407 lines
15 KiB
Python
407 lines
15 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 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=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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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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):
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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)}" |