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https://github.com/farcasclaudiu/TradingAgents.git
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Switch default data vendor
🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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luohy15
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from typing import Annotated
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from datetime import datetime
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from dateutil.relativedelta import relativedelta
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import yfinance as yf
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import os
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from .stockstats_utils import StockstatsUtils
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def get_YFin_data_online(
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symbol: Annotated[str, "ticker symbol of the company"],
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start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
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end_date: Annotated[str, "End date in yyyy-mm-dd format"],
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):
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datetime.strptime(start_date, "%Y-%m-%d")
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datetime.strptime(end_date, "%Y-%m-%d")
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# Create ticker object
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ticker = yf.Ticker(symbol.upper())
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# Fetch historical data for the specified date range
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data = ticker.history(start=start_date, end=end_date)
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# Check if data is empty
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if data.empty:
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return (
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f"No data found for symbol '{symbol}' between {start_date} and {end_date}"
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)
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# Remove timezone info from index for cleaner output
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if data.index.tz is not None:
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data.index = data.index.tz_localize(None)
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# Round numerical values to 2 decimal places for cleaner display
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numeric_columns = ["Open", "High", "Low", "Close", "Adj Close"]
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for col in numeric_columns:
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if col in data.columns:
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data[col] = data[col].round(2)
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# Convert DataFrame to CSV string
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csv_string = data.to_csv()
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# Add header information
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header = f"# Stock data for {symbol.upper()} from {start_date} to {end_date}\n"
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header += f"# Total records: {len(data)}\n"
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header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
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return header + csv_string
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def get_stock_stats_indicators_window(
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symbol: Annotated[str, "ticker symbol of the company"],
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indicator: Annotated[str, "technical indicator to get the analysis and report of"],
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curr_date: Annotated[
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str, "The current trading date you are trading on, YYYY-mm-dd"
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],
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look_back_days: Annotated[int, "how many days to look back"],
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) -> str:
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best_ind_params = {
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# Moving Averages
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"close_50_sma": (
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"50 SMA: A medium-term trend indicator. "
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"Usage: Identify trend direction and serve as dynamic support/resistance. "
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"Tips: It lags price; combine with faster indicators for timely signals."
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),
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"close_200_sma": (
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"200 SMA: A long-term trend benchmark. "
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"Usage: Confirm overall market trend and identify golden/death cross setups. "
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"Tips: It reacts slowly; best for strategic trend confirmation rather than frequent trading entries."
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),
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"close_10_ema": (
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"10 EMA: A responsive short-term average. "
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"Usage: Capture quick shifts in momentum and potential entry points. "
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"Tips: Prone to noise in choppy markets; use alongside longer averages for filtering false signals."
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),
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# MACD Related
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"macd": (
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"MACD: Computes momentum via differences of EMAs. "
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"Usage: Look for crossovers and divergence as signals of trend changes. "
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"Tips: Confirm with other indicators in low-volatility or sideways markets."
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),
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"macds": (
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"MACD Signal: An EMA smoothing of the MACD line. "
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"Usage: Use crossovers with the MACD line to trigger trades. "
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"Tips: Should be part of a broader strategy to avoid false positives."
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),
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"macdh": (
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"MACD Histogram: Shows the gap between the MACD line and its signal. "
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"Usage: Visualize momentum strength and spot divergence early. "
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"Tips: Can be volatile; complement with additional filters in fast-moving markets."
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),
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# Momentum Indicators
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"rsi": (
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"RSI: Measures momentum to flag overbought/oversold conditions. "
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"Usage: Apply 70/30 thresholds and watch for divergence to signal reversals. "
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"Tips: In strong trends, RSI may remain extreme; always cross-check with trend analysis."
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),
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# Volatility Indicators
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"boll": (
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"Bollinger Middle: A 20 SMA serving as the basis for Bollinger Bands. "
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"Usage: Acts as a dynamic benchmark for price movement. "
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"Tips: Combine with the upper and lower bands to effectively spot breakouts or reversals."
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),
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"boll_ub": (
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"Bollinger Upper Band: Typically 2 standard deviations above the middle line. "
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"Usage: Signals potential overbought conditions and breakout zones. "
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"Tips: Confirm signals with other tools; prices may ride the band in strong trends."
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),
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"boll_lb": (
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"Bollinger Lower Band: Typically 2 standard deviations below the middle line. "
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"Usage: Indicates potential oversold conditions. "
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"Tips: Use additional analysis to avoid false reversal signals."
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),
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"atr": (
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"ATR: Averages true range to measure volatility. "
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"Usage: Set stop-loss levels and adjust position sizes based on current market volatility. "
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"Tips: It's a reactive measure, so use it as part of a broader risk management strategy."
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),
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# Volume-Based Indicators
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"vwma": (
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"VWMA: A moving average weighted by volume. "
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"Usage: Confirm trends by integrating price action with volume data. "
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"Tips: Watch for skewed results from volume spikes; use in combination with other volume analyses."
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),
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"mfi": (
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"MFI: The Money Flow Index is a momentum indicator that uses both price and volume to measure buying and selling pressure. "
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"Usage: Identify overbought (>80) or oversold (<20) conditions and confirm the strength of trends or reversals. "
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"Tips: Use alongside RSI or MACD to confirm signals; divergence between price and MFI can indicate potential reversals."
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),
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}
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if indicator not in best_ind_params:
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raise ValueError(
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f"Indicator {indicator} is not supported. Please choose from: {list(best_ind_params.keys())}"
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)
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end_date = curr_date
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curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d")
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before = curr_date_dt - relativedelta(days=look_back_days)
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# online gathering only
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ind_string = ""
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while curr_date_dt >= before:
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indicator_value = get_stockstats_indicator(
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symbol, indicator, curr_date_dt.strftime("%Y-%m-%d")
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)
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ind_string += f"{curr_date_dt.strftime('%Y-%m-%d')}: {indicator_value}\n"
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curr_date_dt = curr_date_dt - relativedelta(days=1)
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result_str = (
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f"## {indicator} values from {before.strftime('%Y-%m-%d')} to {end_date}:\n\n"
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+ ind_string
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+ "\n\n"
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+ best_ind_params.get(indicator, "No description available.")
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)
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return result_str
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def get_stockstats_indicator(
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symbol: Annotated[str, "ticker symbol of the company"],
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indicator: Annotated[str, "technical indicator to get the analysis and report of"],
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curr_date: Annotated[
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str, "The current trading date you are trading on, YYYY-mm-dd"
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],
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) -> str:
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curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d")
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curr_date = curr_date_dt.strftime("%Y-%m-%d")
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try:
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indicator_value = StockstatsUtils.get_stock_stats(
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symbol,
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indicator,
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curr_date,
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)
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except Exception as e:
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print(
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f"Error getting stockstats indicator data for indicator {indicator} on {curr_date}: {e}"
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)
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return ""
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return str(indicator_value)
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