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import datetime |
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from enum import Enum |
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from pathlib import Path |
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import bs4 |
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from typing import Generator, Iterable, Optional |
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from dataclasses import dataclass |
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from .stocks import StockMarketAnalyzer, load_stock_prices |
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def clean_paragraph(paragraph: str): |
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return paragraph.strip().replace("\n", " ").replace("\r", "").replace("--", "") |
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def process_transcript(transcript: str, min_words: int = 30): |
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paragraphs = transcript.split("\n\n") |
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for i, paragraph in enumerate(paragraphs): |
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if len(paragraph.split()) > min_words: |
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yield i, clean_paragraph(paragraph) |
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class Sentiment(str, Enum): |
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positive = "positive" |
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negative = "negative" |
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def __repr__(self) -> str: |
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return self.value |
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def __str__(self) -> str: |
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return self.value |
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@dataclass |
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class EarningsPrompt: |
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company: str |
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date: datetime.datetime |
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para_no: int |
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label: Sentiment |
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text: str |
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def to_dict(self): |
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return { |
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"company": self.company, |
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"date": self.date, |
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"para_no": self.para_no, |
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"label": self.label, |
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"text": self.text, |
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} |
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@dataclass |
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class EarningsCall: |
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company: str |
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date: datetime.datetime |
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file_path: Path |
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sentiment: Optional[Sentiment] = None |
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transcript: Optional[str] = None |
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def load_transcript(self): |
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if self.transcript is None: |
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self.transcript = bs4.BeautifulSoup( |
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self.file_path.read_text(), "html.parser" |
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).text |
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def generate_prompts(self, min_words: int = 30): |
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if self.sentiment is None: |
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raise ValueError("EarningsCall sentiment must be set") |
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if self.transcript is None: |
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self.load_transcript() |
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for para_no, paragraph in process_transcript( |
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self.transcript, min_words=min_words |
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): |
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yield EarningsPrompt( |
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company=self.company, |
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date=self.date, |
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para_no=para_no, |
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label=self.sentiment, |
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text=paragraph, |
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) |
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def set_sentiment( |
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self, |
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market_analyzer: StockMarketAnalyzer, |
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days_before: int = 1, |
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days_after: int = 1, |
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): |
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beats_market = market_analyzer.beats_market( |
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self.company, self.date, days_before=days_before, days_after=days_after |
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) |
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self.sentiment = Sentiment.positive if beats_market else Sentiment.negative |
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@classmethod |
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def from_file(cls, path: Path): |
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""" |
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Given a path to an earnings call transcript file, extracts the company name, date, |
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and file path and returns an EarningsCall object containing this information. |
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Args: |
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path (Path): The path to the earnings call transcript file. |
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Returns: |
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EarningsCall: An object containing the company name, date, and file path. |
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""" |
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company = path.parent.stem |
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date = datetime.datetime.strptime(path.stem[: -(1 + len(company))], "%Y-%b-%d") |
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return cls(company=company, date=date, file_path=path) |
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def load_earnings_calls(files: Iterable[Path]) -> Generator[EarningsCall, None, None]: |
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return (EarningsCall.from_file(f) for f in files) |
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def process_earnings_calls( |
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market_data_directory: Path, days_before: int = 1, days_after: int = 1 |
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) -> Generator[EarningsCall, None, None]: |
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stock_prices = load_stock_prices(market_data_directory.glob("**/*.csv")) |
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earnings_calls = load_earnings_calls(market_data_directory.glob("**/*.txt")) |
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market_analyzer = StockMarketAnalyzer(stock_prices) |
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for call in earnings_calls: |
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call.set_sentiment(market_analyzer, days_before, days_after) |
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yield call |
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def generate_prompts(calls: Iterable[EarningsCall], min_words: int = 30): |
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for call in calls: |
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yield from call.generate_prompts(min_words=min_words) |
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