Quantitative analysis for demanding investors

Data-Driven Edge. Systematic decision-making based on data, not assumptions.

Sivorentix processes large volumes of market data in real time and converts them into structured documents. Predictive modeling, noise reduction and clear documentation of every output.

Analyze markets Review the methodology
Designed for day traders and institutional teams. No promises of returns. Process oriented.
Verifiability
Public performance logs

Each model output is recorded with a time stamp and market context.

Processing
Streamed data in real time

Consolidation of multiple sources into a single analytical view.

Control
Risk first rules

Exposure and limits are part of each recommendation, not an addendum.

Core technology

Predictive analytics built on a verifiable procedure

Sivorentix does not look for shortcuts. It combines statistical models and machine learning to clean, weight and interpret market data. The result is less noise and a clearer basis for your decisions.

01 / Ingestion

Normalization of multiple data sources

Exchanges, order-book data and macroeconomic inputs are unified into a consistent scheme. Duplicates and incomplete records are flagged before further processing.

Technical detail: deduplication, time synchronization, tick completeness checking and validation against reference sources.
02 / Modelling

Predictive modeling with weighted confidence

Models evaluate probabilistic scenarios, not unambiguous predictions. Each output includes a confidence interval and the main factors that influenced it.

Technical detail: probability scores, factor attribution, model versioning and parameter traceability.
03 / Filtering

Noise reduction and quantitative context analysis

Low liquidity, anomalous ticks and irrelevant volatility are filtered out. The signal remains embedded in the context of volatility and market mode.

Technical detail: anomaly detection, market regime classification, liquidity and volatility thresholding.

Data flow from input to decision

The entire chain is documented. You know exactly what data went in, how it was modified, and why the model came to a given conclusion.

  1. 1Collection and cleaning. Data reception, quality control and time series normalization.
  2. 2Model evaluation. Parallel evaluation of scenarios with explicit measurement of uncertainty.
  3. 3Risk filter. Applying exposure limits and checking compliance with your strategy.
  4. 4Delivery and logo. Structured output to the interface or API, writing to the public log.
Access Sivorentix

A tool for experts, not a shortcut to a decision

Sivorentix is the analytical layer between the raw data and your trading decision. It does not replace the trader's judgment. It provides him with a consistent, auditable basis with which he can work systematically.

  • Formal documentation of methodology and model changes.
  • A unified environment for both intraday and swing strategies.
  • Outputs readable for both the trader and the risk team.

The platform is designed for professional use in an environment where latency, accuracy and repeatability of the procedure matter.

Sivorentix overview of data models and analysis interface
Transparency and trust

Public logos instead of performance claims

Community-verified results mean one simple thing: every signal is publicly recorded and auditable. We describe the methodology precisely, including its limits.

Evidence methodology

The log contains signal generation time, model version, input parameters and market context. Records are immutable and sorted chronologically. We do not edit the history or delete failed exits.

The evaluation takes place with a fixed delay after the close of the observed interval, so that the result is comparable and independent of additional adjustments.

Illustrative representation of record structure — not actual performance data. The full log is available to registered users.
Risk management

Capital protection is part of every recommendation

Sivorentix works with risk as a primary parameter. It evaluates volatility, liquidity and position size in relation to your set limit before delivering the recommendation.

The goal is to reduce the risk of unsystematic decisions: clear entry thresholds, defined scenario invalidity and consistent position sizing.

View risk management principles
Approach comparison — systematic filter vs. raw data. Qualitative scheme of the procedure.
ParameterWithout a systematic filterWith Sivorentix
Noise processingManual, inconsistentAutomated mode filtering and classification
Determination of exposureEstimate according to the situationA rule tied to volatility and limit
DocumentationFragmentedUniform logo with model version
EvaluationRetrospective, subjectiveStructured, time bound
Integration into practice

How Sivorentix will be involved in your decision-making process

The integration is designed to not disrupt the existing workflow. You connect the platform to your sources and take the outputs where you need them.

1

Define strategy and limits

You set instruments, timeframes and risk limits. Parameters are saved and versioned.

profil → limity → schválenie
2

Connect data sources

Connection to stock exchange feeds and internal data via REST API or WebSocket. Batch replenishment of historical data is also supported.

REST / WebSocket / CSV import
3

Subscribe to structured outputs

Signals, reliability scores and risk parameters come to the dashboard or directly to your system.

JSON → dashboard / OMS
4

Evaluate and modify

You compare the outputs with the public logo and adjust the thresholds. Every change is recorded for audit.

log → revízia → rekalibrácia
The API documentation describes authentication, data schemas, call limits, and the format of error messages. Technical support communicates in Slovak and English.
Technical questions

Frequently asked questions about data, latency and models

Brief answers to questions asked by experienced traders before deployment.

What data sources does the platform process

We process stock market data, liquidity and volatility data and selected macroeconomic inputs. Each source is recorded, including the time of receipt, so that the output is traceable.

What is the real-time processing latency

Latency depends on the source and the selected interval. Streamed outputs are delivered continuously with the specified timestamp. We will verify the exact values ​​for your use-case during the technical setup.

What models are used for predictive modeling

We use a combination of statistical and machine models according to the type of task. Each model has a version, documentation and defined conditions under which its reliability is reduced.

How the verifiability of the results is ensured

Outputs are written to an immutable log with a time stamp and hash. History cannot be edited retroactively. We provide the evaluation methodology in advance.

The platform replaces the trader's decision

Nope. Sivorentix prepares a structured background and risk framework. The final decision on execution rests with you and your internal rules.

API integration into existing infrastructure is possible

Yes. Outputs are available via REST and WebSocket in documented JSON format. The integration can be tested in a separate environment before production deployment.

Access to the platform

View the logs and test Sivorentix on your own process

We will provide you with access to documentation, public log preview and technical consultation. No strings attached. You make a decision based on data.

Analyze markets View the documentation

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