| Overall Statistics |
|
Total Trades 82 Average Win 9.96% Average Loss -4.59% Compounding Annual Return -1.040% Drawdown 54.400% Expectancy -0.049 Net Profit -10.245% Sharpe Ratio 0.048 Loss Rate 70% Win Rate 30% Profit-Loss Ratio 2.17 Alpha 0.008 Beta 0.024 Annual Standard Deviation 0.187 Annual Variance 0.035 Information Ratio -0.19 Tracking Error 0.259 Treynor Ratio 0.375 Total Fees $84.44 |
namespace QuantConnect.Rotation
{
public class GlobalRotation : QCAlgorithm
{
// we'll use this to tell us when the month has ended
DateTime LastRotationTime = DateTime.MinValue;
TimeSpan RotationInterval = TimeSpan.FromDays(30);
// these are the growth symbols we'll rotate through
List<string> GrowthSymbols = new List<string>
{
"MDY", // US S&P mid cap 400
"EWJ", // Japan
"IEV", // iShares S&P europe 350
"EEM", // iShared MSCI emerging markets
"ILF", // iShares S&P latin america
"EPP", // iShared MSCI Pacific ex-Japan
"IYR", // US REIT
"RWX", // International REIT
"EDV", // Vangaurd TSY 25yr+
"IEF", // Intermediate Treasury
"DBC", // Commodities
"GLD" // Gold
};
// these are the safety symbols we go to when things are looking bad for growth
List<string> SafetySymbols = new List<string>
{
"SHY" // Barclays Low Duration TSY
};
// we'll hold some computed data in these guys
List<SymbolData> SymbolData = new List<SymbolData>();
public override void Initialize()
{
SetCash(10000);
SetStartDate(2007, 1, 1);
SetEndDate(2017, 5, 1);
foreach (var symbol in GrowthSymbols.Union(SafetySymbols))
{
// ideally we would use daily data
AddSecurity(SecurityType.Equity, symbol, Resolution.Minute);
var oneMonthPerformance = MOM(symbol, 30, Resolution.Daily);
var threeMonthPerformance = MOM(symbol, 90, Resolution.Daily);
var sixMonthPerformance = MOM(symbol, 180, Resolution.Daily);
var twelveMonthPerformance = MOM(symbol, 360, Resolution.Daily);
SymbolData.Add(new SymbolData
{
Symbol = symbol,
OneMonthPerformance = oneMonthPerformance,
ThreeMonthPerformance = threeMonthPerformance,
SixMonthPerformance = sixMonthPerformance,
TwelveMonthPerformance = twelveMonthPerformance
});
}
}
private bool first = true;
public void OnData(TradeBars data)
{
try
{
// the first time we come through here we'll need to do some things such as allocation
// and initializing our symbol data
if (first)
{
first = false;
LastRotationTime = data.Time;
return;
}
var delta = Time.Subtract(LastRotationTime);
if (delta > RotationInterval)
{
LastRotationTime = data.Time;
// pick which one is best from growth and safety symbols
var orderedObjScores = SymbolData.OrderByDescending(x => x.ObjectiveScore).ToList();
foreach (var orderedObjScore in orderedObjScores)
{
Log(">>SCORE>>" + orderedObjScore.Symbol + ">>" + orderedObjScore.ObjectiveScore);
}
var bestGrowth = orderedObjScores.First();
if (bestGrowth.ObjectiveScore > 0)
{
if (Portfolio[bestGrowth.Symbol].Quantity == 0)
{
Log("PREBUY>>LIQUIDATE>>");
Liquidate();
}
Log(">>BUY>>" + bestGrowth.Symbol + "@" + (100 * bestGrowth.OneMonthPerformance).ToString("00.00"));
SetHoldings(bestGrowth.Symbol, 1.0);
}
else
{
// if no one has a good objective score then let's hold cash this month to be safe
Log(">>LIQUIDATE>>CASH");
Liquidate();
}
}
}
catch (Exception ex)
{
Error("OnTradeBar: " + ex.Message + "\r\n\r\n" + ex.StackTrace);
}
}
}
class SymbolData
{
public string Symbol;
public Momentum OneMonthPerformance { get; set; }
public Momentum ThreeMonthPerformance { get; set; }
public Momentum SixMonthPerformance { get; set; }
public Momentum TwelveMonthPerformance { get; set; }
public decimal ObjectiveScore
{
get
{
// we weight the one month performance higher
decimal weight1 = 100;
decimal weight2 = 100;
decimal weight3 = 100;
decimal weight4 = 100;
return (weight1 * OneMonthPerformance + weight2 * ThreeMonthPerformance + weight3 * SixMonthPerformance + weight4 * TwelveMonthPerformance) / (weight1 + weight2 + weight3 + weight4);
}
}
}
}