/// <summary>
/// Generate the cumulative return of the backtest, benchmark, and live
/// strategy using the ReportCharts.py python library
/// </summary>
public override string Render()
{
var backtestReturns = ResultsUtil.EquityPoints(_backtest);
var benchmark = ResultsUtil.BenchmarkPoints(_backtest);
var liveReturns = ResultsUtil.EquityPoints(_live);
var liveBenchmark = ResultsUtil.BenchmarkPoints(_live);
var backtestTime = backtestReturns.Keys.ToList();
var backtestStrategy = backtestReturns.Values.ToList();
var benchmarkTime = benchmark.Keys.ToList();
var benchmarkPoints = benchmark.Values.ToList();
var liveTime = liveReturns.Keys.ToList();
var liveStrategy = liveReturns.Values.ToList();
var liveBenchmarkTime = liveBenchmark.Keys.ToList();
var liveBenchmarkStrategy = liveBenchmark.Values.ToList();
var base64 = "";
using (Py.GIL())
{
var backtestList = new PyList();
var liveList = new PyList();
var backtestSeries = new Series <DateTime, double>(backtestTime, backtestStrategy);
var liveSeries = new Series <DateTime, double>(liveTime, liveStrategy);
var backtestBenchmarkSeries = new Series <DateTime, double>(benchmarkTime, benchmarkPoints);
var liveBenchmarkSeries = new Series <DateTime, double>(liveBenchmarkTime, liveBenchmarkStrategy);
// Equivalent in python using pandas for the following operations is:
// --------------------------------------------------
// >>> # note: [...] denotes the data we're passing in
// >>> df = pd.Series([...], index=time)
// >>> df_live = pd.Series([...], index=live_time)
// >>> df_live = df_live.mul(df.iloc[-1] / df_live.iloc[0]).fillna(method='ffill').dropna()
// >>> df_final = pd.concat([df, df_live], axis=0)
// >>> df_cumulative_returns = ((df_final.pct_change().dropna() + 1).cumprod() - 1)
// --------------------------------------------------
//
// We multiply the final value of the backtest and benchmark to have a continuous graph showing the performance out of sample
// as a continuation of the cumulative returns graph. Otherwise, we start plotting from 0% and not the last value of the backtest data
var backtestLastValue = backtestSeries.ValueCount == 0 ? 0 : backtestSeries.LastValue();
var backtestBenchmarkLastValue = backtestBenchmarkSeries.ValueCount == 0 ? 0 : backtestBenchmarkSeries.LastValue();
var liveContinuousEquity = liveSeries;
var liveBenchContinuousEquity = liveBenchmarkSeries;
if (liveSeries.ValueCount != 0)
{
liveContinuousEquity = (liveSeries * (backtestLastValue / liveSeries.FirstValue()))
.FillMissing(Direction.Forward)
.DropMissing();
}
if (liveBenchmarkSeries.ValueCount != 0)
{
liveBenchContinuousEquity = (liveBenchmarkSeries * (backtestBenchmarkLastValue / liveBenchmarkSeries.FirstValue()))
.FillMissing(Direction.Forward)
.DropMissing();
}
var liveStart = liveContinuousEquity.ValueCount == 0 ? DateTime.MaxValue : liveContinuousEquity.DropMissing().FirstKey();
var liveBenchStart = liveBenchContinuousEquity.ValueCount == 0 ? DateTime.MaxValue : liveBenchContinuousEquity.DropMissing().FirstKey();
var finalEquity = backtestSeries.Where(kvp => kvp.Key < liveStart).Observations.ToList();
var finalBenchEquity = backtestBenchmarkSeries.Where(kvp => kvp.Key < liveBenchStart).Observations.ToList();
finalEquity.AddRange(liveContinuousEquity.Observations);
finalBenchEquity.AddRange(liveBenchContinuousEquity.Observations);
var finalSeries = (new Series <DateTime, double>(finalEquity).CumulativeReturns() * 100)
.FillMissing(Direction.Forward)
.DropMissing();
var finalBenchSeries = (new Series <DateTime, double>(finalBenchEquity).CumulativeReturns() * 100)
.FillMissing(Direction.Forward)
.DropMissing();
var backtestCumulativePercent = finalSeries.Where(kvp => kvp.Key < liveStart);
var backtestBenchmarkCumulativePercent = finalBenchSeries.Where(kvp => kvp.Key < liveBenchStart);
var liveCumulativePercent = finalSeries.Where(kvp => kvp.Key >= liveStart);
var liveBenchmarkCumulativePercent = finalBenchSeries.Where(kvp => kvp.Key >= liveBenchStart);
backtestList.Append(backtestCumulativePercent.Keys.ToList().ToPython());
backtestList.Append(backtestCumulativePercent.Values.ToList().ToPython());
backtestList.Append(backtestBenchmarkCumulativePercent.Keys.ToList().ToPython());
backtestList.Append(backtestBenchmarkCumulativePercent.Values.ToList().ToPython());
liveList.Append(liveCumulativePercent.Keys.ToList().ToPython());
liveList.Append(liveCumulativePercent.Values.ToList().ToPython());
liveList.Append(liveBenchmarkCumulativePercent.Keys.ToList().ToPython());
liveList.Append(liveBenchmarkCumulativePercent.Values.ToList().ToPython());
base64 = Charting.GetCumulativeReturns(backtestList, liveList);
}
return(base64);
}
/// <summary>
/// Generate the cumulative return of the backtest, benchmark, and live
/// strategy using the ReportCharts.py python library
/// </summary>
public override string Render()
{
var backtestReturns = ResultsUtil.EquityPoints(_backtest);
var benchmark = ResultsUtil.BenchmarkPoints(_backtest);
var liveReturns = ResultsUtil.EquityPoints(_live);
var liveBenchmark = ResultsUtil.BenchmarkPoints(_live);
var backtestTime = backtestReturns.Keys.ToList();
var backtestStrategy = backtestReturns.Values.ToList();
var benchmarkTime = benchmark.Keys.ToList();
var benchmarkPoints = benchmark.Values.ToList();
var liveTime = liveReturns.Keys.ToList();
var liveStrategy = liveReturns.Values.ToList();
var liveBenchmarkTime = liveBenchmark.Keys.ToList();
var liveBenchmarkStrategy = liveBenchmark.Values.ToList();
var base64 = "";
using (Py.GIL())
{
var backtestList = new PyList();
var liveList = new PyList();
var backtestSeries = new Series <DateTime, double>(backtestTime, backtestStrategy);
var liveSeries = new Series <DateTime, double>(liveTime, liveStrategy);
var backtestBenchmarkSeries = new Series <DateTime, double>(benchmarkTime, benchmarkPoints);
var liveBenchmarkSeries = new Series <DateTime, double>(liveBenchmarkTime, liveBenchmarkStrategy);
// Equivalent in python using pandas for the following operations is:
//
// df.pct_change().cumsum().mul(100)
var backtestCumulativePercent = (backtestSeries.CumulativeReturns() * 100).FillMissing(Direction.Forward).DropMissing();
var backtestBenchmarkCumulativePercent = (backtestBenchmarkSeries.CumulativeReturns() * 100).FillMissing(Direction.Forward).DropMissing();
// Equivalent in python using pandas for the following operations is:
// --------------------------------------------------
// # note: [...] denotes the data we're passing in
// bt = pd.Series([...], index=time)
// df.pct_change().replace([np.inf, -np.inf], np.nan).dropna().cumsum().mul(100).add(bt.iloc[-1])
// --------------------------------------------------
//
// We add the final value of the backtest and benchmark to have a continuous graph showing the performance out of sample
// as a continuation of the cumulative returns graph. Otherwise, we start plotting from 0% and not the last value of the backtest data
var backtestLastValue = backtestCumulativePercent.IsEmpty ? 0 : backtestCumulativePercent.LastValue();
var backtestBenchmarkLastValue = backtestBenchmarkCumulativePercent.IsEmpty ? 0 : backtestBenchmarkCumulativePercent.LastValue();
var liveCumulativePercent = (liveSeries.CumulativeReturns() * 100).FillMissing(Direction.Forward).DropMissing() + backtestLastValue;
var liveBenchmarkCumulativePercent = (liveBenchmarkSeries.CumulativeReturns() * 100).FillMissing(Direction.Forward).DropMissing() + backtestBenchmarkLastValue;
backtestList.Append(backtestCumulativePercent.Keys.ToList().ToPython());
backtestList.Append(backtestCumulativePercent.Values.ToList().ToPython());
backtestList.Append(backtestBenchmarkCumulativePercent.Keys.ToList().ToPython());
backtestList.Append(backtestBenchmarkCumulativePercent.Values.ToList().ToPython());
liveList.Append(liveCumulativePercent.Keys.ToList().ToPython());
liveList.Append(liveCumulativePercent.Values.ToList().ToPython());
liveList.Append(liveBenchmarkCumulativePercent.Keys.ToList().ToPython());
liveList.Append(liveBenchmarkCumulativePercent.Values.ToList().ToPython());
base64 = Charting.GetCumulativeReturns(backtestList, liveList);
}
return(base64);
}
