def calculate_contract_risk(**kwargs):
contract_frame = kwargs['contract_frame']
current_date = kwargs['current_date']
contract_frame['risk'] = np.nan
contract_frame['contract_multiplier'] = np.nan
for i in range(len(contract_frame.index)):
ticker_raw = contract_frame['ticker'].iloc[i]
ticker_output = ticker_raw.split('-')
contract_multiplier = cmi.contract_multiplier[
contract_frame['ticker_head'].iloc[i]]
contract_frame['contract_multiplier'].iloc[i] = contract_multiplier
if len(ticker_output) == 1:
ticker = ticker_output[0]
data_out = gfp.get_futures_price_preloaded(
ticker=ticker, settle_date_to=current_date)
recent_data = data_out.iloc[-10:]
contract_frame['risk'].iloc[i] = contract_multiplier * (
recent_data['close_price'].max() -
recent_data['close_price'].min())
if len(ticker_output) > 1:
aligned_output = opUtil.get_aligned_futures_data(
contract_list=ticker_output,
contracts_back=1,
aggregation_method=12,
date_to=current_date)
aligned_data = aligned_output['aligned_data']
recent_data = aligned_data.iloc[-10:]
spread_price = recent_data['c1']['close_price'] - recent_data[
'c2']['close_price']
contract_frame['risk'].iloc[i] = contract_multiplier * (
spread_price.max() - spread_price.min())
return contract_frame
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_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_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_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_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_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_futures_spread_carry_signals(**kwargs):
ticker_list = kwargs['ticker_list']
date_to = kwargs['date_to']
if 'tr_dte_list' in kwargs.keys():
tr_dte_list = kwargs['tr_dte_list']
else:
tr_dte_list = [exp.get_futures_days2_expiration({'ticker': x,'date_to': date_to}) for x in ticker_list]
if 'aggregation_method' in kwargs.keys() and 'contracts_back' in kwargs.keys():
aggregation_method = kwargs['aggregation_method']
contracts_back = kwargs['contracts_back']
else:
amcb_output = opUtil.get_aggregation_method_contracts_back(cmi.get_contract_specs(ticker_list[0]))
aggregation_method = amcb_output['aggregation_method']
contracts_back = amcb_output['contracts_back']
if 'use_last_as_current' in kwargs.keys():
use_last_as_current = kwargs['use_last_as_current']
else:
use_last_as_current = False
if 'futures_data_dictionary' in kwargs.keys():
futures_data_dictionary = kwargs['futures_data_dictionary']
else:
futures_data_dictionary = {x: gfp.get_futures_price_preloaded(ticker_head=x) for x in [cmi.get_contract_specs(ticker_list[0])['ticker_head']]}
if 'contract_multiplier' in kwargs.keys():
contract_multiplier = kwargs['contract_multiplier']
else:
contract_multiplier = cmi.contract_multiplier[cmi.get_contract_specs(ticker_list[0])['ticker_head']]
if 'datetime5_years_ago' in kwargs.keys():
datetime5_years_ago = kwargs['datetime5_years_ago']
else:
date5_years_ago = cu.doubledate_shift(date_to,5*365)
datetime5_years_ago = cu.convert_doubledate_2datetime(date5_years_ago)
if 'datetime2_months_ago' in kwargs.keys():
datetime2_months_ago = kwargs['datetime2_months_ago']
else:
date2_months_ago = cu.doubledate_shift(date_to,60)
datetime2_months_ago = cu.convert_doubledate_2datetime(date2_months_ago)
aligned_output = opUtil.get_aligned_futures_data(contract_list=ticker_list,
tr_dte_list=tr_dte_list,
aggregation_method=aggregation_method,
contracts_back=contracts_back,
date_to=date_to,
futures_data_dictionary=futures_data_dictionary,
use_last_as_current=use_last_as_current)
aligned_data = aligned_output['aligned_data']
current_data = aligned_output['current_data']
last5_years_indx = aligned_data['settle_date']>=datetime5_years_ago
data_last5_years = aligned_data[last5_years_indx]
ticker1_list = [current_data['c' + str(x+1)]['ticker'] for x in range(len(ticker_list)-1)]
ticker2_list = [current_data['c' + str(x+2)]['ticker'] for x in range(len(ticker_list)-1)]
yield_current_list = [100*(current_data['c' + str(x+1)]['close_price']-
current_data['c' + str(x+2)]['close_price'])/
current_data['c' + str(x+2)]['close_price']
for x in range(len(ticker_list)-1)]
price_current_list = [current_data['c' + str(x+1)]['close_price']-current_data['c' + str(x+2)]['close_price']
for x in range(len(ticker_list)-1)]
yield_history = [100*(aligned_data['c' + str(x+1)]['close_price']-
aligned_data['c' + str(x+2)]['close_price'])/
aligned_data['c' + str(x+2)]['close_price']
for x in range(len(ticker_list)-1)]
change_5_history = [data_last5_years['c' + str(x+1)]['change_5']-
data_last5_years['c' + str(x+2)]['change_5']
for x in range(len(ticker_list)-1)]
change5 = [contract_multiplier*(current_data['c' + str(x+1)]['change5']-
current_data['c' + str(x+2)]['change5'])
for x in range(len(ticker_list)-1)]
change10 = [contract_multiplier*(current_data['c' + str(x+1)]['change10']-
current_data['c' + str(x+2)]['change10'])
for x in range(len(ticker_list)-1)]
change20 = [contract_multiplier*(current_data['c' + str(x+1)]['change20']-
current_data['c' + str(x+2)]['change20'])
for x in range(len(ticker_list)-1)]
front_tr_dte = [current_data['c' + str(x+1)]['tr_dte'] for x in range(len(ticker_list)-1)]
q_list = [stats.get_quantile_from_number({'x': yield_current_list[x],
'y': yield_history[x].values,
'clean_num_obs': max(100, round(3*len(yield_history[x].values)/4))})
for x in range(len(ticker_list)-1)]
percentile_vector = [stats.get_number_from_quantile(y=change_5_history[x].values,
quantile_list=[1, 15, 85, 99],
clean_num_obs=max(100, round(3*len(change_5_history[x].values)/4)))
for x in range(len(ticker_list)-1)]
q1 = [x[0] for x in percentile_vector]
q15 = [x[1] for x in percentile_vector]
q85 = [x[2] for x in percentile_vector]
q99 = [x[3] for x in percentile_vector]
downside = [contract_multiplier*(q1[x]+q15[x])/2 for x in range(len(q1))]
upside = [contract_multiplier*(q85[x]+q99[x])/2 for x in range(len(q1))]
carry = [contract_multiplier*(price_current_list[x]-price_current_list[x+1]) for x in range(len(q_list)-1)]
q_carry = [q_list[x]-q_list[x+1] for x in range(len(q_list)-1)]
reward_risk = [5*carry[x]/((front_tr_dte[x+1]-front_tr_dte[x])*abs(downside[x+1])) if carry[x]>0
else 5*carry[x]/((front_tr_dte[x+1]-front_tr_dte[x])*upside[x+1]) for x in range(len(carry))]
return pd.DataFrame.from_items([('ticker1',ticker1_list),
('ticker2',ticker2_list),
('ticker_head',cmi.get_contract_specs(ticker_list[0])['ticker_head']),
('front_tr_dte',front_tr_dte),
('carry',[np.NAN]+carry),
('q_carry',[np.NAN]+q_carry),
('reward_risk',[np.NAN]+reward_risk),
('price',price_current_list),
('q',q_list),
('upside',upside),
('downside',downside),
('change5',change5),
('change10',change10),
('change20',change20)])
def get_futures_butterfly_signals(**kwargs):
ticker_list = kwargs['ticker_list']
date_to = kwargs['date_to']
if 'tr_dte_list' in kwargs.keys():
tr_dte_list = kwargs['tr_dte_list']
else:
tr_dte_list = [exp.get_futures_days2_expiration({'ticker': x,'date_to': date_to}) for x in ticker_list]
if 'aggregation_method' in kwargs.keys() and 'contracts_back' in kwargs.keys():
aggregation_method = kwargs['aggregation_method']
contracts_back = kwargs['contracts_back']
else:
amcb_output = opUtil.get_aggregation_method_contracts_back(cmi.get_contract_specs(ticker_list[0]))
aggregation_method = amcb_output['aggregation_method']
contracts_back = amcb_output['contracts_back']
if 'use_last_as_current' in kwargs.keys():
use_last_as_current = kwargs['use_last_as_current']
else:
use_last_as_current = False
if 'futures_data_dictionary' in kwargs.keys():
futures_data_dictionary = kwargs['futures_data_dictionary']
else:
futures_data_dictionary = {x: gfp.get_futures_price_preloaded(ticker_head=x) for x in [cmi.get_contract_specs(ticker_list[0])['ticker_head']]}
if 'contract_multiplier' in kwargs.keys():
contract_multiplier = kwargs['contract_multiplier']
else:
contract_multiplier = cmi.contract_multiplier[cmi.get_contract_specs(ticker_list[0])['ticker_head']]
if 'datetime5_years_ago' in kwargs.keys():
datetime5_years_ago = kwargs['datetime5_years_ago']
else:
date5_years_ago = cu.doubledate_shift(date_to,5*365)
datetime5_years_ago = cu.convert_doubledate_2datetime(date5_years_ago)
if 'datetime2_months_ago' in kwargs.keys():
datetime2_months_ago = kwargs['datetime2_months_ago']
else:
date2_months_ago = cu.doubledate_shift(date_to,60)
datetime2_months_ago = cu.convert_doubledate_2datetime(date2_months_ago)
aligned_output = opUtil.get_aligned_futures_data(contract_list=ticker_list,
tr_dte_list=tr_dte_list,
aggregation_method=aggregation_method,
contracts_back=contracts_back,
date_to=date_to,
futures_data_dictionary=futures_data_dictionary,
use_last_as_current=use_last_as_current)
current_data = aligned_output['current_data']
aligned_data = aligned_output['aligned_data']
month_diff_1 = 12*(current_data['c1']['ticker_year']-current_data['c2']['ticker_year'])+(current_data['c1']['ticker_month']-current_data['c2']['ticker_month'])
month_diff_2 = 12*(current_data['c2']['ticker_year']-current_data['c3']['ticker_year'])+(current_data['c2']['ticker_month']-current_data['c3']['ticker_month'])
weight_11 = 2*month_diff_2/(month_diff_1+month_diff_1)
weight_12 = -2
weight_13 = 2*month_diff_1/(month_diff_1+month_diff_1)
price_1 = current_data['c1']['close_price']
price_2 = current_data['c2']['close_price']
price_3 = current_data['c3']['close_price']
linear_interp_price2 = (weight_11*aligned_data['c1']['close_price']+weight_13*aligned_data['c3']['close_price'])/2
butterfly_price = aligned_data['c1']['close_price']-2*aligned_data['c2']['close_price']+aligned_data['c3']['close_price']
price_ratio = linear_interp_price2/aligned_data['c2']['close_price']
linear_interp_price2_current = (weight_11*price_1+weight_13*price_3)/2
price_ratio_current = linear_interp_price2_current/price_2
q = stats.get_quantile_from_number({'x': price_ratio_current, 'y': price_ratio.values, 'clean_num_obs': max(100, round(3*len(price_ratio.values)/4))})
qf = stats.get_quantile_from_number({'x': price_ratio_current, 'y': price_ratio.values[-40:], 'clean_num_obs': 30})
recent_quantile_list = [stats.get_quantile_from_number({'x': x, 'y': price_ratio.values[-40:], 'clean_num_obs': 30}) for x in price_ratio.values[-40:]]
weight1 = weight_11
weight2 = weight_12
weight3 = weight_13
last5_years_indx = aligned_data['settle_date']>=datetime5_years_ago
last2_months_indx = aligned_data['settle_date']>=datetime2_months_ago
data_last5_years = aligned_data[last5_years_indx]
yield1 = 100*(aligned_data['c1']['close_price']-aligned_data['c2']['close_price'])/aligned_data['c2']['close_price']
yield2 = 100*(aligned_data['c2']['close_price']-aligned_data['c3']['close_price'])/aligned_data['c3']['close_price']
yield1_last5_years = yield1[last5_years_indx]
yield2_last5_years = yield2[last5_years_indx]
yield1_current = 100*(current_data['c1']['close_price']-current_data['c2']['close_price'])/current_data['c2']['close_price']
yield2_current = 100*(current_data['c2']['close_price']-current_data['c3']['close_price'])/current_data['c3']['close_price']
butterfly_price_current = current_data['c1']['close_price']\
-2*current_data['c2']['close_price']\
+current_data['c3']['close_price']
yield_regress_output = stats.get_regression_results({'x':yield2, 'y':yield1,'x_current': yield2_current, 'y_current': yield1_current,
'clean_num_obs': max(100, round(3*len(yield1.values)/4))})
yield_regress_output_last5_years = stats.get_regression_results({'x':yield2_last5_years, 'y':yield1_last5_years,
'x_current': yield2_current, 'y_current': yield1_current,
'clean_num_obs': max(100, round(3*len(yield1_last5_years.values)/4))})
bf_qz_frame_short = pd.DataFrame()
bf_qz_frame_long = pd.DataFrame()
if (len(yield1) >= 40)&(len(yield2) >= 40):
recent_zscore_list = [(yield1[-40+i]-yield_regress_output['alpha']-yield_regress_output['beta']*yield2[-40+i])/yield_regress_output['residualstd'] for i in range(40)]
bf_qz_frame = pd.DataFrame.from_items([('bf_price', butterfly_price.values[-40:]),
('q',recent_quantile_list),
('zscore', recent_zscore_list)])
bf_qz_frame = np.round(bf_qz_frame, 8)
bf_qz_frame.drop_duplicates(['bf_price'], take_last=True, inplace=True)
# return bf_qz_frame
bf_qz_frame_short = bf_qz_frame[(bf_qz_frame['zscore'] >= 0.6) & (bf_qz_frame['q'] >= 85)]
bf_qz_frame_long = bf_qz_frame[(bf_qz_frame['zscore'] <= -0.6) & (bf_qz_frame['q'] <= 12)]
if bf_qz_frame_short.empty:
short_price_limit = np.NAN
else:
short_price_limit = bf_qz_frame_short['bf_price'].min()
if bf_qz_frame_long.empty:
long_price_limit = np.NAN
else:
long_price_limit = bf_qz_frame_long['bf_price'].max()
zscore1= yield_regress_output['zscore']
rsquared1= yield_regress_output['rsquared']
zscore2= yield_regress_output_last5_years['zscore']
rsquared2= yield_regress_output_last5_years['rsquared']
second_spread_weight_1 = yield_regress_output['beta']
second_spread_weight_2 = yield_regress_output_last5_years['beta']
butterfly_5_change = data_last5_years['c1']['change_5']\
- (1+second_spread_weight_1)*data_last5_years['c2']['change_5']\
+ second_spread_weight_1*data_last5_years['c3']['change_5']
butterfly_5_change_current = current_data['c1']['change_5']\
- (1+second_spread_weight_1)*current_data['c2']['change_5']\
+ second_spread_weight_1*current_data['c3']['change_5']
butterfly_1_change = data_last5_years['c1']['change_1']\
- (1+second_spread_weight_1)*data_last5_years['c2']['change_1']\
+ second_spread_weight_1*data_last5_years['c3']['change_1']
percentile_vector = stats.get_number_from_quantile(y=butterfly_5_change.values,
quantile_list=[1, 15, 85, 99],
clean_num_obs=max(100, round(3*len(butterfly_5_change.values)/4)))
downside = contract_multiplier*(percentile_vector[0]+percentile_vector[1])/2
upside = contract_multiplier*(percentile_vector[2]+percentile_vector[3])/2
recent_5day_pnl = contract_multiplier*butterfly_5_change_current
residuals = yield1-yield_regress_output['alpha']-yield_regress_output['beta']*yield2
regime_change_ind = (residuals[last5_years_indx].mean()-residuals.mean())/residuals.std()
contract_seasonality_ind = (residuals[aligned_data['c1']['ticker_month'] == current_data['c1']['ticker_month']].mean()-residuals.mean())/residuals.std()
yield1_quantile_list = stats.get_number_from_quantile(y=yield1, quantile_list=[10, 90])
yield2_quantile_list = stats.get_number_from_quantile(y=yield2, quantile_list=[10, 90])
noise_ratio = (yield1_quantile_list[1]-yield1_quantile_list[0])/(yield2_quantile_list[1]-yield2_quantile_list[0])
daily_noise_recent = stats.get_stdev(x=butterfly_1_change.values[-20:], clean_num_obs=15)
daily_noise_past = stats.get_stdev(x=butterfly_1_change.values, clean_num_obs=max(100, round(3*len(butterfly_1_change.values)/4)))
recent_vol_ratio = daily_noise_recent/daily_noise_past
alpha1 = yield_regress_output['alpha']
residuals_last5_years = residuals[last5_years_indx]
residuals_last2_months = residuals[last2_months_indx]
residual_current = yield1_current-alpha1-second_spread_weight_1*yield2_current
z3 = (residual_current-residuals_last5_years.mean())/residuals.std()
z4 = (residual_current-residuals_last2_months.mean())/residuals.std()
yield_change = (alpha1+second_spread_weight_1*yield2_current-yield1_current)/(1+second_spread_weight_1)
new_yield1 = yield1_current + yield_change
new_yield2 = yield2_current - yield_change
price_change1 = 100*((price_2*(new_yield1+100)/100)-price_1)/(200+new_yield1)
price_change2 = 100*((price_3*(new_yield2+100)/100)-price_2)/(200+new_yield2)
theo_pnl = contract_multiplier*(2*price_change1-2*second_spread_weight_1*price_change2)
aligned_data['residuals'] = residuals
aligned_output['aligned_data'] = aligned_data
grouped = aligned_data.groupby(aligned_data['c1']['cont_indx'])
aligned_data['shifted_residuals'] = grouped['residuals'].shift(-5)
aligned_data['residual_change'] = aligned_data['shifted_residuals']-aligned_data['residuals']
mean_reversion = stats.get_regression_results({'x':aligned_data['residuals'].values,
'y':aligned_data['residual_change'].values,
'clean_num_obs': max(100, round(3*len(yield1.values)/4))})
theo_spread_move_output = su.calc_theo_spread_move_from_ratio_normalization(ratio_time_series=price_ratio.values[-40:],
starting_quantile=qf,
num_price=linear_interp_price2_current,
den_price=current_data['c2']['close_price'],
favorable_quantile_move_list=[5, 10, 15, 20, 25])
theo_pnl_list = [x*contract_multiplier*2 for x in theo_spread_move_output['theo_spread_move_list']]
return {'aligned_output': aligned_output, 'q': q, 'qf': qf,
'theo_pnl_list': theo_pnl_list,
'ratio_target_list': theo_spread_move_output['ratio_target_list'],
'weight1': weight1, 'weight2': weight2, 'weight3': weight3,
'zscore1': zscore1, 'rsquared1': rsquared1, 'zscore2': zscore2, 'rsquared2': rsquared2,
'zscore3': z3, 'zscore4': z4,
'zscore5': zscore1-regime_change_ind,
'zscore6': zscore1-contract_seasonality_ind,
'zscore7': zscore1-regime_change_ind-contract_seasonality_ind,
'theo_pnl': theo_pnl,
'regime_change_ind' : regime_change_ind,'contract_seasonality_ind': contract_seasonality_ind,
'second_spread_weight_1': second_spread_weight_1, 'second_spread_weight_2': second_spread_weight_2,
'downside': downside, 'upside': upside,
'yield1': yield1, 'yield2': yield2, 'yield1_current': yield1_current, 'yield2_current': yield2_current,
'bf_price': butterfly_price_current, 'short_price_limit': short_price_limit,'long_price_limit':long_price_limit,
'noise_ratio': noise_ratio,
'alpha1': alpha1, 'alpha2': yield_regress_output_last5_years['alpha'],
'residual_std1': yield_regress_output['residualstd'], 'residual_std2': yield_regress_output_last5_years['residualstd'],
'recent_vol_ratio': recent_vol_ratio, 'recent_5day_pnl': recent_5day_pnl,
'price_1': price_1, 'price_2': price_2, 'price_3': price_3, 'last5_years_indx': last5_years_indx,
'price_ratio': price_ratio,
'mean_reversion_rsquared': mean_reversion['rsquared'],
'mean_reversion_signif' : (mean_reversion['conf_int'][1, :] < 0).all()}
