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depth_avg.py
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depth_avg.py
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from concurrent.futures import ALL_COMPLETED, ThreadPoolExecutor,wait
import datetime
import json
import os
import random
import requests
import pandas as pd
# 从币安API获取深度信息
#每小时从币安获取深度信息快照，取30-98位的均值，再取多个小时的均值，作为深度的均值
class ORDER_DATA_GATHER():
def __init__(self) -> None:
self.symbols = self.get_symbols().keys()
# self.symbols = ['BTC','ETH','TON']
self.csv_dir = 'out'
self.executor = ThreadPoolExecutor(max_workers=100)
self.use_proxy = True
pass
def get_proxy_data(self):
url = 'https://raw.githubusercontent.com/bailiang9966/ccpp/main/out/output.json'
response = requests.get(url)
data = json.loads(response.text)
# result = data['bn_uf']+data['bn_spot']
# result= list(set(result))
return data
def get_symbols(self):
url = 'https://raw.githubusercontent.com/bailiang9966/wtf_work/main/out/data.json'
data = requests.get(url).json()
# data = json.loads(response.text)
return data
def get_depth_avg(self,symbol ,market_type ):
if market_type == 'spot':
depth_url = f'https://fapi.binance.com/fapi/v1/depth?symbol={symbol}USDT&limit=500'
else:
depth_url = f'https://api3.binance.com/api/v3/depth?symbol={symbol}USDT&limit=500'
if self.use_proxy:
# proxy = next(self.proxy_cycle)
proxy_list = self.proxy_datas['bn_'+market_type]
proxy = random.choice(proxy_list)
proxies={
'https':proxy,
'http':proxy,
}
data = requests.get(depth_url,proxies=proxies).json()
else:
data = requests.get(depth_url).json()
# 将深度信息转换为DataFrame
columns = ['price', 'quantity']
bids = pd.DataFrame(data['bids'], columns=columns)
asks = pd.DataFrame(data['asks'], columns=columns)
# 计算买单和卖单的金额
df = pd.concat([asks, bids])
df[columns] = df[columns].astype(float)
df_sorted = df.sort_values('quantity')
num_rows = len(df_sorted)
# 计算 50% 和 99% 位置的索引
start_index = int(num_rows * 0.5)
end_index = int(num_rows * 0.99)
subset_df = df_sorted.iloc[start_index:end_index]
average = subset_df['quantity'].mean()
return average
def update_row(self,symbol,col_name):
depth_avg = self.get_depth_avg(symbol,'spot')
self.spot_df.loc[self.spot_df['symbol'] == symbol, col_name] = depth_avg
depth_avg = self.get_depth_avg(symbol,'uf')
self.uf_df.loc[self.uf_df['symbol'] == symbol, col_name] = depth_avg
def run(self):
self.proxy_datas = self.get_proxy_data()
# proxy_list = self.get_proxy_data()
# print(f"可用代理:{len(proxy_list)}个")
# self.proxy_cycle = cycle(proxy_list)
now = datetime.datetime.now()
fmthr = 'd_'+now.strftime("%m%d%H")
if os.path.exists(self.csv_dir+'/spot.csv'):
self.spot_df = pd.read_csv(self.csv_dir+'/spot.csv',header=0)
self.uf_df = pd.read_csv(self.csv_dir+'/uf.csv',header=0)
self.spot_df = self.spot_df[self.spot_df['symbol'].isin(self.symbols)]
self.uf_df = self.uf_df[self.uf_df['symbol'].isin(self.symbols)]
# 获取不在 df 中的 symbols
add_symbols = [s for s in self.symbols if s not in self.spot_df['symbol'].values]
if len(add_symbols)>0:
self.spot_df = pd.concat([self.spot_df, add_symbols], ignore_index=True)
self.uf_df = pd.concat([self.uf_df, add_symbols], ignore_index=True)
else:
self.spot_df = pd.DataFrame(data=self.symbols,columns=['symbol'])
self.uf_df = pd.DataFrame(data=self.symbols,columns=['symbol'])
self.spot_df[fmthr]=None
self.uf_df[fmthr]=None
futures = [self.executor.submit(self.update_row,s,fmthr) for s in self.symbols]
wait(futures, return_when=ALL_COMPLETED)
col_count = len(self.spot_df.columns)
if col_count > 11:
self.spot_df = self.spot_df.drop(self.spot_df.columns[1], axis=1)
self.uf_df = self.uf_df.drop(self.uf_df.columns[1], axis=1)
self.spot_df.to_csv(self.csv_dir+'/spot.csv',header=True,index=False)
self.uf_df.to_csv(self.csv_dir+'/uf.csv',header=True,index=False)
if __name__ == '__main__':
odg = ORDER_DATA_GATHER()
odg.run()