Your Trading Strategy Makes Sense to You. But Can a Machine Understand It?

Most discretionary traders can describe their strategy surprisingly well.

“I wait for liquidity to be swept, look for a change in structure, then enter on the retracement.”

That sounds like a strategy. But ask a few more questions: What exactly counts as a liquidity sweep? What qualifies as a change of structure? How large does the retracement need to be? Which timeframe confirms the setup? What invalidates it?

Suddenly, a seemingly clear strategy becomes much less precise.

This is one of the biggest problems with discretionary trading: we understand our own trading language intuitively, but intuition is difficult to test consistently.

A machine-readable strategy can change that.

Trading terminology is more ambiguous than it looks

Consider a simple term like breakout.

One trader might define a breakout as price trading above the previous swing high. Another requires a candle close above it. Another requires a close above the level followed by the next candle holding above it.

All three traders can say, “I trade breakouts.” Yet they are trading three different strategies.

The same problem appears with terms such as:

• Break of Structure (BOS)
• Change of Character (CHoCH)
• Liquidity sweep
• Swing high / swing low
• Order block
• Fair Value Gap (FVG)
• Retracement
• Retest
• Consolidation
• Displacement
• Support and resistance

These terms are useful shorthand between humans. But they aren't precise enough for a computer.

And that turns out to be useful.

What is a machine-readable trading strategy?

A machine-readable strategy converts trading ideas into explicit conditions.

Instead of “Buy after a bullish breakout,” you might define a breakout as:

• Price must close above the previous confirmed swing high.
• The following candle must remain above that level on a closing basis.
• If the second candle closes back below the level, the breakout isn't confirmed.

Now “breakout” has an observable definition.

You can do the same thing with the rest of the setup. Instead of “Wait for liquidity to be taken,” the strategy could specify which liquidity level matters, which timeframe identifies it, whether a wick through the level is sufficient, where the candle must close, whether higher-timeframe confirmation is required, and what happens if price takes the same liquidity again.

The goal isn't to turn every trader into an algorithmic trader. The goal is to turn implicit judgment into explicit rules wherever possible.

Why this matters even if you trade manually

This is where machine-readable strategies become interesting. You don't necessarily need a trading bot.

Imagine taking a screenshot before entering a trade and asking:

“How closely does this setup match my strategy?”

The system already knows what your terms mean. It can evaluate the conditions individually:

• Liquidity condition: satisfied.
• Market structure condition: satisfied.
• Higher-timeframe confirmation: missing.
• Entry location: valid.
• Risk/reward requirement: satisfied.
• Strategy match: 82%.

More importantly, it can explain why the score isn't 100%.

That gives the trader something much more useful than a generic AI opinion. The question changes from “Is this a good trade?” to “Does this trade match the strategy I already decided to follow?”

Those are very different questions.

Your journal becomes structured data

Machine-readable strategies become even more powerful after trades accumulate.

Normally a trading journal contains screenshots, notes and outcomes. Useful—but difficult to analyse systematically.

If every trade can also be compared against explicit strategy conditions, the journal becomes a dataset.

You could eventually discover things like:

• Trades with higher-timeframe confirmation: 68% win rate
• Trades without confirmation: 41% win rate
• Liquidity sweep + structural shift: 2.1 average R
• Structural shift without liquidity: 0.7 average R
• Trades scoring above 85% against the strategy: profitable
• Trades scoring below 70%: net negative

Now you're no longer asking whether your strategy “feels good.” You're collecting evidence about which parts of the strategy actually contribute to your edge.

This creates a feedback loop

A trading strategy shouldn't necessarily remain frozen forever. It can evolve through evidence.

Define → Execute → Measure → Review → Refine

You define what your setup means. You execute it. Your trades are graded against those definitions. You analyse which conditions correlate with better outcomes. Then you refine the strategy.

Perhaps you discover that the liquidity sweep you considered mandatory doesn't actually improve results. Or that higher-timeframe confirmation dramatically improves expectancy. Or that your best trades almost always occur after one particular combination of conditions.

The machine isn't discovering your edge for you. It's helping you measure the edge you're trying to build.

AI makes this more practical

Historically, making strategies machine-readable often meant converting everything into code. That works extremely well for strategies based on precise numerical rules.

But discretionary price-action trading is harder. Concepts such as structure, consolidation, meaningful swing points and liquidity can contain visual context that traders recognise quickly but struggle to express as a mathematical formula.

Vision models and language models create another possibility.

A trader can describe:

“I want price to sweep the previous equal highs, reject the level, and then show a bearish structural shift.”

The system can progressively ask what each term means until the rule becomes sufficiently precise. The final strategy can then be stored in structured form while still remaining understandable to the trader.

This creates a bridge between:

human trading language → structured definitions → AI interpretation → measurable execution

Standard definitions aren't enough

Trading communities rarely agree completely on terminology. Two experienced traders may use “BOS” differently.

So a useful system needs both standard definitions and personal definitions.

A trader might begin with the commonly accepted meaning of a liquidity sweep, for example, and then modify it:

“For my strategy, an H1 candle must close back below the liquidity level before I consider the sweep confirmed.”

That modification matters.

The computer shouldn't grade the trader against somebody else's definition. It should grade them against their approved strategy.

From trading vocabulary to an edge

There is a much larger idea hidden inside something as boring as defining trading terminology.

Once trading concepts have explicit definitions, strategies become structured.

Once strategies become structured, trades can be compared against them.

Once trades can be compared, execution can be measured.

And once enough execution data exists, traders can begin identifying which rules actually contribute to expectancy.

So perhaps the first step toward finding an edge isn't another indicator.

It might simply be answering a harder question:

Can you define your strategy clearly enough that someone—or something—other than you could execute the same decision process?

If the answer is no, there may still be valuable information hiding inside your trading intuition. The challenge is extracting it.

That's one of the problems we're exploring with DojiDoggo: turning the language traders already use into structured strategies that can be graded, reviewed and improved using their own trading history.

Doji Doggo is a trade-planning and journaling tool. It does not provide investment advice, trading signals, or predictions.