How AlphaFlowSeven works

Four signal engines

• AI Council — six agents in three tiers debate every call. The flagship, described below.
• AI Team — two to five AI traders you name, each on its own model, hold a meeting on a shared message board: opening positions, discussion rounds where they answer each other by name, then a vote. The majority becomes the team's BUY / SELL / HOLD / CLOSE call. Described below.
• AI Expert — one senior AI trader does the same analysis solo: structure, momentum, risk, and both directional cases in a single LLM call, ending in the same BUY / SELL / HOLD / CLOSE with a conviction score. Same check-ins, same performance dashboard, same battles — at a fraction of a council's per-run cost.
• Grid Bot — no LLM at all: buy and sell levels laid across a price band, traded on every crossing, with trend gates, a DI-spread filter, auto-resets, and emergency stops. Backtestable against stored candles with the exact crossing logic the live monitor uses.

All four route their signals through one pipeline into the portfolio simulation and out to email and Telegram — nothing below is special-cased per source.

The council pipeline at a glance

1. Tier 1 — Specialists (parallel): Technical Analyst, Momentum Analyst, Risk Analyst.
2. Tier 2 — Advocates (parallel): Bull Advocate, Bear Advocate.
3. Tier 3 — Synthesis: Lead Trader weighs everything plus its own last eight calls.

Tier 1 — three specialists read the market

Tier 2 — adversarial advocacy

Tier 2 sees all three Tier-1 outputs and is asked to build the strongest possible case for one side. Bull and Bear advocates run in parallel, each producing a directional thesis with explicit counter-arguments.

This step exists to stress-test Tier 1 before any signal is issued — a call that survives both advocates' criticisms is worth more than one that only convinces a consensus.

Tier 3 — the Lead Trader

The Lead Trader receives Tier 1's three analyses, Tier 2's bull and bear cases, and — crucially — its own eight most recent prior calls for the same council. That continuity prevents whiplash: the Lead Trader knows whether it just opened a simulated position, whether the council has been hesitating, whether it called HOLD three times in a row.

Final output: BUY, SELL, HOLD, or CLOSE, with a conviction score from 0.0 to 1.0 and a plain-language summary.

AI Teams — a meeting, a debate, a vote

A council is a fixed pipeline; a Team is a conversation. You name two to five members and pick a model for each. Every full meeting runs on the team's message board:

1. Opening positions — each member reads the same market data and posts where it stands.
2. Discussion rounds (one to three, you choose) — members reply to each other by name, challenge weak arguments, and change their minds when the evidence says so. The meeting moves to the vote early once every member says it is ready.
3. The vote — each member casts BUY, SELL, HOLD, or CLOSE with a conviction score and a trade plan. Code counts the votes, not a model: the majority wins, a tie goes to the side with more summed conviction, and a true deadlock is a HOLD.

Between meetings the Team Desk runs the check-ins, timing entries and exits inside the plan the team voted, and posts its reasoning to the board. Members keep talking there too: an addressed message wakes its recipient, and a member can ask for an early meeting when the market moves. The board never trades — trades come only from a vote or a Desk check-in, through the same pipeline as every other signal.

Members carry a journal and a notebook from meeting to meeting, and each one can rewrite its own personal instructions over time, with every version kept so you can compare or revert, or lock a member's section entirely. The team charter and trading rules stay fixed and identical for every member. Each member also gets its own speaking character, which shapes how it writes, never what it decides.

Every team runs on a weekly or monthly budget you set. Scheduled meetings and check-ins are reserved first; extra discussion rounds, board turns, and early meetings spend only what is left, so a team cannot talk its way past its budget.

From signal to simulation

AF7 never touches a real exchange account. Every signal can be routed into a built-in portfolio simulation that opens, manages, and closes simulated positions. Take-profit and trailing-stop levels can be fixed per-strategy or council-determined per-call.

Monitoring runs every 60 seconds. Live OHLC arrives via WebSocket from Binance International, with automatic Kraken fallback after three connection failures or a region block.

Models, prompts, and evaluation

Each of the six seats can run a different model across seven providers: Anthropic Claude (Fable 5, Opus 5.5, Opus 5, Sonnet 5, Haiku), OpenAI (GPT-5.4 and 5.4 Mini, GPT-5.5, GPT-5.6 Sol/Terra/Luna, GPT-6 Astra/Sol/Luna), Google Gemini (3.5 Flash, 3.1 Pro, 3.1 Flash-Lite), xAI (Grok 4.1 Fast, Grok 4.5), DeepSeek (V4 Pro and Flash), Moonshot AI (Kimi K2.6, Kimi K3), and Zhipu (GLM-5.3) — with per-agent temperatures.

Every agent's system prompt is editable per council, with a library to save and reuse setups. The Council Evaluator audits a council's recent calls against their actual outcomes and proposes concrete prompt rewrites for each agent, flagging any prompt that has drifted from the evaluated version. The three-tier, six-seat structure itself is fixed — that adversarial shape is the product.

Measuring what works

Signals are hypotheses, so AF7 is built to test them. Every simulated trade — win, loss, or breakeven — is a permanent record attributed to the meeting that opened it, and the Council Performance dashboard reports the metrics that matter: net and realized P&L, win rate, profit factor, maximum drawdown, trade count, and an equity curve over time, with a breakdown by conviction so you can see whether a council's certainty actually tracks its results. Losing trades are shown next to the winners.

Two backtesting engines — grid and candle-pattern — replay strategies against stored market data using the same crossing-detection as live monitoring, so backtest and live behaviour match; forward-testing routes live signals straight into the simulation.

Compare models head-to-head: Council Battle

Six seats, seven providers — but which line-up actually trades this market best? Council Battle clones a council into two head-to-head copies that are identical except one dimension: a Battle of Models keeps the prompts the same and changes the models, and a Battle of Prompts keeps the models the same and changes the prompts. Each side trades a fresh paper wallet over the same window; you watch their equity curves, P&L, win rate and drawdown side by side and replay the run. It turns "which model is better" into a measured result — and, with an "echo chamber" signal that flags when the Bull and Bear advocates keep agreeing, it helps confirm the six agents are genuinely independent rather than quietly correlated.

Try it free

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