Overall Statistics Total Trades 451 Average Win 0.41% Average Loss -0.30% Compounding Annual Return -1.078% Drawdown 11.800% Expectancy -0.087 Net Profit -5.320% Sharpe Ratio -0.209 Loss Rate 62% Win Rate 38% Profit-Loss Ratio 1.39 Alpha -0.006 Beta -0.009 Annual Standard Deviation 0.034 Annual Variance 0.001 Information Ratio -0.716 Tracking Error 0.121 Treynor Ratio 0.823 Total Fees \$1079.77
from QuantConnect.Data.Custom.Tiingo import *
from datetime import datetime, timedelta
import numpy as np

class CompetitionExampleAlgorithm(QCAlgorithm):

def Initialize(self):

self.SetStartDate(2014, 10, 1)
self.SetCash(100000)

## Set Universe Selection Model
self.SetUniverseSelection(TechnologyETFUniverse())

## Set Alpha Model
self.SetAlpha(NewsSentimentAlphaModel())

## Set Portfolio Construction Model
self.SetPortfolioConstruction(InsightWeightingPortfolioConstructionModel())

## Set Execution Model
self.SetExecution(ImmediateExecutionModel())

## Set Risk Management Model
self.SetRiskManagement(NullRiskManagementModel())

class NewsSentimentAlphaModel:

def __init__(self):

# the sample pool of word sentiments
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
}
self.day = -1
self.custom = []

def Update(self, algorithm, data):
insights = []

# Run the model daily
if algorithm.Time.day == self.day:
return insights

self.day = algorithm.Time.day

weights = {}

# Fetch the wordSentiment data for the active securities and trade on any
for security in self.custom:

if not data.ContainsKey(security):
continue

news = data[security]

descriptionWords = news.Description.lower().split(" ")
# Get the intersection words between sentiment sample pool and news description
intersection = set(self.wordSentiment.keys()).intersection(descriptionWords)
# Calculate the score sum of word sentiment
sentimentSum = sum([self.wordSentiment[i] for i in intersection])

if sentimentSum > 0:
weights[security.Underlying] = sentimentSum

# Sort securities by sentiment ranking,
count = min(10, len(weights))
if count == 0:
return insights

# Order the sentiment by value and select the top 10
sortedbyValue = sorted(weights.items(), key = lambda x:x[1], reverse=True)
selected = {kv[0]:kv[1] for kv in sortedbyValue[:count]}

# Populate the list of insights with the selected data where the sentiment sign is the direction and its value is the weight
closeTimeLocal = Expiry.EndOfDay(algorithm.Time)
for symbol, weight in selected.items():
insights.append(Insight.Price(symbol, closeTimeLocal, InsightDirection.Up, None, None, None, abs(weight)))

return insights

def OnSecuritiesChanged(self, algorithm, changes):
self.custom.append(algorithm.AddData(TiingoNews, security.Symbol).Symbol)