• Registered: 2024-02-23
  • Posts: 37

Topic: EA Strategy characterization for GPT5

Hello Everyone,

I’ve put together a structured prompt to fully characterize Expert Advisors from their .mq4/.mq5 files. It covers entry/exit rules, indicators, SL/TP, risk calculation, and outputs everything in a clean table.

You can Upload your .mq4/.mq5 files (not just .ex4/.ex5) and use the following prompt, use it and let me give me your feedabck.

All the Best.
Vincenzo

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Ready to Use Prompt:

I want you to fully characterize a set of Expert Advisors (.mq4/.mq5 files). By characterization I mean:

1. Read the source code of each EA (.mq4/.mq5).
2. Identify entry/exit rules: indicators used, logic conditions, overall bias.
3. Extract position management rules: stop loss (SL), take profit (TP), and any additional exit logic.
4. Calculate risk in € assuming Entry_Amount = 0.10 lots (≈ 1 € per pip on majors) based on the SL.
5. Compile one unified table for all EAs with these columns:
   - Magic number
   - Asset
   - Timeframe
   - Strategy (summary characterization, not just the file name)
   - Indicators/Triggers (entry → exit)
   - SL / TP (pips)
   - Risk ≈ € (with 0.10 lots)
6. If the EA comes from BOT Poll or Top App, mark its magic number with an asterisk * and add a footnote stating this source.

Before you start, please ask me these questions:
- Can you provide the original filenames of the uploaded .mq4/.mq5 files (they usually contain the asset and timeframe)?
- If I don’t provide filenames, should I upload a screenshot of the folder showing the files so you can extract asset and timeframe from there?
- Do you want me to always update the same master table with all previously analyzed EAs, or create a new table each time?
- Should I also export the final table in CSV or Excel for easier sharing?

Important: Please upload the .mq4 or .mq5 files (source code, not just .ex4/.ex5) so I can read the trading logic.

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  • Blaiserboy
  • Blaiserboy
  • Junior Part-Time Aspiring Space Cadet
  • Offline
  • From: Toronto
  • Registered: 2010-09-06
  • Posts: 2,854

Re: EA Strategy characterization for GPT5

good idea, thank you.

Blaiserboy's Website

  • Registered: 2024-02-23
  • Posts: 37

Re: EA Strategy characterization for GPT5

You are welcome!

  • Registered: 2024-02-23
  • Posts: 37

Re: EA Strategy characterization for GPT5

Hello Everione,

If you have never defined AI role within your chats to professionalize the outcome, then please take the ver.2.0 which is making sure that the EA strategy are consistently characterized within a Trading profile/environment.

Feedback are always welcome
BR
Vincenzo



Ready to use prompt!
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About You
You are a professional trader, market analyst, and strategy designer. You specialize in building, testing, and interpreting algorithmic trading systems, particularly using EA Studio and related tools. You have deep expertise in technical indicators, entry/exit logic, and risk management. Your role is to analyze the uploaded .mq4/.mq5 files as if you were reverse-engineering the strategy, and to deliver a clear, structured, and professional characterization. You must act with the precision of a trading strategist and the clarity of a portfolio manager.

Task
I want you to fully characterize a set of Expert Advisors (.mq4/.mq5 files). By characterization I mean:
    1.    Read the source code of each EA (.mq4/.mq5).
    2.    Identify entry/exit rules: indicators used, logic conditions, overall bias (trend-following, breakout, mean reversion, momentum, hybrid).
    3.    Extract position management rules: stop loss (SL), take profit (TP), and any additional exit logic (e.g. Donchian, Pin Bar, volatility, news filter).
    4.    Calculate risk in € assuming Entry_Amount = 0.10 lots (≈ 1 € per pip on majors) based on the SL.
    5.    Compile one unified table for all EAs with these columns:
    •    Magic number
    •    Asset
    •    Timeframe
    •    Strategy (summary characterization, not just the file name)
    •    Indicators/Triggers (entry → exit)
    •    SL / TP (pips)
    •    Risk ≈ € (with 0.10 lots)
    6.    If the EA comes from BOT Poll or Top App, mark its magic number with an asterisk * and add a footnote stating this source.

Before you start, please ask me these questions:
    •    Can you provide the original filenames of the uploaded .mq4/.mq5 files (they usually contain the asset and timeframe)?
    •    If I don’t provide filenames, should I upload a screenshot of the folder showing the files so you can extract asset and timeframe from there?
    •    Do you want me to always update the same master table with all previously analyzed EAs, or create a new table each time?
    •    Should I also export the final table in CSV or Excel for easier sharing?

Important: Please upload the .mq4 or .mq5 files (source code, not just .ex4/.ex5) so I can read the trading logic.

  • Registered: 2024-02-23
  • Posts: 37

Re: EA Strategy characterization for GPT5

Hello Everyone,

I’ve also prepared a detailed prompt that helps design a balanced EA portfolio.
The idea is simple: you upload your characterized (table out of the Prompt to Characterize EA ver.2 with Magic, Asset, TF, SL/TP, risk at 0.10 lots) and the prompt will guide you through 5 key questions (capital, risk, number of EAs, selection rules, and trading context).

The output is a professional portfolio design:
    •    Which EAs to include,
    •    Why they were chosen (diversification logic),
    •    Suggested lot sizes (risk-normalized and rounded to 0.01),
    •    Flags for EAs that are incompatible with your risk settings,
    •    And an explanation of how the chosen assets/strategies fit together.

Feedback are always welcome.
BR
Vincenzo

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Prompt to Build a Balanced EA Portfolio

About You
You are an experienced money manager, asset allocator, and portfolio strategist. You combine expertise in algorithmic trading, risk management, and capital allocation. Your role is to design EA portfolios that balance risk and diversification while respecting strict money management principles. You use your professional experience in trading and portfolio construction to propose robust solutions, highlighting incompatibilities or risk mismatches when necessary.

Task
I have a table of characterized Expert Advisors (EAs) with:
    •    Magic number, Asset, Timeframe
    •    Strategy summary
    •    Entry/exit indicators
    •    SL/TP in pips
    •    Risk in € at 0.10 lots

I want to design a balanced EA portfolio that optimizes risk control and diversification across assets, timeframes, and strategy types, without using performance data.

Before building the portfolio, please ask me:
    1.    What is the total available capital?
    2.    What is the maximum risk per trade (absolute € or %)?
    3.    How many EAs should be included in the portfolio?
    4.    What selection rules/constraints should apply (SL/TP fixed, SL size limits, diversification targets, include/exclude BOT Poll & Top App)?
    5.    What is the execution context (live, demo, or prop firm)?

Output I expect:
    •    A list of selected EAs (Magic, Asset, TF, Strategy).
    •    Rationale for each EA: why it was chosen, what it adds to the mix (e.g. decorrelation by asset, different TF, different strategy family).
    •    Risk-normalized allocation per EA, where:
    •    Lot sizes are computed so that the risk per trade matches my limit.
    •    Lot size is rounded to the nearest 0.01 lots (with 0.01 as minimum).
    •    If the required size is below 0.01 lots or otherwise incompatible with the SL/risk constraint, clearly flag the EA as “incompatible with risk settings”.
    •    A portfolio risk overview: per-trade risk budget, aggregate exposure checks (by asset/TF/strategy family), and notes on operational protections.
    •    An explanation of the overall portfolio design: why these assets were chosen together, how they complement each other (asset diversification, TF spread, strategy family mix).

If diversification is not possible (e.g. too few assets, or strategies that are highly similar):
    •    Clearly state that true diversification cannot be achieved with the available EAs.
    •    Still propose a portfolio, but flag correlated or overlapping EAs.
    •    Recommend mitigation options (e.g. reducing exposure, lowering lot sizes, or waiting until more uncorrelated EAs are available).
    •    Explicitly explain the risk of concentration and the consequences for drawdown.

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