Deep TCN-BiLSTM-Attention Neural Architecture & Local GPU Training Daemon

Author: Quantitative AI & Machine Learning Squad

#Deep Learning#PyTorch#ONNX#CUDA#Quantitative AI

1. Context & Problem Statement

Standard technical indicators suffer from lag and non-stationarity in crypto markets. High-accuracy directional price prediction requires multi-resolution temporal features combining short-term convolutional receptive fields with long-range sequential memory and dynamic feature attention.

2. Decision

We implemented a hybrid deep neural network combining Temporal Convolutional Networks (TCN), 2-layer Bidirectional LSTM, and Multi-Head Self-Attention. Models are trained on local NVIDIA RTX GPU hardware via a scheduled 6-hour retraining daemon with SHA-256 cryptographic verification and exported to ONNX for sub-1.5ms steady-state inference.

3. Consequences & Trade-Offs

Positive Outcomes

Achieved >73% directional validation accuracy across BTC/USDT, ETH/USDT, and SOL/USDT

Sub-1.5ms deterministic ONNX inference execution without cloud GPU latency overhead

Automated model drift mitigation via continuous 6-hour local scheduled retraining

Negative / Trade-Offs

Requires dedicated local GPU memory allocation (16GB GDDR7 VRAM)

Mitigation Strategies

Implemented automatic fallback to cloud CCXT REST and CPU ONNX inference if GPU compute is occupied

Standards & References

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