How StratGeeks tests
We build rules. We don’t train models.
Our strategies are built around market structure, volatility and defined price behaviour — then tested against real historical market data to see how those rules behave across different market conditions.
Section 01
Why we don’t use machine learning
We want the strategy to be understandable.
Machine learning can be useful for certain applications, but it introduces another layer between the trader and the strategy. A model can learn from data and change its behaviour as that data changes. That can make it harder to understand exactly why the system is behaving differently from one period to the next.
That is not how we want StratGeeks systems to work. Our strategies are built from defined rules and measurable market conditions. You should be able to understand what the system is looking for, why it takes a trade, and which parameters control its behaviour.
No hidden model making decisions behind the scenes.
Section 02
We respond to the market
The distinction
We don’t train our strategies to predict the market.We build them to respond to it.
Our strategies are built around observable characteristics of the market:
- Market structure
- Volatility
- Momentum
- Mean reversion
- Opening ranges
- Swing behaviour
The market doesn’t behave exactly the same way every day. Volatility expands and contracts. Price moves between trends and ranges. Market structure develops and changes.
Our systems are designed to respond to those conditions rather than constantly changing the underlying strategy itself.
The rules stay defined. The market determines how those rules behave.
Section 03
What backtesting actually tells us
Backtesting isn’t a crystal ball.
Backtesting isn’t there to prove that tomorrow will look exactly like yesterday. It is there to answer a much simpler question:
What happened when we applied these rules to real market data?
We use historical testing to evaluate how a defined strategy behaved over different periods of actual market movement. That lets us examine things like:
- Entries
- Exits
- Stops
- Targets
- Position sizing
- Trade frequency
- Drawdown
- Profitability
- Behaviour as volatility and structure change
The purpose isn’t to create an impressive-looking number. The purpose is to understand the behaviour of the system.
Tells you whether a result came from steady days or a handful of outliers — and how big the bad days were. A curve on its own hides both.
Read the flat stretches and the dips, not just the end point. How long a system spent going nowhere matters as much as where it finished.
Trade count, drawdown, commission, average win against average loss. This is where a chart that looks good either holds up or does not.
Drag a capture sideways to read it in full.
ORS: Fusion V2.7MNQ 09-26NinjaTrader Strategy Analyzer21 Apr – 20 Jul 2026
Shown as examples of what each output type tells you — the point here is how to read them, not how this particular test performed.
Hypothetical / simulated performanceHypothetical results have inherent limitations and do not represent actual trading results. Past performance does not guarantee future results. Full Risk Disclosure →
Section 04
Real data. Defined rules.
The same four steps behind every StratGeeks system.
Define the rules
We start with specific conditions that determine when the strategy can enter, manage and exit a trade.
Test real market behaviour
Those rules are applied to historical market data so we can see how they actually behaved.
Study the behaviour
We look beyond the final profit number. We examine trades, drawdown, volatility, frequency, and how the strategy behaves under different market conditions.
Take the same rules forward
Once we have a configuration we are comfortable with, the objective is to run that same defined system in the live market.
Section 05
Why this matters
Fully automated doesn’t have to mean fully mysterious.
Automation should remove the need to manually execute every decision. It shouldn’t remove your ability to understand the system.
That is why we build StratGeeks strategies around defined rules. You don’t have to wonder whether a model quietly changed overnight. You don’t have to guess what the system is looking for.
You can understand the rules, configure the parameters, test the behaviour, and then let the system execute them.
Section 06
The StratGeeks approach
- Defined rules.
- Real market data.
- Understandable behaviour.
- Automated execution.
That’s how we build StratGeeks.
