++

2026-01-16 08:59:26 +08:00
parent 6833059711
commit 771b35563e
11 changed files with 1036 additions and 45 deletions
@@ -0,0 +1,404 @@
+# -*- coding: utf-8 -*-
+import os
+import random
+import sys
+import openai
+import httpx
+import csv
+from datetime import datetime
+import jieba
+import time
+
+sys.path.append(os.path.join(os.path.abspath(__file__).split('AlphaGenerator')[0] + 'AlphaGenerator'))
+PROJECT_PATH = os.path.join(os.path.abspath(__file__).split('AlphaGenerator')[0] + 'AlphaGenerator')
+
+PREPARE_PROMPT = os.path.join(PROJECT_PATH, 'prepare_prompt')
+KEYS_TEXT = os.path.join(PREPARE_PROMPT, 'keys_text.txt')
+
+USE_AI = 1
+
+TEMPERATURE = 0.1
+
+RANDOM_DATA_SETS_COUNT = 100
+
+SILICONFLOW_API_KEY = "sk-pvdiisdowmuwkrpnxsrlhxaovicqibmlljwrwwvbbdjaitdl"
+SILICONFLOW_BASE_URL = "https://api.siliconflow.cn/v1"
+MODELS = [
+    'Pro/deepseek-ai/DeepSeek-V3.1-Terminus',
+    'deepseek-ai/DeepSeek-V3.2-Exp',
+    'Qwen/Qwen3-VL-235B-A22B-Instruct',
+    # 'MiniMaxAI/MiniMax-M2',
+    # 'zai-org/GLM-4.6',
+    # 'inclusionAI/Ring-flash-2.0',
+    # 'zai-org/GLM-4.6',
+    # 'inclusionAI/Ling-flash-2.0',
+    # 'inclusionAI/Ring-flash-2.0',
+]
+
+
+def process_text(text):
+    filter_list = ['\n', '\t', '\r', '\b', '\f', '\v', '：', '的', '或', '10', '天', '了', '可', '是', '该', '，', ' ', '、', '让', '和', '集',
+                   '/', '日', '在', '（', '_', '-', ')', '(', '上', '距', '与', '比', '下', '及', '）', '...', '；', '%', '&', '+', ',', '.',
+                   ':', ';', '<', '=', '>', '?', '[', ']', '|', '—', '。'
+                   ]
+
+    text_list = jieba.lcut(text)
+    results = []
+    for tl in text_list:
+        should_include = True
+        for fl in filter_list:
+            if fl == tl:
+                should_include = False
+                break
+        if should_include:
+            results.append(tl.lower())
+
+    results = [item for item in results if item != '"' and len(item) > 2]
+
+    if results:
+        return list(set(results))
+    else:
+        return None
+
+
+def keysTextLoader():
+    if not os.path.exists(KEYS_TEXT):
+        print(f"文件不存在: {KEYS_TEXT}")
+        exit(1)
+    with open(KEYS_TEXT, 'r', encoding='utf-8') as f:
+        text_list = [line.strip() for line in f if line.strip()]
+        if not text_list:
+            print('关键词文本无数据, 程序退出')
+            exit(1)
+
+        result_str = process_text(';'.join(text_list))
+
+        print(f'\n关键词文本处理结果: {result_str}\n')
+
+        return result_str
+
+
+def txtFileLoader(file_path):
+    if not os.path.exists(file_path):
+        print(f"文件不存在: {file_path}")
+        exit(1)
+    with open(file_path, 'r', encoding='utf-8') as f:
+        return [line.strip() for line in f if line.strip()]
+
+
+def csvFileLoader(file_path, keys_text):
+    if not os.path.exists(file_path):
+        print(f"文件不存在: {file_path}")
+        exit(1)
+
+    data_dict = {}  # 使用字典来存储，以id为键
+
+    with open(file_path, 'r', encoding='utf-8') as f:
+        reader = csv.reader(f)
+        for row in reader:
+            for key in keys_text:
+                if key in row[11] or key in row[12]:
+                    item_id = row[0]
+                    # 如果id不存在，或者想要保留第一个出现的记录
+                    if item_id not in data_dict:
+                        data_dict[item_id] = {
+                            'id': int(row[0]),
+                            'data_set_name': f"可以使用:{row[1]}",
+                            'description': f"不可使用,仅供参考:{row[2]}"
+                        }
+
+    # 将字典的值转换为列表
+    return list(data_dict.values())
+
+
+def extend_data_sets(file_path, original_data_sets):
+    result = original_data_sets.copy()
+
+    if not os.path.exists(file_path):
+        print(f"文件不存在: {file_path}")
+        return result
+
+    all_data_sets = []
+    with open(file_path, 'r', encoding='utf-8') as f:
+        reader = csv.reader(f)
+        for row in reader:
+            all_data_sets.append({
+                'id': int(row[0]),
+                'data_set_name': f"可以使用:{row[1]}",
+                'description': f"不可使用,仅供参考:{row[2]}",
+            })
+
+    if RANDOM_DATA_SETS_COUNT and RANDOM_DATA_SETS_COUNT > 0:
+        total_count = len(original_data_sets) + RANDOM_DATA_SETS_COUNT
+
+        # 找出所有可用的数据集（不在原始数据中的）
+        available_datasets = []
+
+        # 先获取原始数据集的所有ID
+        original_ids = []
+        for item in original_data_sets:
+            original_ids.append(item['id'])
+
+        # 找出不在原始数据中的数据集
+        for dataset in all_data_sets:
+            exists = False
+            for oid in original_ids:
+                if dataset['id'] == oid:
+                    exists = True
+                    break
+            if not exists:
+                available_datasets.append(dataset)
+
+        # 计算最多能添加多少个
+        max_can_add = len(available_datasets)
+        need_to_add = RANDOM_DATA_SETS_COUNT
+
+        if need_to_add > max_can_add:
+            print(f"警告：要求添加{need_to_add}个，但只有{max_can_add}个可用")
+            need_to_add = max_can_add
+
+        # 随机选择需要数量的数据集
+        random_selected = random.sample(available_datasets, need_to_add)
+
+        # 添加到结果
+        result.extend(random_selected)
+
+    final_result = []
+    for item in result:
+        final_result.append({
+            'data_set_name': item['data_set_name'],
+            'description': item['description']
+        })
+
+    return final_result
+
+
+def promptLoader(alpha_prompt_path):
+    if not os.path.exists(alpha_prompt_path):
+        print("alpha_prompt.txt文件不存在")
+        exit(1)
+    with open(alpha_prompt_path, 'r', encoding='utf-8') as f:
+        prompt = f.read().strip()
+    if not prompt:
+        print("alpha_prompt.txt是空的")
+        exit(1)
+    return prompt.replace('\n\n', '\n')
+
+
+def operatorLoader(operator_prompt_path):
+    if not os.path.exists(operator_prompt_path):
+        print("operator.txt文件不存在")
+        exit(1)
+    with open(operator_prompt_path, 'r', encoding='utf-8') as f:
+        operator_lines = [line.strip() for line in f.readlines() if line.strip()]
+    if not operator_lines:
+        print("operator.txt是空的")
+        exit(1)
+    return "\n".join(operator_lines)
+
+def mistakesNoteBookLoader(mistakes_notebook_path):
+    if not os.path.exists(mistakes_notebook_path):
+        print("mistakes_notebook.txt文件不存在")
+        return ''
+    with open(mistakes_notebook_path, 'r', encoding='utf-8') as f:
+        mistakes_notebook_lines = [line.strip() for line in f.readlines() if line.strip()]
+    if not mistakes_notebook_lines:
+        print("mistakes_notebook.txt是空的")
+        exit(1)
+    return "\n".join(mistakes_notebook_lines)
+
+def create_result_folder():
+    folder_name = "generated_alpha"
+    if not os.path.exists(folder_name):
+        os.makedirs(folder_name)
+
+    now = datetime.now()
+    year_folder = os.path.join(folder_name, str(now.year))
+    month_folder = os.path.join(year_folder, f"{now.month:02d}")
+    day_folder = os.path.join(month_folder, f"{now.day:02d}")
+
+    if not os.path.exists(year_folder):
+        os.makedirs(year_folder)
+    if not os.path.exists(month_folder):
+        os.makedirs(month_folder)
+    if not os.path.exists(day_folder):
+        os.makedirs(day_folder)
+
+    return day_folder
+
+
+def call_siliconflow(prompt, model):
+    try:
+        client = openai.OpenAI(
+            api_key=SILICONFLOW_API_KEY,
+            base_url=SILICONFLOW_BASE_URL
+        )
+
+        response = client.chat.completions.create(
+            model=model,
+            messages=[
+                {"role": "system", "content": "你是一个专业的量化投资专家，擅长编写Alpha因子。"},
+                {"role": "user", "content": prompt}
+            ],
+            temperature=TEMPERATURE
+        )
+
+        return response.choices[0].message.content
+
+    except openai.AuthenticationError:
+        print("API密钥错误")
+    except openai.RateLimitError:
+        print("调用频率限制")
+    except openai.APIError as e:
+        print(f"API错误: {e}")
+    except Exception as e:
+        print(f"其他错误: {e}")
+    exit(1)
+
+
+def save_result(result, folder, model_name):
+    now = datetime.now()
+    time_filename = now.strftime("%H%M%S")
+
+    filename = f"{model_name}_{time_filename}.txt"
+    filepath = os.path.join(folder, filename)
+
+    with open(filepath, 'w', encoding='utf-8') as f:
+        f.write(result)
+
+    print(f"结果保存到: {filepath}")
+
+
+def get_user_info():
+    headers = {"Authorization": f"Bearer {SILICONFLOW_API_KEY}"}
+    url = "https://api.siliconflow.cn/v1/user/info"
+    response = httpx.get(url, headers=headers)
+    data = response.json()['data']
+    balance = data['totalBalance']
+    print(f"余额: {balance}")
+    return float(balance)
+
+
+def manual_prompt(prompt):
+    manual_prompt_path = os.path.join(PROJECT_PATH, "manual_prompt")
+
+    if not os.path.exists(manual_prompt_path):
+        os.makedirs(manual_prompt_path)
+
+    now = datetime.now()
+    year_folder = os.path.join(manual_prompt_path, str(now.year))
+    month_folder = os.path.join(year_folder, f"{now.month:02d}")
+    day_folder = os.path.join(month_folder, f"{now.day:02d}")
+
+    if not os.path.exists(year_folder):
+        os.makedirs(year_folder)
+    if not os.path.exists(month_folder):
+        os.makedirs(month_folder)
+    if not os.path.exists(day_folder):
+        os.makedirs(day_folder)
+
+    # 文件名后添加保存时间
+    filename = f"manual_prompt_{now.strftime('%Y%m%d%H%M%S')}.txt"
+    filepath = os.path.join(day_folder, filename)
+
+    with open(filepath, 'w', encoding='utf-8') as f:
+        f.write(prompt)
+
+    print(f"手动提示词保存到: {filepath}")
+
+
+def call_ai(prompt, model):
+    balance = get_user_info()
+
+    folder = create_result_folder()
+
+    print(f"正在调用AI...{model}")
+    result = call_siliconflow(prompt, model)
+
+    if result:
+        print(f"AI回复: {result[:200]}...")
+        model_name = model.replace("/", "_")
+        save_result(result, folder, model_name)
+        used_balance = balance - get_user_info()
+        print(f'本次调用 api 使用额度 {used_balance}')
+    else:
+        print("AI调用失败")
+
+
+def prepare_prompt(data_sets):
+    prompt = ''
+
+    # 读取基础提示词
+    alpha_prompt_path = os.path.join(PREPARE_PROMPT, "alpha_prompt.txt")
+    prompt += promptLoader(alpha_prompt_path)
+
+    # 读取操作符
+    prompt += "\n\n以下是我的账号有权限使用的操作符, 请严格按照操作符, 进行生成,组合因子\n\n"
+    prompt += "========================= 操作符开始 =======================================\n"
+    prompt += "注意: Operator: 后面的是操作符(是可以使用的),\nDescription: 此字段后面的是操作符对应的描述或使用说明(禁止使用, 仅供参考), Description字段后面的内容是使用说明, 不是操作符\n"
+    prompt += "特别注意!!!! 必须按照操作符字段Operator的使用说明生成 alpha"
+    operator_prompt_path = os.path.join(PREPARE_PROMPT, "operator.txt")
+    operator = operatorLoader(operator_prompt_path)
+    prompt += operator
+    prompt += "\n========================= 操作符结束 =======================================\n\n"
+
+    prompt += "========================= 数据字段开始 =======================================\n"
+    prompt += "注意: data_set_name: 后面的是数据字段(可以使用), description: 此字段后面的是数据字段对应的描述或使用说明(不能使用), description_cn字段后面的内容是中文使用说明(不能使用)\n\n"
+    for data_set in data_sets:
+        prompt += str(data_set) + '\n'
+
+    prompt += "========================= 数据字段结束 =======================================\n\n"
+
+    prompt += "以上数据字段和操作符, 按照Description说明组合, 但是每一个 alpha 组合的使用的数据字段和操作符不要过于集中, 在符合语法的情况下, 多尝试不同的组合\n\n"
+
+    mistakes_note_book_path = os.path.join(PREPARE_PROMPT, "mistakes_notebook.txt")
+    mistakesNoteBook = mistakesNoteBookLoader(mistakes_note_book_path)
+    if mistakesNoteBook:
+        prompt += mistakesNoteBook
+
+    return prompt
+
+
+def main():
+    # 将金融逻辑, 分割成标签
+    keys_text = keysTextLoader()
+
+    # 分割好的标签, 搜索对应的数据集, 返回匹配到的结果
+    data_sets_path = os.path.join(PREPARE_PROMPT, "all_data_combined.csv")
+    result_data_sets = csvFileLoader(data_sets_path, keys_text)
+
+    if not result_data_sets:
+        print(f'搜索数据集为空, 程序退出')
+        exit(1)
+
+    # 扩展数据集
+    mistakes_notebook_path = os.path.join(PREPARE_PROMPT, "all_data_combined.csv")
+    if RANDOM_DATA_SETS_COUNT:
+        print('=' * 100)
+        print(f'扩展前 {len(result_data_sets)} 条数据')
+        result_data_sets = extend_data_sets(mistakes_notebook_path, result_data_sets)
+        print(f'扩展后 {len(result_data_sets)} 条数据')
+        print('=' * 100)
+
+    print(f'从数据集中提取了 {len(result_data_sets)} 条数据')
+    if len(result_data_sets) > 500:
+        data_sets = random.sample(result_data_sets, 10)
+    else:
+        data_sets = result_data_sets
+
+    # 组合提示词
+    prompt = prepare_prompt(data_sets)
+
+    # # 如果需要手动在页面段模型, 使用提示词, 打开这个, 将生成的提示词存到本地
+    manual_prompt(prompt)
+
+    if USE_AI:
+        for model in MODELS:
+            # 如果需要使用模型, 打开这个
+            call_ai(prompt, model)
+
+            time.sleep(5)
+
+
+if __name__ == "__main__":
+    main()