568 lines
21 KiB
Python
568 lines
21 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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import time
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import logging
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from .stockstats_utils import StockstatsUtils
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from .retry_utils import retry
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logger = logging.getLogger(__name__)
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@retry(max_attempts=3, backoff=2.0, exceptions=(Exception,))
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def get_YFin_data_online(
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symbol: Annotated[str, "公司的股票代碼"],
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start_date: Annotated[str, "開始日期，格式為 yyyy-mm-dd"],
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end_date: Annotated[str, "結束日期，格式為 yyyy-mm-dd"],
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):
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"""
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從 Yahoo Finance 線上獲取股票數據。
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Args:
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symbol (str): 公司的股票代碼。
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start_date (str): 開始日期。
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end_date (str): 結束日期。
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Returns:
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str: 包含股票數據的 CSV 格式字串。
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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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# 使用 yf.download() 獲取指定日期範圍的歷史數據
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try:
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data = yf.download(
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symbol.upper(),
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start=start_date,
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end=end_date,
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multi_level_index=False,
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progress=False,
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auto_adjust=False,
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timeout=30
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)
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except Exception as e:
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raise Exception(f"從 Yahoo Finance 獲取 {symbol} 數據失敗: {e}")
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# 檢查數據是否為空
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if data.empty:
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return (
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f"找不到 '{symbol}' 在 {start_date} 和 {end_date} 之間的數據"
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)
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# 從索引中移除時區資訊以獲得更清晰的輸出
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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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# 將數值四捨五入到小數點後兩位以便更清晰地顯示
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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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# 將 DataFrame 轉換為 CSV 字串
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csv_string = data.to_csv()
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# 新增標頭資訊
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header = f"# {symbol.upper()} 從 {start_date} 到 {end_date} 的股票數據\n"
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header += f"# 總記錄數：{len(data)}\n"
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header += f"# 數據檢索時間：{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, "公司的股票代碼"],
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indicator: Annotated[str, "要獲取分析和報告的技術指標"],
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curr_date: Annotated[
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str, "您正在交易的當前交易日期，格式為 YYYY-mm-dd"
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],
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look_back_days: Annotated[int, "回溯天數"],
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) -> str:
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"""
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獲取給定股票在一個時間窗口內的技術指標。
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Args:
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symbol (str): 公司的股票代碼。
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indicator (str): 技術指標。
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curr_date (str): 當前日期。
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look_back_days (int): 回溯天數。
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Returns:
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str: 包含指標值的格式化字串。
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"""
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best_ind_params = {
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# 移動平均線
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"close_50_sma": (
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"50 SMA：一個中期趨勢指標。"
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"用法：識別趨勢方向並作為動態支撐/阻力。"
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"提示：它滯後於價格；與更快的指標結合以獲得及時信號。"
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),
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"close_200_sma": (
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"200 SMA：一個長期趨勢基準。"
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"用法：確認整體市場趨勢並識別黃金/死亡交叉設置。"
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"提示：它反應緩慢；最適合戰略趨勢確認，而非頻繁的交易入場。"
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),
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"close_10_ema": (
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"10 EMA：一個反應靈敏的短期平均線。"
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"用法：捕捉動能的快速轉變和潛在的入場點。"
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"提示：在震盪市場中容易產生噪音；與較長的平均線一起使用以過濾錯誤信號。"
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),
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# MACD 相關
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"macd": (
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"MACD：通過 EMA 的差異計算動能。"
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"用法：尋找交叉和背離作為趨勢變化的信號。"
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"提示：在低波動性或橫盤市場中與其他指標確認。"
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),
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"macds": (
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"MACD 信號線：MACD 線的 EMA 平滑。"
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"用法：使用與 MACD 線的交叉來觸發交易。"
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"提示：應作為更廣泛策略的一部分以避免誤報。"
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),
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"macdh": (
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"MACD 柱狀圖：顯示 MACD 線與其信號線之間的差距。"
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"用法：可視化動能強度並及早發現背離。"
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"提示：可能不穩定；在快速變動的市場中輔以額外的過濾器。"
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),
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# 動能指標
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"rsi": (
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"RSI：衡量動能以標記超買/超賣狀況。"
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"用法：應用 70/30 閾值並觀察背離以發出反轉信號。"
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"提示：在強勁趨勢中，RSI 可能保持極端；務必與趨勢分析交叉檢查。"
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),
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# 波動性指標
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"boll": (
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"布林帶中軌：作為布林帶基礎的 20 SMA。"
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"用法：作為價格變動的動態基準。"
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"提示：與上下軌結合以有效發現突破或反轉。"
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),
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"boll_ub": (
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"布林帶上軌：通常比中軌高 2 個標準差。"
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"用法：發出潛在超買狀況和突破區域的信號。"
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"提示：與其他工具確認信號；在強勁趨勢中價格可能會沿著軌道運行。"
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),
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"boll_lb": (
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"布林帶下軌：通常比中軌低 2 個標準差。"
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"用法：指示潛在的超賣狀況。"
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"提示：使用額外分析以避免錯誤的反轉信號。"
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),
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"atr": (
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"ATR：平均真實波幅，用於衡量波動性。"
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"用法：根據當前市場波動性設置止損水平和調整頭寸大小。"
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"提示：這是一個反應性指標，因此請將其用作更廣泛風險管理策略的一部分。"
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),
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# 成交量指標
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"vwma": (
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"VWMA：成交量加權移動平均線。"
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"用法：通過將價格行為與成交量數據相結合來確認趨勢。"
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"提示：注意成交量激增導致的結果偏差；與其他成交量分析結合使用。"
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),
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"mfi": (
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"MFI：資金流動指數是一種動能指標，使用價格和成交量來衡量買賣壓力。"
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"用法：識別超買 (>80) 或超賣 (<20) 狀況，並確認趨勢或反轉的強度。"
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"提示：與 RSI 或 MACD 一起使用以確認信號；價格與 MFI 之間的背離可能表示潛在的反轉。"
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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"不支持指標 {indicator}。請從以下選項中選擇：{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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# 優化：一次性獲取股票數據並計算所有日期的指標
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try:
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indicator_data = _get_stock_stats_bulk(symbol, indicator, curr_date)
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# 生成我們需要的日期範圍
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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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# 查找此日期的指標值
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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：非交易日 (週末或假日)"
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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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# 建立結果字串
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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"獲取批量 stockstats 數據時出錯：{e}")
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# 如果批量方法失敗，則回退到原始實現
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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"## 從 {before.strftime('%Y-%m-%d')} 到 {end_date} 的 {indicator} 值：\n\n"
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+ ind_string
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+ "\n\n"
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+ best_ind_params.get(indicator, "無可用描述。")
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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, "公司的股票代碼"],
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indicator: Annotated[str, "要計算的技術指標"],
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curr_date: Annotated[str, "供參考的當前日期"]
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) -> dict:
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"""
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優化的股票統計指標批量計算。
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一次性獲取數據並計算所有可用日期的指標。
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返回將日期字串映射到指標值的字典。
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"""
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from .config import get_config
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import polars as pl
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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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# 本地數據路徑
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try:
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data = pl.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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# stockstats 需要 pandas DataFrame
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data_pd = data.to_pandas()
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df = wrap(data_pd)
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except FileNotFoundError:
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raise Exception("Stockstats 失敗：尚未獲取 Yahoo Finance 數據！")
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else:
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# 帶有快取的線上數據獲取
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from datetime import datetime as dt, timedelta
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today_date = dt.now()
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curr_date_dt = dt.strptime(curr_date, "%Y-%m-%d")
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end_date = today_date
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start_date = today_date - timedelta(days=365*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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# 檢查緩存是否存在且有效（24小時內）
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cache_valid = False
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if os.path.exists(data_file):
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file_mtime = os.path.getmtime(data_file)
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current_time = time.time()
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cache_age_hours = (current_time - file_mtime) / 3600
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if cache_age_hours < 24:
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cache_valid = True
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logger.info(f"{symbol} 緩存有效（年齡：{cache_age_hours:.1f} 小時）")
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else:
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logger.info(f"{symbol} 緩存過期（年齡：{cache_age_hours:.1f} 小時），將重新下載")
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if cache_valid:
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data_pl = pl.read_csv(data_file)
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data_pl = data_pl.with_columns(pl.col("Date").str.to_datetime())
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# stockstats 需要 pandas DataFrame
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data = data_pl.to_pandas()
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else:
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# 使用重試機制下載數據
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@retry(max_attempts=3, backoff=2.0)
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def download_data():
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return 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=False,
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timeout=30
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)
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try:
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data = download_data()
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data = data.reset_index()
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data.to_csv(data_file, index=False)
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logger.info(f"成功下載並緩存 {symbol} 數據到 {data_file}")
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except Exception as e:
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logger.error(f"下載 {symbol} 數據失敗: {e}")
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# 如果下載失敗但有舊緩存，使用舊緩存
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if os.path.exists(data_file):
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logger.warning(f"使用過期緩存作為備援")
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data_pl = pl.read_csv(data_file)
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data_pl = data_pl.with_columns(pl.col("Date").str.to_datetime())
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data = data_pl.to_pandas()
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else:
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raise
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df = wrap(data)
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df["Date"] = df["Date"].dt.strftime("%Y-%m-%d")
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# 一次性計算所有行的指標
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df[indicator] # 這會觸發 stockstats 計算指標
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# 建立一個將日期字串映射到指標值的字典
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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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# 處理 NaN/None 值
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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, "公司的股票代碼"],
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indicator: Annotated[str, "要獲取分析和報告的技術指標"],
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curr_date: Annotated[
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str, "您正在交易的當前交易日期，格式為 YYYY-mm-dd"
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],
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) -> str:
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"""
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獲取單個日期的 stockstats 指標。
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Args:
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symbol (str): 股票代碼。
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indicator (str): 指標名稱。
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curr_date (str): 日期。
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Returns:
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str: 指標值。
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"""
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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"獲取指標 {indicator} 在 {curr_date} 的 stockstats 指標數據時出錯：{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, "公司的股票代碼"],
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freq: Annotated[str, "數據頻率：'annual' 或 'quarterly'"] = "quarterly",
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curr_date: Annotated[str, "當前日期 (yfinance 未使用)"] = None
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):
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"""從 yfinance 獲取資產負債表數據。"""
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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"找不到 '{ticker}' 的資產負債表數據"
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# 為與其他函式保持一致，轉換為 CSV 字串
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csv_string = data.to_csv()
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# 新增標頭資訊
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header = f"# {ticker.upper()} 的資產負債表數據 ({freq})\n"
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header += f"# 數據檢索時間：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
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return header + csv_string
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|
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except Exception as e:
|
||
return f"檢索 {ticker} 的資產負債表時出錯：{str(e)}"
|
||
|
||
|
||
def get_cashflow(
|
||
ticker: Annotated[str, "公司的股票代碼"],
|
||
freq: Annotated[str, "數據頻率：'annual' 或 'quarterly'"] = "quarterly",
|
||
curr_date: Annotated[str, "當前日期 (yfinance 未使用)"] = None
|
||
):
|
||
"""從 yfinance 獲取現金流量數據。"""
|
||
try:
|
||
ticker_obj = yf.Ticker(ticker.upper())
|
||
|
||
if freq.lower() == "quarterly":
|
||
data = ticker_obj.quarterly_cashflow
|
||
else:
|
||
data = ticker_obj.cashflow
|
||
|
||
if data.empty:
|
||
return f"找不到 '{ticker}' 的現金流量數據"
|
||
|
||
# 為與其他函式保持一致，轉換為 CSV 字串
|
||
csv_string = data.to_csv()
|
||
|
||
# 新增標頭資訊
|
||
header = f"# {ticker.upper()} 的現金流量數據 ({freq})\n"
|
||
header += f"# 數據檢索時間：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
|
||
|
||
return header + csv_string
|
||
|
||
except Exception as e:
|
||
return f"檢索 {ticker} 的現金流量時出錯：{str(e)}"
|
||
|
||
|
||
def get_income_statement(
|
||
ticker: Annotated[str, "公司的股票代碼"],
|
||
freq: Annotated[str, "數據頻率：'annual' 或 'quarterly'"] = "quarterly",
|
||
curr_date: Annotated[str, "當前日期 (yfinance 未使用)"] = None
|
||
):
|
||
"""從 yfinance 獲取損益表數據。"""
|
||
try:
|
||
ticker_obj = yf.Ticker(ticker.upper())
|
||
|
||
if freq.lower() == "quarterly":
|
||
data = ticker_obj.quarterly_income_stmt
|
||
else:
|
||
data = ticker_obj.income_stmt
|
||
|
||
if data.empty:
|
||
return f"找不到 '{ticker}' 的損益表數據"
|
||
|
||
# 為與其他函式保持一致，轉換為 CSV 字串
|
||
csv_string = data.to_csv()
|
||
|
||
# 新增標頭資訊
|
||
header = f"# {ticker.upper()} 的損益表數據 ({freq})\n"
|
||
header += f"# 數據檢索時間：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
|
||
|
||
return header + csv_string
|
||
|
||
except Exception as e:
|
||
return f"檢索 {ticker} 的損益表時出錯：{str(e)}"
|
||
|
||
|
||
def get_fundamentals(
|
||
ticker: Annotated[str, "公司的股票代碼"],
|
||
curr_date: Annotated[str, "當前日期 (yfinance 未使用)"] = None
|
||
):
|
||
"""從 yfinance 獲取公司基本面數據。"""
|
||
try:
|
||
ticker_obj = yf.Ticker(ticker.upper())
|
||
info = ticker_obj.info
|
||
|
||
if not info or len(info) == 0:
|
||
return f"找不到 '{ticker}' 的基本面數據"
|
||
|
||
# 提取關鍵基本面指標（與 alpha_vantage 格式相似）
|
||
fundamentals = {
|
||
# 基本資訊
|
||
"Symbol": info.get("symbol", ticker.upper()),
|
||
"Name": info.get("longName", info.get("shortName", "")),
|
||
"Description": (info.get("longBusinessSummary", "") or "")[:300],
|
||
"Sector": info.get("sector", ""),
|
||
"Industry": info.get("industry", ""),
|
||
"MarketCapitalization": info.get("marketCap", ""),
|
||
|
||
# 關鍵財務指標
|
||
"EBITDA": info.get("ebitda", ""),
|
||
"PERatio": info.get("trailingPE", info.get("forwardPE", "")),
|
||
"PEGRatio": info.get("pegRatio", ""),
|
||
"BookValue": info.get("bookValue", ""),
|
||
"DividendPerShare": info.get("dividendRate", ""),
|
||
"DividendYield": info.get("dividendYield", ""),
|
||
"EPS": info.get("trailingEps", ""),
|
||
"RevenuePerShareTTM": info.get("revenuePerShare", ""),
|
||
"ProfitMargin": info.get("profitMargins", ""),
|
||
"OperatingMarginTTM": info.get("operatingMargins", ""),
|
||
"ReturnOnAssetsTTM": info.get("returnOnAssets", ""),
|
||
"ReturnOnEquityTTM": info.get("returnOnEquity", ""),
|
||
"RevenueTTM": info.get("totalRevenue", ""),
|
||
"GrossProfitTTM": info.get("grossProfits", ""),
|
||
|
||
# 交易指標
|
||
"52WeekHigh": info.get("fiftyTwoWeekHigh", ""),
|
||
"52WeekLow": info.get("fiftyTwoWeekLow", ""),
|
||
"50DayMovingAverage": info.get("fiftyDayAverage", ""),
|
||
"200DayMovingAverage": info.get("twoHundredDayAverage", ""),
|
||
|
||
# 財務健康指標
|
||
"QuarterlyEarningsGrowthYOY": info.get("earningsQuarterlyGrowth", ""),
|
||
"QuarterlyRevenueGrowthYOY": info.get("revenueGrowth", ""),
|
||
"AnalystTargetPrice": info.get("targetMeanPrice", ""),
|
||
"Beta": info.get("beta", ""),
|
||
|
||
# 額外的 yfinance 特有指標
|
||
"CurrentPrice": info.get("currentPrice", info.get("regularMarketPrice", "")),
|
||
"DebtToEquity": info.get("debtToEquity", ""),
|
||
"CurrentRatio": info.get("currentRatio", ""),
|
||
"QuickRatio": info.get("quickRatio", ""),
|
||
"FreeCashFlow": info.get("freeCashflow", ""),
|
||
}
|
||
|
||
# 過濾掉空值和 None
|
||
fundamentals = {k: v for k, v in fundamentals.items() if v not in (None, "", "None")}
|
||
|
||
import json
|
||
|
||
# 新增標頭資訊
|
||
header = f"# {ticker.upper()} 的基本面數據 (來源: yfinance)\n"
|
||
header += f"# 數據檢索時間：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
|
||
|
||
return header + json.dumps(fundamentals, ensure_ascii=False, indent=2)
|
||
|
||
except Exception as e:
|
||
return f"檢索 {ticker} 的基本面數據時出錯：{str(e)}"
|
||
|
||
|
||
def get_insider_transactions(
|
||
ticker: Annotated[str, "公司的股票代碼"]
|
||
):
|
||
"""從 yfinance 獲取內部人士交易數據。"""
|
||
try:
|
||
ticker_obj = yf.Ticker(ticker.upper())
|
||
data = ticker_obj.insider_transactions
|
||
|
||
if data is None or data.empty:
|
||
return f"找不到 '{ticker}' 的內部人士交易數據"
|
||
|
||
# 為與其他函式保持一致，轉換為 CSV 字串
|
||
csv_string = data.to_csv()
|
||
|
||
# 新增標頭資訊
|
||
header = f"# {ticker.upper()} 的內部人士交易數據\n"
|
||
header += f"# 數據檢索時間：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
|
||
|
||
return header + csv_string
|
||
|
||
except Exception as e:
|
||
return f"檢索 {ticker} 的內部人士交易時出錯：{str(e)}" |