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已更新 Adaptive Cycle & Trend Strategy

The Adaptive Cycle & Trend Strategy is a fully rules-based trading strategy designed to adapt dynamically to changing market conditions.
Instead of relying on a single static exit model, the strategy continuously evaluates market volatility and trend structure to determine the most appropriate exit behavior for the current regime. This allows it to respond differently in trending environments than in high-volatility or corrective phases.
The strategy is optimized for the 1W timeframe and focuses on capturing meaningful market cycles rather than short-term noise. Entries and exits are generated algorithmically and remain consistent across assets, making the system applicable to both low-volatility markets (such as Bitcoin) and higher-volatility instruments.
Key characteristics:
Adaptive exit logic based on volatility regimes
Cycle- and trend-aware trade management
Fully rule-based, no discretionary inputs
Designed for higher timeframes (1W, 1M) and long-term market structure
No indicators required for interpretation
This strategy is intended for systematic analysis and backtesting purposes only. It does not constitute financial or investment advice.
Instead of relying on a single static exit model, the strategy continuously evaluates market volatility and trend structure to determine the most appropriate exit behavior for the current regime. This allows it to respond differently in trending environments than in high-volatility or corrective phases.
The strategy is optimized for the 1W timeframe and focuses on capturing meaningful market cycles rather than short-term noise. Entries and exits are generated algorithmically and remain consistent across assets, making the system applicable to both low-volatility markets (such as Bitcoin) and higher-volatility instruments.
Key characteristics:
Adaptive exit logic based on volatility regimes
Cycle- and trend-aware trade management
Fully rule-based, no discretionary inputs
Designed for higher timeframes (1W, 1M) and long-term market structure
No indicators required for interpretation
This strategy is intended for systematic analysis and backtesting purposes only. It does not constitute financial or investment advice.
版本注释
The Adaptive Cycle & Trend Strategy is a regime-based, multi-asset analytical framework designed for long-term market evaluation on the weekly timeframe (1W).The strategy classifies market environments exclusively based on price behavior and volatility, without incorporating fundamental data or external research inputs. Its primary objective is to identify market cycles, structural transitions, and volatility regimes across different asset classes.
Core Concept:
Markets behave differently depending on volatility and structural conditions.
This strategy dynamically adapts its internal logic to changing regimes rather than applying static assumptions across all market phases.
Key Characteristics
Regime-based market classification
Volatility-driven risk assessment
Price-action–only methodology
Asset-agnostic framework
Designed for long-term cycle and trend analysis
Risk Framework:
The displayed risk level reflects market risk derived from volatility, not the quality, valuation, or fundamentals of the underlying asset.
It is intended to support contextual market assessment and risk awareness.
Market Coverage:
Cryptocurrencies
Stocks
Indices
Commodities
Timeframe:
Optimized for weekly charts (1W)
Disclaimer:
This script is provided for analytical and educational purposes only and does not constitute financial advice. Past market behavior does not guarantee future results.
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作者的说明
Get access to our strategies at http://www.monarchstrategies.at
免责声明
这些信息和出版物并非旨在提供,也不构成TradingView提供或认可的任何形式的财务、投资、交易或其他类型的建议或推荐。请阅读使用条款了解更多信息。
仅限邀请脚本
只有作者授权的用户才能访问此脚本。您需要申请并获得使用许可。通常情况下,付款后即可获得许可。更多详情,请按照下方作者的说明操作,或直接联系Monarch_Strategies。
TradingView不建议您付费购买或使用任何脚本,除非您完全信任其作者并了解其工作原理。您也可以在我们的社区脚本找到免费的开源替代方案。
作者的说明
Get access to our strategies at http://www.monarchstrategies.at
免责声明
这些信息和出版物并非旨在提供,也不构成TradingView提供或认可的任何形式的财务、投资、交易或其他类型的建议或推荐。请阅读使用条款了解更多信息。