commit 62948d9d81b8df17fe12f9ba430a600b15a1026c Author: jack Date: Wed Nov 12 11:43:31 2025 +0800 first commit diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..c58c029 --- /dev/null +++ b/.gitignore @@ -0,0 +1,41 @@ +# 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/ diff --git a/README.md b/README.md new file mode 100644 index 0000000..d580aba --- /dev/null +++ b/README.md @@ -0,0 +1,142 @@ +# 🚀 WorldQuant Brain Alpha Generator + +
+ +![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 + ____ _____ _____ ____ _ _ _____ + | _ \ |_ _| | ___| / ___| | | | | |_ _| + | |_) | | | | |_ | | _ | |_| | | | + | _ < | | | _| | |_| | | _ | | | + |_| \_\ |_| |_| \____| |_| |_| |_| +``` + +
+ +## 📖 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 + + + + + + Star History Chart + + diff --git a/alpha_strategy.py b/alpha_strategy.py new file mode 100644 index 0000000..37e3c86 --- /dev/null +++ b/alpha_strategy.py @@ -0,0 +1,108 @@ +"""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 diff --git a/brain_batch_alpha.py b/brain_batch_alpha.py new file mode 100644 index 0000000..a0bc636 --- /dev/null +++ b/brain_batch_alpha.py @@ -0,0 +1,398 @@ +"""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"®ion={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 [] diff --git a/build.py b/build.py new file mode 100644 index 0000000..1cbf5ea --- /dev/null +++ b/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)}") diff --git a/build_windows.py b/build_windows.py new file mode 100644 index 0000000..c443310 --- /dev/null +++ b/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)}") diff --git a/create_zipapp.py b/create_zipapp.py new file mode 100644 index 0000000..33333d6 --- /dev/null +++ b/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() diff --git a/dataset_config.py b/dataset_config.py new file mode 100644 index 0000000..a5abf55 --- /dev/null +++ b/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 diff --git a/icon.ico b/icon.ico new file mode 100644 index 0000000..dc15e3c Binary files /dev/null and b/icon.ico differ diff --git a/mac/build_mac.py b/mac/build_mac.py new file mode 100644 index 0000000..64be054 --- /dev/null +++ b/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) diff --git a/mac/create_icns.py b/mac/create_icns.py new file mode 100644 index 0000000..4a090cb --- /dev/null +++ b/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() diff --git a/mac/icon.icns b/mac/icon.icns new file mode 100644 index 0000000..34a8174 Binary files /dev/null and b/mac/icon.icns differ diff --git a/mac/icon.ico b/mac/icon.ico new file mode 100644 index 0000000..c21ba8a Binary files /dev/null and b/mac/icon.ico differ diff --git a/mac/icon.iconset/icon_1024x1024.png b/mac/icon.iconset/icon_1024x1024.png new file mode 100644 index 0000000..c0cc22d Binary files /dev/null and b/mac/icon.iconset/icon_1024x1024.png differ diff --git a/mac/icon.iconset/icon_128x128.png b/mac/icon.iconset/icon_128x128.png new file mode 100644 index 0000000..a5f4fa6 Binary files /dev/null and b/mac/icon.iconset/icon_128x128.png differ diff --git a/mac/icon.iconset/icon_128x128@2x.png b/mac/icon.iconset/icon_128x128@2x.png new file mode 100644 index 0000000..77e700e Binary files /dev/null and b/mac/icon.iconset/icon_128x128@2x.png differ diff --git a/mac/icon.iconset/icon_16x16.png b/mac/icon.iconset/icon_16x16.png new file mode 100644 index 0000000..5f659d2 Binary files /dev/null and b/mac/icon.iconset/icon_16x16.png differ diff --git a/mac/icon.iconset/icon_16x16@2x.png b/mac/icon.iconset/icon_16x16@2x.png new file mode 100644 index 0000000..b63f026 Binary files /dev/null and b/mac/icon.iconset/icon_16x16@2x.png differ diff --git a/mac/icon.iconset/icon_256x256.png b/mac/icon.iconset/icon_256x256.png new file mode 100644 index 0000000..77e700e Binary files /dev/null and b/mac/icon.iconset/icon_256x256.png differ diff --git a/mac/icon.iconset/icon_256x256@2x.png b/mac/icon.iconset/icon_256x256@2x.png new file mode 100644 index 0000000..c0cc22d Binary files /dev/null and b/mac/icon.iconset/icon_256x256@2x.png differ diff --git a/mac/icon.iconset/icon_32x32.png b/mac/icon.iconset/icon_32x32.png new file mode 100644 index 0000000..b63f026 Binary files /dev/null and b/mac/icon.iconset/icon_32x32.png differ diff --git a/mac/icon.iconset/icon_32x32@2x.png b/mac/icon.iconset/icon_32x32@2x.png new file mode 100644 index 0000000..988f198 Binary files /dev/null and b/mac/icon.iconset/icon_32x32@2x.png differ diff --git a/mac/icon.iconset/icon_512x512.png b/mac/icon.iconset/icon_512x512.png new file mode 100644 index 0000000..c0cc22d Binary files /dev/null and b/mac/icon.iconset/icon_512x512.png differ diff --git a/mac/icon.iconset/icon_512x512@2x.png b/mac/icon.iconset/icon_512x512@2x.png new file mode 100644 index 0000000..c0cc22d Binary files /dev/null and b/mac/icon.iconset/icon_512x512@2x.png differ diff --git a/mac/icon.iconset/icon_64x64.png b/mac/icon.iconset/icon_64x64.png new file mode 100644 index 0000000..988f198 Binary files /dev/null and b/mac/icon.iconset/icon_64x64.png differ diff --git a/mac/icon.iconset/icon_64x64@2x.png b/mac/icon.iconset/icon_64x64@2x.png new file mode 100644 index 0000000..a5f4fa6 Binary files /dev/null and b/mac/icon.iconset/icon_64x64@2x.png differ diff --git a/mac/icon.png b/mac/icon.png new file mode 100644 index 0000000..e21025c Binary files /dev/null and b/mac/icon.png differ diff --git a/main.py b/main.py new file mode 100644 index 0000000..e341a45 --- /dev/null +++ b/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() diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..8966a96 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,4 @@ +requests>=2.31.0 +pandas>=2.0.0 +pyinstaller>=5.13.2 +pillow>=10.0.0 diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..e928437 --- /dev/null +++ b/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", + ], +) diff --git a/tor_alpha_browser_logo_icon_152957.ico b/tor_alpha_browser_logo_icon_152957.ico new file mode 100644 index 0000000..dc15e3c Binary files /dev/null and b/tor_alpha_browser_logo_icon_152957.ico differ