Indicators

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

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

var atr = new AverageTrueRange(20);

atr = AverageTrueRange(20)

// Create a dataframe with a date index, and columns are indicator values.
var atrIndicator = qb.Indicator(atr, symbol, 50, Resolution.Daily);

# Create a dataframe with a date index, and columns are indicator values.
atr_dataframe = qb.indicator(atr, symbol, 50, Resolution.DAILY)

// 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;

// Create the William %R indicator with parameters to be studied.
var indicator = new WilliamsPercentR(20);

// Get the history of SPY to update the indicator and trade.
var history = qb.History<TradeBar>(symbol, 252, Resolution.Daily).ToList();

// Obtain the cumulative return curve as a mini-backtest.
var equity = new List<decimal>() { 1m };
var time = new List<DateTime>() { history[0].EndTime };
for (int i = 0; i < history.Count - 1; i++)
{
    var bar = history[i];
    var nextBar = history[i+1];
    // Update the indicator value.
    indicator.Update(bar);

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

    // Buy if the asset is underprice (below -80), sell if overpriced (above -20)
    var order = indicator < -80 ? 1 : (indicator > -20 ? -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

# Get the historical data for trading.
history = qb.history(symbol, 252, Resolution.DAILY).close.unstack(0).iloc[:, 0]

# Create the William %R indicator with parameters to be studied.
indicator = WilliamsPercentR(20)

# Get the indicator history of the indicator.
indicator_dataframe = qb.indicator(indicator, symbol, 252, Resolution.DAILY)

# Create a order record and return column.
# Buy if the asset is underprice (below -80), sell if overpriced (above -20)
indicator_dataframe["position"] = indicator_dataframe.apply(lambda x: 1 if x.current < -80 else -1 if x.current > -20 else 0, axis=1)
# Get the 1-day forward return.
indicator_dataframe["return"] = history.pct_change().shift(-1).fillna(0)
indicator_dataframe["return"] = indicator_dataframe["position"] * indicator_dataframe["return"]

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