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from __future__ import annotations |
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from dataclasses import dataclass |
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from pathlib import Path |
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import pandas as pd |
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from .indicators import rsi, macd |
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@dataclass |
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class DataBundle: |
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df_sentiment: pd.DataFrame |
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df_transcript: pd.DataFrame |
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df_bn_hourly: pd.DataFrame |
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df_news: pd.DataFrame |
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df_bn_1m: pd.DataFrame |
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df_nifty_daily: pd.DataFrame |
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def copy(self) -> "DataBundle": |
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return DataBundle( |
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self.df_sentiment.copy(), |
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self.df_transcript.copy(), |
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self.df_bn_hourly.copy(), |
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self.df_news.copy(), |
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self.df_bn_1m.copy(), |
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self.df_nifty_daily.copy(), |
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) |
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def _read_excel(path: Path) -> pd.DataFrame: |
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if not path.exists(): |
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raise FileNotFoundError(path) |
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return pd.read_excel(path) |
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def load_data(paths) -> DataBundle: |
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df_sent = _read_excel(paths.sentiment_pred) |
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df_tx = _read_excel(paths.zerodha_tx) |
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df_bn_hourly = _read_excel(paths.banknifty_hourly) |
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df_news = _read_excel(paths.bank_news) |
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df_bn_1m = _read_excel(paths.banknifty_1m) |
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df_nifty = _read_excel(paths.nifty_daily) |
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if "predicted_for" in df_sent.columns: |
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df_sent["predicted_for"] = pd.to_datetime(df_sent["predicted_for"]) |
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if "Prediction_for_date" in df_tx.columns: |
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df_tx["Prediction_for_date"] = pd.to_datetime(df_tx["Prediction_for_date"], dayfirst=True) |
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for df in (df_bn_hourly, df_bn_1m, df_news, df_nifty): |
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for c in df.columns: |
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if "date" in c.lower() or c.lower() == "datetime": |
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try: |
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df[c] = pd.to_datetime(df[c]) |
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except Exception: |
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pass |
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df_bn_hourly = rsi(df_bn_hourly) |
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df_bn_hourly = macd(df_bn_hourly) |
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df_nifty = rsi(df_nifty) |
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df_nifty = macd(df_nifty) |
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return DataBundle( |
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df_sentiment=df_sent, |
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df_transcript=df_tx, |
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df_bn_hourly=df_bn_hourly, |
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df_news=df_news, |
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df_bn_1m=df_bn_1m, |
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df_nifty_daily=df_nifty, |
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) |
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