update

requirements.txt

@@ -9,24 +9,22 @@ seaborn>=0.12.0
 # 量化交易
 backtrader>=1.9.78
 yfinance>=0.2.18
-## brew install ta-lib
-ta-lib>=0.4.25
 
 # 机器学习
-scikit-learn>=1.3.0
-xgboost>=1.7.0
+# scikit-learn>=1.3.0
+# xgboost>=1.7.0
 
 # 工具库
 httpx>=0.28.1
 tqdm>=4.65.0
 
 # PyTorch 生态系统
-torch>=2.0.0
-torchvision>=0.15.0
-torchaudio>=2.0.0
-pytorch-lightning>=2.0.0
-torchinfo>=1.7.0
+# torch>=2.0.0
+# torchvision>=0.15.0
+# torchaudio>=2.0.0
+# pytorch-lightning>=2.0.0
+# torchinfo>=1.7.0
 
 # 时间序列深度学习
-pytorch-forecasting>=1.0.0
-pytorch-tabular>=1.0.0
+# pytorch-forecasting>=1.0.0
+# pytorch-tabular>=1.0.0

wqb-demo/brain_credentials.txt

@@ -1 +1 @@
-['uname', 'pw']
+['jack0210_@hotmail.com', '!QAZ2wsx+0913']

wqb-demo/requirements.txt

@@ -1 +0,0 @@
-httpx==0.28.1

wqb-login/brain_credentials.txt

@@ -0,0 +1 @@
+['jack0210_@hotmail.com', '!QAZ2wsx+0913']

wqb-login/login.py

@@ -0,0 +1,33 @@
+import httpx
+from httpx import BasicAuth
+
+def login(credentials_file='brain_credentials.txt'):
+    """登录WorldQuant Brain API"""
+    # 读取本地账号密码
+    with open(credentials_file) as f:
+        credentials = eval(f.read())
+    username, password = credentials[0], credentials[1]
+    
+    print(f"正在登录账户: {username}")
+
+    # 创建客户端并认证
+    client = httpx.Client(auth=BasicAuth(username, password))
+    
+    # 发送登录请求
+    response = client.post('https://api.worldquantbrain.com/authentication')
+    print(f"登录状态: {response.status_code}")
+    
+    if response.status_code == 201:
+        print("登录成功!")
+        print(response.json())
+        return client
+    else:
+        print(f"登录失败: {response.json()}")
+        client.close()
+        return None
+
+# 使用示例
+if __name__ == "__main__":
+    client = login()
+    if client:
+        client.close()

wqb-simulate/account.txt

@@ -0,0 +1 @@
+['jack0210_@hotmail.com', '!QAZ2wsx+0913']

wqb-simulate/alpha.txt

@@ -0,0 +1 @@
+ts_Mean(ts_mean(close - delay(close,1),5) / atr(15), 30)

wqb-simulate/result/simulation_results-1763024938.json

@@ -0,0 +1,10 @@
+[
+  {
+    "expression": "ts_Mean(ts_mean(close - delay(close,1),5) / atr(15), 30)",
+    "time_consuming": 6.7,
+    "formatted_time": "6.70秒",
+    "alpha_id": "/",
+    "status": "error",
+    "description": "Attempted to use inaccessible or unknown operator \"delay\""
+  }
+]

wqb-simulate/simulate.py

@@ -0,0 +1,309 @@
+import os.path
+
+import httpx
+import json
+from httpx import BasicAuth
+import time
+from random import uniform
+import threading
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+
+class WorldQuantBrainSimulate:
+    def __init__(self, credentials_file='account.txt'):
+        self.credentials_file = credentials_file
+        self.client = None
+        self.brain_api_url = 'https://api.worldquantbrain.com'
+
+    def load_credentials(self):
+        """读取本地账号密码"""
+        if not os.path.exists(self.credentials_file):
+            print("未找到 account.txt 文件")
+            with open(self.credentials_file, 'w') as f: f.write("")
+            print("account.txt 文件已创建，请填写账号密码, 格式: ['username', 'password]")
+            exit(1)
+
+        with open(self.credentials_file) as f:
+            credentials = eval(f.read())
+        return credentials[0], credentials[1]
+
+    def login(self):
+        """登录认证"""
+        username, password = self.load_credentials()
+        self.client = httpx.Client(auth=BasicAuth(username, password))
+
+        response = self.client.post(f'{self.brain_api_url}/authentication')
+        print(f"登录状态: {response.status_code}")
+
+        if response.status_code == 201:
+            print("登录成功!")
+            return True
+        else:
+            print(f"登录失败: {response.json()}")
+            return False
+
+    def simulate_alpha(self, expression, settings=None):
+        """模拟Alpha因子"""
+        if self.client is None:
+            raise Exception("请先登录")
+
+        default_settings = {
+            'instrumentType': 'EQUITY',
+            'region': 'USA',
+            'universe': 'TOP3000',
+            'delay': 1,
+            'decay': 0,
+            'neutralization': 'INDUSTRY',
+            'truncation': 0.08,
+            'pasteurization': 'ON',
+            'unitHandling': 'VERIFY',
+            'nanHandling': 'OFF',
+            'language': 'FASTEXPR',
+            'visualization': False,
+        }
+
+        if settings:
+            default_settings.update(settings)
+
+        simulation_data = {
+            'type': 'REGULAR',
+            'settings': default_settings,
+            'regular': expression
+        }
+
+        sim_resp = self.client.post(f'{self.brain_api_url}/simulations', json=simulation_data)
+        print(f"模拟提交状态: {sim_resp.status_code}")
+
+        sim_progress_url = sim_resp.headers['location']
+        print(f"进度URL: {sim_progress_url}")
+
+        while True:
+            sim_progress_resp = self.client.get(sim_progress_url)
+            retry_after_sec = float(sim_progress_resp.headers.get("Retry-After", 0))
+
+            if retry_after_sec == 0:
+                break
+            print(sim_progress_resp.json())
+            print(f"等待 {retry_after_sec} 秒...")
+            time.sleep(retry_after_sec)
+
+        # 如果因子模拟不通过, 获取一下失败信息
+        if sim_progress_resp.json()["status"] == "ERROR":
+            result = sim_progress_resp.json()["message"]
+            print(f"因子模拟失败: {result}")
+            # 返回一个特殊标识，表示模拟失败
+            return {"status": "error", "message": result}
+
+        result = sim_progress_resp.json()["alpha"]
+        print(f"生成的Alpha ID: {result}")
+        return {"status": "success", "alpha_id": result}
+
+    def close(self):
+        """关闭连接"""
+        if self.client:
+            self.client.close()
+
+
+class AlphaSimulationManager:
+    def __init__(self, credentials_file='account.txt'):
+        self.credentials_file = credentials_file
+        self.results = []
+
+    def format_time(self, seconds):
+        """将秒数格式化为 xx分xx秒 格式"""
+        if seconds < 60:
+            return f"{seconds:.2f}秒"
+        else:
+            minutes = int(seconds // 60)
+            remaining_seconds = seconds % 60
+            return f"{minutes}分{remaining_seconds:.2f}秒"
+
+    def simulate_single_alpha(self, api, expression, settings=None):
+        """模拟单个Alpha因子（线程安全）"""
+        alpha_start_time = time.time()
+
+        try:
+            # 模拟Alpha因子
+            simulation_result = api.simulate_alpha(expression, settings)
+            alpha_end_time = time.time()
+            time_consuming = alpha_end_time - alpha_start_time
+
+            # 根据模拟结果类型处理
+            if simulation_result["status"] == "success":
+                # 模拟成功的结果
+                result = {
+                    "expression": expression,
+                    "time_consuming": time_consuming,
+                    "formatted_time": self.format_time(time_consuming),
+                    "alpha_id": simulation_result["alpha_id"],
+                    "status": "success",
+                    "description": "/"
+                }
+                print(f"✓ 因子模拟成功: {expression}")
+                print(f"  耗时: {self.format_time(time_consuming)}，Alpha ID: {simulation_result['alpha_id']}")
+
+            else:
+                # 模拟失败的结果（API返回的错误）
+                result = {
+                    "expression": expression,
+                    "time_consuming": time_consuming,
+                    "formatted_time": self.format_time(time_consuming),
+                    "alpha_id": "/",
+                    "status": "error",
+                    "description": simulation_result["message"]
+                }
+                print(f"✗ 因子模拟失败: {expression}")
+                print(f"  耗时: {self.format_time(time_consuming)}，错误: {simulation_result['message']}")
+
+        except Exception as e:
+            # 其他异常情况
+            alpha_end_time = time.time()
+            time_consuming = alpha_end_time - alpha_start_time
+
+            result = {
+                "expression": expression,
+                "time_consuming": time_consuming,
+                "formatted_time": self.format_time(time_consuming),
+                "alpha_id": "/",
+                "status": "failed",
+                "description": str(e)
+            }
+            print(f"✗ 因子模拟异常: {expression}")
+            print(f"  耗时: {self.format_time(time_consuming)}，异常: {str(e)}")
+
+        return result
+
+    def simulate_alpha_batch(self, alpha_batch, batch_number):
+        """模拟一批Alpha因子（3个一组）"""
+        print(f"\n{'=' * 60}")
+        print(f"开始第 {batch_number} 批因子模拟 (共 {len(alpha_batch)} 个因子)")
+        print(f"因子列表: {alpha_batch}")
+        print(f"{'=' * 60}")
+
+        batch_start_time = time.time()
+        batch_results = []
+
+        # 创建API客户端实例（每个线程独立的客户端）
+        api = WorldQuantBrainSimulate(self.credentials_file)
+
+        try:
+            if api.login():
+                # 使用线程池执行3个因子的模拟
+                with ThreadPoolExecutor(max_workers=3) as executor:
+                    # 提交所有任务
+                    future_to_alpha = {executor.submit(self.simulate_single_alpha, api, alpha): alpha for alpha in alpha_batch}
+
+                    # 等待所有任务完成
+                    for future in as_completed(future_to_alpha):
+                        alpha = future_to_alpha[future]
+                        try:
+                            result = future.result()
+                            batch_results.append(result)
+                        except Exception as e:
+                            print(f"因子 {alpha} 执行异常: {e}")
+        except Exception as e:
+            print(f"第 {batch_number} 批模拟过程中出错: {e}")
+        finally:
+            api.close()
+
+        batch_end_time = time.time()
+        batch_total_time = batch_end_time - batch_start_time
+
+        print(f"\n第 {batch_number} 批模拟完成!")
+        print(f"本批总耗时: {self.format_time(batch_total_time)}")
+        print(f"{'=' * 60}")
+
+        return batch_results
+
+    def run_simulation(self, alpha_list, batch_size=3):
+        """运行批量模拟"""
+        print("开始Alpha因子批量模拟...")
+        total_start_time = time.time()
+
+        # 将因子列表分成每批3个
+        batches = [alpha_list[i:i + batch_size] for i in range(0, len(alpha_list), batch_size)]
+
+        all_results = []
+
+        for i, batch in enumerate(batches, 1):
+            # 模拟当前批次
+            batch_results = self.simulate_alpha_batch(batch, i)
+            all_results.extend(batch_results)
+
+            # 如果不是最后一批，则等待3-5秒
+            if i < len(batches):
+                sleep_time = uniform(3, 5)
+                print(f"\n等待 {sleep_time:.2f} 秒后开始下一批...")
+                time.sleep(sleep_time)
+
+        total_end_time = time.time()
+        total_time = total_end_time - total_start_time
+
+        # 输出最终结果汇总
+        self.print_summary(all_results, total_time)
+
+        # 保存结果到文件
+        self.save_results(all_results)
+
+        return all_results
+
+    def print_summary(self, results, total_time):
+        """打印结果汇总"""
+        print(f"\n{'=' * 60}")
+        print("模拟结果汇总")
+        print(f"{'=' * 60}")
+
+        success_count = sum(1 for r in results if r['status'] == 'success')
+        error_count = sum(1 for r in results if r['status'] == 'error')
+        failed_count = sum(1 for r in results if r['status'] == 'failed')
+
+        print(f"总模拟因子数: {len(results)}")
+        print(f"成功: {success_count} 个")
+        print(f"模拟错误: {error_count} 个")
+        print(f"执行异常: {failed_count} 个")
+        print(f"总耗时: {self.format_time(total_time)}")
+        print(f"{'=' * 60}")
+
+        for i, result in enumerate(results, 1):
+            status_icon = "✓" if result['status'] == 'success' else "✗"
+            print(f"{i}. {status_icon} {result['expression']}")
+            print(f"   状态: {result['status']}")
+            print(f"   耗时: {result['formatted_time']}")
+            print(f"   Alpha ID: {result['alpha_id']}")
+            if result['status'] != 'success':
+                print(f"   原因: {result['description']}")
+            print()
+
+    def save_results(self, results):
+        """保存结果到文件"""
+        # 转换为可序列化的格式
+        serializable_results = []
+        for result in results:
+            serializable_result = result.copy()
+            serializable_result['time_consuming'] = round(serializable_result['time_consuming'], 2)
+            serializable_results.append(serializable_result)
+
+        # 将日志文件, 保存到当前目录下, result 文件夹中
+        if not os.path.exists('./result'):
+            os.makedirs('./result')
+
+        result_name = f"result/simulation_results-{str(int(time.time()))}.json"
+        with open(result_name, 'w', encoding='utf-8') as f:
+            json.dump(serializable_results, f, ensure_ascii=False, indent=2)
+        print(f"结果已保存到 {result_name}")
+
+
+if __name__ == "__main__":
+    # 待模拟因子列表
+    with open('alpha.txt', 'r', encoding='utf-8') as file:
+        alpha_list = [line.strip() for line in file]
+
+    if not alpha_list:
+        print("alpha.txt 文件不存在")
+        with open('alpha.txt', 'w', encoding='utf-8') as file: file.write("")
+        print("已创建 alpha.txt 文件, 请添加因子后重新运行, 一行一个因子")
+        exit(1)
+
+    # 创建模拟管理器并运行
+    manager = AlphaSimulationManager()
+    results = manager.run_simulation(alpha_list, batch_size=3)