FX Value v2 – Real Exchange Rate Changes
Log in to collectAcademic paper
Ahmad Raza; Ben R. Marshall; Nuttawat Visaltanachoti
- NZUniversity of Otago
- ?University of Otago - Department of Accountancy and Finance
Strategy in a nutshell
This strategy trades 39 USD currency pairs using the 5-year real exchange rate change as a value signal. Each month, the most undervalued 20% are bought and the most overvalued 20% are sold, forming an equally weighted, rebalanced portfolio.
Economic rationale
Real exchange rates reflect relative currency values and tend to converge over time. Using 5-year changes provides a robust measure, allowing the strategy to exploit persistent valuation gaps between undervalued and overvalued currencies.
Backtest performance
Annualised return9.57%
Volatility9.51%
Beta-0.114
Sharpe ratio1.01
Sortino ratio-0.375
Win rate41%
Full Python code
from AlgorithmImports import *
import data_tools
from typing import List, Dict
class FXValuev2RealExchangeRateChanges(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2000, 1, 1)
self.SetCash(100000)
self.data:Dict[str, RollingWindow[float]] = {}
self.leverage:int = 3
self.period:int = 12 * 5 # five years of monthly values
self.selected_currencies:int = 3 # select these many of forex futures for long and short each rebalance
# currency future symbol and PPP yearly symbol
self.symbols:Dict[str, str] = {
"CME_AD1" : "AUS_PPP", # Australian Dollar Futures, Continuous Contract #1
"CME_BP1" : "GBR_PPP", # British Pound Futures, Continuous Contract #1
"CME_CD1" : "CAN_PPP", # Canadian Dollar Futures, Continuous Contract #1
"CME_EC1" : "DEU_PPP", # Euro FX Futures, Continuous Contract #1
"CME_JY1" : "JPN_PPP", # Japanese Yen Futures, Continuous Contract #1
"CME_NE1" : "NZL_PPP", # New Zealand Dollar Futures, Continuous Contract #1
"CME_SF1" : "CHE_PPP" # Swiss Franc Futures, Continuous Contract #1
}
self.last_ppp:Dict[str, Union[None, str]] = {
"AUS_PPP" : None,
"GBR_PPP" : None,
"CAN_PPP" : None,
"DEU_PPP" : None,
"JPN_PPP" : None,
"NZL_PPP" : None,
"CHE_PPP" : None,
"USA_PPP" : None
}
for symbol, ppp_symbol in self.symbols.items():
data = self.AddData(data_tools.QuantpediaFutures, symbol, Resolution.Daily)
data.SetFeeModel(data_tools.CustomFeeModel())
data.SetLeverage(self.leverage)
# PPP data
self.AddData(data_tools.PPPData, ppp_symbol, Resolution.Daily)
self.data[symbol] = RollingWindow[float](self.period)
self.AddData(data_tools.PPPData, 'USA_PPP', Resolution.Daily).Symbol
self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
self.selection_flag:bool = False
self.Schedule.On(self.DateRules.MonthStart('CME_AD1'), self.TimeRules.At(0, 0), self.Selection)
def OnData(self, data):
ppp_last_update_date:Dict[str, datetime.date] = data_tools.PPPData.get_last_update_date()
future_last_update_date:Dict[str, datetime.date] = data_tools.QuantpediaFutures.get_last_update_date()
# store PPP values, when they are available
for ppp_symbol, _ in self.last_ppp.items():
if ppp_symbol in data and data[ppp_symbol]:
# only last PPP for each country is stored
self.last_ppp[ppp_symbol] = data[ppp_symbol].Value
if not self.selection_flag:
return
self.selection_flag = False
symbols_to_delete:List[str] = []
# store monthly data
for symbol, ppp_symbol in self.symbols.items():
# data is still coming
if self.Securities[symbol].GetLastData() and self.Time.date() > future_last_update_date[symbol] \
or self.Securities[ppp_symbol].GetLastData() and self.Time.date() > ppp_last_update_date[ppp_symbol]:
symbols_to_delete.append(symbol)
continue
# check if all data are ready for calculation
if symbol in data and data[symbol] and self.last_ppp[ppp_symbol] and self.last_ppp['USA_PPP']:
# forex future price * (forex future PPP / USA PPP)
self.data[symbol].Add(data[symbol].Value * (self.last_ppp[ppp_symbol] / self.last_ppp['USA_PPP']))
if len(symbols_to_delete) != 0:
[self.symbols.pop(symbol) for symbol in symbols_to_delete]
# rebalance
years_return:Dict[Symbol, float] = {}
for symbol, _ in self.symbols.items():
if not self.data[symbol].IsReady:
continue
# calculate years return
values = [x for x in self.data[symbol]]
years_return[symbol] = (values[0] - values[-1]) / values[-1]
if len(years_return) == 0:
return
# sort forex futures by years return
sorted_by_years_return:List[Symbol] = [x[0] for x in sorted(years_return.items(), key=lambda item: item[1])]
# long self.selected_currencies with the lowest past years return
long:List[Symbol] = sorted_by_years_return[:self.selected_currencies]
# short self.selected_currencies with the highest past years return
short:List[Symbol] = sorted_by_years_return[-self.selected_currencies:]
# trade execution
invested:List[str] = [x.Key.Value for x in self.Portfolio if x.Value.Invested]
for symbol in invested:
if symbol not in long + short:
self.Liquidate(symbol)
for i, portfolio in enumerate([long, short]):
for symbol in portfolio:
if symbol in data and data[symbol]:
self.SetHoldings(symbol, ((-1) ** i) / self.selected_currencies)
def Selection(self):
self.selection_flag = True