| Overall Statistics |
|
Total Orders 14894 Average Win 0.77% Average Loss -0.80% Compounding Annual Return 75.490% Drawdown 33.300% Expectancy 0.679 Start Equity 100000 End Equity 541006.43 Net Profit 441.006% Sharpe Ratio 1.538 Sortino Ratio 1.576 Probabilistic Sharpe Ratio 70.334% Loss Rate 14% Win Rate 86% Profit-Loss Ratio 0.95 Alpha 0.467 Beta 0.914 Annual Standard Deviation 0.36 Annual Variance 0.13 Information Ratio 1.451 Tracking Error 0.316 Treynor Ratio 0.607 Total Fees $1253.49 Estimated Strategy Capacity $130000000.00 Lowest Capacity Asset GOOG T1AZ164W5VTX Portfolio Turnover 5.85% |
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