""" 该代码的主要目的是处理Tick数据并生成交易信号。代码中定义了一个tickcome函数，它接收到Tick数据后会进行一系列的处理，包括构建Tick字典、更新上一个Tick的成交量、保存Tick数据、生成K线数据等。其中涉及到的一些函数有： on_tick(tick): 处理单个Tick数据，根据Tick数据生成K线数据。 tickdata(df, symbol): 处理Tick数据，生成K线数据。 orderflow_df_new(df_tick, df_min, symbol): 处理Tick和K线数据，生成订单流数据。 GetOrderFlow_dj(kData): 计算订单流的信号指标。 除此之外，代码中还定义了一个MyTrader类，继承自TraderApiBase，用于实现交易相关的功能。 """ # from multiprocessing import Process, Queue import pandas as pd from datetime import datetime from datetime import time as s_time import operator # import time import numpy as np import os import re tickdatadict = {} # 存储Tick数据的字典 quotedict = {} # 存储行情数据的字典 ofdatadict = {} # 存储K线数据的字典 trader_df = pd.DataFrame({}) # 存储交易数据的DataFrame对象 previous_volume = {} # 上一个Tick的成交量 tsymbollist = {} def tickcome(md_queue): global previous_volume data = md_queue instrument_id = data["InstrumentID"] # 品种代码 # 将 ActionDay 转换为日期字符串 action_day = pd.to_datetime(data["ActionDay"]).strftime("%Y-%m-%d") # 从 UpdateTime 中提取时间部分 update_time = pd.to_datetime(data["UpdateTime"]).strftime("%H:%M:%S") # 组合时间字符串 created_at = f"{action_day} {update_time}.{data['UpdateMillisec']:03d}" # created_at = ActionDay[:4] + '-' + ActionDay[4:6] + '-' + ActionDay[6:] + ' ' + update_time + '.' + update_millisec #创建时间 # 构建tick字典 tick = { "symbol": instrument_id, # 品种代码和交易所ID "created_at": datetime.strptime(created_at, "%Y-%m-%d %H:%M:%S.%f"), # 'created_at': created_at, # 创建时间 "price": float(data["LastPrice"]), # 最新价 "last_volume": int(data["Volume"]) - previous_volume.get(instrument_id, 0) if previous_volume.get(instrument_id, 0) != 0 else 0, # 瞬时成交量 "bid_p": float(data["BidPrice1"]), # 买价 "bid_v": int(data["BidVolume1"]), # 买量 "ask_p": float(data["AskPrice1"]), # 卖价 "ask_v": int(data["AskVolume1"]), # 卖量 "UpperLimitPrice": float(data["UpperLimitPrice"]), # 涨停价 "LowerLimitPrice": float(data["LowerLimitPrice"]), # 跌停价 "TradingDay": data["TradingDay"], # 交易日日期 "cum_volume": int(data["Volume"]), # 最新总成交量 "cum_amount": float(data["Turnover"]), # 最新总成交额 "cum_position": int(data["OpenInterest"]), # 合约持仓量 } # 更新上一个Tick的成交量 previous_volume[instrument_id] = int(data["Volume"]) if tick["last_volume"] > 0: # print(tick['created_at'],'vol:',tick['last_volume']) # 处理Tick数据 on_tick(tick) def can_time(hour, minute): hour = str(hour) minute = str(minute) if len(minute) == 1: minute = "0" + minute return int(hour + minute) def on_tick(tick): # tm = can_time(tick["created_at"].hour, tick["created_at"].minute) # print(tick['symbol']) # print(1) # if tm>1500 and tm<2100 : # return if tick["last_volume"] == 0: return quotes = tick timetick = str(tick["created_at"]).replace("+08:00", "") tsymbol = tick["symbol"] if tsymbol not in tsymbollist.keys(): # 获取tick的买卖价和买卖量 tsymbollist[tsymbol] = tick bid_p = quotes["bid_p"] ask_p = quotes["ask_p"] bid_v = quotes["bid_v"] ask_v = quotes["ask_v"] else: # 获取上一个tick的买卖价和买卖量 rquotes = tsymbollist[tsymbol] bid_p = rquotes["bid_p"] ask_p = rquotes["ask_p"] bid_v = rquotes["bid_v"] ask_v = rquotes["ask_v"] tsymbollist[tsymbol] = tick tick_dt = pd.DataFrame( { "datetime": timetick, "symbol": tick["symbol"], "mainsym": tick["symbol"].rstrip("0123456789").upper(), "lastprice": tick["price"], "vol": tick["last_volume"], "bid_p": bid_p, "ask_p": ask_p, "bid_v": bid_v, "ask_v": ask_v, }, index=[0], ) sym = tick_dt["symbol"][0] # print(tick_dt) tickdata(tick_dt, sym) def data_of(df): global trader_df # 将df数据合并到trader_df中 trader_df = pd.concat([trader_df, df], ignore_index=True) # print('trader_df: ', len(trader_df)) # print(trader_df) def process(bidDict, askDict, symbol): try: # 尝试从quotedict中获取对应品种的报价数据 dic = quotedict[symbol] bidDictResult = dic["bidDictResult"] askDictResult = dic["askDictResult"] except Exception: # 如果获取失败，则初始化bidDictResult和askDictResult为空字典 bidDictResult, askDictResult = {}, {} # 将所有买盘字典和卖盘字典的key合并，并按升序排序 sList = sorted(set(list(bidDict.keys()) + list(askDict.keys()))) # 遍历所有的key，将相同key的值进行累加 for s in sList: if s in bidDict: if s in bidDictResult: bidDictResult[s] = int(bidDict[s]) + bidDictResult[s] else: bidDictResult[s] = int(bidDict[s]) if s not in askDictResult: askDictResult[s] = 0 else: if s in askDictResult: askDictResult[s] = int(askDict[s]) + askDictResult[s] else: askDictResult[s] = int(askDict[s]) if s not in bidDictResult: bidDictResult[s] = 0 # 构建包含bidDictResult和askDictResult的字典，并存入quotedict中 df = {"bidDictResult": bidDictResult, "askDictResult": askDictResult} quotedict[symbol] = df return bidDictResult, askDictResult def tickdata(df, symbol): tickdata = pd.DataFrame( { "datetime": df["datetime"], "symbol": df["symbol"], "lastprice": df["lastprice"], "volume": df["vol"], "bid_p": df["bid_p"], "bid_v": df["bid_v"], "ask_p": df["ask_p"], "ask_v": df["ask_v"], } ) try: if symbol in tickdatadict.keys(): rdf = tickdatadict[symbol] rdftm = pd.to_datetime(rdf["bartime"][0]).strftime("%Y-%m-%d %H:%M:%S") now = str(tickdata["datetime"][0]) if now > rdftm: try: oo = ofdatadict[symbol] data_of(oo) # print('oo',oo) if symbol in quotedict.keys(): quotedict.pop(symbol) if symbol in tickdatadict.keys(): tickdatadict.pop(symbol) if symbol in ofdatadict.keys(): ofdatadict.pop(symbol) except IOError as e: print("rdftm捕获到异常", e) tickdata["bartime"] = pd.to_datetime(tickdata["datetime"]) tickdata["open"] = tickdata["lastprice"] tickdata["high"] = tickdata["lastprice"] tickdata["low"] = tickdata["lastprice"] tickdata["close"] = tickdata["lastprice"] tickdata["starttime"] = tickdata["datetime"] else: tickdata["bartime"] = rdf["bartime"] tickdata["open"] = rdf["open"] tickdata["high"] = max(tickdata["lastprice"].values, rdf["high"].values) tickdata["low"] = min(tickdata["lastprice"].values, rdf["low"].values) tickdata["close"] = tickdata["lastprice"] tickdata["volume"] = df["vol"] + rdf["volume"].values tickdata["starttime"] = rdf["starttime"] else: print("新bar的第一个tick进入") tickdata["bartime"] = pd.to_datetime(tickdata["datetime"]) tickdata["open"] = tickdata["lastprice"] tickdata["high"] = tickdata["lastprice"] tickdata["low"] = tickdata["lastprice"] tickdata["close"] = tickdata["lastprice"] tickdata["starttime"] = tickdata["datetime"] except IOError as e: print("捕获到异常", e) tickdata["bartime"] = pd.to_datetime(tickdata["bartime"]) bardata = ( tickdata.resample(on="bartime", rule="1T", label="right", closed="right") .agg( { "starttime": "first", "symbol": "last", "open": "first", "high": "max", "low": "min", "close": "last", "volume": "sum", } ) .reset_index(drop=False) ) bardata = bardata.dropna().reset_index(drop=True) bardata["bartime"] = pd.to_datetime(bardata["bartime"][0]).strftime( "%Y-%m-%d %H:%M:%S" ) tickdatadict[symbol] = bardata tickdata["volume"] = df["vol"].values # print(bardata['symbol'].values,bardata['bartime'].values) orderflow_df_new(tickdata, bardata, symbol) # time.sleep(0.5) def orderflow_df_new(df_tick, df_min, symbol): # startArray = pd.to_datetime(df_min["starttime"]).values voluememin = df_min["volume"].values highs = df_min["high"].values lows = df_min["low"].values opens = df_min["open"].values closes = df_min["close"].values # endArray = pd.to_datetime(df_min['bartime']).values endArray = df_min["bartime"].values # print(endArray) # deltaArray = np.zeros((len(endArray),)) # tTickArray = pd.to_datetime(df_tick["datetime"]).values bp1TickArray = df_tick["bid_p"].values ap1TickArray = df_tick["ask_p"].values lastTickArray = df_tick["lastprice"].values volumeTickArray = df_tick["volume"].values symbolarray = df_tick["symbol"].values # indexFinal = 0 for index, tEnd in enumerate(endArray): dt = endArray[index] # start = startArray[index] bidDict = {} askDict = {} bar_vol = voluememin[index] bar_close = closes[index] bar_open = opens[index] bar_low = lows[index] bar_high = highs[index] bar_symbol = symbolarray[index] # for indexTick in range(indexFinal,len(df_tick)): # if tTickArray[indexTick] >= tEnd: # break # elif (tTickArray[indexTick] >= start) & (tTickArray[indexTick] < tEnd): Bp = round(bp1TickArray[0], 4) Ap = round(ap1TickArray[0], 4) LastPrice = round(lastTickArray[0], 4) Volume = volumeTickArray[0] if LastPrice >= Ap: if str(LastPrice) in askDict.keys(): askDict[str(LastPrice)] += Volume else: askDict[str(LastPrice)] = Volume if LastPrice <= Bp: if str(LastPrice) in bidDict.keys(): bidDict[str(LastPrice)] += Volume else: bidDict[str(LastPrice)] = Volume # indexFinal = indexTick bidDictResult, askDictResult = process(bidDict, askDict, symbol) bidDictResult = dict(sorted(bidDictResult.items(), key=operator.itemgetter(0))) askDictResult = dict(sorted(askDictResult.items(), key=operator.itemgetter(0))) prinslist = list(bidDictResult.keys()) asklist = list(askDictResult.values()) bidlist = list(bidDictResult.values()) delta = sum(askDictResult.values()) - sum(bidDictResult.values()) # print(prinslist,asklist,bidlist) # print(len(prinslist),len(bidDictResult),len(askDictResult)) df = pd.DataFrame( { "price": pd.Series([prinslist]), "Ask": pd.Series([asklist]), "Bid": pd.Series([bidlist]), } ) # df=pd.DataFrame({'price':pd.Series(bidDictResult.keys()),'Ask':pd.Series(askDictResult.values()),'Bid':pd.Series(bidDictResult.values())}) df["symbol"] = bar_symbol df["datetime"] = dt df["delta"] = str(delta) df["close"] = bar_close df["open"] = bar_open df["high"] = bar_high df["low"] = bar_low df["volume"] = bar_vol # df['ticktime']=tTickArray[0] df["dj"] = GetOrderFlow_dj(df) ofdatadict[symbol] = df def GetOrderFlow_dj(kData): Config = { "Value1": 3, "Value2": 3, "Value3": 3, "Value4": True, } aryData = kData djcout = 0 # 遍历kData中的每一行，计算djcout指标 for index, row in aryData.iterrows(): kItem = aryData.iloc[index] # high = kItem["high"] # low = kItem["low"] # close = kItem["close"] # open = kItem["open"] dtime = kItem["datetime"] price_s = kItem["price"] Ask_s = kItem["Ask"] Bid_s = kItem["Bid"] delta = kItem["delta"] price_s = price_s Ask_s = Ask_s Bid_s = Bid_s gj = 0 xq = 0 gxx = 0 xxx = 0 # 遍历price_s中的每一个元素，计算相关指标 for i in np.arange(0, len(price_s), 1): duiji = { "price": 0, "time": 0, "longshort": 0, } if i == 0: delta = delta order = { "Price": price_s[i], "Bid": {"Value": Bid_s[i]}, "Ask": {"Value": Ask_s[i]}, } # 空头堆积 if i >= 0 and i < len(price_s) - 1: if order["Bid"]["Value"] > Ask_s[i + 1] * int(Config["Value1"]): gxx += 1 gj += 1 if gj >= int(Config["Value2"]) and Config["Value4"] is True: duiji["price"] = price_s[i] duiji["time"] = dtime duiji["longshort"] = -1 if float(duiji["price"]) > 0: djcout += -1 else: gj = 0 # 多头堆积 if i >= 1 and i <= len(price_s) - 1: if order["Ask"]["Value"] > Bid_s[i - 1] * int(Config["Value1"]): xq += 1 xxx += 1 if xq >= int(Config["Value2"]) and Config["Value4"] is True: duiji["price"] = price_s[i] duiji["time"] = dtime duiji["longshort"] = 1 if float(duiji["price"]) > 0: djcout += 1 else: xq = 0 # 返回计算得到的djcout值 return djcout # 交易程序--------------------------------------------------------------------------------------------------------------------------------------------------------------------- class 专享08of: def __init__(self): self.py = 5 # 设置委托价格的偏移，更加容易促成成交。仅螺纹，其他品种根据最小点波动，自己设置 self.cont_df = 0 self.trailing_stop_percent = 0.02 # 跟踪出场参数 self.fixed_stop_loss_percent = 0.01 # 固定出场参数 self.dj_X = 1 # 开仓的堆积参数 self.pos = 0 self.Lots = 1 # 下单手数 self.short_trailing_stop_price = 0 self.long_trailing_stop_price = 0 self.sl_long_price = 0 self.sl_shor_price = 0 self.out_long = 0 self.out_short = 0 self.clearing_executed = False self.kgdata = True # 读取保存的数据 def read_to_csv(self, symbol): # 文件夹路径和文件路径 # 使用正则表达式提取英文字母并重新赋值给symbol symbol = "".join(re.findall("[a-zA-Z]", str(symbol))) folder_path = "traderdata" file_path = os.path.join(folder_path, f"{str(symbol)}traderdata.csv") # 如果文件夹不存在则创建 if not os.path.exists(folder_path): os.makedirs(folder_path) # 读取保留的模型数据CSV文件 if os.path.exists(file_path): df = pd.read_csv(file_path) if not df.empty and self.kgdata is True: # 选择最后一行数据 row = df.iloc[-1] # 根据CSV文件的列名将数据赋值给相应的属性 self.pos = int(row["pos"]) self.short_trailing_stop_price = float(row["short_trailing_stop_price"]) self.long_trailing_stop_price = float(row["long_trailing_stop_price"]) self.sl_long_price = float(row["sl_long_price"]) self.sl_shor_price = float(row["sl_shor_price"]) # self.out_long = int(row['out_long']) # self.out_short = int(row['out_short']) print("找到历史交易数据文件,已经更新持仓，止损止盈数据", df.iloc[-1]) self.kgdata = False else: pass # print("没有找到历史交易数据文件", file_path) # 如果没有找到CSV，则初始化变量 pass # 保存数据 def save_to_csv(self, symbol): # 使用正则表达式提取英文字母并重新赋值给symbol symbol = "".join(re.findall("[a-zA-Z]", str(symbol))) # 创建DataFrame data = { "datetime": [trader_df["datetime"].iloc[-1]], "pos": [self.pos], "short_trailing_stop_price": [self.short_trailing_stop_price], "long_trailing_stop_price": [self.long_trailing_stop_price], "sl_long_price": [self.sl_long_price], "sl_shor_price": [self.sl_shor_price], # 'out_long': [self.out_long], # 'out_short': [self.out_short] } df = pd.DataFrame(data) # 将DataFrame保存到CSV文件 df.to_csv(f"traderdata/{str(symbol)}traderdata.csv", index=False) # 每日收盘重置数据 def day_data_reset(self): # 获取当前时间 current_time = datetime.now().time() # 第一时间范围 clearing_time1_start = s_time(15, 00) clearing_time1_end = s_time(15, 15) # 第二时间范围 clearing_time2_start = s_time(23, 0) clearing_time2_end = s_time(23, 15) # 创建一个标志变量，用于记录是否已经执行过 self.clearing_executed = False # 检查当前时间第一个操作的时间范围内 if ( clearing_time1_start <= current_time <= clearing_time1_end and not self.clearing_executed ): self.clearing_executed = True # 设置标志变量为已执行 trader_df.drop(trader_df.index, inplace=True) # 清除当天的行情数据 # 检查当前时间是否在第二个操作的时间范围内 elif ( clearing_time2_start <= current_time <= clearing_time2_end and not self.clearing_executed ): self.clearing_executed = True # 设置标志变量为已执行 trader_df.drop(trader_df.index, inplace=True) # 清除当天的行情数据 else: self.clearing_executed = False pass return self.clearing_executed def OnRtnTrade(self, pTrade): print("||成交回报||", pTrade) def OnRspOrderInsert(self, pInputOrder, pRspInfo, nRequestID, bIsLast): print("||OnRspOrderInsert||", pInputOrder, pRspInfo, nRequestID, bIsLast) # 订单状态通知 def OnRtnOrder(self, pOrder): print("||订单回报||", pOrder) def Join(self, tickdata): data = tickdata # print(tickdata) instrument_id = data["InstrumentID"] # 品种代码 self.read_to_csv(instrument_id) self.day_data_reset() tickcome(data) # 新K线开始，启动交易程序 and 保存行情数据 if len(trader_df) > self.cont_df: # 检查文件是否存在 csv_file_path = f"traderdata/{instrument_id}_ofdata.csv" if os.path.exists(csv_file_path): # 仅保存最后一行数据 trader_df.tail(1).to_csv( csv_file_path, mode="a", header=False, index=False ) else: # 创建新文件并保存整个DataFrame trader_df.to_csv(csv_file_path, index=False) # 更新跟踪止损价格 if self.long_trailing_stop_price > 0 and self.pos > 0: # print('datetime+sig: ',dt,'旧多头出线',self.long_trailing_stop_price,'low',self.low[0]) self.long_trailing_stop_price = ( trader_df["low"].iloc[-1] if self.long_trailing_stop_price < trader_df["low"].iloc[-1] else self.long_trailing_stop_price ) self.save_to_csv(instrument_id) # print('datetime+sig: ',dt,'多头出线',self.long_trailing_stop_price) if self.short_trailing_stop_price > 0 and self.pos < 0: # print('datetime+sig: ',dt,'旧空头出线',self.short_trailing_stop_price,'high',self.high[0]) self.short_trailing_stop_price = ( trader_df["high"].iloc[-1] if trader_df["high"].iloc[-1] < self.short_trailing_stop_price else self.short_trailing_stop_price ) self.save_to_csv(instrument_id) # print('datetime+sig: ',dt,'空头出线',self.short_trailing_stop_price) self.out_long = self.long_trailing_stop_price * ( 1 - self.trailing_stop_percent ) self.out_short = self.short_trailing_stop_price * ( 1 + self.trailing_stop_percent ) # print('datetime+sig: ',dt,'空头出线',self.out_short) # print('datetime+sig: ',dt,'多头出线',self.out_long) # 跟踪出场 if self.out_long > 0: if ( trader_df["low"].iloc[-1] < self.out_long and self.pos > 0 and self.sl_long_price > 0 and trader_df["low"].iloc[-1] > self.sl_long_price ): print( "datetime+sig: ", trader_df["datetime"].iloc[-1], "多头止盈", "TR", self.out_long, "low", trader_df["low"].iloc[-1], ) # 平多 self.insert_order( data["ExchangeID"], data["InstrumentID"], data["BidPrice1"] - self.py, self.Lots, b"1", b"1", ) self.insert_order( data["ExchangeID"], data["InstrumentID"], data["BidPrice1"] - self.py, self.Lots, b"1", b"3", ) self.long_trailing_stop_price = 0 self.out_long = 0 self.sl_long_price = 0 self.pos = 0 self.save_to_csv(instrument_id) if self.out_short > 0: if ( trader_df["high"].iloc[-1] > self.out_short and self.pos < 0 and self.sl_shor_price > 0 and trader_df["high"].iloc[-1] < self.sl_shor_price ): print( "datetime+sig: ", trader_df["datetime"].iloc[-1], "空头止盈: ", "TR", self.out_short, "high", trader_df["high"].iloc[-1], ) # 平空 self.insert_order( data["ExchangeID"], data["InstrumentID"], data["AskPrice1"] + self.py, self.Lots, b"0", b"1", ) self.insert_order( data["ExchangeID"], data["InstrumentID"], data["AskPrice1"] + self.py, self.Lots, b"0", b"3", ) self.short_trailing_stop_price = 0 self.sl_shor_price = 0 self.out_shor = 0 self.pos = 0 self.save_to_csv(instrument_id) # 固定止损 self.fixed_stop_loss_L = self.sl_long_price * ( 1 - self.fixed_stop_loss_percent ) if ( self.sl_long_price > 0 and self.fixed_stop_loss_L > 0 and self.pos > 0 and trader_df["close"].iloc[-1] < self.fixed_stop_loss_L ): print( "datetime+sig: ", trader_df["datetime"].iloc[-1], "多头止损", "SL", self.fixed_stop_loss_L, "close", trader_df["close"].iloc[-1], ) # 平多 self.insert_order( data["ExchangeID"], data["InstrumentID"], data["BidPrice1"] - self.py, self.Lots, b"1", b"1", ) self.insert_order( data["ExchangeID"], data["InstrumentID"], data["BidPrice1"] - self.py, self.Lots, b"1", b"3", ) self.long_trailing_stop_price = 0 self.sl_long_price = 0 self.out_long = 0 self.pos = 0 self.save_to_csv(instrument_id) self.fixed_stop_loss_S = self.sl_shor_price * ( 1 + self.fixed_stop_loss_percent ) if ( self.sl_shor_price > 0 and self.fixed_stop_loss_S > 0 and self.pos < 0 and trader_df["close"].iloc[-1] > self.fixed_stop_loss_S ): print( "datetime+sig: ", trader_df["datetime"].iloc[-1], "空头止损", "SL", self.fixed_stop_loss_S, "close", trader_df["close"].iloc[-1], ) # 平空 self.insert_order( data["ExchangeID"], data["InstrumentID"], data["AskPrice1"] + self.py, self.Lots, b"0", b"1", ) self.insert_order( data["ExchangeID"], data["InstrumentID"], data["AskPrice1"] + self.py, self.Lots, b"0", b"3", ) self.short_trailing_stop_price = 0 self.sl_shor_price = 0 self.out_short = 0 self.pos = 0 self.save_to_csv(instrument_id) # 日均线 trader_df["dayma"] = trader_df["close"].mean() # 计算累积的delta值 trader_df["delta"] = trader_df["delta"].astype(float) trader_df["delta累计"] = trader_df["delta"].cumsum() # 大于日均线 开多1 = ( trader_df["dayma"].iloc[-1] > 0 and trader_df["close"].iloc[-1] > trader_df["dayma"].iloc[-1] ) # 累计多空净量大于X 开多4 = ( trader_df["delta累计"].iloc[-1] > 2000 and trader_df["delta"].iloc[-1] > 1500 ) # 小于日均线 开空1 = ( trader_df["dayma"].iloc[-1] > 0 and trader_df["close"].iloc[-1] < trader_df["dayma"].iloc[-1] ) # 累计多空净量小于X 开空4 = ( trader_df["delta累计"].iloc[-1] < -2000 and trader_df["delta"].iloc[-1] < -1500 ) 开多组合 = 开多1 and 开多4 and trader_df["dj"].iloc[-1] > self.dj_X 开空条件 = 开空1 and 开空4 and trader_df["dj"].iloc[-1] < -self.dj_X 平多条件 = trader_df["dj"].iloc[-1] < -self.dj_X 平空条件 = trader_df["dj"].iloc[-1] > self.dj_X # 开仓 # 多头开仓条件 if self.pos < 0 and 平空条件: print( "平空： ", "ExchangeID: ", data["ExchangeID"], "InstrumentID", data["InstrumentID"], "AskPrice1", data["AskPrice1"] + self.py, ) # 平空 self.insert_order( data["ExchangeID"], data["InstrumentID"], data["AskPrice1"] + self.py, self.Lots, b"0", b"1", ) self.insert_order( data["ExchangeID"], data["InstrumentID"], data["AskPrice1"] + self.py, self.Lots, b"0", b"3", ) self.pos = 0 self.sl_shor_price = 0 self.short_trailing_stop_price = 0 print( "datetime+sig: ", trader_df["datetime"].iloc[-1], "反手平空：", "平仓价格：", data["AskPrice1"] + self.py, "堆积数：", trader_df["dj"].iloc[-1], ) self.save_to_csv(instrument_id) if self.pos == 0 and 开多组合: print( "开多： ", "ExchangeID: ", data["ExchangeID"], "InstrumentID", data["InstrumentID"], "AskPrice1", data["AskPrice1"] + self.py, ) # 开多 self.insert_order( data["ExchangeID"], data["InstrumentID"], data["AskPrice1"] + self.py, self.Lots, b"0", b"0", ) print( "datetime+sig: ", trader_df["datetime"].iloc[-1], "多头开仓", "开仓价格：", data["AskPrice1"] + self.py, "堆积数：", trader_df["dj"].iloc[-1], ) self.pos = 1 self.long_trailing_stop_price = data["AskPrice1"] self.sl_long_price = data["AskPrice1"] self.save_to_csv(instrument_id) if self.pos > 0 and 平多条件: print( "平多： ", "ExchangeID: ", data["ExchangeID"], "InstrumentID", data["InstrumentID"], "BidPrice1", data["BidPrice1"] - self.py, ) # 平多 self.insert_order( data["ExchangeID"], data["InstrumentID"], data["BidPrice1"] - self.py, self.Lots, b"1", b"1", ) self.insert_order( data["ExchangeID"], data["InstrumentID"], data["BidPrice1"] - self.py, self.Lots, b"1", b"3", ) self.pos = 0 self.long_trailing_stop_price = 0 self.sl_long_price = 0 print( "datetime+sig: ", trader_df["datetime"].iloc[-1], "反手平多", "平仓价格：", data["BidPrice1"] - self.py, "堆积数：", trader_df["dj"].iloc[-1], ) self.save_to_csv(instrument_id) if self.pos == 0 and 开空条件: print( "开空： ", "ExchangeID: ", data["ExchangeID"], "InstrumentID", data["InstrumentID"], "BidPrice1", data["BidPrice1"], ) # 开空 self.insert_order( data["ExchangeID"], data["InstrumentID"], data["BidPrice1"] - self.py, self.Lots, b"1", b"0", ) print( "datetime+sig: ", trader_df["datetime"].iloc[-1], "空头开仓", "开仓价格：", data["BidPrice1"] - self.py, "堆积数：", trader_df["dj"].iloc[-1], ) self.pos = -1 self.short_trailing_stop_price = data["BidPrice1"] self.sl_shor_price = data["BidPrice1"] self.save_to_csv(instrument_id) # print(trader_df) self.cont_df = len(trader_df)