基础基本上写完了，开始写突破逻辑
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148
TurtleOnTime.py
148
TurtleOnTime.py
@ -39,8 +39,16 @@ class TurtleTrading(object):
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Args:
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object (_type_): _description_
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"""
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def __init__(self, TradeCode) -> None:
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def __init__(self, TradeCode, type, riskcoe, Capital, cash) -> None:
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self.TradeCode = TradeCode
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self.type = type
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self.riskcoe = riskcoe
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self.Capital = Capital
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self.cash = cash
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self.TrigerTime = 0
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self.BuyStates = [[0, None, None, 0, 0, self.cash]]
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self.tradeslog = [] # 交易记录
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def GetRecentData(self):
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"""获取某个标的的最近数据，从两年前到今天, 计算后的数据保存在self.CurrentData
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@ -131,7 +139,8 @@ class TurtleTrading(object):
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"""
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Donchian = pd.DataFrame() # 创建一个空的DataFrame用于存储唐奇安通道数据
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# 计算最高价和最低价的N日移动平均线
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Donchian['Upper'] = self.CurrentData['最高'].rolling(n).max() # 使用rolling函数计算n日最高价的移动最大值
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name = 'Donchian_' + str(n) + '_upper'
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Donchian[name] = self.CurrentData['最高'].rolling(n).max() # 使用rolling函数计算n日最高价的移动最大值
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# # 计算中间线
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# Donchian['Middle'] = (self.Donchian['Upper'] + self.Donchian['Lower']) / 2 # 计算上通道和下通道的中间线，但此行代码被注释掉了
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@ -151,13 +160,29 @@ class TurtleTrading(object):
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"""
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Donchian = pd.DataFrame()
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# 计算最高价和最低价的N日移动平均线
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Donchian['lower'] = self.CurrentData['最低'].rolling(n).min()
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name = 'Donchian_' + str(n) + '_lower'
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Donchian[name] = self.CurrentData['最低'].rolling(n).min()
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# # 计算中间线
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# Donchian['Middle'] = (self.Donchian['Upper'] + self.Donchian['Lower']) / 2
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return Donchian
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def calc_atr_donchian_short(self):
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"""计算ATR、短期唐奇安通道
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"""
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# 计算ATR
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self.CalATR(self.CurrentData, 20)
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# 计算唐奇安通道
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self.Donchian_20_ups = self.calculate_donchian_channel_up(20)
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self.Donchian_50_ups = self.calculate_donchian_channel_up(50)
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self.Donchian_downs = self.calculate_donchian_channel_down(10)
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# 画图
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# self.DrawKLine(days)
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# 把self.N, self.Donchian_up, self.Donchian_down, 添加到self.CurrentData后面，保存到mysql数据库
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self.CurrentData = pd.concat([self.CurrentData, self.Donchian_20_ups, self.Donchian_50_ups, self.Donchian_downs], axis=1)
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def get_ready(self, days):
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"""创建一个turtle对象，获取数据，计算ATR，计算唐奇安通道
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@ -165,37 +190,82 @@ class TurtleTrading(object):
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days (_type_): _description_
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n (_type_): _description_
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"""
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# 检查mysql数据库中是否存在该股票的数据 或者数据库最后一条的时间距离今天是否两天以上
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# if 不存在database
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if not mysql_database.check_db_table(f"{self.TradeCode}"):
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self.GetRecentData()
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self.calc_atr_donchian_short()
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Code = f"{self.TradeCode}"
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mysql_database.insert_db(self.CurrentData, Code, True, "日期")
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else:
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# 检查数据库最后一条的时间距离今天是否两天以上
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current_date = date.today()
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threshold_date = current_date - timedelta(days=2)
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last_update = mysql_database.check_db_table_last_date(f"{self.TradeCode}")
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if not mysql_database.check_db_table(f"{self.TradeCode}") or last_update < threshold_date:
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if last_update < threshold_date:
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# 如果不存在，则从akshare获取数据并保存到mysql数据库
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if mysql_database.check_db_table(f"{self.TradeCode}") and last_update < threshold_date:
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mysql_database.delete_table(f"{self.TradeCode}")
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self.GetRecentData()
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self.calc_atr_donchian_short()
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Code = f"{self.TradeCode}"
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mysql_database.insert_db(self.CurrentData, Code, True, "日期")
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else:
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# 如果存在，则从mysql数据库中读取数据
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self.CurrentData = mysql_database.fetch_all_data(f"{self.TradeCode}")
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# 计算ATR
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self.CalATR(self.CurrentData, 20)
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# 计算唐奇安通道
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self.Donchian_up = self.calculate_donchian_channel_up(20)
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self.Donchian_down = self.calculate_donchian_channel_down(10)
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# 画图
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# self.DrawKLine(days)
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def CalPositionSize(self):
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"""根据风险系数、ATR，计算仓位大小, 存于self.IntPositionSize
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"""
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PositionSize = self.riskcoe * self.Capital /(self.N) # 默认用股票形式了 100
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self.IntPositionSize = int(PositionSize // 100) * 100
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# 把self.N, self.Donchian_up, self.Donchian_down, 添加到self.CurrentData后面，保存到mysql数据库
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self.CurrentData = pd.concat([self.CurrentData, self.Donchian_up, self.Donchian_down], axis=1)
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def system1EnterNormal(self, PriceNow, TempDonchian20Upper, BreakOutLog):
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# 没有持仓且价格向上突破---此时包含两种情形：1 对某标的首次使用系统，2 已发生过突破，此时上次突破天然是失败的
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if self.TrigerTime == 0 and PriceNow > TempDonchian20Upper[-1]:
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# 买入
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return True
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elif self.TrigerTime != 0 and PriceNow > TempDonchian20Upper[-1]:
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self.system1BreakoutValid(PriceNow)
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if BreakOutLog[-1][5] == 'Lose': # TT!= 0且突破且上一次突破unseccessful
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return True
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else:
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return False
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else:
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return False
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Code = f"{self.TradeCode}"
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mysql_database.insert_db(self.CurrentData, Code, True, "日期")
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def system1EnterSafe(self, PriceNow, TempDonchian55Upper):
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if PriceNow > TempDonchian55Upper[-1]: # 保底的55日突破
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return True
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else:
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return False
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def system1BreakoutValid(self, priceNow):
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"""判断前一次突破是否成功，是log[-1][5]写入“win”，否则写入“Lose”
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"""
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if priceNow < self.BreakOutLog[-1][3]:
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self.BreakOutLog[-1][5] = 'Lose'
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else:
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self.BreakOutLog[-1][5] = 'None'
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# 一天结束，计算ATR，计算唐奇安通道，追加到已有的mysql数据库中
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def system1Out(self, PriceNow, TempDonchian10Lower):
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# 退出:低于20日最低价（多头方向）,空头以突破20日最高价为止损价格--有持仓且价格向下突破
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if self.TrigerTime != 0 and PriceNow < TempDonchian10Lower[-1]:
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# 退出
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return True
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else:
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return False
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def day_end(self):
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pass
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@ -207,13 +277,14 @@ class TurtleTrading_OnTime(object):
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3、实时监测主流程
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'''
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def __init__(self):
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pass
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def __init__(self, turtle: TurtleTrading):
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self.turtle = turtle
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def get_stocks_data(self):
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"""获取实时股票、基金数据，不保存
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"""
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stock_zh_a_spot_df = ak.stock_zh_a_spot_em()
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stock_zh_a_spot_df = stock_zh_a_spot_df.dropna(subset=['最新价'])
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stock_data = ak.stock_zh_a_spot_em()
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stock_data = stock_data.dropna(subset=['最新价'])
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# # print(stock_zh_a_spot_df)
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# # stock_zh_a_spot_df第一列加上时间，精确到分钟
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# stock_zh_a_spot_df['时间'] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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@ -224,14 +295,43 @@ class TurtleTrading_OnTime(object):
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etf_data = etf_data.dropna(subset=['当前-单位净值'])
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# etf_data['时间'] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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# mysql_database.insert_db(etf_data, "etf_price", True, "代码")
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return stock_data, etf_data
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def Start_S1_system(self):
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"""启动S1系统
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"""
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# ------------------准备阶段--------------------
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# 获取数据或读取数据 -- 计算ATR Donchian 20 50 up, 20 down
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self.turtle.get_ready(100)
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self.turtle.N = self.turtle.CurrentData['ATR'].iloc[-1]
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self.turtle.Donchian_20_up = self.turtle.CurrentData['Donchian_20_upper'].iloc[-1]
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self.turtle.Donchian_50_up = self.turtle.CurrentData['Donchian_50_upper'].iloc[-1]
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self.turtle.Donchian_10_down = self.turtle.CurrentData['Donchian_10_lower'].iloc[-1]
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# ------------------实时监测阶段--------------------
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# 9:00 1、判断是否是新的一周，是则重新计算Position Size
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# 判断是否是新的一周
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if datetime.now().weekday() == 0:
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self.turtle.CalPositionSize()
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# 每分钟获取一次数据，判断是否触发条件 9:30-11:30 13:00-15:00
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stock_data, etf_data = self.get_stocks_data()
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# 根据type，code, 取得实时价格self.turtle.PriceNow
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# ------------------结束阶段--------------------
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# 数据库更新当天数据，增加ATR、donchian数据
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pass
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if __name__ == '__main__':
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# t = TurtleTrading('513300')
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t = TurtleTrading('513300', "etf", 0.25, 100000, 200000)
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# t.get_ready(100)
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a = TurtleTrading_OnTime()
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a.get_stocks_data()
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a = TurtleTrading_OnTime(t)
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a.Start_S1_system()
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# # 全是股票
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# stock_zh_a_spot_df = ak.stock_zh_a_spot_em()
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BIN
__pycache__/mysql_database.cpython-311.pyc
Normal file
BIN
__pycache__/mysql_database.cpython-311.pyc
Normal file
Binary file not shown.
BIN
__pycache__/stock_database.cpython-311.pyc
Normal file
BIN
__pycache__/stock_database.cpython-311.pyc
Normal file
Binary file not shown.
@ -22,7 +22,7 @@ MYSQL_HOST = os.environ.get('MYSQL_HOST') if (os.environ.get('MYSQL_HOST') != No
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MYSQL_USER = os.environ.get('MYSQL_USER') if (os.environ.get('MYSQL_USER') != None) else "root"
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MYSQL_PWD = os.environ.get('MYSQL_PWD') if (os.environ.get('MYSQL_PWD') != None) else "1212"
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MYSQL_DB = os.environ.get('MYSQL_DB') if (os.environ.get('MYSQL_DB') != None) else "stock_data"
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MYSQL_PORT = os.environ.get('MYSQL_PORT') if (os.environ.get('MYSQL_PORT') != None) else "3306"
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MYSQL_PORT = os.environ.get('MYSQL_PORT') if (os.environ.get('MYSQL_PORT') != None) else "3307"
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print("MYSQL_HOST :", MYSQL_HOST, ",MYSQL_USER :", MYSQL_USER, ",MYSQL_DB :", MYSQL_DB)
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MYSQL_CONN_URL = "mysql+mysqldb://" + MYSQL_USER + ":" + MYSQL_PWD + "@" + MYSQL_HOST + ":" + MYSQL_PORT + "/" + MYSQL_DB + "?charset=utf8mb4"
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@ -45,7 +45,12 @@ def engine_to_db(to_db):
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# 通过数据库链接 engine。
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def conn():
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try:
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db = MySQLdb.connect(MYSQL_HOST, MYSQL_USER, MYSQL_PWD, MYSQL_DB, charset="utf8")
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db = MySQLdb.connect(host=MYSQL_HOST,
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user=MYSQL_USER,
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passwd=MYSQL_PWD,
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db=MYSQL_DB,
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port=int(MYSQL_PORT), # 确保转换为整数
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charset="utf8")
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# db.autocommit = True
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except Exception as e:
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print("conn error :", e)
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