Arizmic | Algorithmic Trading Workstation

Why Arizmic

You should not have to choose between fragmented tools and cloud lock-in.

Avoid patching together multiple tools to create one coherent workflow

Separate tools add setup, conversion, and maintenance.
Broad searches and high-fidelity validation often require different engines.
Data, charts, optimization, and deployment create repeated handoffs.

Local open-source products can be powerful, but they are often fragmented across Python libraries with steep learning curves and dependent on a third-party platform for visualization. One product may offer fast, broad vectorized testing for a large search yet lack the event detail needed later. A high-fidelity engine can provide that detail but be impractically slow for the optimization studies used to narrow down the candidates that deserve that level of analysis. Between them, data must be reshaped, charts move to another notebook, optimization needs another package, and live deployment may require rewriting the strategy for a broker or execution platform. While each tool can be excellent at its job, the cost is in the handoffs: more setup, more conversion, and another strategy version to keep aligned.

Read full explanation

Local open-source products can be powerful, but they are often fragmented across Python libraries with steep learning curves and dependent on a third-party platform for visualization. One product may offer fast, broad vectorized testing for a large search yet lack the event detail needed later. A high-fidelity engine can provide that detail but be impractically slow for the optimization studies used to narrow down the candidates that deserve that level of analysis. Between them, data must be reshaped, charts move to another notebook, optimization needs another package, and live deployment may require rewriting the strategy for a broker or execution platform. While each tool can be excellent at its job, the cost is in the handoffs: more setup, more conversion, and another strategy version to keep aligned.

A full stack on the cloud is often expensive and limited.

Hosted workflows can limit compute, bandwidth, and testing fidelity.
Tiered plans meter resources while capable local hardware sits idle.
AI model access may lag current releases, while token usage can carry an added platform markup.

Full-stack cloud platforms can offer data, development, testing, optimization, and deployment, but that convenience is often expensive and limiting. Full functionality may sit behind tiered subscriptions, rate-limited compute plans, resource caps, and constrained bandwidth or testing fidelity, even while capable local hardware sits idle. AI model access may also lag current releases, while token usage can carry an added platform markup instead of letting you use a preferred direct subscription or a local model you control. The workflow may be connected, but the platform still controls the cost, capacity, and available choices.

Read full explanation

Full-stack cloud platforms can offer data, development, testing, optimization, and deployment, but that convenience is often expensive and limiting. Full functionality may sit behind tiered subscriptions, rate-limited compute plans, resource caps, and constrained bandwidth or testing fidelity, even while capable local hardware sits idle. AI model access may also lag current releases, while token usage can carry an added platform markup instead of letting you use a preferred direct subscription or a local model you control. The workflow may be connected, but the platform still controls the cost, capacity, and available choices.

One workflow, optimized stage by stage.

One local workflow connects development, validation, analysis, and deployment.
Each stage uses the engine and fidelity suited to its workload.
Your data, AI models, and supported broker connections remain your choice.

Arizmic keeps the workflow connected on your hardware. Use the data that suits your needs. Author strategies visually, in Python, or with AI assistance while keeping your alpha local and secure. Move from broad vector discovery and Optuna-based optimization to deeper event-driven validation, analysis, and supervised low-latency deployment. Each stage is optimized for its workload and designed to handle large datasets. A data-heavy backtest that might time out on cloud compute can continue locally, adapt to system load, and be paused or resumed as needed. Arizmic uses a Rust foundation for performance-critical pathways. Supported data, LLMs, and broker connections remain your choice.

Read full explanation

Arizmic keeps the workflow connected on your hardware. Use the data that suits your needs. Author strategies visually, in Python, or with AI assistance while keeping your alpha local and secure. Move from broad vector discovery and Optuna-based optimization to deeper event-driven validation, analysis, and supervised low-latency deployment. Each stage is optimized for its workload and designed to handle large datasets. A data-heavy backtest that might time out on cloud compute can continue locally, adapt to system load, and be paused or resumed as needed. Arizmic uses a Rust foundation for performance-critical pathways. Supported data, LLMs, and broker connections remain your choice.

Arizmic brings the full workflow together in one easy-to-use, AI-native workstation.

Inside Arizmic

Follow the workflow from owned data to supervised deployment.

Select any subsystem to inspect its role, operating boundary, and product view without leaving the map.

Data

Build

Studies

Live Deployment

Build subsystems

Research Stack

Your providers. Your terms.

Bring the data services, AI models, and brokers you prefer. Arizmic works with your existing accounts, so you can choose what fits your research instead of being locked into a bundled ecosystem.

Data

Data from the sources you choose

Use your own market-data services or local files without purchasing data through Arizmic.

Your preferred data providers
Your existing subscriptions
Your local datasets

Your provider relationships remain yours. Arizmic gives you one place to work with them without reselling their services or limiting you to a proprietary ecosystem.

Missing a provider you use?

Request an integration

Frequently Asked Questions

Do I need to know how to code?

Basic Python familiarity is useful, but coding is not required. AI Companion turns a plain-English idea into reusable, adjustable signals and strategies; Visual Composer recombines those objects into variations without spending model tokens on every adjustment; and the Python SDK remains the direct-code path when you want it.

Why not just build a strategy with ChatGPT?

Arizmic gives your preferred AI model an integrated, permissioned research harness with direct workstation context, reusable product objects, validation, studies, evidence, and controlled actions. You can connect a ChatGPT subscription through OAuth, use supported API-key or compatible-endpoint routes, or run supported local models; OpenAI Platform API billing remains separate from an ordinary ChatGPT subscription.

What data and provider accounts do I need?

Choose data that matches your markets, strategy, and required fidelity, using local files or your own supported provider accounts. Arizmic supports Databento MBO full-depth (L3) market data and Rithmic futures execution alongside its bar, trade, quote, market-by-price (MBP), and market-by-order (MBO) research paths.

How is Arizmic different from a cloud algorithmic-trading platform?

Arizmic avoids platform lock-in by using your local compute, retaining your alpha and research artifacts locally by default, connecting directly to your provider relationships, and letting you select supported AI models. Every billing interval exposes the same supported functionality, and Arizmic adds no per-backtest or metered AI-relay charges.

How does Arizmic help me test whether a strategy is overfit?

Arizmic layers out-of-sample testing, walk-forward studies, Monte Carlo analysis, parameter surfaces, trial accounting, cost assumptions, deeper execution validation, and established optimization frameworks including Optuna so you can examine fragility from multiple angles. No framework, score, or test proves future performance.

What happens between a backtest and live trading?

Arizmic uses a deliberate progression from validation to sandbox, paper, and supported live deployment. Each stage proves more of the real stack while preserving the versioned strategy contract, risk gates, reconciliation, operator control, and provider-specific availability.

Read all FAQs Pricing

Pricing

Try the complete workstation free for 7 days.

Explore the full Arizmic workflow before you pay. Choose a billing interval now; it begins after your free trial unless you cancel first. Checkout, downloads, and license activation remain unavailable during public pre-release.

Launch access

7-day free trial before your selected billing interval begins
The complete supported backtesting-fidelity range
Two activated installations for the same user

Billing interval

Monthly price equivalent$74.99per month

Billed$899.91 annually

Joining the waitlist does not start a trial or subscription.

Need purchasing for more than one person? Discuss multi-seat pricing

Resources & support

The modern full-stack local workstation for algorithmic trading

Why Arizmic

Avoid patching together multiple tools to create one coherent workflow

A full stack on the cloud is often expensive and limited.

One workflow, optimized stage by stage.

Inside Arizmic

Build subsystems

Research Stack

Data from the sources you choose

AI, on your terms

Brokers you already trust

Frequently Asked Questions

Do I need to know how to code?

Why not just build a strategy with ChatGPT?

What data and provider accounts do I need?

How is Arizmic different from a cloud algorithmic-trading platform?

How does Arizmic help me test whether a strategy is overfit?

What happens between a backtest and live trading?

Pricing

Monthly

Six months

Annual