mirror of
https://github.com/farcasclaudiu/TradingAgents.git
synced 2026-06-22 07:01:21 +03:00
Yijia Xiao
d4dadb82fc
Models added: - OpenAI: GPT-5.2, GPT-5.1, GPT-5, GPT-5 Mini, GPT-5 Nano, GPT-4.1 - Anthropic: Claude Opus 4.5/4.1, Claude Sonnet 4.5/4, Claude Haiku 4.5 - Google: Gemini 3 Pro/Flash, Gemini 2.5 Flash/Flash Lite - xAI: Grok 4, Grok 4.1 Fast (Reasoning/Non-Reasoning) Configs updated: - Add unified thinking_level for Gemini (maps to thinking_level for Gemini 3, thinking_budget for Gemini 2.5; handles Pro's lack of "minimal" support) - Add OpenAI reasoning_effort configuration - Add NormalizedChatGoogleGenerativeAI for consistent response handling Fixes: - Fix Bull/Bear researcher display truncation - Replace ChromaDB with BM25 for memory retrieval
65 lines
2.0 KiB
Python
65 lines
2.0 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
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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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config = get_config()
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today_date = pd.Timestamp.today()
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curr_date_dt = pd.to_datetime(curr_date)
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end_date = today_date
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start_date = today_date - pd.DateOffset(years=15)
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start_date_str = start_date.strftime("%Y-%m-%d")
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end_date_str = end_date.strftime("%Y-%m-%d")
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# Ensure cache directory exists
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os.makedirs(config["data_cache_dir"], exist_ok=True)
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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_str}-{end_date_str}.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_str,
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end=end_date_str,
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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_str = 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_str)]
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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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