Overall Statistics |
Total Trades 2865 Average Win 0.82% Average Loss -0.15% Compounding Annual Return 8.891% Drawdown 10.700% Expectancy 0.325 Net Profit 86.303% Sharpe Ratio 0.84 Loss Rate 79% Win Rate 21% Profit-Loss Ratio 5.31 Alpha 0.069 Beta 0.017 Annual Standard Deviation 0.084 Annual Variance 0.007 Information Ratio -0.272 Tracking Error 0.161 Treynor Ratio 4.096 Total Fees $14469.59 |
from QuantConnect.Data.Custom.Tiingo import * class TiingoNLPDemonstration(QCAlgorithm): def Initialize(self): # Predefine a dictionary of words with scores to scan for in the description # of the Tiingo news article self.wordSentiment = { "bad": -0.5, "good": 0.5, "negative": -0.5, "great": 0.5, "growth": 0.5, "fail": -0.5, "failed": -0.5, "success": 0.5, "nailed": 0.5, "beat": 0.5, "missed": -0.5, "profitable": 0.5, "beneficial": 0.5, "right": 0.5, "positive": 0.5, "large":0.5, "attractive": 0.5, "sound": 0.5, "excellent": 0.5, "wrong": -0.5, "unproductive": -0.5, "lose": -0.5, "missing": -0.5, "mishandled": -0.5, "un_lucrative": -0.5, "up": 0.5, "down": -0.5, "unproductive": -0.5, "poor": -0.5, "wrong": -0.5, "worthwhile": 0.5, "lucrative": 0.5, "solid": 0.5, "scandal": -0.5, "hack": -0.5 } self.SetStartDate(2009, 6, 10) self.SetEndDate(2019, 10, 3) self.SetCash(100000) aapl = self.AddEquity("AAPL", Resolution.Daily).Symbol self.aaplCustom = self.AddData(TiingoNews, aapl).Symbol self.AddEquity("VGT", Resolution.Daily) def OnData(self, data): # Confirm that the data is in the collection if not data.ContainsKey(self.aaplCustom): return # Gets the data from the slice article = data[self.aaplCustom] # Article descriptions come in all caps. Lower and split by word descriptionWords = article.Description.lower().split(" ") # Take the intersection of predefined words and the words in the # description to get a list of matching words intersection = set(self.wordSentiment.keys()).intersection(descriptionWords) # Get the sum of the article's sentiment, and go long or short # depending if it's a positive or negative description sentiment = sum([self.wordSentiment[i] for i in intersection]) if sentiment >= 0: self.SetHoldings("VGT", sentiment)