155 lines
6.2 KiB
Python
155 lines
6.2 KiB
Python
'''
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pip install git+https://github.com/catalyst-team/catalyst@master --upgrade
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cd /www/server/mdserver-web && python3 plugins/cryptocurrency_trade/ccxt/test/p1.py
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cd /Users/midoks/Desktop/mwdev/server/mdserver-web && source bin/activate
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python3 plugins/cryptocurrency_trade/ccxt/test/p1.py
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'''
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from datetime import datetime
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import akshare as ak
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import pandas as pd
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import backtrader as bt
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import sys
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import os
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sys.path.append(os.getcwd() + "/plugins/cryptocurrency_trade/ccxt/strategy")
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import common
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class BollStrategy(bt.Strategy): # BOLL策略程序
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params = (("nk", 13), # 求均值的天数
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('printlog', False),) # 打印log
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def __init__(self): # 初始化
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self.data_close = self.datas[0].close # 指定价格序列
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# 初始化交易指令、买卖价格和手续费
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self.order = None
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self.buy_price = None
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self.buy_comm = None
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# Boll指标计算
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self.top = bt.indicators.BollingerBands(
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self.datas[0], period=self.params.nk).top
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self.bot = bt.indicators.BollingerBands(
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self.datas[0], period=self.params.nk).bot
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# 添加移动均线指标
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self.sma = bt.indicators.SimpleMovingAverage(
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self.datas[0], period=self.params.nk)
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def next(self): # 买卖策略
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if self.order: # 检查是否有指令等待执行
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return
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# 检查是否持仓
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"""
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if not self.position: # 没有持仓
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if self.data_close[0] > self.sma[0]: # 执行买入条件判断：收盘价格上涨突破20日均线
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self.order = self.buy(size=100) # 执行买入
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else:
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if self.data_close[0] < self.sma[0]: # 执行卖出条件判断：收盘价格跌破20日均线
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self.order = self.sell(size=100) # 执行卖出
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"""
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if not self.position: # 没有持仓
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if self.data_close[0] < self.bot[0]: # 收盘价格跌破下轨
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self.log("BUY CREATE, %.2f" % self.data_close[0])
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self.order = self.buy() # 执行买入
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else:
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if self.data_close[0] > self.top[0]: # 收盘价格上涨突破上轨
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self.log("SELL CREATE, %.2f" % self.data_close[0])
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self.order = self.sell() # 执行卖出
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def log(self, txt, dt=None, do_print=False): # 日志函数
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if self.params.printlog or do_print:
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dt = dt or self.datas[0].datetime.date(0)
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print('%s, %s' % (dt.isoformat(), txt))
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def notify_order(self, order): # 记录交易执行情况
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# 如果order为submitted/accepted,返回空
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if order.status in [order.Submitted, order.Accepted]:
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return
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# 指令为buy/sell,报告价格结果
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if order.status in [order.Completed]:
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if order.isbuy():
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self.log(
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f"买入:\n价格:{order.executed.price},\
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成本:{order.executed.value},\
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手续费:{order.executed.comm}"
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)
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self.buyprice = order.executed.price
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self.buycomm = order.executed.comm
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else:
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self.log(
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f"卖出:\n价格：{order.executed.price},\
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成本: {order.executed.value},\
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手续费{order.executed.comm}"
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)
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self.bar_executed = len(self)
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elif order.status in [order.Canceled, order.Margin, order.Rejected]:
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self.log("交易失败") # 指令取消/交易失败, 报告结果
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self.order = None
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def notify_trade(self, trade): # 记录交易收益情况
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if not trade.isclosed:
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return
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self.log(f"策略收益：\n毛收益 {trade.pnl:.2f}, 净收益 {trade.pnlcomm:.2f}")
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def stop(self): # 回测结束后输出结果
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self.log("(BOLL线： %2d日) 期末总资金 %.2f" %
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(self.params.nk, self.broker.getvalue()), do_print=True)
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code = "600036" # 股票代码
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start_cash = 1000000 # 初始自己为1000000
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stake = 100 # 单次交易数量为1手
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commfee = 0.0005 # 佣金为万5
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sdate = '20210101' # 回测时间段
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edate = '20220930'
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cerebro = bt.Cerebro() # 创建回测系统实例
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# 利用AKShare获取股票的前复权数据的前6列
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# df_qfq = ak.stock_zh_a_hist(
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# symbol=code, adjust="qfq", start_date=sdate, end_date=edate).iloc[:, :6]
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# 日期 开盘 收盘 最高 最低 成交量
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# 0 2021-01-04 40.46 40.40 41.01 39.25 1549523
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# 1 2021-01-05 39.99 39.41 40.03 38.67 1387177
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# 2 2021-01-06 39.33 41.38 41.43 39.23 1200646
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# 3 2021-01-07 41.52 43.13 43.20 41.49 1078322
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# 4 2021-01-08 43.52 43.83 44.25 43.04 1299595
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# .. ... ... ... ... ... ...
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# 420 2022-09-26 34.02 33.95 34.52 33.84 525491
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# 421 2022-09-27 33.89 33.90 34.05 33.44 475414
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# 422 2022-09-28 33.79 33.67 34.00 33.42 487299
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# 423 2022-09-29 33.99 33.15 34.32 33.00 617859
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# 424 2022-09-30 33.26 33.65 33.95 33.12 539717
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tag = 'btc'
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symbol = tag.upper() + '/USDT'
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df_qfq = common.getDataFromDb_DF('15m', tag.lower())
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print(df_qfq)
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# # 处理字段命名，以符合Backtrader的要求
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# df_qfq.columns = ['date', 'open', 'close', 'high', 'low', 'volume', ]
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# # 把date作为日期索引，以符合Backtrader的要求
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# df_qfq.index = pd.to_datetime(df_qfq['date'])
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# start_date = datetime.strptime(sdate, "%Y%m%d") # 转换日期格式
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# end_date = datetime.strptime(edate, "%Y%m%d")
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start_date = datetime(2023, 4, 6)
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end_date = datetime(2023, 4, 9)
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data = bt.feeds.PandasData(dataname=df_qfq, fromdate=start_date,
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todate=end_date) # 规范化数据格式
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cerebro.adddata(data) # 加载数据
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cerebro.addstrategy(BollStrategy, nk=13, printlog=True) # 加载交易策略
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cerebro.broker.setcash(start_cash) # broker设置资金
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cerebro.broker.setcommission(commission=commfee) # broker手续费
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cerebro.addsizer(bt.sizers.FixedSize, stake=stake) # 设置买入数量
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print("期初总资金: %.2f" % start_cash)
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cerebro.run() # 运行回测
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end_value = cerebro.broker.getvalue() # 获取回测结束后的总资金
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print("期末总资金: %.2f" % end_value)
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# cerebro.plot(style='candle')
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