Portfolio Strategy Studio.
Define how the portfolio moves.

The six stages on the strategy canvas

The canvas arranges portfolio rules into one connected sequence. Universe, Allocation, Trigger and Execution are required; Filter and Risk sit between them when used as separate stages. The sequence does not branch. Configured risk controls still apply.

1. Universe

   Select the spot assets and any fixed target weights.

2. Allocation

   Set the method, timeframe, lookback and number of selected assets.

3. Filter

   Narrow the selection with trend and momentum conditions.

4. Trigger

   Use a schedule, allocation drift or both to consider a rebalance.

5. Risk

   Define cash reserve, coin caps, budget, turnover limits and the drawdown pause.

6. Execution

   Set minimum and maximum order amounts, fees, slippage and spread limits.

Choose an allocation method for the basket

A portfolio rule decides how capital is distributed across its selected assets. Fixed weights describe a target split; equal weights give the chosen assets the same target; momentum and inverse-volatility approaches use their configured measurements to change the allocation.

These methods express different research assumptions. No allocator is universally best, and a good fit to one historical period does not establish future performance. Use Portfolio Lab and Quant Labs when you want to investigate those assumptions further.

Put reserves and limits inside the rules

Specify the basket and the quote currency, then define the cash reserve, individual asset caps, turnover limits and the conditions for pausing after a drawdown. These checks belong with the allocation logic, because an attractive target distribution is not sufficient evidence that the account can execute it.

• Keep part of the portfolio in the configured cash reserve.
• Constrain individual coin weights and the amount of trading in a rebalance.
• Use drift and schedule conditions to determine when a rebalance should be considered.
• Inspect minimum-order constraints, proposed changes and account context before deployment.

Backtest closed-candle decisions with costs

The portfolio backtest evaluates rules on closed candles and includes configured fees and slippage. Its deterministic allocation logic is shared with deployed portfolio rules; historical prior-close signals are evaluated with next-open execution assumptions in the backtest.

Keep the selected assets, date range, data coverage and cost assumptions with the run. Changing any of them changes the experiment. Compare outcomes with a relevant baseline and inspect turnover and drawdown, not only the ending value. The backtesting guide explains why independent evaluation data matters.

The backtest compares portfolio equity with a buy-and-hold reference that buys the starting allocation once. The results include drawdown, fees, turnover and orders; the trade ledger can be exported.

Review optimisation candidates before promotion

The portfolio optimiser searches within bounded parameter ranges using chronological training, validation and a final holdout. Objectives, scoreboards, progress and cancellation controls help you inspect the search rather than accepting a final number without context.

Scheduled Auto Optimize can look for improvements and save validated candidates as inactive revisions when configured to do so. Search does not silently change a deployed portfolio or select live execution. Review the candidate and explicitly deploy the revision you intend to use.

Build, inspect and deploy a named revision

1. Define the basket

   Choose the spot assets, quote currency, allocation method and portfolio constraints.

2. Test the rules

   Run a closed-candle backtest with fees and slippage and inspect the result across periods.

3. Review a revision

   Inspect optimisation candidates, preserve their settings and choose the revision to deploy.

4. Deploy deliberately

   Start in Paper. Live automation needs Premium and explicit account-owned authorisation. Follow activity in Portfolio Manager.

Keep deployment separate from research

Live portfolio automation is account-owned and tied to the chosen rule revision. A connection can be controlled by one live portfolio deployment at a time, and uncertain fills block further spot writes until reconciled. Saving rules or completing an optimisation is not itself a live-trading instruction.

Use Portfolio Manager to inspect available holdings, actual allocation, drift and activity. The Studio controls the rule definition. Strategy Arena is the separate visual builder for recurring trading strategies, while Trade Planner handles a single conditional plan.

Use Preview allocation before deploying. Save inactive keeps a revision without execution; pause a deployed revision before editing it. Export the strategy as JSON, then select the appropriate spot connection when importing it.

A frozen candidate from Portfolio Manager → Research & SAC stays tied to the allocation and risk rules in its evidence. Train or review new candidates in Research & SAC; the copied revision remains inactive until you deploy it.

Plans and access

The pricing page lists Portfolio Strategy Studio and Portfolio Manager on paper from Client at $49 per month, excluding VAT. Premium adds live portfolio automation at $495 per month, excluding VAT. Quarterly, annual and EUR options are described on the pricing page.

For a walkthrough of the rule builder and revision workflow, request Apex OS access. The public demo remains a read-only market-and-terminal preview rather than a personal portfolio backtest account.

Frequently asked questions

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