'USDT')['available'] / current_price
if amount < 0.001:
continue
log.info(('buy:', cheap_btc_ticker, account_name, current_price,
amount))
accounts[account_name].buy(ticker, current_price, amount * 0.99)
else:
amount = accounts[account_name].get_position('BTC')['available']
if amount < 0.001:
continue
log.info(('sell:', cheap_btc_ticker, ticker, account_name,
current_price, amount))
accounts[account_name].sell(ticker, current_price, amount * 0.99)
# 配置策略参数如：基准、回测数据级别等
my_strategy = ai.create_strategy(initialize,
handle_data,
universe=universe,
benchmark='csi5',
freq='d',
refresh_rate=1)
# 配置回测参数如：回测日期、手续费率
ai.backtest(strategy=my_strategy,
start='2018-03-10',
end='2018-05-29',
commission={
'taker': 0.0002,
'maker': 0.0002
})
# log.info("价格突破10日均线, 买入BTC, 价格：%d" % (asks_1_price))
# 如果跌破5000分钟均线卖出0.2BTC
elif (current_price< MA10_1):
context.account_1.sell("BTC/USDT.poloniex", bids_1_price, 0.2)
# log.info("价格跌破10日均线, 卖出BTC, 价格：%d" % (bids_1_price))
# 可以直接指定universe，或者通过筛选条件选择universe池,这里直接指定binance交易所的BTC/USDT、ETH/USDT
universe = ai.create_universe(['BTC/USDT.poloniex']) #,'ETH/USDT.binance'
# 配置策略参数如：基准、回测数据级别等
my_strategy = ai.create_strategy(
initialize,
handle_data,
universe=universe,
benchmark='csi5',
freq='5m',
refresh_rate=1
)
# 配置回测参数如：回测日期、所在时区、手续费率
#(默认timezone是北京时区，其他时区请手动输入，如timezone='America/New_York',timezone='Asia/Tokyo'等)
ai.backtest(
strategy=my_strategy,
start='2018-10-10 00:00:00',
end='2018-11-10 00:00:00',
commission={'taker': 0.0002, 'maker': 0.0002}
)
current_available_btc = position_btc['available']
position_usdt = context.account.get_position('USDT')
current_available_usdt = position_usdt['available']
current_price = context.get_price('BTC/USDT.binance')
# 当RSI信号小于30，且拥有的BTC数量大于0时，卖出所有BTC
if rsi < context.LOW_RSI and current_available_btc > 0:
context.account.sell_pct('BTC/USDT.binance', current_price, 1)
# 当RSI信号大于70, 且拥有的USDT数量为0时，则全仓买入
elif rsi > context.HIGH_RSI and current_available_usdt > 0:
context.account.buy_pct('BTC/USDT.binance', current_price, 1)
my_strategy = ai.create_strategy(
initialize,
handle_data,
universe=universe,
benchmark='csi5',
freq='d',
refresh_rate=1,
)
ai.backtest(strategy=my_strategy,
start='2017-09-01',
end='2019-11-30',
commission={
'taker': 0.001,
'maker': 0.001
})