ET
Energy Transfer LP Common Units
stockNYSE

I'd suggest speaking with our Workplace Investing team directly to discuss your 401(k) plan and what it permits. Associates are available generally Monday through Friday from 8:30 a.m. to midnight ET. [Contact Us](https://www.fidelity.com/customer-service/contact-us) Additionally, when you complete a Roth conversion a 1099-R Form that shows the funds leaving your retirement account and a 5498 Form showing the funds going into your Roth IRA.

Thank you for reaching out and sharing your feedback regarding the recent updates to your plan's investment names. We completely understand how important it is to have clear, recognizable names when managing and researching your retirement portfolio. Please note that changes to the display names and structure of investment choices within a 401(k) lineup are unique and often made at the direct request of a plan's sponsor. To find the specific underlying mutual funds or investments mapping to these descriptive names, we recommend reviewing your Summary Plan Description (SPD) and additional plan documents (if available) by logging into NetBenefits.com and following these steps: 1. On the NetBenefits home page, click on your 401(k) plan 2. Access your plan's "Summary" page, then click on the "Plan Information" tab 3. Under "Plan Information and Documents," click on "Summary Plan Description (SPD)" If you need any assistance locating these documents or would like a representative to walk you through your specific plan details, please consider connecting with our Workplace Investing team directly. Associates are generally available Monday through Friday from 8:30 a.m. to midnight ET to assist you. [Contact Us ](https://www.fidelity.com/customer-service/contact-us) I hope you enjoy the rest of your evening.

I recently bought $20C for 2029 on ET. I have like 200 or so. Breakeven was like $22.50

Correct. Anything that's considered "market sensitive" is posted after 4 pm ET. Source: I post to cms.gov

**BanBet Created** ▲ | **Record:** 0W - 1L | Ticker | Target | Entry | Move | Expires | |:---:|:---:|:---:|:---:|:---:| | **MSTR** | $160.00 (above) | $151.74 | +5.4% | Fri 4:04 PM ET |

**BanBet Created** ▲ | Ticker | Target | Entry | Move | Expires | |:---:|:---:|:---:|:---:|:---:| | **NVDA** | $240.00 (above) | $230.28 | +4.2% | Thu, Oct 22, 3:15 PM ET |

Hi, happy Thursday! It's great that you're thinking ahead to learn the withdrawal process. I see you chatting in the comments and would also like to confirm that you have separate sources within your 403(b) account: Roth, Traditional, and after-tax. When you're logged into Netbenefits.com, follow these steps to look at what you have invested in each source: 1. Select your account from the homepage 2. Scroll down to the "Current Portfolio" box and click the "Sources" tab 3. Select "Sources Details" From here you can see your percentages, securities, and totals for each source. When it comes time to withdraw, if you're looking to take from a specific source, our Workplace Investing team can assist. Call and say you want a source specific withdrawal, and choose your source. Whatever you pick, it will sell the underlying funds in each source proportionally. Our team hours are Mon. - Fri., 8:00 a.m. - midnight ET, although this can vary by plan. [Contact us](https://www.fidelity.com/customer-service/contact-us) Keep us updated if any other questions arise.

Right at 12:58p ET as they were changing shows.

I currently maintain positions in both EPD and WES. I also hold ET and MPLX.

**BanBet Created** ▼ | Ticker | Target | Entry | Move | Expires | |:---:|:---:|:---:|:---:|:---:| | **QQQ** | $700.00 (below) | $745.53 | -6.1% | Thu, Oct 29, 1:24 PM ET |

PAMP ET!!! DAMP ETTT!!! SNIP SNAP SNIP SNAP!! :kek:

I mean a 24bps swing on QQQ from 2pm ET to EOD was still something lol

Hey, thanks for getting back to us. If you haven't already, I'd definitely recommend clearing both your browser's cache & cookies, as this can often resolve similar issues. If you've tried the above step or continue to experience this issue in another browser, please contact our technical support team for further troubleshooting. You can reach them Monday-Friday from 8:30 a.m. to 9:00 p.m. ET. [Contact us](https://www.fidelity.com/customer-service/contact-us) We appreciate you for choosing Fidelity and engaging with us on the sub.

| Ticker | Target | Entry | Current | Move | Expires | |:---:|:---:|:---:|:---:|:---:|:---:| | **SPY** ▲ | $785.00 (above) | $779.22 | $775.00 | +0.7% | Fri 3:54 PM ET | | **Record** | 0W - 2L | 0% | - | - | - |

**BanBet Created** ▲ | **Record:** 0W - 2L | Ticker | Target | Entry | Move | Expires | |:---:|:---:|:---:|:---:|:---:| | **SPY** | $784.00 (above) | $775.29 | +1.1% | Sat 10:00 AM ET |

⏰ Friendly reminder that there's a **$22 billion 30-year bond auction today**. Results will be announced at 1:00 p.m. ET.

MOBX has an investor call at 4pm ET

**BanBet Created** ▲ | **Record:** 1W - 0L | Ticker | Target | Entry | Move | Expires | |:---:|:---:|:---:|:---:|:---:| | **GOOGL** | $355.00 (above) | $351.22 | +1.1% | Fri 9:05 AM ET |

At 5:00 AM ET, the National Hurricane Center reported that Hurricane Isaias had strengthened to 80 mph, with further intensification expected. Approximately 25.08% of U.S. Gulf of Mexico oil production and 16.37% of natural gas production were already shut in as of Wednesday. This represents approximately 512,000 barrels of daily oil production temporarily offline.

I agree. My theory is that the market makers were taking losses and forced ET to ban a pool of profitable traders (anyone involved in too much trading). Their main issue appears to be short-term trading, but they want define what is acceptable. It’s a guessing game that makes trading impossible on ET.

**BanBet Created** ▼ | **Record:** 5W - 8L | Ticker | Target | Entry | Move | Expires | |:---:|:---:|:---:|:---:|:---:| | **NVDA** | $223.02 (below) | $237.51 | -6.1% | Thu, Oct 15, 7:14 AM ET |

**BanBet Created** ▲ | **Record:** 0W - 1L | Ticker | Target | Entry | Move | Expires | |:---:|:---:|:---:|:---:|:---:| | **MU** | $1200.00 (above) | $1086.74 | +10.4% | Sat 3:21 AM ET |

📅 Oct. 30, 2026: Warrants expire at 5 PM ET... And other dates to know.

**📊 Key U.S. Economic Data (ET)** **8:30 AM** | Unemployment Claims | Forecast: 200K | Previous: 197K **4:30 AM** | Fed Speaker: FOMC Member Waller Speaks ⚠️ For informational purposes only. Not financial advice. 📌 #SPY #SPX #UnemploymentClaims #FOMC

Over the past few months I went fairly deep into testing an Opening Range Breakout + Relative Volume strategy based on the paper *A Profitable Day Trading Strategy for the U.S. Equity Market*. I originally expected this to end with a paper-trading/live system. Instead, it turned into an interesting case study in how a strategy can look very strong in a conventional backtest and then collapse once you model how the trades would actually have to be executed. I thought the results might be useful to others here, particularly anyone working on intraday strategies with tight stops. # The basic strategy The main 5-minute version was approximately: * US equities * opening price > $5 * previous 14-day average volume ≥ 1M shares * ATR14 > $0.50 * first opening range = 09:30–09:35 ET * RVOL = today's first-5-minute volume / average first-5-minute volume over the previous 14 sessions * RVOL ≥ 1 * rank Top 20 by RVOL * bullish first candle → only trade long * bearish first candle → only trade short * long stop-entry at OR high / short stop-entry at OR low * initial stop = 0.10 × ATR * no profit target; remaining positions close near EOD I rebuilt the historical universe and signal pipeline, then eventually added SIP trades/quotes and an execution simulator. Data/execution stack was mostly: * Alpaca SIP historical trades/quotes/bars * point-in-time-ish / survivorship-reduced US equity universe * ticker/entity continuity handling * \~310 ms empirical execution latency * NBBO-based marketable execution * IBKR-style commissions * displayed-liquidity constraints in the later tests * SSR handling * partial fills / exit-liquidity handling One limitation: this is not the paper's exact CRSP + IQFeed dataset, and the universe is not truly survivorship-free. # 1. First surprise: I could reproduce the theoretical edge The conventional/bar-style implementation looked good. Approximate D2 theoretical expectancy: **2016–2023: +0.15R/trade** Later 2024–2026 period: **\~+0.14R/trade** On the exact subset that later received executable fills, the theoretical result was about +0.155R and +0.165R respectively. So this wasn't a case where I simply coded the strategy and immediately got garbage. The idealized strategy really did appear to have an edge. **\[Chart 1 here: theoretical vs executable\]** # 2. Then I replaced theoretical fills with SIP/NBBO execution This was the point where everything changed. Instead of assuming: * breakout entry at exactly the OR level * stop exit at exactly the stop price I used historical SIP trades and NBBO quotes. At approximately **310 ms latency**: |Period|Theoretical D2|Executable| |:-|:-|:-| |2016–2023|\+0.155R|**−0.380R**| |2024–2026|\+0.165R|**−0.616R**| Even the **zero-latency executable bound was negative**: * 2016–2023: −0.184R * later period: −0.420R That was the first major warning that this wasn't something a faster VPS or different broker would magically solve. # Why? The 0.10 ATR stop is extremely tight relative to actual market microstructure. Median quoted spread near the stop execution was approximately: * historical: **0.44R** * later period: **0.58R** In other words, if my entire intended loss is 1R, one side of the bid/ask spread alone is already a meaningful fraction of the whole risk budget. And the strategy trades precisely when the market is moving quickly. Entry moves through the breakout level. Then, when the trade fails, the stop is triggered while price is moving against me. The theoretical backtest effectively assumes: >enter at the breakout level → lose exactly 1R if stopped. Reality was more like: >detect breakout → latency → cross spread → fill beyond breakout → anchor stop to actual fill → adverse move → trigger stop → latency → cross spread again. That completely changed the payoff distribution. # 3. I tried to rescue the execution problem I preregistered 18 execution variants before testing them. Variables included: * 0.10 / 0.20 / 0.30 ATR stops * spread filters * market vs collared marketable-limit entries **0/18 qualified.** There was one interesting pocket: * 0.10 ATR stop * tight spread filter * marketable-limit entry The short side looked substantially better than the long side. That led to one separate short-only hypothesis. # 4. The short-only version was the most promising result I found This version produced: **2016–2026: +0.244R/trade** with roughly: * 1,666 fills * 19% win rate * PF \~1.26 * 7/11 positive years DEV 2016–2020: **+0.303R** 2021–2023: **+0.120R** 2024–2026: **+0.308R** For a moment this looked like the thing that might survive. But there were serious implementation problems. The two hard failures were: **1. Sample size** Only 1,666 fills versus the preregistered 2,500 threshold. **2. Actual displayed liquidity** Median displayed coverage was only: * **46% at entry** * **25% at stop exit** 63.9% of entries wanted more shares than were displayed at the bid, and 79.6% of stop exits wanted more shares than were displayed at the offer. There was also no reliable historical HTB/borrow dataset, which matters a lot for a short strategy selecting volatile, high-RVOL stocks. The return distribution was very right-skewed: * top 10 trades ≈ 66% of total net R I don't consider concentration by itself a reason to reject a positively skewed strategy — tail strategies naturally depend on large winners. But combined with the limited sample, depth problem and borrow uncertainty, it wasn't enough evidence to deploy. # 5. Then I searched the ORB/RVOL design space systematically Instead of continuing to manually tweak things, I built a preregistered search with realistic execution from the start. It tested **20,009 configurations**. Controls included: * capacity-aware NBBO execution * \~310 ms latency * commissions * SSR * frozen search space * no manual “try this after seeing the result” * walk-forward testing * parameter-neighbourhood / plateau testing * Deflated Sharpe * PBO * White Reality Check * Hansen SPA * locked validation/holdout periods Only **one configuration** passed the initial DEV gates. It had approximately: * \+0.071R DEV * \+0.049R OOF * PF \~1.11 * \~1,645 trades But immediate neighbouring parameter settings fell toward zero or negative. It looked like a **parameter spike rather than a stable plateau**. Result: **0 robust candidates out of 20,009 configurations.** Among large-sample configurations, roughly **99.3% lost money OOF**. White Reality Check p-value was approximately 1.0 and Hansen SPA approximately 0.92. At that point I was no longer convinced there was a hidden ORB parameter combination waiting to be discovered. # 6. Was the tight stop the real problem? This became an important question. Maybe the entry was actually good, but the 0.10 ATR stop was destroying it. So I removed the stop entirely. Same canonical ORB/RVOL entry. No target. Enter and simply hold until an executable 15:58 ET exit. 18,169 trades, 2016–2020. Result: **Gross before costs: −3.49 bps/trade** **Net: −11.58 bps/trade** Session-clustered t: **−3.05** 95% bootstrap CI: **−18.8 to −3.8 bps** And: **0/5 years positive.** Longs and shorts were both negative. That changed my interpretation significantly. The tight stop absolutely makes execution worse. But the stop was **not the whole problem**. The canonical ORB/RVOL entry itself did not show robust positive entry→EOD directional alpha in my executable data. # 7. Final experiment: maybe five minutes is simply too early The original paper also tested a published **15-minute opening range**. So instead of inventing another parameter after seeing the failures, I tested that externally specified variant. 09:30–09:45 OR. RVOL recalculated using the first 15 minutes. Everything else kept fixed. This was interesting because waiting until 09:45 genuinely improved market microstructure. # 5-minute vs 15-minute Median spread/R: **0.40 → 0.27** Entry slippage: **0.45R → 0.31R** Entry fill vs OR level: **0.30R → 0.19R** Stop exit cost: **0.39R → 0.27R** Same-minute stops: **27.5% → 15.9%** Gap-through stops: **30.5% → 22.5%** So waiting until 09:45 did almost exactly what I hoped. **\[Chart 3 and Chart 4 here\]** But the actual returns were: |Layer|5-minute|15-minute| |:-|:-|:-| |Paper/bar model|\+0.135R|\+0.073R| |Optimistic executable|−0.415R|−0.327R| |Realistic executable|−0.645R|−0.515R| |Stress|−1.274R|−0.960R| **\[Chart 2 here\]** So the 15-minute version reduced execution damage materially. It just didn't reduce it enough. The signal only had \~+0.07R of theoretical expectancy available to pay for \~0.4–0.6R of executable friction. Stop-hit rate also remained about **87%**. # What I think actually happened My current decomposition is roughly: **Paper/bar backtest** Signal alpha − simplified/absent spread costs − simplified stop fills − simplified queue/depth = attractive result **Executable model** small underlying signal − entry spread − breakout slippage − fill anchoring − stop spread/slippage − fixed commissions − capacity constraints = negative expectancy And after the no-stop test, I would add another issue: >The canonical ORB direction signal itself seems much weaker than the headline backtest suggests. The strategy is extremely positively skewed: lots of losses and rare very large winners. That structure is real. But the average executable loser becomes too expensive relative to the small theoretical edge. # Things I learned the hard way **1. A stop can be tiny statistically but huge microstructurally.** 0.10 ATR sounds reasonable until the spread is 0.4–0.6 of the entire stop distance. **2. “Slippage = 1 tick” is not enough for strategies like this.** The timing of the signal, quote, spread, latency, actual fill and subsequent stop matters. **3. A profitable theoretical signal doesn't guarantee there is enough alpha to pay execution costs.** \+0.15R looked substantial until the real execution waterfall was measured. **4. Backtesting the stop can hide problems in the entry signal.** Removing the stop and holding to EOD was probably one of the most useful diagnostics I ran. **5. Parameter search can find something that looks profitable very easily.** 20,009 configurations produced one interesting spike. Neighbourhood robustness killed it. **6. Displayed liquidity matters even at surprisingly small account sizes.** A $25k account with 1% risk can still ask for thousands of shares when the stop is only 0.10 ATR. **7. A better execution environment cannot manufacture signal alpha.** The 15-minute test is the cleanest example: spreads and stop execution improved substantially, but expectancy remained negative. # Caveats I'm **not claiming the paper is “wrong.”** There are meaningful differences: * paper used CRSP + IQFeed * mine used Alpaca SIP * my universe is survivorship-reduced, not perfectly survivorship-free * there are unavoidable implementation ambiguities around same-bar sequencing * historical borrow availability isn't fully observable * my execution simulator is still a model, not actual historical broker fills Also, after repeated ORB research, I no longer consider the later 2024–2026 data pristine OOS for newly generated hypotheses. What I am comfortable saying is narrower: >Under my data, universe and increasingly realistic SIP/NBBO execution assumptions, I could reproduce the theoretical ORB/RVOL effect, but I could not find robust evidence that the edge was executable. At this point I've closed the ORB/RVOL research track rather than continue parameter mining. The infrastructure is staying — SIP data ingestion, point-in-time mapping, execution simulator, IBKR integration, research framework, etc. — but I'll use it on different signal families. I'd be interested in feedback from anyone who has independently tested this paper or another tight-stop intraday strategy using tick/NBBO data. In particular: * Did you see a similar theory → execution collapse? * How do you model stops when the spread itself is a large fraction of R? * Has anyone replicated this with IQFeed or another consolidated-feed dataset using executable quotes? * Do you see a flaw in the execution assumptions above? Happy to share more methodology/results if useful.