（八九）ArkCompiler 在智能金融中的应用：编译优化与安全性能提升 原创

class ATM:                    def __init__(self, balance):                        self.balance = balance                                     def withdraw(self, amount):                        if amount > self.balance:                            print("余额不足，无法取款。")                        else:                            self.balance -= amount                            print(f"取款成功，当前余额为: {self.balance}")                                 # 初始化ATM机余额                atm = ATM(1000)                                 # 模拟取款操作                atm.withdraw(200)
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import cv2                                 # 读取二维码图片                image = cv2.imread('qrcode.png')                                 # 初始化二维码检测器                qr_detector = cv2.QRCodeDetector()                                 # 检测二维码                retval, decoded_info, points, straight_qrcode = qr_detector.detectAndDecodeMulti(image)                                 if retval:                    for info in decoded_info:                        print(f"识别到的二维码信息: {info}")                else:                    print("未识别到二维码。")
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from cryptography.fernet import Fernet                                 # 生成加密密钥                key = Fernet.generate_key()                cipher_suite = Fernet(key)                                 # 待加密的数据                data = b"敏感金融数据"                                 # 加密数据                encrypted_data = cipher_suite.encrypt(data)                                 # 解密数据                decrypted_data = cipher_suite.decrypt(encrypted_data)                                 print(f"加密后的数据: {encrypted_data}")                print(f"解密后的数据: {decrypted_data}")
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import numpy as np                                 # 模拟历史股票数据                stock_prices = np.array([100, 102, 105, 103, 106])                                 # 计算简单移动平均线                def simple_moving_average(data, window_size):                    weights = np.repeat(1.0, window_size) / window_size                    smas = np.convolve(data, weights, 'valid')                    return smas                                 # 计算5日简单移动平均线                sma_5 = simple_moving_average(stock_prices, 5)                                 print(f"5日简单移动平均线: {sma_5}")
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# 模拟交易数据                transactions = [100, 200, 5000, 300]                                 # 设定风险阈值                risk_threshold = 4000                                 # 监控交易数据                for transaction in transactions:                    if transaction > risk_threshold:                        print(f"警告：检测到高风险交易，交易金额为: {transaction}")
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