Japan's "10 Billion Yen Trade": Can We Replicate Its Winning Edge in EA?

A beginner-friendly summary of the verification: “Japan’s “10 Billion Yen Trade”: Can We Replicate Its Winning Edge in EA?”.

Today, we’re diving into the popular FX trading style of a well-known Japanese educator, Ishin-no-suke, to see if its core principles can be successfully automated as an Expert Advisor (EA).

Who is Ishin-no-suke?

Ishin-no-suke is a prominent Japanese FX educator based in Bangkok. He’s quite well-known for his books (like “Ishin-ryu Trade-jutsu”), blogs (multimillionaire-trade.com, fx-lifeschool.info), and YouTube channel, where he teaches his approach to trading. His focus is primarily on discretionary day trading, meaning he relies on human judgment and skill rather than rigid, automated rules.

What’s the idea?

At the heart of Ishin-no-suke’s method is a strategy built on Dow Theory-based trend following. This means he aims to trade with the prevailing trend. He combines this with Multi-Timeframe (MTF) analysis – looking at charts across different timeframes (e.g., a daily chart for the big picture, a 4-hour chart for entries) to get a comprehensive view of the market. His strategy also incorporates what he calls “4 Ironclad Patterns,” which typically involve identifying pullback buys/sells on the H4 (4-hour) chart and recognizing H4 trend reversals. He uses tools like Moving Averages (MAs), horizontal support/resistance lines, and an understanding of crowd psychology to make his decisions. Here’s the catch: while the general concepts are public, the specific details – like exact MA settings, precise pip targets, or detailed entry/exit rules – are typically reserved for his paid courses or books. This is important context for our test!

How I tested it

My goal was to take the mechanizable core of Ishin-no-suke’s publicly disclosed strategy and try to build an EA around it. I wasn’t able to access his full paid curriculum, so this test focused on what could be reasonably inferred and automated from his general teachings. Here’s how I approached it:

The Engine: I repurposed an existing EA framework (mtf_rsi_sma) that’s good at identifying trends and pullbacks across multiple timeframes.
The Logic: The EA was set up to look for trend-following opportunities based on MTF analysis (specifically H4 and D1 charts). It aimed to enter trades on pullbacks within the established trend.
Scope: I tested this across 8 different currency pairs.
Fair Play: All parameters used were fixed, meaning no “hindsight optimization” or tweaking to make past results look better. This ensures we’re testing for a truly robust edge.

What happened?

The results were quite revealing, and honestly, a bit disappointing from an EA perspective.

Long-Only Test (Pullback Buys):

I first tested a version that only took long trades (pullback buys), as some of my previous research suggests mechanical systems often perform better on the long side.
Total Return: This version resulted in a -2.9% total loss over the testing period.
Profit Factor (PF): The Profit Factor was 0.96. (Quick jargon check: Profit Factor = Gross Profit / Gross Loss. A PF greater than 1 means the strategy is profitable; less than 1 means it’s losing money overall.) So, a 0.96 PF means it barely covered its losses, but ultimately lost.
Consistency: It was profitable in 4 out of 10 years tested. Not terrible, but not consistently profitable either.

Long and Short Test (Pullback Buys + Pullback Sells):

Next, I ran a test that was more faithful to Ishin-no-suke’s overall approach, including both pullback buys and pullback sells (short trades).
Total Return: This version performed significantly worse, resulting in a -19.0% total loss! Ouch.
Profit Factor (PF): The Profit Factor dropped to a low 0.85.
Consistency: It was profitable in only 3 out of 10 years. In other words: The automated version of Ishin-no-suke’s core strategy, especially when trying to include short trades, simply did not show a robust forward edge as an EA. The “pullback sell” or shorting component was a major drag on performance. This aligns with findings from earlier research (Study #42), which suggested that for purely mechanical systems, “long-only” often holds the real edge.

What I learned

This study reinforced some crucial insights about automating trading strategies:

Mechanical Core Lacks Edge: The mechanizable skeleton of Ishin-no-suke’s popular strategy – the MTF trend following, pullbacks, and reversals – did not translate into a profitable or robust EA.
The Edge is in Discretionary Skill: Ishin-no-suke himself famously states, “FX is a game of skill / 95% practice, 5% execution.” This is the key! Our tests strongly suggest that the real edge in his method lies not in the raw rules, but in his discretionary skills. Think of it like a master chef versus a recipe: the recipe gives you the ingredients and steps, but the master chef’s experience, intuition, and subtle adjustments are what make the dish truly exceptional.

These skills include things like drawing accurate trend lines and support/resistance levels, intuitively understanding crowd psychology, reading the nuanced market context, and expertly managing trades as they unfold. These are incredibly difficult, if not impossible, to perfectly replicate in a rigid EA.

Reinforcing a Broader Conclusion: This experiment further strengthens a recurring theme in my research: for truly robust, mechanical trading systems, the edge often boils down to simple, price-based, long-only trend following. When you try to mechanize complex, discretionary strategies from external sources, they often hit the same wall – a lack of consistent, automatable edge. It seems many popular “systems” rely heavily on the human element to succeed, and when that element is removed, the magic fades. This doesn’t mean Ishin-no-suke’s method isn’t effective for those who master it discretionarily. It simply means that, like many other complex trading styles, its core mechanical components, when stripped of human intuition and skill, don’t offer a reliable edge for an automated system.

How this connects

This verification builds on earlier ones (what failed before and what I tried this time, comparisons between approaches).

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