Question 1

How is this different from other algo platforms?

Accepted Answer

Three ways. Your strategy is validated and deterministic, not improvised — broken or ambiguous logic can’t reach the engine, and the same input builds the same strategy every run. Your backtest runs on institutional-grade tick data, not free feeds with the losers deleted. And you keep your own broker — direct, revocable OAuth connections, not a forced brokerage or a webhook chain that fails silently.

Question 2

Do I need to know how to code — or hire someone who does?

Accepted Answer

No, and no. The usual route is paying a developer you can’t audit, waiting weeks, and getting back code that won’t compile, never places a trade, or trades nothing like what you described — then paying again for every tweak. On QuantGenie you describe the strategy the way you’d explain it to a friend. It asks confirming questions, hands you a validated strategy, and you backtest it in the same session. Changing a rule later is a sentence, not another invoice.

Question 3

Can’t I just build this with ChatGPT?

Accepted Answer

You can get code from a chatbot — that’s the problem. A general model hallucinates indicator names and APIs, so the code won’t compile — or compiles and never fires a trade — and you burn hours pasting errors back and forth. Same prompt, different bot every time; we’ve seen “NY open” coded in UTC trade London for three weeks. QuantGenie isn’t a chatbot wrapped around code. It builds on its own quant-trained strategy language (QSDL), runs a hard validation gate so broken or ambiguous logic can’t reach the engine, and builds deterministically: same input, same strategy, every run. There’s no compile loop because there’s no code to fix.

Question 4

What if my description is vague or I leave a rule out?

Accepted Answer

A general AI fills the gap with a guess — a stop, a session window, an exit, a timeframe you never specified — and you find out when the backtest lies to you. QuantGenie never assumes. When a rule is missing or ambiguous it stops and asks (“What should happen at 3:50 PM?”), and only then builds the strategy. Before a single trade you see every rule it locked in as a plain visual logic tree — not 400 lines of code.

Question 5

I don’t have a strategy idea yet. Where do I start?

Accepted Answer

Not from a blank page, and not from a strategy on your feed that nobody has ever backtested. Every QuantGenie user gets free access to a library of backtested strategy templates we keep adding to. Open one, run the backtest as-is to see how it actually behaved, then change a rule in plain English and make it yours. It’s the fastest way to learn which ideas have any evidence behind them.

Question 6

Why does the data matter so much — and do I need to bring my own?

Accepted Answer

You don’t. Long-history intraday data is expensive and arrives as multi-gigabyte files you have to store and query — most traders never get past that step. QuantGenie ships with institutional-grade history already loaded: pick the instrument and timeframe and hit backtest. Quality matters as much as access, because dirty data only ever flatters you. Retail-grade feeds skip splits, dividends, ticker changes and delistings, and blend dark-pool prints into bars you could never have traded, so targets “fill” that never filled — the data section above shows the same strategy backtesting 19 points higher on raw bars than on tradable prices. Every backtest here runs on corporate-action-adjusted data with tradable and non-tradable prints separated, by default.

Question 7

How realistic are the fills, commissions and slippage?

Accepted Answer

More pessimistic than you’re used to. When both your stop and your target are touched inside one 5-minute bar, many retail engines give you the benefit of the doubt and assume the target hit first — a phantom fill. QuantGenie resolves intrabar fills against granular data instead, across years of history rather than one, with nothing to toggle. Then it lets you stress the edge: apply the commission structure of the broker you actually use or type your own, add a slippage percentage, re-run, and see whether the result survives real execution costs.

Question 8

How do I know a backtest isn’t overfit?

Accepted Answer

We assume it might be. Overfitting is the number-one killer of retail strategies: add a filter, add a parameter, add “confluence” until the rules memorize one dataset — perfect on the test they’ve seen, lost on any new one. So every backtest automatically runs a 1,000-simulation Monte Carlo and an in-sample/out-of-sample split, and we don’t show performance stats until 20 closed trades. Then find out whether the edge is real or just one instrument’s personality: duplicate the strategy, change a parameter, or re-run it on ES instead of MNQ or on a different bar size — one click, no rewriting.

Question 9

What does the QuantGenie Score actually measure?

Accepted Answer

Everything a single P&L number hides. Retail judges a backtest by the shape of an equity curve; quants read a full set of metrics and ratios, which is intimidating. QuantGenie shows you all of them — ten metrics across four families — then rolls them into one score for a fast, high-level read on the backtest. You also get time-in-market analysis, a benchmark against simply buying and holding the S&P 500, and beta, so you can tell a strategy from a bull market. The score summarizes a backtest. It doesn’t predict anything.

Question 10

Can I test rules like “skip FOMC days” or “only trade the first 90 minutes”?

Accepted Answer

Yes, in plain English. Most engines can’t test “don’t trade FOMC days,” “only trade 9:30–11:00” or “skip the day after earnings” without custom code and a separate economic-calendar API. QuantGenie has economic calendars and session scheduling built into the backtest — run only on certain days or hours, skip high-impact events, or trade only after them. The same rules carry straight into live trading, so you never toggle them by hand.

Question 11

What happens after I backtest — how does it go live?

Accepted Answer

One click deploys to our cloud. No VPS, no API keys to generate, no endpoints to map, and no alert → webhook → bridge → bridge → broker chain where any link can break. You connect your own broker through a direct, revocable OAuth connection, pick the strategy, and turn it on. It runs 24/7 whether your machine is on or not, handling entries, exits, order management and risk — a live strategy, not an alert you have to act on at 9:31. The only button you press after that is OFF, and it pauses or stops anything instantly.

Question 12

What would it cost to run this myself?

Accepted Answer

More than the subscription, by a lot. Getting anywhere near what QuantGenie does as one platform means assembling it yourself: a virtual environment or cloud instance, a data subscription, a platform license, bridge software, and API or vendor subscriptions — a stack that commonly runs into the thousands of dollars a month before you’ve placed a trade, plus the hours to keep it wired together. QuantGenie builds, backtests and deploys under one login for one flat monthly price. See the pricing above.

Question 13

What happens if the data feed or broker connection drops mid-trade?

Accepted Answer

This is the best-known failure in DIY automation: a disconnect breaks the algo’s communication and leaves floating orders and orphaned positions — and ghost orders can blow an account. QuantGenie is designed so that isn’t your problem. Order management is handled internally, and every deployed strategy runs on two live data feeds with automatic failover: if one drops, the backup keeps managing your trades. We also maintain the broker integrations directly through our broker relationships, monitoring and updating as brokers change their APIs, so a Tuesday API update doesn’t become your Wednesday surprise.

Question 14

Will my live trades match my backtest?

Accepted Answer

They’re the same strategy, run by the same logic on the same engine — not a backtest in one tool re-implemented for a live system in another, which is where “looked great in the backtest” usually dies. The rules you tested are the rules that trade, so the trade in your live ledger is the trade your backtest ledger would have taken. Live fills are still subject to real market conditions, which is exactly why we let you stress slippage and commissions before you deploy.

Question 15

Do I have to handle contract rollovers, holidays and news days myself?

Accepted Answer

No. On a DIY system you switch the strategy to the new futures contract every roll, and forgetting is expensive; holidays can leave a position floating over a long weekend; and news days you’d rather skip mean logging in to flip the strategy off and back on. QuantGenie rolls futures contracts automatically in your live environment, and holiday and economic-event rules are part of the strategy itself — set them once and they apply live, every time, hands-off.

Question 16

Can I run more than one strategy at once — and see everything in one place?

Accepted Answer

That’s the point. Every deployed strategy gets its own broker sub-account, so P&L stays clean per strategy, and one dashboard shows each strategy on its own and all of them combined: live executions in the terminal, the trade ledger, open positions, open risk and the day’s P&L — no tab-hopping between a broker statement, a log file and a VPS. You also get correlation across strategies, so you can catch two of them making the same bet twice. It’s structured the way a fund runs a multi-strategy book.

Question 17

Is there a free trial?

Accepted Answer

Yes — Lite and Pro both come with a free 5-day trial, and for a limited time every Open Beta user gets unlimited token usage. Pick a plan, build your first strategy, and see the full backtest — score, Monte Carlo, metrics and all — before you pay a cent.

Question 18

What’s next on the roadmap?

Accepted Answer

The strategy marketplace: every published strategy shows its backtest and third-party-audited live performance, and publishing hard-locks the logic so what you subscribe to is exactly what runs. Beyond that: strategies that follow institutional money flow and insider filings, options and order-flow data, auto-optimization, and more direct broker integrations.

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In/out-of-sample — the overfit killer.

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Deterministic by design.

Broken logic can't reach the engine

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