| Overall Statistics |
|
Total Trades
75
Average Win
7.71%
Average Loss
-3.87%
Compounding Annual Return
7.938%
Drawdown
34.400%
Expectancy
1.102
Net Profit
295.930%
Sharpe Ratio
0.569
Probabilistic Sharpe Ratio
1.277%
Loss Rate
30%
Win Rate
70%
Profit-Loss Ratio
1.99
Alpha
0.025
Beta
0.414
Annual Standard Deviation
0.106
Annual Variance
0.011
Information Ratio
-0.198
Tracking Error
0.124
Treynor Ratio
0.145
Total Fees
$694.08
Estimated Strategy Capacity
$16000000.00
Lowest Capacity Asset
AGG SSC0EI5J2F6T
|
# https://quantpedia.com/strategies/paired-switching/
#
# This strategy is very flexible. Investors could use stocks, funds, or ETFs as an investment vehicle. We show simple trading rules for a sample strategy
# from the source research paper. The investor uses two Vanguard funds as his investment vehicles – one equity fund (VFINX) and one government bond
# fund (VUSTX). These two funds have a negative correlation as they are proxies for two negatively correlated asset classes. The investor looks at the
# performance of the two funds over the prior quarter and buys the fund that has a higher return during the ranking period. The position is held for one
# quarter (the investment period). At the end of the investment period, the cycle is repeated.
class PairedSwitching(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2004, 1, 1)
self.SetCash(100000)
self.first = self.AddEquity("SPY", Resolution.Daily).Symbol
self.second = self.AddEquity("AGG", Resolution.Daily).Symbol
self.months = 3
self.Schedule.On(self.DateRules.MonthStart(self.first), self.TimeRules.AfterMarketOpen(self.first), self.Rebalance)
def Rebalance(self):
if(self.months % 3 == 0):
if self.Securities.ContainsKey(self.first) and self.Securities.ContainsKey(self.second):
history_call = self.History([self.first, self.second], timedelta(days=90))
if not history_call.empty:
first_bars = history_call.loc[self.first.Value]
last_p1 = first_bars["close"].iloc[0]
second_bars = history_call.loc[self.second.Value]
last_p2 = second_bars["close"].iloc[0]
# Calculates performance of funds over the prior quarter.
first_performance = (float(self.Securities[self.first].Price) - float(last_p1)) / (float(self.Securities[self.first].Price))
second_performance = (float(self.Securities[self.second].Price) - float(last_p2)) / (float(self.Securities[self.second].Price))
# Buys the fund that has the higher return during the period.
if(first_performance > second_performance):
if(self.Securities[self.second].Invested):
self.Liquidate(self.second)
self.SetHoldings(self.first, 1)
else:
if(self.Securities[self.first].Invested):
self.Liquidate(self.first)
self.SetHoldings(self.second, 1)
self.months += 1