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@article{203947,
        author = {Rohan Vijay Gaikwad and Amit Lokhande and Samadhan Ramesh Jadhav and Kiran Pawar and Omkar  Raut and sahil chavan},
        title = {FinanciAi A Hybrid Retrieval Augmented Generation and Market Micro-Simulation Framework for High Frequency Trading Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {3815-3818},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203947},
        abstract = {Standard analytical models are unable to explain the non-linear market dynamics produced by High-Frequency Trading (HFT), which operates in sub-millisecond domains. The most advanced anomaly detectors currently in use, such as Transformers, rely on deep learning and achieve high F1-scores, yet they function as opaque "black boxes" that are unable to reason causally. On the other hand, when used with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) offers explainability but, due to its inability to retrieve past logs for unusual situations, fundamentally fails during fresh, out-of-distribution market events (such as localised flash crashes). To include a discrete-event market micro-simulator directly into the RAG pipeline, we present RAG-MicroSim, a deterministic hybrid architecture. This approach synthesises mathematically constrained limit order book (LOB) states on demand, avoiding static-corpus restrictions. 
RAG-MicroSim produces counterfactual "what-if" evidence using the Hawkes Process for stochastic order flow and Order Book Imbalance (OBI) as a rigorous mathematical trigger. The algorithmic depletion of liquidity is successfully reconstructed by the system when tested against the empirical baseline of the 2010 Flash Crash. With an F1-score of 0.94 in anomaly detection and complete causal interpretability, statistical benchmarking demonstrates how RAG-MicroSim unites semantic AI and quantitative physics.},
        keywords = {Hawkes Process, Order Book Imbalance, Flash Crash, Anomaly Detection, Limit Order Book, High-Frequency Trading, and Retrieval-Augmented Generation},
        month = {June},
        }