class TradingBot(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2020, 1, 1)
self.SetEndDate(datetime.now() - timedelta(10))
self.SetCash(100000)
# *Data Resolution
self.UniverseSettings.Resolution = Resolution.Minute
# *Universe selection model; runs with the above data resolution
# custom universe selection model class created -- from universe_selection -->UniverseSelectionModel()
self.securities = []
self.CustomUniverseSelectionModel = FactorUniverseSelectionModel(self)
self.AddUniverse(self.CustomUniverseSelectionModel.SelectCoarse,
self.CustomUniverseSelectionModel.SelectFine)
# *Alpha model; A
self.CustomAlphaModel = ValueAlphaModel()
# *Portfolio construction model; B
self.CustomPortfolioConstructionModel = OptimisationPortfolioConstructionModel(
turnover=0.05, max_wt=0.05, longshort=True)
#Eexecution model; C
self.CustomExecution = Execution(liq_tol=0.005)
# *Add SPY for trading days data; a
self.AddEquity('SPY', Resolution.Daily)
# *Scheduling rebalancing; b ; we take the a daily resloution and at 2 oclock we execute a rebalance
self.Schedule.On(self.DateRules.EveryDay('SPY'),
self.TimeRules.At(13, 0),
Action(self.RebalancePortfolio))
# Init charting
InitCharts(self)
# Schedule charting
self.Schedule.On(self.DateRules.Every(DayOfWeek.Friday),
self.TimeRules.BeforeMarketClose('SPY', 0),
Action(self.PlotCharts))
def OnData(self, data):
pass
# this controls the A B C ; we chose when we rebalance; we generate our alpha scores ;we pass the alpha scores into our portfolio construction
#next we execute orders based on our portfolio construction
def RebalancePortfolio(self):
alpha_df = self.CustomAlphaModel.GenerateAlphaScores(
self, self.securities)
portfolio = self.CustomPortfolioConstructionModel.GenerateOptimalPortfolio(
self, alpha_df)
self.CustomExecution.ExecutePortfolio(self, portfolio)
def PlotCharts(self):
PlotPerformanceChart(self)
PlotPosConcentrationChart(self)
PlotStockCountChart(self)
PlotExposureChart(self)
class TradingBot(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2019, 1, 1)
self.SetEndDate(2020, 1, 1)
self.SetCash(100000)
# Data resolution
# By default, assets selected by universe selection are requested with minute resolution data.
# https://www.quantconnect.com/docs/algorithm-reference/universes
self.UniverseSettings.Resolution = Resolution.Minute
self.UniverseSettings.Leverage = 1
# Universe selection model
self.securities = []
self.CustomUniverseSelectionModel = FactorUniverseSelectionModel(self)
self.AddUniverse(self.CustomUniverseSelectionModel.SelectCoarse,
self.CustomUniverseSelectionModel.SelectFine)
# Alpha model
self.CustomAlphaModel = ValueAlphaModel()
# Portfolio construction model
self.CustomPortfolioConstructionModel = OptimisationPortfolioConstructionModel(
turnover=0.05, max_wt=0.05, longshort=True)
# Execution model
self.CustomExecution = Execution(liq_tol=0.005)
# Add SPY for trading days data
self.AddEquity('SPY', Resolution.Daily)
# Schedule rebalancing
self.Schedule.On(self.DateRules.EveryDay('SPY'),
self.TimeRules.At(13, 0),
Action(self.RebalancePortfolio))
# Init charting
InitCharts(self)
# Schedule charting
self.Schedule.On(self.DateRules.Every(DayOfWeek.Friday),
self.TimeRules.BeforeMarketClose('SPY', 0),
Action(self.PlotCharts))
def OnData(self, data):
pass
def RebalancePortfolio(self):
alpha_df = self.CustomAlphaModel.GenerateAlphaScores(
self, self.securities)
portfolio = self.CustomPortfolioConstructionModel.GenerateOptimalPortfolio(
self, alpha_df)
self.CustomExecution.ExecutePortfolio(self, portfolio)
def PlotCharts(self):
PlotPerformanceChart(self)
PlotPosConcentrationChart(self)
PlotStockCountChart(self)
PlotExposureChart(self)
class TradingBot(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2003, 1, 1)
self.SetCash(100000)
# Data resolution
self.UniverseSettings.Resolution = Resolution.Minute
# Universe selection model
self.securities = []
self.CustomUniverseSelectionModel = FactorUniverseSelectionModel(self)
self.AddUniverse(self.CustomUniverseSelectionModel.SelectCoarse,
self.CustomUniverseSelectionModel.SelectFine)
# Alpha model
self.CustomAlphaModel = ValueAlphaModel()
# Portfolio construction model
self.CustomPortfolioConstructionModel = OptimisationPortfolioConstructionModel(
)
# Execution model
self.CustomExecution = Execution()
# Add SPY for trading days data
self.AddEquity('SPY', Resolution.Daily)
# Schedule rebalancing
self.Schedule.On(self.DateRules.EveryDay('SPY'),
self.TimeRules.At(13, 0),
Action(self.RebalancePortfolio))
# Init charting
InitCharts(self)
# Schedule charting
self.Schedule.On(self.DateRules.Every(DayOfWeek.Friday),
self.TimeRules.BeforeMarketClose('SPY', 0),
Action(self.PlotCharts))
def OnData(self, data):
pass
def RebalancePortfolio(self):
alpha_df = self.CustomAlphaModel.GenerateAlphaScores(
self, self.securities)
portfolio = self.CustomPortfolioConstructionModel.GenerateOptimalPortfolio(
self, alpha_df)
self.CustomExecution.ExecutePortfolio(self, portfolio)
def PlotCharts(self):
PlotPerformanceChart(self)
PlotPosConcentrationChart(self)
class StockifySentiment(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2017, 1, 1) # Set Start Date
self.SetEndDate(2020, 5, 20)
self.SetCash(100000) # Set Strategy Cash
# Weighting style - normalise or alpha_max (alpha maximisation w/ optimisation)
self.weighting_style = 'normalise'
# Market neutral
self.mkt_neutral = True
# Audio feature to use
self.audio_feature = 'valence'
# Get data
self.data, self.etf_list, self.etf_country = self.DataSetup()
# Add ETFs
for etf in self.etf_list:
self.AddEquity(etf, Resolution.Minute)
# Portfolio construction model
self.CustomPortfolioConstructionModel = OptimisationPortfolioConstructionModel(
turnover=1,
max_wt=0.2,
longshort=True,
mkt_neutral=self.mkt_neutral)
# Execution model
self.CustomExecution = Execution(liq_tol=0.005)
# Schedule rebalancing
self.Schedule.On(self.DateRules.Every(DayOfWeek.Wednesday),
self.TimeRules.BeforeMarketClose('IVV', 210),
Action(self.RebalancePortfolio))
# Init charting
InitCharts(self)
# Schedule charting
self.Schedule.On(self.DateRules.Every(DayOfWeek.Wednesday),
self.TimeRules.BeforeMarketClose('IVV', 5),
Action(self.PlotCharts))
def OnData(self, data):
pass
def RebalancePortfolio(self):
df = self.data.loc[self.Time -
timedelta(7):self.Time].reset_index().set_index(
'symbol')
if self.weighting_style == 'normalise':
portfolio = normalise(df['alpha_score'], equal_ls=self.mkt_neutral)
elif self.weighting_style == 'alpha_max':
df = df[['alpha_score']]
portfolio = self.CustomPortfolioConstructionModel.GenerateOptimalPortfolio(
self, df)
else:
raise Exception('Invalid weighting style')
self.CustomExecution.ExecutePortfolio(self, portfolio)
def PlotCharts(self):
PlotPerformanceChart(self)
PlotExposureChart(self)
PlotCountryExposureChart(self)
def DataSetup(self):
df = pd.read_csv(
StringIO(
self.Download(
'https://raw.githubusercontent.com/Ollie-Hooper/StockifySentiment/master/data/scores.csv'
)))
data = df[['date', 'country', f's_{self.audio_feature}']].copy()
data['date'] = pd.to_datetime(data['date'])
data.rename(columns={f's_{self.audio_feature}': 'alpha_score'},
inplace=True)
etf_df = pd.read_csv(
StringIO(
self.Download(
'https://raw.githubusercontent.com/Ollie-Hooper/StockifySentiment/master/data/etf.csv'
)))
data = pd.merge(data, etf_df)
data = data.sort_values('date')
data.set_index(['date', 'symbol'], inplace=True)
data = data[['alpha_score']]
return data, etf_df['symbol'].to_list(), etf_df.set_index(
'symbol')['country_name'].to_dict()
