A LoRA adapter that turns openai/gpt-oss-120b into a quantitative trading analyst: it audits backtests for bias, extracts implementable strategy specifications from research papers, reasons about market microstructure and derivatives, and designs risk management for live deployment. The adapter is distilled from GLM-5.2 teacher responses over 500 full-text quantitative-finance arXiv papers, real trading-project material, and a curated slice of public finance reasoning data. > ⚠️ Not investment advice. See Limitations & Risks. This model produces > analysis and hypotheses, not trading signals. Nothing it outputs should be traded without independent > validation. The training data is written in an «elite quant strategist and backtest auditor» voice that thinks step by step, flags biases, and distinguishes in-sample from out-of-sample results. Filtering of the external set: from 79,112 rows, corporate-finance/accounting/PE/insurance topics were removed (11,993), and the remainder was balanced across 9 quant themes (~690 each): microstructure & execution · derivatives & options · fixed income & rates · crypto/DeFi · risk & portfolio · strategy & alpha · liquidity & credit · macro & FX…
Модальности:
Генерация текста
Области применения:
Логика и рассуждение Диалог / чат Финансы
Задача: Генерация текста
Автор: dustin2050
Теги: peft, lora, qlora, sft, trl, unsloth, gpt-oss, mixture-of-experts
Лайков: 3 | Загрузок: 15
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
