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Merge pull request #245 from TauricResearch/feat/tooloptim 

Y Finance Tools Optimizations
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Edward Sun 2025-10-09 00:34:10 -07:00 committed by GitHub
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2
README.md
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@ -127,7 +127,7 @@ cp .env.example .env
# Edit .env with your actual API keys
```
**Note:** The default configuration uses [Alpha Vantage](https://www.alphavantage.co/) for fundamental and news data. You can get a free API key from their website, or upgrade to [Alpha Vantage Premium](https://www.alphavantage.co/premium/) for higher rate limits and more stable access. If you prefer to use OpenAI for these data sources instead, you can modify the data vendor settings in `tradingagents/default_config.py`.
**Note:** We are happy to partner with Alpha Vantage to provide robust API support for TradingAgents. You can get a free AlphaVantage API [here](https://www.alphavantage.co/support/#api-key), TradingAgents-sourced requests also have increased rate limits to 60 requests per minute with no daily limits. Typically the quota is sufficient for performing complex tasks with TradingAgents thanks to Alpha Vantage’s open-source support program. If you prefer to use OpenAI for these data sources instead, you can modify the data vendor settings in `tradingagents/default_config.py`.
### CLI Usage

1
pyproject.toml
View File

@ -12,6 +12,7 @@ dependencies = [
"eodhd>=1.0.32",
"feedparser>=6.0.11",
"finnhub-python>=2.4.23",
"grip>=4.6.2",
"langchain-anthropic>=0.3.15",
"langchain-experimental>=0.3.4",
"langchain-google-genai>=2.1.5",

11
test.py Normal file
View File

@ -0,0 +1,11 @@
import time
from tradingagents.dataflows.y_finance import get_YFin_data_online, get_stock_stats_indicators_window, get_balance_sheet as get_yfinance_balance_sheet, get_cashflow as get_yfinance_cashflow, get_income_statement as get_yfinance_income_statement, get_insider_transactions as get_yfinance_insider_transactions
print("Testing optimized implementation with 30-day lookback:")
start_time = time.time()
result = get_stock_stats_indicators_window("AAPL", "macd", "2024-11-01", 30)
end_time = time.time()
print(f"Execution time: {end_time - start_time:.2f} seconds")
print(f"Result length: {len(result)} characters")
print(result)

129
tradingagents/dataflows/y_finance.py
View File

@ -137,16 +137,42 @@ def get_stock_stats_indicators_window(
curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d")
before = curr_date_dt - relativedelta(days=look_back_days)
# online gathering only
ind_string = ""
while curr_date_dt >= before:
indicator_value = get_stockstats_indicator(
symbol, indicator, curr_date_dt.strftime("%Y-%m-%d")
)
ind_string += f"{curr_date_dt.strftime('%Y-%m-%d')}: {indicator_value}\n"
curr_date_dt = curr_date_dt - relativedelta(days=1)
# Optimized: Get stock data once and calculate indicators for all dates
try:
indicator_data = _get_stock_stats_bulk(symbol, indicator, curr_date)
# Generate the date range we need
current_dt = curr_date_dt
date_values = []
while current_dt >= before:
date_str = current_dt.strftime('%Y-%m-%d')
# Look up the indicator value for this date
if date_str in indicator_data:
indicator_value = indicator_data[date_str]
else:
indicator_value = "N/A: Not a trading day (weekend or holiday)"
date_values.append((date_str, indicator_value))
current_dt = current_dt - relativedelta(days=1)
# Build the result string
ind_string = ""
for date_str, value in date_values:
ind_string += f"{date_str}: {value}\n"
except Exception as e:
print(f"Error getting bulk stockstats data: {e}")
# Fallback to original implementation if bulk method fails
ind_string = ""
curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d")
while curr_date_dt >= before:
indicator_value = get_stockstats_indicator(
symbol, indicator, curr_date_dt.strftime("%Y-%m-%d")
)
ind_string += f"{curr_date_dt.strftime('%Y-%m-%d')}: {indicator_value}\n"
curr_date_dt = curr_date_dt - relativedelta(days=1)
result_str = (
f"## {indicator} values from {before.strftime('%Y-%m-%d')} to {end_date}:\n\n"
@ -158,6 +184,89 @@ def get_stock_stats_indicators_window(
return result_str
def _get_stock_stats_bulk(
symbol: Annotated[str, "ticker symbol of the company"],
indicator: Annotated[str, "technical indicator to calculate"],
curr_date: Annotated[str, "current date for reference"]
) -> dict:
"""
Optimized bulk calculation of stock stats indicators.
Fetches data once and calculates indicator for all available dates.
Returns dict mapping date strings to indicator values.
"""
from .config import get_config
import pandas as pd
from stockstats import wrap
import os
config = get_config()
online = config["data_vendors"]["technical_indicators"] != "local"
if not online:
# Local data path
try:
data = pd.read_csv(
os.path.join(
config.get("data_cache_dir", "data"),
f"{symbol}-YFin-data-2015-01-01-2025-03-25.csv",
)
)
df = wrap(data)
except FileNotFoundError:
raise Exception("Stockstats fail: Yahoo Finance data not fetched yet!")
else:
# Online data fetching with caching
today_date = pd.Timestamp.today()
curr_date_dt = pd.to_datetime(curr_date)
end_date = today_date
start_date = today_date - pd.DateOffset(years=15)
start_date_str = start_date.strftime("%Y-%m-%d")
end_date_str = end_date.strftime("%Y-%m-%d")
os.makedirs(config["data_cache_dir"], exist_ok=True)
data_file = os.path.join(
config["data_cache_dir"],
f"{symbol}-YFin-data-{start_date_str}-{end_date_str}.csv",
)
if os.path.exists(data_file):
data = pd.read_csv(data_file)
data["Date"] = pd.to_datetime(data["Date"])
else:
data = yf.download(
symbol,
start=start_date_str,
end=end_date_str,
multi_level_index=False,
progress=False,
auto_adjust=True,
)
data = data.reset_index()
data.to_csv(data_file, index=False)
df = wrap(data)
df["Date"] = df["Date"].dt.strftime("%Y-%m-%d")
# Calculate the indicator for all rows at once
df[indicator] # This triggers stockstats to calculate the indicator
# Create a dictionary mapping date strings to indicator values
result_dict = {}
for _, row in df.iterrows():
date_str = row["Date"]
indicator_value = row[indicator]
# Handle NaN/None values
if pd.isna(indicator_value):
result_dict[date_str] = "N/A"
else:
result_dict[date_str] = str(indicator_value)
return result_dict
def get_stockstats_indicator(
symbol: Annotated[str, "ticker symbol of the company"],
indicator: Annotated[str, "technical indicator to get the analysis and report of"],

92
uv.lock
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