Compare text

Find the difference between two text files

Real-time diff

Unified diff

Collapse lines

Highlight change

Syntax highlighting

Tools

Diffchecker Desktop The most secure way to run Diffchecker. Get the Diffchecker Desktop app: your diffs never leave your computer!Get Desktop

Comparing sensitive data, confidential files or internal emails?

Most legal and privacy policies prohibit uploading sensitive data online. Diffchecker Desktop ensures your confidential information never leaves your computer. Work offline and compare documents securely.

Download Diffchecker Desktop Learn more

Untitled Diff

Created 3 years agoDiff never expires

34 removals

336 lines

118 additions

417 lines

##########################################################################

# Inspired by @nitay-rabinovich at QuqntConnect

# https://www.quantconnect.com/forum/discussion/12768/share-kalman-filter-crossovers-for-crypto-and-smart-rollingwindows/p1/comment-38144

##########################################################################

# EMA Crossover In a Crypto Universe

# ---------------------------------------------

# FOR EDUCATIONAL PURPOSES ONLY. DO NOT DEPLOY.

# Entry:

# -------

# Minimum volume threshold traded

# and

# Price > Fast Daily EMA

# and

# Fast Daily EMA > Slow Daily EMA

# Exit:

# ------

# Price < Slow Daily EMA

# or

# Slow Daily EMA < Fast Daily EMA

# Additional Consideration:

# --------------------------

# Max exposure pct: Total % of available capital to trade with at any time

# Max holdings: Total # of positions that can be held simultaneously

# Rebalance Weekly: If false, only rebalance when we add/remove positions

# UseMomWeight: If true, rebalance w/momentum-based weights (top gainers=more weight)

#########################################################################

from SmartRollingWindow import *

class EMACrossoverUniverse(QCAlgorithm):

def Initialize(self):

self.InitAlgoParams()

self.InitAssets()

self.InitUniverse()

self.InitBacktestParams()

self.ScheduleRoutines()

## Set backtest params: dates, cash, etc. Called from Initialize().

## ----------------------------------------------------------------

def InitBacktestParams(self):

self.SetStartDate(2020, 1, 1)

# self.SetEndDate(2019, 2, 1)

self.SetCash(100000)

self.SetBenchmark(Symbol.Create("BTCUSDT", SecurityType.Crypto, Market.Binance))

def InitUniverse(self):

self.UniverseSettings.Resolution = Resolution.Daily

self.symDataDict = { }

self.UniverseTickers = ["SOLUSDT", "ETHUSDT", "BNBUSDT", "ADAUSDT", "BTCUSDT"]

## More test tickers

# self.UniverseTickers = ["ANTUSDT","BATUSDT","BNBUSDT","BNTUSDT",

# "BTCUSDT", "BTGUSDT",

# "DAIUSDT","DASHUSDT","DGBUSDT",

# "EOSUSDT","ETCUSDT",

# "ETHUSDT","FUNUSDT",

# "IOTAUSDT","KNCUSDT","LRCUSDT",

# "LTCUSDT","MKRUSDT",

# "NEOUSDT","OMGUSDT",

# "PNTUSDT","QTUMUSDT","REQUSDT",

# "STORJUSDT","TRXUSDT","UTKUSDT","VETUSDT",

# "XLMUSDT","XMRUSDT",

# "XRPUSDT","XTZUSDT","XVGUSDT","ZECUSDT",

# "ZILUSDT","ZRXUSDT"]

universeSymbols = []

for symbol in self.UniverseTickers:

universeSymbols.append(Symbol.Create(symbol, SecurityType.Crypto, Market.Binance))

self.SetUniverseSelection(ManualUniverseSelectionModel(universeSymbols))

# --------------------

def InitAlgoParams(self):

self.emaSlowPeriod = int(self.GetParameter('emaSlowPeriod'))

self.emaFastPeriod = int(self.GetParameter('emaFastPeriod'))

self.mompPeriod = int(self.GetParameter('mompPeriod')) # used for momentum based weight

self.minimumVolPeriod = int(self.GetParameter('minimumVolPeriod')) # used for volume threshold

self.warmupPeriod = max(self.emaSlowPeriod, self.mompPeriod, self.minimumVolPeriod)

self.useMomWeight = (int(self.GetParameter("useMomWeight")) == 1)

self.maxExposurePct = float(self.GetParameter("maxExposurePct"))/100

self.rebalanceWeekly = (int(self.GetParameter("rebalanceWeekly")) == 1)

self.minimumVolume = int(self.GetParameter("minimumVolume"))

self.maxHoldings = int(self.GetParameter("maxHoldings"))

## Experimental:

## self.maxSecurityDrawDown = float(self.GetParameter("maxSecurityDrawDown"))

# --------------------

def InitAssets(self):

self.symbol = "BTCUSDT"

self.SetBrokerageModel(BrokerageName.Binance, AccountType.Cash)

self.SetAccountCurrency("USDT")

self.AddCrypto(self.symbol, Resolution.Daily)

self.EnableAutomaticIndicatorWarmUp = True

self.SetWarmUp(timedelta(self.warmupPeriod))

self.SelectedSymbolsAndWeights = {}

## Experimental:

## self.AddRiskManagement(MaximumUnrealizedProfitPercentPerSecurity(self.maxSecurityDrawDown))

# ------------------------

## Schedule routines

## ------------------------

def ScheduleRoutines(self):

## TODO:

## Check if rebalancing has happened in the last 7 days,

## If it has, do not rebalance again

if(self.rebalanceWeekly):

self.Schedule.On( self.DateRules.WeekStart(self.symbol),

self.TimeRules.AfterMarketOpen(self.symbol, 31),

self.RebalanceHoldings )

## Check if we are already holding the max # of open positions.

## TODO:

## When we start using limit orders, include pending holdings

## ------------------------------------------------------------

@property

def PortfolioAtCapacity(self):

numHoldings = len([x.Key for x in self.Portfolio if x.Value.Invested])

return numHoldings >= self.maxHoldings

## In the OnData Event handler, check for signals

## TODO:

## Test logic below for pending holdings

# pendingOrders = len( [x for x in self.Transactions.GetOpenOrders()

# if x.Direction == OrderDirection.Buy

# and x.Type == OrderType.Limit ] )

## Check for signals

## ------------------------------------------------

def OnData(self, dataSlice):

## loop through the symbols in the slice

for symbol in dataSlice.Keys:

## if we have this symbol in our data dictioary

if symbol in self.symDataDict:

symbolData = self.symDataDict[symbol]

## Update the symbol with the data slice data

symbolData.OnSymbolData(self.Securities[symbol].Price, dataSlice[symbol])

## If we're invested in this symbol, manage any open positions

if(self.Portfolio[symbolData.symbol.Value].Invested):

symbolData.ManageOpenPositions()

## otherwise, if we're not invested, check for entry signal

else:

## First check if we are at capacity for new positions.

## TODO:

## For Go-Live, note that the portfolio capacity may not be accurate while

## checking it inside this for-loop. It will be accurate after the positions

## have been open. IE: When the orders are actually filled.

if(not self.PortfolioAtCapacity):

if( symbolData.EntrySignalFired() ):

self.OpenNewPosition(symbolData.symbol)

## TODO:

## For Go-Live, call OnNewPositionOpened only after

## the order is actually filled

symbolData.OnNewPositionOpened()

## Logic to rebalance our portfolio of holdings.

## We will either rebalance with equal weighting,

## or assign weights based on momentum.

## ----------------------------------------------

def RebalanceHoldings(self):

## TODO:

try:

## Check if rebalancing has happened in the last 7 days,

## If it has, do not rebalance again

## -----------------------------------------------------

def RebalanceHoldings(self, rebalanceCurrHoldings=False):

# try:

if self.useMomWeight:

momentumSum = sum(self.symDataDict[symbol].momp.Current.Value for symbol in self.SelectedSymbolsAndWeights)

if (momentumSum == 0):

self.useMomWeight = False

for symbol in self.SelectedSymbolsAndWeights:

if self.useMomWeight:

momentumSum = sum(self.symDataDict[symbol].momp.Current.Value for symbol in self.SelectedSymbolsAndWeights)

symbolWeight = round((self.symDataDict[symbol].momp.Current.Value / momentumSum),4)

else:

symbolWeight = round(1/len(self.SelectedSymbolsAndWeights),4)

for symbol in self.SelectedSymbolsAndWeights:

self.SetWeightedHolding(symbol,symbolWeight)

if self.useMomWeight:

return

symbolWeight = round((self.symDataDict[symbol].momp.Current.Value / momentumSum),4)

else:

symbolWeight = round(1/len(self.SelectedSymbolsAndWeights),4)

## Truncate symbolweight decimal places

truncFactor = 10.0 ** 2

symbolWeight = math.trunc(symbolWeight * truncFactor) / truncFactor

self.SelectedSymbolsAndWeights[symbol] = symbolWeight

## TODO: Calculate order qty instead of using % setholdings

## https://github.com/QuantConnect/Lean/blob/master/Algorithm.Python/BasicTemplateCryptoAlgorithm.py

orderMsg = f"{symbol} | {round(symbolWeight*100,2)}% alloc. | price: {round(self.Securities[symbol].Close,2)}"

if(self.Portfolio[symbol].Invested):

orderMsg = f"[Re-Balancing] {orderMsg}"

else:

orderMsg = f"[NEW Addition] {orderMsg}"

self.SetHoldings(symbol, symbolWeight * self.maxExposurePct, tag=orderMsg)

except:

## Allocate the specified weight (pct) of the portfolio value to

self.Debug(f"Failed to rebalance")

## the specified symbol. This weight will first be adjusted to consider

## cost basis, whether the position is already open and has profit.

## We are doing this to solve the problem where re-balancing causes winners

## to reduce in position size.

## --------–--------–--------–--------–--------–--------–--------–--------–

def SetWeightedHolding(self,symbol,symbolWeight):

## Calculate the basis (the denominator) for rebalancing weights

## This is the sum of costs basis, plus uninvested cash

if( self.Portfolio.Invested ):

# numHoldings = len([x.Key for x in self.Portfolio if x.Value.Invested])

totalCostBasis = sum( [x.Value.HoldingsCost for x in self.Portfolio if x.Value.Invested] )

else:

totalCostBasis = 0.0

## it's okay if this includes cash reserved for pending orders

## because we have alread considered those orders in the symbolsAndWeights list

cashAvailable = self.Portfolio.CashBook["USDT"].Amount

weightingBasis = totalCostBasis + cashAvailable

amtToInvest = weightingBasis * symbolWeight

## if already invested, our adjusted weight needs to account for

## the profits gained, so we adjust the 'amt to invest' based on

## unrealized profit pct of the position.

if(self.Portfolio[symbol].Invested):

profitPct = self.Portfolio[symbol].UnrealizedProfitPercent

adjustedAmtToInvest = amtToInvest * (1 + profitPct)

adjustedWeight = adjustedAmtToInvest / self.Portfolio.TotalPortfolioValue

else:

adjustedWeight = amtToInvest / self.Portfolio.TotalPortfolioValue

symbolWeight = self.GetTruncatedValue(symbolWeight,3)

adjustedWeight = self.GetTruncatedValue(adjustedWeight,3)

self.SelectedSymbolsAndWeights[symbol] = adjustedWeight

## TODO: Calculate order qty instead of using % setholdings

## https://github.com/QuantConnect/Lean/blob/master/Algorithm.Python/BasicTemplateCryptoAlgorithm.py

orderMsg = f"{symbol} | {round(symbolWeight*100,2)}% alloc. ({round(adjustedWeight*100,2)}% adjusted) "

if(self.Portfolio[symbol].Invested):

orderMsg = f"[Re-Balancing] {orderMsg}"

else:

orderMsg = f"[NEW Addition] {orderMsg}"

self.SetHoldings(symbol, adjustedWeight * self.maxExposurePct, tag=orderMsg)

## Adding the symbol to our dictionary will ensure

## that it gets processed in the rebalancing routine

## -------------------------------------------------

def OpenNewPosition(self, symbol):

self.SelectedSymbolsAndWeights[symbol] = 0

self.RebalanceHoldings()

## Removing the symbol from our dictionary will ensure

## that it wont get processed in the rebalancing routine

## -----------------------------------------------------

def ExitPosition(self, symbol, exitMsg=""):

profitPct = round(self.Securities[symbol].Holdings.UnrealizedProfitPercent,2)

self.Liquidate(symbol, tag=f"SELL {symbol.Value} ({profitPct}% profit) [{exitMsg}]")

self.SelectedSymbolsAndWeights.pop(symbol)

## TODO:

## Before go-live, wait until liquidation has happened before rebalancing

## Perhaps Call RebalanceHoldings after an order event has occured.

self.RebalanceHoldings()

return

## Create new symboldata object and add to our dictionary

## ------------------------------------------------------

def OnSecuritiesChanged(self, changes):

for security in changes.AddedSecurities:

symbol = security.Symbol

if( symbol in self.UniverseTickers and \

symbol not in self.symDataDict.keys()):

self.symDataDict[symbol] = SymbolData(symbol, self)

def GetTruncatedValue(self, value, decPlaces):

truncFactor = 10.0 ** decPlaces

return math.trunc(value * truncFactor) / truncFactor

##################################

# SymbolData Class

##################################

class SymbolData():

def __init__(self, theSymbol, algo):

## Algo / Symbol / Price reference

self.algo = algo

self.symbol = theSymbol

self.lastPrice = 0

self.price = 0

## Initialize indicators

self.InitIndicators()

## ----------------------------------------

def InitIndicators(self):

self.indicators = { 'EMA_FAST' : self.algo.EMA(self.symbol,self.algo.emaFastPeriod,Resolution.Daily),

'EMA_SLOW' : self.algo.EMA(self.symbol,self.algo.emaSlowPeriod,Resolution.Daily),

'30DAY_VOL' : IndicatorExtensions.Times( self.algo.SMA(self.symbol,self.algo.minimumVolPeriod, Resolution.Daily, Field.Volume),

self.algo.SMA(self.symbol,self.algo.minimumVolPeriod, Resolution.Daily, Field.Close)),

'MOMP' : self.algo.MOMP(self.symbol,self.algo.mompPeriod,Resolution.Daily)}

## for easy reference from main algo

self.momp = self.indicators['MOMP']

for key, indicator in self.indicators.items():

self.algo.WarmUpIndicator(self.symbol, indicator, Resolution.Minute)

self.emaFastWindow = SmartRollingWindow("float", 2)

self.emaSlowWindow = SmartRollingWindow("float", 2)

self.lastPriceWindow = SmartRollingWindow("float", 2)

## ----------------------------------------

def OnSymbolData(self, lastKnownPrice, tradeBar):

self.lastPrice = lastKnownPrice

self.UpdateRollingWindows()

self.PlotCharts()

## ----------------------------------------

def UpdateRollingWindows(self):

self.emaFastWindow.Add(self.indicators['EMA_FAST'].Current.Value)

self.emaSlowWindow.Add(self.indicators['EMA_SLOW'].Current.Value)

self.lastPriceWindow.Add(self.lastPrice)

## ----------------------------------------

def IsReady(self):

return (self.indicators['EMA_FAST'].IsReady and self.indicators['EMA_SLOW'].IsReady \

and self.indicators['30DAY_VOL'].IsReady)

## ----------------------------------------

def MinimumVolTraded(self):

if( self.indicators['30DAY_VOL'].IsReady ):

dollarVolume = self.indicators['30DAY_VOL'].Current.Value

if( dollarVolume >= self.algo.minimumVolume ):

return True

return False

## ----------------------------------------

def EntrySignalFired(self):

if( self.IsReady() ):

if( self.MinimumVolTraded() ):

if( self.emaFastWindow.isAbove(self.emaSlowWindow) and \

self.lastPriceWindow.isAbove(self.emaFastWindow) ):

return True

return False

## ----------------------------------------

def ExitSignalFired(self):

if( self.IsReady() ):

if ( self.lastPriceWindow.isBelow(self.emaSlowWindow) or \

self.emaSlowWindow.isAbove(self.emaFastWindow) ):

return True

return False

## Logic to run immediately after a new position is opened.

## ---------------------------------------------------------

def OnNewPositionOpened(self):

# self.algo.Log(f"[BOUGHT {self.symbol.Value}] @ ${self.lastPrice:.2f}")

return

## Manage open positions if any. ie: close them, update stops, add to them, etc

## Called periodically, eg: from a scheduled routine

## TODO:

## Consilder also liquidating if volume or liquidity thresholds arent met

## -----------------------------------------------------------------------------

def ManageOpenPositions(self):

## if( not self.MinimumVolTraded() ):

## self.ExitPosition(exitMsg="No longer liquid")

## self.ExitPosition(exitMsg="Trading volume below threshold")

if(self.ExitSignalFired()):

self.ExitPosition(exitMsg="Exit Signal Fired")

# ----------------------------------------

def ExitPosition(self, exitMsg):

# self.algo.Log(f"[SELL {self.symbol.Value}] @ ${self.lastPrice:.2f}")

self.algo.ExitPosition(self.symbol, exitMsg)

# ----------------------------------------

def PlotCharts(self):

## To Plot charts, comment out the below

# self.algo.Plot(f"{self.symbol}-charts", "Price", self.lastPriceWindow[0])

# self.algo.Plot(f"{self.symbol}-charts", "EMA Fast", self.indicators['EMA_FAST'].Current.Value)

# self.algo.Plot(f"{self.symbol}-charts", "EMA Slow", self.indicators['EMA_SLOW'].Current.Value)

return

Saved diffs

Original text

Open file

##########################################################################
# Inspired by @nitay-rabinovich at QuqntConnect
# https://www.quantconnect.com/forum/discussion/12768/share-kalman-filter-crossovers-for-crypto-and-smart-rollingwindows/p1/comment-38144
##########################################################################
#
# EMA Crossover In a Crypto Universe 
# ---------------------------------------------
# FOR EDUCATIONAL PURPOSES ONLY. DO NOT DEPLOY.
#
#
# Entry:
# -------
# Minimum volume threshold traded 
#         and
# Price > Fast Daily EMA 
#         and
# Fast Daily EMA > Slow Daily EMA
#
# Exit:
# ------
# Price < Slow Daily EMA 
#       or 
# Slow Daily EMA < Fast Daily EMA
#
# Additional Consideration:
# --------------------------
# Max exposure pct: Total % of available capital to trade with at any time
# Max holdings: Total # of positions that can be held simultaneously
# Rebalance Weekly: If false,  only rebalance when we add/remove positions  
# UseMomWeight: If true, rebalance w/momentum-based weights (top gainers=more weight)
#
#########################################################################

from SmartRollingWindow import *
class EMACrossoverUniverse(QCAlgorithm):
    
    ## 
    def Initialize(self):
        self.InitAlgoParams()
        self.InitAssets()
        self.InitUniverse()
        self.InitBacktestParams()        
        self.ScheduleRoutines()

## Set backtest params: dates, cash, etc. Called from Initialize().
    ## ----------------------------------------------------------------
    def InitBacktestParams(self):
        self.SetStartDate(2020, 1, 1)
        # self.SetEndDate(2019, 2, 1)  
        self.SetCash(100000)
        self.SetBenchmark(Symbol.Create("BTCUSDT", SecurityType.Crypto, Market.Binance))

def InitUniverse(self):
        self.UniverseSettings.Resolution = Resolution.Daily
        self.symDataDict = { }

self.UniverseTickers = ["SOLUSDT", "ETHUSDT", "BNBUSDT", "ADAUSDT", "BTCUSDT"]
        
        universeSymbols = []
        for symbol in self.UniverseTickers:

universeSymbols.append(Symbol.Create(symbol, SecurityType.Crypto, Market.Binance))
            
        self.SetUniverseSelection(ManualUniverseSelectionModel(universeSymbols))

# --------------------
    def InitAlgoParams(self):
        
        self.emaSlowPeriod    = int(self.GetParameter('emaSlowPeriod'))
        self.emaFastPeriod    = int(self.GetParameter('emaFastPeriod'))
        self.mompPeriod       = int(self.GetParameter('mompPeriod'))       # used for momentum based weight
        self.minimumVolPeriod = int(self.GetParameter('minimumVolPeriod')) # used for volume threshold
        self.warmupPeriod     = max(self.emaSlowPeriod, self.mompPeriod, self.minimumVolPeriod)
        
        self.useMomWeight     = (int(self.GetParameter("useMomWeight")) == 1)                 
        self.maxExposurePct   = float(self.GetParameter("maxExposurePct"))/100
        self.rebalanceWeekly  = (int(self.GetParameter("rebalanceWeekly")) == 1)

self.minimumVolume    = int(self.GetParameter("minimumVolume")) 
        self.maxHoldings      = int(self.GetParameter("maxHoldings")) 
        
        ## Experimental:
        ## self.maxSecurityDrawDown  = float(self.GetParameter("maxSecurityDrawDown"))
    
    # --------------------
    def InitAssets(self):    
        self.symbol = "BTCUSDT" 
        self.SetBrokerageModel(BrokerageName.Binance, AccountType.Cash)
        self.SetAccountCurrency("USDT")
        self.AddCrypto(self.symbol, Resolution.Daily)

self.EnableAutomaticIndicatorWarmUp = True
        self.SetWarmUp(timedelta(self.warmupPeriod))
        self.SelectedSymbolsAndWeights = {}
        
        ## Experimental:
        ## self.AddRiskManagement(MaximumUnrealizedProfitPercentPerSecurity(self.maxSecurityDrawDown))
    
    # ------------------------
    def ScheduleRoutines(self):
        if(self.rebalanceWeekly):
            self.Schedule.On( self.DateRules.WeekStart(self.symbol),
                              self.TimeRules.AfterMarketOpen(self.symbol, 31),
                              self.RebalanceHoldings )

## Check if we are already holding the max # of open positions.
    ## ------------------------------------------------------------
    @property
    def PortfolioAtCapacity(self):
        numHoldings = len([x.Key for x in self.Portfolio if x.Value.Invested])
        return numHoldings >= self.maxHoldings
        
        ## In the OnData Event handler, check for  signals
    ## ------------------------------------------------
    def OnData(self, dataSlice):

## loop through the symbols in the slice
        for symbol in dataSlice.Keys:

## if we have this symbol in our data dictioary
            if symbol in self.symDataDict:

symbolData = self.symDataDict[symbol]

## Update the symbol with the data slice data
                symbolData.OnSymbolData(self.Securities[symbol].Price, dataSlice[symbol])
                
                ## If we're invested in this symbol, manage any open positions
                if(self.Portfolio[symbolData.symbol.Value].Invested):
                    symbolData.ManageOpenPositions()
                
                ## otherwise, if we're not invested, check for entry signal
                else:

## First check if we are at capacity for new positions. 
                    ##
                    ## TODO: 
                    ## For Go-Live, note that the portfolio capacity may not be accurate while
                    ## checking it inside this for-loop. It will be accurate after the positions 
                    ## have been open. IE: When the orders are actually filled.                    
                    if(not self.PortfolioAtCapacity):            
                        if( symbolData.EntrySignalFired() ):
                            self.OpenNewPosition(symbolData.symbol)
                    
                            ## TODO: 
                            ## For Go-Live, call OnNewPositionOpened only after 
                            ## the order is actually filled
                            symbolData.OnNewPositionOpened()
       
    ## Logic to rebalance our portfolio of holdings. 
    ## We will either rebalance with equal weighting, 
    ## or assign weights based on momentum. 
    ## ----------------------------------------------
    def RebalanceHoldings(self):
        try:
            
            if self.useMomWeight:
                momentumSum = sum(self.symDataDict[symbol].momp.Current.Value for symbol in self.SelectedSymbolsAndWeights)
            
            for symbol in self.SelectedSymbolsAndWeights:

if self.useMomWeight:
                    symbolWeight = round((self.symDataDict[symbol].momp.Current.Value / momentumSum),4) 
                else:
                    symbolWeight = round(1/len(self.SelectedSymbolsAndWeights),4)
                    
                ## Truncate symbolweight decimal places
                truncFactor  = 10.0 ** 2
                symbolWeight = math.trunc(symbolWeight * truncFactor) / truncFactor
                
                self.SelectedSymbolsAndWeights[symbol] = symbolWeight

## TODO: Calculate order qty instead of using % setholdings
                ## https://github.com/QuantConnect/Lean/blob/master/Algorithm.Python/BasicTemplateCryptoAlgorithm.py
                orderMsg = f"{symbol} | {round(symbolWeight*100,2)}% alloc. | price: {round(self.Securities[symbol].Close,2)}"
                
                if(self.Portfolio[symbol].Invested): 
                    orderMsg = f"[Re-Balancing] {orderMsg}"
                else:
                    orderMsg = f"[NEW Addition] {orderMsg}"
                    
                self.SetHoldings(symbol, symbolWeight * self.maxExposurePct, tag=orderMsg)

except:
            self.Debug(f"Failed to rebalance")

## Adding the symbol to our dictionary will ensure
    ## that it gets processed in the rebalancing routine
    ## -------------------------------------------------
    def OpenNewPosition(self, symbol):
        self.SelectedSymbolsAndWeights[symbol] = 0
        self.RebalanceHoldings()    
                    
    ## Removing the symbol from our dictionary will ensure
    ## that it wont get processed in the rebalancing routine
    ## -----------------------------------------------------
    def ExitPosition(self, symbol, exitMsg=""):
        profitPct   = round(self.Securities[symbol].Holdings.UnrealizedProfitPercent,2)
        self.Liquidate(symbol, tag=f"SELL {symbol.Value} ({profitPct}% profit) [{exitMsg}]")
        self.SelectedSymbolsAndWeights.pop(symbol)
        self.RebalanceHoldings()
        return

## Create new symboldata object and add to our dictionary
    ## ------------------------------------------------------
    def OnSecuritiesChanged(self, changes):
        for security in changes.AddedSecurities:
            symbol  = security.Symbol
            if( symbol in self.UniverseTickers and \
                symbol not in self.symDataDict.keys()):
                self.symDataDict[symbol] = SymbolData(symbol, self)

##################################
# SymbolData Class
##################################
class SymbolData():
    
    def __init__(self, theSymbol, algo):

## Algo / Symbol / Price reference
        self.algo       = algo
        self.symbol     = theSymbol
        self.lastPrice  = 0
        self.price      = 0

## Initialize indicators
        self.InitIndicators()
            
            
    ## ----------------------------------------
    def InitIndicators(self):

self.indicators = { 'EMA_FAST'  : self.algo.EMA(self.symbol,self.algo.emaFastPeriod,Resolution.Daily),
                            'EMA_SLOW'  : self.algo.EMA(self.symbol,self.algo.emaSlowPeriod,Resolution.Daily),
                            '30DAY_VOL' : IndicatorExtensions.Times( self.algo.SMA(self.symbol,self.algo.minimumVolPeriod, Resolution.Daily, Field.Volume), 
                                                                     self.algo.SMA(self.symbol,self.algo.minimumVolPeriod, Resolution.Daily, Field.Close)),
                            'MOMP'      : self.algo.MOMP(self.symbol,self.algo.mompPeriod,Resolution.Daily)}
        
        ## for easy reference from main algo
        self.momp = self.indicators['MOMP']
                            
        for key, indicator in self.indicators.items():
            self.algo.WarmUpIndicator(self.symbol, indicator, Resolution.Minute)

self.emaFastWindow  = SmartRollingWindow("float", 2)
        self.emaSlowWindow  = SmartRollingWindow("float", 2)
        self.lastPriceWindow    = SmartRollingWindow("float", 2)
        
    ## ----------------------------------------
    def OnSymbolData(self, lastKnownPrice, tradeBar):
        self.lastPrice = lastKnownPrice
        self.UpdateRollingWindows()
        self.PlotCharts()
        
    ## ----------------------------------------
    def UpdateRollingWindows(self):
        self.emaFastWindow.Add(self.indicators['EMA_FAST'].Current.Value)
        self.emaSlowWindow.Add(self.indicators['EMA_SLOW'].Current.Value)
        self.lastPriceWindow.Add(self.lastPrice)
        
    ## ----------------------------------------
    def IsReady(self):
        return (self.indicators['EMA_FAST'].IsReady and self.indicators['EMA_SLOW'].IsReady \
            and self.indicators['30DAY_VOL'].IsReady)

## ----------------------------------------
    def MinimumVolTraded(self):
        if( self.indicators['30DAY_VOL'].IsReady ):
            dollarVolume = self.indicators['30DAY_VOL'].Current.Value
            if( dollarVolume >= self.algo.minimumVolume ):
                return True
                
        return False
        
    ## ----------------------------------------
    def EntrySignalFired(self):
        if( self.IsReady() ):
            if( self.MinimumVolTraded() ):
                if( self.emaFastWindow.isAbove(self.emaSlowWindow) and \
                    self.lastPriceWindow.isAbove(self.emaFastWindow) ):
                    return True
        
        return False

## ----------------------------------------
    def ExitSignalFired(self):
        if( self.IsReady() ):
            if ( self.lastPriceWindow.isBelow(self.emaSlowWindow) or \
                 self.emaSlowWindow.isAbove(self.emaFastWindow) ):
                return True
        
        return False

## Logic to run immediately after a new position is opened.
    ## ---------------------------------------------------------
    def OnNewPositionOpened(self):            
        # self.algo.Log(f"[BOUGHT {self.symbol.Value}] @ ${self.lastPrice:.2f}")    
        return
    
    ## Manage open positions if any. ie: close them, update stops, add to them, etc
    ## Called periodically, eg: from a scheduled routine
    ##
    ## TODO:
    ## Consilder also liquidating if volume or liquidity thresholds arent met
    ## -----------------------------------------------------------------------------
    def ManageOpenPositions(self):

## if( not self.MinimumVolTraded() ):
        ##     self.ExitPosition(exitMsg="No longer liquid")
        
        if(self.ExitSignalFired()):        
            self.ExitPosition(exitMsg="Exit Signal Fired")
        
        
    # ----------------------------------------
    def ExitPosition(self, exitMsg): 
        # self.algo.Log(f"[SELL {self.symbol.Value}] @ ${self.lastPrice:.2f}") 
        self.algo.ExitPosition(self.symbol, exitMsg)

# ----------------------------------------
    def PlotCharts(self):    
    
        ## To Plot charts, comment out the below
        
        # self.algo.Plot(f"{self.symbol}-charts", "Price", self.lastPriceWindow[0])        
        # self.algo.Plot(f"{self.symbol}-charts", "EMA Fast", self.indicators['EMA_FAST'].Current.Value)            
        # self.algo.Plot(f"{self.symbol}-charts", "EMA Slow", self.indicators['EMA_SLOW'].Current.Value)            
        
        return

Changed text

Open file

def InitUniverse(self):
        self.UniverseSettings.Resolution = Resolution.Daily
        self.symDataDict = { }

self.UniverseTickers = ["SOLUSDT", "ETHUSDT", "BNBUSDT", "ADAUSDT", "BTCUSDT"]

## More test tickers
        ## 
        # self.UniverseTickers = ["ANTUSDT","BATUSDT","BNBUSDT","BNTUSDT",
        #                         "BTCUSDT", "BTGUSDT",
        #                         "DAIUSDT","DASHUSDT","DGBUSDT",
        #                         "EOSUSDT","ETCUSDT",
        #                         "ETHUSDT","FUNUSDT",
        #                         "IOTAUSDT","KNCUSDT","LRCUSDT",
        #                         "LTCUSDT","MKRUSDT",
        #                         "NEOUSDT","OMGUSDT",
        #                         "PNTUSDT","QTUMUSDT","REQUSDT",
        #                         "STORJUSDT","TRXUSDT","UTKUSDT","VETUSDT",
        #                         "XLMUSDT","XMRUSDT",
        #                         "XRPUSDT","XTZUSDT","XVGUSDT","ZECUSDT",
        #                         "ZILUSDT","ZRXUSDT"]
        
        universeSymbols = []
        for symbol in self.UniverseTickers:

universeSymbols.append(Symbol.Create(symbol, SecurityType.Crypto, Market.Binance))
            
        self.SetUniverseSelection(ManualUniverseSelectionModel(universeSymbols))

self.EnableAutomaticIndicatorWarmUp = True
        self.SetWarmUp(timedelta(self.warmupPeriod))
        self.SelectedSymbolsAndWeights = {}
        
        ## Experimental:
        ## self.AddRiskManagement(MaximumUnrealizedProfitPercentPerSecurity(self.maxSecurityDrawDown))
    
    ## Schedule routines
    ## ------------------------
    def ScheduleRoutines(self):
        
        ## TODO: 
        ## Check if rebalancing has happened in the last 7 days, 
        ## If it has, do not rebalance again 
        if(self.rebalanceWeekly):
            self.Schedule.On( self.DateRules.WeekStart(self.symbol),
                              self.TimeRules.AfterMarketOpen(self.symbol, 31),
                              self.RebalanceHoldings )

##
    ## Check if we are already holding the max # of open positions.
    ## TODO:
    ## When we start using limit orders, include pending holdings
    ## ------------------------------------------------------------
    @property
    def PortfolioAtCapacity(self):
        numHoldings = len([x.Key for x in self.Portfolio if x.Value.Invested])
        return numHoldings >= self.maxHoldings
    
    ## TODO:
    ## Test logic below for pending holdings
    # pendingOrders  = len( [x for x in self.Transactions.GetOpenOrders()
    #                         if x.Direction == OrderDirection.Buy
    #                             and x.Type == OrderType.Limit ] )
    
                             
    ## Check for  signals
    ## ------------------------------------------------
    def OnData(self, dataSlice):

## loop through the symbols in the slice
        for symbol in dataSlice.Keys:

## if we have this symbol in our data dictioary
            if symbol in self.symDataDict:

symbolData = self.symDataDict[symbol]

## First check if we are at capacity for new positions. 
                    ##
                    ## TODO: 
                    ## For Go-Live, note that the portfolio capacity may not be accurate while
                    ## checking it inside this for-loop. It will be accurate after the positions 
                    ## have been open. IE: When the orders are actually filled.                    
                    if(not self.PortfolioAtCapacity):            
                        if( symbolData.EntrySignalFired() ):
                            self.OpenNewPosition(symbolData.symbol)
                    
                            ## TODO: 
                            ## For Go-Live, call OnNewPositionOpened only after 
                            ## the order is actually filled
                            symbolData.OnNewPositionOpened()
      
       
    ## Logic to rebalance our portfolio of holdings. 
    ## We will either rebalance with equal weighting, 
    ## or assign weights based on momentum. 
    ##
    ## TODO: 
    ## Check if rebalancing has happened in the last 7 days, 
    ## If it has, do not rebalance again    
    ## -----------------------------------------------------
    def RebalanceHoldings(self, rebalanceCurrHoldings=False):
        # try:
            
        if self.useMomWeight:
            momentumSum = sum(self.symDataDict[symbol].momp.Current.Value for symbol in self.SelectedSymbolsAndWeights)
            if (momentumSum == 0):
                self.useMomWeight = False
            
                
        for symbol in self.SelectedSymbolsAndWeights:
            if self.useMomWeight:
                symbolWeight = round((self.symDataDict[symbol].momp.Current.Value / momentumSum),4) 
            else:
                symbolWeight = round(1/len(self.SelectedSymbolsAndWeights),4)
            
            self.SetWeightedHolding(symbol,symbolWeight)

return

## Allocate the specified weight (pct) of the portfolio value to 
    ## the specified symbol. This weight will first be adjusted to consider
    ## cost basis, whether the position is already open and has profit.
    ## We are doing this to solve the problem where re-balancing causes winners
    ## to reduce in position size.
    ## --------–--------–--------–--------–--------–--------–--------–--------–
    def SetWeightedHolding(self,symbol,symbolWeight):

## Calculate the basis (the denominator) for rebalancing weights
        ## This is the sum of costs basis, plus uninvested cash

if( self.Portfolio.Invested ):
            # numHoldings = len([x.Key for x in self.Portfolio if x.Value.Invested])
            totalCostBasis = sum( [x.Value.HoldingsCost for x in self.Portfolio if x.Value.Invested] )
        else: 
            totalCostBasis = 0.0

## it's okay if this includes cash reserved for pending orders
        ## because we have alread considered those orders in the symbolsAndWeights list
        cashAvailable  = self.Portfolio.CashBook["USDT"].Amount
        weightingBasis = totalCostBasis + cashAvailable
        amtToInvest    = weightingBasis * symbolWeight

## if already invested, our adjusted weight needs to account for
        ## the profits gained, so we adjust the 'amt to invest' based on 
        ## unrealized profit pct of the position.

if(self.Portfolio[symbol].Invested):
            profitPct           = self.Portfolio[symbol].UnrealizedProfitPercent
            adjustedAmtToInvest = amtToInvest * (1 + profitPct)
            adjustedWeight      = adjustedAmtToInvest / self.Portfolio.TotalPortfolioValue
            
        else:
            adjustedWeight = amtToInvest / self.Portfolio.TotalPortfolioValue
            
        symbolWeight   = self.GetTruncatedValue(symbolWeight,3)
        adjustedWeight = self.GetTruncatedValue(adjustedWeight,3)

self.SelectedSymbolsAndWeights[symbol] = adjustedWeight

## TODO: Calculate order qty instead of using % setholdings
        ## https://github.com/QuantConnect/Lean/blob/master/Algorithm.Python/BasicTemplateCryptoAlgorithm.py
        orderMsg = f"{symbol} | {round(symbolWeight*100,2)}% alloc. ({round(adjustedWeight*100,2)}% adjusted) "
        
        if(self.Portfolio[symbol].Invested): 
            orderMsg = f"[Re-Balancing] {orderMsg}"
        else:
            orderMsg = f"[NEW Addition] {orderMsg}"
            
        self.SetHoldings(symbol, adjustedWeight * self.maxExposurePct, tag=orderMsg)

def GetTruncatedValue(self, value, decPlaces):
        truncFactor  = 10.0 ** decPlaces
        return math.trunc(value * truncFactor) / truncFactor
    
##################################
# SymbolData Class
##################################
class SymbolData():
    
    def __init__(self, theSymbol, algo):

## Algo / Symbol / Price reference
        self.algo       = algo
        self.symbol     = theSymbol
        self.lastPrice  = 0
        self.price      = 0

## Initialize indicators
        self.InitIndicators()
            
            
    ## ----------------------------------------
    def InitIndicators(self):

## if( not self.MinimumVolTraded() ):
        ##     self.ExitPosition(exitMsg="Trading volume below threshold")
        
        if(self.ExitSignalFired()):        
            self.ExitPosition(exitMsg="Exit Signal Fired")
        
        
    # ----------------------------------------
    def ExitPosition(self, exitMsg): 
        # self.algo.Log(f"[SELL {self.symbol.Value}] @ ${self.lastPrice:.2f}") 
        self.algo.ExitPosition(self.symbol, exitMsg)

# ----------------------------------------
    def PlotCharts(self):    
    
        ## To Plot charts, comment out the below
        # self.algo.Plot(f"{self.symbol}-charts", "Price", self.lastPriceWindow[0])        
        # self.algo.Plot(f"{self.symbol}-charts", "EMA Fast", self.indicators['EMA_FAST'].Current.Value)            
        # self.algo.Plot(f"{self.symbol}-charts", "EMA Slow", self.indicators['EMA_SLOW'].Current.Value)            
        
        return