683 lines
28 KiB
Python
683 lines
28 KiB
Python
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 pandas as pd
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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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# Optimized: Get stock data once and calculate indicators for all dates
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try:
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indicator_data = _get_stock_stats_bulk(symbol, indicator, curr_date)
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# Generate the date range we need
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current_dt = curr_date_dt
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date_values = []
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while current_dt >= before:
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date_str = current_dt.strftime('%Y-%m-%d')
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# Look up the indicator value for this date
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if date_str in indicator_data:
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indicator_value = indicator_data[date_str]
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else:
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indicator_value = "N/A: Not a trading day (weekend or holiday)"
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date_values.append((date_str, indicator_value))
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current_dt = current_dt - relativedelta(days=1)
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# Build the result string
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ind_string = ""
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for date_str, value in date_values:
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ind_string += f"{date_str}: {value}\n"
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except Exception as e:
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print(f"Error getting bulk stockstats data: {e}")
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# Fallback to original implementation if bulk method fails
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ind_string = ""
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curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d")
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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_stock_stats_bulk(
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symbol: Annotated[str, "ticker symbol of the company"],
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indicator: Annotated[str, "technical indicator to calculate"],
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curr_date: Annotated[str, "current date for reference"]
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) -> dict:
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"""
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Optimized bulk calculation of stock stats indicators.
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Fetches data once and calculates indicator for all available dates.
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Returns dict mapping date strings to indicator values.
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"""
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from .config import get_config
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import pandas as pd
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from stockstats import wrap
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import os
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config = get_config()
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online = config["data_vendors"]["technical_indicators"] != "local"
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if not online:
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# Local data path
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try:
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data = pd.read_csv(
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os.path.join(
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config.get("data_cache_dir", "data"),
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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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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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# Online data fetching with caching
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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=2)
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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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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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# Calculate the indicator for all rows at once
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df[indicator] # This triggers stockstats to calculate the indicator
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# Create a dictionary mapping date strings to indicator values
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result_dict = {}
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for _, row in df.iterrows():
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date_str = row["Date"]
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indicator_value = row[indicator]
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# Handle NaN/None values
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if pd.isna(indicator_value):
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result_dict[date_str] = "N/A"
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else:
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result_dict[date_str] = str(indicator_value)
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return result_dict
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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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def get_balance_sheet(
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ticker: Annotated[str, "ticker symbol of the company"],
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freq: Annotated[str, "frequency of data: 'annual' or 'quarterly'"] = "quarterly",
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curr_date: Annotated[str, "current date (not used for yfinance)"] = None
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):
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"""Get balance sheet data from yfinance."""
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try:
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ticker_obj = yf.Ticker(ticker.upper())
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if freq.lower() == "quarterly":
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data = ticker_obj.quarterly_balance_sheet
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else:
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data = ticker_obj.balance_sheet
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if data.empty:
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return f"No balance sheet data found for symbol '{ticker}'"
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# Convert to CSV string for consistency with other functions
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csv_string = data.to_csv()
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# Add header information
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header = f"# Balance Sheet data for {ticker.upper()} ({freq})\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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except Exception as e:
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return f"Error retrieving balance sheet for {ticker}: {str(e)}"
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def get_cashflow(
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ticker: Annotated[str, "ticker symbol of the company"],
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freq: Annotated[str, "frequency of data: 'annual' or 'quarterly'"] = "quarterly",
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curr_date: Annotated[str, "current date (not used for yfinance)"] = None
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):
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"""Get cash flow data from yfinance."""
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try:
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ticker_obj = yf.Ticker(ticker.upper())
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if freq.lower() == "quarterly":
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data = ticker_obj.quarterly_cashflow
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else:
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data = ticker_obj.cashflow
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if data.empty:
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return f"No cash flow data found for symbol '{ticker}'"
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# Convert to CSV string for consistency with other functions
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csv_string = data.to_csv()
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# Add header information
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header = f"# Cash Flow data for {ticker.upper()} ({freq})\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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except Exception as e:
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return f"Error retrieving cash flow for {ticker}: {str(e)}"
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def get_income_statement(
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ticker: Annotated[str, "ticker symbol of the company"],
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freq: Annotated[str, "frequency of data: 'annual' or 'quarterly'"] = "quarterly",
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curr_date: Annotated[str, "current date (not used for yfinance)"] = None
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):
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"""Get income statement data from yfinance."""
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try:
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ticker_obj = yf.Ticker(ticker.upper())
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if freq.lower() == "quarterly":
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data = ticker_obj.quarterly_income_stmt
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else:
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data = ticker_obj.income_stmt
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if data.empty:
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return f"No income statement data found for symbol '{ticker}'"
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# Convert to CSV string for consistency with other functions
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csv_string = data.to_csv()
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# Add header information
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header = f"# Income Statement data for {ticker.upper()} ({freq})\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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except Exception as e:
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return f"Error retrieving income statement for {ticker}: {str(e)}"
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def get_insider_transactions(
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ticker: Annotated[str, "ticker symbol of the company"],
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curr_date: Annotated[str, "current date (not used for yfinance)"] = None
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):
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"""Get insider transactions data from yfinance."""
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try:
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ticker_obj = yf.Ticker(ticker.upper())
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data = ticker_obj.insider_transactions
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if data is None or data.empty:
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return f"No insider transactions data found for symbol '{ticker}'"
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# Convert to CSV string for consistency with other functions
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csv_string = data.to_csv()
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# Add header information
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header = f"# Insider Transactions data for {ticker.upper()}\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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except Exception as e:
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return f"Error retrieving insider transactions for {ticker}: {str(e)}"
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def validate_ticker(symbol: str) -> bool:
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"""
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Validate if a ticker symbol exists and has trading data.
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"""
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try:
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ticker = yf.Ticker(symbol.upper())
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# Try to fetch 1 day of history
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# Suppress yfinance error output
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import sys
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from io import StringIO
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# Redirect stderr to suppress yfinance error messages
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original_stderr = sys.stderr
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sys.stderr = StringIO()
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try:
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history = ticker.history(period="1d")
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return not history.empty
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finally:
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# Restore stderr
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sys.stderr = original_stderr
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except Exception:
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return False
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def get_fundamentals(
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ticker: Annotated[str, "ticker symbol of the company"],
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curr_date: Annotated[str, "current date (for reference)"] = None
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) -> str:
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"""
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Get comprehensive fundamental data for a ticker using yfinance.
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Returns data in a format similar to Alpha Vantage's OVERVIEW endpoint.
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This is a FREE alternative to Alpha Vantage with no rate limits.
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"""
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import json
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try:
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ticker_obj = yf.Ticker(ticker.upper())
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info = ticker_obj.info
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if not info or info.get('regularMarketPrice') is None:
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return f"No fundamental data found for symbol '{ticker}'"
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# Build a structured response similar to Alpha Vantage
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fundamentals = {
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# Company Info
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"Symbol": ticker.upper(),
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"AssetType": info.get("quoteType", "N/A"),
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"Name": info.get("longName", info.get("shortName", "N/A")),
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"Description": info.get("longBusinessSummary", "N/A"),
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"Exchange": info.get("exchange", "N/A"),
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"Currency": info.get("currency", "USD"),
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"Country": info.get("country", "N/A"),
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"Sector": info.get("sector", "N/A"),
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"Industry": info.get("industry", "N/A"),
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"Address": f"{info.get('address1', '')} {info.get('city', '')}, {info.get('state', '')} {info.get('zip', '')}".strip(),
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"OfficialSite": info.get("website", "N/A"),
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"FiscalYearEnd": info.get("fiscalYearEnd", "N/A"),
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# Valuation
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"MarketCapitalization": str(info.get("marketCap", "N/A")),
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"EBITDA": str(info.get("ebitda", "N/A")),
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"PERatio": str(info.get("trailingPE", "N/A")),
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"ForwardPE": str(info.get("forwardPE", "N/A")),
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"PEGRatio": str(info.get("pegRatio", "N/A")),
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"BookValue": str(info.get("bookValue", "N/A")),
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"PriceToBookRatio": str(info.get("priceToBook", "N/A")),
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"PriceToSalesRatioTTM": str(info.get("priceToSalesTrailing12Months", "N/A")),
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"EVToRevenue": str(info.get("enterpriseToRevenue", "N/A")),
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"EVToEBITDA": str(info.get("enterpriseToEbitda", "N/A")),
|
|
|
|
# Earnings & Revenue
|
|
"EPS": str(info.get("trailingEps", "N/A")),
|
|
"ForwardEPS": str(info.get("forwardEps", "N/A")),
|
|
"RevenueTTM": str(info.get("totalRevenue", "N/A")),
|
|
"RevenuePerShareTTM": str(info.get("revenuePerShare", "N/A")),
|
|
"GrossProfitTTM": str(info.get("grossProfits", "N/A")),
|
|
"QuarterlyRevenueGrowthYOY": str(info.get("revenueGrowth", "N/A")),
|
|
"QuarterlyEarningsGrowthYOY": str(info.get("earningsGrowth", "N/A")),
|
|
|
|
# Margins & Returns
|
|
"ProfitMargin": str(info.get("profitMargins", "N/A")),
|
|
"OperatingMarginTTM": str(info.get("operatingMargins", "N/A")),
|
|
"GrossMargins": str(info.get("grossMargins", "N/A")),
|
|
"ReturnOnAssetsTTM": str(info.get("returnOnAssets", "N/A")),
|
|
"ReturnOnEquityTTM": str(info.get("returnOnEquity", "N/A")),
|
|
|
|
# Dividend
|
|
"DividendPerShare": str(info.get("dividendRate", "N/A")),
|
|
"DividendYield": str(info.get("dividendYield", "N/A")),
|
|
"ExDividendDate": str(info.get("exDividendDate", "N/A")),
|
|
"PayoutRatio": str(info.get("payoutRatio", "N/A")),
|
|
|
|
# Balance Sheet
|
|
"TotalCash": str(info.get("totalCash", "N/A")),
|
|
"TotalDebt": str(info.get("totalDebt", "N/A")),
|
|
"CurrentRatio": str(info.get("currentRatio", "N/A")),
|
|
"QuickRatio": str(info.get("quickRatio", "N/A")),
|
|
"DebtToEquity": str(info.get("debtToEquity", "N/A")),
|
|
"FreeCashFlow": str(info.get("freeCashflow", "N/A")),
|
|
"OperatingCashFlow": str(info.get("operatingCashflow", "N/A")),
|
|
|
|
# Trading Info
|
|
"Beta": str(info.get("beta", "N/A")),
|
|
"52WeekHigh": str(info.get("fiftyTwoWeekHigh", "N/A")),
|
|
"52WeekLow": str(info.get("fiftyTwoWeekLow", "N/A")),
|
|
"50DayMovingAverage": str(info.get("fiftyDayAverage", "N/A")),
|
|
"200DayMovingAverage": str(info.get("twoHundredDayAverage", "N/A")),
|
|
"SharesOutstanding": str(info.get("sharesOutstanding", "N/A")),
|
|
"SharesFloat": str(info.get("floatShares", "N/A")),
|
|
"SharesShort": str(info.get("sharesShort", "N/A")),
|
|
"ShortRatio": str(info.get("shortRatio", "N/A")),
|
|
"ShortPercentOfFloat": str(info.get("shortPercentOfFloat", "N/A")),
|
|
|
|
# Ownership
|
|
"PercentInsiders": str(info.get("heldPercentInsiders", "N/A")),
|
|
"PercentInstitutions": str(info.get("heldPercentInstitutions", "N/A")),
|
|
|
|
# Analyst
|
|
"AnalystTargetPrice": str(info.get("targetMeanPrice", "N/A")),
|
|
"AnalystTargetHigh": str(info.get("targetHighPrice", "N/A")),
|
|
"AnalystTargetLow": str(info.get("targetLowPrice", "N/A")),
|
|
"NumberOfAnalysts": str(info.get("numberOfAnalystOpinions", "N/A")),
|
|
"RecommendationKey": info.get("recommendationKey", "N/A"),
|
|
"RecommendationMean": str(info.get("recommendationMean", "N/A")),
|
|
}
|
|
|
|
# Return as formatted JSON string
|
|
return json.dumps(fundamentals, indent=4)
|
|
|
|
except Exception as e:
|
|
return f"Error retrieving fundamentals for {ticker}: {str(e)}"
|
|
|
|
|
|
def get_options_activity(
|
|
ticker: Annotated[str, "ticker symbol of the company"],
|
|
num_expirations: Annotated[int, "number of nearest expiration dates to analyze"] = 3,
|
|
curr_date: Annotated[str, "current date (for reference)"] = None
|
|
) -> str:
|
|
"""
|
|
Get options activity for a specific ticker using yfinance.
|
|
Analyzes volume, open interest, and put/call ratios.
|
|
|
|
This is a FREE alternative to Tradier with no API key required.
|
|
"""
|
|
try:
|
|
ticker_obj = yf.Ticker(ticker.upper())
|
|
|
|
# Get available expiration dates
|
|
expirations = ticker_obj.options
|
|
if not expirations:
|
|
return f"No options data available for {ticker}"
|
|
|
|
# Analyze the nearest N expiration dates
|
|
expirations_to_analyze = expirations[:min(num_expirations, len(expirations))]
|
|
|
|
report = f"## Options Activity for {ticker.upper()}\n\n"
|
|
report += f"**Available Expirations:** {len(expirations)} dates\n"
|
|
report += f"**Analyzing:** {', '.join(expirations_to_analyze)}\n\n"
|
|
|
|
total_call_volume = 0
|
|
total_put_volume = 0
|
|
total_call_oi = 0
|
|
total_put_oi = 0
|
|
|
|
unusual_activity = []
|
|
|
|
for exp_date in expirations_to_analyze:
|
|
try:
|
|
opt = ticker_obj.option_chain(exp_date)
|
|
calls = opt.calls
|
|
puts = opt.puts
|
|
|
|
if calls.empty and puts.empty:
|
|
continue
|
|
|
|
# Calculate totals for this expiration
|
|
call_vol = calls['volume'].sum() if 'volume' in calls.columns else 0
|
|
put_vol = puts['volume'].sum() if 'volume' in puts.columns else 0
|
|
call_oi = calls['openInterest'].sum() if 'openInterest' in calls.columns else 0
|
|
put_oi = puts['openInterest'].sum() if 'openInterest' in puts.columns else 0
|
|
|
|
# Handle NaN values
|
|
call_vol = 0 if pd.isna(call_vol) else int(call_vol)
|
|
put_vol = 0 if pd.isna(put_vol) else int(put_vol)
|
|
call_oi = 0 if pd.isna(call_oi) else int(call_oi)
|
|
put_oi = 0 if pd.isna(put_oi) else int(put_oi)
|
|
|
|
total_call_volume += call_vol
|
|
total_put_volume += put_vol
|
|
total_call_oi += call_oi
|
|
total_put_oi += put_oi
|
|
|
|
# Find unusual activity (high volume relative to OI)
|
|
for _, row in calls.iterrows():
|
|
vol = row.get('volume', 0)
|
|
oi = row.get('openInterest', 0)
|
|
if pd.notna(vol) and pd.notna(oi) and oi > 0 and vol > oi * 0.5 and vol > 100:
|
|
unusual_activity.append({
|
|
'type': 'CALL',
|
|
'expiration': exp_date,
|
|
'strike': row['strike'],
|
|
'volume': int(vol),
|
|
'openInterest': int(oi),
|
|
'vol_oi_ratio': round(vol / oi, 2) if oi > 0 else 0,
|
|
'impliedVolatility': round(row.get('impliedVolatility', 0) * 100, 1)
|
|
})
|
|
|
|
for _, row in puts.iterrows():
|
|
vol = row.get('volume', 0)
|
|
oi = row.get('openInterest', 0)
|
|
if pd.notna(vol) and pd.notna(oi) and oi > 0 and vol > oi * 0.5 and vol > 100:
|
|
unusual_activity.append({
|
|
'type': 'PUT',
|
|
'expiration': exp_date,
|
|
'strike': row['strike'],
|
|
'volume': int(vol),
|
|
'openInterest': int(oi),
|
|
'vol_oi_ratio': round(vol / oi, 2) if oi > 0 else 0,
|
|
'impliedVolatility': round(row.get('impliedVolatility', 0) * 100, 1)
|
|
})
|
|
|
|
except Exception as e:
|
|
report += f"*Error fetching {exp_date}: {str(e)}*\n"
|
|
continue
|
|
|
|
# Calculate put/call ratios
|
|
pc_volume_ratio = round(total_put_volume / total_call_volume, 3) if total_call_volume > 0 else 0
|
|
pc_oi_ratio = round(total_put_oi / total_call_oi, 3) if total_call_oi > 0 else 0
|
|
|
|
# Summary
|
|
report += "### Summary\n"
|
|
report += "| Metric | Calls | Puts | Put/Call Ratio |\n"
|
|
report += "|--------|-------|------|----------------|\n"
|
|
report += f"| Volume | {total_call_volume:,} | {total_put_volume:,} | {pc_volume_ratio} |\n"
|
|
report += f"| Open Interest | {total_call_oi:,} | {total_put_oi:,} | {pc_oi_ratio} |\n\n"
|
|
|
|
# Sentiment interpretation
|
|
report += "### Sentiment Analysis\n"
|
|
if pc_volume_ratio < 0.7:
|
|
report += "- **Volume P/C Ratio:** Bullish (more call volume)\n"
|
|
elif pc_volume_ratio > 1.3:
|
|
report += "- **Volume P/C Ratio:** Bearish (more put volume)\n"
|
|
else:
|
|
report += "- **Volume P/C Ratio:** Neutral\n"
|
|
|
|
if pc_oi_ratio < 0.7:
|
|
report += "- **OI P/C Ratio:** Bullish positioning\n"
|
|
elif pc_oi_ratio > 1.3:
|
|
report += "- **OI P/C Ratio:** Bearish positioning\n"
|
|
else:
|
|
report += "- **OI P/C Ratio:** Neutral positioning\n"
|
|
|
|
# Unusual activity
|
|
if unusual_activity:
|
|
# Sort by volume/OI ratio
|
|
unusual_activity.sort(key=lambda x: x['vol_oi_ratio'], reverse=True)
|
|
top_unusual = unusual_activity[:10]
|
|
|
|
report += "\n### Unusual Activity (High Volume vs Open Interest)\n"
|
|
report += "| Type | Expiry | Strike | Volume | OI | Vol/OI | IV |\n"
|
|
report += "|------|--------|--------|--------|----|---------|----|---|\n"
|
|
for item in top_unusual:
|
|
report += f"| {item['type']} | {item['expiration']} | ${item['strike']} | {item['volume']:,} | {item['openInterest']:,} | {item['vol_oi_ratio']}x | {item['impliedVolatility']}% |\n"
|
|
else:
|
|
report += "\n*No unusual options activity detected.*\n"
|
|
|
|
return report
|
|
|
|
except Exception as e:
|
|
return f"Error retrieving options activity for {ticker}: {str(e)}" |