from AlgoLib import *
class InternationalEquityReversals(XXX):
'''
This class implements an international equity reversal strategy using the XXX framework.
It focuses on identifying and trading based on momentum reversals in international ETFs.
'''
def Initialize(self):
'''
Initializes the algorithm settings, including start date, initial cash,
the list of ETF symbols to trade, momentum score dictionary, analysis period,
and rebalance interval.
'''
self.SetStartDate(2000, 1, 1) # Set the start date of the algorithm
self.SetCash(100000) # Set the initial cash balance
self.momentum_scores = {} # Initialize a dictionary to store momentum scores
self.analysis_period = 36 * 21 # Define the analysis period in trading days (approx. 36 months)
self.SetWarmUp(self.analysis_period, Resolution.Daily) # Warm-up period for indicators
# Define a list of ETF symbols representing various international markets
self.etf_symbols = [
"EWA", # Australia
"EWO", # Austria
"EWK", # Belgium
"EWZ", # Brazil
"EWC", # Canada
"FXI", # China
"EWQ", # France
"EWG", # Germany
"EWH", # Hong Kong
"EWI", # Italy
"EWJ", # Japan
"EWM", # Malaysia
"EWW", # Mexico
"EWN", # Netherlands
"EWS", # Singapore
"EZA", # South Africa
"EWY", # South Korea
"EWP", # Spain
"EWD", # Sweden
"EWL", # Switzerland
"EWT", # Taiwan
"THD", # Thailand
"EWU", # UK
"SPY", # USA
]
# Add each ETF symbol to the algorithm, set fee model and leverage, and initialize momentum scores
for symbol in self.etf_symbols:
equity = self.AddEquity(symbol, Resolution.Daily)
equity.SetFeeModel(EnhancedFeeModel()) # Set a custom fee model
equity.SetLeverage(15) # Set leverage for each equity
# Calculate the Rate of Change (ROC) indicator for each symbol for the analysis period
self.momentum_scores[symbol] = self.ROC(symbol, self.analysis_period, Resolution.Daily)
self.rebalance_interval = 36 # Set rebalance interval in months
self.last_rebalance_month = -1 # Initialize the last rebalance month
def OnData(self, data: Slice):
'''
This method is called whenever new data is received. It checks if the warm-up period is complete,
then rebalances the portfolio based on momentum scores if the rebalance interval has been reached.
'''
if self.IsWarmingUp:
return # Do nothing if the algorithm is still warming up
current_month = self.Time.month # Get the current month
# Check if the current month is the same as the last rebalance month to avoid multiple rebalances within the same month
if current_month == self.last_rebalance_month:
return
self.last_rebalance_month = current_month # Update the last rebalance month
self.rebalance_interval += 1 # Increment the rebalance interval counter
# Check if the rebalance interval is less than or equal to 36 months
if self.rebalance_interval <= 36:
return
self.rebalance_interval = 1 # Reset the rebalance interval
# Sort ETFs by momentum scores and select the top and bottom 4 for long and short positions
momentum_sorted = sorted([x for x in self.momentum_scores.items() if x[1].IsReady and x[0] in data], key=lambda x: x[1].Current.Value, reverse=True)
to_long = [x[0] for x in momentum_sorted[-4:]] # Symbols to take long positions in
to_short = [x[0] for x in momentum_sorted[:4]] # Symbols to take short positions in
# Adjust portfolio by liquidating positions not in the to_long or to_short lists and setting holdings for new positions
for symbol in self.Portfolio:
if symbol.Value not in to_long + to_short:
self.Liquidate(symbol.Value) # Liquidate positions not being held
for symbol in to_long:
self.SetHoldings(symbol, 1 / len(to_long)) # Set long positions equally weighted
for symbol in to_short:
self.SetHoldings(symbol, -1 / len(to_short)) # Set short positions equally weighted
class EnhancedFeeModel(FeeModel):
'''
This class defines a custom fee model for the algorithm, calculating fees based on a specified rate.
'''
def GetOrderFee(self, parameters: OrderFeeParameters):
fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005 # Custom fee rate
return OrderFee(CashAmount(fee, "USD"))
