def sample_817():
"""
8.1.7 使用并行来提升择时运行效率
:return:
"""
# 要关闭沙盒数据环境，因为沙盒里就那几个股票的历史数据, 下面要随机做50个股票
from abupy import EMarketSourceType
abupy.env.g_market_source = EMarketSourceType.E_MARKET_SOURCE_tx
abupy.env.disable_example_env_ipython()
# 关闭沙盒后，首先基准要从非沙盒环境换取，否则数据对不齐，无法正常运行
benchmark = AbuBenchmark()
# 当传入choice_symbols为None时代表对整个市场的所有股票进行回测
# noinspection PyUnusedLocal
choice_symbols = None
# 顺序获取市场后300支股票
# noinspection PyUnusedLocal
choice_symbols = ABuMarket.all_symbol()[-50:]
# 随机获取300支股票
choice_symbols = ABuMarket.choice_symbols(50)
capital = AbuCapital(1000000, benchmark)
sell_factor1 = {'xd': 120, 'class': AbuFactorSellBreak}
sell_factor2 = {
'stop_loss_n': 0.5,
'stop_win_n': 3.0,
'class': AbuFactorAtrNStop
}
sell_factor3 = {'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.0}
sell_factor4 = {'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5}
sell_factors = [sell_factor1, sell_factor2, sell_factor3, sell_factor4]
buy_factors = [{
'xd': 60,
'class': AbuFactorBuyBreak
}, {
'xd': 42,
'class': AbuFactorBuyBreak
}]
orders_pd, action_pd, _ = AbuPickTimeMaster.do_symbols_with_same_factors_process(
choice_symbols, benchmark, buy_factors, sell_factors, capital)
metrics = AbuMetricsBase(orders_pd, action_pd, capital, benchmark)
metrics.fit_metrics()
metrics.plot_returns_cmp(only_show_returns=True)
abupy.env.enable_example_env_ipython()
def sample_823():
"""
8.2.3 使用并行来提升回测运行效率
:return:
"""
from abupy import EMarketSourceType
abupy.env.g_market_source = EMarketSourceType.E_MARKET_SOURCE_tx
abupy.env.disable_example_env_ipython()
benchmark = AbuBenchmark()
capital = AbuCapital(1000000, benchmark)
# 首先随抽取50支股票
choice_symbols = ABuMarket.choice_symbols(50)
# 股价在15-50之间
stock_pickers = [{
'class': AbuPickStockPriceMinMax,
'threshold_price_min': 15.0,
'threshold_price_max': 50.0,
'reversed': False
}]
cs = AbuPickStockMaster.do_pick_stock_with_process(capital, benchmark,
stock_pickers,
choice_symbols)
print('len(cs):', len(cs))
print('cs:\n', cs)
def sample_817():
"""
8.1.7 使用并行来提升择时运行效率
:return:
"""
# 要关闭沙盒数据环境，因为沙盒里就那几个股票的历史数据, 下面要随机做50个股票
from abupy import EMarketSourceType
abupy.env.g_market_source = EMarketSourceType.E_MARKET_SOURCE_tx
abupy.env.disable_example_env_ipython()
# 关闭沙盒后，首先基准要从非沙盒环境换取，否则数据对不齐，无法正常运行
benchmark = AbuBenchmark()
# 当传入choice_symbols为None时代表对整个市场的所有股票进行回测
# noinspection PyUnusedLocal
choice_symbols = None
# 顺序获取市场后300支股票
# noinspection PyUnusedLocal
choice_symbols = ABuMarket.all_symbol()[-50:]
# 随机获取300支股票
choice_symbols = ABuMarket.choice_symbols(50)
capital = AbuCapital(1000000, benchmark)
sell_factor1 = {'xd': 120, 'class': AbuFactorSellBreak}
sell_factor2 = {'stop_loss_n': 0.5, 'stop_win_n': 3.0, 'class': AbuFactorAtrNStop}
sell_factor3 = {'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.0}
sell_factor4 = {'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5}
sell_factors = [sell_factor1, sell_factor2, sell_factor3, sell_factor4]
buy_factors = [{'xd': 60, 'class': AbuFactorBuyBreak},
{'xd': 42, 'class': AbuFactorBuyBreak}]
orders_pd, action_pd, _ = AbuPickTimeMaster.do_symbols_with_same_factors_process(
choice_symbols, benchmark, buy_factors, sell_factors,
capital)
metrics = AbuMetricsBase(orders_pd, action_pd, capital, benchmark)
metrics.fit_metrics()
metrics.plot_returns_cmp(only_show_returns=True)
abupy.env.enable_example_env_ipython()
def sample_823():
"""
8.2.3 使用并行来提升回测运行效率
:return:
"""
from abupy import EMarketSourceType
abupy.env.g_market_source = EMarketSourceType.E_MARKET_SOURCE_tx
abupy.env.disable_example_env_ipython()
benchmark = AbuBenchmark()
capital = AbuCapital(1000000, benchmark)
# 首先随抽取50支股票
choice_symbols = ABuMarket.choice_symbols(50)
# 股价在15-50之间
stock_pickers = [
{'class': AbuPickStockPriceMinMax, 'threshold_price_min': 15.0,
'threshold_price_max': 50.0, 'reversed': False}]
cs = AbuPickStockMaster.do_pick_stock_with_process(capital, benchmark,
stock_pickers,
choice_symbols)
print('len(cs):', len(cs))
print('cs:\n', cs)