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