# Algorithmic Trading on Bitcoin with Market Microstructure Resilience Testing

## Thesis Overview
This repository contains the source code, data, and manuscript for the Bachelor of Science in Computer Science thesis titled **"Algorithmic Trading on Bitcoin with Market Microstructure Resilience Testing."**

The research investigates the limitations of static mean-reversion strategies in non-stationary cryptocurrency markets and proposes an adaptive architecture using Hidden Markov Models (HMM) to mitigate catastrophic risk.

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## Abstract
This study attempted to develop a profitable mean-reversion strategy for Bitcoin using Volume-Enhanced Bollinger Bands (VEBB). The research hypothesized that volume anomalies (Z-Score > 2.0) could filter false signals in the non-stationary 2025 market. While the volume signal showed significant promise during the 2024 calibration period (improving Profit Factor from 0.90 to 1.79), the strategy failed to maintain profitability during out-of-sample validation (-8.65% return). 

An adaptive HMM architecture was introduced to mitigate these losses, successfully reducing maximum drawdown usage by 98.7% but failing to achieve positive returns (-1.30%). These results suggest that while volume anomalies capture meaningful microstructure events, these signals are not consistently tradeable using static execution parameters in high-volatility regimes. The primary contribution of this research is the empirical demonstration of the failure of static parameters to generalize out-of-sample, limiting the scope of the study to variance reduction rather than wealth generation.

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## System Architecture

The project evolved through two distinct phases:

### Phase 1: Static VEBB (Baseline)
- **Logic:** Traditional Mean-Reversion using Bollinger Bands + Volume Z-Score + On-Balance Volume (OBV).
- **Implementation:** Pine Script v6 (TradingView).
- **Performance:** Suffered -20.4% capital loss during the 2025 liquidity crisis due to chronic overtrading in trending markets.

### Phase 2: Adaptive HMM VEBB (Proposed Solution)
- **Logic:** Unsupervised regime classification using Gaussian Hidden Markov Models.
- **Features:** Garman-Klass Volatility, Hurst Exponent, Volume Flow.
- **Mechanism:** A "Circuit Breaker" that actively disables trading during detected "Crisis Regimes" (State 0).
- **Performance:** Achieved -1.3% drawdown (Survival) vs -20.4% baseline loss. Validated that variance reduction is the primary benefit of regime-switching models in crypto.

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## Repository Structure

### /Latex
Contains the full thesis manuscript and build files.
- `sp.pdf`: The final compiled thesis document (73 pages).
- `chapters/`: Source LaTeX files for each chapter.
- `sp.tex`: Main document controller.

### /Code
- `reactive_vebb.py`: The core Python backtesting engine implementing the HMM logic and walk-forward validation.
- `regime_features.py`: Feature engineering module for Garman-Klass and Hurst calculations.

### /Data
- Historical BTCUSDT 15-minute data (2023-2025) sourced from Binance via TradingView.

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## Key Findings & Methodology

1.  **Non-Stationarity:** Static parameters optimized for 2024 failed in 2025, proving the "Stationarity Trap" hypothesis.
2.  **Volume Anomalies:** Volume Z-Scores > 2.0 served as reliable filters for 50th-percentile volatility regimes but failed during liquidity cascades.
3.  **Survival Metric:** The primary metric of success was shifted from "Profit Maximization" to "Drawdown Mitigation" after the HMM demonstrated a 92% reduction in trade frequency during crash periods.
4.  **Statistical Integrity:** The study acknowledges that while the regime-filtered configuration showed a Profit Factor of 1.50, the sample size (N=24) is insufficient for statistical significance, positioning the result as anecdotal evidence for future research rather than a production-ready strategy.

## Authors
- [Author Names Redacted for Repository Privacy]
- Department of Computer Science
- Ateneo de Naga University