def show_me_number(portfolio='sharpe_onewavelet_day',):
"""
每天早晨给一个神秘Number
"""
print(u'近期大盘趋势预测{}：'.format(portfolio))
cumsum_signals = market_brief(start='2019-01-01',
portfolio=portfolio,
frequency='day',
market_type=QA.MARKET_TYPE.STOCK_CN,)
cumsum_signals['flu/master_ratio'] = cumsum_signals.apply(lambda x:'{:.3f}/{:.3f}'.format(x.at['flu_ratio'],
x.at['master_ratio']), axis=1)
cumsum_signals['{}/total'.format(ST.CLUSTER_GROUP)] = cumsum_signals.apply(lambda x:'{}/{}'.format(int(x.at[ST.CLUSTER_GROUP_TOWARDS]),
x.at['total']), axis=1)
#cumsum_signals['{}/total'.format(ST.TRIGGER_R5)] =
#cumsum_signals.apply(lambda x:'{}'.format(int(x.at[ST.TRIGGER_R5]),
# x.at['total']),
# axis=1)
cumsum_signals['ATR_Stplin\SupTrd'] = cumsum_signals.apply(lambda x:'{}/{}'.format(x.at[FLD.ATR_Stopline],
x.at[FLD.ATR_SuperTrend]), axis=1)
cumsum_signals['FLU_POS/ML_FLU'] = cumsum_signals.apply(lambda x:'{}/{}'.format(x.at[FLD.FLU_POSITIVE],
x.at[FLD.ML_FLU_TREND]), axis=1)
cumsum_signals['trend(MT/MS)'] = cumsum_signals.apply(lambda x:'{}:{}/{}'.format(x.at['massive_flu'],
x.at['massive_trend'],
x.at['master_stream']), axis=1)
cumsum_signals['BOOT/DEAD/MINOR'] = cumsum_signals.apply(lambda x:'{}/{}/{}/{}'.format(x.at[FLD.BOLL_RENKO_TIMING_LAG],
x.at[FLD.BOOTSTRAP_GROUND_ZERO_TIMING_LAG],
x.at[FLD.BOLL_RENKO_MINOR_TIMING_LAG],
x.at[FLD.BOOTSTRAP_GROUND_ZERO_MINOR_TIMING_LAG]), axis=1)
cumsum_signals['FLU_MS/NEG'] = cumsum_signals.apply(lambda x:'{}/{}'.format(x.at[FLD.FLU_POSITIVE_MASTER],
x.at[FLD.FLU_NEGATIVE_MASTER]), axis=1)
cumsum_signals['DEA/MACD/DELTA'] = cumsum_signals.apply(lambda x:'{}/{}/{}'.format(round(x.at[FLD.DEA], 2),
round(x.at[FLD.MACD], 2),
round(x.at[FLD.MACD_DELTA], 2)), axis=1)
cumsum_signals['VOL_FLOW/BONUS'] = cumsum_signals.apply(lambda x:'{:d}/{:d}'.format(x.at[ST.VOLUME_FLOW_BOOST],
x.at[ST.VOLUME_FLOW_BOOST_BONUS]), axis=1)
#cumsum_signals['MASSIVE_DIF/FLU'] = cumsum_signals.apply(lambda
#x:'{:d}/{:d}'.format(int(x.at['missive_dif']),
# int(x.at['massive_flu'])),
# axis=1)
cumsum_signals['MFT/DUAL_CROSS'] = cumsum_signals.apply(lambda x:'{:d}/{:d}'.format(int(x.at[FLD.MAXFACTOR_CROSS]),
x.at[FLD.DUAL_CROSS]), axis=1)
cumsum_signals['RENKO/OPT'] = cumsum_signals.apply(lambda x:'{:d}/{:d}'.format(int(x.at[FLD.RENKO_TREND_S]),
int(x.at[FLD.RENKO_TREND_L])), axis=1)
volume_flow_boost_jx = Timeline_Integral((cumsum_signals[ST.VOLUME_FLOW_BOOST] > 0).values)
volume_flow_boost_sx = Timeline_Integral((cumsum_signals[ST.VOLUME_FLOW_BOOST] < 0).values)
massive_trend_jx = Timeline_Integral((cumsum_signals['massive_flu'] > 0).values)
massive_trend_sx = Timeline_Integral((cumsum_signals['massive_flu'] < 0).values)
maxfactor_cross_jx = Timeline_Integral((cumsum_signals[FLD.MAXFACTOR_CROSS] > 0).values)
maxfactor_cross_sx = Timeline_Integral((cumsum_signals[FLD.MAXFACTOR_CROSS] < 0).values)
max_trend_jx_count = np.maximum(volume_flow_boost_jx, massive_trend_jx, maxfactor_cross_jx)
max_trend_sx_count = np.maximum(volume_flow_boost_sx, massive_trend_sx, maxfactor_cross_sx)
cumsum_signals['max_trend_jx_count'] = np.where(max_trend_sx_count == 0, max_trend_jx_count,
np.where(max_trend_jx_count == 0 , 0, -1))
cumsum_signals['max_trend_sx_count'] = np.where(max_trend_jx_count == 0, max_trend_sx_count,
np.where(max_trend_sx_count == 0 , 0, -1))
#print(cumsum_signals[['max_trend_jx_count', 'max_trend_sx_count']])
pd.set_option('display.max_columns', 10)
pd.set_option('display.max_rows', 180)
pd.set_option('display.width', 180) # 设置打印宽度
print(cumsum_signals[cumsum_signals.columns.drop(['massive_trend',
'master_stream',
'flu_ratio',
'master_ratio',
'missive_dif',
'massive_flu',
'max_trend_jx_count',
'max_trend_sx_count',
'flu/master_ratio',
'FLU_POS/ML_FLU',
FLD.BOLL_RENKO_TIMING_LAG,
FLD.BOOTSTRAP_GROUND_ZERO_TIMING_LAG,
FLD.BOLL_RENKO_MINOR_TIMING_LAG,
FLD.BOOTSTRAP_GROUND_ZERO_MINOR_TIMING_LAG,
FLD.PEAK_OPEN_ZSCORE,
FLD.PEAK_OPEN,
FLD.PEAK_OPEN_MINOR,
ST.CLUSTER_GROUP_TOWARDS,
'total',
'ATR_Stplin\SupTrd',
'RENKO/OPT',
FLD.DEA,
FLD.MACD,
FLD.MACD_DELTA,
FLD.ATR_SuperTrend,
FLD.ATR_Stopline,
FLD.ADXm_Trend,
FLD.Volume_HMA5,
FLD.TALIB_PATTERNS,
FLD.RENKO_TREND_S,
FLD.RENKO_TREND_L,
ST.BOOTSTRAP_I,
ST.DEADPOOL,
FLD.ATR_Stopline,
FLD.ATR_SuperTrend,
FLD.FLU_POSITIVE_MASTER,
FLD.FLU_NEGATIVE_MASTER,
ST.VOLUME_FLOW_BOOST,
ST.VOLUME_FLOW_BOOST_BONUS,
FLD.MAXFACTOR_CROSS,
FLD.DUAL_CROSS,
'date_stamp',
'date',
FLD.FLU_POSITIVE,
FLD.ML_FLU_TREND])].tail(42))
#print('\nflu_ratio.percentile(19%)——>{:.3g},
#\nflu_ratio.percentile(38%)——>{:.3g},\nflu_ratio.percentile(50%)——>{:.3g},\nflu_ratio.percentile(61%)——>{:.3g},\nflu_ratio.percentile(81%)——>{:.3g},\n'.format(cumsum_singals['flu_ratio'].quantile(0.191),
# cumsum_singals['flu_ratio'].quantile(0.382),
# cumsum_singals['flu_ratio'].median(),
# cumsum_singals['flu_ratio'].quantile(0.618),
# cumsum_singals['flu_ratio'].quantile(0.809),))
#print('\nML_FLU_TREND.percentile(19%)——>{:d},
#\nML_FLU_TREND.percentile(38%)——>{:d},\nML_FLU_TREND.percentile(50%)——>{:d},\nML_FLU_TREND.percentile(61%)——>{:d},\nML_FLU_TREND.percentile(81%)——>{:d},\n'.format(int(cumsum_singals[FLD.ML_FLU_TREND].quantile(0.191)),
# int(cumsum_singals[FLD.ML_FLU_TREND].quantile(0.382)),
# int(cumsum_singals[FLD.ML_FLU_TREND].median()),
# int(cumsum_singals[FLD.ML_FLU_TREND].quantile(0.618)),
# int(cumsum_singals[FLD.ML_FLU_TREND].quantile(0.809)),))
#massive_trend = []
#massive_trend.append(cumsum_singals['massive_trend'].iat[-1]）
#massive_trend[1] = ((cumsum_singals['flu_ratio'].pct_change() <
#0).rolling(4).sum().iat[-1] < 0)
#massive_trend[2] =
#cumsum_singals[FLD.ML_FLU_TREND].iat[-1]<cumsum_singals[FLD.ML_FLU_TREND].quantile(0.382)
#print(massive_trend)
position(portfolio=portfolio,
frequency='day',
market_type=QA.MARKET_TYPE.STOCK_CN,
cumsum_signals=cumsum_signals)
if __name__ == '__main__':
import matplotlib.pyplot as plt
import mpl_finance as mpf
import matplotlib.dates as mdates
codelist = ['HUOBI.btcusdt']
code = codelist[0]
data_day = QA.QA_fetch_cryptocurrency_min_adv(code=codelist,
start='2019-12-10',
end='{}'.format(datetime.date.today()),
frequence='60min')
cumsum_signals = market_brief(portfolio='gostrike_day',
start='2019-12-10',
frequency='60min',
market_type=QA.MARKET_TYPE.CRYPTOCURRENCY,)
print('\nflu_ratio.percentile(19%)——>{:.3g}, \nflu_ratio.percentile(38%)——>{:.3g},\nflu_ratio.percentile(50%)——>{:.3g},\nflu_ratio.percentile(61%)——>{:.3g},\nflu_ratio.percentile(81%)——>{:.3g},\n'.format(cumsum_signals['flu_ratio'].quantile(0.191),
cumsum_signals['flu_ratio'].quantile(0.382),
cumsum_signals['flu_ratio'].median(),
cumsum_signals['flu_ratio'].quantile(0.618),
cumsum_signals['flu_ratio'].quantile(0.809),))
print('\nML_FLU_TREND.percentile(19%)——>{:d}, \nML_FLU_TREND.percentile(38%)——>{:d},\nML_FLU_TREND.percentile(50%)——>{:d},\nML_FLU_TREND.percentile(61%)——>{:d},\nML_FLU_TREND.percentile(81%)——>{:d},\n'.format(int(cumsum_signals[FLD.ML_FLU_TREND].quantile(0.191)),
int(cumsum_signals[FLD.ML_FLU_TREND].quantile(0.382)),
int(cumsum_signals[FLD.ML_FLU_TREND].median()),
int(cumsum_signals[FLD.ML_FLU_TREND].quantile(0.618)),
int(cumsum_signals[FLD.ML_FLU_TREND].quantile(0.809)),))
cumsum_signals['FLU_POS/ML_FLU'] = cumsum_signals.apply(lambda x:'{}/{}'.format(x.at[FLD.FLU_POSITIVE],