Research Paper on Algorithmic Breakout Detection Via Volume Spike Analysis in Options Trading
Abstract
Breakout trading strategies have long been recognized as fundamental approaches in technical analysis, yet traditional implementations often suffer from imprecise timing mechanisms and inadequate signal validation protocols. This paper presents a novel volume-based breakout detection algorithm specifically engineered for execution within the Think or swim trading platform environment. The algorithm employs a sophisticated detection mechanism that identifies abnormal volume patterns as leading indicators of significant price movements. The research methodology incorporates comprehensive real-world validation using authenticated brokerage data obtained from Charles Schwab's trading records. Performance evaluation encompasses both quantitative metrics and qualitative analysis of trade execution quality. The empirical findings demonstrate exceptional accuracy in breakout prediction, with consistently high returns across diverse market conditions and security types. The algorithm's core innovation lies in its ability to filter false breakout signals through volume confirmation, thereby significantly improving the reliability of traditional price-based breakout detection methods. This approach addresses a critical gap in existing literature where volume anomalies, despite their strong predictive capacity, remain underutilized in systematic trading applications. The research findings confirm the algorithm's value for both academic research and practical trading applications, establishing a new benchmark for volume-based breakout detection methodologies.
Citations
IRE Journals:
Rahul Durgia
"Research Paper on Algorithmic Breakout Detection Via Volume Spike Analysis in Options Trading" Iconic Research And Engineering Journals Volume 9 Issue 2 2025 Page 1169-1174
IEEE:
Rahul Durgia
"Research Paper on Algorithmic Breakout Detection Via Volume Spike Analysis in Options Trading" Iconic Research And Engineering Journals, 9(2)
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