first commit

.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+*.manifest
+*.spec
+WorldQuant_Brain_Alpha_Generator.spec
+
+# Credentials
+brain_credentials.txt
+
+# Generated files
+alpha_ids.txt
+
+# Mac
+.DS_Store
+mac/dist/
+mac/build/
+
+# Windows
+dist/
+build/

README.md

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+# 🚀 WorldQuant Brain Alpha Generator
+
+<div align="center">
+
+![GitHub stars](https://img.shields.io/github/stars/YHYYDS666/WorldQuant-Brain-Alpha?style=social)
+![GitHub forks](https://img.shields.io/github/forks/YHYYDS666/WorldQuant-Brain-Alpha?style=social)
+![GitHub watchers](https://img.shields.io/github/watchers/YHYYDS666/WorldQuant-Brain-Alpha?style=social)
+
+```txt
+  ____    _____   _____    ____   _   _   _____ 
+ |  _ \  |_   _| |  ___|  / ___| | | | | |_   _|
+ | |_) |   | |   | |_    | |  _  | |_| |   | |  
+ |  _ <    | |   |  _|   | |_| | |  _  |   | |  
+ |_| \_\   |_|   |_|      \____| |_| |_|   |_|  
+```
+
+</div>
+
+## 📖 项目介绍
+
+这是一个用于自动生成和提交 WorldQuant Brain Alpha 表达式的工具。它可以帮助用户自动化测试和提交 Alpha 策略。
+
+## 🗂️ 目录结构
+
+```txt
+WorldQuant-Brain-Alpha/
+├── 📜 main.py                # 主程序入口
+├── 🧠 brain_batch_alpha.py   # 核心处理模块
+├── 📊 alpha_strategy.py      # 策略生成模块
+├── ⚙️ dataset_config.py      # 数据集配置
+├── 📋 requirements.txt       # 依赖列表
+├── 🔨 build.py              # 通用构建脚本
+├── 🪟 build_windows.py      # Windows构建脚本
+├── 📦 setup.py              # 打包配置
+├── 🗜️ create_zipapp.py      # ZIP打包脚本
+└── 🍎 mac/                  # Mac相关文件
+    ├── build_mac.py         # Mac构建脚本
+    ├── create_icns.py       # 图标生成
+    └── icon.png             # 图标源文件
+```
+
+## ✨ 功能特点
+
+- 🤖 自动生成 Alpha 策略
+- 📈 自动测试性能指标
+- 🚀 自动提交合格策略
+- 💾 保存策略 ID
+- 🔄 支持多种运行模式
+
+## 🛠️ 安装方法
+
+上传文件出问题了，所有就分开放了两个版本。之后会合并成一个版本。
+
+### Windows 用户
+
+```bash
+# 下载发布版本
+✨ 从 Releases选择Alpha_Tool_v1.0版本 下载 Alpha_.zip
+
+# 从源码构建
+🔨 pip install -r requirements.txt
+🚀 python build_windows.py
+```
+
+### Mac 用户
+
+```bash
+# 下载发布版本
+✨ 从 Releases选择最新版 下载 Alpha_Tool_Mac.zip
+
+  # 解压文件
+  unzip Alpha_Tool_Mac.zip
+
+  # 进入解压目录
+  cd Alpha_Tool_Mac
+
+  # 添加执行权限
+  chmod +x Alpha_Tool
+
+  # 运行程序
+  ./Alpha_Tool
+
+# 从源码构建
+🔨 pip install -r requirements.txt
+🚀 cd mac && python build_mac.py
+```
+
+## 📊 数据集支持
+
+| 数据集 | 描述 | 股票范围 |
+|--------|------|----------|
+| 📈 fundamental6 | 基础财务数据 | TOP3000 |
+| 📊 analyst4 | 分析师预测 | TOP1000 |
+| 📉 pv1 | 成交量数据 | TOP1000 |
+
+## 👍 性能要求
+
+```txt
+     ___________
+    |  METRICS  |
+    |-----------|
+    | ✓ Sharpe  | > 1.5
+    | ✓ Fitness | > 1.0
+    | ✓ Turnover| 0.1-0.9
+    | ✓ IC Mean | > 0.02
+    |___________|
+```
+
+## 🎯 使用流程
+
+1. 📝 配置账号信息
+2. 🎲 选择数据集
+3. 🔄 选择运行模式
+4. 📊 等待结果生成
+5. 🚀 自动提交策略
+
+## 🤝 贡献指南
+
+欢迎提交 Issue 和 Pull Request！
+
+## 📄 许可证
+
+MIT License
+
+## 👨‍💻 联系方式
+
+- 📧 Email: <666@woaiys.filegear-sg.me>
+- 🌟 GitHub: [YHYYDS666](https://github.com/YHYYDS666)
+
+---
+
+⭐ 如果这个项目帮助到你，请给一个 star! ⭐
+
+## Star History
+
+<a href="https://star-history.com/#WorldQuant-Brain-AlphaP/WorldQuant-Brain-AlphaP&Date">
+ <picture>
+   <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=WorldQuant-Brain-AlphaP/WorldQuant-Brain-AlphaP&type=Date&theme=dark" />
+   <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=WorldQuant-Brain-AlphaP/WorldQuant-Brain-AlphaP&type=Date" />
+   <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=WorldQuant-Brain-AlphaP/WorldQuant-Brain-AlphaP&type=Date" />
+ </picture>
+</a>

alpha_strategy.py

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+"""Alpha 策略生成模块"""
+
+
+class AlphaStrategy:
+    def get_simulation_data(self, datafields, mode=1):
+        """根据模式生成策略列表"""
+
+        if mode == 1:
+            return self.generate_basic_strategy(datafields)
+        elif mode == 2:
+            return self.generate_multi_factor_strategy(datafields)
+        else:
+            print("❌ 无效的策略模式")
+            return []
+
+    def generate_basic_strategy(self, datafields):
+        """生成基础策略"""
+
+        strategies = []
+        for field in datafields:
+            # 1. 日内策略
+            strategies.extend([
+                # 日内收益率
+                "group_rank((close - open)/open, subindustry)",
+
+                # 隔夜收益率
+                "group_rank((open - delay(close, 1))/delay(close, 1), subindustry)",
+
+                # 高低价差异
+                "group_rank((high - low)/open, subindustry)"
+            ])
+
+            # 2. 波动率策略
+            strategies.extend([
+                # 波动率偏度
+                f"power(ts_std_dev(abs({field}), 30), 2) - power(ts_std_dev({field}, 30), 2)",
+
+                # 波动率动态调整
+                f"group_rank(std({field}, 20)/mean({field}, 20) * (1/cap), subindustry)"
+            ])
+
+            # 3. 成交量策略
+            if field in ['volume', 'turnover']:
+                strategies.extend([
+                    # 成交量异常
+                    "group_rank((volume/sharesout - mean(volume/sharesout, 20))/std(volume/sharesout, 20), subindustry)",
+
+                    # 成交量趋势
+                    "ts_corr(volume/sharesout, abs(returns), 10)"
+                ])
+
+            # 4. 市场微观结构
+            strategies.extend([
+                # 小单买卖压力
+                f"group_neutralize(power(rank({field} - group_mean({field}, 1, subindustry)), 3), bucket(rank(cap), range='0,1,0.1'))",
+
+                # 流动性压力
+                f"group_rank(correlation({field}, volume/sharesout, 20), subindustry)"
+            ])
+
+            # 5. 条件触发策略
+            strategies.extend([
+                # 条件触发
+                f"trade_when(ts_rank(ts_std_dev(returns, 10), 252) < 0.9, {field}, -1)",
+
+                # 市场状态过滤
+                f"trade_when(volume > mean(volume, 20), {field}, -1)"
+            ])
+
+        return strategies
+
+    def generate_multi_factor_strategy(self, datafields):
+        """生成多因子组合策略"""
+
+        strategies = []
+        n = len(datafields)
+
+        for i in range(0, n-1, 2):
+            field1 = datafields[i]
+            field2 = datafields[i+1]
+
+            # 1. 回归中性化
+            strategies.extend([
+                f"regression_neut(vector_neut({field1}, {field2}), abs(ts_mean(returns, 252)/ts_std_dev(returns, 252)))",
+
+                # 多重回归
+                f"regression_neut(regression_neut({field1}, {field2}), ts_std_dev(returns, 30))"
+            ])
+
+            # 2. 条件组合
+            strategies.extend([
+                # 条件选择
+                f"if_else(rank({field1}) > 0.5, {field2}, -1 * {field2})",
+
+                # 分组组合
+                f"group_neutralize({field1} * {field2}, bucket(rank(cap), range='0.1,1,0.1'))"
+            ])
+
+            # 3. 复杂信号
+            strategies.extend([
+                # 信号强度
+                f"power(rank(group_neutralize(-ts_decay_exp_window(ts_sum(if_else({field1}-group_mean({field1},1,industry)-0.02>0,1,0)*ts_corr({field2},cap,5),3),50),industry)),2)",
+
+                # 市场状态
+                f"trade_when(ts_rank(ts_std_dev(returns,10),252)<0.9, {field1} * {field2}, -1)"
+            ])
+
+        return strategies

brain_batch_alpha.py

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+"""WorldQuant Brain API 批量处理模块"""
+
+import json
+import os
+from datetime import datetime
+from os.path import expanduser
+from time import sleep
+
+import pandas as pd
+import requests
+from requests.auth import HTTPBasicAuth
+
+from alpha_strategy import AlphaStrategy
+from dataset_config import get_api_settings, get_dataset_config
+
+
+class BrainBatchAlpha:
+    API_BASE_URL = 'https://api.worldquantbrain.com'
+
+    def __init__(self, credentials_file='brain_credentials.txt'):
+        """初始化 API 客户端"""
+
+        self.session = requests.Session()
+        self._setup_authentication(credentials_file)
+
+    def _setup_authentication(self, credentials_file):
+        """设置认证"""
+
+        try:
+            with open(expanduser(credentials_file)) as f:
+                credentials = json.load(f)
+            username, password = credentials
+            self.session.auth = HTTPBasicAuth(username, password)
+
+            response = self.session.post(f"{self.API_BASE_URL}/authentication")
+            if response.status_code not in [200, 201]:
+                raise Exception(f"认证失败: HTTP {response.status_code}")
+
+            print("✅ 认证成功!")
+
+        except Exception as e:
+            print(f"❌ 认证错误: {str(e)}")
+            raise
+
+    def simulate_alphas(self, datafields=None, strategy_mode=1, dataset_name=None):
+        """模拟 Alpha 列表"""
+
+        try:
+            datafields = self._get_datafields_if_none(datafields, dataset_name)
+            if not datafields:
+                return []
+
+            alpha_list = self._generate_alpha_list(datafields, strategy_mode)
+            if not alpha_list:
+                return []
+
+            print(f"\n🚀 开始模拟 {len(alpha_list)} 个 Alpha 表达式...")
+
+            results = []
+            for i, alpha in enumerate(alpha_list, 1):
+                print(f"\n[{i}/{len(alpha_list)}] 正在模拟 Alpha...")
+                result = self._simulate_single_alpha(alpha)
+                if result and result.get('passed_all_checks'):
+                    results.append(result)
+                    self._save_alpha_id(result['alpha_id'], result)
+
+                if i < len(alpha_list):
+                    sleep(5)
+
+            return results
+
+        except Exception as e:
+            print(f"❌ 模拟过程出错: {str(e)}")
+            return []
+
+    def _simulate_single_alpha(self, alpha):
+        """模拟单个 Alpha"""
+
+        try:
+            print(f"表达式: {alpha.get('regular', 'Unknown')}")
+
+            # 发送模拟请求
+            sim_resp = self.session.post(
+                f"{self.API_BASE_URL}/simulations",
+                json=alpha
+            )
+
+            if sim_resp.status_code != 201:
+                print(f"❌ 模拟请求失败 (状态码: {sim_resp.status_code})")
+                return None
+
+            try:
+                sim_progress_url = sim_resp.headers['Location']
+                start_time = datetime.now()
+                total_wait = 0
+
+                while True:
+                    sim_progress_resp = self.session.get(sim_progress_url)
+                    retry_after_sec = float(sim_progress_resp.headers.get("Retry-After", 0))
+
+                    if retry_after_sec == 0:  # simulation done!
+                        alpha_id = sim_progress_resp.json()['alpha']
+                        print(f"✅ 获得 Alpha ID: {alpha_id}")
+
+                        # 等待一下让指标计算完成
+                        sleep(3)
+
+                        # 获取 Alpha 详情
+                        alpha_url = f"{self.API_BASE_URL}/alphas/{alpha_id}"
+                        alpha_detail = self.session.get(alpha_url)
+                        alpha_data = alpha_detail.json()
+
+                        # 检查是否有 is 字段
+                        if 'is' not in alpha_data:
+                            print("❌ 无法获取指标数据")
+                            return None
+
+                        is_qualified = self.check_alpha_qualification(alpha_data)
+
+                        return {
+                            'expression': alpha.get('regular'),
+                            'alpha_id': alpha_id,
+                            'passed_all_checks': is_qualified,
+                            'metrics': alpha_data.get('is', {}),
+                            'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+                        }
+
+                    # 更新等待时间和进度
+                    total_wait += retry_after_sec
+                    elapsed = (datetime.now() - start_time).total_seconds()
+                    progress = min(95, (elapsed / 30) * 100)  # 假设通常需要 30 秒完成
+
+                    print(f"⏳ 等待模拟结果... ({elapsed:.1f} 秒 | 进度约 {progress:.0f}%)")
+                    sleep(retry_after_sec)
+
+            except KeyError:
+                print("❌ 无法获取模拟进度 URL")
+                return None
+
+        except Exception as e:
+            print(f"⚠️ Alpha 模拟失败: {str(e)}")
+            return None
+
+    def check_alpha_qualification(self, alpha_data):
+        """检查 Alpha 是否满足所有提交条件"""
+
+        try:
+            # 从 'is' 字段获取指标
+            is_data = alpha_data.get('is', {})
+            if not is_data:
+                print("❌ 无法获取指标数据")
+                return False
+
+            # 获取指标值
+            sharpe = float(is_data.get('sharpe', 0))
+            fitness = float(is_data.get('fitness', 0))
+            turnover = float(is_data.get('turnover', 0))
+            ic_mean = float(is_data.get('margin', 0))  # margin 对应 IC Mean
+
+            # 获取子宇宙 Sharpe
+            sub_universe_check = next(
+                (
+                    check for check in is_data.get('checks', [])
+                    if check['name'] == 'LOW_SUB_UNIVERSE_SHARPE'
+                ),
+                {}
+            )
+            subuniverse_sharpe = float(sub_universe_check.get('value', 0))
+            required_subuniverse_sharpe = float(sub_universe_check.get('limit', 0))
+
+            # 打印指标
+            print("\n📊 Alpha 指标详情:")
+            print(f"  Sharpe: {sharpe:.3f} (>1.5)")
+            print(f"  Fitness: {fitness:.3f} (>1.0)")
+            print(f"  Turnover: {turnover:.3f} (0.1-0.9)")
+            print(f"  IC Mean: {ic_mean:.3f} (>0.02)")
+            print(f"  子宇宙 Sharpe: {subuniverse_sharpe:.3f} (>{required_subuniverse_sharpe:.3f})")
+
+            print("\n📝 指标评估结果:")
+
+            # 检查每个指标并输出结果
+            is_qualified = True
+
+            if sharpe < 1.5:
+                print("❌ Sharpe ratio 不达标")
+                is_qualified = False
+            else:
+                print("✅ Sharpe ratio 达标")
+
+            if fitness < 1.0:
+                print("❌ Fitness 不达标")
+                is_qualified = False
+            else:
+                print("✅ Fitness 达标")
+
+            if turnover < 0.1 or turnover > 0.9:
+                print("❌ Turnover 不在合理范围")
+                is_qualified = False
+            else:
+                print("✅ Turnover 达标")
+
+            if ic_mean < 0.02:
+                print("❌ IC Mean 不达标")
+                is_qualified = False
+            else:
+                print("✅ IC Mean 达标")
+
+            if subuniverse_sharpe < required_subuniverse_sharpe:
+                print(f"❌ 子宇宙 Sharpe 不达标 ({subuniverse_sharpe:.3f} < {required_subuniverse_sharpe:.3f})")
+                is_qualified = False
+            else:
+                print(f"✅ 子宇宙 Sharpe 达标 ({subuniverse_sharpe:.3f} > {required_subuniverse_sharpe:.3f})")
+
+            print("\n🔍 检查项结果:")
+            checks = is_data.get('checks', [])
+            for check in checks:
+                name = check.get('name')
+                result = check.get('result')
+                value = check.get('value', 'N/A')
+                limit = check.get('limit', 'N/A')
+
+                if result == 'PASS':
+                    print(f"✅ {name}: {value} (限制: {limit})")
+                elif result == 'FAIL':
+                    print(f"❌ {name}: {value} (限制: {limit})")
+                    is_qualified = False
+                elif result == 'PENDING':
+                    print(f"⚠️ {name}: 检查尚未完成")
+                    is_qualified = False
+
+            print("\n📋 最终评判:")
+            if is_qualified:
+                print("✅ Alpha 满足所有条件，可以提交!")
+            else:
+                print("❌ Alpha 未达到提交标准")
+
+            return is_qualified
+
+        except Exception as e:
+            print(f"❌ 检查 Alpha 资格时出错: {str(e)}")
+            return False
+
+    def submit_alpha(self, alpha_id):
+        """提交单个 Alpha"""
+
+        submit_url = f"{self.API_BASE_URL}/alphas/{alpha_id}/submit"
+
+        for attempt in range(5):
+            print(f"🔄 第 {attempt + 1} 次尝试提交 Alpha {alpha_id}")
+
+            # POST 请求
+            res = self.session.post(submit_url)
+            if res.status_code == 201:
+                print("✅ POST: 成功，等待提交完成...")
+            elif res.status_code in [400, 403]:
+                print(f"❌ 提交被拒绝 ({res.status_code})")
+                return False
+            else:
+                sleep(3)
+                continue
+
+            # 检查提交状态
+            while True:
+                res = self.session.get(submit_url)
+                retry = float(res.headers.get('Retry-After', 0))
+
+                if retry == 0:
+                    if res.status_code == 200:
+                        print("✅ 提交成功!")
+                        return True
+                    return False
+
+                sleep(retry)
+
+        return False
+
+    def submit_multiple_alphas(self, alpha_ids):
+        """批量提交 Alpha"""
+        successful = []
+        failed = []
+
+        for alpha_id in alpha_ids:
+            if self.submit_alpha(alpha_id):
+                successful.append(alpha_id)
+            else:
+                failed.append(alpha_id)
+
+            if alpha_id != alpha_ids[-1]:
+                sleep(10)
+
+        return successful, failed
+
+    def _get_datafields_if_none(self, datafields=None, dataset_name=None):
+        """获取数据字段列表"""
+
+        try:
+            if datafields is not None:
+                return datafields
+
+            if dataset_name is None:
+                print("❌ 未指定数据集")
+                return None
+
+            config = get_dataset_config(dataset_name)
+            if not config:
+                print(f"❌ 无效的数据集: {dataset_name}")
+                return None
+
+            # 获取数据字段
+            search_scope = {
+                'instrumentType': 'EQUITY',
+                'region': 'USA',
+                'delay': '1',
+                'universe': config['universe']
+            }
+
+            url_template = (
+                f"{self.API_BASE_URL}/data-fields?"
+                f"instrumentType={search_scope['instrumentType']}"
+                f"&region={search_scope['region']}"
+                f"&delay={search_scope['delay']}"
+                f"&universe={search_scope['universe']}"
+                f"&dataset.id={config['id']}"
+                "&limit=50&offset={offset}"
+            )
+
+            # 获取总数
+            initial_resp = self.session.get(url_template.format(offset=0))
+            if initial_resp.status_code != 200:
+                print("❌ 获取数据字段失败")
+                return None
+
+            total_count = initial_resp.json()['count']
+
+            # 获取所有数据字段
+            all_fields = []
+            for offset in range(0, total_count, 50):
+                resp = self.session.get(url_template.format(offset=offset))
+                if resp.status_code != 200:
+                    continue
+                all_fields.extend(resp.json()['results'])
+
+            # 过滤矩阵类型字段
+            matrix_fields = [
+                field['id'] for field in all_fields
+                if field.get('type') == 'MATRIX'
+            ]
+
+            if not matrix_fields:
+                print("❌ 未找到可用的数据字段")
+                return None
+
+            print(f"✅ 获取到 {len(matrix_fields)} 个数据字段")
+            return matrix_fields
+
+        except Exception as e:
+            print(f"❌ 获取数据字段时出错: {str(e)}")
+            return None
+
+    def _generate_alpha_list(self, datafields, strategy_mode):
+        """生成 Alpha 表达式列表"""
+        try:
+            # 初始化策略生成器
+            strategy_generator = AlphaStrategy()
+
+            # 生成策略列表
+            strategies = strategy_generator.get_simulation_data(datafields, strategy_mode)
+
+            print(f"生成了 {len(strategies)} 个Alpha表达式")
+
+            # 转换为 API 所需的格式
+            alpha_list = []
+            for strategy in strategies:
+                simulation_data = {
+                    'type': 'REGULAR',
+                    'settings': {
+                        'instrumentType': 'EQUITY',
+                        'region': 'USA',
+                        'universe': 'TOP3000',
+                        'delay': 1,
+                        'decay': 0,
+                        'neutralization': 'SUBINDUSTRY',
+                        'truncation': 0.08,
+                        'pasteurization': 'ON',
+                        'unitHandling': 'VERIFY',
+                        'nanHandling': 'ON',
+                        'language': 'FASTEXPR',
+                        'visualization': False,
+                    },
+                    'regular': strategy
+                }
+                alpha_list.append(simulation_data)
+
+            return alpha_list
+
+        except Exception as e:
+            print(f"❌ 生成 Alpha 列表失败: {str(e)}")
+            return []

build.py

@@ -0,0 +1,54 @@
+import PyInstaller.__main__
+import os
+import sys
+import shutil
+
+# 确保目录存在
+if not os.path.exists('dist'):
+    os.makedirs('dist')
+
+# 设置命令行参数
+args = [
+    'main.py',  # 主程序入口
+    '--name=Alpha_工具',  # 可执行文件名
+    '--onefile',  # 打包成单个文件
+    '--console',  # 使用控制台窗口（改为控制台模式）
+    '--add-data=dataset_config.py{0}.'.format(os.pathsep),  # 添加配置文件
+    '--add-data=alpha_strategy.py{0}.'.format(os.pathsep),  # 添加策略文件
+    '--add-data=brain_batch_alpha.py{0}.'.format(os.pathsep),  # 添加核心处理文件
+    '--clean',  # 清理临时文件
+    '--noconfirm',  # 不确认覆盖
+]
+
+# 如果有图标文件，添加图标
+if os.path.exists('icon.ico'):
+    args.append('--icon=icon.ico')
+
+# 运行打包命令
+PyInstaller.__main__.run(args)
+
+# 打包完成后，复制或创建配置文件到dist目录
+print("\n正在处理配置文件...")
+try:
+    # 处理认证文件
+    if os.path.exists('brain_credentials.txt'):
+        shutil.copy2('brain_credentials.txt', 'dist/')
+        print("✅ brain_credentials.txt 复制成功")
+    else:
+        # 创建示例认证文件
+        with open('dist/brain_credentials.txt', 'w') as f:
+            f.write('["your_email@example.com","your_password"]')
+        print("✅ 创建了示例 brain_credentials.txt")
+
+    # 处理Alpha ID文件
+    if os.path.exists('alpha_ids.txt'):
+        shutil.copy2('alpha_ids.txt', 'dist/')
+        print("✅ alpha_ids.txt 复制成功")
+    else:
+        # 创建空的alpha_ids.txt
+        with open('dist/alpha_ids.txt', 'w') as f:
+            ...
+        print("✅ 创建了空的 alpha_ids.txt")
+
+except Exception as e:
+    print(f"❌ 处理配置文件时出错: {str(e)}")

build_windows.py

@@ -0,0 +1,52 @@
+import PyInstaller.__main__
+import os
+import sys
+import shutil
+
+# 确保目录存在
+if not os.path.exists('dist'):
+    os.makedirs('dist')
+
+# 设置命令行参数
+args = [
+    'main.py',  # 主程序入口
+    '--name=Alpha_工具',  # 可执行文件名
+    '--onefile',  # 打包成单个文件
+    '--console',  # 使用控制台窗口
+    '--add-data=dataset_config.py{0}.'.format(os.pathsep),  # 添加配置文件
+    '--add-data=alpha_strategy.py{0}.'.format(os.pathsep),  # 添加策略文件
+    '--add-data=brain_batch_alpha.py{0}.'.format(os.pathsep),  # 添加核心处理文件
+    '--clean',  # 清理临时文件
+    '--noconfirm',  # 不确认覆盖
+]
+
+# 如果有图标文件，添加图标
+if os.path.exists('icon.ico'):
+    args.append('--icon=icon.ico')
+
+# 运行打包命令
+PyInstaller.__main__.run(args)
+
+# 打包完成后，复制或创建配置文件到dist目录
+print("\n正在处理配置文件...")
+try:
+    # 处理认证文件
+    if os.path.exists('brain_credentials.txt'):
+        shutil.copy2('brain_credentials.txt', 'dist/')
+        print("✅ brain_credentials.txt 复制成功")
+    else:
+        with open('dist/brain_credentials.txt', 'w') as f:
+            f.write('["your_email@example.com","your_password"]')
+        print("✅ 创建了示例 brain_credentials.txt")
+
+    # 处理Alpha ID文件
+    if os.path.exists('alpha_ids.txt'):
+        shutil.copy2('alpha_ids.txt', 'dist/')
+        print("✅ alpha_ids.txt 复制成功")
+    else:
+        with open('dist/alpha_ids.txt', 'w') as f:
+            ...
+        print("✅ 创建了空的 alpha_ids.txt")
+
+except Exception as e:
+    print(f"❌ 处理配置文件时出错: {str(e)}")

create_zipapp.py

@@ -0,0 +1,88 @@
+import zipapp
+import os
+import shutil
+import subprocess
+import sys
+
+
+def create_zipapp():
+    # 创建临时目录
+    build_dir = "build"
+    if os.path.exists(build_dir):
+        shutil.rmtree(build_dir)
+    os.makedirs(build_dir)
+
+    # 复制源文件
+    source_files = [
+        "main.py",
+        "brain_batch_alpha.py",
+        "alpha_strategy.py",
+        "dataset_config.py",
+    ]
+
+    for file in source_files:
+        shutil.copy2(file, build_dir)
+
+    # 复制配置文件
+    config_files = [
+        "brain_credentials.txt",
+        "alpha_ids.txt",
+    ]
+
+    for file in config_files:
+        if os.path.exists(file):
+            shutil.copy2(file, build_dir)
+        else:
+            print(f"Warning: {file} not found, will be created on first run")
+
+    # 创建 requirements.txt
+    with open(os.path.join(build_dir, "requirements.txt"), "w") as f:
+        f.write("requests>=2.31.0\npandas>=2.0.0\n")
+
+    # 创建 __main__.py
+    with open(os.path.join(build_dir, "__main__.py"), "w") as f:
+        f.write("""
+import sys
+import os
+
+
+def install_deps():
+    import subprocess
+    import pkg_resources
+
+    required = {'requests>=2.31.0', 'pandas>=2.0.0'}
+    installed = {f"{pkg.key}=={pkg.version}" for pkg in pkg_resources.working_set}
+    missing = required - installed
+
+    if missing:
+        subprocess.check_call([sys.executable, '-m', 'pip', 'install', *missing])
+
+
+if __name__ == '__main__':
+    # 安装依赖
+    install_deps()
+
+    # 导入主程序
+    from main import main
+    main()
+""")
+
+    # 创建可执行文件
+    output = "Alpha_Tool.pyz"
+    if os.path.exists(output):
+        os.remove(output)
+
+    zipapp.create_archive(
+        build_dir,
+        output,
+        main="__main__:main",
+        compressed=True
+    )
+
+    print(f"\n✅ 成功创建 {output}")
+    print("使用方法：")
+    print(f"python {output}")
+
+
+if __name__ == "__main__":
+    create_zipapp()

dataset_config.py

@@ -0,0 +1,110 @@
+"""数据集配置管理模块"""
+
+DATASET_CONFIGS = {
+    'fundamental6': {
+        'id': 'fundamental6',
+        'universe': 'TOP3000',
+        'description': '基础财务数据',
+        'api_settings': {
+            'instrumentType': 'EQUITY',
+            'region': 'USA',
+            'delay': 1,
+            'decay': 0,
+            'neutralization': 'SUBINDUSTRY',
+            'truncation': 0.08,
+            'pasteurization': 'ON',
+            'unitHandling': 'VERIFY',
+            'nanHandling': 'ON',
+            'language': 'FASTEXPR'
+        },
+        'fields': [
+            'assets', 'liabilities', 'revenue', 'netincome',
+            'cash', 'debt', 'equity', 'eps', 'pe_ratio',
+            'pb_ratio', 'market_cap', 'dividend_yield'
+        ]
+    },
+    'analyst4': {
+        'id': 'analyst4',
+        'universe': 'TOP1000',
+        'description': '分析师预测数据',
+        'api_settings': {
+            'instrumentType': 'EQUITY',
+            'region': 'USA',
+            'delay': 1,
+            'decay': 0,
+            'neutralization': 'SUBINDUSTRY',
+            'truncation': 0.08,
+            'pasteurization': 'ON',
+            'unitHandling': 'VERIFY',
+            'nanHandling': 'ON',
+            'language': 'FASTEXPR'
+        },
+        'fields': [
+            'anl4_tbvps_low', 'anl4_tbvps_high',
+            'anl4_tbvps_mean', 'anl4_tbvps_median'
+        ]
+    },
+    'pv1': {
+        'id': 'pv1',
+        'universe': 'TOP1000',
+        'description': '股市成交量数据',
+        'api_settings': {
+            'instrumentType': 'EQUITY',
+            'region': 'USA',
+            'delay': 1,
+            'decay': 0,
+            'neutralization': 'SUBINDUSTRY',
+            'truncation': 0.08,
+            'pasteurization': 'ON',
+            'unitHandling': 'VERIFY',
+            'nanHandling': 'ON',
+            'language': 'FASTEXPR'
+        },
+        'fields': [
+            'volume', 'close', 'open', 'high', 'low',
+            'vwap', 'returns', 'turnover', 'volatility'
+        ]
+    }
+}
+
+
+def get_dataset_list():
+    """获取所有可用数据集列表"""
+
+    return [
+        f"{idx+1}: {name} ({config['universe']}) - {config['description']}"
+        for idx, (name, config) in enumerate(DATASET_CONFIGS.items())
+    ]
+
+
+def get_dataset_config(dataset_name):
+    """获取指定数据集的配置"""
+
+    return DATASET_CONFIGS.get(dataset_name)
+
+
+def get_dataset_by_index(index):
+    """通过索引获取数据集名称"""
+
+    try:
+        return list(DATASET_CONFIGS.keys())[int(index)-1]
+    except (IndexError, ValueError):
+        return None
+
+
+def get_dataset_fields(dataset_name):
+    """获取指定数据集的字段列表"""
+
+    config = DATASET_CONFIGS.get(dataset_name)
+    return config['fields'] if config else []
+
+
+def get_api_settings(dataset_name):
+    """获取指定数据集的API设置"""
+
+    config = DATASET_CONFIGS.get(dataset_name)
+    if config and 'api_settings' in config:
+        settings = config['api_settings'].copy()
+        settings['universe'] = config['universe']
+        return settings
+    return None

mac/build_mac.py

@@ -0,0 +1,69 @@
+import PyInstaller.__main__
+import os
+import sys
+import shutil
+
+# 获取项目根目录的绝对路径
+ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+# 创建 mac 目录
+mac_dir = os.path.dirname(os.path.abspath(__file__))
+mac_dist_dir = os.path.join(mac_dir, 'dist')
+if not os.path.exists(mac_dir):
+    os.makedirs(mac_dir)
+if not os.path.exists(mac_dist_dir):
+    os.makedirs(mac_dist_dir)
+
+# 设置命令行参数
+args = [
+    os.path.join(ROOT_DIR, 'main.py'),  # 主程序入口
+    '--name=Alpha_Tool',  # Mac版本名称
+    '--onefile',  # 打包成单个文件
+    '--console',  # 使用控制台窗口
+    f'--add-data={os.path.join(ROOT_DIR, "dataset_config.py")}{os.pathsep}.',
+    f'--add-data={os.path.join(ROOT_DIR, "alpha_strategy.py")}{os.pathsep}.',
+    f'--add-data={os.path.join(ROOT_DIR, "brain_batch_alpha.py")}{os.pathsep}.',
+    '--clean',
+    '--noconfirm',
+    f'--distpath={mac_dist_dir}',
+    f'--workpath={os.path.join(mac_dir, "build")}',
+    f'--specpath={mac_dir}'
+]
+
+# 如果有 Mac 图标文件，添加图标
+icon_path = os.path.join(mac_dir, 'icon.icns')
+if os.path.exists(icon_path):
+    args.append(f'--icon={icon_path}')
+
+try:
+    # 运行打包命令
+    PyInstaller.__main__.run(args)
+
+    # 打包完成后，复制或创建配置文件到 dist 目录
+    print("\n正在处理配置文件...")
+
+    # 处理认证文件
+    credentials_src = os.path.join(ROOT_DIR, 'brain_credentials.txt')
+    if os.path.exists(credentials_src):
+        shutil.copy2(credentials_src, mac_dist_dir)
+        print("✅ brain_credentials.txt 复制成功")
+    else:
+        with open(os.path.join(mac_dist_dir, 'brain_credentials.txt'), 'w') as f:
+            f.write('["your_email@example.com","your_password"]')
+        print("✅ 创建了示例 brain_credentials.txt")
+
+    # 处理 Alpha ID 文件
+    alpha_ids_src = os.path.join(ROOT_DIR, 'alpha_ids.txt')
+    if os.path.exists(alpha_ids_src):
+        shutil.copy2(alpha_ids_src, mac_dist_dir)
+        print("✅ alpha_ids.txt 复制成功")
+    else:
+        with open(os.path.join(mac_dist_dir, 'alpha_ids.txt'), 'w') as f:
+            ...
+        print("✅ 创建了空的 alpha_ids.txt")
+
+    print(f"\n✅ Mac 版本打包完成! 文件位于 {mac_dist_dir}")
+
+except Exception as e:
+    print(f"\n❌ 打包过程出错: {str(e)}")
+    sys.exit(1)

mac/create_icns.py

@@ -0,0 +1,48 @@
+import os
+
+from PIL import Image
+
+
+def create_icns():
+    """创建 .icns 文件"""
+    try:
+        # 检查源图片
+        png_path = os.path.join(os.path.dirname(__file__), 'icon.png')
+        if not os.path.exists(png_path):
+            print("⚠️ 未找到 icon.png，将创建默认图标")
+            # 创建一个简单的默认图标
+            img = Image.new('RGB', (512, 512), color='white')
+            img.save(png_path)
+
+        # 创建临时 iconset 目录
+        iconset_name = "icon.iconset"
+        if not os.path.exists(iconset_name):
+            os.makedirs(iconset_name)
+
+        # 需要的图标尺寸
+        icon_sizes = [16, 32, 64, 128, 256, 512]
+
+        # 打开原始图片
+        img = Image.open(png_path)
+
+        # 生成不同尺寸的图标
+        for size in icon_sizes:
+            img_copy = img.copy()
+            img_copy.thumbnail((size, size), Image.Resampling.LANCZOS)
+            img_copy.save(f"{iconset_name}/icon_{size}x{size}.png")
+
+        # 使用 iconutil 创建 .icns 文件（仅在 Mac 上可用）
+        if os.system('which iconutil') == 0:
+            os.system(f'iconutil -c icns {iconset_name}')
+        else:
+            print("⚠️ iconutil 不可用，跳过 .icns 创建")
+
+        print("✅ 图标处理完成")
+
+    except Exception as e:
+        print(f"❌ 创建图标时出错: {str(e)}")
+        # 不抛出异常，让构建继续进行
+
+
+if __name__ == "__main__":
+    create_icns()

main.py

@@ -0,0 +1,98 @@
+"""WorldQuant Brain 批量 Alpha 生成系统"""
+
+import os
+
+from brain_batch_alpha import BrainBatchAlpha
+from dataset_config import get_dataset_by_index, get_dataset_list
+
+STORAGE_ALPHA_ID_PATH = "alpha_ids.txt"
+
+
+def submit_alpha_ids(brain, num_to_submit=2):
+    """提交保存的 Alpha ID"""
+    try:
+        if not os.path.exists(STORAGE_ALPHA_ID_PATH):
+            print("❌ 没有找到保存的Alpha ID文件")
+            return
+
+        with open(STORAGE_ALPHA_ID_PATH, 'r') as f:
+            alpha_ids = [line.strip() for line in f.readlines() if line.strip()]
+
+        if not alpha_ids:
+            print("❌ 没有可提交的Alpha ID")
+            return
+
+        print("\n📝 已保存的Alpha ID列表:")
+        for i, alpha_id in enumerate(alpha_ids, 1):
+            print(f"{i}. {alpha_id}")
+
+        if num_to_submit > len(alpha_ids):
+            num_to_submit = len(alpha_ids)
+
+        selected_ids = alpha_ids[:num_to_submit]
+        successful, failed = brain.submit_multiple_alphas(selected_ids)
+
+        # 更新 alpha_ids.txt
+        remaining_ids = [id for id in alpha_ids if id not in successful]
+        with open(STORAGE_ALPHA_ID_PATH, 'w') as f:
+            f.writelines([f"{id}\n" for id in remaining_ids])
+
+    except Exception as e:
+        print(f"❌ 提交 Alpha 时出错: {str(e)}")
+
+
+def main():
+    """主程序入口"""
+    try:
+        print("🚀 启动 WorldQuant Brain 批量 Alpha 生成系统")
+
+        print("\n📋 请选择运行模式:")
+        print("1: 自动模式 (测试并自动提交 2 个合格 Alpha)")
+        print("2: 仅测试模式 (测试并保存合格 Alpha ID)")
+        print("3: 仅提交模式 (提交已保存的合格 Alpha ID)")
+
+        mode = int(input("\n请选择模式 (1-3): "))
+        if mode not in [1, 2, 3]:
+            print("❌ 无效的模式选择")
+            return
+
+        brain = BrainBatchAlpha()
+
+        if mode in [1, 2]:
+            print("\n📊 可用数据集列表:")
+            for dataset in get_dataset_list():
+                print(dataset)
+
+            dataset_index = input("\n请选择数据集编号: ")
+            dataset_name = get_dataset_by_index(dataset_index)
+            if not dataset_name:
+                print("❌ 无效的数据集编号")
+                return
+
+            print("\n📈 可用策略模式:")
+            print("1: 基础策略模式")
+            print("2: 多因子组合模式")
+
+            strategy_mode = int(input("\n请选择策略模式 (1-2): "))
+            if strategy_mode not in [1, 2]:
+                print("❌ 无效的策略模式")
+                return
+
+            results = brain.simulate_alphas(None, strategy_mode, dataset_name)
+
+            if mode == 1:
+                submit_alpha_ids(brain, 2)
+
+        elif mode == 3:
+            num_to_submit = int(input("\n请输入要提交的 Alpha 数量: "))
+            if num_to_submit <= 0:
+                print("❌ 无效的提交数量")
+                return
+            submit_alpha_ids(brain, num_to_submit)
+
+    except Exception as e:
+        print(f"❌ 程序运行出错: {str(e)}")
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,4 @@
+requests>=2.31.0
+pandas>=2.0.0
+pyinstaller>=5.13.2
+pillow>=10.0.0

setup.py

@@ -0,0 +1,29 @@
+from setuptools import setup, find_packages
+
+
+setup(
+    name="Alpha_Tool",
+    version="1.0.0",
+    author="YourName",
+    description="WorldQuant Brain Alpha Generator",
+    packages=find_packages(),
+    python_requires=">=3.8",
+    install_requires=[
+        "requests>=2.31.0",
+        "pandas>=2.0.0",
+    ],
+    entry_points={
+        'console_scripts': [
+            'alpha_tool=main:main',
+        ],
+    },
+    # 包含非Python文件
+    package_data={
+        '': ['*.txt', '*.json', '*.ico', '*.png'],
+    },
+    # 额外信息
+    classifiers=[
+        "Programming Language :: Python :: 3",
+        "Operating System :: OS Independent",
+    ],
+)