if len(old_series) == 0:
return new_series
if len(new_series) == 0:
return old_series
joint_data = pd.concat([old_series, new_series], axis=1)
joint_data.columns = ["original", "new"]
# fill to the left
joint_data_filled_across = joint_data.bfill(1)
merged_data = joint_data_filled_across["original"]
return merged_data
all_labels_match = named_object("all labels match")
mismatch_on_last_day = named_object("mismatch_on_last_day")
original_index_matches_new = named_object("original index matches new")
def merge_data_series_with_label_column(original_data,
new_data,
col_names=dict(
data="PRICE",
label="PRICE_CONTRACT")):
"""
For two pd.DataFrames with 2 columns, including a label column, update the data when the labels
start consistently matching
>>> s1=pd.DataFrame(dict(PRICE=[1,2,3,np.nan], PRICE_CONTRACT = ["a", "a", "b", "b"]), index=['a1','a2','a3','a4'])
>>> s2=pd.DataFrame(dict(PRICE=[ 7,3,4], PRICE_CONTRACT = [ "b", "b", "b"]), index=['a2','a3','a4'])
)
from sysproduction.reporting.report_configs import roll_report_config
from sysproduction.reporting.reporting_functions import run_report_with_data_blob
from sysproduction.data.positions import diagPositions, updatePositions
from sysproduction.data.contracts import dataContracts
from sysproduction.data.prices import diagPrices, get_valid_instrument_code_from_user
from sysproduction.reporting.data.rolls import (
rollingAdjustedAndMultiplePrices,
relative_volume_in_forward_contract_versus_price,
)
no_change_required = named_object("No roll required")
EXIT_CODE = "EXIT"
def interactive_update_roll_status():
with dataBlob(log_name="Interactive_Update-Roll-Status") as data:
function_to_call = get_rolling_master_function()
function_to_call(data)
def get_rolling_master_function():
MANUAL_INPUT = "Manually input instrument codes and manually decide when to roll"
MENU_OPTIONS = [
MANUAL_INPUT,
"Cycle through instrument codes automatically, but manually decide when to roll",
import numpy as np
from syscore.genutils import sign
from syscore.objects import named_object
from sysexecution.orders.base_orders import Order
from sysexecution.trade_qty import tradeQuantity
override_close = named_object("Close")
override_no_trading = named_object("No trading")
override_reduce_only = named_object("Reduce only")
override_none = named_object("No override")
override_dict = {
override_close: 0.0,
override_none: 1.0,
override_no_trading: -1.0,
override_reduce_only: -2.0,
}
_override_lookup = []
for key, value in override_dict.items():
_override_lookup.append((value, key))
def lookup_value_and_return_float_or_object(value):
value_list = [entry[1] for entry in _override_lookup if entry[0] == value]
if len(value_list) == 0:
return value
else:
return value_list[0]
from sysobjects.adjusted_prices import futuresAdjustedPrices
from syscore.objects import success, failure, status, named_object
from sysobjects.production.roll_state import default_state, roll_adj_state, explain_roll_state_str, \
allowable_roll_state_from_current_and_position, RollState
from sysproduction.reporting.report_configs import roll_report_config
from sysproduction.reporting.reporting_functions import run_report_with_data_blob, landing_strip
from sysproduction.data.positions import diagPositions, updatePositions
from sysproduction.data.contracts import dataContracts
from sysdata.data_blob import dataBlob
from sysproduction.data.prices import diagPrices, updatePrices, get_valid_instrument_code_from_user
no_state_available = named_object("No state available")
def interactive_update_roll_status():
"""
Update the roll state for a particular instrument
This includes the option, where possible, to switch the adjusted price series on to a new contract
:param instrument_code: str
:return: None
"""
with dataBlob(log_name="Interactive_Update-Roll-Status") as data:
do_another = True
while do_another:
instrument_code = get_valid_instrument_code_from_user(data=data)
return no_change
def get_df_of_data_for_duplicate(mkt_data, all_markets: list) -> pd.DataFrame:
mkt_data_for_duplicates = [
get_market_data_for_duplicate(mkt_data, instrument_code)
for instrument_code in all_markets
]
mkt_data_for_duplicates = pd.DataFrame(mkt_data_for_duplicates,
index=all_markets)
return mkt_data_for_duplicates
no_good_markets = named_object("<No good markets>")
def get_best_market(mkt_data_for_duplicates: pd.DataFrame,
filters: tuple) -> str:
max_cost, min_contracts, min_risk = filters
only_valid = mkt_data_for_duplicates.dropna()
only_valid = only_valid[only_valid.SR_cost <= max_cost]
only_valid = only_valid[only_valid.volume_contracts > min_contracts]
only_valid = only_valid[only_valid.volume_risk > min_risk]
if len(only_valid) == 0:
return no_good_markets
only_valid = only_valid.sort_values("contract_size")
best_market = only_valid.index[0]
- is my status NO-RUN, STOP or GO?
- have I run today and finished running?
"""
from copy import copy
import datetime
import os
import pandas as pd
from syscontrol.list_running_pids import list_of_all_running_pids
from syscore.fileutils import html_table
from syscore.dateutils import SECONDS_PER_DAY, last_run_or_heartbeat_from_date_or_none
from syscore.objects import (success, failure, named_object, missing_data)
process_stop = named_object("process stop")
process_no_run = named_object("process no run")
process_running = named_object("process running")
go_status = "GO"
no_run_status = "NO-RUN"
stop_status = "STOP"
default_process_id = 0
possible_status = [go_status, no_run_status, stop_status]
start_run_idx = 0
end_run_idx = 1
missing_date_str = ""
from collections import namedtuple
import pandas as pd
from syscore.objects import missing_order, named_object
from sysexecution.orders.list_of_orders import listOfOrders
from sysexecution.orders.base_orders import Order
Fill = namedtuple("Fill", ["date", "qty", "price"])
NOT_FILLED = named_object('not filled')
class listOfFills(list):
def __init__(self, list_of_fills):
list_of_fills = [
fill for fill in list_of_fills if fill is not missing_order
]
super().__init__(list_of_fills)
def _as_dict_of_lists(self) -> dict:
qty_list = [fill.qty for fill in self]
price_list = [fill.price for fill in self]
date_list = [fill.date for fill in self]
return dict(qty=qty_list, price=price_list, date=date_list)
def as_pd_df(self) -> pd.DataFrame:
self_as_dict = self._as_dict_of_lists()
date_index = self_as_dict.pop("date")
df = pd.DataFrame(self_as_dict, index=date_index)