Crypto & trading — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-UD-Q6_K_XL, prompt version 1) read this channel’s own recent posts on 8 August 2026 and assigned it the closest of 31 fixed categories, at 95% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.
Growth
6 measurements spanning 5 days, net +2,629. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 532,869–536,286 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
12 Aug 2026, 04:18
535,892
+799
11 Aug 2026, 06:38
535,093
+746
10 Aug 2026, 04:58
534,347
+535
9 Aug 2026, 02:33
533,812
+219
8 Aug 2026, 02:30
533,593
+330
7 Aug 2026, 12:33
533,263
first reading
Engagement
60 posts held, back to 5 August 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 16 pagesof Telegram’s post history, 20 posts per page.
ERR · 30 days
2.14%
avg views ÷ 535,892 subscribers
Avg views / post
11,500
59 posts measured
Reaction rate
0.252%
reactions ÷ views · ER floor
Posts in window
60
of 60 held
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views— note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.
What these figures were computed from
Window
Rolling 30 days · latest post in window 12 August 2026
Posts held
60 (5 August 2026 – 12 August 2026)
Views total
676,640
Reactions total
1,706
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
12 Aug 2026, 17:38 UTC
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
What this channel posts
Photos
≈3,770
Videos
≈6
Links
≈3,770
Lifetime counters from Telegram’s own channel header, read 12 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.
Reaction mix
1,706 reactions across 59 posts, in 17 distinct kinds. The most used accounts for 62.5% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
1,066
62.5%
👍
448
26.3%
👌
93
5.45%
👨💻
22
1.29%
🔥
18
1.06%
⚡
13
0.762%
🎉
7
0.41%
👀
7
0.41%
✍
6
0.352%
🤣
6
0.352%
🏆
4
0.234%
🤔
4
0.234%
🤝
3
0.176%
🤡
3
0.176%
👾
2
0.117%
😁
2
0.117%
🤯
2
0.117%
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 59 of the 60 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 1,706reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 60 most recent posts we hold, published 5 August 2026 to 12 August 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
This article ports level-2 path signatures to MQL5 to measure lead–lag without scanning lags. Instead of shifting series and correlating, it treats two aligned streams as a 2D path and uses the Levy area (the antisymmetric part of level 2) as an ordering-sensitive statistic: sign indicates which channel tends to move first, and zero-crossings mark flips.
The implementation builds a correct incremental engine via Che…
MQL5 backtests often assume ideal fills. Live trading adds slippage, spread widening, requotes, price changes, and latency across the full path: terminal/VPS, network, broker gateway, and liquidity.
A diagnostic EA, Execution Quality Monitor, records execution metrics to separate strategy issues from execution issues. It runs in two modes: an active probe that opens and closes a minimum-lot trade and measures reques…
Slice sampling is presented as an adaptive MCMC alternative that avoids the step-size tuning pain of Metropolis and the conditional-derivation burden of Gibbs. It only needs an unnormalized density (typically evaluated as a log-posterior), then samples by defining a “slice” level, expanding or placing an interval/hyperrectangle, and shrinking it until a valid draw is found.
The article details an MQL5 implementation…
A multi-timeframe trend-agreement indicator consolidates fast/slow moving-average direction across three configurable horizons (default H4, D1, W1) into a single bias score from -3 to +3. Each timeframe contributes +1 when FastMA > SlowMA, -1 when below, and 0 when equal. MA method is selectable (SMA, EMA, SMMA, LWMA) and applied consistently.
The histogram reports only the current live state, avoiding backfilled hi…
Trade Manager is an MT5 Expert Advisor focused on fast manual execution with automated risk controls. Trade parameters are managed on-chart through draggable horizontal lines for entry, stop loss, and take profit, with real-time recalculation as levels are adjusted.
The interface uses native MT5 graphic objects and supports instant minimize/maximize. Two workflows are provided: market execution at current Bid/Ask, a…
A mean-reversion signal indicator pairs Bollinger Bands with an embedded Stochastic RSI and triggers only after a close returns inside the band following an oscillator extreme.
Bearish logic: the prior bar closes above the upper band while StochRSI %K and %D are above the upper limit, then the current bar closes back inside. Bullish logic mirrors this below the lower band with both lines under the lower limit. Arrow…
Indicator architecture follows an event-driven, sequential pipeline split across OnInit, OnCalculate, and OnTimer, with additional tick-level checks for alerts.
OnInit sets chart precision from _Digits, removes legacy objects prefixed with “SMC_OB_”, and initializes buffers plus state arrays.
OnCalculate iterates from prev_calculated - 1 for incremental updates. A price-boundary filter skips non-essential work when…
Reinforcement learning in algo trading shifts strategy creation from fixed rules to continuous updating from trade outcomes. An MQL5/MetaTrader 5 implementation uses a multi-agent layout with experience stored as discrete “memory neurons” and aggregated into independent decision agents.
Each neuron encodes a quantized 32-bit market state, a continuous action value (sell to buy), activation stats, and a score combini…
MQL evolved from early “Expert Advisor” concepts into an integrated environment where strategy code runs inside the terminal, with direct access to market series, indicators, and order execution. The key shift was treating automation as native, not an external add-on.
Early MQL was minimal, but MetaTrader 2 introduced MQL II with loops, arrays, richer types, and math functions, enabling real program logic per tick. …
TimeFound reframes forecasting as a foundation-model problem: a Transformer pre-trained on diverse time series can produce usable predictions even with little or no asset history, enabling zero-shot forecasts for newly traded instruments.
Core mechanics combine encoder–decoder attention with multi-resolution patching, capturing fast spikes and slow trends simultaneously. Standardization aligns scales across domains,…
Backtesting a custom MQL5 indicator in Python often forces a reimplementation that can drift due to bar indexing, rounding, or warm-up rules. A cleaner approach is to export the exact indicator buffers calculated by the terminal and read them in Python as-is.
An MQL5 script can attach via iCustom(), wait using BarsCalculated(), fetch rates with CopyRates() and buffers with CopyBuffer(), then write a locale-stable CS…
Showing the 12 most recent of 60 posts we hold for @mql5dev. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Citation-graph rank
Citation-graph rank — 338,654 of 1,151,006entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
Forward network
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Mentions
Named by 2 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
Domains linked from posts
4 domainsthis channel’s own posts have linked to, measured by scanning the post bodies themselves — not the channel’s description, which is the separate Declared links section below when this entry has one. Appearing here is not a claim about who runs the linked site or why the channel linked to it; an advertisement, a news citation and a malicious link all leave the same kind of row.
A Wikidata item names this Telegram handle as belonging to the entity it describes. This is Wikidata’s claim, not a verification made by this register — nobody here confirmed that the account is genuinely operated by the entity named. Wikidata content is CC0; every fact below is dated to when it was read from Wikidata, not to when the association was first made there.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 12 August 2026 — this
entry's latest reading, not the date you are reading this.
“MQL5 Algo Trading” (@mql5dev), 535,892 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/mql5dev.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.
