Modeling Minimum Cash Requirements in a Brokerage Backtest
Summary
This example shows how a backtest can incorporate brokerage-specific order restrictions and fees through a custom brokerage model. It defines a rule that rejects an order when its estimated cost would reduce portfolio cash below a required minimum, and emits a warning explaining the rejection. A simple buy-and-hold example uses this model while repeatedly attempting to set a target holding; after a failed purchase, it reduces the target allocation and retries.
The sample uses an equity at second-level resolution over a short date range and initializes a cash balance and minimum reserve. It demonstrates a practical way to make simulated order acceptance reflect account constraints, rather than assuming every requested order can be placed. The example does not compare results against a real broker or address broader brokerage rules, such as margin treatment, changing fees, or different asset classes, so the custom check should be treated as a narrow illustration.
Key ideas
- A custom brokerage model can enforce account-specific order acceptance rules during a backtest.
- The example rejects an order if its estimated cost would leave less than the required cash reserve.
- Rejected purchases prompt the sample strategy to reduce its target allocation before retrying.
- Brokerage constraints can improve simulation realism, but this example models only a minimum cash balance rule.
Tags
Full text
# BrokerageModelAlgorithm
# BrokerageModelAlgorithm
Custom brokerage model that requires clients to maintain a minimum cash balance
Demonstrate the usage of the BrokerageModel property to help improve backtesting accuracy through simulation of a specific brokerage's rules around restrictions on submitting orders as well as fee structure.
## Source (Apache-2.0)
```python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from AlgorithmImports import *
### <summary>
### Demonstrate the usage of the BrokerageModel property to help improve backtesting
### accuracy through simulation of a specific brokerage's rules around restrictions
### on submitting orders as well as fee structure.
### </summary>
### <meta name="tag" content="trading and orders" />
### <meta name="tag" content="brokerage models" />
class BrokerageModelAlgorithm(QCAlgorithm):
def initialize(self):
self.set_cash(100000) # Set Strategy Cash
self.set_start_date(2013,10,7) # Set Start Date
self.set_end_date(2013,10,11) # Set End Date
self.add_equity("SPY", Resolution.SECOND)
# there's two ways to set your brokerage model. The easiest would be to call
# self.set_brokerage_model( BrokerageName ) # BrokerageName is an enum
# self.set_brokerage_model(BrokerageName.INTERACTIVE_BROKERS_BROKERAGE)
# self.set_brokerage_model(BrokerageName.DEFAULT)
# the other way is to call SetBrokerageModel( IBrokerageModel ) with your
# own custom model. I've defined a simple extension to the default brokerage
# model to take into account a requirement to maintain 500 cash in the account at all times
self.set_brokerage_model(MinimumAccountBalanceBrokerageModel(self,500.00))
self.last = 1
def on_data(self, slice):
# Simple buy and hold template
if not self.portfolio.invested:
self.set_holdings("SPY", self.last)
if self.portfolio["SPY"].quantity == 0:
# each time we fail to purchase we'll decrease our set holdings percentage
self.debug(str(self.time) + " - Failed to purchase stock")
self.last *= 0.95
else:
self.debug("{} - Purchased Stock @ SetHoldings( {} )".format(self.time, self.last))
class MinimumAccountBalanceBrokerageModel(DefaultBrokerageModel):
'''Custom brokerage model that requires clients to maintain a minimum cash balance'''
def __init__(self, algorithm, minimum_account_balance):
self.algorithm = algorithm
self.minimum_account_balance = minimum_account_balance
def can_submit_order(self,security, order, message):
'''Prevent orders which would bring the account below a minimum cash balance'''
message = None
# we want to model brokerage requirement of minimum_account_balance cash value in account
order_cost = order.get_value(security)
cash = self.algorithm.portfolio.cash
cash_after_order = cash - order_cost
if cash_after_order < self.minimum_account_balance:
# return a message describing why we're not allowing this order
message = BrokerageMessageEvent(BrokerageMessageType.WARNING, "InsufficientRemainingCapital", "Account must maintain a minimum of ${0} USD at all times. Order ID: {1}".format(self.minimum_account_balance, order.id))
self.algorithm.error(str(message))
return False
return True
```Shown in full with attribution under the source's licence. Licence: Apache-2.0
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.