Popular Models
Sentiment Analysis
Introduction
This page explains how to use Hugging Face sentiment analysis models in LEAN trading algorithms. These models classify financial text into sentiment categories like positive, negative, and neutral. The following models are available:
- ahmedrachid/FinancialBERT-Sentiment-Analysis — A BERT model fine-tuned on financial data for sentiment analysis, classifying text as positive, negative, or neutral.
- bardsai/finance-sentiment-fr-base — A French-language financial sentiment classification model.
- cardiffnlp/twitter-roberta-base-sentiment-latest — A RoBERTa model fine-tuned on tweets for sentiment analysis.
- mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis — A DistilRoBERTa model fine-tuned on financial news for sentiment classification.
- nickmuchi/deberta-v3-base-finetuned-finance-text-classification — A DeBERTa model fine-tuned for financial text classification.
- nickmuchi/distilroberta-finetuned-financial-text-classification — A DistilRoBERTa model fine-tuned for financial text classification.
- nickmuchi/sec-bert-finetuned-finance-classification — A SEC-BERT model fine-tuned for financial document classification.
- StephanAkkerman/FinTwitBERT-sentiment — A BERT model fine-tuned on financial tweets for sentiment classification.
All of these models accept text input and return classification labels with confidence scores. You can use them with the Hugging Face transformers library to analyze the sentiment of financial news and social media posts, then use the results to inform trading decisions.
Examples
The following examples demonstrate usage of Hugging Face sentiment analysis models.
Example 1: News Sentiment Trading
The following algorithm selects the most volatile asset at the beginning of each month. It gets the Tiingo News articles that were released for the asset over the previous 10 days and then feeds them into a sentiment analysis model. It aggregates the sentiment scores of all the news releases. If the aggregated sentiment is positive, it enters a long position for the month. If it's negative, it enters a short position. You can replace the model name with any of the sentiment analysis models listed on the introduction page.
from transformers import pipeline, set_seed
class SentimentAnalysisModelAlgorithm(QCAlgorithm):
def initialize(self):
self.set_start_date(2024, 9, 1)
self.set_end_date(2024, 12, 31)
self.set_cash(100_000)
self.universe_settings.resolution = Resolution.DAILY
self.universe_settings.schedule.on(self.date_rules.month_start("SPY"))
self._universe = self.add_universe(
lambda fundamental: [
self.history(
[f.symbol for f in sorted(
fundamental, key=lambda f: f.dollar_volume
)[-10:]],
timedelta(365), Resolution.DAILY
)['close'].unstack(0).pct_change().iloc[1:].std().idxmax()
]
)
set_seed(1, True)
# Load the sentiment analysis pipeline.
# Replace the model name with any supported sentiment model.
self._sentiment_pipeline = pipeline(
"text-classification",
model="mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis"
)
self._last_rebalance_time = datetime.min
self.set_warm_up(30, Resolution.DAILY)
def on_warmup_finished(self):
self._trade()
self.schedule.on(
self.date_rules.month_start("SPY", 1),
self.time_rules.midnight,
self._trade
)
def on_securities_changed(self, changes):
for security in changes.removed_securities:
self.remove_security(security.dataset_symbol)
for security in changes.added_securities:
security.dataset_symbol = self.add_data(
TiingoNews, security.symbol
).symbol
def _trade(self):
if (self.is_warming_up or
self.time - self._last_rebalance_time < timedelta(14)):
return
# Get the target security.
security = self.securities[list(self._universe.selected)[0]]
# Get the latest news articles.
articles = self.history[TiingoNews](
security.dataset_symbol, 10, Resolution.DAILY
)
article_text = [
article.description for article in articles
if article.description
]
if not article_text:
return
# Run sentiment analysis on each article.
# Truncate long articles to the model's max length.
results = self._sentiment_pipeline(
article_text, truncation=True, max_length=512
)
# Aggregate sentiment scores.
positive_score = 0
negative_score = 0
for result in results:
label = result['label'].lower()
score = result['score']
if 'pos' in label:
positive_score += score
elif 'neg' in label:
negative_score += score
self.plot("Sentiment", "Positive", positive_score)
self.plot("Sentiment", "Negative", negative_score)
# Rebalance based on sentiment.
weight = 1 if positive_score > negative_score else -0.25
self.set_holdings(
security.symbol, weight,
liquidate_existing_holdings=True
)
self._last_rebalance_time = self.time
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