Create market_overview.py

market_overview.py

@@ -0,0 +1,58 @@
+import yfinance as yf
+import pandas as pd
+
+
+def get_nifty_trend():
+
+    df = yf.download("^NSEI", period="3mo", interval="1d", progress=False)
+
+    close = df["Close"].squeeze()
+
+    ma20 = close.rolling(20).mean().iloc[-1]
+    price = close.iloc[-1]
+
+    if price > ma20:
+        return "Bullish"
+    else:
+        return "Bearish"
+
+
+def compute_sector_strength(data):
+
+    sector_map = {
+        "BANKING": ["HDFCBANK.NS","ICICIBANK.NS","SBIN.NS","AXISBANK.NS","KOTAKBANK.NS"],
+        "IT": ["TCS.NS","INFY.NS","WIPRO.NS"],
+        "FMCG": ["ITC.NS","HINDUNILVR.NS","NESTLEIND.NS"],
+        "AUTO": ["MARUTI.NS","TATAMOTORS.NS"],
+        "PHARMA": ["SUNPHARMA.NS","DRREDDY.NS"],
+        "INFRA": ["LT.NS","ULTRACEMCO.NS"]
+    }
+
+    sector_scores = {}
+
+    for sector, stocks in sector_map.items():
+
+        returns = []
+
+        for ticker in stocks:
+
+            if ticker not in data:
+                continue
+
+            df = data[ticker]
+
+            close = df["Close"].squeeze()
+
+            if len(close) < 10:
+                continue
+
+            ret = close.pct_change().iloc[-5:].mean()
+
+            returns.append(ret)
+
+        if len(returns) == 0:
+            sector_scores[sector] = 0
+        else:
+            sector_scores[sector] = round(sum(returns) / len(returns) * 100,2)
+
+    return sector_scores