ssz223
/
Stock-Tool
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Tier 1-3 fixes: value inversion, iloc→loc, D/E scale, Piotroski, Sloan accruals, sentiment confidence, early-stage double-count

This commit is contained in:
Nolan Kovacs 2026-03-29 18:36:41 -04:00
parent 5c779efecf
commit f2c8daefba
3 changed files with 134 additions and 61 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

38
cache_builder.py
View File

@ -43,7 +43,9 @@ load_dotenv("/srv/api/.env")
_DB_HOST = "127.0.0.1" _DB_HOST = "127.0.0.1"
_DB_NAME = "verimund" _DB_NAME = "verimund"
_DB_USER = "verimund_user" _DB_USER = "verimund_user"
_DB_PASS = os.environ.get("DB_PASS", "Shlevison2k17") _DB_PASS = os.environ.get("DB_PASS")
if not _DB_PASS:
raise RuntimeError("DB_PASS environment variable is not set")
# TABLE_PREFIX is set by the cron job: "" for stable, "beta_" for beta channel. # TABLE_PREFIX is set by the cron job: "" for stable, "beta_" for beta channel.
_TABLE_PREFIX = os.environ.get("TABLE_PREFIX", "") _TABLE_PREFIX = os.environ.get("TABLE_PREFIX", "")
@ -284,6 +286,7 @@ def _fetch_analyst(ticker: str) -> dict | None:
return { return {
"ticker": ticker, "ticker": ticker,
"name": info.get("longName") or None,
"pe_forward": info.get("forwardPE"), "pe_forward": info.get("forwardPE"),
"eps_forward": info.get("forwardEps"), "eps_forward": info.get("forwardEps"),
"analyst_norm": analyst_norm, "analyst_norm": analyst_norm,
@ -293,6 +296,8 @@ def _fetch_analyst(ticker: str) -> dict | None:
"recommendation": info.get("recommendationKey") or "N/A", "recommendation": info.get("recommendationKey") or "N/A",
"sector": info.get("sector") or None, "sector": info.get("sector") or None,
"industry": info.get("industry") or None, "industry": info.get("industry") or None,
"short_percent": info.get("shortPercentOfFloat"),
"short_ratio": info.get("shortRatio"),
"dividend_yield": info.get("dividendYield"), "dividend_yield": info.get("dividendYield"),
"updated_at": datetime.now(timezone.utc).isoformat(), "updated_at": datetime.now(timezone.utc).isoformat(),
} }
@ -414,6 +419,7 @@ def _fetch_news_sentiment(ticker: str) -> dict | None:
return { return {
"ticker": ticker, "ticker": ticker,
"news_sentiment": sentiment, "news_sentiment": sentiment,
"news_headline_count": len(scores),
"updated_at": datetime.now(timezone.utc).isoformat(), "updated_at": datetime.now(timezone.utc).isoformat(),
} }
except Exception: except Exception:
@ -463,9 +469,9 @@ def run_news_phase(tickers: list[str]) -> None:
# Explicit column allow-lists keep extra fields (e.g. dividend_yield from # Explicit column allow-lists keep extra fields (e.g. dividend_yield from
# yfinance) from causing "column does not exist" errors in PostgreSQL. # yfinance) from causing "column does not exist" errors in PostgreSQL.
_ANALYST_COLS = ( _ANALYST_COLS = (
"ticker", "pe_forward", "eps_forward", "analyst_norm", "analyst_upside", "ticker", "name", "pe_forward", "eps_forward", "analyst_norm", "analyst_upside",
"analyst_count", "analyst_target", "recommendation", "news_sentiment", "analyst_count", "analyst_target", "recommendation", "news_sentiment",
"sector", "industry", "updated_at", "news_headline_count", "sector", "industry", "short_percent", "short_ratio", "updated_at",
) )
_FUND_COLS = ( _FUND_COLS = (
"ticker", "pe_trailing", "pb_ratio", "ev_ebitda", "eps_trailing", "ticker", "pe_trailing", "pb_ratio", "ev_ebitda", "eps_trailing",
@ -473,6 +479,7 @@ _FUND_COLS = (
"debt_to_equity", "total_debt", "total_cash", "book_value", "current_ratio", "debt_to_equity", "total_debt", "total_cash", "book_value", "current_ratio",
"profit_margin", "operating_margin", "fcf_yield", "dividend_yield", "profit_margin", "operating_margin", "fcf_yield", "dividend_yield",
"market_cap", "shares_outstanding", "ev_revenue", "short_percent", "market_cap", "shares_outstanding", "ev_revenue", "short_percent",
"piotroski_score", "accruals_ratio",
"filer_type", "updated_at", "filer_type", "updated_at",
) )
@ -534,8 +541,11 @@ def _fetch_one_frame(concept: str, unit: str, period: str) -> dict[int, float]:
_FRAME_CACHE[key] = result _FRAME_CACHE[key] = result
time.sleep(0.15) time.sleep(0.15)
return result return result
except Exception: except requests.HTTPError as e:
_FRAME_CACHE[key] = {} print(f" [WARN] EDGAR frame {concept}/{unit}/{period}: HTTP {e.response.status_code} — not cached")
return {}
except Exception as e:
print(f" [WARN] EDGAR frame {concept}/{unit}/{period}: {e} — not cached")
return {} return {}
@ -547,7 +557,7 @@ def _sum_frames(concepts: list[str], unit: str, periods: list[str]) -> dict[int,
ttm.setdefault(cik, {}) ttm.setdefault(cik, {})
if period not in ttm[cik]: if period not in ttm[cik]:
ttm[cik][period] = val ttm[cik][period] = val
return {cik: sum(pv.values()) for cik, pv in ttm.items()} 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]: def _best_frame(concepts: list[str], unit: str, periods: list[str]) -> dict[int, float]:
@ -613,6 +623,20 @@ def _compute_fundamentals(
if rev is not None and rev_prev and rev_prev != 0 else None) if rev is not None and rev_prev and rev_prev != 0 else None)
ni_g = ((ni - ni_prev) / abs(ni_prev) ni_g = ((ni - ni_prev) / abs(ni_prev)
if ni is not None and ni_prev and ni_prev != 0 else None) if ni is not None and ni_prev and ni_prev != 0 else None)
# Piotroski partial F-Score (4 signals available at compute time)
p = 0
if ni is not None and tot_a and tot_a > 0 and ni / tot_a > 0: p += 1 # ROA > 0
if ocf is not None and ocf > 0: p += 1 # OCF > 0
if ocf is not None and ni is not None and ocf > ni: p += 1 # OCF > NI (cash-backed)
if rev is not None and rev_prev is not None and rev_prev != 0 and rev > rev_prev: p += 1 # Revenue improving
piotroski = p
# Sloan accruals ratio: (NI - OCF) / Total Assets — negative = higher earnings quality
accruals = None
if ni is not None and ocf is not None and tot_a and tot_a > 0:
accruals = max(-0.3, min(0.3, (ni - ocf) / tot_a))
return { return {
"market_cap": mkt_cap, "market_cap": mkt_cap,
"shares_outstanding": sh, "shares_outstanding": sh,
@ -634,6 +658,8 @@ def _compute_fundamentals(
"profit_margin": pm, "profit_margin": pm,
"operating_margin": om, "operating_margin": om,
"fcf_yield": fcf_yield, "fcf_yield": fcf_yield,
"piotroski_score": piotroski,
"accruals_ratio": accruals,
} }

8
dist/screener_gui.py vendored
View File

@ -1365,10 +1365,14 @@ class ScreenerApp(tk.Tk):
self._status_var.set(f" Rescoring with strategy: {strategy}…") self._status_var.set(f" Rescoring with strategy: {strategy}…")
def _rescore(): def _rescore():
try:
result = score_stocks( result = score_stocks(
self._df_raw, weights=weights, self._df_raw, weights=weights,
sector_stats=self._sector_stats, sector_stats=self._sector_stats,
) )
except Exception:
self.after(0, lambda: setattr(self, "_rescoring", False))
return
def _done(): def _done():
self._rescoring = False self._rescoring = False
if self._rescore_id != current_id: if self._rescore_id != current_id:
@ -1447,9 +1451,9 @@ class ScreenerApp(tk.Tk):
price_min = _to_float(self._filter_price_min) price_min = _to_float(self._filter_price_min)
price_max = _to_float(self._filter_price_max) price_max = _to_float(self._filter_price_max)
if price_min is not None: if price_min is not None:
df = df[df["price"].fillna(0) >= price_min] df = df[df["price"].notna() & (df["price"] >= price_min)]
if price_max is not None: if price_max is not None:
df = df[df["price"].fillna(0) <= price_max] df = df[df["price"].notna() & (df["price"] <= price_max)]
# --- Market cap category --- # --- Market cap category ---
mktcap_cat = self._filter_mktcap.get() mktcap_cat = self._filter_mktcap.get()

135
dist/stock_screener.py vendored
View File

@ -1298,6 +1298,8 @@ def _fetch_fundamentals(
price_now = float(close.iloc[-1]) price_now = float(close.iloc[-1])
def pct_return(days_back: int) -> float | None: def pct_return(days_back: int) -> float | None:
if len(close) <= days_back:
return None
idx = max(0, len(close) - days_back) idx = max(0, len(close) - days_back)
past = float(close.iloc[idx]) past = float(close.iloc[idx])
return (price_now - past) / past if past > 0 else None return (price_now - past) / past if past > 0 else None
@ -1350,7 +1352,7 @@ def _fetch_fundamentals(
"ret_12m": pct_return(252), "ret_12m": pct_return(252),
"roe": info.get("returnOnEquity"), "roe": info.get("returnOnEquity"),
"roa": info.get("returnOnAssets"), "roa": info.get("returnOnAssets"),
"debt_to_equity": info.get("debtToEquity"), "debt_to_equity": (info["debtToEquity"] / 100.0) if info.get("debtToEquity") is not None else None,
"total_debt": info.get("totalDebt"), "total_debt": info.get("totalDebt"),
"total_cash": info.get("totalCash"), "total_cash": info.get("totalCash"),
"book_value": info.get("bookValue"), "book_value": info.get("bookValue"),
@ -1366,6 +1368,7 @@ def _fetch_fundamentals(
"analyst_target": target_price, "analyst_target": target_price,
"recommendation": info.get("recommendationKey", "N/A"), "recommendation": info.get("recommendationKey", "N/A"),
"short_percent": info.get("shortPercentOfFloat"), "short_percent": info.get("shortPercentOfFloat"),
"short_ratio": info.get("shortRatio"),
"beta": info.get("beta"), "beta": info.get("beta"),
"volatility_30d": volatility, "volatility_30d": volatility,
"market_cap": info.get("marketCap"), "market_cap": info.get("marketCap"),
@ -1500,6 +1503,8 @@ def fetch_all(
price_now = float(close.iloc[-1]) price_now = float(close.iloc[-1])
def pct_return(days_back: int) -> float | None: def pct_return(days_back: int) -> float | None:
if len(close) <= days_back:
return None
idx = max(0, len(close) - days_back) idx = max(0, len(close) - days_back)
past = float(close.iloc[idx]) past = float(close.iloc[idx])
return (price_now - past) / past if past > 0 else None return (price_now - past) / past if past > 0 else None
@ -1544,7 +1549,7 @@ def fetch_all(
results.append({ results.append({
"ticker": ticker, "ticker": ticker,
"name": edgar.get("name", ticker), "name": analyst.get("name") or edgar.get("name") or ticker,
"sector": analyst.get("sector") or edgar.get("sector", "N/A"), "sector": analyst.get("sector") or edgar.get("sector", "N/A"),
"industry": analyst.get("industry") or edgar.get("industry", "N/A"), "industry": analyst.get("industry") or edgar.get("industry", "N/A"),
"price": price_now, "price": price_now,
@ -1586,8 +1591,12 @@ def fetch_all(
"analyst_count": analyst.get("analyst_count"), "analyst_count": analyst.get("analyst_count"),
"analyst_target": analyst_target, "analyst_target": analyst_target,
"recommendation": analyst.get("recommendation", "N/A"), "recommendation": analyst.get("recommendation", "N/A"),
# Risk — FINRA cache first, then yfinance shortPercentOfFloat as display fallback # yfinance shortPercentOfFloat (% of float) preferred over FINRA short volume ratio
"short_percent": finra_short.get(ticker) or analyst.get("short_percent"), "short_percent": analyst.get("short_percent") or finra_short.get(ticker),
"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), "beta": beta_map.get(ticker),
"volatility_30d": volatility, "volatility_30d": volatility,
# Market data # Market data
@ -1803,39 +1812,40 @@ _PROFILE_WEIGHTS: dict[str, dict[str, dict[str, float]]] = {
"mature": { "mature": {
"value": {"pe": 0.35, "pb": 0.20, "ev_ebitda": 0.45}, "value": {"pe": 0.35, "pb": 0.20, "ev_ebitda": 0.45},
"growth": {"revenue_growth": 0.30, "earnings_growth": 0.45, "eps_growth": 0.25}, "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}, "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}, "profitability": {"fcf_yield": 0.40, "operating_margin": 0.38, "profit_margin": 0.22, "accruals": 0.15},
}, },
"high_growth": { "high_growth": {
"value": {"pe": 0.00, "pb": 0.20, "ev_ebitda": 0.35, "ev_revenue": 0.45}, "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}, "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}, "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}, "profitability": {"fcf_yield": 0.15, "operating_margin": 0.50, "profit_margin": 0.35, "accruals": 0.15},
}, },
"financial": { "financial": {
"value": {"pe": 0.35, "pb": 0.50, "ev_ebitda": 0.15}, "value": {"pe": 0.35, "pb": 0.50, "ev_ebitda": 0.15},
"growth": {"revenue_growth": 0.30, "earnings_growth": 0.50, "eps_growth": 0.20}, "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}, "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}, "profitability": {"fcf_yield": 0.30, "operating_margin": 0.00, "profit_margin": 0.70, "accruals": 0.15},
}, },
"capital_intensive": { "capital_intensive": {
"value": {"pe": 0.15, "pb": 0.20, "ev_ebitda": 0.65}, "value": {"pe": 0.15, "pb": 0.20, "ev_ebitda": 0.65},
"growth": {"revenue_growth": 0.35, "earnings_growth": 0.40, "eps_growth": 0.25}, "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}, "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}, "profitability": {"fcf_yield": 0.35, "operating_margin": 0.45, "profit_margin": 0.20, "accruals": 0.15},
}, },
"early_stage": { "early_stage": {
"value": {"pe": 0.00, "pb": 0.15, "ev_ebitda": 0.00, "ev_revenue": 0.85}, "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}, "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}, "quality": {"roa": 0.20, "roe": 0.00, "debt_to_equity": 0.00, "current_ratio": 0.80, "piotroski": 0.10},
"profitability": {"fcf_yield": 0.00, "operating_margin": 0.40, "profit_margin": 0.00, # revenue_growth_proxy removed — revenue growth already carries 70% of the growth score
"revenue_growth_proxy": 0.60}, "profitability": {"fcf_yield": 0.00, "operating_margin": 1.00, "profit_margin": 0.00,
"revenue_growth_proxy": 0.00},
}, },
"turnaround": { "turnaround": {
"value": {"pe": 0.00, "pb": 0.30, "ev_ebitda": 0.70}, "value": {"pe": 0.00, "pb": 0.30, "ev_ebitda": 0.70},
"growth": {"revenue_growth": 0.15, "earnings_growth": 0.80, "eps_growth": 0.05}, "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}, "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}, "profitability": {"fcf_yield": 0.25, "operating_margin": 0.45, "profit_margin": 0.30, "accruals": 0.15},
}, },
} }
@ -1871,23 +1881,24 @@ def compute_value_score(df: pd.DataFrame,
""" """
ss = sector_stats or {} ss = sector_stats or {}
sectors = df.get("sector", pd.Series(["N/A"] * len(df), index=df.index)) sectors = df.get("sector", pd.Series(["N/A"] * len(df), index=df.index))
pe = df["pe_forward"].combine_first(df["pe_trailing"]).where(lambda x: x > 0) # 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
# Pre-compute z-scores for each metric fwd_s = 100.0 - _z_series(df["pe_forward"].where(df["pe_forward"] > 0), "pe_forward", sectors, ss)
pe_s = _z_series(pe, "pe_trailing", sectors, ss) trl_s = 100.0 - _z_series(df["pe_trailing"].where(df["pe_trailing"] > 0), "pe_trailing", sectors, ss)
pb_s = _z_series(df["pb_ratio"], "pb_ratio", sectors, ss) pe_s = fwd_s.combine_first(trl_s)
ev_s = _z_series(df["ev_ebitda"], "ev_ebitda", sectors, ss) pb_s = 100.0 - _z_series(df["pb_ratio"], "pb_ratio", sectors, ss)
evr_s = _z_series(df.get("ev_revenue", pd.Series( 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)), [None]*len(df), index=df.index)),
"ev_revenue", sectors, ss) "ev_revenue", sectors, ss)
scores = [] scores = []
for i, row in df.iterrows(): for i, row in df.iterrows():
ms = { ms = {
"pe": float(pe_s.iloc[i]), "pe": float(pe_s.loc[i]),
"pb": float(pb_s.iloc[i]), "pb": float(pb_s.loc[i]),
"ev_ebitda": float(ev_s.iloc[i]), "ev_ebitda": float(ev_s.loc[i]),
"ev_revenue": float(evr_s.iloc[i]), "ev_revenue": float(evr_s.loc[i]),
} }
scores.append(_profile_score_row(row, "value", ms)) scores.append(_profile_score_row(row, "value", ms))
return pd.Series(scores, index=df.index) return pd.Series(scores, index=df.index)
@ -1914,9 +1925,9 @@ def compute_growth_score(df: pd.DataFrame,
scores = [] scores = []
for i, row in df.iterrows(): for i, row in df.iterrows():
ms = { ms = {
"revenue_growth": float(rev_s.iloc[i]), "revenue_growth": float(rev_s.loc[i]),
"earnings_growth": float(ear_s.iloc[i]), "earnings_growth": float(ear_s.loc[i]),
"eps_growth": float(eps_s.iloc[i]), "eps_growth": float(eps_s.loc[i]),
} }
scores.append(_profile_score_row(row, "growth", ms)) scores.append(_profile_score_row(row, "growth", ms))
return pd.Series(scores, index=df.index) return pd.Series(scores, index=df.index)
@ -1967,19 +1978,27 @@ def compute_quality_score(df: pd.DataFrame,
roa_s = _z_series(df["roa"], "roa", sectors, ss) roa_s = _z_series(df["roa"], "roa", sectors, ss)
roe_s = _z_series(df["roe"], "roe", sectors, ss) roe_s = _z_series(df["roe"], "roe", sectors, ss)
de_s = _z_series(df["debt_to_equity"], "debt_to_equity", sectors, ss)
cr_s = _z_series(df["current_ratio"], "current_ratio", 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 # D/E z-score is inverted — lower D/E is better, so flip the z-score
de_s = 100.0 - de_s 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 = [] scores = []
for i, row in df.iterrows(): for i, row in df.iterrows():
ms = { ms = {
"roa": float(roa_s.iloc[i]), "roa": float(roa_s.loc[i]),
"roe": float(roe_s.iloc[i]), "roe": float(roe_s.loc[i]),
"debt_to_equity": float(de_s.iloc[i]), "debt_to_equity": float(de_s.loc[i]),
"current_ratio": float(cr_s.iloc[i]), "current_ratio": float(cr_s.loc[i]),
"piotroski": float(piotroski_s.loc[i]),
} }
scores.append(_profile_score_row(row, "quality", ms)) scores.append(_profile_score_row(row, "quality", ms))
return pd.Series(scores, index=df.index) return pd.Series(scores, index=df.index)
@ -1999,13 +2018,21 @@ def compute_profitability_score(df: pd.DataFrame,
pm_s = _z_series(df["profit_margin"], "profit_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) 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 = [] scores = []
for i, row in df.iterrows(): for i, row in df.iterrows():
ms = { ms = {
"fcf_yield": float(fcf_s.iloc[i]), "fcf_yield": float(fcf_s.loc[i]),
"operating_margin": float(om_s.iloc[i]), "operating_margin": float(om_s.loc[i]),
"profit_margin": float(pm_s.iloc[i]), "profit_margin": float(pm_s.loc[i]),
"revenue_growth_proxy": float(rev_s.iloc[i]), # used by early_stage "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)) scores.append(_profile_score_row(row, "profitability", ms))
return pd.Series(scores, index=df.index) return pd.Series(scores, index=df.index)
@ -2013,12 +2040,27 @@ def compute_profitability_score(df: pd.DataFrame,
def compute_sentiment_score(df: pd.DataFrame) -> pd.Series: def compute_sentiment_score(df: pd.DataFrame) -> pd.Series:
""" """
News Sentiment Score — VADER compound score mapped to 0–100 (absolute scale). News Sentiment Score — VADER compound score mapped to 0–100, confidence-weighted
Sentiment is inherently comparable across all stocks; no z-scoring applied. 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), sent_pts = [(-1.0, 0), (-0.40, 20), (-0.15, 36), (0.0, 50),
(0.15, 64), (0.40, 80), (1.0, 100)] (0.15, 64), (0.40, 80), (1.0, 100)]
return _score_series(df["news_sentiment"], sent_pts) 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, def compute_analyst_score(df: pd.DataFrame,
@ -2092,8 +2134,9 @@ def score_stocks(df: pd.DataFrame, weights: dict | None = None,
"ret_1m", "ret_3m", "ret_6m", "ret_12m", "ret_1m", "ret_3m", "ret_6m", "ret_12m",
"roe", "roa", "debt_to_equity", "current_ratio", "roe", "roa", "debt_to_equity", "current_ratio",
"profit_margin", "operating_margin", "fcf_yield", "profit_margin", "operating_margin", "fcf_yield",
"news_sentiment", "analyst_norm", "analyst_upside", "analyst_count", "news_sentiment", "news_headline_count", "analyst_norm", "analyst_upside", "analyst_count",
"short_percent", "volatility_30d", "beta", "price", "market_cap", "short_percent", "short_ratio", "volatility_30d", "beta", "price", "market_cap",
"piotroski_score", "accruals_ratio",
] ]
for col in _NUMERIC_COLS: for col in _NUMERIC_COLS:
if col in df.columns: if col in df.columns: