Indicators

Working knowledge of C#.

If you use Python, you must understand how to work with pandas DataFrames and Series. If you are not familiar with pandas, refer to the pandas documentation.

Get some historical market data to warm-up and create a historical record of indicator values. For example, to get data for SPY, run:

var qb = new QuantBook();
var symbol = qb.AddEquity("SPY").Symbol;
var history = qb.History(symbol, 70, Resolution.Daily);

qb = QuantBook()
symbol = qb.AddEquity("SPY").Symbol
history = qb.History(symbol, 70, Resolution.Daily).loc[symbol]

Follow these steps to create an indicator timeseries:

1. Create a trade bar indicator (utilizing OHLCV information). In this tutorial, we'll be using a 20-period VolumeWeightedAveragePriceIndicator indicator.

var vwap = new VolumeWeightedAveragePriceIndicator(20);

vwap = VolumeWeightedAveragePriceIndicator(20)

2. Create a RollingWindow for each attribute of the indicator to hold their values.

In this example, save 50 data points.

var time = new RollingWindow<DateTime>(50);
var window = new Dictionary<string, RollingWindow<decimal>>();
window["volumeweightedaveragepriceindicator"] = new RollingWindow<decimal>(50);

window = {}
window['time'] = RollingWindow[DateTime](50)
window['volumeweightedaveragepriceindicator'] = RollingWindow[float](50)

3. Set handler methods to update the RollingWindows.

vwap.Updated += (sender, updated) => 
{
    time.Add(updated.EndTime);
    window["volumeweightedaveragepriceindicator"].Add(updated);
};

def UpdateVWAPWindow(sender: object, updated: IndicatorDataPoint) -> None:
    window['time'].Add(updated.EndTime)
    window["volumeweightedaveragepriceindicator"].Add(updated.Value)

vwap.Updated += UpdateVWAPWindow

When the indicators receive new data, the handler will add the new IndicatorDataPoints into the RollingWindows.

4. Iterate the historical market data to update the indicators and the RollingWindows.

foreach(var bar in history){
    // Update the indicators with the whole TradeBar.
    vwap.Update(bar);
}

for time, row in history.iterrows():
    # Create a TradeBar to update the TradeBar indicator.
    bar = TradeBar(time, symbol, row.open, row.high, row.low, row.close, row.volume)
    # Update the indicator with TradeBar.
    vwap.Update(bar)

5. Display the data.

Console.WriteLine($"time,{string.Join(',', window.Select(kvp => kvp.Key))}");
foreach (var i in Enumerable.Range(0, 5).Reverse())
{
    var data = string.Join(", ", window.Select(kvp => Math.Round(kvp.Value[i],6)));
    Console.WriteLine($"{time[i]:yyyyMMdd}, {data}");
}


6. Convert the RollingWindows' data into pandas.DataFrame.

vwap_dataframe = pd.DataFrame(window).set_index('time')

Indicator Helper Method

Call the qb.Indicator method to create an indicator timeseries.

var vwapIndicator = qb.Indicator(vwap, symbol, 50, Resolution.Daily);

vwap_indicator = qb.Indicator(vwap, symbol, 50, Resolution.Daily)

Jupyter Notebooks don't currently support libraries to plot historical data, but we are working on adding the functionality. Until the functionality is added, use Python to plot historical Equity Options data.

You can call the in-built plot method of pandas.DataFrame to plot the indicators.

1. Call plot to plot the indicator.

vwap_indicator.plot(title="SPY VWAP(20)", figsize=(15, 10))

2. Show the plots.

plt.show()