def generate_futures_list_dataframe(**kwargs): futures_dataframe = gfp.get_futures_prices_4date(**kwargs) futures_dataframe = pd.merge(futures_dataframe, psp.dirty_data_points, on=['settle_date', 'ticker'], how='left') futures_dataframe = futures_dataframe[futures_dataframe['discard'] != True] futures_dataframe = futures_dataframe.drop('discard', 1) futures_dataframe['ticker_class'] = [cmi.ticker_class[ticker_head] for ticker_head in futures_dataframe['ticker_head']] futures_dataframe['multiplier'] = [cmi.contract_multiplier[ticker_head] for ticker_head in futures_dataframe['ticker_head']] additional_tuple = [ut.get_aggregation_method_contracts_back({'ticker_class': ticker_class, 'ticker_head': ticker_head}) for ticker_class, ticker_head in zip(futures_dataframe['ticker_class'],futures_dataframe['ticker_head'])] additional_dataframe = pd.DataFrame(additional_tuple, columns=['aggregation_method', 'contracts_back'],index=futures_dataframe.index) return pd.concat([futures_dataframe, additional_dataframe],axis=1)
def generate_futures_list_dataframe(**kwargs): futures_dataframe = gfp.get_futures_prices_4date(**kwargs) futures_dataframe = pd.merge(futures_dataframe, psp.dirty_data_points, on=["settle_date", "ticker"], how="left") futures_dataframe = futures_dataframe[futures_dataframe["discard"] != True] futures_dataframe = futures_dataframe.drop("discard", 1) futures_dataframe["ticker_class"] = [ cmi.ticker_class[ticker_head] for ticker_head in futures_dataframe["ticker_head"] ] futures_dataframe["multiplier"] = [ cmi.contract_multiplier[ticker_head] for ticker_head in futures_dataframe["ticker_head"] ] additional_tuple = [ ut.get_aggregation_method_contracts_back({"ticker_class": ticker_class, "ticker_head": ticker_head}) for ticker_class, ticker_head in zip(futures_dataframe["ticker_class"], futures_dataframe["ticker_head"]) ] additional_dataframe = pd.DataFrame( additional_tuple, columns=["aggregation_method", "contracts_back"], index=futures_dataframe.index ) return pd.concat([futures_dataframe, additional_dataframe], axis=1)
def get_ics_signals(**kwargs): ticker = kwargs['ticker'] #print(ticker) date_to = kwargs['date_to'] con = msu.get_my_sql_connection(**kwargs) ticker_list = ticker.split('-') #print(ticker_list) ticker_head_list = [ cmi.get_contract_specs(x)['ticker_head'] for x in ticker_list ] ticker_class = cmi.ticker_class[ticker_head_list[0]] 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 list(set(ticker_head_list)) } 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 'num_days_back_4intraday' in kwargs.keys(): num_days_back_4intraday = kwargs['num_days_back_4intraday'] else: num_days_back_4intraday = 5 tr_dte_list = [ exp.get_days2_expiration(ticker=x, date_to=date_to, instrument='futures', con=con)['tr_dte'] for x in ticker_list ] amcb_output = [ opUtil.get_aggregation_method_contracts_back(cmi.get_contract_specs(x)) for x in ticker_list ] aggregation_method = max([x['aggregation_method'] for x in amcb_output]) contracts_back = min([x['contracts_back'] for x in amcb_output]) contract_multiplier = cmi.contract_multiplier[ticker_head_list[0]] 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=True) aligned_data = aligned_output['aligned_data'] last5_years_indx = aligned_data['settle_date'] >= datetime5_years_ago data_last5_years = aligned_data[last5_years_indx] data_last5_years['spread_pnl_1'] = aligned_data['c1'][ 'change_1'] - aligned_data['c2']['change_1'] percentile_vector = stats.get_number_from_quantile( y=data_last5_years['spread_pnl_1'].values, quantile_list=[1, 15, 85, 99], clean_num_obs=max(100, round(3 * len(data_last5_years.index) / 4))) downside = contract_multiplier * (percentile_vector[0] + percentile_vector[1]) / 2 upside = contract_multiplier * (percentile_vector[2] + percentile_vector[3]) / 2 date_list = [ exp.doubledate_shift_bus_days(double_date=date_to, shift_in_days=x) for x in reversed(range(1, num_days_back_4intraday)) ] date_list.append(date_to) intraday_data = opUtil.get_aligned_futures_data_intraday( contract_list=[ticker], date_list=date_list) intraday_data['time_stamp'] = [ x.to_datetime() for x in intraday_data.index ] intraday_data['settle_date'] = intraday_data['time_stamp'].apply( lambda x: x.date()) end_hour = cmi.last_trade_hour_minute[ticker_head_list[0]] start_hour = cmi.first_trade_hour_minute[ticker_head_list[0]] if ticker_class == 'Ag': start_hour1 = dt.time(0, 45, 0, 0) end_hour1 = dt.time(7, 45, 0, 0) selection_indx = [ x for x in range(len(intraday_data.index)) if ((intraday_data['time_stamp'].iloc[x].time() < end_hour1) and (intraday_data['time_stamp'].iloc[x].time() >= start_hour1)) or ((intraday_data['time_stamp'].iloc[x].time() < end_hour) and (intraday_data['time_stamp'].iloc[x].time() >= start_hour)) ] else: selection_indx = [ x for x in range(len(intraday_data.index)) if (intraday_data.index[x].to_datetime().time() < end_hour) and ( intraday_data.index[x].to_datetime().time() >= start_hour) ] intraday_data = intraday_data.iloc[selection_indx] intraday_mean5 = np.nan intraday_std5 = np.nan intraday_mean2 = np.nan intraday_std2 = np.nan intraday_mean1 = np.nan intraday_std1 = np.nan if len(intraday_data.index) > 0: intraday_data['mid_p'] = (intraday_data['c1']['best_bid_p'] + intraday_data['c1']['best_ask_p']) / 2 intraday_mean5 = intraday_data['mid_p'].mean() intraday_std5 = intraday_data['mid_p'].std() intraday_data_last2days = intraday_data[ intraday_data['settle_date'] >= cu.convert_doubledate_2datetime( date_list[-2]).date()] intraday_data_yesterday = intraday_data[ intraday_data['settle_date'] == cu.convert_doubledate_2datetime( date_list[-1]).date()] intraday_mean2 = intraday_data_last2days['mid_p'].mean() intraday_std2 = intraday_data_last2days['mid_p'].std() intraday_mean1 = intraday_data_yesterday['mid_p'].mean() intraday_std1 = intraday_data_yesterday['mid_p'].std() if 'con' not in kwargs.keys(): con.close() return { 'downside': downside, 'upside': upside, 'front_tr_dte': tr_dte_list[0], 'intraday_mean5': intraday_mean5, 'intraday_std5': intraday_std5, 'intraday_mean2': intraday_mean2, 'intraday_std2': intraday_std2, 'intraday_mean1': intraday_mean1, 'intraday_std1': intraday_std1 }
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_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_aligned_option_indicators(**kwargs): ticker_list = kwargs['ticker_list'] settle_datetime = cu.convert_doubledate_2datetime(kwargs['settle_date']) #print(ticker_list) if 'num_cal_days_back' in kwargs.keys(): num_cal_days_back = kwargs['num_cal_days_back'] else: num_cal_days_back = 20*365 settle_datetime_from = settle_datetime-dt.timedelta(num_cal_days_back) contract_specs_output_list = [cmi.get_contract_specs(x) for x in ticker_list] ticker_head_list = [x['ticker_head'] for x in contract_specs_output_list] contract_multiplier_list = [cmi.contract_multiplier[x['ticker_head']] for x in contract_specs_output_list] cont_indx_list = [x['ticker_year']*100+x['ticker_month_num'] for x in contract_specs_output_list] month_seperation_list = [cmi.get_month_seperation_from_cont_indx(x,cont_indx_list[0]) for x in cont_indx_list] if 'option_ticker_indicator_dictionary' in kwargs.keys(): option_ticker_indicator_dictionary = kwargs['option_ticker_indicator_dictionary'] else: con = msu.get_my_sql_connection(**kwargs) unique_ticker_heads = list(set(ticker_head_list)) option_ticker_indicator_dictionary = {x: get_option_ticker_indicators(ticker_head=x, settle_date_to=kwargs['settle_date'], num_cal_days_back=num_cal_days_back, con=con) for x in unique_ticker_heads} if 'con' not in kwargs.keys(): con.close() option_ticker_indicator_dictionary_final = {ticker_list[x]: option_ticker_indicator_dictionary[ticker_head_list[x]] for x in range(len(ticker_list))} max_available_settle_list = [] tr_dte_list = [] cal_dte_list = [] imp_vol_list = [] theta_list = [] close2close_vol20_list = [] volume_list = [] open_interest_list = [] for x in range(len(ticker_list)): ticker_data = option_ticker_indicator_dictionary_final[ticker_list[x]] ticker_data = ticker_data[ticker_data['settle_date'] <= settle_datetime] option_ticker_indicator_dictionary_final[ticker_list[x]] = ticker_data ticker_data = ticker_data[ticker_data['ticker'] == ticker_list[x]] max_available_settle_list.append(ticker_data['settle_date'].iloc[-1]) last_available_settle = min(max_available_settle_list) for x in range(len(ticker_list)): ticker_data = option_ticker_indicator_dictionary_final[ticker_list[x]] ticker_data = ticker_data[(ticker_data['ticker'] == ticker_list[x]) & (ticker_data['settle_date'] == last_available_settle)] tr_dte_list.append(ticker_data['tr_dte'].iloc[0]) cal_dte_list.append(ticker_data['cal_dte'].iloc[0]) imp_vol_list.append(ticker_data['imp_vol'].iloc[0]) theta_list.append(ticker_data['theta'].iloc[0]*contract_multiplier_list[x]) close2close_vol20_list.append(ticker_data['close2close_vol20'].iloc[0]) volume_list.append(ticker_data['volume'].iloc[0]) open_interest_list.append(ticker_data['open_interest'].iloc[0]) current_data = pd.DataFrame.from_items([('ticker',ticker_list), ('tr_dte', tr_dte_list), ('cal_dte', cal_dte_list), ('imp_vol', imp_vol_list), ('theta', theta_list), ('close2close_vol20', close2close_vol20_list), ('volume', volume_list), ('open_interest', open_interest_list)]) current_data['settle_date'] = last_available_settle current_data.set_index('ticker', drop=True, inplace=True) current_data = current_data[['settle_date', 'tr_dte', 'cal_dte', 'imp_vol', 'close2close_vol20', 'theta', 'volume', 'open_interest']] aggregation_method = max([ocu.get_aggregation_method_contracts_back({'ticker_class': x['ticker_class'], 'ticker_head': x['ticker_head']})['aggregation_method'] for x in contract_specs_output_list]) if (current_data['tr_dte'].min() >= 80) and (aggregation_method == 1): aggregation_method = 3 tr_days_half_band_width_selected = ocu.tr_days_half_band_with[aggregation_method] data_frame_list = [] ref_tr_dte_list_list = [] for x in range(len(ticker_list)): ticker_data = option_ticker_indicator_dictionary_final[ticker_list[x]] if ticker_head_list[x] in ['ED', 'E0', 'E2', 'E3', 'E4', 'E5']: model = 'OU' else: model = 'BS' tr_dte_upper_band = current_data['tr_dte'].loc[ticker_list[x]]+tr_days_half_band_width_selected tr_dte_lower_band = current_data['tr_dte'].loc[ticker_list[x]]-tr_days_half_band_width_selected ref_tr_dte_list = [y for y in cmi.aligned_data_tr_dte_list if y <= tr_dte_upper_band and y>=tr_dte_lower_band] if len(ref_tr_dte_list) == 0: return {'hist': [], 'current': [], 'success': False} if aggregation_method == 12: aligned_data = [gop.load_aligend_options_data_file(ticker_head=cmi.aligned_data_tickerhead[ticker_head_list[x]], tr_dte_center=y, contract_month_letter=contract_specs_output_list[x]['ticker_month_str'], model=model) for y in ref_tr_dte_list] ticker_data = ticker_data[ticker_data['ticker_month'] == contract_specs_output_list[x]['ticker_month_num']] else: aligned_data = [gop.load_aligend_options_data_file(ticker_head=cmi.aligned_data_tickerhead[ticker_head_list[x]], tr_dte_center=y, model=model) for y in ref_tr_dte_list] aligned_data = [y[(y['trDTE'] >= current_data['tr_dte'].loc[ticker_list[x]]-tr_days_half_band_width_selected)& (y['trDTE'] <= current_data['tr_dte'].loc[ticker_list[x]]+tr_days_half_band_width_selected)] for y in aligned_data] aligned_data = pd.concat(aligned_data) aligned_data['settle_date'] = pd.to_datetime(aligned_data['settleDates'].astype('str'), format='%Y%m%d') aligned_data.rename(columns={'TickerYear': 'ticker_year', 'TickerMonth': 'ticker_month', 'trDTE': 'tr_dte', 'calDTE': 'cal_dte', 'impVol': 'imp_vol', 'close2CloseVol20': 'close2close_vol20'}, inplace=True) aligned_data.sort(['settle_date', 'ticker_year', 'ticker_month'], ascending=[True,True,True],inplace=True) aligned_data.drop_duplicates(['settle_date','ticker_year','ticker_month'],inplace=True) aligned_data['old_aligned'] = True aligned_data = aligned_data[['settle_date','ticker_month', 'ticker_year', 'cal_dte', 'tr_dte', 'imp_vol', 'close2close_vol20', 'profit5', 'old_aligned']] tr_dte_selection = (ticker_data['tr_dte'] >= current_data['tr_dte'].loc[ticker_list[x]]-tr_days_half_band_width_selected)&\ (ticker_data['tr_dte'] <= current_data['tr_dte'].loc[ticker_list[x]]+tr_days_half_band_width_selected) ticker_data = ticker_data[tr_dte_selection] ticker_data['old_aligned'] = False ticker_data['profit5'] = np.NaN ticker_data = pd.concat([aligned_data, ticker_data[['settle_date', 'ticker_month', 'ticker_year', 'cal_dte', 'tr_dte', 'imp_vol', 'close2close_vol20', 'profit5', 'old_aligned']]]) ticker_data = ticker_data[(ticker_data['settle_date'] <= settle_datetime)&(ticker_data['settle_date'] >= settle_datetime_from)] ticker_data['cont_indx'] = 100*ticker_data['ticker_year']+ticker_data['ticker_month'] ticker_data['cont_indx_adj'] = [cmi.get_cont_indx_from_month_seperation(y,-month_seperation_list[x]) for y in ticker_data['cont_indx']] data_frame_list.append(ticker_data) ref_tr_dte_list_list.append(ref_tr_dte_list) for x in range(len(ticker_list)): data_frame_list[x].set_index(['settle_date','cont_indx_adj'], inplace=True,drop=False) data_frame_list[x]['imp_vol'] = data_frame_list[x]['imp_vol'].astype('float64') merged_dataframe = pd.concat(data_frame_list, axis=1, join='inner',keys=['c'+ str(x+1) for x in range(len(ticker_list))]) merged_dataframe['abs_tr_dte_diff'] = abs(merged_dataframe['c1']['tr_dte']-tr_dte_list[0]) merged_dataframe['settle_date'] = merged_dataframe['c1']['settle_date'] merged_dataframe.sort(['settle_date', 'abs_tr_dte_diff'], ascending=[True,True], inplace=False) merged_dataframe.drop_duplicates('settle_date', inplace=True, take_last=False) merged_dataframe.index = merged_dataframe.index.droplevel(1) return {'hist': merged_dataframe, 'current': current_data, 'success': True}
def get_aligned_option_indicators_legacy(**kwargs): ticker_list = kwargs['ticker_list'] tr_dte_list = kwargs['tr_dte_list'] settle_datetime = cu.convert_doubledate_2datetime(kwargs['settle_date']) if 'num_cal_days_back' in kwargs.keys(): num_cal_days_back = kwargs['num_cal_days_back'] else: num_cal_days_back = 20*365 settle_datetime_from = settle_datetime-dt.timedelta(num_cal_days_back) contract_specs_output_list = [cmi.get_contract_specs(x) for x in ticker_list] ticker_head_list = [x['ticker_head'] for x in contract_specs_output_list] cont_indx_list = [x['ticker_year']*100+x['ticker_month_num'] for x in contract_specs_output_list] month_seperation_list = [cmi.get_month_seperation_from_cont_indx(x,cont_indx_list[0]) for x in cont_indx_list] aggregation_method = max([ocu.get_aggregation_method_contracts_back({'ticker_class': x['ticker_class'], 'ticker_head': x['ticker_head']})['aggregation_method'] for x in contract_specs_output_list]) if (min(tr_dte_list) >= 80) and (aggregation_method == 1): aggregation_method = 3 tr_days_half_band_width_selected = ocu.tr_days_half_band_with[aggregation_method] data_frame_list = [] for x in range(len(ticker_list)): if ticker_head_list[x] in ['ED', 'E0', 'E2', 'E3', 'E4', 'E5']: model = 'OU' else: model = 'BS' tr_dte_upper_band = tr_dte_list[x]+tr_days_half_band_width_selected tr_dte_lower_band = tr_dte_list[x]-tr_days_half_band_width_selected ref_tr_dte_list = [y for y in cmi.aligned_data_tr_dte_list if y <= tr_dte_upper_band and y>=tr_dte_lower_band] if len(ref_tr_dte_list) == 0: return {'hist': [], 'current': [], 'success': False} if aggregation_method == 12: aligned_data = [gop.load_aligend_options_data_file(ticker_head=cmi.aligned_data_tickerhead[ticker_head_list[x]], tr_dte_center=y, contract_month_letter=contract_specs_output_list[x]['ticker_month_str'], model=model) for y in ref_tr_dte_list] else: aligned_data = [gop.load_aligend_options_data_file(ticker_head=cmi.aligned_data_tickerhead[ticker_head_list[x]], tr_dte_center=y, model=model) for y in ref_tr_dte_list] aligned_data = [y[(y['trDTE'] >= tr_dte_lower_band)&(y['trDTE'] <= tr_dte_upper_band)] for y in aligned_data] aligned_data = pd.concat(aligned_data) aligned_data.drop('theta', axis=1, inplace=True) aligned_data['settle_date'] = pd.to_datetime(aligned_data['settleDates'].astype('str'), format='%Y%m%d') aligned_data = aligned_data[(aligned_data['settle_date'] <= settle_datetime)&(aligned_data['settle_date'] >= settle_datetime_from)] aligned_data.rename(columns={'TickerYear': 'ticker_year', 'TickerMonth': 'ticker_month', 'trDTE': 'tr_dte', 'calDTE': 'cal_dte', 'impVol': 'imp_vol', 'close2CloseVol20': 'close2close_vol20', 'dollarTheta': 'theta'}, inplace=True) aligned_data.sort(['settle_date', 'ticker_year', 'ticker_month'], ascending=[True,True,True],inplace=True) aligned_data.drop_duplicates(['settle_date','ticker_year','ticker_month'],inplace=True) aligned_data = aligned_data[['settle_date','ticker_month', 'ticker_year', 'cal_dte', 'tr_dte', 'imp_vol', 'theta', 'close2close_vol20', 'profit5']] aligned_data['cont_indx'] = 100*aligned_data['ticker_year']+aligned_data['ticker_month'] aligned_data['cont_indx_adj'] = [cmi.get_cont_indx_from_month_seperation(y,-month_seperation_list[x]) for y in aligned_data['cont_indx']] data_frame_list.append(aligned_data) for x in range(len(ticker_list)): data_frame_list[x].set_index(['settle_date','cont_indx_adj'], inplace=True,drop=False) merged_dataframe = pd.concat(data_frame_list, axis=1, join='inner',keys=['c'+ str(x+1) for x in range(len(ticker_list))]) merged_dataframe['abs_tr_dte_diff'] = abs(merged_dataframe['c1']['tr_dte']-tr_dte_list[0]) merged_dataframe['settle_date'] = merged_dataframe['c1']['settle_date'] merged_dataframe.sort(['settle_date', 'abs_tr_dte_diff'], ascending=[True,True], inplace=False) merged_dataframe.drop_duplicates('settle_date', inplace=True, take_last=False) merged_dataframe.index = merged_dataframe.index.droplevel(1) tr_dte_list = [] cal_dte_list = [] imp_vol_list = [] theta_list = [] close2close_vol20_list = [] for x in range(len(ticker_list)): selected_data = merged_dataframe['c' + str(x+1)] if settle_datetime in selected_data.index: selected_data = selected_data.loc[settle_datetime] else: return {'hist': [], 'current': [], 'success': False} if selected_data['cont_indx'] != cont_indx_list[x]: return {'hist': [], 'current': [], 'success': False} tr_dte_list.append(selected_data['tr_dte']) cal_dte_list.append(selected_data['cal_dte']) imp_vol_list.append(selected_data['imp_vol']) theta_list.append(selected_data['theta']) close2close_vol20_list.append(selected_data['close2close_vol20']) current_data = pd.DataFrame.from_items([('ticker',ticker_list), ('tr_dte', tr_dte_list), ('cal_dte', cal_dte_list), ('imp_vol', imp_vol_list), ('theta', theta_list), ('close2close_vol20', close2close_vol20_list)]) current_data['settle_date'] = settle_datetime current_data.set_index('ticker', drop=True, inplace=True) return {'hist': merged_dataframe, 'current': current_data, 'success': True}
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_aligned_option_indicators(**kwargs): ticker_list = kwargs['ticker_list'] settle_datetime = cu.convert_doubledate_2datetime(kwargs['settle_date']) if 'num_cal_days_back' in kwargs.keys(): num_cal_days_back = kwargs['num_cal_days_back'] else: num_cal_days_back = 20 * 365 settle_datetime_from = settle_datetime - dt.timedelta(num_cal_days_back) contract_specs_output_list = [ cmi.get_contract_specs(x) for x in ticker_list ] ticker_head_list = [x['ticker_head'] for x in contract_specs_output_list] contract_multiplier_list = [ cmi.contract_multiplier[x['ticker_head']] for x in contract_specs_output_list ] cont_indx_list = [ x['ticker_year'] * 100 + x['ticker_month_num'] for x in contract_specs_output_list ] month_seperation_list = [ cmi.get_month_seperation_from_cont_indx(x, cont_indx_list[0]) for x in cont_indx_list ] if 'option_ticker_indicator_dictionary' in kwargs.keys(): option_ticker_indicator_dictionary = kwargs[ 'option_ticker_indicator_dictionary'] else: con = msu.get_my_sql_connection(**kwargs) unique_ticker_heads = list(set(ticker_head_list)) option_ticker_indicator_dictionary = { x: get_option_ticker_indicators(ticker_head=x, settle_date_to=kwargs['settle_date'], num_cal_days_back=num_cal_days_back, con=con) for x in unique_ticker_heads } if 'con' not in kwargs.keys(): con.close() option_ticker_indicator_dictionary_final = { ticker_list[x]: option_ticker_indicator_dictionary[ticker_head_list[x]] for x in range(len(ticker_list)) } max_available_settle_list = [] tr_dte_list = [] cal_dte_list = [] imp_vol_list = [] theta_list = [] close2close_vol20_list = [] volume_list = [] open_interest_list = [] for x in range(len(ticker_list)): ticker_data = option_ticker_indicator_dictionary_final[ticker_list[x]] ticker_data = ticker_data[ ticker_data['settle_date'] <= settle_datetime] option_ticker_indicator_dictionary_final[ticker_list[x]] = ticker_data ticker_data = ticker_data[ticker_data['ticker'] == ticker_list[x]] max_available_settle_list.append(ticker_data['settle_date'].iloc[-1]) last_available_settle = min(max_available_settle_list) for x in range(len(ticker_list)): ticker_data = option_ticker_indicator_dictionary_final[ticker_list[x]] ticker_data = ticker_data[(ticker_data['ticker'] == ticker_list[x]) & ( ticker_data['settle_date'] == last_available_settle)] tr_dte_list.append(ticker_data['tr_dte'].iloc[0]) cal_dte_list.append(ticker_data['cal_dte'].iloc[0]) imp_vol_list.append(ticker_data['imp_vol'].iloc[0]) theta_list.append(ticker_data['theta'].iloc[0] * contract_multiplier_list[x]) close2close_vol20_list.append(ticker_data['close2close_vol20'].iloc[0]) volume_list.append(ticker_data['volume'].iloc[0]) open_interest_list.append(ticker_data['open_interest'].iloc[0]) current_data = pd.DataFrame.from_dict({ 'ticker': ticker_list, 'tr_dte': tr_dte_list, 'cal_dte': cal_dte_list, 'imp_vol': imp_vol_list, 'theta': theta_list, 'close2close_vol20': close2close_vol20_list, 'volume': volume_list, 'open_interest': open_interest_list }) current_data['settle_date'] = last_available_settle current_data.set_index('ticker', drop=True, inplace=True) current_data = current_data[[ 'settle_date', 'tr_dte', 'cal_dte', 'imp_vol', 'close2close_vol20', 'theta', 'volume', 'open_interest' ]] aggregation_method = max([ ocu.get_aggregation_method_contracts_back({ 'ticker_class': x['ticker_class'], 'ticker_head': x['ticker_head'] })['aggregation_method'] for x in contract_specs_output_list ]) if (current_data['tr_dte'].min() >= 80) and (aggregation_method == 1): aggregation_method = 3 tr_days_half_band_width_selected = ocu.tr_days_half_band_with[ aggregation_method] data_frame_list = [] ref_tr_dte_list_list = [] for x in range(len(ticker_list)): ticker_data = option_ticker_indicator_dictionary_final[ticker_list[x]] if ticker_head_list[x] in ['ED', 'E0', 'E2', 'E3', 'E4', 'E5']: model = 'OU' else: model = 'BS' tr_dte_upper_band = current_data['tr_dte'].loc[ ticker_list[x]] + tr_days_half_band_width_selected tr_dte_lower_band = current_data['tr_dte'].loc[ ticker_list[x]] - tr_days_half_band_width_selected ref_tr_dte_list = [ y for y in cmi.aligned_data_tr_dte_list if y <= tr_dte_upper_band and y >= tr_dte_lower_band ] if len(ref_tr_dte_list) == 0: return {'hist': [], 'current': [], 'success': False} if aggregation_method == 12: aligned_data = [ gop.load_aligend_options_data_file( ticker_head=cmi.aligned_data_tickerhead[ ticker_head_list[x]], tr_dte_center=y, contract_month_letter=contract_specs_output_list[x] ['ticker_month_str'], model=model) for y in ref_tr_dte_list ] ticker_data = ticker_data[ticker_data['ticker_month'] == contract_specs_output_list[x] ['ticker_month_num']] else: aligned_data = [ gop.load_aligend_options_data_file( ticker_head=cmi.aligned_data_tickerhead[ ticker_head_list[x]], tr_dte_center=y, model=model) for y in ref_tr_dte_list ] aligned_data = [ y[(y['trDTE'] >= current_data['tr_dte'].loc[ticker_list[x]] - tr_days_half_band_width_selected) & (y['trDTE'] <= current_data['tr_dte'].loc[ticker_list[x]] + tr_days_half_band_width_selected)] for y in aligned_data ] aligned_data = pd.concat(aligned_data) aligned_data['settle_date'] = pd.to_datetime( aligned_data['settleDates'].astype('str'), format='%Y%m%d') aligned_data.rename(columns={ 'TickerYear': 'ticker_year', 'TickerMonth': 'ticker_month', 'trDTE': 'tr_dte', 'calDTE': 'cal_dte', 'impVol': 'imp_vol', 'close2CloseVol20': 'close2close_vol20' }, inplace=True) aligned_data.sort_values( ['settle_date', 'ticker_year', 'ticker_month'], ascending=[True, True, True], inplace=True) aligned_data.drop_duplicates( ['settle_date', 'ticker_year', 'ticker_month'], inplace=True) aligned_data['old_aligned'] = True aligned_data = aligned_data[[ 'settle_date', 'ticker_month', 'ticker_year', 'cal_dte', 'tr_dte', 'imp_vol', 'close2close_vol20', 'profit5', 'old_aligned' ]] tr_dte_selection = (ticker_data['tr_dte'] >= current_data['tr_dte'].loc[ticker_list[x]]-tr_days_half_band_width_selected)&\ (ticker_data['tr_dte'] <= current_data['tr_dte'].loc[ticker_list[x]]+tr_days_half_band_width_selected) ticker_data = ticker_data[tr_dte_selection] ticker_data['old_aligned'] = False ticker_data['profit5'] = ( ticker_data['option_pnl5'] - ticker_data['delta_pnl5'] ).astype(float) * cmi.contract_multiplier[ticker_head_list[x]] #ticker_data['profit5'] = np.nan ticker_data = pd.concat([ aligned_data, ticker_data[[ 'settle_date', 'ticker_month', 'ticker_year', 'cal_dte', 'tr_dte', 'imp_vol', 'close2close_vol20', 'profit5', 'old_aligned' ]] ]) ticker_data = ticker_data[ (ticker_data['settle_date'] <= settle_datetime) & (ticker_data['settle_date'] >= settle_datetime_from)] ticker_data['cont_indx'] = 100 * ticker_data[ 'ticker_year'] + ticker_data['ticker_month'] ticker_data['cont_indx_adj'] = [ cmi.get_cont_indx_from_month_seperation(y, -month_seperation_list[x]) for y in ticker_data['cont_indx'] ] data_frame_list.append(ticker_data) ref_tr_dte_list_list.append(ref_tr_dte_list) for x in range(len(ticker_list)): data_frame_list[x].set_index(['settle_date', 'cont_indx_adj'], inplace=True, drop=False) data_frame_list[x]['imp_vol'] = data_frame_list[x]['imp_vol'].astype( 'float64') merged_dataframe = pd.concat( data_frame_list, axis=1, join='inner', keys=['c' + str(x + 1) for x in range(len(ticker_list))]) merged_dataframe['abs_tr_dte_diff'] = abs( merged_dataframe['c1']['tr_dte'] - tr_dte_list[0]) merged_dataframe['settle_date'] = merged_dataframe['c1']['settle_date'] merged_dataframe.index.names = [ 'settle_date_index' if x is 'settle_date' else x for x in merged_dataframe.index.names ] merged_dataframe.sort_values(['settle_date', 'abs_tr_dte_diff'], ascending=[True, True], inplace=False) unique_index = np.unique(merged_dataframe['settle_date'], return_index=True)[1] merged_dataframe = merged_dataframe.iloc[unique_index] merged_dataframe.index = merged_dataframe.index.droplevel(1) return {'hist': merged_dataframe, 'current': current_data, 'success': True}
def get_aligned_option_indicators_legacy(**kwargs): ticker_list = kwargs['ticker_list'] tr_dte_list = kwargs['tr_dte_list'] settle_datetime = cu.convert_doubledate_2datetime(kwargs['settle_date']) if 'num_cal_days_back' in kwargs.keys(): num_cal_days_back = kwargs['num_cal_days_back'] else: num_cal_days_back = 20 * 365 settle_datetime_from = settle_datetime - dt.timedelta(num_cal_days_back) contract_specs_output_list = [ cmi.get_contract_specs(x) for x in ticker_list ] ticker_head_list = [x['ticker_head'] for x in contract_specs_output_list] cont_indx_list = [ x['ticker_year'] * 100 + x['ticker_month_num'] for x in contract_specs_output_list ] month_seperation_list = [ cmi.get_month_seperation_from_cont_indx(x, cont_indx_list[0]) for x in cont_indx_list ] aggregation_method = max([ ocu.get_aggregation_method_contracts_back({ 'ticker_class': x['ticker_class'], 'ticker_head': x['ticker_head'] })['aggregation_method'] for x in contract_specs_output_list ]) if (min(tr_dte_list) >= 80) and (aggregation_method == 1): aggregation_method = 3 tr_days_half_band_width_selected = ocu.tr_days_half_band_with[ aggregation_method] data_frame_list = [] for x in range(len(ticker_list)): if ticker_head_list[x] in ['ED', 'E0', 'E2', 'E3', 'E4', 'E5']: model = 'OU' else: model = 'BS' tr_dte_upper_band = tr_dte_list[x] + tr_days_half_band_width_selected tr_dte_lower_band = tr_dte_list[x] - tr_days_half_band_width_selected ref_tr_dte_list = [ y for y in cmi.aligned_data_tr_dte_list if y <= tr_dte_upper_band and y >= tr_dte_lower_band ] if len(ref_tr_dte_list) == 0: return {'hist': [], 'current': [], 'success': False} if aggregation_method == 12: aligned_data = [ gop.load_aligend_options_data_file( ticker_head=cmi.aligned_data_tickerhead[ ticker_head_list[x]], tr_dte_center=y, contract_month_letter=contract_specs_output_list[x] ['ticker_month_str'], model=model) for y in ref_tr_dte_list ] else: aligned_data = [ gop.load_aligend_options_data_file( ticker_head=cmi.aligned_data_tickerhead[ ticker_head_list[x]], tr_dte_center=y, model=model) for y in ref_tr_dte_list ] aligned_data = [ y[(y['trDTE'] >= tr_dte_lower_band) & (y['trDTE'] <= tr_dte_upper_band)] for y in aligned_data ] aligned_data = pd.concat(aligned_data) aligned_data.drop('theta', axis=1, inplace=True) aligned_data['settle_date'] = pd.to_datetime( aligned_data['settleDates'].astype('str'), format='%Y%m%d') aligned_data = aligned_data[ (aligned_data['settle_date'] <= settle_datetime) & (aligned_data['settle_date'] >= settle_datetime_from)] aligned_data.rename(columns={ 'TickerYear': 'ticker_year', 'TickerMonth': 'ticker_month', 'trDTE': 'tr_dte', 'calDTE': 'cal_dte', 'impVol': 'imp_vol', 'close2CloseVol20': 'close2close_vol20', 'dollarTheta': 'theta' }, inplace=True) aligned_data.sort(['settle_date', 'ticker_year', 'ticker_month'], ascending=[True, True, True], inplace=True) aligned_data.drop_duplicates( ['settle_date', 'ticker_year', 'ticker_month'], inplace=True) aligned_data = aligned_data[[ 'settle_date', 'ticker_month', 'ticker_year', 'cal_dte', 'tr_dte', 'imp_vol', 'theta', 'close2close_vol20', 'profit5' ]] aligned_data['cont_indx'] = 100 * aligned_data[ 'ticker_year'] + aligned_data['ticker_month'] aligned_data['cont_indx_adj'] = [ cmi.get_cont_indx_from_month_seperation(y, -month_seperation_list[x]) for y in aligned_data['cont_indx'] ] data_frame_list.append(aligned_data) for x in range(len(ticker_list)): data_frame_list[x].set_index(['settle_date', 'cont_indx_adj'], inplace=True, drop=False) merged_dataframe = pd.concat( data_frame_list, axis=1, join='inner', keys=['c' + str(x + 1) for x in range(len(ticker_list))]) merged_dataframe['abs_tr_dte_diff'] = abs( merged_dataframe['c1']['tr_dte'] - tr_dte_list[0]) merged_dataframe['settle_date'] = merged_dataframe['c1']['settle_date'] merged_dataframe.sort(['settle_date', 'abs_tr_dte_diff'], ascending=[True, True], inplace=False) merged_dataframe.drop_duplicates('settle_date', inplace=True, take_last=False) merged_dataframe.index = merged_dataframe.index.droplevel(1) tr_dte_list = [] cal_dte_list = [] imp_vol_list = [] theta_list = [] close2close_vol20_list = [] for x in range(len(ticker_list)): selected_data = merged_dataframe['c' + str(x + 1)] if settle_datetime in selected_data.index: selected_data = selected_data.loc[settle_datetime] else: return {'hist': [], 'current': [], 'success': False} if selected_data['cont_indx'] != cont_indx_list[x]: return {'hist': [], 'current': [], 'success': False} tr_dte_list.append(selected_data['tr_dte']) cal_dte_list.append(selected_data['cal_dte']) imp_vol_list.append(selected_data['imp_vol']) theta_list.append(selected_data['theta']) close2close_vol20_list.append(selected_data['close2close_vol20']) current_data = pd.DataFrame.from_items([('ticker', ticker_list), ('tr_dte', tr_dte_list), ('cal_dte', cal_dte_list), ('imp_vol', imp_vol_list), ('theta', theta_list), ('close2close_vol20', close2close_vol20_list)]) current_data['settle_date'] = settle_datetime current_data.set_index('ticker', drop=True, inplace=True) return {'hist': merged_dataframe, 'current': current_data, 'success': True}
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_intraday_spread_signals(**kwargs): ticker_list = kwargs['ticker_list'] date_to = kwargs['date_to'] ticker_list = [x for x in ticker_list if x is not None] ticker_head_list = [cmi.get_contract_specs(x)['ticker_head'] for x in ticker_list] ticker_class_list = [cmi.ticker_class[x] for x in ticker_head_list] print('-'.join(ticker_list)) if 'tr_dte_list' in kwargs.keys(): tr_dte_list = kwargs['tr_dte_list'] else: tr_dte_list = [exp.get_days2_expiration(ticker=x,date_to=date_to, instrument='futures')['tr_dte'] for x in ticker_list] weights_output = sutil.get_spread_weights_4contract_list(ticker_head_list=ticker_head_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(x)) for x in ticker_list] aggregation_method = max([x['aggregation_method'] for x in amcb_output]) contracts_back = min([x['contracts_back'] for x in amcb_output]) 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 list(set(ticker_head_list))} if 'use_last_as_current' in kwargs.keys(): use_last_as_current = kwargs['use_last_as_current'] else: use_last_as_current = True 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 'num_days_back_4intraday' in kwargs.keys(): num_days_back_4intraday = kwargs['num_days_back_4intraday'] else: num_days_back_4intraday = 5 contract_multiplier_list = [cmi.contract_multiplier[x] for x in ticker_head_list] 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'] spread_weights = weights_output['spread_weights'] portfolio_weights = weights_output['portfolio_weights'] aligned_data['spread'] = 0 aligned_data['spread_pnl_1'] = 0 aligned_data['spread_pnl1'] = 0 spread_settle = 0 last5_years_indx = aligned_data['settle_date']>=datetime5_years_ago num_contracts = len(ticker_list) for i in range(num_contracts): aligned_data['spread'] = aligned_data['spread']+aligned_data['c' + str(i+1)]['close_price']*spread_weights[i] spread_settle = spread_settle + current_data['c' + str(i+1)]['close_price']*spread_weights[i] aligned_data['spread_pnl_1'] = aligned_data['spread_pnl_1']+aligned_data['c' + str(i+1)]['change_1']*portfolio_weights[i]*contract_multiplier_list[i] aligned_data['spread_pnl1'] = aligned_data['spread_pnl1']+aligned_data['c' + str(i+1)]['change1_instant']*portfolio_weights[i]*contract_multiplier_list[i] aligned_data['spread_normalized'] = aligned_data['spread']/aligned_data['c1']['close_price'] data_last5_years = aligned_data[last5_years_indx] percentile_vector = stats.get_number_from_quantile(y=data_last5_years['spread_pnl_1'].values, quantile_list=[1, 15, 85, 99], clean_num_obs=max(100, round(3*len(data_last5_years.index)/4))) downside = (percentile_vector[0]+percentile_vector[1])/2 upside = (percentile_vector[2]+percentile_vector[3])/2 date_list = [exp.doubledate_shift_bus_days(double_date=date_to,shift_in_days=x) for x in reversed(range(1,num_days_back_4intraday))] date_list.append(date_to) intraday_data = opUtil.get_aligned_futures_data_intraday(contract_list=ticker_list, date_list=date_list) intraday_data['time_stamp'] = [x.to_datetime() for x in intraday_data.index] intraday_data['settle_date'] = intraday_data['time_stamp'].apply(lambda x: x.date()) end_hour = min([cmi.last_trade_hour_minute[x] for x in ticker_head_list]) start_hour = max([cmi.first_trade_hour_minute[x] for x in ticker_head_list]) trade_start_hour = dt.time(9, 30, 0, 0) if 'Ag' in ticker_class_list: start_hour1 = dt.time(0, 45, 0, 0) end_hour1 = dt.time(7, 45, 0, 0) selection_indx = [x for x in range(len(intraday_data.index)) if ((intraday_data['time_stamp'].iloc[x].time() < end_hour1) and(intraday_data['time_stamp'].iloc[x].time() >= start_hour1)) or ((intraday_data['time_stamp'].iloc[x].time() < end_hour) and(intraday_data['time_stamp'].iloc[x].time() >= start_hour))] else: selection_indx = [x for x in range(len(intraday_data.index)) if (intraday_data.index[x].to_datetime().time() < end_hour) and(intraday_data.index[x].to_datetime().time() >= start_hour)] intraday_data = intraday_data.iloc[selection_indx] intraday_data['spread'] = 0 for i in range(num_contracts): intraday_data['c' + str(i+1), 'mid_p'] = (intraday_data['c' + str(i+1)]['best_bid_p'] + intraday_data['c' + str(i+1)]['best_ask_p'])/2 intraday_data['spread'] = intraday_data['spread']+intraday_data['c' + str(i+1)]['mid_p']*spread_weights[i] unique_settle_dates = intraday_data['settle_date'].unique() intraday_data['spread1'] = np.nan for i in range(len(unique_settle_dates)-1): if (intraday_data['settle_date'] == unique_settle_dates[i]).sum() == \ (intraday_data['settle_date'] == unique_settle_dates[i+1]).sum(): intraday_data.loc[intraday_data['settle_date'] == unique_settle_dates[i],'spread1'] = \ intraday_data['spread'][intraday_data['settle_date'] == unique_settle_dates[i+1]].values intraday_data = intraday_data[intraday_data['settle_date'].notnull()] intraday_mean = intraday_data['spread'].mean() intraday_std = intraday_data['spread'].std() intraday_data_last2days = intraday_data[intraday_data['settle_date'] >= cu.convert_doubledate_2datetime(date_list[-2]).date()] intraday_data_yesterday = intraday_data[intraday_data['settle_date'] == cu.convert_doubledate_2datetime(date_list[-1]).date()] intraday_mean2 = intraday_data_last2days['spread'].mean() intraday_std2 = intraday_data_last2days['spread'].std() intraday_mean1 = intraday_data_yesterday['spread'].mean() intraday_std1 = intraday_data_yesterday['spread'].std() intraday_z = (spread_settle-intraday_mean)/intraday_std num_obs_intraday = len(intraday_data.index) num_obs_intraday_half = round(num_obs_intraday/2) intraday_tail = intraday_data.tail(num_obs_intraday_half) num_positives = sum(intraday_tail['spread'] > intraday_data['spread'].mean()) num_negatives = sum(intraday_tail['spread'] < intraday_data['spread'].mean()) recent_trend = 100*(num_positives-num_negatives)/(num_positives+num_negatives) pnl_frame = ifs.get_pnl_4_date_range(date_to=date_to, num_bus_days_back=20, ticker_list=ticker_list) if (len(pnl_frame.index)>15)&(pnl_frame['total_pnl'].std() != 0): historical_sharp = (250**(0.5))*pnl_frame['total_pnl'].mean()/pnl_frame['total_pnl'].std() else: historical_sharp = np.nan return {'downside': downside, 'upside': upside,'intraday_data': intraday_data, 'z': intraday_z,'recent_trend': recent_trend, 'intraday_mean': intraday_mean, 'intraday_std': intraday_std, 'intraday_mean2': intraday_mean2, 'intraday_std2': intraday_std2, 'intraday_mean1': intraday_mean1, 'intraday_std1': intraday_std1, 'aligned_output': aligned_output, 'spread_settle': spread_settle, 'data_last5_years': data_last5_years,'historical_sharp':historical_sharp}
def get_historical_risk_4strategy(**kwargs): con = msu.get_my_sql_connection(**kwargs) alias = kwargs['alias'] #print(alias) if 'as_of_date' in kwargs.keys(): as_of_date = kwargs['as_of_date'] else: as_of_date = exp.doubledate_shift_bus_days() if 'datetime5_years_ago' in kwargs.keys(): datetime5_years_ago = kwargs['datetime5_years_ago'] else: date5_years_ago = cu.doubledate_shift(as_of_date,5*365) datetime5_years_ago = cu.convert_doubledate_2datetime(date5_years_ago) net_position = ts.get_net_position_4strategy_alias(alias=alias,con=con) net_position = net_position[net_position['instrument'] != 'O'] if 'con' not in kwargs.keys(): con.close() if net_position.empty: return {'downside': 0, 'pnl_5_change': []} amcb_output = [opUtil.get_aggregation_method_contracts_back(cmi.get_contract_specs(x)) for x in net_position['ticker']] aggregation_method = pd.DataFrame(amcb_output)['aggregation_method'].max() if aggregation_method == 12: contracts_back = const.annualContractsBack elif aggregation_method == 3: contracts_back = const.quarterlyContractsBack elif aggregation_method == 1: contracts_back = const.monthlyContractsBack aligned_output = opUtil.get_aligned_futures_data(contract_list=net_position['ticker'].values, aggregation_method=aggregation_method, contracts_back=contracts_back,date_to=as_of_date,**kwargs) aligned_data = aligned_output['aligned_data'] last5_years_indx = aligned_data['settle_date'] >= datetime5_years_ago data_last5_years = aligned_data[last5_years_indx] ticker_head_list = [cmi.get_contract_specs(x)['ticker_head'] for x in net_position['ticker']] contract_multiplier_list = [cmi.contract_multiplier[x] for x in ticker_head_list] pnl_5_change_list = [contract_multiplier_list[x]* net_position['qty'].iloc[x]* data_last5_years['c' + str(x+1)]['change_5'] for x in range(len(net_position.index))] pnl_5_change = sum(pnl_5_change_list) percentile_vector = stats.get_number_from_quantile(y=pnl_5_change.values, quantile_list=[1, 15], clean_num_obs=max(100, round(3*len(pnl_5_change.values)/4))) downside = (percentile_vector[0]+percentile_vector[1])/2 unique_ticker_head_list = list(set(ticker_head_list)) ticker_head_based_pnl_5_change = {x: sum([pnl_5_change_list[y] for y in range(len(ticker_head_list)) if ticker_head_list[y] == x]) for x in unique_ticker_head_list} return {'downside': downside, 'pnl_5_change': pnl_5_change,'ticker_head_based_pnl_5_change':ticker_head_based_pnl_5_change}
def get_historical_risk_4strategy(**kwargs): con = msu.get_my_sql_connection(**kwargs) alias = kwargs['alias'] #print(alias) if 'as_of_date' in kwargs.keys(): as_of_date = kwargs['as_of_date'] else: as_of_date = exp.doubledate_shift_bus_days() if 'datetime5_years_ago' in kwargs.keys(): datetime5_years_ago = kwargs['datetime5_years_ago'] else: date5_years_ago = cu.doubledate_shift(as_of_date,5*365) datetime5_years_ago = cu.convert_doubledate_2datetime(date5_years_ago) net_position = ts.get_net_position_4strategy_alias(alias=alias,con=con) net_position = net_position[net_position['instrument'] != 'O'] if 'con' not in kwargs.keys(): con.close() if net_position.empty: return {'downside': 0, 'pnl_5_change': []} amcb_output = [opUtil.get_aggregation_method_contracts_back(cmi.get_contract_specs(x)) for x in net_position['ticker']] aggregation_method = pd.DataFrame(amcb_output)['aggregation_method'].max() if aggregation_method == 12: contracts_back = const.annualContractsBack elif aggregation_method == 3: contracts_back = const.quarterlyContractsBack elif aggregation_method == 1: contracts_back = const.monthlyContractsBack aligned_output = opUtil.get_aligned_futures_data(contract_list=net_position['ticker'].values, aggregation_method=aggregation_method, contracts_back=contracts_back,date_to=as_of_date,**kwargs) aligned_data = aligned_output['aligned_data'] last5_years_indx = aligned_data['settle_date'] >= datetime5_years_ago data_last5_years = aligned_data[last5_years_indx] ticker_head_list = [cmi.get_contract_specs(x)['ticker_head'] for x in net_position['ticker']] contract_multiplier_list = [cmi.contract_multiplier[x] for x in ticker_head_list] pnl_5_change_list = [contract_multiplier_list[x]* net_position['qty'].iloc[x]* data_last5_years['c' + str(x+1)]['change_5'] for x in range(len(net_position.index))] pnl_5_change = sum(pnl_5_change_list) percentile_vector = stats.get_number_from_quantile(y=pnl_5_change.values, quantile_list=[1, 15], clean_num_obs=max(100, round(3*len(pnl_5_change.values)/4))) downside = (percentile_vector[0]+percentile_vector[1])/2 unique_ticker_head_list = list(set(ticker_head_list)) ticker_head_based_pnl_5_change = {x: sum([pnl_5_change_list[y] for y in range(len(ticker_head_list)) if ticker_head_list[y] == x]) for x in unique_ticker_head_list} return {'downside': downside, 'pnl_5_change': pnl_5_change,'ticker_head_based_pnl_5_change':ticker_head_based_pnl_5_change}
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_intraday_spread_signals(**kwargs): ticker_list = kwargs['ticker_list'] date_to = kwargs['date_to'] #print(ticker_list) ticker_list = [x for x in ticker_list if x is not None] ticker_head_list = [ cmi.get_contract_specs(x)['ticker_head'] for x in ticker_list ] ticker_class_list = [cmi.ticker_class[x] for x in ticker_head_list] #print('-'.join(ticker_list)) if 'tr_dte_list' in kwargs.keys(): tr_dte_list = kwargs['tr_dte_list'] else: tr_dte_list = [ exp.get_days2_expiration(ticker=x, date_to=date_to, instrument='futures')['tr_dte'] 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(x)) for x in ticker_list ] aggregation_method = max( [x['aggregation_method'] for x in amcb_output]) contracts_back = min([x['contracts_back'] for x in amcb_output]) 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 list(set(ticker_head_list)) } if 'use_last_as_current' in kwargs.keys(): use_last_as_current = kwargs['use_last_as_current'] else: use_last_as_current = True 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 'num_days_back_4intraday' in kwargs.keys(): num_days_back_4intraday = kwargs['num_days_back_4intraday'] else: num_days_back_4intraday = 10 contract_multiplier_list = [ cmi.contract_multiplier[x] for x in ticker_head_list ] 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'] if ticker_head_list in fixed_weight_future_spread_list: weights_output = sutil.get_spread_weights_4contract_list( ticker_head_list=ticker_head_list) spread_weights = weights_output['spread_weights'] portfolio_weights = weights_output['portfolio_weights'] else: regress_output = stats.get_regression_results({ 'x': aligned_data['c2']['change_1'][-60:], 'y': aligned_data['c1']['change_1'][-60:] }) spread_weights = [1, -regress_output['beta']] portfolio_weights = [ 1, -regress_output['beta'] * contract_multiplier_list[0] / contract_multiplier_list[1] ] aligned_data['spread'] = 0 aligned_data['spread_pnl_1'] = 0 aligned_data['spread_pnl1'] = 0 spread_settle = 0 last5_years_indx = aligned_data['settle_date'] >= datetime5_years_ago num_contracts = len(ticker_list) for i in range(num_contracts): aligned_data['spread'] = aligned_data['spread'] + aligned_data[ 'c' + str(i + 1)]['close_price'] * spread_weights[i] spread_settle = spread_settle + current_data[ 'c' + str(i + 1)]['close_price'] * spread_weights[i] aligned_data[ 'spread_pnl_1'] = aligned_data['spread_pnl_1'] + aligned_data[ 'c' + str(i + 1)]['change_1'] * portfolio_weights[ i] * contract_multiplier_list[i] aligned_data[ 'spread_pnl1'] = aligned_data['spread_pnl1'] + aligned_data[ 'c' + str(i + 1)]['change1_instant'] * portfolio_weights[ i] * contract_multiplier_list[i] aligned_data['spread_normalized'] = aligned_data['spread'] / aligned_data[ 'c1']['close_price'] data_last5_years = aligned_data[last5_years_indx] percentile_vector = stats.get_number_from_quantile( y=data_last5_years['spread_pnl_1'].values, quantile_list=[1, 15, 85, 99], clean_num_obs=max(100, round(3 * len(data_last5_years.index) / 4))) downside = (percentile_vector[0] + percentile_vector[1]) / 2 upside = (percentile_vector[2] + percentile_vector[3]) / 2 date_list = [ exp.doubledate_shift_bus_days(double_date=date_to, shift_in_days=x) for x in reversed(range(1, num_days_back_4intraday)) ] date_list.append(date_to) intraday_data = opUtil.get_aligned_futures_data_intraday( contract_list=ticker_list, date_list=date_list) if len(intraday_data.index) == 0: return { 'downside': downside, 'upside': upside, 'intraday_data': intraday_data, 'trading_data': intraday_data, 'spread_weight': spread_weights[1], 'portfolio_weight': portfolio_weights[1], 'z': np.nan, 'recent_trend': np.nan, 'intraday_mean10': np.nan, 'intraday_std10': np.nan, 'intraday_mean5': np.nan, 'intraday_std5': np.nan, 'intraday_mean2': np.nan, 'intraday_std2': np.nan, 'intraday_mean1': np.nan, 'intraday_std1': np.nan, 'aligned_output': aligned_output, 'spread_settle': spread_settle, 'data_last5_years': data_last5_years, 'ma_spread_lowL': np.nan, 'ma_spread_highL': np.nan, 'ma_spread_low': np.nan, 'ma_spread_high': np.nan, 'intraday_sharp': np.nan } intraday_data['time_stamp'] = [ x.to_datetime() for x in intraday_data.index ] intraday_data['settle_date'] = intraday_data['time_stamp'].apply( lambda x: x.date()) end_hour = min([cmi.last_trade_hour_minute[x] for x in ticker_head_list]) start_hour = max( [cmi.first_trade_hour_minute[x] for x in ticker_head_list]) trade_start_hour = dt.time(9, 30, 0, 0) if 'Ag' in ticker_class_list: start_hour1 = dt.time(0, 45, 0, 0) end_hour1 = dt.time(7, 45, 0, 0) selection_indx = [ x for x in range(len(intraday_data.index)) if ((intraday_data['time_stamp'].iloc[x].time() < end_hour1) and (intraday_data['time_stamp'].iloc[x].time() >= start_hour1)) or ((intraday_data['time_stamp'].iloc[x].time() < end_hour) and (intraday_data['time_stamp'].iloc[x].time() >= start_hour)) ] else: selection_indx = [ x for x in range(len(intraday_data.index)) if (intraday_data.index[x].to_datetime().time() < end_hour) and ( intraday_data.index[x].to_datetime().time() >= start_hour) ] intraday_data = intraday_data.iloc[selection_indx] intraday_data['spread'] = 0 for i in range(num_contracts): intraday_data[ 'c' + str(i + 1), 'mid_p'] = (intraday_data['c' + str(i + 1)]['best_bid_p'] + intraday_data['c' + str(i + 1)]['best_ask_p']) / 2 intraday_data['spread'] = intraday_data['spread'] + intraday_data[ 'c' + str(i + 1)]['mid_p'] * spread_weights[i] unique_settle_dates = intraday_data['settle_date'].unique() intraday_data['spread1'] = np.nan for i in range(len(unique_settle_dates) - 1): if (intraday_data['settle_date'] == unique_settle_dates[i]).sum() == \ (intraday_data['settle_date'] == unique_settle_dates[i+1]).sum(): intraday_data.loc[intraday_data['settle_date'] == unique_settle_dates[i],'spread1'] = \ intraday_data['spread'][intraday_data['settle_date'] == unique_settle_dates[i+1]].values intraday_data = intraday_data[intraday_data['settle_date'].notnull()] intraday_mean10 = intraday_data['spread'].mean() intraday_std10 = intraday_data['spread'].std() intraday_data_last5days = intraday_data[ intraday_data['settle_date'] >= cu.convert_doubledate_2datetime( date_list[-5]).date()] intraday_data_last2days = intraday_data[ intraday_data['settle_date'] >= cu.convert_doubledate_2datetime( date_list[-2]).date()] intraday_data_yesterday = intraday_data[intraday_data['settle_date'] == cu.convert_doubledate_2datetime( date_list[-1]).date()] intraday_mean5 = intraday_data_last5days['spread'].mean() intraday_std5 = intraday_data_last5days['spread'].std() intraday_mean2 = intraday_data_last2days['spread'].mean() intraday_std2 = intraday_data_last2days['spread'].std() intraday_mean1 = intraday_data_yesterday['spread'].mean() intraday_std1 = intraday_data_yesterday['spread'].std() intraday_z = (spread_settle - intraday_mean5) / intraday_std5 num_obs_intraday = len(intraday_data.index) num_obs_intraday_half = round(num_obs_intraday / 2) intraday_tail = intraday_data.tail(num_obs_intraday_half) num_positives = sum( intraday_tail['spread'] > intraday_data['spread'].mean()) num_negatives = sum( intraday_tail['spread'] < intraday_data['spread'].mean()) if num_positives + num_negatives != 0: recent_trend = 100 * (num_positives - num_negatives) / (num_positives + num_negatives) else: recent_trend = np.nan intraday_data_shifted = intraday_data.groupby('settle_date').shift(-60) intraday_data['spread_shifted'] = intraday_data_shifted['spread'] intraday_data[ 'delta60'] = intraday_data['spread_shifted'] - intraday_data['spread'] intraday_data['ewma10'] = pd.ewma(intraday_data['spread'], span=10) intraday_data['ewma50'] = pd.ewma(intraday_data['spread'], span=50) intraday_data['ewma200'] = pd.ewma(intraday_data['spread'], span=200) intraday_data['ma40'] = pd.rolling_mean(intraday_data['spread'], 40) intraday_data[ 'ewma50_spread'] = intraday_data['spread'] - intraday_data['ewma50'] intraday_data[ 'ma40_spread'] = intraday_data['spread'] - intraday_data['ma40'] selection_indx = [ x for x in range(len(intraday_data.index)) if (intraday_data['time_stamp'].iloc[x].time() > trade_start_hour) ] selected_data = intraday_data.iloc[selection_indx] selected_data['delta60Net'] = (contract_multiplier_list[0] * selected_data['delta60'] / spread_weights[0]) selected_data.reset_index(drop=True, inplace=True) selected_data['proxy_pnl'] = 0 t_cost = cmi.t_cost[ticker_head_list[0]] ma_spread_low = np.nan ma_spread_high = np.nan ma_spread_lowL = np.nan ma_spread_highL = np.nan intraday_sharp = np.nan if sum(selected_data['ma40_spread'].notnull()) > 30: quantile_list = selected_data['ma40_spread'].quantile([0.1, 0.9]) down_indx = selected_data['ma40_spread'] < quantile_list[0.1] up_indx = selected_data['ma40_spread'] > quantile_list[0.9] up_data = selected_data[up_indx] down_data = selected_data[down_indx] ma_spread_lowL = quantile_list[0.1] ma_spread_highL = quantile_list[0.9] #return {'selected_data':selected_data,'up_data':up_data,'up_indx':up_indx} selected_data.loc[up_indx, 'proxy_pnl'] = (-up_data['delta60Net'] - 2 * num_contracts * t_cost).values selected_data.loc[down_indx, 'proxy_pnl'] = (down_data['delta60Net'] - 2 * num_contracts * t_cost).values short_term_data = selected_data[ selected_data['settle_date'] >= cu.convert_doubledate_2datetime( date_list[-5]).date()] if sum(short_term_data['ma40_spread'].notnull()) > 30: quantile_list = short_term_data['ma40_spread'].quantile([0.1, 0.9]) ma_spread_low = quantile_list[0.1] ma_spread_high = quantile_list[0.9] if selected_data['proxy_pnl'].std() != 0: intraday_sharp = selected_data['proxy_pnl'].mean( ) / selected_data['proxy_pnl'].std() return { 'downside': downside, 'upside': upside, 'intraday_data': intraday_data, 'trading_data': selected_data, 'spread_weight': spread_weights[1], 'portfolio_weight': portfolio_weights[1], 'z': intraday_z, 'recent_trend': recent_trend, 'intraday_mean10': intraday_mean10, 'intraday_std10': intraday_std10, 'intraday_mean5': intraday_mean5, 'intraday_std5': intraday_std5, 'intraday_mean2': intraday_mean2, 'intraday_std2': intraday_std2, 'intraday_mean1': intraday_mean1, 'intraday_std1': intraday_std1, 'aligned_output': aligned_output, 'spread_settle': spread_settle, 'data_last5_years': data_last5_years, 'ma_spread_lowL': ma_spread_lowL, 'ma_spread_highL': ma_spread_highL, 'ma_spread_low': ma_spread_low, 'ma_spread_high': ma_spread_high, 'intraday_sharp': intraday_sharp }
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()}