| Overall Statistics |
|
Total Orders 16647 Average Win 0.62% Average Loss -0.89% Compounding Annual Return 55.152% Drawdown 33.300% Expectancy 0.426 Start Equity 100000 End Equity 580169.94 Net Profit 480.170% Sharpe Ratio 1.209 Sortino Ratio 1.308 Probabilistic Sharpe Ratio 53.617% Loss Rate 16% Win Rate 84% Profit-Loss Ratio 0.70 Alpha 0.304 Beta 1.02 Annual Standard Deviation 0.353 Annual Variance 0.124 Information Ratio 1.005 Tracking Error 0.305 Treynor Ratio 0.418 Total Fees $1882.77 Estimated Strategy Capacity $56000000.00 Lowest Capacity Asset NFLX SEWJWLJNHZDX Portfolio Turnover 6.37% |
from AlgorithmImports import *
class EMAMovingAverageStrategy(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2018, 1, 1)
self.SetEndDate(2022, 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