> ## Documentation Index
> Fetch the complete documentation index at: https://docs.askfutures.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Your first strategy
> Build and read a 15-minute opening-range breakout on Micro Nasdaq, screen by screen, then refine it with one sentence.
Let's build a real strategy together: a **15-minute opening-range breakout on
Micro Nasdaq (MNQ)**. The idea is simple and popular — many traders believe the
first 15 minutes of the session hint at where the market wants to go for the
rest of the day. We'll describe it, read the backtest, then add one filter and
watch the result change.
Past performance does not guarantee future results. Always test before you trade.
Sign in at [askfutures.com](https://askfutures.com) and start a new chat. If
you haven't started your trial yet, the
[quickstart](/get-started/quickstart) covers sign-up in two steps.
Type the idea exactly as you'd say it out loud:
```text Prompt theme={null}
Buy Micro Nasdaq when price breaks above the first 15-minute high,
and exit if it closes back below the midpoint of that range
```
Send it. Notice what you *didn't* specify — no bar size, no date range, no
direction. AskFutures fills those in with sensible defaults and tells you
what it assumed.
AskFutures turns your sentence into a precise, rule-based strategy. On the
**strategy card** you'll see it written out:
* **Market:** Micro Nasdaq (MNQ)
* **Entry:** buy when price breaks above the high of the first 15-minute
range
* **Exit:** close if price closes back below the midpoint of that range
* **Strategy Flow:** a chart of the logic, so you can see the rules connect
Because this is a day-trading idea, AskFutures also adds an **end-of-day
exit** — any open trade is closed at the session close rather than held
overnight. That's one of the assumptions it states for you.
AskFutures runs the strategy through a fixed, deterministic backtest over the
last year of real 1-minute prices and shows you the **equity curve** plus the
headline numbers — net of modeled slippage (1 tick) and commission. The
equity curve is your at-a-glance read: a line drifting up means the rule made
money over the period; chop or a slide downward tells you the opposite.
These results are **hypothetical and simulated** — no real trades were
placed, and simulated programs are designed with the benefit of hindsight.
Past performance, actual or simulated, does not guarantee future results.
Always test before you trade.
Here's the part that makes AskFutures feel like a conversation. The thinking:
a *small* opening range might just be noise, while a *large* range can signal
stronger intent — so maybe we only want to trade the bigger ranges. Tell it
so, in plain English:
```text Refinement theme={null}
Trade only if the opening range is over $400
```
Send it. AskFutures adds the filter — keeping the same entry and exit — and
saves it as a **new version**, then reruns the backtest automatically. The
new equity curve appears beside the old one so you can judge whether the
filter actually helped.
## What you learned
In a few sentences you built a complete strategy, backtested it on real data,
and refined it — without writing a single line of code. The AI only translated
your words into rules; every number came from the same deterministic engine, so
the same rules on the same data always produce the same result. That's the whole
loop: **describe, strategy, backtest, iterate.**
From here you can keep going — add a stop, widen the date range, swap MNQ for a
different market, or [optimize](/concepts/optimization) the \$400 threshold to see
which value held up best.
## Next steps
More phrasing patterns for changing a strategy by chatting.
How to read the equity curve and the metrics that matter.
Stack your versions and compare before and after.
Why these numbers are an honest replay, not a guess.