Rally

Research, score, and plan long-only portfolio allocations and trades for public markets with risk constraints and explainable decisions.

Security Scan

The skill is internally consistent with a long-only portfolio research and allocation planner; its declared storage, optional API use, and runtime behaviors align with the described purpose — but it can register scheduled jobs and self-update from GitHub, so review the remote source and cron/self-update behavior before enabling execution or giving credentials.

Purpose & Capability

Name, description, commands, and declared filesystem read/write paths all match a portfolio research/plan workflow. Optional QUIVERQUANT_API_KEY (for congressional trades) is appropriate for the described congressional_flow feature. No unrelated credentials or binaries are requested.

Instruction Scope

SKILL.md limits actions to portfolio ingestion, universe screening, signal computation, plan generation, journaling, and local JSONL persistence in ~/openclaw/data/ocas-rally and journals. It also documents cron registration (daily jobs) and a self-update command that pulls from GitHub — these are consistent with the operating model but are side-effectful (network and system-scheduling access) and worth reviewing.

Install Mechanism

There is no formal install spec in the registry (instruction-only), though the SKILL.md/frontmatter suggests installing from a GitHub URL. Instruction-only reduces upfront install risk, but the self-update mechanism (rally.update) implies future code pulled from GitHub may be executed or used by the skill; verify the upstream repo before enabling auto-updates.

Credentials

The only declared optional credential is QUIVERQUANT_API_KEY for an optional congressional-flow signal. No broad or unrelated environment variables or secrets are requested. Filesystem access is scoped to the skill's own data and journal directories under the user's home, which is proportionate for persistent portfolio state.

Persistence & Privilege

always:false and execution is disabled by default; however, the skill expects to create local data files and register cron jobs (daily reporting and self-update). Combined with the self-update capability, this gives the skill ongoing presence and the ability to fetch code — acceptable for this use case but warrants manual review and conservative defaults (keep execution disabled, require explicit deployment approvals).

Assessment

This skill looks coherent for portfolio research and constrained allocation planning, but take these precautions before installing or enabling: 1) Inspect the GitHub repository referenced by the skill (https://github.com/indigokarasu/rally) so you know exactly what the self-update will fetch. 2) Keep config.execution.enabled = false until you review broker integrations and confirm any broker credentials are only provided to explicit, audited integration steps. 3) If you do not want automated runs or auto-updates, disable cron registration / rally.update or run them manually. 4) Only provide QUIVERQUANT_API_KEY if you want the congressional_flow signal; it is optional. 5) Verify that writing to ~/openclaw/data/ocas-rally/ and ~/openclaw/journals/ocas-rally/ matches your storage/permission policies and back up any sensitive files. If you want higher assurance, ask the publisher for a signed release tarball or review the repo history and install steps before enabling self-update or scheduling.

Rally turns public-market research into explainable, risk-bounded allocation and trade plans — screening a universe of candidates, computing composite signals, and solving for constrained allocations that respect hard limits including long-only positioning, max position size, and maximum drawdown. Execution is disabled by default, so Rally is fully useful as a research and planning tool even without brokerage integration.

When to use

• Research and rank an investable universe
• Generate constrained allocation plans from ranked candidates
• Derive trade plans from current-vs-target deltas
• Run daily or monthly portfolio reporting
• Validate portfolio state against risk constraints

When not to use

• Meme-stock speculation or hype-driven picks
• Margin, shorting, or leveraged products (unless explicitly enabled)
• Budget or personal finance planning
• Generic financial news summarization — use Sift

Responsibility boundary

Rally owns governed portfolio research, scoring, allocation, and trade planning.

Rally does not own: general web research (Sift), knowledge graph (Elephas), communications (Dispatch), pattern analysis (Corvus).

Commands

• rally.ingest.portfolio — ingest or update portfolio state
• rally.universe.refresh — rebuild the investable universe with filters
• rally.research.signals — compute signals and composite rankings
• rally.candidates.rank — ranked candidate list with scores
• rally.plan.allocation — constrained allocation plan
• rally.plan.trade — trade plan from current-vs-target deltas
• rally.execute.trades — execute trades (disabled by default)
• rally.report.daily — daily portfolio report
• rally.report.monthly — monthly performance attribution
• rally.validate — run risk and constraint validation checks
• rally.status — portfolio summary, active plan, risk check status
• rally.journal — write journal for the current run; called at end of every run
• rally.update — pull latest from GitHub source; preserves journals and data

Run completion

After every Rally command:

1. Persist portfolio state, research events, signals, plans to local JSONL files
2. Log material decisions (allocation changes, trade plans) to decisions.jsonl
3. Write journal via rally.journal — Observation Journal for research/scoring runs, Action Journal for trade execution runs

Hard boundaries

• Long-only unless explicitly configured otherwise
• No margin, shorting, leverage, or derivatives unless enabled
• No assumed external deposits — growth from returns and reallocation only
• Execution disabled by default and never required for the skill to be useful
• Every target has rationale and evidence references
• Risk check failure halts the plan

Storage layout

~/openclaw/data/ocas-rally/
  config.json
  portfolio_state.jsonl
  research_events.jsonl
  signals.jsonl
  decisions.jsonl
  allocation_plans.jsonl
  trade_plans.jsonl
  wash_sale_exclusions.jsonl
  factor_ic.jsonl
  congressional_flow_cache.jsonl
  reports/

~/openclaw/journals/ocas-rally/
  YYYY-MM-DD/
    {run_id}.json

Default config.json:

{
  "skill_id": "ocas-rally",
  "skill_version": "3.0.0",
  "config_version": "2",
  "created_at": "",
  "updated_at": "",
  "benchmark": "SPY",
  "execution": {
    "enabled": false,
    "broker_integration": null
  },
  "constraints": {
    "long_only": true,
    "max_position_pct": 0.15,
    "max_sector_pct": 0.30,
    "max_drawdown": 0.10,
    "max_pairwise_corr": 0.85,
    "corr_lookback_days": 60,
    "corr_penalty_factor": 0.50
  },
  "cash": {
    "normal_pct": 0.05,
    "cautious_pct": 0.15,
    "defensive_pct": 0.30
  },
  "universe": {
    "min_market_cap_bn": 2.0,
    "min_adv_mn": 5.0,
    "target_positions": 15,
    "include_foreign": false
  },
  "position_sizing": "score_vol_weighted",
  "position_sizing_options": {
    "vol_lookback_days": 60,
    "vol_min_days": 20,
    "floor_pct": 0.02,
    "cap_pct": 0.15
  },
  "scoring": {
    "quality_weight": 0.40,
    "momentum_weight": 0.20,
    "safety_weight": 0.25,
    "reversion_weight": 0.10,
    "congressional_flow_weight": 0.05,
    "reversion_lookback_days": 5,
    "confidence_floor": 0.40,
    "sector_blend_weight": 0.50,
    "min_sector_size": 5
  },
  "regime": {
    "signals": ["trend", "credit", "volatility", "breadth"],
    "weights": [0.25, 0.25, 0.25, 0.25],
    "thresholds": {
      "normal_floor": 0.65,
      "cautious_floor": 0.40
    },
    "elevated_confidence_floor": 0.55,
    "trend": {
      "sma_days": 200
    },
    "credit": {
      "source": "HY_OAS",
      "bullish_ceiling_bp": 350,
      "bearish_floor_bp": 500
    },
    "volatility": {
      "metric": "VIX_TERM_STRUCTURE",
      "contango_threshold_pct": 5.0
    },
    "breadth": {
      "index": "SP500",
      "ma_days": 50,
      "bullish_floor_pct": 65,
      "bearish_ceiling_pct": 40
    },
    "reentry_deploy_max_pct": 0.50
  },
  "earnings_overlay": {
    "enabled": true,
    "lookahead_trading_days": 5,
    "reduce_pct": 0.50,
    "hold_through_percentile": 0.80
  },
  "rebalance": {
    "drift_threshold": 0.05,
    "min_interval_days": 14,
    "displacement_threshold": 0.35,
    "max_turnover_pct": 0.30,
    "emergency_drop_pct": 0.15,
    "atr_stop_multiplier": 3.0,
    "atr_lookback_days": 14,
    "stop_type": "dual",
    "cost_aware_turnover": true,
    "estimated_cost_model": "adv_based"
  },
  "drawdown_risk_budget": {
    "enabled": true,
    "trailing_days": 20,
    "reduction_threshold_pct": -0.08,
    "reduction_factor": 0.25,
    "recovery_requires_regime_normal": true
  },
  "factor_monitor": {
    "enabled": true,
    "ic_lookback_runs": 30,
    "alert_threshold_ic": 0.0,
    "alert_consecutive_days": 60
  },
  "congressional_flow": {
    "enabled": true,
    "api_source": "quiverquant",
    "api_key_env_var": "QUIVERQUANT_API_KEY",
    "flow_lookback_days": 30,
    "committee_relevance_multiplier": 1.5,
    "cluster_threshold": 3,
    "cluster_bonus_multiplier": 1.5,
    "max_data_age_days": 3
  },
  "retention": {
    "days": 0,
    "max_records": 10000
  }
}

OKRs

Universal OKRs from spec-ocas-journal.md apply to all runs.

skill_okrs:
  - name: decision_accuracy
    metric: fraction of allocation decisions outperforming benchmark over evaluation window
    direction: maximize
    target: 0.60
    evaluation_window: 30_runs
  - name: risk_adjusted_return
    metric: Sharpe ratio relative to benchmark
    direction: maximize
    target: 1.0
    evaluation_window: 30_runs
  - name: max_drawdown
    metric: maximum portfolio drawdown from peak
    direction: minimize
    target: 0.10
    evaluation_window: 30_runs

Optional skill cooperation

• Sift — web research for signal enrichment
• Vesper — emits portfolio outcome signals for briefings

Journal outputs

• Observation Journal — research and scoring runs
• Action Journal — trade execution runs

Initialization

On first invocation of any Rally command, run rally.init:

1. Create ~/openclaw/data/ocas-rally/ and subdirectories (reports/)
2. Write default config.json with ConfigBase fields if absent
3. Create empty JSONL files: portfolio_state.jsonl, research_events.jsonl, signals.jsonl, decisions.jsonl, allocation_plans.jsonl, trade_plans.jsonl, wash_sale_exclusions.jsonl, factor_ic.jsonl, congressional_flow_cache.jsonl
4. Create ~/openclaw/journals/ocas-rally/
5. Register cron jobs rally:daily and rally:update if not already present (check openclaw cron list first)
6. Log initialization as a DecisionRecord in decisions.jsonl

Background tasks

Job name	Mechanism	Schedule	Command
rally:daily	cron	0 6 * * 1-5 (weekdays 6am)	Full daily sequence: universe refresh, update stops, check emergency sells, fetch congressional data, compute signals, regime score, drawdown check, factor monitor, rebalance triggers, (if triggered: earnings overlay, correlation penalty, allocation, turnover budget, trade plan), daily report, journal
rally:update	cron	0 0 * * * (midnight daily)	rally.update

Cron options: sessionTarget: isolated, lightContext: true, wakeMode: next-heartbeat.

Registration during rally.init:

openclaw cron list
# If rally:daily absent:
openclaw cron add --name rally:daily --schedule "0 6 * * 1-5" --command "rally.universe.refresh && rally.research.signals && rally.report.daily && rally.journal" --sessionTarget isolated --lightContext true --wakeMode next-heartbeat --timezone America/Los_Angeles
# If rally:update absent:
openclaw cron add --name rally:update --schedule "0 0 * * *" --command "rally.update" --sessionTarget isolated --lightContext true --timezone America/Los_Angeles

Self-update

rally.update pulls the latest package from the source: URL in this file's frontmatter. Runs silently — no output unless the version changed or an error occurred.

1. Read source: from frontmatter → extract {owner}/{repo} from URL
2. Read local version from skill.json
3. Fetch remote version: gh api "repos/{owner}/{repo}/contents/skill.json" --jq '.content' | base64 -d | python3 -c "import sys,json;print(json.load(sys.stdin)['version'])"
4. If remote version equals local version → stop silently
5. Download and install:

   TMPDIR=$(mktemp -d)
   gh api "repos/{owner}/{repo}/tarball/main" > "$TMPDIR/archive.tar.gz"
   mkdir "$TMPDIR/extracted"
   tar xzf "$TMPDIR/archive.tar.gz" -C "$TMPDIR/extracted" --strip-components=1
   cp -R "$TMPDIR/extracted/"* ./
   rm -rf "$TMPDIR"
   

6. On failure → retry once. If second attempt fails, report the error and stop.
7. Output exactly: I updated Rally from version {old} to {new}

Visibility

public

Support file map

File	When to read
references/data-model.md	Before creating portfolio state, candidates, or plans
references/research-and-scoring.md	Before universe filtering, signal computation, or scoring
references/operating-model.md	Before running the full ingest-to-report loop
references/journal.md	Before rally.journal; at end of every run