Methodology
How the bot works

Two strategies. One bot. Plain English.

No black-box claims. Both strategies the bot trades are openly documented setups from published market-structure research, refined and backtested across 24 months of real candle data.

Strategy #1 — Paired-asset divergence

The concept

Two correlated instruments — Nasdaq and Russell 2000, Gold and Silver, EUR and GBP — almost always move in step. When one of them sweeps a recent high or low and the other refuses to confirm, that asymmetry frequently marks a session reversal.

The pattern itself is well-known. The edge is what's been done with it — years of refinement against real market data, multi-tier filtering, and chunked stability testing on independent out-of-sample windows. The specific filter thresholds, session gates, per-pair direction rules, and risk-multiplier triggers are not published — that's the work we've put in.

Pairs traded

XAU vs XAGGold / Silver
NQ vs RTYNasdaq / Russell
DAX vs CACGermany / France
BTC vs ETHCrypto, direction-filtered
EUR vs GBPFX, mode-filtered

Strategy #2 — 4-hour structural reversal

The concept

When a recent 4-hour high or low gets swept and the move fails immediately, the last opposing candle that held that level often becomes a high-probability reversal zone — price frequently retests it on the way back the other direction.

Our variant runs this setup on the 4-hour reference candle with a fixed reward target. As with the divergence strategy, the framework is well-known. The specific filtering — which instruments pass quality screening, which direction edges hold per pair, how the entry is timed, how risk is sized when it stacks with the paired-asset signals — is the result of years of testing, and isn't published.

Instruments covered

This setup runs as a single-instrument strategy on the same instrument universe as the divergence strategy, plus a few additional pairs. Some instruments execute live; others are watched but not auto-executed pending further out-of-sample validation. Subscribers see the live ones as actionable signals; the rest as observation-only data.

Risk management is hard-coded

Every trade is structurally protected — not by hope, by code:

Backtest results (24 months, raw market data)

+796.7RStrategy 1 total return
0.92R per signal (strategy 1)
864Strategy 1 signals (24mo)
+424RStrategy 2 total return
0.46R per signal (strategy 2)
3/3Chunked stability (8mo windows)

"R" is the standard risk-adjusted unit: 1R = the dollar amount risked on the trade. A +0.92R-per-signal edge across 864 trades means that on average, every trade returned 92% of what it risked — losers are clipped at -1R, winners run to +3R-9R depending on the pair.

Both strategies were validated through chunked-stability testing: the 24-month dataset was split into three 8-month chunks, and the strategy had to produce positive returns in all three independently. This is to filter out strategies that look profitable overall because of one freak month — and is far more conservative than typical "show the equity curve" backtest reporting.

What this is NOT

What it is: a research bot publishing every signal it generates, transparently, with the methodology openly documented. Subscribers can use it as one input in their own informed decision-making.

Mr Algo FX publishes algorithmic trading signals from a fully-automated research bot. All content is for educational and informational purposes only. It does not constitute personalised investment advice, financial promotion, or asset management services. We do not manage funds or execute trades on behalf of subscribers. All trading carries risk; past performance is not a guide to future results.