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)}
+
+# ==============================
+# Icons
+# ==============================
+SUBGROUP_ICONS = {
+    "Live Price": "💹",
+    "Volume": "📊",
+    "Moving Avg": "📈",
+    "Range / Vol": "📉",
+    "Bid / Analyst": "📝",
+    "Other": "ℹ️"
+}
+
+MAIN_ICONS = {
+    "Price / Volume": "📈",
+    "Fundamentals": "📊",
+    "Company Profile": "🏢"
+}
+
+# ==============================
+# Column layout wrapper
+# ==============================
+def column_layout(html, min_width=260):
+    return f"""
+    <div style="
+        display:grid;
+        grid-template-columns:repeat(auto-fit,minmax({min_width}px,1fr));
+        gap:8px;
+        align-items:start;
+    ">
+        {html}
+    </div>
+    """
+
+# ==============================
+# Card renderer
+# ==============================
+def html_card(title, body, mini=False, shade=0):
+    font = "12px" if mini else "14px"
+    pad  = "6px" if mini else "10px"
+
+    shades = ["#e6f0fa", "#d7e3f5", "#c8d6f0"]
+    grads = [
+        "linear-gradient(to right,#1a4f8a,#4a7ac7)",
+        "linear-gradient(to right,#1f5595,#5584d6)",
+        "linear-gradient(to right,#205ca0,#6192e0)"
+    ]
+
+    return f"""
+    <div style="
+        background:{shades[shade%3]};
+        border:1px solid #a3c0e0;
+        border-radius:8px;
+        padding:{pad};
+        font-size:{font};
+        box-shadow:0 2px 6px rgba(0,0,0,.08);
+    ">
+        <div style="
+            background:{grads[shade%3]};
+            color:white;
+            padding:4px 8px;
+            border-radius:6px;
+            font-weight:600;
+            margin-bottom:6px;
+        ">
+            {title}
+        </div>
+        {body}
+    </div>
+    """
+
+# ==============================
+# Number formatting
+# ==============================
+def format_number(x):
+    try:
+        x = float(x)
+        if abs(x) >= 100:
+            return f"{x:,.0f}"
+        if abs(x) >= 1:
+            return f"{x:,.2f}"
+        return f"{x:.4f}"
+    except:
+        return str(x)
+
+# ==============================
+# Compact inline table (Field : Value)
+# ==============================
+def make_table(df):
+    rows = ""
+    for _, r in df.iterrows():
+        val = r[1]
+        color = "#0d1f3c"
+
+        # Highlight change values
+        if any(x in r[0].lower() for x in ["chg", "%"]):
+            try:
+                color = "#0a7d32" if float(val) >= 0 else "#b00020"
+            except:
+                pass
+
+        rows += f"""
+        <div style="
+            display:flex;
+            justify-content:space-between;
+            gap:6px;
+            padding:2px 0;
+            border-bottom:1px dashed #bcd0ea;
+        ">
+            <span style="color:#1a4f8a;font-weight:500;white-space:nowrap;">
+                {r[0]}
+            </span>
+            <span style="
+                color:{color};
+                font-weight:600;
+                background:#f1f6ff;
+                padding:1px 6px;
+                border-radius:4px;
+                white-space:nowrap;
+            ">
+                {val}
+            </span>
+        </div>
+        """
+
+    return f"<div>{rows}</div>"
+
+# ==============================
+# Noise keys
+# ==============================
+NOISE_KEYS = {
+    "maxAge","priceHint","triggerable",
+    "customPriceAlertConfidence",
+    "sourceInterval","exchangeDataDelayedBy",
+    "esgPopulated"
+}
+
+def is_noise(k): 
+    return k in NOISE_KEYS
+
+# ==============================
+# Duplicate resolution
+# ==============================
+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.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
+
+# ==============================
+# Short 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","twoHundredDayAverage":"200DMA",
+    "fiftyTwoWeekLow":"52WL","fiftyTwoWeekHigh":"52WH",
+    "beta":"Beta","targetMeanPrice":"Target"
+}
+
+def pretty_key(k):
+    return SHORT_NAMES.get(k, k[:12])
+
+# ==============================
+# Classifiers
+# ==============================
+def classify_price_volume_subgroup(key):
+    k = key.lower()
+    if "volume" in k: return "Volume"
+    if "average" in k or "dma" in k: return "Moving Avg"
+    if "week" in k or "beta" in k: return "Range / Vol"
+    if "target" in k or "recommend" in k: return "Bid / Analyst"
+    return "Live Price"
+
+def classify_key(key, value):
+    k = key.lower()
+    if isinstance(value,(int,float)) and any(x in k for x in [
+        "price","volume","avg","change","percent","market","week","beta","target"
+    ]):
+        return "price_volume"
+    if any(x in k for x in [
+        "revenue","income","profit","margin","pe","pb","roe","roa","debt","equity"
+    ]):
+        return "fundamental"
+    if isinstance(value,str) and len(value)>80:
+        return "long_text"
+    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
+        groups[classify_key(k,v)][k] = v
+    return groups
+
+# ==============================
+# DataFrame builder
+# ==============================
+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)
+        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 "No data"
+
+        groups = build_grouped_info(info)
+        html = ""
+
+        # -------- PRICE / VOLUME --------
+        pv = resolve_duplicates(groups["price_volume"])
+        sub = {}
+        for k,v in pv.items():
+            sg = classify_price_volume_subgroup(k)
+            sub.setdefault(sg,{})[k] = v
+
+        cards = ""
+        for i,(t,d) in enumerate(sub.items()):
+            df = build_df_from_dict(d)
+            if not df.empty:
+                cards += html_card(
+                    f"{SUBGROUP_ICONS.get(t,'ℹ️')} {t}",
+                    make_table(df),
+                    mini=True,
+                    shade=i
+                )
+
+        if cards:
+            html += html_card(
+                f"{MAIN_ICONS['Price / Volume']} Price / Volume",
+                column_layout(cards),
+                shade=0
+            )
+
+        # -------- FUNDAMENTALS --------
+        if groups["fundamental"]:
+            html += html_card(
+                f"{MAIN_ICONS['Fundamentals']} Fundamentals",
+                make_table(build_df_from_dict(groups["fundamental"])),
+                shade=1
+            )
+
+        # -------- PROFILE --------
+        if groups["profile"]:
+            html += html_card(
+                f"{MAIN_ICONS['Company Profile']} Company Profile",
+                make_table(build_df_from_dict(groups["profile"])),
+                shade=2
+            )
+
+        # -------- LONG TEXT --------
+        for k,v in groups["long_text"].items():
+            html += html_card(pretty_key(k), v, shade=2)
+
+        return html
+
+    except Exception as e:
+        return f"<pre>{traceback.format_exc()}</pre>"