finrag-trading-expert-qwen3-32b

FinRAG Trading Expert — Qwen3-32B (Distilled)

Overview

A domain-specific fine-tuned model distillation of Qwen3-32B for quantitative trading strategy analysis, backtest auditing, and risk management. The model was trained on 6,305 high-quality examples generated by GLM-5.2 as teacher, covering forex, crypto, stocks, and commodities markets.

Training Details

Training Data

The training dataset contains 6,305 examples from 7 sources:

Data Quality Pipeline

1. Paper Collection: 6,089 unique papers from arXiv (q-fin.*) and OpenAlex
2. Fulltext Download: 200 top papers downloaded as PDF, extracted to text (~15k chars each)
3. Teacher Generation: GLM-5.2 generates detailed answers with chain-of-thought reasoning
4. Post-Processing:
   • <think> tags for reasoning separation (99% tagged)
   • Deduplication (max 30 per template for Self-Instruct, 50 for papers)
   • Truncation to max 14k chars (seq_length compatible)
   • Removal of generic finance Q&A (FinAlpaca removed — not trading-relevant)

Evaluation Results

Evaluated on 40 trading-specific questions comparing Base Qwen3-32B vs. Distilled:

Qualitative Improvements

The distilled model shows:

1. Direct Skepticism: Immediately flags suspicious CAGR (>100%) as red flag, unlike base model which explains basic concepts first
2. Structured Output: Uses numbered lists with concrete bias types (look-ahead, survivorship, data-snooping)
3. Domain Precision: Mentions specific concepts (Kelly criterion, McLean & Pontiff alpha decay, walk-forward validation, OOS degradation)
4. Compact Expertise: Less introductory text, more actionable analysis
5. Real Failure Patterns: Draws on training data from actual failed trading strategies (Trading-Fable5 tracks, Forex_Renko phases)

Category Coverage

Usage

With Unsloth

from unsloth import FastLanguageModel
import torch

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="dustin2050/finrag-trading-expert-qwen3-32b",
    max_seq_length=2048,
    dtype=None,
    load_in_4bit=True,
)
FastLanguageModel.for_inference(model)

messages = [{"role": "user", "content": "Design a momentum strategy for EUR/USD with walk-forward validation."}]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
outputs = model.generate(input_ids=inputs, max_new_tokens=2048, temperature=0.6, top_p=0.95)
print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))

With PEFT (transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-32B", torch_dtype=torch.bfloat16, device_map="auto")
model = PeftModel.from_pretrained(base_model, "dustin2050/finrag-trading-expert-qwen3-32b")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-32B")

messages = [{"role": "user", "content": "Audit this backtest: 567% CAGR, 30% DD. What are the red flags?"}]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
outputs = model.generate(input_ids=inputs, max_new_tokens=2048, temperature=0.6, top_p=0.95)
print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))

Recommended Inference Settings

Limitations

1. Not Financial Advice: This model generates analysis for research purposes only. Do not use for real trading decisions without independent validation.
2. No Live Trading: The model cannot execute trades or access real-time market data.
3. Knowledge Cutoff: Training data covers papers up to July 2026.
4. Reasoning Depth: While the model shows structured trading analysis, it cannot replace full backtesting or professional risk management.
5. GGUF Export: Currently only LoRA adapter is available. GGUF export pending.

Training Infrastructure

• Cloud Provider: Vast.ai (RTX PRO 6000 Blackwell, 96GB VRAM)
• Training Framework: Unsloth 2026.7.5 (2x faster, 70% less VRAM)
• Teacher API: OpenRouter (GLM-5.2 / z-ai/glm-5.2)
• Data Pipeline: Custom Python pipeline (arXiv API + OpenAlex API + PyMuPDF)

Acknowledgments

• Unsloth for the optimized training framework
• Qwen Team for the base model
• OpenRouter for API access to GLM-5.2
• arXiv and OpenAlex for paper access

License

Apache 2.0 (inherited from Qwen3-32B base model)