Building a Futures Algorithm with Modular Framework Models
Summary
This example shows how to assemble a futures trading algorithm from separate framework components. It selects a futures chain each day, filters the universe to front-month contracts, emits constant upward price insights for futures, converts those insights into targets, and sends orders through immediate execution. Risk management is left to a null model, so the example does not demonstrate protective limits or position controls.
The chain selector changes its choice from an equity index future to a gold future on a stated date, illustrating how the selected market can vary over time. The algorithm uses minute-resolution data and a short sample period, but provides no performance results or evidence that its signals are profitable. Its constant bullish signal and single-unit targets are demonstration choices, not a tested trading strategy. The example is most useful for understanding how universe selection, alpha, portfolio construction, execution, and risk management fit together in a futures system.
Key ideas
- A futures framework algorithm connects universe selection, alpha, portfolio construction, execution, and risk management models.
- The universe model selects front-month contracts and can choose different futures chains over time.
- The alpha model emits constant upward insights only for futures symbols.
- Portfolio targets follow the insight direction, while execution is immediate and risk management is disabled.
- The example defines system structure rather than evidence of a profitable trading strategy.
Tags
Full text
# BasicTemplateFuturesFrameworkAlgorithm
# BasicTemplateFuturesFrameworkAlgorithm
Creates futures chain universes that select the front month contract and runs a user
defined future_chain_symbol_selector every day to enable choosing different futures chains
Basic template futures framework algorithm uses framework components to define an algorithm that trades futures.
## 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 *
from Alphas.ConstantAlphaModel import ConstantAlphaModel
from Selection.FutureUniverseSelectionModel import FutureUniverseSelectionModel
### <summary>
### Basic template futures framework algorithm uses framework components
### to define an algorithm that trades futures.
### </summary>
class BasicTemplateFuturesFrameworkAlgorithm(QCAlgorithm):
def initialize(self):
self.universe_settings.resolution = Resolution.MINUTE
self.universe_settings.extended_market_hours = self.get_extended_market_hours()
self.set_start_date(2013, 10, 7)
self.set_end_date(2013, 10, 11)
self.set_cash(100000)
# set framework models
self.set_universe_selection(FrontMonthFutureUniverseSelectionModel(self.select_future_chain_symbols))
self.set_alpha(ConstantFutureContractAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(1)))
self.set_portfolio_construction(SingleSharePortfolioConstructionModel())
self.set_execution(ImmediateExecutionModel())
self.set_risk_management(NullRiskManagementModel())
def select_future_chain_symbols(self, utc_time):
new_york_time = Extensions.convert_from_utc(utc_time, TimeZones.NEW_YORK)
if new_york_time.date() < date(2013, 10, 9):
return [ Symbol.create(Futures.Indices.SP_500_E_MINI, SecurityType.FUTURE, Market.CME) ]
else:
return [ Symbol.create(Futures.Metals.GOLD, SecurityType.FUTURE, Market.COMEX) ]
def get_extended_market_hours(self):
return False
class FrontMonthFutureUniverseSelectionModel(FutureUniverseSelectionModel):
'''Creates futures chain universes that select the front month contract and runs a user
defined future_chain_symbol_selector every day to enable choosing different futures chains'''
def __init__(self, select_future_chain_symbols):
super().__init__(timedelta(1), select_future_chain_symbols)
def filter(self, filter):
'''Defines the futures chain universe filter'''
return (filter.front_month()
.only_apply_filter_at_market_open())
class ConstantFutureContractAlphaModel(ConstantAlphaModel):
'''Implementation of a constant alpha model that only emits insights for future symbols'''
def __init__(self, _type, direction, period):
super().__init__(_type, direction, period)
def should_emit_insight(self, utc_time, symbol):
# only emit alpha for future symbols and not underlying equity symbols
if symbol.security_type != SecurityType.FUTURE:
return False
return super().should_emit_insight(utc_time, symbol)
class SingleSharePortfolioConstructionModel(PortfolioConstructionModel):
'''Portfolio construction model that sets target quantities to 1 for up insights and -1 for down insights'''
def create_targets(self, algorithm, insights):
targets = []
for insight in insights:
targets.append(PortfolioTarget(insight.symbol, insight.direction))
return targets
```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.