Indicators

var qb = new QuantBook();
var symbol = qb.AddEquity("SPY").Symbol;

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

// Load the assembly files and data types in their own cell.
#load "../Initialize.csx"

// Load the necessary assembly files.
#load "../QuantConnect.csx"
#r "../Plotly.NET.dll"
#r "../Plotly.NET.Interactive.dll"

// Import the QuantConnect, Plotly.NET, and Accord packages for calculation and plotting.
using QuantConnect;
using QuantConnect.Indicators;
using QuantConnect.Research;
            
using Plotly.NET;
using Plotly.NET.Interactive;
using Plotly.NET.LayoutObjects;

// Instantiate the QuantBook instance for researching.
var qb = new QuantBook();
// Request SPY data to work with the indicator.
var symbol = qb.AddEquity("SPY").Symbol;

// Request historical trading data with the daily resolution.
var history = qb.History(symbol, 1000, Resolution.Minute).ToList();

// Create a window dictionary to store RollingWindow objects.
var window = new Dictionary<string, RollingWindow<decimal>>();
// Store the RollingWindow objects, index by key is the property of the indicator.
var time = new RollingWindow<DateTime>(50);
window["bollingerbands"] = new RollingWindow<decimal>(50);
window["lowerband"] = new RollingWindow<decimal>(50);
window["middleband"] = new RollingWindow<decimal>(50);
window["upperband"] = new RollingWindow<decimal>(50);
window["price"] = new RollingWindow<decimal>(50);

// Create the Bollinger Band indicator with parameters to be studied.
var bb = new BollingerBands(20, 2);
// Define an update function to add the indicator values to the RollingWindow object.
bb.Updated += (sender, updated) => 
{
    var indicator = (BollingerBands)sender;
    time.Add(updated.EndTime);
    window["bollingerbands"].Add(updated);
    window["lowerband"].Add(indicator.LowerBand);
    window["middleband"].Add(indicator.MiddleBand);
    window["upperband"].Add(indicator.UpperBand);
    window["price"].Add(indicator.Price);
};

// Create a TradeBarConsolidator to consolidate data into the custom resolution.
var consolidator = new TradeBarConsolidator(TimeSpan.FromMinutes(5));
// Attach a handler method to feed data into the consolidator and updates the indicator with the consolidated bars.
consolidator.DataConsolidated += (sender, consolidated) =>
{
    bb.Update(consolidated.EndTime, consolidated.Close);
};

// Iterate through the historical market data and update the indicator.
foreach(var bar in history)
{
    consolidator.Update(bar);
}

// Obtain the cumulative return curve as a mini-backtest.
var equity = new List<decimal>() { 1m };
var time_ = new List<DateTime>() { history[0].EndTime };
var lowerBand = window["lowerband"].ToList();
var upperBand = window["upperband"].ToList();
for (int i = history.Count - 50; i < history.Count - 1; i++)
{
    var bar = history[i];
    var nextBar = history[i+1];

    // Get 1-day forward return.
    var pctChg = (nextBar.Close - bar.Close) / bar.Close;

    // Buy if the asset is underprice (below the lower band), sell if overpriced (above the upper band)
    var order = bar.Close < lowerBand[i - (history.Count - 50)] ? 1 : (bar.Close > upperBand[i - (history.Count - 50)] ? -1 : 0);
    var equityChg = order * pctChg;
    
    equity.Add((1m + equityChg) * equity[^1]);
    time_.Add(nextBar.EndTime);
}

// Create line chart of the equity curve.
var chart = Chart2D.Chart.Line<DateTime, decimal, string>(
    time_,
    equity
);

// Create a Layout as the plot settings.
LinearAxis xAxis = new LinearAxis();
xAxis.SetValue("title", "Time");
LinearAxis yAxis = new LinearAxis();
yAxis.SetValue("title", "Equity");
Title title = Title.init($"Equity by Time of {symbol}");

Layout layout = new Layout();
layout.SetValue("xaxis", xAxis);
layout.SetValue("yaxis", yAxis);
layout.SetValue("title", title);
// Assign the Layout to the chart.
chart.WithLayout(layout);

// Display the plot.
HTML(GenericChart.toChartHTML(chart))

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

# Request historical trading data with the minute resolution.
history = qb.history[TradeBar](symbol, 1000, Resolution.MINUTE)

# 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["bollingerbands"] = RollingWindow[float](50)
window["lowerband"] = RollingWindow[float](50)
window["middleband"] = RollingWindow[float](50)
window["upperband"] = RollingWindow[float](50)
window["price"] = RollingWindow[float](50)

# Define an update function to add the indicator values to the RollingWindow object.
def update_bollinger_band_window(sender: object, updated: IndicatorDataPoint) -> None:
    indicator = sender
    window['time'].add(updated.end_time)
    window["bollingerbands"].add(updated.value)
    window["lowerband"].add(indicator.lower_band.current.value)
    window["middleband"].add(indicator.middle_band.current.value)
    window["upperband"].add(indicator.upper_band.current.value)
    window["price"].add(indicator.price.current.value)

# Create the Bollinger Band indicator with parameters to be studied.
bb = BollingerBands(20, 2)
bb.updated += update_bollinger_band_window

# Create a TradeBarConsolidator to consolidate data into the custom resolution.
consolidator = TradeBarConsolidator(timedelta(minutes=5))
# Attach a handler method to feed data into the consolidator and updates the indicator with the consolidated bars.
def on_data_consolidated(sender, consolidated):
    bb.update(consolidated.end_time, consolidated.close)
consolidator.data_consolidated += on_data_consolidated

# Iterate through the historical market data and update the indicator.
for bar in history:
    consolidator.update(bar)

# Populate a DataFrame with the data in the RollingWindow objects.
bb_dataframe = pd.DataFrame(window).set_index('time')

# Create a order record and return column.
# Buy if the asset is underprice (below the lower band), sell if overpriced (above the upper band)
bb_dataframe["position"] = bb_dataframe.apply(lambda x: 1 if x.price < x.lowerband else -1 if x.price > x.upperband else 0, axis=1)
# Get the 1-day forward return.
bb_dataframe["return"] = bb_dataframe["price"].pct_change().shift(-1).fillna(0)
bb_dataframe["return"] = bb_dataframe["position"] * bb_dataframe["return"]

# Obtain the cumulative return curve as a mini-backtest.
equity_curve = (bb_dataframe["return"] + 1).cumprod()
equity_curve.plot(title="Equity Curve on BBand Mean Reversal", ylabel="Equity", xlabel="time")