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Execution

Interactive Brokers

from pathlib import Path
import pandas as pd
from pwb_toolbox import execution as pwb_exec

STRATEGIES = {"my_strategy": {"path": "my_package.my_strategy", "weight": 1.0}}
logs_dir = Path("logs")
ACCOUNT_REFERENCE_NAV_VALUE = 100_000
ACCOUNT_REFERENCE_NAV_DATE = pd.Timestamp("2023-01-01")
LEVERAGE = 1.0
MARKET_DATA_TYPE = 1

ibc = pwb_exec.IBConnector(market_data_type=MARKET_DATA_TYPE)
ibc.connect()

account_nav_value = ibc.get_account_nav()
account_nav_date = pd.Timestamp.today().normalize()

nav_entry = pwb_exec.append_nav_history(logs_dir, account_nav_value)
nav_series, raw_positions = pwb_exec.run_strategies(STRATEGIES)
strategies_positions, theoretical_positions = pwb_exec.scale_positions(
    STRATEGIES,
    raw_positions,
    nav_series,
    ACCOUNT_REFERENCE_NAV_VALUE,
    LEVERAGE,
    ACCOUNT_REFERENCE_NAV_DATE,
)

ib_positions = ibc.get_positions()
orders = pwb_exec.compute_orders(theoretical_positions, ib_positions)

execution_time = 5 * 60  # five minutes
trades = pwb_exec.execute_and_log_orders(ibc, orders, execution_time)

pwb_exec.log_current_state(
    logs_dir,
    account_nav_value,
    strategies_positions,
    theoretical_positions,
    ib_positions,
    orders,
    account_nav_date,
    trades=trades,
    nav_history_entry=nav_entry,
)

ibc.disconnect()

CCXT Exchanges

from pathlib import Path
import pandas as pd
from pwb_toolbox import execution as pwb_exec

STRATEGIES = {"my_strategy": {"path": "my_package.my_strategy", "weight": 1.0}}
logs_dir = Path("logs")
ACCOUNT_REFERENCE_NAV_VALUE = 100_000
ACCOUNT_REFERENCE_NAV_DATE = pd.Timestamp("2023-01-01")
LEVERAGE = 1.0

cc = pwb_exec.CCXTConnector(
    exchange="binance",
    api_key="YOUR_API_KEY",
    api_secret="YOUR_API_SECRET",
)
cc.connect()

account_nav_value = cc.get_account_nav()
account_nav_date = pd.Timestamp.today().normalize()

nav_entry = pwb_exec.append_nav_history(logs_dir, account_nav_value)
nav_series, raw_positions = pwb_exec.run_strategies(STRATEGIES)
strategies_positions, theoretical_positions = pwb_exec.scale_positions(
    STRATEGIES,
    raw_positions,
    nav_series,
    ACCOUNT_REFERENCE_NAV_VALUE,
    LEVERAGE,
    ACCOUNT_REFERENCE_NAV_DATE,
)

cc_positions = cc.get_positions()
orders = pwb_exec.compute_orders(theoretical_positions, cc_positions)

execution_time = 5 * 60
trades = pwb_exec.execute_and_log_orders(cc, orders, execution_time)

pwb_exec.log_current_state(
    logs_dir,
    account_nav_value,
    strategies_positions,
    theoretical_positions,
    cc_positions,
    orders,
    account_nav_date,
    trades=trades,
    nav_history_entry=nav_entry,
)

cc.disconnect()

Optimal Limit Order

The module pwb_toolbox.execution.optimal_limit_order implements the optimal limit‑order placement framework described in Optimal Portfolio Liquidation with Limit Orders by Guéant, Lehalle, and Tapia.

Given a target quantity and time horizon, get_optimal_quote solves the associated system of differential equations to return the price offset from the mid‑price that maximises the expected utility of execution. Market parameters such as volatility (sigma), arrival rate of market orders (A), liquidity impact (k), trader risk aversion (gamma) and liquidation penalty (b) can be supplied to model different scenarios. Setting is_plot=True visualises the optimal quote path over time.