國信證券學習系列（8）

????????我為什么要用國信，就是這個原因，可以做期權，期貨，股票，etf，可轉債的回測。滿足了我所有的需要，我要做指數增強。通常的做法是股票和期貨。但實際上，股票和期權做組合，成本更低。

（1）認沽期權和認購期權的隱含波動率大于50etf波動率時，
以標的資產當前的價格作為行權價買入相同數量、相同標的資產和相同行權日的認購和認沽期權開倉
（2）認沽期權和認購期權的隱含波動率小于50etf波動率且有市場時，平倉
獲取數據：

import datetime
from math import (log, pow, sqrt, exp)from scipy import stats
import numpy as np
import talibcdf = stats.norm.cdf
STEP_CHANGE = 0.001
STEP_UP = 1 + STEP_CHANGE
STEP_DOWN = 1 - STEP_CHANGE
STEP_DIFF = STEP_CHANGE * 2
DX_TARGET = 0.00001
'''
變量說明
s：標的物股票價格
k：行權價
r：無風險利率
t：剩余到期時間（年）
v：隱含波動率
cp：期權類型，+1/-1對應call/put
price：期權價格
'''

初始參數：

def init(ContextInfo):ContextInfo.contract_unit = 10000#隨著時間推移該對合約可能不存了，根據需求手動修改更新ContextInfo.optOne="10001713" ContextInfo.optTwo="10001714"#上面合約的到期年月，根據需求手動修改更新ContextInfo.aa= get_week_of_month(2019, 3)ContextInfo.holdings = {ContextInfo.optOne: 0, ContextInfo.optTwo: 0}ContextInfo.accountID = '8861103625'ContextInfo.s_volatility = []

計算隱含波動率：

def calculateImpv(price, s, k, r, t, cp):"""計算隱含波動率"""# 檢查期權價格必須為正數if price <= 0:return 0# 檢查期權價格是否滿足最小價值（即到期行權價值）meet = Falseif cp == 1 and (price > (s - k) * exp(-r * t)):meet = Trueelif cp == -1 and (price > k * exp(-r * t) - s):meet = True# 若不滿足最小價值，則直接返回0if not meet:return 0# 采用Newton Raphson方法計算隱含波動率v = 0.29 # 初始波動率猜測for i in range(50):# 計算當前猜測波動率對應的期權價格和vega值p = calculatePrice(s, k, r, t, v, cp)vega = calculateOriginalVega(s, k, r, t, v, cp)# 如果vega過小接近0，則直接返回if not vega:break# 計算誤差dx = (price - p) / vega# 檢查誤差是否滿足要求，若滿足則跳出循環if abs(dx) < DX_TARGET:break# 計算新一輪猜測的波動率v += dx# 檢查波動率計算結果非負if v <= 0:return 0# 保留4位小數v = round(v, 4)return v

計算vega：

def calculateOriginalVega(s, k, r, t, v, cp):"""計算原始vega值"""    price1 = calculatePrice(s, k, r, t, v*STEP_UP, cp)price2 = calculatePrice(s, k, r, t, v*STEP_DOWN, cp)vega = (price1 - price2) / (v * STEP_DIFF)return vega

計算期權價格：

def calculatePrice(s, k, r, t, v, cp):"""計算期權價格"""# 如果波動率為0，則直接返回期權空間價值if v <= 0:return max(0, cp * (s - k))d1 = (log(s / k) + (r + 0.5 * pow(v, 2)) * t) / (v * sqrt(t))d2 = d1 - v * sqrt(t)price = cp * (s * cdf(cp * d1) - k * cdf(cp * d2) * exp(-r * t))return price

獲取日期：

def get_week_of_month(year, month):"""獲取指定的某天是某個月中的第幾周周一作為一周的開始"""begin = datetime.datetime(year, month, 1).weekday()delta = datetime.timedelta(days=21+begin)exercise_date = datetime.datetime(year, month, 1) + deltareturn  exercise_date.strftime('%Y-%m-%d')

策略代碼：

#coding:gbk
"""
跨式期權策略
回測模型示例（非實盤交易策略）（1）認沽期權和認購期權的隱含波動率大于50etf波動率時，
以標的資產當前的價格作為行權價買入相同數量、相同標的資產和相同行權日的認購和認沽期權開倉
（2）認沽期權和認購期權的隱含波動率小于50etf波動率且有市場時，平倉
"""
import datetime
from math import (log, pow, sqrt, exp)from scipy import stats
import numpy as np
import talibcdf = stats.norm.cdf
STEP_CHANGE = 0.001
STEP_UP = 1 + STEP_CHANGE
STEP_DOWN = 1 - STEP_CHANGE
STEP_DIFF = STEP_CHANGE * 2
DX_TARGET = 0.00001
'''
變量說明
s：標的物股票價格
k：行權價
r：無風險利率
t：剩余到期時間（年）
v：隱含波動率
cp：期權類型，+1/-1對應call/put
price：期權價格
'''def init(ContextInfo):ContextInfo.contract_unit = 10000#隨著時間推移該對合約可能不存了，根據需求手動修改更新ContextInfo.optOne="10001713" ContextInfo.optTwo="10001714"#上面合約的到期年月，根據需求手動修改更新ContextInfo.aa= get_week_of_month(2019, 3)ContextInfo.holdings = {ContextInfo.optOne: 0, ContextInfo.optTwo: 0}ContextInfo.accountID = '8861103625'ContextInfo.s_volatility = []
def handlebar(ContextInfo):#當前K線的對應的下標從0開始index = ContextInfo.barpos#當前K線對應的時間：毫秒realtime = ContextInfo.get_bar_timetag(index)current_time = timetag_to_datetime(realtime,'%Y-%m-%d')#當前周期period = ContextInfo.period#取當前K線圖對應的合約當前K線的當前主圖復權方式下的收盤價price_call = ContextInfo.get_market_data(['close'],period=period, stock_code = [ContextInfo.optOne+".SHO"]) price_put = ContextInfo.get_market_data(['close'],period=period, stock_code = [ContextInfo.optTwo+".SHO"])s = ContextInfo.get_market_data(["close"],stock_code = ["510050.SH"],period=period)#print price_call,price_putContextInfo.s_volatility.append(s)s_volatility = np.std(np.array(ContextInfo.s_volatility))ContextInfo.paint("s_volatility", s_volatility*10, -1, 0)k = 2.55r = ContextInfo.get_risk_free_rate(index)/100t = ((datetime.datetime.strptime(ContextInfo.aa, "%Y-%m-%d") - datetime.datetime.strptime(current_time, "%Y-%m-%d")).days) / 365.0cp_call = 1cp_put = -1implied_volatility_call = calculateImpv(price_call, s, k, r, t, cp_call)implied_volatility_put = calculateImpv(price_put, s, k, r, t, cp_put)#print "implied_volatility_call", implied_volatility_call#print "implied_volatility_put", implied_volatility_putContextInfo.paint("implied_volatility", (implied_volatility_put + implied_volatility_call), -1, 0)if (implied_volatility_put + implied_volatility_call) > s_volatility*10 :passorder(50,1101,ContextInfo.accountID ,ContextInfo.optOne,5,-1,1,ContextInfo)passorder(50,1101,ContextInfo.accountID ,ContextInfo.optTwo,5,-1,1,ContextInfo)ContextInfo.holdings[ContextInfo.optOne] = 1ContextInfo.holdings[ContextInfo.optTwo] = 1elif ContextInfo.holdings[ContextInfo.optOne] == 1 and ContextInfo.holdings[ContextInfo.optTwo] == 1 and (implied_volatility_put + implied_volatility_call) < s_volatility*10 :passorder(53,1101,ContextInfo.accountID ,ContextInfo.optOne,5,-1,1,ContextInfo)passorder(53,1101,ContextInfo.accountID ,ContextInfo.optTwo,5,-1,1,ContextInfo)ContextInfo.holdings[ContextInfo.optOne] = 0ContextInfo.holdings[ContextInfo.optTwo] = 0def calculateImpv(price, s, k, r, t, cp):"""計算隱含波動率"""# 檢查期權價格必須為正數if price <= 0:return 0# 檢查期權價格是否滿足最小價值（即到期行權價值）meet = Falseif cp == 1 and (price > (s - k) * exp(-r * t)):meet = Trueelif cp == -1 and (price > k * exp(-r * t) - s):meet = True# 若不滿足最小價值，則直接返回0if not meet:return 0# 采用Newton Raphson方法計算隱含波動率v = 0.29 # 初始波動率猜測for i in range(50):# 計算當前猜測波動率對應的期權價格和vega值p = calculatePrice(s, k, r, t, v, cp)vega = calculateOriginalVega(s, k, r, t, v, cp)# 如果vega過小接近0，則直接返回if not vega:break# 計算誤差dx = (price - p) / vega# 檢查誤差是否滿足要求，若滿足則跳出循環if abs(dx) < DX_TARGET:break# 計算新一輪猜測的波動率v += dx# 檢查波動率計算結果非負if v <= 0:return 0# 保留4位小數v = round(v, 4)return vdef calculateOriginalVega(s, k, r, t, v, cp):"""計算原始vega值"""    price1 = calculatePrice(s, k, r, t, v*STEP_UP, cp)price2 = calculatePrice(s, k, r, t, v*STEP_DOWN, cp)vega = (price1 - price2) / (v * STEP_DIFF)return vegadef calculatePrice(s, k, r, t, v, cp):"""計算期權價格"""# 如果波動率為0，則直接返回期權空間價值if v <= 0:return max(0, cp * (s - k))d1 = (log(s / k) + (r + 0.5 * pow(v, 2)) * t) / (v * sqrt(t))d2 = d1 - v * sqrt(t)price = cp * (s * cdf(cp * d1) - k * cdf(cp * d2) * exp(-r * t))return pricedef get_week_of_month(year, month):"""獲取指定的某天是某個月中的第幾周周一作為一周的開始"""begin = datetime.datetime(year, month, 1).weekday()delta = datetime.timedelta(days=21+begin)exercise_date = datetime.datetime(year, month, 1) + deltareturn  exercise_date.strftime('%Y-%m-%d')