Category: MT5 Strategy Testing & Optimization
If you trade MT5 EAs long enough, you will eventually see it.
A backtest report proudly showing:
Modeling quality: 99.0%
For many traders, that number feels like a guarantee.
At 1kPips, we see this assumption destroy more confidence than almost any other misunderstanding in EA development. Modeling quality is one of the most misunderstood metrics in MT5, and ironically, one of the most dangerous when taken at face value.
This article explains what MT5 modeling quality actually measures, what it does not measure, and how professional EA traders interpret it without falling into false confidence.
Why Modeling Quality Exists at All
MT5 modeling quality exists to answer one basic question:
How accurately did the Strategy Tester simulate price movement for this test?
It does not answer:
- Whether your strategy is good
- Whether execution assumptions are realistic
- Whether results will repeat live
Modeling quality measures how complete and consistent the historical price construction was inside the tester, not how tradable the outcome is.
The Core Concept: MT5 Simulates, It Does Not Replay
This is the most important idea to understand.
MT5 does not simply replay the market tick by tick in most cases. Instead, it:
- Uses available historical data
- Generates missing ticks mathematically
- Builds price paths that obey OHLC constraints
Modeling quality tells you how much of that simulation was based on real data versus generated data.
It does not tell you whether those generated ticks match reality in behavior.
What “Tick Data” Means in MT5
The phrase “tick data” is often used loosely.
In MT5, tick data can mean several things:
- Real ticks from a broker or data source
- Synthetic ticks generated from minute bars
- A hybrid of real and generated ticks
Modeling quality increases when the tester can rely more heavily on actual recorded ticks rather than reconstructing price movement from OHLC bars.
However, even real tick data has limitations:
- Different brokers have different tick streams
- Historical tick depth varies by symbol
- Spread and liquidity conditions are not uniform
High-quality tick data improves precision, but it does not eliminate modeling assumptions.
What the Modeling Quality Percentage Actually Represents
The modeling quality percentage reflects:
- Completeness of historical data
- Consistency between ticks and bars
- Accuracy of tick reconstruction
A higher percentage means fewer gaps and fewer reconstructed segments.
It does not mean:
- Orders were filled realistically
- Spreads behaved like live trading
- Latency and slippage were simulated correctly
This is where many traders make the critical mistake of overtrusting the number.
Why 99% Modeling Quality Can Still Be Misleading
An EA can achieve high modeling quality and still fail live for several reasons:
- Signals rely on current-bar values
- Execution logic ignores spread changes
- Strategy depends on micro price movements
Scalping EAs are especially vulnerable.
A few points of difference in tick sequencing, spread timing, or order filling can flip expectancy from positive to negative without affecting modeling quality at all.
The tester may model price accurately, while still modeling execution optimistically.
Real Ticks Mode: Better, But Not Magic
MT5 offers a “real ticks” testing mode, which many traders treat as a silver bullet.
It is an improvement, but it is not perfect.
Real ticks mode:
- Uses recorded tick-by-tick price changes
- Improves realism for scalping strategies
- Reduces artificial smoothness
But it still cannot simulate:
- Broker-specific execution delays
- Liquidity shortages
- News-driven spread explosions
Real ticks increase backtest precision, not certainty.
Backtest Precision vs Backtest Truth
This distinction matters.
Precision refers to how accurately prices were simulated.
Truth refers to whether the trading experience matches live conditions.
High modeling quality improves precision.
It does not guarantee truth.
Professional EA developers treat modeling quality as a prerequisite, not a conclusion.
How to Use Modeling Quality the Right Way
Instead of asking:
“Is modeling quality high?”
Ask:
- Does my strategy depend on tick-level behavior?
- Are entries based on closed bars?
- Would small execution delays change outcomes?
Modeling quality should be interpreted relative to strategy type:
- Higher importance for scalpers
- Moderate importance for intraday systems
- Lower importance for higher timeframe strategies
Common Misuses of Modeling Quality
- Using it as proof of profitability
- Ignoring poor live results because modeling was “good”
- Optimizing aggressively on high-quality backtests
A beautiful backtest with high modeling quality can still be curve-fitted.
Modeling quality does not protect against over-optimization.
A Practical Testing Workflow
At 1kPips, we encourage a layered approach:
- High modeling quality as a baseline
- Conservative assumptions on spread and execution
- Forward testing inside the Strategy Tester
- Small-scale live validation
Each layer removes a different type of illusion.
Why Modeling Quality Still Matters
Despite its limitations, modeling quality is not useless.
Low modeling quality often means:
- Missing historical data
- Inconsistent price construction
- Unreliable indicator calculations
Such tests should not be trusted at all.
High modeling quality earns your EA the right to be tested further. Nothing more.
Final Thought: Numbers Do Not Trade, Systems Do
Modeling quality is a number.
Markets are systems.
Confusing the two leads to false confidence.
At 1kPips, we believe strong EAs are built by understanding what metrics mean, and more importantly, what they hide.
Use modeling quality as a tool, not a shield, and your testing process becomes honest instead of hopeful.