def backtest_spread_carry(**kwargs): date_list = kwargs['date_list'] risk = 1000 futures_data_dictionary = {x: gfp.get_futures_price_preloaded(ticker_head=x) for x in sc.max_tr_dte_limits.keys()} ticker_head_list = list(sc.max_tr_dte_limits.keys()) total_pnl_frame = pd.DataFrame({'report_date': date_list}) total_pnl_frame['portfolio'] = 0 backtest_output = [] for i in range(len(ticker_head_list)): total_pnl_frame[ticker_head_list[i]] = 0 for i in range(len(date_list)): spread_carry_output = sc.generate_spread_carry_sheet_4date(report_date=date_list[i],futures_data_dictionary=futures_data_dictionary) if spread_carry_output['success']: daily_sheet = spread_carry_output['spread_report'] else: continue backtest_output.append(daily_sheet) daily_sheet['q_carry_abs'] = abs(daily_sheet['q_carry']) pnl_tickerhead_frame = pd.DataFrame({'ticker_head': ticker_head_list}) pnl_tickerhead_frame['total_pnl'] = 0 for j in range(len(ticker_head_list)): ticker_head_results = daily_sheet[daily_sheet['tickerHead'] == ticker_head_list[j]] if len(ticker_head_results.index)<=1: continue max_q_carry_abs = ticker_head_results['q_carry_abs'].max() if np.isnan(max_q_carry_abs): continue selected_spread = ticker_head_results.ix[ticker_head_results['q_carry_abs'].idxmax()] if selected_spread['q_carry']>0: total_pnl_frame[ticker_head_list[j]][i] = selected_spread['change5']*risk/abs(selected_spread['downside']) pnl_tickerhead_frame['total_pnl'][j] = total_pnl_frame[ticker_head_list[j]][i] elif selected_spread['q_carry']<0: total_pnl_frame[ticker_head_list[j]][i] = -selected_spread['change5']*risk/abs(selected_spread['upside']) pnl_tickerhead_frame['total_pnl'][j] = total_pnl_frame[ticker_head_list[j]][i] total_pnl_frame['portfolio'][i] = pnl_tickerhead_frame['total_pnl'].sum() big_data = pd.concat(backtest_output) big_data['pnl_long5'] = big_data['change5']*risk/abs(big_data['downside']) big_data['pnl_short5'] = -big_data['change5']*risk/abs(big_data['upside']) big_data['pnl_final'] = big_data['pnl_long5'] big_data.loc[big_data['q_carry'] < 0, 'pnl_final'] = big_data.loc[big_data['q_carry'] <0, 'pnl_short5'] return {'total_pnl_frame': total_pnl_frame, 'big_data': big_data}
def construct_spread_portfolio(**kwargs): date_list = kwargs['date_list'] risk = 1000 futures_data_dictionary = {x: gfp.get_futures_price_preloaded(ticker_head=x) for x in sc.max_tr_dte_limits.keys()} ticker_head_list = list(sc.max_tr_dte_limits.keys()) 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)): spread_carry_output = sc.generate_spread_carry_sheet_4date(report_date=date_list[i],futures_data_dictionary=futures_data_dictionary) if spread_carry_output['success']: daily_sheet = spread_carry_output['spread_report'] else: continue 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 = \ daily_sheet[(np.isfinite(daily_sheet['change5']))& (np.isfinite(daily_sheet['upside']))& (np.isfinite(daily_sheet['downside']))] 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_spread_carry_filters(data_frame_input=ticker_head_results, filter_list=['long1']) filter_output_short = sf.get_spread_carry_filters(data_frame_input=ticker_head_results, filter_list=['short1']) selected_short_trades = ticker_head_results[filter_output_short['selection_indx']] selected_long_trades = ticker_head_results[filter_output_long['selection_indx']] if len(selected_short_trades.index) > 0: short_pnl = -selected_short_trades['change5']*risk/abs(selected_short_trades['upside']) pnl_tickerhead_frame['sell_mean_pnl'][j] = short_pnl.mean() if len(selected_long_trades.index) > 0: long_pnl = selected_long_trades['change5']*risk/abs(selected_long_trades['downside']) pnl_tickerhead_frame['buy_mean_pnl'][j] = long_pnl.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() return total_pnl_frame
def generate_spread_carry_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='spread_carry', report_date=report_date) spread_carry_output = sc.generate_spread_carry_sheet_4date( report_date=report_date) spread_report = spread_carry_output['spread_report'] writer = pd.ExcelWriter(output_dir + '/' + futil.get_xls_file_name('spread_carry') + '.xlsx', engine='xlsxwriter') spread_report.to_excel(writer, sheet_name='summary')
writer_out = sff.generate_vcs_followup_report(as_of_date=report_date, con=con) sff.generate_ocs_followup_report(as_of_date=report_date, con=con, broker='ib', writer=writer_out) prep.move_from_dated_folder_2daily_folder(ext='ta', file_name='followup', folder_date=report_date) except Exception: log.error('generate_followup_report', exc_info=True) quit() try: log.info('generate_spread_carry_sheet_4date...') sc.generate_spread_carry_sheet_4date(report_date=report_date) except Exception: log.error('generate_spread_carry_sheet_4date failed', exc_info=True) quit() try: log.info('generate_overnight_spreads_sheet_4date...') ocs.generate_overnight_spreads_sheet_4date(date_to=report_date) except Exception: log.error('generate_overnight_spreads_sheet_4date failed', exc_info=True) quit() try: log.info('generate_ocs_formatted_output...') fsf.generate_ocs_formatted_output(report_date=report_date) except Exception:
def get_results_4strategy(**kwargs): signal_input = dict() if 'futures_data_dictionary' in kwargs.keys(): signal_input['futures_data_dictionary'] = kwargs[ 'futures_data_dictionary'] if 'date_to' in kwargs.keys(): date_to = kwargs['date_to'] else: date_to = exp.doubledate_shift_bus_days() if 'datetime5_years_ago' in kwargs.keys(): signal_input['datetime5_years_ago'] = kwargs['datetime5_years_ago'] if 'strategy_info_output' in kwargs.keys(): strategy_info_output = kwargs['strategy_info_output'] else: strategy_info_output = ts.get_strategy_info_from_alias(**kwargs) if 'broker' in kwargs.keys(): broker = kwargs['broker'] else: broker = 'abn' con = msu.get_my_sql_connection(**kwargs) strategy_info_dict = sc.convert_from_string_to_dictionary( string_input=strategy_info_output['description_string']) strategy_class = strategy_info_dict['strategy_class'] pnl_frame = tpm.get_daily_pnl_snapshot(as_of_date=date_to, broker=broker) pnl_frame = pnl_frame[pnl_frame['alias'] == kwargs['alias']] strategy_position = ts.get_net_position_4strategy_alias( alias=kwargs['alias'], as_of_date=date_to) if strategy_class == 'futures_butterfly': ticker_head = cmi.get_contract_specs( strategy_info_dict['ticker1'])['ticker_head'] if not strategy_position.empty: total_contracts2trade = strategy_position['qty'].abs().sum() t_cost = cmi.t_cost[ticker_head] QF_initial = float(strategy_info_dict['QF']) z1_initial = float(strategy_info_dict['z1']) bf_signals_output = fs.get_futures_butterfly_signals( ticker_list=[ strategy_info_dict['ticker1'], strategy_info_dict['ticker2'], strategy_info_dict['ticker3'] ], aggregation_method=int(strategy_info_dict['agg']), contracts_back=int(strategy_info_dict['cBack']), date_to=date_to, **signal_input) if bf_signals_output['success']: aligned_output = bf_signals_output['aligned_output'] current_data = aligned_output['current_data'] holding_tr_dte = int( strategy_info_dict['trDte1']) - current_data['c1']['tr_dte'] success_status = True QF = bf_signals_output['qf'] z1 = bf_signals_output['zscore1'] short_tr_dte = current_data['c1']['tr_dte'] second_spread_weight = bf_signals_output['second_spread_weight_1'] if strategy_position.empty: recommendation = 'CLOSE' elif (z1_initial>0)&(holding_tr_dte > 5) &\ (bf_signals_output['qf']<QF_initial-20)&\ (pnl_frame['total_pnl'].iloc[0] > 3*t_cost*total_contracts2trade): recommendation = 'STOP' elif (z1_initial<0)&(holding_tr_dte > 5) &\ (bf_signals_output['qf']>QF_initial+20)&\ (pnl_frame['total_pnl'].iloc[0] > 3*t_cost*total_contracts2trade): recommendation = 'STOP' elif (current_data['c1']['tr_dte'] < 35)&\ (pnl_frame['total_pnl'].iloc[0] > 3*t_cost*total_contracts2trade): recommendation = 'STOP' elif (current_data['c1']['tr_dte'] < 35)&\ (pnl_frame['total_pnl'].iloc[0] < 3*t_cost*total_contracts2trade): recommendation = 'WINDDOWN' else: recommendation = 'HOLD' else: success_status = False QF = np.nan z1 = np.nan short_tr_dte = np.nan holding_tr_dte = np.nan second_spread_weight = np.nan recommendation = 'MISSING DATA' result_output = { 'success': success_status, 'ticker_head': ticker_head, 'QF_initial': QF_initial, 'z1_initial': z1_initial, 'QF': QF, 'z1': z1, 'short_tr_dte': short_tr_dte, 'holding_tr_dte': holding_tr_dte, 'second_spread_weight': second_spread_weight, 'recommendation': recommendation } elif strategy_class == 'spread_carry': trades4_strategy = ts.get_trades_4strategy_alias(**kwargs) grouped = trades4_strategy.groupby('ticker') net_position = pd.DataFrame() net_position['ticker'] = (grouped['ticker'].first()).values net_position['qty'] = (grouped['trade_quantity'].sum()).values net_position = net_position[net_position['qty'] != 0] net_position['ticker_head'] = [ cmi.get_contract_specs(x)['ticker_head'] for x in net_position['ticker'] ] price_output = [ gfp.get_futures_price_preloaded(ticker=x, settle_date=date_to) for x in net_position['ticker'] ] net_position['tr_dte'] = [ np.nan if x.empty else x['tr_dte'].values[0] for x in price_output ] results_frame = pd.DataFrame() unique_tickerhead_list = net_position['ticker_head'].unique() results_frame['tickerHead'] = unique_tickerhead_list results_frame['ticker1'] = [None] * len(unique_tickerhead_list) results_frame['ticker2'] = [None] * len(unique_tickerhead_list) results_frame['qty'] = [None] * len(unique_tickerhead_list) results_frame['pnl'] = [None] * len(unique_tickerhead_list) results_frame['downside'] = [None] * len(unique_tickerhead_list) results_frame['indicator'] = [None] * len(unique_tickerhead_list) results_frame['timeHeld'] = [None] * len(unique_tickerhead_list) results_frame['recommendation'] = [None] * len(unique_tickerhead_list) spread_carry_output = osc.generate_spread_carry_sheet_4date( report_date=date_to) spread_report = spread_carry_output['spread_report'] pnl_output = tpnl.get_strategy_pnl(**kwargs) pnl_per_tickerhead = pnl_output['pnl_per_tickerhead'] for i in range(len(unique_tickerhead_list)): net_position_per_tickerhead = net_position[ net_position['ticker_head'] == unique_tickerhead_list[i]] net_position_per_tickerhead.sort_values('tr_dte', ascending=True, inplace=True) selected_spread = spread_report[ (spread_report['ticker1'] == net_position_per_tickerhead['ticker'].values[0]) & (spread_report['ticker2'] == net_position_per_tickerhead['ticker'].values[1])] results_frame['qty'][i] = net_position_per_tickerhead[ 'qty'].values[0] if selected_spread.empty: results_frame['ticker1'][i] = net_position_per_tickerhead[ 'ticker'].values[0] results_frame['ticker2'][i] = net_position_per_tickerhead[ 'ticker'].values[1] else: results_frame['ticker1'][i] = selected_spread[ 'ticker1'].values[0] results_frame['ticker2'][i] = selected_spread[ 'ticker2'].values[0] selected_trades = trades4_strategy[ trades4_strategy['ticker'] == results_frame['ticker1'].values[i]] price_output = gfp.get_futures_price_preloaded( ticker=results_frame['ticker1'].values[i], settle_date=pd.to_datetime( selected_trades['trade_date'].values[0])) results_frame['timeHeld'][i] = price_output['tr_dte'].values[ 0] - net_position_per_tickerhead['tr_dte'].values[0] results_frame['pnl'][i] = pnl_per_tickerhead[ unique_tickerhead_list[i]].sum() if unique_tickerhead_list[i] in ['CL', 'B', 'ED']: results_frame['indicator'][i] = selected_spread[ 'reward_risk'].values[0] if results_frame['qty'][i] > 0: results_frame['recommendation'][i] = 'STOP' elif results_frame['qty'][i] < 0: if results_frame['indicator'][i] > -0.06: results_frame['recommendation'][i] = 'STOP' else: results_frame['recommendation'][i] = 'HOLD' else: results_frame['indicator'][i] = selected_spread[ 'q_carry'].values[0] if results_frame['qty'][i] > 0: if results_frame['indicator'][i] < 19: results_frame['recommendation'][i] = 'STOP' else: results_frame['recommendation'][i] = 'HOLD' elif results_frame['qty'][i] < 0: if results_frame['indicator'][i] > -9: results_frame['recommendation'][i] = 'STOP' else: results_frame['recommendation'][i] = 'HOLD' if results_frame['qty'][i] > 0: results_frame['downside'][i] = selected_spread[ 'downside'].values[0] * results_frame['qty'][i] else: results_frame['downside'][i] = selected_spread[ 'upside'].values[0] * results_frame['qty'][i] return {'success': True, 'results_frame': results_frame} elif strategy_class == 'vcs': greeks_out = sg.get_greeks_4strategy_4date(alias=kwargs['alias'], as_of_date=date_to) ticker_portfolio = greeks_out['ticker_portfolio'] options_position = greeks_out['options_position'] if ticker_portfolio.empty and not options_position.empty: result_output = { 'success': False, 'net_oev': np.NaN, 'net_theta': np.NaN, 'long_short_ratio': np.NaN, 'recommendation': 'MISSING DATA', 'last_adjustment_days_ago': np.NaN, 'min_tr_dte': np.NaN, 'long_oev': np.NaN, 'short_oev': np.NaN, 'favQMove': np.NaN } elif ticker_portfolio.empty and options_position.empty: result_output = { 'success': False, 'net_oev': np.NaN, 'net_theta': np.NaN, 'long_short_ratio': np.NaN, 'recommendation': 'EMPTY', 'last_adjustment_days_ago': np.NaN, 'min_tr_dte': np.NaN, 'long_oev': np.NaN, 'short_oev': np.NaN, 'favQMove': np.NaN } else: min_tr_dte = min([ exp.get_days2_expiration(ticker=x, date_to=date_to, instrument='options', con=con)['tr_dte'] for x in ticker_portfolio['ticker'] ]) net_oev = ticker_portfolio['total_oev'].sum() net_theta = ticker_portfolio['theta'].sum() long_portfolio = ticker_portfolio[ ticker_portfolio['total_oev'] > 0] short_portfolio = ticker_portfolio[ ticker_portfolio['total_oev'] < 0] short_portfolio['total_oev'] = abs(short_portfolio['total_oev']) long_oev = long_portfolio['total_oev'].sum() short_oev = short_portfolio['total_oev'].sum() if (not short_portfolio.empty) & (not long_portfolio.empty): long_short_ratio = 100 * long_oev / short_oev long_portfolio.sort_values('total_oev', ascending=False, inplace=True) short_portfolio.sort_values('total_oev', ascending=False, inplace=True) long_ticker = long_portfolio['ticker'].iloc[0] short_ticker = short_portfolio['ticker'].iloc[0] long_contract_specs = cmi.get_contract_specs(long_ticker) short_contract_specs = cmi.get_contract_specs(short_ticker) if 12*long_contract_specs['ticker_year']+long_contract_specs['ticker_month_num'] < \ 12*short_contract_specs['ticker_year']+short_contract_specs['ticker_month_num']: front_ticker = long_ticker back_ticker = short_ticker direction = 'long' else: front_ticker = short_ticker back_ticker = long_ticker direction = 'short' if 'vcs_output' in kwargs.keys(): vcs_output = kwargs['vcs_output'] else: vcs_output = ovcs.generate_vcs_sheet_4date(date_to=date_to) vcs_pairs = vcs_output['vcs_pairs'] selected_result = vcs_pairs[ (vcs_pairs['ticker1'] == front_ticker) & (vcs_pairs['ticker2'] == back_ticker)] if selected_result.empty: favQMove = np.NaN else: current_Q = selected_result['Q'].iloc[0] q_limit = of.get_vcs_filter_values( product_group=long_contract_specs['ticker_head'], filter_type='tickerHead', direction=direction, indicator='Q') if direction == 'long': favQMove = current_Q - q_limit elif direction == 'short': favQMove = q_limit - current_Q else: long_short_ratio = np.NaN favQMove = np.NaN trades_frame = ts.get_trades_4strategy_alias(**kwargs) trades_frame_options = trades_frame[trades_frame['instrument'] == 'O'] last_adjustment_days_ago = len( exp.get_bus_day_list( date_to=date_to, datetime_from=max( trades_frame_options['trade_date']).to_pydatetime())) if favQMove >= 10 and last_adjustment_days_ago > 10: recommendation = 'STOP-ratio normalized' elif min_tr_dte < 25: recommendation = 'STOP-close to expiration' elif np.isnan(long_short_ratio): recommendation = 'STOP-not a proper calendar' else: if long_short_ratio < 80: if favQMove < 0: recommendation = 'buy_options_to_grow' else: recommendation = 'buy_options_to_shrink' elif long_short_ratio > 120: if favQMove < 0: recommendation = 'sell_options_to_grow' else: recommendation = 'sell_options_to_shrink' else: recommendation = 'HOLD' result_output = { 'success': True, 'net_oev': net_oev, 'net_theta': net_theta, 'long_short_ratio': long_short_ratio, 'recommendation': recommendation, 'last_adjustment_days_ago': last_adjustment_days_ago, 'min_tr_dte': min_tr_dte, 'long_oev': long_oev, 'short_oev': short_oev, 'favQMove': favQMove } elif strategy_class == 'ocs': datetime_to = cu.convert_doubledate_2datetime(date_to) time_held = (datetime_to.date() - strategy_info_output['created_date'].date()).days notes = '' strategy_position = ts.get_net_position_4strategy_alias( alias=kwargs['alias'], as_of_date=date_to, con=con) if len(strategy_position.index) == 0: tpnl.close_strategy(alias=kwargs['alias'], close_date=date_to, broker=broker, con=con) result_output = { 'success': True, 'time_held': time_held, 'dollar_noise': np.nan, 'notes': 'closed' } elif strategy_position['qty'].sum() != 0: result_output = { 'success': True, 'time_held': time_held, 'dollar_noise': np.nan, 'notes': 'check position' } else: strategy_position['cont_indx'] = [ cmi.get_contract_specs(x)['cont_indx'] for x in strategy_position['ticker'] ] strategy_position.sort_values('cont_indx', ascending=True, inplace=True) ocs_output = ocs.generate_overnight_spreads_sheet_4date( date_to=date_to) overnight_calendars = ocs_output['overnight_calendars'] selection_indx = (overnight_calendars['ticker1'] == strategy_position['ticker'].iloc[0])&\ (overnight_calendars['ticker2'] == strategy_position['ticker'].iloc[1]) if sum(selection_indx) > 0: dollar_noise = (overnight_calendars.loc[ selection_indx, 'dollarNoise100'].values[0]) * abs( strategy_position['qty'].iloc[0]) else: dollar_noise = np.nan result_output = { 'success': True, 'time_held': time_held, 'dollar_noise': dollar_noise, 'notes': 'hold' } elif strategy_class == 'skpt': long_ticker = strategy_position.loc[strategy_position['qty'] > 0, 'ticker'].iloc[0] short_ticker = strategy_position.loc[strategy_position['qty'] < 0, 'ticker'].iloc[0] long_data = gsp.get_stock_price_preloaded(ticker=long_ticker, data_source='iex', settle_date_to=date_to) short_data = gsp.get_stock_price_preloaded(ticker=short_ticker, data_source='iex', settle_date_to=date_to) merged_data = pd.merge(long_data[['close', 'settle_datetime']], short_data[['close', 'settle_datetime']], how='inner', on='settle_datetime') merged_data.set_index('settle_datetime', drop=True, inplace=True) intaday_output_long = pweb.DataReader(long_ticker, 'iex-tops') intaday_output_short = pweb.DataReader(short_ticker, 'iex-tops') merged_data = merged_data.append( pd.DataFrame( { 'close_x': intaday_output_long.iloc[4].values[0], 'close_y': intaday_output_short.iloc[4].values[0] }, index=[dt.datetime.now()])) signal_output = spt.backtest(merged_data, 'close_x', 'close_y') return { 'long_ticker': long_ticker, 'short_ticker': short_ticker, 'zScoreC': signal_output['data_frame']['zScore'].iloc[-1], 'zScore': signal_output['data_frame']['zScore'].iloc[-2] } else: result_output = {'success': False} if 'con' not in kwargs.keys(): con.close() return result_output
def get_overnight_calendar_signals(**kwargs): ticker_list = kwargs['ticker_list'] date_to = kwargs['date_to'] #print(ticker_list) if 'tr_dte_list' in kwargs.keys(): tr_dte_list = kwargs['tr_dte_list'] else: tr_dte_list = [exp.get_futures_days2_expiration({'ticker': x,'date_to': date_to}) for x in ticker_list] if 'aggregation_method' in kwargs.keys() and 'contracts_back' in kwargs.keys(): aggregation_method = kwargs['aggregation_method'] contracts_back = kwargs['contracts_back'] else: amcb_output = opUtil.get_aggregation_method_contracts_back(cmi.get_contract_specs(ticker_list[0])) aggregation_method = amcb_output['aggregation_method'] contracts_back = amcb_output['contracts_back'] if 'use_last_as_current' in kwargs.keys(): use_last_as_current = kwargs['use_last_as_current'] else: use_last_as_current = False if 'futures_data_dictionary' in kwargs.keys(): futures_data_dictionary = kwargs['futures_data_dictionary'] else: futures_data_dictionary = {x: gfp.get_futures_price_preloaded(ticker_head=x) for x in [cmi.get_contract_specs(ticker_list[0])['ticker_head']]} if 'contract_multiplier' in kwargs.keys(): contract_multiplier = kwargs['contract_multiplier'] else: contract_multiplier = cmi.contract_multiplier[cmi.get_contract_specs(ticker_list[0])['ticker_head']] aligned_output = opUtil.get_aligned_futures_data(contract_list=ticker_list, tr_dte_list=tr_dte_list, aggregation_method=aggregation_method, contracts_back=contracts_back, date_to=date_to, futures_data_dictionary=futures_data_dictionary, use_last_as_current=use_last_as_current) ticker1L = '' ticker2L = '' q_carry = np.nan butterfly_q = np.nan butterfly_z = np.nan butterfly_q10 = np.nan butterfly_q25 = np.nan butterfly_q35 = np.nan butterfly_q50 = np.nan butterfly_q65 = np.nan butterfly_q75 = np.nan butterfly_q90 = np.nan butterfly_noise = np.nan butterfly_mean = np.nan if not aligned_output['success']: return {'success': False, 'ticker1L': ticker1L, 'ticker2L': ticker2L, 'q_carry': q_carry, 'butterfly_q': butterfly_q, 'butterfly_z': butterfly_z, 'spread_price': np.nan, 'butterfly_q10': butterfly_q10, 'butterfly_q25': butterfly_q25, 'butterfly_q35': butterfly_q35, 'butterfly_q50': butterfly_q50, 'butterfly_q65': butterfly_q65, 'butterfly_q75': butterfly_q75, 'butterfly_q90': butterfly_q90, 'butterfly_mean': butterfly_mean, 'butterfly_noise': butterfly_noise, 'noise_100': np.nan, 'dollar_noise_100': np.nan, 'pnl1': np.nan, 'pnl1_instant': np.nan, 'pnl2': np.nan, 'pnl5': np.nan, 'pnl10': np.nan} aligned_data = aligned_output['aligned_data'] current_data = aligned_output['current_data'] aligned_data['spread_change_1'] = aligned_data['c1']['change_1']-aligned_data['c2']['change_1'] aligned_data['spread_price'] = aligned_data['c1']['close_price']-aligned_data['c2']['close_price'] spread_price_current = current_data['c1']['close_price']-current_data['c2']['close_price'] pnl1 = (current_data['c1']['change1'] - current_data['c2']['change1'])*contract_multiplier pnl1_instant = (current_data['c1']['change1_instant'] - current_data['c2']['change1_instant']) * contract_multiplier pnl2 = (current_data['c1']['change2'] - current_data['c2']['change2']) * contract_multiplier pnl5 = (current_data['c1']['change5'] - current_data['c2']['change5']) * contract_multiplier pnl10 = (current_data['c1']['change10'] - current_data['c2']['change10']) * contract_multiplier noise_100 = np.std(aligned_data['spread_change_1'].iloc[-100:]) if noise_100 == 0: noise_100 = np.nan spread_carry_output = sc.generate_spread_carry_sheet_4date(report_date=date_to) if spread_carry_output['success']: spread_report = spread_carry_output['spread_report'] selected_line = spread_report[(spread_report['ticker1']==ticker_list[0])&(spread_report['ticker2']==ticker_list[1])] if not selected_line.empty: q_carry = selected_line['q_carry'].iloc[0] butterfly_q = selected_line['butterfly_q'].iloc[0] butterfly_z = selected_line['butterfly_z'].iloc[0] butterfly_q10 = selected_line['butterfly_q10'].iloc[0] butterfly_q25 = selected_line['butterfly_q25'].iloc[0] butterfly_q35 = selected_line['butterfly_q35'].iloc[0] butterfly_q50 = selected_line['butterfly_q50'].iloc[0] butterfly_q65 = selected_line['butterfly_q65'].iloc[0] butterfly_q75 = selected_line['butterfly_q75'].iloc[0] butterfly_q90 = selected_line['butterfly_q90'].iloc[0] butterfly_mean = selected_line['butterfly_mean'].iloc[0] butterfly_noise = selected_line['butterfly_noise'].iloc[0] ticker1L = selected_line['ticker1L'].iloc[0] ticker2L = selected_line['ticker2L'].iloc[0] return {'success': True, 'ticker1L': ticker1L, 'ticker2L': ticker2L, 'q_carry': q_carry, 'butterfly_q': butterfly_q, 'butterfly_z': butterfly_z, 'spread_price': spread_price_current, 'butterfly_q10': butterfly_q10, 'butterfly_q25': butterfly_q25, 'butterfly_q35': butterfly_q35, 'butterfly_q50': butterfly_q50, 'butterfly_q65': butterfly_q65, 'butterfly_q75': butterfly_q75, 'butterfly_q90': butterfly_q90, 'butterfly_mean': butterfly_mean, 'butterfly_noise': butterfly_noise, 'noise_100': noise_100, 'dollar_noise_100': noise_100*contract_multiplier, 'pnl1': pnl1, 'pnl1_instant': pnl1_instant, 'pnl2': pnl2, 'pnl5': pnl5, 'pnl10': pnl10}
def get_results_4strategy(**kwargs): signal_input = dict() if 'futures_data_dictionary' in kwargs.keys(): signal_input['futures_data_dictionary'] = kwargs['futures_data_dictionary'] if 'date_to' in kwargs.keys(): date_to = kwargs['date_to'] else: date_to = exp.doubledate_shift_bus_days() if 'datetime5_years_ago' in kwargs.keys(): signal_input['datetime5_years_ago'] = kwargs['datetime5_years_ago'] if 'strategy_info_output' in kwargs.keys(): strategy_info_output = kwargs['strategy_info_output'] else: strategy_info_output = ts.get_strategy_info_from_alias(**kwargs) con = msu.get_my_sql_connection(**kwargs) strategy_info_dict = sc.convert_from_string_to_dictionary(string_input=strategy_info_output['description_string']) #print(kwargs['alias']) strategy_class = strategy_info_dict['strategy_class'] pnl_frame = tpm.get_daily_pnl_snapshot(as_of_date=date_to) pnl_frame = pnl_frame[pnl_frame['alias']==kwargs['alias']] strategy_position = ts.get_net_position_4strategy_alias(alias=kwargs['alias'],as_of_date=date_to) if strategy_class == 'futures_butterfly': ticker_head = cmi.get_contract_specs(strategy_info_dict['ticker1'])['ticker_head'] if not strategy_position.empty: total_contracts2trade = strategy_position['qty'].abs().sum() t_cost = cmi.t_cost[ticker_head] QF_initial = float(strategy_info_dict['QF']) z1_initial = float(strategy_info_dict['z1']) bf_signals_output = fs.get_futures_butterfly_signals(ticker_list=[strategy_info_dict['ticker1'], strategy_info_dict['ticker2'], strategy_info_dict['ticker3']], aggregation_method=int(strategy_info_dict['agg']), contracts_back=int(strategy_info_dict['cBack']), date_to=date_to,**signal_input) aligned_output = bf_signals_output['aligned_output'] current_data = aligned_output['current_data'] holding_tr_dte = int(strategy_info_dict['trDte1'])-current_data['c1']['tr_dte'] if strategy_position.empty: recommendation = 'CLOSE' elif (z1_initial>0)&(holding_tr_dte > 5) &\ (bf_signals_output['qf']<QF_initial-20)&\ (pnl_frame['total_pnl'].iloc[0] > 3*t_cost*total_contracts2trade): recommendation = 'STOP' elif (z1_initial<0)&(holding_tr_dte > 5) &\ (bf_signals_output['qf']>QF_initial+20)&\ (pnl_frame['total_pnl'].iloc[0] > 3*t_cost*total_contracts2trade): recommendation = 'STOP' elif (current_data['c1']['tr_dte'] < 35)&\ (pnl_frame['total_pnl'].iloc[0] > 3*t_cost*total_contracts2trade): recommendation = 'STOP' elif (current_data['c1']['tr_dte'] < 35)&\ (pnl_frame['total_pnl'].iloc[0] < 3*t_cost*total_contracts2trade): recommendation = 'WINDDOWN' else: recommendation = 'HOLD' result_output = {'success': True,'ticker_head': ticker_head, 'QF_initial':QF_initial,'z1_initial': z1_initial, 'QF': bf_signals_output['qf'],'z1': bf_signals_output['zscore1'], 'short_tr_dte': current_data['c1']['tr_dte'], 'holding_tr_dte': holding_tr_dte, 'second_spread_weight': bf_signals_output['second_spread_weight_1'],'recommendation': recommendation} elif strategy_class == 'spread_carry': trades4_strategy = ts.get_trades_4strategy_alias(**kwargs) grouped = trades4_strategy.groupby('ticker') net_position = pd.DataFrame() net_position['ticker'] = (grouped['ticker'].first()).values net_position['qty'] = (grouped['trade_quantity'].sum()).values net_position = net_position[net_position['qty'] != 0] net_position['ticker_head'] = [cmi.get_contract_specs(x)['ticker_head'] for x in net_position['ticker']] price_output = [gfp.get_futures_price_preloaded(ticker=x, settle_date=date_to) for x in net_position['ticker']] net_position['tr_dte'] = [x['tr_dte'].values[0] for x in price_output] results_frame = pd.DataFrame() unique_tickerhead_list = net_position['ticker_head'].unique() results_frame['tickerHead'] = unique_tickerhead_list results_frame['ticker1'] = [None]*len(unique_tickerhead_list) results_frame['ticker2'] = [None]*len(unique_tickerhead_list) results_frame['qty'] = [None]*len(unique_tickerhead_list) results_frame['pnl'] = [None]*len(unique_tickerhead_list) results_frame['downside'] = [None]*len(unique_tickerhead_list) results_frame['indicator'] = [None]*len(unique_tickerhead_list) results_frame['timeHeld'] = [None]*len(unique_tickerhead_list) results_frame['recommendation'] = [None]*len(unique_tickerhead_list) spread_carry_output = osc.generate_spread_carry_sheet_4date(report_date=date_to) spread_report = spread_carry_output['spread_report'] pnl_output = tpnl.get_strategy_pnl(**kwargs) pnl_per_tickerhead = pnl_output['pnl_per_tickerhead'] for i in range(len(unique_tickerhead_list)): net_position_per_tickerhead = net_position[net_position['ticker_head'] == unique_tickerhead_list[i]] net_position_per_tickerhead.sort('tr_dte',ascending=True,inplace=True) selected_spread = spread_report[(spread_report['ticker1'] == net_position_per_tickerhead['ticker'].values[0]) & (spread_report['ticker2'] == net_position_per_tickerhead['ticker'].values[1])] results_frame['ticker1'][i] = selected_spread['ticker1'].values[0] results_frame['ticker2'][i] = selected_spread['ticker2'].values[0] results_frame['qty'][i] = net_position_per_tickerhead['qty'].values[0] selected_trades = trades4_strategy[trades4_strategy['ticker'] == results_frame['ticker1'].values[i]] price_output = gfp.get_futures_price_preloaded(ticker=results_frame['ticker1'].values[i], settle_date=pd.to_datetime(selected_trades['trade_date'].values[0])) results_frame['timeHeld'][i] = price_output['tr_dte'].values[0]-net_position_per_tickerhead['tr_dte'].values[0] results_frame['pnl'][i] = pnl_per_tickerhead[unique_tickerhead_list[i]].sum() if unique_tickerhead_list[i] in ['CL', 'B', 'ED']: results_frame['indicator'][i] = selected_spread['reward_risk'].values[0] if results_frame['qty'][i] > 0: results_frame['recommendation'][i] = 'STOP' elif results_frame['qty'][i] < 0: if results_frame['indicator'][i] > -0.06: results_frame['recommendation'][i] = 'STOP' else: results_frame['recommendation'][i] = 'HOLD' else: results_frame['indicator'][i] = selected_spread['q_carry'].values[0] if results_frame['qty'][i] > 0: if results_frame['indicator'][i] < 19: results_frame['recommendation'][i] = 'STOP' else: results_frame['recommendation'][i] = 'HOLD' elif results_frame['qty'][i] < 0: if results_frame['indicator'][i] > -9: results_frame['recommendation'][i] = 'STOP' else: results_frame['recommendation'][i] = 'HOLD' if results_frame['qty'][i] > 0: results_frame['downside'][i] = selected_spread['downside'].values[0]*results_frame['qty'][i] else: results_frame['downside'][i] = selected_spread['upside'].values[0]*results_frame['qty'][i] return {'success': True, 'results_frame': results_frame} elif strategy_class == 'vcs': greeks_out = sg.get_greeks_4strategy_4date(alias=kwargs['alias'], as_of_date=date_to) ticker_portfolio = greeks_out['ticker_portfolio'] if ticker_portfolio.empty: min_tr_dte = np.NaN result_output = {'success': False, 'net_oev': np.NaN, 'net_theta': np.NaN, 'long_short_ratio': np.NaN, 'recommendation': 'EMPTY', 'last_adjustment_days_ago': np.NaN, 'min_tr_dte': np.NaN, 'long_oev': np.NaN, 'short_oev': np.NaN, 'favQMove': np.NaN} else: min_tr_dte = min([exp.get_days2_expiration(ticker=x,date_to=date_to,instrument='options',con=con)['tr_dte'] for x in ticker_portfolio['ticker']]) net_oev = ticker_portfolio['total_oev'].sum() net_theta = ticker_portfolio['theta'].sum() long_portfolio = ticker_portfolio[ticker_portfolio['total_oev'] > 0] short_portfolio = ticker_portfolio[ticker_portfolio['total_oev'] < 0] short_portfolio['total_oev']=abs(short_portfolio['total_oev']) long_oev = long_portfolio['total_oev'].sum() short_oev = short_portfolio['total_oev'].sum() if (not short_portfolio.empty) & (not long_portfolio.empty): long_short_ratio = 100*long_oev/short_oev long_portfolio.sort('total_oev', ascending=False, inplace=True) short_portfolio.sort('total_oev', ascending=False, inplace=True) long_ticker = long_portfolio['ticker'].iloc[0] short_ticker = short_portfolio['ticker'].iloc[0] long_contract_specs = cmi.get_contract_specs(long_ticker) short_contract_specs = cmi.get_contract_specs(short_ticker) if 12*long_contract_specs['ticker_year']+long_contract_specs['ticker_month_num'] < \ 12*short_contract_specs['ticker_year']+short_contract_specs['ticker_month_num']: front_ticker = long_ticker back_ticker = short_ticker direction = 'long' else: front_ticker = short_ticker back_ticker = long_ticker direction = 'short' if 'vcs_output' in kwargs.keys(): vcs_output = kwargs['vcs_output'] else: vcs_output = ovcs.generate_vcs_sheet_4date(date_to=date_to) vcs_pairs = vcs_output['vcs_pairs'] selected_result = vcs_pairs[(vcs_pairs['ticker1'] == front_ticker) & (vcs_pairs['ticker2'] == back_ticker)] if selected_result.empty: favQMove = np.NaN else: current_Q = selected_result['Q'].iloc[0] q_limit = of.get_vcs_filter_values(product_group=long_contract_specs['ticker_head'], filter_type='tickerHead',direction=direction,indicator='Q') if direction == 'long': favQMove = current_Q-q_limit elif direction == 'short': favQMove = q_limit-current_Q else: long_short_ratio = np.NaN favQMove = np.NaN trades_frame = ts.get_trades_4strategy_alias(**kwargs) trades_frame_options = trades_frame[trades_frame['instrument'] == 'O'] last_adjustment_days_ago = len(exp.get_bus_day_list(date_to=date_to,datetime_from=max(trades_frame_options['trade_date']).to_datetime())) if favQMove >= 10 and last_adjustment_days_ago > 10: recommendation = 'STOP-ratio normalized' elif min_tr_dte<25: recommendation = 'STOP-close to expiration' elif np.isnan(long_short_ratio): recommendation = 'STOP-not a proper calendar' else: if long_short_ratio < 80: if favQMove < 0: recommendation = 'buy_options_to_grow' else: recommendation = 'buy_options_to_shrink' elif long_short_ratio > 120: if favQMove < 0: recommendation = 'sell_options_to_grow' else: recommendation = 'sell_options_to_shrink' else: recommendation = 'HOLD' result_output = {'success': True, 'net_oev': net_oev, 'net_theta': net_theta, 'long_short_ratio': long_short_ratio, 'recommendation': recommendation, 'last_adjustment_days_ago': last_adjustment_days_ago, 'min_tr_dte': min_tr_dte, 'long_oev': long_oev, 'short_oev': short_oev, 'favQMove': favQMove} else: result_output = {'success': False} if 'con' not in kwargs.keys(): con.close() return result_output