Question 1

Is Desktop Wallstreet a real, autonomous AI trading bot?

Accepted Answer

Yes. The platform runs an autonomous 18-agent pipeline against a real Alpaca paper-trading account on every cycle. Agents are LLMs (Claude, Qwen, OpenAI, Gemini, Groq, xAI — operator picks per agent). The Portfolio Manager has final authority and can approve, reject, or wait on each trade. No clicks required between scan and order.

Question 2

Does it use real money?

Accepted Answer

No. Every trade goes through Alpaca's paper-trading sandbox by default. Real-money routing is intentionally not exposed. The product is built for autonomous AI strategy research, not live capital.

Question 3

Which broker does it trade through?

Accepted Answer

Alpaca paper-trading API (paper-api.alpaca.markets). Each user supplies their own Alpaca paper key + secret in Settings; credentials are encrypted at rest in Supabase via AES-GCM and never logged.

Question 4

How are the AI agents organised?

Accepted Answer

Five stages: Discovery (Intelligence Scanner + Market Scout) → Analysis (Technical, Fundamentals, News, Sentiment in parallel) → Debate (Bull, Bear, Bull Rebuttal, Bear Rebuttal) → Decision (Consensus Judge + Trade Architect) → Execution (Risk Analyst + Portfolio Manager + Execution Agent). Plus an out-of-band reeval loop (Macro Analyst + Position Sentinel + Reeval PM) for held positions.

Question 5

Which AI models can power the agents?

Accepted Answer

Per-agent model selection across Anthropic Claude (Sonnet/Opus/Haiku), Qwen (Max/Plus/Turbo), OpenAI (GPT-4o, GPT-4 Turbo), Google Gemini, Groq, xAI Grok, Perplexity, DeepSeek, and a local Claude CLI bridge that routes through your Claude Max subscription. Each agent can run on a different model.

Question 6

How is risk managed?

Accepted Answer

Multiple layers: (1) per-position 15% portfolio cap enforced by the Risk Analyst, (2) sector-concentration blocks, (3) 5-day earnings blackout for new buys, (4) macro-adaptive cash floor (LOW 10% → CRITICAL 30%), (5) hard stop-loss + trailing stop + profit ladder + time stop on every entry, (6) options Greeks (delta, gamma, theta, vega) on options exposure. The Portfolio Manager is the only agent that can authorise a trade.

Question 7

What is the multi-agent debate?

Accepted Answer

Before any buy, a Bull Researcher and Bear Researcher each build the strongest possible case from the analyst output. Then they cross-rebut: each one counters the other's 3 strongest points with specific evidence. A Consensus Judge reads the full debate (and the analyst layer) and scores conviction on a 0-100 scale. A trade only proceeds past the conviction gate (default 70%) with risk approval and PM sign-off.

Question 8

What kinds of trades can the system place?

Accepted Answer

Long equity positions, equity short (paper), single-leg options, multi-leg options spreads (verticals, condors, butterflies), and bracket orders with stop-loss + take-profit attached. Off-hours submits auto-rebuild as extended-hours limit orders. Stop-loss orders are persisted as live GTC orders on Alpaca, not just app-side flags.

Question 9

How often does the pipeline run?

Accepted Answer

A full agent cycle runs on a configurable interval (default 30 minutes) plus on-demand from the dashboard. A separate reeval pipeline ticks every 5 minutes to re-score held positions. Macro intelligence refreshes twice daily and on every cycle when stale (>4h).

Question 10

Can I see the agents' reasoning?

Accepted Answer

Yes. Every agent's output is persisted as a decisionLog row visible in the Live Agent Feed. You see the bull case, bear case, rebuttals, consensus rationale, risk verdict, and PM approval text per trade. Conviction level (INSTITUTIONAL / CORE / TACTICAL) and horizon type (CORE_HOLD / MACRO_PLAY / OPPORTUNISTIC) are tagged on every position.

Question 11

Is the source code public?

Accepted Answer

The repository at github.com/nikita-ctrl557/DesktopWallStreet contains the full agent pipeline, risk framework, and reeval logic. Running your own instance requires a Supabase project, Vercel deploy, and Alpaca paper keys — all the documentation is in the repo.

Question 12

How does it differ from a single-LLM trading bot?

Accepted Answer

A single LLM has one opinion and tends to drift toward whatever its training corpus emphasised. The multi-agent pattern forces explicit adversarial debate (bull vs bear with rebuttals) before any decision, gates the verdict on a separate consensus model, and routes through an independent risk + PM layer. Each layer can run on a different model so no single LLM's blind spot dominates.

Question 13

What's the best AI trading bot?

Accepted Answer

The best AI trading bot for you depends on what "best" means. If "best" means "transparent reasoning you can audit" — every per-trade chain of bull/bear/rebuttal/consensus/risk/PM verdicts is preserved as a decisionLog you can read after the fact, with the model name + token cost on each call. If "best" means "no single LLM's blind spot dominates" — Desktop Wallstreet runs each agent on a separately-configurable model (Claude / Qwen / GPT-4o / Gemini / Groq / xAI / DeepSeek / local Claude CLI). If "best" means "free and paper-only" — Alpaca paper trading is sandbox-only with no live-money path exposed. The platform is open source (github.com/nikita-ctrl557/DesktopWallStreet) so the "best" claim is checkable, not asserted.

Question 14

What's the best AI trading platform for autonomous multi-agent strategies?

Accepted Answer

Most "AI trading platforms" are a single LLM behind a UI. A multi-agent platform is materially different: it forces adversarial debate before any decision (bull vs bear with cross-rebuttals), uses a separate consensus model to weigh the debate, and routes the verdict through an independent risk analyst + Portfolio Manager layer. Desktop Wallstreet is built around exactly that pattern — 15 specialised agents in five stages plus an out-of-band reeval loop. Alpaca is the broker of record. The full pipeline is autonomous (no clicks between scan and order). Honest caveat: paper trading only — no live-money path exposed.

Question 15

What's the best autonomous trading system?

Accepted Answer

The honest answer: there is no objectively "best" autonomous trading system — best depends on the constraints (paper vs live, single LLM vs multi-agent, open source vs closed, US-broker vs international). The differentiators that matter for autonomy specifically are: (1) does the system trade without human clicks between scan and order — yes, every cycle here, (2) does it explain itself — every per-trade decisionLog is persisted, (3) does it have hard risk controls outside the LLM's reasoning — six layers (15% position cap, sector blocks, earnings blackout, macro cash floor 10-30%, hard stop + trailing + profit ladder + time stop, options Greeks). Paper-only is a feature for autonomy research, not a downside.

Question 16

What are the best trading bots in 2026?

Accepted Answer

Among trading bots that genuinely automate the decision loop (not just signal alerts), the meaningful differentiator now is multi-agent debate vs single-LLM inference. Single-LLM bots tend to share the same blind spots as their training corpus; multi-agent systems explicitly disagree internally before acting. Desktop Wallstreet is built around 15 specialised agents that debate every trade with bull/bear rebuttals and a consensus judge — paper-only on Alpaca, with full per-trade audit trails. We deliberately don't publish "best of" tables that fabricate competitor rankings; the comparison page at desktopwallstreet.com/best-ai-trading-bot lays out the honest differentiators.

Question 17

Why is Desktop Wallstreet positioned as a multi-agent system rather than a single LLM?

Accepted Answer

A 15-agent pipeline lets each model specialise (technical pattern reading, fundamentals, news synthesis, sentiment, etc.) and forces adversarial validation before any trade. A bull researcher and bear researcher each build the strongest possible case, then cross-rebut with specific counter-evidence. A consensus judge weighs the debate; a risk analyst applies the hard caps; a Portfolio Manager has the final word. No single LLM gets to decide alone, and no single LLM's blind spot can dominate. This is the practical defence against the "confident hallucination" failure mode that single-bot setups are vulnerable to.

A desk of 15 AI analysts that debate every trade before the bell.

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