misbah-dev/trade-parse-qlora

TradeParse-QLoRA

QLoRA adapter for Qwen2.5-1.5B-Instruct (4-bit, MLX), fine-tuned to convert natural language trade instructions into structured, schema-validated JSON.

Example

Input: "Buy Reliance if it drops below 2800, sell above 3000, intraday"

Output:

{
  "symbol": "RELIANCE",
  "action_conditions": [
    {"trigger": "price_below", "value": 2800, "action": "buy"},
    {"trigger": "price_above", "value": 3000, "action": "sell"}
  ],
  "strategy_type": "intraday"
}

Technique

• Base model: mlx-community/Qwen2.5-1.5B-Instruct-4bit
• Method: QLoRA (LoRA adapters trained on top of a 4-bit quantized base) via mlx-lm, chosen for feasible memory usage on Apple Silicon
• Training data: synthetic instruction → JSON pairs, including normal trade parses and structured error cases for missing/ambiguous information

Eval results

V2 adapter eval on 240 generated validation examples:

Metric	Result
Valid JSON	100.00%
Schema valid	100.00%
Success exact match	92.08%
Error exact match	68.42%
Overall exact match	88.33%

The split matters: success exact match is measured over trade-parse cases only, while error exact match is measured over invalid/ambiguous instruction cases only.

Three representative cases

1. Correct trade JSON

Input:

Buy Infosys if it drops below 1500, sell above 1700, intraday

Expected/model output:

{
  "symbol": "INFY",
  "action_conditions": [
    {"trigger": "price_below", "value": 1500, "action": "buy"},
    {"trigger": "price_above", "value": 1700, "action": "sell"}
  ],
  "strategy_type": "intraday"
}

This shows the normal target behavior: company-name normalization, two price conditions, and strategy extraction.

2. Correct structured error

Input:

Buy ONGC when it's cheap, sell when it's expensive

Expected/model output:

{
  "error": "missing_price_condition",
  "symbol": "ONGC",
  "message": "No numeric price condition found."
}

This should not be force-parsed. The instruction has a symbol and actions, but no concrete price levels.

3. Known wrong / limitation case

Input:

buy this stock below 500, sell above 600, futures

model output:

{
  "symbol": "AAPL", 
  "action_conditions": [
    {"trigger": "price_below", "value": 500, "action": "buy"}, 
    {"trigger": "price_above", "value": 600, "action": "sell"}], 
  "strategy_type": "futures"
}

Expected output:

{
  "error": "missing_symbol",
  "symbol": null,
  "message": "No symbol or company name found."
}

This is a useful failure case to track. The model may produce valid-looking trade JSON even though the instruction never identifies which stock to trade. In a real trading workflow, this should be rejected or sent back for clarification.

Known failure modes:

• Vague price language like "cheap" or "expensive" requires a structured error unless another price-resolution system exists.
• Missing-symbol instructions can still tempt the model to hallucinate a symbol.
• Error-case accuracy is lower than normal trade parsing, so negative/error examples need more data and harder evaluation.

Usage

Requires the base model + this adapter loaded together via mlx-lm:

from mlx_lm import load, generate

model, tokenizer = load(
    "mlx-community/Qwen2.5-1.5B-Instruct-4bit",
    adapter_path="path/to/downloaded/adapter"
)

Full code, training data generation script, and eval scripts: GitHub repo

Scope

This is a translation layer (natural language → structured JSON), not a trading execution engine. Output is intended to be consumed by a downstream order/strategy system.