def generate_futures_butterfly_formatted_output(**kwargs):
if 'report_date' in kwargs.keys():
report_date = kwargs['report_date']
else:
report_date = exp.doubledate_shift_bus_days()
output_dir = ts.create_strategy_output_dir(
strategy_class='futures_butterfly', report_date=report_date)
butterfly_output = fb.generate_futures_butterfly_sheet_4date(
date_to=report_date)
butterflies = butterfly_output['butterflies']
filter_out = ff.get_futures_butterfly_filters(
data_frame_input=butterflies, filter_list=['long7', 'short7'])
good_butterflies = filter_out['selected_frame']
good_butterflies = good_butterflies[
(good_butterflies['second_spread_weight_1'] <= 2.5)
& (good_butterflies['second_spread_weight_1'] >= 0.4)]
butterflies_w_selected_columns = butterflies[[
'ticker1', 'ticker2', 'ticker3', 'tickerHead', 'trDte1', 'trDte2',
'trDte3', 'Q', 'QF', 'z1', 'z2', 'z3', 'z4', 'theo_pnl', 'r1', 'r2',
'bf_price', 'RC', 'seasonality', 'second_spread_weight_1', 'upside',
'downside', 'recent_vol_ratio', 'recent_5day_pnl', 'bf_sell_limit',
'bf_buy_limit'
]]
good_butterflies_w_selected_columns = good_butterflies[[
'ticker1', 'ticker2', 'ticker3', 'tickerHead', 'trDte1', 'trDte2',
'trDte3', 'Q', 'QF', 'z1', 'z2', 'z3', 'z4', 'theo_pnl', 'r1', 'r2',
'bf_price', 'RC', 'seasonality', 'second_spread_weight_1', 'upside',
'downside', 'recent_vol_ratio', 'recent_5day_pnl', 'bf_sell_limit',
'bf_buy_limit'
]]
writer = pd.ExcelWriter(
output_dir + '/' + futil.xls_file_names['futures_butterfly'] + '.xlsx',
engine='xlsxwriter')
butterflies_w_selected_columns.to_excel(writer, sheet_name='all')
good_butterflies_w_selected_columns.to_excel(writer, sheet_name='good')
worksheet_good = writer.sheets['good']
worksheet_all = writer.sheets['all']
worksheet_good.freeze_panes(1, 0)
worksheet_all.freeze_panes(1, 0)
worksheet_good.autofilter(0, 0,
len(good_butterflies_w_selected_columns.index),
len(good_butterflies_w_selected_columns.columns))
worksheet_all.autofilter(0, 0, len(butterflies_w_selected_columns.index),
len(butterflies_w_selected_columns.columns))
writer.save()
def construct_futures_butterfly_portfolio(**kwargs):
rule_no = kwargs['rule_no']
backtest_output = kwargs['backtest_output']
pnl_field = kwargs['pnl_field']
if rule_no in [1, 3, 4, 5, 6, 7, 8, 9]:
stop_loss = -100000000000
elif rule_no == 2:
stop_loss = -1000
backtest_results_folder = dn.get_directory_name(ext='backtest_results')
if os.path.isfile(backtest_results_folder +
'/futures_butterfly/portfolio' + str(rule_no) + '.pkl'):
return pd.read_pickle(backtest_results_folder +
'/futures_butterfly/portfolio' + str(rule_no) +
'.pkl')
elif not os.path.exists(backtest_results_folder + '/futures_butterfly'):
os.makedirs(backtest_results_folder + '/futures_butterfly')
date_list = kwargs['date_list']
ticker_head_list = cmi.futures_butterfly_strategy_tickerhead_list
total_pnl_frame = pd.DataFrame({'report_date': date_list})
total_pnl_frame['portfolio'] = 0
for i in range(len(ticker_head_list)):
total_pnl_frame[ticker_head_list[i]] = 0
for i in range(len(date_list)):
pnl_tickerhead_frame = pd.DataFrame({'ticker_head': ticker_head_list})
pnl_tickerhead_frame['buy_mean_pnl'] = 0
pnl_tickerhead_frame['sell_mean_pnl'] = 0
pnl_tickerhead_frame['total_pnl'] = 0
daily_sheet = backtest_output[i]
for j in range(len(ticker_head_list)):
ticker_head_results = daily_sheet[daily_sheet['tickerHead'] ==
ticker_head_list[j]]
filter_output_long = sf.get_futures_butterfly_filters(
data_frame_input=ticker_head_results,
filter_list=['long' + str(rule_no)])
filter_output_short = sf.get_futures_butterfly_filters(
data_frame_input=ticker_head_results,
filter_list=['short' + str(rule_no)])
selected_short_trades = ticker_head_results[
filter_output_short['selection_indx']
& (np.isfinite(ticker_head_results[pnl_field]))]
selected_long_trades = ticker_head_results[
filter_output_long['selection_indx']
& (np.isfinite(ticker_head_results[pnl_field]))]
if len(selected_short_trades.index) > 0:
selected_short_trades.loc[selected_short_trades['hold_pnl1short'] < stop_loss, pnl_field]= \
selected_short_trades.loc[selected_short_trades['hold_pnl1short'] < stop_loss, 'hold_pnl2short']
pnl_tickerhead_frame['sell_mean_pnl'][
j] = selected_short_trades[pnl_field].mean()
if len(selected_long_trades.index) > 0:
selected_long_trades.loc[selected_long_trades['hold_pnl1long'] <stop_loss, pnl_field] = \
selected_long_trades.loc[selected_long_trades['hold_pnl1long'] <stop_loss, 'hold_pnl2long']
pnl_tickerhead_frame['buy_mean_pnl'][j] = selected_long_trades[
pnl_field].mean()
pnl_tickerhead_frame['total_pnl'][j] = pnl_tickerhead_frame[
'buy_mean_pnl'][j] + pnl_tickerhead_frame['sell_mean_pnl'][j]
total_pnl_frame[
ticker_head_list[j]][i] = pnl_tickerhead_frame['total_pnl'][j]
total_pnl_frame['portfolio'][i] = pnl_tickerhead_frame[
'total_pnl'].sum()
total_pnl_frame.to_pickle(backtest_results_folder +
'/futures_butterfly/portfolio' + str(rule_no) +
'.pkl')
return total_pnl_frame
def construct_futures_butterfly_portfolio(**kwargs):
rule_no = kwargs['rule_no']
backtest_output = kwargs['backtest_output']
pnl_field = kwargs['pnl_field']
if rule_no in [1, 3, 4, 5, 6]:
stop_loss = -100000000000
elif rule_no == 2:
stop_loss = -1000
backtest_results_folder = dn.get_directory_name(ext='backtest_results')
if os.path.isfile(backtest_results_folder + '/futures_butterfly/portfolio' + str(rule_no) + '.pkl'):
return pd.read_pickle(backtest_results_folder + '/futures_butterfly/portfolio' + str(rule_no) + '.pkl')
elif not os.path.exists(backtest_results_folder + '/futures_butterfly'):
os.makedirs(backtest_results_folder + '/futures_butterfly')
date_list = kwargs['date_list']
ticker_head_list = cmi.futures_butterfly_strategy_tickerhead_list
total_pnl_frame = pd.DataFrame({'report_date': date_list})
total_pnl_frame['portfolio'] = 0
for i in range(len(ticker_head_list)):
total_pnl_frame[ticker_head_list[i]] = 0
for i in range(len(date_list)):
pnl_tickerhead_frame = pd.DataFrame({'ticker_head': ticker_head_list})
pnl_tickerhead_frame['buy_mean_pnl'] = 0
pnl_tickerhead_frame['sell_mean_pnl'] = 0
pnl_tickerhead_frame['total_pnl'] = 0
daily_sheet = backtest_output[i]
for j in range(len(ticker_head_list)):
ticker_head_results = daily_sheet[daily_sheet['tickerHead'] == ticker_head_list[j]]
filter_output_long = sf.get_futures_butterfly_filters(data_frame_input=ticker_head_results, filter_list=['long'+str(rule_no)])
filter_output_short = sf.get_futures_butterfly_filters(data_frame_input=ticker_head_results, filter_list=['short'+str(rule_no)])
selected_short_trades = ticker_head_results[filter_output_short['selection_indx'] &
(np.isfinite(ticker_head_results[pnl_field]))]
selected_long_trades = ticker_head_results[filter_output_long['selection_indx'] &
(np.isfinite(ticker_head_results[pnl_field]))]
if len(selected_short_trades.index) > 0:
selected_short_trades.loc[selected_short_trades['hold_pnl1short'] < stop_loss, pnl_field]= \
selected_short_trades.loc[selected_short_trades['hold_pnl1short'] < stop_loss, 'hold_pnl2short']
pnl_tickerhead_frame['sell_mean_pnl'][j] = selected_short_trades[pnl_field].mean()
if len(selected_long_trades.index) > 0:
selected_long_trades.loc[selected_long_trades['hold_pnl1long'] <stop_loss, pnl_field] = \
selected_long_trades.loc[selected_long_trades['hold_pnl1long'] <stop_loss, 'hold_pnl2long']
pnl_tickerhead_frame['buy_mean_pnl'][j] = selected_long_trades[pnl_field].mean()
pnl_tickerhead_frame['total_pnl'][j] = pnl_tickerhead_frame['buy_mean_pnl'][j] + pnl_tickerhead_frame['sell_mean_pnl'][j]
total_pnl_frame[ticker_head_list[j]][i] = pnl_tickerhead_frame['total_pnl'][j]
total_pnl_frame['portfolio'][i] = pnl_tickerhead_frame['total_pnl'].sum()
total_pnl_frame.to_pickle(backtest_results_folder + '/futures_butterfly/portfolio' + str(rule_no) + '.pkl')
return total_pnl_frame
def generate_futures_butterfly_formatted_output(**kwargs):
if "report_date" in kwargs.keys():
report_date = kwargs["report_date"]
else:
report_date = exp.doubledate_shift_bus_days()
output_dir = ts.create_strategy_output_dir(strategy_class="futures_butterfly", report_date=report_date)
butterfly_output = fb.generate_futures_butterfly_sheet_4date(date_to=report_date)
butterflies = butterfly_output["butterflies"]
filter_out = ff.get_futures_butterfly_filters(data_frame_input=butterflies, filter_list=["long1", "short1"])
good_butterflies = filter_out["selected_frame"]
butterflies_w_selected_columns = butterflies[
[
"ticker1",
"ticker2",
"ticker3",
"tickerHead",
"trDte1",
"trDte2",
"trDte3",
"Q",
"QF",
"z1",
"z2",
"z3",
"z4",
"theo_pnl",
"r1",
"r2",
"bf_price",
"RC",
"seasonality",
"second_spread_weight_1",
"upside",
"downside",
"recent_vol_ratio",
"recent_5day_pnl",
"bf_sell_limit",
"bf_buy_limit",
]
]
good_butterflies_w_selected_columns = good_butterflies[
[
"ticker1",
"ticker2",
"ticker3",
"tickerHead",
"trDte1",
"trDte2",
"trDte3",
"Q",
"QF",
"z1",
"z2",
"z3",
"z4",
"theo_pnl",
"r1",
"r2",
"bf_price",
"RC",
"seasonality",
"second_spread_weight_1",
"upside",
"downside",
"recent_vol_ratio",
"recent_5day_pnl",
"bf_sell_limit",
"bf_buy_limit",
]
]
writer = pd.ExcelWriter(output_dir + "/" + futil.xls_file_names["futures_butterfly"] + ".xlsx", engine="xlsxwriter")
butterflies_w_selected_columns.to_excel(writer, sheet_name="all")
good_butterflies_w_selected_columns.to_excel(writer, sheet_name="good")
worksheet_good = writer.sheets["good"]
worksheet_all = writer.sheets["all"]
worksheet_good.freeze_panes(1, 0)
worksheet_all.freeze_panes(1, 0)
worksheet_good.autofilter(
0, 0, len(good_butterflies_w_selected_columns.index), len(good_butterflies_w_selected_columns.columns)
)
worksheet_all.autofilter(
0, 0, len(butterflies_w_selected_columns.index), len(butterflies_w_selected_columns.columns)
)
