---
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