---
title: "Combining Indicators"
description: "Learn how to create, combine, and update the ensembled indicators in the research environment in QuantConnect."
canonical: https://www.quantconnect.com/docs/v2/research-environment/indicators/combining-indicators
markdown: https://www.quantconnect.com/docs/v2/research-environment/indicators/combining-indicators.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/04%20Research%20Environment/06%20Indicators/05%20Combining%20Indicators
modified: 2026-10-29
index: https://www.quantconnect.com/llms.txt
---

# Combining Indicators

[Documentation](https://www.quantconnect.com/docs/v2.md) > [Research Environment](https://www.quantconnect.com/docs/v2/research-environment.md) > [Indicators](https://www.quantconnect.com/docs/v2/research-environment/indicators.md) > Combining Indicators

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

## Introduction

This page explains how to create, update, and visualize LEAN Composite indicators.

## Create Subscriptions

You need to [subscribe to some market data](https://www.quantconnect.com/docs/v2/research-environment/datasets/us-equity.md#03-Create-Subscriptions) in order to calculate indicator values.

```python
qb = QuantBook()
symbol = qb.add_equity("SPY").symbol
```

## Create Indicator Timeseries

You need to [subscribe to some market data](https://www.quantconnect.com/docs/v2/research-environment/indicators/combining-indicators.md#03-Create-Subscriptions) and create a composite indicator in order to calculate a timeseries of indicator values. In this example, use a 10-period `SimpleMovingAverage` of a 10-period `RelativeStrengthIndex` indicator.

```python
# Create 10-period RSI and 10-period SMA indicator objects.
rsi = RelativeStrengthIndex(10)
sma = SimpleMovingAverage(10)
# Create a composite indicator by feeding the value of 10-period RSI to the 10-period SMA indicator.
sma_of_rsi = IndicatorExtensions.of(sma, rsi)
```

Follow these steps to create an indicator timeseries:

1. Get some [historical data](https://www.quantconnect.com/docs/v2/research-environment/datasets/key-concepts.md).
   ```python
   # Request historical trading data with the daily resolution.
   history = qb.history[TradeBar](symbol, 70, Resolution.DAILY)
   ```
2. Create a `RollingWindow` for each attribute of the indicator to hold their values.
   In this example, save 50 data points.
   ```python
   # Create a window dictionary to store RollingWindow objects.
   window = {}
   # Store the RollingWindow objects, index by key is the property of the indicator.
   window['time'] = RollingWindow(50)
   window["SMA Of RSI"] = RollingWindow(50)
   window["rollingsum"] = RollingWindow(50)
   ```
3. Attach a handler method to the indicator that updates the `RollingWindow` objects.
   ```python
   # Define an update function to add the indicator values to the RollingWindow object.
   def update_sma_of_rsi_window(sender: object, updated: IndicatorDataPoint) -> None:
       indicator = sender
       window['time'].add(updated.end_time)
       window["SMA Of RSI"].add(updated.value)
       window["rollingsum"].add(indicator.rolling_sum.current.value)

   sma_of_rsi.updated += UpdateSmaOfRsiWindow
   ```
   When the indicator receives new data, the preceding handler method adds the new `IndicatorDataPoint` values into the respective `RollingWindow`.
4. Iterate the [historical market data](https://www.quantconnect.com/docs/v2/research-environment/datasets/key-concepts.md#03-Request-Data) to update the indicators and the `RollingWindow`s.
   ```python
   for bar in history:
       # Update the base indicators, the composite indicator will update automatically when the base indicator is updated.
       rsi.update(bar.end_time, bar.close)
   ```
5. Populate a `DataFrame` with the data in the `RollingWindow` objects.
   ```python
   sma_of_rsi_dataframe = pd.DataFrame(window).set_index('time')
   ```
   ![Historical data of 10-day SMA of 10-day RSI](https://cdn.quantconnect.com/i/tu/indicator-composite-py-classic-smaofrsi.png)

## Plot Indicators

Follow these steps to plot the indicator values:

1. Select the columns/features to plot.
   ```python
   sma_of_rsi_plot = sma_of_rsi_dataframe[["SMA Of RSI"]]
   ```
2. Call the `plot` method.
   ```python
   sma_of_rsi_plot.plot(title="SPY SMA(10) of RSI(10)", figsize=(15, 10))
   ```
3. Show the plots.
   ```python
   plt.show()
   ```
   ![Line plot of 10-day SMA of 10-day RSI](https://cdn.quantconnect.com/i/tu/indicator-composite-py-plot.png)

## Examples

The following examples demonstrate some common practices for researching with combining indicators.

### Example 1: Plot Standard Deviation Of Return

The following example demonstrates a quick backtest to testify the effectiveness of a Standard Deviation On Return mean-reversal under the research enviornment.

```python
# Instantiate the QuantBook instance for researching.
qb = QuantBook()
# Request SPY data to work with the indicator.
symbol = qb.add_equity("SPY").symbol

# Get the historical data for trading.
history = qb.history(symbol, 500, Resolution.DAILY).close.unstack(0)
history = history.groupby(history.index.date).sum()

# Create the SD on Return with parameters to be studied.
roc = RateOfChange(1)
sd = StandardDeviation(252)
indicator = IndicatorExtensions.of(sd, roc)

# Update and obtain the indicator value
def update(row):
    roc.update(row.name, row.iloc[0])
    return indicator.current.value
indicator_dataframe = history.apply(update, axis=1).iloc[252:]

indicator_dataframe.plot(title=f"Return SD of {symbol}", ylabel="%", xlabel="Time")
```