP points (For Ex TP1.), then index goes down and goes up again same TP (above TP1 in scenario) after 4 candle, system gives a close order signal again in the same TP.
3. There is a Profit Factor value had been shown at Order Info table. This information shows how profitable is the setup regarding to given FP and PP values.
In general market conditions, A Profit Factor above 1.50 is considered good enough and above 2.0 it is considered ideal. A strategy with profit factor less than 1.20 suggests too bigger a risk taken for making money.
In some cases automatic ADR and DC calculations are not good enough. so if you want to find a good Profit Factor value, you can change the system automatic calculation to manual value entering and you can see the results directly with in this field.
kama
█ Description
An adaptive indicator could be defined as market conditions following indicator, in summary, the parameter of the indicator would be adjusted to fit its optimum value to the current price action. KAMA, Kaufman's Adaptive Moving Average, an adaptive trendline indicator developed by Perry J. Kaufman, with the notion of using the fastest trend possible based on the smallest calculation period for the existing market conditions, by applying an exponential smoothing formula to vary the speed of the trend (changing smoothing constant each period), as cited from Trading Systems and Methods p.g. 780 (Perry J. Kaufman). In this indicator, the proposed notion is on the Efficiency Ratio within the computation of KAMA, which will use a Dominant Cycle instead, an adaptive filter developed by John F. Ehlers, on determining the n periods, aiming to achieve an optimum lookback period, with respect to the original Efficiency Ratio calculation period of less than 14, and 8 to 10 is preferable.
█ Kaufman's Adaptive Moving Average
kama_ = kama + smoothing_constant * (price - kama )
where:
price = current price (source)
smoothing_constant = (efficiency_ratio * (fastest - slowest) + slowest)^2
fastest = 2/(fastest length + 1)
slowest = 2/(slowest length + 1)
efficiency_ratio = price - price /sum(abs(src - src , int(dominant_cycle))
█ Feature
The indicator will have a specified default parameter of: length = 14; fast_length = 2; slow_length = 30; hp_period = 48; source = ohlc4
KAMA trendline i.e. output value if price above the trendline and trendline indicates with green color, consider to buy/long position
while, if the price is below the trendline and the trendline indicates red color, consider to sell/short position
Hysteresis Band
Bar Color
other example
