| Overall Statistics |
|
Total Orders 10001 Average Win 0.59% Average Loss -1.23% Compounding Annual Return 104.016% Drawdown 29.000% Expectancy 0.325 Start Equity 100000 End Equity 671657.72 Net Profit 571.658% Sharpe Ratio 2.062 Sortino Ratio 2.189 Probabilistic Sharpe Ratio 87.707% Loss Rate 11% Win Rate 89% Profit-Loss Ratio 0.48 Alpha 0.646 Beta 0.811 Annual Standard Deviation 0.348 Annual Variance 0.121 Information Ratio 2.016 Tracking Error 0.312 Treynor Ratio 0.883 Total Fees $971.99 Estimated Strategy Capacity $340000000.00 Lowest Capacity Asset AMZN R735QTJ8XC9X Portfolio Turnover 5.48% |
from AlgorithmImports import *
class EMAMovingAverageStrategy(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2018, 1, 1)
self.SetEndDate(2021, 1, 1)
self.SetCash(100000)
# Add all symbols in S&P 500
self.symbols = ["AAPL", "MSFT", "GOOGL", "AMZN", "FB", "JPM", "V", "PG", "DIS", "HD", # Add more symbols as needed
"VZ", "KO", "INTC", "NFLX", "TSLA", "NVDA", "UNH", "PYPL", "PEP", "ABT",
"BAC", "CMCSA", "ADBE", "XOM", "MRK", "PFE", "WMT", "NKE", "CSCO", "MCD",
"MA", "ABNB", "CRM", "AVGO", "T", "ORCL", "ACN", "CVX", "LMT", "MDT",
"IBM", "TXN", "QCOM", "LOW", "AMGN", "SBUX", "TMO", "COST", "GILD", "UPS"]
# Initialize indicators and rolling windows for each symbol
self.indicators = {}
self.ema60_tracks = {}
for symbol in self.symbols:
equity = self.AddEquity(symbol, Resolution.Daily)
self.indicators[symbol] = {
"ema9": self.EMA(equity.Symbol, 9, Resolution.Daily),
"ema15": self.EMA(equity.Symbol, 15, Resolution.Daily),
"ema65": self.EMA(equity.Symbol, 65, Resolution.Daily),
"ema200": self.EMA(equity.Symbol, 150, Resolution.Daily),
"ema35": self.EMA(equity.Symbol, 35 , Resolution.Daily),
"rsi": self.RSI(equity.Symbol, 14, Resolution.Daily)
}
self.ema60_tracks[symbol] = RollingWindow[IndicatorDataPoint](60)
self.SetWarmUp(200)
def OnData(self, data):
if self.IsWarmingUp:
return
for symbol in self.symbols:
if data.ContainsKey(symbol):
current_data = data[symbol]
if current_data:
self.ema60_tracks[symbol].Add(self.indicators[symbol]["ema35"].Current)
for symbol in self.symbols:
if self.ema60_tracks[symbol].IsReady:
self.TradeSymbol(symbol)
def TradeSymbol(self, symbol):
indicators = self.indicators[symbol]
ema60_track = self.ema60_tracks[symbol] # Corrected attribute name
if self.IsBuyCondition(indicators, ema60_track): # Pass ema60_track to IsBuyCondition
self.SetHoldings(symbol, 1)
elif self.IsSellCondition(indicators, ema60_track): # Pass ema60_track to IsSellCondition
self.Liquidate(symbol)
elif self.Portfolio[symbol].Invested:
if self.IsExitBuyCondition(indicators):
self.Liquidate(symbol)
elif self.IsExitSellCondition(indicators):
self.Liquidate(symbol)
def IsBuyCondition(self, indicators, ema60_track): # Added ema60_track parameter
ema_condition = (indicators["ema9"].Current.Value > indicators["ema15"].Current.Value > indicators["ema65"].Current.Value > indicators["ema200"].Current.Value)
ema35_condition = all(x.Value > 60 for x in ema60_track)
return ema_condition and ema35_condition
def IsSellCondition(self, indicators, ema60_track): # Added ema60_track parameter
ema_condition = (indicators["ema9"].Current.Value < indicators["ema15"].Current.Value < indicators["ema65"].Current.Value < indicators["ema200"].Current.Value)
ema35_condition = all(x.Value < 60 for x in ema60_track)
return ema_condition and ema35_condition
def IsExitBuyCondition(self, indicators):
ema_cross_condition = (indicators["ema9"].Current.Value < indicators["ema65"].Current.Value or indicators["ema15"].Current.Value < indicators["ema65"].Current.Value)
rsi_condition = indicators["rsi"].Current.Value < 40
return ema_cross_condition and rsi_condition
def IsExitSellCondition(self, indicators):
ema_cross_condition = (indicators["ema9"].Current.Value > indicators["ema65"].Current.Value or indicators["ema15"].Current.Value > indicators["ema65"].Current.Value)
rsi_condition = indicators["rsi"].Current.Value > 60
return ema_cross_condition and rsi_condition