Momentum Fusion X Strategy (with data filter) Momentum Fusion X Strategy — Conceptual Overview
Momentum Fusion X Strategy is a selectively reactive market-participation system designed to engage only when price behavior exhibits structured directional clarity across multiple internal evaluation layers.
Rather than responding to isolated indicators or short-term noise, the strategy operates on a confirmation-based framework where trades are activated only when the broader internal state of the market demonstrates sufficient directional agreement.
This approach intentionally prioritizes quality of participation over frequency, allowing the system to remain inactive during uncertain or low-information phases.
The strategy has demonstrated its strongest performance characteristics on MIDCAPNIFTY, while remaining structurally adaptable to other instruments and market environments.
Structural Philosophy
The internal architecture is built around a multi-layered decision model:
Directional Consensus Framework
Trades are considered only when multiple independent internal components align in the same directional bias. No single condition is capable of triggering a trade independently.
Momentum Confirmation Logic
Price movement is evaluated in a manner that emphasizes continuation strength rather than short-lived reactions. This helps reduce participation during false breakouts or choppy market behavior.
Noise Suppression
The system actively filters out ambiguous conditions, remaining flat during periods where directional intent lacks clarity or conviction.
This structural discipline ensures consistency and prevents over-trading, particularly on lower timeframes.
Trade Behaviour & Execution
Trade Initiation
Positions are initiated only when internal directional alignment reaches a predefined quality threshold. Partial or conflicting conditions are intentionally ignored.
Trade Exit
Positions are closed dynamically when internal momentum deteriorates or when directional alignment weakens, helping limit exposure during transitions or reversals.
Market Inactivity
The strategy may display selective trade participation during backtests. This behavior is intentional and reflects strict filtering logic rather than missed opportunities.
Backtesting & Configuration Notes
Data Filter:
The strategy includes a data filter input, allowing controlled backtesting from a specific start date.
Backtest Start Date:
Historical evaluation has been conducted starting from 5 August 2025.
Initial Capital:
Backtests are configured using an initial capital of 10,000.
Primary Timeframe:
The strategy is intended to be evaluated and used on the 15-minute timeframe for optimal structural behavior.
Automation & Execution Support
The script includes a built-in Dhan webhook alert system, enabling optional automation of execution workflows.
Alerts are synchronized directly with the strategy’s internal execution logic to ensure alignment between backtest results and live alert behavior.
Commission and slippage are incorporated into the strategy configuration to provide a more realistic simulation of execution conditions.
Commission: 0.01%
Slippage: 2 points
Intended Usage Context
Instrument Focus: MIDCAPNIFTY (best observed performance)
Trading Style: Intraday, momentum-based participation
Trade Frequency: Selective by design
Timeframe: 15-Minute
Users are encouraged to apply independent capital allocation, position sizing, and risk controls appropriate to their individual trading plan.
Intellectual Property Notice
The internal construction, scoring logic, alignment thresholds, and execution conditions are intentionally abstracted.
This description explains the conceptual philosophy without exposing implementation details, preserving the originality and intellectual integrity of the strategy.
Direct replication of the internal logic is neither implied nor supported by this publication.
Disclaimer
This strategy is provided strictly for educational, research, and analytical purposes.
Market behavior evolves over time, and historical performance does not guarantee future results.
Users are responsible for forward testing, execution decisions, and risk management when applying this strategy in live market conditions.
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