diff --git a/.gitignore b/.gitignore
new file mode 100644
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--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,4 @@
+.env
+__pycache__/
+.venv
+data/
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diff --git a/README.md b/README.md
index fdc3cc6..e0c33d0 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,12 @@
-# stock-finance-analyzer
+# Stock Finance Analyzer
 
-A small Python finance project that fetches historical stock prices from Alpaca and calculates some indicators.
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+A small Python finance project that fetches historical stock prices from Alpaca and calculates:
+
+- highest close price
+- lowest close price
+- average close price
+- daily returns
+- average return
+- 3-day moving average
+
+This project was built as part of my Python/pandas finance learning path.
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diff --git a/requirements.txt b/requirements.txt
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--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,3 @@
+pandas
+python-dotenv
+alpaca-py
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diff --git a/stock_analysis.py b/stock_analysis.py
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index 0000000..32375fd
--- /dev/null
+++ b/stock_analysis.py
@@ -0,0 +1,58 @@
+from datetime import datetime
+import os
+
+import pandas as pd
+from dotenv import load_dotenv
+
+from alpaca.data.historical import StockHistoricalDataClient
+from alpaca.data.requests import StockBarsRequest
+from alpaca.data.timeframe import TimeFrame
+
+
+load_dotenv()
+
+API_KEY = os.getenv("ALPACA_API_KEY")
+SECRET_KEY = os.getenv("ALPACA_SECRET_KEY")
+
+
+def fetch_stock_data(symbol, start_date, end_date):
+    client = StockHistoricalDataClient(API_KEY, SECRET_KEY)
+
+    request = StockBarsRequest(
+        symbol_or_symbols=symbol,
+        timeframe=TimeFrame.Day,
+        start=datetime.fromisoformat(start_date),
+        end=datetime.fromisoformat(end_date),
+    )
+
+    bars = client.get_stock_bars(request)
+    df = bars.df.reset_index()
+
+    return df
+
+
+def analyze_stock(df):
+    df["Return"] = df["close"].pct_change()
+    df["MA_3"] = df["close"].rolling(window=3).mean()
+
+    print("\nFirst rows:")
+    print(df.head())
+
+    print("\nStock analysis:")
+    print(f"Highest price: {df['close'].max():.2f}")
+    print(f"Lowest price: {df['close'].min():.2f}")
+    print(f"Average price: {df['close'].mean():.2f}")
+    print(f"Average return: {df['Return'].mean():.4f}")
+
+    print("\nClose, Return, MA_3:")
+    print(df[["timestamp", "close", "Return", "MA_3"]])
+
+
+if __name__ == "__main__":
+    symbol = input("Enter stock symbol, for example AAPL: ").upper()
+
+    start_date = input("Start date YYYY-MM-DD: ")
+    end_date = input("End date YYYY-MM-DD: ")
+
+    df = fetch_stock_data(symbol, start_date, end_date)
+    analyze_stock(df)
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