Investing 14: How I Use AI in Trading

Building an algorithmic trading system used to require a strong programming background, quantitative knowledge, and a significant amount of development work.

Today, AI has changed that.

I can describe a strategy in plain language, ask AI to help convert the idea into measurable rules, test the logic, rank opportunities, and even assist with trade execution based on predefined instructions.

This does not mean AI should make every investment decision for me.

Instead, I use AI as a tool to make my investment process more structured, measurable, and consistent.

Many investment strategies begin as ideas.

For example, I may say: I want to buy strong stocks after a pullback, but only when the rebound is confirmed.

That sounds clear conceptually, but it is still subjective.

AI can help convert the idea into measurable conditions.

For my Second Mover Strategy, or SMS, I can define rules such as:

AI can then help translate these ideas into formulas, indicators, conditions, and scoring rules.

For example:

Trend Score

Pullback Score

Confirmation Score

Instead of simply saying, "This chart looks good," I can build a numerical score.

That makes the strategy easier to test and easier to repeat.

One of the most useful ways I use AI is to rank signals.

Not every SMS signal is equally attractive.

Two stocks may both satisfy the technical conditions, but one may have:

AI can combine these factors into a ranking system.

For example, I might create a score out of 100:

Then the system may produce:

Stock A: 86/100
Stock B: 78/100
Stock C: 69/100

This allows me to focus on the highest-quality setups instead of treating every signal equally.

The important part is that the scoring rules come from my strategy.

AI helps me structure and calculate them, but I still decide what matters.

My SMS strategy is not purely technical.

Technical analysis helps identify timing, but fundamental analysis helps determine whether the company deserves my capital.

AI is useful here because it can help organize fundamental information quickly.

For example, after a stock generates an SMS signal, I can ask AI to review:

The system can then add a fundamental score to the technical score.

For example:

A stock may have an excellent chart but weak fundamentals.

Another stock may have a slightly weaker setup but much stronger earnings growth, margins, and competitive positioning.

AI helps me compare these signals more systematically.

This is especially useful when many stocks trigger at the same time.

Instead of manually researching every company from zero, AI can help narrow the list.

Once technical and fundamental factors are quantified, AI can help rank the signals.

This is where the process becomes much more useful.

Imagine 20 stocks trigger my SMS conditions on the same day.

Without a ranking system, I may simply choose the stock that looks most exciting.

That introduces emotion and bias.

Instead, AI can rank them based on my predefined framework.

For example:

1. Stock A — Score 91
Strong trend, controlled pullback, high-volume rebound, strong sector, improving earnings.

2. Stock B — Score 85
Excellent fundamentals and strong trend, but rebound confirmation is slightly weaker.

3. Stock C — Score 80
Good technical setup, but sector strength is only average.

Now I am not asking: Which stock do I feel like buying?

I am asking: Which stock best matches my system?

That is a much more disciplined question.

AI can also help after the signal is generated.

Once I decide the rules, the system can assist with execution based on my instructions.

For example, I can define:

A simplified instruction might be: Buy only if the stock trades above the confirmation level, risk no more than 1% of portfolio capital, and place the stop below the support zone adjusted for normal volatility.

AI can then calculate the appropriate position size.

For example:

Portfolio size: $100,000
Maximum risk per trade: 1%
Maximum acceptable loss: $1,000

Entry price: $50
Stop loss: $47.50
Risk per share: $2.50

Position size:

$1,000 ÷ $2.50 = 400 shares

Instead of guessing the position size, the calculation becomes rule-based.

This is where AI can reduce execution mistakes.

One of the biggest problems in trading is not strategy.

It is execution.

Investors often break their own rules because of:

AI does not experience these emotions.

If I define the rules clearly, AI can help remind me when I am deviating from them.

For example:

My strategy says not to buy before rebound confirmation.

But I see the stock rising quickly and want to enter early.

AI can flag: The current setup does not yet meet your confirmation rule.

Or perhaps I want to increase a position after a large rally.

The system may show: Increasing the position would exceed your maximum portfolio risk.

This does not remove human judgment.

But it creates an additional layer of discipline.

AI can also help me study whether the strategy actually works.

Once the rules are quantified, I can test questions such as:

For example, I may discover:

SMS signals with strong volume confirmation have a 62% win rate.

Signals without volume confirmation have only a 51% win rate.

That information allows me to improve the strategy.

The process becomes:

Idea → Quantify → Test → Execute → Record → Improve

AI can assist at every stage.

This is the most important point.

AI itself is not my investment edge.

My edge still comes from:

AI helps me make those things more systematic.

If the original strategy is bad, AI can simply automate a bad strategy faster.

Therefore, I do not ask AI: What stock should I buy today?

Instead, I ask: Based on my strategy, which opportunities best match my rules?

That is a very different use of AI.

My current workflow can be summarized as:

Market Data → SMS Signal → AI Scoring → Fundamental Analysis → Ranking → Risk Check → Trade Execution → Trade Journal → Review

AI helps connect each step.

It allows me to turn a trading philosophy into a measurable process.

And that is where I believe AI creates the most value in investing.

The biggest advantage of AI in trading is not prediction.

It is systemization.

AI makes it easier to convert ideas into rules, rules into scores, scores into rankings, and rankings into disciplined execution.

For me, AI does not replace the investor.

It helps the investor become more consistent.

The real power of AI in trading is not letting AI decide for me. It is using AI to help me follow my own strategy better.