ET
Energy Transfer LP Common Units
stockNYSE

**BanBet Created** ▼ | **Record:** 0W - 1L | Ticker | Target | Entry | Move | Expires | |:---:|:---:|:---:|:---:|:---:| | **ASTS** | $39.06 (below) | $39.45 | -1.0% | Wed 8:12 AM ET |

View in your timezone: [November 3, 2026 8:00 AM ET][1] [1]: https://timee.io/20261103T1300?tl=CMPS+earnings+announcement

| Ticker | Target | Entry | Current | Move | Expires | |:---:|:---:|:---:|:---:|:---:|:---:| | **GOOGL** ▲ | $350.00 (above) | $321.58 | $317.70 | +8.8% | Thu 2:19 PM ET |

Peace deal announcement at 9am ET

| Ticker | Target | Entry | Current | Move | Expires | |:---:|:---:|:---:|:---:|:---:|:---:| | **MU** ▲ | $987.42 (above) | $914.28 | $905.05 | +8.0% | Wed 6:37 PM ET | | **Record** | 1W - 2L | 33% | - | - | - |

| Ticker | Target | Entry | Current | Move | Expires | |:---:|:---:|:---:|:---:|:---:|:---:| | **ASTS** ▲ | $100.00 (above) | $40.67 | $39.45 | +145.9% | Tue, Nov 17, 4:10 PM ET | | **Record** | 0W - 1L | 0% | - | - | - |

My predictions for markets this week is that The stock market will open at 9:30 AM ET and close at 4:00 PM ET

VZ, MO, PFE, O, ENB, ET, MPLX, GSL, LTC, UPS, SCHD, SPYD Last couple weeks I've been buying VZ every time it dips from overreactions to SpaceX news. Current price gets me a 6.5+% yield. Feels like buying a high-yield bond without the associated default risk, whose NAV won't erode as much in an increasing rate environment as bonds do. Payout ratio is a comfortable 56%.

https://preview.redd.it/uop0l7hxpwxh1.png?width=1920&format=png&auto=webp&s=3c898c486b857f040385aa2d0b7b4f2046bc1a6e **📊 Key U.S. Economic Data (ET)** **10:00 AM** | CB Consumer Confidence | Forecast: 81.2 | Previous: 81.9 ⚠️ For informational purposes only. Not financial advice. 📌 #SPY #SPX #StockMarket #TechnicalAnalysis

From puts to call or the other way around? Cuz you could get 770 spy call or 720 qqq calls tomorrow at 10 am ET and see what happens

Peace deal announcement at 9pm ET

**BanBet Created** ▼ | **Record:** 10W - 9L | Ticker | Target | Entry | Move | Expires | |:---:|:---:|:---:|:---:|:---:| | **SNDK** | $1225.00 (below) | $1256.07 | -2.5% | Tue 6:56 PM ET |

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I would treat this as a feed-reconciliation problem before choosing another vendor. IB, Alpaca SIP and Massive can all be internally valid yet disagree because they apply different trade-condition filters, odd-lot handling, corrections, timestamp conventions and one-second bar boundaries. Capture a small representative symbol set from both feeds simultaneously around 09:30:55–09:31:15 ET. Preserve raw trades and quotes, exchange timestamps, receipt timestamps, sequence numbers and condition codes. Then construct the one-second bars yourself with one documented rule set. Compare in layers: raw event coverage, event ordering, bar OHLCV, screener membership, signal timestamp, and finally the simulated fill. That tells you whether the disagreement comes from missing events, aggregation, latency or execution assumptions. Replay the same captured event stream through the production signal code rather than comparing one vendor’s historical bars with another vendor’s paper fills. For symbols where the decision changes, record the smallest input difference that flipped it. If ordinary feed differences regularly change the signal, the strategy is feed-specific: validate and deploy it on the same feed and processing path, or redesign the trigger so it is stable within an empirically measured tolerance. A limited live shadow test is still necessary, but it should answer a narrower question. Send tiny or non-marketable test orders while logging decision time, order-arrival time, quote state, acknowledgement, partial fills and cancellations. Use those observations to calibrate latency and fill assumptions; paper trading will not establish queue position or live slippage. I would evaluate Massive only after this dual-feed capture, because otherwise a third provider may add another unexplained difference rather than resolve the first two.

One frustrating change Schwab has made to thinkorswim is how stock prices are displayed after regular market hours. Previously, thinkorswim continued to display the **official 4:00 PM ET closing price** after the market closed. Traders could then look at the after-hours bid and ask prices to determine where the stock was currently trading. Now, Schwab appears to have replaced that closing price with the latest traded price, including after-hours transactions. This is a significant step backward in usability. For example, if a stock closes at $200 and subsequently trades at $210 after hours, the platform now displays $210 instead of retaining the official $200 closing price. This makes it unnecessarily difficult to quickly determine how much the stock has moved relative to its regular-session close. For active traders monitoring dozens of stocks, particularly during earnings season, having the official closing price readily available is extremely useful. We shouldn't have to remember closing prices or manually look them up every time we want to assess an after-hours move. **My request to Schwab is simple: Restore the previous functionality where the official closing price remains displayed after hours, while traders can use the bid and ask to evaluate extended-hours price movements.** At the very least, make this a configurable option so traders can choose between displaying the official closing price and the latest traded price. I would be interested to know whether other thinkorswim users find this change equally frustrating.

TONIGHT: Jim Cramer interviews the CEOs of Thermo Fisher, Blackberry and Crowdstrike on "Mad Money." Tune in to CNBC at 6 p.m. ET to watch live.

**Day 2 in the books!** \*\*\*Glitch with GitHub today causing 1 hour delay in getting in the markets. Very rare with this system but we added better watchdog signals to make sure this doesn't happen again. **Changes before today:** \- **Promoted** Stretched Below VWAP Bounce and RSI Collapse Dip Buy (all day) to **Primary**(My overzealous decision which was not good today) \- **Demoted** Early Sellers Above VWAP moved to **Testing** (Claude reco) \- **Demoted** Gap-Down VWAP Loss Short to **Graveyard** (Claude reco) Quick answers to common questions: - Claude (Opus 5.5) - Python - Alpaca API, paper trading, SIP market data (all US exchanges). - US stocks + ETFs, intraday only (9:50 AM-3:55 PM ET), long and short, nothing held overnight. - Setups built by basic indicators: RSI, VWAP, volume / buy-sell pressure, gaps, opening range, prior-day highs/lows, ATR. - Setup Generator - A bot that invents 10 new setups a night and trades them on its own paper account. New concept, setups built randomly on random indicators. - Setup Life Cycle - Idea or generator -> 2-yr backtest (costs in) -> test through replay sessions(20+ of them) -> testing (paper) -> Primary (paper) -> real money after the milestones. Losers move down to the Graveyard, never deleted. - Avg R - Average result per trade in units of the risk taken, after costs, fees and slippage( factored in already). - Break-even win rate - Win rate a setup needs just to not lose money. - Avg R w/o best day? Same, with its single best session removed - spots records carried by one big day. - Accounts meant to start at 100k, could not reset accounts during other testing before starting this without conflicts so some accounts started in a deficit. **Things I am still wanting**: \- Better system for more live setups. Reps and exposure to the market make the data wheels churn faster. Love the ideas if you have them! \- Quality setup ideas - Always looking to add! Especially LONGs as you can see! **Quick thoughts**: \- Slowly realizing that removing bad setups from **Primary** is probably 2x better than adding potentially good setups. Maybe we should all not be so eager to think our system/setup works? \- Patience \- What I'm not posting is all of the operational decisions and testing that has to be worked through on the backend to mitigate things like duplicate orders from setups if signals re-trigger, setup limits per ticker, stop setup after certain R, etc. It's boring stuff but makes a big difference and takes a lot of time, tokens(usage), and sessions ofr reps.  As always, thanks so much for the support and input! Fun stuff!

Thank you for choosing to invest with Fidelity. Trades placed after 4:00 p.m. ET, or at any time during the weekend, will receive the price of the next business day since mutual funds only trade once a day. When you enter a trade to buy, sell, or exchange mutual fund shares, the system will execute the trade at the next available price, which is calculated after trading ends that day and is usually posted by 6:00 p.m. ET. Additionally, keep in mind that it may take a few hours for mutual funds to calculate and report the updated price for the day. Orders submitted before 4:00 p.m. ET will show as "open" after the markets close until our systems are updated to reflect the executed order. Do you have any other questions for us today?

I’m working on a long-only ML stock-trading strategy and could use some outside perspective. The historical results looked promising, but the more recent results have been much weaker. How it trades It uses one-minute price/volume data, usually enters around 11:30 a.m.–12:30 p.m. ET, and closes everything at 3:55 p.m. No leverage or overnight holds. It generally takes a small number of trades per day. There’s only one entry per stock per session, and each cash allocation gets used once that day. I’ve tried engineered features, traditional ML, and neural networks. The current version averages predictions from several model fits. Results (model | SPY) \- 2020–2024 research backtest | +157.4% | +96.4% | \- 2025 OOS validation | +6.8% | +17.7% | \- 2026 YTD simulated paper trading | −0.4% | +13.7% | For 2020–2024, Sharpe was 0.90, beta to SPY 0.18, and max drawdown 20.2%. It beat SPY in three of five years. The edge per trade is thin: roughly 0.1–0.15% net, depending on the period. Average winners are around +2.5%, and average losers around −2.25%. The backtest includes modeled transaction costs at twice our base assumptions. The weakness also started before the OOS period: 2024 returned −10.6% versus SPY’s +24.9%. Averaging predictions made results less dependent on a particular model fit, but didn’t fix the weak periods. My concern is that it might be picking relatively good stocks while the broader midday-to-close opportunity has deteriorated. An initial attempt to predict when that trading window would be favorable didn’t show useful skill. What would you investigate first? \- What simple baseline would best separate stock-picking skill from just trading that time window? \- Would you revisit the fixed end-of-day exit, the training target, or the entry timing first? \- How would you test a “don’t trade today” rule without overfitting? \- With an edge this thin, which execution assumptions would you stress-test hardest? \- After extensive experimentation, what would make you stop researching this approach? I also have a separate multiweek strategy with stronger historical growth but higher market exposure and drawdown. Combining them improves the historical portfolio metrics, but we want to understand whether the intraday strategy holds up on its own. Would appreciate specific test ideas, useful controls, or a blunt assessment of what we might be missing. Happy to share more information if needed. If helpful: Here’s the yearly breakdown, strategy vs. SPY: **2020:** \+60.0% vs. +18.3% **2021:** \+31.4% vs. +28.7% **2022:** \+19.0% vs. −18.2% **2023:** \+15.1% vs. +26.2% **2024:** −10.6% vs. +24.9%

I’m working on a long-only ML stock-trading strategy and could use some outside perspective. The historical results looked promising, but the more recent results have been much weaker. **How it trades** It uses one-minute price/volume data, usually enters around 11:30 a.m.–12:30 p.m. ET, and closes everything at 3:55 p.m. No leverage or overnight holds. It generally takes a small number of trades per day. There’s only one entry per stock per session, and each cash allocation gets used once that day. I’ve tried engineered features, traditional ML, and neural networks. The current version averages predictions from several model fits. **Results (model | SPY)** \- 2020–2024 research backtest | +157.4% | +96.4% | \- 2025 OOS validation | +6.8% | +17.7% | \- 2026 YTD simulated paper trading | −0.4% | +13.7% | For 2020–2024, Sharpe was 0.90, beta to SPY 0.18, and max drawdown 20.2%. It beat SPY in three of five years. The edge per trade is thin: roughly 0.1–0.15% net, depending on the period. Average winners are around +2.5%, and average losers around −2.25%. The backtest includes modeled transaction costs at twice our base assumptions. The weakness also started before the OOS period: 2024 returned −10.6% versus SPY’s +24.9%. Averaging predictions made results less dependent on a particular model fit, but didn’t fix the weak periods. My concern is that it might be picking relatively good stocks while the broader midday-to-close opportunity has deteriorated. An initial attempt to predict when that trading window would be favorable didn’t show useful skill. **What would you investigate first?** \- What simple baseline would best separate stock-picking skill from just trading that time window? \- Would you revisit the fixed end-of-day exit, the training target, or the entry timing first? \- How would you test a “don’t trade today” rule without overfitting? \- With an edge this thin, which execution assumptions would you stress-test hardest? \- After extensive experimentation, what would make you stop researching this approach? I also have a separate multiweek strategy with stronger historical growth but higher market exposure and drawdown. Combining them improves the historical portfolio metrics, but we want to understand whether the intraday strategy holds up on its own. Would appreciate specific test ideas, useful controls, or a blunt assessment of what we might be missing. Happy to share more information if needed.

If you trade NQ or ES, most scheduled spikes land at a few fixed ET times. Here's what I keep in front of me: - 8:30 AM ET: CPI, Core CPI, Non-Farm Payrolls, Unemployment Claims (Thursdays), Retail Sales, PPI, GDP, PCE - 9:45 AM ET: Flash Manufacturing and Services PMI - 10:00 AM ET: ISM, UoM Consumer Sentiment, JOLTS - 2:00 PM ET: FOMC statement (press conference usually 2:30 PM ET) This week, FOMC is Wednesday at 2:00 PM ET, and GDP, PCE and claims come out Thursday at 8:30 AM ET. Europe already changed its clocks, but the US changes on Nov 1, so double check any time you see in CET. Habits that help: 1. Check the next trading day's red and orange folders the night before, not at 8:25. 2. Convert everything to ET once. 3. Decide in advance whether you'll be flat, reduced, or trading the release. Not financial advice.

We appreciate you letting us know. Since you're experiencing this on multiple platforms, we recommend reaching out to our Technical Support team so they can troubleshoot the issue further with you. Associates are available Monday through Friday from 8:30 a.m. to 9:00 p.m. ET. Please say "technical support" when prompted by the automated system to be connected to the right group. [Contact us](https://www.fidelity.com/customer-service/contact-us) Let us know if anything else comes up.

| Ticker | Target | Entry | Current | Move | Expires | |:---:|:---:|:---:|:---:|:---:|:---:| | **AMZN** ▲ | $274.37 (above) | $249.43 | $248.33 | +10.0% | Thu, Nov 5, 7:24 AM ET | | **Record** | 1W - 3L | 25% | - | - | - |

| Ticker | Target | Entry | Current | Move | Expires | |:---:|:---:|:---:|:---:|:---:|:---:| | **SOXL** ▲ | $95.00 (above) | $82.70 | $91.84 | +14.9% | Tue 4:16 PM ET | | **Record** | 2W - 1L | 67% | - | - | - |

The 10/30 $30C's got a big bump as well, about 3,500 more contracts at the about the same time. Between 11:30-12:00 ET today, 11k ITM calls were traded, versus only 500 for the same time period on Friday.