Here’s a quick review of John Ehlers’ inverse fisher transform.

You’re right about attributing this trading theorem or whatever you choose to call it to this Engineer.

Is it worth the time you’ll spend learning it?

Definitely!

It enables you see the market in one more light.

And that could improve the way you trade significantly.

Therefore, sit back and give this concept great thought.

I’ll try as much as I can to use simple English to describe the inverse fisher transform.

And its application to the Relative Strength Index, Stochastic, as with other oscillating indicators.

Because it can get a bit tacky at some point.

Let’s get right into the Inverse fisher stochastic.

## What is Stochastic?

Stochastic is a trading indicator that hints at trend reversals.

You can easily enable it on trading apps like TradingView, TabTrader, Binance, etc.

Now, this indicator shows you overbought and oversold regions.

I’ll explain.

Overbought simply means price is trading higher than it should within that session or timeframe.

So let’s say Bitcoin was trading between $29,400 and $29,800 on the 5 minutes timeframe.

All of a sudden it rallies to $30,000 and even trades as high as $32,000.

What would you say has happened?

Bitcoin is overpriced at that moment since its range on that timeframe is exceptionally high.

If you understand this concept for overbought, then compare it to oversold or undersold pairs.

Price is trading significantly lower than it had in certain timeframes.

Read Also: Weekly Pivot Points for Swing Trading Cryptos

As I’d earlier mentioned, stochastics indicator shows you these overpriced and underpriced levels.

The stochastic’s oscillator ranges between 80 and 100 showing overbought prices.

It oscillates below 20 to indicate undersold prices.

It’s worth mentioning the RSI as well since it is majorly used in this theorem.

The RSI’s range around 70 hints overbought and below 30 is undersold.

Now that you understand this concept, let’s check the inverse theorem concept is about.

## Overview of the Inverse Fisher Transform Concept

Ehlers’ remarks that indicators do help us time our entries and exits in the market.

Yet we often times don’t take their signals confidently.

Why’s that?

It could be because we’re uncertain if the indicator is right.

But what happens if the indicator is actually right and you’re the one misreading its signals?

You’d take the trade in the wrong direction and probably blame the indicator for false signals.

That could get worse if you’re new to trading. Or have little or no knowledge on candlesticks and generally price action trading.

So, Ehlers has proferred a solution that could go a long way to give your stochastic indicator a greater reading accuracy.

Let’s find out what that is.

### Theorem’s Focus

Ehlers’ focus is on timely and clear signals from indicators.

Because these two could be everything.

You need to read the actual signal the indicator gives and on time.

Misreading it could just be as bad as false signals.

And finally realizing it’s time to buy or sell after the market has advanced or declined from a good entry isn’t ideal.

That’s to say losses could be due to the trader as a result of not really understanding what the indicator says.

I’ve been going on about indicators but this concept specifically focuses on indicators that oscilate.

What are these?

There’s the Relative Strenghth Index (RSI), Moving Average Convergence Divergence (MACD), and the stochastic.

## What is Inverse Fisher Transform?

An inverse fisher transform is a formula used to modify the probability distribution function (PDF) of indicators.

Probability distribution is a function that shows the likelihood for certain results to occur during an experiment.

Accordingly, Inverse fisher transform is applied to prices’ and indicators’ probability distribution function.

Ehlers applied the inverse fisher transform to the PDF of RSI.

According to the writer, the same can be done for oscillating indicators.

All of this is made possible using an inverse fisher transform formula.

This formula is:

Check this online document for more clarity on the Inverse Fisher Theorem and even its relation to Gaussian normal distribution.

## How the Inverse Fisher Transform is Created

Here’s a summary of what you’ll find in this concept.

- There is a transfer response of the inverse fisher.
- An input around –0.5 and +0.5 yields an output that is similar to the input.
- Input values above 2 result in compressed outputs.
- Inverse Fisher Transform produces a result that could be +1 or –1.

And this result helps to create indicators that give clear signals.

An RSI of price is taken before a stochastic of the RSI is done.

The latter gives the new indicator a ranging value between 0 and 100.

This range can be equated to -1 and +1 values.

The writer recommends using the probability PDF to make your Stochastic perform better.

This differs from the regular way of only modifying the values of the indicator.

The latter is often in a bid to make it faster or slower.

## How to Trade the Inverse Fisher Transform Stochastic Oscillator

The inverse fisher transform was applied to the RSI indicator to create a new indicator.

The indicator is divided into three parts.

One part is above +0.5, the second is between +0.5 and -0.5 and the last is below -0.5.

Here’s how to use the Inverse transform indicator.:

- Long when oscillator goes above -0.5
- Long when oscillator goes above +0.5
- Short when oscillator goes below +0.5
- Short when oscillator goes below -0.5

## 1. Long When oscillator Goes Above -0.5:

Around -0.5 it is believed that price is neither overbought nor oversold.

Also, it is not in the bearish zone which is below -0.5.

Longing around this region would be an early entry.

As such, there’s a lot of profit margin to cover.

You’d also have a good risk to reward.

**Read Also: Higher High Lower Low Pattern Trading Strategy**

Remember that you could also trade spot positions such as buying Bitcoin, Ethereum, Binance Coin, Matic, etc.

This signal would also serve as a good indicator to close short positions.

### 2. Long When oscillator Goes Above +0.5:

You can also buy or make long entries when price goes above +0.5.

But then your profit margin will be small.

This is because the oscillator is close to the overbought region. As such, there could be corrections in price soon.

Nonetheless, price can remain oversold for days if not weeks.

## 3. Short When Price Goes Below +0.5:

The oscillator below +0.5 means that it’s close to overbought level.

This means you’d be getting an early entry since you could hold the trade until price gets to -0.5 or goes below it.

The wide profit margin could also mean a better risk to reward.

On the other hand, this signal can be used to exit a long position or take profit.

## 4. Short When Oscillator Goes Below -0.5:

It’s still possible to cart profit from the market when the oscillator trades below -0.5.

You’d be using a tight stop in this case since price is already close to being extremely oversold.

## Frequently Asked Questions

Here are answers to some frequently asked questions

### 1. Stochastic Oscillator Success Rate

The stochastic oscillator has a high accuracy even though it can’t be tied to a certain number.

The indicator’s success rate also relies on your ability to read its signals clearly.

2. What is the Opposite of Stochastic

RSI can be called the opposite of stochastic.

It yields overbought and oversold regions just like the stochastic.

However, the market is considered overbought when the oscillator goes above 70 and not 80 as is the case of stochastic.

Also, oversold readings on RSI begin from 30 and not 20.

## Final Words

The Inverse Fisher Transform stochastic is a bit of a complex concept.

That’s why you don’t have to modify your indicator from scratch.

You can simply use an indicator that has already been tweaked to offer clearer signals.

It’ll go a long way to improve your confidence in the signals offered by the indicator.

But try not to have a heavy reliance on these indicators.

Because at the end of the day, price is what you should really be paying attention to.

Got contributions regarding the Inverse Fisher Transform in relation to RSI, Stochastic, or other oscillators?

Let me know in the comment section.