def run(self, trX, trY, tsX, tsY, maxiter, validating=False):
self.scale(trX, tsX)
weights = self.model.weights(trX, seed=1)
gradients = zeros(weights.shape)
trR = zeros(maxiter+1)
tsR = zeros(maxiter+1)
costs = []
for i in range(maxiter):
#rs = RandomState(i)
#r = rs.randint(0, trX.shape[0]/2)
#tempX = trX[r:, :]
#tempY = trY[r:]
gradient = self.model.grad(weights, trX, trY)
gradients += power(gradient, 2)
rate = 1. / sqrt(gradients)
weights = weights - rate*gradient
if validating:
costs.append(self.model.cost(weights, tsX, tsY))
# Calculate returns and decisions on test set.
returns, decisions = self.model.returns(weights, tsX, tsY)
s = sharpe(returns)
w = wealth(returns)
return returns, decisions
def run(self, trX, trY, tsX, tsY, maxiter, validating=False):
self.scale(trX, tsX)
weights = self.model.weights(trX, seed=1)
gradients = zeros(weights.shape)
trR = zeros(maxiter + 1)
tsR = zeros(maxiter + 1)
costs = []
for i in range(maxiter):
#rs = RandomState(i)
#r = rs.randint(0, trX.shape[0]/2)
#tempX = trX[r:, :]
#tempY = trY[r:]
gradient = self.model.grad(weights, trX, trY)
gradients += power(gradient, 2)
rate = 1. / sqrt(gradients)
weights = weights - rate * gradient
if validating:
costs.append(self.model.cost(weights, tsX, tsY))
# Calculate returns and decisions on test set.
returns, decisions = self.model.returns(weights, tsX, tsY)
s = sharpe(returns)
w = wealth(returns)
return returns, decisions
# filename = 'figures/synthetic_noside_single_w_%i_s_%i_l_%i_d_%f.pdf' % (
# window, slide, lookback, delta)
# title = 'Single Layer - Synthetic (No Side Information)'
# print "%s\tWealth: %f\tSharpe: %f" % (filename,
# wealth(returns)[-1], sharpe(returns)[-1])
# plotter.save(filename, title, series, returns, decisions)
for window in [200]:
for slide in [5]:
for lookback in [10]:
for delta in [0.001]:
models = []
for hidden in [4, 6, 8]:
for lmb in [0.0, 0.0001, 0.001, 0.01]:
models.append(
Nonlinear(delta=delta, lmb=lmb, hidden=hidden))
trainer = ValidatingTrainer(data, models)
returns, decisions = trainer.train(window=window,
lookback=lookback,
slide=slide,
maxiter=20)
filename = 'figures/synthetic_noside_multi_w_%i_s_%i_l_%i_d_%f.pdf' % (
window, slide, lookback, delta)
title = 'Multiple Layer - Synthetic (No Side Information)'
print "%s\tWealth: %f\tSharpe: %f" % (
filename, wealth(returns)[-1], sharpe(returns)[-1])
plotter.save(filename, title, series, returns, decisions)
def test_calculates_sharpe(self):
self.assertAlmostEqual(sharpe(self.returns)[-1], 1.03209369)
def exp_symbols_statistics(fout_path=os.path.join(
DATA_DIR, 'exp_symbols_statistics.xlsx')):
"""
statistics of experiment symbols
output the results to xlsx
"""
t0 = time()
fin_path = os.path.join(SYMBOLS_PKL_DIR,
'TAIEX_2005_largest50cap_panel.pkl')
# shape: (n_exp_period, n_stock, ('simple_roi', 'close_price'))
panel = pd.read_pickle(fin_path)
assert panel.major_axis.tolist() == EXP_SYMBOLS
panel = panel.loc[date(2005, 1, 3):date(2014, 12, 31)]
# the roi in the first experiment date is zero
panel.loc[date(2005, 1, 3), :, 'simple_roi'] = 0.
stat_indices = (
# basic information
'start_date', 'end_date',
'n_exp_period', 'n_period_up', 'n_period_down',
# roi
'cum_roi', 'daily_roi', 'daily_mean_roi',
'daily_std_roi', 'daily_skew_roi', 'daily_kurt_roi',
# roi/risk indices
'sharpe', 'sortino_full', 'sortino_full_semi_std',
'sortino_partial', 'sortino_partial_semi_std',
'max_abs_drawdown',
# normal tests
'JB', 'JB_pvalue',
# uni-root tests
'ADF_c',
'ADF_c_pvalue',
'ADF_ct',
'ADF_ct_pvalue',
'ADF_ctt',
'ADF_ctt_pvalue',
'ADF_nc',
'ADF_nc_pvalue',
'DFGLS_c',
'DFGLS_c_pvalue',
'DFGLS_ct',
'DFGLS_ct_pvalue',
'PP_c',
'PP_c_pvalue',
'PP_ct',
'PP_ct_pvalue',
'PP_nc',
'PP_nc_pvalue',
'KPSS_c',
'KPSS_c_pvalue',
'KPSS_ct',
'KPSS_ct_pvalue',
# performance
'SPA_l_pvalue', 'SPA_c_pvalue', 'SPA_u_pvalue'
)
stat_df = pd.DataFrame(np.zeros((len(stat_indices), len(EXP_SYMBOLS))),
index=stat_indices,
columns=EXP_SYMBOLS)
for rdx, symbol in enumerate(EXP_SYMBOLS):
t1 = time()
rois = panel[:, symbol, 'simple_roi']
# basic
stat_df.loc['start_date', symbol] = rois.index[0].strftime("%Y/%b/%d")
stat_df.loc['end_date', symbol] = rois.index[-1].strftime("%Y/%b/%d")
stat_df.loc['n_exp_period', symbol] = len(rois)
stat_df.loc['n_period_up', symbol] = (rois > 0).sum()
stat_df.loc['n_period_down', symbol] = (rois < 0).sum()
# roi
stat_df.loc['cum_roi', symbol] = (rois + 1.).prod() - 1
stat_df.loc['daily_roi', symbol] = np.power((rois + 1.).prod(),
1. / len(rois)) - 1
stat_df.loc['daily_mean_roi', symbol] = rois.mean()
stat_df.loc['daily_std_roi', symbol] = rois.std()
stat_df.loc['daily_skew_roi', symbol] = rois.skew()
stat_df.loc['daily_kurt_roi', symbol] = rois.kurt() # excess
# roi/risk indices
stat_df.loc['sharpe', symbol] = sharpe(rois)
(stat_df.loc['sortino_full', symbol],
stat_df.loc['sortino_full_semi_std', symbol]) = sortino_full(rois)
(stat_df.loc['sortino_partial', symbol],
stat_df.loc['sortino_partial_semi_std', symbol]) = sortino_partial(
rois)
stat_df.loc['max_abs_drawdown', symbol] = maximum_drawdown(rois)
# normal tests
jb = jarque_bera(rois)
stat_df.loc['JB', symbol] = jb[0]
stat_df.loc['JB_pvalue', symbol] = jb[1]
# uniroot tests
adf_c = adfuller(rois, regression='c')
stat_df.loc['ADF_c', symbol] = adf_c[0]
stat_df.loc['ADF_c_pvalue', symbol] = adf_c[1]
adf_ct = adfuller(rois, regression='ct')
stat_df.loc['ADF_ct', symbol] = adf_ct[0]
stat_df.loc['ADF_ct_pvalue', symbol] = adf_ct[1]
adf_ctt = adfuller(rois, regression='ctt')
stat_df.loc['ADF_ctt', symbol] = adf_ctt[0]
stat_df.loc['ADF_ctt_pvalue', symbol] = adf_ctt[1]
adf_nc = adfuller(rois, regression='nc')
stat_df.loc['ADF_nc', symbol] = adf_nc[0]
stat_df.loc['ADF_nc_pvalue', symbol] = adf_nc[1]
dfgls_c_instance = DFGLS(rois, trend='c')
dfgls_c, dfgls_c_pvalue = (dfgls_c_instance.stat,
dfgls_c_instance.pvalue)
stat_df.loc['DFGLS_c', symbol] = dfgls_c
stat_df.loc['DFGLS_c_pvalue', symbol] = dfgls_c_pvalue
dfgls_ct_instance = DFGLS(rois, trend='ct')
dfgls_ct, dfgls_ct_pvalue = (dfgls_ct_instance.stat,
dfgls_ct_instance.pvalue)
stat_df.loc['DFGLS_ct', symbol] = dfgls_ct
stat_df.loc['DFGLS_ct_pvalue', symbol] = dfgls_ct_pvalue
pp_c_instance = PhillipsPerron(rois, trend='c')
pp_c, pp_c_pvalue = (pp_c_instance.stat, pp_c_instance.pvalue)
stat_df.loc['PP_c', symbol] = pp_c
stat_df.loc['PP_c_pvalue', symbol] = pp_c_pvalue
pp_ct_instance = PhillipsPerron(rois, trend='ct')
pp_ct, pp_ct_pvalue = (pp_ct_instance.stat, pp_ct_instance.pvalue)
stat_df.loc['PP_ct', symbol] = pp_ct
stat_df.loc['PP_ct_pvalue', symbol] = pp_ct_pvalue
pp_nc_instance = PhillipsPerron(rois, trend='nc')
pp_nc, pp_nc_pvalue = (pp_nc_instance.stat, pp_nc_instance.pvalue)
stat_df.loc['PP_nc', symbol] = pp_nc
stat_df.loc['PP_nc_pvalue', symbol] = pp_nc_pvalue
kpss_c_instance = KPSS(rois, trend='c')
kpss_c, kpss_c_pvalue = (kpss_c_instance.stat, kpss_c_instance.pvalue)
stat_df.loc['KPSS_c', symbol] = kpss_c
stat_df.loc['KPSS_c_pvalue', symbol] = kpss_c_pvalue
kpss_ct_instance = KPSS(rois, trend='ct')
kpss_ct, kpss_ct_pvalue = (kpss_ct_instance.stat,
kpss_ct_instance.pvalue)
stat_df.loc['KPSS_ct', symbol] = kpss_ct
stat_df.loc['KPSS_ct_pvalue', symbol] = kpss_ct_pvalue
# performance
spa = SPA(rois, np.zeros(len(rois)), reps=5000)
spa.seed(np.random.randint(0, 2 ** 31 - 1))
spa.compute()
stat_df.loc['SPA_l_pvalue', symbol] = spa.pvalues[0]
stat_df.loc['SPA_c_pvalue', symbol] = spa.pvalues[1]
stat_df.loc['SPA_u_pvalue', symbol] = spa.pvalues[2]
print ("[{}/{}] {} roi statistics OK, {:.3f} secs".format(
rdx + 1, len(EXP_SYMBOLS), symbol, time() - t1
))
# write to excel
writer = pd.ExcelWriter(fout_path, engine='xlsxwriter')
stat_df = stat_df.T
stat_df.to_excel(writer, sheet_name='stats')
# Get the xlsxwriter workbook and worksheet objects.
workbook = writer.book
worksheet = writer.sheets['stats']
# basic formats.
# set header
header_fmt = workbook.add_format()
header_fmt.set_text_wrap()
worksheet.set_row(0, 15, header_fmt)
# set date
date_fmt = workbook.add_format({'num_format': 'yy/mmm/dd'})
date_fmt.set_align('right')
worksheet.set_column('B:C', 12, date_fmt)
# set percentage
percent_fmt = workbook.add_format({'num_format': '0.00%'})
worksheet.set_column('G:J', 8, percent_fmt)
worksheet.set_column('M:Q', 8, percent_fmt)
worksheet.set_column('T:T', 8, percent_fmt)
worksheet.set_column('V:V', 8, percent_fmt)
worksheet.set_column('X:X', 8, percent_fmt)
worksheet.set_column('Z:Z', 8, percent_fmt)
worksheet.set_column('AB:AB', 8, percent_fmt)
worksheet.set_column('AD:AD', 8, percent_fmt)
worksheet.set_column('AF:AF', 8, percent_fmt)
worksheet.set_column('AH:AH', 8, percent_fmt)
worksheet.set_column('AJ:AJ', 8, percent_fmt)
worksheet.set_column('AL:AL', 8, percent_fmt)
worksheet.set_column('AN:AN', 8, percent_fmt)
worksheet.set_column('AP:AP', 8, percent_fmt)
worksheet.set_column('AQ:AS', 8, percent_fmt)
writer.save()
print ("all roi statistics OK, {:.3f} secs".format(time() - t0))
def test_calculates_sharpe(self):
self.assertAlmostEqual(sharpe(self.returns)[-1], 1.03209369)
# slide=slide, maxiter=20)
#
# filename = 'figures/synthetic_noside_single_w_%i_s_%i_l_%i_d_%f.pdf' % (
# window, slide, lookback, delta)
# title = 'Single Layer - Synthetic (No Side Information)'
# print "%s\tWealth: %f\tSharpe: %f" % (filename,
# wealth(returns)[-1], sharpe(returns)[-1])
# plotter.save(filename, title, series, returns, decisions)
for window in [200]:
for slide in [5]:
for lookback in [10]:
for delta in [0.001]:
models = []
for hidden in [4, 6, 8]:
for lmb in [0.0, 0.0001, 0.001, 0.01]:
models.append(Nonlinear(delta=delta, lmb=lmb,
hidden=hidden))
trainer = ValidatingTrainer(data, models)
returns, decisions = trainer.train(window=window, lookback=lookback,
slide=slide, maxiter=20)
filename = 'figures/synthetic_noside_multi_w_%i_s_%i_l_%i_d_%f.pdf' % (
window, slide, lookback, delta)
title = 'Multiple Layer - Synthetic (No Side Information)'
print "%s\tWealth: %f\tSharpe: %f" % (filename,
wealth(returns)[-1], sharpe(returns)[-1])
plotter.save(filename, title, series, returns, decisions)
window=window, lookback=lookback, slide=slide, maxiter=40)
padding = zeros(len(series)-len(returns))
returns = append(padding, returns)
decisions = append(padding, decisions)
# Linear : 2,618,987,047.18
# Nonlin (15): 2,272,781,214.71
# Nonlin (10): 2,884,104,773.60
# Nonlin (5) : 1,232,632,146.82
# Linear:
print "Wealth: ", wealth(returns)[-1]
x_axis = range(len(series))
# Two subplots, the axes array is 1-d
f, axarr = plt.subplots(5, sharex=True)
axarr[0].plot(x_axis, series)
axarr[0].set_title('Prices')
axarr[1].plot(x_axis, wealth(returns))
axarr[1].set_title('Wealth')
axarr[2].plot(x_axis, sharpe(returns))
axarr[2].set_title('Sharpe')
axarr[3].plot(x_axis, returns)
axarr[3].set_title('Returns')
axarr[4].plot(x_axis, decisions)
axarr[4].set_title('Decisions')
plt.show()
def exp_symbols_statistics(fout_path=os.path.join(
DATA_DIR, 'exp_symbols_statistics.xlsx')):
"""
statistics of experiment symbols
output the results to xlsx
"""
t0 = time()
fin_path = os.path.join(SYMBOLS_PKL_DIR,
'TAIEX_2005_largest50cap_panel.pkl')
# shape: (n_exp_period, n_stock, ('simple_roi', 'close_price'))
panel = pd.read_pickle(fin_path)
assert panel.major_axis.tolist() == EXP_SYMBOLS
panel = panel.loc[date(2005, 1, 3):date(2014, 12, 31)]
# the roi in the first experiment date is zero
panel.loc[date(2005, 1, 3), :, 'simple_roi'] = 0.
stat_indices = (
# basic information
'start_date',
'end_date',
'n_exp_period',
'n_period_up',
'n_period_down',
# roi
'cum_roi',
'daily_roi',
'daily_mean_roi',
'daily_std_roi',
'daily_skew_roi',
'daily_kurt_roi',
# roi/risk indices
'sharpe',
'sortino_full',
'sortino_full_semi_std',
'sortino_partial',
'sortino_partial_semi_std',
'max_abs_drawdown',
# normal tests
'JB',
'JB_pvalue',
# uni-root tests
'ADF_c',
'ADF_c_pvalue',
'ADF_ct',
'ADF_ct_pvalue',
'ADF_ctt',
'ADF_ctt_pvalue',
'ADF_nc',
'ADF_nc_pvalue',
'DFGLS_c',
'DFGLS_c_pvalue',
'DFGLS_ct',
'DFGLS_ct_pvalue',
'PP_c',
'PP_c_pvalue',
'PP_ct',
'PP_ct_pvalue',
'PP_nc',
'PP_nc_pvalue',
'KPSS_c',
'KPSS_c_pvalue',
'KPSS_ct',
'KPSS_ct_pvalue',
# performance
'SPA_l_pvalue',
'SPA_c_pvalue',
'SPA_u_pvalue')
stat_df = pd.DataFrame(np.zeros((len(stat_indices), len(EXP_SYMBOLS))),
index=stat_indices,
columns=EXP_SYMBOLS)
for rdx, symbol in enumerate(EXP_SYMBOLS):
t1 = time()
rois = panel[:, symbol, 'simple_roi']
# basic
stat_df.loc['start_date', symbol] = rois.index[0].strftime("%Y/%b/%d")
stat_df.loc['end_date', symbol] = rois.index[-1].strftime("%Y/%b/%d")
stat_df.loc['n_exp_period', symbol] = len(rois)
stat_df.loc['n_period_up', symbol] = (rois > 0).sum()
stat_df.loc['n_period_down', symbol] = (rois < 0).sum()
# roi
stat_df.loc['cum_roi', symbol] = (rois + 1.).prod() - 1
stat_df.loc['daily_roi', symbol] = np.power(
(rois + 1.).prod(), 1. / len(rois)) - 1
stat_df.loc['daily_mean_roi', symbol] = rois.mean()
stat_df.loc['daily_std_roi', symbol] = rois.std()
stat_df.loc['daily_skew_roi', symbol] = rois.skew()
stat_df.loc['daily_kurt_roi', symbol] = rois.kurt() # excess
# roi/risk indices
stat_df.loc['sharpe', symbol] = sharpe(rois)
(stat_df.loc['sortino_full',
symbol], stat_df.loc['sortino_full_semi_std',
symbol]) = sortino_full(rois)
(stat_df.loc['sortino_partial',
symbol], stat_df.loc['sortino_partial_semi_std',
symbol]) = sortino_partial(rois)
stat_df.loc['max_abs_drawdown', symbol] = maximum_drawdown(rois)
# normal tests
jb = jarque_bera(rois)
stat_df.loc['JB', symbol] = jb[0]
stat_df.loc['JB_pvalue', symbol] = jb[1]
# uniroot tests
adf_c = adfuller(rois, regression='c')
stat_df.loc['ADF_c', symbol] = adf_c[0]
stat_df.loc['ADF_c_pvalue', symbol] = adf_c[1]
adf_ct = adfuller(rois, regression='ct')
stat_df.loc['ADF_ct', symbol] = adf_ct[0]
stat_df.loc['ADF_ct_pvalue', symbol] = adf_ct[1]
adf_ctt = adfuller(rois, regression='ctt')
stat_df.loc['ADF_ctt', symbol] = adf_ctt[0]
stat_df.loc['ADF_ctt_pvalue', symbol] = adf_ctt[1]
adf_nc = adfuller(rois, regression='nc')
stat_df.loc['ADF_nc', symbol] = adf_nc[0]
stat_df.loc['ADF_nc_pvalue', symbol] = adf_nc[1]
dfgls_c_instance = DFGLS(rois, trend='c')
dfgls_c, dfgls_c_pvalue = (dfgls_c_instance.stat,
dfgls_c_instance.pvalue)
stat_df.loc['DFGLS_c', symbol] = dfgls_c
stat_df.loc['DFGLS_c_pvalue', symbol] = dfgls_c_pvalue
dfgls_ct_instance = DFGLS(rois, trend='ct')
dfgls_ct, dfgls_ct_pvalue = (dfgls_ct_instance.stat,
dfgls_ct_instance.pvalue)
stat_df.loc['DFGLS_ct', symbol] = dfgls_ct
stat_df.loc['DFGLS_ct_pvalue', symbol] = dfgls_ct_pvalue
pp_c_instance = PhillipsPerron(rois, trend='c')
pp_c, pp_c_pvalue = (pp_c_instance.stat, pp_c_instance.pvalue)
stat_df.loc['PP_c', symbol] = pp_c
stat_df.loc['PP_c_pvalue', symbol] = pp_c_pvalue
pp_ct_instance = PhillipsPerron(rois, trend='ct')
pp_ct, pp_ct_pvalue = (pp_ct_instance.stat, pp_ct_instance.pvalue)
stat_df.loc['PP_ct', symbol] = pp_ct
stat_df.loc['PP_ct_pvalue', symbol] = pp_ct_pvalue
pp_nc_instance = PhillipsPerron(rois, trend='nc')
pp_nc, pp_nc_pvalue = (pp_nc_instance.stat, pp_nc_instance.pvalue)
stat_df.loc['PP_nc', symbol] = pp_nc
stat_df.loc['PP_nc_pvalue', symbol] = pp_nc_pvalue
kpss_c_instance = KPSS(rois, trend='c')
kpss_c, kpss_c_pvalue = (kpss_c_instance.stat, kpss_c_instance.pvalue)
stat_df.loc['KPSS_c', symbol] = kpss_c
stat_df.loc['KPSS_c_pvalue', symbol] = kpss_c_pvalue
kpss_ct_instance = KPSS(rois, trend='ct')
kpss_ct, kpss_ct_pvalue = (kpss_ct_instance.stat,
kpss_ct_instance.pvalue)
stat_df.loc['KPSS_ct', symbol] = kpss_ct
stat_df.loc['KPSS_ct_pvalue', symbol] = kpss_ct_pvalue
# performance
spa = SPA(rois, np.zeros(len(rois)), reps=5000)
spa.seed(np.random.randint(0, 2**31 - 1))
spa.compute()
stat_df.loc['SPA_l_pvalue', symbol] = spa.pvalues[0]
stat_df.loc['SPA_c_pvalue', symbol] = spa.pvalues[1]
stat_df.loc['SPA_u_pvalue', symbol] = spa.pvalues[2]
print("[{}/{}] {} roi statistics OK, {:.3f} secs".format(
rdx + 1, len(EXP_SYMBOLS), symbol,
time() - t1))
# write to excel
writer = pd.ExcelWriter(fout_path, engine='xlsxwriter')
stat_df = stat_df.T
stat_df.to_excel(writer, sheet_name='stats')
# Get the xlsxwriter workbook and worksheet objects.
workbook = writer.book
worksheet = writer.sheets['stats']
# basic formats.
# set header
header_fmt = workbook.add_format()
header_fmt.set_text_wrap()
worksheet.set_row(0, 15, header_fmt)
# set date
date_fmt = workbook.add_format({'num_format': 'yy/mmm/dd'})
date_fmt.set_align('right')
worksheet.set_column('B:C', 12, date_fmt)
# set percentage
percent_fmt = workbook.add_format({'num_format': '0.00%'})
worksheet.set_column('G:J', 8, percent_fmt)
worksheet.set_column('M:Q', 8, percent_fmt)
worksheet.set_column('T:T', 8, percent_fmt)
worksheet.set_column('V:V', 8, percent_fmt)
worksheet.set_column('X:X', 8, percent_fmt)
worksheet.set_column('Z:Z', 8, percent_fmt)
worksheet.set_column('AB:AB', 8, percent_fmt)
worksheet.set_column('AD:AD', 8, percent_fmt)
worksheet.set_column('AF:AF', 8, percent_fmt)
worksheet.set_column('AH:AH', 8, percent_fmt)
worksheet.set_column('AJ:AJ', 8, percent_fmt)
worksheet.set_column('AL:AL', 8, percent_fmt)
worksheet.set_column('AN:AN', 8, percent_fmt)
worksheet.set_column('AP:AP', 8, percent_fmt)
worksheet.set_column('AQ:AS', 8, percent_fmt)
writer.save()
print("all roi statistics OK, {:.3f} secs".format(time() - t0))
def step(self, t, action_t):
self.action[t] = action_t
# end = t+2
if self.trading_rule == 'daytrading0':
if action_t != 0:
self.reward[t] = utils.sharpe(self.action[t + 1 - self.L:t + 1] \
* self.r_t[t + 2 - self.L:t +2] \
- self.transaction_cost)
else:
self.reward[t] = self.no_trade_reward
# end = t+2
elif self.trading_rule == 'daytrading1':
if action_t != 0:
self.reward[t] = utils.sharpe(
self.action[t] * self.r_t[t + 2 - self.L:t + 2] \
- self.transaction_cost)
else:
self.reward[t] = self.no_trade_reward
# start = t+1
elif self.trading_rule == 'daytrading2':
if action_t != 0:
self.reward[t] = utils.sharpe(
self.action[t] * self.r_t[t + 1:t + self.L + 1] \
- self.transaction_cost)
else:
self.reward[t] = self.no_trade_reward
# start = t+1
elif self.trading_rule == 'daytrading3':
if action_t != 0:
self.reward[t] = utils.sharpe_short_term(
self.action[t] * self.r_t[t + 1:t + self.L + 1] \
- self.transaction_cost)
else:
self.reward[t] = self.no_trade_reward
elif self.trading_rule == 'daytrading4':
if action_t != 0:
self.reward[t] = utils.sharpe_short_term2(
self.action[t] * self.r_t[t + 2 - self.L:t + 2] \
- self.transaction_cost)
else:
self.reward[t] = self.no_trade_reward
# start = t+1
elif self.trading_rule == 'fixed_period':
if action_t != 0 and t >= self.last_position_time + self.L:
self.reward[t] = utils.sharpe(
self.action[t] * self.r_t[t + 1:t + self.L + 1] \
- self.transaction_cost)
self.last_position_time = t
else:
self.reward[t] = self.no_trade_reward
# start = t+1
elif self.trading_rule == 'hold0':
if self.action[t] != self.action[t - 1] and self.action[t] != 0:
self.reward[t] = utils.sharpe_short_term(
self.action[t] * self.r_t[t + 1:t + self.L + 1] \
- self.transaction_cost)
else:
self.reward[t] = self.no_trade_reward
# start = t+1
elif self.trading_rule == 'hold1':
if self.action[t] != self.action[t - 1] and self.action[t] != 0:
self.reward[t] = utils.sharpe(
self.action[t] * self.r_t[t + 1:t + self.L + 1] \
- self.transaction_cost)
else:
self.reward[t] = self.no_trade_reward
elif self.trading_rule == 'hold2':
if self.action[t] != self.action[t - 1] and self.action[t] != 0:
self.reward[t] = utils.sharpe(self.action[t + 1 - self.L:t + 1] \
* self.r_t[t + 2 - self.L:t + 2] - self.transaction_cost)
else:
self.reward[t] = self.no_trade_reward
else:
raise ValueError(
'invalid trading rule: supported daytrading, fixed_period and hold'
)
next_state = self.rl_method.update(t, self.action)
return (next_state, self.reward[t])
