def plot_gain_prod(df_gain, time_col='日期', N=None, show_gain=True, **kwargs):
'''
绘制盈亏净值曲线图
df_gain须包含列[time_col, '盈亏%']
**kwargs为plot_Series可接受参数
'''
N = df_gain.shape[0] if N is None or N < 1 or N > df_gain.shape[0] else N
df = df_gain.reindex(columns=[time_col, '盈亏%'])
if N >= df.shape[0]:
df.sort_values(time_col, ascending=True, inplace=True)
tmp = pd.DataFrame(columns=[time_col, '盈亏%'])
tmp.loc['tmp'] = ['start', 0]
df = pd.concat((tmp, df), axis=0)
else:
df = df.sort_values(time_col, ascending=True).iloc[-N - 1:, :]
df.set_index(time_col, inplace=True)
df.loc[df.index[0], '盈亏%'] = 0
df['净值'] = (1 + df['盈亏%'] / 100).cumprod()
gain_pct = round(100 * (df['净值'].iloc[-1] - 1), 2)
if not 'title' in kwargs:
if N == df_gain.shape[0]:
kwargs['title'] = f'期权账户净值曲线\n(收益率: {gain_pct}%)' \
if show_gain else '期权账户净值曲线'
else:
kwargs['title'] = \
f'期权账户近{N}个交易日净值曲线\n(收益率: {gain_pct}%)' \
if show_gain else f'期权账户近{N}个交易日净值曲线'
plot_Series(df, {'净值': '-ro'}, **kwargs)
def plot_gain_act(df_gain, time_col='日期', N=None, **kwargs):
'''
绘制实际盈亏曲线图
df_gain须包含列[time_col, '实际总盈亏%']
**kwargs为plot_Series可接受参数
'''
N = df_gain.shape[0] if N is None or N < 1 or N > df_gain.shape[0] else N
df = df_gain.reindex(columns=[time_col, '实际总盈亏%'])
if N == df_gain.shape[0]:
df.sort_values(time_col, ascending=True, inplace=True)
tmp = pd.DataFrame(columns=['日期', '实际总盈亏%'])
tmp.loc['tmp'] = ['start', 0]
df = pd.concat((tmp, df), axis=0)
else:
df = df.sort_values(time_col, ascending=True).iloc[-N - 1:, :]
df.set_index(time_col, inplace=True)
if not 'title' in kwargs:
if N == df_gain.shape[0]:
kwargs['title'] = '期权账户实际总盈亏(%)走势'
else:
kwargs['title'] = '期权账户近{}个交易日实际总盈亏(%)走势'.format(N)
plot_Series(df, {'实际总盈亏%': '-ro'}, **kwargs)
objf = TestFuncs.F5
parms_func = {
'func_name': objf.__name__,
'x_lb': -10,
'x_ub': 10,
'dim': 5,
'kwargs': {}
}
parms_opter = {'opter_name': 'GWO-test', 'PopSize': 20, 'Niter': 1000}
# logger = simple_logger()
logger = get_logger('./test/GWO_test.txt', screen_show=True)
# parms_log = {'logger': logger, 'nshow': 10}
parms_log = {'logger': logger, 'nshow': 100}
func_opter_parms = FuncOpterInfo(parms_func, parms_opter, parms_log)
func_opter_parms = GWO(objf, func_opter_parms)
vals = pd.DataFrame({
'fval_best': func_opter_parms.convergence_curve,
'fval_mean': func_opter_parms.convergence_curve_mean
})
plot_Series(vals, {'fval_best': '-r', 'fval_mean': '-b'}, figsize=(10, 6))
# best_x = func_opter_parms.best_x
# func_opter_parms.parms_log['logger'].info(f'best x: {best_x}')
close_log_file(logger)
print(f'used time: {round(time.time()-strt_tm, 6)}.')
if __name__ == '__main__':
strt_tm = time.time()
objf = TestFuncs.F1
parms_func = {'func_name': objf.__name__,
'x_lb': -10, 'x_ub': 10, 'dim': 5, 'kwargs': {}}
parms_opter = {'opter_name': 'GA-test',
'PopSize': 20, 'Niter': 1000,
'Pcrs': 0.7, 'Pmut': 0.1, 'Ntop': 2}
# logger = simple_logger()
logger = get_logger('./test/GA_test.txt', screen_show=True)
# parms_log = {'logger': logger, 'nshow': 10}
parms_log = {'logger': logger, 'nshow': 100}
func_opter_parms = FuncOpterInfo(parms_func, parms_opter, parms_log)
func_opter_parms = GA(objf, func_opter_parms)
vals = pd.DataFrame({'fval_best': func_opter_parms.convergence_curve,
'fval_mean': func_opter_parms.convergence_curve_mean})
plot_Series(vals, {'fval_best': '-r', 'fval_mean': '-b'}, figsize=(10, 6),
title='GA优化目标函数值收敛过程')
# best_x = func_opter_parms.best_x
# func_opter_parms.parms_log['logger'].info(f'best x: {best_x}')
close_log_file(logger)
print(f'used time: {round(time.time()-strt_tm, 6)}.')
from utils_hoo.utils_plot.plot_Common import plot_Series
# 50ETF日线行情------------------------------------------------------------
fpath = './test/510050_daily_pre_fq.csv'
data = load_csv(fpath)
data.set_index('date', drop=False, inplace=True)
data['cci'] = CCI(data)
data['cci_100'] = data['cci'].apply(lambda x: 1 if x > 100 else \
(-1 if x < -100 else 0))
plot_Series(
data.iloc[-200:, :], {'close': ('.-k', False)},
cols_styl_low_left={'cci': ('.-c', False)},
cols_to_label_info={
'cci': [['cci_100', (-1, 1), ('r^', 'bv'), False]]
},
xparls_info={'cci': [(100, 'r', '-', 1.3), (-100, 'r', '-', 1.3)]},
figsize=(8, 7),
grids=True)
start_ends_1 = get_con_start_end(data['cci_100'], lambda x: x == -1)
start_ends1 = get_con_start_end(data['cci_100'], lambda x: x == 1)
data['cci_100_'] = 0
for start, end in start_ends_1:
if end + 1 < data.shape[0]:
data.loc[data.index[end + 1], 'cci_100_'] = -1
for start, end in start_ends1:
if end + 1 < data.shape[0]:
data.loc[data.index[end + 1], 'cci_100_'] = 1
#%%
# 参数汇总
Results = pd.DataFrame({'ga': ga_parms.best_x,
'pso': pso_parms.best_x,
'cs': cs_parms.best_x,
'gwo': gwo_parms.best_x,
'woa': woa_parms.best_x,
'hho': hho_parms.best_x})
Results.index = ['w1', 'w2', 'b']
Results = Results.transpose()
print(Results)
for n in Results.index:
w_b = list(Results.loc[n])
w, b = w_b[:-1], w_b[-1]
plot_result(data, w, b, n)
#%%
# 作图比较
plot_Series(fvals.iloc[:, :],
{'GA': '-', 'PSO': '-', 'CS': '-', 'GWO': '-', 'WOA': '-',
'HHO': '-'},
figsize=(10, 6))
#%%
close_log_file(logger)
#%%
print(f'used time: {round(time.time()-strt_tm, 6)}s.')
1.19, 1.17, 1.17, 1.18, 1.19, 1.23, 1.24, 1.25, 1.24, 1.25, 1.24, 1.25,
1.24, 1.25, 1.24, 1.27, 1.23, 1.22, 1.18, 1.2, 1.22, 1.25, 1.25, 1.27,
1.26, 1.31, 1.32, 1.31, 1.33, 1.33, 1.36, 1.33, 1.35, 1.38, 1.4, 1.42,
1.45, 1.43, 1.46, 1.48, 1.52, 1.53, 1.52, 1.55, 1.54, 1.53, 1.55, 1.54,
1.52, 1.53, 1.53, 1.5, 1.45, 1.43, 1.42, 1.41, 1.43, 1.42, 1.45, 1.45,
1.49, 1.49, 1.51, 1.54, 1.53, 1.56, 1.52, 1.53, 1.58, 1.58, 1.58, 1.61,
1.63, 1.61, 1.59
]
data = pd.DataFrame(values, columns=['values'])
# maxDown, (strt_idx, end_idx) = get_MaxDown(data['values'])
maxDown, (strt_idx, end_idx) = get_MaxUp(data['values'])
data['in_maxDown'] = 0
data.loc[data.index[strt_idx:end_idx + 1], 'in_maxDown'] = 1
plot_Series(data, {'values': '.-b'},
cols_to_label_info={
'values': [['in_maxDown', (1, ), ('.-r', ), ('最大回撤区间', )]]
},
grids=True,
figsize=(11, 7))
fpath = '../test/510050_daily_pre_fq.csv'
data = load_csv(fpath)
data.set_index('date', drop=False, inplace=True)
data = data.iloc[:, :][['close']]
plot_Series(data, {'close': '.-b'}, grids=True, figsize=(11, 7))
maxDown, _, (strt_idx, end_idx) = get_MaxDown_pd(data['close'])
maxDown = str(round(maxDown, 4))
data['in_maxDown'] = 0
data.loc[data.index[strt_idx:end_idx + 1], 'in_maxDown'] = 1
plot_Series(data, {'close': '.-b'},
strt_tm = time.time()
# 50ETF日线行情
fpath = '../test/510050_daily_pre_fq.csv'
df = load_csv(fpath)
df.set_index('date', drop=False, inplace=True)
# df = df.reindex(columns=['high', 'low', 'close'])
# CCI --------------------------------------------------------------------
N = 14
r = 0.015
df['cci'] = CCI(df, N=N, r=r)
plot_Series(
df.iloc[-200:, :], {'close': ('.-k', False)},
cols_styl_low_left={'cci': ('.-b', False)},
xparls_info={'cci': [(100, 'r', '-', 1.3), (-100, 'r', '-', 1.3)]},
figsize=(8.5, 7),
grids=True)
# EXPMA-------------------------------------------------------------------
N = 5
df['expma' + str(N)] = EXPMA(df, N, 'close')
# MACD--------------------------------------------------------------------
macds = MACD(df['close'])
df = df.merge(macds, how='left', left_index=True, right_index=True)
# 九转序列------------------------------------------------------------------
N, Lag = 9, 4
df['dmktd'] = DeMarkTD(df['close'], N, Lag)