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_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_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_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)
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_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)
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;
ABuTradeExecute.apply_action_to_capital(capital, action_pd, kl_pd_manager);
capital.capital_pd.capital_blance.plot();
'''
# 2) simple way
from abupy import ABuPickTimeExecute
orders_pd, action_pd, _ = ABuPickTimeExecute.do_symbols_with_same_factors(
[
'usTSLA', 'usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG',
'usWUBA', 'usVIPS'
],
benchmark,
buy_factors,
sell_factors,
capital,
show=False)
print(orders_pd[:10])
# 3) metric the profit
from abupy import AbuMetricsBase
metrics = AbuMetricsBase(orders_pd, action_pd, capital, benchmark)
metrics.fit_metrics()
metrics.plot_returns_cmp(only_show_returns=True)
# 4) evaluate win_rate(为了计算胜率)
win_rate = metrics.win_rate
gains_mean = metrics.gains_mean
losses_mean = -metrics.losses_mean
# 5) add position control
orders_pd, action_pd, _ = ABuTradeProxy.trade_summary(abu_worker.orders,
kl_pd,
draw=False)
# 上面是从股价角度，下面从我们的资金来看
# ABuTradeExecute.apply_action_to_capital(capital, action_pd, kl_pd_manager)
# print(capital.capital_pd.head())
# capital.capital_pd.capital_blance.plot()
# plt.show()
# 上面只是实现了买入突破，下面我们用卖出突破来试试
# 2、这里是卖出突破
sell_factor1 = {'xd': 120, 'class': AbuFactorSellBreak}
sell_factors = [sell_factor1]
capital = AbuCapital(1000000, benchmark)
orders_pd, action_pd, _ = ABuPickTimeExecute.do_symbols_with_same_factors(
['usTSLA'], benchmark, buy_factors, sell_factors, capital, show=False)
'''
下面会用到ATR这个指标，解释一下这个指标。
1. 设当前交易日的最高价减最低价为x1，
2. 前一个交易日的收盘件减去当前交易日的最高价的绝对值为x2
3. 前一个交易日的收盘件减去当前交易日的最低价的绝对值为x3
4. 那么真实波动TR=max(x1,x2,x3)
5. ATR,就是求N天这个值（TR）的平均，ATR=(∑TR）/n
作用：
1. 用来衡量价格的波动强度，和价格的绝对值没有关系
'''
'''
2）
上面给出了买入点和卖出点的定义，以及他们在历史数据上的表现
但是现在很多操作会有止盈和止损的操作，我们使用ATR来协助判断止盈和止损
具体的止盈止损策略：
from abupy import AbuFactorAtrNStop
from abupy import ABuPickTimeExecute, AbuBenchmark, AbuCapital
# 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
}
# 两个卖出因子策略并行同时生效
sell_factors = [sell_factor1, sell_factor2]
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)
from abupy import AbuFactorPreAtrNStop
# 暴跌止损卖出因子形成dict
sell_factor3 = {'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.0}
# 三个卖出因子同时生效，组成sell_factors
sell_factors = [sell_factor1, sell_factor2, sell_factor3]
capital = AbuCapital(1000000, benchmark)
orders_pd, action_pd, _ = ABuPickTimeExecute.do_symbols_with_same_factors(
['usTSLA'], benchmark, buy_factors, sell_factors, capital, show=True)
from abupy import AbuFactorCloseAtrNStop
# 保护止盈卖出因子组成dict
sell_factor4 = {'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5}
'stop_loss_n': 0.5,
'stop_win_n': 3.0,
'class': AbuFactorAtrNStop
}, {
'class': AbuFactorPreAtrNStop,
'pre_atr_n': 1.0
}, {
'class': AbuFactorCloseAtrNStop,
'close_atr_n': 1.5
}]
benchmark = AbuBenchmark()
capital = AbuCapital(1000000, benchmark)
# 我们假定choice_symbols是我们选股模块的结果，
choice_symbols = [
'usTSLA', 'usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG', 'usWUBA',
'usVIPS'
]
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()
metrics.plot_returns_cmp(only_show_returns=True)
print(all_fit_symbols_cnt)