def sample_b3_4():
"""
【示例4】abu量化系统选股结合相关性，编写相关性选股策略
AbuPickSimilarNTop源代码请自行阅读，只简单示例使用。
:return:
"""
from abupy import AbuPickSimilarNTop
from abupy import AbuPickStockWorker
from abupy import AbuBenchmark, AbuCapital, AbuKLManager
benchmark = AbuBenchmark()
# 选股因子AbuPickSimilarNTop， 寻找与usTSLA相关性不低于0.95的股票
# 这里内部使用以整个市场作为观察者方式计算，即取值范围0-1
stock_pickers = [{
'class': AbuPickSimilarNTop,
'similar_stock': 'usTSLA',
'threshold_similar_min': 0.95
}]
# 从这几个股票里进行选股，只是为了演示方便，一般的选股都会是数量比较多的情况比如全市场股票
choice_symbols = [
'usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG', 'usTSLA', 'usWUBA',
'usVIPS'
]
capital = AbuCapital(1000000, benchmark)
kl_pd_manager = AbuKLManager(benchmark, capital)
stock_pick = AbuPickStockWorker(capital,
benchmark,
kl_pd_manager,
choice_symbols=choice_symbols,
stock_pickers=stock_pickers)
stock_pick.fit()
print('stock_pick.choice_symbols:\n', stock_pick.choice_symbols)
"""
通过选股因子first_choice属性执行批量优先选股操作，具体阅读源代码
"""
# 选股因子AbuPickSimilarNTop， 寻找与usTSLA相关性不低于0.95的股票
# 通过设置'first_choice':True，进行优先批量操作，默认从对应市场选股
stock_pickers = [{
'class': AbuPickSimilarNTop,
'first_choice': True,
'similar_stock': 'usTSLA',
'threshold_similar_min': 0.95
}]
benchmark = AbuBenchmark()
capital = AbuCapital(1000000, benchmark)
kl_pd_manager = AbuKLManager(benchmark, capital)
stock_pick = AbuPickStockWorker(capital,
benchmark,
kl_pd_manager,
choice_symbols=None,
stock_pickers=stock_pickers)
stock_pick.fit()
print('stock_pick.choice_symbols:\n', stock_pick.choice_symbols)
def sample_821_2():
"""
8.2.1_2 ABuPickStockExecute
:return:
"""
stock_pickers = [{
'class': AbuPickRegressAngMinMax,
'threshold_ang_min': 0.0,
'threshold_ang_max': 10.0,
'reversed': False
}]
choice_symbols = [
'601398', '601988', '601939', '603993', '600999', '300059', '600900',
'601328', '601288', '600887', '600029', '000002'
]
benchmark = AbuBenchmark()
capital = AbuCapital(1000000, benchmark)
kl_pd_manager = AbuKLManager(benchmark, capital)
print(
'ABuPickStockExecute.do_pick_stock_work:\n',
ABuPickStockExecute.do_pick_stock_work(choice_symbols, benchmark,
capital, stock_pickers))
kl_pd_sfun = kl_pd_manager.get_pick_stock_kl_pd('601398')
print('sfun 选股周期内角度={}'.format(
round(ABuRegUtil.calc_regress_deg(kl_pd_sfun.close), 3)))
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 __init__(self, read_cash, buy_factors, sell_factors):
# self.read_cash = read_cash
# self.read_cash = 200000
# 买入因子使用60日向上和42日突破因子
# self.buy_factors = [{'fast': 5, 'slow': 20, 'class': AbuDoubleMaBuy}, {'xd': 60, 'class': AbuFactorBuyBreak},
# {'xd': 42, 'class': AbuFactorBuyBreak,
# 'position': {'class': AbuKellyPosition}, }]
self.buy_factors = buy_factors
# 趋势跟踪策略止盈要大于止损设置值，这里1.0，3.0
# 卖出因子并行生效
# self.sell_factors = [
# {'stop_loss_n': 1.0, 'stop_win_n': 3.0,
# 'class': AbuFactorAtrNStop},
# {'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.5},
# {'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5}
# ]
self.sell_factors = sell_factors
# self.sell_factors = [{'fast': 5, 'slow': 20, 'class': AbuDoubleMaSell},
# {'stop_loss_n': 1.0, 'stop_win_n': 3.0,
# 'class': AbuFactorAtrNStop},
# {'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.5},
# {'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5}]
abupy.env.g_enable_ml_feature = True
abupy.env.g_market_target = EMarketTargetType.E_MARKET_TARGET_CN
self.commission_dict = {
'buy_commission_func': self.buy_commission_ch,
'sell_commission_func': self.sell_commission_ch
}
self.benchmark = AbuBenchmark()
self.capital = AbuCapital(self.read_cash, self.benchmark,
self.commission_dict)
self.kl_pd_manager = AbuKLManager(self.benchmark, self.capital)
def sample_821_1():
"""
8.2.1_1 选股使用示例
:return:
"""
# 选股条件threshold_ang_min=0.0, 即要求股票走势为向上上升趋势
stock_pickers = [{'class': AbuPickRegressAngMinMax,
'threshold_ang_min': 0.0, 'reversed': False}]
# 从这几个股票里进行选股，只是为了演示方便
# 一般的选股都会是数量比较多的情况比如全市场股票
choice_symbols = ['usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG',
'usTSLA', 'usWUBA', 'usVIPS']
benchmark = AbuBenchmark()
capital = AbuCapital(1000000, benchmark)
kl_pd_manager = AbuKLManager(benchmark, capital)
stock_pick = AbuPickStockWorker(capital, benchmark, kl_pd_manager,
choice_symbols=choice_symbols,
stock_pickers=stock_pickers)
stock_pick.fit()
# 打印最后的选股结果
print('stock_pick.choice_symbols:', stock_pick.choice_symbols)
# 从kl_pd_manager缓存中获取选股走势数据，注意get_pick_stock_kl_pd为选股数据，get_pick_time_kl_pd为择时
kl_pd_noah = kl_pd_manager.get_pick_stock_kl_pd('usNOAH')
# 绘制并计算角度
deg = ABuRegUtil.calc_regress_deg(kl_pd_noah.close)
print('noah 选股周期内角度={}'.format(round(deg, 3)))
def sample_815():
"""
8.1.5 自定义仓位管理策略的实现
:return:
"""
metrics = sample_814(False)
print('\nmetrics.gains_mean:{}, -metrics.losses_mean:{}'.format(metrics.gains_mean, -metrics.losses_mean))
from abupy import AbuKellyPosition
# 42d使用AbuKellyPosition，60d仍然使用默认仓位管理类
buy_factors2 = [{'xd': 60, 'class': AbuFactorBuyBreak},
{'xd': 42, 'position': AbuKellyPosition, 'win_rate': metrics.win_rate,
'gains_mean': metrics.gains_mean, 'losses_mean': -metrics.losses_mean,
'class': AbuFactorBuyBreak}]
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]
benchmark = AbuBenchmark()
choice_symbols = ['usTSLA', 'usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG', 'usWUBA', 'usVIPS']
capital = AbuCapital(1000000, benchmark)
orders_pd, action_pd, all_fit_symbols_cnt = ABuPickTimeExecute.do_symbols_with_same_factors(choice_symbols,
benchmark, buy_factors2,
sell_factors, capital,
show=False)
print(orders_pd[:10].filter(['symbol', 'buy_cnt', 'buy_factor', 'buy_pos']))
def sample_816():
"""
8.1.6 多支股票使用不同的因子进行择时
:return:
"""
# 选定noah和sfun
target_symbols = ['usSFUN', 'usNOAH']
# 针对sfun只使用42d向上突破作为买入因子
buy_factors_sfun = [{'xd': 42, 'class': AbuFactorBuyBreak}]
# 针对sfun只使用60d向下突破作为卖出因子
sell_factors_sfun = [{'xd': 60, 'class': AbuFactorSellBreak}]
# 针对noah只使用21d向上突破作为买入因子
buy_factors_noah = [{'xd': 21, 'class': AbuFactorBuyBreak}]
# 针对noah只使用42d向下突破作为卖出因子
sell_factors_noah = [{'xd': 42, 'class': AbuFactorSellBreak}]
factor_dict = dict()
# 构建SFUN独立的buy_factors，sell_factors的dict
factor_dict['usSFUN'] = {'buy_factors': buy_factors_sfun, 'sell_factors': sell_factors_sfun}
# 构建NOAH独立的buy_factors，sell_factors的dict
factor_dict['usNOAH'] = {'buy_factors': buy_factors_noah, 'sell_factors': sell_factors_noah}
# 初始化资金
benchmark = AbuBenchmark()
capital = AbuCapital(1000000, benchmark)
# 使用do_symbols_with_diff_factors执行
orders_pd, action_pd, all_fit_symbols = ABuPickTimeExecute.do_symbols_with_diff_factors(
target_symbols, benchmark, factor_dict, capital)
print('pd.crosstab(orders_pd.buy_factor, orders_pd.symbol):\n', pd.crosstab(orders_pd.buy_factor, orders_pd.symbol))
def sample_812():
"""
8.1.2 卖出因子的实现
:return:
"""
# 120天向下突破为卖出信号
sell_factor1 = {'xd': 120, 'class': AbuFactorSellBreak}
# 趋势跟踪策略止盈要大于止损设置值，这里0.5，3.0
sell_factor2 = {
'stop_loss_n': 0.5,
'stop_win_n': 3.0,
'class': AbuFactorAtrNStop
}
# 暴跌止损卖出因子形成dict
sell_factor3 = {'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.0}
# 保护止盈卖出因子组成dict
sell_factor4 = {'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5}
# 四个卖出因子同时生效，组成sell_factors
sell_factors = [sell_factor1, sell_factor2, sell_factor3, sell_factor4]
# buy_factors 60日向上突破，42日向上突破两个因子
buy_factors = [{
'xd': 60,
'class': AbuFactorBuyBreak
}, {
'xd': 42,
'class': AbuFactorBuyBreak
}]
benchmark = AbuBenchmark()
capital = AbuCapital(1000000, benchmark)
orders_pd, action_pd, _ = ABuPickTimeExecute.do_symbols_with_same_factors(
['usTSLA'], benchmark, buy_factors, sell_factors, capital, show=True)
def sample_814(show=True):
"""
8.1.4 对多支股票进行择时
:return:
"""
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]
benchmark = AbuBenchmark()
buy_factors = [{'xd': 60, 'class': AbuFactorBuyBreak},
{'xd': 42, 'class': AbuFactorBuyBreak}]
choice_symbols = ['usTSLA', 'usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG', 'usWUBA', 'usVIPS']
capital = AbuCapital(1000000, benchmark)
orders_pd, action_pd, all_fit_symbols_cnt = ABuPickTimeExecute.do_symbols_with_same_factors(choice_symbols,
benchmark, buy_factors,
sell_factors, capital,
show=False)
metrics = AbuMetricsBase(orders_pd, action_pd, capital, benchmark)
metrics.fit_metrics()
if show:
print('orders_pd[:10]:\n', orders_pd[:10].filter(
['symbol', 'buy_price', 'buy_cnt', 'buy_factor', 'buy_pos', 'sell_date', 'sell_type_extra', 'sell_type',
'profit']))
print('action_pd[:10]:\n', action_pd[:10])
metrics.plot_returns_cmp(only_show_returns=True)
return metrics
def sample_811():
"""
8.1.1 买入因子的实现
:return:
"""
# buy_factors 60日向上突破，42日向上突破两个因子
buy_factors = [{
'xd': 60,
'class': AbuFactorBuyBreak
}, {
'xd': 42,
'class': AbuFactorBuyBreak
}]
benchmark = AbuBenchmark()
capital = AbuCapital(1000000, benchmark)
kl_pd_manager = AbuKLManager(benchmark, capital)
# 获取TSLA的交易数据
kl_pd = kl_pd_manager.get_pick_time_kl_pd('usTSLA')
abu_worker = AbuPickTimeWorker(capital, kl_pd, benchmark, buy_factors,
None)
abu_worker.fit()
orders_pd, action_pd, _ = ABuTradeProxy.trade_summary(abu_worker.orders,
kl_pd,
draw=True)
ABuTradeExecute.apply_action_to_capital(capital, action_pd, kl_pd_manager)
capital.capital_pd.capital_blance.plot()
plt.show()
def sample_822():
"""
8.2.2 多个选股因子并行执行
:return:
"""
# 选股list使用两个不同的选股因子组合，并行同时生效
stock_pickers = [{
'class': AbuPickRegressAngMinMax,
'threshold_ang_min': 0.0,
'reversed': False
}, {
'class': AbuPickStockPriceMinMax,
'threshold_price_min': 50.0,
'reversed': False
}]
choice_symbols = [
'usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG', 'usTSLA', 'usWUBA',
'usVIPS'
]
benchmark = AbuBenchmark()
capital = AbuCapital(1000000, benchmark)
print(
'ABuPickStockExecute.do_pick_stock_work:\n',
ABuPickStockExecute.do_pick_stock_work(choice_symbols, benchmark,
capital, stock_pickers))
def sample_821_2():
"""
8.2.1_2 ABuPickStockExecute
:return:
"""
stock_pickers = [{
'class': AbuPickRegressAngMinMax,
'threshold_ang_min': 0.0,
'threshold_ang_max': 10.0,
'reversed': False
}]
choice_symbols = [
'usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG', 'usTSLA', 'usWUBA',
'usVIPS'
]
benchmark = AbuBenchmark()
capital = AbuCapital(1000000, benchmark)
kl_pd_manager = AbuKLManager(benchmark, capital)
print(
'ABuPickStockExecute.do_pick_stock_work:\n',
ABuPickStockExecute.do_pick_stock_work(choice_symbols, benchmark,
capital, stock_pickers))
kl_pd_sfun = kl_pd_manager.get_pick_stock_kl_pd('usSFUN')
print('sfun 选股周期内角度={}'.format(
round(ABuRegUtil.calc_regress_deg(kl_pd_sfun.close), 3)))
def batchpick():
start = '20190101'
end = '2020-11-06'
# 策略 选取符合基本财务业绩指标，且短期与相关系数为负的，思路，庄股，跟大盘反着来的。这种策略适合在大盘下跌时买入
stock_pickers = [{
'class': KPickStockValue,
'first_choice': True,
'start': start,
'end': end.replace('-', ''),
'roe_dt_2': 13,
'grossprofit_margin': 20,
'ocf_to_opincome': 0.7
}, {
'class': KPickStockStrongShake,
'first_choice': True,
'start': start,
'end': end.replace('-', ''),
'short_relation': -0.25,
'short_range': 30,
'long_range': 300,
'short_scope': 1.15,
'long_scope': 1.7,
'short_range_deg': 5
}]
# 策略 选取符合基本财务业绩指标，最近1，2，3周成交量较大盘放大的，思路，选取资金涌入个股，改进（根据涌入个股分析所属板块，再延申选取相应板块优秀个股），自上而下的顺势根据当前资金涌入板块选择标的冲浪
stock_pickers = [{
'class': KPickStockValue,
'first_choice': True,
'start': start,
'end': end.replace('-', ''),
'roe_dt_2': 8,
'grossprofit_margin': 18,
'ocf_to_opincome': 0.5
}, {
'class': KPickStockVolume,
'first_choice': True,
'start': '20200801',
'end': end.replace('-', '')
}]
fin_manager = KFinManager()
choice_symbols = fin_manager.get_stock_basic().ts_code
#choice_symbols = ['688179.SH']
benchmark = AbuBenchmark(start='2018-01-01', end=end)
capital = AbuCapital(1000000, benchmark)
print(
'ABuPickStockExecute.do_pick_stock_work:\n',
ABuPickStockExecute.do_pick_stock_work(choice_symbols, benchmark,
capital, stock_pickers))
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_821_3():
"""
8.2.1_3 reversed
:return:
"""
# 和上面的代码唯一的区别就是reversed=True
stock_pickers = [{'class': AbuPickRegressAngMinMax,
'threshold_ang_min': 0.0, 'threshold_ang_max': 10.0, 'reversed': True}]
choice_symbols = ['usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG',
'usTSLA', 'usWUBA', 'usVIPS']
benchmark = AbuBenchmark()
capital = AbuCapital(1000000, benchmark)
print('ABuPickStockExecute.do_pick_stock_work:\n',
ABuPickStockExecute.do_pick_stock_work(choice_symbols, benchmark, capital, stock_pickers))
def sample_813():
"""
8.1.3 滑点买入卖出价格确定及策略实现
:return:
"""
from abupy import AbuSlippageBuyBase
# 修改g_open_down_rate的值为0.02
g_open_down_rate = 0.02
# noinspection PyClassHasNoInit
class AbuSlippageBuyMean2(AbuSlippageBuyBase):
def fit_price(self):
if (self.kl_pd_buy.open /
self.kl_pd_buy.pre_close) < (1 - g_open_down_rate):
# 开盘下跌K_OPEN_DOWN_RATE以上，单子失效
print(self.factor_name + 'open down threshold')
return np.inf
# 买入价格为当天均价
self.buy_price = np.mean(
[self.kl_pd_buy['high'], self.kl_pd_buy['low']])
return self.buy_price
# 只针对60使用AbuSlippageBuyMean2
buy_factors2 = [{
'slippage': AbuSlippageBuyMean2,
'xd': 60,
'class': AbuFactorBuyBreak
}, {
'xd': 42,
'class': AbuFactorBuyBreak
}]
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]
benchmark = AbuBenchmark()
capital = AbuCapital(1000000, benchmark)
orders_pd, action_pd, _ = ABuPickTimeExecute.do_symbols_with_same_factors(
['usTSLA'], benchmark, buy_factors2, sell_factors, capital, show=True)
sell_factors = [{
'xd': 120,
'class': AbuFactorSellBreak
}, {
'stop_loss_n': 0.5,
'stop_win_n': 3.0,
'class': AbuFactorAtrNStop
}, {
'pre_atr_n': 1.0,
'class': AbuFactorPreAtrNStop
}, {
'close_atr_n': 1.5,
'class': AbuFactorCloseAtrNStop
}]
benchmark = AbuBenchmark()
capital = AbuCapital(1000000, benchmark)
# assign money
'''
# 1) tedious way
from abupy import AbuPickTimeWorker;
from abupy import AbuKLManager;
kl_pd_manager = AbuKLManager(benchmark, capital);
kl_pd = kl_pd_manager.get_pick_time_kl_pd('usTSLA'); # load k line of Tesla
# simulate with buy factor and sell factor
abu_worker = AbuPickTimeWorker(capital, kl_pd, benchmark, buy_factors = buy_factors, sell_factors = sell_factors);
abu_worker.fit();
# visualize orders
from abupy import ABuTradeProxy;
orders_pd, action_pd, _ = ABuTradeProxy.trade_summary(abu_worker.orders, kl_pd, draw = True, show_info = False);
# visualize capitalism
from abupy import ABuTradeExecute;
def __init__(self, symbol, period, money):
self.symbol = symbol
self.buy_factors = [{'xd': period, 'class': AbuFactorBuyBreak}]
self.benchmark = AbuBenchmark()
self.capital = AbuCapital(money, self.benchmark)
def sample_821_3():
"""
8.2.1_2 ABuPickStockExecute
:return:
"""
stock_pickers = [{
'class': abupy.FuWeekVolumeBoll,
'threshold_ang_min': 0.0,
'threshold_ang_max': 10.0,
'reversed': False
}]
choice_symbols = [
'601398', '601988', '601939', '603993', '600999', '300059', '600900',
'601328', '601288', '600887', '600029', '000002'
]
choice_symbols = [
'sz000983', 'sh600338', 'sh600511', 'sh600196', 'sh600423', 'sz399136',
'sz002044', 'sh601800', 'sz300132', 'sz300133', 'sh000821', 'sz300003',
'sz300009', 'sz200045', 'sh600998', 'sz300313', 'sh601607', 'sz002644',
'sh600697', 'sz000627', 'sh000003', 'sz399302', 'sh600984', 'sz399301',
'sz000916', 'sz000911', 'sz000912', 'sz000688', 'sh600079', 'sh601101',
'sz000861', 'sz000736', 'sz002053', 'sz000048', 'sh600703', 'sh000814',
'sz300015', 'sh000818', 'sz399352', 'sz399356', 'sh900911', 'sh600395',
'sh000075', 'sz002323', 'sh000101', 'sh600285', 'sh600882', 'sz000789',
'sh601398', 'sz000898', 'sh601390', 'sh601009', 'sh601001', 'sz000525',
'sh600713', 'sh601628', 'sz399299', 'sz399298', 'sh600800', 'sh000808',
'sh900909', 'sh900908', 'sh000061', 'sh000068', 'sh000116', 'sz000617',
'sh600535', 'sz000792', 'sz000889', 'sz000065', 'sh601015', 'sz000089',
'sh600871', 'sz002412', 'sz399400', 'sz399402', 'sz399404', 'sh000057',
'sh900930', 'sh900936', 'sh900934', 'sh900935', 'sh600267', 'sz000650',
'sz399978', 'sh600485', 'sh601021', 'sh601601', 'sh600208', 'sh601288',
'sh600062', 'sh600015', 'sh600016', 'sz300197', 'sz300199', 'sz399413',
'sz399411', 'sz399416', 'sh000134', 'sh000136', 'sh000139', 'sz002007',
'sh600258', 'sh600123', 'sz000511', 'sh601618', 'sh600745', 'sz399170',
'sh000923', 'sz399319', 'sz399554', 'sz399555', 'sz002530', 'sh000145',
'sz002070', 'sh000149', 'sz399220', 'sh601998', 'sh600111', 'sh600023',
'sz000560', 'sh601699', 'sz399305', 'sz399431', 'sz000766', 'sz399436',
'sz399230', 'sz399237', 'sz002661', 'sz002599', 'sh000155', 'sh000152',
'sh000151', 'sh600806', 'sh601988', 'sh600693', 'sh600699', 'sh600582',
'sz000995', 'sh600566', 'sh601318', 'sz399150', 'sz399441', 'sz399200',
'sh000841', 'sh600917', 'sz002128', 'sh600176', 'sz000968', 'sh600771',
'sh600579', 'sh600578', 'sh600572', 'sh600681', 'sh600680', 'sz399140',
'sz000540', 'sz000545', 'sz200022', 'sz200026', 'sz200025', 'sz399210',
'sz200029', 'sz002601', 'sz002656', 'sz002204', 'sz002737', 'sz000748',
'sh600965', 'sz002135', 'sh000934', 'sh601169', 'sh601899', 'sh601898',
'sh600549', 'sh600546', 'sh600545', 'sz000778', 'sh600141', 'sh600145',
'sh601231', 'sz399139', 'sz000630', 'sz000613', 'sz399137', 'sz399130',
'sz399131', 'sz399132', 'sz399133', 'sz200019', 'sz300146', 'sz300144',
'sz399661', 'sz002701', 'sh600971', 'sz002382', 'sz002385', 'sh600085',
'sh603158', 'sz002602', 'sh601939', 'sh600007', 'sz399645', 'sh600000',
'sh601339', 'sh601336', 'sh000125', 'sz399674', 'sh000974', 'sz399160',
'sz002653', 'sz002717', 'sz200726', 'sz399647', 'sz300294', 'sh000100',
'sz300347', 'sh600348', 'sh000933', 'sh600401', 'sh000109', 'sz000034',
'sh600623', 'sz000581', 'sz000672', 'sz300028', 'sh603368', 'sh000023',
'sh000021', 'sz002198', 'sh600432', 'sh603989', 'sz000937', 'sh600508',
'sh600500', 'sh000159', 'sz000732', 'sh600188', 'sz000598', 'sz000029',
'sz000028', 'sz399394', 'sh000832', 'sz300036', 'sz200053', 'sz300326',
'sz002742', 'sz300253', 'sh000013', 'sh000011'
]
# choice_symbols = ['002656', '000903']
benchmark = AbuBenchmark(n_folds=15)
capital = AbuCapital(1000000, benchmark)
kl_pd_manager = AbuKLManager(benchmark, capital)
stock_pickers = ABuPickStockExecute.do_pick_stock_work(
None,
benchmark,
# stock_pickers = ABuPickStockExecute.do_pick_stock_work(choice_symbols, benchmark,
capital,
stock_pickers)
print('ABuPickvStockExecute.do_pick_stock_work:\n', stock_pickers)
for stock_symbol in stock_pickers:
if ~fetch_stock_base_info(stock_symbol):
continue
draw_candle(stock_symbol, 15)
}
sell_factor3 = {'class': AbuFactorPreAtrNStop, 'pre_art_n': 1.0}
sell_factor4 = {'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5}
# buy_factors 60日向上突破，42日向上突破两个因子
buy_factors = [{
'slippage': AbuSlippageBuyMean2,
'xd': 60,
'class': AbuFactorBuyBreak
}, {
'xd': 42,
'class': AbuFactorBuyBreak
}]
# 只使用120天向下突破为卖出因子
sell_factors = [sell_factor1, sell_factor2, sell_factor3, sell_factor4]
benchmark = AbuBenchmark()
# 构造一个字典key='buy_commission_func', value=自定义的手续费方法函数
commission_dict = {'buy_commission_func': calc_commission_us}
# 将commission_dict做为参数传入AbuCapital
capital = AbuCapital(1000000,
benchmark,
user_commission_dict=commission_dict)
print(benchmark.__str__())
orders_pd, action_pd, _ = ABuPickTimeExecute.do_symbols_with_same_factors(
['usTSLA'], benchmark, buy_factors, sell_factors, capital, show=False)
print(orders_pd)
print(action_pd)
print(capital.commission.commission_df)
