Strategy Builder

Strategy Builder settings define when the portfolio can change state and what it holds in each state. What matters is whether each rule expresses a defensible decision.

Set the dollar scale and price mode

The scenario row anchors what every dollar figure in the result represents.

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Starting principal: The dollar amount the strategy starts with. Drives the scale of every value, drawdown, and cashflow chart.

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Price Mode: Total return includes dividends and distributions. Raw prices keep them separate. Most rule-driven strategies use total return.

Choose the backtest window

The window decides which market regimes your rules get tested against.

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Backtest window: Span at least one major drawdown when you can. A calm window flatters rule-based strategies.

Order the rules from most specific to fallback

Rules evaluate top-to-bottom, and the first match wins.

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Rule cards: Ordered conditions with per-rule allocations. Put narrower conditions higher. The last rule has no condition and catches all remaining states.

Make execution explicit

The defaults are reasonable, but a high-frequency strategy needs costs set realistically.

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Trading settings: Evaluation frequency, per-trade cost, slippage, and the inflation lens applied to charts.

Validate parameters out-of-sample

Walk-forward retrains on rolling train windows and evaluates on the next test window.

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Walk-forward validation: Rolling train and test window lengths, the step between iterations, and the handling of windows with too little data.

• Composite signals with AND/OR/NOT logic
• Regime-based allocation switching
• Ranked allocation rules that pick the top or bottom N tickers from a universe by a ranking metric
• Trading frequency and cooldown controls to reduce whipsaw
• Signal state timeline visualization
• In-sample / out-of-sample holdout split with per-slice metric tables
• Save and share tactical configurations

• Allocation rule: a named asset mix whose fixed and ranked sleeves sum to 100%.
• Signal: a boolean condition that determines whether a rule is active.
• Rule order: the engine checks rules from top to bottom. The first rule whose signal is true becomes active.
• Fallback rule: the last rule should have no condition. It applies when no earlier rule is active.
• Trading frequency: how often the engine can switch between rules.
• Signal return basis: whether signals use total-return series or raw closes. Total return is the default. Uploaded U.* aliases and unsupported .SIM raw signal series use total_return with a warning. Execution prices still use the selected mode.
• Execution lag: rule changes trade no earlier than the next trading day. Same-day execution is not modeled in Strategy Builder backtests.
• Ranked rule: an allocation rule that selects the top or bottom N tickers from a universe by a ranking metric.

Any rule can select tickers instead of using fixed weights. Set its allocation type to ranked. Configure the universe, ranking metric, lookback, and weighting method. At each rerank, the engine scores the universe. It drops tickers that fail optional score thresholds or have insufficient price history. It then selects N tickers until the next rerank.

• Universe: 2 to 29 plain tickers to rank. The fallback is separate, so the total pool is up to 30.
• Metric: total return, volatility, price-over-SMA ratio, or RSI. The same metric applies to every ticker in the universe.
• Lookback: primary window in trading rows, with up to three optional secondary lookbacks. The engine averages scores across lookbacks, or combines them with per-lookback weights when you specify them. VAA, BAA, and HAA use a 12/4/2/1 weighting.
• Select and N: hold the top or bottom N tickers after sorting by score.
• Weighting: equal weight across the selected tickers, or inverse-volatility weighting proportional to 1 / annualized volatility over the primary lookback.
• Score thresholds: optional min and max scores that exclude tickers before selection. Useful for absolute-momentum filters that fall to cash when nothing passes.
• Fallback ticker: held for the ranked sleeve whenever zero tickers survive filtering.
• Rerank cadence: follows the strategy's trading frequency. Monthly reranks occur every 21 trading rows, and quarterly reranks occur every 63 rows. Between reranks, the model carries the selection forward.
• Ranked sleeve %: percent of the rule assigned to the ranked sleeve, in the range 1 to 100. Use 100 to keep the rule ranked-only. When set below 100, the remainder fills a fixed sleeve defined by the rule's static weights.

Composing a fixed sleeve with the ranked sleeve. Below 100%, the rule's weights field becomes the fixed sleeve. The realized allocation equals ranked picks times sleeve_fraction, plus the fixed sleeve at its configured weights. The sleeve_fraction equals the ranked sleeve percentage divided by 100. Aggregation is additive when tickers overlap. The schema rejects a fixed ticker that is in the universe or equals the replacement ticker.

Only the fallback ticker can receive both ranked and fixed contributions. Fixed sleeve tickers are calendar anchors. A missing rebalance price makes the rule use the default rule for that day. A late-inception fixed ticker moves the effective backtest start date to that ticker's inception date. A missing universe ticker drops out of ranking.

Example. A rule sets ranked sleeve % = 70, universe = [SPY, QQQ, EFA], fallback = BIL, n = 2, and fixed sleeve = [{AGG, 30%}]. It holds 35% of each of the top-2 ranked picks plus 30% AGG on every rebalance.

Ranked rotations count as rebalance events, so trading-cost and slippage haircuts apply on the decision row the same way they do for fixed-weight rule transitions. What-if price overrides cannot target ranked universe or fallback tickers.

1. Add a Risk-on rule with the assets you want to hold when the signal is true.
2. Add a fallback rule with the assets you want to hold when the signal is false.
3. Choose one signal, such as price above or below a moving average.
4. Set the date range, principal, benchmark, and trading frequency.
5. Run the analysis and review the equity curve, transition count, drawdowns, and rule timeline.

A good first build is one risk-on rule, one fallback rule, and one signal. Add more conditions only when the research question requires them.

• Date range: historical sample used in the run.
• Principal: starting portfolio value.
• Benchmark: reference series used for comparison.
• Trading frequency: daily, weekly, biweekly, monthly, quarterly, or annual switching cadence.
• Trading costs and slippage: implementation costs applied to rule transitions.
• Cashflows: recurring or one-time contributions and withdrawals.
• In-sample / out-of-sample holdout: optional split date for separate metric tables before and after the date. Both tables use the same realized strategy run.

Strategy Builder supports atomic signals and composite operators:

• Trend: moving average and related price-based rules, including dual-moving-average hysteresis
• Momentum: return momentum, composite momentum, and relative strength
• Volatility and risk: VIX, volatility, RSI, drawdown, and correlation rules
• Macro and calendar: yield curve and date-based filters
• Composites: AND, OR, and NOT over other signals

• Equity curve: portfolio and benchmark value through time.
• Trading stats: transition count, time in each rule, and trading costs.
• Metrics: return and risk summary statistics.
• Monthly returns heatmap: monthly and annual return breakdowns.
• Rule attribution: return contribution by rule.
• Condition stability: signal and rule behavior through time.
• Signal history: historical time-series of each atomic signal's numeric indicator value and boolean state. Threshold lines and transition markers show how signal evaluations align with rule changes. Uses the same evaluation path as the backtest, so charted values match the decisions the engine made.
• Ranking: appears when any rule uses ranked allocation. Shows the latest selection and per-ticker scores, when the fallback ticker fired, and the realized rerank history.
• Current status: latest fully-observed rule, signal state, and active weights. Use this to confirm the strategy is doing what you expect on the latest data.
• In-sample / out-of-sample: paired metric tables for the in-sample window and the later out-of-sample window when you enable a holdout split.

For a step-by-step debugging workflow, see Debug A Strategy below.

Strategy Builder supports an in-sample / out-of-sample holdout. Choose a split date to compare metrics before and after that date on the same backtest path.

• The split does not refit signal thresholds or change rule order, ranked selections, transition timing, costs, or cashflows.
• The requested date snaps to the nearest prior trading day. Both sides need enough history. Otherwise, the result shows a warning instead of partial metrics.
• For rolling or expanding train/test schedules, use Walk-Forward Validation in the Strategy Builder configuration section.

When a tactical strategy produces unexpected results, work through these steps:

1. Open the Strategy Builder with the strategy loaded.
2. Check the Condition stability panel. Frequent switching may indicate a noisy signal or a threshold set too close to the boundary.
3. Check the Rule attribution panel. If one rule contributes most of the return, the other rules may not be firing at useful times.
4. Check the Trading stats panel. High transition counts combined with trading costs may erode performance.
5. Open Signal Analyzer. Test the signal in isolation for forward predictive value on the target asset.

The typical signal-to-strategy workflow moves through three tools:

1. Signal Analyzer: test whether an indicator predicts a later outcome. Evaluate hit rate, return spread, and forward-return distributions by signal regime.
2. Strategy Builder: if the signal shows predictive value, build a tactical strategy that switches allocations based on that signal. Review transition behavior and rule attribution.
3. Portfolio Backtest: load the tactical strategy into a portfolio allocation and run a full historical backtest with cash flows, rebalancing, and optional tax-aware accounting.

See Signal Analyzer and Portfolio Backtest for the individual tool references.