Research Paper
Beyond Real Weights
Progressive PHM reparameterization compresses multimodal language models by 35%, achieving 48% faster inference while preserving output quality — accepted at WACV 2026.
RobotTrader
Welcome to
RobotTrader
RobotTrader
Research into Market dynamics. Explore patterns, test hypotheses, and uncover insights with state-of-the-art neural architectures.
Explore the frontier of
in crypto markets
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AI-Powered Analysis
Neural architectures surface
market patterns around the clock.
Deep Pattern Analysis
Uncover structural signals across
price, volume, and on-chain data.
Multi-Market Data
Aggregate and cross-reference data
from all major exchanges.
Hypothesis Testing
Validate research findings against
years of historical market data.
LSTM
Short-term memory
xLSTM
Extended memory
Transformer
Parallel processing
Three AI Architectures, One Research Engine
It starts simple
A basic neural network that connects market data to actionable insights. Information flows forward, layer by layer.
Processing:
Sequential, one direction
LSTM adds memory loops
Now the network remembers. Loops allow information to persist, catching patterns that unfold across hours of market activity.
Memory span:
24-48 hours of market data
Best for:
Intraday pattern research
xLSTM extends the memory
Extended LSTM stretches memory across weeks. It tracks major structural levels, regime changes, and multi-week cycles.
Memory span:
30+ days of context
Best for:
Medium-term regime analysis
Transformers see everything at once
No more sequential processing. Every data point connects to every other instantly — like having 100 researchers working in parallel.
Processing:
Parallel, all at once
Best for:
Cross-market correlation research
All three work together
LSTM captures momentum. xLSTM tracks structural trends. Transformers reveal hidden correlations. Together, they surface insights no single model could.
67
Data inputs analyzed
3
AI models voting
10ms
Analysis speed
What's New
The latest updates and breakthroughs from RobotTrader
Research Paper
Bidirectional Reasoning
Novel bidirectional fine-tuning method integrating positive and negative rationales with PEFT, enabling 3B models to surpass label-only 70B models on multilingual financial sustainability classification.
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Research Paper
Hybrid Decomposition
Revolutionary multi-scale decomposition combining wavelet transforms with neural attention mechanisms. Achieved 38.7% improvement in trend prediction accuracy.
Explore Research
Research Paper
Language Guided Forecasting
Revolutionary approach combining natural language semantics with time series analysis for 41.2% reduction in forecasting errors.
Explore Research