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- ZIG 右侧突破回补策略：MyTT 新增 ZIG 之字转向（未来函数，含前视偏差警示）； 波谷启动建仓挂硬止损（OCO）→ 见顶清仓记前高 → 右侧突破回补；路径依赖不实现 entry_exit_masks（向量化守护测试白名单）；含寻优预设网格与 --strategy-file 独立文件 - 交易时段感知刷新：realtime/session.py（09:15~11:30:30 / 13:00~15:05）+ GET /market/session；看板 30/60/120s 轮询休市自动暂停（三态状态栏 + 开关持久化 + 手动刷新不受限）；SSE/WS 既有会话语义不动 - 120 分钟 K 线：/bars?category=MIN_120（MAC 原生 Period.MINS×120 优先， 2×60M 相邻聚合兜底，标准客户端上限 400 根）；前端周期选择器同步 - 逐 bar 衍生字段：/bars 与 /bars/index 附带 pre_close/change/change_pct/ amplitude_pct（pre_close≤0.01 兜底防除零） - 159 只核心龙头池：数据资产取自 Fork（东财全行业龙头名单，四组分层）； universe=core 接入 screen scan / SignalScanner / StrengthRanker / market strength； GET /market/core-leaders + WebUI「龙头池」页（搜索/个股详情） - 多 Provider LLM 直连：easy_tdx.ai + /llm/*（DeepSeek/通义/智谱/Kimi/MiniMax/ OpenAI/Claude/Ollama/自定义，openai 兼容 + anthropic 原生双协议）； 配置落盘 ~/.easy_tdx/llm.json（WebUI「AI 设置」页 ⇆ 手工编辑双向兼容， 文件>环境变量>预设；key 脱敏回显/CLEAR 清除）；「AI 解读」后台任务化 （复用 task_runner，提交+轮询，不占 HTTP 连接）；思考型模型空白正文防御 （reasoning_content 耗尽 max_tokens → 可操作报错；默认 16000）； AI 解读历史页（自动归档 Prompt/正文/策略上下文 + 去回测带参引导） - WebUI 加固：SPA fallback 对未知 /api/* 返回 JSON 404（不再 200 HTML 伪装解析错）； index.html 一律 Cache-Control: no-store（防缓存旧资源引用）；路由兜底重定向； 全局风险提示常驻底栏 + 龙头池/AI 解读针对性免责声明 - 测试：新增 9 个单测文件共 59 例；黄金基线仅新增 zig_breakout 条目（其余零漂移）； 全量 1448 例通过
415 lines
17 KiB
Python
415 lines
17 KiB
Python
"""LLM 客户端与配置单元测试（ai/llm.py，v1.29）。
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覆盖：配置文件读写、环境变量兜底、Provider 预设补齐、api_key 脱敏、
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未配置 key 的友好报错、openai/anthropic 两种协议的请求组装与响应解析
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（HTTP 层 monkeypatch，零真实网络调用）。
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"""
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from __future__ import annotations
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import asyncio
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import pytest
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from easy_tdx.ai import llm as llm_mod
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from easy_tdx.ai.llm import (
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PROVIDER_PRESETS,
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LlmClient,
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LlmConfig,
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LlmError,
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load_config,
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mask_key,
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resolve_config,
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save_config,
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)
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@pytest.fixture()
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def config_dir(tmp_path, monkeypatch):
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monkeypatch.setenv("EASY_TDX_CONFIG_DIR", str(tmp_path))
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# 清掉可能存在的兜底环境变量，保证用例间互不干扰
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for var in ("LLM_PROVIDER", "LLM_API_KEY", "LLM_BASE_URL", "LLM_MODEL"):
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monkeypatch.delenv(var, raising=False)
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return tmp_path
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class TestConfigFile:
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def test_default_when_no_file(self, config_dir):
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cfg = load_config()
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assert cfg.provider == "deepseek" and cfg.api_key == ""
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def test_save_and_load_roundtrip(self, config_dir):
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save_config(LlmConfig(provider="zhipu", api_key="sk-test1234567890", model="glm-4.6"))
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cfg = load_config()
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assert cfg.provider == "zhipu"
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assert cfg.api_key == "sk-test1234567890"
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assert cfg.model == "glm-4.6"
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def test_corrupt_file_returns_default(self, config_dir):
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(config_dir / "llm.json").write_text("{not json", encoding="utf-8")
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assert load_config().provider == "deepseek" # 不抛异常
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def test_env_fills_missing_fields(self, config_dir, monkeypatch):
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monkeypatch.setenv("LLM_PROVIDER", "kimi")
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monkeypatch.setenv("LLM_API_KEY", "sk-env-key-123456")
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cfg = load_config()
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assert cfg.provider == "kimi" and cfg.api_key == "sk-env-key-123456"
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def test_file_overrides_env(self, config_dir, monkeypatch):
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monkeypatch.setenv("LLM_API_KEY", "sk-env")
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save_config(LlmConfig(provider="deepseek", api_key="sk-file-12345678"))
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assert load_config().api_key == "sk-file-12345678"
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class TestResolve:
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def test_preset_fills_url_and_model(self, config_dir):
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save_config(LlmConfig(provider="qwen"))
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r = resolve_config()
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assert r.api_url == "https://dashscope.aliyuncs.com/compatible-mode/v1"
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assert r.model == "qwen-plus"
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def test_explicit_values_win(self, config_dir):
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save_config(LlmConfig(provider="deepseek", api_url="http://gw.local/v1", model="my-model"))
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r = resolve_config()
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assert r.api_url == "http://gw.local/v1" and r.model == "my-model"
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def test_custom_requires_url_and_model(self, config_dir):
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with pytest.raises(ValueError, match="不完整"):
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resolve_config(LlmConfig(provider="custom"))
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class TestMaskKey:
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def test_mask(self):
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assert mask_key("") == ""
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assert mask_key("short") == "*****"
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assert mask_key("sk-abcdef1234567890") == "sk-***7890"
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class TestClient:
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def test_missing_key_friendly_error(self, config_dir):
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client = LlmClient(LlmConfig(provider="deepseek", api_key=""))
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with pytest.raises(LlmError, match="API Key"):
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asyncio.run(client.chat("hi"))
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def test_ollama_needs_no_key(self, config_dir, monkeypatch):
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captured = {}
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def fake_post(url, headers, payload, timeout):
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captured.update(url=url, headers=headers, payload=payload)
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return {"choices": [{"message": {"content": "OK"}}]}
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monkeypatch.setattr(llm_mod, "_post_json", fake_post)
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client = LlmClient(LlmConfig(provider="ollama", timeout=5))
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reply = asyncio.run(client.chat("ping"))
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assert reply == "OK"
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assert captured["url"].startswith("http://localhost:11434/v1/chat/completions")
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assert "Authorization" not in captured["headers"] # 免 key 不带鉴权头
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def test_openai_style_request_and_parse(self, config_dir, monkeypatch):
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captured = {}
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def fake_post(url, headers, payload, timeout):
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captured.update(url=url, headers=headers, payload=payload)
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return {"choices": [{"message": {"content": "解读完成"}}]}
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monkeypatch.setattr(llm_mod, "_post_json", fake_post)
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client = LlmClient(LlmConfig(provider="zhipu", api_key="sk-zhipu-123456789"))
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reply = asyncio.run(client.chat("报告…", system_prompt="SYS"))
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assert reply == "解读完成"
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assert captured["url"] == "https://open.bigmodel.cn/api/paas/v4/chat/completions"
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assert captured["headers"]["Authorization"] == "Bearer sk-zhipu-123456789"
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msgs = captured["payload"]["messages"]
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assert msgs[0] == {"role": "system", "content": "SYS"}
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assert msgs[1]["content"] == "报告…"
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assert captured["payload"]["model"] == "glm-4-flash"
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def test_anthropic_style_request_and_parse(self, config_dir, monkeypatch):
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captured = {}
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def fake_post(url, headers, payload, timeout):
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captured.update(url=url, headers=headers, payload=payload)
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return {"content": [{"type": "text", "text": "Claude 回复"}]}
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monkeypatch.setattr(llm_mod, "_post_json", fake_post)
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client = LlmClient(LlmConfig(provider="claude", api_key="sk-ant-123456789"))
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reply = asyncio.run(client.chat("hi"))
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assert reply == "Claude 回复"
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assert captured["url"] == "https://api.anthropic.com/v1/messages"
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assert captured["headers"]["x-api-key"] == "sk-ant-123456789"
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assert captured["headers"]["anthropic-version"] == "2023-06-01"
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assert captured["payload"]["system"] # system 走顶层字段而非 messages
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def test_http_error_wrapped(self, config_dir, monkeypatch):
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"""_post_json 把 HTTPError（带响应体）包装成带状态码的 LlmError。"""
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import io
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import urllib.error
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def fake_urlopen(req, timeout):
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body = io.BytesIO(b'{"error":"bad key"}')
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raise urllib.error.HTTPError(
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req.full_url, 401, "Unauthorized", hdrs=None, fp=body
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)
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monkeypatch.setattr(llm_mod.urllib.request, "urlopen", fake_urlopen)
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with pytest.raises(LlmError, match="401") as ei:
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llm_mod._post_json("https://x/v1/chat/completions", {}, {"m": 1}, 5.0)
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assert ei.value.status == 401
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assert "bad key" in str(ei.value)
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def test_test_endpoint_reports_failure(self, config_dir):
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client = LlmClient(LlmConfig(provider="deepseek", api_key=""))
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result = asyncio.run(client.test())
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assert result["ok"] is False and "API Key" in result["error"]
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def test_provider_presets_cover_major_vendors():
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vendors = [
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"deepseek", "qwen", "zhipu", "kimi", "minimax",
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"openai", "claude", "ollama", "custom",
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]
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for pid in vendors:
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assert pid in PROVIDER_PRESETS, pid
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assert PROVIDER_PRESETS["claude"].api_style == "anthropic"
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assert PROVIDER_PRESETS["ollama"].needs_key is False
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assert PROVIDER_PRESETS["zhipu"].base_url.startswith("https://open.bigmodel.cn")
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class TestTimeoutSemantics:
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def test_default_timeout_is_generous(self):
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"""默认超时 ≥120s：非流式接口需等模型生成完整段回复（大报告 1-3 分钟）。"""
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assert LlmConfig().timeout >= 120
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def test_read_timeout_actionable_message(self, monkeypatch):
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"""读超时单独成类报错，文案给出「调大超时」动作而非裸异常。"""
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def fake_urlopen(req, timeout):
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raise TimeoutError("The read operation timed out")
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monkeypatch.setattr(llm_mod.urllib.request, "urlopen", fake_urlopen)
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with pytest.raises(LlmError, match="请求超时（180s"):
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llm_mod._post_json("https://x/v1/chat/completions", {}, {"m": 1}, 180.0)
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def test_connect_timeout_via_urlerror(self, monkeypatch):
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"""连接期超时（URLError.reason=TimeoutError）同样走超时文案。"""
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import urllib.error
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def fake_urlopen(req, timeout):
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raise urllib.error.URLError(TimeoutError("timed out"))
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monkeypatch.setattr(llm_mod.urllib.request, "urlopen", fake_urlopen)
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with pytest.raises(LlmError, match="请求超时"):
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llm_mod._post_json("https://x/v1/chat/completions", {}, {"m": 1}, 30.0)
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class TestAsyncChatTask:
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"""POST /llm/chat/async + GET /llm/chat/tasks/{id} 的提交-轮询闭环。"""
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@pytest.fixture(autouse=True)
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def _fresh_history_store(self, config_dir):
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"""每个用例用独立的 llm_history.db（模块级单例绑定了首个用例的临时目录）。"""
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import easy_tdx.web.llm_history_store as hs
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hs._store = None
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yield
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hs._store = None
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def test_submit_and_poll_done(self, config_dir, monkeypatch):
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import time
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from fastapi.testclient import TestClient
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from easy_tdx.web.app import _create_app
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def fake_chat(self, prompt, system_prompt=None):
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async def _slow():
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await asyncio.sleep(0.05)
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return f"解读:{prompt[:8]}"
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return _slow()
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monkeypatch.setattr(LlmClient, "chat", fake_chat)
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app = _create_app(enable_mac=False, enable_ui=False)
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with TestClient(app) as c:
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r = c.post("/api/v1/llm/chat/async", json={"prompt": "整份回测报告…" * 10})
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assert r.status_code == 202, r.text
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task_id = r.json()["task_id"]
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assert r.json()["status"] in ("pending", "running")
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state = None
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for _ in range(50):
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state = c.get(f"/api/v1/llm/chat/tasks/{task_id}").json()
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if state["status"] in ("done", "failed"):
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break
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time.sleep(0.05)
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assert state["status"] == "done", state
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assert state["result"]["reply"].startswith("解读:")
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assert state["result"]["elapsed"] >= 0.0
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def test_task_failure_surfaces_error(self, config_dir, monkeypatch):
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import time
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from fastapi.testclient import TestClient
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from easy_tdx.web.app import _create_app
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def fake_chat(self, prompt, system_prompt=None):
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async def _boom():
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raise LlmError("请求超时（180s 内无响应）")
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return _boom()
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monkeypatch.setattr(LlmClient, "chat", fake_chat)
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app = _create_app(enable_mac=False, enable_ui=False)
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with TestClient(app) as c:
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task_id = c.post("/api/v1/llm/chat/async", json={"prompt": "x"}).json()["task_id"]
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state = None
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for _ in range(50):
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state = c.get(f"/api/v1/llm/chat/tasks/{task_id}").json()
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if state["status"] in ("done", "failed"):
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break
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time.sleep(0.05)
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assert state["status"] == "failed"
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assert "请求超时" in state["error"]
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def test_unknown_task_rejected(self, config_dir):
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"""未知 task → 400（与 GET /backtest/tasks/{id} 的 ValueError 约定一致）。"""
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from fastapi.testclient import TestClient
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from easy_tdx.web.app import _create_app
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app = _create_app(enable_mac=False, enable_ui=False)
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with TestClient(app) as c:
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r = c.get("/api/v1/llm/chat/tasks/nonexistent")
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assert r.status_code == 400
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assert "未知任务" in r.json()["detail"]
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def test_async_success_records_history(self, config_dir, monkeypatch):
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"""异步解读成功 → 自动落历史库（含策略上下文），供历史页查询。"""
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import time as _time
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from fastapi.testclient import TestClient
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from easy_tdx.web.app import _create_app
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async def fake_chat(self, prompt, system_prompt=None):
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return "解读正文"
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monkeypatch.setattr(LlmClient, "chat", fake_chat)
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app = _create_app(enable_mac=False, enable_ui=False)
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with TestClient(app) as c:
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ctx = {
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"strategy": "ma_cross",
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"strategy_label": "双均线交叉",
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"symbol": "600519",
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"category": "DAY",
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"params": {"fast": 5, "slow": 20},
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"start_date": "2024-01-01",
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"end_date": "2025-01-01",
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}
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tid = c.post("/api/v1/llm/chat/async",
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json={"prompt": "报告", "context": ctx}).json()["task_id"]
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for _ in range(50):
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st = c.get(f"/api/v1/llm/chat/tasks/{tid}").json()
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if st["status"] in ("done", "failed"):
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break
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_time.sleep(0.05)
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assert st["status"] == "done", st
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hist = c.get("/api/v1/llm/history").json()
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assert hist["count"] >= 1
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item = hist["items"][0]
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assert item["reply"] == "解读正文"
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assert item["strategy"] == "ma_cross" and item["symbol"] == "600519"
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assert item["params"] == {"fast": 5, "slow": 20}
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# 删除一条
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r = c.delete(f"/api/v1/llm/history/{item['id']}")
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assert r.json()["ok"] is True
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assert c.get("/api/v1/llm/history").json()["count"] == hist["count"] - 1
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def test_async_failure_not_recorded(self, config_dir, monkeypatch):
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"""解读失败 → 不落历史（历史只归档成功解读）。"""
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import time as _time
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from fastapi.testclient import TestClient
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from easy_tdx.web.app import _create_app
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async def fake_chat(self, prompt, system_prompt=None):
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raise LlmError("boom")
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monkeypatch.setattr(LlmClient, "chat", fake_chat)
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app = _create_app(enable_mac=False, enable_ui=False)
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with TestClient(app) as c:
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tid = c.post("/api/v1/llm/chat/async", json={"prompt": "x"}).json()["task_id"]
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for _ in range(50):
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st = c.get(f"/api/v1/llm/chat/tasks/{tid}").json()
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if st["status"] in ("done", "failed"):
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break
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_time.sleep(0.05)
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assert st["status"] == "failed"
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assert c.get("/api/v1/llm/history").json()["count"] == 0
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def test_submit_rejects_incomplete_config(self, config_dir):
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"""custom 未填 url/model：提交期即 400（不等任务跑起来才失败）。"""
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from fastapi.testclient import TestClient
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from easy_tdx.web.app import _create_app
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app = _create_app(enable_mac=False, enable_ui=False)
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with TestClient(app) as c:
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r = c.post(
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"/api/v1/llm/chat/async",
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json={"prompt": "x", "override": {"provider": "custom"}},
|
||
)
|
||
assert r.status_code == 400
|
||
assert "不完整" in r.json()["detail"]
|
||
|
||
|
||
class TestThinkingModelBlankContent:
|
||
"""思考型模型正文空白（reasoning_content 耗尽 max_tokens）的防御。
|
||
|
||
v1.29.1 实测：GLM-5.x 思考链计入 max_tokens，预算耗尽时 content 为
|
||
空白——truthy 但渲染为空（状态条报成功、正文空白）。解析层必须把
|
||
这类响应转成可操作的错误，绝不返回空白字符串。
|
||
"""
|
||
|
||
def _client(self, max_tokens: int = 4000) -> LlmClient:
|
||
return LlmClient(LlmConfig(provider="zhipu", api_key="sk-x-1234567890",
|
||
model="glm-5.3-flash", max_tokens=max_tokens))
|
||
|
||
def test_normal_content_wins_over_reasoning(self):
|
||
msg = {"content": "正文", "reasoning_content": "思考…", "role": "assistant"}
|
||
assert self._client()._extract_reply_openai(msg, "stop") == "正文"
|
||
|
||
def test_blank_content_with_reasoning_raises_actionable(self):
|
||
msg = {"content": " ", "reasoning_content": "思考" * 500, "role": "assistant"}
|
||
with pytest.raises(LlmError, match="思考链.*4000.*16000"):
|
||
self._client()._extract_reply_openai(msg, "length")
|
||
|
||
def test_null_content_with_reasoning(self):
|
||
msg = {"content": None, "reasoning_content": "思考", "role": "assistant"}
|
||
with pytest.raises(LlmError, match="思考链"):
|
||
self._client()._extract_reply_openai(msg, "length")
|
||
|
||
def test_blank_content_without_reasoning(self):
|
||
with pytest.raises(LlmError, match="content 为空"):
|
||
self._client()._extract_reply_openai({"content": ""}, "stop")
|
||
|
||
def test_length_finish_without_content(self):
|
||
with pytest.raises(LlmError, match="截断"):
|
||
self._client()._extract_reply_openai({"content": ""}, "length")
|
||
|
||
def test_whitespace_reply_rejected_end_to_end(self, config_dir, monkeypatch):
|
||
"""端到端：伪 HTTP 返回空白正文 → chat() 抛错（任务态 failed 而非 done 空回复）。"""
|
||
|
||
def fake_post(url, headers, payload, timeout):
|
||
blank = chr(10) + " " + chr(10)
|
||
return {"choices": [{"message": {"content": blank, "reasoning_content": "r"},
|
||
"finish_reason": "length"}]}
|
||
|
||
monkeypatch.setattr(llm_mod, "_post_json", fake_post)
|
||
with pytest.raises(LlmError, match="思考链"):
|
||
asyncio.run(self._client().chat("报告"))
|
||
|
||
def test_default_max_tokens_generous_for_thinking(self):
|
||
assert LlmConfig().max_tokens >= 16000
|