Popular Models
Text Generation
Introduction
This page explains how to use Hugging Face text generation models in LEAN trading algorithms. These models generate text given an input prompt, which you can use for tasks like summarizing financial data or generating structured analysis. The following models are available:
- openai-community/gpt2 — The GPT-2 language model by OpenAI, a lightweight model suitable for text generation tasks.
- google/gemma-7b — A 7B parameter model from Google's Gemma family, offering strong text generation capabilities. Requires a GPU node.
- deepseek-ai/DeepSeek-R1-Distill-Llama-70B — A 70B parameter reasoning model distilled from DeepSeek-R1 into a Llama architecture. Requires a GPU node with significant memory.
Text generation models can analyze market context and generate structured outputs. You can prompt them to classify market conditions or extract trading signals from financial text. Note that larger models like Gemma-7B and DeepSeek-70B require GPU nodes with sufficient memory.
Examples
The following examples demonstrate usage of Hugging Face text generation models.
Example 1: GPT-2 Market Condition Classifier
The following algorithm uses GPT-2 to classify market conditions based on recent price data. At the beginning of each month, it calculates trailing returns, volatility, and momentum for the universe of the 5 most liquid assets. It then prompts GPT-2 to complete a structured market analysis template and parses the generated text to determine position sizing.
from transformers import pipeline, set_seed
class GPT2MarketAnalysisAlgorithm(QCAlgorithm):
def initialize(self):
self.set_start_date(2024, 9, 1)
self.set_end_date(2024, 12, 31)
self.set_cash(100_000)
self.settings.min_absolute_portfolio_target_percentage = 0
set_seed(1, True)
# Load the text generation pipeline with GPT-2.
self._generator = pipeline(
"text-generation",
model="openai-community/gpt2"
)
# Define the universe.
spy = Symbol.create("SPY", SecurityType.EQUITY, Market.USA)
self.universe_settings.schedule.on(self.date_rules.month_start(spy))
self.universe_settings.resolution = Resolution.DAILY
self._universe = self.add_universe(
self.universe.top(
self.get_parameter('universe_size', 5)
)
)
self._last_rebalance = datetime.min
self.schedule.on(
self.date_rules.month_start(spy, 1),
self.time_rules.midnight,
self._trade
)
self.set_warm_up(timedelta(31))
def _trade(self):
if self.is_warming_up:
return
if self.time - self._last_rebalance < timedelta(25):
return
self._last_rebalance = self.time
symbols = list(self._universe.selected)
if not symbols:
return
# Get trailing 60-day price data.
history = self.history(
symbols, 60, Resolution.DAILY
)['close'].unstack(0)
scores = {}
for symbol in symbols:
prices = history[symbol].dropna()
if len(prices) < 20:
continue
# Calculate features.
returns_20d = (prices.iloc[-1] / prices.iloc[-20] - 1) * 100
volatility = prices.pct_change().std() * np.sqrt(252) * 100
# Create a structured prompt.
prompt = (
f"Stock analysis: 20-day return {returns_20d:.1f}%, "
f"annualized volatility {volatility:.1f}%. "
f"Market outlook:"
)
# Generate text.
result = self._generator(
prompt, max_new_tokens=30, num_return_sequences=1,
do_sample=True, temperature=0.7
)
generated = result[0]['generated_text'].lower()
# Parse sentiment from generated text.
bullish_words = ['bullish', 'growth', 'strong', 'positive', 'upward', 'buy', 'rally']
bearish_words = ['bearish', 'decline', 'weak', 'negative', 'downward', 'sell', 'crash']
bull_count = sum(1 for w in bullish_words if w in generated)
bear_count = sum(1 for w in bearish_words if w in generated)
# Combine model signal with momentum.
momentum_signal = 1 if returns_20d > 0 else -1
model_signal = bull_count - bear_count
scores[symbol] = momentum_signal + model_signal * 0.5
if not scores:
return
# Normalize scores to portfolio weights.
total = sum(abs(v) for v in scores.values())
if total == 0:
return
weights = {s: v / total for s, v in scores.items()}
# Rebalance.
self.set_holdings(
[
PortfolioTarget(symbol, weight)
for symbol, weight in weights.items()
],
True
)
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