def view_instrument_config(data):
instrument_code = get_valid_instrument_code_from_user(data)
diag_instruments = diagInstruments(data)
meta_data = diag_instruments.get_meta_data(instrument_code)
print(meta_data)
return None
def last_currency_fx(data: dataBlob, instrument_code: str) -> float:
data_currency = dataCurrency(data)
diag_instruments = diagInstruments(data)
currency = diag_instruments.get_currency(instrument_code)
fx_rate = data_currency.get_last_fx_rate_to_base(currency)
return fx_rate
def get_perc_pandl_series_for_strategy_instrument_vs_total_capital(
data, instrument_strategy: instrumentStrategy):
print("Data for %s" % (instrument_strategy))
instrument_code = instrument_strategy.instrument_code
strategy_name = instrument_strategy.strategy_name
capital = get_total_capital_series(data)
fx = get_fx_series_for_instrument(data, instrument_code)
diag_instruments = diagInstruments(data)
value_per_point = diag_instruments.get_point_size(instrument_code)
positions = get_position_series_for_instrument_strategy(
data, instrument_code=instrument_code, strategy_name=strategy_name)
prices = get_current_contract_price_series_for_instrument(
data, instrument_code=instrument_code)
fills = get_fills_for_instrument(data,
instrument_code=instrument_code,
strategy_name=strategy_name)
calculator = pandlCalculationWithFills.using_positions_and_prices_merged_from_fills(
prices,
positions=positions,
fills=fills,
fx=fx,
capital=capital,
value_per_point=value_per_point,
)
perc_pandl = calculator.percentage_pandl()
return perc_pandl
def get_fx_series_for_instrument(data, instrument_code):
diag_instruments = diagInstruments(data)
currency = diag_instruments.get_currency(instrument_code)
currency_data = currencyData(data)
fx_series = currency_data.get_fx_prices_to_base(currency)
return fx_series
def get_pandl_series_in_local_ccy_for_contract(data, instrument_code, contract_id):
diag_instruments = diagInstruments(data)
pandl_in_points = get_pandl_series_in_points_for_contract(data, instrument_code, contract_id)
point_size = diag_instruments.get_point_size(instrument_code)
pandl_in_local = point_size * pandl_in_points
return pandl_in_local
def view_instrument_config(data):
instrument_code = get_valid_instrument_code_from_user(data)
diag_instruments = diagInstruments(data)
meta_data = diag_instruments.get_meta_data(instrument_code)
print(meta_data)
data_broker = dataBroker(data)
instrument_broker_data = data_broker.get_brokers_instrument_with_metadata(instrument_code)
print(instrument_broker_data)
def get_pandl_series_in_local_ccy_for_strategy_instrument(
data, strategy_name, instrument_code):
diag_instruments = diagInstruments(data)
pandl_in_points = get_pandl_series_in_points_for_instrument_strategy(
data, instrument_code, strategy_name)
point_size = diag_instruments.get_point_size(instrument_code)
pandl_in_local = point_size * pandl_in_points
return pandl_in_local
def get_period_perc_pandl_for_sector_in_date_range(self, asset_class: str):
diag_instruments = diagInstruments(self.data)
list_of_instruments = diag_instruments.get_all_instruments_in_asset_class(
asset_class)
instrument_pandl = [
self.
get_period_perc_pandl_for_instrument_all_strategies_in_date_range(
instrument_code) for instrument_code in list_of_instruments
]
asset_class_pandl = sum(instrument_pandl)
return asset_class_pandl
def get_period_perc_pandl_for_all_sectors_in_date_range(self):
diag_instruments = diagInstruments(self.data)
asset_classes = diag_instruments.get_all_asset_classes()
list_pandl = [
PandL(
asset_class,
self.get_period_perc_pandl_for_sector_in_date_range(
asset_class),
) for asset_class in asset_classes
]
return list_pandl
def get_percentage_cost_for_instrument(data: dataBlob, instrument_code: str):
diag_instruments = diagInstruments(data)
costs_object = diag_instruments.get_cost_object(instrument_code)
blocks_traded = 1
block_price_multiplier = get_block_size(data, instrument_code)
price = recent_average_price(data, instrument_code)
percentage_cost = \
costs_object.calculate_cost_percentage_terms(blocks_traded=blocks_traded,
block_price_multiplier=block_price_multiplier,
price=price)
return percentage_cost
def get_current_configured_spread_cost(data):
diag_instruments = diagInstruments(data)
list_of_instruments = diag_instruments.get_list_of_instruments()
spreads_as_list = [
get_configured_spread_cost_for_instrument(data, instrument_code)
for instrument_code in list_of_instruments
]
spreads_as_df = pd.Series(spreads_as_list, index=list_of_instruments)
return spreads_as_df
def get_period_perc_pandl_for_all_sectors_in_date_range(
data, start_date, end_date):
diag_instruments = diagInstruments(data)
asset_classes = diag_instruments.get_all_asset_classes()
list_pandl = [
PandL(
asset_class,
get_period_perc_pandl_for_sector_in_date_range(
asset_class, data, start_date, end_date),
) for asset_class in asset_classes
]
return list_pandl
def get_period_perc_pandl_for_sector_in_date_range(asset_class, data,
start_date, end_date):
print("Getting data for %s" % asset_class)
diag_instruments = diagInstruments(data)
list_of_instruments = diag_instruments.get_all_instruments_in_asset_class(
asset_class)
instrument_pandl = [
get_period_perc_pandl_for_instrument_all_strategies_in_date_range(
data, instrument_code, start_date, end_date)
for instrument_code in list_of_instruments
]
asset_class_pandl = sum(instrument_pandl)
return asset_class_pandl * 100
def cash_calculations_for_slippage_row(slippage_row, data):
## What's a tick worth in base currency?
diag_instruments = diagInstruments(data)
value_of_price_point = diag_instruments.get_point_size_base_currency(
slippage_row.instrument_code)
input_items = [
'delay', 'bid_ask', 'execution', 'versus_limit', 'versus_parent_limit',
'total_trading'
]
output = [
value_of_price_point * slippage_row[input_name]
for input_name in input_items
]
return tuple(output + [value_of_price_point])
def get_cash_cost_in_base_for_instrument(data: dataBlob, instrument_code: str):
diag_instruments = diagInstruments(data)
costs_object = diag_instruments.get_cost_object(instrument_code)
blocks_traded = 1
block_price_multiplier = get_block_size(data, instrument_code)
price = recent_average_price(data, instrument_code)
cost_instrument_ccy = costs_object.calculate_cost_instrument_currency(
blocks_traded=blocks_traded,
block_price_multiplier=block_price_multiplier,
price=price,
)
fx = last_currency_fx(data, instrument_code)
cost_base_ccy = cost_instrument_ccy * fx
return cost_base_ccy
def get_perc_pandl_series_for_contract(data, instrument_code, contract_id):
capital = get_total_capital_series(data)
fx = get_fx_series_for_instrument(data, instrument_code)
diag_instruments = diagInstruments(data)
value_per_point = diag_instruments.get_point_size(instrument_code)
positions = get_position_series_for_contract(
data, instrument_code, contract_id)
prices = get_price_series_for_contract(
data, instrument_code, contract_id)
fills = get_fills_for_contract(data, instrument_code, contract_id)
calculator = pandlCalculationWithFills.using_positions_and_prices_merged_from_fills(prices,
positions=positions,
fills=fills,
fx=fx,
capital = capital,
value_per_point=value_per_point)
perc_pandl = calculator.percentage_pandl()
return perc_pandl
def get_base_currency_point_size_per_contract(data, instrument_code):
diag_instruments = diagInstruments(data)
point_size_base_currency = diag_instruments.get_point_size_base_currency(
instrument_code)
return point_size_base_currency
def get_block_size(data, instrument_code):
diag_instruments = diagInstruments(data)
return diag_instruments.get_point_size(instrument_code)
def get_configured_spread_cost_for_instrument(data, instrument_code):
diag_instruments = diagInstruments(data)
meta_data = diag_instruments.get_meta_data(instrument_code)
return meta_data.Slippage
