import requests
from binance.spot import Spot
import pandas as pd
from binance.um_futures import UMFutures

import urllib3

api_key = "HCpeel8g6fsTK2630b7BvGBcS09Z3qfXkLVcAY2JkpaiMm1J6DWRvoQZBQlElDJg"
api_secret= "TySs6onlHOTrGzV8fMdDxLKTWWYnQ4rCHVAmjrcHby17acKflmo7xVTWVsbqtxe7"

client = Spot(api_key, api_secret)
future_client = UMFutures(api_key, api_secret)

# 获取市值前top的币种
def _get_top_coins_by_market_cap(top):
    
    coingecko_url = "https://api.coingecko.com/api/v3/coins/markets"
    params = {
        'vs_currency': 'usd',
        'order': 'market_cap_desc',
        'per_page': top,
        'page': 1
    }
    urllib3.disable_warnings(category=urllib3.exceptions.InsecureRequestWarning)
    response = requests.get(coingecko_url, params=params, verify=False)
    coins = response.json()
    return coins

# 获取Binance上的USDT交易对信息
def _get_binance_usdt_pairs():
    url = "https://api.binance.com/api/v3/exchangeInfo"
    urllib3.disable_warnings(category=urllib3.exceptions.InsecureRequestWarning)
    response = requests.get(url, verify=False)
    data = response.json()

    usdt_pairs = [symbol['symbol'] for symbol in data['symbols'] if (symbol['quoteAsset'] == 'USDT' and symbol['status'] == 'TRADING')]
    return usdt_pairs

def get_top_binance_usdt_pairs(top):
    # 获取市值前10的币种
    top_coins = _get_top_coins_by_market_cap(top)
    
    # 获取Binance上的USDT交易对
    usdt_pairs = _get_binance_usdt_pairs()


    # 筛选出前10币种中与USDT有交易对的币种
    top_pairs = []
    for coin in top_coins:
        coin_symbol = coin['symbol'].upper()
        pair = f"{coin_symbol}USDT"
        if pair in usdt_pairs:
            top_pairs.append(pair)
    
    return top_pairs

## 获取所有现货交易对
def get_symbols():
    data = client.exchange_info()["symbols"]

    # 创建DataFrame
    columns = ['symbol', 'status', 'baseAsset', 'quoteAsset']
    df = pd.DataFrame(data, columns=columns)
    df = df[(df['status'] == 'TRADING') & (df['quoteAsset'] == 'USDT')]

    return df['symbol']

def get_future_symbols():
    data = future_client.exchange_info()["symbols"]

    # 创建DataFrame
    columns = ['symbol', 'status','contractType', 'baseAsset', 'quoteAsset']
    df = pd.DataFrame(data, columns=columns)
    # 过滤出交易对
    df = df[(df['status'] == 'TRADING') & (df['contractType'] == 'PERPETUAL')]

    return df['symbol']

## 根据交易对和周期获取数据集
def get_klines(symbol,interval, future = False,limit=1000):
    # 获取 k 线数据
    data = {}
    if future == True:
        data = future_client.klines(symbol, interval, limit=limit)
    else:
        data = client.klines(symbol, interval,limit=limit)

    # 将数据转换为DataFrame
    columns = ['timestamp', 'open', 'high', 'low', 'close', 'volume', 
           'close_time', 'quote_asset_volume', 'number_of_trades', 
           'taker_buy_base_asset_volume', 'taker_buy_quote_asset_volume', 'ignore']

    df = pd.DataFrame(data, columns=columns)

    # 转化成 float
    df['open'] = df['open'].astype('float64')
    df['high'] = df['high'].astype('float64')
    df['low'] = df['low'].astype('float64')
    df['close'] = df['close'].astype('float64')
    df['volume'] = df['volume'].astype('float64')

    # 将时间戳转换为日期时间格式
    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms', utc=True).map(lambda x: x.tz_convert('Asia/Shanghai'))
    df['close_time'] = pd.to_datetime(df['close_time'], unit='ms', utc=True).map(lambda x: x.tz_convert('Asia/Shanghai'))

    return df