| Overall Statistics |
|
Total Orders 118 Average Win 0.99% Average Loss -0.81% Compounding Annual Return 45.365% Drawdown 10.600% Expectancy 0.237 Start Equity 100000 End Equity 124520.40 Net Profit 24.520% Sharpe Ratio 1.542 Sortino Ratio 1.881 Probabilistic Sharpe Ratio 71.216% Loss Rate 44% Win Rate 56% Profit-Loss Ratio 1.23 Alpha 0.372 Beta -0.7 Annual Standard Deviation 0.171 Annual Variance 0.029 Information Ratio 0.486 Tracking Error 0.227 Treynor Ratio -0.378 Total Fees $682.94 Estimated Strategy Capacity $7200000.00 Lowest Capacity Asset AAPL R735QTJ8XC9X Portfolio Turnover 109.33% |
from AlgorithmImports import *
from QuantConnect.DataSource import *
class TiingoNewsDataAlgorithm(QCAlgorithm):
current_holdings = 0
target_holdings = 0
word_scores = {'good': 1, 'great': 1, 'best': 1, 'growth': 1,
'bad': -1, 'terrible': -1, 'worst': -1, 'loss': -1}
def initialize(self) -> None:
self.set_start_date(2023, 9, 1)
self.set_end_date(2024, 4, 1)
self.set_cash(100000)
# Requesting data
self.aapl = self.add_equity("AAPL", Resolution.MINUTE).symbol
self.tiingo_symbol = self.add_data(TiingoNews, self.aapl).symbol
# Historical data
history = self.history(self.tiingo_symbol, 14, Resolution.DAILY)
self.debug(f"We got {len(history)} items from our history request")
def on_data(self, slice: Slice) -> None:
if slice.contains_key(self.tiingo_symbol):
# Assign a sentiment score to the news article
title_words = slice[self.tiingo_symbol].description.lower()
score = 0
for word, word_score in self.word_scores.items():
if word in title_words:
score += word_score
if score > 0:
self.target_holdings = -1
elif score < 0:
self.target_holdings = 1
# Buy or short sell if the sentiment has changed from our current holdings
if slice.contains_key(self.aapl) and self.current_holdings != self.target_holdings:
self.set_holdings(self.aapl, self.target_holdings)
self.current_holdings = self.target_holdings