87 lines
3.1 KiB
Python
87 lines
3.1 KiB
Python
import pandas as pd
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import yfinance as yf
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from stockstats import wrap
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from typing import Annotated
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import os
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from .config import get_config, DATA_DIR
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class StockstatsUtils:
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@staticmethod
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def get_stock_stats(
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symbol: Annotated[str, "ticker symbol for the company"],
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indicator: Annotated[
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str, "quantitative indicators based off of the stock data for the company"
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],
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curr_date: Annotated[
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str, "curr date for retrieving stock price data, YYYY-mm-dd"
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],
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):
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# Get config and set up data directory path
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config = get_config()
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online = config["data_vendors"]["technical_indicators"] != "local"
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df = None
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data = None
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# CRITICAL: Use curr_date as end date to prevent future data leakage
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# This ensures backtest doesn't see data beyond the analysis date
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curr_date_dt = pd.to_datetime(curr_date)
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end_date_dt = curr_date_dt
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start_date_dt = curr_date_dt - pd.DateOffset(years=2) # Reduced from 15 years for faster fetching
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if not online:
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try:
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data = pd.read_csv(
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os.path.join(
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DATA_DIR,
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f"{symbol}-YFin-data-2015-01-01-2025-03-25.csv",
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)
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)
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# CRITICAL: Filter local data to prevent future data leakage
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data["Date"] = pd.to_datetime(data["Date"])
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data = data[data["Date"] <= curr_date_dt]
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df = wrap(data)
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except FileNotFoundError:
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raise Exception("Stockstats fail: Yahoo Finance data not fetched yet!")
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else:
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start_date = start_date_dt.strftime("%Y-%m-%d")
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end_date = end_date_dt.strftime("%Y-%m-%d")
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# Get config and ensure cache directory exists
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os.makedirs(config["data_cache_dir"], exist_ok=True)
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# Cache file now uses curr_date (end_date), not today's date
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data_file = os.path.join(
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config["data_cache_dir"],
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f"{symbol}-YFin-data-{start_date}-{end_date}.csv",
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)
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if os.path.exists(data_file):
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data = pd.read_csv(data_file)
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data["Date"] = pd.to_datetime(data["Date"])
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else:
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data = yf.download(
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symbol,
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start=start_date,
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end=end_date,
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multi_level_index=False,
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progress=False,
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auto_adjust=True,
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)
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data = data.reset_index()
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data.to_csv(data_file, index=False)
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df = wrap(data)
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df["Date"] = df["Date"].dt.strftime("%Y-%m-%d")
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curr_date = curr_date_dt.strftime("%Y-%m-%d")
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df[indicator] # trigger stockstats to calculate the indicator
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matching_rows = df[df["Date"].str.startswith(curr_date)]
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if not matching_rows.empty:
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indicator_value = matching_rows[indicator].values[0]
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return indicator_value
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else:
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return "N/A: Not a trading day (weekend or holiday)"
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