def update(portfolio: Tuple[str, ...]) -> None:
StockDAO.create_if_not_exists(portfolio)
rows: List[IntradayEntity] = IntradayEntity.query.with_entities(
IntradayEntity.ticker).filter(
IntradayEntity.ticker.in_(portfolio)).distinct(
IntradayEntity.ticker).all()
if len(rows) < len(portfolio):
tickers: List[str] = list(map(lambda r: r.ticker, rows))
difference: str = Utils.first(
sorted(list(set(portfolio) - set(tickers))))
if difference is not None:
IntradayDAO.create_ticker(difference)
else:
rows: List[IntradayEntity] = db.session.query(
IntradayEntity.ticker,
db.func.max(IntradayEntity.date)).group_by(
IntradayEntity.ticker).all()
if rows is not None:
latest_date: datetime = max(list(map(lambda r: r[1], rows)))
ticker: str = Utils.first(
sorted(
list(
map(
lambda r: r.ticker,
list(
filter(
lambda f:
(f[1].date() != latest_date.date()),
rows))))))
if ticker is not None:
IntradayDAO.create_ticker(ticker)
def start() -> None:
latest_date: datetime = ForwardDAO.read_latest_date()
evaluation: EvaluationEntity = EvaluationDAO.read_order_by_sum()
if evaluation is None or Utils.is_today(latest_date) or not Utils.is_working_day_ny():
return
read_latest_date: List[IntradayEntity] = IntradayDAO.read_latest_date()
latest_date_dict: Dict[str, str] = {r.ticker: r[0] for r in read_latest_date}
rows: List[IntradayEntity] = IntradayDAO.read_order_by_date_asc()
frame: DataFrame = IntradayDAO.dataframe(rows)
inventory, cash, fee = ForwardBO.init()
broker: BrokerBO = ForwardBrokerBO(cash, fee, inventory)
statistic: StatisticBO = StatisticBO('forward')
attempt: AttemptDTO = AttemptDTOConverter.from_evaluation(evaluation)
AnalyserBO.analyse(frame, StrategyBO.counter_cyclical, broker, statistic, attempt, latest_date_dict)
def update(inventory: Dict[str, InventoryBO], cash: Decimal) -> Tuple[Dict[str, InventoryBO], Decimal, Decimal]:
total: Decimal = ZERO
total_value: Decimal = ZERO
for ticker, entry in inventory.items():
intraday: IntradayEntity = IntradayDAO.read_filter_by_ticker_first(ticker)
if intraday is None:
continue
entry.price = Decimal(intraday.close)
total_value += entry.value()
total += cash + total_value
return inventory, total_value, total
def to_file() -> List[Dict[str, str]]:
rows: List[IntradayEntity] = IntradayDAO.read_order_by_date_asc()
rows_dict: List[Dict[str, str]] = list(
map(
lambda row: dict(
list(
map(
lambda i:
(str(i[0]), str(float(i[1]))
if isinstance(i[1], Decimal) else str(i[1])),
filter(lambda e: not e[0].startswith('_'),
row.__dict__.items())))), rows))
return rows_dict
def intraday_view() -> str:
return render_template('intraday.html', tables=[IntradayDAO.dataframe_ticker().to_html(classes='data',
header='true')])
def stock_intraday_view(ticker: str) -> str:
return render_template('stock-intraday.html', intradays=IntradayDAO.read_filter_by_ticker(ticker))
def start(portfolio: List[str], number: int, group_number: int) -> None:
group_size: int = int(number / group_number)
groups: Tuple[Tuple[str]] = Utils.group(group_size, portfolio[:number])
OptimizationBO.optimise(IntradayDAO.dataframe_group(groups))
def from_file(request: Request) -> None:
IntradayDAO.create_from_file(request.files['file'].read())