Footprint & order flow
Analysis modules examine diagonal and stacked imbalances and absorption patterns. These inputs help structure a testable market hypothesis; they do not establish certainty about the next market move.
Product overview · in development
From market inputs
to testable decisions.
Mascarelo AI is developing a Python-powered automated trading system for independent traders who want to examine strategy behavior, risk and execution assumptions before using automation.
Built on Python and NautilusTrader, the codebase connects order-flow research, historical simulation and configurable trading safeguards. Licensed customer access is the intended product.
Discuss future accessInside the development code
Research, test and inspect.
These are capabilities supported by the development codebase. Their presence does not establish customer readiness, strategy profitability or live execution certification.
Historical simulation
NautilusTrader-based backtesting connects strategy definitions to historical data and execution assumptions. Evaluation artifacts make it possible to inspect the configuration and outcome of a test.
Configurable risk checks
Position sizing, drawdown checks, trade admission and trading-time constraints are represented in the codebase. Settings and behavior require validation for the intended account and execution environment.
Reviewable decisions
Signal inputs and rejection reasons support examination of why an attempted trade was admitted or blocked. A useful record includes strategy version, dataset, costs and effective settings.
How the system is being developed
An explicit decision workflow.
Prepare market inputs
Define the data source and market context. Data integrity and instrument assumptions affect everything that follows.
Evaluate the strategy
Use footprint and strategy conditions to form a candidate decision. Inspect the signal inputs instead of treating a label as an explanation.
Apply risk and account constraints
Check sizing, drawdown and trading-time rules against the configured profile. Inspect rejected decisions as well as admitted ones.
Review behavior and evidence
Compare historical results across conditions, costs and configurations. Live execution and customer delivery are separate validation steps.
The current research baseline uses gold futures. This is a research focus, not a promise that any specific customer market, broker, account or platform is already supported.
The intended commercial offer
Licensed access.
Two delivery paths under evaluation.
The planned offer combines access to the trading system with documented setup and configuration guidance. Delivery is being designed around the customer’s environment; neither option below is a released service.
Executable on your machine or VPS
A packaged executable running in an agreed local or VPS environment. Before release, installation, updates, compatibility, licence enforcement and operational support need validation.
The customer’s supported environment and execution permissions would be agreed explicitly.
Client bridge to a server
A bridge connected to the client’s account, receiving order instructions from Franco’s server. This model needs validated authentication, permissions, connection recovery and order reconciliation.
Controls for stale or duplicate instructions, emergency stopping and customer authorization must be defined and tested before release.
What will be agreed before a purchase?
- Supported instruments, data sources, platform and account configuration.
- Execution authority, risk settings and customer responsibilities.
- Installation or bridge connection, updates and support scope.
- Availability, price, licence terms and permitted use.
- Applicable privacy, security and service terms for the chosen delivery model.
Production access is not available today. No payment or account connection is requested on this website. Do not email brokerage passwords, API keys or other credentials.