AI will not replace the seasoned programmer, well not yet.

Can AI solve 10 lines of EasyLanguage code and fix the subtle order-execution flaws hiding inside? EasyLanguage’s small footprint makes it a known weak spot for LLMs, which rely on sheer volume to learn edge cases. That weakness shows up clearly when entry and exit criteria are triggered at the same time. The strategy exits and re-enters at the exact same price and time. While trade management stays intact on paper, this simultaneous exit and re-entry cannot be truly realized in real-world trading, leaving you paying extra execution costs and commissions for phantom backtest trades.

Exit after N bars

Many predictive models utilize an “exit after N bars” rule to determine objective function fitness.

The problem with testing a “no matter what happens, you exit after N bars” strategy in TradeStation is that the engine will immediately re-enter the same trade at the exact same exit price and bar if the entry criteria remain true.

Above we had four occurrences of this problem.  In reality this is just one trade.

inputs:exitBars(7),stoploss$(2000),profitTarg$(3500);
if close > close[1] and close > average(close,20) Then
	buy("simple buy") next bar at open;
if barsSinceEntry >= exitBars then
begin
	if close > close[1] then sell("xitBars") next bar at market;
	sell("xitBars-ll") next bar at lowest(l,2) stop;
end;
setStopLoss(stoploss$);
setProfitTarget(profitTarg$);

The AI Response

I fed all of this into an LLM. Before going completely down the rabbit hole of a seemingly infinite prompt, test, feedback loop, I programmed the fix myself.

Then, I showed the LLM my code and asked a simple question: If we had stayed in that loop, would you have ever derived this solution?

Its answer was a clear “no.”

Here is why the AI got stuck, and why real trading and programming experience still matters.

The EasyLanguage Challenge

EasyLanguage is a specialized, niche script. Mainstream languages like Python or JavaScript have massive amounts of code online for LLMs to train on, but EasyLanguage has a much smaller footprint.

Because of that limited training data, AI models rely on broad programming rules. They simply do not understand how TradeStation’s state machine processes order fills, bar updates, and variable evaluation line-by-line during an execution pass.

How I Solved It with a PreLaunch Paradigm

Instead of trying to force built-in engine stops that kept breaking, I stepped back and built a custom execution flow using three practical techniques:

• The Hybrid Stop Switch (setStopLoss(-1)): Standard AI models treat native engine tools and custom order logic as an either-or choice. By passing -1 into setStopLoss, I actively suppressed the native stop engine the moment my custom price targets took over, stopping order conflicts in their tracks.
• State Priming (barsInTrade = -1): An LLM looks at setting a counter to -1 as a mistake and tries to “correct” it to 0. But because TradeStation increments variables at the very bottom of the script pass, priming barsInTrade to -1 ensured the trade clock started at exactly 0 when the next bar opened.
• Forward-Looking Execution (open of next bar): By evaluating open of next bar inside the end-of-pass calculation, the code figures out theoretical fill prices (theoEntryPrice) and re-establishes profit targets before the new bar starts printing. That handles simultaneous exit-and-reentry collisions cleanly in a single pass.

The Bottom Line

AI has a strong bias toward fixing what is already on the screen. It tries to stretch built-in functions past their limits instead of stepping back and rebuilding the architecture.

AI writes code based on how a system is supposed to work on paper. It still takes a seasoned programmer to write code based on how execution engines actually behave in the real world.

It admitted that generative models have a strong “fix what’s there” bias. They try to bend standard built-in functions to their limit rather than stepping back, discarding broken abstractions, and manually rebuilding the architecture.

It takes a seasoned programmer; someone who truly understands platform execution loops, bar-evaluation timing, and manual state overrides to see past the standard toolkit and engineer a real solution.

My code grew from ten lines to 40.  Platform nuances and misbehavior usually needs much more code