Data acquisition and normalization
Third-party futures history is normalized into consistent tables with session-aware timestamps and raw contract lineage.
A governed research-to-execution lifecycle.
The Trade Framework is organized as a layered private software architecture. Each layer has a narrow mandate, explicit inputs, auditable outputs, and a defined handoff to the next stage of the operating lifecycle.
Operating model
The architecture is strongest where most trading stacks are weakest: boundaries, reproducibility, and parity.
Data Management builds the trusted market-data substrate. Research searches and validates feasible systematic strategies. Shared modules preserve feature-generation semantics. Execution converts approved logic into broker-aware operation under portfolio, state, reconciliation, and supervision controls.
Layered design
Five layers form the production path.
Continuous futures construction
Proprietary roll events and additive adjustment produce stable symbol-level histories for quantitative research.
Research discovery and validation
Candidate systems are generated, simulated, validated across robustness gates, and converted into reportable evidence.
Shared signal contract
Research and Execution rely on shared feature semantics rather than separate implementations that can drift.
Execution and runtime control
Broker connectivity, order lifecycle management, portfolio allocation, state, reconciliation, and supervision surround live operation.
Architectural strengths
Designed for evidence preservation from raw data through live decisions.
Configuration as control surface
YAML-driven configuration defines data policy, research windows, search boundaries, validation regimes, reporting sections, execution behavior, and portfolio controls.
Deterministic research posture
Seeded search, caching, deduplication, schema validation, and deterministic report artifacts support repeatable investigation and regression review.
Shared signal semantics
The shared signal frame is the architectural bridge between backtest evidence, forward testing, and live runtime interpretation.
Data integrity gates
Roll invariant checks, inventory auditing, quarantine/flag policy, session alignment, and usable-range reports reduce hidden data defects.
Durable state and ledgers
Runtime state, account and position truth, trade lifecycle events, allocation decisions, and reconciliation evidence are treated as primary records.
Fail-closed operating posture
Execution is guarded by readiness checks, admission gates, stale-data controls, restart logic, and supervised recovery boundaries.
Method flow
The framework is a closed loop, not a loose collection of scripts.
Each stage produces artifacts consumed by the next stage: normalized bars, roll evidence, candidate systems, validation results, shared signal definitions, allocation decisions, execution records, and reconciliation snapshots.
NormalizeRaw futures history becomes structured research data.
DiscoverFeasible systems are searched through multi-objective optimization.
ValidateSurvivors pass robustness gates and reporting review.
AllocatePortfolio controls govern admission and exposure.
ExecuteBroker-aware runtime handles orders, state, and reconciliation.
Disclosure boundary
This public architecture summary intentionally excludes source code, strategy rules, candidate parameters, database credentials, private endpoints, live account data, and production deployment identifiers.