Do not buy unless these five signs tools are all present

■ Why I wrote this book

Nice to meet you.

I am a developer who creates “sign tools” for binary options (a trading where you predict whether the price will be higher or lower after a fixed period). The tool detects changes in price movement and notifies you.

On the sales page, you may sometimes see only the win rate displayed prominently, like “90% win rate” or “in a winning streak.”

How should you read those numbers? This article handles only that part.

The problem is that the viewer (someone who is undecided about buying) does not have a clue to verify whether those numbers are genuine.

Even if you’re shown a win rate, it matters whether it’s 9 wins out of 10 or 900 wins out of 1000—the level of trust changes completely.

There can be a clear difference between theoretical results (simulations) and actual results achieved through automated trading.

I realized this difference while validating my own tool.

There are three things I want to say up front.

First, this article is not a book recommending a particular broker or service.

Second, I will not provide any trading instructions that tell you to enter a trade under certain conditions to win.

Third, I will not explain the internal mechanism of the tool I developed (i.e., under what conditions it outputs signals).

What this article covers is what is missing when you rely only on win rate, and a concrete checklist to verify the performance shown on sales pages.

Details such as why theoretical results diverge from real automated trading will be covered in the following volumes.

From the next chapter, we will look at concrete numbers.

Chapter summary in 3 lines

1. The author is a developer who creates sign tools for binary options.

2. This article is not intended to promote a specific tool or to provide buy/sell instructions.

3. We will focus only on how to read the numbers on a sales page and how to verify them.

■ You can’t tell anything from “Win Rate” alone (the denominator matters)

First, let’s confirm the terminology.

Binary options are trades where you predict whether the price will be higher or lower after a fixed time.

If you’re correct, you get back a fixed amount (the stake multiplied by the payout rate).

If you’re wrong, the stake is not returned.

It’s not a complicated mechanism. It’s a binary choice: up or down.

“Win rate” is the number of correct predictions divided by the total number of trades.

What matters here is the bottom number in the division, i.e., the “count” (the denominator).

For example, suppose you see a page that says, “9 wins out of 10, win rate 90%.”

That looks good.

However, flipping a coin 10 times and getting heads 9 times is not that rare probabilistically (about 1% chance).

With only 10 trades, it’s not yet enough to determine whether it’s skill or luck.

On the other hand, what about “530 wins out of 1000, win rate 53%”?

Seen by itself, 53% is less impressive than 90%.

But having 1000 trades makes the 53% figure harder to dismiss as luck.

As the number of trades increases, the win rate approaches the true ability.

In short, win rate should always be viewed in conjunction with the number of trades.

A win rate that isn’t accompanied by the count is not a reliable basis for judgment.

Another important figure is the “payout rate.”

This is the multiplier you receive when you win.

If you know it, you can calculate the break-even win rate (the point where you neither gain nor lose).

For example, if the payout is 1.90 times.

If you bet 10,000 yen and win, you get back 19,000 yen, for a net profit of 9,000 yen.

If you lose, your 10,000 yen is not returned.

Under these conditions, a win rate of exactly 52.63% makes wins and losses balance (this 52.63% is calculated by 1 ÷ 1.90).

If the win rate is higher than that, you’ll be profitable in the long run; if lower, you’ll lose.

One caveat: the payout rate can change depending on the decision time (how many seconds after you place the trade the outcome is determined).

If you look at a short-term payout example from a broker (not a recommendation), the payout tends to be higher for shorter decision times and lower for longer times.

From this table, you can see something interesting.

When the decision time is short at 15 seconds, the break-even point is only 48.78%.

In other words, even with a 50% win rate like flipping a coin, you can be profitable on average (note that payout rates tend to vary by account and time, so treat this as a representative value).

Conversely, with longer decision times like 180 or 300 seconds, you need to exceed 52.63% to avoid a loss.

Most importantly, do not assume that a higher win rate automatically means superior performance.

The same win rate of 55% can be highly profitable with a 15-second decision time, and only slightly profitable with 180 seconds.

Conversely, a win rate of 50% is slightly profitable at 15 seconds, but clearly unprofitable at 180 seconds.

Unless you consider the decision time (payout rate), win rate alone cannot determine quality.

When looking at a sales page’s performance, always check the following three items together.

① Win rate, ② the number of trades that underlie that win rate (the denominator), ③ the decision time (payout rate and break-even point).

If any one of these is missing from the performance display, it is not enough to judge.

Chapter summary in 3 lines

1. Win rate cannot determine skill vs. luck unless paired with the number of trades.

2. From payout rate, you can calculate the break-even win rate, and shorter decision times lower the break-even point (e.g., 15 seconds 48.78%, 180 seconds 52.63%).

3. When looking at a sales page, always confirm the three items: win rate, count, and decision time.

■ What to look for on sales page performance displays (checklist)

In the previous chapter, we looked at how to read win rate.

The detailed reasons why theoretical results diverge from actual results will be covered in the next volumes.

In this chapter, I summarize the win-rate reading into a practical “checklist” for reading sales pages.

There are five items to check.

① The period: from when to when

Behind any “actual results” figures, there is always a period indicating the start and end dates.

If this period isn’t stated, it could be showing only a few good days or weeks, which cannot be dismissed.

Highlighting only good periods is called “cherry-picking.”

Under the Fair Advertising Law, presenting results that are significantly more favorable than reality may be considered “misleading advertising.”

It’s worth doubting performance without a clearly defined period.

② Count (denominator) is stated

As seen in Chapter 2.

Win rate alone does not tell you whether it’s 10 trades or 1000 trades.

A win rate without a count is not sufficient.

③ Are there losing days?

If you see pages with only winning days or a winning-streak sequence, look for where the losing day records are.

Displays that only show positive experiences or results are called “unsubstantiated advertising” and are regarded as problematic for lack of evidence.

Even on official media from platforms that run consumer services, it is noted that showing only positive testimonials and data without basis is prohibited.

What is allowed is a “reasonable, well-founded performance display,” and when multiple results are shown, the breakdown should also be specified.

A performance that shows no trace of losing days is itself a warning sign.

④ Are decision time and payout clearly stated?

As seen in Chapter 2, the break-even win rate changes with decision time (e.g., 15 seconds 48.78%, 180 seconds 52.63%).

If a performance display only says “60% win rate” without indicating decision time, you cannot determine whether that 60% is strongly positive or only slightly positive.

⑤ Is it automated trading performance or manual (self-recorded) performance?

Self-evaluation that only records the moment a signal appears (later matching hits/misses) and actual orders placed by the system and filled by the broker can yield different numbers even with the same logic.

Many pages simply say “actual results” without specifying at which stage the numbers are.

If possible, ask and confirm whether it is an actual filled order from automated trading.

If any one of the five items is missing, please treat the performance display as insufficient for making a judgment.

Let’s look at a more concrete example.

Suppose there is a display: “In the most recent year, 720 trades with 463 hits, win rate 64.3%” (fictional example).

This satisfies items ① period and ② count being stated, so the first two checklist items are met.

However, if ③ records of losing periods, ④ breakdown of decision time and payout, and ⑤ distinction between automated executions vs. self-recorded scores are not stated, the remaining three items are still unverified.

Don’t judge a good tool based only on the first two items; search for the remaining three items as well. That is how you use the checklist.

Even if some numbers look good, if the latter half of the checklist is incomplete, use this as a reference for caution.

In another product (one-time purchase), the product page embeds a live stream directly to show ongoing operation, which helps with period cues for ① period.

Multiple dated performance images also help indicate period cues for ①.

However, whether those are actual automated-trading executions or manually recorded self-evaluations by the streamer cannot be determined from the page alone.

If item ⑤ remains ambiguous, some checklist items remain unfilled.

For reference, I also checked the product page for the author’s own signal tool “SpikeEdge” using the same criteria.

They publish payouts per decision time and performances with stated periods and counts.

Daily records including losing days and actual executions through automated trading are also clearly stated.

Thus, a sign tool that publicly discloses information ①–⑤ can be a good material for considering a purchase.

Conversely, pages that lack these details do not yet provide enough basis for judgment.

Finally, a note on disclaimers.

A sentence like “Past results do not guarantee future profits” is usually found on conscientious sales pages.

Whether this sentence exists or not does not in itself determine quality.

However, it can be a clue that the author is aware of “excessive claims.”

Chapter summary in 3 lines

1. Sales page performance should be checked for five items: period, count, losing days, decision time and payout, and whether it’s automated trading or self-recorded.

2. Cherry-picking or focusing only on favorable testimonials is viewed critically under regulations and law.

3. A performance display with any one missing of the five items is insufficient for judgment.

■ In conclusion, what’s free to try

So far we have looked at how to read win rates and the sales-page checklist.

When looking at sales-page numbers, check the five points: period, count, losing days, decision time and payout, and whether the performance is from automated trading or self-recorded.

That is the main message I wanted to convey in this article.

I will now point you to where you can actually verify this checklist yourself.

From here, the guidance will differ slightly depending on the source of this article.

I publish a free tool on Gogojungle’s product page that applies this sales-page checklist to real numbers.

You can also experiment with martingale (increasing bet size after consecutive losses to recover) and pyramiding (increasing bets after wins) by adjusting numbers yourself, though these features are planned as paid in the future.

The free option is a one-shot simulation that validates each trade individually.

→ https://www.gogojungle.co.jp/tools/ebooks/85729

If you try something for free and then recall what you read in this article, you will benefit.

Do not take numbers at face value; verify with your own eyes. That is the core message I wanted to convey with this article.

This series is planned to be a total of 3 volumes. Volume 2 will cover “Why theoretical values diverge from actual executions” (how win rate drops when automated trading is used and examples of ineffective filters). Volume 3 will cover “The mechanism of loss by Martingale and the ‘count × expected value’ approach” (how bet sizes grow after losses and how to leverage counts rather than trying to raise win rate).

Chapter summary in 3 lines

1. The aim of this article is to help readers doubt and read performance numbers themselves.

2. A free one-shot simulation tool is available from Gogojungle’s product page.

3. In the follow-up volumes, we will discuss “divergence between theoretical values and actual executions” and “how Martingale works.”

*This article is a draft based on records and verifications by the developer themselves and does not constitute investment advice or a guarantee of profit. Please conduct actual trading with your own judgment and responsibility.*

■ The continuation is in paid e-books

This article is the free release of Volume 1 of the series.

Volume 2, “Why theoretical values diverge from actual executions” (how many points win rate drops when placed in automated trading; a collection of filters that didn’t work) and Volume 3, “The mechanism that Martingale melts down and ‘count × expected value’” will be released as paid e-books on Gogojungle.

A free tool to try profit and loss calculations (single-run verification) is available here.

https://www.gogojungle.co.jp/tools/ebooks/85729