Ubuntu
564 lines
24 KiB
Python
564 lines
24 KiB
Python
"""
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akshare data adapter for TradingAgents.
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Replaces yfinance as the data vendor for Chinese (A-share), US, and HK stocks.
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Uses akshare (https://github.com/akfamily/akshare) which sources data from
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East Money (东方财富), providing reliable access without Yahoo Finance rate limits.
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"""
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import pandas as pd
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from datetime import datetime, timedelta
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from typing import Optional
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from dateutil.relativedelta import relativedelta
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import os
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# Suppress akshare's internal tqdm progress bars
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os.environ.setdefault('TQDM_DISABLE', '1')
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import akshare as ak
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# ═══════════════════════════════════════════════════════════════
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# Symbol detection helpers
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# ═══════════════════════════════════════════════════════════════
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def _detect_market(symbol: str) -> str:
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"""Detect market: 'cn' (A-share), 'us' (US), or 'hk' (HK).
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A-share: 6-digit numeric (000001, 600519)
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US: alphabetic tickers (AAPL, TSLA)
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HK: 5-digit numeric (00700, 09988)
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"""
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sym = symbol.strip().upper().replace(".", "").replace("-", "")
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if sym.isdigit():
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if len(sym) == 6:
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return "cn"
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elif len(sym) == 5:
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return "hk"
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return "us"
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def _get_exchange_prefix(symbol: str) -> str:
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"""Get exchange prefix for A-share financial statement APIs.
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Shanghai: 60xxxx, 68xxxx → 'SH'
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Shenzhen: 00xxxx, 30xxxx → 'SZ'
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Beijing: 8xxxxx, 4xxxxx → 'BJ'
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"""
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sym = symbol.strip()
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first = sym[0]
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if first == '6':
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return "SH"
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elif first in ('0', '3'):
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return "SZ"
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elif first in ('8', '4'):
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return "BJ"
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return "SZ"
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# ═══════════════════════════════════════════════════════════════
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# Data fetching helpers
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# ═══════════════════════════════════════════════════════════════
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COLUMN_MAP = {
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"日期": "date",
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"开盘": "open",
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"收盘": "close",
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"最高": "high",
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"最低": "low",
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"成交量": "volume",
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"成交额": "amount",
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"振幅": "amplitude",
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"涨跌幅": "pct_change",
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"涨跌额": "change",
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"换手率": "turnover",
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}
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def _get_cn_stock_data(symbol: str, start_date: str, end_date: str) -> pd.DataFrame:
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"""Fetch A-share OHLCV via akshare stock_zh_a_hist."""
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df = ak.stock_zh_a_hist(
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symbol=symbol.strip(),
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period="daily",
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start_date=start_date.replace("-", ""),
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end_date=end_date.replace("-", ""),
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adjust="qfq",
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)
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df = df.rename(columns=COLUMN_MAP)
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needed = ["date", "open", "close", "high", "low", "volume"]
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return df[[c for c in needed if c in df.columns]]
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def _get_us_stock_data(symbol: str, start_date: str, end_date: str) -> pd.DataFrame:
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"""Fetch US stock OHLCV via akshare stock_us_daily (no market code needed).
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stock_us_daily returns all historical data; we filter by date range here.
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"""
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ticker = symbol.strip().upper()
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# akshare US tickers use dot notation (BRK.B), not dash (BRK-B)
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if "-" in symbol:
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ticker = ticker.replace("-", ".")
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df = ak.stock_us_daily(symbol=ticker, adjust="qfq")
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df["date"] = pd.to_datetime(df["date"])
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mask = (df["date"] >= pd.Timestamp(start_date)) & (df["date"] <= pd.Timestamp(end_date))
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df = df[mask]
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needed = ["date", "open", "close", "high", "low", "volume"]
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return df[[c for c in needed if c in df.columns]]
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def _get_hk_stock_data(symbol: str, start_date: str, end_date: str) -> pd.DataFrame:
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"""Fetch HK stock OHLCV via akshare stock_hk_hist."""
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df = ak.stock_hk_hist(
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symbol=symbol.strip(),
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period="daily",
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start_date=start_date.replace("-", ""),
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end_date=end_date.replace("-", ""),
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adjust="qfq",
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)
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df = df.rename(columns=COLUMN_MAP)
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needed = ["date", "open", "close", "high", "low", "volume"]
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return df[[c for c in needed if c in df.columns]]
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# ═══════════════════════════════════════════════════════════════
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# Formatting helpers
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# ═══════════════════════════════════════════════════════════════
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def _fmt_stock_data(df: pd.DataFrame, symbol: str) -> str:
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"""Format OHLCV DataFrame as Markdown table string."""
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if df.empty:
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return f"No stock data found for {symbol} in the given date range."
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df = df.tail(60).copy()
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df["date"] = df["date"].astype(str)
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return df.to_markdown(index=False)
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def _fmt_financial(df: pd.DataFrame, statement: str, symbol: str, freq: str) -> str:
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"""Format financial statement DataFrame.
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Handles both wide format (A-share: rows=periods, cols=items) and
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long format (US: rows=item×period, cols=ITEM_NAME+AMOUNT).
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"""
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if df.empty:
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return f"No {freq} {statement} data found for {symbol}."
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# Long format (US stocks) — pivot to wide
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if "ITEM_NAME" in df.columns and "AMOUNT" in df.columns:
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pivot = df.pivot_table(
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index="REPORT_DATE",
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columns="ITEM_NAME",
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values="AMOUNT",
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aggfunc="first",
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)
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pivot = pivot.sort_index(ascending=False)
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return pivot.head(8).to_markdown()
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# Wide format (A-share) — select date + key items
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key_items = {
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"balance sheet": [
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"REPORT_DATE", "TOTAL_ASSETS", "TOTAL_LIABILITIES",
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"TOTAL_EQUITY", "MONETARYFUNDS", "INVENTORY",
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"TOTAL_CURRENT_ASSETS", "TOTAL_CURRENT_LIAB",
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"TOTAL_NONCURRENT_ASSETS", "TOTAL_NONCURRENT_LIAB",
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"SHORT_LOAN", "LONG_LOAN", "GOODWILL",
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],
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"cash flow": [
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"REPORT_DATE", "NETCASH_OPERATE", "NETCASH_INVEST",
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"NETCASH_FINANCE", "CASH_EQUIVALENT_BEGIN",
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"CASH_EQUIVALENT_END", "OPERATE_INFLOW",
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"OPERATE_OUTFLOW", "INVEST_INFLOW", "INVEST_OUTFLOW",
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"FINANCE_INFLOW", "FINANCE_OUTFLOW",
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],
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"income statement": [
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"REPORT_DATE", "TOTAL_OPERATE_INCOME", "OPERATE_PROFIT",
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"TOTAL_PROFIT", "NET_PROFIT", "BASIC_EPS",
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"TOTAL_OPERATE_COST", "OPERATE_COST",
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"RESEARCH_EXPENSE", "SELL_EXPENSE", "MANAGE_EXPENSE",
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"FINANCE_EXPENSE",
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],
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}
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stat_key = statement.lower().replace("_", " ")
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items = key_items.get(stat_key, [])
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available = [c for c in items if c in df.columns]
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if not available:
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# Fallback: show all columns but limit columns to first 15
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result = df.head(8).iloc[:, :15]
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else:
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result = df[available].head(8)
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return result.to_markdown(index=False)
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# ═══════════════════════════════════════════════════════════════
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# Public API — matches y_finance.py interface
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# ═══════════════════════════════════════════════════════════════
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def get_stock_data(symbol: str, start_date: str, end_date: str) -> str:
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"""Get stock OHLCV price data. Returns Markdown table string."""
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try:
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market = _detect_market(symbol)
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if market == "cn":
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df = _get_cn_stock_data(symbol, start_date, end_date)
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elif market == "us":
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df = _get_us_stock_data(symbol, start_date, end_date)
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else:
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df = _get_hk_stock_data(symbol, start_date, end_date)
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return _fmt_stock_data(df, symbol)
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except Exception as e:
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return f"[akshare] Error fetching stock data for {symbol}: {e}"
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def get_indicators(
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symbol: str,
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indicator: str,
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curr_date: str,
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look_back_days: int = 365,
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) -> str:
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"""Get technical indicator values. Returns formatted string with date: value lines.
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Uses stockstats for calculation (same as yfinance adapter).
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"""
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from stockstats import wrap
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# Indicator descriptions (same as yfinance)
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best_ind_params = {
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"close_5_sma": (
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"5-day SMA: A short-term simple moving average. "
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"Usage: Identify short-term trend direction and potential entry/exit signals. "
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"Tips: Often combined with longer SMAs (e.g., 20-day) for crossover strategies."
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),
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"close_10_sma": (
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"10-day SMA: A short to medium-term simple moving average. "
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"Usage: Useful for gauging immediate trend strength and potential support/resistance levels."
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),
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"close_20_sma": (
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"20-day SMA: A medium-term simple moving average. "
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"Usage: Commonly used to identify the intermediate trend and key swing levels."
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),
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"close_50_sma": (
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"50-day SMA: A medium to long-term simple moving average. "
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"Usage: Watched by many traders to gauge the intermediate trend strength."
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),
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"close_200_sma": (
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"200-day SMA: A long-term simple moving average. "
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"Usage: Often used to determine the overall bull/bear market trend."
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),
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"close_5_ema": (
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"5-day EMA: A short-term exponential moving average giving more weight to recent prices. "
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"Usage: Provides early signals for short-term trend changes."
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),
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"close_10_ema": (
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"10-day EMA: A short to medium-term exponential moving average. "
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"Usage: Reacts faster to price changes than SMA; good for active trading strategies."
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),
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"close_20_ema": (
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"20-day EMA: A medium-term exponential moving average. "
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"Usage: Commonly watched by swing and short-term traders for trend confirmation."
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),
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"close_50_ema": (
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"50-day EMA: A medium to long-term exponential moving average. "
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"Usage: Offers a balance between responsiveness and smoothing for trend analysis."
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),
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"close_200_ema": (
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"200-day EMA: A long-term exponential moving average. "
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"Usage: Often used by longer-term traders to gauge major trend turns."
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),
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"rsi": (
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"RSI (14): The Relative Strength Index measures momentum on a scale of 0 to 100. "
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"Usage: Identify overbought (>70) or oversold (<30) conditions. "
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"Tips: Combine with trend indicators; divergences between RSI and price can signal reversals."
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),
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"rsi_6": (
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"RSI (6): A faster-setting RSI. "
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"Usage: More sensitive and generates earlier signals, ideal for quick momentum shifts."
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),
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"rsi_14": (
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"RSI (14): The standard Relative Strength Index. "
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"Usage: Same as RSI; widely used for identifying overbought/oversold and reversals."
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),
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"macd": (
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"MACD (Moving Average Convergence Divergence): Trend-following momentum indicator. "
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"Usage: Shows the relationship between two EMAs. "
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"Tips: Crossovers of the MACD and signal line, as well as divergence from price, can indicate trades."
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),
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"macds": (
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"MACD Signal: The 9-day EMA of the MACD line. "
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"Usage: Acts as a trigger for buy/sell signals when the MACD line crosses above or below the signal line."
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),
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"macdh": (
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"MACD Histogram: The difference between MACD and signal line. "
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"Usage: Illustrates changes in momentum; a rising histogram indicates increasing momentum."
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),
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"boll": (
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"Bollinger Bands Middle: 20-day SMA central line. "
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"Usage: The baseline for volatility bands. "
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"Tips: Prices reverting to the middle may suggest fading momentum."
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),
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"boll_ub": (
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"Bollinger Bands Upper: Upper volatility band (2 std dev above middle). "
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"Usage: May indicate overbought conditions. "
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"Tips: Used with lower band to identify squeeze and breakout patterns."
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),
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"boll_lb": (
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"Bollinger Bands Lower: Lower volatility band (2 std dev below middle). "
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"Usage: May indicate oversold conditions. "
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"Tips: Price touching/extending beyond the band can signal strong trend continuation."
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),
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"atr": (
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"ATR: Average True Range measures market volatility. "
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"Usage: High ATR indicates increased volatility; useful for setting stop-losses and position sizing."
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),
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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 = datetime.strptime(curr_date, "%Y-%m-%d")
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start_date = end_date - relativedelta(days=look_back_days + 200) # buffer for SMA calc
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try:
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# Fetch OHLCV via akshare
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market = _detect_market(symbol)
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if market == "cn":
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df = _get_cn_stock_data(symbol, start_date.strftime("%Y-%m-%d"), curr_date)
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elif market == "us":
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df = _get_us_stock_data(symbol, start_date.strftime("%Y-%m-%d"), curr_date)
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else:
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df = _get_hk_stock_data(symbol, start_date.strftime("%Y-%m-%d"), curr_date)
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# Prepare for stockstats (expects capitalized column names)
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df = df.rename(columns={
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"date": "Date", "open": "Open", "close": "Close",
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"high": "High", "low": "Low", "volume": "Volume",
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})
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sdf = wrap(df)
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sdf["Date"] = pd.to_datetime(sdf["Date"]).dt.strftime("%Y-%m-%d")
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# Trigger calculation
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sdf[indicator]
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# Build dict: date → value
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result_map = {}
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for _, row in sdf.iterrows():
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val = row[indicator]
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result_map[row["Date"]] = f"{val:.4f}" if not pd.isna(val) and isinstance(val, float) else str(val) if not pd.isna(val) else "N/A"
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except Exception as e:
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# Fallback: date-by-date calculation (less efficient but more resilient)
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print(f"[akshare] Bulk indicator calc failed: {e}, falling back to per-date")
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ind_string = ""
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curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d")
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fallback_dt = curr_date_dt
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while fallback_dt >= end_date - relativedelta(days=look_back_days):
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indicator_value = _get_indicator_single(symbol, indicator, fallback_dt.strftime("%Y-%m-%d"))
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ind_string += f"{fallback_dt.strftime('%Y-%m-%d')}: {indicator_value}\n"
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fallback_dt -= relativedelta(days=1)
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result_str = (
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f"## {indicator} values from "
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f"{end_date - relativedelta(days=look_back_days):%Y-%m-%d} to {curr_date}:\n\n"
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+ ind_string
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+ "\n\n"
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+ best_ind_params.get(indicator, "")
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)
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return result_str
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# Build output string
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ind_string = ""
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current_dt = end_date
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before = end_date - relativedelta(days=look_back_days)
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while current_dt >= before:
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date_str = current_dt.strftime("%Y-%m-%d")
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value = result_map.get(date_str, "N/A: Not a trading day (weekend or holiday)")
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ind_string += f"{date_str}: {value}\n"
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current_dt -= relativedelta(days=1)
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result_str = (
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f"## {indicator} values from {before.strftime('%Y-%m-%d')} to {curr_date}:\n\n"
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+ ind_string
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+ "\n\n"
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+ best_ind_params.get(indicator, "")
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)
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return result_str
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def _get_indicator_single(symbol: str, indicator: str, date_str: str) -> str:
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"""Fallback: calculate a single indicator value for one date."""
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from stockstats import wrap
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try:
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end_date = datetime.strptime(date_str, "%Y-%m-%d")
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start_date = end_date - relativedelta(days=400)
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market = _detect_market(symbol)
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if market == "cn":
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df = _get_cn_stock_data(symbol, start_date.strftime("%Y-%m-%d"), date_str)
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elif market == "us":
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df = _get_us_stock_data(symbol, start_date.strftime("%Y-%m-%d"), date_str)
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else:
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df = _get_hk_stock_data(symbol, start_date.strftime("%Y-%m-%d"), date_str)
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df = df.rename(columns={
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"date": "Date", "open": "Open", "close": "Close",
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"high": "High", "low": "Low", "volume": "Volume",
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})
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sdf = wrap(df)
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sdf["Date"] = pd.to_datetime(sdf["Date"]).dt.strftime("%Y-%m-%d")
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sdf[indicator]
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matching = sdf[sdf["Date"].str.startswith(date_str)]
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if not matching.empty:
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val = matching[indicator].values[0]
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return f"{val:.4f}" if isinstance(val, float) else str(val)
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return "N/A: Not a trading day (weekend or holiday)"
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except Exception as e:
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return f"N/A (error: {e})"
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def get_fundamentals(ticker: str, curr_date: str = None) -> str:
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"""Get company fundamentals. Returns Markdown string."""
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try:
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market = _detect_market(ticker)
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if market == "cn":
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df = ak.stock_individual_info_em(symbol=ticker.strip())
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lines = [f"# Fundamentals for {ticker}\n"]
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for _, row in df.head(30).iterrows():
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lines.append(f"- **{row['item']}**: {row['value']}")
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return "\n".join(lines)
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elif market == "us":
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t = ticker.strip().upper().replace("-", ".")
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df = ak.stock_individual_basic_info_us_xq(symbol=t)
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||
lines = [f"# Fundamentals for {ticker}\n"]
|
||
for _, row in df.head(25).iterrows():
|
||
lines.append(f"- **{row['item']}**: {row['value']}")
|
||
return "\n".join(lines)
|
||
|
||
else: # HK
|
||
try:
|
||
df = ak.stock_hk_company_profile_em(symbol=ticker.strip())
|
||
return df.to_markdown(index=False)
|
||
except Exception:
|
||
return f"Fundamentals not available for HK stock {ticker} via akshare."
|
||
|
||
except Exception as e:
|
||
return f"[akshare] Error fetching fundamentals for {ticker}: {e}"
|
||
|
||
|
||
def get_balance_sheet(ticker: str, freq: str = "annual", curr_date: str = None) -> str:
|
||
"""Get balance sheet. freq: 'annual' or 'quarterly'. Returns Markdown string."""
|
||
try:
|
||
market = _detect_market(ticker)
|
||
|
||
if market == "cn":
|
||
prefix = _get_exchange_prefix(ticker)
|
||
df = ak.stock_balance_sheet_by_report_em(symbol=f"{prefix}{ticker.strip()}")
|
||
if freq in ("annual", "yearly"):
|
||
df = df[df["REPORT_TYPE"] == "年报"]
|
||
else:
|
||
df = df[df["REPORT_TYPE"].isin(["一季报", "中报", "三季报"])]
|
||
if curr_date and not df.empty:
|
||
df = df[pd.to_datetime(df["REPORT_DATE"]) <= pd.Timestamp(curr_date)]
|
||
return _fmt_financial(df, "balance sheet", ticker, freq)
|
||
|
||
elif market == "us":
|
||
indicator = "年报" if freq in ("annual", "yearly") else "单季报"
|
||
t = ticker.strip().upper().replace("-", ".")
|
||
df = ak.stock_financial_us_report_em(
|
||
stock=t, symbol="资产负债表", indicator=indicator,
|
||
)
|
||
if curr_date and not df.empty:
|
||
df = df[pd.to_datetime(df["REPORT_DATE"]) <= pd.Timestamp(curr_date)]
|
||
return _fmt_financial(df, "balance sheet", ticker, freq)
|
||
|
||
else:
|
||
return f"Balance sheet not supported for HK stock {ticker} via akshare."
|
||
|
||
except Exception as e:
|
||
return f"[akshare] Error fetching balance sheet for {ticker}: {e}"
|
||
|
||
|
||
def get_cashflow(ticker: str, freq: str = "annual", curr_date: str = None) -> str:
|
||
"""Get cash flow statement. freq: 'annual' or 'quarterly'. Returns Markdown string."""
|
||
try:
|
||
market = _detect_market(ticker)
|
||
|
||
if market == "cn":
|
||
prefix = _get_exchange_prefix(ticker)
|
||
df = ak.stock_cash_flow_sheet_by_report_em(symbol=f"{prefix}{ticker.strip()}")
|
||
if freq in ("annual", "yearly"):
|
||
df = df[df["REPORT_TYPE"] == "年报"]
|
||
else:
|
||
df = df[df["REPORT_TYPE"].isin(["一季报", "中报", "三季报"])]
|
||
if curr_date and not df.empty:
|
||
df = df[pd.to_datetime(df["REPORT_DATE"]) <= pd.Timestamp(curr_date)]
|
||
return _fmt_financial(df, "cash flow", ticker, freq)
|
||
|
||
elif market == "us":
|
||
indicator = "年报" if freq in ("annual", "yearly") else "单季报"
|
||
t = ticker.strip().upper().replace("-", ".")
|
||
df = ak.stock_financial_us_report_em(
|
||
stock=t, symbol="现金流量表", indicator=indicator,
|
||
)
|
||
if curr_date and not df.empty:
|
||
df = df[pd.to_datetime(df["REPORT_DATE"]) <= pd.Timestamp(curr_date)]
|
||
return _fmt_financial(df, "cash flow", ticker, freq)
|
||
|
||
else:
|
||
return f"Cash flow statement not supported for HK stock {ticker} via akshare."
|
||
|
||
except Exception as e:
|
||
return f"[akshare] Error fetching cash flow for {ticker}: {e}"
|
||
|
||
|
||
def get_income_statement(ticker: str, freq: str = "annual", curr_date: str = None) -> str:
|
||
"""Get income statement. freq: 'annual' or 'quarterly'. Returns Markdown string."""
|
||
try:
|
||
market = _detect_market(ticker)
|
||
|
||
if market == "cn":
|
||
prefix = _get_exchange_prefix(ticker)
|
||
df = ak.stock_profit_sheet_by_report_em(symbol=f"{prefix}{ticker.strip()}")
|
||
if freq in ("annual", "yearly"):
|
||
df = df[df["REPORT_TYPE"] == "年报"]
|
||
else:
|
||
df = df[df["REPORT_TYPE"].isin(["一季报", "中报", "三季报"])]
|
||
if curr_date and not df.empty:
|
||
df = df[pd.to_datetime(df["REPORT_DATE"]) <= pd.Timestamp(curr_date)]
|
||
return _fmt_financial(df, "income statement", ticker, freq)
|
||
|
||
elif market == "us":
|
||
indicator = "年报" if freq in ("annual", "yearly") else "单季报"
|
||
t = ticker.strip().upper().replace("-", ".")
|
||
df = ak.stock_financial_us_report_em(
|
||
stock=t, symbol="综合损益表", indicator=indicator,
|
||
)
|
||
if curr_date and not df.empty:
|
||
df = df[pd.to_datetime(df["REPORT_DATE"]) <= pd.Timestamp(curr_date)]
|
||
return _fmt_financial(df, "income statement", ticker, freq)
|
||
|
||
else:
|
||
return f"Income statement not supported for HK stock {ticker} via akshare."
|
||
|
||
except Exception as e:
|
||
return f"[akshare] Error fetching income statement for {ticker}: {e}"
|
||
|
||
|
||
def get_insider_transactions(ticker: str) -> str:
|
||
"""Insider transactions not supported by akshare.
|
||
Raise AlphaVantageRateLimitError to trigger fallback to yfinance."""
|
||
from .alpha_vantage_common import AlphaVantageRateLimitError
|
||
raise AlphaVantageRateLimitError(
|
||
f"akshare does not support insider transactions for {ticker}. "
|
||
"Falling back to next vendor."
|
||
)
|