AlphaLedger engagement report
@byalphaledger - 369K followers on X
Measured over 1 original post from a 30-day window, last computed on September 12, 2026.
Engagement
Early reading. We have captured 1 original post for this account, below the 8 we require before treating a median as settled. The numbers above describe what we have seen so far, not a finished profile of the account.
A typical post picks up 1 interaction against 369K followers, an engagement rate of 0%. Posts are seen about 1.6K times each, and 0.062% of those impressions turn into an interaction. That is about 0.44% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.03 posts a day over the last 30 days, though only 3% of days saw any activity at all. Most posts go out around 14:00 UTC, and Friday is the busiest day of the week. Of the 1 posts sampled, 100% carry an image or video. The account's strongest tracked post pulled 5 interactions, about 5.0x its own typical post. Only 1 original post have been captured so far, fewer than the 8 posts we want behind a median before treating it as settled. Read the figures above as an early measurement of this account, not as a finished profile of it.
Measured over 1 original post from a 30-day window, last computed on September 12, 2026.
Where this sits in the catalog
At under 0.001%, AlphaLedger sits below the 10th percentile of the 205,048 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.024%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.024% |
| 50th percentile | 0.143% |
| 75th percentile | 0.678% |
| 90th percentile | 2.55% |
| 99th percentile | 82.4% |
This ruler is the whole measured catalog, not a size-matched group: it shows where the raw rate falls across every account we can measure, all of which are large. For a like-for-like comparison, read the size-band percentile above instead. See how the bands are built
Posting timing
This account posts most often around 14:00 UTC, and Friday is its busiest day of the week. The bars below are the catalog-wide pattern, with this account's own busiest slot marked. They do not show how this account performs at each hour: we keep one aggregate per account, not one per hour, so that measurement does not exist in our data.
Show engagement by hour posted, utc as a table
| Hour (UTC) | Vs author median | Posts |
|---|---|---|
| 00:00 UTC | -1% | 89K |
| 01:00 UTC | -2% | 90K |
| 02:00 UTC | -3% | 88K |
| 03:00 UTC | -4% | 94K |
| 04:00 UTC | -5% | 76K |
| 05:00 UTC | -4% | 75K |
| 06:00 UTC | -5% | 86K |
| 07:00 UTC | -5% | 93K |
| 08:00 UTC | -4% | 108K |
| 09:00 UTC | -4% | 124K |
| 10:00 UTC | -3% | 129K |
| 11:00 UTC | -3% | 141K |
| 12:00 UTC | -3% | 154K |
| 13:00 UTC | -3% | 167K |
| 14:00 UTC | -4% | 173K |
| 15:00 UTC | -2% | 176K |
| 16:00 UTC | -3% | 171K |
| 17:00 UTC | -3% | 159K |
| 18:00 UTC | -2% | 149K |
| 19:00 UTC | -2% | 141K |
| 20:00 UTC | -1% | 131K |
| 21:00 UTC | 0% | 116K |
| 22:00 UTC | -2% | 100K |
| 23:00 UTC | -1% | 90K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 393K |
| Monday | +1% | 483K |
| Tuesday | -2% | 520K |
| Wednesday | -3% | 472K |
| Thursday | -2% | 430K |
| Friday | -3% | 447K |
| Saturday | +2% | 393K |
Formats this account uses
Its own posting mix on the left, and what each of those formats does across every account we track on the right. Only formats where the effect clears our publish test appear here, so an empty row is a format we could not measure rather than one that does nothing.
| Format | This account | Catalog effect | 95% interval | Accounts behind it |
|---|---|---|---|---|
| Image or video | 100% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 0% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | -3% | -3% to -2% | 54K |
- 100% of this account's sampled posts carry an image or video. Across the catalog, posts with an image or video run 111% above the same accounts' other posts.
- 0% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts.
- Its average post runs 80 characters, which falls in the 80 - 180 characters band. Across the catalog, posts of 80 to 180 characters run 3% below the same accounts' other posts.
These are catalog-wide differences applied to this account's own posting mix, not a measurement of how each format performs for this account specifically. We keep one median per account, not one per format per account, so the second thing is not something this data can tell you.
Best tweets
- Jul 1, 20265.0x their median
How the CPA audit in AlphaLedger works - without marketing language. Step 1: Direct broker integration. Data flows directly from the broker. No manual entry - the source cannot be compromised. Step 2: Blockchain hashing. Each trade receives a cryptographic hash at execution. Changing it retroactively is mathematically impossible. Step 3: Independent US CPA audit. A certified public accountant verifies record integrity and signs the opinion. Step 4: Publication of verified profile. The track record becomes accessible to qualified investors through the leaderboard. This is the new standard of transparency → https://t.co/WKUSVS0cvf
- Jun 17, 20262.0x their median
5 metrics an investor checks first. If you're raising capital - you must know how an allocator thinks. Sharpe Ratio - risk-adjusted return. Entry threshold: 1.2+ Maximum Drawdown - your worst period. Above 20% - serious conversation in question. Calmar Ratio - annual return vs maximum drawdown. Target: 2.0+ Consistency Score - not one lucky month, but systematic stability. Verified Status - without independent audit, everything above is just words. AlphaLedger tracks all these metrics automatically and adds verification that no ordinary trading journal can provide. Know your numbers. Prove them → https://t.co/WKUSVS0cvf
- Jun 3, 20262.0x their median
One metric that separates "good trader" from "capital manager". It's Sharpe Ratio. Most traders optimize returns. Institutional investors optimize risk-adjusted returns. Formula: Return + Volatility - Sharpe < 1.0: more risk than return per unit - Sharpe 1.0-1.5: competitive level. You're on the radar. - Sharpe > 1.5: family offices and prop firms are looking for you AlphaLedger tracks Sharpe in real time - among 8,000+ analytical data points. You can't manage what you don't measure.
- Jun 1, 20262.0x their median
Every day without a verified track record is missed capital - investors aren't looking for good traders, they're looking for proven results. AlphaLedger bridges this gap with blockchain-verified trades, CPA-certified audits, and an investor leaderboard that brings capital to you. If you've traded consistently for 6+ months, you already have a track record - you just haven't verified it yet.
- May 13, 20262.0x their median
Two years of strong performance is not proof of edge. It might be. But it might not be. Markets run in regimes. Conditions favor certain approaches and punish others - sometimes for extended periods. A strategy that's been running in favorable conditions for two years hasn't been tested. It's been lucky enough not to need testing yet. The questions that matter: does the strategy hold when volatility shifts, are returns consistent across different regimes, is the sample size large enough to separate skill from a prolonged favorable period? AlphaLedger tracks performance across time and volatility regimes - so traders can see whether what's working is genuinely repeatable, or whether it's been riding conditions that won't last. Find out which one you actually have → https://t.co/WKUSVS0cvf Or comment "EDGE" and we'll show you how your strategy holds up across changing conditions.
- Apr 15, 20262.0x their median
A journal entry doesn't explain a losing trade. It just documents one. Documentation without analysis is just a log - it captures the surface and stops there. Why does the strategy perform differently across conditions? What behavioral pattern keeps showing up in the losing clusters? AlphaLedger is built to answer them - analyzing behavioral patterns across full trading history, measuring capital efficiency in risk-adjusted outcomes, revealing why performance looks the way it does, not just when it changed. The difference between a journal and a performance system is the difference between recording and understanding. Move beyond the log → https://t.co/WKUSVS0cvf Or comment "INTELLIGENCE" and we'll show you what static tracking has been missing.
- Aug 28, 2026
The investigation has begun. AlphaLedger is online. 🚨 https://t.co/KNLQ9ZQhYJ
- Jul 29, 2026
Win rate is one of the most intuitive metrics in trading and one of the most frequently misread by allocators. What matters is mathematical expectancy: what the system does to capital over a long series of trades. 75% win rate with a weak risk-reward can slowly destroy capital. 40% win rate with a strong risk-reward can steadily compound it. The allocator thinks forward. That’s why expectancy matters more than a pretty win frequency.
- Jul 22, 2026
An institutional investor doesn't look at an aggregated P&L curve. He breaks it down by market regime. What he checks: → how the strategy behaves in volatility → how it moves through drawdowns → what explains the best periods The final question: will the investor receive the same system after capital allocation. How do you explain your strategy's behavior across different market regimes to investors? Share your approach in the comments.
- Jul 8, 2026
Your data already exists. The only question is - is it verified? Every trader who trades through a broker already has a track record. The problem: only the trader sees it. An investor sees only what the trader chooses to show and may not trust it. AlphaLedger bridges the gap - direct broker integration, automatic blockchain verification of every trade. One step turns a trade history into an institutional-grade track record → https://t.co/WKUSVS0cvf
Ranked by total interactions across everything we have tracked for this account, which is a longer history than the 30-day window the rates above use. The multiple compares each post to this account's own median.
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Reading these numbers
A typical post picks up 1 interaction against 369K followers, an engagement rate of 0%. Posts are seen about 1.6K times each, and 0.062% of those impressions turn into an interaction. That is about 0.44% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.03 posts a day over the last 30 days, though only 3% of days saw any activity at all. Most posts go out around 14:00 UTC, and Friday is the busiest day of the week. Of the 1 posts sampled, 100% carry an image or video. The account's strongest tracked post pulled 5 interactions, about 5.0x its own typical post. Only 1 original post have been captured so far, fewer than the 8 posts we want behind a median before treating it as settled. Read the figures above as an early measurement of this account, not as a finished profile of it.
- What is AlphaLedger's engagement rate on X?
- AlphaLedger (@byalphaledger) has an engagement rate of 0%, based on the median interactions across 1 original posts from the last 30 days against 368,944 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At under 0.001%, AlphaLedger sits below the 10th percentile of the 205,048 accounts in this comparison. Those comparison accounts are all large ones, because our scanning cadence is weighted towards big accounts, so this is a ranking among peers of similar scale rather than a ranking across X.
- Does @byalphaledger have real engagement?
- There is not yet enough sample to rank this account against others of its size.
- When does @byalphaledger post?
- Most posts go out around 14:00 UTC, and Friday is its busiest day, at roughly 0.03 posts per day across the measured window.