---
title: "Fisher Transform"
description: "The Fisher transform is a mathematical process which is used to convert any data set to a modified data set whose Probability D..."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/fisher-transform
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/fisher-transform.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/28%20Indicators/01%20Supported%20Indicators/041%20Fisher%20Transform
modified: 2026-10-31
index: https://www.quantconnect.com/llms.txt
---

# Fisher Transform

[Documentation](https://www.quantconnect.com/docs/v2.md) > [Writing Algorithms](https://www.quantconnect.com/docs/v2/writing-algorithms.md) > Indicators > [Supported Indicators](https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators.md) > Fisher Transform

> Code examples are shown in Python. Append `?lang=csharp` to this URL for the C# version of this page.

## Introduction

The Fisher transform is a mathematical process which is used to convert any data set to a modified data set whose Probability Distribution Function is approximately Gaussian. Once the Fisher transform is computed, the transformed data can then be analyzed in terms of it's deviation from the mean. The equation is y = .5 * ln [ 1 + x / 1 - x ] where x is the input y is the output ln is the natural logarithm The Fisher transform has much sharper turning points than other indicators such as MACD For more info, read chapter 1 of Cybernetic Analysis for Stocks and Futures by John F. Ehlers We are implementing the latest version of this indicator found at Fig. 4 of http://www.mesasoftware.com/papers/UsingTheFisherTransform.pdf

To view the implementation of this indicator, see the [LEAN GitHub repository](https://github.com/QuantConnect/Lean/tree/master/Indicators/FisherTransform.cs).

## Using FISH Indicator

To create an automatic indicator for `FisherTransform`, call the `fish` helper method from the `QCAlgorithm` class. The `fish` method creates a `FisherTransform` object, hooks it up for automatic updates, and returns it so you can used it in your algorithm. In most cases, you should call the helper method in the `initialize` method.

```python
class FisherTransformAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._fish = self.fish(self._symbol, 20)

    def on_data(self, slice: Slice) -> None:

        if self._fish.is_ready:
            # The current value of self._fish is represented by self._fish.current.value
            self.plot("FisherTransform", "fish", self._fish.current.value)
```

For more information about this method, see the [QCAlgorithm class](https://www.lean.io/docs/v2/lean-engine/class-reference/py/QuantConnect/Algorithm/QCAlgorithm/#QuantConnect.Algorithm.QCAlgorithm.fish).

You can manually create a `FisherTransform` indicator, so it doesn't automatically update. Manual indicators let you update their values with any data you choose.

Updating your indicator manually enables you to control when the indicator is updated and what data you use to update it. To manually update the indicator, call the `update` method. The indicator will only be ready after you prime it with enough data.

```python
class FisherTransformAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._fishertransform = FisherTransform(20)

    def on_data(self, slice: Slice) -> None:
        bar = slice.bars.get(self._symbol)
        if bar:
            self._fishertransform.update(bar)

        if self._fishertransform.is_ready:
            # The current value of self._fishertransform is represented by self._fishertransform.current.value
            self.plot("FisherTransform", "fishertransform", self._fishertransform.current.value)
```

For more information about this indicator, see its [reference](https://www.lean.io/docs/v2/lean-engine/class-reference/py/QuantConnect/Indicators/FisherTransform).

## Visualization

The following plot shows values for some of the `FisherTransform` indicator properties:

![FisherTransform line plot.](https://cdn.quantconnect.com/docs/i/writing-algorithms/indicators/supported-indicators/fisher-transform.png)

## Indicator History

To get the historical data of the `FisherTransform` indicator, call the `self.indicator_history` method. This method resets your indicator, makes a [history request](https://www.quantconnect.com/docs/v2/writing-algorithms/historical-data/history-requests.md), and updates the indicator with the historical data. Just like with regular history requests, the `indicator_history` method supports time periods based on a trailing number of bars, a trailing period of time, or a defined period of time. If you don't provide a `resolution` argument, it defaults to match the resolution of the security subscription.

```python
class FisherTransformAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
        self._fish = self.fish(self._symbol, 20)

        indicator_history = self.indicator_history(self._fish, self._symbol, 100, Resolution.MINUTE)
        timedelta_indicator_history = self.indicator_history(self._fish, self._symbol, timedelta(days=10), Resolution.MINUTE)
        time_period_indicator_history = self.indicator_history(self._fish, self._symbol, datetime(2024, 7, 1), datetime(2024, 7, 5), Resolution.MINUTE)
```