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from __future__ import print_function
from __future__ import division
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
sns.set_context(rc={'figure.figsize': (14, 7) } )
figzize_me = figsize =(14, 7)
# import warnings; warnings.filterwarnings('ignore')
import os
import sys
# 使用insert 0即只使用github，避免交叉使用了pip安装的abupy，导致的版本不一致问题
sys.path.insert(0, os.path.abspath('../'))
import abupy
# 本章不使用沙盒数据
abupy.env.disable_example_env_ipython()
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from abupy import ABuSymbolPd
# 表A-1所示
ABuSymbolPd.make_kl_df('601398').tail()
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In [5]:
from abupy import EMarketDataFetchMode, abu
# 强制使用本地缓存数据
abupy.env.g_data_fetch_mode = \
EMarketDataFetchMode.E_DATA_FETCH_FORCE_LOCAL
from abupy import AbuFactorBuyBreak
from abupy import AbuFactorAtrNStop
from abupy import AbuFactorPreAtrNStop
from abupy import AbuFactorCloseAtrNStop
# 设置初始资金数
read_cash = 1000000
# 设置选股因子，None为不使用选股因子
stock_pickers = None
# 买入因子依然延用向上突破因子
buy_factors = [{'xd': 60, 'class': AbuFactorBuyBreak},
{'xd': 42, 'class': AbuFactorBuyBreak}]
# 卖出因子继续使用上一章使用的因子
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}
]
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# 择时股票池
choice_symbols = ['usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG', 'usTSLA', 'usWUBA', 'usVIPS']
# 使用run_loop_back运行策略
abupy.env.enable_example_env_ipython()
abu_result_tuple, _ = abu.run_loop_back(read_cash,
buy_factors, sell_factors, stock_pickers, choice_symbols=choice_symbols, n_folds=2)
abupy.env.disable_example_env_ipython()
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from abupy import AbuMetricsBase
metrics = AbuMetricsBase(*abu_result_tuple)
metrics.fit_metrics()
metrics.plot_returns_cmp()
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from abupy import EMarketSourceType
abupy.env.g_market_source = EMarketSourceType.E_MARKET_SOURCE_tx
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# 强制走网络数据源
abupy.env.g_data_fetch_mode = EMarketDataFetchMode.E_DATA_FETCH_FORCE_NET
# 择时股票池
choice_symbols = ['601398', '600028', '601857', '601318', '600036', '000002', '600050', '600030']
# 使用run_loop_back运行策略
abu_result_tuple, _ = abu.run_loop_back(read_cash,
buy_factors, sell_factors, stock_pickers, choice_symbols=choice_symbols, n_folds=2)
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from abupy import AbuMetricsBase
metrics = AbuMetricsBase(*abu_result_tuple)
metrics.fit_metrics()
metrics.plot_returns_cmp()
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