Use case · Investing

Compose a specialist agent for every layer of investing.

One agent runs quant signals, another reads filings, another manages risk, another executes trades through your brokerage, another runs exit strategy — every agent loaded with your investment thesis, every gated trade auditable, your humans hold the deal-review gate.

Best fit for · Investors and trading desks running multi-strategy analysis with deal review, risk gates, and brokerage execution
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packwolf.app · Investing desk
Live
Portfolio · Day 142YTD +12.4% · 14 OPEN · 3 PENDING · STRATEGY-LOADEDDAYWEEKYTDILMANAGERInvestment LeadQAQuantSIGNALS: 14FAFundamentalsFILINGS: 6RMRisk ManagerLIMITS OKTRTraderLIVE · IBKRDEAL REVIEW3 awaitingSIGNALS · TRIANGULATED7NVDAEarnings beat + AI capexQUANT + FUNDBUY82%TSLAVolatility regime shiftQUANTSELL71%JPMNIM compression riskFUNDHOLD55%AAPLBuyback announce + iPhone cycleQUANT + FUNDBUY76%METAMargin expansion confirmedFUNDBUY79%AMDInventory unwindQUANTSELL68%GOOGLSearch share stable + CloudFUNDHOLD58%OPEN POSITIONS · LIVE P&L6TICKERQTYCOSTLASTP&LNVDA240$542.10$612.40+$16872+13.0%QAOWNS · STOP -8%AAPL480$178.20$198.70+$9840+11.5%FAOWNS · STOP -8%META320$401.50$502.80+$32416+25.2%FAOWNS · STOP -8%MSFT220$386.00$412.10+$5742+6.8%QAOWNS · STOP -8%AMZN180$142.50$168.90+$4752+18.5%FAOWNS · STOP -8%TSM300$102.10$108.40+$1890+6.2%QAOWNS · STOP -8%ORDERS · APPROVAL + EXECUTION5NFLXDEAL REVIEWBUY80 @ $612.00QAOWNS· THESIS · STOP · TARGETORCLDEAL REVIEWBUY150 @ $142.40FAOWNS· THESIS · STOP · TARGETASMLDRAFTINGBUY60 @ $856.50QAOWNS· THESIS · STOP · TARGETAMDQUEUEDSELL200 @ $152.00EXOWNS· THESIS · STOP · TARGETTSLAFILLEDSELL90 @ $248.00EXOWNS· THESIS · STOP · TARGETSTATEBUY / WINHOLD / REVIEWSELL / STOPQUEUEDDRAFTING
An example investing pack's portfolio + signals desk. Yours can look different — you define which agents own quant, fundamentals, technicals, risk, execution, and exit strategy.
What the pack does

Five concrete tasks.

  1. 01

    Specialist agents for every layer of investing.

    Compose the pack you need: a Quant for algorithmic signals, a Fundamentals analyst for filings + earnings, a Risk Manager for exposure + drawdown, a Trader for brokerage execution, an Exit Strategy specialist for stop-loss and take-profit. Add or drop specialists per strategy — long-only, market-neutral, event-driven all want different shapes.

  2. 02

    Strategy thesis loads into every agent.

    A strategy custodian (or your thesis memory) holds the investment thesis, risk limits, position sizing rules, and disqualification criteria. Every other agent — Quant, Fundamentals, Trader, Exit — loads it before acting. Trades that violate the thesis don't get past the gate.

  3. 03

    Multi-strategy signal generation in parallel.

    Quant runs algorithmic signals (momentum, mean-reversion, factor models). Fundamentals reads filings + earnings + cash flow. Technical patterns surface from chart analysis. The pack triangulates across strategies before recommending a position — single-strategy bets don't pass the deal-review gate.

  4. 04

    Automated execution with human-gated approvals.

    Trader executes through your brokerage API (IBKR, Alpaca, Tradier, Robinhood) once a trade clears the deal-review gate. Position size + stop loss attached at entry. Risk Manager monitors drawdown live; trips a circuit breaker when limits hit.

  5. 05

    Exit strategy that doesn't forget the plan.

    Exit Strategy agent watches every open position against the original thesis. Stop-loss, take-profit, trailing stop, time-based exit — defined at entry, executed without sentiment. Performance Analyst attributes P&L back to which signals worked.

How a team uses this

Setup once, then watch it run.

Concrete operator setup, the phases the pack moves through, and where you stay in the loop.

Compose your pack: pick the agents your strategy needs (lead, quant, fundamentals, risk, trader, exit, analyst).
Author the investment thesis, risk limits, and position sizing rules as durable memory every agent reads.
Connect your brokerage (IBKR / Alpaca / Tradier / Schwab / etc) and your market-data feeds via MCP.
Set the Investment Lead as the deal-review approver. Auto-execute stays off until you're confident in the pack.
Phase 1

Universe + signals

Quant scans the universe against algorithmic signals; Fundamentals reads filings + earnings + cash flow. Market Data feeds both. Triangulated candidates surface to the deal-review queue.

Phase 2

Deal review

Investment Lead + Risk Manager review thesis fit, position size, hedge requirements. Three-tier approval before any trade executes.

Phase 3

Execution

Approved trades execute through your brokerage API. Stop-loss + take-profit attached at entry; Brokerage Sync tracks fills.

Phase 4

Live monitoring

Risk Manager watches every open position against drawdown limits; Exit Strategy fires take-profits and stop-losses without sentiment. Circuit breakers on limit breach.

Phase 5

Performance

Performance Analyst attributes P&L to signals + strategies weekly. Calls out which patterns worked. Proposes thesis refinements.

PHASE 01Universe + signalsPHASE 02Deal reviewPHASE 03ExecutionPHASE 04Live monitoringPHASE 05PerformanceApprovedCloseRefineOperatorSETS THESISStrategy thesisPROCEDURAL MEMORYQuantSIGNAL SCANNERFundamentalsFILINGS + EARNINGSCandidatesTRIANGULATEDInvestment LeadREVIEWS FITRisk ManagerLIMITS + HEDGESDeal reviewTHREE-TIERTraderROUTES ORDERSBrokerageIBKR / ALPACA / ETCFillsEXECUTED POSITIONSHeartbeatEVERY TICKExit StrategySL · TP · TRAILINGRisk ManagerDRAWDOWN WATCHERPerformanceP&L ATTRIBUTIONWeekly recapWINS + THESIS TWEAKS
An example pack + workflow

Your pack, your workflow.

Workflows are markdown that reference the roles in your pack. Below is one example shape - yours can have different agents, different steps, different cadence.

  1. Step 01Continuous

    Universe + signals

    ReceivesInvestment thesis + universe
    QAQuantFAFundamentalsSCStrategy

    Quant scans algorithmic signals (momentum, mean-reversion, factor models). Fundamentals reads filings + earnings + cash flow. Triangulated candidates surface.

    Hands offTriangulated candidate list
  2. Step 02Per candidate

    Deal review

    ReceivesTriangulated candidates
    ILInv LeadRMRisk Manager

    Investment Lead + Risk Manager review thesis fit, position size, hedge requirements. Three-tier approval before any trade executes.

    Hands offApproved trade with size + stops
  3. Step 03On approval

    Execution

    ReceivesApproved trade
    TRTraderBSBrokerage

    Trader places orders through your brokerage API. Stop-loss + take-profit attached at entry; Brokerage Sync tracks fills as they print.

    Hands offOpen position with risk params
  4. Step 04Live

    Monitoring + exit

    ReceivesOpen position
    RMRisk ManagerEXExit Strategy

    Risk Manager watches drawdown, exposure, correlation against limits. Exit Strategy fires take-profits, stop-losses, trailing stops without sentiment.

    Hands offClosed position + P&L
  5. Step 05Weekly

    Performance + iteration

    ReceivesWeek's closed positions
    PAPerformance

    Performance Analyst attributes P&L to signals + strategies. Calls out which patterns worked. Proposes thesis refinements back to the strategy memory.

    Hands offWeekly recap → feeds next cycle's thesis
Example workflow · 5 steps · per-trade handoff chain
Workflow · markdownExample investing cycle
# Example investing cycle
# Workflows are markdown — yours can swap agents,
# add strategies, change risk limits, or define
# a different deal-review gate.

Match: products labelled "investing"
Required tools: web_search, http_request (broker + data via MCP), file_read
Required skills: deep-research, statistical-verification, risk-discipline

## Strategy context — always-on
Every agent loads from your strategy memory: investment thesis,
risk limits, position sizing rules, disqualification criteria,
target return + drawdown. No agent acts without strategy context.

## Step 1 — Universe + signals (continuous)
Quant Analyst scans the universe against algorithmic signals
(momentum, mean-reversion, factor models). Fundamentals reads
filings, earnings, cash flow. Market Data feeds both.

## Step 2 — Deal review
Triangulated candidates surface to the deal-review queue.
Investment Lead + Risk Manager review thesis fit, position
size, hedge requirements. Three-tier approval before any trade
executes.

## Step 3 — Execution
On approval, Trader places orders through your brokerage API
(IBKR / Alpaca / Tradier / etc). Stop-loss + take-profit attached
at entry. Brokerage Sync tracks fills.

## Step 4 — Live monitoring
Risk Manager watches every open position against drawdown limits,
exposure caps, correlation. Circuit breakers trip if limits hit.
Exit Strategy fires take-profits and stop-losses without sentiment.

## Step 5 — Performance + iteration
Performance Analyst attributes P&L to signals and strategies
weekly. Calls out which patterns worked, which didn't. Proposes
strategy refinements back to the thesis.

Approvals: Investment Lead signs off on every trade above threshold.
Risk Manager has hard veto on any position breaching limits.
The ROI

A multi-strategy analyst desk with brokerage execution — composable, thesis-loaded, with risk gates and audit trail on every trade.

Common questions

Things teams actually ask.

No. Investing teams shape per strategy — long-only equity needs a different pack than market-neutral, event-driven, or systematic options. Some teams want a dedicated earnings-call agent; others want a separate macro-watcher; others want a gamification layer that scores trader skill against the pack's signals. PACKWOLF lets you compose the specialists your strategy actually needs.

Run this pack on your team's work.

Closed-beta cohorts are small. Tell us about your work and we'll configure the pack for what you actually do.

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