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