quantconnect--lean/Algorithm.Python/BubbleAlgorithm.py

# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from clr import AddReference
AddReference("System")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Common")

from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Data import SubscriptionDataSource
from QuantConnect.Python import PythonData
from datetime import date, timedelta, datetime
import decimal
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(1998,1,1)
        self.SetEndDate(2014,6,1)
        self._symbols = []
        self._macdDic, self._rsiDic = {},{}
        self._newLow, self._currCape = None, None
        self._counter, self._counter2 = 0, 0
        self._c, self._cCopy = np.empty([4]), np.empty([4])
        #self._symbols.append("FB"), self._symbols.append("LNKD"),self._symbols.append("GRPN"), self._symbols.append("TWTR")
        self._symbols.append("SPY")
        self.AddData(Cape, "CAPE")
        for stock in self._symbols:
            self.AddSecurity(SecurityType.Equity, stock, Resolution.Minute)
            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
        
    ### <summary>
    ### Trying to find if current Cape is the lowest Cape in three months to indicate selling period
    ### </summary>
    def OnData(self, data):
        
        if self._currCape and self._newLow is not None:   
            try:
                # Bubble territory
                if self._currCape > 20 and self._newLow == False:
                    self.Log(" Time " + str(self.Time))
                    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:
                            self.BuyStock(stock)
                    # 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:
                            self.BuyStock(stock)
                
                # 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 "CAPE" not in data: return
        self._newLow = False
        # Adds first four Cape Ratios to array c
        self._currCape = data["CAPE"].Cape
        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))
        if self._newLow:
            self.Debug("New Low has been hit on " + str(self.Time))

    ### <summary>
    ### Buy this symbol
    ### </summary>
    def BuyStock(self,symbol):
        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))
    ### <summary>
    ### Sell this symbol
    ### </summary>
    ### <param name="symbol"></param>  
    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))

### <summary>
### 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
### </summary>

class Cape(PythonData):
    
    ### <summary>
    ### Return the URL string source of the file. This will be converted to a stream
    ### </summary>
    ### <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):
        # Remember to add the "?dl=1" for dropbox links
        return SubscriptionDataSource("https://www.dropbox.com/s/ggt6blmib54q36e/CAPE.csv?dl=1", SubscriptionTransportMedium.RemoteFile)
    
    
    ### <summary>
    ### 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.
    ### </summary>
    ### <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.Value = decimal.Decimal(data[10])
            
    
        except ValueError:
                # Do nothing
                return None
    
        return index