Overall Statistics
Total Trades
4
Average Win
8.96%
Average Loss
-10.27%
Compounding Annual Return
-2.224%
Drawdown
10.700%
Expectancy
-0.063
Net Profit
-2.222%
Sharpe Ratio
-0.106
Loss Rate
50%
Win Rate
50%
Profit-Loss Ratio
0.87
Alpha
0.149
Beta
-8.168
Annual Standard Deviation
0.131
Annual Variance
0.017
Information Ratio
-0.258
Tracking Error
0.131
Treynor Ratio
0.002
Total Fees
$7.56
 
import pandas as pd
import nltk
# for details of NLTK, please visit https://www.nltk.org/index.html

class NLTKSentimentTradingAlgorithm(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2018, 1, 1)  # Set Start Date
        self.SetEndDate(2019, 1, 1) # Set End Date
        self.SetCash(100000)  # Set Strategy Cash
        
        spy = self.AddEquity("SPY", Resolution.Minute)
        self.text = self.get_text() # Get custom text data for creating trading signals
        self.symbols = [spy.Symbol] # This can be extended to multiple symbols
        
        # for what extra models needed to download, please use code nltk.download()
        nltk.download('punkt')
        self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 30), self.Trade)
    
    def Trade(self):
        current_time = f'{self.Time.year}-{self.Time.month}-{self.Time.day}'
        current_text = self.text.loc[current_time][0]
        words = nltk.word_tokenize(current_text)
        
        # users should decide their own positive and negative words
        positive_word = 'Up'
        negative_word = 'Down'
        
        for holding in self.Portfolio.Values:
            # liquidate if it contains negative words
            if negative_word in words and holding.Invested:
                self.Liquidate(holding.Symbol)
            
            # buy if it contains positive words
            if positive_word in words and not holding.Invested:
                self.SetHoldings(holding.Symbol, 1 / len(self.symbols))
        
    def get_text(self):
        # import custom data
        # Note: dl must be 1, or it will not download automatically
        url = 'https://www.dropbox.com/s/7xgvkypg6uxp6xl/EconomicNews.csv?dl=1'
        data = self.Download(url).split('\n')

        headline = [x.split(',')[1] for x in data][1:]
        date = [x.split(',')[0] for x in data][1:]
        
        # create a pd dataframe with 1st col being date and 2nd col being headline (content of the text)
        df = pd.DataFrame(headline, index = date, columns = ['headline'])
        return df