abel-invest

name	abel-invest
description	Use when the user asks how to invest, trade, buy or sell, find alpha, find or improve a trading strategy, backtest or stress a signal, screen candidates, optimize Sharpe/return/drawdown, run graph-enriched feature/model/ensemble search, or continue/prepare/debug an Abel strategy-discovery workspace — even if they don't say "Abel" and even when they just ask for "a good strategy for X" or "is there alpha in Y". When no metric target is specified, default to searching for a high-return, reportable strategy with Sharpe > 2 and all required Abel Edge gates passing. Prefer this over ad-hoc hand-designed strategy work.
metadata	{"openclaw":{"requires":{"bins":["python3"]}}}

Abel Invest Alpha Search

Use this skill for:

• alpha search and candidate screening
• continuing an existing Abel strategy discovery workspace
• creating sessions and branches
• preparing, debugging, recording, and reviewing strategy rounds
• interpreting evidence_ledger.json, frontier.md, agent_context.md, and exploration_path.md

Activation Checklist

Always start by resolving workspace state before strategy work.

1. Read references/workspace-bootstrap.md.
2. Resolve the workspace location:
   • if alpha.workspace.yaml is in the current directory, use the current directory
   • else if abel-invest-workspace/alpha.workspace.yaml exists under the current directory, use that child workspace
   • else bootstrap a workspace before deep strategy work
3. Run the active skill bootstrap shim for the resolved or default workspace: python3 <abel-invest-skill-root>/scripts/bootstrap_workspace.py --path <workspace-root>. Use --runtime-python /path/to/python only when the local machine cannot create a venv and the user intentionally provides an existing interpreter.
4. Baseline-first: before from-scratch search, check whether a validated strategy for this target already exists in any baseline / strategy catalog the user maintains. If one exists, treat it as a benchmark and launchpad; iterate from it when useful rather than wasting rounds rediscovering it.
5. If bootstrap reports auth_missing, use abel-auth, then rerun the active bootstrap shim. If it reports scaffold_stale, runtime_stale, env_missing, edge_missing, or edge_contract_missing, rerun the active bootstrap shim after fixing the stated blocker.
6. Only start or continue session/branch work after bootstrap readiness is ready, unless the user explicitly asks you to inspect or repair setup.

Reference Routing

• New workspace, workspace reuse, auth, generated-file refresh, or setup repair: read references/workspace-bootstrap.md.
• New session, normal round loop, or resuming a session: read references/experiment-loop.md.
• Live graph discovery, graph frontier expansion, or graph-informed alpha context: read references/discovery-protocol.md.
• Creating or revising branch.yaml, reviewing evidence labels, path coverage, input realization, or exploration path use: read references/branch-authoring.md.
• Writing engine.py, handling semantic/runtime failures, or checking temporal legality: read references/constraints.md.
• Handling hosted paper contract requests, promoted strategy source edits, paper-contract-report.json, packaged strategy assets, or hosted paper state: read the emitted paper-contract-request.json first; read contractGuide.referencePath from this active skill when the request requires stateful continuation, source edits, or deeper gate diagnosis.
• Explaining why the workflow is data-led, graph-informed, or evidence-boundary oriented: optionally read references/methodology.md.
• Choosing concrete constructions while writing the engine: read references/proven-patterns.md (battle-tested patterns). Core path.
• A hard Sharpe / MaxDD / PnL target is set: read references/guarded-optimization.md (performance-target search and reportability rules). Core path — not optional — when a performance bar is set.
• Before writing "exhausted / ceiling / no edge": read references/experiment-loop.md and check the ledger requirements there.
• Ordinary alpha search, data-driven candidate construction, or the next idea risks becoming another simple hand-written rule: read references/data-driven-construction.md before the first broad candidate. Core path.
• No explicit metric target: use the normal experiment loop and default objective; do not treat this as a separate mode.

Operating Rules

Always:

• Work workspace-first. Resolve workspace_root, research_root, and bootstrap readiness before session or branch work.
• Reuse the default workspace when it already exists; reuse any resolved existing workspace before bootstrapping another one.
• Bootstrap the workspace before deep strategy work when no workspace exists.
• Use Abel Invest commands through the workspace command_prefix when available, not old aliases.
• On a fresh install where abel-invest is not installed, run python3 <abel-invest-skill-root>/scripts/bootstrap_workspace.py --path abel-invest-workspace. Do not import abel_invest with the system interpreter for first-run bootstrap.
• If a skill update changed the workspace scaffold, runtime contract, or generated workspace docs, rerun the active bootstrap shim before strategy work. Do not use workspace-local lifecycle commands to repair setup.
• Reuse existing Abel auth first. If live access is missing, use abel-auth and rerun the active bootstrap shim.
• Treat abel-auth/.env.skill as the normal shared auth/profile source. Workspace .env is only an explicit per-workspace override; do not copy API keys there unless the user intentionally wants this workspace to use different credentials or endpoints. Trust bootstrap's effective profile/CAP URL report.
• Report to the user with the current workspace/session/branch path, bootstrap readiness, blockers, what evidence exists, and the next action you will take.
• Treat agent_context.md as the compact factual resume surface, exploration_path.md as the human-facing chosen-path and Edge-feedback log.
• Treat the terse checkpoint printed by prepare-branch, debug-branch, and run-branch as the normal loop feedback. Use compact artifact-digest only for resume, blocker detail, branch backtrack, or insufficient checkpoint state. Treat full digest --json, raw artifacts, --verbose, and --audit as audit/debug surfaces, not the standard loop.
• On a fresh or unfamiliar ticker, use the compact first-look data scout in experiment-loop.md before the first serious recorded alpha candidate unless the user gave a narrow path or continuation. Expect the scout to take roughly 5 minutes: score plausible target, graph, and construction shapes, then rank what looks worth formal validation before broad recorded work. If the scout script is still making progress, let it finish naturally before deciding what to validate. Put temporary scout scripts or summaries under research/<ticker>/<session_id>/scratch/ when files are useful.

Never:

• Do not create sessions before bootstrap readiness is confirmed.
• Do not use --root unless intentionally creating a legacy/offline session; then pass --allow-outside-workspace.
• Do not treat branch.yaml as evidence. It is an audit declaration.
• Do not treat evidence_ledger.json, frontier.md, or agent_context.md as generated strategy advice. They are factual surfaces.
• Do not hide parameter, sizing, threshold, filter, model, factor, or node-subset search inside one "single" strategy. Name search width honestly.
• Do not report a raw-metric winner as a robust strategy before required validation and honest search-width accounting support that claim.
• Do not optimize only for gate-passing at the expense of Sharpe, return, or the user's objective. Gates estimate reliability and reportability; they are not the user-facing purpose of the search.
• Do not treat --selection-trials as a strategy-quality shortcut; it is reportability accounting, not a brake on empirical search.
• Do not run-branch a flat/no-signal branch solely to warm cache or make a scout feel official. prepare-branch is enough for data materialization; use recorded runs for meaningful candidates, controls, diagnostics, or ablations.
• Do not treat a diagnostic table such as IC, correlation, or feature importance as a completed first-look scout when graph/model construction remains available. Pair diagnostics with scored candidate-shaped variants.
• Never pass a running/cumulative total to --selection-trials; pass this round's search width only.
• Do not depend on any external skill for guarded optimization; abel-invest runs it self-contained.

Core search invariants:

• User objective first. If the user gives no metric target, search for a strong tradable strategy: high return, Sharpe > 2, and all required Abel Edge gates passing. This is the internal completion target; do not stop at a mediocre branch or a promising near-pass while useful graph-informed search axes remain.
• Follow experiment-loop.md as the single detailed source for the round loop, completion check, stop report, visualization prompt, and interrupted/blocked note boundary.
• Stay in Exploring until a normal ending is justified: the user objective/default target is achieved, or the ledger supports that the bounded search is unlikely to reach the target. Either normal ending enters Completed. If a concrete next search action remains, keep searching.
• If the user explicitly interrupts or an external blocker prevents continuation, do not enter Completed; give only a brief interrupted/blocked note and do not ask for visualization.
• Search hard, then explain. Let observed results, failure modes, and metric shape choose the next candidate family. Mechanism stories are useful after evidence appears; they are not admission tickets.
• Ordinary alpha search has a default posture: high-capacity empirical construction over a scoped target + graph-derived universe. Use the graph, target behavior, feature construction, model comparison, denoise, subset search, regimes, sizing, filters, or ensembles as data calls for them; these are degrees of freedom, not a scripted route.
• Fresh or unfamiliar tickers should normally use the prepared first-look scout sequence in experiment-loop.md before the first broad recorded candidate. Its practical output is scored target, graph, and construction shapes ranked by what looks worth formal validation, not only an analysis memo. Direct recorded branches remain valid for user-specified strategies, existing leads, baselines, controls, continuations, or very narrow diagnostics.
• Live graph discovery is the default high-value alpha universe when available. Use discovery-protocol.md for graph semantics and expansion; use data-driven-construction.md for feature factories, model comparison, denoise, node subsets, lags, regimes, sizing, filters, and ensembles.
• Target-only work is a baseline, seed, ablation, or competitor. A graph-supported branch is not automatically data-driven: runtime graph reads prove input realization, not construction breadth. Hand-written single-mechanism branches are diagnostics, controls, ablations, or refinements around empirical construction, not the default search posture when live graph-derived data is available.
• A hard user metric target (Sharpe / MaxDD / PnL) is an optimization request. Search is expected: use target/baseline context, graph-derived features, feature factories, ensembles, parameter search, model-family comparison, HPO, regime/sizing/filter search, and node-subset search when useful. Then report candidates according to their objective quality and validation reliability.
• Gates measure reliability and reportability; they are not the user-facing goal. High return and high Sharpe remain the product objective. A high-ceiling near-pass is a lead, not waste or final success.
• Edge failures are diagnostics, not the next objective. After a failed round, keep choosing the next action by objective quality and upside; do not only repair gates into conservative branches when return or Sharpe remain weak.
• Treat conservative preferences such as no leverage, lower drawdown, or simple return as ordinary user constraints inside the alpha-search loop, not as a separate product mode.
• Record the effective width of any search that materially selected the submitted candidate. Search-width accounting should not make the agent timid about pursuing a high-ceiling empirical lead.
• Exhaustion is ledger-proven. Do not write "exhausted", "ceiling", or "no edge" unless experiment-loop.md's ledger requirements are satisfied, including materially different search axes, graph-derived and target/baseline contrasts where useful, and all attempted width. One validated candidate does not certify search exhaustiveness.
• CAP graph nodes are model-supported causal priors, not trading instructions. Do not infer hidden weight, exact lag, signed effect, or tradable direction from graph role alone. Expand the graph or use narrative scout context only when it helps the empirical search question.
• The framework defines legality, evidence validity, search-width accounting, and reportability. The agent owns the alpha search.

Completion, reporting, and artifacts:

• After every recorded run-branch, follow the printed loop_checkpoint and its next_boundary. Continue a concrete exploration action or enter final report; do not send a final user report that says exploration is incomplete while also naming the next experiment.
• Completed is the only normal final-answer state, whether the target was reached or the ledger supports unable-to-reach. A completed stop report uses <command_prefix> best-strategy --session <session> --json as the read-only final-report handoff. Follow that payload's report guidance, report its selected strategy exactly, and compose the user-facing result naturally from the selected strategy, metrics, robustness notes, and session review guidance.
• Keep internal completion evidence out of the default user-facing goal: translate validation checks into confidence and limitations, and do not lead with internal validation labels, diagnostic acronyms, selector details, file paths, or live quote context unless the user asks for technical details.
• Do not run visualize-session or export-strategy-artifact merely to compute the stop report, and do not manually rank results.tsv, frontier.json, or branch outputs. The read-only handoff already owns strategy selection.
• There is no third reporting state. If still Exploring, continue the search; only explicit interruption or a blocker justifies a non-completed note, and that note must not ask for visualization.
• Do not create or refresh an online session view automatically. If the user agrees or explicitly asks, run <command_prefix> visualize-session --session <session>.
• If the user asks to upload or visualize a specific strategy branch/round, run <command_prefix> visualize-session --session <session> --strategy <branch> --round <round>. Keep local artifact export and explicit promotion commands for internal debug probes only; if a hosted paper paper-contract-request.json appears, read it first and follow its reportTemplate / contractGuide.
• The default Abel router base URL is https://api.abel.ai/router/. abel-auth owns API key setup; do not ask for a router URL unless the user is testing a non-default router.

Glossary:

• CAP: Abel causal graph surface used as a prior.
• Edge: Abel runtime that prepares data and validates strategies.
• DSR: deflated Sharpe ratio accounting; --selection-trials records effective search width and does not replace final validation.
• Ledger: evidence_ledger.json, the evidence record.
• Frontier: frontier.md / frontier.json, factual search coverage.
• Artifact digest: artifact-digest, a compact read-only summary of session or branch artifacts for ordinary loop decisions.
• PASS/FAIL: Edge validation verdicts, not instructions to stop thinking.
• Narrative scout: Abel Ask/domain-context pass used for candidate generation, not validation.