forked from paperswithbacktest/awesome-systematic-trading
-
Notifications
You must be signed in to change notification settings - Fork 0
/
pairs-trading-with-country-etfs.py
182 lines (155 loc) · 9.45 KB
/
pairs-trading-with-country-etfs.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
# https://quantpedia.com/strategies/pairs-trading-with-country-etfs/
#
# The investment universe consists of 22 international ETFs. A normalized cumulative total return index is created for each ETF (dividends
# included), and the starting price during the formation period is set to $1 (price normalization). The selection of pairs is made after
# a 120 day formation period. Pair’s distance for all ETF pairs is calculated as the sum of squared deviations between two normalized
# price series. The top 5 pairs with the smallest distance are used in the subsequent 20 day trading period. The strategy is monitored
# daily, and trade is opened when the divergence between the pairs exceeds 0.5x the historical standard deviation. Investors go long
# on the undervalued ETF and short on the overvalued ETF. The trade is exited if a pair converges or after 20 days (if the pair does
# not converge within the next 20 business days). Pairs are weighted equally, and the portfolio is rebalanced on a daily basis.
#
# QC Implementation:
import numpy as np
from AlgorithmImports import *
import itertools as it
class PairsTradingwithCountryETFs(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2000, 1, 1)
self.SetCash(100000)
self.symbols = [
"EWA", # iShares MSCI Australia Index ETF
"EWO", # iShares MSCI Austria Investable Mkt Index ETF
"EWK", # iShares MSCI Belgium Investable Market Index ETF
"EWZ", # iShares MSCI Brazil Index ETF
"EWC", # iShares MSCI Canada Index ETF
"FXI", # iShares China Large-Cap ETF
"EWQ", # iShares MSCI France Index ETF
"EWG", # iShares MSCI Germany ETF
"EWH", # iShares MSCI Hong Kong Index ETF
"EWI", # iShares MSCI Italy Index ETF
"EWJ", # iShares MSCI Japan Index ETF
"EWM", # iShares MSCI Malaysia Index ETF
"EWW", # iShares MSCI Mexico Inv. Mt. Idx
"EWN", # iShares MSCI Netherlands Index ETF
"EWS", # iShares MSCI Singapore Index ETF
"EZA", # iShares MSCI South Africe Index ETF
"EWY", # iShares MSCI South Korea ETF
"EWP", # iShares MSCI Spain Index ETF
"EWD", # iShares MSCI Sweden Index ETF
"EWL", # iShares MSCI Switzerland Index ETF
"EWT", # iShares MSCI Taiwan Index ETF
"THD", # iShares MSCI Thailand Index ETF
"EWU", # iShares MSCI United Kingdom Index ETF
"SPY", # SPDR S&P 500 ETF
]
self.period = 120
self.max_traded_pairs = 5 # The top 5 pairs with the smallest distance are used.
self.history_price = {}
self.traded_pairs = []
self.traded_quantity = {}
for symbol in self.symbols:
data = self.AddEquity(symbol, Resolution.Daily)
data.SetFeeModel(CustomFeeModel())
data.SetLeverage(5)
symbol_obj = data.Symbol
if symbol not in self.history_price:
self.history_price[symbol] = RollingWindow[float](self.period)
history = self.History(self.Symbol(symbol), self.period, Resolution.Daily)
if history.empty:
self.Log(f"Note enough data for {symbol} yet")
else:
closes = history.loc[symbol].close[:-1]
for time, close in closes.iteritems():
self.history_price[symbol].Add(close)
self.sorted_pairs = []
self.symbol_pairs = list(it.combinations(self.symbols, 2))
self.days = 20
def OnData(self, data):
# Update the price series everyday
for symbol in self.history_price:
symbol_obj = self.Symbol(symbol)
if symbol_obj in data and data[symbol_obj]:
price = data[symbol_obj].Value
self.history_price[symbol].Add(price)
# Start of trading period.
if self.days == 20:
# minimize the sum of squared deviations
distances = {}
for pair in self.symbol_pairs:
if self.history_price[pair[0]].IsReady and self.history_price[pair[1]].IsReady:
if (self.Time.date() - self.Securities[pair[0]].GetLastData().Time.date()).days <= 3 and (self.Time.date() - self.Securities[pair[1]].GetLastData().Time.date()).days <= 3:
distances[pair] = self.Distance([x for x in self.history_price[pair[0]]], [x for x in self.history_price[pair[1]]])
if len(distances) != 0:
self.sorted_pairs = sorted(distances.items(), key = lambda x: x[1])[:self.max_traded_pairs]
self.sorted_pairs = [x[0] for x in self.sorted_pairs]
self.Liquidate()
self.traded_pairs.clear()
self.traded_quantity.clear()
self.days = 0
self.days += 1
if self.sorted_pairs is None: return
pairs_to_remove = []
for pair in self.sorted_pairs:
# Calculate the spread of two price series.
price_a = [x for x in self.history_price[pair[0]]]
price_b = [x for x in self.history_price[pair[1]]]
norm_a = np.array(price_a) / price_a[-1]
norm_b = np.array(price_b) / price_b[-1]
spread = norm_a - norm_b
mean = np.mean(spread)
std = np.std(spread)
actual_spread = spread[0]
# Long-short position is opened when pair prices have diverged by two standard deviations.
traded_portfolio_value = self.Portfolio.TotalPortfolioValue / self.max_traded_pairs
if actual_spread > mean + 0.5*std or actual_spread < mean - 0.5*std:
if pair not in self.traded_pairs:
# open new position for pair, if there's place for it.
if len(self.traded_pairs) < self.max_traded_pairs:
symbol_a = pair[0]
symbol_b = pair[1]
a_price_norm = norm_a[0]
b_price_norm = norm_b[0]
a_price = price_a[0]
b_price = price_b[0]
# a etf's price > b etf's price
if a_price_norm > b_price_norm:
long_q = traded_portfolio_value / b_price # long b etf
short_q = -traded_portfolio_value / a_price # short a etf
if self.Securities.ContainsKey(symbol_a) and self.Securities.ContainsKey(symbol_b) and \
self.Securities[symbol_a].Price != 0 and self.Securities[symbol_a].IsTradable and \
self.Securities[symbol_b].Price != 0 and self.Securities[symbol_b].IsTradable:
self.MarketOrder(symbol_a, short_q)
self.MarketOrder(symbol_b, long_q)
self.traded_quantity[pair] = (short_q, long_q)
self.traded_pairs.append(pair)
# b etf's price > a etf's price
else:
long_q = traded_portfolio_value / a_price # long a etf
short_q = -traded_portfolio_value / b_price # short b etf
if self.Securities.ContainsKey(symbol_a) and self.Securities.ContainsKey(symbol_b) and \
self.Securities[symbol_a].Price != 0 and self.Securities[symbol_a].IsTradable and \
self.Securities[symbol_b].Price != 0 and self.Securities[symbol_b].IsTradable:
self.MarketOrder(symbol_a, long_q)
self.MarketOrder(symbol_b, short_q)
self.traded_quantity[pair] = (long_q, short_q)
self.traded_pairs.append(pair)
# The position is closed when prices revert back.
else:
if pair in self.traded_pairs and pair in self.traded_quantity:
# make opposite order to opened position
self.MarketOrder(pair[0], -self.traded_quantity[pair][0])
self.MarketOrder(pair[1], -self.traded_quantity[pair][1])
pairs_to_remove.append(pair)
for pair in pairs_to_remove:
self.traded_pairs.remove(pair)
del self.traded_quantity[pair]
def Distance(self, price_a, price_b):
# Calculate the sum of squared deviations between two normalized price series.
norm_a = np.array(price_a) / price_a[-1]
norm_b = np.array(price_b) / price_b[-1]
return sum((norm_a - norm_b)**2)
# Custom fee model.
class CustomFeeModel(FeeModel):
def GetOrderFee(self, parameters):
fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))