def calc_delta_with_decays(row, risk_pd, risk_pids):
pid = row['positionId']
result = row['deltaWithDecays']
if pid in risk_pids:
result = [get_val(risk_pd.loc[pid]['deltas'], 0) / get_val(row['underlyerMultipliers'], 0),
get_val(risk_pd.loc[pid]['deltas'], 1) / get_val(row['underlyerMultipliers'], 1)]
return [np.nan, np.nan] if is_nan(result) else result
def process_positions(positions):
multi_asset_position_criteria = positions.productType.isin(['RATIO_SPREAD_EUROPEAN', 'SPREAD_EUROPEAN'])
single_asset_positions = positions[~multi_asset_position_criteria]
multi_asset_positions = positions[multi_asset_position_criteria]
if not multi_asset_positions.empty:
multi_asset_rows = list()
for index, row in multi_asset_positions.iterrows():
row2 = row.copy()
row['underlyerPrice'] = get_val(row.get('underlyerPrices'), 0)
row2['underlyerPrice'] = get_val(row.get('underlyerPrices'), 1)
row['underlyerInstrumentId'] = get_val(row.get('underlyerInstrumentIds'), 0)
row2['underlyerInstrumentId'] = get_val(row.get('underlyerInstrumentIds'), 1)
multi_asset_rows.append(row)
multi_asset_rows.append(row2)
multi_asset_positions = pd.DataFrame(multi_asset_rows)
single_asset_positions = pd.concat([multi_asset_positions, single_asset_positions], sort=False)
return single_asset_positions
def process_multi_asset_pos(positions, cash_flows, risks, domain, headers, params):
all_data = positions.merge(cash_flows, on='positionId', how='left').merge(risks, on='positionId', how='left')
rpt = all_data[['positionId', 'bookName', 'counterPartyName', 'tradeId', 'asset.underlyerInstrumentId1', 'vegas',
'asset.underlyerInstrumentId2', 'productType', 'initialNumber1', 'initialNumber2', 'gammas', 'r',
'unwindNumber1', 'unwindNumber2', 'tradeDate', 'asset.expirationDate', 'message', 'deltas', 'qs',
'asset.underlyerMultiplier1', 'asset.underlyerMultiplier2', 'price', 'theta', 'settle', 'vols',
'quantity1', 'quantity2', 'actualPremium', 'open', 'underlyerPrices', 'rhoR', 'unwind']]
rpt.rename(columns={'counterPartyName': 'partyName', 'asset.expirationDate': 'expirationDate'}, inplace=True)
rpt['marketValue'] = rpt['price']
rpt['underlyerInstrumentIds'] = rpt.apply(
lambda r: [r['asset.underlyerInstrumentId1'], r['asset.underlyerInstrumentId2']], axis=1)
# TODO: 这种写法可能会有性能问题，但考虑到只是针对多资产交易，数量少，暂时这样做。若以后交易量大，后台应提供相应批量查询接口以避免频繁调用接口
rpt['correlation'] = rpt.apply(lambda r: get_correlation(r['underlyerInstrumentIds'], domain, headers), axis=1)
rpt['underlyerMultipliers'] = rpt.apply(
lambda r: [r['asset.underlyerMultiplier1'], r['asset.underlyerMultiplier2']], axis=1)
rpt['initialNumbers'] = rpt.apply(lambda r: [r['initialNumber1'], r['initialNumber2']], axis=1)
rpt['unwindNumbers'] = rpt.apply(lambda r: [r['unwindNumber1'], r['unwindNumber2']], axis=1)
rpt['numbers'] = rpt.apply(lambda row: [np.float64(row['quantity1']) / row['underlyerMultipliers'][0],
np.float64(row['quantity2']) / row['underlyerMultipliers'][1]], axis=1)
rpt['premium'] = rpt.apply(
lambda row: np.float64(row['actualPremium']) if is_nan(row['open']) else np.float64(row['open']), axis=1)
rpt['unwindAmount'] = rpt.apply(
lambda row: 0 if is_nan(row['open']) else np.float64(row['unwind']) + np.float64(row['settle']), axis=1)
rpt['pnl'] = np.float64(rpt['marketValue']) + rpt['premium'] + rpt['unwindAmount']
rpt['deltas'] = rpt.apply(lambda r: [get_val(r['deltas'], 0) / get_val(r['underlyerMultipliers'], 0),
get_val(r['deltas'], 1) / get_val(r['underlyerMultipliers'], 1)], axis=1)
rpt['deltaCashes'] = rpt.apply(lambda r: [np.float64(r['deltas'][0]) * get_val(r['underlyerPrices'], 0),
np.float64(r['deltas'][1]) * get_val(r['underlyerPrices'], 1)], axis=1)
rpt['gammas'] = rpt.apply(
lambda r: [get_val(r['gammas'], 0, 0) * get_val(r['underlyerPrices'], 0) / r['underlyerMultipliers'][0] / 100,
get_val(r['gammas'], 1, 1) * get_val(r['underlyerPrices'], 1) / r['underlyerMultipliers'][1] / 100],
axis=1)
rpt['gammaCashes'] = rpt.apply(lambda r: [np.float64(r['gammas'][0]) * get_val(r['underlyerPrices'], 0),
np.float64(r['gammas'][1]) * get_val(r['underlyerPrices'], 1)], axis=1)
rpt['vegas'] = rpt.apply(lambda r: [get_val(r['vegas'], 0) / 100, get_val(r['vegas'], 1) / 100], axis=1)
rpt['theta'] = np.float64(rpt['theta']) / 365
rpt['rho'] = np.float64(rpt['rhoR']) / 100
rpt['deltaDecays'] = np.nan
rpt['deltaWithDecays'] = np.nan
rpt.drop(['asset.underlyerInstrumentId1', 'asset.underlyerInstrumentId2', 'asset.underlyerMultiplier1', 'quantity1',
'asset.underlyerMultiplier2', 'initialNumber1', 'initialNumber2', 'rhoR', 'quantity2', 'actualPremium',
'open', 'settle', 'unwindNumber1', 'unwindNumber2', 'underlyerPrices', 'unwind'], axis=1, inplace=True)
params['tradeIds'] = list(rpt.tradeId.unique())
decay_data = call_request(domain, 'pricing-service', 'prcPrice', params, headers)
if 'result' in decay_data:
diagnostics_pd = pd.DataFrame(decay_data['diagnostics'])
if not diagnostics_pd.empty:
diagnostics_pids = list(diagnostics_pd.key.unique())
diagnostics_pd.set_index('key', inplace=True)
diagnostics_pd.drop_duplicates(inplace=True)
rpt['message'] = rpt.apply(lambda row: get_error_msg(row, diagnostics_pd, diagnostics_pids), axis=1)
risk_pd = pd.DataFrame(decay_data['result'])
if not risk_pd.empty:
risk_pids = list(risk_pd.positionId.unique())
risk_pd = risk_pd.set_index('positionId')
rpt['deltaWithDecays'] = rpt.apply(lambda row: calc_delta_with_decays(row, risk_pd, risk_pids), axis=1)
rpt['deltaDecays'] = rpt.apply(lambda r: [get_val(r['deltaWithDecays'], 0) - get_val(r['deltas'], 0),
get_val(r['deltaWithDecays'], 1) - get_val(r['deltas'], 1)],
axis=1)
rpt['listedOption'] = False
return rpt
def massage_data(risks, yst_positions, cash_flows_today, position_index):
spread_types = ['RATIO_SPREAD_EUROPEAN', 'SPREAD_EUROPEAN']
if not position_index.empty:
multi_asset_pid_criteria = position_index.productType.isin(
spread_types)
multi_asset_pids = position_index[multi_asset_pid_criteria]
position_index = position_index[~multi_asset_pid_criteria]
if not multi_asset_pids.empty:
multi_asset_rows = list()
multi_asset_pids.reset_index(inplace=True)
multi_asset_position_ids = list(
multi_asset_pids.positionId.unique())
for index, row in multi_asset_pids.iterrows():
row2 = row.copy()
row['positionId'] = row['positionId'] + '_1'
row2['positionId'] = row2['positionId'] + '_2'
row['asset.underlyerInstrumentId'] = row[
'asset.underlyerInstrumentId1']
row2['asset.underlyerInstrumentId'] = row[
'asset.underlyerInstrumentId2']
row['asset.underlyerMultiplier'] = row[
'asset.underlyerMultiplier1']
row2['asset.underlyerMultiplier'] = row[
'asset.underlyerMultiplier2']
multi_asset_rows.append(row)
multi_asset_rows.append(row2)
multi_asset_pids = pd.DataFrame(multi_asset_rows).set_index(
'positionId')
position_index = pd.concat([position_index, multi_asset_pids],
sort=False)
if not risks.empty:
multi_asset_risks_criteria = risks.index.isin(
multi_asset_position_ids)
multi_asset_risks = risks[multi_asset_risks_criteria]
risks = risks[~multi_asset_risks_criteria]
if not multi_asset_risks.empty:
multi_asset_rows = list()
multi_asset_risks.reset_index(inplace=True)
for index, row in multi_asset_risks.iterrows():
row2 = row.copy()
row['positionId'] = row['positionId'] + '_1'
row2['positionId'] = row2['positionId'] + '_2'
row['vol'] = get_val(row.get('vols'), 0)
row2['vol'] = get_val(row.get('vols'), 1)
row['underlyerPrice'] = get_val(
row.get('underlyerPrices'), 0)
row2['underlyerPrice'] = get_val(
row.get('underlyerPrices'), 1)
multi_asset_rows.append(row)
multi_asset_rows.append(row2)
multi_asset_risks = pd.DataFrame(
multi_asset_rows).set_index('positionId')
risks = pd.concat([risks, multi_asset_risks], sort=False)
if not cash_flows_today.empty:
multi_asset_cash_flow_criteria = cash_flows_today.positionId.isin(
multi_asset_position_ids)
multi_asset_cash_flow = cash_flows_today[
multi_asset_cash_flow_criteria]
cash_flows_today = cash_flows_today[
~multi_asset_cash_flow_criteria]
if not multi_asset_cash_flow.empty:
multi_asset_rows = list()
for index, row in multi_asset_cash_flow.iterrows():
row2 = row.copy()
row['positionId'] = row['positionId'] + '_1'
row2['positionId'] = row2['positionId'] + '_2'
multi_asset_rows.append(row)
multi_asset_rows.append(row2)
multi_asset_cash_flow = pd.DataFrame(multi_asset_rows)
cash_flows_today = pd.concat(
[cash_flows_today, multi_asset_cash_flow], sort=False)
if not yst_positions.empty:
multi_asset_position_criteria = yst_positions.productType.isin(
spread_types)
multi_asset_positions = yst_positions[multi_asset_position_criteria]
yst_positions = yst_positions[~multi_asset_position_criteria]
if not multi_asset_positions.empty:
multi_asset_rows = list()
for index, row in multi_asset_positions.iterrows():
row2 = row.copy()
row['positionId'] = row['positionId'] + '_1'
row2['positionId'] = row2['positionId'] + '_2'
row['vol'] = get_val(row.get('vols'), 0)
row2['vol'] = get_val(row.get('vols'), 1)
row['delta'] = get_val(row.get('deltas'), 0)
row2['delta'] = get_val(row.get('deltas'), 1)
row['gamma'] = get_val(row.get('gammas'), 0)
row2['gamma'] = get_val(row.get('gammas'), 1)
row['vega'] = get_val(row.get('vegas'), 0)
row2['vega'] = get_val(row.get('vegas'), 1)
row['number'] = get_val(row.get('numbers'), 0)
row2['number'] = get_val(row.get('numbers'), 1)
row['underlyerPrice'] = get_val(row.get('underlyerPrices'), 0)
row2['underlyerPrice'] = get_val(row.get('underlyerPrices'), 1)
row['underlyerInstrumentId'] = get_val(
row.get('underlyerInstrumentIds'), 0)
row2['underlyerInstrumentId'] = get_val(
row.get('underlyerInstrumentIds'), 1)
row['underlyerMultiplier'] = get_val(
row.get('underlyerMultipliers'), 0)
row2['underlyerMultiplier'] = get_val(
row.get('underlyerMultipliers'), 1)
multi_asset_rows.append(row)
multi_asset_rows.append(row2)
multi_asset_positions = pd.DataFrame(multi_asset_rows)
yst_positions = pd.concat([yst_positions, multi_asset_positions],
sort=False)
return risks, yst_positions, cash_flows_today, position_index