Pergent platform for AI-powered data analysis and investment strategy
AI-powered investment analysis

Well-founded investment decisions based on back-tested models

Pergent connects real-time market data with historically validated AI models and translates large amounts of data into concrete, risk-adjusted recommendations for action for your portfolio.

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Historical return distribution

Why gut feeling is no longer enough with today's amount of data

Today, financial markets generate more information than an individual can meaningfully evaluate in a reasonable amount of time. Price movements, macro data, company reports and sentiment indicators arrive continuously - parallel to your own everyday working life.

For young professionals who want to build up a second source of wealth in addition to their core business, this does not create a security problem, but rather an efficiency problem: time that goes into manual research is missing elsewhere. Decisions are then made out of habit rather than analysis.

The real cost factor is not the risk itself, but the time that is missing to systematically assess it.
Pergent analysis environment for data-based decision-making processes

An analysis tool, not a forecast promise

Pergent was developed to bridge the gap between raw data and an understandable investment decision. The platform does not replace your own judgment, but rather structures the underlying data so that patterns and risks become visible before a decision is made.

All models are first run against historical market data before being used to make current recommendations. This order — first back-checking, then application — is the methodological basis of the entire platform.

How the analysis engine works in the background

01

Data collection

Price data, trading volumes and publicly available market information are continuously recorded and converted into a uniform format so that different sources remain comparable.

02

Model analysis

The incoming data is checked against historical market trends. Models that did not show stable performance in past market phases are excluded from the current recommendation logic.

03

Risk-adjusted output

The remaining models create a recommendation that, in addition to the expected development, also shows the historical fluctuation range and possible loss scenarios.

Tools for measurable results instead of pure forecasts

Predictive Analytics

Forecasting module

Recognizes statistically relevant patterns in price trends and assigns them to historical comparison cases. This means that market movements are not evaluated in isolation, but rather in the context of similar historical data.

Risk management

Risk dashboard

Displays the historical fluctuation range and possible loss scenarios for each position so that decisions can be made based on probabilities rather than assumptions.

Backtesting

Automated strategy backtester

Tests a proposed strategy against multiple historical market phases before deploying capital. This reduces the research effort that would otherwise be necessary for manual comparison analyzes and provides a basis that remains scalable even as the portfolio grows.

Practical scenarios for building wealth alongside your job

A

Classify market volatility

In the event of sudden price swings, the risk dashboard shows whether a movement is within historical fluctuations or represents an unusual deviation. This is what distinguishes a short-term correction from a structural signal.

b

Plan long-term wealth creation

For savings plans with a multi-year horizon, the backtester provides an assessment of how a strategy would have performed in different market cycles in the past - as a guide, not a guarantee for future development.

C

Diversify portfolio

The forecast module shows correlations between existing positions. If you want to build up a second income from investments, you can see where additional diversification actually reduces the overall risk.

Answers to technical and methodological questions

What data sources does Pergent use?

The platform uses publicly available market data such as price trends, trading volumes and historical volatility metrics. All sources are checked for consistency before processing and converted into a uniform data format.

How reliable are the models?

Each model is backtested against historical market data before productive use. Past results are not evidence of future developments; they serve to exclude obviously unstable models before use. Models are regularly re-evaluated as market conditions structurally change.

How can Pergent be integrated into existing processes?

Pergent can be used as an independent analysis tool parallel to existing depots and reporting processes. A connection takes place without interfering with existing accounts; The platform provides recommendations that users implement independently in their usual environment.

Start reviewing your strategy based on data

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