2025-04-02 23:06:11 +08:00
|
|
|
|
import numpy as np
|
|
|
|
|
|
import math
|
|
|
|
|
|
import akshare as ak
|
|
|
|
|
|
import os
|
|
|
|
|
|
from datetime import datetime, timedelta
|
|
|
|
|
|
import pandas as pd
|
|
|
|
|
|
import mplfinance as mpf
|
|
|
|
|
|
|
|
|
|
|
|
def CalTrueFluc(data, day):
|
|
|
|
|
|
|
|
|
|
|
|
H_L = data.iloc[day]['最高'] - data.iloc[day]['最低']
|
|
|
|
|
|
H_PDC = data.iloc[day]['最高'] - data.iloc[day-1]['收盘']
|
|
|
|
|
|
PDC_L = data.iloc[day-1]['收盘'] - data.iloc[day]['最低']
|
|
|
|
|
|
TrueFluc = np.max([H_L, H_PDC, PDC_L])
|
|
|
|
|
|
print('high', data.iloc[day]['最高'], 'low', data.iloc[day]['最低'], 'TrueRange', TrueFluc)
|
|
|
|
|
|
|
|
|
|
|
|
return TrueFluc
|
|
|
|
|
|
|
|
|
|
|
|
def calc_sma_atr_pd(kdf,period):
|
|
|
|
|
|
"""计算TR与ATR
|
|
|
|
|
|
|
|
|
|
|
|
Args:
|
|
|
|
|
|
kdf (_type_): 历史数据
|
|
|
|
|
|
period (_type_): ATR周期
|
|
|
|
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
|
|
_type_: 返回kdf，增加TR与ATR列
|
|
|
|
|
|
"""
|
|
|
|
|
|
kdf['HL'] = kdf['最高'] - kdf['最低']
|
|
|
|
|
|
kdf['HC'] = np.abs(kdf['最高'] - kdf['收盘'].shift(1))
|
|
|
|
|
|
kdf['LC'] = np.abs(kdf['最低'] - kdf['收盘'].shift(1))
|
|
|
|
|
|
kdf['TR'] = np.round(kdf[['HL','HC','LC']].max(axis=1), 3)
|
|
|
|
|
|
# ranges = pd.concat([high_low, high_close, low_close], axis=1)
|
|
|
|
|
|
# true_range = np.max(ranges, axis=1)
|
|
|
|
|
|
kdf['ATR'] = np.round(kdf['TR'].rolling(period).mean(), 3)
|
|
|
|
|
|
|
|
|
|
|
|
return kdf.drop(['HL','HC','LC'], axis = 1)
|
|
|
|
|
|
|
|
|
|
|
|
# A股数据 东方财富网
|
|
|
|
|
|
# all_data = ak.stock_zh_a_spot_em()
|
|
|
|
|
|
|
|
|
|
|
|
# 基金实时数据 东方财富网
|
|
|
|
|
|
# fund_etf_spot_em_df = ak.fund_etf_spot_em()
|
|
|
|
|
|
|
|
|
|
|
|
# 后复权历史数据
|
|
|
|
|
|
# fund_etf_hist_em_df = ak.fund_etf_hist_em(symbol="513300", period="daily", start_date="20130101", end_date="20240408", adjust="hfq")
|
|
|
|
|
|
# fund_etf_hist_em_df.to_csv('513300data.csv', index=False)
|
|
|
|
|
|
|
|
|
|
|
|
# data = pd.read_csv('513300data.csv')
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# # 一、计算头寸规模
|
|
|
|
|
|
|
|
|
|
|
|
# # 真实波动幅度 = max (H-L, H-pdc, pdc-L)
|
|
|
|
|
|
|
|
|
|
|
|
# today = datetime.today()
|
|
|
|
|
|
# # print(today)
|
|
|
|
|
|
|
|
|
|
|
|
# # print(data.iloc[-1]['成交额'])
|
|
|
|
|
|
|
|
|
|
|
|
# TrueFlucs = []
|
|
|
|
|
|
# Nserious = np.zeros(101)
|
|
|
|
|
|
# last120days = np.arange(-120, -100)
|
|
|
|
|
|
# for i in last120days:
|
|
|
|
|
|
# H_L = data.iloc[i]['最高'] - data.iloc[i]['最低']
|
|
|
|
|
|
# H_PDC = data.iloc[i]['最高'] - data.iloc[i-1]['收盘']
|
|
|
|
|
|
# PDC_L = data.iloc[i-1]['收盘'] - data.iloc[i]['最低']
|
|
|
|
|
|
# TrueFlucs.append(np.max([H_L, H_PDC, PDC_L]))
|
|
|
|
|
|
|
|
|
|
|
|
# # 求简单平均,放入N序列第一个
|
|
|
|
|
|
# Nsimple = np.average(TrueFlucs)
|
|
|
|
|
|
# Nserious[0] = Nsimple
|
|
|
|
|
|
# # 计算-21到-1的N
|
|
|
|
|
|
# last100days = np.arange(-100, 0)
|
|
|
|
|
|
|
|
|
|
|
|
# for i in range(0,100):
|
|
|
|
|
|
# day = last100days[i]
|
|
|
|
|
|
# H_L = data.iloc[day]['最高'] - data.iloc[day]['最低']
|
|
|
|
|
|
# H_PDC = data.iloc[day]['最高'] - data.iloc[day-1]['收盘']
|
|
|
|
|
|
# PDC_L = data.iloc[day-1]['收盘'] - data.iloc[day]['最低']
|
|
|
|
|
|
# TrueFluc = np.max([H_L, H_PDC, PDC_L])
|
|
|
|
|
|
|
|
|
|
|
|
# Ntemp = (19 * Nserious[i] + TrueFluc)/20
|
|
|
|
|
|
# Nserious[i+1] = Ntemp
|
|
|
|
|
|
|
|
|
|
|
|
# # print(Nserious)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# total_rows = len(data)
|
|
|
|
|
|
# Ndata = np.zeros(total_rows)
|
|
|
|
|
|
# Ndata[total_rows-101:] = Nserious
|
|
|
|
|
|
# # NewColumn = [0]*(total_rows-101) + Nserious
|
|
|
|
|
|
# data['N'] = Ndata
|
|
|
|
|
|
|
|
|
|
|
|
# data.to_csv('513300data-N.csv', index=False)
|
|
|
|
|
|
# pass
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# -----------------------更新atr----------------------
|
|
|
|
|
|
"""已有数据与新数据对比，补充新的N,同时更新数据库
|
|
|
|
|
|
"""
|
|
|
|
|
|
|
|
|
|
|
|
# Today = datetime.today()
|
|
|
|
|
|
# # print(Today)
|
|
|
|
|
|
# formatted_date = Today.strftime("%Y%m%d")
|
|
|
|
|
|
|
|
|
|
|
|
# # print(formatted_date)
|
|
|
|
|
|
# CurrentData = ak.fund_etf_hist_em(symbol="513300", period="daily", start_date="20130101", end_date=formatted_date, adjust="hfq")
|
|
|
|
|
|
|
|
|
|
|
|
# CurrentData = calc_sma_atr_pd(CurrentData, 20)
|
|
|
|
|
|
# CurrentData.to_csv('513300data-N.csv', index=False)
|
|
|
|
|
|
# pass
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# ------------------计算头寸规模 资金10w, 1%波动------------
|
|
|
|
|
|
|
|
|
|
|
|
# money = 100000
|
|
|
|
|
|
# OldData = pd.read_csv('513300data-N.csv')
|
|
|
|
|
|
|
|
|
|
|
|
# N = OldData.iloc[-1]['ATR']
|
|
|
|
|
|
# # N = 0.473
|
|
|
|
|
|
# Price = OldData.iloc[-1]['收盘']
|
|
|
|
|
|
# # Price = 5.60
|
|
|
|
|
|
# EveryUnit = 0.0025 * money /(N*100*Price)
|
|
|
|
|
|
|
|
|
|
|
|
# print('单位',EveryUnit)
|
|
|
|
|
|
|
|
|
|
|
|
# print(113*100*Price)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class TurtleTrading(object):
|
|
|
|
|
|
"""对象范围较小，对某一个标的创建一个海龟，如513300，
|
|
|
|
|
|
计算ATR、
|
|
|
|
|
|
Position Size，
|
|
|
|
|
|
买入、卖出、加仓等行为
|
|
|
|
|
|
Args:
|
|
|
|
|
|
object (_type_): _description_
|
|
|
|
|
|
"""
|
|
|
|
|
|
def __init__(self, TradeCode) -> None:
|
|
|
|
|
|
self.TradeCode = TradeCode
|
|
|
|
|
|
|
|
|
|
|
|
def GetRecentData(self):
|
|
|
|
|
|
Today = datetime.today()
|
|
|
|
|
|
# print(Today)
|
|
|
|
|
|
formatted_date = Today.strftime("%Y%m%d")
|
|
|
|
|
|
|
|
|
|
|
|
# print(formatted_date)
|
|
|
|
|
|
Code = f"{self.TradeCode}"
|
|
|
|
|
|
CurrentData = ak.fund_etf_hist_em(symbol=Code, period="daily", start_date="20130101", end_date=formatted_date, adjust="")
|
|
|
|
|
|
return CurrentData
|
|
|
|
|
|
|
|
|
|
|
|
def CalATR(self, data, ATRday, SaveOrNot):
|
|
|
|
|
|
"""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
|
|
|
|
|
|
|
|
|
|
|
|
Args:
|
|
|
|
|
|
ATRday: 多少日ATR
|
|
|
|
|
|
SaveOrNot (_type_): 是否保存.csv数据
|
|
|
|
|
|
"""
|
|
|
|
|
|
|
|
|
|
|
|
self.CurrentData = calc_sma_atr_pd(data, ATRday)
|
|
|
|
|
|
self.N = self.CurrentData['ATR']
|
|
|
|
|
|
if SaveOrNot:
|
|
|
|
|
|
self.CurrentData.to_csv('513300data-N.csv', index=False)
|
|
|
|
|
|
print("csv保存成功")
|
|
|
|
|
|
|
|
|
|
|
|
return self.N
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def CalPositionSize(self, RiskCoef, Capital):
|
|
|
|
|
|
"""计算PosizionSize 持有的单位，该单位某标的，1N波动对应RiskCoef * Capital资金
|
|
|
|
|
|
|
|
|
|
|
|
Args:
|
|
|
|
|
|
RiskCoef (_type_): 风险系数
|
|
|
|
|
|
Capital (_type_): 资金
|
|
|
|
|
|
"""
|
|
|
|
|
|
|
|
|
|
|
|
N = self.CurrentData.iloc[-1]['ATR']
|
|
|
|
|
|
# N = 0.473
|
|
|
|
|
|
Price = self.CurrentData.iloc[-1]['收盘']
|
|
|
|
|
|
# Price = 5.60
|
|
|
|
|
|
self.PositionSize = RiskCoef * Capital /( N*100*Price) # 默认用股票形式了 100
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
return self.PositionSize
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def ReadExistData(self, data):
|
|
|
|
|
|
"""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
|
|
|
|
|
|
|
|
|
|
|
|
Args:
|
|
|
|
|
|
data (_type_): 本地csv名称
|
|
|
|
|
|
"""
|
|
|
|
|
|
self.CurrentData = pd.read_csv(data)
|
|
|
|
|
|
|
|
|
|
|
|
def DrawKLine(self, days):
|
|
|
|
|
|
"""画出k线图看看,画出最近days天的K线图
|
|
|
|
|
|
"""
|
|
|
|
|
|
|
|
|
|
|
|
# 日期部分
|
|
|
|
|
|
|
|
|
|
|
|
dates = pd.to_datetime(self.CurrentData['日期'][-days:])
|
|
|
|
|
|
# Klinedf['Data'] = pd.to_datetime(self.CurrentData['日期'])
|
|
|
|
|
|
Klinedf = pd.DataFrame()
|
|
|
|
|
|
# Klinedf.set_index = Klinedf['Data']
|
|
|
|
|
|
|
|
|
|
|
|
# 其他数据
|
|
|
|
|
|
Klinedf['Open'] = self.CurrentData['开盘'][-days:]
|
|
|
|
|
|
Klinedf['High'] = self.CurrentData['最高'][-days:]
|
|
|
|
|
|
Klinedf['Low'] = self.CurrentData['最低'][-days:]
|
|
|
|
|
|
Klinedf['Close'] = self.CurrentData['收盘'][-days:]
|
|
|
|
|
|
Klinedf['Volume'] = self.CurrentData['成交量'][-days:]
|
|
|
|
|
|
|
|
|
|
|
|
Klinedf.set_index(dates, inplace=True)
|
|
|
|
|
|
# 画图
|
|
|
|
|
|
mpf.plot(Klinedf, type='candle', style='yahoo', volume=False, mav=(5,), addplot=[mpf.make_addplot(self.Donchian[['Upper', 'Lower']])])
|
|
|
|
|
|
|
|
|
|
|
|
def calculate_donchian_channel(self, days, n):
|
|
|
|
|
|
"""
|
|
|
|
|
|
计算唐奇安通道days一共多少日， n多少日唐奇安
|
|
|
|
|
|
|
|
|
|
|
|
参数:
|
|
|
|
|
|
self.CurrentData (DataFrame): 包含价格数据的Pandas DataFrame，至少包含"High"和"Low"列
|
|
|
|
|
|
n (int): 时间周期
|
|
|
|
|
|
|
|
|
|
|
|
返回:self.Donchian
|
|
|
|
|
|
DataFrame: 唐奇安通道的DataFrame，包含"Upper", "Lower", 和 "Middle"列
|
|
|
|
|
|
"""
|
|
|
|
|
|
Donchian = pd.DataFrame()
|
|
|
|
|
|
# 计算最高价和最低价的N日移动平均线
|
|
|
|
|
|
Donchian['Upper'] = self.CurrentData['最高'][-days:].rolling(n).max()
|
|
|
|
|
|
Donchian['Lower'] = self.CurrentData['最低'][-days:].rolling(n).min()
|
|
|
|
|
|
|
|
|
|
|
|
# # 计算中间线
|
|
|
|
|
|
# Donchian['Middle'] = (self.Donchian['Upper'] + self.Donchian['Lower']) / 2
|
|
|
|
|
|
|
|
|
|
|
|
return Donchian
|
|
|
|
|
|
|
|
|
|
|
|
class Trade(object):
|
|
|
|
|
|
"""具有以下功能：
|
|
|
|
|
|
接收Turtle Class作为输入
|
|
|
|
|
|
1 数据准备：
|
|
|
|
|
|
a 获取数据
|
|
|
|
|
|
b 计算atr
|
|
|
|
|
|
c 计算55日、20日、10日Donchian
|
|
|
|
|
|
|
|
|
|
|
|
2 总持有单位判断
|
|
|
|
|
|
3 定义系统2 超过55日xxx，分段加仓
|
|
|
|
|
|
4 回测功能
|
|
|
|
|
|
5 实时功能 问题实时功能如何同时监控几个item
|
|
|
|
|
|
|
|
|
|
|
|
78
|
|
|
|
|
|
|
|
|
|
|
|
Args:
|
|
|
|
|
|
object (_type_): _description_
|
|
|
|
|
|
"""
|
|
|
|
|
|
def __init__(self, turtles, riskcoe, cash, StartTime, EndTime) -> None:
|
|
|
|
|
|
"""接收所有的turtles
|
|
|
|
|
|
|
|
|
|
|
|
Args:
|
|
|
|
|
|
turtles (_type_): _description_
|
|
|
|
|
|
"""
|
|
|
|
|
|
self.turtles = turtles
|
|
|
|
|
|
self.riskcoe = riskcoe
|
|
|
|
|
|
self.cash = cash
|
|
|
|
|
|
self.Capital = cash
|
|
|
|
|
|
|
|
|
|
|
|
self.StartTime = StartTime
|
|
|
|
|
|
self.EndTime = EndTime
|
|
|
|
|
|
self.TrigerTime = 0
|
|
|
|
|
|
# self.BuyStates = {
|
|
|
|
|
|
# "trigger_count": 0, # 触发次数
|
|
|
|
|
|
# "BuyPrice": None, # 买入/持有价格
|
|
|
|
|
|
# "StopPrice":None, # 止损价格
|
|
|
|
|
|
# "quantity": 0, # 持有份数
|
|
|
|
|
|
# "N": 0, # ATR
|
|
|
|
|
|
# "available_cash": self.cash # 可用资金
|
|
|
|
|
|
# }
|
|
|
|
|
|
# 0"trigger_count", 1"BuyPrice", 2"StopPrice", 3"quantity", 4"N", 5"available_cash"
|
|
|
|
|
|
self.BuyStates = [[0, None, None, 0, 0, self.cash]]
|
|
|
|
|
|
|
|
|
|
|
|
self.tradeslog = [] # 交易记录
|
|
|
|
|
|
|
|
|
|
|
|
self.current_week = None # 当前周数
|
|
|
|
|
|
# def TurtleDataPre(self):
|
|
|
|
|
|
# for turtle in self.turtles:
|
|
|
|
|
|
# turtle.CalATR(20, True)
|
|
|
|
|
|
|
|
|
|
|
|
# turtle.Donchian20 = turtle.calculate_donchian_channel(500, 20)
|
|
|
|
|
|
# turtle.Donchian10 = turtle.calculate_donchian_channel(500, 10)
|
|
|
|
|
|
# turtle.Donchian55 = turtle.calculate_donchian_channel(500, 55)
|
|
|
|
|
|
# pass
|
|
|
|
|
|
|
|
|
|
|
|
def PortfolioPositon(self):
|
|
|
|
|
|
"""总共能持有多少个单位
|
|
|
|
|
|
"""
|
|
|
|
|
|
pass
|
|
|
|
|
|
|
|
|
|
|
|
def TestBuyStocks(self, PriceNow, date):
|
|
|
|
|
|
# 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
|
|
|
|
|
|
# 返回self.BuyStates
|
|
|
|
|
|
# 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
|
|
|
|
|
|
|
|
|
|
|
|
# 更新BuyStates:"触发次数"、"买入/持有价格"、"持有份数"、"N"、"可用资金"
|
|
|
|
|
|
N = self.ThisWeekN
|
|
|
|
|
|
Shares = self.ThisWeekPosizionSize
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# 更新 BuyStates
|
|
|
|
|
|
if self.TrigerTime == 0: # 第一次买入是直接修改
|
|
|
|
|
|
# if self.BuyStates[0] == 0:
|
|
|
|
|
|
|
|
|
|
|
|
self.TrigerTime += 1
|
|
|
|
|
|
BuyPrice = PriceNow
|
|
|
|
|
|
AddPrice = PriceNow + 1/2 * N
|
|
|
|
|
|
StopPrice = PriceNow - 2 * N
|
|
|
|
|
|
available_cash = self.cash - Shares * BuyPrice
|
|
|
|
|
|
|
|
|
|
|
|
# 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]]
|
|
|
|
|
|
|
|
|
|
|
|
# 更新log
|
|
|
|
|
|
#
|
|
|
|
|
|
AllShares = sum(row[4] for row in self.BuyStates)
|
|
|
|
|
|
NetValue = available_cash + AllShares * BuyPrice
|
|
|
|
|
|
self.tradeslog.append([date, 'Buy', Shares, PriceNow, N, available_cash, NetValue])
|
|
|
|
|
|
|
|
|
|
|
|
else: # 加仓的操作，在BuyStates后边追加
|
|
|
|
|
|
self.TrigerTime += 1
|
|
|
|
|
|
BuyPrice = PriceNow
|
|
|
|
|
|
AddPrice = PriceNow + 1/2 * N
|
|
|
|
|
|
StopPrice = PriceNow - 2 * N
|
|
|
|
|
|
available_cash = self.BuyStates[self.TrigerTime-2][6] - Shares * BuyPrice
|
|
|
|
|
|
|
|
|
|
|
|
self.BuyStates.append([self.TrigerTime, BuyPrice, AddPrice, StopPrice, Shares, N, available_cash])
|
|
|
|
|
|
|
|
|
|
|
|
# 更新log
|
|
|
|
|
|
AllShares = sum(row[4] for row in self.BuyStates)
|
|
|
|
|
|
NetValue = available_cash + AllShares * BuyPrice
|
|
|
|
|
|
self.tradeslog.append([date, 'Buy', Shares, PriceNow, N, available_cash, NetValue])
|
|
|
|
|
|
|
|
|
|
|
|
pass
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def TestStopSaleStocks(self, PriceNow, date):
|
|
|
|
|
|
# 回测中的卖出函数，仓位全卖
|
|
|
|
|
|
N = self.ThisWeekN
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# Shares应该等于所有持仓的和
|
|
|
|
|
|
Shares = sum(row[4] for row in self.BuyStates)
|
|
|
|
|
|
# Shares = sum(self.BuyStates[:, 4])
|
|
|
|
|
|
available_cash = self.BuyStates[-1][6] + Shares * PriceNow
|
|
|
|
|
|
# 更新log
|
|
|
|
|
|
NetValue = available_cash
|
|
|
|
|
|
self.tradeslog.append([date, 'StopSale', Shares, PriceNow, N, available_cash, NetValue])
|
|
|
|
|
|
# self.trades.append((date, 'Sale', Shares, PriceNow, N, available_cash))
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# TrigerTime归0
|
|
|
|
|
|
self.TrigerTime = 0
|
|
|
|
|
|
# self.cash更新
|
|
|
|
|
|
self.cash = available_cash
|
|
|
|
|
|
# 回到初始状态
|
|
|
|
|
|
self.BuyStates = [[0, None, None, None, 0, 0, self.cash]]
|
|
|
|
|
|
|
|
|
|
|
|
def TestOutSaleStocks(self, PriceNow, date):
|
|
|
|
|
|
# 回测中的卖出函数，仓位全卖
|
|
|
|
|
|
N = self.ThisWeekN
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# Shares应该等于所有持仓的和
|
|
|
|
|
|
Shares = sum(row[4] for row in self.BuyStates)
|
|
|
|
|
|
# Shares = sum(self.BuyStates[:, 4])
|
|
|
|
|
|
available_cash = self.BuyStates[-1][6] + Shares * PriceNow
|
|
|
|
|
|
# 更新log
|
|
|
|
|
|
NetValue = available_cash
|
|
|
|
|
|
self.tradeslog.append([date, 'OutSale', Shares, PriceNow, N, available_cash, NetValue])
|
|
|
|
|
|
# self.trades.append((date, 'Sale', Shares, PriceNow, N, available_cash))
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# TrigerTime归0
|
|
|
|
|
|
self.TrigerTime = 0
|
|
|
|
|
|
# self.cash更新
|
|
|
|
|
|
self.cash = available_cash
|
|
|
|
|
|
# 回到初始状态
|
|
|
|
|
|
self.BuyStates = [[0, None, None, None, 0, 0, self.cash]]
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def system2Enter(self, PriceNow, TempDonchian55Upper):
|
|
|
|
|
|
"""以50日突破为基础的较简单的长线系统
|
|
|
|
|
|
入市：价格超过了前55日的最高价或最低价就建立头寸。
|
|
|
|
|
|
- 如果价格超过55日最高价，买入一个单位建立多头头寸。
|
|
|
|
|
|
- 如果价格跌破55日最低价，卖出一个单位建立空头头寸。
|
|
|
|
|
|
退出：
|
|
|
|
|
|
|
|
|
|
|
|
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
|
|
|
|
|
|
单个品种，最大4个单位。
|
|
|
|
|
|
|
|
|
|
|
|
以多头编写
|
|
|
|
|
|
"""
|
|
|
|
|
|
|
|
|
|
|
|
# # 触发次数
|
|
|
|
|
|
# Trigertime = 0
|
|
|
|
|
|
|
|
|
|
|
|
# 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
|
|
|
|
|
|
if self.TrigerTime == 0 and PriceNow > TempDonchian55Upper[-1]:
|
|
|
|
|
|
# 买入
|
|
|
|
|
|
return True
|
|
|
|
|
|
|
|
|
|
|
|
else:
|
|
|
|
|
|
return False
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def system1EnterNormal(self, PriceNow, TempDonchian20Upper, BreakOutLog):
|
|
|
|
|
|
# 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
|
|
|
|
|
|
if self.TrigerTime == 0 and PriceNow > TempDonchian20Upper[-1]:
|
|
|
|
|
|
# 买入
|
|
|
|
|
|
return True
|
|
|
|
|
|
elif self.TrigerTime != 0 and PriceNow > TempDonchian20Upper[-1]:
|
|
|
|
|
|
self.system1BreakoutValid(PriceNow)
|
2025-05-16 20:20:58 +08:00
|
|
|
|
if BreakOutLog[-1][5] == 'Lose': # TrigerTime != 0且突破且上一次突破unseccessful
|
2025-04-02 23:06:11 +08:00
|
|
|
|
return True
|
|
|
|
|
|
else:
|
|
|
|
|
|
return False
|
|
|
|
|
|
else:
|
|
|
|
|
|
return False
|
|
|
|
|
|
|
|
|
|
|
|
def system1EnterSafe(self, PriceNow, TempDonchian55Upper):
|
|
|
|
|
|
|
|
|
|
|
|
if PriceNow > TempDonchian55Upper[-1]: # 保底的55日突破
|
|
|
|
|
|
return True
|
|
|
|
|
|
else:
|
|
|
|
|
|
return False
|
|
|
|
|
|
|
|
|
|
|
|
def system1BreakoutValid(self, priceNow):
|
|
|
|
|
|
"""判断前一次突破是否成功，是log[-1][5]写入“win”，否则写入“Lose”
|
|
|
|
|
|
"""
|
|
|
|
|
|
if priceNow < self.BreakOutLog[-1][3]:
|
|
|
|
|
|
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()
|
