from btalib.indicators import sma
import pandas as pd
import backtrader as bt
import os.path #To manage paths
import sys # to find out the script name
import datetime
import matplotlib as plt
from backtrader import cerebro
from numpy import mod #for datetime object
df = pd.read_csv('C:/Users/User/Desktop/programming/dataset/coin_Bitcoin.csv',parse_dates=True, index_col='Date')
sma14 = btalib.sma(df, period = 14)
sma5 = btalib.sma(df, period=5)
class TestStrategy(bt.Strategy):
params = (
('exitbars', 5),
)
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
self.order = None
self.buyprice = None
self.buycomm = None
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
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/Reject')
# Write down: no pending order
self.order = None
# Check if an order has been completed
# Attention: broker could reject order if not enough cash
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):
#sma = btalib.sma(df, period=30)
# 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 sma5[0] > sma14[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:
# Already in the market ... we might sell
if sma5[0] < sma14[0]:
# SELL, SELL, SELL!!! (with all possible default parameters)
self.log('S[enter image description here][1]ELL CREATE, %.2f' % self.dataclose[0])
self.order = self.sell()
if __name__ == '__main__':
# Create a cerebro entity
cerebro = bt.Cerebro()
# Add a strategy
cerebro.addstrategy(TestStrategy)
modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
datapath = os.path.join(modpath, 'C:/programming/AlgoTrading/backtest/BTC-USD-YF.csv')
data = bt.feeds.YahooFinanceCSVData(
dataname = datapath,
fromdate = datetime.datetime(2020,5,1),
todate = datetime.datetime(2021,6,1),
reverse = False)
#Add the Data Feed to Cerebro
cerebro.adddata(data)
cerebro.broker.setcash(100000.0)
# Add a FixedSize sizer according to the stake
#cerebro.addsizer(bt.sizers.FixedSize, stake=10)
cerebro.addsizer(bt.sizers.FixedSize)
# 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(df(data))
# Print out the final result
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
cerebro.plot()
当sma5>sma14并以sma5<sma14，但它不起作用我使用backtrader作为回溯测试库，并使用btalib作为指标来生成信号；btalib.sma(df，周期("；
大脑功能是什么反测试模块
有时是每天买卖，今天买明天卖
也许你必须颠倒df的顺序，这是我用btalib计算RSI时的问题。示例：df = df.iloc[::-1]