AlphaMindQuantitative & Fundamental Stock Analysis Platform\

Verbatim Code / Config

def derive_roe(data: dict) -> float:
    if data.get('roe'): return data['roe']
    if pb := data.get('pb_ratio') and (pe := data.get('pe_ratio')) and pe > 0:
        return pb / pe  # (Price/Book) / (Price/EPS) = EPS/Book = ROE
    if eps := data.get('eps') and (bv := data.get('book_value')) and bv > 0:
        return eps / bv
    if roa := data.get('roa') and (lev := data.get('leverage_ratio')):
        return roa * lev
    return fallback_catalog.get(data['symbol'], {}).get('roe', 0.0)

Verbatim Code / Config

def compute_technicals(df: pd.DataFrame) -> dict:
    delta = df['Close'].diff()
    gain = delta.where(delta > 0, 0).rolling(14).mean()
    loss = (-delta.where(delta < 0, 0)).rolling(14).mean()
    rs = gain / (loss + 1e-9)
    rsi = 100 - (100 / (1 + rs))
    sma_50 = df['Close'].rolling(50).mean()
    sma_200 = df['Close'].rolling(200).mean()
    return {'rsi': rsi.iloc[-1], 'golden_cross': sma_50.iloc[-1] > sma_200.iloc[-1]}

Verbatim Code / Config

class NumberedCanvas(canvas.Canvas):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self._saved_page_states = []
    def showPage(self):
        self._saved_page_states.append(dict(self.__dict__))
        self._startPage()
    def save(self):
        num_pages = len(self._saved_page_states)
        for state in self._saved_page_states:
            self.__dict__.update(state)
            self.draw_page_number(num_pages)
            super().showPage()
        super().save()

Verbatim Code / Config

app = FastAPI(title='AlphaMind Engine')
app.include_router(auth_router, prefix='/api/auth')
app.include_router(analysis_router, prefix='/api/stocks')
app.include_router(digest_router, prefix='/api/reports')