Update Readme

README.md

@@ -1,26 +1,46 @@
 ---
-dataset_info:
-  features:
-  - name: instruction
-    dtype: string
-  - name: input
-    dtype: string
-  - name: output
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 1230221
-    num_examples: 926
-  - name: test
-    num_bytes: 137322
-    num_examples: 103
-  download_size: 380623
-  dataset_size: 1367543
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+license: mit
+language: en
 ---
+
+# Bitcoin Price Prediction Dataset (60d History -> 4d Forecast)
+
+This dataset is designed for fine-tuning Large Language Models (LLMs) on a time-series forecasting task. Each sample contains 60 days of historical financial and social media data to predict the next 4 days of Bitcoin's closing price.
+
+## Dataset Description
+
+The dataset was generated by combining two primary sources:
+1.  **Financial Data:** Historical daily prices for Bitcoin (BTC), Gold (GC=F), Crude Oil (CL=F), S&P 500 (^GSPC), and the US Dollar Index (DX-Y.NYB) were fetched from Yahoo Finance.
+2.  **Social Media Data:** The [Bitcoin Tweets Dataset](https://www.kaggle.com/datasets/kaushiksuresh147/bitcoin-tweets) was used to extract daily tweet volume and sample tweet texts.
+
+### Features Included in the Prompt:
+- **60-Day Historical BTC Prices**: The core time-series data.
+- **Technical Indicators**: 14-day RSI, 50-day EMA, and 200-day EMA for Bitcoin.
+- **Macroeconomic Context**: Daily closing prices of Gold, Oil, S&P 500, and the US Dollar Index.
+- **Social Media Sentiment**: A sample of 4 tweets for the most recent day in the historical window.
+
+## Data Fields
+
+The dataset is structured for instruction fine-tuning and contains three columns:
+
+- `instruction`: A string containing the comma-separated closing prices of Bitcoin for the last 60 days.
+- `input`: A detailed text block containing all the contextual information (technical indicators, macro data, sample tweets) for the most recent day of the historical period.
+- `output`: A string containing the comma-separated closing prices of Bitcoin for the next 4 days (the prediction target).
+
+## Splits
+
+- **train**: The training split (`train_dataset.json`).
+- **test**: The validation/test split (`val_dataset.json`).
+
+## Intended Use
+
+This dataset is intended to be used with the `SFTTrainer` from the TRL library or similar frameworks to fine-tune models like Llama 3 for complex, multi-modal time-series forecasting tasks.
+
+Example prompt structure:
+```json
+{{
+  "instruction": "45123.45, 45321.89, ... (60 days of prices)",
+  "input": "Based on the historical data from the last 60 days, predict the Bitcoin closing prices for the next 4 days. The prediction period starts on 2023-11-20 (Weekday). The most recent day's technical analysis (2023-11-19): ...",
+  "output": "46123.78, 46050.12, 46300.50, 45987.32"
+}}
+```