Overall Statistics Total Trades289Average Win4.21%Average Loss-1.51%Compounding Annual Return23.393%Drawdown23.100%Expectancy1.365Net Profit1422.383%Sharpe Ratio1.539Probabilistic Sharpe Ratio90.950%Loss Rate38%Win Rate62%Profit-Loss Ratio2.78Alpha0.238Beta0.108Annual Standard Deviation0.163Annual Variance0.027Information Ratio0.537Tracking Error0.245Treynor Ratio2.327Total Fees\$7641.89
```"""
Based on 'In & Out' strategy by Peter Guenther 4 Oct 2020
expanded/inspired by Tentor Testivis, Dan Whitnable (Quantopian), Vladimir, and Thomas Chang.

https://www.quantopian.com/posts/new-strategy-in-and-out
https://www.quantconnect.com/forum/discussion/9597/the-in-amp-out-strategy-continued-from-quantopian/p1
"""

# Import packages
import numpy as np
import pandas as pd
import scipy as sc

class InOut(QCAlgorithm):

def Initialize(self):

self.SetStartDate(2008, 1, 1)  # Set Start Date
self.SetCash(100000)  # Set Strategy Cash
self.UniverseSettings.Resolution = Resolution.Daily
res = Resolution.Hour

# Feed-in constants
self.INI_WAIT_DAYS = 15  # out for 3 trading weeks

# Holdings
### 'Out' holdings and weights
self.BNDselect = self.BND1
self.HLD_OUT = {self.BNDselect: 1}

### 'In' holdings and weights (static stock selection strategy)
self.STKselect = self.STK1
self.HLD_IN = {self.STKselect: 1}

# Market and list of signals based on ETFs
self.MRKT = self.AddEquity('SPY', res).Symbol  # market
self.PRDC = self.AddEquity('XLI', res).Symbol  # production (industrials)
self.METL = self.AddEquity('DBB', res).Symbol  # input prices (metals)
self.NRES = self.AddEquity('IGE', res).Symbol  # input prices (natural res)
#self.DEBT = self.AddEquity('SHY', res).Symbol  # cost of debt (bond yield)
self.USDX = self.AddEquity('UUP', res).Symbol  # safe haven (USD)
self.GOLD = self.AddEquity('GLD', res).Symbol  # gold
self.SLVA = self.AddEquity('SLV', res).Symbol  # vs silver
self.UTIL = self.AddEquity('XLU', res).Symbol  # utilities
self.INDU = self.PRDC  # vs industrials
self.SHCU = self.AddEquity('FXF', res).Symbol  # safe haven currency (CHF)
self.RICU = self.AddEquity('FXA', res).Symbol  # vs risk currency (AUD)

self.FORPAIRS = [self.GOLD, self.SLVA, self.UTIL, self.SHCU, self.RICU]
self.SIGNALS = [self.PRDC, self.METL, self.NRES, self.USDX] #, self.DEBT]

# Initialize variables
## 'In'/'out' indicator
self.be_in = 999 #initially, set to an arbitrary value different from 1 (in) and 0 (out)
## Day count variables
self.dcount = 0  # count of total days since start
self.outday = 0  # dcount when self.be_in=0
## Flexi wait days
self.returnWindowLength = 100

# set a warm-up period to initialize the indicator
self.SetWarmUp(timedelta(350))

self.Schedule.On(
self.DateRules.EveryDay(),
self.TimeRules.AfterMarketOpen('SPY', 1),
self.calculate_signal
)

self.Schedule.On(
self.DateRules.EveryDay(),
self.TimeRules.AfterMarketOpen('SPY', 120),
self.rebalance_when_out_of_the_market
)

self.Schedule.On(
self.DateRules.WeekEnd(),
self.TimeRules.AfterMarketOpen('SPY', 121),
self.rebalance_when_in_the_market
)

def Returns(self, symbol, period):
closingBars = self.History(symbol, TimeSpan.FromDays(period),Resolution.Daily).close
return (closingBars[-1] - closingBars[0])/closingBars[-1]

def calculate_signal(self):
# Returns sample to detect extreme observations
hist = self.History(
self.SIGNALS + [self.MRKT] + self.FORPAIRS, 252, Resolution.Daily)['close'].unstack(level=0).dropna()
hist_shift = hist.apply(lambda x: (x.shift(65) + x.shift(64) + x.shift(63) + x.shift(62) + x.shift(
61) + x.shift(60) + x.shift(59) + x.shift(58) + x.shift(57) + x.shift(56) + x.shift(55)) / 11)

returns_sample = (hist / hist_shift - 1)
# Reverse code USDX: sort largest changes to bottom
returns_sample[self.USDX] = returns_sample[self.USDX] * (-1)
# For pairs, take returns differential, reverse coded
returns_sample['G_S'] = -(returns_sample[self.GOLD] - returns_sample[self.SLVA])
returns_sample['U_I'] = -(returns_sample[self.UTIL] - returns_sample[self.INDU])
returns_sample['C_A'] = -(returns_sample[self.SHCU] - returns_sample[self.RICU])
self.pairlist = ['G_S', 'U_I', 'C_A']

# Extreme observations; statist. significance = 1%
pctl_b = np.nanpercentile(returns_sample, 1, axis=0)
extreme_b = returns_sample.iloc[-1] < pctl_b

# Determine waitdays empirically via safe haven excess returns, 50% decay
self.INI_WAIT_DAYS * max(1,
np.where((returns_sample[self.GOLD].iloc[-1]>0) & (returns_sample[self.SLVA].iloc[-1]<0) & (returns_sample[self.SLVA].iloc[-2]>0), self.INI_WAIT_DAYS, 1),
np.where((returns_sample[self.UTIL].iloc[-1]>0) & (returns_sample[self.INDU].iloc[-1]<0) & (returns_sample[self.INDU].iloc[-2]>0), self.INI_WAIT_DAYS, 1),
np.where((returns_sample[self.SHCU].iloc[-1]>0) & (returns_sample[self.RICU].iloc[-1]<0) & (returns_sample[self.RICU].iloc[-2]>0), self.INI_WAIT_DAYS, 1)
))
)

# Determine whether 'in' or 'out' of the market
if (extreme_b[self.SIGNALS + self.pairlist]).any():
self.be_in = False
self.outday = self.dcount
if self.dcount >= self.outday + adjwaitdays:
self.be_in = True
self.dcount += 1

self.Plot("In Out", "in_market", int(self.be_in))
self.Plot("In Out", "num_out_signals", extreme_b[self.SIGNALS + self.pairlist].sum())

# select momentumin asset
if self.Returns(self.BND1,self.returnWindowLength) < self.Returns(self.BND2,self.returnWindowLength):
self.BNDselect =  self.BND2
elif self.Returns(self.BND1,self.returnWindowLength) > self.Returns(self.BND2,self.returnWindowLength):
self.BNDselect =  self.BND1

# select momentumin asset
if self.Returns(self.STK1,self.returnWindowLength) < self.Returns(self.STK2,self.returnWindowLength):
self.STKselect =  self.STK2
elif self.Returns(self.STK1,self.returnWindowLength) > self.Returns(self.STK2,self.returnWindowLength):
self.STKselect =  self.STK1

self.HLD_IN = {self.STKselect: 1}
self.HLD_OUT = {self.BNDselect: 1}

def rebalance_when_out_of_the_market(self):
# Swap to 'out' assets if applicable
if not self.be_in:
# Only trade when changing from in to out

def rebalance_when_in_the_market(self):
# Swap to 'in' assets if applicable
if self.be_in:
# Only trade when changing from out to in
self.Log(f"TotalPortfolioValue: {self.Portfolio.TotalPortfolioValue}, TotalMarginUsed: {self.Portfolio.TotalMarginUsed}, MarginRemaining: {self.Portfolio.MarginRemaining}, Cash:  {self.Portfolio.Cash}")
for key in sorted(self.Portfolio.keys()):
if self.Portfolio[key].Quantity > 0.0:
self.Log(f"Symbol/Qty: {key} / {self.Portfolio[key].Quantity}, Avg: {self.Portfolio[key].AveragePrice}, Curr: { self.Portfolio[key].Price}, Profit(\$): {self.Portfolio[key].UnrealizedProfit}")

if self.Portfolio.Invested:
for symbol in self.Portfolio.Keys:
if symbol not in weight_by_sec:
self.Liquidate(symbol)

for sec, weight in weight_by_sec.items():
# Check that we have data in the algorithm to process a trade
if not self.CurrentSlice.ContainsKey(sec) or self.CurrentSlice[sec] is None:
continue

cond1 = weight == 0 and self.Portfolio[sec].IsLong
cond2 = weight > 0 and not self.Portfolio[sec].Invested
if cond1 or cond2:
quantity = self.CalculateOrderQuantity(sec, weight)
if quantity > 0:
elif quantity < 0:
self.Order(sec, quantity)