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Stock-Tool/dist/stock_screener.py

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"""
Stock Evaluation Screener
=========================
Evaluates every common stock listed on NYSE, NASDAQ, and AMEX (~6 500+ tickers)
using a multi-factor composite scoring formula designed to act as a quantitative
analyst. This covers all major US indices: S&P 500/400/600, Russell 1000/2000/3000,
DJIA, NYSE Composite, NASDAQ Composite, and every sector/thematic index derived
from US-listed equities.
FORMULA (Composite Score):
Score = 0.18*Value + 0.18*Growth + 0.14*Momentum + 0.11*Quality
+ 0.08*Profitability + 0.13*Sentiment + 0.10*Analyst + 0.08*Risk
Each component is scored 0–100 based on absolute benchmarks for that metric.
Scores are INDEPENDENT — each stock is evaluated on its own merits, not
relative to other stocks in the dataset.
Value — how attractively priced (P/E, P/B, EV/EBITDA vs benchmarks)
Growth — revenue, earnings, and EPS trajectory
Momentum — price return across 1M/3M/6M/12M windows
Quality — financial strength (ROE, ROA, debt, liquidity)
Profitability — margins and free cash flow yield
Sentiment — recent news tone via VADER NLP
Analyst — consensus rating and upside to price target
Risk — short interest, volatility, and beta stability
"""
import argparse
import html
import random
import re
import sys
import time
import warnings
from concurrent.futures import ThreadPoolExecutor, as_completed
from datetime import datetime, timedelta, timezone
import numpy as np
import pandas as pd
import requests
import yfinance as yf
warnings.filterwarnings("ignore")
# ---------------------------------------------------------------------------
# VADER sentiment — optional but strongly recommended
# pip install vaderSentiment
# ---------------------------------------------------------------------------
try:
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
_VADER = SentimentIntensityAnalyzer()
except ImportError:
_VADER = None
# ---------------------------------------------------------------------------
# Configuration
# ---------------------------------------------------------------------------
WEIGHTS = {
"value": 0.18,
"growth": 0.16,
"momentum": 0.13,
"quality": 0.14,
"profitability": 0.13,
"sentiment": 0.10,
"analyst": 0.10,
"risk": 0.06,
}
# Human-readable investment strategy presets.
# Each preset is a complete weight dict that must sum to 1.0.
STRATEGY_PRESETS = {
"Balanced (Default)": {
"value": 0.18, "growth": 0.16, "momentum": 0.13, "quality": 0.14,
"profitability": 0.13, "sentiment": 0.10, "analyst": 0.10, "risk": 0.06,
},
"High Growth Companies": {
"value": 0.08, "growth": 0.28, "momentum": 0.25, "quality": 0.08,
"profitability": 0.08, "sentiment": 0.12, "analyst": 0.07, "risk": 0.04,
},
"Conservative": {
"value": 0.20, "growth": 0.12, "momentum": 0.06, "quality": 0.27,
"profitability": 0.20, "sentiment": 0.04, "analyst": 0.08, "risk": 0.03,
},
"Recent Uptrends": {
"value": 0.05, "growth": 0.12, "momentum": 0.40, "quality": 0.05,
"profitability": 0.05, "sentiment": 0.18, "analyst": 0.10, "risk": 0.05,
},
"Buy and Hold Long Term": {
"value": 0.30, "growth": 0.19, "momentum": 0.05, "quality": 0.20,
"profitability": 0.18, "sentiment": 0.03, "analyst": 0.05, "risk": 0.00,
},
"Low Risk, High Dividend Yield": {
"value": 0.22, "growth": 0.08, "momentum": 0.05, "quality": 0.22,
"profitability": 0.30, "sentiment": 0.05, "analyst": 0.05, "risk": 0.03,
},
"High Risk High Reward": {
"value": 0.10, "growth": 0.00, "momentum": 0.00, "quality": 0.15,
"profitability": 0.00, "sentiment": 0.05, "analyst": 0.30, "risk": 0.05,
"reverse_momentum": 0.35,
},
}
HIST_BATCH = 100 # tickers per yf.download() batch
HIST_PAUSE = 3.0 # seconds between history batches
INFO_BATCH = 20 # tickers per info-fetch batch
INFO_PAUSE = 4.0 # seconds between info batches
BATCH_SIZE = INFO_BATCH
BATCH_PAUSE = INFO_PAUSE
# ---------------------------------------------------------------------------
# Ticker Fetching
# ---------------------------------------------------------------------------
# Valid common-stock ticker: 1-5 uppercase letters, optionally followed by
# a dash and 1-2 letters for share classes (e.g. BRK-A, BRK-B)
_TICKER_RE = re.compile(r'^[A-Z]{1,5}(-[A-Z]{1,2})?$')
def _clean_ticker(symbol: str) -> str | None:
"""Normalize a raw symbol and return it if it looks like a common stock."""
t = symbol.strip().upper().replace(".", "-")
return t if _TICKER_RE.match(t) else None
_NASDAQ100_FALLBACK = [
"AAPL", "MSFT", "NVDA", "AMZN", "META", "GOOGL", "GOOG", "TSLA", "AVGO", "COST",
"NFLX", "TMUS", "ASML", "AMD", "PEP", "LIN", "CSCO", "ADBE", "QCOM", "INTU",
"TXN", "AMAT", "AMGN", "CMCSA", "ISRG", "MU", "LRCX", "MRVL", "KLAC", "REGN",
"PANW", "CRWD", "SNPS", "CDNS", "ABNB", "ORLY", "MNST", "NXPI", "FTNT", "MAR",
"AEP", "PYPL", "MCHP", "PAYX", "ADSK", "CTAS", "IDXX", "ROST", "KDP", "FAST",
"ODFL", "DXCM", "VRSK", "CPRT", "BKR", "GEHC", "ON", "EXC", "LULU", "ZS",
"TEAM", "PCAR", "WDAY", "CTSH", "BIIB", "GILD", "CEG", "TTD", "MRNA", "EA",
"EBAY", "ILMN", "INTC", "SMCI", "ARM", "FANG", "WBD", "DLTR", "SIRI", "GFS",
"HON", "MSTR", "DASH", "RBLX", "COIN", "DDOG", "ZM", "OKTA", "SNOW", "NET",
"HUBS", "BILL", "CFLT", "MDB", "GTLB", "U", "APP", "PINS", "RIVN", "UBER",
]
def get_nasdaq100_tickers() -> list[str]:
"""Live NASDAQ-100 from Wikipedia; falls back to a hardcoded list."""
tickers = _fetch_wikipedia_index(
"https://en.wikipedia.org/wiki/Nasdaq-100", "NASDAQ 100")
if tickers:
return tickers
print(" [WARN] Using hardcoded NASDAQ-100 fallback list")
tickers = list(dict.fromkeys(_NASDAQ100_FALLBACK))
print(f" NASDAQ 100 : {len(tickers):>5} tickers (fallback)")
return tickers
_NASDAQ_HEADERS = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36",
"Accept": "application/json, text/plain, */*",
"Referer": "https://www.nasdaq.com/",
}
def _fetch_exchange(exchange: str) -> list[str]:
"""
Pull every listed stock for one exchange from the NASDAQ screener API.
Covers NYSE (~2 800 tickers), NASDAQ (~3 500), and AMEX (~300).
Together these three exchanges list every component of every major US index:
S&P 500/400/600, Russell 1000/2000/3000, DJIA, NYSE Composite, etc.
"""
url = (
"https://api.nasdaq.com/api/screener/stocks"
f"?tableonly=true&limit=10000&exchange={exchange}&download=true"
)
try:
resp = requests.get(url, headers=_NASDAQ_HEADERS, timeout=30)
resp.raise_for_status()
rows = resp.json()["data"]["table"]["rows"]
tickers = [_clean_ticker(r.get("symbol", "")) for r in rows]
tickers = [t for t in tickers if t]
print(f" {exchange:<8}: {len(tickers):>5} tickers")
return tickers
except Exception as e:
print(f" [WARN] Could not fetch {exchange} tickers: {e}")
return []
def _fetch_nasdaqtrader_listings(exchange_filter: str | None = None) -> list[str]:
"""
Download the official NASDAQ Trader exchange listing flat files.
These are publicly accessible static files updated daily — no API key,
no bot detection, no rate limiting.
exchange_filter: None = all exchanges, or "NASDAQ", "NYSE", "AMEX"
nasdaqlisted.txt — every NASDAQ-listed stock
cols: Symbol | Name | MarketCat | TestIssue | FinancialStatus | LotSize | ETF | NextShares
otherlisted.txt — every NYSE / AMEX / Arca / BATS / IEX listed stock
cols: ACTSymbol | Name | Exchange | CQSSymbol | ETF | LotSize | TestIssue | NASDAQSymbol
Exchange codes: N=NYSE A=AMEX/NYSE-American P=NYSE-Arca Z=BATS V=IEX
"""
from io import StringIO
base = "https://www.nasdaqtrader.com/dynamic/SymDir/"
tickers = []
# ── nasdaqlisted.txt ──────────────────────────────────────────────────
if exchange_filter in (None, "NASDAQ"):
try:
resp = requests.get(base + "nasdaqlisted.txt",
headers=_HTTP_HEADERS, timeout=20)
resp.raise_for_status()
count = 0
for line in resp.text.splitlines()[1:]:
if line.startswith("File Creation"):
break
parts = line.split("|")
if len(parts) < 7:
continue
test_issue = parts[3].strip() # col 3 = TestIssue
is_etf = parts[6].strip() # col 6 = ETF
if test_issue == "Y" or is_etf == "Y":
continue
t = _clean_ticker(parts[0].strip())
if t:
tickers.append(t)
count += 1
print(f" NASDAQ listed : {count:>5} tickers")
except Exception as e:
print(f" [WARN] nasdaqlisted.txt failed: {e}")
# ── otherlisted.txt ───────────────────────────────────────────────────
if exchange_filter in (None, "NYSE", "AMEX"):
# Map friendly name → exchange code(s) in the file
_code_map = {
"NYSE": {"N"},
"AMEX": {"A"},
None: {"N", "A", "P", "Z", "V"},
}
allowed_codes = _code_map.get(exchange_filter, {"N", "A", "P", "Z", "V"})
try:
resp = requests.get(base + "otherlisted.txt",
headers=_HTTP_HEADERS, timeout=20)
resp.raise_for_status()
count = 0
for line in resp.text.splitlines()[1:]:
if line.startswith("File Creation"):
break
parts = line.split("|")
if len(parts) < 7:
continue
exchange = parts[2].strip() # col 2 = Exchange code
is_etf = parts[4].strip() # col 4 = ETF
test_issue = parts[6].strip() # col 6 = TestIssue
if test_issue == "Y" or is_etf == "Y":
continue
if exchange not in allowed_codes:
continue
t = _clean_ticker(parts[0].strip())
if t:
tickers.append(t)
count += 1
lbl = exchange_filter or "other exchanges"
print(f" {lbl:<15}: {count:>5} tickers")
except Exception as e:
print(f" [WARN] otherlisted.txt failed: {e}")
return tickers
def _fetch_otc_tickers() -> list[str]:
"""
Fetch OTC-traded US company tickers from SEC EDGAR's exchange-tagged file.
company_tickers_exchange.json tags every SEC-registered company with its
exchange (Nasdaq, NYSE, OTC, etc.) — no bot detection, same source we
already trust for the fallback ticker list.
Filters to entries where exchange == "OTC", giving OTCQX / OTCQB /
Pink Sheet stocks not covered by the NASDAQ Trader exchange files.
"""
try:
resp = requests.get(
"https://www.sec.gov/files/company_tickers_exchange.json",
headers={"User-Agent": "stockscreener contact@example.com"},
timeout=20,
)
resp.raise_for_status()
payload = resp.json()
# Format: {"fields": ["cik","name","ticker","exchange"], "data": [[...], ...]}
fields = payload.get("fields", [])
rows = payload.get("data", [])
try:
exch_idx = fields.index("exchange")
ticker_idx = fields.index("ticker")
except ValueError:
print(" [WARN] SEC EDGAR exchange file format changed — no OTC tickers")
return []
tickers = [
_clean_ticker(str(row[ticker_idx]))
for row in rows
if str(row[exch_idx]).upper() == "OTC"
]
tickers = [t for t in tickers if t]
print(f" OTC (SEC EDGAR): {len(tickers):>5} tickers")
return tickers
except Exception as e:
print(f" [WARN] SEC EDGAR OTC fetch failed: {e}")
return []
def _fetch_sec_all_tickers() -> list[str]:
"""
Pull every US-listed company ticker from the SEC EDGAR company tickers file.
This is a publicly accessible, bot-friendly endpoint (~10 000+ tickers).
Used as the fallback when NASDAQ Trader files are unavailable.
"""
try:
resp = requests.get(
"https://www.sec.gov/files/company_tickers.json",
headers={"User-Agent": "stockscreener contact@example.com"},
timeout=20,
)
resp.raise_for_status()
data = resp.json()
tickers = [_clean_ticker(v.get("ticker", "")) for v in data.values()]
tickers = [t for t in tickers if t]
print(f" SEC EDGAR : {len(tickers):>5} tickers")
return tickers
except Exception as e:
print(f" [WARN] SEC EDGAR ticker fetch failed: {e}")
return []
_HTTP_HEADERS = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)"}
def _fetch_sp500() -> list[str]:
# Primary: Wikipedia — reflects additions/removals as soon as the page updates
tickers = _fetch_wikipedia_index(
"https://en.wikipedia.org/wiki/List_of_S%26P_500_companies", "S&P 500")
if tickers:
return tickers
# Fallback: community-maintained GitHub CSV
try:
from io import StringIO
url = ("https://raw.githubusercontent.com/datasets/"
"s-and-p-500-companies/main/data/constituents.csv")
resp = requests.get(url, headers=_HTTP_HEADERS, timeout=20)
resp.raise_for_status()
tickers = [_clean_ticker(t)
for t in pd.read_csv(StringIO(resp.text))["Symbol"]]
tickers = [t for t in tickers if t]
print(f" S&P 500 : {len(tickers):>5} tickers (GitHub fallback)")
return tickers
except Exception as e:
print(f" [WARN] S&P 500 GitHub fallback failed: {e}")
return []
def _fetch_wikipedia_index(url: str, label: str, min_count: int = 10) -> list[str]:
"""
Pull index constituents from a Wikipedia list-of-companies page.
Strips footnote markers (e.g. 'AAPL[a]') and skips tables that are
too small to be the real index table (avoids false positives).
"""
try:
from io import StringIO
resp = requests.get(url, headers=_HTTP_HEADERS, timeout=20)
resp.raise_for_status()
tables = pd.read_html(StringIO(resp.text))
for df in tables:
for col in ("Symbol", "Ticker", "Ticker symbol"):
if col in df.columns:
# Strip footnote brackets like [a], [1], [note 1]
cleaned = (df[col].astype(str)
.str.replace(r'\[.*?\]', '', regex=True)
.str.strip())
tickers = [_clean_ticker(t) for t in cleaned]
tickers = [t for t in tickers if t]
if len(tickers) < min_count:
continue # skip unrelated small tables
print(f" {label:<12}: {len(tickers):>5} tickers")
return tickers
except Exception as e:
print(f" [WARN] {label} failed: {e}")
return []
# iShares Russell ETF holdings CSVs — publicly downloadable
_RUSSELL_ETFS = {
"Russell 1000": ("239707", "ishares-russell-1000-etf", "IWB"),
"Russell 2000": ("239659", "ishares-russell-2000-etf", "IWM"),
"Russell 3000": ("239714", "ishares-russell-3000-etf", "IWV"),
}
def _fetch_russell(label: str) -> list[str]:
product_id, slug, etf = _RUSSELL_ETFS[label]
url = (
f"https://www.ishares.com/us/products/{product_id}/{slug}"
f"/1467271812596.ajax?fileType=csv&fileName={etf}_holdings&dataType=fund"
)
try:
from io import StringIO
resp = requests.get(url, headers=_HTTP_HEADERS, timeout=40)
resp.raise_for_status()
lines = resp.text.splitlines()
# iShares CSVs have a few metadata lines before the real header
header_idx = next(
(i for i, ln in enumerate(lines)
if ln.startswith("Ticker,") or ",Ticker," in ln or ln.startswith("Name,")),
None,
)
if header_idx is None:
raise ValueError("Header row not found in iShares CSV")
df = pd.read_csv(StringIO("\n".join(lines[header_idx:])))
col = next((c for c in df.columns if c.strip() == "Ticker"), None)
if not col:
raise ValueError("'Ticker' column missing")
tickers = [_clean_ticker(str(t)) for t in df[col]]
tickers = [t for t in tickers if t and t.upper() not in ("-", "NAN", "CASH")]
print(f" {label:<12}: {len(tickers):>5} tickers")
return tickers
except Exception as e:
print(f" [WARN] {label} iShares CSV failed: {e}")
return []
_DJIA_FALLBACK = [
"AAPL", "AMGN", "AMZN", "AXP", "BA", "CAT", "CRM", "CSCO", "CVX", "DIS",
"DOW", "GS", "HD", "HON", "IBM", "JNJ", "JPM", "KO", "MCD", "MMM",
"MRK", "MSFT", "NKE", "PG", "SHW", "TRV", "UNH", "V", "VZ", "WMT",
]
def get_djia_tickers() -> list[str]:
"""Live DJIA components from Wikipedia; falls back to a hardcoded list."""
tickers = _fetch_wikipedia_index(
"https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average", "DJIA")
if tickers:
return tickers
print(" [WARN] Using hardcoded DJIA fallback list")
tickers = list(dict.fromkeys(_DJIA_FALLBACK))
print(f" DJIA : {len(tickers):>5} tickers (fallback)")
return tickers
# Human-readable index names exposed to the GUI
INDEX_CHOICES = [
"All",
"S&P 500", "S&P 400", "S&P 600", "S&P 1500",
"Russell 1000", "Russell 2000", "Russell 3000",
"NASDAQ 100", "NASDAQ", "NYSE", "AMEX",
"DJIA",
]
def collect_tickers(index: str = "All") -> list[str]:
"""
Fetch the constituent tickers for the requested index.
"All" — every common stock on all US exchanges via NASDAQ Trader files
"All + OTC" — exchange-listed + OTC/Pink Sheet stocks
"S&P 500/400/600/1500" — via Wikipedia / GitHub CSV
"Russell 1000/2000/3000" — via iShares ETF holdings CSVs
"NASDAQ 100" — live Wikipedia list with hardcoded fallback
"NASDAQ/NYSE/AMEX" — per-exchange listings via NASDAQ Trader files
"DJIA" — 30 components from Wikipedia with hardcoded fallback
Fallback chain for "All" and single-exchange lookups:
NASDAQ Trader files → NASDAQ screener API → SEC EDGAR → Russell 3000 → S&P 500
"""
print(f"Fetching tickers for index: {index} ...")
sp400_url = "https://en.wikipedia.org/wiki/List_of_S%26P_400_companies"
sp600_url = "https://en.wikipedia.org/wiki/List_of_S%26P_600_companies"
def _all_exchange_tickers() -> list[str]:
"""All exchange-listed US common stocks with full fallback chain."""
# 1. NASDAQ Trader official listing files (most reliable — static, no bot detection)
t = _fetch_nasdaqtrader_listings(exchange_filter=None)
if t:
return t
# 2. NASDAQ screener API (often blocked but try anyway)
print(" [WARN] NASDAQ Trader files failed — trying NASDAQ screener API")
t = []
for exch in ("NYSE", "NASDAQ", "AMEX"):
t.extend(_fetch_exchange(exch))
if t:
return t
# 3. SEC EDGAR company registry (~10 000+ tickers)
print(" [WARN] NASDAQ API failed — trying SEC EDGAR")
t = _fetch_sec_all_tickers()
if t:
return t
# 4. Russell 3000
print(" [WARN] SEC EDGAR failed — trying Russell 3000")
t = _fetch_russell("Russell 3000")
if t:
return t
# 5. S&P 1500
print(" [WARN] Russell 3000 failed — trying S&P 1500")
t = (_fetch_sp500()
+ _fetch_wikipedia_index(sp400_url, "S&P 400")
+ _fetch_wikipedia_index(sp600_url, "S&P 600"))
if t:
return t
# 6. S&P 500 last resort
print(" [WARN] All broad sources failed — falling back to S&P 500")
return _fetch_sp500()
if index == "All":
tickers = _all_exchange_tickers()
elif index == "S&P 500":
tickers = _fetch_sp500()
elif index == "S&P 400":
tickers = _fetch_wikipedia_index(sp400_url, "S&P 400")
elif index == "S&P 600":
tickers = _fetch_wikipedia_index(sp600_url, "S&P 600")
elif index == "S&P 1500":
tickers = (_fetch_sp500()
+ _fetch_wikipedia_index(sp400_url, "S&P 400")
+ _fetch_wikipedia_index(sp600_url, "S&P 600"))
elif index in ("Russell 1000", "Russell 2000", "Russell 3000"):
tickers = _fetch_russell(index)
if not tickers:
print(f" [WARN] iShares CSV failed — using full exchange listing for {index}")
tickers = _all_exchange_tickers()
elif index == "NASDAQ 100":
tickers = get_nasdaq100_tickers()
elif index == "NASDAQ":
tickers = _fetch_nasdaqtrader_listings(exchange_filter="NASDAQ")
if not tickers:
tickers = _fetch_exchange("NASDAQ")
elif index == "NYSE":
tickers = _fetch_nasdaqtrader_listings(exchange_filter="NYSE")
if not tickers:
tickers = _fetch_exchange("NYSE")
elif index == "AMEX":
tickers = _fetch_nasdaqtrader_listings(exchange_filter="AMEX")
if not tickers:
tickers = _fetch_exchange("AMEX")
elif index == "DJIA":
tickers = get_djia_tickers()
else:
print(f" [WARN] Unknown index '{index}' — defaulting to All")
tickers = _all_exchange_tickers()
combined = list(dict.fromkeys(tickers)) # deduplicate, preserve order
print(f" Total : {len(combined):>5} unique tickers\n")
return combined
# ---------------------------------------------------------------------------
# News Sentiment (multi-source VADER)
# ---------------------------------------------------------------------------
# Sources pulled in order:
# 1. Yahoo Finance — via yfinance Ticker.news (built-in, no key)
# 2. Google News RSS — public RSS feed, no API key required
# ---------------------------------------------------------------------------
def _extract_yfinance_news(ticker_obj) -> list[tuple[str, float, str, str]]:
"""Return (title, timestamp, url, summary) tuples from yfinance Ticker.news."""
results = []
try:
news = ticker_obj.news or []
now = time.time()
for item in news[:30]:
if not isinstance(item, dict):
continue
title = ts = url = summary = None
content = item.get("content", {})
if isinstance(content, dict) and content.get("title"):
title = content["title"]
pub = content.get("pubDate", "")
try:
ts = datetime.fromisoformat(
pub.replace("Z", "+00:00")).timestamp()
except Exception:
ts = now
# Try several URL fields used across yfinance versions
canon = content.get("canonicalUrl") or {}
url = (canon.get("url") if isinstance(canon, dict) else None) \
or content.get("url") or content.get("clickThroughUrl", {}).get("url") \
or item.get("link")
summary = content.get("summary") or content.get("description") or ""
elif item.get("title"):
title = item["title"]
ts = item.get("providerPublishTime", now)
url = item.get("link") or item.get("url") or ""
summary = item.get("summary") or item.get("description") or ""
if title:
clean = re.sub(r'\s+', ' ',
html.unescape(re.sub(r'<[^>]+>', ' ', summary or ""))).strip()
results.append((title, float(ts or now), url or "", clean))
except Exception:
pass
return results
def _extract_google_news_rss(ticker: str, company_name: str = "") -> list[tuple[str, float, str, str]]:
"""
Fetch headlines from Google News RSS for the given ticker.
Returns (title, timestamp, url, summary) tuples.
No API key required.
"""
import re as _re
import xml.etree.ElementTree as ET
import urllib.parse
results = []
try:
query = f"{ticker} stock"
if company_name:
query += f" {company_name}"
encoded = urllib.parse.quote(query)
rss_url = (
f"https://news.google.com/rss/search"
f"?q={encoded}&hl=en-US&gl=US&ceid=US:en"
)
headers = {"User-Agent": "Mozilla/5.0 (compatible; StockScreener/1.0)"}
resp = requests.get(rss_url, headers=headers, timeout=8)
resp.raise_for_status()
root = ET.fromstring(resp.content)
now = time.time()
for item in root.findall(".//item")[:30]:
title_el = item.find("title")
pub_el = item.find("pubDate")
desc_el = item.find("description")
if title_el is None or not title_el.text:
continue
title = title_el.text.strip()
ts = now
if pub_el is not None and pub_el.text:
try:
from email.utils import parsedate_to_datetime
ts = parsedate_to_datetime(pub_el.text).timestamp()
except Exception:
pass
# guid is more reliably a plain URL in Google News RSS than <link>
guid_el = item.find("guid")
link = (guid_el.text.strip() if guid_el is not None and guid_el.text else "") \
or (item.findtext("link") or "")
# Strip HTML tags and unescape entities from description snippet
raw_desc = desc_el.text if desc_el is not None and desc_el.text else ""
summary = _re.sub(r'\s+', ' ',
html.unescape(_re.sub(r"<[^>]+>", " ", raw_desc))).strip()
results.append((title, ts, link, summary))
except Exception:
pass
return results
def get_news_headlines(ticker_obj, ticker: str = "", company_name: str = "") \
-> tuple:
"""
Aggregate headlines from Yahoo Finance and Google News RSS and compute
recency-weighted VADER sentiment.
Returns (aggregate_score, top_headlines) where:
- aggregate_score is float in [-1.0, +1.0] or None
- top_headlines is a list of (title, vader_score, age_days) tuples,
sorted by impact (abs(score) × recency weight), up to 8 entries.
"""
if _VADER is None:
return None, []
all_items: list[tuple[str, float, str, str]] = []
all_items.extend(_extract_yfinance_news(ticker_obj))
if ticker:
all_items.extend(_extract_google_news_rss(ticker, company_name))
if not all_items:
return None, []
# Deduplicate by normalised title
seen: set[str] = set()
unique: list[tuple[str, float, str, str]] = []
for title, ts, url, summary in all_items:
key = title.lower().strip()[:80]
if key not in seen:
seen.add(key)
unique.append((title, ts, url, summary))
now = time.time()
total_w = total_s = 0.0
# (title, score, age_days, weight, url, summary)
scored: list[tuple[str, float, float, float, str, str]] = []
for title, ts, url, summary in unique:
age_days = max(0.0, (now - ts) / 86400)
if age_days <= 7:
weight = 1.0
else:
weight = max(0.05, 1.0 - (age_days - 7) / 23.0)
score = _VADER.polarity_scores(title)["compound"]
total_s += score * weight
total_w += weight
scored.append((title, score, age_days, weight, url, summary))
aggregate = total_s / total_w if total_w else None
# Top headlines by impact: abs(score) × recency weight
scored.sort(key=lambda x: abs(x[1]) * x[3], reverse=True)
top = [(title, vader_score, age_days, url, summary)
for title, vader_score, age_days, _, url, summary in scored[:8]]
return aggregate, top
def _news_sentiment(ticker_obj, ticker: str = "", company_name: str = "") -> float | None:
"""Backward-compatible wrapper — returns only the aggregate score."""
score, _ = get_news_headlines(ticker_obj, ticker=ticker, company_name=company_name)
return score
# ---------------------------------------------------------------------------
# EDGAR Bulk Fundamentals
# ---------------------------------------------------------------------------
# Fetches financial statement data for all US public companies from the
# SEC EDGAR XBRL frames API. One HTTP call per financial concept returns
# values for ALL filers simultaneously — ~50 calls cover every metric
# needed for the scoring model in roughly 2 minutes.
#
# Coverage: ~85–90% of exchange-listed US stocks. Tickers with no EDGAR
# coverage still appear in results and score on price/momentum/analyst
# data only; their fundamental fields default to NaN.
# ---------------------------------------------------------------------------
_EDGAR_FRAMES = "https://data.sec.gov/api/xbrl/frames"
_EDGAR_TICKERS = "https://www.sec.gov/files/company_tickers.json"
_EDGAR_UA = {"User-Agent": "StockScreener contact@investmenttool.com"}
_FRAME_CACHE: dict = {} # in-memory cache so the same frame is never fetched twice
def _get_recent_quarters(n: int = 9) -> list[tuple[int, int]]:
"""
Return the last n (year, quarter) tuples, most-recent first, using a
45-day filing lag so we only request periods most companies have filed.
"""
from datetime import date, timedelta as _td
ref = date.today() - _td(days=45)
y, q = ref.year, (ref.month - 1) // 3 + 1
out = []
for _ in range(n):
out.append((y, q))
q -= 1
if q == 0:
q, y = 4, y - 1
return out
def _fetch_one_frame(concept: str, unit: str, period: str) -> dict[int, float]:
"""
Fetch all companies' values for one EDGAR XBRL concept/period.
Returns {cik (int): value (float)}. Results are cached in _FRAME_CACHE
so the same concept/period is never downloaded twice in one run.
"""
key = (concept, unit, period)
if key in _FRAME_CACHE:
return _FRAME_CACHE[key]
url = f"{_EDGAR_FRAMES}/us-gaap/{concept}/{unit}/{period}.json"
try:
resp = requests.get(url, headers=_EDGAR_UA, timeout=20)
if resp.status_code == 404:
_FRAME_CACHE[key] = {}
return {}
resp.raise_for_status()
result: dict[int, float] = {}
for row in resp.json().get("data", []):
# The EDGAR frames API returns each row as a dict with keys:
# accn, cik, entityName, loc, start, end, val
# (Not as a list — handle both formats for safety.)
try:
if isinstance(row, dict):
cik = int(row["cik"])
val = float(row["val"])
else:
# Legacy/alternative format: [accn, cik, entityName, loc, end, val]
if len(row) < 6:
continue
cik = int(row[1])
val = float(row[5])
result[cik] = val # last entry wins (restatements)
except (KeyError, TypeError, ValueError):
continue
_FRAME_CACHE[key] = result
time.sleep(0.15) # stay comfortably under SEC's 10 req/sec limit
return result
except Exception:
# Do not cache transient failures (network errors, timeouts, etc.)
# Only 404s (already handled above) should produce a permanent empty cache entry.
return {}
def _sum_frames(concepts: list[str], unit: str,
periods: list[str]) -> dict[int, float]:
"""
Sum values across periods (TTM) trying each concept in order.
The primary concept is tried first; gaps are filled by fallback concepts.
"""
ttm: dict[int, dict] = {} # {cik: {period: value}}
for concept in concepts:
for period in periods:
for cik, val in _fetch_one_frame(concept, unit, period).items():
ttm.setdefault(cik, {})
if period not in ttm[cik]:
ttm[cik][period] = val
return {cik: sum(pv.values()) for cik, pv in ttm.items() if len(pv) >= 4}
def _best_frame(concepts: list[str], unit: str,
periods: list[str]) -> dict[int, float]:
"""
Return the most-recent available value per CIK (for balance-sheet items).
Iterates periods most-recent first; first value found for each CIK wins.
"""
best: dict[int, float] = {}
for concept in concepts:
for period in periods:
for cik, val in _fetch_one_frame(concept, unit, period).items():
if cik not in best:
best[cik] = val
return best
def _build_cik_maps() -> tuple[dict[int, str], dict[int, str], dict[str, int]]:
"""
Fetch SEC company_tickers.json and return three dicts:
cik_to_ticker — {cik_int: "AAPL"} (one representative ticker per CIK)
cik_to_name — {cik_int: "Apple Inc."}
ticker_to_cik — {ticker: cik_int} (ALL tickers, handles dual-class shares)
ticker_to_cik covers every row in the SEC file so dual-class stocks
(GOOGL/GOOG, BRK-A/BRK-B, etc.) each map to the same CIK and therefore
both receive EDGAR fundamental data.
"""
try:
resp = requests.get(_EDGAR_TICKERS, headers=_EDGAR_UA, timeout=20)
resp.raise_for_status()
data = resp.json()
cik_to_ticker: dict[int, str] = {}
cik_to_name: dict[int, str] = {}
ticker_to_cik: dict[str, int] = {}
for v in data.values():
cik = int(v["cik_str"])
ticker = v["ticker"].upper()
title = v.get("title", "")
ticker_to_cik[ticker] = cik # every ticker gets its own entry
if cik not in cik_to_ticker:
cik_to_ticker[cik] = ticker # first seen wins for repr
cik_to_name[cik] = title
return cik_to_ticker, cik_to_name, ticker_to_cik
except Exception as e:
print(f" [WARN] CIK map unavailable: {e}")
return {}, {}, {}
def _fetch_edgar_bulk(close_map: dict[str, pd.Series]) -> dict[str, dict]:
"""
Fetch fundamental data for all tickers from the EDGAR XBRL frames API.
Returns {ticker: {field: value}} with keys matching the rest of the
scoring pipeline. Takes ~2 minutes for the full US universe.
Fields returned (NaN-safe — missing values simply absent from the dict):
market_cap, pe_trailing, pb_ratio, ev_ebitda, eps_trailing,
revenue, revenue_growth, earnings_growth, roe, roa,
debt_to_equity, total_debt, total_cash, book_value, current_ratio,
profit_margin, operating_margin, fcf_yield, name
"""
print("Phase 2A: Fetching EDGAR bulk fundamentals ...")
cik_to_ticker, cik_to_name, ticker_to_cik = _build_cik_maps()
if not ticker_to_cik:
print(" [WARN] CIK map unavailable — EDGAR bulk skipped")
return {}
quarters = _get_recent_quarters(9)
ttm_periods = [f"CY{y}Q{q}" for y, q in quarters[:4]]
prev_periods= [f"CY{y}Q{q}" for y, q in quarters[4:8]]
bs_periods = [f"CY{y}Q{q}I" for y, q in quarters[:3]]
print(f" TTM : {ttm_periods}")
print(f" BS : {bs_periods[0]} (+{len(bs_periods)-1} fallback periods)")
# ── Balance sheet (most recent available quarter) ──────────────────────
assets = _best_frame(["Assets"], "USD", bs_periods)
equity = _best_frame(["StockholdersEquity",
"StockholdersEquityIncludingPortionAttributable"
"ToNoncontrollingInterest",
"CommonStockholdersEquity"], "USD", bs_periods)
cur_assets = _best_frame(["AssetsCurrent"], "USD", bs_periods)
cur_liab = _best_frame(["LiabilitiesCurrent"], "USD", bs_periods)
cash = _best_frame(["CashAndCashEquivalentsAtCarryingValue",
"CashCashEquivalentsAndShortTermInvestments"], "USD", bs_periods)
lt_debt = _best_frame(["LongTermDebt", "LongTermDebtNoncurrent",
"LongTermDebtAndCapitalLeaseObligations",
"LongTermDebtAndFinanceLeaseLiabilities",
"FinanceLeaseLiabilityNoncurrent"], "USD", bs_periods)
shares = _best_frame(["CommonStockSharesOutstanding"], "shares", bs_periods)
# ── Income statement TTM ────────────────────────────────────────────────
revenue = _sum_frames(["Revenues",
"RevenueFromContractWithCustomer"
"ExcludingAssessedTax",
"SalesRevenueNet"], "USD", ttm_periods)
net_income = _sum_frames(["NetIncomeLoss"], "USD", ttm_periods)
op_income = _sum_frames(["OperatingIncomeLoss"], "USD", ttm_periods)
dna = _sum_frames(["DepreciationDepletionAndAmortization",
"DepreciationAndAmortization"], "USD", ttm_periods)
# ── Cash flow TTM ───────────────────────────────────────────────────────
op_cf = _sum_frames(["NetCashProvidedByUsedInOperatingActivities"], "USD", ttm_periods)
capex = _sum_frames(["PaymentsToAcquirePropertyPlantAndEquipment"], "USD", ttm_periods)
# ── Prior-year TTM for growth rates ────────────────────────────────────
rev_prev = _sum_frames(["Revenues",
"RevenueFromContractWithCustomer"
"ExcludingAssessedTax",
"SalesRevenueNet"], "USD", prev_periods)
ni_prev = _sum_frames(["NetIncomeLoss"], "USD", prev_periods)
print(f" Revenue data : {len(revenue)} companies")
# ── Assemble per-ticker records ─────────────────────────────────────────
result: dict[str, dict] = {}
for ticker, close in close_map.items():
cik = ticker_to_cik.get(ticker)
if cik is None:
continue # no SEC filing found for this ticker
price = float(close.iloc[-1])
rev = revenue.get(cik); ni = net_income.get(cik)
op_i = op_income.get(cik); d_a = dna.get(cik)
ocf = op_cf.get(cik); cx = capex.get(cik)
tot_a = assets.get(cik); eq = equity.get(cik)
ca = cur_assets.get(cik); cl = cur_liab.get(cik)
csh = cash.get(cik); ltd = lt_debt.get(cik)
sh = shares.get(cik)
r_p = rev_prev.get(cik); n_p = ni_prev.get(cik)
mkt_cap = (price * sh) if sh and sh > 0 else None
eps_trail = (ni / sh) if ni is not None and sh and sh > 0 else None
pe_trail = (price / eps_trail) if eps_trail and eps_trail > 0 else None
pb = (price / (eq / sh)) if eq and sh and sh > 0 and eq > 0 else None
ebitda = ((op_i + d_a) if op_i is not None and d_a is not None else op_i)
ev = ((mkt_cap + (ltd or 0) - (csh or 0)) if mkt_cap is not None else None)
ev_ebitda = (ev / ebitda) if ev and ebitda and ebitda > 0 else None
ev_rev = (ev / rev) if ev and rev and rev > 0 else None
roe = (ni / eq) if ni is not None and eq and eq > 0 else None
roa = (ni / tot_a) if ni is not None and tot_a and tot_a > 0 else None
de = (ltd / eq) if ltd is not None and eq and eq > 0 else None
curr_r = (ca / cl) if ca and cl and cl > 0 else None
pm = (ni / rev) if ni is not None and rev and rev > 0 else None
om = (op_i / rev) if op_i is not None and rev and rev > 0 else None
fcf = ((ocf - cx) if ocf is not None and cx is not None else ocf)
fcf_yield = (fcf / mkt_cap) if fcf is not None and mkt_cap and mkt_cap > 0 else None
rev_g = ((rev - r_p) / abs(r_p) if rev is not None and r_p and r_p != 0 else None)
ni_g = ((ni - n_p) / abs(n_p) if ni is not None and n_p and n_p != 0 else None)
result[ticker] = {
"name": cik_to_name.get(cik, ticker),
"sector": "N/A",
"industry": "N/A",
"market_cap": mkt_cap,
"shares_outstanding": sh,
"pe_trailing": pe_trail,
"pb_ratio": pb,
"ev_ebitda": ev_ebitda,
"ev_revenue": ev_rev,
"eps_trailing": eps_trail,
"revenue": rev,
"revenue_growth": rev_g,
"earnings_growth": ni_g,
"roe": roe,
"roa": roa,
"debt_to_equity": de,
"total_debt": ltd,
"total_cash": csh,
"book_value": (eq / sh) if eq and sh and sh > 0 else None,
"current_ratio": curr_r,
"profit_margin": pm,
"operating_margin": om,
"fcf_yield": fcf_yield,
}
print(f" EDGAR matched : {len(result)} tickers\n")
return result
# ---------------------------------------------------------------------------
# Analyst + Fundamentals Cache (Verimund API)
# ---------------------------------------------------------------------------
try:
from license_check import _API_URL as _API_URL, get_api_headers as _get_api_headers
except ImportError:
_API_URL = None
_get_api_headers = lambda: {}
def _load_analyst_cache() -> dict[str, dict]:
"""
Load analyst consensus and news sentiment from the Verimund API cache.
Returns {ticker: {field: value}} or {} on any failure.
Warns (but still proceeds) if cache data is more than 48 hours old.
"""
if not _API_URL:
print(" [WARN] API config unavailable — analyst cache skipped")
return {}
try:
resp = requests.get(
f"{_API_URL}/cache/analyst",
headers=_get_api_headers(),
timeout=30,
)
resp.raise_for_status()
rows = resp.json().get("data", [])
if not rows:
print(" [WARN] Analyst cache is empty — analyst scores will be neutral")
return {}
valid_ts = [r["updated_at"] for r in rows if r.get("updated_at")]
if valid_ts:
age_h = (
datetime.now(timezone.utc)
- datetime.fromisoformat(max(valid_ts).replace("Z", "+00:00"))
).total_seconds() / 3600
if age_h > 48:
print(f" [WARN] Analyst cache is {age_h:.0f}h old "
"(refresh runs tonight)")
else:
print(f" Analyst cache : {len(rows)} tickers "
f"(updated {age_h:.1f}h ago)")
return {r["ticker"]: r for r in rows}
except Exception as e:
print(f" [WARN] Could not load analyst cache: {e}")
return {}
def _load_fundamentals_cache() -> dict[str, dict]:
"""
Load pre-computed fundamental metrics from the Verimund API cache.
Returns {ticker: {field: value}} or {} on any failure.
"""
if not _API_URL:
print(" [WARN] API config unavailable — fundamentals cache skipped")
return {}
try:
resp = requests.get(
f"{_API_URL}/cache/fundamentals",
headers=_get_api_headers(),
timeout=30,
)
resp.raise_for_status()
rows = resp.json().get("data", [])
if not rows:
print(" [WARN] Fundamentals cache is empty — falling back to live EDGAR")
return {}
valid_ts = [r["updated_at"] for r in rows if r.get("updated_at")]
if valid_ts:
age_h = (
datetime.now(timezone.utc)
- datetime.fromisoformat(max(valid_ts).replace("Z", "+00:00"))
).total_seconds() / 3600
if age_h > 48:
print(f" [WARN] Fundamentals cache is {age_h:.0f}h old "
"(refresh runs tonight)")
else:
print(f" Fundamentals cache: {len(rows)} tickers "
f"(updated {age_h:.1f}h ago)")
return {r["ticker"]: r for r in rows}
except Exception as e:
print(f" [WARN] Could not load fundamentals cache: {e}")
return {}
# ---------------------------------------------------------------------------
# FINRA Short Interest
# ---------------------------------------------------------------------------
def _fetch_finra_short() -> dict[str, float]:
"""
Download the FINRA RegSHO daily short-volume file.
Returns {ticker: short_volume_ratio} where ratio = short_vol / total_vol.
This is a short-volume ratio, not short % of float, but it is a reliable
intraday bearish-pressure signal and a suitable proxy for the Risk score.
Files are published each trading day at:
https://cdn.finra.org/equity/regsho/daily/
"""
from datetime import date as _date, timedelta as _td
for days_back in range(1, 6):
d = _date.today() - _td(days=days_back)
if d.weekday() >= 5: # skip Saturday / Sunday
continue
url = (
"https://cdn.finra.org/equity/regsho/daily/"
f"CNMSshvol{d.strftime('%Y%m%d')}.txt"
)
try:
resp = requests.get(url, headers=_HTTP_HEADERS, timeout=15)
if resp.status_code == 404:
continue
resp.raise_for_status()
result: dict[str, float] = {}
for line in resp.text.splitlines()[1:]:
parts = line.split("|")
if len(parts) < 4:
continue
try:
sv = float(parts[1]); tv = float(parts[3])
if tv > 0:
result[parts[0].strip()] = sv / tv
except (ValueError, IndexError):
continue
if result:
print(f" FINRA short : {len(result)} tickers ({d})")
return result
except Exception:
continue
print(" [WARN] FINRA short interest unavailable")
return {}
# ---------------------------------------------------------------------------
# Beta Calculation
# ---------------------------------------------------------------------------
def _calc_beta_map(close_map: dict[str, pd.Series],
start_date: str) -> dict[str, float]:
"""
Calculate 1-year beta vs SPY for every ticker in close_map.
Downloads SPY history once, then computes cov(stock, SPY) / var(SPY)
in pandas — pure vectorised math, takes ~10–30 seconds for 6,500 tickers.
"""
try:
spy_raw = yf.download("SPY", start=start_date,
auto_adjust=True, progress=False)
if spy_raw is None or spy_raw.empty:
return {}
spy_col = next(
(c for c in ("Close", "Adj Close") if c in spy_raw.columns), None
)
if not spy_col:
return {}
spy_close = spy_raw[spy_col]
# Newer yfinance returns MultiIndex columns even for single tickers —
# squeeze a single-column DataFrame to a Series.
if isinstance(spy_close, pd.DataFrame):
spy_close = spy_close.squeeze(axis=1)
spy_ret = spy_close.dropna().pct_change().dropna()
beta_map: dict[str, float] = {}
for ticker, close in close_map.items():
try:
s_ret = close.pct_change().dropna()
common = s_ret.index.intersection(spy_ret.index)
if len(common) < 50:
continue
m = spy_ret.loc[common]
var_m = float(m.var())
if var_m > 0:
beta_map[ticker] = float(s_ret.loc[common].cov(m) / var_m)
except Exception:
continue
print(f" Beta : {len(beta_map)} tickers calculated")
return beta_map
except Exception as e:
print(f" [WARN] Beta calculation failed: {e}")
return {}
# ---------------------------------------------------------------------------
# Data Fetching
# ---------------------------------------------------------------------------
# Sentinel so the rest of the file still compiles if something references these
_yf_session = None
_yf_crumb = None
def _download_history_batch(tickers: list[str], start: str) -> dict[str, pd.Series]:
"""
Download price history for a batch of tickers via yf.download().
yfinance manages cookies and crumb internally — no custom auth needed.
Returns {ticker: Close Series} for tickers with ≥ 20 trading days.
"""
if not tickers:
return {}
result: dict[str, pd.Series] = {}
try:
# yf.download handles auth/crumb internally
raw = yf.download(
tickers if len(tickers) > 1 else tickers[0],
start=start,
auto_adjust=True,
progress=False,
)
if raw is None or raw.empty:
return result
if len(tickers) == 1:
# Single ticker — newer yfinance returns MultiIndex columns even for
# a single ticker, so raw["Close"] may be a single-column DataFrame.
# Squeeze it to a Series so downstream float(close.iloc[-1]) works.
t = tickers[0]
col = next((c for c in ("Close", "Adj Close") if c in raw.columns), None)
if col:
s = raw[col]
if isinstance(s, pd.DataFrame):
s = s.squeeze(axis=1) # DataFrame → Series
s = s.dropna()
if len(s) >= 20:
result[t] = s
else:
# Multiple tickers → MultiIndex columns
cols = raw.columns
if isinstance(cols, pd.MultiIndex):
lvl0 = cols.get_level_values(0)
if "Close" in lvl0:
close_df = raw["Close"]
for t in tickers:
if t in close_df.columns:
s = close_df[t].dropna()
if len(s) >= 20:
result[t] = s
else:
# (ticker, field) layout used by some yfinance versions
for t in tickers:
if t in lvl0:
try:
sub = raw[t]
col = next((c for c in ("Close", "Adj Close")
if c in sub.columns), None)
if col:
s = sub[col].dropna()
if len(s) >= 20:
result[t] = s
except (KeyError, TypeError):
pass
except Exception as e:
print(f"\n [WARN] yf.download batch failed: {e}")
return result
def _fetch_fundamentals(
ticker: str,
close: pd.Series,
sector_filter: tuple | None = None,
mktcap_range: tuple | None = None,
) -> dict | None:
"""
Fetch fundamental/analyst/sentiment data for one ticker via yf.Ticker.
yfinance handles all Yahoo Finance auth internally.
Price history is supplied from Phase 1 so we only need the info call.
sector_filter: (column, value) tuple — e.g. ("sector", "Technology") or
("industry", "Aerospace & Defense"). None = no filter.
mktcap_range: (min, max) in dollars, either bound may be None.
"""
# Small random delay to spread concurrent worker requests
time.sleep(random.uniform(0.0, 1.0))
try:
stock = yf.Ticker(ticker)
info = stock.info
if not info or not isinstance(info, dict) or len(info) < 3:
return None
# ── Screen-time sector filter ──────────────────────────────────────
if sector_filter is not None:
col, val = sector_filter
actual = info.get("sector") if col == "sector" else info.get("industry")
if actual != val:
return None
# ── Screen-time market-cap filter ──────────────────────────────────
if mktcap_range is not None:
try:
mkt = float(info.get("marketCap") or 0)
except (TypeError, ValueError):
mkt = 0.0
lo, hi = mktcap_range
if lo is not None and mkt < lo:
return None
if hi is not None and mkt >= hi:
return None
price_now = float(close.iloc[-1])
def pct_return(days_back: int) -> float | None:
if len(close) <= days_back:
return None
idx = max(0, len(close) - days_back)
past = float(close.iloc[idx])
return (price_now - past) / past if past > 0 else None
returns_30d = close.iloc[-30:].pct_change().dropna()
volatility = float(returns_30d.std() * (252 ** 0.5)) if len(returns_30d) >= 5 else None
try:
rec_mean = float(info.get("recommendationMean")) if info.get("recommendationMean") is not None else None
except (TypeError, ValueError):
rec_mean = None
analyst_norm = (5.0 - rec_mean) / 4.0 if rec_mean is not None and 1 <= rec_mean <= 5 else None
try:
target_price = float(info.get("targetMeanPrice")) if info.get("targetMeanPrice") is not None else None
except (TypeError, ValueError):
target_price = None
analyst_upside = (target_price / price_now - 1.0
if target_price is not None and price_now > 0 else None)
try:
fcf = float(info.get("freeCashflow")) if info.get("freeCashflow") is not None else None
except (TypeError, ValueError):
fcf = None
try:
mkt = float(info.get("marketCap")) if info.get("marketCap") is not None else None
except (TypeError, ValueError):
mkt = None
fcf_yield = (fcf / mkt) if fcf is not None and mkt is not None and mkt > 0 else None
company_name = info.get("longName", "")
news_sent, news_headlines = get_news_headlines(stock, ticker=ticker, company_name=company_name)
return {
"ticker": ticker,
"name": info.get("longName", ticker),
"sector": info.get("sector", "N/A"),
"industry": info.get("industry", "N/A"),
"price": price_now,
"pe_forward": info.get("forwardPE"),
"pe_trailing": info.get("trailingPE"),
"pb_ratio": info.get("priceToBook"),
"ev_ebitda": info.get("enterpriseToEbitda"),
"revenue_growth": info.get("revenueGrowth"),
"earnings_growth": info.get("earningsGrowth"),
"eps_forward": info.get("forwardEps"),
"eps_trailing": info.get("trailingEps"),
"ret_1m": pct_return(21),
"ret_3m": pct_return(63),
"ret_6m": pct_return(126),
"ret_12m": pct_return(252),
"roe": info.get("returnOnEquity"),
"roa": info.get("returnOnAssets"),
"debt_to_equity": (info["debtToEquity"] / 100.0) if info.get("debtToEquity") is not None else None,
"total_debt": info.get("totalDebt"),
"total_cash": info.get("totalCash"),
"book_value": info.get("bookValue"),
"current_ratio": info.get("currentRatio"),
"profit_margin": info.get("profitMargins"),
"operating_margin": info.get("operatingMargins"),
"fcf_yield": fcf_yield,
"news_sentiment": news_sent,
"news_headlines": news_headlines,
"analyst_norm": analyst_norm,
"analyst_upside": analyst_upside,
"analyst_count": info.get("numberOfAnalystOpinions"),
"analyst_target": target_price,
"recommendation": info.get("recommendationKey", "N/A"),
"short_percent": info.get("shortPercentOfFloat"),
"short_ratio": info.get("shortRatio"),
"beta": info.get("beta"),
"volatility_30d": volatility,
"market_cap": info.get("marketCap"),
"52w_high": info.get("fiftyTwoWeekHigh"),
"52w_low": info.get("fiftyTwoWeekLow"),
"dividend_yield": info.get("dividendYield"),
"revenue": info.get("totalRevenue"),
"business_summary": info.get("longBusinessSummary", "") or "",
}
except Exception:
return None
def fetch_all(
tickers: list[str],
max_workers: int = 5,
screen_filters: dict | None = None,
) -> pd.DataFrame:
"""
Multi-source data fetch replacing the old per-ticker yf.Ticker.info loop.
Phase 1 — yf.download() batch price history (unchanged, ~7 min)
Phase 2A — EDGAR XBRL frames: bulk fundamentals for all tickers (~2 min)
Phase 2B — Supabase analyst_cache: analyst consensus + news sentiment (~2s)
Phase 2C — FINRA RegSHO daily file: short-volume ratios (~5s)
Phase 2D — Beta calculated locally vs SPY from price history (~20s)
Phase 3 — Assemble one record per ticker, apply any active filters
Tickers with no EDGAR coverage (typically micro/nano-cap OTC stocks) still
appear in results — they score on price momentum, risk, and analyst data;
fundamental fields default to NaN and receive the 50-point neutral score.
screen_filters keys (all optional):
sector_filter (col, val) tuple | None
mktcap_range (lo, hi) tuple | None
"""
filters = screen_filters or {}
total = len(tickers)
start_date = (datetime.today() - timedelta(days=380)).strftime("%Y-%m-%d")
start_time = time.time()
# ── Phase 1: batch price history ──────────────────────────────────────
print(f"Phase 1: downloading price history for {total} tickers ...")
close_map: dict[str, pd.Series] = {}
for i in range(0, total, HIST_BATCH):
batch = tickers[i: i + HIST_BATCH]
closes = _download_history_batch(batch, start_date)
close_map.update(closes)
scanned = min(i + HIST_BATCH, total)
print(
f"\r {scanned}/{total} scanned valid={len(close_map)}"
f" ({scanned / total * 100:.0f}%) ",
end="", flush=True,
)
if i + HIST_BATCH < total:
time.sleep(HIST_PAUSE)
valid = list(close_map.keys())
print(f"\n Phase 1 done — {len(valid)}/{total} tickers have price history.\n")
if not valid:
print(" [WARN] yf.download returned no data; trying individual history calls ...")
for t in tickers[:500]:
try:
hist = yf.Ticker(t).history(start=start_date, auto_adjust=True)
if not hist.empty and len(hist) >= 20:
col = "Close" if "Close" in hist.columns else "Adj Close"
if col in hist.columns:
s = hist[col].dropna()
if len(s) >= 20:
close_map[t] = s
except Exception:
pass
time.sleep(random.uniform(0.3, 0.7))
valid = list(close_map.keys())
if not valid:
print("ERROR: No tickers returned valid price history.")
return pd.DataFrame()
print(f" Fallback found {len(valid)} tickers with history.\n")
# ── Phase 2A: Fundamentals from Supabase cache (nightly EDGAR+FINRA) ──
print("Phase 2A: Loading fundamentals cache ...")
edgar_data = _load_fundamentals_cache()
if not edgar_data:
# Cache empty (first run before nightly job) — fall back to live EDGAR
print(" Falling back to live EDGAR bulk fetch ...")
edgar_data = _fetch_edgar_bulk(close_map)
# ── Phase 2B: Analyst + news cache from Supabase ──────────────────────
print("Phase 2B: Loading analyst cache ...")
analyst_cache = _load_analyst_cache()
# ── Phase 2C: FINRA short-volume ratios ───────────────────────────────
# Primary: pull from fundamentals_cache (populated by nightly FINRA fetch).
# Fallback: live FINRA fetch when cache coverage < 60% of screened tickers.
# The two sources are always merged so no ticker misses due to cache gaps.
finra_short: dict[str, float] = {
t: d["short_percent"]
for t, d in edgar_data.items()
if d.get("short_percent") is not None
}
coverage = len(finra_short) / max(len(valid), 1)
print(f"Phase 2C: FINRA short interest loaded from cache ({len(finra_short)} tickers, {coverage:.0%} coverage)")
# ── Phase 2D: Beta vs SPY ─────────────────────────────────────────────
print("Phase 2D: Calculating beta ...")
beta_map = _calc_beta_map(close_map, start_date)
# ── Phase 3: assemble per-ticker records ──────────────────────────────
sector_filter = filters.get("sector_filter")
mktcap_range = filters.get("mktcap_range")
results: list[dict] = []
filter_labels = []
if sector_filter:
filter_labels.append(f"sector={sector_filter[1]}")
if mktcap_range:
lo, hi = mktcap_range
filter_labels.append(
f"mktcap={'$'+str(int(lo//1e9))+'B+' if lo else ''}"
f"{'–$'+str(int(hi//1e9))+'B' if hi else ''}"
)
filter_note = f" [{', '.join(filter_labels)}]" if filter_labels else ""
print(f"\nPhase 3: assembling {len(valid)} records{filter_note} ...")
for ticker in valid:
close = close_map[ticker]
edgar = edgar_data.get(ticker, {})
analyst = analyst_cache.get(ticker, {})
price_now = float(close.iloc[-1])
def pct_return(days_back: int) -> float | None:
if len(close) <= days_back:
return None
idx = max(0, len(close) - days_back)
past = float(close.iloc[idx])
return (price_now - past) / past if past > 0 else None
returns_30d = close.iloc[-30:].pct_change().dropna()
volatility = (float(returns_30d.std() * (252 ** 0.5))
if len(returns_30d) >= 5 else None)
# Live market cap: price_now × shares_outstanding beats stale cached value
sh_out = edgar.get("shares_outstanding")
mkt_cap = (price_now * sh_out) if sh_out else edgar.get("market_cap")
# Track data source for N/A display in GUI
_data_source = "cache" if edgar else "none"
# EV/Revenue for growth/early-stage value scoring
ev_rev = edgar.get("ev_revenue")
# ── Filters ───────────────────────────────────────────────────────
if sector_filter is not None:
col, val = sector_filter
field = "sector" if col == "sector" else "industry"
actual = analyst.get(field) or edgar.get(field, "N/A")
if actual != val:
continue
if mktcap_range is not None:
lo, hi = mktcap_range
mkt = float(mkt_cap or 0)
if lo is not None and mkt < lo:
continue
if hi is not None and mkt >= hi:
continue
# ── Analyst fields ────────────────────────────────────────────────
analyst_target = analyst.get("analyst_target")
analyst_upside = analyst.get("analyst_upside")
if analyst_upside is None and analyst_target and price_now > 0:
try:
analyst_upside = float(analyst_target) / price_now - 1.0
except (TypeError, ValueError):
analyst_upside = None
results.append({
"ticker": ticker,
"name": analyst.get("name") or edgar.get("name") or ticker,
"sector": analyst.get("sector") or edgar.get("sector", "N/A"),
"industry": analyst.get("industry") or edgar.get("industry", "N/A"),
"price": price_now,
"_data_source": _data_source,
# Valuation
"pe_forward": analyst.get("pe_forward"),
"pe_trailing": edgar.get("pe_trailing"),
"pb_ratio": edgar.get("pb_ratio"),
"ev_ebitda": edgar.get("ev_ebitda"),
"ev_revenue": ev_rev,
# Growth
"revenue_growth": edgar.get("revenue_growth"),
"earnings_growth": edgar.get("earnings_growth"),
"eps_forward": analyst.get("eps_forward"),
"eps_trailing": edgar.get("eps_trailing"),
# Momentum (price-derived)
"ret_1m": pct_return(21),
"ret_3m": pct_return(63),
"ret_6m": pct_return(126),
"ret_12m": pct_return(252),
# Quality
"roe": edgar.get("roe"),
"roa": edgar.get("roa"),
"debt_to_equity": edgar.get("debt_to_equity"),
"total_debt": edgar.get("total_debt"),
"total_cash": edgar.get("total_cash"),
"book_value": edgar.get("book_value"),
"current_ratio": edgar.get("current_ratio"),
# Profitability
"profit_margin": edgar.get("profit_margin"),
"operating_margin": edgar.get("operating_margin"),
"fcf_yield": edgar.get("fcf_yield"),
# Sentiment (from nightly cache)
"news_sentiment": analyst.get("news_sentiment"),
"news_headlines": [],
# Analyst (from nightly cache)
"analyst_norm": analyst.get("analyst_norm"),
"analyst_upside": analyst_upside,
"analyst_count": analyst.get("analyst_count"),
"analyst_target": analyst_target,
"recommendation": analyst.get("recommendation", "N/A"),
# Bug 10 fix: use only shortPercentOfFloat (0.0–1.0 float fraction) from yfinance.
# FINRA short-volume ratio (sv/total_vol) is on a different scale (typically 0.3–0.6)
# and is incompatible with the short_pts breakpoints which expect % of float.
# Do NOT fall back to finra_short here; leave as None when yfinance data is absent.
"short_percent": analyst.get("short_percent"),
"short_ratio": analyst.get("short_ratio"),
"piotroski_score": edgar.get("piotroski_score"),
"accruals_ratio": edgar.get("accruals_ratio"),
"news_headline_count": analyst.get("news_headline_count"),
"beta": beta_map.get(ticker),
"volatility_30d": volatility,
# Market data
"market_cap": mkt_cap,
"52w_high": float(close.max()),
"52w_low": float(close.min()),
"dividend_yield": edgar.get("dividend_yield"),
"revenue": edgar.get("revenue"),
"business_summary": "",
})
print(f"\nDone. Assembled {len(results)} stocks in"
f" {time.time() - start_time:.0f}s.\n")
df = pd.DataFrame(results)
if not df.empty:
df = df.drop_duplicates(subset=["ticker"], keep="first").reset_index(drop=True)
return df
# ---------------------------------------------------------------------------
# Sector Stats Loader
# ---------------------------------------------------------------------------
def _load_sector_stats() -> dict[str, dict]:
"""
Load pre-computed sector statistics from the Verimund API.
Returns {sector: {metric: (median, MAD)}} or {} on failure.
Used by z-score scoring to normalize metrics relative to sector peers.
"""
if not _API_URL:
return {}
try:
resp = requests.get(
f"{_API_URL}/cache/sector",
headers=_get_api_headers(),
timeout=15,
)
resp.raise_for_status()
rows = resp.json().get("data", [])
result: dict[str, dict] = {}
for row in rows:
sector = row.get("sector")
if not sector:
continue
stats: dict[str, tuple] = {}
for key, val in row.items():
if key.endswith("_med"):
metric = key[:-4]
mad_key = f"{metric}_mad"
mad = row.get(mad_key)
if val is not None and mad is not None and mad > 0:
stats[metric] = (float(val), float(mad))
result[sector] = stats
print(f" Sector stats loaded: {len(result)} sectors")
return result
except Exception as e:
print(f" [WARN] Sector stats unavailable: {e}")
return {}
# ---------------------------------------------------------------------------
# Scoring
# ---------------------------------------------------------------------------
def _abs_score(v, breakpoints: list, default: float = 50.0) -> float:
"""
Piecewise-linear interpolation of a scalar value v against a list of
(metric_value, score) breakpoints sorted by metric_value ascending.
Returns `default` when v is NaN/None.
Example: breakpoints=[(0,0),(10,50),(20,100)]
v=5 → 25.0
v=15 → 75.0
v=25 → 100.0 (clamped)
"""
if not breakpoints:
return default
if v is None:
return default
try:
if np.isnan(v):
return default
except (TypeError, ValueError):
return default
pts = breakpoints
if v <= pts[0][0]:
return float(pts[0][1])
if v >= pts[-1][0]:
return float(pts[-1][1])
for i in range(len(pts) - 1):
x0, y0 = pts[i]
x1, y1 = pts[i + 1]
if x0 <= v <= x1:
t = (v - x0) / (x1 - x0)
return float(y0 + t * (y1 - y0))
return default
def _score_series(series: pd.Series, breakpoints: list, default: float = 50.0) -> pd.Series:
"""Apply _abs_score element-wise to a Series."""
return series.apply(lambda v: _abs_score(v, breakpoints, default))
def _z_score(value, metric: str, sector: str, sector_stats: dict,
default: float = 50.0) -> float:
"""
Normalize value relative to sector peers using median and MAD, then map
the resulting z-score to a 0–100 scale via piecewise-linear interpolation.
Falls back to default (50 = neutral) when value or stats are unavailable.
Z-score curve: z ≤ -2.5 → 3, z=0 → 50, z ≥ +2.5 → 97
"""
if value is None:
return default
try:
if np.isnan(float(value)):
return default
except (TypeError, ValueError):
return default
stats = sector_stats.get(sector, {})
pair = stats.get(metric)
if pair is None:
# Try market-wide fallback
pair = sector_stats.get("__market__", {}).get(metric)
if pair is None:
return default
med, mad = pair
if mad <= 0:
return default
z = (float(value) - med) / mad
z = max(-3.0, min(3.0, z)) # winsorize
z_pts = [(-2.5, 3), (-1.5, 18), (-0.5, 38), (0.0, 50),
(0.5, 62), (1.5, 82), (2.5, 97)]
return _abs_score(z, z_pts, default)
def _z_series(series: pd.Series, metric: str, sectors: pd.Series,
sector_stats: dict, default: float = 50.0) -> pd.Series:
"""Apply _z_score element-wise using per-row sector labels."""
return pd.Series(
[_z_score(v, metric, s, sector_stats, default)
for v, s in zip(series, sectors)],
index=series.index,
)
def _classify_profile(row: pd.Series) -> dict:
"""
Classify a stock into one or more company profiles with blend weights.
Returns {profile_name: weight} summing to 1.0.
Supports soft blending for ambiguous cases.
"""
sector = str(row.get("sector") or "").lower()
rev_g = row.get("revenue_growth")
pm = row.get("profit_margin")
de = row.get("debt_to_equity")
revenue = row.get("revenue")
mktcap = row.get("market_cap")
earn_g = row.get("earnings_growth")
# Financial: banks, insurance, credit — D/E irrelevant, use ROA/P/B
fin_sectors = ("financial", "bank", "insurance", "credit")
if any(s in sector for s in fin_sectors):
return {"financial": 1.0}
# Capital intensive: utilities, energy, industrials with meaningful leverage
cap_sectors = ("utilities", "energy", "industrials", "basic materials")
if any(s in sector for s in cap_sectors) and de is not None and de > 0.8:
return {"capital_intensive": 1.0}
# Score membership in remaining profiles
scores = {}
# Early stage: very small revenue or deeply negative margins
es_score = 0.0
if revenue is not None and revenue < 30_000_000:
es_score += 0.6
if pm is not None and pm < -0.30 and mktcap is not None and mktcap < 1_000_000_000:
es_score += 0.4
if es_score > 0:
scores["early_stage"] = min(es_score, 1.0)
# High growth: fast revenue expansion, possibly thin margins
hg_score = 0.0
if rev_g is not None and rev_g > 0.25:
hg_score += 0.7
elif rev_g is not None and rev_g > 0.15 and pm is not None and pm < 0.05:
hg_score += 0.5
if hg_score > 0:
scores["high_growth"] = min(hg_score, 1.0)
# Turnaround: strong earnings recovery from a loss position
if earn_g is not None and earn_g > 0.40 and pm is not None and pm < 0.0:
scores["turnaround"] = 0.8
# Mature: default for profitable, slower-growth companies
if not scores:
return {"mature": 1.0}
# Normalize to sum = 1.0
total = sum(scores.values())
return {k: v / total for k, v in scores.items()}
# Profile-specific sub-component weights for each scoring dimension.
# Structure: {profile: {dimension: {metric: weight}}}
_PROFILE_WEIGHTS: dict[str, dict[str, dict[str, float]]] = {
"mature": {
"value": {"pe": 0.35, "pb": 0.20, "ev_ebitda": 0.45},
"growth": {"revenue_growth": 0.30, "earnings_growth": 0.45, "eps_growth": 0.25},
"quality": {"roa": 0.40, "roe": 0.20, "debt_to_equity": 0.30, "current_ratio": 0.10, "piotroski": 0.20},
"profitability": {"fcf_yield": 0.40, "operating_margin": 0.38, "profit_margin": 0.22, "accruals": 0.15},
},
"high_growth": {
"value": {"pe": 0.00, "pb": 0.20, "ev_ebitda": 0.35, "ev_revenue": 0.45},
"growth": {"revenue_growth": 0.50, "earnings_growth": 0.30, "eps_growth": 0.20},
"quality": {"roa": 0.35, "roe": 0.15, "debt_to_equity": 0.15, "current_ratio": 0.35, "piotroski": 0.20},
"profitability": {"fcf_yield": 0.15, "operating_margin": 0.50, "profit_margin": 0.35, "accruals": 0.15},
},
"financial": {
"value": {"pe": 0.35, "pb": 0.50, "ev_ebitda": 0.15},
"growth": {"revenue_growth": 0.30, "earnings_growth": 0.50, "eps_growth": 0.20},
"quality": {"roa": 0.50, "roe": 0.30, "debt_to_equity": 0.00, "current_ratio": 0.20, "piotroski": 0.15},
"profitability": {"fcf_yield": 0.30, "operating_margin": 0.00, "profit_margin": 0.70, "accruals": 0.15},
},
"capital_intensive": {
"value": {"pe": 0.15, "pb": 0.20, "ev_ebitda": 0.65},
"growth": {"revenue_growth": 0.35, "earnings_growth": 0.40, "eps_growth": 0.25},
"quality": {"roa": 0.30, "roe": 0.15, "debt_to_equity": 0.40, "current_ratio": 0.15, "piotroski": 0.15},
"profitability": {"fcf_yield": 0.35, "operating_margin": 0.45, "profit_margin": 0.20, "accruals": 0.15},
},
"early_stage": {
"value": {"pe": 0.00, "pb": 0.15, "ev_ebitda": 0.00, "ev_revenue": 0.85},
"growth": {"revenue_growth": 0.70, "earnings_growth": 0.20, "eps_growth": 0.10},
"quality": {"roa": 0.20, "roe": 0.00, "debt_to_equity": 0.00, "current_ratio": 0.80, "piotroski": 0.10},
# revenue_growth_proxy removed — revenue growth already carries 70% of the growth score
"profitability": {"fcf_yield": 0.00, "operating_margin": 1.00, "profit_margin": 0.00,
"revenue_growth_proxy": 0.00},
},
"turnaround": {
"value": {"pe": 0.00, "pb": 0.30, "ev_ebitda": 0.70},
"growth": {"revenue_growth": 0.15, "earnings_growth": 0.80, "eps_growth": 0.05},
"quality": {"roa": 0.35, "roe": 0.15, "debt_to_equity": 0.35, "current_ratio": 0.15, "piotroski": 0.20},
"profitability": {"fcf_yield": 0.25, "operating_margin": 0.45, "profit_margin": 0.30, "accruals": 0.15},
},
}
def _profile_score_row(row: pd.Series, dimension: str,
metric_scores: dict[str, float]) -> float:
"""
Compute a single dimension score for one row using profile-blended weights.
metric_scores: {metric_name: 0-100 score} — pre-computed for this row.
"""
profile_blend = _classify_profile(row)
total = 0.0
for profile, profile_weight in profile_blend.items():
pw = _PROFILE_WEIGHTS.get(profile, _PROFILE_WEIGHTS["mature"])
dim_weights = pw.get(dimension, {})
# Normalize weights to only available metrics
available = {m: w for m, w in dim_weights.items()
if m in metric_scores and w > 0}
if not available:
total += profile_weight * 50.0
continue
w_sum = sum(available.values())
dim_score = sum(metric_scores[m] * (w / w_sum) for m, w in available.items())
total += profile_weight * dim_score
return total
def compute_value_score(df: pd.DataFrame,
sector_stats: dict | None = None) -> pd.Series:
"""
Value Score — sector-relative z-score for each valuation metric, then
profile-blended sub-weights applied per row.
"""
ss = sector_stats or {}
sectors = df.get("sector", pd.Series(["N/A"] * len(df), index=df.index))
# Z-score forward and trailing PE against their own sector distributions, then combine
# Lower valuation ratios are better — invert z-scores so cheap stocks rank high
fwd_s = 100.0 - _z_series(df["pe_forward"].where(df["pe_forward"] > 0), "pe_forward", sectors, ss)
trl_s = 100.0 - _z_series(df["pe_trailing"].where(df["pe_trailing"] > 0), "pe_trailing", sectors, ss)
pe_s = fwd_s.combine_first(trl_s)
pb_s = 100.0 - _z_series(df["pb_ratio"], "pb_ratio", sectors, ss)
ev_s = 100.0 - _z_series(df["ev_ebitda"], "ev_ebitda", sectors, ss)
evr_s = 100.0 - _z_series(df.get("ev_revenue", pd.Series(
[None]*len(df), index=df.index)),
"ev_revenue", sectors, ss)
scores = []
for i, row in df.iterrows():
ms = {
"pe": float(pe_s.loc[i]),
"pb": float(pb_s.loc[i]),
"ev_ebitda": float(ev_s.loc[i]),
"ev_revenue": float(evr_s.loc[i]),
}
scores.append(_profile_score_row(row, "value", ms))
return pd.Series(scores, index=df.index)
def compute_growth_score(df: pd.DataFrame,
sector_stats: dict | None = None) -> pd.Series:
"""
Growth Score — sector-relative z-scores for revenue growth, earnings growth,
and EPS growth rate (clamped to ±150% to avoid breakeven-crossing distortion).
"""
ss = sector_stats or {}
sectors = df.get("sector", pd.Series(["N/A"] * len(df), index=df.index))
# EPS growth rate: (forward - trailing) / abs(trailing), clamped ±1.5
eps_t = pd.to_numeric(df["eps_trailing"], errors="coerce")
eps_f = pd.to_numeric(df["eps_forward"], errors="coerce")
eps_g = ((eps_f - eps_t) / eps_t.abs()).clip(-1.5, 1.5).where(eps_t.abs() > 1e-9)
rev_s = _z_series(df["revenue_growth"], "revenue_growth", sectors, ss)
ear_s = _z_series(df["earnings_growth"], "earnings_growth", sectors, ss)
eps_s = _z_series(eps_g, "eps_growth", sectors, ss)
scores = []
for i, row in df.iterrows():
ms = {
"revenue_growth": float(rev_s.loc[i]),
"earnings_growth": float(ear_s.loc[i]),
"eps_growth": float(eps_s.loc[i]),
}
scores.append(_profile_score_row(row, "growth", ms))
return pd.Series(scores, index=df.index)
def compute_momentum_score(df: pd.DataFrame) -> pd.Series:
"""
Momentum Score — absolute breakpoints (price returns are inherently comparable).
1M weight reduced to 5% to minimise short-term reversal noise.
"""
r1_pts = [(-0.15, 5), (-0.05, 25), (0.0, 45), (0.05, 60), (0.10, 75), (0.20, 90), (0.30, 100)]
r3_pts = [(-0.20, 5), (-0.05, 25), (0.0, 42), (0.08, 58), (0.15, 72), (0.25, 88), (0.40, 100)]
r6_pts = [(-0.25, 5), (-0.05, 22), (0.0, 38), (0.10, 55), (0.20, 70), (0.35, 87), (0.55, 100)]
r12_pts = [(-0.30, 5), (-0.05, 20), (0.0, 35), (0.12, 52), (0.25, 68), (0.40, 85), (0.65, 100)]
return (
_score_series(df["ret_1m"], r1_pts) * 0.05 +
_score_series(df["ret_3m"], r3_pts) * 0.25 +
_score_series(df["ret_6m"], r6_pts) * 0.45 +
_score_series(df["ret_12m"], r12_pts) * 0.25
)
def compute_reverse_momentum_score(df: pd.DataFrame) -> pd.Series:
"""
Reverse Momentum Score — rewards stocks that have fallen the most recently.
Used by the High Risk High Reward strategy preset.
"""
r1_pts = [(-0.30, 100), (-0.20, 88), (-0.10, 75), (-0.05, 62), (0.0, 48), (0.05, 32), (0.10, 18), (0.20, 8), (0.30, 3)]
r3_pts = [(-0.40, 100), (-0.25, 88), (-0.15, 75), (-0.05, 60), (0.0, 45), (0.08, 30), (0.20, 15), (0.35, 5)]
r6_pts = [(-0.55, 100), (-0.35, 88), (-0.20, 72), (-0.05, 55), (0.0, 42), (0.10, 28), (0.25, 15), (0.40, 5)]
r12_pts = [(-0.65, 100), (-0.40, 85), (-0.25, 68), (-0.05, 52), (0.0, 38), (0.12, 22), (0.30, 10), (0.50, 3)]
return (
_score_series(df["ret_1m"], r1_pts) * 0.35 +
_score_series(df["ret_3m"], r3_pts) * 0.35 +
_score_series(df["ret_6m"], r6_pts) * 0.20 +
_score_series(df["ret_12m"], r12_pts) * 0.10
)
def compute_quality_score(df: pd.DataFrame,
sector_stats: dict | None = None) -> pd.Series:
"""
Quality Score — sector-relative z-scores with profile-blended sub-weights.
ROA promoted to primary metric; ROE demoted to reduce leverage-gaming bias.
"""
ss = sector_stats or {}
sectors = df.get("sector", pd.Series(["N/A"] * len(df), index=df.index))
roa_s = _z_series(df["roa"], "roa", sectors, ss)
roe_s = _z_series(df["roe"], "roe", sectors, ss)
cr_s = _z_series(df["current_ratio"], "current_ratio", sectors, ss)
# Negative D/E means negative equity (insolvency risk) — treat as extremely high leverage
de_raw = df["debt_to_equity"].where(df["debt_to_equity"] >= 0, 999.0)
# D/E z-score is inverted — lower D/E is better, so flip the z-score
de_s = 100.0 - _z_series(de_raw, "debt_to_equity", sectors, ss)
# Piotroski partial F-Score (0-4) → 0-100 via absolute breakpoints
p_raw = pd.to_numeric(df.get("piotroski_score",
pd.Series([None] * len(df), index=df.index)), errors="coerce")
p_pts = [(0, 5), (1, 30), (2, 50), (3, 72), (4, 95)]
piotroski_s = _score_series(p_raw, p_pts)
scores = []
for i, row in df.iterrows():
ms = {
"roa": float(roa_s.loc[i]),
"roe": float(roe_s.loc[i]),
"debt_to_equity": float(de_s.loc[i]),
"current_ratio": float(cr_s.loc[i]),
"piotroski": float(piotroski_s.loc[i]),
}
scores.append(_profile_score_row(row, "quality", ms))
return pd.Series(scores, index=df.index)
def compute_profitability_score(df: pd.DataFrame,
sector_stats: dict | None = None) -> pd.Series:
"""
Profitability Score — sector-relative z-scores. FCF yield promoted to 40%
for mature companies. Early stage profile substitutes revenue growth.
"""
ss = sector_stats or {}
sectors = df.get("sector", pd.Series(["N/A"] * len(df), index=df.index))
fcf_s = _z_series(df["fcf_yield"], "fcf_yield", sectors, ss)
om_s = _z_series(df["operating_margin"], "operating_margin", sectors, ss)
pm_s = _z_series(df["profit_margin"], "profit_margin", sectors, ss)
rev_s = _z_series(df["revenue_growth"], "revenue_growth", sectors, ss)
# Sloan accruals ratio: more negative = earnings are cash-backed = better quality
acc_raw = pd.to_numeric(df.get("accruals_ratio",
pd.Series([None] * len(df), index=df.index)), errors="coerce")
acc_pts = [(-0.25, 95), (-0.10, 78), (-0.03, 62), (0.0, 50),
(0.03, 38), (0.10, 22), (0.25, 5)]
accruals_s = _score_series(acc_raw, acc_pts)
scores = []
for i, row in df.iterrows():
ms = {
"fcf_yield": float(fcf_s.loc[i]),
"operating_margin": float(om_s.loc[i]),
"profit_margin": float(pm_s.loc[i]),
"revenue_growth_proxy": float(rev_s.loc[i]), # used by early_stage
"accruals": float(accruals_s.loc[i]),
}
scores.append(_profile_score_row(row, "profitability", ms))
return pd.Series(scores, index=df.index)
def compute_sentiment_score(df: pd.DataFrame) -> pd.Series:
"""
News Sentiment Score — VADER compound score mapped to 0–100, confidence-weighted
by headline count. Fewer headlines blend toward neutral (50) to avoid overreacting
to a single article.
"""
sent_pts = [(-1.0, 0), (-0.40, 20), (-0.15, 36), (0.0, 50),
(0.15, 64), (0.40, 80), (1.0, 100)]
base = _score_series(df["news_sentiment"], sent_pts)
# Derive headline count from stored field or from news_headlines list
if "news_headline_count" in df.columns:
count = pd.to_numeric(df["news_headline_count"], errors="coerce").fillna(0)
elif "news_headlines" in df.columns:
count = df["news_headlines"].apply(
lambda h: len(h) if isinstance(h, list) else 0
).astype(float)
else:
return base
# 5+ headlines = full confidence; 0 headlines = 5% confidence (nearly neutral)
confidence = (count.clip(upper=5) / 5.0).clip(lower=0.05)
return base * confidence + 50.0 * (1.0 - confidence)
def compute_analyst_score(df: pd.DataFrame,
sector_stats: dict | None = None) -> pd.Series:
"""
Analyst Score — consensus (absolute) + upside (sector z-score) + count (absolute).
Upside top breakpoints compressed to dampen analyst optimism bias.
"""
ss = sector_stats or {}
sectors = df.get("sector", pd.Series(["N/A"] * len(df), index=df.index))
norm_pts = [(0.0, 0), (0.25, 25), (0.50, 50), (0.65, 65),
(0.75, 78), (0.875, 90), (1.0, 100)]
count_pts = [(0, 0), (1, 20), (3, 40), (5, 55), (10, 70), (20, 85), (30, 100)]
# Upside: sector z-score with dampened ceiling for extreme claims
upside_pts = [(-0.25, 3), (-0.08, 20), (0.0, 35), (0.05, 48),
(0.12, 62), (0.25, 80), (0.40, 88), (0.60, 92)]
norm_s = _score_series(df["analyst_norm"], norm_pts)
count_s = _score_series(df["analyst_count"], count_pts)
upside_s = _z_series(df["analyst_upside"], "analyst_upside", sectors, ss)
# Fall back to absolute breakpoints when sector stats unavailable
upside_abs = _score_series(df["analyst_upside"], upside_pts)
has_stats = sectors.map(lambda s: s in ss and "analyst_upside" in ss.get(s, {}))
upside_s = upside_s.where(has_stats, upside_abs)
return (
norm_s * 0.43 +
upside_s * 0.42 +
count_s * 0.15
)
def compute_risk_score(df: pd.DataFrame) -> pd.Series:
"""
Risk Score — higher score = lower risk.
Dynamic short interest weighting: contributes 35% when data is present,
drops out (weight redistributed to volatility/beta) when absent.
Beta: asymmetric scoring peaking at beta 0.2-0.4 (genuine defensive).
"""
vol_pts = [(0.05, 100), (0.10, 88), (0.15, 78), (0.25, 62),
(0.35, 45), (0.50, 25), (0.70, 8), (1.0, 3)]
beta_pts = [(0.0, 88), (0.2, 96), (0.4, 90), (0.7, 78), (0.85, 70),
(1.0, 62), (1.3, 45), (1.6, 28), (2.0, 12), (3.0, 3)]
short_pts= [(0.0, 100), (0.03, 88), (0.05, 75), (0.10, 55),
(0.15, 35), (0.20, 18), (0.30, 5)]
beta_clipped = df["beta"].fillna(1.0).clip(lower=0.0)
vol_s = _score_series(df["volatility_30d"], vol_pts)
beta_s = _score_series(beta_clipped, beta_pts)
short_s = _score_series(df["short_percent"], short_pts)
has_short = df["short_percent"].notna()
# Dynamic weighting
score_with = short_s * 0.35 + vol_s * 0.40 + beta_s * 0.25
score_without = vol_s * 0.65 + beta_s * 0.35
return score_with.where(has_short, score_without)
def score_stocks(df: pd.DataFrame, weights: dict | None = None,
sector_stats: dict | None = None) -> pd.DataFrame:
df = df.copy()
df = df.drop_duplicates(subset=["ticker"], keep="first").reset_index(drop=True)
# Load sector stats if not provided (allows GUI re-scoring to pass cached stats)
ss = sector_stats if sector_stats is not None else _load_sector_stats()
_NUMERIC_COLS = [
"pe_forward", "pe_trailing", "pb_ratio", "ev_ebitda", "ev_revenue",
"revenue_growth", "earnings_growth", "eps_forward", "eps_trailing",
"ret_1m", "ret_3m", "ret_6m", "ret_12m",
"roe", "roa", "debt_to_equity", "current_ratio",
"profit_margin", "operating_margin", "fcf_yield",
"news_sentiment", "news_headline_count", "analyst_norm", "analyst_upside", "analyst_count",
"short_percent", "short_ratio", "volatility_30d", "beta", "price", "market_cap",
"piotroski_score", "accruals_ratio",
]
for col in _NUMERIC_COLS:
if col in df.columns:
df[col] = pd.to_numeric(df[col], errors="coerce")
df["score_value"] = compute_value_score(df, ss)
df["score_growth"] = compute_growth_score(df, ss)
df["score_momentum"] = compute_momentum_score(df)
df["score_reverse_momentum"] = compute_reverse_momentum_score(df)
df["score_quality"] = compute_quality_score(df, ss)
df["score_profitability"] = compute_profitability_score(df, ss)
df["score_sentiment"] = compute_sentiment_score(df)
df["score_analyst"] = compute_analyst_score(df, ss)
df["score_risk"] = compute_risk_score(df)
w = weights if weights is not None else WEIGHTS
df["composite_score"] = sum(
df[f"score_{k}"] * v for k, v in w.items()
)
return df.sort_values("composite_score", ascending=False).reset_index(drop=True)
# ---------------------------------------------------------------------------
# Display
# ---------------------------------------------------------------------------
def fmt_pct(val) -> str:
if val is None or (isinstance(val, float) and np.isnan(val)):
return "N/A"
return f"{val*100:.1f}%"
def fmt_float(val, decimals=2) -> str:
if val is None or (isinstance(val, float) and np.isnan(val)):
return "N/A"
return f"{val:.{decimals}f}"
def fmt_mcap(val) -> str:
if val is None or (isinstance(val, float) and np.isnan(val)):
return "N/A"
if val >= 1e12: return f"${val/1e12:.1f}T"
if val >= 1e9: return f"${val/1e9:.1f}B"
if val >= 1e6: return f"${val/1e6:.1f}M"
return f"${val:.0f}"
def print_results(df: pd.DataFrame, top_n: int = 50) -> None:
top = df.head(top_n).copy()
top.index = range(1, len(top) + 1)
W = 140
print("=" * W)
print(f" TOP {top_n} STOCKS — {datetime.today().strftime('%Y-%m-%d %H:%M')}")
print("=" * W)
print(
f"{'#':>3} {'Ticker':<7} {'Name':<26} {'Sector':<20} "
f"{'Score':>6} {'Val':>5} {'Grw':>5} {'Mom':>5} {'Qlt':>5} "
f"{'Pft':>5} {'Sent':>5} {'Anlst':>6} {'Risk':>5} "
f"{'P/E':>6} {'RevGrw':>7} {'ROE':>6} {'MktCap':>8}"
)
print("-" * W)
for rank, row in top.iterrows():
print(
f"{rank:>3} {row['ticker']:<7} {str(row['name'])[:25]:<26} "
f"{str(row['sector'])[:19]:<20} "
f"{row['composite_score']:>6.1f} "
f"{row['score_value']:>5.1f} {row['score_growth']:>5.1f} "
f"{row['score_momentum']:>5.1f} {row['score_quality']:>5.1f} "
f"{row['score_profitability']:>5.1f} {row['score_sentiment']:>5.1f} "
f"{row['score_analyst']:>6.1f} {row['score_risk']:>5.1f} "
f"{fmt_float(row.get('pe_forward') or row.get('pe_trailing')):>6} "
f"{fmt_pct(row.get('revenue_growth')):>7} "
f"{fmt_pct(row.get('roe')):>6} "
f"{fmt_mcap(row.get('market_cap')):>8}"
)
print("=" * W)
w = WEIGHTS
print(
f"\nFormula: {w['value']*100:.0f}%*Val + {w['growth']*100:.0f}%*Grw + "
f"{w['momentum']*100:.0f}%*Mom + {w['quality']*100:.0f}%*Qlt + "
f"{w['profitability']*100:.0f}%*Pft + {w['sentiment']*100:.0f}%*Sent + "
f"{w['analyst']*100:.0f}%*Anlst + {w['risk']*100:.0f}%*Risk\n"
)
def save_csv(df: pd.DataFrame, top_n: int) -> str:
cols = [
"ticker", "name", "sector", "price", "market_cap",
"composite_score", "score_value", "score_growth", "score_momentum",
"score_quality", "score_profitability", "score_sentiment",
"score_analyst", "score_risk",
"pe_forward", "pe_trailing", "pb_ratio", "ev_ebitda",
"revenue_growth", "earnings_growth",
"ret_1m", "ret_3m", "ret_6m", "ret_12m",
"roe", "roa", "debt_to_equity", "current_ratio",
"profit_margin", "operating_margin", "fcf_yield",
"news_sentiment", "analyst_norm", "analyst_upside",
"analyst_count", "analyst_target", "recommendation",
"short_percent", "beta", "volatility_30d",
]
out_cols = [c for c in cols if c in df.columns]
filename = f"top{top_n}_stocks_{datetime.today().strftime('%Y%m%d')}.csv"
df.head(top_n)[out_cols].to_csv(filename, index=False)
return filename
# ---------------------------------------------------------------------------
# Entry Point
# ---------------------------------------------------------------------------
def parse_args():
p = argparse.ArgumentParser(description="Multi-factor stock screener")
p.add_argument("--top", type=int, default=50)
p.add_argument("--workers", type=int, default=15)
p.add_argument("--output", choices=["console", "csv", "both"], default="console")
p.add_argument("--index", default="All", choices=INDEX_CHOICES,
help="Index universe to screen (default: All)")
return p.parse_args()
def main():
args = parse_args()
print("\n" + "=" * 60)
print(" MULTI-FACTOR STOCK SCREENER (8-Factor Analyst Model)")
print("=" * 60 + "\n")
if _VADER is None:
print(" [INFO] vaderSentiment not installed — sentiment scores will be neutral.")
print(" Run: pip install vaderSentiment\n")
tickers = collect_tickers(index=args.index)
if not tickers:
print("ERROR: Could not retrieve any ticker lists.")
sys.exit(1)
df_raw = fetch_all(tickers, max_workers=args.workers)
if df_raw.empty:
print("ERROR: No data returned.")
sys.exit(1)
print("Computing scores...")
df_scored = score_stocks(df_raw)
print(f"Scored {len(df_scored)} stocks.\n")
if args.output in ("console", "both"):
print_results(df_scored, top_n=args.top)
if args.output in ("csv", "both"):
print(f"Results saved to: {save_csv(df_scored, top_n=args.top)}")
# ---------------------------------------------------------------------------
# Insider Trading — SEC EDGAR Form 4
# ---------------------------------------------------------------------------
_FORM4_TX_CODES: dict[str, str] = {
"P": "Buy", "S": "Sale",
"A": "Award", "D": "Disposition",
"F": "Tax Withholding","G": "Gift",
"M": "Option Exercise","X": "Option Exercise",
"O": "Option Exercise","J": "Other",
"I": "Plan Trade", "C": "Conversion",
}
def _parse_form4_xml(xml_bytes: bytes, filing_url: str, filed_at: str) -> list[dict]:
"""Parse a Form 4 XML document and return a list of transaction dicts."""
import xml.etree.ElementTree as ET
try:
root = ET.fromstring(xml_bytes)
except ET.ParseError:
return []
def _val(node, *tags):
"""Return stripped text from the first matching .//tag/value or .//tag."""
for tag in tags:
n = node.find(f".//{tag}/value")
if n is not None and n.text:
return n.text.strip()
n = node.find(f".//{tag}")
if n is not None and n.text:
return n.text.strip()
return ""
ticker = _val(root, "issuerTradingSymbol")
company = _val(root, "issuerName")
insider = _val(root, "rptOwnerName")
period = _val(root, "periodOfReport")
title = ""
rel = root.find(".//reportingOwnerRelationship")
if rel is not None:
ot = _val(rel, "officerTitle")
if ot:
title = ot
elif _val(rel, "isDirector") in ("1", "true"):
title = "Director"
elif _val(rel, "isTenPercentOwner") in ("1", "true"):
title = "10% Owner"
elif _val(rel, "isOfficer") in ("1", "true"):
title = "Officer"
transactions: list[dict] = []
for tx in root.findall(".//nonDerivativeTransaction"):
tx_date = _val(tx, "transactionDate") or period
tx_code = _val(tx, "transactionCode")
tx_type = _FORM4_TX_CODES.get(tx_code, tx_code or "Other")
try: shares = float(_val(tx, "transactionShares"))
except: shares = None
try: price = float(_val(tx, "transactionPricePerShare"))
except: price = None
try: owned = float(_val(tx, "sharesOwnedFollowingTransaction"))
except: owned = None
value = (shares * price) if (shares and price) else None
transactions.append({
"filed_date": (filed_at or "")[:10] or tx_date,
"tx_date": tx_date,
"ticker": ticker.upper() if ticker else "—",
"company": company,
"insider": insider,
"title": title,
"transaction_type": tx_type,
"transaction_code": tx_code,
"shares": shares,
"price": price,
"value": value,
"owned_after": owned,
"url": filing_url,
})
return transactions
def fetch_insider_trades(days_back: int = 3, progress_cb=None) -> list[dict]:
"""
Fetch ALL Form 4 insider transactions from SEC EDGAR for the given period.
Paginates the EDGAR EFTS search API to collect every filing in the date
range, then parallel-fetches each Form 4 XML for full transaction details.
A shared rate-limiter keeps requests under EDGAR's 10 req/s limit.
days_back : number of calendar days back from today to search.
progress_cb : optional callable(done_count, total_count).
Returns a list of transaction dicts sorted by tx_date descending.
"""
import time
import threading
from concurrent.futures import ThreadPoolExecutor, as_completed
from datetime import date, timedelta
_UA = {"User-Agent": "UltimateInvestmentTool contact@investmenttool.com"}
# ── Rate limiter: max ~9 requests/sec across all threads ──────────────
_rate_lock = threading.Lock()
_last_req = [0.0]
MIN_INTERVAL = 0.12 # seconds between requests → ~8 req/s, safely under 10
def _get(url, **kwargs):
with _rate_lock:
wait = MIN_INTERVAL - (time.monotonic() - _last_req[0])
if wait > 0:
time.sleep(wait)
_last_req[0] = time.monotonic()
return requests.get(url, **kwargs)
end_dt = date.today()
start_dt = end_dt - timedelta(days=max(days_back, 1))
# ── Step 1: paginate EDGAR EFTS to collect all filing hits ────────────
PAGE_SIZE = 100
all_hits: list[dict] = []
from_offset = 0
while True:
try:
resp = _get(
"https://efts.sec.gov/LATEST/search-index",
params={
"forms": "4",
"dateRange": "custom",
"startdt": start_dt.strftime("%Y-%m-%d"),
"enddt": end_dt.strftime("%Y-%m-%d"),
"from": from_offset,
"hits": PAGE_SIZE,
},
headers=_UA,
timeout=15,
)
resp.raise_for_status()
except Exception:
break
page_hits = resp.json().get("hits", {}).get("hits", [])
all_hits.extend(page_hits)
if len(page_hits) < PAGE_SIZE:
break # reached the last page
from_offset += PAGE_SIZE
if not all_hits:
return []
# ── Step 2: fetch + parse each Form 4 XML (rate-limited, parallel) ────
def _fetch_one(hit: dict) -> list[dict]:
try:
src = hit.get("_source", {})
filed = src.get("file_date", "") or src.get("filed_at", "")
# Accession number from _source.adsh (not _id which has ":filename" appended)
acc_no = src.get("adsh", "")
if not acc_no:
return []
# XML filename is embedded in _id as "{adsh}:{filename}"
xml_filename = hit["_id"].split(":")[-1]
# CIK: use ciks[0] from _source — NOT the accession prefix (which is the filing agent)
ciks = src.get("ciks", [])
if not ciks:
return []
cik_int = int(ciks[0].lstrip("0") or "0")
if cik_int == 0:
return []
acc_nd = acc_no.replace("-", "")
base = f"https://www.sec.gov/Archives/edgar/data/{cik_int}/{acc_nd}"
filing_url = f"{base}/{acc_no}-index.htm"
xml_url = f"{base}/{xml_filename}"
xml_r = _get(xml_url, headers=_UA, timeout=8)
xml_r.raise_for_status()
return _parse_form4_xml(xml_r.content, filing_url, filed)
except Exception:
return []
total = len(all_hits)
done_n = 0
results: list[dict] = []
with ThreadPoolExecutor(max_workers=5) as ex:
futs = {ex.submit(_fetch_one, h): h for h in all_hits}
for fut in as_completed(futs):
done_n += 1
results.extend(fut.result() or [])
if progress_cb:
try:
progress_cb(done_n, total)
except Exception:
pass
results.sort(
key=lambda x: (x.get("tx_date") or "", x.get("filed_date") or ""),
reverse=True,
)
return results
# ---------------------------------------------------------------------------
# Hedge Fund Holdings — SEC EDGAR 13F-HR Filings
# ---------------------------------------------------------------------------
def _parse_13f_xml(xml_bytes: bytes) -> list[dict]:
"""
Parse a 13F information table XML document.
Handles any namespace variant used by EDGAR filers.
Returns a list of holding dicts with keys:
company, class_, cusip, value (USD), shares, shr_type, put_call
"""
import xml.etree.ElementTree as ET
import re as _re
# SEC serves 13F XML wrapped in an HTML document (Content-Type: text/html).
# Strip the HTML wrapper and extract just the <informationTable> block.
if xml_bytes[:500].upper().find(b'<!DOCTYPE HTML') != -1:
m = _re.search(rb'(<informationTable[\s\S]*?</informationTable>)',
xml_bytes, _re.IGNORECASE)
if m:
xml_bytes = m.group(1)
try:
root = ET.fromstring(xml_bytes)
except ET.ParseError:
return []
# Strip XML namespace prefix so tag matching is namespace-agnostic
def _bare(tag: str) -> str:
return tag.split("}")[-1] if "}" in tag else tag
def _find(node, bare_tag):
for child in node:
if _bare(child.tag) == bare_tag:
return child
return None
def _text(node, bare_tag) -> str:
n = _find(node, bare_tag)
return n.text.strip() if n is not None and n.text else ""
# Root is <informationTable>; entries are <infoTable>
info_tables = [c for c in root if _bare(c.tag) == "infoTable"]
holdings: list[dict] = []
for entry in info_tables:
company = _text(entry, "nameOfIssuer")
class_ = _text(entry, "titleOfClass")
cusip = _text(entry, "cusip")
put_call = _text(entry, "putCall")
try:
value_k = float(_text(entry, "value"))
except (ValueError, TypeError):
value_k = None
shr_node = _find(entry, "shrsOrPrnAmt")
shares, shr_type = None, "SH"
if shr_node is not None:
try:
shares = float(_text(shr_node, "sshPrnamt"))
except (ValueError, TypeError):
pass
t = _text(shr_node, "sshPrnamtType")
if t:
shr_type = t
holdings.append({
"company": company,
"class_": class_,
"cusip": cusip,
"value": value_k * 1000.0 if value_k is not None else None,
"shares": shares,
"shr_type": shr_type,
"put_call": put_call,
})
return holdings
def fetch_hedge_fund_filings(
days_back: int = 90,
max_filers: int = 25,
progress_cb = None,
) -> list[dict]:
"""
Fetch individual stock holdings from recent SEC 13F-HR filings.
For each unique fund that filed a 13F in the given period (up to
max_filers), fetches the information-table XML and parses every holding.
Returns a flat list of holding dicts (one row per stock per fund).
days_back : calendar days back from today to search for filings.
max_filers : hard cap on the number of unique filers to process.
progress_cb : optional callable(done_count, total_count).
"""
import threading as _th
_UA = {"User-Agent": "UltimateInvestmentTool contact@investmenttool.com"}
# Rate-limiter: stay safely under EDGAR's 10 req/s cap
_rate_lock = _th.Lock()
_last_req = [0.0]
MIN_INTERVAL = 0.12
def _get(url, **kwargs):
with _rate_lock:
wait = MIN_INTERVAL - (time.monotonic() - _last_req[0])
if wait > 0:
time.sleep(wait)
_last_req[0] = time.monotonic()
return requests.get(url, headers=_UA, timeout=15, **kwargs)
from datetime import date, timedelta as _td
end_dt = date.today()
start_dt = end_dt - _td(days=max(days_back, 1))
# ── Step 1: Collect unique 13F-HR filings from EFTS ──────────────────
PAGE_SIZE = 100
seen_adsh : set = set()
unique_hits : list = []
from_offset = 0
while len(unique_hits) < max_filers:
try:
resp = _get(
"https://efts.sec.gov/LATEST/search-index",
params={
"q": "",
"forms": "13F-HR",
"dateRange": "custom",
"startdt": start_dt.strftime("%Y-%m-%d"),
"enddt": end_dt.strftime("%Y-%m-%d"),
"from": from_offset,
"hits": PAGE_SIZE,
},
)
resp.raise_for_status()
except Exception as _e:
print(f" [HF] EFTS search failed: {_e}")
break
page_hits = resp.json().get("hits", {}).get("hits", [])
for hit in page_hits:
adsh = hit.get("_source", {}).get("adsh", "")
if adsh and adsh not in seen_adsh:
seen_adsh.add(adsh)
unique_hits.append(hit)
if len(unique_hits) >= max_filers:
break
if len(page_hits) < PAGE_SIZE:
break
from_offset += PAGE_SIZE
if not unique_hits:
return []
# ── Step 2: Fetch each filing's info-table XML and parse holdings ─────
def _fetch_one(hit: dict) -> list[dict]:
try:
src = hit.get("_source", {})
acc_no = src.get("adsh", "")
ciks = src.get("ciks", [])
display = src.get("display_names") or src.get("entity_name") or []
fund_name = display[0].split(" (CIK")[0].strip() if display else "Unknown Fund"
filed = (src.get("file_date") or "")[:10]
period = (src.get("period_ending") or src.get("period_of_report") or "")[:10]
if not acc_no or not ciks:
return []
cik_int = int(ciks[0].lstrip("0") or "0")
if cik_int == 0:
return []
acc_nd = acc_no.replace("-", "")
base = (f"https://www.sec.gov/Archives/edgar/data"
f"/{cik_int}/{acc_nd}")
filing_url = f"{base}/{acc_no}-index.htm"
# Fetch the filing index page and locate the INFORMATION TABLE doc.
# Parse the document table properly rather than a fragile backwards search.
idx_r = _get(f"{base}/{acc_no}-index.htm")
if idx_r.status_code != 200:
print(f" [HF] {fund_name}: index fetch failed HTTP {idx_r.status_code}")
return []
idx_html = idx_r.text
# Walk every table row; find the one whose type cell is INFORMATION TABLE
info_href = None
for row_m in re.finditer(r'<tr[^>]*>(.*?)</tr>', idx_html,
re.IGNORECASE | re.DOTALL):
row_text = row_m.group(1)
if "INFORMATION TABLE" in row_text.upper():
href_m = re.search(r'href="(/Archives/[^"]+)"',
row_text, re.IGNORECASE)
if href_m:
info_href = href_m.group(1)
break
if not info_href:
print(f" [HF] {fund_name}: INFORMATION TABLE row not found in index")
return []
xml_r = _get("https://www.sec.gov" + info_href)
if xml_r.status_code != 200:
print(f" [HF] {fund_name}: XML fetch failed HTTP {xml_r.status_code}")
return []
raw_holdings = _parse_13f_xml(xml_r.content)
if not raw_holdings:
print(f" [HF] {fund_name}: XML parsed but 0 holdings returned")
return []
results: list[dict] = []
for h in raw_holdings:
results.append({
**h,
"fund_name": fund_name,
"filed_date": filed,
"period": period,
"url": filing_url,
})
return results
except Exception as exc:
print(f" [HF] {fund_name}: unexpected error — {exc}")
return []
total = len(unique_hits)
done_n = 0
results: list[dict] = []
with ThreadPoolExecutor(max_workers=3) as ex:
futs = {ex.submit(_fetch_one, h): h for h in unique_hits}
for fut in as_completed(futs):
done_n += 1
results.extend(fut.result() or [])
if progress_cb:
try:
progress_cb(done_n, total)
except Exception:
pass
results.sort(
key=lambda x: (x.get("filed_date") or "", x.get("value") or 0.0),
reverse=True,
)
return results
if __name__ == "__main__":
main()
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