I have been looking at different crypto trading bots across chains like Solana and ETH and noticed they all seem to promise similar things around automation and signal execution. What I am not sure about is how much real edge these systems actually provide once market conditions get unstable. It feels like in highly volatile environments, automation might just speed up decision-making rather than actually improve outcomes. For people who actively use trading bots, do you find they genuinely improve performance over manual trading, or is the main benefit just execution speed and discipline? via /r/CryptoTradingBot https://ift.tt/ajInyth

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I have 3 Ariana Grande tickets for the Crypto com Arena show ......Please DM me if interested, i can send you proof. Thank you!!! via /r/Tickets https://ift.tt/a4NCi5K

Ticker: NEARUSDExchange: CRYPTOTime: 11 Jun 2026 @ 18:24Price: $2.06Link: https://ift.tt/rwAsO6n via /r/getagraph https://ift.tt/7RGFvoY

So today i was in a desperate need for an answer went to oranum, just so you guys know i have used oranum for over a year now so today i logged in back added funds and the moment i was about to join a private session they banned my account and locked me out , now they won't let me login and when tried to reset it shows no account registered with the mail but I was desperate and needed to talk to someone so created a new account thinking payment failed or something like that added funds via crypto which went through credits were added and boom banned again this is messed up , please don't use oranum and they won't even answer my mails now .Title mistake ; please don't use oranum via /r/psychics https://ift.tt/jnAi6Kw

I'm trying to find a pretty famous crypto trading course from past months. Tried my best and even searched via FMHY but can't find it. Do you guys suggest any forum or niche site which can have it? via /r/PiracyBackup https://ift.tt/Nvgdw2i

I’ve been building an NBA props model for a while, and the biggest improvement did not come from adding another player stat. It came from changing the order of the whole process. (if this helps let me know)I used to start with the player: minutes, usage, recent form, matchup, line, projection. That got me around 54% accuracy over my tracked plays on OOS testing. Not terrible, but not good enough to feel like there was a real edge. So I reconfigured my system for capturing pregame context before every slate.I’m a little over 1,000 games logged now. Not just box scores. The whole pre-game itinerary from injuries, starting lineups, late scratches and pre-game beat report notes, rotation changes, last 3 games both rolling recent three and current matchup previous three results. I've been tracking coach quotes, matchup style, pace expectations, defensive scheme, who gets doubled, who brings the ball up, who loses touches when someone comes back and who gains shots when a role player sits and how the previous game changed the next game plan. & morePoint is after about 500 games of testing with this newer process I have large enough sample and proof, the model moved from about 54% to 61% hence my post.The main change was this: I stopped modeling player props first. Now I model the game first. The game layer of the system is built to identify the environment before the player projection ever runs. It looks at pace, coverage, injury impact, rotation changes, shot profile, defensive pressure, and how the matchup is likely to shape possessions. A fast game with weak transition defense creates completely different player opportunities than a slow half-court game where both teams switch and force late-clock shots. Same thing with injuries. A starter being out does not automatically mean every replacement gains usage. Sometimes the ball shifts to a second creator. Sometimes the bench absorbs the minutes but not the shots. Sometimes the defense changes its coverage because the spacing is different.That is why I started treating the game as the first prediction. Once the model has a projected game script, then the player layer distributes usage, shots, assists, rebounds, and defensive pressure inside that script. That helped a lot because players do not create stats in isolation. Their stats come from the environment they are dropped into.That is where my old model was weak. It saw player-level trends, but it didn’t understand why those trends happened.For the stack, I’ve been testing a few things: XGBoost handles most of the structured slate features. Random forest is useful as a sanity check because it exposes when the model is overreacting. TGNN-style graph features help with player/team interaction stuff, especially usage redistribution. Monte Carlo simulation helps map different game scripts instead of pretending there is one clean projection. I’ve also tested a quantum simulation weighting layer, but honestly most of the real gain came from better pregame capture, not from making the model simulations any deeper.The biggest lesson from logging all of this: Reverse engineer your systems and you need to know where to dig to find gold.(Golden Nugget: I automated a pipeline that pulls videos from YouTube channels that post player/game updates close to tip, transcribes them, and feeds the useful parts into my database. Did the same thing with X accounts, injury/news posts, beat reporters, and other pregame info sources. It is basically a sports version of the crypto news bots people use for memecoin trading back in 2024.)Curious if anyone else builds this way. Do you start with the player projection and adjust for the game, or do you model the game first and let the player outcomes come from that? via /r/NBATalk https://ift.tt/vlpPA9s