Momentum Strategy for No-Load US Equity Mutual Funds

from AlgoLib import *
#endregion

class MomentuminMutualFundReturns(XXX):

def Initialize(self):
# NOTE: most of the data start from 2014 and until 2015 there wasn't any trade
self.SetStartDate(2014, 1, 1)
self.SetCash(100000)

self.data = {}
self.symbols = []

self.period = 21 * 6 # Storing 6 months of daily prices
self.quantile = 10

self.symbol = self.AddEquity('SPY', Resolution.Daily).Symbol

# Load csv file with etf symbols and split line with semi-colon
etf_symbols_csv = self.Download("data.quantpedia.com/backtesting_data/equity/mutual_funds/symbols.csv")
splitted_csv = etf_symbols_csv.split(';')

for symbol in splitted_csv:
    self.symbols.append(symbol)
    
    # Subscribe for QuantpediaETF by etf symbol, then set fee model and leverage
    data = self.AddData(QuantpediaETF, symbol, Resolution.Daily)
    data.SetFeeModel(CustomFeeModel())
    data.SetLeverage(5)
    
    self.data[symbol] = RollingWindow[float](self.period)

self.recent_month = -1

def OnData(self, data):
# Update daily prices of etfs
for symbol in self.symbols:
    if symbol in data and data[symbol]:
        price = data[symbol].Value
        self.data[symbol].Add(price)

if self.recent_month == self.Time.month:
    return
self.recent_month = self.Time.month

# Rebalance quarterly
if self.recent_month % 3 != 0:
    return
    
performance = {}

for symbol in self.symbols:
    # If data for etf are ready calculate it's 6 month performance
    if self.data[symbol].IsReady:
        if self.Securities[symbol].GetLastData() and (self.Time.date() - self.Securities[symbol].GetLastData().Time.date()).days <= 3:
            prices = [x for x in self.data[symbol]]
            performance[symbol] = (prices[0] - prices[-1]) / prices[-1]
        
if len(performance) < self.quantile:
    self.Liquidate()
    return

decile = int(len(performance) / self.quantile)
# sort dictionary by performance and based on it create sorted list
sorted_by_perf = [x[0] for x in sorted(performance.items(), key=lambda item: item[1], reverse=True)]
# select top decile etfs for investment based on performance
long = sorted_by_perf[:decile]

# Trade execution
invested_etfs = [x.Key for x in self.Portfolio if x.Value.Invested]
for symbol in invested_etfs:
    if symbol not in long:
        self.Liquidate(symbol)
    
long_length = len(long)

for symbol in long:
    self.SetHoldings(symbol, 1 / long_length)    

# Quantpedia data
# NOTE: IMPORTANT: Data order must be ascending (datewise)
class QuantpediaETF(PythonData):
def GetSource(self, config, date, isLiveMode):
return SubscriptionDataSource("data.quantpedia.com/backtesting_data/equity/mutual_funds/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)

def Reader(self, config, line, date, isLiveMode):
data = QuantpediaETF()
data.Symbol = config.Symbol

if not line[0].isdigit(): return None
split = line.split(';')

data.Time = datetime.strptime(split[0], "%d.%m.%Y") + timedelta(days=1)
data['settle'] = float(split[1])
data.Value = float(split[1])

return data

# Custom fee model
class CustomFeeModel(FeeModel):
def GetOrderFee(self, parameters):
fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))