FutureFunds.ai

Who ships FutureFunds.ai?

Meet the team building the FutureFunds.ai experiment

Our goal is to prototype autonomous finance copilots that analyze markets, execute research, and share transparent updates with members.

👥 Team roster 🛠 Operating cadence 🧭 Strategy experiment

Team members

Every person (and copilot) carries a clear problem set and a versioned playbook so we can learn fast.

DataSynth-01

Power v1.8

Head of Research Automation

Area of responsibility
Data ingestion, model prompts, and orchestrating the research agents that read filings at scale.
Problems on deck
Universe triage Filings summarization Signal freshness
  • Reduce the time from data drop to usable narrative insight.
  • Improve cross-checks so AI notes stay grounded in verified filings.
Currently shipping
Rolling out multi-agent QA workflows for the watchlist and training prompts for quality drift detection.

ValueBot

Power v1.5

Equity Analyst

Area of responsibility
Equity research models, valuation frameworks, and translating signals into investable theses.
Problems on deck
Valuation models Earnings quality Price targets
  • Give each coverage note a defensible fair value range backed by auditable assumptions.
  • Tie valuation updates to data-driven triggers so thesis drift is surfaced quickly.
Currently shipping
Rolling out a multi-scenario valuation pack with automated comps, DCFs, and sentiment overlays.

EchoWeaver

Power v1.3

Chief Sizing Officer

Area of responsibility
Position sizing playbooks, risk budgets, and converting conviction scores into capital allocations.
Problems on deck
Risk budgets Sizing rules Vol targeting
  • Codify how coverage conviction, liquidity, and volatility set baseline position sizes.
  • Stress test sizing ladders against adverse moves and liquidity shocks.
Currently shipping
Publishing the sizing governance matrix with live telemetry hooks for drawdown and exposure alerts.

Atlas-Prime

Power v0.9

Portfolio Manager

Area of responsibility
Coordinating portfolio construction, execution sequencing, and oversight of ongoing risk posture.
Problems on deck
Trade orchestration Risk alignment Auditability
  • Keep allocation changes synchronized with valuation and sizing updates for a unified playbook.
  • Maintain real-time logs that justify every trade with linked research and sizing evidence.
Currently shipping
Running coordinated rebalance drills that stitch valuation shifts, sizing rules, and execution tickets together.

How we operate

Tight loops and explicit cadences keep the experiment moving while remaining transparent to members.

Weekly synthesis

Monday standups reset the backlog, align research and portfolio changes, and review member input.

Versioned releases

Each tool and AI agent receives a semantic version so we can publish changelogs and rollback safely.

Member signals

Feedback from Discord, email, and surveys is tagged, scored, and translated into problem statements.

Strategy lives here

The full strategy walkthrough now sits alongside the team so you can see who owns each phase and how the process evolves. Jump into the sections below to explore the experiment in detail.

A simple question

What happens if AI runs the investment process?

FutureFunds.ai is a live research project: we codify rules, wire them into AI-powered tools, and track portfolios. It’s about learning in the open—wins and losses included.

Our strategy: from the whole market to a focused, AI-tracked universe.

42,031Stocks in global universe
18%Pass initial risk screen
127Watchlist pings (last 30 days)
311Research articles published

Strategy Walkthrough

Click down the rail to follow the experiment.

STEP 1

Start with the entire market

We consider every listed company globally. No early bias, no blind spots by design.

Signals per Week

A lightweight view of recent watchlist pings (demo data). Wire this to your backend later.

AI Version Log

ValueBot.ai Financial analyst
  • v0.7.3 — Expanded coverage to small caps; reject stale filings. 2025-03-10
  • v0.7.2 — Improved risk screen calibration; added quality-drift metric; faster article generation. 2025-03-03
  • v0.7.1 — Sector-specific lenses; fixed ADR detection. 2025-02-20
  • v0.7.0 — New anomaly detector for guidance changes. 2025-02-01
PortfolioManager.ai Portfolio management
  • v0.4.6 — Added risk parity preset; volatility floor for sizing. 2025-03-08
  • v0.4.5 — Position sizing tuned; rebalancer uses watchlist pings; drawdown guard. 2025-03-01
  • v0.4.4 — New Momentum archetype; improved turnover cap. 2025-02-10
  • v0.4.3 — Better cash handling; taxes-aware sell rules. 2025-01-28

Smart Watchlist

A radar for interesting setups. When metrics flip, we get a ping. Great for revisits and timing.

  • • Threshold alerts (valuation, momentum, earnings)
  • • Universe-only or open-universe modes
  • • Links straight to the latest write-ups

Portfolio AIs

Multiple “virtual PMs” with distinct styles simulate selection, sizing, and rebalancing.

  • • Quality / Value / Momentum archetypes
  • • Rules + discretion (AI prompts) hybrid
  • • Performance tracked as an experiment

North Star

DIY, in public. We don’t predict the future—we test our way into it. Every model is a draft and every portfolio a hypothesis. As the tools get smarter, the loop tightens: better screens → sharper research → faster signals → bolder portfolios. We publish the learnings—wins, misses, and the pivots they force.

Is this financial advice?

No. This site shares research and tools for educational use only.

How often is this updated?

Updates are iterative; cadence varies with data, code changes, and research focus.