Overall Statistics
Total Trades
0
Average Win
0%
Average Loss
0%
Compounding Annual Return
0%
Drawdown
0%
Expectancy
0
Net Profit
0%
Sharpe Ratio
0
Probabilistic Sharpe Ratio
0%
Loss Rate
0%
Win Rate
0%
Profit-Loss Ratio
0
Alpha
0
Beta
0
Annual Standard Deviation
0
Annual Variance
0
Information Ratio
-6.603
Tracking Error
0.079
Treynor Ratio
0
Total Fees
$0.00
Estimated Strategy Capacity
$0
Lowest Capacity Asset
 
#1. Import Tiingo Data 
from QuantConnect.Data.Custom.Tiingo import *
from datetime import datetime, timedelta
import numpy as np

class TiingoNewsSentimentAlgorithm(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2016, 11, 1)
        self.SetEndDate(2017, 3, 1)  
        
        #2. Add AAPL and NKE symbols to a Manual Universe 
        symbols = [Symbol.Create("AAPL", SecurityType.Equity, Market.USA), 
            Symbol.Create("NKE", SecurityType.Equity, Market.USA)]
        self.SetUniverseSelection(ManualUniverseSelectionModel(symbols))
        
        # 3. Add an instance of the NewsSentimentAlphaModel
        self.SetAlpha(NewsSentimentAlphaModel())
        
        self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel()) 
        self.SetExecution(ImmediateExecutionModel()) 
        self.SetRiskManagement(NullRiskManagementModel())
        
# 4. Create a NewsSentimentAlphaModel class with Update() and OnSecuritiesChanged() methods
class NewsSentimentAlphaModel(AlphaModel):
    def __init__(self): 
        pass
    def Update(self, algorithm, data):
        insights = []
        return insights
    def OnSecuritiesChanged(self, algorithm, changes):
        pass