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('-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('-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 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 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)))