Abstract
The pricing mechanics of Bitcoin (BTC) are characterized by extreme non-stationarity, heteroskedastic volatility, and a distinct decoupling from traditional fundamental valuation anchors. This paper introduces CryptoSent, a high-frequency forecasting architecture that addresses these stochastic challenges by synthesizing autoregressive price signals with high-dimensional natural language features. Utilizing a cloud-native infrastructure for minute-level inferencing, the system deploys a hierarchical stacked ensemble. The architecture is novel in its application of a Temporal Fusion Transformer (TFT) to integrate a decomposable NeuralProphet-LSTM time-series branch with a regime-switching sentiment branch powered by FinBERT and ModernBERT embeddings. Empirical analysis demonstrates that this approach, which treats text as a leading indicator for distributional tail events, achieves a Mean Absolute Percentage Error (MAPE) of approximately 0.31% over a 12-hour horizon. The system significantly outperforms a random walk baseline, providing robust evidence for the efficacy of attention-based mechanisms in capturing the latent interplay between social signal propagation and asset price discovery.
1. Introduction
The valuation of Bitcoin presents a distinct anomaly in financial econometrics. Lacking the intrinsic cash flow streams required for Discounted Cash Flow (DCF) analysis, BTC functions as a purely speculative asset where price formation is heavily dependent on behavioral economics and information diffusion. The market is inefficient, trading continuously and reacting with high elasticity to exogenous shocks from social media and news wires.
The CryptoSent project operationalizes the hypothesis that high-frequency price predictability is attainable not through technical analysis alone, but through the fusion of market microstructure data with unstructured semantic signals. By quantifying the velocity and sentiment of information flow, the system attempts to model the "epistemic uncertainty" of the market. This paper outlines the system's rigorous methodology, focusing on the sophisticated coupling of deep learning architectures to minimize directional error in a high-latency-cost environment.
2. Data Engineering and Cloud Architecture
The system's robustness relies on a low-latency, cloud-native pipeline capable of ingesting and processing high-velocity data streams.
2.1 Data Ingestion
The architecture integrates three distinct data vectors:
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Price Telemetry: Minute-level BTC-USD pricing data is sourced primarily from the Coinbase API to ensure liquidity representation, with Binance US serving as a redundancy layer.
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Textual Corpus: The system ingests unstructured text data from Twitter (X) and news headlines to capture market sentiment.
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Feature Engineering: Raw text is transformed into quantitative signals through the extraction of engagement metrics and topic-specific sentiment labels.
2.2 Orchestration
The end-to-end workflow is hosted on Google Cloud Platform (GCP) to ensure scalability and reproducibility. Data ingestion lands in BigQuery for warehousing, while transformation logic is executed on Compute Engine clusters. The pipeline orchestration is managed by Cloud Composer (built on Apache Airflow), ensuring strict temporal dependencies are met. Finally, model artifacts are serialized in Cloud Storage, with inference results visualized via Looker for analytical interpretation. This architecture supports a rolling window evaluation strategy, essential for mitigating concept drift in non-stationary markets.
3. Methodological Framework
The core forecasting engine utilizes a hierarchical meta-learning structure. The problem space is decomposed into two orthogonal branches: intrinsic time-series dynamics (Branch A) and extrinsic sentiment-induced regime shifts (Branch B), which are subsequently harmonized via a Temporal Fusion Transformer.
3.1 Branch A: Hybrid Autoregressive Neural Decomposition
Branch A is engineered to capture the distinct low-frequency trend components and high-frequency volatility clusters inherent in crypto-asset data. This is achieved through a sequential hybrid architecture:
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Spectral Decomposition via AR-Net: The system first initializes a NeuralProphet module, an enhanced Auto-Regressive Network (AR-Net). Unlike standard ARIMA models, this component utilizes Fourier series expansion to approximate seasonal periodicities and piecewise linear regression to isolate the underlying trend, T(t). This effectively acts as a high-pass filter, separating the macro-structure from the stochastic noise.
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Residual Learning via LSTM: The residuals, defined as ε = y(t) − T(t), containing the non-linear, short-term dependencies, are fed into a Long Short-Term Memory (LSTM) network. The LSTM addresses the vanishing gradient problem common in Recurrent Neural Networks (RNNs) by utilizing a specific gating mechanism where the cell state C(t) is updated via: C(t) = f(t) × C(t−1) + i(t) × tanh(W · [h(t−1), x(t)] + b). This allows the model to maintain a memory state that captures long-range temporal dependencies in the residual volatility.
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Temporal Fusion Transformer (TFT) Integration: The outputs of the NeuralProphet (trend) and LSTM (residual) are not merely summed; they are ingested by a Temporal Fusion Transformer. The TFT employs a multi-head attention mechanism to learn the optimal temporal relationships and weights between the trend and residual signals, dynamically attending to the most relevant historical time-steps. This allows for interpretable variable selection and ensures that the model can differentiate between transient noise and significant structural breaks.
3.2 Branch B: NLP-Driven Regime Switching & Gradient Boosting
Branch B operates on the premise that market sentiment acts as a precursor to distributional tail events (i.e., "Bull Runs"). This branch functions as a sophisticated classification-regression hybrid.
Semantic Vectorization (BERT Architecture)
The system bypasses traditional lexicon-based approaches (e.g., Bag-of-Words) in favor of contextual embeddings. It utilizes FinBERT, a BERT-base model pre-trained on a massive financial corpus, alongside a supervised ModernBERT classifier. These transformer models map discrete text tokens into a continuous high-dimensional vector space, preserving syntactic and semantic relationships.
Tail Event Definition
The target variable is defined as a binary classification of a positive tail event (price appreciation ≥ 0.8% over 12 hours). This threshold serves as a proxy for momentum ignition and was selected via hyperparameter search.
Gradient Boosted Decision Trees (XGBoost)
The semantic embeddings and engineered engagement metrics serve as input features for an XGBoost classifier. The model utilizes an ensemble of weak prediction trees, optimizing a regularized objective function L(φ): L(φ) = Σ l(ŷ, y) + Σ Ω(fk). Where Ω represents the regularization term penalizing model complexity (L1 and L2 regularization) to prevent overfitting on noisy social data.
Probabilistic Adjustment Factor
The classifier outputs a probability score P(Bull). This score is transformed into a scalar adjustment factor via a calibrated sensitivity function. This factor acts as a Bayesian prior, tilting the baseline forecast output from Branch A. Effectively, Branch B serves to bias the manifold of possible price trajectories based on the "psychological state" of the market.
3.3 Meta-Learner: Adaptive Stacking
The final inference is generated by a stacked meta-learner. Rather than a static weighted average, which fails during regime shifts, the meta-learner employs an adaptive weighting mechanism. This allows the system to dynamically reallocate influence between the technical (Branch A) and sentiment (Branch B) signals based on recent error metrics, effectively learning to trust the text signals during high-hype cycles and revert to mean-reversion technicals during consolidation phases.
4. Empirical Results
4.1 Predictive Accuracy
The primary evaluation metric is Mean Absolute Percentage Error (MAPE). The ensemble demonstrates clear performance gains through its stacked architecture:
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Branch A (Time Series): Achieves a MAPE of approximately 0.42%.
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Branch B (Sentiment): Reduces error to approximately 0.36%.
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Meta-Learner: Further optimizes performance, achieving a MAPE of approximately 0.31%.
4.2 Benchmark Comparison
Given that low MAPE can be misleading in high-value assets, the system is benchmarked against a Random Walk model.
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Relative Performance: Branch B reduces error by roughly 15% relative to the random walk, while the complete meta-learner yields a reduction of approximately 25%.
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Trajectory Smoothness: Visual analysis indicates that while standalone LSTM and GRU models often lag during rapid price rallies, the weighted ensemble tracks price action with superior smoothness and reduced latency.
5. Limitations and Critical Discussion
While the CryptoSent project establishes a robust baseline, several theoretical and practical limitations warrant discussion.
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Metric Validity: The reliance on MAPE, while interpretable, does not inherently capture financial risk or tradability. It treats positive and negative errors symmetrically, which may not align with the risk profile of a long-only or market-neutral strategy.
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Label Sensitivity: The "Bull Run" definition (0.8% threshold) introduces a hyperparameter that significantly influences class balance and model calibration. A more sophisticated approach would involve predicting a full probability distribution over future returns to support risk-aware decision-making.
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Data Stationarity and Noise: Social media text is inherently noisy and subject to platform-specific rate limits. While the pipeline mitigates this via backup feeds and rolling retraining, the stochastic nature of "hype" cycles requires continuous noise reduction efforts.
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Generalizability: The current model is scoped to BTC-USD and English-language text. Scaling to other cryptocurrencies would require re-weighting sentiment sources and mapping creator influence graphs specific to those ecosystems.
Future development vectors include the integration of creator weighting algorithms, scenario-based forecasting, and extending the predictive horizon to 24–48 hours.
6. Conclusion
The CryptoSent project validates the necessity of multimodal modeling in modern financial forecasting. By coupling a trend-residual time series decomposition with a sentiment-conditioned adjustment layer, the system outperforms strong random walk benchmarks and demonstrates that language is a leading indicator for crypto-asset pricing. The implementation of this logic within a minute-level, cloud-native pipeline underscores the practical viability of this approach for high-frequency trading environments where reaction time is the primary determinant of alpha.