Quantitative trading · Full automation · Walk-Forward validated

Strategies that
survive live markets.

We build trading systems — data-driven, walk-forward validated and honest about the limits. From indicators to semi-automated assistants to fully automated EAs: MetaTrader 5, cTrader, TradingView, Python.

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01 — Services

Your strategy as a finished system.

Custom development — from an indicator to a semi-automated assistant to a fully automated EA/bot. We pick the automation level that fits the strategy and ship it with backtest, walk-forward validation and documentation — to the same standards we trade our own capital with.

MetaTrader 5 · MQL5TradingView · PinecTrader · C#Python
View services
Open Research

All our research — open & searchable.

Every study is free to read — with data basis, methodology and open limitations, searchable like documentation. Search your topic and see whether there are already solid numbers on it. Research this open is rare in trading.

Search your topic — DAX, open, walk-forward…
DAX · ≥1R hit rate per year
'18
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Stable across the full sample (47–64%) — through crash, war & bull market.

02 — Approach

Most of the work is about disproving your own strategy.

Most trading systems don't fail because of a bad idea — they fail for lack of validation. A backtest on coarse data with optimistic fills almost always produces a nice, smooth curve, and that is exactly the one that falls apart in a real account. We invert this: before we consider a strategy good, we try everything to break it. What survives is allowed to go live.

It starts with the data

Not with the strategy. We work with real tick / M1 data straight from the broker and check where spread and quotes come from — because a fill that never realistically existed makes every downstream statistic worthless. The logic on top stays deliberately lean: clear rules for entry, exit, stop and position size, instead of a stack of optimised indicators that has merely memorised the past.

The real test: walk-forward

Parameters are optimised on one window and tested on the next, unseen one — rolling across years. A strategy that only shines in the optimisation window fails here. On top come Monte-Carlo simulations over trade order, parameter sensitivity and a look across market regimes: a setup has to hold through crashes, inflation bear markets and grinding sideways phases — not just the one good year.

Conservative into live

Only once that holds does capital come in — with fixed risk per trade in R, a defined maximum drawdown, and no martingale or grid that keeps a losing system artificially alive. In operation the systems run on a VPS, with clean magic-number separation, session force-close and a continuous reconciliation of live results against the backtest. If the two diverge, we investigate — we don't keep trading.

And we say where the limits are

Every one of our research studies states its limitations openly: sample size, single-source data, what wasn't modelled. We promise no returns. We show what the data supports — and what it doesn't. That sells worse than '+300% in backtest', but it is the difference between a system you understand and a black box you can only hope in.

What this looks like in practice — our research studies
03 — Trading Assistants

MetaTrader 5 · Semi-automated

The assistant does the mechanical work.
The decision stays with you.

Place orders at the breakout candle, trail stops, manage positions — automatically and in milliseconds, without constant manual adjusting. You choose setup, context and exit.

Tools for your own trading — they handle the fast, mechanical execution, not the decision. Part of the Elite Plan (€99/mo): all assistants, updates and support. No return promises; trading carries risk of loss.

04 — Open Source

Test the code quality.

Free indicators for TradingView and MetaTrader. Experience the quality before starting a project.

  • Overnight Range Indicator
    TradingView · Pine Script
  • Internal SL Manager
    MetaTrader 5 · Hidden Stop-Loss
  • Lot Size Calculator
    MetaTrader 5 · Visual Risk Tool
Go to Downloads
OvernightRange.pine
// @version=5
indicator("Overnight Range", overlay=true)
// Configuration
var sessionStart = "1800"
var sessionEnd = "0800"
// Calculate range
calcOvernightRange() =>
high_val = ta.highest(high, 14)
low_val = ta.lowest(low, 14)
[high_val, low_val]
// ... more code
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Analyze requirements, get an honest assessment.

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