def get_futures_spread_carry_signals(**kwargs): ticker_list = kwargs['ticker_list'] date_to = kwargs['date_to'] 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']] if 'datetime5_years_ago' in kwargs.keys(): datetime5_years_ago = kwargs['datetime5_years_ago'] else: date5_years_ago = cu.doubledate_shift(date_to, 5 * 365) datetime5_years_ago = cu.convert_doubledate_2datetime(date5_years_ago) 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) aligned_data = aligned_output['aligned_data'] current_data = aligned_output['current_data'] last5_years_indx = aligned_data['settle_date'] >= datetime5_years_ago data_last5_years = aligned_data[last5_years_indx] ticker1_list = [ current_data['c' + str(x + 1)]['ticker'] for x in range(len(ticker_list) - 1) ] ticker2_list = [ current_data['c' + str(x + 2)]['ticker'] for x in range(len(ticker_list) - 1) ] yield_current_list = [ 100 * (current_data['c' + str(x + 1)]['close_price'] - current_data['c' + str(x + 2)]['close_price']) / current_data['c' + str(x + 2)]['close_price'] for x in range(len(ticker_list) - 1) ] butterfly_current_list = [ 100 * (current_data['c' + str(x + 1)]['close_price'] - 2 * current_data['c' + str(x + 2)]['close_price'] + current_data['c' + str(x + 3)]['close_price']) / current_data['c' + str(x + 2)]['close_price'] for x in range(len(ticker_list) - 2) ] price_current_list = [ current_data['c' + str(x + 1)]['close_price'] - current_data['c' + str(x + 2)]['close_price'] for x in range(len(ticker_list) - 1) ] yield_history = [ 100 * (aligned_data['c' + str(x + 1)]['close_price'] - aligned_data['c' + str(x + 2)]['close_price']) / aligned_data['c' + str(x + 2)]['close_price'] for x in range(len(ticker_list) - 1) ] butterfly_history = [ 100 * (aligned_data['c' + str(x + 1)]['close_price'] - 2 * aligned_data['c' + str(x + 2)]['close_price'] + aligned_data['c' + str(x + 3)]['close_price']) / aligned_data['c' + str(x + 2)]['close_price'] for x in range(len(ticker_list) - 2) ] change_5_history = [ data_last5_years['c' + str(x + 1)]['change_5'] - data_last5_years['c' + str(x + 2)]['change_5'] for x in range(len(ticker_list) - 1) ] change5 = [ contract_multiplier * (current_data['c' + str(x + 1)]['change5'] - current_data['c' + str(x + 2)]['change5']) for x in range(len(ticker_list) - 1) ] change10 = [ contract_multiplier * (current_data['c' + str(x + 1)]['change10'] - current_data['c' + str(x + 2)]['change10']) for x in range(len(ticker_list) - 1) ] change20 = [ contract_multiplier * (current_data['c' + str(x + 1)]['change20'] - current_data['c' + str(x + 2)]['change20']) for x in range(len(ticker_list) - 1) ] front_tr_dte = [ current_data['c' + str(x + 1)]['tr_dte'] for x in range(len(ticker_list) - 1) ] q_list = [ stats.get_quantile_from_number({ 'x': yield_current_list[x], 'y': yield_history[x].values, 'clean_num_obs': max(100, round(3 * len(yield_history[x].values) / 4)) }) for x in range(len(ticker_list) - 1) ] butterfly_q_list = [ stats.get_quantile_from_number({ 'x': butterfly_current_list[x], 'y': butterfly_history[x].values[-40:], 'clean_num_obs': round(3 * len(butterfly_history[x].values[-40:]) / 4) }) for x in range(len(ticker_list) - 2) ] extreme_quantiles_list = [ stats.get_number_from_quantile( y=x.values[:-40], quantile_list=[10, 25, 35, 50, 65, 75, 90]) for x in butterfly_history ] butterfly_q10 = [x[0] for x in extreme_quantiles_list] butterfly_q25 = [x[1] for x in extreme_quantiles_list] butterfly_q35 = [x[2] for x in extreme_quantiles_list] butterfly_q50 = [x[3] for x in extreme_quantiles_list] butterfly_q65 = [x[4] for x in extreme_quantiles_list] butterfly_q75 = [x[5] for x in extreme_quantiles_list] butterfly_q90 = [x[6] for x in extreme_quantiles_list] butterfly_noise_list = [ stats.get_stdev(x=butterfly_history[i].values[-20:]) for i in range(len(ticker_list) - 2) ] butterfly_mean_list = [ stats.get_mean(x=butterfly_history[i].values[-10:]) for i in range(len(ticker_list) - 2) ] butterfly_z_list = [(butterfly_current_list[i] - butterfly_mean_list[i]) / butterfly_noise_list[i] for i in range(len(ticker_list) - 2)] percentile_vector = [ stats.get_number_from_quantile( y=change_5_history[x].values, quantile_list=[1, 15, 85, 99], clean_num_obs=max(100, round(3 * len(change_5_history[x].values) / 4))) for x in range(len(ticker_list) - 1) ] q1 = [x[0] for x in percentile_vector] q15 = [x[1] for x in percentile_vector] q85 = [x[2] for x in percentile_vector] q99 = [x[3] for x in percentile_vector] downside = [ contract_multiplier * (q1[x] + q15[x]) / 2 for x in range(len(q1)) ] upside = [ contract_multiplier * (q85[x] + q99[x]) / 2 for x in range(len(q1)) ] carry = [ contract_multiplier * (price_current_list[x] - price_current_list[x + 1]) for x in range(len(q_list) - 1) ] q_carry = [q_list[x] - q_list[x + 1] for x in range(len(q_list) - 1)] q_average = np.cumsum(q_list) / range(1, len(q_list) + 1) q_series = pd.Series(q_list) q_min = q_series.cummin().values q_max = q_series.cummax().values q_carry_average = [ q_average[x] - q_list[x + 1] for x in range(len(q_list) - 1) ] q_carry_max = [q_max[x] - q_list[x + 1] for x in range(len(q_list) - 1)] q_carry_min = [q_min[x] - q_list[x + 1] for x in range(len(q_list) - 1)] reward_risk = [ 5 * carry[x] / ((front_tr_dte[x + 1] - front_tr_dte[x]) * abs(downside[x + 1])) if carry[x] > 0 else 5 * carry[x] / ((front_tr_dte[x + 1] - front_tr_dte[x]) * upside[x + 1]) for x in range(len(carry)) ] return pd.DataFrame.from_dict({ 'ticker1': ticker1_list, 'ticker2': ticker2_list, 'ticker1L': [''] + ticker1_list[:-1], 'ticker2L': [''] + ticker2_list[:-1], 'ticker_head': cmi.get_contract_specs(ticker_list[0])['ticker_head'], 'front_tr_dte': front_tr_dte, 'front_tr_dteL': [np.NAN] + front_tr_dte[:-1], 'carry': [np.NAN] + carry, 'q_carry': [np.NAN] + q_carry, 'q_carry_average': [np.NAN] + q_carry_average, 'q_carry_max': [np.NAN] + q_carry_max, 'q_carry_min': [np.NAN] + q_carry_min, 'butterfly_q': [np.NAN] + butterfly_q_list, 'butterfly_z': [np.NAN] + butterfly_z_list, 'reward_risk': [np.NAN] + reward_risk, 'price': price_current_list, 'priceL': [np.NAN] + price_current_list[:-1], 'butterfly_q10': [np.NAN] + butterfly_q10, 'butterfly_q25': [np.NAN] + butterfly_q25, 'butterfly_q35': [np.NAN] + butterfly_q35, 'butterfly_q50': [np.NAN] + butterfly_q50, 'butterfly_q65': [np.NAN] + butterfly_q65, 'butterfly_q75': [np.NAN] + butterfly_q75, 'butterfly_q90': [np.NAN] + butterfly_q90, 'butterfly_mean': [np.NAN] + butterfly_mean_list, 'butterfly_noise': [np.NAN] + butterfly_noise_list, 'q': q_list, 'upside': upside, 'downside': downside, 'upsideL': [np.NAN] + upside[:-1], 'downsideL': [np.NAN] + downside[:-1], 'change5': change5, 'change10': change10, 'change20': change20 })
def get_fm_signals(**kwargs): ticker_head = kwargs['ticker_head'] date_to = kwargs['date_to'] #print(ticker_head) ticker_class = cmi.ticker_class[ticker_head] datetime_to = cu.convert_doubledate_2datetime(date_to) date5_years_ago = cu.doubledate_shift(date_to,5*365) datetime5_years_ago = cu.convert_doubledate_2datetime(date5_years_ago) data_out = gfp.get_futures_price_preloaded(ticker_head=ticker_head,settle_date_to=datetime_to) data4day = data_out[data_out['settle_date']==datetime_to] data4day = data4day[data4day['tr_dte']>=20] if len(data4day.index)<2: return {'ticker': '', 'comm_net': np.nan, 'spec_net': np.nan, 'comm_cot_index_slow': np.nan, 'comm_cot_index_fast': np.nan, 'trend_direction': np.nan,'curve_slope': np.nan, 'rsi_3': np.nan, 'rsi_7': np.nan, 'rsi_14': np.nan, 'change1': np.nan, 'change1_instant': np.nan, 'change5': np.nan, 'change10': np.nan, 'change20': np.nan, 'change1_dollar': np.nan, 'change1_instant_dollar': np.nan, 'change5_dollar': np.nan, 'change10_dollar': np.nan, 'change20_dollar': np.nan} data4day.sort_values('volume', ascending=False, inplace=True) data4day = data4day.iloc[:2] data4day.sort_values('tr_dte',ascending=True,inplace=True) ticker1 = data4day['ticker'].iloc[0] ticker2 = data4day['ticker'].iloc[1] tr_dte_list = [data4day['tr_dte'].iloc[0], data4day['tr_dte'].iloc[1]] amcb_output = opUtil.get_aggregation_method_contracts_back({'ticker_head': ticker_head, 'ticker_class': cmi.ticker_class[ticker_head]}) aggregation_method = amcb_output['aggregation_method'] contracts_back = amcb_output['contracts_back'] futures_data_dictionary = {ticker_head: data_out} aligned_output = opUtil.get_aligned_futures_data(contract_list=[ticker1,ticker2], 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=False) aligned_data = aligned_output['aligned_data'] current_data = aligned_output['current_data'] yield1_current = 100*(current_data['c1']['close_price']-current_data['c2']['close_price'])/current_data['c2']['close_price'] yield1 = 100*(aligned_data['c1']['close_price']-aligned_data['c2']['close_price'])/aligned_data['c2']['close_price'] last5_years_indx = aligned_data['settle_date']>=datetime5_years_ago yield1_last5_years = yield1[last5_years_indx] curve_slope = stats.get_quantile_from_number({'x':yield1_current,'y': yield1_last5_years}) ticker_head_data = gfp.get_futures_price_preloaded(ticker_head=ticker_head) ticker_head_data = ticker_head_data[ticker_head_data['settle_date'] <= datetime_to] if ticker_class in ['Index', 'FX', 'Metal', 'Treasury', 'STIR']: merged_data = ticker_head_data[ticker_head_data['tr_dte'] >= 10] merged_data.sort_values(['settle_date', 'tr_dte'],ascending=True,inplace=True) merged_data.drop_duplicates(subset=['settle_date'], keep='first', inplace=True) merged_data['ma200'] = merged_data['close_price'].rolling(200).mean() merged_data['ma200_10'] = merged_data['ma200']-merged_data['ma200'].shift(10) else: data_out_front = ticker_head_data[ticker_head_data['tr_dte'] <= 60] data_out_front.drop_duplicates(subset=['settle_date'], keep='last', inplace=True) data_out_back = ticker_head_data[ticker_head_data['tr_dte'] > 60] data_out_back.drop_duplicates(subset=['settle_date'], keep='last', inplace=True) merged_data = pd.merge(data_out_front[['settle_date','tr_dte','close_price']],data_out_back[['tr_dte','close_price','settle_date','ticker','change_1']],how='inner',on='settle_date') merged_data['const_mat']=((merged_data['tr_dte_y']-60)*merged_data['close_price_x']+ (60-merged_data['tr_dte_x'])*merged_data['close_price_y'])/\ (merged_data['tr_dte_y']-merged_data['tr_dte_x']) merged_data['ma200'] = merged_data['const_mat'].rolling(200).mean() merged_data['ma200_10'] = merged_data['ma200']-merged_data['ma200'].shift(10) merged_data = merged_data[merged_data['settle_date']==datetime_to] if len(merged_data.index) == 0: trend_direction = np.nan elif merged_data['ma200_10'].iloc[0]>=0: trend_direction = 1 else: trend_direction = -1 ticker_data = gfp.get_futures_price_preloaded(ticker=ticker2,settle_date_to=datetime_to) ticker_data = ti.rsi(data_frame_input=ticker_data, change_field='change_1', period=3) ticker_data = ti.rsi(data_frame_input=ticker_data, change_field='change_1', period=7) ticker_data = ti.rsi(data_frame_input=ticker_data, change_field='change_1', period=14) cot_output = cot.get_cot_data(ticker_head=ticker_head, date_to=date_to) daily_noise = np.std(ticker_data['change_1'].iloc[-60:]) if len(cot_output.index)>0: if ticker_class in ['FX','STIR','Index','Treasury']: cot_output['comm_long'] = cot_output['Asset Manager Longs']+cot_output['Dealer Longs'] cot_output['comm_short'] = cot_output['Asset Manager Shorts']+cot_output['Dealer Shorts'] cot_output['comm_net'] = cot_output['comm_long']-cot_output['comm_short'] cot_output['spec_long'] = cot_output['Leveraged Funds Longs'] cot_output['spec_short'] = cot_output['Leveraged Funds Shorts'] cot_output['spec_net'] = cot_output['spec_long']-cot_output['spec_short'] else: cot_output['comm_long'] = cot_output['Producer/Merchant/Processor/User Longs']+cot_output['Swap Dealer Longs'] cot_output['comm_short'] = cot_output['Producer/Merchant/Processor/User Shorts']+cot_output['Swap Dealer Shorts'] cot_output['comm_net'] = cot_output['comm_long']-cot_output['comm_short'] cot_output['spec_long'] = cot_output['Money Manager Longs']+cot_output['Other Reportable Longs'] cot_output['spec_short'] = cot_output['Money Manager Shorts']+cot_output['Other Reportable Shorts'] cot_output['spec_net'] = cot_output['spec_long']-cot_output['spec_short'] if (datetime_to-cot_output['settle_date'].iloc[-1]).days>=10: comm_net = np.nan spec_net = np.nan else: comm_net = cot_output['comm_net'].iloc[-1] spec_net = cot_output['spec_net'].iloc[-1] comm_net_min_slow = cot_output['comm_net'].iloc[-156:].min() comm_net_max_slow = cot_output['comm_net'].iloc[-156:].max() comm_cot_index_slow = 100*(comm_net-comm_net_min_slow)/(comm_net_max_slow-comm_net_min_slow) comm_net_min_fast = cot_output['comm_net'].iloc[-52:].min() comm_net_max_fast = cot_output['comm_net'].iloc[-52:].max() comm_cot_index_fast = 100*(comm_net-comm_net_min_fast)/(comm_net_max_fast-comm_net_min_fast) else: comm_net = np.nan spec_net = np.nan comm_cot_index_slow = np.nan comm_cot_index_fast = np.nan contract_multiplier = cmi.contract_multiplier[ticker_head] return {'ticker': ticker2, 'comm_net': comm_net, 'spec_net': spec_net, 'comm_cot_index_slow': comm_cot_index_slow, 'comm_cot_index_fast': comm_cot_index_fast, 'trend_direction': trend_direction,'curve_slope': curve_slope, 'rsi_3': ticker_data['rsi_3'].iloc[-1], 'rsi_7': ticker_data['rsi_7'].iloc[-1], 'rsi_14': ticker_data['rsi_14'].iloc[-1], 'change1': ticker_data['change1'].iloc[-1]/daily_noise, 'change1_instant': ticker_data['change1_instant'].iloc[-1]/daily_noise, 'change5': ticker_data['change5'].iloc[-1]/daily_noise, 'change10': ticker_data['change10'].iloc[-1]/daily_noise, 'change20': ticker_data['change20'].iloc[-1]/daily_noise, 'change1_dollar': ticker_data['change1'].iloc[-1]*contract_multiplier, 'change1_instant_dollar': ticker_data['change1_instant'].iloc[-1]*contract_multiplier, 'change5_dollar': ticker_data['change5'].iloc[-1]*contract_multiplier, 'change10_dollar': ticker_data['change10'].iloc[-1]*contract_multiplier, 'change20_dollar': ticker_data['change20'].iloc[-1]*contract_multiplier}
def get_futures_butterfly_signals(**kwargs): ticker_list = kwargs['ticker_list'] date_to = kwargs['date_to'] 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']] if 'datetime5_years_ago' in kwargs.keys(): datetime5_years_ago = kwargs['datetime5_years_ago'] else: date5_years_ago = cu.doubledate_shift(date_to, 5 * 365) datetime5_years_ago = cu.convert_doubledate_2datetime(date5_years_ago) if 'datetime2_months_ago' in kwargs.keys(): datetime2_months_ago = kwargs['datetime2_months_ago'] else: date2_months_ago = cu.doubledate_shift(date_to, 60) datetime2_months_ago = cu.convert_doubledate_2datetime( date2_months_ago) 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) if not aligned_output['success']: return {'success': False} current_data = aligned_output['current_data'] aligned_data = aligned_output['aligned_data'] month_diff_1 = 12 * (current_data['c1']['ticker_year'] - current_data['c2']['ticker_year']) + ( current_data['c1']['ticker_month'] - current_data['c2']['ticker_month']) month_diff_2 = 12 * (current_data['c2']['ticker_year'] - current_data['c3']['ticker_year']) + ( current_data['c2']['ticker_month'] - current_data['c3']['ticker_month']) weight_11 = 2 * month_diff_2 / (month_diff_1 + month_diff_1) weight_12 = -2 weight_13 = 2 * month_diff_1 / (month_diff_1 + month_diff_1) price_1 = current_data['c1']['close_price'] price_2 = current_data['c2']['close_price'] price_3 = current_data['c3']['close_price'] linear_interp_price2 = (weight_11 * aligned_data['c1']['close_price'] + weight_13 * aligned_data['c3']['close_price']) / 2 butterfly_price = aligned_data['c1']['close_price'] - 2 * aligned_data[ 'c2']['close_price'] + aligned_data['c3']['close_price'] price_ratio = linear_interp_price2 / aligned_data['c2']['close_price'] linear_interp_price2_current = (weight_11 * price_1 + weight_13 * price_3) / 2 price_ratio_current = linear_interp_price2_current / price_2 q = stats.get_quantile_from_number({ 'x': price_ratio_current, 'y': price_ratio.values, 'clean_num_obs': max(100, round(3 * len(price_ratio.values) / 4)) }) qf = stats.get_quantile_from_number({ 'x': price_ratio_current, 'y': price_ratio.values[-40:], 'clean_num_obs': 30 }) recent_quantile_list = [ stats.get_quantile_from_number({ 'x': x, 'y': price_ratio.values[-40:], 'clean_num_obs': 30 }) for x in price_ratio.values[-40:] ] weight1 = weight_11 weight2 = weight_12 weight3 = weight_13 last5_years_indx = aligned_data['settle_date'] >= datetime5_years_ago last2_months_indx = aligned_data['settle_date'] >= datetime2_months_ago data_last5_years = aligned_data[last5_years_indx] yield1 = 100 * ( aligned_data['c1']['close_price'] - aligned_data['c2']['close_price']) / aligned_data['c2']['close_price'] yield2 = 100 * ( aligned_data['c2']['close_price'] - aligned_data['c3']['close_price']) / aligned_data['c3']['close_price'] yield1_last5_years = yield1[last5_years_indx] yield2_last5_years = yield2[last5_years_indx] yield1_current = 100 * ( current_data['c1']['close_price'] - current_data['c2']['close_price']) / current_data['c2']['close_price'] yield2_current = 100 * ( current_data['c2']['close_price'] - current_data['c3']['close_price']) / current_data['c3']['close_price'] butterfly_price_current = current_data['c1']['close_price']\ -2*current_data['c2']['close_price']\ +current_data['c3']['close_price'] #return {'yield1': yield1, 'yield2': yield2, 'yield1_current':yield1_current, 'yield2_current': yield2_current} yield_regress_output = stats.get_regression_results({ 'x': yield2, 'y': yield1, 'x_current': yield2_current, 'y_current': yield1_current, 'clean_num_obs': max(100, round(3 * len(yield1.values) / 4)) }) yield_regress_output_last5_years = stats.get_regression_results({ 'x': yield2_last5_years, 'y': yield1_last5_years, 'x_current': yield2_current, 'y_current': yield1_current, 'clean_num_obs': max(100, round(3 * len(yield1_last5_years.values) / 4)) }) bf_qz_frame_short = pd.DataFrame() bf_qz_frame_long = pd.DataFrame() if (len(yield1) >= 40) & (len(yield2) >= 40): recent_zscore_list = [ (yield1[-40 + i] - yield_regress_output['alpha'] - yield_regress_output['beta'] * yield2[-40 + i]) / yield_regress_output['residualstd'] for i in range(40) ] bf_qz_frame = pd.DataFrame.from_dict({ 'bf_price': butterfly_price.values[-40:], 'q': recent_quantile_list, 'zscore': recent_zscore_list }) bf_qz_frame = np.round(bf_qz_frame, 8) bf_qz_frame.drop_duplicates(['bf_price'], keep='last', inplace=True) # return bf_qz_frame bf_qz_frame_short = bf_qz_frame[(bf_qz_frame['zscore'] >= 0.6) & (bf_qz_frame['q'] >= 85)] bf_qz_frame_long = bf_qz_frame[(bf_qz_frame['zscore'] <= -0.6) & (bf_qz_frame['q'] <= 12)] if bf_qz_frame_short.empty: short_price_limit = np.NAN else: short_price_limit = bf_qz_frame_short['bf_price'].min() if bf_qz_frame_long.empty: long_price_limit = np.NAN else: long_price_limit = bf_qz_frame_long['bf_price'].max() zscore1 = yield_regress_output['zscore'] rsquared1 = yield_regress_output['rsquared'] zscore2 = yield_regress_output_last5_years['zscore'] rsquared2 = yield_regress_output_last5_years['rsquared'] second_spread_weight_1 = yield_regress_output['beta'] second_spread_weight_2 = yield_regress_output_last5_years['beta'] butterfly_5_change = data_last5_years['c1']['change_5']\ - (1+second_spread_weight_1)*data_last5_years['c2']['change_5']\ + second_spread_weight_1*data_last5_years['c3']['change_5'] butterfly_5_change_current = current_data['c1']['change_5']\ - (1+second_spread_weight_1)*current_data['c2']['change_5']\ + second_spread_weight_1*current_data['c3']['change_5'] butterfly_1_change = data_last5_years['c1']['change_1']\ - (1+second_spread_weight_1)*data_last5_years['c2']['change_1']\ + second_spread_weight_1*data_last5_years['c3']['change_1'] percentile_vector = stats.get_number_from_quantile( y=butterfly_5_change.values, quantile_list=[1, 15, 85, 99], clean_num_obs=max(100, round(3 * len(butterfly_5_change.values) / 4))) downside = contract_multiplier * (percentile_vector[0] + percentile_vector[1]) / 2 upside = contract_multiplier * (percentile_vector[2] + percentile_vector[3]) / 2 recent_5day_pnl = contract_multiplier * butterfly_5_change_current residuals = yield1 - yield_regress_output[ 'alpha'] - yield_regress_output['beta'] * yield2 regime_change_ind = (residuals[last5_years_indx].mean() - residuals.mean()) / residuals.std() seasonal_residuals = residuals[aligned_data['c1']['ticker_month'] == current_data['c1']['ticker_month']] seasonal_clean_residuals = seasonal_residuals[np.isfinite( seasonal_residuals)] clean_residuals = residuals[np.isfinite(residuals)] contract_seasonality_ind = ( seasonal_clean_residuals.mean() - clean_residuals.mean()) / clean_residuals.std() yield1_quantile_list = stats.get_number_from_quantile( y=yield1, quantile_list=[10, 90]) yield2_quantile_list = stats.get_number_from_quantile( y=yield2, quantile_list=[10, 90]) noise_ratio = (yield1_quantile_list[1] - yield1_quantile_list[0]) / ( yield2_quantile_list[1] - yield2_quantile_list[0]) daily_noise_recent = stats.get_stdev(x=butterfly_1_change.values[-20:], clean_num_obs=15) daily_noise_past = stats.get_stdev( x=butterfly_1_change.values, clean_num_obs=max(100, round(3 * len(butterfly_1_change.values) / 4))) recent_vol_ratio = daily_noise_recent / daily_noise_past alpha1 = yield_regress_output['alpha'] residuals_last5_years = residuals[last5_years_indx] residuals_last2_months = residuals[last2_months_indx] residual_current = yield1_current - alpha1 - second_spread_weight_1 * yield2_current z3 = (residual_current - residuals_last5_years.mean()) / residuals.std() z4 = (residual_current - residuals_last2_months.mean()) / residuals.std() yield_change = (alpha1 + second_spread_weight_1 * yield2_current - yield1_current) / (1 + second_spread_weight_1) new_yield1 = yield1_current + yield_change new_yield2 = yield2_current - yield_change price_change1 = 100 * ( (price_2 * (new_yield1 + 100) / 100) - price_1) / (200 + new_yield1) price_change2 = 100 * ( (price_3 * (new_yield2 + 100) / 100) - price_2) / (200 + new_yield2) theo_pnl = contract_multiplier * ( 2 * price_change1 - 2 * second_spread_weight_1 * price_change2) aligned_data['residuals'] = residuals aligned_output['aligned_data'] = aligned_data grouped = aligned_data.groupby(aligned_data['c1']['cont_indx']) aligned_data['shifted_residuals'] = grouped['residuals'].shift(-5) aligned_data['residual_change'] = aligned_data[ 'shifted_residuals'] - aligned_data['residuals'] mean_reversion = stats.get_regression_results({ 'x': aligned_data['residuals'].values, 'y': aligned_data['residual_change'].values, 'clean_num_obs': max(100, round(3 * len(yield1.values) / 4)) }) theo_spread_move_output = su.calc_theo_spread_move_from_ratio_normalization( ratio_time_series=price_ratio.values[-40:], starting_quantile=qf, num_price=linear_interp_price2_current, den_price=current_data['c2']['close_price'], favorable_quantile_move_list=[5, 10, 15, 20, 25]) theo_pnl_list = [ x * contract_multiplier * 2 for x in theo_spread_move_output['theo_spread_move_list'] ] return { 'success': True, 'aligned_output': aligned_output, 'q': q, 'qf': qf, 'theo_pnl_list': theo_pnl_list, 'ratio_target_list': theo_spread_move_output['ratio_target_list'], 'weight1': weight1, 'weight2': weight2, 'weight3': weight3, 'zscore1': zscore1, 'rsquared1': rsquared1, 'zscore2': zscore2, 'rsquared2': rsquared2, 'zscore3': z3, 'zscore4': z4, 'zscore5': zscore1 - regime_change_ind, 'zscore6': zscore1 - contract_seasonality_ind, 'zscore7': zscore1 - regime_change_ind - contract_seasonality_ind, 'theo_pnl': theo_pnl, 'regime_change_ind': regime_change_ind, 'contract_seasonality_ind': contract_seasonality_ind, 'second_spread_weight_1': second_spread_weight_1, 'second_spread_weight_2': second_spread_weight_2, 'downside': downside, 'upside': upside, 'yield1': yield1, 'yield2': yield2, 'yield1_current': yield1_current, 'yield2_current': yield2_current, 'bf_price': butterfly_price_current, 'short_price_limit': short_price_limit, 'long_price_limit': long_price_limit, 'noise_ratio': noise_ratio, 'alpha1': alpha1, 'alpha2': yield_regress_output_last5_years['alpha'], 'residual_std1': yield_regress_output['residualstd'], 'residual_std2': yield_regress_output_last5_years['residualstd'], 'recent_vol_ratio': recent_vol_ratio, 'recent_5day_pnl': recent_5day_pnl, 'price_1': price_1, 'price_2': price_2, 'price_3': price_3, 'last5_years_indx': last5_years_indx, 'price_ratio': price_ratio, 'mean_reversion_rsquared': mean_reversion['rsquared'], 'mean_reversion_signif': (mean_reversion['conf_int'][1, :] < 0).all() }
def get_vcs_signals_legacy(**kwargs): aligned_indicators_output = get_aligned_option_indicators_legacy(**kwargs) if not aligned_indicators_output['success']: return {'atm_vol_ratio': np.NaN, 'q': np.NaN, 'q2': np.NaN, 'q1': np.NaN, 'q5': np.NaN, 'fwd_vol_q': np.NaN, 'fwd_vol_q2': np.NaN, 'fwd_vol_q1': np.NaN, 'fwd_vol_q5': np.NaN, 'atm_real_vol_ratio': np.NaN, 'q_atm_real_vol_ratio': np.NaN, 'atm_real_vol_ratio_ratio': np.NaN, 'q_atm_real_vol_ratio_ratio': np.NaN, 'tr_dte_diff_percent': np.NaN,'downside': np.NaN, 'upside': np.NaN, 'theta1': np.NaN, 'theta2': np.NaN, 'hist': []} hist = aligned_indicators_output['hist'] current = aligned_indicators_output['current'] settle_datetime = cu.convert_doubledate_2datetime(kwargs['settle_date']) settle_datetime_1year_back = settle_datetime-dt.timedelta(360) settle_datetime_5year_back = settle_datetime-dt.timedelta(5*360) hist['atm_vol_ratio'] = hist['c1']['imp_vol']/hist['c2']['imp_vol'] fwd_var = hist['c2']['cal_dte']*(hist['c2']['imp_vol']**2)-hist['c1']['cal_dte']*(hist['c1']['imp_vol']**2) fwd_vol_sq = fwd_var/(hist['c2']['cal_dte']-hist['c1']['cal_dte']) fwd_vol_adj = np.sign(fwd_vol_sq)*((abs(fwd_vol_sq)).apply(np.sqrt)) hist['fwd_vol_adj'] = fwd_vol_adj fwd_var = current['cal_dte'][1]*(current['imp_vol'][1]**2)-current['cal_dte'][0]*(current['imp_vol'][0]**2) fwd_vol_sq = fwd_var/(current['cal_dte'][1]-current['cal_dte'][0]) fwd_vol_adj = np.sign(fwd_vol_sq)*(np.sqrt(abs(fwd_vol_sq))) atm_vol_ratio = current['imp_vol'][0]/current['imp_vol'][1] hist['atm_real_vol_ratio'] = hist['c1']['imp_vol']/hist['c1']['close2close_vol20'] atm_real_vol_ratio = current['imp_vol'][0]/current['close2close_vol20'][0] hist['atm_real_vol_ratio_ratio'] = (hist['c1']['imp_vol']/hist['c1']['close2close_vol20'])/(hist['c2']['imp_vol']/hist['c2']['close2close_vol20']) atm_real_vol_ratio_ratio = (current['imp_vol'][0]/current['close2close_vol20'][0])/(current['imp_vol'][0]/current['close2close_vol20'][0]) hist_1year = hist[hist.index >= settle_datetime_1year_back] hist_5year = hist[hist.index >= settle_datetime_5year_back] q = stats.get_quantile_from_number({'x': atm_vol_ratio, 'y': hist['atm_vol_ratio'].values, 'clean_num_obs': max(100, round(3*len(hist.index)/4))}) q2 = stats.get_quantile_from_number({'x': atm_vol_ratio, 'y': hist['atm_vol_ratio'].values[-40:], 'clean_num_obs': 30}) q1 = stats.get_quantile_from_number({'x': atm_vol_ratio, 'y': hist_1year['atm_vol_ratio'].values, 'clean_num_obs': max(50, round(3*len(hist_1year.index)/4))}) q5 = stats.get_quantile_from_number({'x': atm_vol_ratio, 'y': hist_5year['atm_vol_ratio'].values, 'clean_num_obs': max(100, round(3*len(hist_5year.index)/4))}) fwd_vol_q = stats.get_quantile_from_number({'x': fwd_vol_adj, 'y': hist['fwd_vol_adj'].values, 'clean_num_obs': max(100, round(3*len(hist.index)/4))}) fwd_vol_q2 = stats.get_quantile_from_number({'x': fwd_vol_adj, 'y': hist['fwd_vol_adj'].values[-40:], 'clean_num_obs': 30}) fwd_vol_q1 = stats.get_quantile_from_number({'x': fwd_vol_adj, 'y': hist_1year['fwd_vol_adj'].values, 'clean_num_obs': max(50, round(3*len(hist_1year.index)/4))}) fwd_vol_q5 = stats.get_quantile_from_number({'x': fwd_vol_adj, 'y': hist_5year['fwd_vol_adj'].values, 'clean_num_obs': max(100, round(3*len(hist_5year.index)/4))}) q_atm_real_vol_ratio = stats.get_quantile_from_number({'x': atm_real_vol_ratio, 'y': hist['atm_real_vol_ratio'].values, 'clean_num_obs': max(100, round(3*len(hist.index)/4))}) q_atm_real_vol_ratio_ratio = stats.get_quantile_from_number({'x': atm_real_vol_ratio_ratio, 'y': hist['atm_real_vol_ratio_ratio'].values, 'clean_num_obs': max(100, round(3*len(hist.index)/4))}) tr_dte_diff_percent = round(100*(current['tr_dte'][1]-current['tr_dte'][0])/current['tr_dte'][0]) profit5 = hist['c1']['profit5']-hist['c2']['profit5'] clean_indx = profit5.notnull() clean_data = hist[clean_indx] if clean_data.empty: downside = np.NaN upside = np.NaN else: last_available_align_date = clean_data.index[-1] clean_data = clean_data[clean_data.index >= last_available_align_date-dt.timedelta(5*365)] profit5 = clean_data['c1']['profit5']-clean_data['c2']['profit5'] percentile_vector = stats.get_number_from_quantile(y=profit5.values, quantile_list=[1, 15, 85, 99], clean_num_obs=max(100, round(3*len(profit5.values)/4))) downside = (percentile_vector[0]+percentile_vector[1])/2 upside = (percentile_vector[2]+percentile_vector[3])/2 return {'atm_vol_ratio': atm_vol_ratio, 'q': q, 'q2': q2, 'q1': q1, 'q5': q5, 'fwd_vol_q': fwd_vol_q, 'fwd_vol_q2': fwd_vol_q2, 'fwd_vol_q1': fwd_vol_q1, 'fwd_vol_q5': fwd_vol_q5, 'atm_real_vol_ratio': atm_real_vol_ratio, 'q_atm_real_vol_ratio': q_atm_real_vol_ratio, 'atm_real_vol_ratio_ratio': atm_real_vol_ratio_ratio, 'q_atm_real_vol_ratio_ratio': q_atm_real_vol_ratio_ratio, 'tr_dte_diff_percent': tr_dte_diff_percent, 'downside': downside, 'upside': upside, 'theta1': current['theta'][0], 'theta2': current['theta'][1], 'hist': hist}
def get_vcs_signals(**kwargs): aligned_indicators_output = get_aligned_option_indicators(**kwargs) if not aligned_indicators_output['success']: return { 'hist': [], 'current': [], 'atm_vol_ratio': np.NaN, 'fwd_vol': np.NaN, 'downside': np.NaN, 'upside': np.NaN, 'real_vol_ratio': np.NaN, 'atm_real_vol_ratio': np.NaN, 'theta': np.NaN, 'q': np.NaN, 'q1': np.NaN, 'fwd_vol_q': np.NaN } hist = aligned_indicators_output['hist'] current = aligned_indicators_output['current'] settle_datetime = cu.convert_doubledate_2datetime(kwargs['settle_date']) settle_datetime_1year_back = settle_datetime - dt.timedelta(360) hist['atm_vol_ratio'] = hist['c1']['imp_vol'] / hist['c2']['imp_vol'] if 'atm_vol_ratio' in kwargs.keys(): atm_vol_ratio = kwargs['atm_vol_ratio'] else: atm_vol_ratio = current['imp_vol'][0] / current['imp_vol'][1] hist_1year = hist[hist.index >= settle_datetime_1year_back] q = stats.get_quantile_from_number({ 'x': atm_vol_ratio, 'y': hist['atm_vol_ratio'].to_numpy(), 'clean_num_obs': max(100, round(3 * len(hist.index) / 4)) }) q1 = stats.get_quantile_from_number({ 'x': atm_vol_ratio, 'y': hist_1year['atm_vol_ratio'].to_numpy(), 'clean_num_obs': max(50, round(3 * len(hist_1year.index) / 4)) }) fwd_var = hist['c2']['cal_dte'] * (hist['c2']['imp_vol']** 2) - hist['c1']['cal_dte'] * ( hist['c1']['imp_vol']**2) fwd_vol_sq = fwd_var / (hist['c2']['cal_dte'] - hist['c1']['cal_dte']) fwd_vol_adj = np.sign(fwd_vol_sq) * ((abs(fwd_vol_sq)).apply(np.sqrt)) hist['fwd_vol_adj'] = fwd_vol_adj fwd_var = current['cal_dte'][1] * (current['imp_vol'][1]** 2) - current['cal_dte'][0] * ( current['imp_vol'][0]**2) fwd_vol_sq = fwd_var / (current['cal_dte'][1] - current['cal_dte'][0]) fwd_vol_adj = np.sign(fwd_vol_sq) * (np.sqrt(abs(fwd_vol_sq))) fwd_vol_q = stats.get_quantile_from_number({ 'x': fwd_vol_adj, 'y': hist['fwd_vol_adj'].to_numpy(), 'clean_num_obs': max(100, round(3 * len(hist.index) / 4)) }) clean_indx = hist['c1']['profit5'].notnull() clean_data = hist[clean_indx] if clean_data.empty: downside = np.NaN upside = np.NaN else: last_available_align_date = clean_data.index[-1] clean_data = clean_data[clean_data.index >= last_available_align_date - dt.timedelta(5 * 365)] profit5 = clean_data['c1']['profit5'] - clean_data['c2']['profit5'] percentile_vector = stats.get_number_from_quantile( y=profit5.to_numpy(), quantile_list=[1, 15, 85, 99], clean_num_obs=max(100, round(3 * len(profit5.to_numpy()) / 4))) downside = (percentile_vector[0] + percentile_vector[1]) / 2 upside = (percentile_vector[2] + percentile_vector[3]) / 2 return { 'hist': hist, 'current': current, 'atm_vol_ratio': atm_vol_ratio, 'fwd_vol': fwd_vol_adj, 'downside': downside, 'upside': upside, 'real_vol_ratio': current['close2close_vol20'][0] / current['close2close_vol20'][1], 'atm_real_vol_ratio': current['imp_vol'][0] / current['close2close_vol20'][0], 'theta': current['theta'][1] - current['theta'][0], 'q': q, 'q1': q1, 'fwd_vol_q': fwd_vol_q }
def get_futures_spread_carry_signals(**kwargs): ticker_list = kwargs['ticker_list'] date_to = kwargs['date_to'] 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']] if 'datetime5_years_ago' in kwargs.keys(): datetime5_years_ago = kwargs['datetime5_years_ago'] else: date5_years_ago = cu.doubledate_shift(date_to,5*365) datetime5_years_ago = cu.convert_doubledate_2datetime(date5_years_ago) if 'datetime2_months_ago' in kwargs.keys(): datetime2_months_ago = kwargs['datetime2_months_ago'] else: date2_months_ago = cu.doubledate_shift(date_to,60) datetime2_months_ago = cu.convert_doubledate_2datetime(date2_months_ago) 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) aligned_data = aligned_output['aligned_data'] current_data = aligned_output['current_data'] last5_years_indx = aligned_data['settle_date']>=datetime5_years_ago data_last5_years = aligned_data[last5_years_indx] ticker1_list = [current_data['c' + str(x+1)]['ticker'] for x in range(len(ticker_list)-1)] ticker2_list = [current_data['c' + str(x+2)]['ticker'] for x in range(len(ticker_list)-1)] yield_current_list = [100*(current_data['c' + str(x+1)]['close_price']- current_data['c' + str(x+2)]['close_price'])/ current_data['c' + str(x+2)]['close_price'] for x in range(len(ticker_list)-1)] price_current_list = [current_data['c' + str(x+1)]['close_price']-current_data['c' + str(x+2)]['close_price'] for x in range(len(ticker_list)-1)] yield_history = [100*(aligned_data['c' + str(x+1)]['close_price']- aligned_data['c' + str(x+2)]['close_price'])/ aligned_data['c' + str(x+2)]['close_price'] for x in range(len(ticker_list)-1)] change_5_history = [data_last5_years['c' + str(x+1)]['change_5']- data_last5_years['c' + str(x+2)]['change_5'] for x in range(len(ticker_list)-1)] change5 = [contract_multiplier*(current_data['c' + str(x+1)]['change5']- current_data['c' + str(x+2)]['change5']) for x in range(len(ticker_list)-1)] change10 = [contract_multiplier*(current_data['c' + str(x+1)]['change10']- current_data['c' + str(x+2)]['change10']) for x in range(len(ticker_list)-1)] change20 = [contract_multiplier*(current_data['c' + str(x+1)]['change20']- current_data['c' + str(x+2)]['change20']) for x in range(len(ticker_list)-1)] front_tr_dte = [current_data['c' + str(x+1)]['tr_dte'] for x in range(len(ticker_list)-1)] q_list = [stats.get_quantile_from_number({'x': yield_current_list[x], 'y': yield_history[x].values, 'clean_num_obs': max(100, round(3*len(yield_history[x].values)/4))}) for x in range(len(ticker_list)-1)] percentile_vector = [stats.get_number_from_quantile(y=change_5_history[x].values, quantile_list=[1, 15, 85, 99], clean_num_obs=max(100, round(3*len(change_5_history[x].values)/4))) for x in range(len(ticker_list)-1)] q1 = [x[0] for x in percentile_vector] q15 = [x[1] for x in percentile_vector] q85 = [x[2] for x in percentile_vector] q99 = [x[3] for x in percentile_vector] downside = [contract_multiplier*(q1[x]+q15[x])/2 for x in range(len(q1))] upside = [contract_multiplier*(q85[x]+q99[x])/2 for x in range(len(q1))] carry = [contract_multiplier*(price_current_list[x]-price_current_list[x+1]) for x in range(len(q_list)-1)] q_carry = [q_list[x]-q_list[x+1] for x in range(len(q_list)-1)] reward_risk = [5*carry[x]/((front_tr_dte[x+1]-front_tr_dte[x])*abs(downside[x+1])) if carry[x]>0 else 5*carry[x]/((front_tr_dte[x+1]-front_tr_dte[x])*upside[x+1]) for x in range(len(carry))] return pd.DataFrame.from_items([('ticker1',ticker1_list), ('ticker2',ticker2_list), ('ticker_head',cmi.get_contract_specs(ticker_list[0])['ticker_head']), ('front_tr_dte',front_tr_dte), ('carry',[np.NAN]+carry), ('q_carry',[np.NAN]+q_carry), ('reward_risk',[np.NAN]+reward_risk), ('price',price_current_list), ('q',q_list), ('upside',upside), ('downside',downside), ('change5',change5), ('change10',change10), ('change20',change20)])
def get_futures_butterfly_signals(**kwargs): ticker_list = kwargs['ticker_list'] date_to = kwargs['date_to'] 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']] if 'datetime5_years_ago' in kwargs.keys(): datetime5_years_ago = kwargs['datetime5_years_ago'] else: date5_years_ago = cu.doubledate_shift(date_to,5*365) datetime5_years_ago = cu.convert_doubledate_2datetime(date5_years_ago) if 'datetime2_months_ago' in kwargs.keys(): datetime2_months_ago = kwargs['datetime2_months_ago'] else: date2_months_ago = cu.doubledate_shift(date_to,60) datetime2_months_ago = cu.convert_doubledate_2datetime(date2_months_ago) 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) current_data = aligned_output['current_data'] aligned_data = aligned_output['aligned_data'] month_diff_1 = 12*(current_data['c1']['ticker_year']-current_data['c2']['ticker_year'])+(current_data['c1']['ticker_month']-current_data['c2']['ticker_month']) month_diff_2 = 12*(current_data['c2']['ticker_year']-current_data['c3']['ticker_year'])+(current_data['c2']['ticker_month']-current_data['c3']['ticker_month']) weight_11 = 2*month_diff_2/(month_diff_1+month_diff_1) weight_12 = -2 weight_13 = 2*month_diff_1/(month_diff_1+month_diff_1) price_1 = current_data['c1']['close_price'] price_2 = current_data['c2']['close_price'] price_3 = current_data['c3']['close_price'] linear_interp_price2 = (weight_11*aligned_data['c1']['close_price']+weight_13*aligned_data['c3']['close_price'])/2 butterfly_price = aligned_data['c1']['close_price']-2*aligned_data['c2']['close_price']+aligned_data['c3']['close_price'] price_ratio = linear_interp_price2/aligned_data['c2']['close_price'] linear_interp_price2_current = (weight_11*price_1+weight_13*price_3)/2 price_ratio_current = linear_interp_price2_current/price_2 q = stats.get_quantile_from_number({'x': price_ratio_current, 'y': price_ratio.values, 'clean_num_obs': max(100, round(3*len(price_ratio.values)/4))}) qf = stats.get_quantile_from_number({'x': price_ratio_current, 'y': price_ratio.values[-40:], 'clean_num_obs': 30}) recent_quantile_list = [stats.get_quantile_from_number({'x': x, 'y': price_ratio.values[-40:], 'clean_num_obs': 30}) for x in price_ratio.values[-40:]] weight1 = weight_11 weight2 = weight_12 weight3 = weight_13 last5_years_indx = aligned_data['settle_date']>=datetime5_years_ago last2_months_indx = aligned_data['settle_date']>=datetime2_months_ago data_last5_years = aligned_data[last5_years_indx] yield1 = 100*(aligned_data['c1']['close_price']-aligned_data['c2']['close_price'])/aligned_data['c2']['close_price'] yield2 = 100*(aligned_data['c2']['close_price']-aligned_data['c3']['close_price'])/aligned_data['c3']['close_price'] yield1_last5_years = yield1[last5_years_indx] yield2_last5_years = yield2[last5_years_indx] yield1_current = 100*(current_data['c1']['close_price']-current_data['c2']['close_price'])/current_data['c2']['close_price'] yield2_current = 100*(current_data['c2']['close_price']-current_data['c3']['close_price'])/current_data['c3']['close_price'] butterfly_price_current = current_data['c1']['close_price']\ -2*current_data['c2']['close_price']\ +current_data['c3']['close_price'] yield_regress_output = stats.get_regression_results({'x':yield2, 'y':yield1,'x_current': yield2_current, 'y_current': yield1_current, 'clean_num_obs': max(100, round(3*len(yield1.values)/4))}) yield_regress_output_last5_years = stats.get_regression_results({'x':yield2_last5_years, 'y':yield1_last5_years, 'x_current': yield2_current, 'y_current': yield1_current, 'clean_num_obs': max(100, round(3*len(yield1_last5_years.values)/4))}) bf_qz_frame_short = pd.DataFrame() bf_qz_frame_long = pd.DataFrame() if (len(yield1) >= 40)&(len(yield2) >= 40): recent_zscore_list = [(yield1[-40+i]-yield_regress_output['alpha']-yield_regress_output['beta']*yield2[-40+i])/yield_regress_output['residualstd'] for i in range(40)] bf_qz_frame = pd.DataFrame.from_items([('bf_price', butterfly_price.values[-40:]), ('q',recent_quantile_list), ('zscore', recent_zscore_list)]) bf_qz_frame = np.round(bf_qz_frame, 8) bf_qz_frame.drop_duplicates(['bf_price'], take_last=True, inplace=True) # return bf_qz_frame bf_qz_frame_short = bf_qz_frame[(bf_qz_frame['zscore'] >= 0.6) & (bf_qz_frame['q'] >= 85)] bf_qz_frame_long = bf_qz_frame[(bf_qz_frame['zscore'] <= -0.6) & (bf_qz_frame['q'] <= 12)] if bf_qz_frame_short.empty: short_price_limit = np.NAN else: short_price_limit = bf_qz_frame_short['bf_price'].min() if bf_qz_frame_long.empty: long_price_limit = np.NAN else: long_price_limit = bf_qz_frame_long['bf_price'].max() zscore1= yield_regress_output['zscore'] rsquared1= yield_regress_output['rsquared'] zscore2= yield_regress_output_last5_years['zscore'] rsquared2= yield_regress_output_last5_years['rsquared'] second_spread_weight_1 = yield_regress_output['beta'] second_spread_weight_2 = yield_regress_output_last5_years['beta'] butterfly_5_change = data_last5_years['c1']['change_5']\ - (1+second_spread_weight_1)*data_last5_years['c2']['change_5']\ + second_spread_weight_1*data_last5_years['c3']['change_5'] butterfly_5_change_current = current_data['c1']['change_5']\ - (1+second_spread_weight_1)*current_data['c2']['change_5']\ + second_spread_weight_1*current_data['c3']['change_5'] butterfly_1_change = data_last5_years['c1']['change_1']\ - (1+second_spread_weight_1)*data_last5_years['c2']['change_1']\ + second_spread_weight_1*data_last5_years['c3']['change_1'] percentile_vector = stats.get_number_from_quantile(y=butterfly_5_change.values, quantile_list=[1, 15, 85, 99], clean_num_obs=max(100, round(3*len(butterfly_5_change.values)/4))) downside = contract_multiplier*(percentile_vector[0]+percentile_vector[1])/2 upside = contract_multiplier*(percentile_vector[2]+percentile_vector[3])/2 recent_5day_pnl = contract_multiplier*butterfly_5_change_current residuals = yield1-yield_regress_output['alpha']-yield_regress_output['beta']*yield2 regime_change_ind = (residuals[last5_years_indx].mean()-residuals.mean())/residuals.std() contract_seasonality_ind = (residuals[aligned_data['c1']['ticker_month'] == current_data['c1']['ticker_month']].mean()-residuals.mean())/residuals.std() yield1_quantile_list = stats.get_number_from_quantile(y=yield1, quantile_list=[10, 90]) yield2_quantile_list = stats.get_number_from_quantile(y=yield2, quantile_list=[10, 90]) noise_ratio = (yield1_quantile_list[1]-yield1_quantile_list[0])/(yield2_quantile_list[1]-yield2_quantile_list[0]) daily_noise_recent = stats.get_stdev(x=butterfly_1_change.values[-20:], clean_num_obs=15) daily_noise_past = stats.get_stdev(x=butterfly_1_change.values, clean_num_obs=max(100, round(3*len(butterfly_1_change.values)/4))) recent_vol_ratio = daily_noise_recent/daily_noise_past alpha1 = yield_regress_output['alpha'] residuals_last5_years = residuals[last5_years_indx] residuals_last2_months = residuals[last2_months_indx] residual_current = yield1_current-alpha1-second_spread_weight_1*yield2_current z3 = (residual_current-residuals_last5_years.mean())/residuals.std() z4 = (residual_current-residuals_last2_months.mean())/residuals.std() yield_change = (alpha1+second_spread_weight_1*yield2_current-yield1_current)/(1+second_spread_weight_1) new_yield1 = yield1_current + yield_change new_yield2 = yield2_current - yield_change price_change1 = 100*((price_2*(new_yield1+100)/100)-price_1)/(200+new_yield1) price_change2 = 100*((price_3*(new_yield2+100)/100)-price_2)/(200+new_yield2) theo_pnl = contract_multiplier*(2*price_change1-2*second_spread_weight_1*price_change2) aligned_data['residuals'] = residuals aligned_output['aligned_data'] = aligned_data grouped = aligned_data.groupby(aligned_data['c1']['cont_indx']) aligned_data['shifted_residuals'] = grouped['residuals'].shift(-5) aligned_data['residual_change'] = aligned_data['shifted_residuals']-aligned_data['residuals'] mean_reversion = stats.get_regression_results({'x':aligned_data['residuals'].values, 'y':aligned_data['residual_change'].values, 'clean_num_obs': max(100, round(3*len(yield1.values)/4))}) theo_spread_move_output = su.calc_theo_spread_move_from_ratio_normalization(ratio_time_series=price_ratio.values[-40:], starting_quantile=qf, num_price=linear_interp_price2_current, den_price=current_data['c2']['close_price'], favorable_quantile_move_list=[5, 10, 15, 20, 25]) theo_pnl_list = [x*contract_multiplier*2 for x in theo_spread_move_output['theo_spread_move_list']] return {'aligned_output': aligned_output, 'q': q, 'qf': qf, 'theo_pnl_list': theo_pnl_list, 'ratio_target_list': theo_spread_move_output['ratio_target_list'], 'weight1': weight1, 'weight2': weight2, 'weight3': weight3, 'zscore1': zscore1, 'rsquared1': rsquared1, 'zscore2': zscore2, 'rsquared2': rsquared2, 'zscore3': z3, 'zscore4': z4, 'zscore5': zscore1-regime_change_ind, 'zscore6': zscore1-contract_seasonality_ind, 'zscore7': zscore1-regime_change_ind-contract_seasonality_ind, 'theo_pnl': theo_pnl, 'regime_change_ind' : regime_change_ind,'contract_seasonality_ind': contract_seasonality_ind, 'second_spread_weight_1': second_spread_weight_1, 'second_spread_weight_2': second_spread_weight_2, 'downside': downside, 'upside': upside, 'yield1': yield1, 'yield2': yield2, 'yield1_current': yield1_current, 'yield2_current': yield2_current, 'bf_price': butterfly_price_current, 'short_price_limit': short_price_limit,'long_price_limit':long_price_limit, 'noise_ratio': noise_ratio, 'alpha1': alpha1, 'alpha2': yield_regress_output_last5_years['alpha'], 'residual_std1': yield_regress_output['residualstd'], 'residual_std2': yield_regress_output_last5_years['residualstd'], 'recent_vol_ratio': recent_vol_ratio, 'recent_5day_pnl': recent_5day_pnl, 'price_1': price_1, 'price_2': price_2, 'price_3': price_3, 'last5_years_indx': last5_years_indx, 'price_ratio': price_ratio, 'mean_reversion_rsquared': mean_reversion['rsquared'], 'mean_reversion_signif' : (mean_reversion['conf_int'][1, :] < 0).all()}