/// <summary>
/// Computes the average value
/// </summary>
/// <param name="time"></param>
/// <param name="input">The data for the calculation</param>
/// <returns>The average value</returns>
protected override DoubleArray Forward(long time, DoubleArray input)
{
var price = (input[HighIdx] + input[LowIdx]) / 2;
_high.Add(input[HighIdx]);
_low.Add(input[LowIdx]);
// our first data point just return identity
if (_high.Samples <= _high.Size)
{
return(price);
}
var hh = _high.Take(_n / 2).Max();
var ll = _low.Take(_n / 2).Min();
var n1 = (hh - ll) / (_n / 2);
hh = _high.Skip(_n / 2).Take(_n / 2).Max();
ll = _low.Skip(_n / 2).Take(_n / 2).Min();
var n2 = (hh - ll) / (_n / 2);
var n3 = (_high.Max() - _low.Min()) / _n;
double dimen = 0;
if (n1 + n2 > 0 && n3 > 0)
{
dimen = Math.Log((n1 + n2) / n3) / Math.Log(2);
}
var alpha = Math.Exp(_w * (dimen - 1));
if (alpha < .01d)
{
alpha = .01d;
}
if (alpha > 1)
{
alpha = 1;
}
return(alpha * price + (1 - alpha) * Current.Value);
}
public override void Initialize()
{
SetStartDate(2015, 1, 1);
SetEndDate(DateTime.Now);
SetCash(10000);
foreach (var equity in _equities)
{
AddSecurity(SecurityType.Equity, equity, Resolution.Minute);
}
_s = new int[_stocks.Length];
_x0 = new int[_stocks.Length];
_x1 = Enumerable.Repeat(1d / _stocks.Length, _stocks.Length).ToArray();
_symbolData = new Dictionary <Symbol, SymbolData>();
_bb = new BollingerBands(_spy, 22, 1.05m, MovingAverageType.Simple);
_spyWvf = new RollingWindow <decimal>(22);
foreach (var stock in _stocks)
{
var closes = new RollingWindow <decimal>(17 * 390);
var dailyCloses = new RollingWindow <TradeBar>(29);
var dailyConsolidator = new TradeBarConsolidator(TimeSpan.FromDays(1));
dailyConsolidator.DataConsolidated += (sender, consolidated) => _symbolData[consolidated.Symbol].UpdateDaily(consolidated);
SubscriptionManager.AddConsolidator(stock, dailyConsolidator);
_symbolData.Add(stock, new SymbolData(closes, dailyCloses));
}
var spyDailyCloses = new RollingWindow <TradeBar>(28);
var spyDailyConsolidator = new TradeBarConsolidator(TimeSpan.FromDays(1));
spyDailyConsolidator.DataConsolidated += (sender, consolidated) => _symbolData[consolidated.Symbol].UpdateDaily(consolidated);
SubscriptionManager.AddConsolidator(_spy, spyDailyConsolidator);
_symbolData.Add(_spy, new SymbolData(null, spyDailyCloses));
var vxxDailyConsolidator = new TradeBarConsolidator(TimeSpan.FromDays(1));
vxxDailyConsolidator.DataConsolidated += (sender, consolidated) => _symbolData[consolidated.Symbol].UpdateDaily(consolidated);
SubscriptionManager.AddConsolidator(_vxx, vxxDailyConsolidator);
_symbolData.Add(_spy, new SymbolData(null, spyDailyCloses));
Schedule.On(DateRules.EveryDay(), TimeRules.AfterMarketOpen("SPY", 15d), () =>
{
if (_symbolData[_spy].IsReady)
{
return;
}
var vxxCloses = _symbolData[_vxx].DailyHistory.Select(tb => tb.Close);
var vxxLows = _symbolData[_vxx].DailyHistory.Select(tb => tb.Low);
// William's VIX Fix indicator a.k.a. the Synthetic VIX
var previousMax = vxxCloses.Take(28).Max();
var previousWvf = 100 * (previousMax - vxxLows.Skip(27).First()) / previousMax;
var max = vxxCloses.Skip(1).Max();
var wvf = 100 * (max - vxxLows.Last()) / max;
if (previousWvf < WvfLimit && wvf >= WvfLimit)
{
SetHoldings(_vxx, 0);
SetHoldings(_xiv, 0.07);
}
else if (previousWvf > WvfLimit && wvf <= WvfLimit)
{
SetHoldings(_vxx, 0.07);
SetHoldings(_xiv, 0);
}
});
Schedule.On(DateRules.EveryDay(), TimeRules.AfterMarketOpen("SPY", 15d), () =>
{
if (_symbolData[_spy].IsReady)
{
return;
}
var spyCloses = _symbolData[_spy].NDaysDailyHistory(22).Select(tb => tb.Close);
var spyLows = _symbolData[_spy].NDaysDailyHistory(22).Select(tb => tb.Low);
var max = spyCloses.Max();
var wvf = 100 * (max - spyLows.Last()) / max;
_bb.Update(DateTime.Now, wvf);
_spyWvf.Add(wvf);
var rangeHigh = _spyWvf.Max() * 0.9m;
var latestClose = _symbolData[_spy].NDaysDailyHistory(1).First().Close;
var spy_higher_then_Xdays_back = latestClose > _symbolData[_spy].NDaysDailyHistory(3).First().Close;
var spy_lower_then_longterm = latestClose > _symbolData[_spy].NDaysDailyHistory(40).First().Close;
var spy_lower_then_midterm = latestClose > _symbolData[_spy].NDaysDailyHistory(14).First().Close;
// Alerts Criteria
var alert2 = !(_spyWvf[0] >= _bb.UpperBand && _spyWvf[0] >= rangeHigh) &&
(_spyWvf[1] >= _bb.UpperBand && _spyWvf[2] >= rangeHigh);
if ((alert2 || spy_higher_then_Xdays_back) && (spy_lower_then_longterm || spy_lower_then_midterm))
{
SetHoldings(_spy, 0.3);
SetHoldings(_shortSpy, 0);
}
else
{
SetHoldings(_spy, 0);
SetHoldings(_shortSpy, 0.3);
}
});
Schedule.On(DateRules.EveryDay(), TimeRules.AfterMarketOpen("SPY", 15d), () =>
{
if (_symbolData[_spy].IsReady)
{
return;
}
var returns = new List <double[]>(); // 28?
foreach (var stock in _stocks)
{
_symbolData[stock].UpdateWeights();
returns.Add(_symbolData[stock].PercentReturn.Select(pr => (double)(pr / _symbolData[stock].Norm)).ToArray());
}
var retNorm = _symbolData.Select(s => s.Value.Norm);
var retNormMax = retNorm.Max();
var epsFactor = retNormMax > 0 ? 0.9m : 1.0m;
var eps = (double)(epsFactor * retNormMax);
var constraints = new List <LinearConstraint>();
constraints.Add(new LinearConstraint(Enumerable.Repeat(1.0, _stocks.Length).ToArray())
{
ShouldBe = ConstraintType.EqualTo,
Value = 1
});
constraints.Add(new LinearConstraint(retNorm.Select(x => (double)x).ToArray())
{
ShouldBe = ConstraintType.GreaterThanOrEqualTo,
Value = eps + eps * 0.01
}); // Add and subtract small bounce to achieve inequality?
constraints.Add(new LinearConstraint(retNorm.Select(x => (double)x).ToArray())
{
ShouldBe = ConstraintType.LesserThanOrEqualTo,
Value = eps - eps * 0.01
}); // Add and subtract small bounce to achieve inequality?
constraints.Add(new LinearConstraint(_stocks.Length)
{
VariablesAtIndices = Enumerable.Range(0, _stocks.Length).ToArray(),
ShouldBe = ConstraintType.GreaterThanOrEqualTo,
Value = 0
});
constraints.Add(new LinearConstraint(_stocks.Length)
{
VariablesAtIndices = Enumerable.Range(0, _stocks.Length).ToArray(),
ShouldBe = ConstraintType.LesserThanOrEqualTo,
Value = 1
});
var initialGuess = new double[_stocks.Length];
var f = new QuadraticObjectiveFunction(() => Variance(initialGuess, returns.ToMatrix()));
var solver = new GoldfarbIdnani(f, constraints);
solver.Minimize();
var weights = _x1;
var totalWeight = weights.Sum();
if (solver.Status == GoldfarbIdnaniStatus.Success)
{
weights = solver.Solution;
}
for (var i = 0; i < _stocks.Length; i++)
{
SetHoldings(_stocks[i], weights[i] / totalWeight);
}
});
}