def main():
parser = argparse.ArgumentParser()
parser.add_argument('-s', '--sharpe', help='minimum sharpe', default=1)
parser.add_argument('-t', '--train_result', default='SM0_21')
parser.add_argument('-p', '--portfolio_name', default='smin1')
args = parser.parse_args()
sharpe = float(args.sharpe)
train_result_dir = os.path.join(CommonDir.train_output_dir,
args.train_result)
instMaster = InstMaster()
json_ = instMaster.getInstrumentJson()
selected_inst_json = []
for inst in json_['instruments']:
symbol = inst['canonical_name']
fp = os.path.join(train_result_dir, symbol, 'optimize', 'final',
'strategy.csv')
if os.path.exists(fp) == False:
continue
stgy_df = pd.read_csv(fp)
if stgy_df['sharpe'].iloc[0] >= sharpe:
inst['pos'] = 0
inst['order_size'] = 1
inst['pnl'] = 0
inst['bounds'] = [1, 30]
selected_inst_json.append(inst)
print(fp)
if len(selected_inst_json) > 0:
instMaster.writePortfolioJson(args.portfolio_name, selected_inst_json)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-a',
'--max_allocation',
help='max allocation',
default=10000000)
parser.add_argument('-j', '--pname', default='smin0')
parser.add_argument('--tag', default='SM0_21')
args = parser.parse_args()
date = '2021-01-07'
max_allocation = args.max_allocation
pname = args.pname
instMaster = InstMaster()
inst_json = instMaster.getInstrumentJson()
inst_update = []
for idx, inst in enumerate(inst_json[pname]):
print(inst['symbol'])
ss_df = pdr.get_data_yahoo(inst['symbol'], start=date, end=date)
ss_df['d'] = ss_df.index
ss_df = ss_df[ss_df['d'] == date]
lprice = ss_df['Close'].iloc[0]
max_alloc = int(max_allocation / lprice)
inst['bounds'] = [1, max_alloc]
inst_update.append(inst)
inst_json[pname] = inst_update
print(json.dumps(inst_json, indent=2))
instMaster.writePortfolioJson(pname, inst_json)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-t', '--type', help='intra, inter', default='inter')
parser.add_argument('-i',
'--intruments',
help='instruments_kospi200, instruments_sp500',
default='instruments_kospi200')
args = parser.parse_args()
output_dir = '/home/smin/StockEngine/data'
start_date = '2016-01-01'
end_date = '2020-12-25'
instMaster = InstMaster(args.type, args.instruments)
inst_json = instMaster.getInstrumentJson()
if instMaster.type() == 0:
for inst in inst_json['instruments']:
ss_df = pdr.get_data_yahoo(inst['symbol'],
start=start_date,
end=end_date)
fp = '{}.csv'.format(
os.path.join(output_dir, inst['canonical_name']))
ss_df.to_csv(fp)
print(fp)
else:
for inst in inst_json['instruments']:
symbol_ = inst['symbol']
ts = TimeSeries(key='YOUR_API_KEY', output_format='pandas')
data, meta_data = ts.get_intraday(symbol=symbol_,
interval='1min',
outputsize='full')
os.makedirs(
os.path.join(CommonDir.sampled_us_dir, '{}'.format(symbol_)))
data.to_csv(symbol and date.csv)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-p',
'--tradingPeriod',
help='tradingPeriod',
default=126)
parser.add_argument('-t', '--sim_type', default=1)
parser.add_argument('-b', '--begin_date', default='2010-01-01')
parser.add_argument('-e', '--end_date', default='2020-12-01')
parser.add_argument('-j', '--pname', default='instruments')
parser.add_argument('--tag', default='SM0_21')
args = parser.parse_args()
sim_output = CommonDir.sim_output_dir
portfolio_config_dir = CommonDir.train2_output_dir
trading_period = int(args.tradingPeriod)
begin_date = args.begin_date
end_date = args.end_date
sim_type = int(args.sim_type)
prtf_name = args.pname
tickers = InstMaster().getTickerList(prtf_name)
result_dfs = pd.DataFrame()
result_detail_df = pd.DataFrame()
for train_dir in tickers:
config_fp = os.path.join(portfolio_config_dir, args.tag, train_dir,
'optimize', 'final', 'strategy.csv')
if os.path.exists(config_fp) == False:
continue
symbol = train_dir
data_frame_df = pd.read_csv(
os.path.join(CommonDir.sampled_dir, '{}.csv'.format(symbol)))
sim = Simulator(data_frame_df, config_fp, begin_date, end_date, symbol,
trading_period, sim_type)
result_df, rdfs = sim.run()
if result_df.shape[0] > 0:
result_dfs = result_dfs.append(result_df)
result_detail_df = result_detail_df.append(rdfs)
else:
print('{} has no result '.format(train_dir))
summary_df = summary_portfolio(result_detail_df, prtf_name, trading_period)
summary_df.to_csv(
os.path.join(
sim_output,
'summary_{}_{}_{}.csv'.format(prtf_name,
begin_date.replace('-', ''),
end_date.replace('-', ''))))
result_dfs.to_csv(
os.path.join(
sim_output, 'sim_{}_{}_{}.csv'.format(prtf_name,
begin_date.replace('-', ''),
end_date.replace('-', ''))))
display(summary_df)
display(result_dfs)
def updateBuy(pname, ticker, qty, price, date, cprice):
instMaster = InstMaster()
portfolio_json = instMaster.getInstrumentJson()[pname]
ticker_idx = 0
for idx, s in enumerate(portfolio_json):
if s['symbol'] == ticker:
ticker_idx = idx
break
t_json = portfolio_json[ticker_idx]
# portfolio_json[ticker_idx]['pnl'] = t_json['pnl'] + (cprice - price)* qty if 'pnl' in t_json else (cprice - price)* qty
portfolio_json[ticker_idx]['pos'] = t_json['pos'] + qty if 'pos' in t_json else qty
orders = t_json['orders'] if 'orders' in t_json else []
o = { 'price':price, 'qty':qty, 'date':date }
orders .append(o)
portfolio_json[ticker_idx]['orders'] = orders
instMaster.writePortfolioJson(pname, portfolio_json)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'-j',
'--train_config',
help='training config json',
default=
'/home/smin/eclipse-workspace/IntraStrategy/models/US_model.json')
args = parser.parse_args()
if os.path.exists(args.train_config) == False:
print('{} does not exist'.format(args.train_config))
return
json_ = ReadFile.read_json(args.train_config)
tradingPeriod = int(json_['period'])
sim_type = int(json_['sim_type'])
begin_date = json_['begin_date']
end_date = json_['end_date']
intra_trading = not json_['daily']
train_output_dir = CommonDir.train_output_dir
if sim_type == 1:
train_output_dir = CommonDir.train2_output_dir
train_output_dir = os.path.join(train_output_dir, json_['output_dir'])
instMaster = InstMaster(json_['daily'], json_['instruments'])
cnt = multiprocessing.cpu_count() - 3
symbols = instMaster.getInstNames()
symbols = ['testdata'] #TODO testing
sampled_dir = '/home/smin/dataDir' #TODO sampling directory
for s in symbols:
run_simOptimizer(os.path.join(sampled_dir, s),
os.path.join(train_output_dir,
'{}'.format(s)), tradingPeriod, s,
sim_type, begin_date, end_date, json_['obj_func'],
copy.deepcopy(json_), intra_trading)
print('end of optimization')
def updateSell(pname, ticker, qty, price, date, cprice):
instMaster = InstMaster()
portfolio_json = instMaster.getInstrumentJson()[pname]
ticker_idx = 0
for idx, s in enumerate(portfolio_json):
if s['symbol'] == ticker:
ticker_idx = idx
break
t_json = portfolio_json[ticker_idx]
# orders = sorted(t_json['orders'], key=lambda k:k['price'], reverse=True) # sort worst
orders = sorted(t_json['orders'], key=lambda k:k['price']) # sort best
portfolio_json[ticker_idx]['pos'] = t_json['pos'] - qty
for idx, o in enumerate(orders):
portfolio_json[ticker_idx]['pnl'] = t_json['pnl'] + (price - o['price'])* o['qty'] if 'pnl' in t_json else (price - o['price'])* o['qty']
qty = qty - o['qty']
orders[idx]['qty'] = 0
if qty == 0:
break
portfolio_json[ticker_idx]['orders'] = [ o for o in orders if o['qty'] > 0 ]
instMaster.writePortfolioJson(pname, portfolio_json)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-d',
'--trd_date',
help='trading_date',
default='2021-01-15')
parser.add_argument('-j', '--pname', default='smin0')
parser.add_argument('--tag', default='SM0_21')
args = parser.parse_args()
date = args.trd_date
pname = args.pname
instMaster = InstMaster()
inst_json = instMaster.getInstrumentJson()
portfolio_config_dir = os.path.join(CommonDir.train2_output_dir, args.tag)
today_orders = pd.DataFrame(columns=[
'date', 'type', 'ticker', 'ticker_id', 'is_buy', 'price', 'qty',
'start_price'
])
for inst in inst_json[pname]:
ticker = inst['canonical_name']
ss_df = pdr.get_data_yahoo(inst['symbol'], start=date, end=date)
ss_df['d'] = ss_df.index
ss_df = ss_df[ss_df['d'] == date]
config_fp = os.path.join(portfolio_config_dir, ticker, 'optimize',
'final', 'strategy.csv')
if os.path.exists(config_fp) == False:
continue
config_df = pd.read_csv(config_fp)
c_edge = config_df['c_edge'].iloc[0]
s_edge = config_df['s_edge'].iloc[0]
f_edge = config_df['f_edge'].iloc[0]
pos = inst['pos'] if 'pos' in inst else 0
orders = inst['orders'] if 'orders' in inst else []
realized_pnl = inst['pnl'] if 'pnl' in inst else 0
order_size = inst['order_size'] if 'order_size' in inst else 1
lprice = ss_df['Close'].iloc[0]
if pos == 0:
propBid = lprice - s_edge * instMaster.getTickSize(lprice)
propAsk = propBid + c_edge * instMaster.getTickSize(lprice)
today_orders.loc[today_orders.shape[0]] = [
date, 'normal', inst['symbol'], inst['canonical_name'], True,
propBid, order_size, lprice
]
today_orders.loc[today_orders.shape[0]] = [
date, 'stop', inst['symbol'], inst['canonical_name'], False,
propAsk, order_size, lprice
]
else:
cov_notion = 0
cov_qty = 0
low_price = np.NAN
for o in inst['orders']:
cov_qty = cov_qty + o['qty']
cov_notion = cov_notion + o['qty'] * o['price']
if np.isnan(low_price) or low_price > o['price']:
low_price = o['price']
propAsk = cov_notion / cov_qty + c_edge * instMaster.getTickSize(
lprice)
buy_price = low_price - f_edge * instMaster.getTickSize(lprice)
propBid = lprice if buy_price > lprice else buy_price
today_orders.loc[today_orders.shape[0]] = [
date, 'normal', inst['symbol'], inst['canonical_name'], True,
propBid, inst['order_size'], lprice
]
today_orders.loc[today_orders.shape[0]] = [
date, 'stop', inst['symbol'], inst['canonical_name'], False,
propAsk, o['qty'], lprice
]
display(today_orders)
today_orders.to_csv(
os.path.join(CommonDir.trading_dir, '{}.{}.csv'.format(pname, date)))