| 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)