| Overall Statistics |
|
Total Trades 0 Average Win 0% Average Loss 0% Compounding Annual Return 0% Drawdown 0% Expectancy 0 Net Profit 0% Sharpe Ratio 0 Probabilistic Sharpe Ratio 0% Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0 Beta 0 Annual Standard Deviation 0 Annual Variance 0 Information Ratio -2.783 Tracking Error 0.157 Treynor Ratio 0 Total Fees $0.00 Estimated Strategy Capacity $0 Lowest Capacity Asset |
class SymbolData:
def __init__(self,algorithm,symbol):
self.symbol = symbol
self.algo = algorithm
self.rsi = RelativeStrengthIndex(10, MovingAverageType.Simple)
self.ema = ExponentialMovingAverage(15)
consolidator = TradeBarConsolidator(Resolution.Hour)
self.algo.SubscriptionManager.AddConsolidator(self.symbol, consolidator)
self.algo.RegisterIndicator(self.symbol, self.rsi, consolidator)
self.algo.RegisterIndicator(self.symbol, self.ema, consolidator)
class ResistanceMultidimensionalCompensator(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2020, 6, 25) # Set Start Date
self.SetCash(100000) # Set Strategy Cash
self.AddEquity("SPY", Resolution.Minute)
self.AddAlpha(MyAlphaModel())
def OnData(self, data):
'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
Arguments:
data: Slice object keyed by symbol containing the stock data
'''
# if not self.Portfolio.Invested:
# self.SetHoldings("SPY", 1)
class MyAlphaModel(AlphaModel):
def __init__(self):
self.algo = None
self.Symbol_Data = {}
def Update(self, algorithm, data):
insights = []
return insights
def OnSecuritiesChanged(self, algorithm, changes):
if self.algo is None:
self.algo = algorithm
for security in changes.AddedSecurities:
symbol = security.Symbol
if symbol not in self.Symbol_Data:
self.Symbol_Data[symbol] = SymbolData(algorithm,symbol)
def HourBarHandler(self, sender, updated):
symbol = self.consolidator2symbol[sender]
close = updated.Close
self.algo.Plot('Custom', symbol.Value + " close: ", str(close))
self.rw.Add(updated)
self.updated = True