Overall Statistics Total Trades 40 Average Win 4.82% Average Loss -2.91% Compounding Annual Return 6.599% Drawdown 12.200% Expectancy 0.593 Net Profit 37.668% Sharpe Ratio 0.683 Loss Rate 40% Win Rate 60% Profit-Loss Ratio 1.65 Alpha 0.156 Beta -4.35 Annual Standard Deviation 0.101 Annual Variance 0.01 Information Ratio 0.485 Tracking Error 0.101 Treynor Ratio -0.016 Total Fees \$191.99
```from datetime import datetime

### <summary>
### Simple indicator demonstration algorithm of MACD
### </summary>
### <meta name="tag" content="indicators" />
### <meta name="tag" content="indicator classes" />
### <meta name="tag" content="plotting indicators" />
class MACDTrendAlgorithm(QCAlgorithm):

def Initialize(self):
'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''

self.SetStartDate(2010, 1, 1)    #Set Start Date
self.SetEndDate(2015, 1, 1)      #Set End Date
self.SetCash(100000)             #Set Strategy Cash
# Find more symbols here: http://quantconnect.com/data

# define our daily macd(12,26) with a 9 day signal
self.__macd = self.MACD("SPY", 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily)
self.__previous = datetime.min
self.PlotIndicator("MACD", True, self.__macd, self.__macd.Signal)
self.PlotIndicator("SPY", self.__macd.Fast, self.__macd.Slow)

overlayPlot = Chart("Overlay Plot")

def OnData(self, data):
'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.'''
# wait for our macd to fully initialize

# only once per day
if self.__previous.date() == self.Time.date(): return

# define a small tolerance on our checks to avoid bouncing
tolerance = 0.0025

holdings = self.Portfolio["SPY"].Quantity

signalDeltaPercent = (self.__macd.Current.Value - self.__macd.Signal.Current.Value)/self.__macd.Fast.Current.Value

# if our macd is greater than our signal, then let's go long
if holdings <= 0 and signalDeltaPercent > tolerance:  # 0.01%
# longterm says buy as well
self.SetHoldings("SPY", 1.0)

# of our macd is less than our signal, then let's go short
elif holdings >= 0 and signalDeltaPercent < -tolerance:
self.Liquidate("SPY")
sell_signal_triggered = True

self.__previous = self.Time