Delete yahooinfo3.py

yahooinfo3.py

@@ -1,351 +0,0 @@
-# ==============================
-# 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>")