Overall Statistics Total Trades235Average Win18.14%Average Loss-8.17%Compounding Annual Return57.098%Drawdown70.600%Expectancy0.652Net Profit13415.466%Sharpe Ratio1.605Probabilistic Sharpe Ratio73.186%Loss Rate49%Win Rate51%Profit-Loss Ratio2.22Alpha0.678Beta0.578Annual Standard Deviation0.481Annual Variance0.231Information Ratio1.284Tracking Error0.476Treynor Ratio1.334Total Fees\$44369.28
"""
Based on 'In & Out' strategy by Peter Guenther 4 Oct 2020
expanded/inspired by Tentor Testivis, Dan Whitnable (Quantopian), Vladimir, Thomas Chang,
Derek Melchin (QuantConnect), Nathan Swenson, and Goldie Yalamanchi.

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(2010, 2, 10)  # Set Start Date
self.SetCash(100000)  # Set Strategy Cash
res = Resolution.Minute
self.highestPrice = {}
self.stopMarketTicket = {}
self.entryPrices = {}

# Percentage offset of trailing stop loss
self.stopLoss = 0.15
self.stopMarketOrderFillTime = datetime.min
# Feed-in constants
self.INI_WAIT_DAYS = 5  # out for 3 trading weeks

# Holdings
### 'Out' holdings and weights
self.BND1 = self.AddEquity('TMF', res).Symbol #TLT; TMF for 3xlev
self.BND2 = self.AddEquity('IEF', res).Symbol #IEF; TYD for 3xlev
self.HLD_OUT = {self.BND1: 1, self.BND2: 0}
### 'In' holdings and weights (static stock selection strategy)
self.STKS = self.AddEquity('TQQQ', res).Symbol #SPY or QQQ; TQQQ for 3xlev
self.HLD_IN = {self.STKS: 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.DEBT, self.USDX]
self.pairlist = ['G_S', 'U_I', 'C_A']

# 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.Schedule.On(
self.DateRules.EveryDay(),
self.TimeRules.AfterMarketOpen('SPY', 5),
self.rebalance_when_out_of_the_market
)

self.Schedule.On(self.DateRules.EveryDay("SPY"),
self.TimeRules.At(10, 00),
self.EveryMarketOpen)

self.Schedule.On(
self.DateRules.WeekEnd(),
self.TimeRules.AfterMarketOpen('SPY', 360),
self.rebalance_when_in_the_market
)

# Setup daily consolidation
symbols = self.SIGNALS + [self.MRKT] + self.FORPAIRS
for symbol in symbols:
self.consolidator.DataConsolidated += self.consolidation_handler

# Warm up history
self.lookback = 252
self.history = self.History(symbols, self.lookback, Resolution.Daily)
if self.history.empty or 'close' not in self.history.columns:
return
self.history = self.history['close'].unstack(level=0).dropna()
self.update_history_shift()

def consolidation_handler(self, sender, consolidated):
self.history.loc[consolidated.EndTime, consolidated.Symbol] = consolidated.Close
self.history = self.history.iloc[-self.lookback:]
self.update_history_shift()

def update_history_shift(self):
self.history_shift = self.history.rolling(11, center=True).mean().shift(60)

def rebalance_when_out_of_the_market(self):
# Returns sample to detect extreme observations
returns_sample = (self.history / self.history_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])

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

# Re-assess double-edged signals
median = np.nanmedian(returns_sample, axis=0)
abovemedian = returns_sample.iloc[-1] > median
### Interest rate expectations (cost of debt) may increase because the economic outlook improves (showing in input prices)  which actually is not a negative signal
extreme_b.loc[[self.DEBT]] = np.where((extreme_b.loc[[self.DEBT]].any()) & (abovemedian[[self.METL, self.NRES]].any()), False, extreme_b.loc[[self.DEBT]])

# 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.be_in = True # for testing; sets the algo to being always in

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

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

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

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)
self.Order(sec, quantity)

def EveryMarketOpen(self):
positions = [sec.Symbol for sec in self.Portfolio.Values if self.Portfolio[sec.Symbol].Invested]
for symbol in positions:
# If no order exists, send stop-loss
if not self.Transactions.GetOpenOrders(symbol):
self.stopMarketTicket[symbol] = self.StopMarketOrder(symbol, -self.Portfolio[symbol].Quantity, (1-self.stopLoss) * self.entryPrices[symbol])

# Check if the asset's price is higher than earlier highestPrice
elif self.Securities[symbol].Close > self.highestPrice[symbol]:
# Save the new high to highestPrice
self.highestPrice[symbol] = self.Securities[symbol].Close
# Update the stop price
updateFields = UpdateOrderFields()
updateFields.StopPrice = self.Securities[symbol].Close * (1-self.stopLoss)
self.stopMarketTicket[symbol].Update(updateFields)

def OnOrderEvent(self, orderEvent):
if orderEvent.Status == OrderStatus.Filled:
self.entryPrices[orderEvent.Symbol] = orderEvent.FillPrice
self.highestPrice[orderEvent.Symbol] = orderEvent.FillPrice