Bitcoin Price Direction Prediction via Regime-Aware Multi-Modal Fusion of Social Sentiment and Technical Features

Muhammad Abdullah Haroon

Source-linked AI summary

Bitcoin Price Direction Prediction via Regime-Aware Multi-Modal Fusion of Social Sentiment and Technical Features

Muhammad Abdullah Haroon

arXiv:2607.23370v1Jul 25, 2026cs.LGcs.CEecon.EMstat.CO

TL;DR

Sub-daily Bitcoin prediction is difficult, while fixed fusion of technical and sentiment signals ignores market-regime dependence. RAML uses a regime-conditioned fusion gate and achieves the highest F1 across both horizons, with the highest 3-hour AUC.

Problem
Standard models apply fixed technical-sentiment fusion weights across market conditions despite evidence that sentiment informativeness varies by regime.
Method
RAML uses a learnable sigmoid gate conditioned on a binary volatility regime to weight social sentiment against OHLCV technical features.
Results
RAML achieved the highest F1 on both horizons—0.5474 at 3h and 0.5513 at 6h—and the highest 3h AUC at 0.5084.
Takeaways & Limitations
Fusion architectures for multi-modal financial forecasting should condition modality weighting on an interpretable market-state signal rather than use fixed weights.
Takeaways & Limitations
The effective training set contains 2,146 hourly observations, limiting generalisation, particularly at the longer 6-hour horizon.

Abstract

from arXiv · show

Bitcoin price prediction on sub-daily timescales is a hard open problem in computational finance. Bitcoin exhibits fat-tailed returns, non-stationary dynamics, and a price discovery process influenced by social discourse on Reddit and Twitter. Conventional approaches fuse OHLCV technical features with sentiment via static concatenation, applying identical fusion weights regardless of market state. This is inconsistent with the behavioural finance literature, which shows that retail sentiment is most predictive during volatile periods and noisy during calm ones. This paper proposes Regime-Aware Multi-Modal Learning (RAML), which conditions fusion of sentiment and price features on a dynamically detected binary market regime. Rolling 24-hour volatility partitions observations into stable and volatile regimes; a learnable sigmoid gate adjusts the weight of the sentiment embedding relative to the price embedding, trusting sentiment more during volatility and price dynamics more during stable phases. The system is evaluated on 3,491 hourly observations (July 2024-September 2025), combining Bitcoin OHLCV data with Reddit /r/Bitcoin FinBERT sentiment. Four models are compared - price-only BiLSTM, sentiment-only classifier, static-concatenation BiLSTM, and RAML - across 3-hour and 6-hour horizons, with an ablation study isolating the sentiment branch, regime detection, and adaptive fusion. RAML achieves macro-F1 of 0.5474 (3h) and 0.5513 (6h), with the highest AUC at 3 hours (0.5084), indicating better calibration. Ablation confirms every component is necessary, and replacing adaptive weighting with concatenation causes recall collapse at 6 hours (F1: 0.14). These results establish regime-conditioned adaptive fusion as a necessary design principle for multi-modal financial forecasting.

I. INTRODUCTION … A. Technical Analysis and Sequential Models

The paper frames Bitcoin forecasting as difficult because of heavy-tailed, continuously traded dynamics and socially driven price movements. It motivates regime-aware fusion, then situates the approach within sequential OHLCV modeling and multimodal extensions.

I. INTRODUCTION: Bitcoin forecasting is difficult because returns are leptokurtic and its fragmented, continuously operating market is exposed to liquidity shocks and social contagion.These conditions create sudden dislocations and non-fundamental price movements.
A. The Role of Social Discourse: Reddit and Twitter sentiment can provide Granger-causal information about near-term Bitcoin movements as retail herding transmits signals before arbitrage corrects deviations.The passage links this mechanism to noise-trader theory and social-network transmission.
B. The Regime Modulation Problem: Standard multimodal models concatenate technical features and sentiment with fixed weights, treating all market conditions as equivalent.This design conflicts with evidence that sentiment predictability varies by regime.
B. The Regime Modulation Problem: Sentiment has stronger predictive power during uncertainty and high volatility but is largely uninformative during calm phases governed by fundamental anchors.Applying identical weights can conflate sentiment-as-signal with sentiment-as-noise, degrading calibration and directional accuracy.
C. Contributions: The proposed architecture uses a binary market state to adaptively adjust the relative contributions of sentiment and price embeddings.The gate is presented as a single interpretable architectural innovation for the regime modulation problem.
C. Contributions: The fusion gate computes e(fused) = w_t · e(s) + (1 − w_t) · e(p), with w_t = σ(θ_r · r_t).Here r_t is a volatility-derived binary regime label, θ_r is learnable, and σ is the logistic sigmoid.
C. Contributions: The work contributes a volatility-conditioned fusion architecture requiring no additional labelled data and a reproducible hourly pipeline aligning 14 months of OHLCV with Reddit /r/Bitcoin FinBERT scores.The pipeline spans bull, bear, and consolidation market phases.
A. Technical Analysis and Sequential Models: Prior cryptocurrency forecasting established sequential OHLCV modeling, with LSTM outperforming ARIMA and BiLSTM outperforming unidirectional models on directional classification.Related work also introduced social activity volume and temporal attention, while the price branch adopts the two-layer sequential modeling direction.

B. Sentiment Analysis in Cryptocurrency Markets … A. Data Sources

The paper positions regime-aware, adaptive fusion of social sentiment and price features as a response to static multimodal methods, using interpretable volatility regimes and aligned heterogeneous data. It distinguishes this approach from prior regime-switching and fusion work through hourly resolution, trainable gating, financial-language-model sentiment, and systematic ablation.

B. Sentiment Analysis in Cryptocurrency Markets: Reddit activity and sentiment show statistically significant associations with near-term Bitcoin price direction, extending earlier evidence linking social mood to financial returns.Prior work reports analogous Bitcoin-specific correlations between Reddit post volume, sentiment polarity, and subweekly price direction.
B. Sentiment Analysis in Cryptocurrency Markets: FinBERT replaces lexicon-based sentiment analysis by capturing contextual nuance in financial language and outperforming traditional financial sentiment lexicons.CryptoBERT further adapts transformer pretraining to cryptocurrency-specific discussion data, although the supplied passage is truncated before its reported result.
C. Multi-Modal Fusion Strategies: Static concatenation appends sentiment embeddings to price vectors but applies identical implicit modality weights regardless of market condition.The paper instead translates learnable gating into financial time series with explicit regime supervision.
C. Multi-Modal Fusion Strategies: Cross-modal attention improves over concatenation in prior cryptocurrency prediction work, while this paper uses explicit regime-supervised gating for financial time series.The supplied passage describes attention-based alternatives but truncates details of the cited multi-head mechanism.
D. Market Regime Detection: A median-split volatility threshold provides balanced, interpretable binary regimes while avoiding overfitting risks associated with machine-learning regime detection on limited windows.The choice is motivated by rolling volatility as the dominant differentiator between market states and by practitioner interpretability.
E. Regime-Conditioned Prediction: Prior regime-switching cryptocurrency work selects among separate models for detected states at daily resolution, whereas this approach continuously modulates a fusion gate.The distinction motivates regime-conditioned fusion at finer temporal resolution.
F. Literature Comparison: The paper claims no prior work combines hourly prediction, explicit binary regimes, trainable adaptive gating, financial-language-model sentiment, and systematic ablation.Table I situates the contribution across five dimensions of related work.
A. Data Sources: The final dataset aligns three heterogeneous sources at hourly UTC timestamps through an inner join, producing the feature matrix used for experiments.The sources include yfinance Bitcoin OHLCV, Reddit FinBERT sentiment, and Treasury/Google News CryptoBERT daily sentiment; the effective aligned window is July 2024–September 2025.

B. Feature Engineering

The paper engineers five technical features from hourly OHLCV data to capture momentum, trend, and volatility dynamics. Rolling 24-hour volatility provides the primary signal for market-regime detection, alongside RSI and short- and long-window moving averages.

Technical features: Five technical features are derived from hourly OHLCV data to represent momentum, trend, and volatility dynamics.The feature set is introduced as the technical input derived from hourly price and volume observations.
Regime signal: Rolling 24-hour volatility serves as the primary regime signal.It is computed over a 24-hour window using returns relative to the mean return over that same window.
Momentum features: The feature set includes a 14-period Relative Strength Index based on exponentially weighted gains and losses from the preceding 14 hours.RSI captures recent price momentum using separate exponentially weighted averages of gains and losses.
Trend features: Short-window and long-window moving averages are included to capture trend information.The passages identify both moving-average features but do not specify their window lengths.

C. Regime Labelling · D. Target Variable · E. Feature Correlation Analysis

The paper defines binary volatility regimes and forward-looking direction targets at 3-hour and 6-hour horizons, then uses correlation analysis to motivate returns and volatility over raw price levels. Test-window regimes are volatility-skewed, while sentiment features show weak price-level relationships and complementary bullish/bearish outputs.

C. Regime Labelling: Each timestep receives a binary regime label by comparing rolling volatility σt with the global dataset median.The median is computed over the full dataset.
C. Regime Labelling: 59.4% of test-window hours are volatile and 40.6% are stable under the median-split regime construction.The full dataset remains balanced by the global median, while the July–September 2025 test window reflects elevated volatility.
D. Target Variable: Binary direction labels are constructed for two forward-looking prediction horizons: 3 hours and 6 hours.These horizons are selected to preserve predictive relevance while remaining longer than minute-level noise intervals.
D. Target Variable: The target labels encode whether the closing price h hours ahead is strictly higher than the current close, with 1 denoting an upward move.The study uses 3-hour and 6-hour horizons as actionable intraday windows that reduce minute-level bid-ask noise.
E. Feature Correlation Analysis: Price levels including open, high, low, close, MA-7, and MA-30 exhibit near-perfect pairwise correlations approaching +1.00.This motivates using returns and volatility as operative price features, while raw levels remain inputs to the BiLSTM sliding window.
E. Feature Correlation Analysis: Rolling volatility σt has moderate negative correlation with absolute price levels at r ≈−0.37.The passage links this relationship to the empirical tendency for elevated volatility to coincide with lower absolute price levels.
E. Feature Correlation Analysis: Sentiment correlates weakly positively with price levels at r ≈+0.08 for mean_finbert, while bullish and bearish probabilities correlate at r = −0.74.The weak sentiment relationship is consistent with a noise-trader channel, whereas the probability features are complementary FinBERT outputs.

F. Dataset Statistics … C. Sentiment Branch

The study forms a near-balanced aligned dataset and evaluates RAML through symmetric price and sentiment BiLSTM branches whose regime-conditioned gate controls modality contributions. Each branch uses 24-hour windows and produces a 32-dimensional embedding, while the price branch processes ten features and the sentiment branch five.

F. Dataset Statistics: Approximately 51–52% Up labels indicate near-balanced horizons without systematic directional bias, making random guessing approximately 50% accuracy and 0.50 F1.The final dataset follows feature computation, NaN removal from rolling-window initialization, and inner-join alignment across three sources.
A. Architecture Overview: RAML encodes price and sentiment streams in parallel before dynamically gated fusion conditioned on the detected market regime.The dual-branch design and adaptive gate are presented as the framework’s core architecture.
B. Price Branch: The price branch processes the latest L = 24 hourly observations of ten price features with a two-layer bidirectional LSTM.Its hidden dimension is d = 64 per direction, yielding 2d = 128 dimensions before projection to a 32-dimensional embedding; dropout probability is p_d = 0.3.
B. Price Branch: 59.4% of test-window hours are volatile and 40.6% stable under the rolling-volatility median split.The volatile skew is attributed to the corrective market phase following the late-2024 bull run.
C. Sentiment Branch: The sentiment branch processes the same 24-hour window of five FinBERT sentiment features through a separate two-layer BiLSTM with d = 64.The branch produces a 32-dimensional sentiment embedding using architectural hyperparameters matching the price branch.
C. Sentiment Branch: Equal representational capacity across branches ensures that the adaptive gate, rather than a capacity imbalance, determines each modality’s inference-time contribution.This symmetry is deliberate and supports interpreting fusion weights as adaptive modality contributions.
C. Sentiment Branch: The 24-hour rolling-volatility bands coincide with the late-2024 price surge, early-2025 correction, and a July 2024 spike associated with an intra-day drawdown.Volatile periods are defined by σ_t > median(σ), illustrating market-state transitions used by the regime-aware architecture.

D. Regime Detection · E. Adaptive Fusion Gate

The paper detects stable and volatile market regimes with a deterministic rolling 24-hour volatility threshold, then uses a learnable sigmoid gate to adaptively fuse price and sentiment embeddings. The gate increases sentiment weighting in volatile conditions while remaining parsimonious, requiring one parameter and no additional labelled data.

D. Regime Detection: Rolling 24-hour volatility σ_t is binarised into a pre-training regime signal that provides contextual labels rather than a learned parameter.The threshold-based design prioritises interpretability for practitioners.
D. Regime Detection: Threshold-based detection produces deterministic, reproducible stable/volatile labels and provides adequate separability for the fusion gate.This avoids stochastic EM-based alternatives while retaining the operationally relevant distinction.
E. Adaptive Fusion Gate: The regime label r_t ∈ {0, 1} is transformed linearly and passed through a sigmoid to generate the continuous fusion weight.The resulting weight controls the combination of the price and sentiment branch embeddings.
E. Adaptive Fusion Gate: Rolling volatility correlates +0.74 with the regime label and −0.37 with price levels, while absolute price features form a near-unity collinearity cluster.These relationships motivate normalisation, sequence modelling, and volatility-based regime context.
E. Adaptive Fusion Gate: When r_t = 1 and θ_r > 0, w_t = σ(θ_r) > 0.5, upweighting sentiment; when r_t = 0, w_t = σ(0) = 0.5, leaving price effectively dominant.The stable-regime setting defaults to equal weighting before the stronger price signal determines the effective contribution.
E. Adaptive Fusion Gate: The learned θ_r is positive, supporting the hypothesis that sentiment is more informative during volatility.A negative θ_r would invert the interpretation of the gate.
E. Adaptive Fusion Gate: The gate adds only one learnable parameter, θ_r, and zero additional labelled data, making it a parsimonious extension of static fusion.Its parameter and data requirements are explicitly limited despite introducing regime-conditioned weighting.
E. Adaptive Fusion Gate: The architecture encodes 24-hour OHLCV and FinBERT Reddit aggregates in symmetric BiLSTM branches, then applies a regime-conditioned sigmoid gate before binary classification.Each branch produces a 32-dimensional embedding, and the gate is w_t = σ(θ_r · r_t).

F. Classification Head · G. Training Procedure · H. Model Summary

RAML converts the fused embedding into an upward-move probability through a two-layer ReLU classification head and trains both branches, the regime gate, and classifier jointly by backpropagation. The documented procedure uses 24-step sequences, 32-sample batches, 30 epochs, and a 10^-3 learning rate, with adaptive fusion weighting sentiment and price embeddings by regime.

F. Classification Head: The fused embedding enters a two-layer feedforward classifier with an intermediate ReLU activation.
F. Classification Head: The head outputs the probability of an upward price move, thresholds predictions at 0.5, and minimizes binary cross-entropy end-to-end.
G. Training Procedure: Joint backpropagation trains both BiLSTM branches, the fusion-gate parameter θr, and the classification head through the fused embedding.The classification gradient simultaneously shapes regime sensitivity and branch-specific embedding representations.
H. Model Summary: The training inputs are price sequences, sentiment sequences, regime labels, and targets, configured with sequence length L=24, batch size B=32, epochs E=30, and learning rate η=10^-3.
H. Model Summary: The procedure computes a global median volatility threshold on the training set and assigns binary regime labels for all training observations.
H. Model Summary: The adaptive gate computes w=σ(θr · rB) and forms the fused embedding as w · e(s) + (1 −w) · e(p).
H. Model Summary: During stable periods, the gate assigns approximately 0.8 weight to price and 0.2 to sentiment, while volatile periods reverse emphasis toward approximately 0.9 sentiment and 0.1 price.

V. EXPERIMENTAL SETUP … C. Evaluation Metrics

The experiments use a strict chronological, out-of-distribution test split and compare RAML with price-only, sentiment-only, and static-concatenation baselines. Models are evaluated on held-out data using five binary-classification metrics, with F1 as the primary ranking metric.

A. Train/Test Split: 2,146 observations from July 2024 to June 2025 form the training set, while 1,345 observations from July 2025 to September 2025 form the test set.The split is 61.5/38.5, with no random shuffling.
A. Train/Test Split: The held-out test period follows the late-2024 bull run’s corrective phase and represents a distinct market environment from training.This makes the evaluation genuinely out of distribution.
B. Baseline Models: B1 uses the same two-layer BiLSTM architecture as RAML’s price branch but trains solely on ten price features without sentiment input.This isolates the predictive contribution of technical analysis.
B. Baseline Models: B2 is a three-layer 128-64-1 feedforward classifier trained on five FinBERT sentiment features without price input.It measures standalone Reddit sentiment predictive power and tests whether sentiment alone suffices for cryptocurrency forecasting.
B. Baseline Models: B3 is a BiLSTM that concatenates ten price and five sentiment features without regime-aware gating.It provides the direct comparison for adaptive fusion versus naive feature-level fusion.
C. Evaluation Metrics: Five complementary binary-classification metrics are reported: Accuracy, Precision, Recall, F1 score, and AUC.They measure correctness, predicted-up reliability, actual-up recovery, the precision–recall harmonic mean, and probability-ranking quality, respectively.
C. Evaluation Metrics: All metrics are computed only on the held-out test set, with F1 selected as the primary ranking metric.AUC measures the quality of predicted probability ranking.

D. Ablation Study Design … B. F1 and AUC Trajectory Analysis

The study isolates sentiment, regime gating, and adaptive fusion through three ablations, then compares model trajectories across F1 and AUC. RAML is the only model exceeding random performance on both metrics at 3 hours while maintaining balanced recall across horizons.

D. Ablation Study Design: Three ablations separately remove the sentiment branch, regime gate, or adaptive fusion weighting to decompose RAML’s architectural contributions.A1 tests incremental social-signal value, A2 tests adaptive gating against equal weighting, and A3 tests adaptive weighting against direct concatenation.
D. Ablation Study Design: A1 removes the sentiment BiLSTM, leaving only the price embedding to test whether social signals add information beyond price features.The classifier receives only the price embedding.
D. Ablation Study Design: A2 fixes the fusion weight at wt = 0.5 for every t, retaining both modalities while eliminating regime-adaptive weighting.This tests regime detection against simple equal-weight averaging.
D. Ablation Study Design: A3 concatenates the two 32-dimensional embeddings into a 64-dimensional classifier input, testing whether RAML’s gain comes specifically from adaptive weighting.This reproduces the static-concatenation strategy within the dual-branch architecture.
E. Computational Environment: Approximately 45 seconds are required to train one model variant for 30 epochs on the 2,146-row training set using the stated laptop hardware.The environment includes PyTorch with CUDA 12.x acceleration and an NVIDIA GeForce RTX 5060 GPU.
A. Baseline Comparison: 0.6579 is B2’s highest raw 3-hour F1, but its 3-hour AUC is 0.4938 below the 0.5 random baseline because recall reaches 0.9067.B3 records F1: 0.4366 vs. 0.6579 at 3 hours and F1: 0.5844, AUC: 0.5253 at 6 hours; B1 collapses to Rec: 0.0916, F1: 0.1563 at 6 hours.
A. Baseline Comparison: 0.5474 and 0.5513 are RAML’s F1 scores at 3-hour and 6-hour horizons, while 0.5084 is its highest 3-hour AUC.RAML’s recall is 0.5778 at 3h and 0.6090 at 6h, described as the most balanced among models exceeding random AUC.
B. F1 and AUC Trajectory Analysis: RAML is the unique model above random on both F1 and AUC at 3 hours, whereas B2 falls below 0.5 AUC despite its high F1.The trajectory analysis treats simultaneous above-random F1 and AUC as the most demanding joint criterion for practical deployment.

C. Confusion Matrix Analysis … F. Component Contribution Summary

The analyses show that RAML avoids the directional biases of the baseline models and maintains the most balanced prediction profile. Ablations identify adaptive fusion, sentiment, and regime conditioning as independently necessary components, with adaptive fusion especially critical at 6 hours.

C. Confusion Matrix Analysis: B1 predicts Up almost equally for both true classes, while B2 and B3 exploit severe class biases toward Up and Down, respectively.B1 has 277 Pred Up counts for each true class; B2 makes only 77 Down predictions out of 1,345, whereas B3 makes 845 Down predictions.
C. Confusion Matrix Analysis: RAML is the only model with a reasonably symmetric confusion matrix, reaching recall 0.577 and specificity 0.446.It predicts Up correctly for 396 of 686 true-Up instances and Down correctly for 294 of 659 true-Down instances.
D. Radar Chart Analysis: RAML’s 3-hour radar profile is larger and more symmetric, whereas sentiment-only performance is distorted toward recall and the other baselines form smaller profiles.The radar comparison visualizes all five metrics simultaneously and shows that RAML does not sacrifice a single dimension to increase another.
E. Ablation Study: Removing sentiment reduces 3-hour F1 from 0.5474 to 0.4441 and AUC from 0.5084 to 0.4766, while 6-hour F1 falls from 0.5513 to 0.3099.The reported F1 changes are ∆F1 = −0.1033 at 3 hours and ∆F1 = −0.2414 at 6 hours; the 3-hour AUC change is ∆AUC = −0.0318.
E. Ablation Study: Replacing the regime gate with equal weighting raises 3-hour F1 to 0.6180 but lowers AUC to 0.4841, reflecting a high-recall majority-class strategy.Recall rises to 0.7896 versus 0.5778 for the full model, while AUC falls from 0.5084 to 0.4841.
E. Ablation Study: Direct concatenation causes the worst failure: at 6 hours, A3 reaches recall 0.0828 and F1 0.1418.The passage identifies adaptive weighting versus concatenation as the most critical fusion decision in the dual-branch design.
F. Component Contribution Summary: Component impacts rank adaptive fusion as critical at 6 hours, sentiment as critical across both horizons, and the regime gate as necessary for calibration.Reported degradations are ∆F1 = −0.4095 for adaptive fusion, ∆F13h = −0.1033 and ∆F16h = −0.2414 for sentiment, and ∆AUC = −0.0243 at 3 hours plus ∆F16h = −0.1814 for the regime gate.

VII. DISCUSSION … C. On the Modest Absolute Performance Values

The discussion argues that regime-conditioned fusion improves the relative use of sentiment and price signals, while modest absolute scores remain meaningful in an efficient, noisy sub-daily Bitcoin market.

A. Interpretation of Regime-Conditioned Fusion: RAML supports the thesis that sentiment and technical features have regime-dependent informativeness, outperforming models that treat market conditions equivalently.
A. Interpretation of Regime-Conditioned Fusion: Without the regime gate, A2 defaults to constant high-recall behavior that succeeds in high-recall environments but fails to generalise.
A. Interpretation of Regime-Conditioned Fusion: Without adaptive weighting, A3 cannot arbitrate conflicting embeddings, causing catastrophic prediction collapse on the 6-hour task.
A. Interpretation of Regime-Conditioned Fusion: RAML achieves the best AUC on both horizons, indicating probability calibration across the full ROC curve rather than only at the 0.5 threshold.Calibration supports risk-weighted position sizing and dynamic threshold adjustment despite modest absolute performance.
B. Behavioural Finance Grounding: Stable phases favor OHLCV patterns, whereas volatile or uncertain phases temporarily increase the predictive content of retail social-media sentiment.This interpretation is grounded in the noise-trader hypothesis and investor sentiment literature.
B. Behavioural Finance Grounding: 0.16: Price-only B1 records the most extreme F1 degradation on the 6-hour task, while RAML maintains the most consistent F1 trajectory.B3 achieves marginally higher AUC at this horizon.
B. Behavioural Finance Grounding: FinBERT Reddit sentiment shows sharp transitions around major price events, while sentiment features have weak but positive correlation with price levels.The late-2024 bull run above $120,000 exemplifies alignment during high-conviction, high-volatility phases.
C. On the Modest Absolute Performance Values: AUC values are modest because hourly Bitcoin is highly informationally efficient, while F1 > 0.90 claims often reflect leakage, favorable horizons, in-sample evaluation, or cherry-picked tests.The paper contextualizes its results through a strict chronological and out-of-distribution evaluation design.

D. Error Analysis · E. Limitations and Future Directions · VIII. CONCLUSION

RAML’s errors concentrate on true-down hours, while limitations include limited data, binary regimes, single-asset evaluation, coarse sentiment timing, and omitted exogenous events. Despite these constraints, RAML improves held-out directional prediction and motivates interpretable regime-conditioned fusion as a general forecasting design principle.

D. Error Analysis: RAML correctly identifies 294 of 659 true-down hours (specificity: 44.6%) and 396 of 686 true-up hours (recall: 57.7%), revealing its primary difficulty with True Down.Downward moves are harder to predict because exogenous shocks may not appear in historical price or sentiment features.
E. Limitations and Future Directions: Several limitations constrain the current work and motivate future investigation, including data volume, regime representation, asset scope, sentiment ordering, and exogenous events.These limitations define the main directions for extending the framework.
E. Limitations and Future Directions: 2,146 hourly observations limit dual-branch BiLSTM generalisation, especially at the 6-hour horizon, because of the 730-day yfinance limit and Reddit-overlap requirement.Extending the pipeline with Twitter/X or Telegram data could reach the full 2017–2025 Reddit window.
E. Limitations and Future Directions: The binary regime detector cannot distinguish trending, mean-reverting, and crisis states, motivating three-state Hidden Markov Models or continuous volatility embeddings.A richer conditioning signal may become appropriate as training data volume grows.
E. Limitations and Future Directions: Evaluation exclusively on BTC-USD leaves generalisation to ETH, SOL, BNB, traditional equities, and cross-asset settings unvalidated.Regime dynamics and sentiment-price coupling may differ substantially across assets.
E. Limitations and Future Directions: Hourly arithmetic-mean sentiment discards within-hour post ordering, motivating within-hour attention or 15-minute resolution.Posts published at different minutes contribute equally to the hourly aggregate.
E. Limitations and Future Directions: No macro event features are included, although FOMC announcements, regulatory filings, and exchange incidents can drive sharp dislocations; structured news could incorporate them.Such events are by construction unpredictable from past OHLCV or historical sentiment alone.
VIII. CONCLUSION: RAML conditions OHLCV–sentiment fusion on a dynamically detected binary state through a learnable sigmoid gate, routing authority toward sentiment in volatility and price dynamics in stable phases.The framework presents regime-aware multi-modal learning as its central architecture and contribution.