307 lines
12 KiB
Python
307 lines
12 KiB
Python
import json
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import os
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import sqlite3
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import sys
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import pytest
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from fastapi.testclient import TestClient
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PROJECT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
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if PROJECT_DIR not in sys.path:
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sys.path.insert(0, PROJECT_DIR)
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from app.db import altcoin_db
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from app.db.llm_insights import compute_input_hash, get_cached_insight, repair_mojibake_json, upsert_insight
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from app.services import event_driven_screener, llm_insights
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from app.web import web_server
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@pytest.fixture
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def temp_db(monkeypatch, tmp_path):
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db_path = tmp_path / "altcoin_monitor.db"
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monkeypatch.setattr(altcoin_db, "DB_PATH", str(db_path))
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monkeypatch.setattr(web_server, "init_db", altcoin_db.init_db)
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altcoin_db.init_db()
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return db_path
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def _insert_recommendation(db_path, **kwargs):
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defaults = dict(
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symbol="AAA/USDT",
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rec_time="2026-05-01T10:00:00",
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rec_state="加速",
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rec_score=80,
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entry_price=100.0,
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stop_loss=95.0,
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tp1=110.0,
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tp2=120.0,
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sector="AI",
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signals=json.dumps(["15min 即刻入场信号"], ensure_ascii=False),
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signal_codes_json=json.dumps(["vp_fly_1h_current"], ensure_ascii=False),
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signal_labels_json=json.dumps(["15min 即刻入场信号"], ensure_ascii=False),
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is_meme=0,
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status="active",
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current_price=100.0,
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max_price=104.0,
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min_price=98.0,
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pnl_pct=0.0,
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max_pnl_pct=4.0,
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max_drawdown_pct=-1.0,
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hit_tp1_time="",
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hit_tp2_time="",
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stopped_out_time="",
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expired_time="",
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last_track_time="2026-05-01T10:10:00",
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entry_plan_json=json.dumps({"entry_price": 100.0, "entry_action": "可即刻买入", "entry_trigger_confirmed": True}, ensure_ascii=False),
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action_status="可即刻买入",
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direction="多头启动",
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execution_status="buy_now",
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display_bucket="realtime",
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lifecycle_state="position",
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entry_triggered=1,
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state_reason="推荐时就是可即刻买入",
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strategy_version="v1.0",
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)
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defaults.update(kwargs)
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conn = sqlite3.connect(db_path)
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cols = ",".join(defaults.keys())
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qs = ",".join(["?"] * len(defaults))
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conn.execute(f"INSERT INTO recommendation ({cols}) VALUES ({qs})", tuple(defaults.values()))
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conn.commit()
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conn.close()
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def _fetch_llm_row(db_path):
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conn = sqlite3.connect(db_path)
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conn.row_factory = sqlite3.Row
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row = conn.execute("SELECT * FROM llm_insights ORDER BY id DESC LIMIT 1").fetchone()
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conn.close()
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return dict(row) if row else None
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def test_disabled_llm_skips_and_does_not_change_strategy_state(monkeypatch, temp_db):
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_insert_recommendation(temp_db)
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monkeypatch.setattr(llm_insights, "get_llm_module_enabled", lambda module: False)
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result = llm_insights.run(scope="recommendations", limit=10)
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assert result["status"] == "skipped"
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rows = altcoin_db.get_active_recommendations_deduped(actionable_only=False)
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target = next(r for r in rows if r["symbol"] == "AAA/USDT")
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assert target["execution_status"] == "buy_now"
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assert "llm_insight" not in target
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def test_same_input_hash_is_cached_without_repeated_call(monkeypatch, temp_db):
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_insert_recommendation(temp_db)
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monkeypatch.setattr(llm_insights, "get_llm_module_enabled", lambda module: True)
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monkeypatch.setattr(llm_insights, "_call_llm_json", lambda prompt, payload: {"status": "success", "content": {"summary": "ok"}, "model": "m"})
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calls = []
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original_upsert = llm_insights.upsert_insight
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def wrapped_upsert(*args, **kwargs):
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calls.append((args, kwargs))
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return original_upsert(*args, **kwargs)
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monkeypatch.setattr(llm_insights, "upsert_insight", wrapped_upsert)
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first = llm_insights.run(scope="recommendations", limit=10)
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second = llm_insights.run(scope="recommendations", limit=10)
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assert first["processed"] == 1
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assert second["processed"] == 0
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assert len(calls) == 1
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rows = altcoin_db.get_active_recommendations_deduped(actionable_only=False)
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target = next(r for r in rows if r["symbol"] == "AAA/USDT")
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assert target["llm_insight"]["content"]["summary"] == "ok"
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def test_invalid_json_is_marked_failed(monkeypatch, temp_db):
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_insert_recommendation(temp_db, symbol="BBB/USDT", rec_time="2026-05-01T11:00:00")
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monkeypatch.setattr(llm_insights, "get_llm_module_enabled", lambda module: True)
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monkeypatch.setattr(llm_insights, "_call_llm_json", lambda prompt, payload: {"status": "failed", "error": "invalid_json", "model": "m"})
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llm_insights.run(scope="recommendations", limit=10)
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row = _fetch_llm_row(temp_db)
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assert row["status"] == "failed"
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def test_only_key_samples_generate_insights(monkeypatch, temp_db):
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_insert_recommendation(temp_db, symbol="CCC/USDT", action_status="观察", execution_status="observe", display_bucket="watch_pool", state_reason="普通观察")
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_insert_recommendation(temp_db, symbol="DDD/USDT", action_status="等回踩", execution_status="wait_pullback", display_bucket="realtime", rec_time="2026-05-01T12:00:00")
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monkeypatch.setattr(llm_insights, "get_llm_module_enabled", lambda module: True)
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seen = []
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monkeypatch.setattr(llm_insights, "_call_llm_json", lambda prompt, payload: seen.append(payload["symbol"]) or {"status": "success", "content": {"summary": "ok"}, "model": "m"})
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llm_insights.run(scope="recommendations", limit=10)
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assert "CCC/USDT" not in seen
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assert "DDD/USDT" in seen
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def test_api_exposes_cached_ai_fields(monkeypatch, temp_db):
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_insert_recommendation(temp_db)
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monkeypatch.setattr(llm_insights, "get_llm_module_enabled", lambda module: True)
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monkeypatch.setattr(llm_insights, "_call_llm_json", lambda prompt, payload: {"status": "success", "content": {"summary": "AI 摘要", "key_evidence": ["量能增强"]}, "model": "m"})
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llm_insights.run(scope="recommendations", limit=10)
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client = TestClient(web_server.app)
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resp = client.get("/api/recommendations/active?actionable_only=false")
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assert resp.status_code == 200
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data = resp.json()
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target = next(r for r in data if r["symbol"] == "AAA/USDT")
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assert target["llm_insight"]["content"]["summary"] == "AI 摘要"
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def test_invalid_json_gets_cached_as_failed(monkeypatch, temp_db):
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_insert_recommendation(temp_db)
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monkeypatch.setattr(llm_insights, "get_llm_module_enabled", lambda module: True)
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monkeypatch.setattr(llm_insights, "_call_llm_json", lambda prompt, payload: {"status": "failed", "error": "invalid_json:bad", "model": "m"})
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llm_insights.run(scope="recommendations", limit=10)
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row = _fetch_llm_row(temp_db)
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assert row["status"] == "failed"
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assert "invalid_json" in row["error"]
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def test_llm_insights_api_exposes_input_and_output(temp_db):
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payload = {"symbol": "AAA/USDT", "action_status": "可即刻买入", "signals": ["15min 即刻入场信号"]}
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upsert_insight(
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"recommendation",
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"7",
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llm_insights.PROMPTS["recommendation_explain_v1"],
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llm_insights.PROMPTS["recommendation_explain_v1"],
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compute_input_hash(payload),
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"success",
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input_payload=payload,
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content={"summary": "AI 判断现在处于入场窗口", "key_evidence": ["量能增强"]},
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model="test-model",
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)
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client = TestClient(web_server.app)
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resp = client.get("/api/llm/insights")
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assert resp.status_code == 200
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data = resp.json()
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assert data["items"][0]["model"] == "test-model"
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assert data["items"][0]["input"]["symbol"] == "AAA/USDT"
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assert data["items"][0]["content"]["summary"] == "AI 判断现在处于入场窗口"
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detail = client.get(f"/api/llm/insights/{data['items'][0]['id']}").json()
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assert detail["input"]["signals"] == ["15min 即刻入场信号"]
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def test_sentiment_batch_analysis_api_returns_cached_result(temp_db):
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payload = {
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"target_type": "sentiment_batch",
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"target_id": "sentiment_batch:24h",
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"hours": 24,
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"event_count": 1,
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"events": [{"title": "Binance Will List ABCUSDT", "related_symbol": "ABC/USDT"}],
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}
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upsert_insight(
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"sentiment_batch",
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"sentiment_batch:24h",
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llm_insights.PROMPTS["sentiment_batch_analyze_v1"],
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llm_insights.PROMPTS["sentiment_batch_analyze_v1"],
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compute_input_hash(payload),
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"success",
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input_payload=payload,
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content={
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"market_mood": "risk_on",
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"summary": "上币事件带动短线风险偏好",
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"coin_impacts": [{"symbol": "ABC/USDT", "direction": "positive", "reason": "Binance listing", "need_technical_check": True}],
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},
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model="test-model",
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)
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client = TestClient(web_server.app)
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resp = client.get("/api/sentiment/analysis")
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assert resp.status_code == 200
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data = resp.json()
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assert data["analysis"]["summary"] == "上币事件带动短线风险偏好"
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assert data["source_events"][0]["related_symbol"] == "ABC/USDT"
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def test_sentiment_batch_enqueues_technical_check_candidates(monkeypatch, temp_db):
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event_driven_screener.init_event_tables()
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conn = sqlite3.connect(temp_db)
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conn.execute(
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"""
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INSERT INTO event_news (
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event_hash, source, symbol, title, url, published_at, detected_at,
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importance, event_type, processed
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) VALUES (?, ?, ?, ?, ?, datetime('now'), datetime('now'), ?, ?, 1)
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""",
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(
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"seed-llm-candidate",
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"binance_latest",
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"AAA/USDT",
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"Binance announces AAA ecosystem update",
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"",
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"A",
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"important_catalyst",
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),
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)
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conn.commit()
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conn.close()
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monkeypatch.setattr(llm_insights, "get_llm_module_enabled", lambda module: True)
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monkeypatch.setattr(
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llm_insights,
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"_call_llm_json",
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lambda prompt, payload: {
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"status": "success",
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"model": "m",
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"content": {
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"summary": "AAA 需要进入技术检查",
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"coin_impacts": [
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{
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"symbol": "AAA/USDT",
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"direction": "positive",
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"reason": "公告催化且主题关注升温",
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"confidence": 88,
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"need_technical_check": True,
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},
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{
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"symbol": "BBB/USDT",
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"direction": "positive",
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"reason": "置信度不足",
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"confidence": 40,
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"need_technical_check": True,
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},
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],
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},
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},
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)
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result = llm_insights.generate_sentiment_batch_analysis(limit=10)
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assert result["candidate_events"]["queued"] == 1
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assert result["candidate_events"]["symbols"] == ["AAA/USDT"]
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conn = sqlite3.connect(temp_db)
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conn.row_factory = sqlite3.Row
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rows = conn.execute(
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"SELECT source, symbol, event_type, processed, decision, rec_id FROM event_news WHERE source='llm_sentiment'"
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).fetchall()
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conn.close()
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assert len(rows) == 1
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assert rows[0]["symbol"] == "AAA/USDT"
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assert rows[0]["event_type"] == "llm_sentiment_candidate"
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assert rows[0]["processed"] == 0
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assert rows[0]["decision"] in ("", None)
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assert rows[0]["rec_id"] == 0
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def test_llm_insights_page_route(temp_db):
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client = TestClient(web_server.app)
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resp = client.get("/llm-insights")
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assert resp.status_code == 200
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assert "AI 记录" in resp.text
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def test_mojibake_json_is_repaired_for_display():
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raw = {"ç»è®º": "ç\x89å\x9b\x9e踩ï¼\x8cä¸\x8d追é«\x98", "list": ["æ\xa0¸å¿\x83å\x8e\x9få\x9b\xa0"]}
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fixed = repair_mojibake_json(raw)
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assert fixed["结论"] == "等回踩，不追高"
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assert fixed["list"][0] == "核心原因"
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def test_mixed_mojibake_text_is_repaired():
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raw = "AI舆情候选 ABC/USDT: Binance Futuresæ\x96°ä¸\x8a线永ç»\xadå\x90\x88约"
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fixed = repair_mojibake_json(raw)
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assert fixed == "AI舆情候选 ABC/USDT: Binance Futures新上线永续合约"
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