Channel
@metabacktest
On this record: Topic · Growth · Engagement · Posts · Cite this entry
6subscribers
+0 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1002213058839 |
|---|---|
| Type | Channel |
| Username | @metabacktest |
| Created | 6 August 2024 — measured — dated from the channel’s first post |
| First recorded | 12 August 2026 |
| Last confirmed live | 2 October 2026 |
| Measurements held | 4 |
| Confirmed unchanged | 3 times, most recently 2 October 2026 |
| On Telegram | t.me/metabacktest |
Crypto & trading — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 2 October 2026 and assigned it the closest of 31 fixed categories, at 96% 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.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 9 Sept 2026, 16:21 | 6 | +1 |
| 15 Aug 2026, 05:25 | 5 | -1 |
| 12 Aug 2026, 22:46 | 6 | no change |
| 7 Aug 2026, 17:04 | 6 | first reading |
14 posts held, back to 6 August 2024 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 page of Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 14 posts for this entry, the most recent from 29 October 2025. An engagement rate over an empty window would be a number about nothing.
MQL4 Hidden Markov Random Adaptive Forex Model with Continuous Emissions This work demonstrates how a Hidden Markov–based adaptive layer can transform random trade outcomes into probabilistic state estimates guiding position size. The concept offers an experimental path for traders and researchers to investigate the relationship between randomness, inference, and profitability . Read More on MetaBacktest .c…
Adaptive Randomized Bernoulli Cascade System The Bernoulli Cascade System demonstrates a new paradigm in algorithmic design — one that replaces market prediction with statistical validation. By structuring a random process and evaluating its outputs through mathematically sound filters, The Bernoulli Cascade System turns randomness itself into a tool of adaptation. While unconventional, its beauty lies in its …
How to Build a Probability-Aware EMA Trading Strategy Using the Binomial Distribution in MQL4 This guide explores how to: •Combine EMA8/EMA21 signals with binomial probability logic •Measure confidence in your recent trade performance •Dynamically adjust lot sizes based on statistical evidence — not emotion •Implement everything directly in MQL4 The Binomial distribution offers a statistical measure of confidence…
Backtesting the Evening Star & Morning Star Patterns In this article, we’ll break down both patterns in detail, explain their trading significance, and then show how to automate the strategy with a clean MQL4 Expert Advisor (EA) that applies modern money management, time filters, and flexible detection parameters. The Morning Star and Evening Star are timeless candlestick reversal patterns, and with the help…
Signed dh
Channel photo updated
Piercing Pattern & Dark Cloud Cover EA - Candlestick Trading in Forex... At MetaBacktest, we aim to blend elegance with effectiveness in our trading solutions. Our "Piercing Pattern & Dark Cloud Cover EA" exemplifies this approach: it automatically identifies key reversal candlestick formations and executes trades with advanced risk management, precise money control, and smart filtering. This EA focuses on Piercin…
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Backtesting the “Three White Soldiers” and “Three Black Crows” with an Automated Expert Advisor... In this article, we’ll walk through an Expert Advisor (MQL4 EA) that automatically trades the Three White Soldiers (bullish reversal) and the Three Black Crows (bearish reversal) patterns. We’ll also explain its smart money management features, the ATR-based stop loss logic , and how you can tune it to your…
Signed dh
Does the MACD Zero Line Strategy Work? Backtest Results Revealed At MetaBacktest, we love testing simple trading strategies that are easy to understand, quick to implement, and suitable for both beginners and advanced traders. In this article, we’ll take a closer look at the MACD Zero Line Strategy, build an Expert Advisor (EA) in MQL4, and share backtest results so you can see how it performs in real market cond…
Signed dh
Backtesting Stochastic Strategy with Smart Money Management and Time Filtering via MetaBacktest: The Stochastic Oscillator is one of the most popular momentum indicators in trading. It helps traders identify overbought and oversold conditions, where markets may be due for a reversal. To automate this approach, we designed a custom Expert Advisor (EA) for MetaTrader 4 that combines Stochastic signals with smart money…
Signed dh
Backtesting 'EMA-CrossOver' strategy with a custom setting file: In this section, we will create a customized settings file and conduct a backtest using MetaBacktest. Read More https://www.metabacktest.com/articles/2/Backtesting-EMA-CrossOver-strategy-with-a-custom-setting-file
Signed dh
Comparing backtests on various symbols with MetaBacktest: we’re diving into the mystical realm of backtesting—where dreams of profits go to dance with the ghosts of trades past. Now, grab your digital compass because we’re embarking on a thrilling expedition to explore our very own EMA-Crossover strategy! Read More
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Is a simple 'EMA-CrossOver' strategy, a winning one for the long term? we will develop an MQL4 Expert Advisor from the ground up, designed to execute automatic trades based on exponential moving average (EMA) crossovers. Subsequently, we will conduct a backtest utilizing high-quality data spanning over a decade across various currency pairs. This analysis aims to determine the long-term efficacy of this straightforw…
Signed dh
Showing the 12 most recent of 14 posts we hold for @metabacktest. 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.
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 9 September 2026 — this entry's latest reading, not the date you are reading this.
“Metabacktest” (@metabacktest), 6 subscribers as measured 9 September 2026. Telegram Register, tgregister.com/channel/metabacktest.
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.