Update README.md

How It Works:
Data Fetching:

Historical data for ETH/USDT is fetched via Binance API.
Data Preparation:

Past price windows are converted into Llama documents for indexing.
LlamaIndex Integration:

Using the SimpleKeywordTableIndex from LlamaIndex, documents are indexed and a simple prompt is queried to simulate market prediction or pattern detection.
Response Interpretation:

The language model's response provides insights or possible trends based on indexed price data.
Prerequisites:
Install the required libraries:

bash
Kodu kopyala
pip install pandas numpy llama-index requests
Sample Prompt Result:
A sample response might look like:

vbnet
Kodu kopyala
Prediction response from LlamaIndex:
Based on the trend from 2023-10-01 12:00 to 2023-10-01 17:00, the next price is expected to be $12

README.md

@@ -1,3 +1,84 @@
 ---
 license: llama3.3
+datasets:
+- HuggingFaceTB/finemath
+base_model:
+- Datou1111/shou_xin
 ---
+from llama_index import SimpleKeywordTableIndex, Document
+from datetime import datetime, timedelta
+import requests
+import numpy as np
+import pandas as pd
+
+# Fetch historical price data
+def fetch_price_data(symbol="ETHUSDT", interval="1h", limit=500):
+    """
+    Fetch historical price data from Binance API.
+    """
+    url = f"https://api.binance.com/api/v3/klines?symbol={symbol}&interval={interval}&limit={limit}"
+    response = requests.get(url)
+    data = response.json()
+
+    # Parse to DataFrame
+    df = pd.DataFrame(data, columns=[
+        "timestamp", "open", "high", "low", "close", "volume", "close_time",
+        "quote_asset_volume", "number_of_trades", "taker_buy_base",
+        "taker_buy_quote", "ignore"
+    ])
+    df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms")
+    df["close"] = df["close"].astype(float)
+    return df[["timestamp", "close"]]
+
+# Prepare price history as documents
+def prepare_documents(df, window_size=5):
+    """
+    Convert sliding price windows into Llama documents.
+    """
+    prices = df["close"].values
+    timestamps = df["timestamp"].values
+
+    documents = []
+    for i in range(len(prices) - window_size):
+        time_range = f"{timestamps[i]} to {timestamps[i + window_size]}"
+        price_window = prices[i:i + window_size]
+        content = f"Price trend from {time_range}: {price_window.tolist()}"
+        documents.append(Document(content))
+    return documents
+
+# LlamaIndex-based predictive task
+def train_with_llama_index(documents):
+    """
+    Create a Simple Keyword Table Index to simulate trend prediction using LlamaIndex.
+    """
+    index = SimpleKeywordTableIndex.from_documents(documents)
+    
+    # Simulating a market prediction
+    prompt = (
+        "Based on historical trends, what might be the next ETH/USDT price, "
+        "assuming consistent linear progression? Focus on patterns."
+    )
+    response = index.query(prompt)
+    return response
+
+# Main pipeline
+def main():
+    # Step 1: Fetch historical data
+    symbol = "ETHUSDT"
+    df = fetch_price_data(symbol)
+    print("Fetched historical data:")
+    print(df.head())
+
+    # Step 2: Prepare documents for LlamaIndex
+    window_size = 5
+    documents = prepare_documents(df, window_size)
+
+    # Step 3: Train a Simple Keyword Table Index and predict trends
+    prediction_response = train_with_llama_index(documents)
+    
+    # Step 4: Display response
+    print(f"\nPrediction response from LlamaIndex:\n{prediction_response}")
+
+# Entry point
+if __name__ == "__main__":
+    main()