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xiangxnxiangxn
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‎.gitignore

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# rope configuration
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.cache/
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.DS_Store
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*.log
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__pycache__
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.pytest_cache
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# Python
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*.py[co]
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## Packages
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*.egg
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*.egg-info
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dist
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venv
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log
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docs/_build
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docs/api
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## paver generated files
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paver-minilib.zip
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config.json
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.env

‎.vscode/settings.json

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{
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"python.defaultInterpreterPath": "venv/bin/python",
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"python.formatting.provider": "yapf",
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"python.formatting.yapfArgs": [
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"--style",
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"{column_limit:160,space_inside_brackets:false,arithmetic_precedence_indication:true,blank_line_before_nested_class_or_def:true,spaces_around_dict_delimiters:true}"
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]
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}

‎BOLL+KDJ.txt

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空仓
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买入: 由boll触发主判断，如果close大于上轨并且kdj最后一次是买入信号则开多
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卖出: 由boll触发主判断，如果close小于下轨并且kdj最后一次是卖出信号则开空
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持仓
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空仓: 由kdj触发主判断，如果kdj信号为买入信号并且close大于下轨则平仓
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多仓: 由kdj触发主判断，如果kdj信号为卖出信号并且close小于上轨则平仓

‎binance_data.py

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from datetime import datetime
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import backtrader as bt
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from data.dataset import CustomDataset
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from strategies.abbration import Abbration
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from strategies.bollkdj import BOLLKDJ
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from strategies.bollema import BollEMA
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from strategies.boll import Boll
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class PrintClose(bt.Strategy):
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def log(self, txt, dt=None):
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dt = dt or self.datas[0].datetime.datetime(0)
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print(f'{dt} {txt}') # Print date and close
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def next(self):
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self.log('Close: %.3f' % self.data0.close[0])
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if __name__ == '__main__':
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cerebro = bt.Cerebro(maxcpus=1)
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# cerebro.addstrategy(PrintClose)
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cerebro.addstrategy(Boll, period_boll=275, boll_diff=40)
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# cerebro.addstrategy(BollEMA)
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# cerebro.addstrategy(Abbration, boll_period=200)
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# cerebro.addstrategy(BOLLKDJ, boll_period=200, kdj_period=72, kdj_ma1=24, kdj_ma2=24)
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# cerebro.optstrategy(Boll, period_boll=range(250, 300,5), debug=False)
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# 加载数据
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data = CustomDataset(name="ETH",
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dataname="data/ETHUSDT-1m-2022-07.csv",
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dtformat=lambda x: datetime.utcfromtimestamp(int(x) / 1000),
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timeframe=bt.TimeFrame.Minutes,
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fromdate=datetime(2022, 5, 1),
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todate=datetime(2022, 11, 1),
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nullvalue=0.0)
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cerebro.resampledata(data, timeframe=bt.TimeFrame.Minutes, compression=5)
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cerebro.broker.setcash(100.0)
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# 配置滑点费用,2跳
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# cerebro.broker.set_slippage_fixed(slippage*1)
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cerebro.broker.setcommission(commission=0.0004, margin=0.1, mult=1.0)
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# cerebro.broker.setcommission(commission=0.00075)
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cerebro.addsizer(bt.sizers.FixedSize, stake=1)
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cerebro.run()
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cerebro.plot()

‎binance_live.py

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from ccxtbt import CCXTStore
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import backtrader as bt
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from datetime import datetime, timedelta
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from strategies.bollema import BollEMA
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from strategies.boll import Boll
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from utils.helper import init_env, get_env
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class TestStrategy(bt.Strategy):
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def __init__(self):
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self.sma = bt.indicators.SMA(self.data, period=21)
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def next(self):
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# Get cash and balance
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# New broker method that will let you get the cash and balance for
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# any wallet. It also means we can disable the getcash() and getvalue()
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# rest calls before and after next which slows things down.
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# NOTE: If you try to get the wallet balance from a wallet you have
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# never funded, a KeyError will be raised! Change LTC below as approriate
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if self.live_data:
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cash, value = self.broker.get_wallet_balance('USDT')
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else:
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# Avoid checking the balance during a backfill. Otherwise, it will
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# Slow things down.
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cash = 'NA'
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# return # 仍然处于历史数据回填阶段，不执行逻辑，返回
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for data in self.datas:
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print('{} - {} | Cash {} | O: {} H: {} L: {} C: {} V:{} SMA:{}'.format(data.datetime.datetime(), data._name, cash, data.open[0], data.high[0],
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data.low[0], data.close[0], data.volume[0], self.sma[0]))
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def notify_data(self, data, status, *args, **kwargs):
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dn = data._name
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dt = datetime.utcnow()
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msg = 'Data Status: {}'.format(data._getstatusname(status))
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print(dt, dn, msg)
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if data._getstatusname(status) == 'LIVE':
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self.live_data = True
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else:
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self.live_data = False
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if __name__ == '__main__':
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init_env()
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cerebro = bt.Cerebro(quicknotify=True)
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# Add the strategy
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# cerebro.addstrategy(TestStrategy)
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# cerebro.addstrategy(BollEMA, period_boll=200, period_ema=99, production=True)
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cerebro.addstrategy(Boll)
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# Create our store
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config = { 'apiKey': get_env('B_APIKEY'), 'secret': get_env('B_SECRET'), 'enableRateLimit': True }
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if get_env("PROXY") == '1':
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config['proxies'] = { 'https': "http://127.0.0.1:8001", 'http': "http://127.0.0.1:8001"}
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# IMPORTANT NOTE - Kraken (and some other exchanges) will not return any values
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# for get cash or value if You have never held any BNB coins in your account.
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# So switch BNB to a coin you have funded previously if you get errors
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store = CCXTStore(exchange='binanceusdm', currency='USDT', config=config, retries=5, debug=False)
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# Get the broker and pass any kwargs if needed.
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# ----------------------------------------------
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# Broker mappings have been added since some exchanges expect different values
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# to the defaults. Case in point, Kraken vs Bitmex. NOTE: Broker mappings are not
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# required if the broker uses the same values as the defaults in CCXTBroker.
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broker_mapping = {
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'order_types': {
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bt.Order.Market: 'market',
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bt.Order.Limit: 'limit',
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bt.Order.Stop: 'stop-loss', #stop-loss for kraken, stop for bitmex
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bt.Order.StopLimit: 'stop limit'
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},
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'mappings': {
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'closed_order': {
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'key': 'status',
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'value': 'closed'
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},
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'canceled_order': {
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'key': 'status',
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'value': 'canceled'
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}
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}
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}
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broker = store.getbroker(broker_mapping=broker_mapping)
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cerebro.setbroker(broker)
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# Get our data
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# Drop newest will prevent us from loading partial data from incomplete candles
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hist_start_date = datetime.utcnow() - timedelta(minutes=1600)
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data = store.getdata(
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dataname='ETH/USDT',
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name="ETHUSDT",
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timeframe=bt.TimeFrame.Minutes,
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fromdate=hist_start_date,
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compression=5,
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ohlcv_limit=99999,
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drop_newest=True,
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# historical=True
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)
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# Add the feed
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cerebro.adddata(data)
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cerebro.broker.setcommission(commission=0.0004, margin=0.1, mult=1.0)
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cerebro.addsizer(bt.sizers.FixedSize, stake=1)
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# Run the strategy
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cerebro.run()

‎custom_data.py

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from datetime import datetime
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import backtrader as bt
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import pandas as pd
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class PrintClose(bt.Strategy):
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def log(self, txt, dt=None):
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dt = dt or self.datas[0].datetime.datetime(0)
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print(f'{dt} {txt}') # Print date and close
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def next(self):
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self.log('Close: %.3f' % self.data0.close[0])
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self.log('turnover: %.8f' % self.data0.turnover[0])
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class CustomData(bt.feeds.PandasData):
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# class CustomData(bt.feeds.GenericCSVData):
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lines = ("turnover", )
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params = (("turnover", -1), )
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if __name__ == '__main__':
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cerebro = bt.Cerebro()
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cerebro.addstrategy(PrintClose)
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datapath = './data/t.csv'
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# 加载数据
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stock_hfq_df = pd.read_csv(datapath, index_col="date", parse_dates=True, usecols=["date", "open", "high", "low", "close", "volume", "turnover"])
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start_date = datetime(2016, 7, 4) # 回测开始时间
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end_date = datetime(2021, 12, 4) # 回测结束时间
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data = CustomData(dataname=stock_hfq_df, fromdate=start_date, todate=end_date)
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# 加载数据
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# data = CustomData(dataname=datapath, dtformat=('%Y-%m-%d %H:%M:%S%z'), datetime=0, open=1, high=2, low=3, close=4, volume=5, turnover=6, openinterest=-1)
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cerebro.adddata(data)
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cerebro.run()

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