1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
|
"""News feed service for Prism v2 — Finnhub company news + sentiment, FMP fallback."""
from __future__ import annotations
import hashlib
import logging
import os
from datetime import datetime, timedelta, timezone
from typing import Any
from urllib.parse import urlparse
import httpx
from cachetools import TTLCache, cached
FINNHUB_BASE = "https://finnhub.io/api/v1"
FMP_BASE = "https://financialmodelingprep.com/api/v3"
NEWS_CACHE: TTLCache = TTLCache(maxsize=128, ttl=600)
logger = logging.getLogger(__name__)
POSITIVE_WORDS = [
"beats", "surges", "rises", "gains", "profit", "record", "upgrade", "buy", "outperform",
"growth", "strong", "higher", "rally", "raises", "expands", "upside",
]
NEGATIVE_WORDS = [
"misses", "falls", "drops", "loss", "cut", "downgrade", "sell", "underperform", "weak",
"lower", "decline", "warning", "layoff", "lawsuit", "probe", "cuts",
]
def _finnhub_api_key() -> str:
return os.getenv("FINNHUB_API_KEY", "")
def _fmp_api_key() -> str:
return os.getenv("FMP_API_KEY", "")
def _today_window(days: int = 30) -> tuple[str, str]:
end = datetime.now(timezone.utc).date()
start = end - timedelta(days=days)
return start.isoformat(), end.isoformat()
def _http_get_json(url: str, params: dict[str, Any] | None = None, timeout: float = 10.0) -> Any:
try:
with httpx.Client(timeout=timeout) as client:
resp = client.get(url, params=params)
resp.raise_for_status()
return resp.json()
except Exception as exc: # noqa: BLE001
logger.warning("news upstream request failed: %s - %s", url, exc)
return None
def _finnhub_company_news(symbol: str) -> list[dict]:
start, end = _today_window(30)
data = _http_get_json(
f"{FINNHUB_BASE}/company-news",
params={"symbol": symbol.upper(), "from": start, "to": end, "token": _finnhub_api_key()},
)
if not isinstance(data, list):
return []
return data[:100]
def _finnhub_news_sentiment(symbol: str) -> dict | None:
data = _http_get_json(
f"{FINNHUB_BASE}/news-sentiment",
params={"symbol": symbol.upper(), "token": _finnhub_api_key()},
)
if isinstance(data, dict):
return data
return None
def _fmp_stock_news(symbol: str) -> list[dict]:
data = _http_get_json(
f"{FMP_BASE}/stock_news",
params={"tickers": symbol.upper(), "limit": 100, "apikey": _fmp_api_key()},
)
if not isinstance(data, list):
return []
return data[:100]
def _classify_sentiment(article: dict) -> str:
headline = str(article.get("headline") or article.get("title") or "").lower()
summary = str(article.get("summary") or article.get("text") or "").lower()
text = headline + " " + summary
pos = sum(1 for w in POSITIVE_WORDS if w in text)
neg = sum(1 for w in NEGATIVE_WORDS if w in text)
if pos > neg:
return "bullish"
if neg > pos:
return "bearish"
return "neutral"
def _normalize_source(raw: Any) -> str:
text = str(raw or "").strip().lower()
if text.startswith("www."):
text = text[4:]
# If it looks like a domain, drop TLD.
if "." in text:
text = text.split(".")[0]
return text or "unknown"
def _parse_published_at(raw: Any) -> str | None:
if raw is None:
return None
if isinstance(raw, (int, float)):
try:
dt = datetime.fromtimestamp(float(raw), tz=timezone.utc)
return dt.strftime("%Y-%m-%dT%H:%M:%SZ")
except Exception: # noqa: BLE001
return None
text = str(raw).strip()
if not text:
return None
# ISO strings
norm = text.replace("Z", "+00:00")
try:
dt = datetime.fromisoformat(norm)
if dt.tzinfo is None:
dt = dt.replace(tzinfo=timezone.utc)
else:
dt = dt.astimezone(timezone.utc)
return dt.strftime("%Y-%m-%dT%H:%M:%SZ")
except Exception: # noqa: BLE001
pass
# FMP-style "YYYY-MM-DD HH:MM:SS"
for fmt in ("%Y-%m-%d %H:%M:%S", "%Y-%m-%d"):
try:
dt = datetime.strptime(text[: len(fmt)], fmt).replace(tzinfo=timezone.utc)
return dt.strftime("%Y-%m-%dT%H:%M:%SZ")
except Exception: # noqa: BLE001
pass
return None
def _is_safe_url(url: Any) -> bool:
if not url:
return False
parsed = urlparse(str(url))
return parsed.scheme in ("http", "https")
def _build_item(article: dict) -> dict | None:
title = str(article.get("headline") or article.get("title") or "").strip()
if not title:
title = "Untitled item"
summary = str(article.get("summary") or article.get("text") or "").strip()
source = _normalize_source(article.get("source") or article.get("site"))
url = article.get("url") or article.get("newsURL")
if not _is_safe_url(url):
return None
published_at = _parse_published_at(article.get("datetime") or article.get("publishedDate"))
if published_at is None:
return None
item_id = hashlib.sha1(str(url).encode("utf-8")).hexdigest()[:16]
return {
"id": item_id,
"title": title,
"summary": summary,
"source": source,
"url": str(url),
"sentiment": _classify_sentiment(article),
"published_at": published_at,
}
def _build_aggregate(raw: dict | None) -> dict | None:
if not raw:
return None
buzz = raw.get("buzz") or {}
score = raw.get("sentiment") or {}
weekly = buzz.get("articlesInLastWeek")
bull = score.get("bullishPercent")
bear = score.get("bearishPercent")
neutral = score.get("neutralPercent")
if weekly is None and bull is None and bear is None and neutral is None:
return None
return {
"buzz_articles_last_week": int(weekly) if weekly is not None else 0,
"bullish_pct": float(bull) if bull is not None else 0.0,
"bearish_pct": float(bear) if bear is not None else 0.0,
"neutral_pct": float(neutral) if neutral is not None else 0.0,
}
@cached(NEWS_CACHE, key=lambda symbol: symbol.upper())
def get_news(symbol: str) -> dict:
"""Return recent news articles and aggregate sentiment for a ticker."""
symbol = symbol.upper()
provider = "finnhub"
raw_articles = _finnhub_company_news(symbol)
if not raw_articles:
raw_articles = _fmp_stock_news(symbol)
provider = "fmp" if raw_articles else None
items = []
for article in raw_articles:
if not article:
continue
item = _build_item(article)
if item:
items.append(item)
items.sort(key=lambda i: i["published_at"], reverse=True)
items = items[:100]
aggregate = _build_aggregate(_finnhub_news_sentiment(symbol))
return {
"items": items,
"aggregate": aggregate,
"provider": provider,
"fetched_at": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"),
}
|
