pip install alpaca-trade-api pandas numpy matplotlib

APCA_API_KEY_ID=your_key_here
APCA_API_SECRET_KEY=your_secret_here
APCA_API_BASE_URL=https://paper-api.alpaca.markets

import alpaca_trade_api as tradeapi
import pandas as pd
import os
from datetime import datetime, timedelta

# Initialize the API client
api = tradeapi.REST(
    os.getenv('APCA_API_KEY_ID'),
    os.getenv('APCA_API_SECRET_KEY'),
    os.getenv('APCA_API_BASE_URL', 'https://paper-api.alpaca.markets'),
    api_version='v2'
)

# Verify connection
account = api.get_account()
print(f"Account status: {account.status}")
print(f"Buying power: ${float(account.buying_power):,.2f}")
print(f"Portfolio value: ${float(account.portfolio_value):,.2f}")

def get_historical_bars(symbol: str, days: int = 365) -> pd.DataFrame:
    """Fetch daily bars for backtesting."""
    end = datetime.now()
    start = end - timedelta(days=days)

    bars = api.get_bars(
        symbol,
        tradeapi.TimeFrame.Day,
        start=start.strftime('%Y-%m-%d'),
        end=end.strftime('%Y-%m-%d'),
        limit=None
    ).df

    bars.index = bars.index.tz_localize(None)
    return bars

# Example: Get 1 year of AAPL data
df = get_historical_bars('AAPL', 365)
print(f"Loaded {len(df)} bars for AAPL")
print(df.tail())

def backtest_sma_crossover(df: pd.DataFrame, fast: int = 10, slow: int = 30) -> dict:
    """
    SMA crossover backtest.
    Buy when fast SMA crosses above slow SMA.
    Sell when fast SMA crosses below slow SMA.
    """
    df = df.copy()
    df['sma_fast'] = df['close'].rolling(fast).mean()
    df['sma_slow'] = df['close'].rolling(slow).mean()
    df['signal'] = 0

    # Generate signals
    df.loc[df['sma_fast'] > df['sma_slow'], 'signal'] = 1
    df.loc[df['sma_fast'] <= df['sma_slow'], 'signal'] = -1

    # Calculate returns
    df['position'] = df['signal'].shift(1)  # avoid look-ahead bias
    df['daily_return'] = df['close'].pct_change()
    df['strategy_return'] = df['position'] * df['daily_return']

    # Performance metrics
    total_return = (1 + df['strategy_return'].dropna()).prod() - 1
    sharpe = df['strategy_return'].mean() / df['strategy_return'].std() * (252 ** 0.5)
    max_dd = (df['strategy_return'].cumsum() - df['strategy_return'].cumsum().cummax()).min()

    trades = (df['position'].diff().abs() > 0).sum()

    return {
        'total_return': round(total_return * 100, 2),
        'sharpe_ratio': round(sharpe, 2),
        'max_drawdown': round(max_dd * 100, 2),
        'total_trades': int(trades),
        'win_rate': round(
            (df[df['strategy_return'] > 0].shape[0] /
             df[df['strategy_return'] != 0].shape[0]) * 100, 1
        )
    }

results = backtest_sma_crossover(df, fast=10, slow=30)
print(f"Return: {results['total_return']}%")
print(f"Sharpe: {results['sharpe_ratio']}")
print(f"Max DD: {results['max_drawdown']}%")
print(f"Trades: {results['total_trades']}")

def place_order(symbol: str, qty: int, side: str = 'buy'):
    """Place a market order on Alpaca."""
    try:
        order = api.submit_order(
            symbol=symbol,
            qty=qty,
            side=side,
            type='market',
            time_in_force='day'
        )
        print(f"Order placed: {side} {qty} {symbol} (ID: {order.id})")
        return order
    except Exception as e:
        print(f"Order failed: {e}")
        return None

# Example: Buy 10 shares of AAPL
# place_order('AAPL', 10, 'buy')