| Overall Statistics |
|
Total Orders 11 Average Win 5.25% Average Loss -5.14% Compounding Annual Return -3.650% Drawdown 23.700% Expectancy -0.326 Start Equity 100000.00 End Equity 78769.10 Net Profit -21.231% Sharpe Ratio -0.421 Sortino Ratio -0.548 Probabilistic Sharpe Ratio 0.003% Loss Rate 67% Win Rate 33% Profit-Loss Ratio 1.02 Alpha -0.038 Beta -0.273 Annual Standard Deviation 0.074 Annual Variance 0.005 Information Ratio -0.053 Tracking Error 0.121 Treynor Ratio 0.114 Total Fees $0.00 Estimated Strategy Capacity $460000.00 Lowest Capacity Asset EURUSD 8G Portfolio Turnover 0.73% |
#region imports
from AlgorithmImports import *
from scipy import sparse
from scipy.sparse.linalg import spsolve
#endregion
class TrendFollowingAlgorithm(QCAlgorithm):
def initialize(self):
self.set_start_date(2011,1,1)
self.set_end_date(2017,5,30)
self.set_cash(100000)
self._numdays = 360*5 # set the length of training period
self._syl = self.add_forex("EURUSD", Resolution.DAILY).symbol
self._n,self.m = 2, 1
self._trend = None
self.set_benchmark(self._syl)
history = self.history(self._numdays,Resolution.DAILY)
self._close = [slice[self._syl].close for slice in history]
def _hpfilter(self,X, lamb=1600):
X = np.asarray(X, float)
if X.ndim > 1:
X = X.squeeze()
nobs = len(X)
I = sparse.eye(nobs,nobs)
offsets = np.array([0,1,2])
data = np.repeat([[1.],[-2.],[1.]], nobs, axis=1)
K = sparse.dia_matrix((data, offsets), shape=(nobs-2,nobs))
use_umfpack = True
self._trend = spsolve(I+lamb*K.T.dot(K), X, use_umfpack=use_umfpack)
def on_data(self,data):
self._close.append(self.portfolio[self._syl].price)
self._hpfilter(self._close[-self._numdays:len(self._close)+1], 100)
m_a_rules_today = (np.mean(self._trend[-self.m : len(self._trend)]) - np.mean(self._trend[-self._n : len(self._trend)]))
m_a_rules_yesterday = (np.mean(self._trend[-self.m-1: len(self._trend)-1]) - np.mean(self._trend[-self._n-1 : len(self._trend)-1]))
holdings = self.portfolio[self._syl].quantity
if m_a_rules_today > 0 and m_a_rules_yesterday < 0:
self.set_holdings(self._syl, 1)
elif m_a_rules_today < 0 and m_a_rules_yesterday > 0:
self.set_holdings(self._syl, -1)