| Overall Statistics |
|
Total Trades 686 Average Win 2.10% Average Loss -0.80% Compounding Annual Return 33.957% Drawdown 16.100% Expectancy 1.235 Net Profit 4377.298% Sharpe Ratio 1.678 Probabilistic Sharpe Ratio 96.913% Loss Rate 38% Win Rate 62% Profit-Loss Ratio 2.63 Alpha 0.283 Beta 0.082 Annual Standard Deviation 0.174 Annual Variance 0.03 Information Ratio 0.796 Tracking Error 0.244 Treynor Ratio 3.533 Total Fees $6420.70 |
"""
SEL(stock selection part)
Based on the 'Momentum Strategy with Market Cap and EV/EBITDA' strategy introduced by Jing Wu, 6 Feb 2018
adapted and recoded by Jack Simonson, Goldie Yalamanchi, Vladimir, Peter Guenther, and Leandro Maia
https://www.quantconnect.com/forum/discussion/3377/momentum-strategy-with-market-cap-and-ev-ebitda/p1
https://www.quantconnect.com/forum/discussion/9678/quality-companies-in-an-uptrend/p1
https://www.quantconnect.com/forum/discussion/9632/amazing-returns-superior-stock-selection-strategy-superior-in-amp-out-strategy/p1
I/O(in & out part)
Based on the 'In & Out' strategy introduced by Peter Guenther, 4 Oct 2020
expanded/inspired by Tentor Testivis, Dan Whitnable (Quantopian), Vladimir, Thomas Chang,
Mateusz Pulka, Derek Melchin (QuantConnect), Nathan Swenson, Goldie Yalamanchi, and Sudip Sil
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
code version: In_out_flex_v5_disambiguate_v2
"""
'''from QuantConnect.Data.UniverseSelection import *
import math
import numpy as np
import pandas as pd
import scipy as sp
class EarningsFactorWithMomentum_InOut(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2008, 1, 1) #Set Start Date
#self.SetEndDate(2009, 12, 31) #Set End Date
self.cap = 100000
self.SetCash(self.cap)
res = Resolution.Minute
# Holdings
### 'Out' holdings and weights
self.BND1 = self.AddEquity('TLT', res).Symbol #TLT; TMF for 3xlev
self.HLD_OUT = {self.BND1: 1}
### 'In' holdings and weights (static stock selection strategy)
##### These are determined flexibly via sorting on fundamentals
##### In & Out parameters #####
# Feed-in constants
self.INI_WAIT_DAYS = 15 # out for 3 trading weeks
# 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.INFL = self.AddEquity('RINF', res).Symbol # disambiguate GPLD/SLVA pair via inflaction expectations
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.INFL]
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)
self.be_in_prior = 999
## 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.WDadjvar = self.INI_WAIT_DAYS
# set a warm-up period to initialize the indicator
self.SetWarmUp(timedelta(350))
##### Momentum & fundamentals strategy parameters #####
self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.UniverseCoarseFilter, self.UniverseFundamentalsFilter)
self.num_screener = 100
self.num_stocks = 10
self.formation_days = 70
self.lowmom = False
# rebalance the universe selection once a month
self.rebalance_flag = 0
# make sure to run the universe selection at the start of the algorithm even if it's not the month start
self.flip_flag = 0
self.first_month_trade_flag = 1
self.trade_flag = 0
self.symbols = None
self.Schedule.On(
self.DateRules.EveryDay(),
self.TimeRules.AfterMarketOpen('SPY', 120),
self.rebalance_when_out_of_the_market
)
self.Schedule.On(
self.DateRules.EveryDay(),
self.TimeRules.BeforeMarketClose('SPY', 0),
self.record_vars
)
# Setup daily consolidation
symbols = self.SIGNALS + [self.MRKT] + self.FORPAIRS
for symbol in symbols:
self.consolidator = TradeBarConsolidator(timedelta(days=1))
self.consolidator.DataConsolidated += self.consolidation_handler
self.SubscriptionManager.AddConsolidator(symbol, self.consolidator)
# 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()
# Benchmark = record SPY
self.spy = []
def UniverseCoarseFilter(self, coarse):
#self.Debug(str(self.Time) + "UniverseCoarseFilter: be_in:" + str(self.be_in) + " flip_flag:" + str(self.flip_flag))
if (self.rebalance_flag or self.first_month_trade_flag) and (self.be_in or self.flip_flag):
# drop stocks which have no fundamental data or have too low prices
selected = [x for x in coarse if (x.HasFundamentalData) and (float(x.Price) > 5)]
# rank the stocks by dollar volume
filtered = sorted(selected, key=lambda x: x.DollarVolume, reverse=True)
return [x.Symbol for x in filtered[:200]]
else:
return self.symbols
def UniverseFundamentalsFilter(self, fundamental):
#self.Debug(str(self.Time) + "UniverseFundamentalsFilter: be_in:" + str(self.be_in) + " flip_flag:" + str(self.flip_flag))
if (self.rebalance_flag or self.first_month_trade_flag) and (self.be_in or self.flip_flag):
try:
filtered_fundamental = [x for x in fundamental if (x.ValuationRatios.EVToEBITDA > 0)
and (x.EarningReports.BasicAverageShares.ThreeMonths > 0)
and x.EarningReports.BasicAverageShares.ThreeMonths * x.Price > 2e9]
except:
filtered_fundamental = [x for x in fundamental if (x.ValuationRatios.EVToEBITDA > 0)
and (x.EarningReports.BasicAverageShares.ThreeMonths > 0)]
top = sorted(filtered_fundamental, key = lambda x: x.ValuationRatios.EVToEBITDA, reverse=True)[:self.num_screener]
self.symbols = [x.Symbol for x in top]
self.rebalance_flag = 0
self.first_month_trade_flag = 0
self.trade_flag = 1
return self.symbols
else:
return self.symbols
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/disambiguate 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 rising input prices) = actually 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]])
### GOLD/SLVA differential may increase due to inflation expectations which actually suggest an economic improvement = actually not a negative signal
try:
extreme_b.loc['G_S'] = np.where((extreme_b.loc[['G_S']].any()) & (abovemedian.loc[[self.INFL]].any()), False, extreme_b.loc['G_S'])
except:
pass
# Determine waitdays empirically via safe haven excess returns, 50% decay
self.WDadjvar = int(
max(0.50 * self.WDadjvar,
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)
))
)
adjwaitdays = min(60, self.WDadjvar)
# Determine whether 'in' or 'out' of the market
if (extreme_b[self.SIGNALS + self.pairlist]).any():
self.be_in = False
self.outday = self.dcount
self.trade({**dict.fromkeys(self.Portfolio.Keys, 0), **self.HLD_OUT})
if self.dcount >= self.outday + adjwaitdays:
self.be_in = True
self.dcount += 1
# Only re-shuffle stock allocation when switching from out to in, not in-between
if not self.be_in_prior and self.be_in:
self.flip_flag = 1
self.rebalance()
self.flip_flag = 0
self.Plot("In Out", "in_market", int(self.be_in))
self.Plot("In Out", "num_out_signals", extreme_b[self.SIGNALS + self.pairlist].sum())
self.Plot("Wait Days", "waitdays", adjwaitdays)
self.be_in_prior = self.be_in
def rebalance(self):
self.rebalance_flag = 1
#self.Debug(str(self.Time) + "rebalance: be_in:" + str(self.be_in) + " flip_flag:" + str(self.flip_flag))
if self.symbols is None: return
chosen_df = self.calc_return(self.symbols)
chosen_df = chosen_df.iloc[:self.num_stocks]
for symbol in chosen_df.index:
self.AddEquity(symbol)
weight = 0.99/len(chosen_df)
self.trade({**dict.fromkeys(chosen_df.index.tolist(), weight), **dict.fromkeys(list(dict.fromkeys(set(self.Portfolio.Keys) - set(chosen_df.index))), 0), **dict.fromkeys(self.HLD_OUT, 0)})
def calc_return(self, stocks):
hist = self.History(stocks, self.formation_days, Resolution.Daily)['close'].unstack(level=0)
current = self.History(stocks, 1, Resolution.Minute)['close'].unstack(level=0)
ret = (current.iloc[-1]/hist.iloc[0] - 1).dropna()
ret = pd.DataFrame.from_dict(ret)
ret.columns = ['return']
sort_return = ret.sort_values(by = ['return'], ascending = self.lowmom)
return sort_return
def trade(self, weight_by_sec):
buys = []
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:
buys.append((sec, quantity))
elif quantity < 0:
self.Order(sec, quantity)
for sec, quantity in buys:
self.Order(sec, quantity)
def record_vars(self):
self.spy.append(self.history[self.MRKT].iloc[-1])
spy_perf = self.spy[-1] / self.spy[0] * self.cap
self.Plot('Strategy Equity', 'SPY', spy_perf)
account_leverage = self.Portfolio.TotalHoldingsValue / self.Portfolio.TotalPortfolioValue
self.Plot('Holdings', 'leverage', round(account_leverage, 2))'''"""
SEL(stock selection part)
Based on the 'Momentum Strategy with Market Cap and EV/EBITDA' strategy introduced by Jing Wu, 6 Feb 2018
adapted and recoded by Jack Simonson, Goldie Yalamanchi, Vladimir, Peter Guenther, and Leandro Maia
https://www.quantconnect.com/forum/discussion/3377/momentum-strategy-with-market-cap-and-ev-ebitda/p1
https://www.quantconnect.com/forum/discussion/9678/quality-companies-in-an-uptrend/p1
https://www.quantconnect.com/forum/discussion/9632/amazing-returns-superior-stock-selection-strategy-superior-in-amp-out-strategy/p1
I/O(in & out part)
Based on the 'In & Out' strategy introduced by Peter Guenther, 4 Oct 2020
expanded/inspired by Tentor Testivis, Dan Whitnable (Quantopian), Vladimir, Thomas Chang,
Mateusz Pulka, Derek Melchin (QuantConnect), Nathan Swenson, Goldie Yalamanchi, and Sudip Sil
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
code version: In_out_flex_v5_disambiguate_v2
"""
from QuantConnect.Data.UniverseSelection import *
import math
import numpy as np
import pandas as pd
import scipy as sp
class EarningsFactorWithMomentum_InOut(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2008, 1, 1) #Set Start Date
#self.SetEndDate(2009, 12, 31) #Set End Date
self.cap = 100000
self.SetCash(self.cap)
self.averages = { }
res = Resolution.Minute
# Holdings
### 'Out' holdings and weights
self.BND1 = self.AddEquity('TLT', res).Symbol #TLT; TMF for 3xlev
self.HLD_OUT = {self.BND1: 1}
### 'In' holdings and weights (static stock selection strategy)
##### These are determined flexibly via sorting on fundamentals
##### In & Out parameters #####
# Feed-in constants
self.INI_WAIT_DAYS = 15 # out for 3 trading weeks
# 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.INFL = self.AddEquity('RINF', res).Symbol # disambiguate GPLD/SLVA pair via inflaction expectations
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.INFL]
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)
self.be_in_prior = 999
## 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.WDadjvar = self.INI_WAIT_DAYS
# set a warm-up period to initialize the indicator
self.SetWarmUp(timedelta(350))
##### Momentum & fundamentals strategy parameters #####
self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.UniverseCoarseFilter, self.UniverseFundamentalsFilter)
self.num_screener = 100
self.num_stocks = 10
self.formation_days = 70
self.lowmom = False
# rebalance the universe selection once a month
self.rebalance_flag = 0
# make sure to run the universe selection at the start of the algorithm even if it's not the month start
self.flip_flag = 0
self.first_month_trade_flag = 1
self.trade_flag = 0
self.symbols = None
self.Schedule.On(
self.DateRules.EveryDay(),
self.TimeRules.AfterMarketOpen('SPY', 120),
self.rebalance_when_out_of_the_market
)
self.Schedule.On(
self.DateRules.EveryDay(),
self.TimeRules.BeforeMarketClose('SPY', 0),
self.record_vars
)
# Setup daily consolidation
symbols = self.SIGNALS + [self.MRKT] + self.FORPAIRS
for symbol in symbols:
self.consolidator = TradeBarConsolidator(timedelta(days=1))
self.consolidator.DataConsolidated += self.consolidation_handler
self.SubscriptionManager.AddConsolidator(symbol, self.consolidator)
# 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()
# Benchmark = record SPY
self.spy = []
def UniverseCoarseFilter(self, coarse):
#self.Debug(str(self.Time) + "UniverseCoarseFilter: be_in:" + str(self.be_in) + " flip_flag:" + str(self.flip_flag))
if (self.rebalance_flag or self.first_month_trade_flag) and (self.be_in or self.flip_flag):
# drop stocks which have no fundamental data or have too low prices
selected = [x for x in coarse if (x.HasFundamentalData) and (float(x.Price) > 5)]
# rank the stocks by dollar volume
#filtered = sorted(selected, key=lambda x: x.DollarVolume, reverse=True)
# We are going to use a dictionary to refer the object that will keep the moving averages
for cf in selected:
symbol = cf.Symbol
if cf.Symbol not in self.averages:
self.averages[cf.Symbol] = SymbolDataVolume(cf.Symbol, 21, 5)
# Updates the SymbolData object with current EOD price
avg = self.averages[cf.Symbol]
avg.update(cf.EndTime, cf.AdjustedPrice, cf.DollarVolume)
# Filter the values of the dict: we only want up-trending securities
#values = list(filter(lambda x: x.is_uptrend and sd.volume > sd.sma.Current.Value and sd.volume_ratio > 0, self.averages.values()))
#values = list(filter(lambda sd: sd.smaw.Current.Value > sd.sma.Current.Value and sd.volume_ratio > 0.1, self.averages.values()))
values = list(filter(lambda sd: sd.smaw.Current.Value > 0, self.averages.values()))
values.sort(key=lambda x: x.smaw.Current.Value, reverse=True)
# we need to return only the symbol objects
return [ x.symbol for x in values[:200] ]
#return [x.Symbol for x in filtered[:200]]
else:
return self.symbols
def UniverseFundamentalsFilter(self, fundamental):
#self.Debug(str(self.Time) + "UniverseFundamentalsFilter: be_in:" + str(self.be_in) + " flip_flag:" + str(self.flip_flag))
if (self.rebalance_flag or self.first_month_trade_flag) and (self.be_in or self.flip_flag):
try:
filtered_fundamental = [x for x in fundamental if (x.ValuationRatios.EVToEBITDA > 0)
and (x.EarningReports.BasicAverageShares.ThreeMonths > 0)
and x.EarningReports.BasicAverageShares.ThreeMonths * x.Price > 2e9]
except:
filtered_fundamental = [x for x in fundamental if (x.ValuationRatios.EVToEBITDA > 0)
and (x.EarningReports.BasicAverageShares.ThreeMonths > 0)]
top = sorted(filtered_fundamental, key = lambda x: x.ValuationRatios.EVToEBITDA, reverse=True)[:self.num_screener]
self.symbols = [x.Symbol for x in top]
self.rebalance_flag = 0
self.first_month_trade_flag = 0
self.trade_flag = 1
return self.symbols
else:
return self.symbols
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/disambiguate 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 rising input prices) = actually 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]])
### GOLD/SLVA differential may increase due to inflation expectations which actually suggest an economic improvement = actually not a negative signal
try:
extreme_b.loc['G_S'] = np.where((extreme_b.loc[['G_S']].any()) & (abovemedian.loc[[self.INFL]].any()), False, extreme_b.loc['G_S'])
except:
pass
# Determine waitdays empirically via safe haven excess returns, 50% decay
self.WDadjvar = int(
max(0.50 * self.WDadjvar,
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)
))
)
adjwaitdays = min(60, self.WDadjvar)
# Determine whether 'in' or 'out' of the market
if (extreme_b[self.SIGNALS + self.pairlist]).any():
self.be_in = False
self.outday = self.dcount
self.trade({**dict.fromkeys(self.Portfolio.Keys, 0), **self.HLD_OUT})
if self.dcount >= self.outday + adjwaitdays:
self.be_in = True
self.dcount += 1
# Only re-shuffle stock allocation when switching from out to in, not in-between
if not self.be_in_prior and self.be_in:
self.flip_flag = 1
self.rebalance()
self.flip_flag = 0
self.Plot("In Out", "in_market", int(self.be_in))
self.Plot("In Out", "num_out_signals", extreme_b[self.SIGNALS + self.pairlist].sum())
self.Plot("Wait Days", "waitdays", adjwaitdays)
self.be_in_prior = self.be_in
def rebalance(self):
self.rebalance_flag = 1
#self.Debug(str(self.Time) + "rebalance: be_in:" + str(self.be_in) + " flip_flag:" + str(self.flip_flag))
if self.symbols is None: return
chosen_df = self.calc_return(self.symbols)
chosen_df = chosen_df.iloc[:self.num_stocks]
for symbol in chosen_df.index:
self.AddEquity(symbol)
weight = 0.99/len(chosen_df)
self.trade({**dict.fromkeys(chosen_df.index.tolist(), weight), **dict.fromkeys(list(dict.fromkeys(set(self.Portfolio.Keys) - set(chosen_df.index))), 0), **dict.fromkeys(self.HLD_OUT, 0)})
def calc_return(self, stocks):
hist = self.History(stocks, self.formation_days, Resolution.Daily)['close'].unstack(level=0)
current = self.History(stocks, 1, Resolution.Minute)['close'].unstack(level=0)
ret = (current.iloc[-1]/hist.iloc[0] - 1).dropna()
ret = pd.DataFrame.from_dict(ret)
ret.columns = ['return']
sort_return = ret.sort_values(by = ['return'], ascending = self.lowmom)
return sort_return
def trade(self, weight_by_sec):
buys = []
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:
buys.append((sec, quantity))
elif quantity < 0:
self.Order(sec, quantity)
for sec, quantity in buys:
self.Order(sec, quantity)
def record_vars(self):
self.spy.append(self.history[self.MRKT].iloc[-1])
spy_perf = self.spy[-1] / self.spy[0] * self.cap
self.Plot('Strategy Equity', 'SPY', spy_perf)
account_leverage = self.Portfolio.TotalHoldingsValue / self.Portfolio.TotalPortfolioValue
self.Plot('Holdings', 'leverage', round(account_leverage, 2))
class SymbolDataVolume(object):
def __init__(self, symbol, period, periodw):
self.symbol = symbol
#self.tolerance = 1.01
self.tolerance = 0.95
self.fast = ExponentialMovingAverage(10)
self.slow = ExponentialMovingAverage(21)
self.is_uptrend = False
self.scale = 0
self.volume = 0
self.volume_ratio = 0
self.volume_ratiow = 0
self.sma = SimpleMovingAverage(period)
self.smaw = SimpleMovingAverage(periodw)
def update(self, time, value, volume):
self.volume = volume
if self.smaw.Update(time, volume):
# get ratio of this volume bar vs previous 10 before it.
if self.smaw.Current.Value != 0:
self.volume_ratiow = volume / self.smaw.Current.Value
if self.sma.Update(time, volume):
# get ratio of this volume bar vs previous 10 before it.
if self.sma.Current.Value != 0:
self.volume_ratio = self.smaw.Current.Value / self.sma.Current.Value
if self.fast.Update(time, value) and self.slow.Update(time, value):
fast = self.fast.Current.Value
slow = self.slow.Current.Value
#self.is_uptrend = fast > slow * self.tolerance
self.is_uptrend = (fast > (slow * self.tolerance)) and (value > (fast * self.tolerance))
if self.is_uptrend:
self.scale = (fast - slow) / ((fast + slow) / 2.0)