Create yahooinfo.py

app/yahooinfo.py

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+# ==============================
+# Imports
+# ==============================
+import yfinance as yf
+import pandas as pd
+import traceback
+from datetime import datetime, timezone
+
+from .persist import exists, load, save
+
+# ==============================
+# Icons
+# ==============================
+MAIN_ICONS = {
+    "Price / Volume": "📈",
+    "Fundamentals": "📊",
+    "Trend": "📈",
+    "Signals": "🧠",
+    "Company Profile": "🏢",
+    "Management": "👔"
+}
+
+# ==============================
+# Short names
+# ==============================
+SHORT_NAMES = {
+    "regularMarketPrice": "Price",
+    "regularMarketChange": "Chg",
+    "regularMarketChangePercent": "Chg%",
+    "regularMarketPreviousClose": "Prev",
+    "regularMarketOpen": "Open",
+    "regularMarketDayHigh": "High",
+    "regularMarketDayLow": "Low",
+    "regularMarketVolume": "Vol",
+    "averageDailyVolume10Day": "Avg Vol 10D",
+    "averageDailyVolume3Month": "Avg Vol 3M",
+    "fiftyDayAverage": "50DMA",
+    "twoHundredDayAverage": "200DMA",
+    "fiftyTwoWeekLow": "52W Low",
+    "fiftyTwoWeekHigh": "52W High",
+    "mostRecentQuarter":"Recent Q",
+    "lastFiscalYearEnd":"FY End"
+}
+
+# ==============================
+# Price / Volume Sub-Groups
+# ==============================
+PRICE_VOLUME_GROUPS = {
+    "Market Price": ["Price","Chg","Chg%","Prev","Open"],
+    "Intraday Range": ["High","Low"],
+    "Volume & Liquidity": ["Vol","Avg Vol 10D","Avg Vol 3M"],
+    "Moving Averages": ["50DMA","200DMA"],
+    "52W Range": ["52W Low","52W High"]
+}
+
+PIN_PRICE = ["Price","Chg","Chg%","Prev","Open"]
+PIN_FUND  = ["MCap","PE","PB","EPS","ROE","ROA","Margin","D/E","Recent Q","FY End"]
+
+# ==============================
+# Noise keys
+# ==============================
+NOISE_KEYS = {
+    "maxAge","priceHint","triggerable",
+    "customPriceAlertConfidence",
+    "sourceInterval","exchangeDataDelayedBy",
+    "esgPopulated"
+}
+
+# ==============================
+# Low-level Yahoo fetch
+# ==============================
+def yfinfo(symbol):
+    try:
+        t = yf.Ticker(symbol + ".NS")
+        info = t.info
+        return info if isinstance(info, dict) else {}
+    except Exception as e:
+        return {"__error__": str(e)}
+
+# ==============================
+# Formatting
+# ==============================
+def human_number(n):
+    try:
+        n = float(n)
+        if abs(n) >= 1e7: return f"{n/1e7:.2f}Cr"
+        if abs(n) >= 1e5: return f"{n/1e5:.2f}L"
+        if abs(n) >= 1e3: return f"{n/1e3:.2f}K"
+        return f"{n:,.2f}"
+    except:
+        return str(n)
+
+DATE_KEYWORDS = ("date", "time", "timestamp", "fiscal", "quarter","earnings","dividend")
+def looks_like_unix_ts(v):
+    try:
+        v = int(v)
+        return (946684800 <= v <= 4102444800 or 946684800000 <= v <= 4102444800000)
+    except:
+        return False
+def unix_to_dt(v):
+    v = int(v)
+    if v > 10**12: v //= 1000
+    return datetime.fromtimestamp(v, tz=timezone.utc)
+def fy_quarter_label(dt):
+    y, m = dt.year, dt.month
+    if m >= 4:
+        fy = y + 1
+        q = (m - 1)//3
+    else:
+        fy = y
+        q = (m + 8)//3
+    return f"Q{q} FY{str(fy)[-2:]}"
+def format_value(k, v):
+    lk = k.lower()
+    # Date / Quarter
+    if isinstance(v,(int,float)) and looks_like_unix_ts(v):
+        if any(x in lk for x in DATE_KEYWORDS):
+            dt = unix_to_dt(v)
+            if "quarter" in lk:
+                return fy_quarter_label(dt)
+            return dt.strftime("%d %b %Y")
+    # Numbers
+    if isinstance(v,(int,float)):
+        cls = "pos" if v>0 else "neg" if v<0 else ""
+        if "percent" in lk:
+            return f'<span class="{cls}">{v:.2f}%</span>'
+        if any(x in lk for x in ["marketcap","revenue","income","value","cap","enterprise"]):
+            return f'<span class="{cls}">₹{human_number(v)}</span>'
+        return f'<span class="{cls}">{human_number(v)}</span>'
+    return v
+
+# ==============================
+# HTML Helpers
+# ==============================
+def column_layout(html):
+    return f"""
+    <style>
+        .grid{{display:grid;gap:10px;grid-template-columns:repeat(3,1fr);}}
+        @media(max-width:1024px){{.grid{{grid-template-columns:repeat(2,1fr);}}}}
+        @media(max-width:640px){{.grid{{grid-template-columns:1fr;}}}}
+        .pos{{color:#0a7d32;font-weight:600;}}
+        .neg{{color:#b00020;font-weight:600;}}
+    </style>
+    <div class="grid">{html}</div>
+    """
+def html_card(title,body,mini=False):
+    font = "12px" if mini else "14px"
+    pad  = "6px" if mini else "10px"
+    return f"""
+    <div style="background:#e6f0fa;border:1px solid #a3c0e0;border-radius:8px;padding:{pad};
+                font-size:{font};margin-bottom:6px;">
+        <div style="font-weight:600;margin-bottom:6px;">{title}</div>{body}
+    </div>
+    """
+def make_table(df):
+    return "".join(
+        f"""<div style="display:flex;justify-content:space-between;border-bottom:1px dashed #bcd0ea;padding:2px 0;">
+            <span>{r.Field}</span><span>{r.Value}</span></div>"""
+        for r in df.itertuples()
+    )
+def collapsible(title,body):
+    return f"""
+    <details open>
+        <summary style="cursor:pointer;font-weight:600;font-size:15px;padding:6px 0;">
+            {title}
+        </summary>{body}
+    </details>
+    """
+
+# ==============================
+# Data Helpers
+# ==============================
+def build_df_from_dict(data):
+    rows = [(SHORT_NAMES.get(k,k[:16]), format_value(k,v)) for k,v in data.items() if k not in NOISE_KEYS]
+    return pd.DataFrame(rows,columns=["Field","Value"])
+
+def resolve_duplicates(data):
+    DUP = {
+        "price":["regularMarketPrice","currentPrice"],
+        "prev":["regularMarketPreviousClose","previousClose"],
+        "open":["regularMarketOpen","open"],
+        "high":["regularMarketDayHigh","dayHigh"],
+        "low":["regularMarketDayLow","dayLow"],
+        "volume":["regularMarketVolume","volume"]
+    }
+    resolved, used = {}, set()
+    for keys in DUP.values():
+        for k in keys:
+            if k in data:
+                resolved[k] = data[k]
+                used.update(keys)
+                break
+    for k,v in data.items():
+        if k not in used:
+            resolved[k] = v
+    return resolved
+
+def classify(k,v):
+    lk = k.lower()
+    if k=="companyOfficers": return "management"
+    if any(x in lk for x in ["pe","pb","roe","roa","margin","debt","revenue","profit","eps","cap"]):
+        return "fundamental"
+    if isinstance(v,(int,float)): return "price_volume"
+    if isinstance(v,str) and len(v)>80: return "long_text"
+    return "profile"
+
+def group_info(info):
+    g = {"price_volume":{}, "fundamental":{}, "profile":{}, "management":{}, "long_text":{}}
+    for k,v in info.items():
+        if k in NOISE_KEYS or v in [None,"",[],{}]: continue
+        g[classify(k,v)][k] = v
+    return g
+
+def split_df(df):
+    n = len(df)
+    cols = 1 if n<=6 else 2 if n<=14 else 3
+    size = (n+cols-1)//cols
+    return [df.iloc[i:i+size] for i in range(0,n,size)]
+
+# ==============================
+# Derived Metrics
+# ==============================
+def build_price_volume_derived(info):
+    out={}
+    price=info.get("regularMarketPrice")
+    dma50=info.get("fiftyDayAverage")
+    dma200=info.get("twoHundredDayAverage")
+    low52=info.get("fiftyTwoWeekLow")
+    high52=info.get("fiftyTwoWeekHigh")
+    if price and dma50: out["vs 50DMA"]="Above ↑" if price>dma50 else "Below ↓"
+    if price and dma200: out["vs 200DMA"]="Above ↑" if price>dma200 else "Below ↓"
+    if price and low52 and high52 and high52!=low52: out["52W Pos"]=f"{(price-low52)/(high52-low52)*100:.1f}%"
+    return out
+
+def build_smart_signals(info):
+    rows=[]
+    pe=info.get("trailingPE")
+    roe=info.get("returnOnEquity")
+    debt=info.get("debtToEquity")
+    price=info.get("regularMarketPrice")
+    dma50=info.get("fiftyDayAverage")
+    dma200=info.get("twoHundredDayAverage")
+    if pe: rows.append(("Valuation","Expensive" if pe>35 else "Cheap" if pe<15 else "Fair"))
+    if roe: rows.append(("Quality","High" if roe>0.18 else "Average"))
+    if debt: rows.append(("Balance Sheet","Weak" if debt>1 else "Healthy"))
+    if price and dma50 and dma200:
+        trend = "Bullish" if price>dma50>dma200 else "Bearish" if price<dma50<dma200 else "Neutral"
+        rows.append(("Momentum",trend))
+    return pd.DataFrame(rows,columns=["Field","Value"])
+
+# ==============================
+# Build Price/Volume Section
+# ==============================
+def build_price_volume_section(info,pv_data):
+    df=build_df_from_dict(pv_data)
+    derived=build_price_volume_derived(info)
+    if derived: df=pd.concat([df,pd.DataFrame(derived.items(),columns=["Field","Value"])],ignore_index=True)
+    cards=""
+    for title,fields in PRICE_VOLUME_GROUPS.items():
+        sub=df[df["Field"].isin(fields)]
+        if not sub.empty: cards+=html_card(title,make_table(sub),mini=True)
+    trend_df=df[df["Field"].isin(["vs 50DMA","vs 200DMA","52W Pos"])]
+    if not trend_df.empty: cards+=html_card("Trend & Momentum",make_table(trend_df),mini=True)
+    signals=build_smart_signals(info)
+    if not signals.empty: cards+=html_card("Smart Signals",make_table(signals),mini=True)
+    return column_layout(cards)
+
+# ==============================
+# Main Function
+# ==============================
+def fetch_info(symbol):
+    key=f"info_{symbol}"
+    if exists(key,"html"):
+        cached=load(key,"html")
+        if cached: return cached
+    try:
+        info=yfinfo(symbol)
+        if "__error__" in info: return "No data"
+        groups=group_info(info)
+        html=""
+        # Price / Volume
+        pv=resolve_duplicates(groups["price_volume"])
+        if pv: html+=html_card(f"{MAIN_ICONS['Price / Volume']} Price / Volume",build_price_volume_section(info,pv),shade=0)
+        # Fundamentals
+        if groups["fundamental"]:
+            df=build_df_from_dict(groups["fundamental"])
+            html+=html_card(f"{MAIN_ICONS['Fundamentals']} Fundamentals",
+                            column_layout("".join(html_card("Fundamentals",make_table(c),mini=True) for c in split_df(df))),
+                            shade=1)
+        # Company Profile
+        if groups["profile"]:
+            df=build_df_from_dict(groups["profile"])
+            html+=html_card(f"{MAIN_ICONS['Company Profile']} Company Profile",
+                            column_layout("".join(html_card("Profile",make_table(c),mini=True) for c in split_df(df))),
+                            shade=2)
+        # Management
+        if groups["management"].get("companyOfficers"):
+            cards=""
+            for o in groups["management"]["companyOfficers"]:
+                cards+=html_card(o.get("name",""),o.get("title",""),mini=True)
+            html+=html_card(f"{MAIN_ICONS['Management']} Management",column_layout(cards),shade=2)
+        # Long Text
+        for k,v in groups["long_text"].items():
+            html+=html_card(SHORT_NAMES.get(k,k[:16]),v)
+        save(key,html,"html")
+        return html
+    except Exception:
+        return f"<pre>{traceback.format_exc()}</pre>"