def get_list_of_last_futures_price_updates(data):
list_of_instruments = get_list_of_instruments(data)
updates = [
get_last_futures_price_update_for_instrument(data, instrument_code)
for instrument_code in list_of_instruments
]
return updates
def get_list_of_position_updates_for_strategy(data, strategy_name):
instrument_list = get_list_of_instruments(data)
list_of_updates = [get_last_position_update_for_strategy_instrument(data, strategy_name, instrument_code)
for instrument_code in instrument_list]
list_of_updates = [update for update in list_of_updates if update is not None]
return list_of_updates
def auto_populate_position_limits(data: dataBlob):
instrument_list = get_list_of_instruments(data)
auto_parameters = get_auto_population_parameters()
[
set_position_limit_for_instrument(data,
instrument_code=instrument_code,
auto_parameters=auto_parameters)
for instrument_code in instrument_list
]
return None
def auto_populate_position_limits(data: dataBlob):
instrument_list = get_list_of_instruments(data)
risk_multiplier, max_leverage = get_risk_multiplier_and_max_leverage()
[
set_position_limit_for_instrument(
data,
instrument_code=instrument_code,
risk_multiplier=risk_multiplier,
max_leverage=max_leverage,
) for instrument_code in instrument_list
]
return None
def get_market_moves_for_dates(self,
start_date: datetime.datetime,
end_date: datetime.datetime):
self._end_date = end_date
self._start_date = start_date
list_of_instruments = get_list_of_instruments(self.data, source="multiple")
all_moves = [self.get_market_move_for_instrument_and_dates(
instrument_code=instrument_code)
for instrument_code in list_of_instruments]
all_moves_as_df = pd.DataFrame(all_moves)
all_moves_as_df = all_moves_as_df.dropna()
return all_moves_as_df
def get_market_moves_for_period(self,
period: str) -> pd.DataFrame:
self._end_date = datetime.datetime.now()
print("Getting data for %s" % period)
# ['name', 'change', 'vol_adjusted']
list_of_instruments = get_list_of_instruments(self.data, source="multiple")
all_moves = [self.get_market_move_for_instrument_and_period(
instrument_code=instrument_code,
period=period)
for instrument_code in list_of_instruments]
all_moves_as_df = pd.DataFrame(all_moves)
all_moves_as_df = all_moves_as_df.dropna()
return all_moves_as_df
def get_instrument_risk_table(data, only_held_instruments=True):
## INSTRUMENT RISK (daily %, annual %, return space daily and annual, base currency per contract daily and annual, positions)
if only_held_instruments:
instrument_list = get_instruments_with_positions_all_strategies(data)
else:
instrument_list = get_list_of_instruments()
p = progressBar(len(instrument_list))
risk_data_list = []
for instrument_code in instrument_list:
risk_this_instrument = get_risk_data_for_instrument(
data, instrument_code)
risk_data_list.append(risk_this_instrument)
p.iterate()
p.finished()
risk_df = pd.DataFrame(risk_data_list, index=instrument_list).transpose()
risk_df = sorted_clean_df(risk_df, "annual_risk_perc_capital")
return risk_df
def auto_populate_limits(data: dataBlob):
instrument_list = get_list_of_instruments(data)
auto_parameters = get_auto_population_parameters()
trade_multiplier = get_and_convert(
"Higgest proportion of standard position expected to trade daily?",
type_expected=float,
default_value=MAX_POSITION_TRADED_DAILY,
)
period_days = get_and_convert("What period in days to set limit for?",
type_expected=int,
default_value=1)
_ = [
set_trade_limit_for_instrument(
data,
instrument_code=instrument_code,
auto_parameters=auto_parameters,
trade_multiplier=trade_multiplier,
period_days=period_days,
) for instrument_code in instrument_list
]
return None
def auto_populate_limits(data: dataBlob):
instrument_list = get_list_of_instruments(data)
risk_multiplier, max_leverage = get_risk_multiplier_and_max_leverage()
trade_multiplier = get_and_convert(
"Higgest proportion of standard position expected to trade daily?",
type_expected=float,
default_value=0.33,
)
period_days = get_and_convert("What period in days to set limit for?",
type_expected=int,
default_value=1)
_ = [
set_trade_limit_for_instrument(
data,
instrument_code=instrument_code,
risk_multiplier=risk_multiplier,
trade_multiplier=trade_multiplier,
period_days=period_days,
max_leverage=max_leverage,
) for instrument_code in instrument_list
]
return None
