Security Options Strategy — 安全选项策略

期权策略大师——覆盖期权定价、Greeks计算、多腿策略分析、波动率交易。适用：期权交易者、风险管理人。

行业痛点 / 行业痛点 痛点 / 痛点 | 影响 | 本Skill解决方案 定价复杂 | BS模型参数选择困难 | 参数解释+敏感性分析 希腊字母难懂 | Delta/Gamma/Vega傻傻分不清 | 可视化Greeks图形 策略选择困难 | 不知道用什么策略应对市场 | 情景化策略推荐 保证金占用 | 资金效率低 | 最优保证金管理 风险敞口不清晰 | 极端行情爆仓 | 实时风险监控+预警

触发关键词 / 触发关键词 English Triggers: options strategy, options pricing, Greeks, volatility trading, China options, covered call, protective put, spread trading, straddles, collars 中文触发词（优先）：期权策略 / 期权定价 / 希腊字母 / Delta / Gamma / Vega / 备兑开仓 / 保护性看跌 / 价差策略 / 跨式策略 / 勒式策略 / 波动率交易 / 50ETF期权 / 沪深300期权 / 期权开户 / 期权权限 / 权利仓 / 义务仓 / 做市商 / 波动率曲面

核心能力 / 核心能力

• 期权定价引擎 / Options Pricing Engine

import numpy as np
from scipy.stats import norm
class OptionsPricer:
    """期权定价引擎"""
    @staticmethod
    def black_scholes(S, K, T, r, sigma, option_type='call'):
        """ Black-Scholes期权定价
        Args:
            S: 标的资产价格
            K: 行权价
            T: 到期时间（年）
            r: 无风险利率
            sigma: 波动率
            option_type: 'call' 或 'put'
        """
        if T <= 0:
            if option_type == 'call':
                return max(S - K, 0)
            else:
                return max(K - S, 0)
        d1 = (np.log(S / K) + (r + 0.5  sigma  2)  T) / (sigma  np.sqrt(T))
        d2 = d1 - sigma  np.sqrt(T)
        if option_type == 'call':
            price = S  norm.cdf(d1) - K  np.exp(-r  T)  norm.cdf(d2)
            delta = norm.cdf(d1)
        else:
            price = K  np.exp(-r  T)  norm.cdf(-d2) - S  norm.cdf(-d1)
            delta = norm.cdf(d1) - 1
        # Greeks计算
        gamma = norm.pdf(d1) / (S  sigma  np.sqrt(T))
        vega = S  norm.pdf(d1)  np.sqrt(T) / 100
        theta_call = (-S  norm.pdf(d1)  sigma / (2  np.sqrt(T)) - r  K  np.exp(-r  T)  norm.cdf(d2)) / 365
        theta_put = (-S  norm.pdf(d1)  sigma / (2  np.sqrt(T)) + r  K  np.exp(-r  T)  norm.cdf(-d2)) / 365
        return {
            'price': round(price, 4),
            'delta': round(delta, 4),
            'gamma': round(gamma, 6),
            'vega': round(vega, 4),
            'theta': round(theta_call if option_type == 'call' else theta_put, 4),
            'd1': round(d1, 4),
            'd2': round(d2, 4)
        }
    @staticmethod
    def implied_volatility(market_price, S, K, T, r, option_type='call'):
        """计算隐含波动率（牛顿迭代法）"""
        sigma = 0.3
        for _ in range(100):
            bs_price = OptionsPricer.black_scholes(S, K, T, r, sigma, option_type)['price']
            vega = OptionsPricer.black_scholes(S, K, T, r, sigma, option_type)['vega']  100
            diff = market_price - bs_price
            if abs(diff) < 1e-6:
                break
            sigma = sigma + diff / vega
        if sigma < 0.01 or sigma > 5:
            return None
        return round(sigma  100, 2)
期权策略分析 / Options Strategy Analysis

python class OptionsStrategy: """期权策略分析""" STRATEGIES = { "covered_call": { "name": "备兑开仓", "description": "持有标的+卖出看涨期权", "use_case": "预期横盘/小幅上涨，想增加收益", "max_profit": "股价 - 行权价 + 权利金", "max_loss": "无限（理论上）" }, "protective_put": { "name": "保护性看跌", "description": "持有标的+买入看跌期权", "use_case": "担心下跌，想锁定损失", "max_profit": "无限", "max_loss": "行权价 - 股价 + 权利金" }, "bull_call_spread": { "name": "牛市看涨价差", "description": "买入低行权价看涨+卖出高行权价看涨", "use_case": "看涨但涨幅有限", "max_profit": "行权价差 - 净权利金", "max_loss": "净权利金" }, "bear_put_spread": { "name": "熊市看跌价差", "description": "买入高行权价看跌+卖出低行权价看跌", "use_case": "看跌但跌幅有限", "max_profit": "行权价差 - 净权利金", "max_loss": "净权利金" }, "straddle": { "name": "跨式策略", "description": "同时买入同行权价的看涨+看跌", "use_case": "预期大幅波动但方向不明", "max_profit": "无限（上涨）或标的 - 行权价（下跌）", "max_loss": "两倍权利金" }, "strangle": { "name": "勒式策略", "description": "买入高行权价看涨+低行权价看跌", "use_case": "预期大幅波动但方向不明（成本低于跨式）", "max_profit": "无限", "max_loss": "两倍权利金" }, "iron_condor": { "name": "铁鹰策略", "description": "卖出价差+买入更宽价差保护", "use_case": "预期标的在一定范围内波动", "max_profit": "净权利金", "max_loss": "两个价差幅度 - 净权利金" } }

def analyze_strategy(self, strategy_name): ```