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LABELS = ["Fall", "Neutral", "Rise"] |
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LABEL_MAP = {"Rise": 2, "Neutral": 1, "Fall": 0} |
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NUMERIC_LABEL_MAP = {v: k for k, v in LABEL_MAP.items()} |
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SEEDS = [0, 13, 42] |
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SYSTEM_ROLE_DEF_1 = "You are a helpful assistant and a financial technical analyst." |
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SYSTEM_ROLE_DEF_2 = ("You are a helpful financial market technical analyst. " |
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"You specialize in financial stock and equities market, a top expert in assessing market index movement direction from events and news. ") |
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def get_truncated_user_prompt_for_nifty(user_prompt: str, drop_percent: float = 0.5) -> str: |
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"""Keeps instruction and context unchanged, drops p% of news headlines randomly |
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Usage e.g.: |
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user_prompt = get_truncated_user_prompt_for_nifty(user_prompt, drop_percent=drop_percent) |
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""" |
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import random |
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splits = user_prompt.split("\n\n") |
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context, news = splits[:-1], splits[-1] |
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news_headlines = news.split("\n") |
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news_headlines, suffix = news_headlines[:-1], news_headlines[-1] |
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N = len(news_headlines) |
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N_truncated = int(N * drop_percent) |
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random.shuffle(news_headlines) |
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truncated_news_headlines = news_headlines[:N_truncated] + [suffix] |
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truncated_news_string = "\n".join(truncated_news_headlines) |
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truncated_user_prompt = context + [truncated_news_string] |
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truncated_user_prompt = "\n\n".join(truncated_user_prompt) |
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return truncated_user_prompt |
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