Published on: April 2026
DEVELOP COMPREHENSIVE MARKET ANALYSIS FOR STRATEGIC DECISION MAKING
Manav Paul
Dr. Nirmal Kaur
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Abstract
As Researched that Traditional technical analysis has long relied on lagging oscillators such as the Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD). However, recent empirical studies demonstrate that these linear models often yield a high rate of false signals due to inherent temporal lag and an inability to account for institutional liquidity shifts. This research introduces the MANAVPAUL70 Logic Engine, a novel algorithmic framework implemented via Pine Script that transitions from reactive indicators to structural market heuristics. By codifying non-linear concepts such as Order Blocks (OB), Liquidity Sweeps, and Market Structure Shifts (MSS), the proposed model identifies high-probability entry zones based on institutional order flow rather than price derivatives. Experimental backtesting reveals that while legacy indicators struggle with a 1:2 risk-reward ratio and frequent "whipsaw" losses, the MANAVPAUL70 algorithm achieves superior precision with a targeted 1:12 risk-reward profile. The findings suggest that structural feature extraction significantly enhances signal-to-noise ratios (SNR) in volatile Forex environments, offering a robust engineering solution for automated high-frequency trading systems.
How to Cite this Paper
Paul, M. (2026). Develop Comprehensive Market Analysis for Strategic Decision Making. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.862
Paul, Manav. "Develop Comprehensive Market Analysis for Strategic Decision Making." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.862.
Paul, Manav. "Develop Comprehensive Market Analysis for Strategic Decision Making." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.862.
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References
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- Housel, (2020). The Psychology of Money: Timeless lessons on wealth, greed, and happiness. Harriman House. (Source for the behavioral finance principles and the necessity of the "psychological buffer" in trading systems.)
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- Smart Money Concepts (SMC) (2026). Structural Feature Extraction: Order Blocks and Market Structure Shifts in Algorithmic Environments. [Internal Technical Manual]. (Technical definitions for the non-linear heuristics: OB, Sweep, and MSS.)
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- •Published on: Apr 30 2026
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