wehub-resource-sync
77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
from __future__ import annotations
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import math
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import matplotlib.pyplot as plt
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import pandas as pd
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import yfinance as yf
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_DARK_BG = "#0f172a"
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_AXES_BG = "#1e293b"
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_GRID_COLOR = "#334155"
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_TEXT_COLOR = "#cbd5e1"
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_TICK_COLOR = "#94a3b8"
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def apply_dark_style(fig: plt.Figure, *axes) -> None:
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fig.patch.set_facecolor(_DARK_BG)
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for ax in axes:
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ax.set_facecolor(_AXES_BG)
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ax.tick_params(colors=_TICK_COLOR)
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ax.xaxis.label.set_color(_TEXT_COLOR)
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ax.yaxis.label.set_color(_TEXT_COLOR)
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ax.title.set_color(_TEXT_COLOR)
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for s in ax.spines.values():
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s.set_color(_GRID_COLOR)
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ax.grid(True, color=_GRID_COLOR, alpha=0.45)
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lg = ax.get_legend()
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if lg is not None:
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lg.get_frame().set_facecolor(_AXES_BG)
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lg.get_frame().set_edgecolor(_GRID_COLOR)
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for t in lg.get_texts():
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t.set_color(_TEXT_COLOR)
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def close_series(ticker: str, start: str, end: str) -> pd.Series:
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df = yf.download(ticker, start=start, end=end, auto_adjust=True, progress=False)
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if df.empty:
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raise RuntimeError(f"未获取到 {ticker} 数据")
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close = df["Close"]
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if isinstance(close, pd.DataFrame):
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close = close.iloc[:, 0]
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s = close.dropna()
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s.name = ticker
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return s
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def annual_to_daily(rate: float, periods: int = 252) -> float:
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return (1.0 + rate) ** (1.0 / periods)
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def rsi(series: pd.Series, period: int = 14) -> pd.Series:
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d = series.diff()
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up = d.clip(lower=0).ewm(alpha=1.0 / period, adjust=False).mean()
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dn = (-d.clip(upper=0)).ewm(alpha=1.0 / period, adjust=False).mean()
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rs = up / dn.replace(0, math.nan)
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return 100.0 - (100.0 / (1.0 + rs))
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def norm_cdf(x: float) -> float:
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return 0.5 * (1.0 + math.erf(x / math.sqrt(2.0)))
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def bs_price(spot: float, strike: float, t_years: float, rate: float, sigma: float, kind: str) -> float:
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if t_years <= 0:
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if kind == "put":
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return max(strike - spot, 0.0)
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return max(spot - strike, 0.0)
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sigma = max(sigma, 1e-6)
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if spot <= 0 or strike <= 0:
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return 0.0
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d1 = (math.log(spot / strike) + (rate + 0.5 * sigma * sigma) * t_years) / (sigma * math.sqrt(t_years))
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d2 = d1 - sigma * math.sqrt(t_years)
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if kind == "put":
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return strike * math.exp(-rate * t_years) * norm_cdf(-d2) - spot * norm_cdf(-d1)
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return spot * norm_cdf(d1) - strike * math.exp(-rate * t_years) * norm_cdf(d2)
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