def get_signals_4date(**kwargs): report_date = kwargs['report_date'] date_from = cu.doubledate_shift(report_date, 365) report_datetime = cu.convert_doubledate_2datetime(report_date) fund_price_dictionary = { x: gsp.get_stock_price_preloaded(ticker=x, settle_date_from=date_from, settle_date_to=report_date) for x in symbol_list } data_current_dictionary = { x: fund_price_dictionary[x]['settle_datetime'].iloc[-1] == report_datetime for x in symbol_list } symbols2update = [] for key, value in data_current_dictionary.items(): if not value: symbols2update.append(key) if symbols2update: ssd.save_stock_data(symbol_list=symbols2update) fund_price_dictionary = { x: gsp.get_stock_price_preloaded(ticker=x, settle_date_from=date_from, settle_date_to=report_date) for x in symbol_list } performance_dictionary = {} for j in range((len(symbol_list))): price_data = fund_price_dictionary[symbol_list[j]] if price_data['settle_datetime'].iloc[-1] != report_datetime: return {'success': False, 'performance_dictionary': {}} price_data.reset_index(drop=True, inplace=True) #split_envents = price_data[price_data['split_coefficient'] != 1] #split_index_list = split_envents.index #for i in range(len(split_index_list)): # price_data['close'].iloc[:split_index_list[i]] = \ # price_data['close'].iloc[:split_index_list[i]] / price_data['split_coefficient'].iloc[split_index_list[i]] # price_data['dividend_amount'].iloc[:split_index_list[i]] = \ # price_data['dividend_amount'].iloc[:split_index_list[i]] / price_data['split_coefficient'].iloc[split_index_list[i]] performance_dictionary[symbol_list[j]] = 100 * ( price_data['close'].iloc[-1] - price_data['close'].iloc[0]) / price_data['close'].iloc[0] return {'success': True, 'performance_dictionary': performance_dictionary}
def get_summary(**kwargs): symbol1 = kwargs['symbol1'] symbol2 = kwargs['symbol2'] report_date = kwargs['report_date'] if 'get_diagnosticQ' in kwargs.keys(): get_diagnosticQ = kwargs['get_diagnosticQ'] else: get_diagnosticQ = False report_datetime = cu.convert_doubledate_2datetime(report_date) data1 = gsp.get_stock_price_preloaded(ticker=symbol1, data_source='iex', settle_date_to = report_date) data2 = gsp.get_stock_price_preloaded(ticker=symbol2, data_source='iex', settle_date_to = report_date) merged_data = pd.merge(data1[['close','settle_datetime']], data2[['close','settle_datetime']], how='inner', on='settle_datetime') split = int(len(merged_data) * .4) if split<200 or report_datetime!=merged_data['settle_datetime'].iloc[-1]: return {'price1': np.nan,'price2': np.nan, 'p_value_2': np.nan,'p_value_1': np.nan, 'beta_1': np.nan, 'beta_2': np.nan, 'corr': np.nan, 'cagr1': np.nan, 'cagr2': np.nan, 'kalpha': np.nan, 'kbeta': np.nan, 'meanSpread': np.nan, 'stdSpread': np.nan, 'zScore': np.nan} training_data = merged_data[:split] test_data = merged_data[split:] cointegration_output_2 = sm.tsa.stattools.coint(training_data['close_x'], training_data['close_y']) cointegration_output_1 = sm.tsa.stattools.coint(test_data['close_x'], test_data['close_y']) regress_output_1 = ss.get_regression_results({'y': test_data['close_y'].values, 'x': test_data['close_x'].values}) regress_output_2 = ss.get_regression_results({'y': training_data['close_y'].values, 'x': training_data['close_x'].values}) regress_output_3 = ss.get_regression_results({'y': test_data['close_y'].diff().values, 'x': test_data['close_x'].diff().values}) merged_data.set_index('settle_datetime', drop=True, inplace=True) backtest_output_1 = backtest(merged_data[split:], 'close_x', 'close_y') backtest_output_2 = backtest(merged_data[:split], 'close_x', 'close_y') if get_diagnosticQ: return {'backtest_output': backtest_output_1, 'cagr2': backtest_output_2['cagr']} else: return {'price1': merged_data['close_x'].iloc[-1],'price2': merged_data['close_y'].iloc[-1], 'p_value_2': cointegration_output_2[1],'p_value_1': cointegration_output_1[1], 'beta_1': regress_output_1['beta'], 'beta_2': regress_output_2['beta'], 'corr': np.sqrt(regress_output_3['rsquared']/100), 'cagr1': backtest_output_1['cagr'], 'cagr2': backtest_output_2['cagr'], 'kalpha': backtest_output_1['kalpha'], 'kbeta': backtest_output_1['kbeta'], 'meanSpread': backtest_output_1['meanSpread'], 'stdSpread': backtest_output_1['stdSpread'], 'zScore': backtest_output_1['zScore']}
def get_strategy_pnl(**kwargs): alias = kwargs['alias'] con = msu.get_my_sql_connection(**kwargs) strategy_info = ts.get_strategy_info_from_alias(alias=alias, con=con) if 'as_of_date' in kwargs.keys(): as_of_date = kwargs['as_of_date'] else: as_of_date = exp.doubledate_shift_bus_days() if 'broker' in kwargs.keys(): broker = kwargs['broker'] else: broker = 'abn' open_date = int(strategy_info['open_date'].strftime('%Y%m%d')) #open_date = 20160920 close_date = int(strategy_info['close_date'].strftime('%Y%m%d')) if close_date > as_of_date: close_date = as_of_date bus_day_list = exp.get_bus_day_list(date_from=open_date, date_to=close_date) trades_frame = ts.get_trades_4strategy_alias(alias=alias, con=con) if sum(trades_frame['instrument'] == 'S') > 0: stock_strategy_Q = True else: stock_strategy_Q = False if stock_strategy_Q: return { 'pnl_frame': pd.DataFrame(), 'daily_pnl': np.nan, 'total_pnl': np.nan } unique_ticker_list = trades_frame['ticker'].unique() stock_data_dictionary = { x: gsp.get_stock_price_preloaded(ticker=x) for x in unique_ticker_list } trades_frame['t_cost'] = [ smi.get_ib_t_cost(price=trades_frame['trade_price'].iloc[x], quantity=trades_frame['trade_quantity'].iloc[x]) for x in range(len(trades_frame.index)) ] pnl_path = [ get_stock_strategy_pnl_4day( alias=alias, pnl_date=x, con=con, trades_frame=trades_frame, stock_data_dictionary=stock_data_dictionary) for x in bus_day_list ] nan_price_q_list = [x['nan_price_q'] for x in pnl_path] good_price_q_list = [not i for i in nan_price_q_list] bus_day_after_nan_list = [ bus_day_list[x + 1] for x in range(len(bus_day_list) - 1) if nan_price_q_list[x] ] pnl_path = [ pnl_path[x] for x in range(len(pnl_path)) if good_price_q_list[x] ] bus_day_list = [ bus_day_list[x] for x in range(len(bus_day_list)) if good_price_q_list[x] ] # print(bus_day_list) # print(bus_day_after_nan_list) if len(bus_day_after_nan_list) > 0: pnl_path_after_nan = [ get_stock_strategy_pnl_4day( alias=alias, pnl_date=x, con=con, trades_frame=trades_frame, broker=broker, shift_in_days=2, stock_data_dictionary=stock_data_dictionary) for x in bus_day_after_nan_list ] for i in range(len(bus_day_after_nan_list)): if bus_day_after_nan_list[i] in bus_day_list: index_val = bus_day_list.index(bus_day_after_nan_list[i]) pnl_path[index_val] = pnl_path_after_nan[i] pnl_frame = pd.DataFrame(pnl_path) pnl_frame['settle_date'] = bus_day_list output_dictionary = { 'pnl_frame': pnl_frame[[ 'settle_date', 'position_pnl', 'intraday_pnl', 't_cost', 'total_pnl' ]], 'daily_pnl': pnl_frame['total_pnl'].values[-1], 'total_pnl': pnl_frame['total_pnl'].sum() } else: ticker_head_list = [ cmi.get_contract_specs(x)['ticker_head'] for x in trades_frame['ticker'] ] unique_ticker_head_list = list(set(ticker_head_list)) 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 unique_ticker_head_list } trades_frame['contract_multiplier'] = [ cmi.contract_multiplier[x] for x in ticker_head_list ] trades_frame['t_cost'] = [ cmi.get_t_cost(ticker_head=x, broker=broker) for x in ticker_head_list ] pnl_path = [ get_strategy_pnl_4day( alias=alias, pnl_date=x, con=con, trades_frame=trades_frame, broker=broker, futures_data_dictionary=futures_data_dictionary) for x in bus_day_list ] nan_price_q_list = [x['nan_price_q'] for x in pnl_path] good_price_q_list = [not i for i in nan_price_q_list] bus_day_after_nan_list = [ bus_day_list[x + 1] for x in range(len(bus_day_list) - 1) if nan_price_q_list[x] ] pnl_path = [ pnl_path[x] for x in range(len(pnl_path)) if good_price_q_list[x] ] bus_day_list = [ bus_day_list[x] for x in range(len(bus_day_list)) if good_price_q_list[x] ] #print(bus_day_list) #print(bus_day_after_nan_list) if len(bus_day_after_nan_list) > 0: pnl_path_after_nan = [ get_strategy_pnl_4day( alias=alias, pnl_date=x, con=con, trades_frame=trades_frame, broker=broker, shift_in_days=2, futures_data_dictionary=futures_data_dictionary) for x in bus_day_after_nan_list ] for i in range(len(bus_day_after_nan_list)): index_val = bus_day_list.index(bus_day_after_nan_list[i]) pnl_path[index_val] = pnl_path_after_nan[i] pnl_per_tickerhead_list = [x['pnl_per_tickerhead'] for x in pnl_path] pnl_per_tickerhead = pd.concat(pnl_per_tickerhead_list, axis=1, sort=True) pnl_per_tickerhead = pnl_per_tickerhead[['pnl_total']] pnl_per_tickerhead = pnl_per_tickerhead.transpose() if len(unique_ticker_head_list) > 1: zero_indx = [[x not in y.index for y in pnl_per_tickerhead_list] for x in pnl_per_tickerhead.columns] for i in range(len(pnl_per_tickerhead.columns)): pnl_per_tickerhead.iloc[:, i][zero_indx[i]] = 0 pnl_per_tickerhead['settle_date'] = bus_day_list pnl_per_tickerhead.reset_index(inplace=True, drop=True) pnl_frame = pd.DataFrame(pnl_path) pnl_frame['settle_date'] = bus_day_list daily_index = pnl_frame['settle_date'] == as_of_date if sum(daily_index) == 0: daily_pnl = np.nan else: daily_pnl = pnl_frame['total_pnl'].values[-1] output_dictionary = { 'pnl_frame': pnl_frame[[ 'settle_date', 'position_pnl', 'intraday_pnl', 't_cost', 'total_pnl' ]], 'pnl_per_tickerhead': pnl_per_tickerhead, 'daily_pnl': daily_pnl, 'total_pnl': pnl_frame['total_pnl'].sum() } if 'con' not in kwargs.keys(): con.close() return output_dictionary
def get_stock_strategy_pnl_4day(**kwargs): alias = kwargs['alias'] pnl_date = kwargs['pnl_date'] if 'shift_in_days' in kwargs.keys(): shift_in_days = kwargs['shift_in_days'] else: shift_in_days = 1 pnl_datetime = cu.convert_doubledate_2datetime(pnl_date) con = msu.get_my_sql_connection(**kwargs) if 'trades_frame' in kwargs.keys(): trades_frame = kwargs['trades_frame'] else: trades_frame = ts.get_trades_4strategy_alias(alias=alias, con=con) trades_frame['t_cost'] = [ smi.get_ib_t_cost(price=trades_frame['trade_price'].iloc[x], quantity=trades_frame['trade_quantity'].iloc[x]) for x in range(len(trades_frame.index)) ] position_frame_aux = trades_frame[ trades_frame['trade_date'] < pnl_datetime] intraday_frame_aux = trades_frame[trades_frame['trade_date'] == pnl_datetime] grouped = position_frame_aux.groupby(['ticker']) net_position = pd.DataFrame() net_position['qty'] = grouped['trade_quantity'].sum() net_position['ticker'] = grouped['ticker'].first() net_position = net_position[abs(net_position['qty']) > 0.1] useful_generalized_ticker_list = list( set(net_position['ticker'].values) | set(intraday_frame_aux['ticker'].unique())) trades_frame = trades_frame[trades_frame['ticker'].isin( useful_generalized_ticker_list)] if 'stock_data_dictionary' in kwargs.keys(): stock_data_dictionary = kwargs['stock_data_dictionary'] else: unique_ticker_list = trades_frame['ticker'].unique() stock_data_dictionary = { x: gsp.get_stock_price_preloaded(ticker=x) for x in unique_ticker_list } pnl_date_1 = exp.doubledate_shift_bus_days(double_date=pnl_date, shift_in_days=shift_in_days) stock_price_out_1 = [ gsp.get_stock_price_preloaded( ticker=x, stock_data_dictionary=stock_data_dictionary, settle_date=pnl_date_1) for x in trades_frame['ticker'] ] stock_price_out = [ gsp.get_stock_price_preloaded( ticker=x, stock_data_dictionary=stock_data_dictionary, settle_date=pnl_date) for x in trades_frame['ticker'] ] trades_frame['price_1'] = [ np.NaN if x.empty else x['close'].values[0] for x in stock_price_out_1 ] trades_frame['price'] = [ np.NaN if x.empty else x['close'].values[0] for x in stock_price_out ] position_frame = trades_frame[trades_frame['trade_date'] < pnl_datetime] nan_price_q = position_frame['price'].isnull().values.any() intraday_frame = trades_frame[trades_frame['trade_date'] == pnl_datetime] position_pnl_per_ticker = pd.DataFrame(columns=['ticker', 'pnl_position']) intraday_pnl_per_ticker = pd.DataFrame(columns=['ticker', 'pnl_intraday']) if len(position_frame) == 0: position_pnl = 0 else: position_frame['pnl'] = position_frame['trade_quantity'] * \ (position_frame['price'] - position_frame['price_1']) position_pnl = position_frame['pnl'].sum() position_grouped_per_ticker = position_frame.groupby('ticker') position_pnl_per_ticker['pnl_position'] = ( position_grouped_per_ticker['pnl'].sum()).values position_pnl_per_ticker['ticker'] = ( position_grouped_per_ticker['ticker'].first()).values if len(intraday_frame) == 0: intraday_pnl = 0 t_cost = 0 else: intraday_frame['pnl'] = intraday_frame['trade_quantity'] * \ (intraday_frame['price'] - intraday_frame['trade_price']) intraday_frame[ 'pnl_wtcost'] = intraday_frame['pnl'] - intraday_frame['t_cost'] intraday_pnl = intraday_frame['pnl'].sum() t_cost = intraday_frame['t_cost'].sum() intraday_grouped_per_ticker = intraday_frame.groupby('ticker') intraday_pnl_per_ticker['pnl_intraday'] = ( intraday_grouped_per_ticker['pnl_wtcost'].sum()).values intraday_pnl_per_ticker['ticker'] = ( intraday_grouped_per_ticker['ticker'].first()).values pnl_per_ticker = pd.merge(position_pnl_per_ticker, intraday_pnl_per_ticker, how='outer', on='ticker') intraday_zero_indx = [ x not in intraday_pnl_per_ticker['ticker'].values for x in pnl_per_ticker['ticker'] ] position_zero_indx = [ x not in position_pnl_per_ticker['ticker'].values for x in pnl_per_ticker['ticker'] ] pnl_per_ticker['pnl_position'][position_zero_indx] = 0 pnl_per_ticker['pnl_intraday'][intraday_zero_indx] = 0 pnl_per_ticker['pnl_total'] = pnl_per_ticker[ 'pnl_position'] + pnl_per_ticker['pnl_intraday'] pnl_per_ticker.set_index('ticker', drop=True, inplace=True) if 'con' not in kwargs.keys(): con.close() return { 'total_pnl': int(position_pnl + intraday_pnl - t_cost), 'position_pnl': int(position_pnl), 'intraday_pnl': int(intraday_pnl), 't_cost': int(t_cost), 'nan_price_q': nan_price_q, 'pnl_per_ticker': pnl_per_ticker }
def get_results_4strategy(**kwargs): signal_input = dict() if 'futures_data_dictionary' in kwargs.keys(): signal_input['futures_data_dictionary'] = kwargs[ 'futures_data_dictionary'] if 'date_to' in kwargs.keys(): date_to = kwargs['date_to'] else: date_to = exp.doubledate_shift_bus_days() if 'datetime5_years_ago' in kwargs.keys(): signal_input['datetime5_years_ago'] = kwargs['datetime5_years_ago'] if 'strategy_info_output' in kwargs.keys(): strategy_info_output = kwargs['strategy_info_output'] else: strategy_info_output = ts.get_strategy_info_from_alias(**kwargs) if 'broker' in kwargs.keys(): broker = kwargs['broker'] else: broker = 'abn' con = msu.get_my_sql_connection(**kwargs) strategy_info_dict = sc.convert_from_string_to_dictionary( string_input=strategy_info_output['description_string']) strategy_class = strategy_info_dict['strategy_class'] pnl_frame = tpm.get_daily_pnl_snapshot(as_of_date=date_to, broker=broker) pnl_frame = pnl_frame[pnl_frame['alias'] == kwargs['alias']] strategy_position = ts.get_net_position_4strategy_alias( alias=kwargs['alias'], as_of_date=date_to) if strategy_class == 'futures_butterfly': ticker_head = cmi.get_contract_specs( strategy_info_dict['ticker1'])['ticker_head'] if not strategy_position.empty: total_contracts2trade = strategy_position['qty'].abs().sum() t_cost = cmi.t_cost[ticker_head] QF_initial = float(strategy_info_dict['QF']) z1_initial = float(strategy_info_dict['z1']) bf_signals_output = fs.get_futures_butterfly_signals( ticker_list=[ strategy_info_dict['ticker1'], strategy_info_dict['ticker2'], strategy_info_dict['ticker3'] ], aggregation_method=int(strategy_info_dict['agg']), contracts_back=int(strategy_info_dict['cBack']), date_to=date_to, **signal_input) if bf_signals_output['success']: aligned_output = bf_signals_output['aligned_output'] current_data = aligned_output['current_data'] holding_tr_dte = int( strategy_info_dict['trDte1']) - current_data['c1']['tr_dte'] success_status = True QF = bf_signals_output['qf'] z1 = bf_signals_output['zscore1'] short_tr_dte = current_data['c1']['tr_dte'] second_spread_weight = bf_signals_output['second_spread_weight_1'] if strategy_position.empty: recommendation = 'CLOSE' elif (z1_initial>0)&(holding_tr_dte > 5) &\ (bf_signals_output['qf']<QF_initial-20)&\ (pnl_frame['total_pnl'].iloc[0] > 3*t_cost*total_contracts2trade): recommendation = 'STOP' elif (z1_initial<0)&(holding_tr_dte > 5) &\ (bf_signals_output['qf']>QF_initial+20)&\ (pnl_frame['total_pnl'].iloc[0] > 3*t_cost*total_contracts2trade): recommendation = 'STOP' elif (current_data['c1']['tr_dte'] < 35)&\ (pnl_frame['total_pnl'].iloc[0] > 3*t_cost*total_contracts2trade): recommendation = 'STOP' elif (current_data['c1']['tr_dte'] < 35)&\ (pnl_frame['total_pnl'].iloc[0] < 3*t_cost*total_contracts2trade): recommendation = 'WINDDOWN' else: recommendation = 'HOLD' else: success_status = False QF = np.nan z1 = np.nan short_tr_dte = np.nan holding_tr_dte = np.nan second_spread_weight = np.nan recommendation = 'MISSING DATA' result_output = { 'success': success_status, 'ticker_head': ticker_head, 'QF_initial': QF_initial, 'z1_initial': z1_initial, 'QF': QF, 'z1': z1, 'short_tr_dte': short_tr_dte, 'holding_tr_dte': holding_tr_dte, 'second_spread_weight': second_spread_weight, 'recommendation': recommendation } elif strategy_class == 'spread_carry': trades4_strategy = ts.get_trades_4strategy_alias(**kwargs) grouped = trades4_strategy.groupby('ticker') net_position = pd.DataFrame() net_position['ticker'] = (grouped['ticker'].first()).values net_position['qty'] = (grouped['trade_quantity'].sum()).values net_position = net_position[net_position['qty'] != 0] net_position['ticker_head'] = [ cmi.get_contract_specs(x)['ticker_head'] for x in net_position['ticker'] ] price_output = [ gfp.get_futures_price_preloaded(ticker=x, settle_date=date_to) for x in net_position['ticker'] ] net_position['tr_dte'] = [ np.nan if x.empty else x['tr_dte'].values[0] for x in price_output ] results_frame = pd.DataFrame() unique_tickerhead_list = net_position['ticker_head'].unique() results_frame['tickerHead'] = unique_tickerhead_list results_frame['ticker1'] = [None] * len(unique_tickerhead_list) results_frame['ticker2'] = [None] * len(unique_tickerhead_list) results_frame['qty'] = [None] * len(unique_tickerhead_list) results_frame['pnl'] = [None] * len(unique_tickerhead_list) results_frame['downside'] = [None] * len(unique_tickerhead_list) results_frame['indicator'] = [None] * len(unique_tickerhead_list) results_frame['timeHeld'] = [None] * len(unique_tickerhead_list) results_frame['recommendation'] = [None] * len(unique_tickerhead_list) spread_carry_output = osc.generate_spread_carry_sheet_4date( report_date=date_to) spread_report = spread_carry_output['spread_report'] pnl_output = tpnl.get_strategy_pnl(**kwargs) pnl_per_tickerhead = pnl_output['pnl_per_tickerhead'] for i in range(len(unique_tickerhead_list)): net_position_per_tickerhead = net_position[ net_position['ticker_head'] == unique_tickerhead_list[i]] net_position_per_tickerhead.sort_values('tr_dte', ascending=True, inplace=True) selected_spread = spread_report[ (spread_report['ticker1'] == net_position_per_tickerhead['ticker'].values[0]) & (spread_report['ticker2'] == net_position_per_tickerhead['ticker'].values[1])] results_frame['qty'][i] = net_position_per_tickerhead[ 'qty'].values[0] if selected_spread.empty: results_frame['ticker1'][i] = net_position_per_tickerhead[ 'ticker'].values[0] results_frame['ticker2'][i] = net_position_per_tickerhead[ 'ticker'].values[1] else: results_frame['ticker1'][i] = selected_spread[ 'ticker1'].values[0] results_frame['ticker2'][i] = selected_spread[ 'ticker2'].values[0] selected_trades = trades4_strategy[ trades4_strategy['ticker'] == results_frame['ticker1'].values[i]] price_output = gfp.get_futures_price_preloaded( ticker=results_frame['ticker1'].values[i], settle_date=pd.to_datetime( selected_trades['trade_date'].values[0])) results_frame['timeHeld'][i] = price_output['tr_dte'].values[ 0] - net_position_per_tickerhead['tr_dte'].values[0] results_frame['pnl'][i] = pnl_per_tickerhead[ unique_tickerhead_list[i]].sum() if unique_tickerhead_list[i] in ['CL', 'B', 'ED']: results_frame['indicator'][i] = selected_spread[ 'reward_risk'].values[0] if results_frame['qty'][i] > 0: results_frame['recommendation'][i] = 'STOP' elif results_frame['qty'][i] < 0: if results_frame['indicator'][i] > -0.06: results_frame['recommendation'][i] = 'STOP' else: results_frame['recommendation'][i] = 'HOLD' else: results_frame['indicator'][i] = selected_spread[ 'q_carry'].values[0] if results_frame['qty'][i] > 0: if results_frame['indicator'][i] < 19: results_frame['recommendation'][i] = 'STOP' else: results_frame['recommendation'][i] = 'HOLD' elif results_frame['qty'][i] < 0: if results_frame['indicator'][i] > -9: results_frame['recommendation'][i] = 'STOP' else: results_frame['recommendation'][i] = 'HOLD' if results_frame['qty'][i] > 0: results_frame['downside'][i] = selected_spread[ 'downside'].values[0] * results_frame['qty'][i] else: results_frame['downside'][i] = selected_spread[ 'upside'].values[0] * results_frame['qty'][i] return {'success': True, 'results_frame': results_frame} elif strategy_class == 'vcs': greeks_out = sg.get_greeks_4strategy_4date(alias=kwargs['alias'], as_of_date=date_to) ticker_portfolio = greeks_out['ticker_portfolio'] options_position = greeks_out['options_position'] if ticker_portfolio.empty and not options_position.empty: result_output = { 'success': False, 'net_oev': np.NaN, 'net_theta': np.NaN, 'long_short_ratio': np.NaN, 'recommendation': 'MISSING DATA', 'last_adjustment_days_ago': np.NaN, 'min_tr_dte': np.NaN, 'long_oev': np.NaN, 'short_oev': np.NaN, 'favQMove': np.NaN } elif ticker_portfolio.empty and options_position.empty: result_output = { 'success': False, 'net_oev': np.NaN, 'net_theta': np.NaN, 'long_short_ratio': np.NaN, 'recommendation': 'EMPTY', 'last_adjustment_days_ago': np.NaN, 'min_tr_dte': np.NaN, 'long_oev': np.NaN, 'short_oev': np.NaN, 'favQMove': np.NaN } else: min_tr_dte = min([ exp.get_days2_expiration(ticker=x, date_to=date_to, instrument='options', con=con)['tr_dte'] for x in ticker_portfolio['ticker'] ]) net_oev = ticker_portfolio['total_oev'].sum() net_theta = ticker_portfolio['theta'].sum() long_portfolio = ticker_portfolio[ ticker_portfolio['total_oev'] > 0] short_portfolio = ticker_portfolio[ ticker_portfolio['total_oev'] < 0] short_portfolio['total_oev'] = abs(short_portfolio['total_oev']) long_oev = long_portfolio['total_oev'].sum() short_oev = short_portfolio['total_oev'].sum() if (not short_portfolio.empty) & (not long_portfolio.empty): long_short_ratio = 100 * long_oev / short_oev long_portfolio.sort_values('total_oev', ascending=False, inplace=True) short_portfolio.sort_values('total_oev', ascending=False, inplace=True) long_ticker = long_portfolio['ticker'].iloc[0] short_ticker = short_portfolio['ticker'].iloc[0] long_contract_specs = cmi.get_contract_specs(long_ticker) short_contract_specs = cmi.get_contract_specs(short_ticker) if 12*long_contract_specs['ticker_year']+long_contract_specs['ticker_month_num'] < \ 12*short_contract_specs['ticker_year']+short_contract_specs['ticker_month_num']: front_ticker = long_ticker back_ticker = short_ticker direction = 'long' else: front_ticker = short_ticker back_ticker = long_ticker direction = 'short' if 'vcs_output' in kwargs.keys(): vcs_output = kwargs['vcs_output'] else: vcs_output = ovcs.generate_vcs_sheet_4date(date_to=date_to) vcs_pairs = vcs_output['vcs_pairs'] selected_result = vcs_pairs[ (vcs_pairs['ticker1'] == front_ticker) & (vcs_pairs['ticker2'] == back_ticker)] if selected_result.empty: favQMove = np.NaN else: current_Q = selected_result['Q'].iloc[0] q_limit = of.get_vcs_filter_values( product_group=long_contract_specs['ticker_head'], filter_type='tickerHead', direction=direction, indicator='Q') if direction == 'long': favQMove = current_Q - q_limit elif direction == 'short': favQMove = q_limit - current_Q else: long_short_ratio = np.NaN favQMove = np.NaN trades_frame = ts.get_trades_4strategy_alias(**kwargs) trades_frame_options = trades_frame[trades_frame['instrument'] == 'O'] last_adjustment_days_ago = len( exp.get_bus_day_list( date_to=date_to, datetime_from=max( trades_frame_options['trade_date']).to_pydatetime())) if favQMove >= 10 and last_adjustment_days_ago > 10: recommendation = 'STOP-ratio normalized' elif min_tr_dte < 25: recommendation = 'STOP-close to expiration' elif np.isnan(long_short_ratio): recommendation = 'STOP-not a proper calendar' else: if long_short_ratio < 80: if favQMove < 0: recommendation = 'buy_options_to_grow' else: recommendation = 'buy_options_to_shrink' elif long_short_ratio > 120: if favQMove < 0: recommendation = 'sell_options_to_grow' else: recommendation = 'sell_options_to_shrink' else: recommendation = 'HOLD' result_output = { 'success': True, 'net_oev': net_oev, 'net_theta': net_theta, 'long_short_ratio': long_short_ratio, 'recommendation': recommendation, 'last_adjustment_days_ago': last_adjustment_days_ago, 'min_tr_dte': min_tr_dte, 'long_oev': long_oev, 'short_oev': short_oev, 'favQMove': favQMove } elif strategy_class == 'ocs': datetime_to = cu.convert_doubledate_2datetime(date_to) time_held = (datetime_to.date() - strategy_info_output['created_date'].date()).days notes = '' strategy_position = ts.get_net_position_4strategy_alias( alias=kwargs['alias'], as_of_date=date_to, con=con) if len(strategy_position.index) == 0: tpnl.close_strategy(alias=kwargs['alias'], close_date=date_to, broker=broker, con=con) result_output = { 'success': True, 'time_held': time_held, 'dollar_noise': np.nan, 'notes': 'closed' } elif strategy_position['qty'].sum() != 0: result_output = { 'success': True, 'time_held': time_held, 'dollar_noise': np.nan, 'notes': 'check position' } else: strategy_position['cont_indx'] = [ cmi.get_contract_specs(x)['cont_indx'] for x in strategy_position['ticker'] ] strategy_position.sort_values('cont_indx', ascending=True, inplace=True) ocs_output = ocs.generate_overnight_spreads_sheet_4date( date_to=date_to) overnight_calendars = ocs_output['overnight_calendars'] selection_indx = (overnight_calendars['ticker1'] == strategy_position['ticker'].iloc[0])&\ (overnight_calendars['ticker2'] == strategy_position['ticker'].iloc[1]) if sum(selection_indx) > 0: dollar_noise = (overnight_calendars.loc[ selection_indx, 'dollarNoise100'].values[0]) * abs( strategy_position['qty'].iloc[0]) else: dollar_noise = np.nan result_output = { 'success': True, 'time_held': time_held, 'dollar_noise': dollar_noise, 'notes': 'hold' } elif strategy_class == 'skpt': long_ticker = strategy_position.loc[strategy_position['qty'] > 0, 'ticker'].iloc[0] short_ticker = strategy_position.loc[strategy_position['qty'] < 0, 'ticker'].iloc[0] long_data = gsp.get_stock_price_preloaded(ticker=long_ticker, data_source='iex', settle_date_to=date_to) short_data = gsp.get_stock_price_preloaded(ticker=short_ticker, data_source='iex', settle_date_to=date_to) merged_data = pd.merge(long_data[['close', 'settle_datetime']], short_data[['close', 'settle_datetime']], how='inner', on='settle_datetime') merged_data.set_index('settle_datetime', drop=True, inplace=True) intaday_output_long = pweb.DataReader(long_ticker, 'iex-tops') intaday_output_short = pweb.DataReader(short_ticker, 'iex-tops') merged_data = merged_data.append( pd.DataFrame( { 'close_x': intaday_output_long.iloc[4].values[0], 'close_y': intaday_output_short.iloc[4].values[0] }, index=[dt.datetime.now()])) signal_output = spt.backtest(merged_data, 'close_x', 'close_y') return { 'long_ticker': long_ticker, 'short_ticker': short_ticker, 'zScoreC': signal_output['data_frame']['zScore'].iloc[-1], 'zScore': signal_output['data_frame']['zScore'].iloc[-2] } else: result_output = {'success': False} if 'con' not in kwargs.keys(): con.close() return result_output
def get_tms(**kwargs): date_to = kwargs['date_to'] feature_list = ['obv', 'rsi_6', 'rsi_12', 'atr_14', 'mfi_14', 'adx_14', 'adx_20', 'rocr_1', 'rocr_3', 'rocr_12', 'cci_12', 'cci_20', 'macd_12_26', 'macd_signal_12_26_9', 'macd_hist_12_26_9', 'williams_r_10', 'tsf_10_3', 'tsf_20_3', 'trix_15'] if 'ticker_head' in kwargs.keys(): ticker_head = kwargs['ticker_head'] panel_data = gfp.get_futures_price_preloaded(ticker_head=ticker_head, settle_date_to=date_to, settle_date_from=cu.doubledate_shift(date_to, 11 * 365)) panel_data.rename(columns={'open_price': 'open', 'high_price': 'high', 'low_price': 'low', 'close_price': 'close'},inplace=True) panel_data.sort_values(['cont_indx', 'settle_date'], ascending=[True, True], inplace=True) unique_cont_indx_list = panel_data['cont_indx'].unique() contract_data_list = [] for i in range(len(unique_cont_indx_list)): contract_data = panel_data[panel_data['cont_indx'] == unique_cont_indx_list[i]] if len(contract_data.index) < 30: continue contract_data = calc_tms(contract_data=contract_data) contract_data_list.append(contract_data) merged_frame = pd.concat(contract_data_list) merged_frame.dropna(subset=feature_list, inplace=True) merged_frame = merged_frame[merged_frame['cal_dte'] > 30] merged_frame.sort_values(['settle_date', 'cont_indx'], ascending=[True, True], inplace=True) data_out = merged_frame.drop_duplicates('settle_date', inplace=False, keep='first') elif 'stock_ticker' in kwargs.keys(): contract_data = gsp.get_stock_price_preloaded(ticker='AAPL', settle_date_to=date_to, settle_date_from=cu.doubledate_shift(date_to, 11 * 365)) contract_data = contract_data[['close', 'settle_datetime', 'volume', 'high', 'low', 'split_coefficient']] contract_data.reset_index(drop=True,inplace=True) split_envents = contract_data[contract_data['split_coefficient'] != 1] split_index_list = split_envents.index for i in range(len(split_index_list)): contract_data['close'].iloc[:split_index_list[i]] = \ contract_data['close'].iloc[:split_index_list[i]] / contract_data['split_coefficient'].iloc[split_index_list[i]] contract_data['high'].iloc[:split_index_list[i]] = \ contract_data['high'].iloc[:split_index_list[i]] / contract_data['split_coefficient'].iloc[ split_index_list[i]] contract_data['low'].iloc[:split_index_list[i]] = \ contract_data['low'].iloc[:split_index_list[i]] / contract_data['split_coefficient'].iloc[ split_index_list[i]] contract_data.rename(columns={'settle_datetime': 'settle_date'}, inplace=True) contract_data['change_1'] = contract_data.close - contract_data.close.shift(1) data_out = calc_tms(contract_data=contract_data) data_out.dropna(subset=feature_list, inplace=True) return data_out