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)