def run_all_assets(self):
for idx, asset in enumerate(self.sim_assets):
output = self.load_curr_results(idx)
if len(output.keys()) == len(self.scenarios):
continue
self.set_config(idx)
self.load_data(idx)
for ix, s in enumerate(self.scenarios):
if str(ix) in output:
continue
res = {'asset': '_'.join(asset), 'scen_id': ix,\
'sim_name': self.sim_name, 'sim_class': self.sim_class.__name__, 'sim_func': self.sim_func,
'end_date': str(self.config['end_date'])}
for i in range(5):
res['par_name' + str(i)] = ''
res['par_value' + str(i)] = 0
for i, (key, seq) in enumerate(zip(self.scen_keys, s)):
self.config[key] = self.scen_param[key][seq]
res['par_name' + str(i)] = key
res['par_value' + str(i)] = str(self.scen_param[key][seq])
self.prepare_data(idx, cont_idx=0)
sim_strat = self.sim_class(self.config)
sim_dfs, closed_trades = getattr(sim_strat, self.sim_func)()
(res_pnl, ts) = get_pnl_stats(sim_dfs,
self.config['marginrate'],
self.sim_freq,
self.pnl_tenors,
cost_ratio=self.cost_ratio)
res_trade = get_trade_stats(closed_trades)
res.update(dict(res_pnl.items() + res_trade.items()))
file_prefix = self.file_prefix + '_' + '_'.join(
[self.sim_mode] + asset)
res['trade_file'] = file_prefix + '_' + str(ix) + '_trades.csv'
res['pnl_file'] = file_prefix + '_' + str(
ix) + '_dailydata.csv'
output[str(ix)] = res
all_trades = {}
for i, tradepos in enumerate(closed_trades):
all_trades[i] = trade_position.tradepos2dict(tradepos)
trades = pd.DataFrame.from_dict(all_trades).T
trades.to_csv(res['trade_file'])
ts.to_csv(res['pnl_file'])
fname = file_prefix + '_stats.json'
with open(fname, 'w') as ofile:
json.dump(output, ofile)
cnx = dbaccess.connect(**self.dbconfig)
#cnx.set_converter_class(mysql_helper.NumpyMySQLConverter)
dbaccess.insert_row_by_dict(cnx,
self.dbtable,
res,
is_replace=True)
cnx.close()
print 'The results for asset = %s, scen = %s are saved' % (
asset, str(ix))
def run_all_assets(self):
self.restart()
for idx, asset in enumerate(self.sim_assets):
output = self.load_curr_results(idx)
if len(output.keys()) == len(self.scenarios):
continue
self.set_config(idx)
self.load_data(idx)
for ix, s in enumerate(self.scenarios):
if str(ix) in output:
continue
file_prefix = self.file_prefix + '_' + '_'.join([self.sim_mode] + asset)
fname1 = file_prefix + '_'+ str(ix) + '_trades.csv'
fname2 = file_prefix + '_'+ str(ix) + '_dailydata.csv'
for key, seq in zip(self.scen_keys, s):
self.config[key] = self.scen_param[key][seq]
self.prepare_data(idx, cont_idx = 0)
sim_strat = self.sim_class(self.config)
sim_df, closed_trades = getattr(sim_strat, self.sim_func)()
(res_pnl, ts) = get_pnl_stats( [sim_df], self.config['marginrate'], 'm')
res_trade = get_trade_stats(closed_trades)
res = dict( res_pnl.items() + res_trade.items())
res.update(dict(zip(self.scen_keys, s)))
res['asset'] = '_'.join(asset)
output[ix] = res
print 'saving results for asset = %s, scen = %s' % (asset, str(ix))
all_trades = {}
for i, tradepos in enumerate(closed_trades):
all_trades[i] = trade_position.tradepos2dict(tradepos)
trades = pd.DataFrame.from_dict(all_trades).T
trades.to_csv(fname1)
ts.to_csv(fname2)
fname = file_prefix + '_stats.json'
with open(fname, 'w') as ofile:
json.dump(output, ofile)
res = pd.DataFrame.from_dict(output, orient = 'index')
res.index.name = 'scenario'
res = res.sort_values(by = ['sharp_ratio'], ascending=False)
res = res.reset_index()
res.set_index(['asset', 'scenario'])
out_res = res[self.output_columns()]
if len(self.summary_df)==0:
self.summary_df = out_res[:30].copy(deep = True)
else:
self.summary_df = self.summary_df.append(out_res[:30])
fname = self.file_prefix + 'summary.csv'
self.summary_df.to_csv(fname)
def run_all_assets(self):
for idx, asset in enumerate(self.sim_assets):
output = self.load_curr_results(idx)
if len(output.keys()) == len(self.scenarios):
continue
self.set_config(idx)
self.load_data(idx)
for ix, s in enumerate(self.scenarios):
if str(ix) in output:
continue
res = {'asset': '_'.join(asset), 'scen_id': ix,\
'sim_name': self.sim_name, 'sim_class': self.sim_class.__name__, 'sim_func': self.sim_func,
'end_date': str(self.config['end_date'])}
for i in range(5):
res['par_name' + str(i)] = ''
res['par_value' + str(i)] = 0
for i, (key, seq) in enumerate(zip(self.scen_keys, s)):
self.config[key] = self.scen_param[key][seq]
res['par_name' + str(i)] = key
res['par_value' + str(i)] = str(self.scen_param[key][seq])
self.prepare_data(idx, cont_idx = 0)
sim_strat = self.sim_class(self.config)
sim_dfs, closed_trades = getattr(sim_strat, self.sim_func)()
(res_pnl, ts) = get_pnl_stats( sim_dfs, self.config['marginrate'], self.sim_freq, self.pnl_tenors, cost_ratio = self.cost_ratio)
res_trade = get_trade_stats(closed_trades)
res.update(dict( res_pnl.items() + res_trade.items()))
file_prefix = self.file_prefix + '_' + '_'.join([self.sim_mode] + asset)
res['trade_file'] = file_prefix + '_'+ str(ix) + '_trades.csv'
res['pnl_file'] = file_prefix + '_'+ str(ix) + '_dailydata.csv'
output[str(ix)] = res
all_trades = {}
for i, tradepos in enumerate(closed_trades):
all_trades[i] = trade_position.tradepos2dict(tradepos)
trades = pd.DataFrame.from_dict(all_trades).T
trades.to_csv(res['trade_file'])
ts.to_csv(res['pnl_file'])
fname = file_prefix + '_stats.json'
with open(fname, 'w') as ofile:
json.dump(output, ofile)
cnx = dbaccess.connect(**self.dbconfig)
#cnx.set_converter_class(mysql_helper.NumpyMySQLConverter)
dbaccess.insert_row_by_dict(cnx, self.dbtable, res, is_replace=True)
cnx.close()
print 'The results for asset = %s, scen = %s are saved' % (asset, str(ix))
def run_all_assets(self):
for idx, asset in enumerate(self.sim_assets):
cont_map = self.cont_maplist[idx]
output = self.load_curr_results(idx)
if len(output) == 0:
output = {'total': {}, 'cont': {}}
elif len(output.keys()) == len(self.scenarios):
continue
self.set_config(idx)
self.load_data(idx)
for ix, s in enumerate(self.scenarios):
file_prefix = self.file_prefix + '_' + '_'.join(self.sim_mode + asset)
fname1 = file_prefix + str(ix) + '_trades.csv'
fname2 = file_prefix + str(ix) + '_dailydata.csv'
if os.path.isfile(fname1) and os.path.isfile(fname2):
continue
for key, seq in zip(self.scen_keys, s):
self.config[key] = self.scen_param[key][seq]
df_list = []
trade_list = []
for idy in range(abs(min(cont_map)), len(self.contlist[asset[0]]) - max(cont_map)):
cont = self.contlist[asset[0]][idy]
self.prepare_data(idx, cont_idx = idy)
sim_strat = self.sim_class(self.config)
sim_df, closed_trades = getattr(sim_strat, self.sim_func)()
df_list.append(sim_df)
trade_list = trade_list + closed_trades
(res_pnl, ts) = get_pnl_stats( [sim_df], self.config['marginrate'], 'm')
res_trade = get_trade_stats(closed_trades)
res = dict( res_pnl.items() + res_trade.items())
res.update(dict(zip(self.scen_keys, s)))
res['asset'] = cont
if cont not in output['cont']:
output['cont'][cont] = {}
output['cont'][cont][ix] = res
(res_pnl, ts) = get_pnl_stats(df_list, self.config['marginrate'], 'm')
output[ix] = res
res_trade = get_trade_stats(trade_list)
res = dict(res_pnl.items() + res_trade.items())
res.update(dict(zip(self.scen_keys, s)))
res['asset'] = '_'.join(asset)
output['total'][ix] = res
print 'saving results for asset = %s, scen = %s' % (asset, str(ix))
all_trades = {}
for i, tradepos in enumerate(trade_list):
all_trades[i] = trade_position.tradepos2dict(tradepos)
trades = pd.DataFrame.from_dict(all_trades).T
trades.to_csv(fname1)
ts.to_csv(fname2)
fname = file_prefix + '_stats.json'
with open(fname, 'w') as ofile:
json.dump(output, ofile)
cont_df = pd.DataFrame()
for idy in range(abs(min(cont_map)), len(self.contlist[asset[0]]) - max(cont_map)):
cont = self.contlist[asset[0]][idy]
if cont not in output['cont']:
continue
res = scen_dict_to_df(output['cont'][cont])
out_res = res[self.output_columns()]
if len(cont_df) == 0:
cont_df = out_res[:30].copy(deep=True)
else:
cont_df = cont_df.append(out_res[:30])
fname = file_prefix + 'cont_stat.csv'
cont_df.to_csv(fname)
res = scen_dict_to_df(output['total'])
out_res = res[self.output_columns()]
if len(summary_df)==0:
summary_df = out_res[:30].copy(deep = True)
else:
summary_df = summary_df.append(out_res[:30])
fname = self.file_prefix + 'summary.csv'
summary_df.to_csv(fname)
def run_all_assets(self):
for idx, asset in enumerate(self.sim_assets):
cont_map = self.cont_maplist[idx]
output = self.load_curr_results(idx)
if len(output) == 0:
output = {'total': {}, 'cont': {}}
elif len(output.keys()) == len(self.scenarios):
continue
self.set_config(idx)
self.load_data(idx)
for ix, s in enumerate(self.scenarios):
file_prefix = self.file_prefix + '_' + '_'.join(self.sim_mode + asset)
fname1 = file_prefix + str(ix) + '_trades.csv'
fname2 = file_prefix + str(ix) + '_dailydata.csv'
if os.path.isfile(fname1) and os.path.isfile(fname2):
continue
for key, seq in zip(self.scen_keys, s):
self.config[key] = self.scen_param[key][seq]
df_list = []
trade_list = []
for idy in range(abs(min(cont_map)), len(self.contlist[asset[0]]) - max(cont_map)):
cont = self.contlist[asset[0]][idy]
self.prepare_data(idx, cont_idx = idy)
sim_strat = self.sim_class(self.config)
sim_df, closed_trades = getattr(sim_strat, self.sim_func)()
df_list.append(sim_df)
trade_list = trade_list + closed_trades
(res_pnl, ts) = get_pnl_stats( sim_df, self.config['marginrate'], 'm', cost_ratio = self.cost_ratio)
res_trade = get_trade_stats(closed_trades)
res = dict( res_pnl.items() + res_trade.items())
res.update(dict(zip(self.scen_keys, s)))
res['asset'] = cont
if cont not in output['cont']:
output['cont'][cont] = {}
output['cont'][cont][ix] = res
(res_pnl, ts) = get_pnl_stats(df_list, self.config['marginrate'], 'm', cost_ratio = self.cost_ratio)
output[ix] = res
res_trade = get_trade_stats(trade_list)
res = dict(res_pnl.items() + res_trade.items())
res.update(dict(zip(self.scen_keys, s)))
res['asset'] = '_'.join(asset)
output['total'][ix] = res
print 'saving results for asset = %s, scen = %s' % (asset, str(ix))
all_trades = {}
for i, tradepos in enumerate(trade_list):
all_trades[i] = trade_position.tradepos2dict(tradepos)
trades = pd.DataFrame.from_dict(all_trades).T
trades.to_csv(fname1)
ts.to_csv(fname2)
fname = file_prefix + '_stats.json'
with open(fname, 'w') as ofile:
json.dump(output, ofile)
cont_df = pd.DataFrame()
for idy in range(abs(min(cont_map)), len(self.contlist[asset[0]]) - max(cont_map)):
cont = self.contlist[asset[0]][idy]
if cont not in output['cont']:
continue
res = scen_dict_to_df(output['cont'][cont])
out_res = res[self.output_columns()]
if len(cont_df) == 0:
cont_df = out_res[:30].copy(deep=True)
else:
cont_df = cont_df.append(out_res[:30])
fname = file_prefix + 'cont_stat.csv'
cont_df.to_csv(fname)
res = scen_dict_to_df(output['total'])
out_res = res[self.output_columns()]
if len(summary_df)==0:
summary_df = out_res[:30].copy(deep = True)
else:
summary_df = summary_df.append(out_res[:30])
fname = self.file_prefix + 'summary.csv'
summary_df.to_csv(fname)