Overall Statistics Total Trades3Average Win0.11%Average Loss0%Compounding Annual Return14.218%Drawdown5.900%Expectancy0Net Profit5.652%Sharpe Ratio1.243Probabilistic Sharpe Ratio56.042%Loss Rate0%Win Rate100%Profit-Loss Ratio0Alpha0.022Beta0.964Annual Standard Deviation0.11Annual Variance0.012Information Ratio0.893Tracking Error0.02Treynor Ratio0.142Total Fees\$9.17
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
#
# you may not use this file except in compliance with the License.
#
# Unless required by applicable law or agreed to in writing, software
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and

from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Indicators import *
from QuantConnect.Data import SubscriptionDataSource
from QuantConnect.Python import PythonData
from datetime import date, timedelta, datetime
import numpy as np
import math
import json

### <summary>
### Strategy example algorithm using CAPE - a bubble indicator dataset saved in dropbox. CAPE is based on a macroeconomic indicator(CAPE Ratio),
### we are looking for entry/exit points for momentum stocks CAPE data: January 1990 - December 2014
### Goals:
### Capitalize in overvalued markets by generating returns with momentum and selling before the crash
### Capitalize in undervalued markets by purchasing stocks at bottom of trough
### </summary>
### <meta name="tag" content="strategy example" />
### <meta name="tag" content="custom data" />
class BubbleAlgorithm(QCAlgorithm):

def Initialize(self):

self.SetCash(100000)
self.SetStartDate(2014,1,1)
self.SetEndDate(2014,6,1)
self._symbols = []
self._macdDic, self._rsiDic = {},{}
self._newLow, self._currCape, self._realEarnings = None, None, None
self._counter, self._counter2 = 0, 0
self._c, self._cCopy = np.empty([4]), np.empty([4])
self._symbols.append("SPY")

# # Present Social Media Stocks:
# self._symbols.append("FB"), self._symbols.append("LNKD"),self._symbols.append("GRPN"), self._symbols.append("TWTR")
# self.SetStartDate(2011, 1, 1)
# self.SetEndDate(2014, 12, 1)

# # 2008 Financials
# self._symbols.append("C"), self._symbols.append("AIG"), self._symbols.append("BAC"), self._symbols.append("HBOS")
# self.SetStartDate(2003, 1, 1)
# self.SetEndDate(2011, 1, 1)

# # 2000 Dot.com
# self._symbols.append("IPET"), self._symbols.append("WBVN"), self._symbols.append("GCTY")
# self.SetStartDate(1998, 1, 1)
# self.SetEndDate(2000, 1, 1)

for stock in self._symbols:
self._macd = self.MACD(stock, 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily)
self._macdDic[stock] = self._macd
self._rsi = self.RSI(stock, 14, MovingAverageType.Exponential, Resolution.Daily)
self._rsiDic[stock] = self._rsi

# Trying to find if current Cape is the lowest Cape in three months to indicate selling period
def OnData(self, data):

if self._currCape and self._newLow is not None:
try:
# Bubble territory
if self._currCape > 20 and self._newLow == False:
for stock in self._symbols:
# Order stock based on MACD
# During market hours, stock is trading, and sufficient cash
if self.Securities[stock].Holdings.Quantity == 0 and self._rsiDic[stock].Current.Value < 70 \
and self.Securities[stock].Price != 0 \
and self.Portfolio.Cash > self.Securities[stock].Price * 100 \
and self.Time.hour == 9 and self.Time.minute == 31:
# Utilize RSI for overbought territories and liquidate that stock
if self._rsiDic[stock].Current.Value > 70 and self.Securities[stock].Holdings.Quantity > 0 \
and self.Time.hour == 9 and self.Time.minute == 31:
self.SellStock(stock)

# Undervalued territory
elif self._newLow:
for stock in self._symbols:
# Sell stock based on MACD
if self.Securities[stock].Holdings.Quantity > 0 and self._rsiDic[stock].Current.Value > 30 \
and self.Time.hour == 9 and self.Time.minute == 31:
self.SellStock(stock)
# Utilize RSI and MACD to understand oversold territories
elif self.Securities[stock].Holdings.Quantity == 0 and self._rsiDic[stock].Current.Value < 30 \
and Securities[stock].Price != 0 and self.Portfolio.Cash > self.Securities[stock].Price * 100 \
and self.Time.hour == 9 and self.Time.minute == 31:

# Cape Ratio is missing from orignial data
# Most recent cape data is most likely to be missing
elif self._currCape == 0:
self.Debug("Exiting due to no CAPE!")
self.Quit("CAPE ratio not supplied in data, exiting.")

except:
# Do nothing
return None

if not data.ContainsKey("CAPE"): return
self._newLow = False
# Adds first four Cape Ratios to array c
self._currCape = data["CAPE"].Cape
#self._realEarnings = data["CAPE"].RealEarnings

if self._counter < 4:
self._c[self._counter] = self._currCape
self._counter +=1
# Replaces oldest Cape with current Cape
# Checks to see if current Cape is lowest in the previous quarter
# Indicating a sell off
else:
self._cCopy = self._c
self._cCopy = np.sort(self._cCopy)
if self._cCopy[0] > self._currCape:
self._newLow = True
self._c[self._counter2] = self._currCape
self._counter2 += 1
if self._counter2 == 4: self._counter2 = 0
#self.Debug("Current Cape: " + str(self._currCape) + " on " + str(self.Time) + " realEarnings:" + str(self._realEarnings))
self.Debug("Current Cape: " + str(self._currCape) + " on " + str(self.Time))
if self._newLow:
self.Debug("New Low has been hit on " + str(self.Time))

s = self.Securities[symbol].Holdings
if self._macdDic[symbol].Current.Value>0:
self.SetHoldings(symbol, 1)
self.Debug("Purchasing: " + str(symbol) + "   MACD: " + str(self._macdDic[symbol]) + "   RSI: " + str(self._rsiDic[symbol])
+ "   Price: " + str(round(self.Securities[symbol].Price, 2)) + "   Quantity: " + str(s.Quantity))

# Sell this symbol
def SellStock(self,symbol):
s = self.Securities[symbol].Holdings
if s.Quantity > 0 and self._macdDic[symbol].Current.Value < 0:
self.Liquidate(symbol)
self.Debug("Selling: " + str(symbol) + " at sell MACD: " + str(self._macdDic[symbol]) + "   RSI: " + str(self._rsiDic[symbol])
+ "   Price: " + str(round(self.Securities[symbol].Price, 2)) + "   Profit from sale: " + str(s.LastTradeProfit))

# CAPE Ratio for SP500 PE Ratio for avg inflation adjusted earnings for previous ten years Custom Data from DropBox
# Original Data from: http://www.econ.yale.edu/~shiller/data.htm
class Cape(PythonData):

# Return the URL string source of the file. This will be converted to a stream
# <param name="config">Configuration object</param>
# <param name="date">Date of this source file</param>
# <param name="isLiveMode">true if we're in live mode, false for backtesting mode</param>
# <returns>String URL of source file.</returns>

def GetSource(self, config, date, isLiveMode):
return SubscriptionDataSource("https://www.dropbox.com/s/ggt6blmib54q36e/CAPE.csv?dl=1", SubscriptionTransportMedium.RemoteFile)

''' Reader Method : using set of arguements we specify read out type. Enumerate until
the end of the data stream or file. E.g. Read CSV file line by line and convert into data types. '''

# <returns>BaseData type set by Subscription Method.</returns>
# <param name="config">Config.</param>
# <param name="line">Line.</param>
# <param name="date">Date.</param>
# <param name="isLiveMode">true if we're in live mode, false for backtesting mode</param>

def Reader(self, config, line, date, isLiveMode):
if not (line.strip() and line[0].isdigit()): return None

# New Nifty object
index = Cape()
index.Symbol = config.Symbol

try:
# Example File Format:
# Date   |  Price |  Div  | Earning | CPI  | FractionalDate | Interest Rate | RealPrice | RealDiv | RealEarnings | CAPE
# 2014.06  1947.09  37.38   103.12   238.343    2014.37          2.6           1923.95     36.94        101.89     25.55
data = line.split(',')
# Dates must be in the format YYYY-MM-DD. If your data source does not have this format, you must use
# DateTime.ParseExact() and explicit declare the format your data source has.
index.Time = datetime.strptime(data[0], "%Y-%m")
index["Cape"] = float(data[10])
index["RealEarnings"] = float(data[9])
index.Value = data[10]

except ValueError:
# Do nothing
return None

return index