Instruments, not signals
We do not trade opinions. We measure mechanics.
You build your own strategies? Then you are in the right place: measurement instruments on real futures data, risk utilities as one-time purchases — and custom development when you want it built for you.
Measure → Validate → Build
Start here
What is this place?
Three ways in, depending on where you are — everything rests on the same measurement base.
Tools
What do you actually get?
Three flagships — each built on its own openly documented measurement.
MQL5 Market tools
MetaTrader 5 · one-time
Risk utilities for live and prop accounts: enforce drawdown limits, mirror accounts — built from our own live operations.
Guardian live: hard cap, equity trailing, risk multiplier
TradingView instruments
10 indicators · open source
Real futures volume, dealer gamma and session structure right on your chart.
VWAP with σ bands from real futures volume, Dow M15
Custom Suites
Desktop & app · own applications
Standalone applications from daily operations: analog-day analysis (Studio), market context and alerts (MarketPulse) — and whatever comes next.
Studio: 295 of 3,590 days match the condition
How it works
How does a tool get made here?
Five steps, always the same — each with the picture from the work it came from.
- 01
Idea
Every tool starts with an observation from our own trading: behaviour that seems to recur. At this point an idea is worth nothing — it is a candidate, no more.
The desk where the observation happens: DAX and FTSE, same session, same setup. - 02
Concept & evaluation
The idea is phrased so it can be falsified: which rule, which market, which period? A first rough evaluation decides whether the full test bench is even worth it — most candidates end here.
Study: second candle on trend days →
First evaluation: hit rate across the confirmation offset, four indices — confirmation helps, but not equally everywhere. - 03
Measure
Now the base rate, properly: thousands of sessions on real M1 data, costs from the first number on. Not three good weeks — the full history including the phases that hurt.
Study: edge persistence →
Measured, not assumed: a fresh anomaly does not decay slowly — it falls off a cliff after one half-year. - 04
Validate
Out-of-sample, walk-forward, Monte Carlo — the gate that separates the past from a mechanism. What fails is published anyway; the no is part of the result.
Study: validation gate →
The gate in one picture: profit factor per rolling one-year window — no market stays above the threshold. - 05
Build
Only what survives becomes an instrument: open source on TradingView, a utility on the MQL5 Market — or, as an engagement, your EA with the same test bench behind it.
Study: MT5 algo reference →
The finished EA in its reference backtest, out-of-sample 2023–2026 — a historical curve, not a return forecast.
Research
A no is a result, too.
42 open studies, 7 of them with a confirmed edge and 16 without — each with its data basis, cost assumption and verdict. The failed ideas stand next to the confirmed ones; that is what makes the confirmed ones credible.
Edge confirmed
Edge Persistence: Does a Measured Edge Survive — and Which Part of It?
How fast an edge decays
No edge
The Doji Trap on the 2-Minute Chart: Trapped Sellers or Just Noise?
Doji pattern versus benchmark per year — the two series move together for eleven years
Measured
After the EU Close: How Often Does a US Sell-Off Actually Turn Around?
How often a US decline still reverses
Platforms
One standard, five platforms.
Built natively instead of lowest common denominator — MQL5, Pine, C#, Python. NinjaTrader and the cTrader tools are in preparation.
The execution layer: runs unattended on live and prop accounts, with full access to orders, history and account state. MQL5 is close to C — fast but unforgiving: mistakes cost real money, which is why restart safety beats elegance.
Example: a drawdown guardian that enforces limits, or a recovery EA whose accounting nets out swap and commission.
The chart cockpit: Pine is deliberately limited — no file system, no external computation — which makes it fast to review and hard to obfuscate. Ideal for making context visible; unsuitable for heavy backtesting.
Example: projecting futures volume into CFD coordinates, or dealer-gamma levels as a chart overlay with alerts.
The research layer everything else stands on: data engineering, backtest pipelines, walk-forward and Monte Carlo. Anything is possible — which is why methodological discipline matters more than the language itself.
Example: the 123,193-episode base behind our studies, or a walk-forward test bench for your own rule.
Full execution control in a grown-up language: C# with a real type system, proper testing and direct API access to every order. The choice when order logic gets complex and has to stay traceable.
Example: fine-grained partial executions with complete logging of every fill.
The standard of the futures prop scene, technically C# family. In preparation — the risk utilities get ported first.
About
Who is behind this?
KruegerAlgorithms grew out of our own trading: live in the markets for several years, research-driven from day one — because an idea is worth nothing until it has been quantified and tested. The systems here run by the same rules that get published.
What you see on this site is the part of the work that can be open: 42 studies on 123,193 measured episodes across 10 markets since 2015, 10 open instruments, 4 delivered projects — including the ideas that did not survive the test. You will not find signals here. Tools to measure for yourself — that you will.
- Every number names its source: market, period, sample, cost assumption.
- Negative results are published and filterable by verdict.
- The instruments are open — recomputable, not a black box.
- No return promises. No subscriptions.
"Really cleanly solved! Congratulations!"
Your own strategy? First call free.
Whether and how your rule set can be automated — an honest assessment before any money changes hands. If the idea will not hold, I tell you in the first call.