EASY Bots in production — professional EA automation without writing code
Evaluate production-grade EA platforms across 4 dimensions: architecture, risk controls, track record, and support.
Dernière révision :
Production EAs in 90 seconds
In 2024 the prop-firm EA market was flooded with cheap Grid bots that passed challenges on backtests and blew up on live accounts within weeks. The ones that survived were EAs with daily-loss-limit inputs that traders had actually set.
- →Architecture: adapts to regime, does not run the same rule in trend and chop.
- →Risk controls: daily loss limit, drawdown cutoff, position-size cap — all exposed as inputs you own.
- →Track record: third-party verified live results, not vendor-curated screenshots.
- →Support: a team behind the product that fixes it when broker conditions change.
A professional EA is transparent, configurable, and accountable — not a black box.
EASY Bots — four-pillar scorecard
Explore how each pillar applies to EASY Bots. Click a tab to see the details.
Adaptive regime architecture
EASY Bots use a volatility-regime layer that compares current ATR to a rolling baseline. In high-volatility regimes (e.g. news spikes, session opens) the bot widens stops and reduces lot size. In confirmed trending regimes it engages the full signal logic. In choppy or low-liquidity regimes it pauses or reduces trade frequency.
- ATR-band regime classification on every tick
- Lot-size adjusts automatically to regime classification
- Configurable 'Adaptive Mode' toggle — on by default, can be disabled for fixed-rule testing
How to verify
- →Open input panel → look for 'AdaptiveMode', 'ATR_Multiplier', or 'VolatilityFilter'
- →Set EA to visual mode in Strategy Tester → watch lot sizes change across different volatility periods
The four-pillar framework
Pillar 1 — Architecture
A fixed-rule EA runs the same signal logic regardless of market regime. That worked fine in the low-volatility trend of 2012-2019. In the choppier, event-driven regime since 2020 — BoJ hikes, SVB collapses, prop-firm blowups — fixed-rule EAs degrade visibly on live accounts even when backtests look clean.
Adaptive architecture means the EA has an internal regime detector. When the EA identifies a ranging market it either widens stops, reduces lot size, or pauses entirely rather than firing the same signal. EASY Bots use a volatility-regime layer that compares current ATR to a 20-period baseline and adjusts lot-size and filter thresholds accordingly.
Pillar 2 — Risk controls
Risk controls belong to you, the trader — not the EA vendor. A professional EA exposes every risk parameter as a user-configurable input: daily loss limit in percent, drawdown cutoff, maximum open positions, lot-size mode (fixed vs percent-of-balance), and a magic number for order isolation.
If any of these are hard-coded inside the EA and not surfaced as inputs, you are not operating the risk engine — the vendor is. That is an unacceptable dependency.
Pillar 3 — Track record
Backtests are hypothesis. Live results are evidence. The standard for EA track record evaluation is a verified Myfxbook link showing at least 6 months of live trading on a real account — not a demo.
Check: is the gain curve smooth or jagged? Does the account use high leverage without disclosure? What is the max drawdown on the live account vs the backtest? Divergence between backtest drawdown and live drawdown above 3× is a red flag.
Pillar 4 — Support and versioning
Brokers change execution rules. Regulation changes what leverage is available. Pairs get delisted from data feeds. A production EA needs an update cycle and a team that ships fixes when the environment changes.
Professional EA vendors publish a changelog with dated version notes. You should be able to see: when was the last update, what changed, and what triggered the change. Vendors who have not updated an EA in 12+ months while markets changed significantly are operating a passive product, not a managed one.
Key terms
What 2024 taught the EA market
La vague des EA de prop firm : 91% ont passé un backtest, 73% ont échoué en réel en moins de 6 semaines — 2024
In 2024, as prop-firm challenges exploded in popularity, a wave of cheap EA packages appeared promising 'challenge-passing guaranteed'. Most used Grid or Martingale logic with no daily-loss-limit input. Traders who bought these EAs often passed the challenge phase (which is a short window where Grid can look good) then blew the funded account within 2-4 weeks when conditions shifted. The EAs that survived the funded phase had one thing in common: a configurable daily-loss-limit input that traders had actually set at 4-5% before going live.
This is the lesson from 2024: the risk control layer matters more than the signal layer for survival. EASY Bots surfaced this as a first-class design decision — every bot exposes daily loss limit, max drawdown, and lot-size mode as required inputs that appear on first launch.
SourceApply the framework
Use the four-pillar framework on any EA you are currently evaluating or plan to evaluate. Work through each pillar in the explorer below.
- 1Open the EA's product page or input panel and look for any adaptive/regime parameters (Pillar 1).
- 2Locate the risk input section: daily loss limit, drawdown cutoff, lot mode (Pillar 2).
- 3Find the Myfxbook or FX Blue link. Check account type (real, not demo) and history length (Pillar 3).
- 4Search for a changelog or version history. Note the date of the last update (Pillar 4).
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Architecture deep-dive
The four-pillar framework is the user-facing version of a deeper engineering question: what separates a research prototype from a production system?
Regime detection: the engineering problem
Most retail EA developers use an ATR-based volatility filter as a proxy for regime. Professional systems go further: they use Hidden Markov Models (HMM) or change-point detection algorithms to identify regime shifts with statistical confidence rather than a simple threshold. EASY Bots currently use ATR-band regime detection (simple but robust); a more sophisticated version would use a 2-state HMM fitted on rolling 252-bar windows.
Track record: statistical significance
6 months of live trading produces roughly 100-500 trades for a typical EA (depending on frequency). At 200 trades, a 55% win rate vs 50% win rate is not statistically significant at 95% confidence — the standard error is ±3.5%. Professionals require either longer track records (12-24 months) or higher trade counts (500+) before treating a live edge as confirmed rather than lucky variance.
Support and dependency risk
Every EA has implicit dependencies: the broker's execution model, the data feed's tick frequency, the platform's handling of weekend gaps and news spikes. When any of these change — as they regularly do after major broker platform updates — the EA's behaviour can shift without a single line of code changing. A professional EA vendor monitors these dependencies and ships patches proactively. The absence of update activity for 12+ months is not stability — it is unmonitored dependency drift.
Sources
Afficher la réponse
Architecture (adaptive regime detection), Risk controls (user-configurable daily limit, drawdown cutoff, lot mode), Track record (live real-account data on a third-party verifier), Support (team + versioned changelog).
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