合约跟单系统开发（方案及策略）丨合约跟单交易所系统开发（详细及源码）

　What is the termfollow orders,which means placing orders with traders(professionals with trading experience/market analysis)and setting stop loss risk controls to avoid losses as much as possible;The tracking system has gradually become one of the standard configurations for contract trading,and contracts are a trading system that can buy up or down based on market conditions,operate in both directions,and can also enter and exit at any time,avoiding risks to a certain extent;The tracking system can assist investors in stopping one-to-many,forward and reverse tracking,automatically completing position tracking,automatically completing net position tracking,and controlling risk of tracking accounts and total positions.　　Functions of contract quantification strategy tracking system:　　1.When traders settle in,users apply to become platform traders to carry orders,providing them with some revenue sharing,which can bring good contract direction to platform users while making profits;　　2.Social functions,with social functions,allow users to freely post,chat,and other operations,thereby increasing the user viscosity of the platform;　　　3.Market docking,which allows traders and users to choose between multiple mainstream exchanges;　　4.Recommendation mechanism,inviting people to settle on the platform,bringing greater benefits;　　5.The tracking community can check the trading history of traders(recent profit and loss ratio,accuracy rate,etc.)to better select tracking traders,thereby　　常见跟单分自动跟单和手动跟单，自动跟单可以选择要跟的币种，下单的手数，和杠杆倍数；手动跟单需要选择跟单资产跟的价格止盈止损，下单手数和杠杆倍数；　　合约跟单的角色大致可分为两种，即跟单者和交易员：　　1.跟单者　　系统生成交易员榜单，展现交易员的交易记录，例如累计收益率、近三周交易胜率、交易天数、交易笔数、累计跟随人数等，跟随者可在此查看相关记录，选择自己想要跟随的交易员，选择跟单方式（固定金额跟单/比例跟单），设置好相应的策略来进行跟单即可。improving the opportunity to obtain profits;　　2.交易员　　申请成为交易员，经过后台审核通过才能成为交易员，审核相对严格，通过平台展现自己的交易能力，Collect subscription fees or trading volume from traders to earn commissions;Trader recommendation mechanism,through which users/investors are recommended to obtain corresponding shares;　　#载入 ImageNet 校准数据集　　import os　　import torchvision.transforms as transforms　　from PIL import Image　　import numpy as np　　import torch　　input_path=xxxxxxx/xxxxx/x#【改】数据集路径　　for file in os.listdir(input_path):　　filename=os.fsdecode(file)　　img=Image.open(os.path.join(input_path,filename)).convert('RGB')　　scaler=transforms.Resize((224,224))　　normalize=transforms.Normalize(mean=[0.485,0.456,0.406],　　std=[0.229,0.224,0.225])　　to_tensor=transforms.ToTensor()　　device=torch.device(cpu)　　image=normalize(to_tensor(scaler(img))).unsqueeze(0).to(device)　　np.save(file=f'working/data/{filename[:-4]}',arr=image)#【改】注意图像格式是.jpg 还是.jpeg，.jpeg 则 filename[:-5]　　print('{}已完成，进度{}/{}'.format(filename[:-5],os.listdir(input_path).index(file),len(os.listdir(input_path))))