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Indicators

Combining Indicators

Introduction

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

Create Subscriptions

You need to subscribe to some market data in order to calculate indicator values.

Select Language:
qb = QuantBook()
symbol = qb.add_equity("SPY").symbol

Create Indicator Timeseries

You need to subscribe to some market data 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.

Select Language:
# 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.
  2. Select Language:
    # Request historical trading data with the daily resolution.
    history = qb.history[TradeBar](symbol, 70, Resolution.DAILY)
  3. Create a RollingWindow for each attribute of the indicator to hold their values.
  4. In this example, save 50 data points.

    Select Language:
    # 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[DateTime](50)
    window["SMA Of RSI"] = RollingWindow[float](50)
    window["rollingsum"] = RollingWindow[float](50)
  5. Attach a handler method to the indicator that updates the RollingWindow objects.
  6. Select Language:
    # 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.

  7. Iterate the historical market data to update the indicators and the RollingWindows.
  8. Select Language:
    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)
  9. Populate a DataFrame with the data in the RollingWindow objects.
  10. sma_of_rsi_dataframe = pd.DataFrame(window).set_index('time')
    Historical data of 10-day SMA of 10-day RSI

Plot Indicators

Follow these steps to plot the indicator values:

  1. Select the columns/features to plot.
  2. sma_of_rsi_plot = sma_of_rsi_dataframe[["SMA Of RSI"]]
  3. Call the plot method.
  4. sma_of_rsi_plot.plot(title="SPY SMA(10) of RSI(10)", figsize=(15, 10))
  5. Show the plots.
  6. plt.show()
    Line plot of 10-day SMA of 10-day RSI

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.

Select Language:
# 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")

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