Algorithmic Trading Robot for Prop Firm Evaluations

Discover a new algorithmic trading robot designed to pass prop firm evaluations. Learn how it uses scaling management to boost success in funding challenges.

Published · Updated · Methodology: Mixed

Part of: Algorithmic & Automated Trading

  • Methodology: Mixed
  • Content type: educational

Source video

Decoded from: El Robot que me va a Pasar muchas Evaluaciones de Fondeo este 2025 by Hobbiecode — watch the original

Key timestamps:

  • 0:00 - Introduction to the new trading robot
  • 0:10 - Mention of scaling management strategy
  • 0:20 - Promise to explain the robot in detail and show results

Strategy overview

A robot built for prop firm evaluations is designed against a rulebook rather than against a market: the target is to reach a profit objective without breaching a daily loss or maximum drawdown limit inside a defined window. This entry decodes Hobbiecode's video "El Robot que me va a Pasar muchas Evaluaciones de Fondeo este 2025" ("The Robot That Will Pass Me Many Funding Evaluations This 2025"), and the plural in that title is the first thing worth noticing — the robot is framed as something to be run repeatedly across accounts and across a year, not as a single setup to be traded once. That framing changes what "working" means: the success metric on offer is evaluations passed, a throughput figure, rather than per-trade quality.

The second thing worth noticing is where the published index stops. The available timestamps cover only the opening twenty seconds — an introduction to the robot, a mention of a scaling management approach, and a promise to explain the system in detail and show results afterwards. So the indexed portion of this source is an announcement, and the one design commitment it surfaces before the detail begins is that position sizing is treated as part of the robot rather than as something bolted on later. In an evaluation account that ordering is not cosmetic: the sizing schedule is the component that actually meets the drawdown rule, because it determines how much of the allowance a losing sequence consumes.

No rules were extracted for this entry — no timeframes, no indicators, no entry or exit logic — and the methodology is catalogued only as mixed. What this page holds is context around the video and the channel, not a decoded ruleset, and results referenced in a video title are claims made by its author ahead of the period they describe rather than a track record. For the specifics of how the robot is built and sized, the source video itself is the reference.

Topics

algorithmic trading · trading robot · prop firm evaluation · prop firm passing bot · funding challenge strategy · ea trading strategy · tradingview strategy · pine script · automated trading · scalping strategy · swing trading

Frequently asked questions

What is an algorithmic trading robot for prop firm evaluations?

It is an automated system whose design constraints come from a funding provider's rulebook — a profit target plus daily loss and maximum drawdown limits inside a set window — rather than from open-ended profit seeking. The rulebook, not the market, defines what a passing outcome looks like.

What does 'scaling management' mean in this context?

The video's opening index mentions a scaling management strategy without defining it at that point. In general the term refers to how position size is adjusted as the account balance or the evaluation progresses; the source video is where its specific meaning here is explained.

Why does position sizing matter more in an evaluation than in a personal account?

Because the drawdown limit is enforced externally and ends the attempt when breached. Entry logic decides which trades happen; the sizing schedule decides how much of the allowed drawdown a losing sequence consumes, which is what determines whether the evaluation survives it.

Does this page include the robot's rules?

No. Nothing was extracted for this entry — the indexed portion of the source covers only its opening announcement, so no entry, exit, timeframe or indicator details are available here. Strategy Decoder catalogues video sources like this one and extracts structure where the source makes it explicit.

About this strategy page

This trading strategy was decoded by Strategy Decoder's AI from a public YouTube trading video and turned into a structured, reviewable specification. In the interactive app this page shows the full entry and exit logic, risk management settings, the indicators involved with their parameters, AlgoWizard-compatible logic and a Pine Script export ready for TradingView backtesting — plus an automated backtest verdict when one has been computed for this strategy.

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