247 lines
8.7 KiB
Python
247 lines
8.7 KiB
Python
import json
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import os
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import sys
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from datetime import datetime, timedelta
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from unittest.mock import patch
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import pandas as pd
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sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
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from app.services import event_driven_screener as ed
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def test_symbol_extraction_filters_usdt_suffix_and_pollution():
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title = "Binance Futures Will Launch ABCUSDT, XYZUSDT and USD1USDT USDⓈ-Margined Perpetual Contracts"
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symbols = ed._symbol_from_title(title)
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assert "ABC/USDT" in symbols
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assert "XYZ/USDT" in symbols
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assert "ABCUSDT/USDT" not in symbols
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assert "USD1/USDT" not in symbols
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def test_recent_time_window_rejects_old_news():
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assert ed._is_recent(datetime.now() - timedelta(hours=2), 3) is True
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assert ed._is_recent(datetime.now() - timedelta(hours=8), 3) is False
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assert ed._is_recent(None, 3) is False
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def test_classify_major_listing_as_s_level_and_negative_as_risk():
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level, event_type = ed.classify_event("Binance Futures Will Launch ABCUSDT USDⓈ-Margined Perpetual Contracts")
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assert level == "S"
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assert event_type == "major_listing_or_contract"
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level, event_type = ed.classify_event("Binance Will Delist ABC on 2026-05-07")
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assert level == "RISK"
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assert event_type == "risk_negative"
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def test_store_events_deduplicates_by_hash(tmp_path):
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# 使用真实DB表，但同一事件重复插入只保留一次
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event = {
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"source": "binance_listing",
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"symbol": "ABC/USDT",
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"title": "Binance Futures Will Launch ABCUSDT USDⓈ-Margined Perpetual Contracts",
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"url": "https://example.com",
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"published_at": datetime.now(),
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"importance": "S",
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"event_type": "major_listing_or_contract",
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"raw": {"id": 1},
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}
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first = ed.store_events([event])
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second = ed.store_events([event])
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assert len(first) in (0, 1) # 若本地DB已有同事件，允许0
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assert second == []
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def test_quick_technical_check_rejects_old_overheated_gain():
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event = {
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"source": "binance_listing",
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"symbol": "ABC/USDT",
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"title": "Binance Futures Will Launch ABCUSDT USDⓈ-Margined Perpetual Contracts",
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"importance": "S",
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}
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with patch.object(ed, "_ticker_info", return_value={"price": 1.0, "change_24h": 35.0, "volume_24h": 10000000}):
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result = ed.quick_technical_check(event)
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assert result["decision"] == "risk"
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assert "不追高" in result["reason"]
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def test_theme_expansion_spreads_ton_news_to_ecosystem_symbols():
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event = {
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"source": "coingecko_trending",
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"symbol": "TON/USDT",
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"title": "Telegram becomes the main driver of the TON ecosystem and cuts TON fees",
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"url": "https://example.com/ton",
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"published_at": datetime.now(),
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"importance": "B",
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"event_type": "market_heat",
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"raw": {},
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}
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expanded = ed.expand_theme_events([event])
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by_symbol = {e["symbol"]: e for e in expanded}
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assert "TON/USDT" in by_symbol
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assert "NOT/USDT" in by_symbol
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assert "DOGS/USDT" in by_symbol
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assert by_symbol["DOGS/USDT"]["importance"] == "A"
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assert by_symbol["DOGS/USDT"]["event_type"] == "theme_expansion"
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assert "主题扩散:ton_ecosystem" in by_symbol["DOGS/USDT"]["title"]
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def test_theme_expansion_respects_max_expanded_symbols(monkeypatch):
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event = {
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"source": "coingecko_trending",
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"symbol": "TON/USDT",
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"title": "Telegram becomes the main driver of the TON ecosystem and cuts TON fees",
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"url": "https://example.com/ton",
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"published_at": datetime.now(),
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"importance": "B",
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"event_type": "market_heat",
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"raw": {},
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}
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original_cfg = ed._cfg()
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limited_cfg = json.loads(json.dumps(original_cfg))
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limited_cfg["theme_expansion"]["max_expanded_symbols"] = 2
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monkeypatch.setattr(ed, "_cfg", lambda: limited_cfg)
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expanded = ed.expand_theme_events([event])
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assert len(expanded) <= 3 # 原始事件 + 最多 2 个扩散事件
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def test_wublock_atom_feed_events_are_parsed_and_symbolized(monkeypatch):
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xml = """<?xml version="1.0" encoding="utf-8"?>
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<feed xmlns="http://www.w3.org/2005/Atom">
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<entry>
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<title>SUI ecosystem project announces major upgrade and airdrop</title>
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<link href="https://www.wublock123.com/example"/>
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<updated>2026-05-16T10:00:00Z</updated>
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</entry>
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</feed>
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"""
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class Resp:
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status_code = 200
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content = xml.encode("utf-8")
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monkeypatch.setattr(ed, "_now", lambda: datetime(2026, 5, 16, 18, 0, 0))
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with patch.object(ed.requests, "get", return_value=Resp()):
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events = ed.fetch_rss_events("wublock123", {
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"enabled": True,
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"type": "rss",
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"url": "https://www.wublock123.com/feed",
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"weight": "A",
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"symbol_aliases": {"sui": "SUI"},
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})
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assert len(events) == 1
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assert events[0]["source"] == "wublock123"
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assert events[0]["symbol"] == "SUI/USDT"
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assert events[0]["importance"] == "A"
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assert events[0]["url"] == "https://www.wublock123.com/example"
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def test_panews_rss_feed_events_are_parsed_and_symbolized(monkeypatch):
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xml = """<?xml version="1.0" encoding="utf-8"?>
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<rss version="2.0">
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<channel>
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<item>
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<title>Solana 生态 Meme 项目成交量大幅上升，SUI 同步活跃</title>
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<link>https://www.panewslab.com/example</link>
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<pubDate>Sat, 16 May 2026 10:00:00 GMT</pubDate>
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</item>
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</channel>
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</rss>
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"""
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class Resp:
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status_code = 200
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content = xml.encode("utf-8")
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monkeypatch.setattr(ed, "_now", lambda: datetime(2026, 5, 16, 18, 0, 0))
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with patch.object(ed.requests, "get", return_value=Resp()):
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events = ed.fetch_rss_events("panewslab", {
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"enabled": True,
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"type": "rss",
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"url": "https://www.panewslab.com/rss.xml?lang=zh&type=NORMAL%2CNEWS",
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"weight": "A",
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"symbol_aliases": {"solana": "SOL", "sui": "SUI"},
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})
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assert {e["symbol"] for e in events} == {"SOL/USDT", "SUI/USDT"}
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assert {e["source"] for e in events} == {"panewslab"}
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assert all(e["importance"] == "A" for e in events)
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def _fake_ohlcv(rows=60):
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return pd.DataFrame({
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"timestamp": pd.date_range("2026-05-01", periods=rows, freq="h"),
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"open": [1.0] * rows,
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"high": [1.02] * rows,
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"low": [0.98] * rows,
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"close": [1.0] * rows,
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"volume": [1000.0] * rows,
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})
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def test_theme_static_accumulation_bonus_can_upgrade_to_recommend():
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event = {
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"source": "coingecko_trending",
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"symbol": "DOGS/USDT",
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"title": "[主题扩散:ton_ecosystem] Telegram becomes the main driver of the TON ecosystem",
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"importance": "A",
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"event_type": "theme_expansion",
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}
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with patch.object(ed, "_ticker_info", return_value={"price": 0.001, "change_24h": 5.0, "volume_24h": 10000000}), \
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patch.object(ed, "fetch_klines", return_value=_fake_ohlcv()), \
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patch.object(ed, "detect_volume_price_fly", return_value=None), \
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patch.object(ed, "detect_static_accumulation", return_value={"static_count": 23, "vol_ratio": 1.4}), \
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patch.object(ed, "full_pa_analysis", return_value={"ignition_points": []}), \
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patch.object(ed, "fetch_derivatives_context", return_value={"funding_rate": 0, "top_trader_long_pct": 56}), \
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patch.object(ed, "calc_atr", return_value=0.00005):
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result = ed.quick_technical_check(event)
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assert result["score"] >= 6
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assert result["decision"] == "recommend"
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assert any("生态主题+强静K蓄力升权" in s for s in result["signals"])
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def test_run_once_caps_processed_events(monkeypatch):
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events = [
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{
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"source": "unit",
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"symbol": f"T{i}/USDT",
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"title": f"event {i}",
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"url": "",
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"published_at": datetime.now(),
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"importance": "A",
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"event_type": "news",
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"event_hash": f"h{i}",
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"raw": {},
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}
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for i in range(6)
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]
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processed = []
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monkeypatch.setattr(ed, "init_db", lambda: None)
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monkeypatch.setattr(ed, "init_event_tables", lambda: None)
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monkeypatch.setattr(ed, "collect_events", lambda: events)
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monkeypatch.setattr(ed, "store_events", lambda collected: collected)
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monkeypatch.setattr(ed, "process_event", lambda event: processed.append(event["symbol"]) or {
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"event": event,
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"result": {"decision": "ignore", "score": 0, "reason": ""},
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"rec_id": 0,
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"pushed": False,
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})
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monkeypatch.setattr(ed, "log_cron_run", lambda **kwargs: None)
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result = ed.run_once(limit=2, max_seconds=30)
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assert result["processed_count"] == 2
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assert result["skipped_due_to_limit"] == 4
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assert processed == ["T0/USDT", "T1/USDT"]
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