Test your rules before you trust them
NACH Quant Lab tests a trading strategy you already have. You describe it in your own words, we write it out as explicit rules, backtest it on historical data, and send you a standard report. Research and education: no signals, no advice.
Describe your strategy to our AI assistant: it turns it into rules and builds a plan for your report.
Conceptual illustration. Not a real strategy or real data.
The problem
Most trading strategies live in someone's head, or in a note that reads clearly until you try to run it. "Buy the pullback in a rising market" leaves a lot open. Rising for how long? A pullback of how much? And when do you get out if it keeps falling?
The common fix makes things worse. Adjust the rules until the past looks good, and you get a strategy fitted to one stretch of history, which is the kind most likely to fall apart on data it hasn't seen.
Who it's for
Anyone who trades or invests by rules they can put into words and wants to see how those rules held up before trusting them further, whether you trade on your own or with a group. If you're looking for someone to tell you what to buy, this isn't it, and the chat will say so.
How we approach it
It starts with your words. You describe the strategy in the chat or on a call, the way you'd explain it to a friend: when you get in and out, and how much you risk. We write it out as explicit rules and ask about every place where your words leave a choice open, until the rules mean exactly what you meant. You see the full set before anything runs.
Data the rules never saw
We run the rules on the market and the period you choose, and keep a stretch of that history apart. The first part is where the rules were built and checked; the second shows how they behave on data they never saw. The report puts the two side by side, with trading costs and slippage counted, and breaks the results down by market conditions.
What we won't do
We test the rules you have. We don't tune them until the past looks good. We don't tell you what to buy or sell, and we don't trade them for you. The report describes how your rules behaved on past data and says nothing about what they will do next.
Conceptual illustration. Not a real strategy or real data.
A worked example
Say you write: "I buy when the market has been rising and today it pulls back toward its average. I sell when it gets back to where the pullback started."
Before anything runs, we ask. Rising for how long, and measured how? Which average? A pullback of how much? And if it never gets back, when do you cut the trade? Each answer becomes one line of the rule set.
Then the rules run on the market and period you chose, with the last stretch kept apart. The report comes back with results for both parts, with costs counted, broken down by market conditions. If the rules only looked good on the history they were built on, that's the most useful thing the report can tell you, and it's better learned here than with real money.
Our research system, up close
Demo account with simulated execution, no real money. This is an example of how the system works, not an investment result. Simulated results have limitations and do not predict future results.
We run our own research on a system we built in Python and connected to MetaTrader 5. It runs around the clock on a hosted machine in London, on a demo account: live market data, simulated execution, and no real money.
Every decision it makes is written to an audit log, so any trade can be traced back to the rules that produced it. Below is a recreation of that audit view, drawn from synthetic data with generic labels, because the real one would show how the strategies work.
Trade lifecycle audit
Showing one lifecycle in isolation · 10:12:00 to 10:59:00 · 48 1 minute bars
- Signal detected
- Pending order opened
- Filled
- Closed
- Rule 1 passed
- Rule 2 passed
- Rule 3 failed
- Rule 4 passed
| Timestamp | Event | Side | Size | Reference | Note |
|---|---|---|---|---|---|
| 10:21:00 | Signal detected | — | — | — | Criteria met |
| 10:23:00 | Rule evaluation | — | — | — | Rule 3 failed, logged |
| 10:26:00 | Pending order opened | Long | 1 unit | entry reference | Placed above level |
| 10:31:00 | Order amended | Long | 1 unit | entry reference | Stop reference moved |
| 10:36:00 | Filled | Long | 1 unit | entry reference | Order filled |
| 10:50:00 | Closed | Long | 1 unit | exit reference | Exit criteria met |
Illustrative recreation of our audit dashboard. Synthetic data.
Built with Python and MetaTrader 5. Demo account, live market data, simulated execution. Research and education: no signals, no advice.
Your report holds your rules to the same standard: written down exactly, with every result traceable. How we built the system.
Fixed scope
Quant Lab Research Report
What you get
- Your strategy written out as explicit rules a computer can run, starting from the way you describe it
- A standard backtest report on the market and the period you choose
- Equity and drawdown: how the account would have grown, and how far it would have fallen from a peak
- Expectancy: what one trade made or lost, on average
- How the result changes once trading costs and slippage are counted
- In-sample and out-of-sample results: the history the rules were built on, and a stretch they never saw
- A breakdown by market regime: how the rules behaved in different market conditions
What we need from you
- Your rules, in your own words: when you get in, when you get out, and how much you risk
- The market and the period you want tested
- Answers to our questions wherever your words leave a choice open
What is out of scope
- Signals: we never tell you what to buy, sell or hold
- Investment advice of any kind
- Live trading, or running the rules for you
- Tuning your strategy until the past looks better
- How long it takes
- One to two weeks. Start dates depend on current availability.
- Price
- We give you a fixed price before we start, usually within two business days.
What it costs: ask NACH in the chat, and it tells you the starting price for this report.
Need something bigger?
Some work needs more than one report: a research pipeline you can rerun yourself, or the data engineering behind a trading system. Our own research system is an example of that kind of work: it runs on a demo account, with live market data and simulated execution, and logs every decision it makes. We scope work like that separately.
Every project is handled directly by a member of our team.
Questions people ask
Will you tell me whether my strategy is good?
We'll show you how it behaved on the history you chose, including the stretch it never saw, with costs counted. Whether to trade it is your decision. We don't advise on that, and the report doesn't either.
Can you find a strategy for me, or improve mine?
No. We test rules you already have. Finding a strategy for you would be a signal service, and tuning yours until the past looks good is the mistake the report exists to catch. We don't offer either.
What if part of my strategy can't be written down exactly?
Then that part is a judgment you make in the moment, and a backtest can't test your judgment. We ask until every rule is exact. If something stays a judgment call, we tell you, and we test the part that can be written down.
Who sees my rules?
The people on our team doing the work. Before you share anything sensitive, we sign an NDA.
Is this investment advice?
No. The report describes how your rules behaved on past data. It is not a recommendation to buy, sell or hold anything, and NACH Analytics is not an investment adviser, a broker or a signal provider.
What rules would you like to test?
Tell us the decision you're trying to make. If we're not the right people, we'll say so.
Our AI assistant asks about your project and can build you a free plan.