What is the term"follow 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;

　　開發(fā)案例唯：MrsFu123

　　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

　　常見跟單分自動跟單和手動跟單，開發(fā)策略I35模式7O98詳細O7I8自動跟單可以選擇要跟的幣種，下單的手數(shù)，和杠桿倍數(shù)；手動跟單需要選擇跟單資產(chǎn)跟的價格止盈止損，下單手數(shù)和杠桿倍數(shù)；

　　合約跟單的角色大致可分為兩種，即跟單者和交易員：

　　1.跟單者

　　系統(tǒng)生成交易員榜單，展現(xiàn)交易員的交易記錄，例如累計收益率、近三周交易勝率、交易天數(shù)、交易筆數(shù)、累計跟隨人數(shù)等，跟隨者可在此查看相關(guān)記錄，選擇自己想要跟隨的交易員，選擇跟單方式（固定金額跟單/比例跟單），設(shè)置好相應(yīng)的策略來進行跟單即可。improving the opportunity to obtain profits;

　　2.交易員

　　申請成為交易員，經(jīng)過后臺審核通過才能成為交易員，審核相對嚴格，通過平臺展現(xiàn)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;

　　#載入ImageNet校準數(shù)據(jù)集

　　import os

　　import torchvision.transforms as transforms

　　from PIL import Image

　　import numpy as np

　　import torch

　　input_path="xxxxxxx/xxxxx/x"#【改】數(shù)據(jù)集路徑

　　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】注意圖像格式是.jpg還是.jpeg，.jpeg則filename[:-5]

　　print('{}已完成，進度{}/{}'.format(filename[:-5],os.listdir(input_path).index(file),len(os.listdir(input_path))))