88 lines
2.8 KiB
Python
88 lines
2.8 KiB
Python
import requests
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from binance.spot import Spot
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import pandas as pd
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api_key = "HCpeel8g6fsTK2630b7BvGBcS09Z3qfXkLVcAY2JkpaiMm1J6DWRvoQZBQlElDJg"
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api_secret= "TySs6onlHOTrGzV8fMdDxLKTWWYnQ4rCHVAmjrcHby17acKflmo7xVTWVsbqtxe7"
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client = Spot(api_key, api_secret)
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# 获取市值前top的币种
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def _get_top_coins_by_market_cap(top):
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coingecko_url = "https://api.coingecko.com/api/v3/coins/markets"
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params = {
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'vs_currency': 'usd',
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'order': 'market_cap_desc',
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'per_page': top,
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'page': 1
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}
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response = requests.get(coingecko_url, params=params)
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coins = response.json()
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return coins
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# 获取Binance上的USDT交易对信息
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def _get_binance_usdt_pairs():
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url = "https://api.binance.com/api/v3/exchangeInfo"
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response = requests.get(url)
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data = response.json()
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usdt_pairs = [symbol['symbol'] for symbol in data['symbols'] if (symbol['quoteAsset'] == 'USDT' and symbol['status'] == 'TRADING')]
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return usdt_pairs
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def get_top_binance_usdt_pairs(top):
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# 获取市值前10的币种
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top_coins = _get_top_coins_by_market_cap(top)
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# 获取Binance上的USDT交易对
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usdt_pairs = _get_binance_usdt_pairs()
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# 筛选出前10币种中与USDT有交易对的币种
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top_pairs = []
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for coin in top_coins:
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coin_symbol = coin['symbol'].upper()
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pair = f"{coin_symbol}USDT"
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if pair in usdt_pairs:
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top_pairs.append(pair)
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return top_pairs
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## 获取所有现货交易对
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def get_symbols():
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data = client.exchange_info()["symbols"]
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# 创建DataFrame
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columns = ['symbol', 'status', 'baseAsset', 'quoteAsset']
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df = pd.DataFrame(data, columns=columns)
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# 过滤出在架交易对
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df_in_trade = df[df['status'] == 'TRADING']
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df_in_USDT = df_in_trade[df_in_trade['quoteAsset'] == 'USDT']
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return df_in_USDT['symbol']
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## 根据交易对和周期获取数据集
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def get_klines(symbol,interval):
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# 获取 k 线数据
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data = client.klines(symbol, interval,limit=500)
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# 将数据转换为DataFrame
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columns = ['timestamp', 'open', 'high', 'low', 'close', 'volume',
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'close_time', 'quote_asset_volume', 'number_of_trades',
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'taker_buy_base_asset_volume', 'taker_buy_quote_asset_volume', 'ignore']
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df = pd.DataFrame(data, columns=columns)
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# 转化成 float
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df['open'] = df['open'].astype('float64')
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df['high'] = df['high'].astype('float64')
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df['low'] = df['low'].astype('float64')
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df['close'] = df['close'].astype('float64')
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df['volume'] = df['volume'].astype('float64')
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# 将时间戳转换为日期时间格式
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df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms', utc=True).map(lambda x: x.tz_convert('Asia/Shanghai'))
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df['close_time'] = pd.to_datetime(df['close_time'], unit='ms', utc=True).map(lambda x: x.tz_convert('Asia/Shanghai'))
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return df |