def archive_models():
df = pd.DataFrame(columns=['id', 'model_name', 'ref_object_type'])
df.loc[0] = [1, 'vix_curve', 'strategy']
df.loc[1] = [2, 'equity_vs_vol', 'strategy']
df.loc[2] = [3, 'vol_rv', 'equity']
existing_data = get_models()
ind = df.index[~df['model_name'].isin(existing_data['model_name'].tolist()
)]
df = df.loc[ind]
table = db.get_table('models')
db.execute_bulk_insert(df=df, table=table)
def archive_model_param_config(model_name=None, settings=None):
export_settings = model_settings_to_json(settings)
model_id = get_model_id(model_name)
model_params = get_model_param_configs(model_name)
if export_settings not in model_params['model_params'].tolist():
new_id = db.read_sql('select max(id) from model_param_config')
try:
new_id = new_id['max'][0] + 1
except:
new_id = 0
model_params = pd.DataFrame(columns=['id', 'model_id', 'model_params'])
model_params.loc[0] = [new_id, model_id, export_settings]
table = db.get_table('model_param_config')
db.execute_bulk_insert(df=model_params, table=table)
def archive_strategy_signals(model=None, signal_data=None):
signal_names = signal_data.columns
model_id = get_model_id(model.name)
existing_signals = get_strategy_signals(model_name=model.name)
df = pd.DataFrame(columns=['model_id', 'signal_name'])
df['signal_name'] = signal_names
df['model_id'] = model_id
# Filter down now
ind = df.index[~df['signal_name'].isin(existing_signals['signal_name'].
tolist())]
df = df.loc[ind]
if len(df) > 0:
table = db.get_table('model_signals')
db.execute_bulk_insert(df=df, table=table)