Hey everyone,

"The economy, stupid!" -- originally used as a political phrase in the 90s to explain what determined voter behavior, we're using it here to emphasize the power of macroeconomic data in finding alpha. We've written up an example of how you can incorporate Trading Economics data in your algorithms. The Trading Economics data available covers 28 countries and countless macroeconomic fields -- consumer prices, government bonds, inflation, personal spending, etc.

In this algorithm we use the Energy ETF basket and import three Trading Economics datasets: Natural Gas Stocks Change, API Crude Oil Stock Change, and Gasoline Stocks Change. Finally, we add an Insight Weighting Portfolio Construction Model, and Immediate Execution Model, and a Scheduled Event in which we'll perform our analysis and generate Insights.

def Initialize(self): #1. Required: Five years of backtest history self.SetStartDate(2014, 1, 1) #2. Required: Alpha Streams Models: self.SetBrokerageModel(BrokerageName.AlphaStreams) #3. Required: Significant AUM Capacity self.SetCash(1000000) #4. Required: Benchmark to SPY self.SetBenchmark("SPY") #5. Use InsightWeightingPCM since we will compute the weights self.SetPortfolioConstruction(InsightWeightingPortfolioConstructionModel()) self.SetExecution(ImmediateExecutionModel()) # Add TradingEconomicsCalendar for Energy Data us = TradingEconomics.Calendar.UnitedStates self.nat = self.AddData(TradingEconomicsCalendar, us.NaturalGasStocksChange).Symbol self.oli = self.AddData(TradingEconomicsCalendar, us.ApiCrudeOilStockChange).Symbol self.gas = self.AddData(TradingEconomicsCalendar, us.GasolineStocksChange).Symbol # Energy Basket tickers = ["XLE", "IYE", "VDE", "USO", "XES", "XOP", "UNG", "ICLN", "ERX", "ERY", "SCO", "UCO", "AMJ", "BNO", "AMLP", "OIH", "DGAZ", "UGAZ", "TAN"] # Add Equity ---------------------------------------------- self.symbols = [self.AddEquity(x).Symbol for x in tickers] self.factor = 0 # Emit insights 10 minutes after market open to # try to ensure all price data is from the current day self.Schedule.On(self.DateRules.EveryDay("XLE"), self.TimeRules.AfterMarketOpen("XLE", 10), self.EveryDayAfterMarketOpen)

In OnData, we iterate over the Trading Economics data and compare the forecasted production values against the actual production values for each of our supplementary data sources. If the actual production was worse than the forecast, then we set the weighting factor to our minimum of 0.1. Otherwise, we use 1 - (actual production / forecasted production). If this is greater than 1, then we cap the factor at 1 so we don't try to assign weights that don't sum to 1. The worst-case floor of 0.1 this provides us with some investment but minimizes our exposure.

def OnData(self, data): # Discard updates before 10 to avoid EveryDayAfterMarketOpen running with today's data if self.Time.hour < 10: return # Compute the factor based on the Actual vs Forecast values for kvp in data.Get(TradingEconomicsCalendar): calendar = kvp.Value actual = calendar.Actual # The reference will be the Forecast, but if not available, use the Previous reference = calendar.Forecast if reference is None or reference == 0: reference = calendar.Previous if reference is None or reference == 0: reference = actual # Actual was worse than the reference. # Bad. Reduce all positions to a minimum if actual < reference: self.factor = 0.1 continue self.factor = max(0.1, min(1, 1 - actual / reference))

In the Scheduled Event EveryDayAfterMarketOpen we create weights for each symbol based on the calculated factor.

def EveryDayAfterMarketOpen(self): if self.factor == 0: return # The weight is factor normialized by the number of symbols weight = self.factor / len(self.symbols) self.factor = 0 # Emit Up Price insight self.EmitInsights([ Insight.Price(x, timedelta(15), InsightDirection.Up, None, None, None, weight) for x in self.symbols])

This data is certainly a different beast than conventional price data, but it is also immensely informative. Adding macroeconomic data to inform your algorithm could be a huge source of untapped alpha!