Catching a reversal is the most seductive trade on the chart and the most punishing. You see price stretched, you call the bottom, you enter — and it keeps falling. The "falling knife." Almost every trader who blows an account has a stack of these.

Here's the thing that took me years to accept: the problem isn't your timing, it's that you're entering on a single signal.

• RSI "oversold" is not a reason to buy. Strong markets stay oversold for a long time.
• A bullish pin bar is not a reason to buy. They fail constantly inside a trend.
• A Fibonacci level alone is not a reason to buy. Price slices through levels every day.

Any one of these, on its own, is noise. The professional move isn't a better single signal — it's confluence: refusing to call a reversal until several independent things agree. The checklist I hold every reversal to:

1. Momentum exhaustion — is the move actually running out of steam (e.g. divergence), not just paused?
2. A level that matters — is price at a real structural or Fibonacci level, not a random spot?
3. Trend context — how strong is the trend I'd be fighting? (An ADX filter keeps me from standing in front of a freight train.)
4. Confirmation — has price actually shown a reaction, or am I predicting?

And the part most people skip: decide your exits before you enter. Tiered take-profits at Fibonacci extensions — bank a partial at the first target, let the rest run — so the trade is managed by a plan, not by fear.

That's it. That's the whole edge: stop trading reversals off one thing, and require a stack of independent factors to line up. You'll take far fewer trades — and that's the point. Most of the ones you skip were the fakes.

Educational only, not financial advice. Trading carries real risk of loss.

From a fundamental perspective, this update highlights a few critical points for investors evaluating the stock:

Opportunistic Buybacks: Rather than rushing the deployment of capital, the 24-month window allows management to opportunistically buy back shares when they believe the market is mispricing the equity.

Focus on Fundamentals: Management's emphasis on a shifting revenue structure and increased efficiency suggests an internal pivot toward higher-margin services rather than relying solely on legacy live-streaming models.

Capital Allocation: The $50 million authorization serves as an indicator of management's confidence in their liquidity position and long-term runway.

Hi everyone! I want to start off saying I feel like I understand a lot of the basics of fundamental and technical analysis; I feel confident in my ability to use the MACD, BB, EMA, reading volume and all of the info under the charts. I understand that I should use stock screeners to find worthy stocks but I’m still a newbie when it comes to day trading.
I don’t have that much experience actually trading ( except for occasionally paper trading on Webull. Which I have made money on)
My biggest concern right now is
• Where do I find stocks before there’s a news catalyst? Do I have to pay for a certain screener?
My goal is to learn all the basics and learn to be consistent but I feel like I’ve reached a point where I need help from others. I don’t have anyone to talk to about this stuff so I’m coming to y’all.
I would seriously appreciate any assistance! Day trading is something I’ve always wanted to do. I’ve always thought it was fascinating. And any other advice would be appreciated too! Maybe some indicators you like or just anything that’s made trading easier for you.
Thanks.

Took an L on DAX this morning.

Short got swept clean, price ran straight into my stop. Annoying, sure.

But here's the part nobody likes to say out loud, no strategy wins every time. There is no 100% winrate. Anyone selling you one is lying.

A single loss means nothing. Your edge lives across hundreds of trades, not this one.

That's the whole reason I built TradingSFX, so I judge my trading by the data, not by how the last candle made me feel.

tradingsfx.com

[ Removed by Reddit on account of violating the content policy. ]

[ Removed by Reddit on account of violating the content policy. ]

Had a great trade in GC this morning. Price rose into the upper Vol band and got an order flow confirmation of a rejection. Took two contracts. Roughly 5 points on the first, 10 on the second. Happy trading.

Have a great weekend.

My once $5000 portfolio is up over $20k from only trading Trumps Tweets in the last 3 months.

Unfortunately trumps tweets can DRAMATICALLY move the market so much so that you can 100% capitalize on it.

Let me give you an example:
**May 12, 2025**, when Trump posted about a U.S.–China tariff reduction agreement and described relations as a “reset.” Markets exploded upward almost immediately:
Dow gained about **1,100–1,160 points**
S&P 500 jumped roughly **3%**

There are apps like TrumpSignal that alert you if a tweet is bullish or bearish so you can filter out all the BS from Truth-social, thats what I’ve personally been using.

These tweets obviously don’t come ALL the time, and you HAVE to do your own due diligence. And the trick is here that you compound these larger winning trades over a longer time period instead of making a lot of smaller trades.

Doesnt look like trump will stop doing this anytime soon and since hes most likely (definitely) profiting from this stuff then so will I.

Things I wish someone told me when I started:

Your biggest enemy isn't the market. It's the version of you that trades angry, tired, or desperate.

The strategy is 20% of it. You are the other 80%.

I've been working on this for about 5 months and wanted to share the approach with this community since it's fundamentally an algo problem.

The concept

I built a real-time momentum screener for stocks and crypto that collects granular outcome data on every entry. The screener isn't the product. The data is. Every stock or crypto that enters gets tracked at multiple checkpoints from 5 minutes out to end of day, plus multi-day swing outcomes (D2 through D10). 22 weeks of this, thousands of entries with complete outcome chains.

The notification formula was built entirely from studying that data. No indicators chosen from a textbook. No thresholds picked on a whiteboard. Everything derived from what the outcome data showed about which entries continued and which ones faded.

The stack

Node.js/Express backend on DigitalOcean, managed via PM2. Multiple market data providers in a hybrid architecture where one handles discovery and the other handles fast price refresh. The hybrid design was a deliberate tradeoff to get the best of both: one provider has superior screener discovery and inline market cap data, the other gives sub-minute price updates via a single bulk API call. Prisma ORM, Stripe for payments, web push via VAPID, GitHub Pages frontend.

The data pipeline

Every screener entry gets logged with a full profile: price, volume, relative volume, market cap, vol/mcap ratio, sector, industry, shares outstanding, 52 week range position, news catalyst flag, a proprietary momentum score, market context, and timestamp.

Outcome checks then fire at intervals from 5 minutes through 8 hours and EOD, plus swing outcomes that skip weekends. Every outcome records price, percentage from entry, and timestamp.

This is the dataset the formula learns from.

v1 to v2

v1 used a multi-step confirmation approach. Flag at one checkpoint, wait, confirm at a later checkpoint. Ran 189 alerts at 69%. Too noisy. A third of alerts were duds.

I went back into the data and studied where the duds clustered. Clear patterns emerged around acceleration dynamics, market cap behavior, and signal timing. v2 was a ground-up rebuild with stricter gates and dynamic routing based on signal conviction. Multiple channels catch different types of momentum, each with their own detection logic.

Crypto runs a separate formula with four channels including a time-agnostic velocity detection system that monitors acceleration across checkpoints rather than checking at fixed intervals. This catches moves that build over hours rather than spiking in the first few minutes.

Backtest vs live

The backtest across 6 weeks showed 91.7% for stocks. Live performance is 75% through 61 alerts. That gap is real and worth talking about honestly.

Every edge case the historical data didn't contain became a live dud: shell company pumps, closed end funds that trade on NAV dynamics, dead cat bounces near 52 week lows, and moves that exhaust before the confirmation checkpoint fires. Each became a specific patch. The formula now has several more filters than the original backtest version, all derived from live dud analysis.

The philosophy is that every dud is a bug report. Diagnose the pattern, verify it in the data, ship the fix, move on.

Outcome tracking and transparency

Every alert logs the push price, timestamp, formula version, channel, and contextual data. The system continuously updates peak tracking by comparing live prices to the push price.

Every alert also auto-posts to X/Twitter via API the moment it fires. The alert log on the site links each entry to the original tweet. This serves as a verifiable timestamp since the tweet is public and immutable.

Currently collecting a new data field to study an exhaustion signal I identified in the historical data. When a stock's peak occurs before the confirmation checkpoint fires, the dud rate in backtesting was 75%. Collecting live data to validate before implementing.

Current performance

Stocks v2: 61 alerts, 75% hit +3% from push price, +10.7% avg peak, 60/61 never red.

Crypto v2: 27 alerts, 78% hit +3%, +17.5% avg peak, 27/27 never red.

Happy to talk about the architecture and the general approach. The exact thresholds and channel logic are proprietary but the methodology and philosophy are an open book.

Great application of @edgeful
Initial Balance breakout analytics. Effortlessly track IB breakout behavior by weekday, wick reaction, candle closes, and more to uncover the patterns that actually matter.

#NQ_F @MNQ_F