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Whether you are developing novel reinforcement learning trading policies, building financial LLMs, or have inquiries regarding our published benchmarks, we'd love to hear from you.
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Review Cycles
Our editorial committee evaluates submitted manuscripts within 5 business days. Code reproducibility in PyTorch or JAX is required for all empirical alpha claims.
Understanding AI in Trading
Can I submit our quantitative research paper or model for publication?
Yes. We welcome submissions from quants, PhD candidates, and machine learning engineers. Papers must include reproducible methodologies, backtest parameters, and mathematical definitions.
Are the AI trading code snippets production-ready?
Our code examples are reference architectures designed to demonstrate algorithmic concepts (such as PPO reward shaping or Transformer volatility heads). For production use, you should integrate proper order routing, risk management, and broker APIs.
What market data frequencies and formats are analyzed in Techbast research?
Our research covers tick-level Level 2/Level 3 Limit Order Books (LOB), millisecond trades, aggregate OHLCV candles, and unstructured text corpora including FOMC speeches and SEC filings.
What is the Techbast policy on proprietary trading secrets?
Techbast publishes open scientific principles, foundational neural architectures, and robust statistical evaluation frameworks without disclosing proprietary private fund keys or client strategies.