CHF20k+
Institutional terminal
Per seat, per year, on a two-year contract. Top-tier data, but the quantitative models still have to be built.
AION — quantitative analysis infrastructure
Quantitative analysis infrastructure
Single asset · Macro · Screening · Strategies
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Each particle = one simulated daily return · n = —
Return distribution
Markets
are not normal.
Returns have heavier tails than the normal curve allows, volatility comes in clusters, regimes shift. These are structural properties of markets: measured and understood, they become opportunities.
01
Context
01 · Context
What quantitative analysis
costs today.
CHF8–65k
Research and newsletters
Per year, for access to a bank's research. Static reports, identical for every client: they cannot be queried or adapted to a portfolio.
CHF100k+
In-house quant
Per year for a single analyst, before data and infrastructure. Custom models, but months of development and ongoing maintenance.
Sources: Bloomberg Terminal 2026 pricing, $24–32k (≈ CHF 20–26k) per user per year · bank research pricing under MiFID II (Bloomberg, 2017) · median base salary of a quant analyst in Switzerland, CHF 107k (PayScale, 2026) · USD and EUR figures converted at September 2026 rates (1 USD ≈ 0.82 CHF, 1 EUR ≈ 0.94 CHF).
Analytical depth is concentrated at the top. The tools that support a quantitative investment process remain reserved for firms that can pay per-seat licence fees. Independent advisors, family offices and emerging managers are excluded.
Fragmentation
Anyone can buy data.
Few can read it.
Prices, option chains, financial statements and macro series are now within anyone's reach. The hard part is choosing the right data, running the right analyses, and having all of them in one place.
The missing toolbox
Many models.
No toolbox.
Anyone can build a GARCH, a Kalman filter or a regime model. What is missing is one toolbox that brings them all together: ready to use, continuously updated and validated.
x̂t = x̂t−1 + Kt ( zt − x̂t−1 )
Kalman filter · latent state estimation
The market
More quantitative.
Faster.
91%
of asset managers already use (54%) or plan to use (37%) artificial intelligence in their investment strategy or research. Not just quant funds.
Mercer, AI integration in investment management, 2024
59%
of S&P 500 index options volume in 2025 was zero-day (0DTE), up from 5% in 2016. Same-day options now dominate index options trading: reading their flows takes advanced analytics.
Cboe Global Markets, 0DTE share of annual SPX options volume · 2016–2022: Cboe Insights, Aug. 2023 · 2023–2025: Cboe annual results and releases
More models, faster decisions, no in-house quant team: managers need tools that are validated and ready to use.
Why now
01
Institutional tools, affordable
Professional data on prices, options, fundamentals and macro now costs a fraction of what it did. For the first time, an independent manager can work with the same analytical tools as an institution, without an institutional budget.
02
Compute is no longer a constraint
Models that once required dedicated infrastructure run on pay-as-you-go cloud infrastructure, precomputed outside the request path and served from cache.
03
Demand has shifted
Fee pressure and reporting obligations are pushing independent advisors and managers toward an analytical process of their own that they can document.
02
The platform
The problem
Fragmented data.
No consistent view.
Prices, options, fundamentals, macro, sentiment and technical data sit in separate systems, with different formats and update cycles. Without a common layer, results cannot be compared across models or assets.
Lorenz system · illustration
The solution
One tool.
Every approach.
AION brings every quantitative approach into a single tool, ready to use, on the same data.
One suite
The quantitative universe,
in its entirety.
From a single stock to the whole market: models, data and results in one environment, consistent with each other.
Continue
The areas
Every angle
of the market.
- 01
Single asset
Price, ±2σ bands and outliers, realized and GARCH volatility, return distribution, seasonality.
- 02
Fundamental
Price against P/E bands on forward earnings, quarterly EPS and revenue growth.
- 03
Technical
Moving averages, momentum, support and resistance levels on the price series.
- 04
Options
Implied volatility surface by strike and expiry, ATM term structure and 25Δ skew.
- 05
Multivariate
Correlation matrix, regression on the S&P 500 and rolling 60-day beta.
- 06
Sentiment
News and market sentiment, scored daily and compared with price.
- 07
Macro
Economic regimes estimated with a Markov chain, with the probabilities of moving between them.
- 08
On-chain
Exchange flows, holder distribution and network activity for digital assets.
- 09
Screener
The investable universe filtered by quantitative criteria and ranked by score.
- 10
Proprietary strategies
Internally developed strategies: equity curve, drawdown and risk metrics from backtests.
Simulated data for illustration purposes
Validated models, open parameters
Every parameter
is adjustable.
In the suite, no parameter is hidden. Here, a GARCH(1,1): change the reaction to shocks and their persistence, and see how the model responds.
σ²t = ω + α ε²t−1 + β σ²t−1
returns εₜ± 2 σₜ conditionalbeyond 3 σ long-run
The method
01 · Research
Academic and market models
From the literature to practice, from classical to advanced: GARCH, Kalman filters, regime models, option pricing.
02 · Validation
All validated
Every new method goes through the experimental area and enters the suite only after passing a documented review.
03 · Suite
Open parameters
Managers adapt each model to their own process and compare the results. The library grows without changing tools.
Artificial intelligence
Aion Brain.
From the question
to the analysis.
An advanced language model built into AION. It works on the suite's own data and models.
- 01Chooses the right models and charts
- 02Builds the manager's personal dashboard
- 03Guides the investment process
Simulated data for illustration purposes
Artificial intelligence · Reinforcement learning
A model that grows
with the manager.
The manager logs their own trades, paper trades included. The model gradually learns their style (horizon, risk, entries and exits) and proposes trade ideas consistent with it.
- 01The manager logs trades
- 02The model learns the style
- 03It proposes ideas, the manager decides
Artificial intelligence
AI informs.
The manager decides.
The platform produces analysis, not personalized recommendations.
Simulated data for illustration purposes
03
Market and business model
Product status
50+
Analysis modules already live in the beta. The investment does not fund development: the platform is already built.
6independent containerized services: heavy computation never enters the interface path
5normalized data providers behind a single layer: no critical vendor dependency
msprecomputation and caching: recurring workloads run outside the request path
100%of the API covered by automated tests, with a documented audit cycle on every release
What AION offers
One platform.
Every quantitative service.
Every module runs on the same normalized data layer, so results are comparable across models, assets and time.
Analysis and models
Quantitative models on any asset
Equities, indices, ETFs and digital assets: statistical, structural and volatility models, from classical to advanced.
Risk models
Volatility, tail risk, confidence bands, exposures and factor sensitivities.
Options models
Pricing, implied volatility surface and term structure, dealer positioning and flows.
Fundamental, technical, sentiment, macro, on-chain
Valuation and financial statements, market structure, news and market sentiment, regimes and systemic risk, blockchain activity.
Screener
From quantitative criteria to the instruments that meet them, ranked by score across the investable universe.
Intelligence
Aion Brain
A language model built into AION: it selects the right models and charts and follows the manager through the analysis.
Personalization
A personal dashboard of the views and parameters each manager works with.
Learning from the manager
The reinforcement-learning module learns the manager’s style from their logged trades and proposes ideas consistent with it.
Proprietary strategies
Internally developed quantitative strategies, with backtests and risk metrics.
Integration
API
Programmatic access to the calculation engines, for teams that already have a process of their own.
MCP server
Connect AION to language models and other tools, so the analysis can be used where the manager already works.
Normalized data layer
Five providers behind a single layer: aligned, cleaned and ready for the models.
Who we serve
For professionals
who must justify every decision,
without a research desk.
01
Advisors and family offices
They manage assets on behalf of clients. They pay for analytical depth and a traceable process.
02
Emerging managers and research firms
They produce analysis for their own clients. They pay for the analytical layer they do not want to build.
03
Quant teams and integrators
They already have their own process. They need calculation engines and normalized data, consumed via API.
Versus the alternatives
The gap in the market.
Depth of analysis against annual cost, on a logarithmic scale. Above roughly CHF 10k a year, depth requires an institutional seat or a dedicated team; below it, the offer is limited to charts and static reports.
The model
Four
subscription plans.
Basic
CHF99/month
Historical, fundamental and macro
Sophisticated private investors
Pro
CHF299/month
Full single-asset suite and screener
Advisors and individual managers
Quant
CHF999/month
All modules, strategies and experimental area
Quant desks, family offices, research firms
API
CHF499/month
Programmatic access to the calculation engines
Integrators and internal teams
Proposed monthly prices, per user.
Institutional-grade quantitative analysis.
In one environment.
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