外汇价值 v3 – 实际汇率水平

“该策略每周交易39种货币对，使用名义汇率与实际汇率的比率，做多被低估的货币，做空被高估的货币，采用等权重、再平衡的投资组合，以捕捉估值差距。”

资产类别: 差价合约、远期、期货、掉期 | 地区: 全球 | 周期: 每周 | 市场: 外汇 | 关键词: 汇率

I. 策略概要

该策略以美元为目标，交易39种货币对，使用当前名义汇率与实际汇率的比率作为价值指标。实际汇率纳入了名义汇率，并根据本地与美国消费品价格水平的比率进行调整。每周，货币根据该比率从最被低估到最被高估进行排名。投资者做多前20%（最被低估的），做空后20%（最被高估的）。投资组合等权重，并每周进行再平衡，利用估值差异优化回报。

II. 策略合理性

实际汇率水平是货币市场中衡量基本价值的常用指标。理论认为，国家之间实际汇率的差异应该在长期内消除；因此，实际汇率低于1的货币可能被视为“被低估”，而高于1的货币则被视为“被高估”。

III. 来源论文

外汇市场中的价值策略是否有利可图？ [点击查看论文]

• 艾哈迈德·拉扎，奥塔哥大学 – 会计与金融系。

<摘要>

本文研究了基于四种不同货币估值指标的价值策略的盈利能力。实际汇率水平策略在长达1个月的区间内产生最大的超额回报，而实际汇率变化在1-12个月的区间内产生最大的超额回报。基于购买力平价和巨无霸指数的策略表现不佳。这些回报无法用经济状态变量或货币风险因素解释，并且大于估计的交易成本。基于所有四种估值方法的综合策略产生更高的平均回报，但由于投资组合集中度，波动性也更高。

IV. 回测表现

年化回报	6.91%
波动率	9.53%
β值	-0.024
夏普比率	0.73
索提诺比率	-0.534
最大回撤	N/A
胜率	51%

V. 完整的 Python 代码

from AlgorithmImports import *
import data_tools
from typing import Dict, List
from dateutil.relativedelta import relativedelta
class FXValuev3RealExchangeRateLevels(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(2000, 1, 1)
        self.SetCash(100000)
        
        self.leverage:int = 3
        self.selected_currencies:int = 3 # We 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 quandl data.
            self.AddData(data_tools.PPPData, ppp_symbol, Resolution.Daily)
            
        self.AddData(data_tools.PPPData, 'USA_PPP', Resolution.Daily).Symbol
        
        self.selection_flag:bool = False
        self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
        self.Schedule.On(self.DateRules.WeekStart('CME_AD1'), self.TimeRules.At(0, 0), self.Selection)
    def OnData(self, data: Slice) -> None:
        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
        
        exchange_ratio:Dict[str, float] = {}
        symbols_to_delete:List[str] = []
        
        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] + relativedelta(months=12):
                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']:
                
                # real exchange rate = forex future price * (forex future PPP / USA PPP)
                real_exchange_rate:float = data[symbol].Value * (self.last_ppp[ppp_symbol] / self.last_ppp['USA_PPP'])
                nominal_exchange_rate:float = data[symbol].Value
                    
                exchange_ratio[symbol] = nominal_exchange_rate / real_exchange_rate
        if len(symbols_to_delete) != 0:
            [self.symbols.pop(symbol) for symbol in symbols_to_delete]
        # currency with the highest (lowest) past 5-year return is considered most overvalued (undervalued)
        sorted_by_ratio:List[str] = [x[0] for x in sorted(exchange_ratio.items(), key=lambda item: item[1])]
        # Investor ranks all currencies from the most undervalued to the most overvalued and goes long top 20% (the most undervalued) currencies and
        # goes short 20% (the most overvalued) currencies.
        
        # Go long self.selected_currencies with the lowest ratio
        long:List[str] = sorted_by_ratio[:self.selected_currencies]
        
        # Go short self.selected_currencies with the highest ratio
        short:List[str] = sorted_by_ratio[-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) -> None:
        self.selection_flag = True