| --- |
| base_model: Qwen/Qwen3-8B |
| library_name: peft |
| license: apache-2.0 |
| tags: |
| - finance |
| - trading |
| - sft |
| - lora |
| - qwen3 |
| --- |
| |
| # ActiveTrader — SFT Fine-Tuned Trader Agent |
|
|
| LoRA adapter for **Qwen3-8B**, fine-tuned to generate structured stock trading recommendations from analyst and risk manager reports. |
|
|
| ## What This Model Does |
|
|
| Takes two inputs from upstream agents: |
| 1. **Analyst Report** — fundamentals, news, social sentiment, macro context |
| 2. **Risk Manager Report** — technical indicators, support/resistance, risk assessment |
|
|
| Outputs a structured **Trading Recommendation**: Buy / Hold / Sell with entry zone, stop loss, target price, reasoning, and key risks. |
|
|
| ## Training Details |
|
|
| | Parameter | Value | |
| |-----------|-------| |
| | Base model | Qwen/Qwen3-8B | |
| | Method | QLoRA (4-bit NF4) | |
| | LoRA rank | 16 | |
| | LoRA alpha | 32 | |
| | Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj | |
| | Training examples | 150 (30 tickers × 5 question variants) | |
| | Train/eval split | 135 / 15 | |
| | Epochs | 3 | |
| | Batch size | 2 × 4 grad accum = 8 effective | |
| | Learning rate | 2e-4 (cosine schedule) | |
| | Hardware | NVIDIA RTX 4070 (8GB VRAM) | |
| | Training time | ~9 hours | |
| | Trainable params | 43.6M / 8.2B (0.53%) | |
| |
| ## Training Results |
| |
| | Metric | Value | |
| |--------|-------| |
| | Initial train loss | 1.845 | |
| | Final train loss | 0.481 | |
| | Final eval loss | 0.534 | |
| |
| ## Training Data |
| |
| 150 SFT examples generated by: |
| 1. Running Analyst (Qwen2.5-7B) + Risk Manager (Qwen2.5-7B) on 30 tickers across sectors (tech, finance, healthcare, energy, consumer, industrial) |
| 2. Sending report pairs to **GPT-4o** with varied user questions to generate gold-standard trader recommendations |
| 3. Formatting as chat-style JSONL (system + user + assistant) |
| |
| ## Usage |
| |
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig |
| from peft import PeftModel |
| import torch |
| |
| # Load base model in 4-bit |
| base_model = AutoModelForCausalLM.from_pretrained( |
| "Qwen/Qwen3-8B", |
| quantization_config=BitsAndBytesConfig( |
| load_in_4bit=True, |
| bnb_4bit_compute_dtype=torch.bfloat16, |
| bnb_4bit_quant_type="nf4", |
| ), |
| device_map="cuda:0", |
| ) |
| |
| # Load LoRA adapter |
| model = PeftModel.from_pretrained(base_model, "abababab2003/trader-sft-lora") |
| model = model.merge_and_unload() |
|
|
| tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B") |
| ``` |
| |
| ## Project |
| |
| **ActiveTrader** — a multi-agent trading system built with LangGraph for CS 496 (Agent AI) at Northwestern University. Three agents collaborate: an Analyst, a Risk Manager, and this SFT-trained Trader. |
| |
| - GitHub: [Vio1etV/Trading-Agent](https://github.com/Vio1etV/Trading-Agent) |
| |
| ## Framework Versions |
| |
| - PEFT: 0.17.1 |
| - Transformers: 4.57.6 |
| - PyTorch: 2.6.0+cu124 |
| - bitsandbytes: 0.48.2 |
| |
