861 lines
37 KiB
Python
Executable File
861 lines
37 KiB
Python
Executable File
import numpy as np
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import math
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import akshare as ak
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import os
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from datetime import datetime, timedelta
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import pandas as pd
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import mplfinance as mpf
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def CalTrueFluc(data, day):
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H_L = data.iloc[day]['最高'] - data.iloc[day]['最低']
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H_PDC = data.iloc[day]['最高'] - data.iloc[day-1]['收盘']
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PDC_L = data.iloc[day-1]['收盘'] - data.iloc[day]['最低']
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TrueFluc = np.max([H_L, H_PDC, PDC_L])
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print('high', data.iloc[day]['最高'], 'low', data.iloc[day]['最低'], 'TrueRange', TrueFluc)
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return TrueFluc
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def calc_sma_atr_pd(kdf,period):
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"""计算TR与ATR
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Args:
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kdf (_type_): 历史数据
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period (_type_): ATR周期
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Returns:
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_type_: 返回kdf，增加TR与ATR列
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"""
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kdf['HL'] = kdf['最高'] - kdf['最低']
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kdf['HC'] = np.abs(kdf['最高'] - kdf['收盘'].shift(1))
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kdf['LC'] = np.abs(kdf['最低'] - kdf['收盘'].shift(1))
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kdf['TR'] = np.round(kdf[['HL','HC','LC']].max(axis=1), 3)
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# ranges = pd.concat([high_low, high_close, low_close], axis=1)
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# true_range = np.max(ranges, axis=1)
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kdf['ATR'] = np.round(kdf['TR'].rolling(period).mean(), 3)
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return kdf.drop(['HL','HC','LC'], axis = 1)
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# A股数据 东方财富网
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# all_data = ak.stock_zh_a_spot_em()
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# 基金实时数据 东方财富网
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# fund_etf_spot_em_df = ak.fund_etf_spot_em()
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# 后复权历史数据
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# fund_etf_hist_em_df = ak.fund_etf_hist_em(symbol="513300", period="daily", start_date="20130101", end_date="20240408", adjust="hfq")
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# fund_etf_hist_em_df.to_csv('513300data.csv', index=False)
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# data = pd.read_csv('513300data.csv')
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# # 一、计算头寸规模
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# # 真实波动幅度 = max (H-L, H-pdc, pdc-L)
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# today = datetime.today()
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# # print(today)
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# # print(data.iloc[-1]['成交额'])
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# TrueFlucs = []
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# Nserious = np.zeros(101)
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# last120days = np.arange(-120, -100)
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# for i in last120days:
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# H_L = data.iloc[i]['最高'] - data.iloc[i]['最低']
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# H_PDC = data.iloc[i]['最高'] - data.iloc[i-1]['收盘']
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# PDC_L = data.iloc[i-1]['收盘'] - data.iloc[i]['最低']
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# TrueFlucs.append(np.max([H_L, H_PDC, PDC_L]))
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# # 求简单平均,放入N序列第一个
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# Nsimple = np.average(TrueFlucs)
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# Nserious[0] = Nsimple
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# # 计算-21到-1的N
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# last100days = np.arange(-100, 0)
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# for i in range(0,100):
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# day = last100days[i]
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# H_L = data.iloc[day]['最高'] - data.iloc[day]['最低']
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# H_PDC = data.iloc[day]['最高'] - data.iloc[day-1]['收盘']
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# PDC_L = data.iloc[day-1]['收盘'] - data.iloc[day]['最低']
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# TrueFluc = np.max([H_L, H_PDC, PDC_L])
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# Ntemp = (19 * Nserious[i] + TrueFluc)/20
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# Nserious[i+1] = Ntemp
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# # print(Nserious)
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# total_rows = len(data)
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# Ndata = np.zeros(total_rows)
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# Ndata[total_rows-101:] = Nserious
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# # NewColumn = [0]*(total_rows-101) + Nserious
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# data['N'] = Ndata
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# data.to_csv('513300data-N.csv', index=False)
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# pass
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# -----------------------更新atr----------------------
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"""已有数据与新数据对比，补充新的N,同时更新数据库
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"""
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# Today = datetime.today()
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# # print(Today)
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# formatted_date = Today.strftime("%Y%m%d")
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# # print(formatted_date)
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# CurrentData = ak.fund_etf_hist_em(symbol="513300", period="daily", start_date="20130101", end_date=formatted_date, adjust="hfq")
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# CurrentData = calc_sma_atr_pd(CurrentData, 20)
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# CurrentData.to_csv('513300data-N.csv', index=False)
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# pass
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# ------------------计算头寸规模 资金10w, 1%波动------------
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# money = 100000
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# OldData = pd.read_csv('513300data-N.csv')
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# N = OldData.iloc[-1]['ATR']
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# # N = 0.473
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# Price = OldData.iloc[-1]['收盘']
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# # Price = 5.60
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# EveryUnit = 0.0025 * money /(N*100*Price)
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# print('单位',EveryUnit)
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# print(113*100*Price)
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class TurtleTrading(object):
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"""对象范围较小，对某一个标的创建一个海龟，如513300，
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计算ATR、
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Position Size，
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买入、卖出、加仓等行为
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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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self.TradeCode = TradeCode
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def GetRecentData(self):
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Today = datetime.today()
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# print(Today)
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formatted_date = Today.strftime("%Y%m%d")
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# print(formatted_date)
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Code = f"{self.TradeCode}"
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CurrentData = ak.fund_etf_hist_em(symbol=Code, period="daily", start_date="20130101", end_date=formatted_date, adjust="")
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return CurrentData
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def CalATR(self, data, ATRday, SaveOrNot):
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"""计算某个标的的ATR，从上市日到今天, 计算后的数据保存在self.CurrentData
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Args:
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ATRday: 多少日ATR
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SaveOrNot (_type_): 是否保存.csv数据
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"""
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self.CurrentData = calc_sma_atr_pd(data, ATRday)
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self.N = self.CurrentData['ATR']
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if SaveOrNot:
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self.CurrentData.to_csv('513300data-N.csv', index=False)
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print("csv保存成功")
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return self.N
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def CalPositionSize(self, RiskCoef, Capital):
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"""计算PosizionSize 持有的单位，该单位某标的，1N波动对应RiskCoef * Capital资金
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Args:
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RiskCoef (_type_): 风险系数
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Capital (_type_): 资金
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"""
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N = self.CurrentData.iloc[-1]['ATR']
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# N = 0.473
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Price = self.CurrentData.iloc[-1]['收盘']
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# Price = 5.60
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self.PositionSize = RiskCoef * Capital /( N*100*Price) # 默认用股票形式了 100
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return self.PositionSize
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def ReadExistData(self, data):
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"""除了通过发请求获取数据，也可以读本地的数据库，赋值给self.CurrentData
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Args:
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data (_type_): 本地csv名称
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"""
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self.CurrentData = pd.read_csv(data)
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def DrawKLine(self, days):
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"""画出k线图看看,画出最近days天的K线图
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"""
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# 日期部分
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dates = pd.to_datetime(self.CurrentData['日期'][-days:])
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# Klinedf['Data'] = pd.to_datetime(self.CurrentData['日期'])
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Klinedf = pd.DataFrame()
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# Klinedf.set_index = Klinedf['Data']
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# 其他数据
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Klinedf['Open'] = self.CurrentData['开盘'][-days:]
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Klinedf['High'] = self.CurrentData['最高'][-days:]
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Klinedf['Low'] = self.CurrentData['最低'][-days:]
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Klinedf['Close'] = self.CurrentData['收盘'][-days:]
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Klinedf['Volume'] = self.CurrentData['成交量'][-days:]
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Klinedf.set_index(dates, inplace=True)
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# 画图
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mpf.plot(Klinedf, type='candle', style='yahoo', volume=False, mav=(5,), addplot=[mpf.make_addplot(self.Donchian[['Upper', 'Lower']])])
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def calculate_donchian_channel(self, days, n):
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"""
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计算唐奇安通道days一共多少日， n多少日唐奇安
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参数:
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self.CurrentData (DataFrame): 包含价格数据的Pandas DataFrame，至少包含"High"和"Low"列
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n (int): 时间周期
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返回:self.Donchian
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DataFrame: 唐奇安通道的DataFrame，包含"Upper", "Lower", 和 "Middle"列
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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['Upper'] = self.CurrentData['最高'][-days:].rolling(n).max()
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Donchian['Lower'] = self.CurrentData['最低'][-days:].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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class Trade(object):
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"""具有以下功能：
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接收Turtle Class作为输入
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1 数据准备：
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a 获取数据
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b 计算atr
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c 计算55日、20日、10日Donchian
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2 总持有单位判断
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3 定义系统2 超过55日xxx，分段加仓
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4 回测功能
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5 实时功能 问题实时功能如何同时监控几个item
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78
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Args:
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object (_type_): _description_
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"""
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def __init__(self, turtles, riskcoe, cash, StartTime, EndTime) -> None:
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"""接收所有的turtles
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Args:
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turtles (_type_): _description_
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"""
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self.turtles = turtles
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self.riskcoe = riskcoe
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self.cash = cash
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self.Capital = cash
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self.StartTime = StartTime
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self.EndTime = EndTime
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self.TrigerTime = 0
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# self.BuyStates = {
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# "trigger_count": 0, # 触发次数
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# "BuyPrice": None, # 买入/持有价格
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# "StopPrice":None, # 止损价格
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# "quantity": 0, # 持有份数
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# "N": 0, # ATR
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# "available_cash": self.cash # 可用资金
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# }
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# 0"trigger_count", 1"BuyPrice", 2"StopPrice", 3"quantity", 4"N", 5"available_cash"
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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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self.current_week = None # 当前周数
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# def TurtleDataPre(self):
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# for turtle in self.turtles:
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# turtle.CalATR(20, True)
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# turtle.Donchian20 = turtle.calculate_donchian_channel(500, 20)
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# turtle.Donchian10 = turtle.calculate_donchian_channel(500, 10)
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# turtle.Donchian55 = turtle.calculate_donchian_channel(500, 55)
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# pass
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def PortfolioPositon(self):
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"""总共能持有多少个单位
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"""
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pass
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def TestBuyStocks(self, PriceNow, date):
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# 回测中的买入函数 如果开盘价大于55日，最高价大于四份价格，执行加满
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# 返回self.BuyStates
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# 实盘中应该是触发买入信号，发送买入邮件，价格，份数。当前Turtle程序暂停，收到邮件返回，更新买入价格，份数
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# 更新BuyStates:"触发次数"、"买入/持有价格"、"持有份数"、"N"、"可用资金"
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N = self.ThisWeekN
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Shares = self.ThisWeekPosizionSize
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# 更新 BuyStates
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if self.TrigerTime == 0: # 第一次买入是直接修改
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# if self.BuyStates[0] == 0:
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self.TrigerTime += 1
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BuyPrice = PriceNow
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AddPrice = PriceNow + 1/2 * N
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StopPrice = PriceNow - 2 * N
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available_cash = self.cash - Shares * BuyPrice
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# 0"trigger_count", 1"BuyPrice", 2"AddPrice", 3"StopPrice", 4"quantity", 5"N", 6"available_cash"
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self.BuyStates = [[self.TrigerTime, BuyPrice, AddPrice, StopPrice, Shares, N, available_cash]]
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# 更新log
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#
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AllShares = sum(row[4] for row in self.BuyStates)
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NetValue = available_cash + AllShares * BuyPrice
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self.tradeslog.append([date, 'Buy', Shares, PriceNow, N, available_cash, NetValue])
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else: # 加仓的操作，在BuyStates后边追加
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self.TrigerTime += 1
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BuyPrice = PriceNow
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AddPrice = PriceNow + 1/2 * N
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StopPrice = PriceNow - 2 * N
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available_cash = self.BuyStates[self.TrigerTime-2][6] - Shares * BuyPrice
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self.BuyStates.append([self.TrigerTime, BuyPrice, AddPrice, StopPrice, Shares, N, available_cash])
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# 更新log
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AllShares = sum(row[4] for row in self.BuyStates)
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NetValue = available_cash + AllShares * BuyPrice
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self.tradeslog.append([date, 'Buy', Shares, PriceNow, N, available_cash, NetValue])
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pass
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def TestStopSaleStocks(self, PriceNow, date):
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# 回测中的卖出函数，仓位全卖
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N = self.ThisWeekN
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# Shares应该等于所有持仓的和
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Shares = sum(row[4] for row in self.BuyStates)
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# Shares = sum(self.BuyStates[:, 4])
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available_cash = self.BuyStates[-1][6] + Shares * PriceNow
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# 更新log
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NetValue = available_cash
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self.tradeslog.append([date, 'StopSale', Shares, PriceNow, N, available_cash, NetValue])
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# self.trades.append((date, 'Sale', Shares, PriceNow, N, available_cash))
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# TrigerTime归0
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self.TrigerTime = 0
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# self.cash更新
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self.cash = available_cash
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# 回到初始状态
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self.BuyStates = [[0, None, None, None, 0, 0, self.cash]]
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def TestOutSaleStocks(self, PriceNow, date):
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# 回测中的卖出函数，仓位全卖
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N = self.ThisWeekN
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# Shares应该等于所有持仓的和
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Shares = sum(row[4] for row in self.BuyStates)
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# Shares = sum(self.BuyStates[:, 4])
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available_cash = self.BuyStates[-1][6] + Shares * PriceNow
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# 更新log
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NetValue = available_cash
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self.tradeslog.append([date, 'OutSale', Shares, PriceNow, N, available_cash, NetValue])
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# self.trades.append((date, 'Sale', Shares, PriceNow, N, available_cash))
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# TrigerTime归0
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self.TrigerTime = 0
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# self.cash更新
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self.cash = available_cash
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# 回到初始状态
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self.BuyStates = [[0, None, None, None, 0, 0, self.cash]]
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def system2Enter(self, PriceNow, TempDonchian55Upper):
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"""以50日突破为基础的较简单的长线系统
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入市：价格超过了前55日的最高价或最低价就建立头寸。
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- 如果价格超过55日最高价，买入一个单位建立多头头寸。
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- 如果价格跌破55日最低价，卖出一个单位建立空头头寸。
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退出：
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增加单位：突破时只建立一个单位，建立后以1/2N的间隔增加头寸，以前面指令的实际成交价为基础。实际成交价+1/2N
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单个品种，最大4个单位。
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以多头编写
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"""
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# # 触发次数
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# Trigertime = 0
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# 只有没买入过，且 现价超过55日最高价，是一次突破，直接以突破价格买入--没有持仓且价格向上突破
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if self.TrigerTime == 0 and PriceNow > TempDonchian55Upper[-1]:
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# 买入
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return True
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else:
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return False
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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': # TrigerTime != 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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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'
|
||
else:
|
||
self.BreakOutLog[-1][5] = 'None'
|
||
pass
|
||
|
||
def system2Out(self, PriceNow, TempDonchian20Lower):
|
||
# 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
|
||
if self.TrigerTime != 0 and PriceNow < TempDonchian20Lower[-1]:
|
||
# 退出
|
||
return True
|
||
|
||
else:
|
||
return False
|
||
|
||
def system1Out(self, PriceNow, TempDonchian10Lower):
|
||
# 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
|
||
if self.TrigerTime != 0 and PriceNow < TempDonchian10Lower[-1]:
|
||
# 退出
|
||
return True
|
||
|
||
else:
|
||
return False
|
||
|
||
def system2Add(self, PriceNow):
|
||
"""加仓判断：如果当前价格>上一次买入后的加仓价格则加仓
|
||
"""
|
||
if self.TrigerTime < 4 and PriceNow > self.BuyStates[self.TrigerTime - 1][2]:
|
||
# 买入
|
||
return True
|
||
else:
|
||
return False
|
||
|
||
def system2Stop(self, PriceNow):
|
||
"""止损判断：如果当前价格<上一次买入后的止损价格则止损
|
||
"""
|
||
if PriceNow < self.BuyStates[self.TrigerTime - 1][3]:
|
||
# 买入
|
||
return True
|
||
else:
|
||
return False
|
||
|
||
def system2CalDonchian(self):
|
||
# 按照system2的要求计算唐奇安通道上下边界--55日上界，20日下界
|
||
|
||
Donchian = pd.DataFrame()
|
||
# 计算最高价和最低价的N日移动平均线
|
||
Donchian['Upper'] = self.RecentAlldata['最高'].rolling(55).max()
|
||
Donchian['Lower'] = self.RecentAlldata['最低'].rolling(20).min()
|
||
|
||
# # 计算中间线
|
||
# Donchian['Middle'] = (self.Donchian['Upper'] + self.Donchian['Lower']) / 2
|
||
|
||
return Donchian
|
||
|
||
def system1CalDonchian(self):
|
||
# 按照system2的要求计算唐奇安通道上下边界--55日上界，20日下界
|
||
|
||
Donchian = pd.DataFrame()
|
||
# 计算最高价和最低价的N日移动平均线
|
||
Donchian['Upper55'] = self.RecentAlldata['最高'].rolling(55).max()
|
||
Donchian['Upper20'] = self.RecentAlldata['最高'].rolling(20).max()
|
||
Donchian['Lower'] = self.RecentAlldata['最低'].rolling(10).min()
|
||
|
||
# # 计算中间线
|
||
# Donchian['Middle'] = (self.Donchian['Upper'] + self.Donchian['Lower']) / 2
|
||
|
||
return Donchian
|
||
|
||
def CalPositionSize(self, N, price):
|
||
PositionSize = self.riskcoe * self.Capital /(N) # 默认用股票形式了 100
|
||
IntPositionSize = int(PositionSize // 100) * 100
|
||
return IntPositionSize
|
||
|
||
def TestSys2Function(self):
|
||
"""回测函数，对于某个标的，
|
||
准备:获取价格数据，计算ATR,N,头寸单位，唐奇安通道上下边界
|
||
运行过程:按时间推进，开始时间-结束时间，按照系统1或2或组合形式，执行买入、加仓、退出、止损
|
||
操作记录:形成一个log
|
||
"""
|
||
|
||
# ------------------准备阶段----------------
|
||
# 1、获取标的价格
|
||
self.RecentAlldata = self.turtles.GetRecentData()
|
||
self.RecentAlldata['日期'] = pd.to_datetime(self.RecentAlldata['日期'])
|
||
self.RecentAlldata.set_index('日期', inplace=True)
|
||
|
||
# 2、计算ATR
|
||
self.N = self.turtles.CalATR(self.RecentAlldata, 20, None)
|
||
# self.PositionSize = self.turtles.CalPositionSize(self.riskcoe, self.cash)
|
||
# 3、计算唐奇安通道上下边界--system2
|
||
self.Donchian = self.system2CalDonchian()
|
||
|
||
# 开始、结束日期
|
||
RowStart = self.RecentAlldata.index.get_loc(self.StartTime)
|
||
RowEnd = self.RecentAlldata.index.get_loc(self.EndTime)
|
||
|
||
# 准备迭代使用的数据
|
||
TempData = pd.DataFrame() # 存储最高、最低价等信息
|
||
TempN = []
|
||
TempDonchian55Upper = []
|
||
TempDonchian20Lower = []
|
||
# ------------------运行阶段----------------
|
||
|
||
for date, row in self.RecentAlldata[RowStart:RowEnd].iterrows():
|
||
|
||
# 增加一个迭代更新的数据
|
||
TempData = pd.concat([TempData, row.to_frame().T], ignore_index=True)
|
||
|
||
# day <=55，不执行任何操作，跳过,等待
|
||
|
||
# day < 55,<20刚开始运行，N、Donchian数据从总数据拿, 不用框时间，直接从总数据拿
|
||
# if TempData.shape[0] < 20:
|
||
# 每天更新上下边界
|
||
TempDonchian20Lower.append(self.Donchian['Lower'].iloc[RowStart + TempData.shape[0]-2])
|
||
|
||
# if TempData.shape[0] < 55:
|
||
TempDonchian55Upper.append(self.Donchian['Upper'].iloc[RowStart + TempData.shape[0]-2])
|
||
|
||
# 检查是否是新的一周
|
||
if self.current_week is None or date.week != self.current_week:
|
||
self.current_week = date.week
|
||
|
||
# 更新atr、头寸规模
|
||
self.ThisWeekN = self.N.iloc[RowStart + TempData.shape[0]-2]
|
||
TempN.append(self.ThisWeekN)
|
||
|
||
self.ThisWeekPosizionSize = self.CalPositionSize(self.ThisWeekN, TempData.iloc[-1]['收盘'])
|
||
|
||
|
||
# 如果空仓，判断当天开盘价是否突破，收盘价是否突破，突破则买入
|
||
if self.TrigerTime == 0:
|
||
if self.system2Enter(row["开盘"], TempDonchian55Upper):
|
||
self.TestBuyStocks(row["开盘"], date)
|
||
|
||
# 开盘突破后，最高价能否加仓？
|
||
# 价格差取整是能加仓几手，循环加仓
|
||
delta = math.floor((row['最高']-row['开盘']) / (1/2 * self.ThisWeekN))
|
||
if 1 <= delta <= 3:
|
||
for i in range(delta):
|
||
self.TestBuyStocks((row["开盘"] + (i+1) * 1/2 * self.ThisWeekN +0.001), date)
|
||
elif 3 < delta:
|
||
for i in range(3):
|
||
self.TestBuyStocks((row["开盘"] + (i+1) * 1/2 * self.ThisWeekN +0.001), date)
|
||
# 最高价突破,买入
|
||
elif self.system2Enter(row["最高"], TempDonchian55Upper):
|
||
self.TestBuyStocks(TempDonchian55Upper[-1]+0.001, date)
|
||
|
||
delta = math.floor((row['最高']-TempDonchian55Upper[-1]) / (1/2 * self.ThisWeekN))
|
||
if 1 <= delta <= 3:
|
||
for i in range(delta):
|
||
self.TestBuyStocks((TempDonchian55Upper[-1] + (i+1) * 1/2 * self.ThisWeekN +0.001), date)
|
||
elif 3 < delta:
|
||
for i in range(3):
|
||
self.TestBuyStocks((TempDonchian55Upper[-1] + (i+1) * 1/2 * self.ThisWeekN +0.001), date)
|
||
|
||
# 0"trigger_count", 1"BuyPrice", 2"AddPrice", 3"StopPrice", 4"quantity", 5"N", 6"available_cash"
|
||
# self.BuyStates = [self.TrigerTime, BuyPrice, AddPrice, StopPrice, Shares, N, available_cash]
|
||
elif 1 <= self.TrigerTime < 4: # 加仓1-3次，考虑止损和加仓
|
||
# 开盘价加仓
|
||
if self.system2Add(row["开盘"]):
|
||
self.TestBuyStocks(row["开盘"], date)
|
||
|
||
if self.TrigerTime == 4:
|
||
# 满仓了，不能再加
|
||
pass
|
||
# 开盘加仓后，最高价能否继续加仓？
|
||
else:
|
||
delta = math.floor((row['最高']-row['开盘']) / (1/2 * self.ThisWeekN))
|
||
|
||
if 1 <= delta <= 4-self.TrigerTime:
|
||
for i in range(delta):
|
||
self.TestBuyStocks((row["开盘"] + (i+1) * 1/2 * self.ThisWeekN +0.001), date)
|
||
|
||
# 最高价加仓
|
||
elif self.system2Add(row["最高"]):
|
||
self.TestBuyStocks(self.BuyStates[self.TrigerTime - 1][2] + 0.001, date)
|
||
|
||
|
||
if self.TrigerTime == 4:
|
||
# 满仓了，不能再加
|
||
pass
|
||
# 开盘加仓后，最高价能否继续加仓？
|
||
else:
|
||
delta = math.floor((row['最高']-self.BuyStates[self.TrigerTime - 1][2]) / (1/2 * self.ThisWeekN))
|
||
|
||
if 1 <= delta <= 4-self.TrigerTime:
|
||
for i in range(delta):
|
||
self.TestBuyStocks((self.BuyStates[self.TrigerTime - 1][2] + (i+1) * 1/2 * self.ThisWeekN +0.001), date)
|
||
|
||
|
||
# 止损
|
||
elif self.system2Stop(row["收盘"]):
|
||
self.TestStopSaleStocks(self.BuyStates[self.TrigerTime - 1][3] - 0.001, date)
|
||
elif self.system2Stop(row["最低"]):
|
||
self.TestStopSaleStocks(self.BuyStates[self.TrigerTime - 1][3] - 0.001, date)
|
||
# 止盈
|
||
elif self.system2Out(row["收盘"], TempDonchian20Lower):
|
||
self.TestOutSaleStocks(TempDonchian20Lower[-1] - 0.001, date)
|
||
elif self.system2Out(row["最低"], TempDonchian20Lower):
|
||
self.TestOutSaleStocks(TempDonchian20Lower[-1] - 0.001, date)
|
||
|
||
elif self.TrigerTime == 4: # 满仓 考虑止损和退出
|
||
# 止损
|
||
if self.system2Stop(row["收盘"]):
|
||
self.TestStopSaleStocks(self.BuyStates[self.TrigerTime - 1][3] - 0.001, date)
|
||
elif self.system2Stop(row["最低"]):
|
||
self.TestStopSaleStocks(self.BuyStates[self.TrigerTime - 1][3] - 0.001, date)
|
||
# 止盈
|
||
elif self.system2Out(row["收盘"], TempDonchian20Lower):
|
||
self.TestOutSaleStocks(TempDonchian20Lower[-1] - 0.001, date)
|
||
elif self.system2Out(row["最低"], TempDonchian20Lower):
|
||
self.TestOutSaleStocks(TempDonchian20Lower[-1] - 0.001, date)
|
||
|
||
# 交易日结束，重新计算高低突破点：
|
||
|
||
print("---------------回测结束，回测日志如下----------------")
|
||
|
||
for sublist in self.tradeslog:
|
||
print(sublist)
|
||
|
||
|
||
def TestSys1Function(self):
|
||
"""回测函数，对于某个标的，
|
||
准备:获取价格数据，计算ATR,N,头寸单位，唐奇安通道上下边界
|
||
运行过程:按时间推进，开始时间-结束时间，按照系统1或2或组合形式，执行买入、加仓、退出、止损
|
||
操作记录:形成一个log
|
||
"""
|
||
|
||
# ------------------准备阶段----------------
|
||
# 1、获取标的价格
|
||
self.RecentAlldata = self.turtles.GetRecentData()
|
||
self.RecentAlldata['日期'] = pd.to_datetime(self.RecentAlldata['日期'])
|
||
self.RecentAlldata.set_index('日期', inplace=True)
|
||
|
||
# 2、计算ATR
|
||
self.N = self.turtles.CalATR(self.RecentAlldata, 20, None)
|
||
# self.PositionSize = self.turtles.CalPositionSize(self.riskcoe, self.cash)
|
||
# 3、计算唐奇安通道上下边界--system2
|
||
self.Donchian = self.system1CalDonchian()
|
||
|
||
# 开始、结束日期
|
||
RowStart = self.RecentAlldata.index.get_loc(self.StartTime)
|
||
RowEnd = self.RecentAlldata.index.get_loc(self.EndTime)
|
||
|
||
# 准备迭代使用的数据
|
||
TempData = pd.DataFrame() # 存储最高、最低价等信息
|
||
TempN = []
|
||
TempDonchian55Upper = []
|
||
TempDonchian20Upper = []
|
||
TempDonchian10Lower = []
|
||
self.BreakOutLog = [['date', 'breakout', 'BOprice', 'LosePrice', 'ValidOrNot', 'WinOrLose']]
|
||
# ------------------运行阶段----------------
|
||
|
||
for date, row in self.RecentAlldata[RowStart:RowEnd].iterrows():
|
||
|
||
# 增加一个迭代更新的数据
|
||
TempData = pd.concat([TempData, row.to_frame().T], ignore_index=True)
|
||
|
||
# day <=55，不执行任何操作，跳过,等待
|
||
|
||
# day < 55,<20刚开始运行，N、Donchian数据从总数据拿, 不用框时间，直接从总数据拿
|
||
# if TempData.shape[0] < 20:
|
||
# 每天更新上下边界
|
||
TempDonchian10Lower.append(self.Donchian['Lower'].iloc[RowStart + TempData.shape[0]-2])
|
||
|
||
# if TempData.shape[0] < 55:
|
||
TempDonchian55Upper.append(self.Donchian['Upper55'].iloc[RowStart + TempData.shape[0]-2])
|
||
TempDonchian20Upper.append(self.Donchian['Upper20'].iloc[RowStart + TempData.shape[0]-2])
|
||
|
||
# 检查是否是新的一周
|
||
if self.current_week is None or date.week != self.current_week:
|
||
self.current_week = date.week
|
||
|
||
# 更新atr、头寸规模
|
||
self.ThisWeekN = self.N.iloc[RowStart + TempData.shape[0]-2]
|
||
TempN.append(self.ThisWeekN)
|
||
|
||
self.ThisWeekPosizionSize = self.CalPositionSize(self.ThisWeekN, TempData.iloc[-1]['收盘'])
|
||
|
||
|
||
# 如果空仓，判断当天开盘价是否突破，收盘价是否突破，突破则买入
|
||
if self.TrigerTime == 0:
|
||
if self.system1EnterNormal(row["开盘"], TempDonchian20Upper, self.BreakOutLog):
|
||
self.TestBuyStocks(row["开盘"], date)
|
||
# 写入BreakOut状态
|
||
self.BreakOutLog.append([date, 'breakout', row["开盘"], row["开盘"] - 2*self.ThisWeekN, 'Valid','None'])
|
||
# 开盘突破后，最高价能否加仓？
|
||
# 价格差取整是能加仓几手，循环加仓
|
||
delta = math.floor((row['最高']-row['开盘']) / (1/2 * self.ThisWeekN))
|
||
if 1 <= delta <= 3:
|
||
for i in range(delta):
|
||
self.TestBuyStocks((row["开盘"] + (i+1) * 1/2 * self.ThisWeekN + 0.001), date)
|
||
elif 3 < delta:
|
||
for i in range(3):
|
||
self.TestBuyStocks((row["开盘"] + (i+1) * 1/2 * self.ThisWeekN + 0.001), date)
|
||
|
||
elif self.system1EnterSafe(row["开盘"], TempDonchian55Upper):
|
||
self.TestBuyStocks(row["开盘"], date)
|
||
# 价格差取整是能加仓几手，循环加仓
|
||
delta = math.floor((row['最高']-row['开盘']) / (1/2 * self.ThisWeekN))
|
||
if 1 <= delta <= 3:
|
||
for i in range(delta):
|
||
self.TestBuyStocks((row["开盘"] + (i+1) * 1/2 * self.ThisWeekN + 0.001), date)
|
||
elif 3 < delta:
|
||
for i in range(3):
|
||
self.TestBuyStocks((row["开盘"] + (i+1) * 1/2 * self.ThisWeekN + 0.001), date)
|
||
|
||
# 最高价突破买入
|
||
elif self.system1EnterNormal(row["最高"], TempDonchian20Upper, self.BreakOutLog):
|
||
self.TestBuyStocks(TempDonchian20Upper[-1] + 0.001, date)
|
||
# 写入BreakOut状态
|
||
self.BreakOutLog.append([date, 'breakout', TempDonchian20Upper[-1] + 0.001, TempDonchian20Upper[-1] + 0.001 - 2*self.ThisWeekN, 'Valid','None'])
|
||
|
||
# 价格差取整是能加仓几手，循环加仓
|
||
delta = math.floor((row['最高']-TempDonchian20Upper[-1]) / (1/2 * self.ThisWeekN))
|
||
if 1 <= delta <= 3:
|
||
for i in range(delta):
|
||
self.TestBuyStocks((TempDonchian20Upper[-1] + (i+1) * 1/2 * self.ThisWeekN + 0.001), date)
|
||
elif 3 < delta:
|
||
for i in range(3):
|
||
self.TestBuyStocks((TempDonchian20Upper[-1] + (i+1) * 1/2 * self.ThisWeekN + 0.001), date)
|
||
|
||
elif self.system1EnterSafe(row["最高"], TempDonchian55Upper):
|
||
self.TestBuyStocks(TempDonchian55Upper[-1] + 0.001, date)
|
||
|
||
# 0"trigger_count", 1"BuyPrice", 2"AddPrice", 3"StopPrice", 4"quantity", 5"N", 6"available_cash"
|
||
# self.BuyStates = [self.TrigerTime, BuyPrice, AddPrice, StopPrice, Shares, N, available_cash]
|
||
elif 1 <= self.TrigerTime < 4: # 加仓1-3次，考虑止损和加仓
|
||
# 开盘价突破
|
||
if self.system1EnterNormal(row["开盘"], TempDonchian20Upper, self.BreakOutLog):
|
||
self.TestBuyStocks(row["开盘"], date)
|
||
self.BreakOutLog.append([date, 'breakout', TempDonchian20Upper[-1] + 0.001, TempDonchian20Upper[-1] + 0.001 - 2*self.ThisWeekN, 'Valid','None'])
|
||
elif self.system1EnterSafe(row["开盘"], TempDonchian55Upper):
|
||
self.TestBuyStocks(row["开盘"], date)
|
||
|
||
# 开盘价加仓
|
||
if self.system2Add(row["开盘"]):
|
||
self.TestBuyStocks(row["开盘"], date)
|
||
|
||
if self.TrigerTime == 4:
|
||
# 满仓了，不能再加
|
||
pass
|
||
# 开盘加仓后，最高价能否继续加仓？
|
||
else:
|
||
delta = math.floor((row['最高']-row['开盘']) / (1/2 * self.ThisWeekN))
|
||
|
||
if 1 <= delta <= 4-self.TrigerTime:
|
||
for i in range(delta):
|
||
self.TestBuyStocks((row["开盘"] + (i+1) * 1/2 * self.ThisWeekN + 0.001), date)
|
||
|
||
# 最高价加仓
|
||
elif self.system2Add(row["最高"]):
|
||
self.TestBuyStocks(self.BuyStates[self.TrigerTime - 1][2] + 0.001, date)
|
||
|
||
if self.TrigerTime == 4:
|
||
# 满仓了，不能再加
|
||
pass
|
||
# 开盘加仓后，最高价能否继续加仓？
|
||
else:
|
||
delta = math.floor((row['最高']-self.BuyStates[self.TrigerTime - 1][2]) / (1/2 * self.ThisWeekN))
|
||
|
||
if 1 <= delta <= 4-self.TrigerTime:
|
||
for i in range(delta):
|
||
self.TestBuyStocks((self.BuyStates[self.TrigerTime - 1][2] + (i+1) * 1/2 * self.ThisWeekN + 0.001), date)
|
||
|
||
# 止损
|
||
elif self.system2Stop(row["收盘"]):
|
||
self.TestStopSaleStocks(self.BuyStates[self.TrigerTime - 1][3] - 0.001, date)
|
||
elif self.system2Stop(row["最低"]):
|
||
self.TestStopSaleStocks(self.BuyStates[self.TrigerTime - 1][3] - 0.001, date)
|
||
# 止盈
|
||
elif self.system2Out(row["收盘"], TempDonchian10Lower):
|
||
self.TestOutSaleStocks(TempDonchian10Lower[-1] - 0.001, date)
|
||
elif self.system2Out(row["最低"], TempDonchian10Lower):
|
||
self.TestOutSaleStocks(TempDonchian10Lower[-1] - 0.001, date)
|
||
|
||
elif self.TrigerTime == 4: # 满仓 考虑止损和退出
|
||
# 止损
|
||
if self.system2Stop(row["收盘"]):
|
||
self.TestStopSaleStocks(self.BuyStates[self.TrigerTime - 1][3] - 0.001, date)
|
||
elif self.system2Stop(row["最低"]):
|
||
self.TestStopSaleStocks(self.BuyStates[self.TrigerTime - 1][3] - 0.001, date)
|
||
# 止盈
|
||
elif self.system2Out(row["收盘"], TempDonchian10Lower):
|
||
self.TestOutSaleStocks(TempDonchian10Lower[-1] - 0.001, date)
|
||
elif self.system2Out(row["最低"], TempDonchian10Lower):
|
||
self.TestOutSaleStocks(TempDonchian10Lower[-1] - 0.001, date)
|
||
|
||
# 交易日结束，重新计算高低突破点：
|
||
|
||
print("---------------回测结束，回测日志如下----------------")
|
||
|
||
for sublist in self.tradeslog:
|
||
print(sublist)
|
||
for sublist in self.BreakOutLog:
|
||
print(sublist)
|
||
|
||
|
||
# nsdk513300 = TurtleTrading(513300)
|
||
|
||
# # 每周更新
|
||
# nsdk513300.GetRecentData()
|
||
# nsdk513300.CalATR(20, True)
|
||
# nsdk513300.ReadExistData('513300data-N.csv')
|
||
# nsdk513300.CalPositionSize(0.0025, 100000)
|
||
|
||
# # 每天更新
|
||
# nsdk513300.Donchian20 = nsdk513300.calculate_donchian_channel(500, 20)
|
||
# nsdk513300.Donchian10 = nsdk513300.calculate_donchian_channel(500, 10)
|
||
# nsdk513300.Donchian55 = nsdk513300.calculate_donchian_channel(500, 55)
|
||
# nsdk513300.DrawKLine(500)
|
||
|
||
# print(nsdk513300.PositionSize)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
nsdk513300 = TurtleTrading(513300)
|
||
# nsdk513300test = Trade(nsdk513300, 0.0025, 100000, '2023-1-3', '2024-05-9')
|
||
nsdk513300test = Trade(nsdk513300, 0.0025, 100000, '2014-1-15', '2025-02-18')
|
||
|
||
nsdk513300test.TestSys2Function()
|
||
# nsdk513300test.TestSys1Function() |