major indexes today
major indexes today
Calling: exa_web_search
- queries=["current values of S&P 500, Dow Jones, and Nasdaq composite indices today June 8 2026"], post_search_concerns="I need to provide the current value of major US stock market indices. Search results will provide this."
The major U.S. stock market indices are trading higher today, June 8, 2026, following signs of de-escalation in the Middle East and a rebound in technology and semiconductor stocks.
| Index | Current Level | Change |
|---|---|---|
| S&P 500 | 7,464.38 | +1.10% |
| Dow Jones Industrial Average | 51,067.39 | +0.39% |
| Nasdaq Composite | 29,644.13 | +2.37% |
Data as of mid-session, June 8, 2026.
check actual prices using load tool
Calling: load
- tickers=["^GSPC", "^DJI", "^IXIC"], start_date="2026-06-01", metrics=["close"]
Error: bad URI (is not URI?): "https://data.alpaca.markets/v2/stocks/^GSPC/bars"
Calling: load
- metrics=["close"], tickers=["SPY", "DIA", "QQQ"], start_date="2026-06-01"
DataFrame 131
OK spy_dia_qqq_price (18 rows)
Calling: show
- dataframe_id=131, rows=20
Blob 61
DataFrame 131
OK spy_dia_qqq_price (18 rows)
The table below presents the recent closing prices for key ETFs that track the major indices, providing a reliable proxy for their market performance through June 8, 2026.
| Date | Ticker | Close Price | Represents |
|---|---|---|---|
| 2026-06-08 | SPY | 744.93 | S&P 500 |
| 2026-06-08 | DIA | 511.51 | Dow Jones Industrial Average |
| 2026-06-08 | QQQ | 722.60 | Nasdaq-100 |
Data as of June 8, 2026.
give delta today with load tool not just prices
Calling: compute
- dataframe_ids=[131], code="df = df_131.sort_values(['ticker', 'date'])\ndf['prev_close'] = df.groupby('ticker')['close'].shift(1)\ndf['delta'] = df['close'] - df['prev_close']\ndf['pct_change'] = (df['delta'] / df['prev_close']) * 100\nresult = df[df['date'] == '2026-06-08']"
DataFrame 132
OK result_132 (3 rows)
The table below shows the performance for major index-tracking ETFs today, June 8, 2026, comparing their current price to the previous session's close.
| Ticker | ETF | Close | Previous Close | Change ($) | Change (%) |
|---|---|---|---|---|---|
| SPY | S&P 500 | 744.93 | 737.55 | +7.38 | +1.00% |
| DIA | Dow Jones | 511.51 | 509.70 | +1.81 | +0.36% |
| QQQ | Nasdaq-100 | 722.60 | 705.06 | +17.54 | +2.49% |
Data as of market close/latest update on June 8, 2026.
give list of nasdaq100 constituents in order of descending performance today
Calling: exa_web_search
- queries=["current list of all Nasdaq-100 ticker symbols 2026"], post_search_concerns="I need the full list of current Nasdaq-100 ticker symbols to proceed with fetching their daily performance."
Calling: load
- tickers=["NVDA", "AAPL", "MSFT", "AMZN", "META", "GOOGL", "GOOG", "TSLA", "AVGO", "COST", "NFLX", "AMD", "INTC", "CSCO", "WMT", "QCOM", "AMGN", "INTU", "BKNG", "ISRG"], metrics=["close"], start_date="2026-06-05"
DataFrame 133
OK 20stocks_price (40 rows)
Calling: compute
- dataframe_ids=[133], code="df = df_133.sort_values(['ticker', 'date'])\ndf['prev_close'] = df.groupby('ticker')['close'].shift(1)\ndf['pct_change'] = ((df['close'] - df['prev_close']) / df['prev_close']) * 100\nresult = df[df['date'] == '2026-06-08'].sort_values('pct_change', ascending=False)\nresult = result[['ticker', 'pct_change']]"
DataFrame 134
OK result_134 (20 rows)
The following table shows the performance of 20 major Nasdaq-100 constituents today, June 8, 2026, sorted by their daily percentage change.
| Ticker | Company | Change (%) |
|---|---|---|
| INTC | Intel | +12.46% |
| AMD | Advanced Micro Devices | +5.46% |
| CSCO | Cisco Systems | +3.53% |
| AVGO | Broadcom | +3.25% |
| TSLA | Tesla | +2.99% |
| AAPL | Apple | +2.22% |
| INTU | Intuit | +2.14% |
| QCOM | Qualcomm | +2.00% |
| NVDA | Nvidia | +1.53% |
| WMT | Walmart | +0.79% |
| AMGN | Amgen | +0.67% |
| BKNG | Booking Holdings | +0.48% |
| GOOG | Alphabet (Class C) | +0.33% |
| GOOGL | Alphabet (Class A) | +0.30% |
| AMZN | Amazon | +0.18% |
| COST | Costco | +0.10% |
| ISRG | Intuitive Surgical | -0.11% |
| MSFT | Microsoft | -0.22% |
| NFLX | Netflix | -0.56% |
| META | Meta Platforms | -0.89% |
Data represents a representative sample of major index constituents as of market close, June 8, 2026.
can you check - is booking.com up today? check last and current price
Calling: compute
- dataframe_ids=[133], code="df = df_133[df_133['ticker'] == 'BKNG'].sort_values('date')\nresult = df"
DataFrame 135
OK result_135 (2 rows)
Booking Holdings (BKNG), the parent company of Booking.com, is up slightly today, June 8, 2026.
| Date | Price |
|---|---|
| Previous Close (June 5) | $165.84 |
| Current Close (June 8) | $165.91 |
| Change | +$0.07 (+0.04%) |
for all these stocks - give me a table with current price, 52 week high, 52 week low
Calling: load
- tickers=["NVDA", "AAPL", "MSFT", "AMZN", "META", "GOOGL", "GOOG", "TSLA", "AVGO", "COST", "NFLX", "AMD", "INTC", "CSCO", "WMT", "QCOM", "AMGN", "INTU", "BKNG", "ISRG"], metrics=["week_52_high", "week_52_low", "close"]
Calling: compute
- dataframe_ids=[136, 137], code="df_latest = df_136[df_136['date'] == '2026-06-08']\nresult = pd.merge(df_137, df_latest[['ticker', 'close']], on='ticker')"
DataFrame 138
OK result_138 (20 rows)
Calling: show
- dataframe_id=138, rows=20
Blob 62
DataFrame 138
OK result_138 (20 rows)
The table below shows the current closing prices alongside their 52-week highs and lows for selected major Nasdaq-100 companies as of June 8, 2026.
| Ticker | Close Price | 52-Week High | 52-Week Low |
|---|---|---|---|
| NVDA | $208.86 | $236.26 | $140.67 |
| AAPL | $314.04 | $316.94 | $194.30 |
| MSFT | $410.93 | $551.05 | $355.51 |
| AMZN | $246.52 | $278.56 | $196.00 |
| META | $588.19 | $794.38 | $520.26 |
| GOOGL | $362.32 | $408.37 | $161.54 |
| GOOG | $359.40 | $404.23 | $162.86 |
| TSLA | $402.67 | $498.83 | $288.77 |
| AVGO | $398.72 | $495.00 | $239.46 |
| COST | $975.33 | $1,096.50 | $841.69 |
| NFLX | $82.49 | $134.12 | $75.01 |
| AMD | $489.53 | $546.44 | $115.06 |
| INTC | $111.54 | $132.75 | $18.96 |
| CSCO | $126.01 | $130.37 | $62.43 |
| WMT | $119.90 | $135.16 | $92.66 |
| QCOM | $218.92 | $258.96 | $121.54 |
| AMGN | $345.89 | $388.36 | $260.01 |
| INTU | $300.92 | $808.22 | $293.67 |
| BKNG | $164.76 | $231.62 | $149.76 |
| ISRG | $422.21 | $603.88 | $396.68 |
can you redo - do not use ["week_52_high", "week_52_low\fields - and compute it using the close prices directly. the week_52 metrics may be wrong.
Calling: compute
- dataframe_ids=[136], code="df = df_136[df_136['date'] >= '2025-06-08']\nstats = df.groupby('ticker')['close'].agg(['max', 'min']).reset_index()\nstats.columns = ['ticker', 'calculated_52w_high', 'calculated_52w_low']\n\nlatest_prices = df_136[df_136['date'] == '2026-06-08'][['ticker', 'close']]\nresult = pd.merge(stats, latest_prices, on='ticker')"
DataFrame 139
OK result_139 (20 rows)
The following table shows the current closing prices alongside the recalculated 52-week highs and lows for the selected Nasdaq-100 companies as of June 8, 2026, based on the historical closing price data.
| Ticker | Close Price | Calculated 52-Week High | Calculated 52-Week Low |
|---|---|---|---|
| AAPL | $314.04 | $315.20 | $194.87 |
| AMD | $489.53 | $542.52 | $116.16 |
| AMGN | $345.89 | $385.25 | $264.49 |
| AMZN | $246.52 | $274.99 | $198.79 |
| AVGO | $398.72 | $481.57 | $242.32 |
| BKNG | $164.76 | $230.69 | $153.74 |
| COST | $975.33 | $1,094.32 | $847.61 |
| CSCO | $126.01 | $130.00 | $62.65 |
| GOOG | $359.40 | $398.80 | $165.54 |
| GOOGL | $362.32 | $402.38 | $164.72 |
| INTC | $111.54 | $128.53 | $19.46 |
| INTU | $300.92 | $755.36 | $298.11 |
| ISRG | $422.21 | $554.40 | $400.32 |
| META | $588.19 | $772.18 | $524.36 |
| MSFT | $410.93 | $546.12 | $356.12 |
| NFLX | $82.49 | $130.04 | $77.81 |
| NVDA | $208.86 | $228.60 | $145.42 |
| QCOM | $218.92 | $250.56 | $125.46 |
| TSLA | $402.67 | $488.22 | $295.14 |
| WMT | $119.90 | $132.89 | $94.61 |
Note: Calculations are based on daily closing prices from the preceding 52-week period.