backtrader3.py

# -*- coding: utf-8 -*-
"""
Created on 2019-10-12 21:50:26

author: huangyunbin

email: huangyunbin@sina.com

QQ: 592440193

https://blog.csdn.net/qtlyx/article/details/70306256

"""

# -*- coding: utf-8 -*-
 
from __future__ import (absolute_import, division, print_function,
                        unicode_literals)
 
import datetime  # For datetime objects
import pandas as pd
import backtrader as bt
import numpy as np
 
class ssa_index_ind(bt.Indicator):
    lines = ('ssa',)
    def __init__(self, ssa_window):
        self.params.ssa_window = ssa_window
        # 这个很有用，会有 not maturity生成
        self.addminperiod(self.params.ssa_window * 2)
 
    def get_window_matrix(self, input_array, t, m):
        # 将时间序列变成矩阵
        temp = []
        n = t - m + 1
        for i in range(n):
            temp.append(input_array[i:i + m])
        window_matrix = np.array(temp)
        return window_matrix
 
    def svd_reduce(self, window_matrix):
        # svd分解
        u, s, v = np.linalg.svd(window_matrix)
        m1, n1 = u.shape
        m2, n2 = v.shape
        index = s.argmax()  # get the biggest index
        u1 = u[:, index]
        v1 = v[index]
        u1 = u1.reshape((m1, 1))
        v1 = v1.reshape((1, n2))
        value = s.max()
        new_matrix = value * (np.dot(u1, v1))
        return new_matrix
 
    def recreate_array(self, new_matrix, t, m):
        # 时间序列重构
        ret = []
        n = t - m + 1
        for p in range(1, t + 1):
            if p < m:
                alpha = p
            elif p > t - m + 1:
                alpha = t - p + 1
            else:
                alpha = m
            sigma = 0
            for j in range(1, m + 1):
                i = p - j + 1
                if i > 0 and i < n + 1:
                    sigma += new_matrix[i - 1][j - 1]
            ret.append(sigma / alpha)
        return ret
 
    def SSA(self, input_array, t, m):
        window_matrix = self.get_window_matrix(input_array, t, m)
        new_matrix = self.svd_reduce(window_matrix)
        new_array = self.recreate_array(new_matrix, t, m)
        return new_array
 
    def next(self):
        data_serial = self.data.get(size=self.params.ssa_window * 2)
        self.lines.ssa[0] = self.SSA(data_serial, len(data_serial), int(len(data_serial) / 2))[-1]
 
# Create a Stratey
class MyStrategy(bt.Strategy):
    params = (
        ('ssa_window', 15),
        ('maperiod', 15),
    )
 
    def log(self, txt, dt=None):
        ''' Logging function fot this strategy'''
        dt = dt or self.datas[0].datetime.date(0)
        print('%s, %s' % (dt.isoformat(), txt))
 
    def __init__(self):
        # Keep a reference to the "close" line in the data[0] dataseries
        self.dataclose = self.datas[0].close
 
        # To keep track of pending orders and buy price/commission
        self.order = None
        self.buyprice = None
        self.buycomm = None
 
        # Add a MovingAverageSimple indicator
        self.ssa = ssa_index_ind(ssa_window=self.params.ssa_window, subplot=False)
        # bt.indicator.LinePlotterIndicator(self.ssa, name='ssa')
        self.sma = bt.indicators.SimpleMovingAverage(period=self.params.maperiod)
    def start(self):
        print("the world call me!")
 
    def prenext(self):
        print("not mature")
 
    def notify_order(self, order):
        if order.status in [order.Submitted, order.Accepted]:
            # Buy/Sell order submitted/accepted to/by broker - Nothing to do
            return
 
        # Check if an order has been completed
        # Attention: broker could reject order if not enougth cash
        if order.status in [order.Completed]:
            if order.isbuy():
                self.log(
                    'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
                    (order.executed.price,
                     order.executed.value,
                     order.executed.comm))
 
                self.buyprice = order.executed.price
                self.buycomm = order.executed.comm
            else:  # Sell
                self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
                         (order.executed.price,
                          order.executed.value,
                          order.executed.comm))
 
            self.bar_executed = len(self)
 
        elif order.status in [order.Canceled, order.Margin, order.Rejected]:
            self.log('Order Canceled/Margin/Rejected')
 
        self.order = None
 
    def notify_trade(self, trade):
        if not trade.isclosed:
            return
 
        self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %
                 (trade.pnl, trade.pnlcomm))
 
    def next(self):
        # Simply log the closing price of the series from the reference
        self.log('Close, %.2f' % self.dataclose[0])
 
        # Check if an order is pending ... if yes, we cannot send a 2nd one
        if self.order:
            return
 
        # Check if we are in the market
        if not self.position:
 
            # Not yet ... we MIGHT BUY if ...
            if self.dataclose[0] > self.ssa[0]:
 
                # BUY, BUY, BUY!!! (with all possible default parameters)
                self.log('BUY CREATE, %.2f' % self.dataclose[0])
 
                # Keep track of the created order to avoid a 2nd order
                self.order = self.buy()
 
        else:
 
            if self.dataclose[0] < self.ssa[0]:
                # SELL, SELL, SELL!!! (with all possible default parameters)
                self.log('SELL CREATE, %.2f' % self.dataclose[0])
 
                # Keep track of the created order to avoid a 2nd order
                self.order = self.sell()
    def stop(self):
        print("death")
 
if __name__ == '__main__':
    # Create a cerebro entity
    cerebro = bt.Cerebro()
    # Add a strategy
    cerebro.addstrategy(MyStrategy)
    # 本地数据，笔者用Wind获取的东风汽车数据以csv形式存储在本地。
    # parase_dates = True是为了读取csv为dataframe的时候能够自动识别datetime格式的字符串，big作为index
    # 注意，这里最后的pandas要符合backtrader的要求的格式
    dataframe = pd.read_csv('sz000651.csv', index_col=0, parse_dates=True)
    dataframe['openinterest'] = 0
    data = bt.feeds.PandasData(dataname=dataframe,
                            fromdate = datetime.datetime(2018, 1, 1),
                            todate = datetime.datetime(2019, 12, 31)
                            )
    # Add the Data Feed to Cerebro
    cerebro.adddata(data)
    # Set our desired cash start
    cerebro.broker.setcash(10000.0)
    # 设置每笔交易交易的股票数量
    cerebro.addsizer(bt.sizers.FixedSize, stake=10)
    # Set the commission
    cerebro.broker.setcommission(commission=0.0)
    # Print out the starting conditions
    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
    # Run over everything
    cerebro.run()
    # Print out the final result
    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
    cerebro.plot()