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Channel

Trading Algorítmico MQL5

@mql5es

On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Telegram's recommendations · Cite this entry

44,758subscribers

+998 since we began measuring on 7 August 2026

Risers and fallers across the register · movement among entries of 31,623–100,000.

Register entry

Telegram ID-1002196668706
TypeChannel
Username@mql5es
CreatedBetween 1 June 2024 and 30 September 2024— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded7 August 2026
Last confirmed live28 August 2026
Measurements held20
Confirmed unchanged1 time, most recently 28 August 2026
On Telegramt.me/mql5es

Growth

43,76044,75844,2597 August 2026 — 43,760 subscribers8 August 2026 — 43,788 subscribers9 August 2026 — 43,810 subscribers10 August 2026 — 43,827 subscribers11 August 2026 — 43,869 subscribers12 August 2026 — 43,948 subscribers13 August 2026 — 43,997 subscribers14 August 2026 — 44,067 subscribers15 August 2026 — 44,136 subscribers17 August 2026 — 44,168 subscribers18 August 2026 — 44,223 subscribers19 August 2026 — 44,251 subscribers20 August 2026 — 44,305 subscribers21 August 2026 — 44,343 subscribers23 August 2026 — 44,420 subscribers24 August 2026 — 44,488 subscribers25 August 2026 — 44,592 subscribers26 August 2026 — 44,663 subscribers27 August 2026 — 44,702 subscribers28 August 2026 — 44,758 subscribers7 August 202628 August 2026
20 measurements spanning 21 days, net +998. 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 43,610–44,908 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
28 Aug 2026, 19:4744,758+56
27 Aug 2026, 22:1744,702+39
26 Aug 2026, 23:4844,663+71
25 Aug 2026, 22:0344,592+104
24 Aug 2026, 19:3544,488+68
23 Aug 2026, 06:3644,420+77
21 Aug 2026, 16:2844,343+38
20 Aug 2026, 17:1844,305+54
19 Aug 2026, 13:4244,251+28
18 Aug 2026, 13:1344,223+55
17 Aug 2026, 10:1644,168+32
15 Aug 2026, 19:3544,136+69
14 Aug 2026, 08:1744,067+70
13 Aug 2026, 02:5643,997+49
12 Aug 2026, 05:4243,948+79
11 Aug 2026, 02:2543,869+42
10 Aug 2026, 02:2143,827+17
9 Aug 2026, 04:1243,810+22
8 Aug 2026, 05:2043,788+28
7 Aug 2026, 16:1643,760first reading

Engagement

95 posts held, back to 2 August 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 46 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
4.65%
avg views ÷ 44,758 subscribers
Avg views / post
2,080
95 posts measured
Reaction rate
0.331%
reactions ÷ views · ER floor
Posts in window
95
of 95 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
WindowRolling 30 days · latest post in window 28 August 2026
Posts held95 (2 August 202628 August 2026)
Views total197,724
Reactions total654
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken28 Aug 2026, 16:01 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

Video runtime
2m 31s
Average length
2m 31s

Measured directly from 1 video with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no mark.

Reaction mix

654 reactions across 95 posts, in 11 distinct kinds. The most used accounts for 36.9% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
24136.9%
19129.2%
👌10716.4%
👍8212.5%
🔥192.91%
🏆40.612%
👏30.459%
20.306%
👀20.306%
🤣20.306%
💔10.153%

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 95 of the 95 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 654reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 95 most recent posts we hold, published 2 August 2026 to 28 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.

Recent posts

28 Aug 2026, 10:00 UTC724 views2 reactionsread 28 August 2026
Photo

Serie técnica sobre reducción de latencia en señales de cruce de medias móviles. Tras probar predicción estadística de cruces y un esquema con mismo período aplicado a apertura/cierre, se evalúa un doble cruce multi‑timeframe. El sesgo se define en un marco superior (D1) y la entrada se valida en uno inferior (M15). Se contemplan dos modos: seguimiento de tendencia o reversión, y dos reglas de salida: por cruce en m

1👌1

28 Aug 2026, 08:00 UTC713 views2 reactionsread 28 August 2026
Photo

Se evalúa la combinación de Awesome Oscillator (AO) y Envelopes para unir confirmación de tendencia con zonas dinámicas de soporte/resistencia. Se diseñan 10 patrones y se prueban en USD/JPY M30: 2023 para entrenamiento y 2024 como forward. AO mide impulso como diferencia de SMA(5) y SMA(34) del precio medio; los cruces de cero, extremos y divergencias aportan lectura de tendencia y reversión. Envelopes crea bandas

👌1👍1

28 Aug 2026, 06:00 UTC745 views3 reactionsread 28 August 2026
Photo

Value Area Levels calcula la distribución de precio y volumen a partir de velas de marcos inferiores y publica niveles clave del perfil de mercado: VAH, POC y VAL. También mantiene niveles en desarrollo (DVAH, DPOC, DVAL) y niveles previos ya completados (PDVAH, PDPOC, PDVAL) visibles durante la sesión actual como referencia operativa. El periodo del perfil es configurable e independiente del marco del gráfico: diar

11👌1

28 Aug 2026, 04:00 UTC912 views3 reactionsread 28 August 2026
Photo

Indicador de flujo de órdenes tipo footprint y huella institucional, portado de Pine Script a MQL5. Descompone barras de marcos principales usando volumen real y/o tick histórico de marcos inferiores para estimar delta compra/venta por nivel de precio. Incluye selección automática de timeframe, resaltado de POC, desequilibrios apilados, detección de absorción extrema y seguimiento de órdenes pendientes (UB) con miti

2👌1

27 Aug 2026, 12:00 UTC≈1,740 views6 reactionsread 28 August 2026
Photo

Lang y Gao publicaron en marzo de 2025 el Dream Optimization Algorithm (DOA) en Computer Methods in Applied Mechanics and Engineering (vol. 436). Es un metaheurístico poblacional aplicable a optimización de parámetros, incluido trading algorítmico. DOA separa la población en grupos con distintos niveles de memoria. En exploración, cada agente se reubica en el mejor del grupo y luego aplica olvido selectivo o compart

32👌1

27 Aug 2026, 10:00 UTC≈1,490 views4 reactionsread 28 August 2026
Photo

TQNet aborda la predicción multivariante en finanzas combinando dos necesidades opuestas: reaccionar a cambios intradía y conservar relaciones estables entre activos a lo largo del historial. La clave es Temporal Query: en la atención multi-cabeza, las consultas no salen del dato, sino de vectores entrenables que se desplazan periódicamente (t mod W). Esto introduce “memoria” de ciclos (sesiones, estacionalidad), mi

11👌1👍1

27 Aug 2026, 08:00 UTC≈1,330 views4 reactionsread 28 August 2026
Photo

El Duelist Algorithm propone una optimización poblacional distinta a los genéticos clásicos para ajustar parámetros de estrategias en MT5: separa el comportamiento de ganadores y perdedores para reducir mutaciones y cruces “a ciegas”. Los perdedores mejoran copiando parcialmente parámetros del ganador según una probabilidad de aprendizaje. Los ganadores exploran mediante innovación: mutaciones discretizadas por rang

2👌1👍1

27 Aug 2026, 06:00 UTC≈1,260 views3 reactionsread 28 August 2026
Photo

Muchos operadores en MT5 dimensionan posiciones de forma aislada y no evalúan el efecto conjunto como cartera. Instrumentos aparentemente independientes pueden moverse de forma similar ante el mismo evento, y varias posiciones pequeñas pueden acumular un margen combinado relevante. Un indicador procesa listas de símbolos y lotajes y genera un informe en “Expertos” y, opcionalmente, en el gráfico. Incluye matriz de c

1🏆1👌1

27 Aug 2026, 04:00 UTC≈1,340 views8 reactionsread 28 August 2026
Photo

Se presenta un prototipo de negociación ligero basado en gestión interactiva de zonas de oferta y demanda. No pretende ser una metodología completa ni un sistema automatizado final; sirve para mostrar la integración de análisis de zonas, validación de señales, gestión de operaciones y evaluación del contexto de mercado. El marco se controla mediante parámetros configurables: automatización de señales e identificador

33👌1👍1

26 Aug 2026, 10:00 UTC≈2,030 views5 reactionsread 28 August 2026
Photo

Se presenta una implementación en MQL5 inspirada en requests de Python para simplificar WebRequest en MetaTrader 5, encapsulando método HTTP, cabeceras, timeout y envío de datos. La función central unifica cargas JSON y no JSON, ajusta automáticamente Content-Type y devuelve una respuesta estructurada con status, texto, bytes, JSON parseado, headers, cookies y tiempos. Sobre esa base se exponen wrappers GET/POST/PUT

21👌1👍1

26 Aug 2026, 08:00 UTC≈1,720 views4 reactionsread 28 August 2026
Photo

SSCNN se consolida como arquitectura para series temporales financieras al combinar dependencias espaciales y secuenciales, con Attention-based Normalization para priorizar señales relevantes y estabilizar el entrenamiento. La modularidad permite ajustar el modelo a horizontes cortos o ciclos largos, con ejecución integrada en MQL5 y OpenCL. La fase actual conecta los bloques en un codificador unificado (CNeuronSSCN

21👌1

26 Aug 2026, 06:00 UTC≈1,510 views5 reactionsread 28 August 2026
Photo

Una sola media móvil describe la tendencia de un único marco temporal y puede generar lecturas contradictorias cuando el corto plazo sube y el contexto general sigue bajista. Un panel basado en SMMA por marcos múltiples permite ver la alineación entre M15, H1, H4 u otros desde un solo gráfico. Precio por encima de varias SMMA sugiere sesgo alcista coordinado. Por debajo de todas, sesgo bajista. Por encima solo en el

3👌1👍1

Showing the 12 most recent of 95 posts we hold for @mql5es. 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.

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Telegram’s own answer, not this register’s.When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.

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Read from Telegram’s recommendation API, most recently 28 August 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.

Appears in Telegram’s recommendations for other channels

The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.

MQL5 Algo Trading
@mql5dev · 546,191
Telegram ranks this channel #7 of 66 here — alongside 65 others — read 15 August 2026

This channel appears in 1 seed channel's Telegram-generated recommendation list in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.

Cite this entry

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 28 August 2026 — this entry's latest reading, not the date you are reading this.

“Trading Algorítmico MQL5” (@mql5es), 44,758 subscribers as measured 28 August 2026. Telegram Register, tgregister.com/channel/mql5es.

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.