Regime replay
Select environments such as Goldilocks Rotation, AI Mega-Cap Melt-Up, Rates Shock, Soft Landing Grind, and Range-Bound Chop.
See how the decision path changes with the market backdrop.Institutional AI decision infrastructure
Not another black-box model. A governed decision layer.
AlphaWeave Capital plugs into the strategies, market data, risk controls, execution records, and reporting systems investment teams already use, then governs which assets, strategies, sleeves, and AI-assisted sizing changes deserve capital.
Measure AISeparate where AI helps returns from where it damages performance.
Govern capitalRoute every trade through policy, risk, and entitlement controls.
Plug inUse institutional components already in place instead of replacing the stack.
What it is
The control layer between model output and portfolio capital.
AlphaWeave Capital is designed for asset managers, bank trading teams, and quantamental groups that need AI-assisted allocation without losing governance. It turns signals, historical context, sleeve rules, risk caps, and model review into auditable trade decisions.
Strategy and asset switchingCompare candidates before replacing the incumbent exposure.
The platform can evaluate which strategy and asset should carry the sleeve at each decision point.
AI/PSM reviewUse AI to adjust conviction, not to bypass discipline.
PSM means position size multiple. The overlay can press, reduce, or hold size inside policy and risk limits.
Replay and attributionShow exactly what changed returns and drawdown.
Trade tracker events, risk-capped weights, and run reports make the decision path inspectable after the fact.
Organize your work
Why use a workspace?
A workspace gives a client or research project a home within your desk. It groups its runs, reports, and discussions so you can tell which work belongs together, find earlier results, and continue the same investigation without mixing unrelated projects.
Create one for a separate body of work
Use a new workspace for a new client or a project that needs its own run history and discussions. For example, keep Client A research separate from an internal equity research project. Access follows configured user permissions and desk entitlements; creating a workspace does not grant access by itself.
Reuse it for the next experiment
Keep the current workspace when testing another strategy, changing assets, adjusting dates, or comparing backtests for the same project. You do not need a new workspace for every configuration or run.
Keep organization separate from investment choices
The workspace identifies where the work belongs. The run configuration defines the portfolio through sleeves, basket legs, allocation groups, assets, and strategies. A run executes that configuration and produces results.
In the application: choose Create backtest to start the wizard. Select a workspace and click Next: Configuration, or create one with Create workspace and Next. Then choose or edit a configuration, review readiness, and start. My runs groups accessible runs by workspace; editing a run copy retains its original workspace without repeating the selection step. Workspace maintenance is separate: creating a workspace there does not automatically select it.
Risk Management
Keep portfolio decisions within configured exposure, position-size, drawdown, and mandate constraints. Risk controls constrain proposed allocations before execution, independently of AI-generated recommendations.
Capital Management
Decide where capital goes, how much is deployed, and why.
AlphaWeave connects asset and strategy selection to portfolio allocation. The capital management layer translates selected opportunities into position sizes within the configured capital budget, sleeve allocations, and portfolio limits.
Capital and risk management in practice
These shared controls connect capital allocation with portfolio protection, from the initial budget through sizing, limits, and outcome review.
Set the capital budget
Start with the run's configured capital and cash reserve. These settings establish the budget for portfolio allocation.
Allocate across sleeves
A sleeve is a portion of the portfolio with its own assets and strategies. Allocation groups define how capital is distributed within the configured portfolio structure.
Size each position
Translate the allocation into position weights. A position size multiplier (PSM) can increase or reduce the proposed size according to the configured policy and decision review.
Apply portfolio limits
Check proposed exposure against position, gross and net exposure, and other configured risk constraints. Sizing recommendations remain subject to the risk management layer.
Measure the outcome
Use trade events, portfolio weights, and capital-weighted reports to follow capital changes and assess the contribution of selection, sizing, and execution.
Capital management and risk management work together. Capital management determines the proposed allocation and size; risk management constrains what the portfolio is allowed to hold. AI-assisted conviction does not override those limits.
Why it matters
AI can add alpha, but unmanaged AI can destroy it.
The institutional problem is not whether an LLM can produce an opinion. The problem is whether the opinion should alter capital, how much size it should receive, and whether the outcome can be explained to portfolio management, risk, compliance, and clients.
AlphaWeave makes the AI contribution measurable. It compares baseline strategy performance, asset switching, policy-only overlays, compact triage, and AI/PSM sizing so teams can keep what is economically useful and remove what is not.
Forward trade blotter
Play forward a single rotatable sleeve across named market regimes.
The full blotter replay shows how a sleeve can rotate among candidate assets and strategies, resize through position size multiple, update risk-capped weights, and leave an auditable trail of BUY, SELL, RESIZE, and NO_EXEC decisions.
Sleeve state
Follow one sleeve through asset changes, strategy labels, risk-capped weights, PSM changes, and simulated position state.
Understand the difference between selection, sizing, and execution.Decision record
Replay timestamped trade events and inspect the blotter rows, current event, event feed, capital path, and risk metrics.
Review what changed and why it was recorded.Enterprise deployment
Designed for SaaS, internal deployment, and white-labeled strategy workflows.
The platform can run as a hosted application or inside a customer-controlled environment. Authentication, entitlements, usage metering, control databases, flat-file run exports, and dashboard replay support both operating models.
Asset Managers
White-label strategy overlays, generate client-facing evidence, and show how asset selection, strategy selection, risk caps, and AI/PSM changed outcomes.
White-label allocation intelligenceBanks
Deploy behind the firewall with existing data, risk, execution, and compliance controls while preserving audit records for production runs.
Internal AI governance layerQuantamental Teams
Combine systematic signals with AI review, policy simulation, risk-capped sizing, and replayable trade records.
Research-to-forward-test workflowGive every AI-assisted allocation decision a policy, a weight, a risk check, and a replayable record.
A WalkthroughExplore the platform
Open a topic for the complete explanation, illustrations, and interactive detail cards.
Product capabilitiesAsset and strategy selection, portfolio controls, and attribution.
Product
Give every allocation change a reason, a risk check, and an attribution trail.
AlphaWeave Capital is plug-and-play decision infrastructure for systematic allocation. It connects to the components institutions already have, then helps teams decide when a signal is worth capital, which asset or strategy should carry the exposure, and how much size the portfolio should allow.
Reduce noiseProtect teams from acting on weak rotations and short-lived signal noise.
Improve governancePut market context, mandate constraints, risk, and prior outcomes in one review.
Explain performanceShow leadership what improved returns: asset selection, strategy selection, AI sizing, or execution.
The problem
Signals are easy to generate. Allocation decisions are harder to govern.
Too many dashboards stop at BUY, SELL, or HOLD. AlphaWeave Capital asks which strategy to run, which assets to trade, and how much capital to deploy.
Good strategies still need allocation judgment. The platform checks market context, sleeve mandate, risk budget, similar history, and execution quality.
Performance needs attribution. See whether asset selection, strategy rotation, AI-assisted sizing, or execution actually added value.
Core value
A practical allocation intelligence layer between model output and deployed capital.
AlphaWeave Capital gives research, portfolio, and risk teams a shared operating language across existing institutional systems: what changed, why the action is justified, what could go wrong, how much capital is allowed, and how the result will be measured.
Click a capability to see the business value.
Decision discipline
Fewer impulsive changes. More accountable allocation moves.
The platform is designed to avoid noisy churn. It behaves like a disciplined investment committee for systematic portfolios: change only when the evidence is strong enough, and preserve the reason when capital moves.
Click a policy area to see the target behavior.
AI controls
Use AI to sharpen judgment without pretending it guarantees alpha.
- Gate entries: suppress weak entries when market context conflicts with raw signal.
- Adjust duration: shorten or extend holding expectations based on regime and risk.
- Adjust size: allow conviction-aware position size multipliers.
- Conviction sizing: scale realized exposure subject to risk caps and measured attribution.
Organizational value
Make research, portfolio construction, and risk review work from the same evidence.
AlphaWeave Capital is not just an execution engine and not a packaged exposure product. It is designed to sit above existing strategies, data feeds, risk systems, and reporting workflows so teams can select the active strategy set, select assets, size conviction, enforce portfolio mandates, and measure which stage actually added return.
Not just executionIt decides how much capital a reviewed signal deserves.
Execution is downstream of conviction, portfolio construction, and risk.
Not fixed exposureStrategy and asset composition can adapt with regime and evidence.
Rotations are evaluated against realized and shadow edge.
Not black-box AIPrompt trails, cache matches, risk packets, and outcomes are inspectable.
AI contributes review context inside a controlled process.
Where it fits
A focused wedge between heavyweight institutional platforms and black-box AI.
Large investment platforms prove that institutions value analytics, attribution, risk, and operating control. AlphaWeave Capital focuses that idea on systematic allocation decisions while remaining modular: connect the components already in place, then govern which assets, which strategies, how much conviction, and what actually improved performance.
Not just execution
It helps decide how much capital a reviewed signal deserves.
Execution quality matters, but the bigger question is whether the strategy, asset, mandate, and conviction justify the allocation.
Not a static product
It can adapt the active strategy and asset set as conditions change.
Instead of relying on fixed universes and fixed weights, the system reviews strategy fit, asset fit, and current market context.
Not an AI oracle
It keeps risk, constraints, prompts, outcomes, and attribution visible.
AI helps summarize and compare evidence; deterministic controls preserve the audit trail and portfolio discipline.
White-label ready
Asset managers can wrap the decision layer around their own strategies.
Use AlphaWeave Capital behind the manager's brand to support proprietary strategies, model portfolios, risk oversight, and client-facing attribution.
White-label infrastructure
Govern the manager's strategy without replacing the manager's identity.
For asset managers, AlphaWeave Capital can operate as a branded allocation intelligence layer: ingest existing signals, apply approved risk and mandate constraints, produce BUY/SELL/HOLD/RESIZE rationale, and generate attribution that explains whether return and drawdown improvement came from strategy choice, asset choice, sizing, execution, or risk control.
Mandates
Support directional and market-neutral workflows.
A sleeve is a portfolio mandate or allocation bucket with its own assets, strategy rules, risk limits, and execution intent.
Click a sleeve type to see how AlphaWeave Capital treats it.
Make every allocation change easier to understand, defend, and improve.
How it worksFollow the review process from market data to outcome memory.
Decision workflow
A calmer path from signal to capital.
AlphaWeave Capital gives teams a repeatable decision layer between signal and trade without forcing a rip-and-replace architecture. It uses existing institutional inputs, then reviews each idea for evidence, asset fit, strategy fit, risk, history, and sizing so capital moves with more discipline and less improvisation.
BeforeKnow why a signal deserves attention before capital is committed.
DuringConvert the review into an action, size, and risk-aware portfolio intent.
AfterTurn the result into organizational memory instead of another forgotten trade.
The review loop
A trading-desk review process for systematic strategies.
Review the signal
Start with the strategy output, then ask whether today's context supports acting on it.
Respect the guardrails
Before capital moves, exposure, drawdown, liquidity, net, gross, and beta constraints stay visible.
Remember the outcome
Each decision becomes part of the research memory, so the next review starts smarter.
Operating loop
One repeatable route, every time capital is considered.
The workflow creates consistency across researchers, portfolio managers, and risk reviewers. Everyone can see the same chain from evidence to action to outcome.
Click a workflow stage to see what it contributes.
Why it matters
A signal is only the start of the decision.
AlphaWeave Capital separates the spark, the decision, the trade, and the realized result. That makes performance easier to explain because the team can see whether the idea was wrong, the decision was late, or the execution got in the way.
Pipeline and reportingInspect decision records, reports, validation, and diagnostics.
Operations
Run allocation research with an institutional-grade decision trail.
AlphaWeave Capital gives research and portfolio teams operating infrastructure for long allocation experiments. It plugs into existing configuration, data, run, report, and recovery workflows so teams can inspect what changed, reuse evidence safely, and explain whether asset selection, strategy selection, or AI-assisted sizing helped performance.
RecoverProtect long-running research from restarts, interruptions, and lost context.
InspectGive teams the exact evidence, model inputs, and outputs behind each decision.
AttributeShow whether asset selection, strategy selection, AI sizing, or execution added value.
Click an operations surface to see what it controls.
Audit trail
A flight recorder for every reviewed decision.
When a run succeeds or disappoints, the team can review what happened instead of guessing. The pipeline preserves the context needed for research review, risk oversight, and management reporting.
Click a report artifact to see why it matters.
Diagnostics
Diagnostics that explain the result, not just the return.
The organization gets more than a performance number. It gets evidence about model behavior, cache reuse, chart interpretation, handoffs, execution state, and monthly contribution by stage.
Click a diagnostic report to see what it verifies.
Market-neutral portfoliosReview long-short relationships, basket behavior, and neutrality controls.
Basket sleeves
Treat relative-value trades like relationships, not tickers.
AlphaWeave Capital extends allocation intelligence to market-neutral sleeves while plugging into the institution's existing data, hedge logic, risk limits, and reporting components. It reviews the long side, short side, spread, hedge, and realized contribution together so relative-value decisions stay balanced, explainable, and aligned with the mandate.
Pair-awareKnow whether the relationship is still strong enough to trade.
NeutralityKeep net, gross, beta, benchmark, and hedge intent visible before exposure changes.
OutcomeExplain whether the basket relationship produced alpha or only looked balanced.
Organizational value
Make market-neutral allocation decisions easier to defend.
Relative-value strategies can fail quietly when the hedge stops behaving like a hedge. AlphaWeave Capital keeps the relationship, neutrality targets, and realized spread contribution visible to research, portfolio, and risk teams.
Click a basket capability to see how it supports relative-value decisions.
Example
Long thesis, short hedge, one combined story.
A market-neutral sleeve is a portfolio mandate for a balanced basket. Instead of judging each asset alone, AlphaWeave Capital asks whether the relationship worked: did the long side help, did the short side hedge, did beta stay within mandate, and did the spread improve the portfolio?
Adaptive neutrality
Do not assume the pair still works. Re-validate it.
Traditional market-neutral workflows can treat a pair as valid because it was valid before. AlphaWeave Capital keeps re-checking whether the relationship is stable, degrading, or improving, then adjusts basket construction, strategy choice, and conviction sizing.
Basket qualityScreen for correlation stability, volatility similarity, liquidity compatibility, and spread mean reversion.
A weak pair should not be treated as neutral just because it has two sides.
Strategy fitRotate between spread, mean-reversion, relative momentum, and regime-sensitive behavior.
The same relationship can require different logic as the regime changes.
Capital balanceUse conviction sizing to adjust relative exposure while respecting sleeve constraints.
PSM helps balance opportunity and risk across long and short legs.
Architecture and AI controlsUnderstand reviewers, model routing, memory, and safeguards.
Architecture
Allocation intelligence your organization can inspect.
AlphaWeave Capital breaks the path from signal to capital into visible reviewers, deterministic controls, and audit-ready reports. It is built to plug into institutional data, model, risk, execution, and reporting components so AI can help summarize and compare evidence without hiding the handoffs, attribution, or final allocation logic.
VisibleTeams can see who reviewed what, and what reached the decision.
ControlledRules handle mandates, risk, parsing, caching, and routing where precision matters.
AuditablePrompts, responses, cache matches, and outcomes stay inspectable for review.
Select an architecture block to inspect its role in the system.
Why it matters
Specialized reviewers, not one giant black box.
An agent is a software reviewer responsible for one part of the workflow. An LLM, or large language model, is an AI model trained to read and generate text; AlphaWeave Capital uses LLMs where judgment and summarization help, while rules handle the parts that must be exact.
Click an agent to see what it contributes to the chain.
Operational value
Every evaluation follows the same route from data to memory.
At each bar, AlphaWeave Capital loads data, selects assets, selects strategies, generates raw signals, triages whether deeper AI review is needed, converts conviction into portfolio intent, applies risk controls, and stores the outcome for future attribution.
Click a flow checkpoint to see why it matters.
Control plane
The guardrails that keep the reviewers honest.
These controls make AI useful inside a governed process: outputs are parsed, expensive review is routed only when justified, and reused evidence is traceable instead of mysterious.
Click a control-plane block to see how it protects the system.
Why this architecture matters
AI in trading needs controls, not just predictions.
Industry guidance keeps pointing to the same tension: AI can help process information faster, but trading teams still need explainability, model risk controls, data integrity, supervision, and decision attribution. AlphaWeave Capital is built around that reality.
Industry guidance
Model risk management matters.
AI workflows need review around explainability, data integrity, and privacy.
Trading controls Real-time controls stay central.AI trading needs monitoring and control frameworks, especially as models become less transparent.
Supervision Testing and explainability are not optional.AI use in financial workflows should be evaluated through governance, testing, and supervision.
AlphaWeave Capital is not trying to be a one-click trading oracle. It is strategy-agnostic, plug-and-play decision infrastructure: connect existing institutional components, review evidence, select assets and strategies, apply sleeve constraints, scale conviction through PSM, preserve the audit trail, and attribute what actually changed returns.
AI routing
Use AI where judgment helps, and rules where control matters.
A large language model, or LLM, is an AI model trained to read and generate text. AlphaWeave Capital uses LLMs like specialist analysts for summarizing evidence and comparing context, while rules handle selection, risk, caching, and reporting.
Click an item to see why it matters.