def load_latest_tt_fills(**kwargs):
file_list = os.listdir(dna.tt_fill_directory)
num_files = len(file_list)
time_list = []
for i in range(num_files):
time_list.append(os.path.getmtime(dna.tt_fill_directory + '/' + file_list[i]))
loc_latest_file = time_list.index(max(time_list))
dna.get_directory_name(ext='daily') + '/ttFills.xlsx'
tt_export_frame = pd.read_excel(dna.get_directory_name(ext='daily') + '/ttFills.xlsx',header=None,
names=['trade_date','trade_time','exchange','product','contract','prod_type','B/S','qty','price','P/F', 'route','account','order_tag',
'originator','current_user','tt_order_id','parent_id'])
tt_export_frame_filtered = tt_export_frame[tt_export_frame['prod_type']=='Future']
if 'tags2exclude' in kwargs.keys():
tt_export_frame_filtered['order_tag'] = tt_export_frame_filtered['order_tag'].astype('str')
tt_export_frame_filtered = tt_export_frame_filtered[[not any([y in x for y in kwargs['tags2exclude']]) for x in tt_export_frame_filtered['order_tag']]]
return tt_export_frame_filtered
def assign_trades_2strategies(**kwargs):
trade_source = kwargs['trade_source']
if trade_source == 'tt':
formatted_fills = get_formatted_tt_fills(**kwargs)
elif trade_source == 'cme_direct':
formatted_fills = get_formatted_cme_direct_fills()
elif trade_source == 'manual_entry':
formatted_fills = get_formatted_manual_entry_fills()
aggregate_trades = formatted_fills['aggregate_trades']
allocation_frame = pd.read_excel(dna.get_directory_name(ext='daily') + '/' + 'trade_allocation.xlsx')
combined_list = [None]*len(allocation_frame.index)
for i in range(len(allocation_frame.index)):
if allocation_frame['criteria'][i]=='tickerhead':
selected_trades = aggregate_trades[aggregate_trades['ticker_head'] == allocation_frame['value'][i]]
elif allocation_frame['criteria'][i]=='ticker':
selected_trades = aggregate_trades[aggregate_trades['ticker'] == allocation_frame['value'][i]]
combined_list[i] = selected_trades[['ticker','option_type','strike_price','trade_price','trade_quantity','instrument','real_tradeQ']]
combined_list[i]['alias'] = allocation_frame['alias'][i]
return pd.concat(combined_list).reset_index(drop=True)
def send_hrsn_report(**kwargs):
daily_dir = dna.get_directory_name(ext='daily')
if 'report_date' in kwargs.keys():
report_date = kwargs['report_date']
else:
report_date = exp.doubledate_shift_bus_days()
ibo_dir = ts.create_strategy_output_dir(strategy_class='os', report_date=report_date)
cov_data_integrity = ''
try:
with open(ibo_dir + '/' + 'covDataIntegrity.txt','r') as text_file:
cov_data_integrity = text_file.read()
except Exception:
pass
try:
expiration_report = ef.get_expiration_report(report_date=report_date, con=kwargs['con'])
expiration_report = expiration_report[expiration_report['tr_days_2roll'] < 5]
if expiration_report.empty:
expiration_text = 'No near expirations.'
else:
expiration_text = 'Check for approaching expirations!'
except Exception:
expiration_text = 'Check expiration report for errors!'
se.send_email_with_attachment(subject='hrsn_' + str(report_date),
email_text='cov_data_integrity: ' + cov_data_integrity + "\r\n" + expiration_text,
attachment_list = [daily_dir + '/' + 'pnl_final_' + str(report_date) + '.xlsx', daily_dir +
'/' + 'followup_' + str(report_date) + '.xlsx'])
def send_wh_report(**kwargs):
if 'report_date' in kwargs.keys():
report_date = kwargs['report_date']
else:
report_date = exp.doubledate_shift_bus_days()
ta_output_dir = dn.get_dated_directory_extension(folder_date=report_date, ext='ta')
strategy_frame = tpm.get_daily_pnl_snapshot(as_of_date=report_date, name='final')
total_pnl_row = strategy_frame[strategy_frame.alias == 'TOTAL']
report_date_str = cu.convert_datestring_format({'date_string': str(report_date), 'format_from': 'yyyymmdd', 'format_to': 'dd/mm/yyyy'})
config_output = su.read_config_file(file_name=dna.get_directory_name(ext='daily') + '/riskAndMargin.txt')
email_text = "Expected Maximum Drawdown: " + config_output['emd'] + "K" + \
"\nMargin: " + str(int(config_output['iceMargin']) + int(config_output['cmeMargin'])) + "K" + \
"\nNet Liquidating Value: " + config_output['pnl'] + "K" + \
"\n \nSee attached for individual strategy pnls."
se.send_email_with_attachment(send_from='*****@*****.**',
send_to=['*****@*****.**','*****@*****.**'],
sender_account_alias='wh_trading',
subject='Daily PnL for ' + report_date_str + ' is: ' + '${:,}'.format(total_pnl_row['daily_pnl'].iloc[0]),
email_text=email_text,
attachment_list = [ta_output_dir + '/' + 'pnl_final.xlsx'],
attachment_name_list=['PnLs.xlsx'])
def get_futures_price_preloaded(**kwargs):
if 'ticker_head' in kwargs.keys():
ticker_head = kwargs['ticker_head']
else:
ticker = kwargs['ticker']
contract_specs_output = cmi.get_contract_specs(ticker)
ticker_head = contract_specs_output['ticker_head']
file_ticker = cmi.mini_contract_dictionary.get(
ticker_head,
ticker_head) + contract_specs_output['ticker_month_str'] + str(
contract_specs_output['ticker_year'])
if 'futures_data_dictionary' in kwargs.keys():
data_out = kwargs['futures_data_dictionary'][ticker_head]
else:
presaved_futures_data_folder = dna.get_directory_name(
ext='presaved_futures_data')
file_ticker_head = cmi.mini_contract_dictionary.get(
ticker_head, ticker_head)
if os.path.isfile(presaved_futures_data_folder + '/' +
file_ticker_head + '.pkl'):
data_out = pd.read_pickle(presaved_futures_data_folder + '/' +
file_ticker_head + '.pkl')
else:
data_out = pd.DataFrame()
return data_out
if 'settle_date' in kwargs.keys():
settle_date = kwargs['settle_date']
if isinstance(settle_date, int):
data_out = data_out[data_out['settle_date'] ==
cu.convert_doubledate_2datetime(settle_date)]
elif isinstance(settle_date, dt.datetime):
data_out = data_out[data_out['settle_date'] == settle_date]
if 'settle_date_from_exclusive' in kwargs.keys():
data_out = data_out[
data_out['settle_date'] > cu.convert_doubledate_2datetime(
kwargs['settle_date_from_exclusive'])]
if 'settle_date_from' in kwargs.keys():
data_out = data_out[
data_out['settle_date'] >= cu.convert_doubledate_2datetime(
kwargs['settle_date_from'])]
if 'settle_date_to' in kwargs.keys():
settle_date_to = kwargs['settle_date_to']
if isinstance(settle_date_to, int):
data_out = data_out[
data_out['settle_date'] <= cu.convert_doubledate_2datetime(
kwargs['settle_date_to'])]
elif isinstance(settle_date_to, dt.datetime):
data_out = data_out[data_out['settle_date'] <= settle_date_to]
if 'ticker' in kwargs.keys():
data_out = data_out[data_out['ticker'] == file_ticker]
return data_out
def get_strategy_target_size(**kwargs):
config_output = su.read_text_file(
file_name=dna.get_directory_name(ext='c#config') + '/BetSize.txt')
bet_size = float(config_output[0])
return bet_size * target_size_multiplier_dictionary[
kwargs['strategy_class']]
def load_cme__fills(**kwargs):
fill_frame = pd.read_csv(dna.get_directory_name(ext='daily') + '/' + cme_direct_fill_file_name, header=1)
fill_frame_filtered = fill_frame[fill_frame['IsStrategy'] == False]
fill_frame_filtered.reset_index(inplace=True,drop=True)
return fill_frame_filtered[['ContractCode', 'Side', 'Price', 'FilledQuantity']]
def get_stock_price_preloaded(**kwargs):
ticker = kwargs['ticker']
if 'data_source' in kwargs.keys():
data_source = kwargs['data_source']
else:
data_source = 'iex'
if data_source == 'iex':
file_dir = dna.get_directory_name(ext='iex_stock_data')
else:
file_dir = dna.get_directory_name(ext='stock_data')
if 'stock_data_dictionary' in kwargs.keys():
data_out = kwargs['stock_data_dictionary'][ticker]
else:
if not os.path.isfile(file_dir + '/' + ticker + '.pkl'):
ssd.save_stock_data(symbol_list=[ticker],data_source=data_source)
data_out = pd.read_pickle(file_dir + '/' + ticker + '.pkl')
report_date = exp.doubledate_shift_bus_days()
if cu.convert_doubledate_2datetime(report_date)>data_out['settle_datetime'].iloc[-1].to_pydatetime():
ssd.save_stock_data(symbol_list=[ticker],data_source=data_source)
data_out = pd.read_pickle(file_dir + '/' + ticker + '.pkl')
if 'settle_date' in kwargs.keys():
settle_date = kwargs['settle_date']
if isinstance(settle_date,int):
data_out = data_out[data_out['settle_datetime'] == cu.convert_doubledate_2datetime(settle_date)]
elif isinstance(settle_date,dt.datetime):
data_out = data_out[data_out['settle_datetime'] == settle_date]
if 'settle_date_from' in kwargs.keys():
data_out = data_out[data_out['settle_datetime']>=cu.convert_doubledate_2datetime(kwargs['settle_date_from'])]
if 'settle_date_to' in kwargs.keys():
data_out = data_out[data_out['settle_datetime']<=cu.convert_doubledate_2datetime(kwargs['settle_date_to'])]
return data_out
def get_pca_seasonality_adjustments(**kwargs):
ticker_head = kwargs['ticker_head']
if 'file_date_to' in kwargs.keys():
file_date_to = kwargs['file_date_to']
else:
file_date_to = 20160219
if 'years_back' in kwargs.keys():
years_back = kwargs['years_back']
else:
years_back = 10
if 'date_to' in kwargs.keys():
date_to = kwargs['date_to']
else:
date_to = file_date_to
date5_years_ago = cu.doubledate_shift(date_to, 5 * 365)
backtest_output_dir = dna.get_directory_name(ext='backtest_results')
file_name = ticker_head + '_' + str(file_date_to) + '_' + str(
years_back) + '_z'
if os.path.isfile(backtest_output_dir + '/curve_pca/' + file_name +
'.pkl'):
backtest_results = pd.read_pickle(backtest_output_dir + '/curve_pca/' +
file_name + '.pkl')
else:
return pd.DataFrame.from_items([('monthSpread', [1] * 12 + [6] * 2),
('ticker_month_front',
list(range(1, 13)) + [6] + [12]),
('z_seasonal_mean', [0] * 14)])
entire_report = pd.concat(backtest_results['report_results_list'])
selected_report = entire_report[
(entire_report['report_date'] <= date_to)
& (entire_report['report_date'] >= date5_years_ago)]
selected_report = selected_report[(selected_report['tr_dte_front'] > 80)
& (selected_report['monthSpread'] < 12)]
grouped = selected_report.groupby(['monthSpread', 'ticker_month_front'])
seasonality_adjustment = pd.DataFrame()
seasonality_adjustment['monthSpread'] = (
grouped['monthSpread'].first()).values
seasonality_adjustment['ticker_month_front'] = (
grouped['ticker_month_front'].first()).values
seasonality_adjustment['z_seasonal_mean'] = (grouped['z'].mean()).values
return seasonality_adjustment
def get_daily_price_data4ticker(**kwargs):
date_to = kwargs['date_to']
datetime_to = cu.convert_doubledate_2datetime(date_to)
try:
data_out = pd.read_pickle(
dna.get_directory_name(ext='binance') + '/daily/' +
kwargs['ticker'] + '.pkl')
except:
return pd.DataFrame()
#data_out['pydate'] = [x.to_pydatetime().date() for x in data_out['openDatetime']]
return data_out[data_out['openDate'] <= datetime_to.date()]
def get_strategy_class_historical_pnls(**kwargs):
con = msu.get_my_sql_connection(**kwargs)
if 'as_of_date' in kwargs.keys():
as_of_date = kwargs['as_of_date']
else:
as_of_date = exp.doubledate_shift_bus_days()
strategy_frame = ts.select_strategies(con=con, open_date_to=as_of_date)
strategy_frame['strategy_class'] = [
sc.convert_from_string_to_dictionary(
string_input=strategy_frame['description_string'][x])
['strategy_class'] for x in range(len(strategy_frame.index))
]
unique_strategy_class_list = strategy_frame['strategy_class'].unique()
time_series_list = [None] * len(unique_strategy_class_list)
for i in range(len(unique_strategy_class_list)):
strategy_frame_selected = strategy_frame[
strategy_frame['strategy_class'] == unique_strategy_class_list[i]]
pnl_out = [
tpnl.get_strategy_pnl(
alias=x, as_of_date=as_of_date,
con=con)['pnl_frame'][['settle_date', 'total_pnl']]
for x in strategy_frame_selected['alias']
]
[x.set_index('settle_date', drop=True, inplace=True) for x in pnl_out]
time_series_list[i] = pd.concat(pnl_out, axis=1).fillna(0).sum(axis=1)
if 'con' not in kwargs.keys():
con.close()
merged_pnl = pd.concat(time_series_list,
axis=1,
keys=unique_strategy_class_list).fillna(0)
merged_pnl['total'] = merged_pnl.sum(axis=1)
output_dir = dna.get_directory_name(ext='daily')
writer = pd.ExcelWriter(output_dir + '/historical_performance_' +
str(as_of_date) + '.xlsx',
engine='xlsxwriter')
merged_pnl.to_excel(writer, sheet_name='timeSeries')
writer.save()
return merged_pnl
def get_formatted_manual_entry_fills(**kwargs):
fill_frame = pd.read_csv(dna.get_directory_name(ext='daily') + '/' + manual_trade_entry_file_name)
formatted_frame = fill_frame
formatted_frame.rename(columns={'optionType': 'option_type',
'strikePrice': 'strike_price',
'tradePrice': 'trade_price',
'quantity': 'trade_quantity'},
inplace=True)
formatted_frame['strike_price'] = formatted_frame['strike_price'].astype('float64')
formatted_frame['PQ'] = formatted_frame['trade_price']*formatted_frame['trade_quantity']
formatted_frame['instrument'] = 'O'
formatted_frame.loc[formatted_frame['option_type'].isnull(),'instrument'] = 'F'
formatted_frame.loc[[cmi.is_stockQ(x) for x in formatted_frame['ticker']], 'instrument'] = 'S'
option_type = formatted_frame['option_type']
formatted_frame['option_type']= option_type.where(pd.notnull(option_type),None)
option_indx = formatted_frame['instrument'] == 'O'
formatted_frame['generalized_ticker'] = formatted_frame['ticker']
formatted_frame['generalized_ticker'][option_indx] = formatted_frame['ticker'][option_indx] + '-' + \
formatted_frame['option_type'][option_indx] + '-' + \
formatted_frame['strike_price'][option_indx].astype(str)
formatted_frame['side'] = np.sign(formatted_frame['trade_quantity'])
formatted_frame['ticker_head'] = [cmi.get_contract_specs(x)['ticker_head'] for x in formatted_frame['ticker']]
grouped = formatted_frame.groupby(['generalized_ticker', 'side'])
aggregate_trades = pd.DataFrame()
aggregate_trades['trade_price'] = grouped['PQ'].sum()/grouped['trade_quantity'].sum()
aggregate_trades['trade_quantity'] = grouped['trade_quantity'].sum()
aggregate_trades['ticker'] = grouped['ticker'].first()
aggregate_trades['ticker_head'] = grouped['ticker_head'].first()
aggregate_trades['instrument'] = grouped['instrument'].first()
aggregate_trades['option_type'] = grouped['option_type'].first()
aggregate_trades['strike_price'] = grouped['strike_price'].first()
aggregate_trades['real_tradeQ'] = True
return {'raw_trades': fill_frame, 'aggregate_trades': aggregate_trades }
def load_aligend_options_data_file(**kwargs):
ticker_head = kwargs['ticker_head']
tr_dte_center = kwargs['tr_dte_center']
option_data_dir = dna.get_directory_name(ext='aligned_time_series_output')
if 'delta_center' in kwargs.keys():
delta_center = kwargs['delta_center']
else:
delta_center = 0.5
if 'model' in kwargs.keys():
model = kwargs['model']
else:
model = 'BS'
if 'contract_month_letter' in kwargs.keys():
contract_month_str = '_' + kwargs['contract_month_letter']
else:
contract_month_str = ''
if 'column_names' in kwargs.keys():
column_names = kwargs['column_names']
else:
column_names = get_column_names_4option_data()
file_dir = ticker_head + '_' + str(
delta_center) + '_' + model + '_20_510204060_' + str(
tr_dte_center) + contract_month_str + '.mat'
if os.path.isfile(option_data_dir + '/' + file_dir):
try:
mat_output = scipy.io.loadmat(option_data_dir + '/' + file_dir)
data_frame_out = pd.DataFrame(mat_output['alignedDataMatrix'],
columns=column_names)
except Exception:
mat_output = h5py.File(option_data_dir + '/' + file_dir)
data_frame_out = pd.DataFrame(
mat_output['alignedDataMatrix'][()].transpose(),
columns=column_names)
else:
data_frame_out = pd.DataFrame(columns=column_names)
return data_frame_out
def get_dated_directory_extension(**kwargs):
if 'folder_date' in kwargs.keys():
folder_date = kwargs['folder_date']
else:
folder_date = exp.doubledate_shift_bus_days()
if 'ext' not in kwargs.keys():
print('Need to provide a valid ext !')
return
directory_name = dna.get_directory_name(**kwargs)
dated_directory_name = directory_name + '/' + cu.get_directory_extension(
folder_date)
if not os.path.exists(dated_directory_name):
os.makedirs(dated_directory_name)
return dated_directory_name
def create_strategy_output_dir(**kwargs):
strategy_class = kwargs['strategy_class']
report_date = kwargs['report_date']
strategy_output_folder = dna.get_directory_name(ext='strategy_output')
if strategy_class == 'futures_butterfly':
output_dir = strategy_output_folder + '/futures_butterfly/' + cu.get_directory_extension(
report_date)
elif strategy_class == 'intraday_futures_experimental':
output_dir = strategy_output_folder + '/ife/' + cu.get_directory_extension(
report_date)
else:
output_dir = strategy_output_folder + '/' + strategy_class + '/' + cu.get_directory_extension(
report_date)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
return output_dir
def save_ib_data(**kwargs):
if 'duration_str' in kwargs.keys():
duration_str = kwargs['duration_str']
else:
duration_str = '2 M'
app = algo.Algo()
con = msu.get_my_sql_connection()
date_now = cu.get_doubledate()
datetime_now = dt.datetime.now()
report_date = exp.doubledate_shift_bus_days()
ticker_head_list = cmi.cme_futures_tickerhead_list
data_list = [
gfp.get_futures_price_preloaded(ticker_head=x, settle_date=report_date)
for x in ticker_head_list
]
ticker_frame = pd.concat(data_list)
ticker_frame = ticker_frame[~((ticker_frame['ticker_head'] == 'ED') &
(ticker_frame['tr_dte'] < 250))]
ticker_frame = ticker_frame[~((ticker_frame['ticker_head'] == 'GC') |
(ticker_frame['ticker_head'] == 'SI'))]
ticker_frame.sort_values(['ticker_head', 'volume'],
ascending=[True, False],
inplace=True)
ticker_frame.drop_duplicates(subset=['ticker_head'],
keep='first',
inplace=True)
app.ticker_list = list(ticker_frame['ticker'])
app.output_dir = dna.get_directory_name(ext='ib_data')
app.durationStr = duration_str
app.con = con
app.connect(client_id=5)
app.run()
def save_daily_price_data4ticker(**kwargs):
if 'client' in kwargs.keys():
client = kwargs['client']
else:
client = btu.get_binance_client()
ticker = kwargs['ticker']
file_name = dna.get_directory_name(
ext='binance') + '/daily/' + ticker + '.pkl'
if os.path.isfile(file_name):
old_frame = pd.read_pickle(file_name)
new_frame = gbp.get_klines(
ticker=ticker,
interval='1d',
start_str=(old_frame['openDatetime'].iloc[-1] -
dt.timedelta(days=5)).strftime('%m/%d/%y'))
new_frame['frame_indx'] = 1
old_frame['frame_indx'] = 0
merged_data = pd.concat([old_frame, new_frame], ignore_index=True)
merged_data.sort_values(['openDate', 'frame_indx'],
ascending=[True, False],
inplace=True)
merged_data.drop_duplicates(subset=['openDate'],
keep='first',
inplace=True)
price_frame = merged_data.drop('frame_indx', 1, inplace=False)
else:
price_frame = gbp.get_klines(ticker=ticker,
interval='1d',
start_str='01/01/2017',
client=client)
price_frame.to_pickle(file_name)
def get_spreads_4date(**kwargs):
futures_dataframe = cl.generate_futures_list_dataframe(**kwargs)
if 'volume_filter' in kwargs.keys():
volume_filter = kwargs['volume_filter']
futures_dataframe = futures_dataframe[futures_dataframe['volume'] > volume_filter]
futures_dataframe.reset_index(drop=True, inplace=True)
futures_dataframe['yearMonth'] = 12*futures_dataframe['ticker_year']+futures_dataframe['ticker_month']
futures_dataframe['yearMonthMerge'] = futures_dataframe['yearMonth']
futures_dataframe = futures_dataframe[['ticker','yearMonth','yearMonthMerge','ticker_head','volume']]
spread_frame = pd.read_excel(dna.get_directory_name(ext='python_file') + '/opportunity_constructs/user_defined_spreads.xlsx')
output_frame = pd.DataFrame()
for i in range(len(spread_frame.index)):
tickerhead1 = spread_frame['tickerHead1'].iloc[i]
tickerhead2 = spread_frame['tickerHead2'].iloc[i]
tickerhead3 = spread_frame['tickerHead3'].iloc[i]
tickerhead4 = spread_frame['tickerHead4'].iloc[i]
frame1 = futures_dataframe[futures_dataframe['ticker_head'] == tickerhead1]
frame2 = futures_dataframe[futures_dataframe['ticker_head'] == tickerhead2]
frame3 = futures_dataframe[futures_dataframe['ticker_head'] == tickerhead3]
frame4 = futures_dataframe[futures_dataframe['ticker_head'] == tickerhead4]
merged11 = pd.merge(frame1, frame2, how='inner', on='yearMonthMerge')
frame2['yearMonthMerge'] = frame2['yearMonth']+1
merged12 = pd.merge(frame1, frame2, how='inner', on='yearMonthMerge')
frame2['yearMonthMerge'] = frame2['yearMonth']-1
merged10 = pd.merge(frame1, frame2, how='inner', on='yearMonthMerge')
if frame3.empty:
output_frame2 = pd.concat([merged11, merged12, merged10])
spread_i = pd.DataFrame()
spread_i['contract1'] = output_frame2['ticker_x']
spread_i['contract2'] = output_frame2['ticker_y']
spread_i['contract3'] = None
spread_i['ticker_head1'] = tickerhead1
spread_i['ticker_head2'] = tickerhead2
spread_i['ticker_head3'] = None
spread_i['volume1'] = output_frame2['volume_x']
spread_i['volume2'] = output_frame2['volume_y']
spread_i['volume3'] = None
output_frame = pd.concat([output_frame, spread_i])
elif frame4.empty:
frame3 = futures_dataframe[futures_dataframe['ticker_head'] == tickerhead3]
merged111 = pd.merge(merged11, frame3, how='inner', on='yearMonthMerge')
merged121 = pd.merge(merged12, frame3, how='inner', on='yearMonthMerge')
merged101 = pd.merge(merged10, frame3, how='inner', on='yearMonthMerge')
frame3['yearMonthMerge'] = frame3['yearMonth']+1
merged112 = pd.merge(merged11, frame3, how='inner', on='yearMonthMerge')
merged122 = pd.merge(merged12, frame3, how='inner', on='yearMonthMerge')
frame3['yearMonthMerge'] = frame3['yearMonth']-1
merged110 = pd.merge(merged11, frame3, how='inner', on='yearMonthMerge')
merged100 = pd.merge(merged10, frame3, how='inner', on='yearMonthMerge')
output_frame3 = pd.concat([merged111,merged121,merged101,
merged112,merged122,merged110,merged100],sort=True)
spread_i = pd.DataFrame()
spread_i['contract1'] = output_frame3['ticker_x']
spread_i['contract2'] = output_frame3['ticker_y']
spread_i['contract3'] = output_frame3['ticker']
spread_i['ticker_head1'] = tickerhead1
spread_i['ticker_head2'] = tickerhead2
spread_i['ticker_head3'] = tickerhead3
spread_i['volume1'] = output_frame3['volume_x']
spread_i['volume2'] = output_frame3['volume_y']
spread_i['volume3'] = output_frame3['volume']
output_frame = pd.concat([output_frame,spread_i])
output_frame['spread_description'] = output_frame.apply(lambda x: x['ticker_head1']+ '_' +x['ticker_head2'] if x['ticker_head3'] is None else x['ticker_head1']+ '_' +x['ticker_head2'] + '_' + x['ticker_head3'] , axis=1)
output_frame['min_volume'] = output_frame.apply(lambda x: min(x['volume1'],x['volume2']) if x['ticker_head3'] is None else min(x['volume1'],x['volume2'],x['volume3']),axis=1)
output_frame.sort_values(['spread_description','min_volume'],ascending=[True, False],inplace=True)
output_frame.drop_duplicates('spread_description',inplace=True)
output_frame.reset_index(drop=True,inplace=True)
return output_frame
def get_cme_direct_prices(**kwargs):
cme_frame = pd.read_csv(
dna.get_directory_name(ext='daily') + '/' + price_file_name)
return cme_frame
import quandl_data.get_data_quandl as gdq
import shared.directory_names_aux as dna
import contract_utilities.contract_meta_info as cmi
import shared.calendar_utilities as cu
import datetime as dt
import os.path
import pandas as pd
presaved_cot_data_folder = dna.get_directory_name(
ext='commitments_of_traders_data')
db_2_quandl_dictionary = {
'GC': '088691',
'SI': '084691',
'EC': '099741',
'BP': '096742',
'JY': '097741',
'AD': '232741',
'CD': '090741',
'TU': '042601',
'FV': '044601',
'TY': '043602',
'US': '020601',
'ED': '132741',
'ES': '13874A',
'NQ': '209742',
'CL': '067651',
'HO': '022651',
'RB': '111659',
'NG': '023651',
'C': '002602',
def main():
app = algo.Algo()
report_date = exp.doubledate_shift_bus_days()
todays_date = cu.get_doubledate()
con = msu.get_my_sql_connection()
vcs_output = vcs.generate_vcs_sheet_4date(date_to=report_date)
vcs_pairs = vcs_output['vcs_pairs']
filter_out = of.get_vcs_filters(data_frame_input=vcs_pairs,
filter_list=['long2', 'short2'])
vcs_pairs = filter_out['selected_frame']
vcs_pairs = vcs_pairs[vcs_pairs['downside'].notnull()
& vcs_pairs['upside'].notnull()]
# &(vcs_pairs.tickerClass!='Energy')
vcs_pairs = vcs_pairs[(vcs_pairs['trDte1'] >= 50)
& (vcs_pairs.tickerClass != 'Metal') &
(vcs_pairs.tickerClass != 'FX') &
(vcs_pairs.tickerClass != 'Energy')]
vcs_pairs = vcs_pairs[((vcs_pairs['Q'] <= 30) &
(vcs_pairs['fwdVolQ'] >= 30)) |
((vcs_pairs['Q'] >= 70) &
(vcs_pairs['fwdVolQ'] <= 70))]
vcs_pairs.reset_index(drop=True, inplace=True)
vcs_pairs['underlying_ticker1'] = [
omu.get_option_underlying(ticker=x) for x in vcs_pairs['ticker1']
]
vcs_pairs['underlying_ticker2'] = [
omu.get_option_underlying(ticker=x) for x in vcs_pairs['ticker2']
]
vcs_pairs['underlying_tickerhead'] = [
cmi.get_contract_specs(x)['ticker_head']
for x in vcs_pairs['underlying_ticker1']
]
futures_data_dictionary = {
x: gfp.get_futures_price_preloaded(ticker_head=x)
for x in vcs_pairs['underlying_tickerhead'].unique()
}
proxy_output_list1 = [
up.get_underlying_proxy_ticker(
ticker=x,
settle_date=report_date,
futures_data_dictionary=futures_data_dictionary)
for x in vcs_pairs['underlying_ticker1']
]
vcs_pairs['proxy_ticker1'] = [x['ticker'] for x in proxy_output_list1]
vcs_pairs['add_2_proxy1'] = [x['add_2_proxy'] for x in proxy_output_list1]
proxy_output_list2 = [
up.get_underlying_proxy_ticker(
ticker=x,
settle_date=report_date,
futures_data_dictionary=futures_data_dictionary)
for x in vcs_pairs['underlying_ticker2']
]
vcs_pairs['proxy_ticker2'] = [x['ticker'] for x in proxy_output_list2]
vcs_pairs['add_2_proxy2'] = [x['add_2_proxy'] for x in proxy_output_list2]
vcs_pairs['expiration_date1'] = [
int(
exp.get_expiration_from_db(instrument='options', ticker=x,
con=con).strftime('%Y%m%d'))
for x in vcs_pairs['ticker1']
]
vcs_pairs['expiration_date2'] = [
int(
exp.get_expiration_from_db(instrument='options', ticker=x,
con=con).strftime('%Y%m%d'))
for x in vcs_pairs['ticker2']
]
vcs_pairs['interest_date1'] = [
grfs.get_simple_rate(as_of_date=report_date, date_to=x)['rate_output']
for x in vcs_pairs['expiration_date1']
]
vcs_pairs['interest_date2'] = [
grfs.get_simple_rate(as_of_date=report_date, date_to=x)['rate_output']
for x in vcs_pairs['expiration_date2']
]
vcs_pairs['exercise_type'] = [
cmi.get_option_exercise_type(ticker_head=x)
for x in vcs_pairs['tickerHead']
]
admin_dir = dna.get_directory_name(ext='admin')
risk_file_out = su.read_text_file(file_name=admin_dir +
'/RiskParameter.txt')
vcs_risk_parameter = 5 * 2 * float(risk_file_out[0])
vcs_pairs['long_quantity'] = vcs_risk_parameter / abs(
vcs_pairs['downside'])
vcs_pairs['short_quantity'] = vcs_risk_parameter / vcs_pairs['upside']
vcs_pairs['long_quantity'] = vcs_pairs['long_quantity'].round()
vcs_pairs['short_quantity'] = vcs_pairs['short_quantity'].round()
vcs_pairs['alias'] = [
generate_vcs_alias(vcs_row=vcs_pairs.iloc[x])
for x in range(len(vcs_pairs.index))
]
vcs_pairs['call_mid_price1'] = np.nan
vcs_pairs['put_mid_price1'] = np.nan
vcs_pairs['call_mid_price2'] = np.nan
vcs_pairs['put_mid_price2'] = np.nan
vcs_pairs['call_iv1'] = np.nan
vcs_pairs['put_iv1'] = np.nan
vcs_pairs['call_iv2'] = np.nan
vcs_pairs['put_iv2'] = np.nan
vcs_pairs['underlying_mid_price1'] = np.nan
vcs_pairs['underlying_mid_price2'] = np.nan
vcs_pairs['proxy_mid_price1'] = np.nan
vcs_pairs['proxy_mid_price2'] = np.nan
vcs_pairs['current_strike1'] = np.nan
vcs_pairs['current_strike2'] = np.nan
ta_folder = dn.get_dated_directory_extension(folder_date=todays_date,
ext='ta')
app.vcs_pairs = vcs_pairs
app.con = con
app.futures_data_dictionary = futures_data_dictionary
app.report_date = report_date
app.todays_date = todays_date
app.log = lg.get_logger(file_identifier='vcs', log_level='INFO')
app.trade_file = ta_folder + '/trade_dir.csv'
app.vcs_risk_parameter = vcs_risk_parameter
app.connect(client_id=3)
app.run()
import contract_utilities.expiration as exp
import ta.strategy as ts
import shutil as sutil
import shared.directory_names_aux as dna
import shared.directory_names as dn
import formats.utils as futil
daily_dir = dna.get_directory_name(ext='daily')
def prepare_strategy_daily(**kwargs):
strategy_class = kwargs['strategy_class']
if 'report_date' in kwargs.keys():
report_date = kwargs['report_date']
else:
report_date = exp.doubledate_shift_bus_days()
output_dir = ts.create_strategy_output_dir(strategy_class=strategy_class,
report_date=report_date)
sutil.copyfile(
output_dir + '/' + futil.xls_file_names[strategy_class] + '.xlsx',
daily_dir + '/' + futil.xls_file_names[strategy_class] + '_' +
str(report_date) + '.xlsx')
def move_from_dated_folder_2daily_folder(**kwargs):
ext = kwargs['ext']
def load_and_convert_man_position_file(**kwargs):
positions_directory = dna.get_directory_name(ext='man_positions')
file_list = os.listdir(positions_directory)
num_files = len(file_list)
time_list = []
for i in range(num_files):
time_list.append(
os.path.getmtime(positions_directory + '/' + file_list[i]))
loc_latest_file = time_list.index(max(time_list))
man_frame = pd.read_csv(positions_directory + '/' +
file_list[loc_latest_file])
man_frame.rename(columns={
'Strike': 'strike_price',
'Option Type': 'option_type',
'Net Qty': 'qty',
'Futures Code': 'Instrument',
'Contract Maturity': 'Prompt'
},
inplace=True)
man_frame['ticker_head'] = [
conversion_from_man_ticker_head[x] for x in man_frame['Instrument']
]
man_frame['strike_multiplier'] = [
man_strike_multiplier.get(x, 1) for x in man_frame['ticker_head']
]
man_frame['ticker'] = [
man_frame['ticker_head'].iloc[x] +
cmi.full_letter_month_list[int(man_frame['Prompt'].iloc[x] % 100) - 1]
+ str(m.floor(man_frame['Prompt'].iloc[x] / 100))
for x in range(len(man_frame.index))
]
man_frame.rename(columns={
'Strike': 'strike_price',
'OptionType': 'option_type',
'NetQty': 'qty'
},
inplace=True)
man_frame['strike_price'] = round(
man_frame['strike_multiplier'] * man_frame['strike_price'], 4)
man_frame['instrumet'] = 'F'
option_indx = (man_frame['option_type'] == 'C') | (man_frame['option_type']
== 'P')
man_frame['instrumet'][option_indx] = 'O'
man_frame['generalized_ticker'] = man_frame['ticker']
man_frame['generalized_ticker'][option_indx] = man_frame['ticker'][option_indx] + '-' + \
man_frame['option_type'][option_indx] + '-' + \
man_frame['strike_price'][option_indx].astype(str)
man_frame = man_frame[man_frame['qty'] != 0]
man_frame['generalized_ticker'] = [
x.rstrip('0').rstrip('.') for x in man_frame['generalized_ticker']
]
return man_frame[['generalized_ticker', 'qty']]
def load_and_convert_wh_position_file(**kwargs):
positions_directory = dna.get_directory_name(ext='wh_positions')
file_name = 'open pos .tul01 5-1.xlsx'
double_date = cu.get_doubledate()
century_mark = m.floor(double_date / 1e6) * 100
wh_frame = pd.read_excel(positions_directory + '/' + file_name,
header=None,
names=['raw_symbol', 'qty'])
raw_symbol_list = []
qty_list = []
instrument_list = []
current_instrument = ''
direction_list = []
current_direction = ''
strike_price_list = []
option_type_list = []
ticker_list = []
ticker_head_list = []
for i in range(len(wh_frame.index) - 1):
if len(str(wh_frame['raw_symbol'].iloc[i])) > 4 and (str(
wh_frame['qty'].iloc[i]).isnumeric()):
raw_symbol_list.append(wh_frame['raw_symbol'].iloc[i])
instrument_list.append(current_instrument)
split_out = wh_frame['raw_symbol'].iloc[i].split(' ')
if split_out[0] in ['CALL', 'PUT']:
strike_price_list.append(float(split_out[-1]))
option_type_list.append(split_out[0][0])
if split_out[0] == 'CALL':
month_indx = 1
year_indx = 2
else:
month_indx = 2
year_indx = 3
else:
strike_price_list.append(np.nan)
option_type_list.append(None)
month_indx = 0
year_indx = 1
ticker_head = conversion_from_man_ticker_head[current_instrument]
ticker_head_list.append(ticker_head)
ticker_list.append(ticker_head + cmi.full_letter_month_list[
cu.three_letter_month_dictionary[split_out[month_indx]] - 1] +
str(century_mark + int(split_out[year_indx])))
if current_direction == 'B':
qty_list.append(wh_frame['qty'].iloc[i])
elif current_direction == 'S':
qty_list.append(-wh_frame['qty'].iloc[i])
else:
print(current_direction)
elif wh_frame['raw_symbol'].iloc[i] in ['B', 'S']:
current_direction = wh_frame['raw_symbol'].iloc[i]
else:
current_instrument = str(wh_frame['raw_symbol'].iloc[i])
wh_frame = pd.DataFrame()
wh_frame['Instrument'] = instrument_list
wh_frame['raw_symbol'] = raw_symbol_list
wh_frame['strike_price'] = strike_price_list
wh_frame['option_type'] = option_type_list
wh_frame['qty'] = qty_list
wh_frame['ticker_head'] = ticker_head_list
wh_frame['ticker'] = ticker_list
wh_frame['strike_multiplier'] = [
wh_strike_multiplier.get(x, 1) for x in wh_frame['ticker_head']
]
wh_frame['strike_price'] = round(
wh_frame['strike_multiplier'] * wh_frame['strike_price'], 4)
wh_frame['instrumet'] = 'F'
option_indx = (wh_frame['option_type'] == 'C') | (wh_frame['option_type']
== 'P')
wh_frame['instrumet'][option_indx] = 'O'
wh_frame['generalized_ticker'] = wh_frame['ticker']
wh_frame['generalized_ticker'][option_indx] = wh_frame['ticker'][option_indx] + '-' + \
wh_frame['option_type'][option_indx] + '-' + \
wh_frame['strike_price'][option_indx].astype(str)
wh_frame['generalized_ticker'] = [
x.rstrip('0').rstrip('.') for x in wh_frame['generalized_ticker']
]
return wh_frame[['generalized_ticker', 'qty']]
__author__ = 'kocat_000'
import os.path
import get_price.get_futures_price as gfp
import contract_utilities.contract_meta_info as cmi
import my_sql_routines.my_sql_utilities as msu
import contract_utilities.expiration as exp
from pandas.tseries.offsets import CustomBusinessDay
import shared.calendar_utilities as cu
import shared.directory_names_aux as dna
import pandas as pd
import numpy as np
import datetime as dt
pd.options.mode.chained_assignment = None
presaved_futures_data_folder = dna.get_directory_name(
ext='presaved_futures_data')
dirty_data_points = pd.DataFrame([('BM2006', dt.datetime(2005, 11, 18), True),
('BM2006', dt.datetime(2005, 11, 21), True),
('BM2006', dt.datetime(2005, 11, 25), True),
('BM2006', dt.datetime(2005, 11, 28), True),
('BM2006', dt.datetime(2005, 11, 30), True),
('BM2006', dt.datetime(2005, 12, 1), True),
('BM2006', dt.datetime(2005, 12, 2), True),
('BM2006', dt.datetime(2005, 12, 6), True),
('BM2006', dt.datetime(2005, 12, 8), True),
('BM2006', dt.datetime(2005, 12, 9), True),
('BM2006', dt.datetime(2005, 12, 12), True),
('BM2006', dt.datetime(2005, 12, 13), True),
('BM2006', dt.datetime(2005, 12, 14), True),
('BM2006', dt.datetime(2005, 12, 15), True),
def main():
app = algo.Algo()
admin_dir = dna.get_directory_name(ext='admin')
risk_file_out = su.read_text_file(file_name=admin_dir +
'/RiskParameter.txt')
app.bet_size = float(risk_file_out[0])
con = msu.get_my_sql_connection()
date_now = cu.get_doubledate()
report_date = exp.doubledate_shift_bus_days()
report_date_list = [
exp.doubledate_shift_bus_days(shift_in_days=x) for x in range(1, 10)
]
overnight_calendars_list = []
for i in range(len(report_date_list)):
ocs_output = ocs.generate_overnight_spreads_sheet_4date(
date_to=report_date_list[i])
overnight_calendars = ocs_output['overnight_calendars']
overnight_calendars = \
overnight_calendars[overnight_calendars['tickerHead'].isin(['CL', 'HO', 'NG', 'C', 'W', 'KW', 'S', 'SM', 'BO', 'LC', 'LN', 'FC'])]
#isin(['CL', 'HO','NG', 'C', 'W', 'KW', 'S', 'SM', 'BO', 'LC', 'LN', 'FC'])]
overnight_calendars = overnight_calendars[
(overnight_calendars['ticker1L'] != '')
& (overnight_calendars['ticker2L'] != '')]
overnight_calendars['back_spread_price'] = np.nan
overnight_calendars['front_spread_price'] = np.nan
overnight_calendars['mid_ticker_price'] = np.nan
overnight_calendars['back_spread_ticker'] = [
overnight_calendars['ticker1'].iloc[x] + '-' +
overnight_calendars['ticker2'].iloc[x]
for x in range(len(overnight_calendars.index))
]
overnight_calendars['front_spread_ticker'] = [
overnight_calendars['ticker1L'].iloc[x] + '-' +
overnight_calendars['ticker2L'].iloc[x]
for x in range(len(overnight_calendars.index))
]
overnight_calendars['target_quantity'] = [
min(mth.ceil(app.bet_size / x),
app.total_traded_volume_max_before_user_confirmation)
for x in overnight_calendars['dollarNoise100']
]
overnight_calendars['alias'] = [
overnight_calendars['ticker1'].iloc[x] + '_' +
overnight_calendars['ticker2'].iloc[x] + '_ocs'
for x in range(len(overnight_calendars.index))
]
overnight_calendars['total_quantity'] = 0
overnight_calendars['total_risk'] = 0
overnight_calendars['holding_period'] = 0
#overnight_calendars['expiring_position_q'] = 0
overnight_calendars.reset_index(drop=True, inplace=True)
overnight_calendars_list.append(overnight_calendars)
overnight_calendars = overnight_calendars_list.pop(0)
open_strategy_frame = ts.get_filtered_open_strategies(
strategy_class_list=['ocs'], as_of_date=date_now)
for i in range(len(open_strategy_frame.index)):
position_manager_output = pm.get_ocs_position(
alias=open_strategy_frame['alias'].iloc[i],
as_of_date=date_now,
con=con)
trades_frame = ts.get_trades_4strategy_alias(
alias=open_strategy_frame['alias'].iloc[i], con=con)
datetime_now = cu.convert_doubledate_2datetime(date_now)
holding_period = (datetime_now - trades_frame['trade_date'].min()).days
if (not position_manager_output['empty_position_q']) & (
not position_manager_output['correct_position_q']):
print('Check ' + open_strategy_frame['alias'].iloc[i] +
' ! Position may be incorrect')
elif position_manager_output['correct_position_q']:
ticker_head = cmi.get_contract_specs(
position_manager_output['sorted_position']
['ticker'].iloc[0])['ticker_head']
position_name = ''
if position_manager_output['scale'] > 0:
position_name = ticker_head + '_long'
else:
position_name = ticker_head + '_short'
app.ocs_portfolio.order_send(ticker=position_name,
qty=abs(
position_manager_output['scale']))
app.ocs_portfolio.order_fill(ticker=position_name,
qty=abs(
position_manager_output['scale']))
ticker1 = position_manager_output['sorted_position'][
'ticker'].iloc[0]
ticker2 = position_manager_output['sorted_position'][
'ticker'].iloc[1]
selection_indx = overnight_calendars[
'back_spread_ticker'] == ticker1 + '-' + ticker2
if sum(selection_indx) == 1:
overnight_calendars.loc[
selection_indx,
'total_quantity'] = position_manager_output['scale']
overnight_calendars.loc[
selection_indx, 'total_risk'] = position_manager_output[
'scale'] * overnight_calendars.loc[selection_indx,
'dollarNoise100']
overnight_calendars.loc[
selection_indx,
'alias'] = open_strategy_frame['alias'].iloc[i]
overnight_calendars.loc[selection_indx,
'holding_period'] = holding_period
app.ocs_risk_portfolio.order_send(
ticker=position_name,
qty=abs(
position_manager_output['scale'] *
overnight_calendars.loc[selection_indx,
'dollarNoise100'].values[0]))
app.ocs_risk_portfolio.order_fill(
ticker=position_name,
qty=abs(
position_manager_output['scale'] *
overnight_calendars.loc[selection_indx,
'dollarNoise100'].values[0]))
else:
for j in range(len(overnight_calendars_list)):
overnight_calendars_past = overnight_calendars_list[j]
selection_indx = overnight_calendars_past[
'back_spread_ticker'] == ticker1 + '-' + ticker2
if sum(selection_indx) == 1:
overnight_calendars_past.loc[
selection_indx,
'total_quantity'] = position_manager_output[
'scale']
overnight_calendars_past.loc[
selection_indx,
'total_risk'] = position_manager_output[
'scale'] * overnight_calendars_past.loc[
selection_indx, 'dollarNoise100']
overnight_calendars_past.loc[
selection_indx,
'alias'] = open_strategy_frame['alias'].iloc[i]
overnight_calendars_past.loc[
selection_indx, 'holding_period'] = holding_period
app.ocs_risk_portfolio.order_send(
ticker=position_name,
qty=abs(position_manager_output['scale'] *
overnight_calendars_past.loc[
selection_indx,
'dollarNoise100'].values[0]))
app.ocs_risk_portfolio.order_fill(
ticker=position_name,
qty=abs(position_manager_output['scale'] *
overnight_calendars_past.loc[
selection_indx,
'dollarNoise100'].values[0]))
if j > 1:
overnight_calendars_past.loc[
selection_indx, 'butterflyMean'] = np.nan
overnight_calendars_past.loc[
selection_indx, 'butterflyNoise'] = np.nan
overnight_calendars = overnight_calendars.append(
overnight_calendars_past[selection_indx])
break
overnight_calendars.reset_index(drop=True, inplace=True)
overnight_calendars['working_order_id'] = np.nan
spread_ticker_list = list(
set(overnight_calendars['back_spread_ticker']).union(
overnight_calendars['front_spread_ticker']))
back_spread_ticker_list = list(overnight_calendars['back_spread_ticker'])
theme_name_list = set([
x + '_long' for x in back_spread_ticker_list
]).union(set([x + '_short' for x in back_spread_ticker_list]))
ocs_alias_portfolio = aup.portfolio(ticker_list=theme_name_list)
for i in range(len(overnight_calendars.index)):
if overnight_calendars.loc[i, 'total_quantity'] > 0:
position_name = overnight_calendars.loc[
i, 'back_spread_ticker'] + '_long'
ocs_alias_portfolio.order_send(
ticker=position_name,
qty=overnight_calendars.loc[i, 'total_quantity'])
ocs_alias_portfolio.order_fill(
ticker=position_name,
qty=overnight_calendars.loc[i, 'total_quantity'])
elif overnight_calendars.loc[i, 'total_quantity'] < 0:
position_name = overnight_calendars.loc[
i, 'back_spread_ticker'] + '_short'
ocs_alias_portfolio.order_send(
ticker=position_name,
qty=-overnight_calendars.loc[i, 'total_quantity'])
ocs_alias_portfolio.order_fill(
ticker=position_name,
qty=-overnight_calendars.loc[i, 'total_quantity'])
app.price_request_dictionary['spread'] = spread_ticker_list
app.price_request_dictionary['outright'] = overnight_calendars[
'ticker1'].values
app.overnight_calendars = overnight_calendars
app.open_strategy_list = list(open_strategy_frame['alias'])
app.ocs_alias_portfolio = ocs_alias_portfolio
app.ticker_list = list(
set(overnight_calendars['ticker1']).union(
overnight_calendars['ticker2']).union(
set(overnight_calendars['ticker1L'])).union(
set(overnight_calendars['ticker2L'])))
app.output_dir = ts.create_strategy_output_dir(strategy_class='ocs',
report_date=report_date)
app.log = lg.get_logger(file_identifier='ib_ocs', log_level='INFO')
app.con = con
app.pnl_frame = tpm.get_daily_pnl_snapshot(as_of_date=report_date,
name='final')
print('Emre')
app.connect(client_id=2)
app.run()
