import pandas as pd import numpy as np import talib as tb from pandas.plotting import table from jqdata import * # 期权合约信息 class OptionContract: close: 0 #现价 volume: 0 #成交量 position: 0 #当前持仓量 priceChangePct: 0 #收盘价涨跌幅 intrinsic_value: 0 #内在价值 time_value: 0 #时间价值 def __init__(self,close, volume, position, priceChangePct, intrinsic_value=0, time_value=0): self.close = close self.volume = volume self.position = position self.priceChangePct = priceChangePct self.intrinsic_value = intrinsic_value self.time_value = time_value # 同一行权价的一组期权 class OptionGroup: exercisePrice: 0 ##行权价 call: None ##提醒的品种 put: None ##提醒的类型 def __init__(self, exercisePrice, call, put): self.exercisePrice = exercisePrice self.call = call self.put = put def __init__(self, exercisePrice): self.exercisePrice = exercisePrice #数据内容 CONTENT = [ 'close', 'volume', 'position', 'priceChangePct', ] ##数据域 CONTENT_HEADER_MAP = { 'prePosition': '昨日持仓', 'close': '现价', 'volume': '成交量', 'position': '持仓量', 'priceChangePct': '涨跌幅' } # 豆粕期权 symbol = 'AU' starttime = '2025-06-30' endtime = '2025-07-14' SUBJECT_MATTER = get_dominant_future(symbol,date = starttime) print(f"SUBJECT_MATTER: {SUBJECT_MATTER}") #m2407 匹配 2024-04-19 EXPIRE_MONTH = '2025-09-24' ## 行权价间隔 比如3800 3750 每50元一档 gap = 8 # 获取期货价格 try: # 获取期货价格数据 future_price_data = get_price(SUBJECT_MATTER, end_date=endtime, count=1, fields=['close']) current_future_price = float(future_price_data['close'].iloc[-1]) except Exception as e: print(f"获取期货价格失败: {e}") current_future_price = 781.4 # 使用模拟价格 print(f"标的期货价格: {current_future_price}") #查询相关的合约,适用于商品 qy = query(opt.OPT_CONTRACT_INFO).filter( opt.OPT_CONTRACT_INFO.underlying_symbol == SUBJECT_MATTER, ##期权标的物 opt.OPT_CONTRACT_INFO.expire_date == EXPIRE_MONTH,##期权到期日 ).order_by(opt.OPT_CONTRACT_INFO.exercise_price, opt.OPT_CONTRACT_INFO.contract_type) optList = opt.run_query(qy) optList code = SUBJECT_MATTER.split('.')[0] print(f"code: {code}") # optList.to_csv(f'price_list_{code}.csv') # 获取实时数据 optionGroups = {} for index, row in optList.iterrows(): code = row['code'] #key - 期权代码 # print(f"检查{code}的数据") #查询具体合约的信息,商品需要去掉开盘前的静态信息 realTimeQuery = query(opt.OPT_DAILY_PRICE).filter( opt.OPT_DAILY_PRICE.code==code, opt.OPT_DAILY_PRICE.date==endtime ).order_by(opt.OPT_DAILY_PRICE.date.desc()).limit(1) realTimeData = opt.run_query(realTimeQuery) # print(f"realTimeQuery: {realTimeQuery}") # print(f"realTimeData: {realTimeData}") #期权基本信息 exercisePrice = row['exercise_price'] #行权价 contractType = row['contract_type'] #合约类型。CO-认购期权，PO-认沽期权 #实时表查询 close = realTimeData.loc[0].at['close'] #现价 volume = int(realTimeData.loc[0].at['volume']) #成交量 position = realTimeData.loc[0].at['position'] #当前持仓量 priceChangePct = str(round(realTimeData.loc[0].at['change_pct_close'], 2)) + '%' #收盘价涨跌幅 #去除非标准的带A合约 if(exercisePrice % gap != 0): continue # 计算内在价值和时间价值 if contractType == 'CO': # 认购期权 intrinsic_value = max(current_future_price - exercisePrice, 0) time_value = close - intrinsic_value print(f"处理认购期权, 期货价格: {current_future_price}, 行权价：{exercisePrice}") print(f"获得内在价值: {intrinsic_value}, 时间价值: {time_value}") else: # 认沽期权 intrinsic_value = max(exercisePrice - current_future_price, 0) time_value = close - intrinsic_value print(f"处理认沽期权, 期货价格: {current_future_price}, 行权价：{exercisePrice}") print(f"获得内在价值: {intrinsic_value}, 时间价值: {time_value}") optionContract = OptionContract(close, volume, position, priceChangePct, intrinsic_value, time_value) if(exercisePrice in optionGroups): if(contractType == 'CO'): optionGroups[exercisePrice].call = optionContract else: optionGroups[exercisePrice].put = optionContract else: optionGroup = OptionGroup(exercisePrice) if(contractType == 'CO'): optionGroup.call = optionContract else: optionGroup.put = optionContract optionGroups[exercisePrice] = optionGroup # 使用optionGroups构造T型报价表 data = [] for exercisePrice in sorted(optionGroups.keys()): group = optionGroups[exercisePrice] # print(f"exercisePrice: {exercisePrice}, group: {group}") # 认购期权数据 call_price = group.call.close if group.call else None call_volume = group.call.volume if group.call else None call_position = group.call.position if group.call else None call_change = group.call.priceChangePct if group.call else None call_intrinsic = group.call.intrinsic_value if group.call else None # print(f"exercisePrice: {exercisePrice}, call_intrinsic: {call_intrinsic}") call_time = group.call.time_value if group.call else None # 认沽期权数据 put_price = group.put.close if group.put else None put_volume = group.put.volume if group.put else None put_position = group.put.position if group.put else None put_change = group.put.priceChangePct if group.put else None put_intrinsic = group.put.intrinsic_value if group.put else None put_time = group.put.time_value if group.put else None data.append({ '认购涨跌幅': call_change, # '认购持仓量': call_position, # '认购成交量': call_volume, '认购时间价值': call_time, '认购内在价值': call_intrinsic, '认购价格': call_price, '行权价': exercisePrice, '认沽价格': put_price, '认沽内在价值': put_intrinsic, '认沽时间价值': put_time, # '认沽成交量': put_volume, # '认沽持仓量': put_position, '认沽涨跌幅': put_change }) # 创建T型报价DataFrame df = pd.DataFrame(data) # 确保df的列顺序为：'认购涨跌幅'，'认购持仓量'，'认购成交量'，'认购时间价值'，'认购内在价值'，'认购价格'，'行权价'，'认沽价格'，'认沽内在价值'，'认沽时间价值'，'认沽成交量'，'认沽持仓量'，'认沽涨跌幅' df = df[['认购涨跌幅', '认购时间价值', '认购内在价值', '认购价格', '行权价', '认沽价格', '认沽内在价值', '认沽时间价值', '认沽涨跌幅']] print("\n" + "="*100) print("期权T型报价表（含内在价值和时间价值）") print("="*100) print(f"标的期货价格: {current_future_price}") print("="*100) # 显示T型报价 print(df.to_string(index=False))