Update README.md

README.md

@@ -1,3 +1,105 @@
----
-license: mit
----
+---
+license: mit
+language:
+- en
+tags:
+- stockmarket
+- trading
+pretty_name: sunny thakur
+size_categories:
+- 10K<n<100K
+---
+
+📈 LLM Trading Instruction Dataset – V2 (2023–2025)
+```
+Dataset Version: 2
+Filename: llm_trading_dataset_20250629_115817.jsonl
+Entries: 20,306
+Period Covered: 2023–2025
+Format: JSON Lines (.jsonl)
+Task: Instruction Tuning for Financial Signal Classification
+Target Models: LLaMA, Mistral, GPT-J, Falcon, Zephyr, DeepSeek, Qwen
+```
+
+🧠 Overview
+
+This second version of the dataset expands the time horizon and depth of training data for instruction-tuned LLMs by covering real-world market indicators from 2023 through 2025. It enables financial models to learn patterns, sentiment, and timing in Buy/Sell signal generation.
+
+
+
+📁 Dataset Format
+
+Each entry follows the instruction tuning schema:
+```
+{
+  "instruction": "Given technical indicators, predict if it's a Buy or Sell signal.",
+  "input": "AAPL on 2025-03-20 with indicators: EMA20=224.55, EMA50=232.09, BB_upper=254.87, BB_lower=203.31, MACD=-6.81, MACD_signal=-4.88, RSI=33.61, CCI=-85.31, STOCH_K=15.95, STOCH_D=15.7",
+  "output": "Buy"
+}
+```
+📌 Fields:
+```
+
+    instruction – Prompt for LLM task (uniform for all entries)
+
+    input – Date, stock symbol, and associated technical indicators
+
+    output – Predicted trading signal: "Buy" or "Sell"
+```
+
+
+
+🔍 Technical Indicators Used
+```
+Indicator	Description
+EMA20 / EMA50	Short and medium-term exponential MA
+BB_upper/lower	Bollinger Bands – price volatility zones
+MACD, MACD_sig	Momentum crossover indicators
+RSI	Overbought/Oversold indicator (0–100)
+CCI	Momentum-based deviation indicator
+STOCH_K / D	Stochastic oscillator %K/%D lines
+```
+🔧 Example Usage
+```
+import json
+
+with open("llm_trading_dataset_20250629_115817.jsonl") as f:
+    for line in f:
+        ex = json.loads(line)
+        print("Prompt:", ex["instruction"])
+        print("Indicators:", ex["input"])
+        print("Decision:", ex["output"])
+```
+
+
+🧪 Use Cases
+
+    Finetune instruction-tuned LLMs for trading automation
+
+    Evaluate transformer models for financial decision tasks
+
+    Build explainable AI advisors using LLM-based logic
+
+    Backtest models on realistic multi-year indicators
+
+    Create copilot assistants for traders & hedge funds
+
+
+
+📌 Version Info
+Version	Range Covered	Notes
+v1	2025 only	Initial dataset release
+v2	2023–2025	Extended multi-year training set
+
+📜 License
+
+
+MIT License – Open for use, distribution, and modification.
+
+Attribution recommended for research and commercial tools.
+
+🤝 Contact
+
+📧 [email protected]
+
+🧠 AI/Trading Collab: DM for finetuning support or strategy model help