The **Fisher Transform Indicator** converts price into a normal distribution, and alongside other tools can find price reversals for traders to act on.

The Fisher Transform indicator turns a price into a Gaussian normal distribution, and it’s usually used to uncover over buying and overselling situations in the market which could point towards possible reversal points that can be exploited by the trader.

Price is converted into a normal distribution (or bell curve) which is where the trailing ends are thin (and in theory, this means that the chances of them occurring are not very likely). This means large swings or excessive values in the indicator shouldn’t be all that common.

In the event that this kind of behavior does take place it might augur an imminent reversal in price. Because of this, traders like the Fisher Transform because it can sniff out possible opportunities like this virtually ‘live’ in the moment, with more up-to-the-minute accuracy than laggy indicators can provide, and the peaks it uses to alert the trader are prominent and hard to ignore, a boon to those who favor the efficiencies of automated trading.

The Fisher Transform can (and probably should) be used in tandem with other technical indicators like the moving average convergence divergence (MACD) and relative strength index (RSI), boosting the quality an accuracy of predictions.

Calculating the Fisher Transform:

Fisher Transform = ½ * ln [(1 + X) / (1 – X)]

Where:

*ln* is the shorthand form of the natural logarithm.

*X* stands for price change to a level between -1 and 1 to make for easier calculation.

Fisher Transform signals include the touching or breaching of a particular level, and for anyone doing it this way, the idea behind it is that you should act quickly with reversals because if you wait too long for confirmation that the indicator has peaked, you’re likely to have missed the moment for that asset. The level you choose will vary according to the market, the duration, and your own preferences.

A reversal in the indicator itself can also signal a potential trade opportunity. Rather than sticking to a set breach of a level, you could just take a trade once that happens. Of course, the caveat here is that you should never use a single indicator in isolation since it’s much more effective to factor in the agreement of other technical indicators and fundamental analysis results.

We’ll look at trade signals based on these rules:

Fisher Transform must be positive (for instance, price thought of as overly bullish)

The trade occurs after a reversal in the direction of the Fisher Transform

But if we take a look at how it fares on its own using this daily chart for the S&P 500, it tends to come unstuck. Inside the white vertical lines, you can see buy (“long”) and sell (“short”) trades. The trade opens at the white line where the candle on the left closes, and the exit point is at the white line on the candle on the right.

The result would have been four successes and four failures which overall would have meant a ‘breakeven’ result.

Revising the system so it includes the rules we set out along with the stipulation that trades are only taken in the direction of the prevailing trend – as stipulated by a 50-period moving average – there will be a big improvement in precision.

The initial trade doesn’t do so well, the second more or less breaks even while the last two do succeed. Once again, use the Fisher transform with other indicators and you get much more pertinent results.

Fisher Transform has to be negative (which is to say that the more negative the indicator becomes the more price will be “stretched” or overly bearish)

Taken after a reversal of the Fisher Transform from negatively sloped to positively sloped (for example, rate of change from negative to positive)

Financial information doesn’t usually fit the normal distribution all that well. A few markets, like developed market equities, will have a tendency to consistently rise over time because the companies that underpin them make money.

Normal distributions mirror each other around the mean. Even assets like commodities, which don’t produce cash in the same way that companies do, still go up in line with inflation, showing slight directionality over time instead of symmetry in their price fluctuations.

Also, financial info has a tendency to be more evenly distributed, which is to say that the “tails” of the curve will be broader compared to a typical distribution. But it’s inadvisable for us to work under the assumption that financial markets follow a normal distribution because this can lull the trader into underestimating the chance of outliers showing up.

The Fisher Transform converts a price into a Gaussian normal distribution and isolates possible reversals of price in the market. The clarity of the signals is a definite benefit. It’s limited by the fact that application of the normal distribution to financial information will most likely give inaccurate results, but it does have value as an adjunct to other indicators and tools.