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288 lines
9.4 KiB
288 lines
9.4 KiB
# -*- coding: utf-8 -*-
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import os
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import random
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import sys
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import json
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import openai
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import httpx
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import csv
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from datetime import datetime
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import jieba
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sys.path.append(os.path.join(os.path.abspath(__file__).split('AlphaGenerator')[0] + 'AlphaGenerator'))
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PROJECT_PATH = os.path.join(os.path.abspath(__file__).split('AlphaGenerator')[0] + 'AlphaGenerator')
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PREPARE_PROMPT = os.path.join(PROJECT_PATH, 'prepare_prompt')
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KEYS_TEXT = os.path.join(PREPARE_PROMPT, 'keys_text.txt')
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SELECT_DATA_SET_QTY = 30
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SILICONFLOW_API_KEY = "sk-pvdiisdowmuwkrpnxsrlhxaovicqibmlljwrwwvbbdjaitdl"
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SILICONFLOW_BASE_URL = "https://api.siliconflow.cn/v1"
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MODELS = [
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'Pro/deepseek-ai/DeepSeek-V3.1-Terminus',
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# 'deepseek-ai/DeepSeek-V3.2-Exp',
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# 'Qwen/Qwen3-VL-235B-A22B-Instruct',
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# 'MiniMaxAI/MiniMax-M2',
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# 'zai-org/GLM-4.6',
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# 'inclusionAI/Ring-flash-2.0',
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# 'zai-org/GLM-4.6',
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# 'inclusionAI/Ling-flash-2.0',
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# 'inclusionAI/Ring-flash-2.0',
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]
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def process_text(text):
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filter_list = ['\n', '\t', '\r', '\b', '\f', '\v', '：', '的', '或', '10', '天', '了', '可', '是', '该', '，', ' ', '、', '让', '和', '集',
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'/', '日', '在', '（', '_', '-', ')', '(', '上', '距', '与', '比', '下', '及', '）', '...', '；', '%', '&', '+', ',', '.',
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':', ';', '<', '=', '>', '?', '[', ']', '|', '—', '。'
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]
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text_list = jieba.lcut(text)
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results = []
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for tl in text_list:
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should_include = True
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for fl in filter_list:
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if fl == tl:
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should_include = False
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break
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if should_include:
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results.append(tl)
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if results:
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return list(set(results))
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else:
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return None
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def load_keys_text():
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if not os.path.exists(KEYS_TEXT):
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print(f"文件不存在: {KEYS_TEXT}")
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exit(1)
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with open(KEYS_TEXT, 'r', encoding='utf-8') as f:
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text_list = [line.strip() for line in f if line.strip()]
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if not text_list:
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print('关键词文本无数据, 程序退出')
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exit(1)
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result_str = process_text(';'.join(text_list))
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print(f'\n关键词文本处理结果: {result_str}\n')
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return result_str
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def txtFileLoader(file_path):
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if not os.path.exists(file_path):
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print(f"文件不存在: {file_path}")
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exit(1)
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with open(file_path, 'r', encoding='utf-8') as f:
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return [line.strip() for line in f if line.strip()]
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def csvFileLoader(file_path, keys_text):
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if not os.path.exists(file_path):
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print(f"文件不存在: {file_path}")
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exit(1)
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data_dict = {} # 使用字典来存储，以id为键
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with open(file_path, 'r', encoding='utf-8') as f:
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reader = csv.reader(f)
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for row in reader:
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for key in keys_text:
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if key in row[11] or key in row[12]:
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item_id = row[0]
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# 如果id不存在，或者想要保留第一个出现的记录
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if item_id not in data_dict:
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data_dict[item_id] = {
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'id': item_id,
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'data_set_name': row[1],
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'description': row[2],
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'description_cn': row[11],
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}
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# 将字典的值转换为列表
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return list(data_dict.values())
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def read_prompt(alpha_prompt_path):
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if not os.path.exists(alpha_prompt_path):
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print("alpha_prompt.txt文件不存在")
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exit(1)
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with open(alpha_prompt_path, 'r', encoding='utf-8') as f:
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prompt = f.read().strip()
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if not prompt:
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print("alpha_prompt.txt是空的")
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exit(1)
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return prompt.replace('\n\n', '\n')
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def read_operator(operator_prompt_path):
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if not os.path.exists(operator_prompt_path):
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print("wqb_operator.txt文件不存在")
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exit(1)
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with open(operator_prompt_path, 'r', encoding='utf-8') as f:
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operator_lines = [line.strip() for line in f.readlines() if line.strip()]
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if not operator_lines:
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print("wqb_operator.txt是空的")
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exit(1)
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return "\n".join(operator_lines)
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def create_result_folder():
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folder_name = "generated_alpha"
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if not os.path.exists(folder_name):
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os.makedirs(folder_name)
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return folder_name
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def call_siliconflow(prompt, model):
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try:
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client = openai.OpenAI(
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api_key=SILICONFLOW_API_KEY,
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base_url=SILICONFLOW_BASE_URL
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)
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response = client.chat.completions.create(
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model=model,
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messages=[{"role": "user", "content": prompt}]
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)
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return response.choices[0].message.content
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except openai.AuthenticationError:
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print("API密钥错误")
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except openai.RateLimitError:
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print("调用频率限制")
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except openai.APIError as e:
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print(f"API错误: {e}")
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except Exception as e:
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print(f"其他错误: {e}")
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exit(1)
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def save_result(result, folder):
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now = datetime.now()
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date_folder = now.strftime("%Y-%m-%d")
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time_filename = now.strftime("%H%M%S")
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full_folder_path = os.path.join(folder, date_folder)
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if not os.path.exists(full_folder_path):
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os.makedirs(full_folder_path)
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print(f"创建文件夹: {full_folder_path}")
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filename = f"{time_filename}.txt"
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filepath = os.path.join(full_folder_path, filename)
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with open(filepath, 'w', encoding='utf-8') as f:
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f.write(result)
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print(f"结果保存到: {filepath}")
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def get_user_info():
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headers = {"Authorization": f"Bearer {SILICONFLOW_API_KEY}"}
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url = "https://api.siliconflow.cn/v1/user/info"
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response = httpx.get(url, headers=headers)
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data = response.json()['data']
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balance = data['totalBalance']
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print(f"余额: {balance}")
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return float(balance)
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def manual_prompt(prompt):
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manual_prompt_path = os.path.join(PROJECT_PATH, "manual_prompt")
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if not os.path.exists(manual_prompt_path):
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os.makedirs(manual_prompt_path)
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print(f"创建文件夹: {manual_prompt_path}")
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# 文件名后添加保存时间
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now = datetime.now()
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filename = f"manual_prompt_{now.strftime('%Y%m%d%H%M%S')}.txt"
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filepath = os.path.join(manual_prompt_path, filename)
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with open(filepath, 'w', encoding='utf-8') as f:
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f.write(prompt)
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print(f"手动提示词保存到: {filepath}")
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def call_ai(prompt, model):
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balance = get_user_info()
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folder = create_result_folder()
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print(f"正在调用AI...{model}")
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result = call_siliconflow(prompt, model)
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if result:
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print(f"AI回复: {result[:200]}...")
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save_result(result, folder)
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used_balance = balance - get_user_info()
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print(f'本次调用 api 使用额度 {used_balance}')
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else:
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print("AI调用失败")
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def prepare_prompt(data_sets):
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prompt = ''
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# 读取基础提示词
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alpha_prompt_path = os.path.join(PREPARE_PROMPT, "alpha_prompt.txt")
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prompt += read_prompt(alpha_prompt_path)
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# 读取操作符
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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"
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prompt += "========================= 操作符开始 ======================================="
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prompt += "注意: Operator: 后面的是操作符,\nDescription: 此字段后面的是操作符对应的描述或使用说明, Description字段后面的内容是使用说明, 不是操作符\n"
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prompt += "特别注意!!!! 必须按照操作符字段Operator的使用说明生成 alpha"
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operator_prompt_path = os.path.join(PREPARE_PROMPT, "operator.txt")
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operator = read_operator(operator_prompt_path)
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prompt += operator
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prompt += "\n========================= 操作符结束 =======================================\n\n"
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prompt += "========================= 数据字段开始 =======================================\n"
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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"
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for data_set in data_sets:
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prompt += str(data_set) + '\n'
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prompt += "========================= 数据字段结束 =======================================\n\n"
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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"
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return prompt
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def main():
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# 将金融逻辑, 分割成标签
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keys_text = load_keys_text()
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# 分割好的标签, 搜索对应的数据集, 返回匹配到的结果
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data_sets_path = os.path.join(PREPARE_PROMPT, "all_data_combined.csv")
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result_data_sets = csvFileLoader(data_sets_path, keys_text)
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if not result_data_sets:
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print(f'搜索数据集为空, 程序退出')
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exit(1)
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data_sets = 0
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print(f'从数据集中提取了 {len(result_data_sets)} 条数据')
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if len(result_data_sets) > 500:
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data_sets = random.sample(result_data_sets, 10)
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else:
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data_sets = result_data_sets
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# 组合提示词
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prompt = prepare_prompt(data_sets)
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# # 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
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manual_prompt(prompt)
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for model in MODELS:
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# 如果需要使用模型, 打开这个
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call_ai(prompt, model)
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if __name__ == "__main__":
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main()
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