Our Automated Trade Methodology
Learn more about our transparent, layered process
Our system blends AI automation with human supervision to deliver timely, reviewed recommendations. We value transparency and compliance, with each signal vetted by both technology and experienced professionals. Past performance doesn't guarantee future results. Results may vary.
How We Develop Signals
Our process starts by gathering a wide array of market data relevant to the Australian trading environment. We implement advanced AI models to scan this data, flagging trends and anomalies in real time. Each flagged opportunity is assessed by our human experts, who filter insights through local compliance standards and practical insights. Every trade recommendation is the result of this layered review—AI initiates, humans validate, and only then is a recommendation shared. This ensures each signal is representative of up-to-date conditions and relevant market context. Throughout, we prioritise transparency by providing the rationale and analytics that power every recommendation, helping you understand risk and context. While we aim to offer robust support for your trading decisions, results may vary and past trends offer no promises for the future.
Stepwise Review for Actionable Signals
Orathenquiva’s approach combines automation, oversight, and transparency at every stage to ensure every trade signal meets strict compliance and contextual standards. Results may vary and no signal can guarantee a particular outcome.
Collecting Market Data and Trends
Our AI systems scan multiple data sources, focusing on real-time shifts and patterns specific to Australia.
Project Goals
To gather broad, accurate market data for effective analysis.
Our Actions
Aggregate and process data from licensed and reputable Australian sources, ensuring the dataset reflects current regulations and local factors.
Execution
Deploy proprietary algorithms that automatically filter high-volume inputs for relevance and compliance.
Key Tools
AI data miners, secure local APIs.
Expected Results
Catalogue of real-time data and market indicators for further analysis.
Flagging and Initial Signal Identification
AI models scan the cleaned data for actionable patterns and possibilities in the marketplace.
Project Goals
To identify emerging trends or outlier activity promptly.
Our Actions
AI analyses filtered data to flag potentially relevant opportunities based on pattern recognition and pre-set compliance criteria.
Execution
Use advanced detection logic and pattern-matching against local financial market norms.
Key Tools
Proprietary pattern-recognition models.
Expected Results
Flagged data points and rationale for review.
Expert Human Review and Localisation
Professional analysts review all flagged signals, considering their applicability to Australian standards.
Project Goals
To confirm legitimacy and contextual accuracy of every automated signal.
Our Actions
Compare flagged insights with regulatory requirements and local trading context; remove anything misaligned or unsuitable.
Execution
Experienced analysts evaluate flagged data with a compliance checklist, adding findings or comments where needed.
Key Tools
Manual analytics dashboard, compliance review toolkit.
Expected Results
Curated, locally relevant recommendations ready for user delivery.
Transparent Delivery to Clients
Each recommendation is delivered with its rationale, analytics, and compliance notes attached.
Project Goals
To provide users with contextualised, actionable trade recommendations.
Our Actions
Send reviewed signals to the user platform with accompanying explanations, key metrics, and compliance documentation.
Execution
Automated delivery pipeline with a transparent audit trail and notification system.
Key Tools
Client interface, signal delivery software.
Expected Results
Actionable, traceable recommendations presented to users.