| Overall Statistics |
|
Total Trades 108 Average Win 0.28% Average Loss -0.30% Compounding Annual Return 7.961% Drawdown 1.600% Expectancy 0.288 Net Profit 4.747% Sharpe Ratio 2.477 Loss Rate 33% Win Rate 67% Profit-Loss Ratio 0.93 Alpha 0.192 Beta -8.058 Annual Standard Deviation 0.025 Annual Variance 0.001 Information Ratio 1.831 Tracking Error 0.025 Treynor Ratio -0.008 Total Fees $0.00 |
#import cpattern
#class PerformTrade():
#def __init__():
#if self.algo.Portfolio[self.symbol].Quantity == 0:
#self.SetHoldings(self.symbol, 1000)
#self.algo.StopMarketOrder('EURUSD', -1000, float(data['EURUSD'].Close) - 0.0050)
#self.algo.LimitOrder('EURUSD', -1000, float(data['EURUSD'].Close) + 0.0050)
# Your New Python Fileimport numpy as np
import yaml
from functools import reduce
import datastorage
class CPattern():
def __init__(self, allData, algo, patLength = 30):
self.algo = algo
self.patLength = patLength
self.avgLine = allData['close'].values.tolist()
self.patForRec = []
self.plotPatAr = [] # Array containing recognized patterns
#self.performanceAr = []
self.predictedOutcomes = []
self.predArray = [] # Array containing 1 for good outcomes and -1 for bad outcomes
history = self.algo.History(self.algo.Securities.Keys, 4000, Resolution.Hour)
self.data = datastorage.Data(history, 3700, self) # Object of type Data containing information
self.lastPattern()
self.algo.Debug(str(self.patForRec))
def percentChange(self, startPoint, currentPoint):
try:
x = ((float(currentPoint)-startPoint)/abs(startPoint))*100.00
if x == 0.0:
return 0.0000001
else:
return x
except:
return 0.0000001
def lastPattern(self):
self.patForRec.clear()
last = self.algo.History(self.algo.Securities.Keys, 35, Resolution.Hour)['close'].values.tolist()
for i in range(0, self.patLength):
self.patForRec.append(self.percentChange(last[-31], last[-30+i]))
self.algo.Debug("PatForRec: " + str(self.patForRec))
def patternRecognition(self, data):
patFound = 0
self.plotPatAr = []
self.lastPattern()
for pattern in self.data.patternArray:
sim = []
totalSim = 0
for i in range(0, self.patLength):
sim.append(100.00 - abs(self.percentChange(pattern[i], self.patForRec[i])))
totalSim += sim[i]
totalSim = totalSim/self.patLength
#self.algo.Debug("Sim len: " + str(len(sim)))
#self.algo.Debug("Total Sim: " + str(totalSim))
if totalSim > 65:
patternIndex = self.data.patternArray.index(pattern)
patFound = 1
xp = [i for i in range(1, self.patLength + 1)]
self.plotPatAr.append(pattern)
self.algo.Debug("Recognized pattern: " + str(pattern))
self.algo.Debug("Recognized patterns: " + str(len(self.plotPatAr)))
#self.algo.Debug("Patterns: " + str(self.plotPatAr))
return patFound
def determinePattern(self, data):
self.predArray = []
self.predictedOutcomes = []
if self.patternRecognition(data) == 1:
for eachPatt in self.plotPatAr:
#if len(eachPatt) == 1:
#self.algo.Debug("Only one pattern")
#return
futurePoints = self.data.patternArray.index(eachPatt)
if self.data.performanceArray[futurePoints] > self.patForRec[29]:
self.predArray.append(1.000)
else:
self.predArray.append(-1.000)
self.predictedOutcomes.append(self.data.performanceArray[futurePoints])
self.algo.Debug("PredArray: " + str(self.predArray))
self.algo.Debug("PredOutcomes: " + str(self.predictedOutcomes))
def predictOutcome(self, data):
self.determinePattern(data)
if len(self.predictedOutcomes) < 5:
return None
predictedAvgOutcome = reduce(lambda x, y: x+y, self.predictedOutcomes) / len(self.predictedOutcomes)
predictionAverage = reduce(lambda x, y: x+y, self.predArray) / len(self.predArray)
self.algo.Debug("PredictedAvgOutcome: " + str(predictedAvgOutcome))
if predictionAverage < 0:
self.algo.Debug("Drop predicted")
return -1
if predictionAverage > 0:
self.algo.Debug("Rise predicted")
return 1import numpy as np
import yaml
from functools import reduce
class Data():
def __init__(self, allData, limit, algo):
self.algo = algo
self.patternArray = []
self.performanceArray = []
self.toWhat = limit
self.avgLine = allData['close'].values.tolist()
self.avgLine = self.avgLine[:limit]
self.avgLine = self.avgLine[::-1]
self.patternStorage()
def percentChange(self, startPoint, currentPoint):
try:
x = ((float(currentPoint)-startPoint)/abs(startPoint))*100.00
if x == 0.0:
return 0.0000001
else:
return x
except:
return 0.0000001
def patternStorage(self):
x = self.toWhat - 30
y = 31
n = 0
while y < x:
pattern = []
for i in range(0, 30):
pattern.append(self.percentChange(self.avgLine[y-30], self.avgLine[y-(29-i)]))
outcomeRange = self.avgLine[y+20:y+30]
currentPoint = self.avgLine[y]
try:
avgOutcome = (reduce(lambda x,y: x+y, outcomeRange) / len(outcomeRange))
except:
avgOutcome = 0
futureOutcome = self.percentChange(currentPoint, avgOutcome)
self.patternArray.append(pattern)
self.performanceArray.append(futureOutcome)
y += 1
def getPatLen(self):
return len(self.patternArray)
def getPerfLen(self):
return len(self.performanceArray)
def getAvgLen(self):
return len(self.avgLine)from clr import AddReference
AddReference("System")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Indicators")
AddReference("QuantConnect.Common")
from System import *
from QuantConnect import *
from QuantConnect.Data import *
from QuantConnect.Algorithm import *
from QuantConnect.Indicators import *
from decimal import Decimal
from datetime import datetime, timedelta
import numpy as np
import yaml
import datastorage
import cpattern
#from functools import reduce
### <summary>
### Basic template algorithm simply initializes the date range and cash. This is a skeleton
### framework you can use for designing an algorithm.
### </summary>
class BasicTemplateAlgorithm(QCAlgorithm):
'''Basic template algorithm simply initializes the date range and cash'''
def __init__(self):
self.symbol = "EURUSD"
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(2013,10,7) #Set Start Date
self.SetEndDate(2014,5,15) #Set End Date
self.SetCash(100000) #Set Strategy Cash
# Find more symbols here: http://quantconnect.com/data
self.AddForex(self.symbol, Resolution.Minute)
self.tradePrice = 0
self.tradeTime = self.UtcTime;
self.results = 0;
#self.closeWindow = RollingWindow[Decimal](4)
self.Debug("numpy test >>> print numpy.pi: " + str(np.pi))
history = self.History(self.Securities.Keys, 4000, Resolution.Hour)
self.Debug("History length: " + str(len(history)))
self.Debug("History in 1" + str(history['close'].values))
self.cPat = cpattern.CPattern(history, self)
#self.Debug("Data: " + str(self.cPat.data.patternArray))
hourConsolidator = QuoteBarConsolidator(timedelta(minutes=60))
hourConsolidator.DataConsolidated += self.HourBarHandler
self.SubscriptionManager.AddConsolidator(self.symbol, hourConsolidator)
def HourBarHandler(self, sender, bar):
self.results = 0
try:
self.Debug("============================================================================")
self.Debug("============================================================================")
self.Debug("============================================================================")
self.Debug("============================================================================")
self.results = self.cPat.predictOutcome(bar)
except:
self.Debug("Error")
def OnData(self, data):
'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
Arguments:
data: Slice object keyed by symbol containing the stock data
'''
#if not self.Portfolio.Invested:
#self.SetHoldings(self.symbol, 1)
#self.closeWindow.Add(slice["EURUSD"].Close)
#if len(self.closeWindow) < 33:
#return
try:
#if self.UtcTime > self.tradeTime + timedelta(hours=1):
if self.UtcTime.replace(tzinfo=None) < datetime.strptime("12/28/13", "%m/%d/%y") or self.UtcTime.replace(tzinfo=None) > datetime.strptime("1/3/14", "%m/%d/%y"):
if self.results == 1:
if self.Portfolio['EURUSD'].Quantity == 0:
self.tradeTime = self.UtcTime
self.SetHoldings('EURUSD', 1)
self.tradePrice = data['EURUSD'].Close
#self.StopMarketOrder('USDJPY', -1, float(slice['USDJPY'].Close) - 0.0030)
#self.LimitOrder('USDJPY', -1, float(slice['USDJPY'].Close) + 0.0030)
if self.UtcTime > self.tradeTime + timedelta(hours=20):
if self.Portfolio['EURUSD'].Quantity > 0:
if (float(data['EURUSD'].Close)) > (float(self.tradePrice) + 0.0030):
self.Liquidate()
self.tradeTime = self.UtcTime
if (float(data['EURUSD'].Close)) < (float(self.tradePrice) - 0.0030):
self.Liquidate()
self.tradeTime = self.UtcTime
if self.Portfolio['EURUSD'].Quantity > 0:
if (float(data['EURUSD'].Close)) < (float(self.tradePrice) - 0.0060):
self.Liquidate()
self.tradeTime = self.UtcTime
"""
#SHORTING STARTS HERE
if self.UtcTime.replace(tzinfo=None) < datetime.strptime("12/28/13", "%m/%d/%y") or self.UtcTime.replace(tzinfo=None) > datetime.strptime("1/3/14", "%m/%d/%y"):
if self.results == -1:
if self.Portfolio['EURUSD'].Quantity == 0:
self.tradeTime = self.UtcTime
self.SetHoldings('EURUSD', -1)
self.tradePrice = data['EURUSD'].Close
#self.StopMarketOrder('EURUSD', -1, float(slice['EURUSD'].Close) - 0.0030)
#self.LimitOrder('EURUSD', -1, float(slice['EURUSD'].Close) + 0.0030)
if self.UtcTime > self.tradeTime + timedelta(hours=20):
if self.Portfolio['EURUSD'].Quantity < 0:
if (float(data['EURUSD'].Close)) > (float(self.tradePrice) + 0.0030):
self.Liquidate()
self.tradeTime = self.UtcTime
if (float(data['EURUSD'].Close)) < (float(self.tradePrice) - 0.0030):
self.Liquidate()
self.tradeTime = self.UtcTime
if self.Portfolio['EURUSD'].Quantity < 0:
if (float(data['EURUSD'].Close)) > (float(self.tradePrice) + 0.0060):
self.Liquidate()
self.tradeTime = self.UtcTime
#SHORTING STOPS HERE
"""
except:
self.Debug("Error")