Quant Apple Scavenger

Quant Scavenger FBS – Apple Series EA

Overview

Quant Scavenger – Apple Series EA is the first release in a structured series of single-asset, ML-based quantitative Expert Advisors.

Each EA in the series is dedicated to one broker-offered asset only. This edition is specifically built for trading Apple Inc. (AAPL CFD).

The embedded quantitative model is trained exclusively on H1 (1-hour timeframe) data. It is not a multi-timeframe system and is designed to operate on H1 only.

Core Design Principles

  • Single asset, single timeframe specialization
  • An ensemble of four independently trained model types — CatBoost, XGBoost, Gradient Boosting, and LightGBM — vote on trade direction. A trade only fires when a configurable minimum number of models agree, reducing single-model overfit risk.
  • A Follow-the-Leader style selection policy periodically re-scores which model combination has performed best on recent closed trades (by win rate, Sharpe, or Sortino) and can reallocate to a different combination live — the system is not statically frozen at purchase time.
  • Stop-loss distance is derived from MAE/MFE analysis on historical trades (how far winning and losing trades actually moved before resolving), rather than an arbitrary fixed-multiple stop.
  • Optional signal gates (confidence threshold, gap size, session hour) let you tighten entry conditions beyond the base ensemble vote.
  • Quantitative, model-driven entries with a fixed holding-period exit — not a discretionary TP/SL chase.
  • Portfolio-based risk allocation
  • Structured, non-scalping execution

This EA is not a generalized multi-market robot. It is engineered specifically for Apple CFD price structure.

Model & Updates

The quantitative model:

  • Is trained on historical H1 data using a walk-forward validation methodology, with evaluation designed to avoid the accuracy inflation that comes from overlapping test windows
  • Uses structured feature inputs derived from price behavior
  • Is embedded into the EA logic via ONNX for fast in-EA inference
  • Is reviewed and retrained 

Monthly/yearly updates allow:

  • Adaptation to evolving volatility regimes
  • Recalibration to structural market changes
  • Improved robustness over time

Each update reflects refreshed training and optimization based on recent market data. 

Verified Performance

The performance are verified for FBS only, due to data availability.

Broker Consideration

Primary optimization performed using FBS CFD data.

Because quantitative systems are sensitive to tick data differences, spread structure, and execution model — and most CFD brokers are also market makers — backtesting on your own broker is recommended before live deployment.

Risk sizing (stop-loss/take-profit) is defined in fixed USD amounts rather than raw price points, so behavior stays consistent across brokers with different tick sizes. A USD-denominated account is assumed for the default risk settings; non-USD accounts should re-check position sizing before going live.

The MAE and MFE analysis advise 600 points as stop loss.

Trading Conditions

  • Required timeframe: H1 only
  • Minimum deposit: 350$ advised; a cent account is a reasonable alternative if you're not ready to commit that amount
  • Instrument: Apple CFD (AAPL)

Not designed for:

  • M1/M5 scalping
  • Multi-timeframe stacking
  • Multi-asset trading inside one EA

Inputs

Risk Management

  • max_lot — hard ceiling on position size regardless of other sizing settings
  • base_lot — starting lot size for fixed-lot mode
  • tpl_usd / sll_usd — take-profit and stop-loss expressed as a fixed USD amount per trade, broker-agnostic

Position Sizing

  • fraction_of_account — percentage of account equity used for dynamic position sizing. Not implemented yet
  • Fix_Lot_Mode — if true, a constant lot size is used instead of dynamic sizing.
  • Fix_Lot — used only when Fix_Lot_Mode = true . If set to 0, the broker's minimum lot size is used.

Model Configuration

  • EnsembleMask — which of the four models are active in the voting pool
  • EnsembleMinAgree — minimum number of active models that must agree before a trade fires (0 = full consensus required)
  • UseLazyFTL / UseLazyFTLModel — enable adaptive pair/model reallocation

Signal Gates (all optional, off by default unless noted)

  • UseConfGate / ConfThreshold — only trade when ensemble confidence exceeds this level, ideal value from calibration lies between 0.55 and 0.58 for higher accuracy
  • UseGapGate / GapGate — filter entries by opening gap size
  • UseHourGate — restrict trading to specific session hours


Trial Version

Contact Me if you have broker in mind and want a free demo.

Operational Notes

  • Not intended to guarantee immediate profits
  • Not immune to market shocks or slippage
  • Manual supervision during major news events is advisable
  • Past performance, whether backtested or live, does not guarantee future results

Series Structure

The Quant Scavenger Series will expand to include one EA per asset, each trained and updated independently. This Apple Series edition is the first in that structure.

Support

Contact me at frankmichel022@gmail.com — or https://www.mql5.com/en/users/frankmichel022-gmail , if you went through the trouble of downloading this, you might as well write the developer for suggestions.

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