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投资者情绪与货币市场的动量效应

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学术论文

Investor Sentiment, Attention and Profitability of Currency Momentum Strategies

作者Pawee Maryniak

机构

Wrocław University of Science and Technology
Wroclaw University of Economics and Business
?Uniwersytet Ekonomiczny we Wrocławiu
?Wroclaw University of Technology

论文链接https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2862161

策略概要

投资范围包括71种与美元交易的货币。情绪通过贝克-伍格勒（BW）指数衡量，该指数将情绪分为高、中、低水平。货币也按其过去表现进行排名，前六分之一和后六分之一分别归类为“赢家”和“输家”。根据上个月的情绪水平，投资者采取不同的仓位：在低情绪期间做多赢家，在高情绪期间做空输家，在中等情绪期间同时做多和做空。投资组合等权重，并每月重新平衡。

II. 策略合理性

贝克-伍格勒（BW）指数用于衡量情绪，并解释市场中的动量效应。根据该理论，在情绪低落时期，投资者通常寻求安全资产，由于资本转移到更安全的市场，这些资产往往被高估。这种避险行为通常导致外币多头的高回报和空头的低回报，从而形成动量。相反，在高情绪时期，资本流入风险较高的资产导致美元升值，与多头相比，空头获得高回报。

回测表现

波动率10.36%

夏普比率0.56

胜率37%

完整 Python 代码

1from AlgorithmImports import *
2import numpy as np
3from dateutil.relativedelta import relativedelta
4#endregion
5class InvestorSentiment(QCAlgorithm):
6def Initialize(self):
7self.SetStartDate(2011, 1, 1)
8self.SetCash(100000)
9self.period:int = 21
10self.SetWarmUp(self.period, Resolution.Daily)
11
12self.symbols = [
13    "USDAUD", "USDCAD", "USDCHF", "USDCZK", "USDDKK", "USDEUR",
14    "USDGBP", "USDHKD", "USDHUF", "USDJPY", "USDMXN", "USDPLN", 
15    "USDNOK", "USDSAR", "USDSGD", "USDTHB", "USDTRY", "USDTWD", 
16    "USDZAR", "USDSEK"
17] 
18
19self.data:dict[Symbol, SymbolData] = {}
20self.sentiment_warmup_period:int = 12
21self.sentiment_history = RollingWindow[float](self.sentiment_warmup_period)
22self.max_missing_days:int = 31
23self.quantile:int = 6
24
25for symbol in self.symbols:
26    data = self.AddForex(symbol, Resolution.Daily, Market.Oanda)
27    data.SetLeverage(5)
28    
29    self.data[symbol] = SymbolData(symbol, self.period)
30
31# Import custom data 
32self.sentimet_index:Symbol = self.AddData(SentimentData, "sentiment", Resolution.Daily).Symbol
33self.recent_month:int = -1
34
35def OnData(self, data:Slice) -> None:
36perf:dict[str, float] = {}
37# store daily prices
38for symbol in self.symbols:
39    if symbol in data and data[symbol]:
40        self.data[symbol].Update(data[symbol].Value)
41    
42    if self.recent_month != self.Time.month and not self.IsWarmingUp:
43        if self.data[symbol].IsReady():
44            perf[symbol] = self.data[symbol].Return()
45if self.IsWarmingUp: return
46
47# monthly rebalance
48if self.recent_month == self.Time.month:
49    return
50self.recent_month = self.Time.month
51# check sentiment index data arrival
52if self.Securities[self.sentimet_index].GetLastData() and (self.Time.date() - self.Securities[self.sentimet_index].GetLastData().Time.date()).days > self.max_missing_days:
53    if self.Portfolio.Invested:
54        self.Liquidate()
55    self.sentiment_history.Reset()
56else:
57    sentiment_index:float = self.Securities[self.sentimet_index].Price
58    self.sentiment_history.Add(sentiment_index)
59
60    if not self.sentiment_history.IsReady: return
61    sorted_by_ret:List = sorted([x for x in self.data.items() if x[1].IsReady()], key=lambda x: x[1].Return(), reverse = True)
62    if len(sorted_by_ret) < self.quantile:
63        self.Liquidate()
64        return
65    quantile:int = int(len(self.symbols) / self.quantile)
66    winners:List[str] = [x[0] for x in sorted_by_ret[:quantile]]
67    losers:List[str] = [x[0] for x in sorted_by_ret[-quantile:]]
68    
69    percentile_33:float = np.percentile(list(self.sentiment_history), 0.33)
70    percentile_66:float = np.percentile(list(self.sentiment_history), 0.66)
71    long:List[str] = []
72    short:List[str] = []
73    # When the sentiment level in the previous month is low then only long position in winners is taken. When the sentiment level is high then only short position is taken. When sentiment level is medium then both short and long positions are taken.
74    if sentiment_index < percentile_33:
75        long = winners
76    if sentiment_index > percentile_66:
77        short = losers
78    else:
79        long = winners
80        short = losers
81    # liquidate
82    invested = [x.Key.Value for x in self.Portfolio if x.Value.Invested]
83    for symbol in invested:
84        if symbol not in long + short:
85            self.Liquidate(symbol)
86    # market execution
87    for symbol in long:
88        self.SetHoldings(symbol, 1 / len(long))
89    for symbol in short:
90        self.SetHoldings(symbol, -1 / len(short))
91class SymbolData:
92def __init__(self, symbol:str, lookback:int) -> None:
93self.Symbol:str = symbol
94self.History:RollingWindow = RollingWindow[float](lookback)
95self.Lookback = lookback
96def Update(self, value:float) -> None:
97self.History.Add(value)
98def IsReady(self) -> bool:
99return self.History.IsReady
100
101# Monthly return
102def Return(self) -> float:
103prices:List[float] = list(self.History)
104return (prices[0] / prices[self.Lookback - 1]) - 1
105
106class SentimentData(PythonData):
107def GetSource(self, config, date, isLiveMode):
108return SubscriptionDataSource("data.quantpedia.com/backtesting_data/index/baker_wurgler_sentiment_index.csv", SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)
109def Reader(self, config, line, date, isLiveMode):
110index = SentimentData()
111index.Symbol = config.Symbol
112
113try:
114    data = line.split(';')
115    index.Time = datetime.strptime(data[0], "%Y%m") + relativedelta(months=1)
116    index.Value = data[1]
117except:
118    return None
119    
120return index