Create yahooinfo.py

yahooinfo.py

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+# ==============================
+# Imports
+# ==============================
+import yfinance as yf
+import pandas as pd
+import traceback
+
+# ==============================
+# Yahoo Finance info fetch
+# ==============================
+def yfinfo(symbol):
+    try:
+        t = yf.Ticker(symbol+".NS")
+        info = t.info
+        if not info or not isinstance(info, dict):
+            return {}
+        return info
+    except Exception as e:
+        return {"__error__": str(e)}
+
+# ==============================
+# Subgroup icons
+# ==============================
+SUBGROUP_ICONS = {
+    "Live Price": "💹",
+    "Volume": "📊",
+    "Moving Avg": "📈",
+    "Range / Vol": "📉",
+    "Bid / Analyst": "📝",
+    "Other": "ℹ️"
+}
+
+# Main section icons
+MAIN_ICONS = {
+    "Price / Volume": "📈",
+    "Fundamentals": "📊",
+    "Company Profile": "🏢"
+}
+
+# ==============================
+# HTML card renderer
+# ==============================
+def html_card(title, body, mini=False, shade=0):
+    font = "12px" if mini else "14px"
+    pad  = "6px" if mini else "10px"
+
+    # Card background shades
+    shades = ["#e6f0fa", "#d7e3f5", "#c8d6f0", "#b9c9eb"]
+    bg = shades[shade % len(shades)]
+
+    # Gradient headers for background
+    header_gradients = [
+        "linear-gradient(to right, #1a4f8a, #4a7ac7)",
+        "linear-gradient(to right, #1f5595, #5584d6)",
+        "linear-gradient(to right, #205ca0, #6192e0)",
+        "linear-gradient(to right, #2360ab, #6ba0e5)"
+    ]
+    header_bg = header_gradients[shade % len(header_gradients)]
+
+    return f"""
+    <div style="
+        background:{bg};
+        border:1px solid #a3c0e0;
+        border-radius:8px;
+        padding:{pad};
+        margin:6px 0;
+        color:#0d1f3c;
+        font-size:{font};
+        box-shadow: 0 2px 6px rgba(0,0,0,0.1);
+        transition: transform 0.1s ease, box-shadow 0.2s ease;
+    " onmouseover="this.style.transform='scale(1.02)';this.style.boxShadow='0 4px 12px rgba(0,0,0,0.15)';" 
+      onmouseout="this.style.transform='scale(1)';this.style.boxShadow='0 2px 6px rgba(0,0,0,0.1)';">
+        <div style="
+            font-weight:600;
+            background:{header_bg};
+            color:white;
+            padding:4px 8px;
+            border-radius:6px 6px 0 0;
+            margin:-{pad}px -{pad}px {pad}px -{pad}px;
+        ">
+            {title}
+        </div>
+        <div>{body}</div>
+    </div>
+    """
+
+# ==============================
+# DataFrame → HTML table
+# ==============================
+def make_table(df, compact=False):
+    if df is None or df.empty:
+        return "<i>No data</i>"
+
+    font = "11px" if compact else "13px"
+    pad  = "4px 8px" if compact else "6px 10px"
+
+    th = "".join(
+        f"<th style='padding:{pad};border-bottom:2px solid #a3c0e0;text-align:left;color:#1a4f8a;'>"
+        f"{c}</th>"
+        for c in df.columns
+    )
+
+    rows = ""
+    for i, r in df.iterrows():
+        bg = "#f5f9ff" if i%2==0 else "#e6f0fa"  # alternate row colors
+        tds = "".join(
+            f"<td style='padding:{pad};border-bottom:1px solid #c0d4ee;background:{bg}'>{v}</td>"
+            for v in r
+        )
+        rows += f"<tr>{tds}</tr>"
+
+    return f"""
+    <table style="
+        width:100%;
+        border-collapse:collapse;
+        font-size:{font};
+        color:#0d1f3c;
+    ">
+        <thead style='background:#c0d4ee'>{th}</thead>
+        <tbody>{rows}</tbody>
+    </table>
+    """
+
+# ==============================
+# Number formatting
+# ==============================
+def format_number(x):
+    try:
+        if x is None:
+            return "-"
+        x = float(x)
+        if abs(x) >= 100:
+            return f"{x:,.0f}"
+        if abs(x) >= 1:
+            return f"{x:,.2f}"
+        return f"{x:.4f}"
+    except Exception:
+        return str(x)
+
+def format_large_number(x):
+    try:
+        x = float(x)
+        for u in ["", "K", "M", "B", "T"]:
+            if abs(x) < 1000:
+                return f"{x:.2f}{u}"
+            x /= 1000
+        return f"{x:.2f}P"
+    except Exception:
+        return str(x)
+
+# ==============================
+# HTML error block
+# ==============================
+def html_error(msg):
+    return f"""
+    <div style="
+        background:#fdecea;
+        color:#a00;
+        border:1px solid #f5c0c0;
+        border-radius:8px;
+        padding:10px;
+        font-weight:600;
+        box-shadow: 0 1px 4px rgba(0,0,0,0.05);
+    ">
+        ❌ {msg}
+    </div>
+    """
+
+# ==============================
+# Noise keys
+# ==============================
+NOISE_KEYS = {
+    "maxAge", "priceHint", "triggerable",
+    "customPriceAlertConfidence",
+    "sourceInterval", "exchangeDataDelayedBy",
+    "esgPopulated"
+}
+def is_noise(k):
+    return k in NOISE_KEYS
+
+# ==============================
+# Duplicate resolution priority
+# ==============================
+DUPLICATE_PRIORITY = {
+    "price": ["regularMarketPrice", "currentPrice"],
+    "prev": ["regularMarketPreviousClose", "previousClose"],
+    "open": ["regularMarketOpen", "open"],
+    "high": ["regularMarketDayHigh", "dayHigh"],
+    "low": ["regularMarketDayLow", "dayLow"],
+    "volume": ["regularMarketVolume", "volume"],
+}
+def resolve_duplicates(data):
+    resolved = {}
+    used = set()
+    for _, keys in DUPLICATE_PRIORITY.items():
+        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
+
+# ==============================
+# Short display names
+# ==============================
+SHORT_NAMES = {
+    "regularMarketPrice": "Price",
+    "regularMarketChange": "Chg",
+    "regularMarketChangePercent": "Chg%",
+    "regularMarketPreviousClose": "Prev",
+    "regularMarketOpen": "Open",
+    "regularMarketDayHigh": "High",
+    "regularMarketDayLow": "Low",
+    "regularMarketVolume": "Vol",
+    "averageDailyVolume10Day": "AvgV10",
+    "averageDailyVolume3Month": "AvgV3M",
+    "fiftyDayAverage": "50DMA",
+    "fiftyDayAverageChangePercent": "50DMA%",
+    "twoHundredDayAverage": "200DMA",
+    "twoHundredDayAverageChangePercent": "200DMA%",
+    "fiftyTwoWeekLow": "52WL",
+    "fiftyTwoWeekHigh": "52WH",
+    "fiftyTwoWeekRange": "52WR",
+    "beta": "Beta",
+    "targetHighPrice": "TgtH",
+    "targetLowPrice": "TgtL",
+    "targetMeanPrice": "Tgt",
+    "recommendationMean": "Reco",
+}
+def pretty_key(k):
+    return SHORT_NAMES.get(k, k[:12])
+
+# ==============================
+# Subgroup classifier
+# ==============================
+def classify_price_volume_subgroup(key):
+    k = key.lower()
+    if any(x in k for x in ["price", "open", "close", "change", "day"]):
+        return "Live Price"
+    if "volume" in k:
+        return "Volume"
+    if "average" in k or "fiftyday" in k or "twohundredday" in k:
+        return "Moving Avg"
+    if any(x in k for x in ["week", "range", "high", "low", "alltime", "beta"]):
+        return "Range / Vol"
+    if any(x in k for x in ["bid", "ask", "target", "recommendation", "analyst"]):
+        return "Bid / Analyst"
+    return "Other"
+
+def build_price_volume_subgroups(data):
+    sub = {}
+    for k, v in data.items():
+        sg = classify_price_volume_subgroup(k)
+        sub.setdefault(sg, {})[k] = v
+    return sub
+
+# ==============================
+# Main key classifier
+# ==============================
+def classify_key(key, value):
+    k = key.lower()
+    if isinstance(value, str) and len(value) > 80:
+        return "long_text"
+    if isinstance(value, (int, float)) and any(x in k for x in [
+        "price", "volume", "avg", "average", "change",
+        "percent", "market", "day", "week", "bid",
+        "ask", "beta", "target", "recommendation"
+    ]):
+        return "price_volume"
+    if any(x in k for x in [
+        "revenue", "income", "earnings", "profit",
+        "margin", "pe", "pb", "roe", "roa",
+        "cash", "debt", "equity", "dividend",
+        "ebitda", "growth", "ratio", "shares"
+    ]):
+        return "fundamental"
+    return "profile"
+
+# ==============================
+# Group builder
+# ==============================
+def build_grouped_info(info):
+    groups = {"price_volume": {}, "fundamental": {}, "profile": {}, "long_text": {}}
+    for k, v in info.items():
+        if v in [None, "", [], {}]:
+            continue
+        grp = classify_key(k, v)
+        groups[grp][k] = v
+    return groups
+
+# ==============================
+# Build DataFrame
+# ==============================
+def build_df_from_dict(data):
+    rows = []
+    for k, v in data.items():
+        if is_noise(k):
+            continue
+        if isinstance(v, (int, float)):
+            v = format_number(v)
+        elif isinstance(v, list):
+            v = ", ".join(map(str, v[:5]))
+        rows.append([pretty_key(k), v])
+    return pd.DataFrame(rows, columns=["Field", "Value"])
+
+# ==============================
+# MAIN FUNCTION
+# ==============================
+def fetch_info(symbol):
+    try:
+        info = yfinfo(symbol)
+        if not info:
+            return html_error(f"No information found for {symbol}")
+
+        groups = build_grouped_info(info)
+        final_html = ""
+
+        # ---------------- PRICE / VOLUME ----------------
+        price_data = groups["price_volume"]
+        price_data = resolve_duplicates(price_data)
+        price_subgroups = build_price_volume_subgroups(price_data)
+        price_html = ""
+        for i, (title, data) in enumerate(price_subgroups.items()):
+            df = build_df_from_dict(data)
+            if not df.empty:
+                icon = SUBGROUP_ICONS.get(title, "ℹ️")
+                price_html += html_card(f"{icon} {title}", make_table(df, compact=True), mini=True, shade=i)
+        if price_html:
+            final_html += html_card(f"{MAIN_ICONS['Price / Volume']} Price / Volume", price_html, shade=0)
+
+        # ---------------- FUNDAMENTALS ----------------
+        if groups["fundamental"]:
+            df = build_df_from_dict(groups["fundamental"])
+            final_html += html_card(f"{MAIN_ICONS['Fundamentals']} Fundamentals", make_table(df, compact=True), shade=1)
+
+        # ---------------- PROFILE ----------------
+        if groups["profile"]:
+            df = build_df_from_dict(groups["profile"])
+            final_html += html_card(f"{MAIN_ICONS['Company Profile']} Company Profile", make_table(df, compact=True), shade=2)
+
+        # ---------------- LONG TEXT ----------------
+        for i, (k, v) in enumerate(groups["long_text"].items()):
+            final_html += html_card(pretty_key(k), f"<div class='long-text'>{v}</div>", shade=3)
+
+        return final_html
+
+    except Exception as e:
+        return html_error(f"INFO ERROR: {e}<br><pre>{traceback.format_exc()}</pre>")