62 lines
2.0 KiB
Python
62 lines
2.0 KiB
Python
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"""基金深度分析的数据整理层。"""
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from __future__ import annotations
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from decimal import Decimal
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from typing import Iterable
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def _number(value):
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if isinstance(value, Decimal):
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return float(value)
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return value
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def _display_value(value):
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if value is None:
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return "暂无"
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if isinstance(value, float):
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return f"{value:g}"
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return str(value)
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def _build_analysis_text(fund: dict, metrics: list[dict]) -> str:
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fund_name = fund.get("fund_name") or fund.get("fund_code") or "该基金"
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if not metrics:
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return f"{fund_name}暂无足够业绩数据，暂无法形成完整解读。"
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latest = metrics[-1]
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period = _display_value(latest.get("period"))
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return_rate = _display_value(latest.get("return_rate"))
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max_drawdown = _display_value(latest.get("max_drawdown"))
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sharpe = _display_value(latest.get("sharpe"))
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return (
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f"{fund_name}在{period}的历史收益率为{return_rate}%，"
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f"最大回撤为{max_drawdown}%，夏普比率为{sharpe}。"
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"以上仅基于历史业绩数据，不代表未来收益。"
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)
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def build_fund_analysis(fund: dict, performance_rows: Iterable[dict]) -> dict:
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metrics = []
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for row in performance_rows:
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metrics.append(
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{
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key: _number(value)
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for key, value in row.items()
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}
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)
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return {
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"fund_code": fund.get("fund_code"),
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"fund_name": fund.get("fund_name"),
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"risk_level": fund.get("risk_level"),
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"metrics": metrics,
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"analysis_text": _build_analysis_text(fund, metrics),
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"chart_data": {
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"periods": [row.get("period") for row in metrics],
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"return_rate": [row.get("return_rate") for row in metrics],
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"annual_volatility": [row.get("annual_volatility") for row in metrics],
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"max_drawdown": [row.get("max_drawdown") for row in metrics],
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"sharpe": [row.get("sharpe") for row in metrics],
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},
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}
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