| """ |
| Light-weight predictor that fuses price momentum with model-based |
| financial sentiment. |
| |
| Usage: |
| from finance.quick_predict import predict_signal |
| signal = predict_signal("AAPL", "Earnings were upbeat...") |
| # -> "Buy", "Hold" or "Sell" |
| """ |
| from datetime import date, timedelta |
|
|
| import pandas as pd |
| import yfinance as yf |
|
|
| from finance.processor import get_classifier, _label_to_score |
|
|
|
|
| def _yesterday_pct(ticker: str) -> float: |
| """Return the most recent day's % change.""" |
| today = date.today() |
| data = yf.download( |
| ticker, start=today - timedelta(days=5), end=today, progress=False |
| ) |
| if len(data) < 2: |
| return 0.0 |
| |
| if isinstance(data.columns, pd.MultiIndex): |
| data.columns = data.columns.get_level_values(0) |
| close = float(data["Close"].iloc[-1]) |
| prev = float(data["Close"].iloc[-2]) |
| return (close - prev) / prev * 100.0 |
|
|
|
|
| def _sentiment_score(text: str) -> float: |
| """Signed sentiment score in [-1, 1] weighted by model confidence.""" |
| classifier = get_classifier() |
| result = classifier(text, truncation=True, max_length=128)[0] |
| return _label_to_score(result["label"]) * float(result["score"]) |
|
|
|
|
| def predict_signal(ticker: str, news_text: str) -> str: |
| """Buy / Hold / Sell based on momentum + sentiment.""" |
| pct = _yesterday_pct(ticker) |
| emo = _sentiment_score(news_text) |
| blended = 0.6 * pct + 40 * emo |
|
|
| if blended > 2.0: |
| return "Buy" |
| if blended < -2.0: |
| return "Sell" |
| return "Hold" |
|
|
