怎加更多LLM provider适配

.env.example

@@ -0,0 +1,34 @@
+# Copy to .env and fill in your keys:
+#   cp .env.example .env
+
+# LLM API Keys — 变量名须与 config.yaml 中的 base_url 对应：
+#   DeepSeek  -> DEEPSEEK_API_KEY
+#   Kimi Code  -> KIMI_API_KEY, base_url=https://api.kimi.com/coding/v1
+#   Kimi Open Platform -> MOONSHOT_API_KEY, base_url=https://api.moonshot.ai/v1
+#   StepFun   -> STEPFUN_API_KEY
+#   通用（任意 OpenAI 兼容接口）-> LLM_API_KEY
+DEEPSEEK_API_KEY=
+KIMI_API_KEY=
+MOONSHOT_API_KEY=
+STEPFUN_API_KEY=
+OPENAI_API_KEY=
+LLM_API_KEY=
+GEMINI_API_KEY=
+GOOGLE_API_KEY=
+
+# Optional LLM base URLs
+# DEEPSEEK_BASE_URL=https://api.deepseek.com
+# STEPFUN_BASE_URL=https://api.stepfun.com/v1
+# OPENAI_BASE_URL=https://api.openai.com/v1
+
+# ==================================================
+# Data source keys (optional)
+# ==================================================
+# TUSHARE_KEY=
+# BOCHA_KEY=
+# SERP_KEY=
+# FMP_KEY=
+# FINNHUB_KEY=
+
+# Market: CN-Stock | US-Stock
+# CONTEST_TRADE_MARKET=CN-Stock

cli/main.py

@@ -1,12 +1,28 @@
 """
 ContestTrade: 基于内部竞赛机制的Multi-Agent交易系统
 """
+from pathlib import Path
+
+
+def _bootstrap_dotenv() -> None:
+    """Load .env before any config/LLM imports."""
+    env_path = Path(__file__).resolve().parent.parent / ".env"
+    if not env_path.is_file():
+        return
+    try:
+        from dotenv import load_dotenv
+        load_dotenv(env_path, override=False)
+    except ImportError:
+        pass
+
+
+_bootstrap_dotenv()
+
 import asyncio
 import sys
 import json
 import re
 import os
-from pathlib import Path
 from typing import Optional, Dict
 from datetime import datetime
 from collections import deque

cli/utils.py

@@ -57,16 +57,52 @@ def validate_tushare_connection():
 def validate_llm_connection():
     """验证LLM连接"""
     try:
-        # Import LLM model when needed
-        from contest_trade.models.llm_model import GLOBAL_LLM
-
+        from contest_trade.models.llm_model import GLOBAL_LLM, GLOBAL_LLM_CONFIG
+        from contest_trade.models.provider_auth import (
+            format_missing_key_error,
+            get_env_config_hints,
+            get_env_var_candidates,
+            resolve_api_key,
+        )
+
         console.print("🔍 [cyan]正在验证LLM配置...[/cyan]")
+        console.print(
+            f"  Provider: {GLOBAL_LLM_CONFIG.provider} | "
+            f"Model: Kimi K3 ({GLOBAL_LLM_CONFIG.model_name}) | "
+            f"Base URL: {GLOBAL_LLM_CONFIG.base_url or '—'}"
+        )
+
+        resolved_key = resolve_api_key(
+            GLOBAL_LLM_CONFIG.provider,
+            config_key=GLOBAL_LLM_CONFIG.api_key,
+            base_url=GLOBAL_LLM_CONFIG.base_url,
+        )
+        if not resolved_key:
+            env_vars = get_env_var_candidates(
+                GLOBAL_LLM_CONFIG.provider,
+                GLOBAL_LLM_CONFIG.base_url,
+            )
+            console.print(
+                f"❌ [red]{format_missing_key_error(GLOBAL_LLM_CONFIG.provider, GLOBAL_LLM_CONFIG.base_url)}[/red]"
+            )
+            console.print(f"   请在 .env 中设置: {', '.join(env_vars[:3])}")
+            for hint in get_env_config_hints(
+                GLOBAL_LLM_CONFIG.provider,
+                GLOBAL_LLM_CONFIG.base_url,
+            ):
+                console.print(f"   ⚠️  [yellow]{hint}[/yellow]")
+            return False
+
         test_messages = [
             {"role": "user", "content": "请回复'连接测试成功'，不要添加任何其他内容。"}
         ]
-        result = GLOBAL_LLM.run(test_messages, max_tokens=1, temperature=0.1, max_retries=0)
-        if result and hasattr(result, 'content') and result.content:
-            console.print(f"✅ [green]LLM连接成功[/green] - 模型: {GLOBAL_LLM.model_name}")
+        result = GLOBAL_LLM.run(test_messages, max_tokens=32, temperature=1, max_retries=0)
+        has_content = result and (
+            (hasattr(result, 'content') and result.content)
+            or (hasattr(result, 'reasoning_content') and result.reasoning_content)
+        )
+        if has_content:
+            console.print(f"✅ [green]LLM连接成功[/green] - 模型: Kimi K3 ({GLOBAL_LLM.model_name})")
             return True
         else:
             console.print("❌ [red]LLM连接失败 - 无响应内容[/red]")

config.yaml

@@ -23,12 +23,28 @@ finnhub_key: ""
 #
 # 配置示例 / Configuration examples:
 #
+# 也可通过 .env 或环境变量设置 API Key（优先级低于 config.yaml）:
+#   1. 复制 .env.example 为 .env 并填入密钥
+#   2. DeepSeek  -> DEEPSEEK_API_KEY
+#   3. StepFun   -> STEPFUN_API_KEY
+#   4. OpenAI    -> OPENAI_API_KEY
+#   5. 通用      -> LLM_API_KEY
+#
 # OpenAI:
 #   provider: "openai"
 #   base_url: "https://api.openai.com/v1"
 #   api_key: "your_openai_api_key"
 #   model_name: "gpt-4"
 #
+# Kimi Code (OpenAI compatible):
+#   provider: "openai"
+#   base_url: "https://api.kimi.com/coding/v1"
+#   api_key: null   # 在 .env 中设置 KIMI_API_KEY（Kimi Code 控制台密钥）
+#   model_name: "kimi-k3"   # API model id；显示名称: Kimi K3
+#
+# Kimi Open Platform:
+#   base_url: "https://api.moonshot.ai/v1"
+#   model_name: "kimi-k3"
 # DeepSeek (OpenAI compatible):
 #   provider: "openai"
 #   base_url: "https://api.deepseek.com"
@@ -57,23 +73,20 @@ finnhub_key: ""
 llm:
   provider: "openai"
   base_url: "https://api.deepseek.com"
-  #YOUR_DEEPSEEK_API_KEY
-  api_key: null 
+  api_key: null
   model_name: "deepseek-chat"
 
 llm_thinking:
   provider: "openai"
   base_url: "https://api.deepseek.com"
-  #YOUR_DEEPSEEK_API_KEY
-  api_key: null 
+  api_key: null
   model_name: "deepseek-reasoner"
 
 vlm:
   provider: "openai"
-  base_url: "https://api.stepfun.com/v1"
-  #YOUR_STEPFUN_API_KEY
-  api_key: null 
-  model_name: "step-1o-turbo-vision"
+  base_url: "https://api.kimi.com/coding/v1"
+  api_key: null
+  model_name: "kimi-k3"
 
 
 data_agents_config:
@@ -92,7 +105,7 @@ data_agents_config:
 
 research_agent_config:
   belief_list_path: "config/belief_list.json"
-  max_react_step: 10
+  max_react_step: 5
   output_language: "中文"
   tools:
     - "tools.stock_symbol_search_akshare.stock_symbol_search"

config_us.yaml

@@ -16,9 +16,11 @@ finnhub_key: ""
 #YOUR_ALPHA_VANTAGE_KEY
 alpha_vantage_key: ""
 
-# YOUR_POLYGON_KEY
+#YOUR_POLYGON_KEY
 polygon_key: ""
 
+# API keys: set in config.yaml, or copy .env.example to .env (see DEEPSEEK_API_KEY, STEPFUN_API_KEY, LLM_API_KEY)
+
 llm:
   provider: "openai"
   base_url: "https://api.deepseek.com"

contest_trade/config/config.py

@@ -6,6 +6,26 @@
 import os
 
 PROJECT_ROOT = Path(__file__).parent.parent.resolve()
+REPO_ROOT = PROJECT_ROOT.parent
+
+
+def _load_dotenv() -> None:
+    """
+    Load .env from project root. Existing shell env vars take precedence.
+
+    @generated AI Assistant - 2026-08-04 11:42:00
+    """
+    env_path = REPO_ROOT / ".env"
+    if not env_path.is_file():
+        return
+    try:
+        from dotenv import load_dotenv
+        load_dotenv(env_path, override=False)
+    except ImportError:
+        print(f"Warning: .env found at {env_path} but python-dotenv is not installed.")
+
+
+_load_dotenv()
 
 
 class ProjectConfig:
@@ -20,7 +40,7 @@ def __init__(self) -> None:
         else:
             config_filename = "config.yaml"
 
-        yaml_path = PROJECT_ROOT.parent / config_filename
+        yaml_path = REPO_ROOT / config_filename
         print(f"Loading config from: {yaml_path} (Market: {market_type})")
 
         with open(yaml_path, "r", encoding="utf-8") as fr:

contest_trade/contest/researcher/research_contest.py

@@ -19,6 +19,7 @@
 sys.path.append(str(PROJECT_ROOT))
 
 from models.llm_model import GLOBAL_LLM
+from models.provider_auth import resolve_api_key, resolve_base_url, resolve_chat_completions_url
 from utils.market_manager import GLOBAL_MARKET_MANAGER
 from config.config import cfg
 from research_contest_types import SignalData, ResearchContestResult
@@ -316,10 +317,17 @@ async def _get_current_judge_scores(self, current_signals: Dict[str, SignalData]
         """获取当天信号的judge评分"""
         logger.info(f"获取当天信号judge评分 - {len(current_signals)} 个信号")
 
+        llm_section = cfg.llm if isinstance(cfg.llm, dict) else vars(cfg.llm)
+        provider = llm_section.get("provider", "openai")
+        base_url = resolve_base_url(provider, llm_section.get("base_url"))
         llm_config = {
-            "api_key": cfg.llm.api_key,
-            "api_base": cfg.llm.api_base,
-            "model_name": cfg.llm.model_name
+            "api_key": resolve_api_key(
+                provider,
+                config_key=llm_section.get("api_key"),
+                base_url=base_url,
+            ),
+            "api_base": resolve_chat_completions_url(base_url),
+            "model_name": llm_section.get("model_name"),
         }
 
         judge_scores = await self.signal_judger.judge_signals(

contest_trade/data_source/sina_news_crawl.py

@@ -21,7 +21,7 @@
 
 
 class SinaNewsCrawl(DataSourceBase):
-    def __init__(self, start_page=1, end_page=50):
+    def __init__(self, start_page=1, end_page=20):
         super().__init__("sina_news_crawl")
         self.start_page = start_page
         self.end_page = end_page
@@ -35,7 +35,7 @@ def __init__(self, start_page=1, end_page=50):
         }
         self.all_items = []
         self.fetch_full_intro = True  # 是否抓取文章页以补全 intro
-        self.article_concurrency = 2 # 控制抓取文章页的并发
+        self.article_concurrency = 10 # 控制抓取文章页的并发
 
     async def fetch_page(self, session, page):
         """异步获取单个页面的数据"""