👋 Welcome to Ascent Terminal

You're in. Here's how to get started:

📡 Observer members:
The Signal Feed is your home base — signal snapshots, market scans and daily reports land there. Jump into Community Chat to connect with other traders.

🕹️ Operator members:
Open the Ascent Terminal app, connect your own exchange API keys, and you're live — non-custodial, on your keys. Use Strategy Lab to backtest your edge, then run up to 3 automated bots.

🏗️ Architect members:
Everything in Operator, plus up to 8 bots, the highest AI and automation limits, and priority support via the Priority Dev Support chat. (API / SDK access is on the roadmap.)

Every tier gets Community Chat — that's where the real conversations happen.

Let's build.

Most trading bots are built on a flawed premise: static rules in a dynamic market.

You set a moving average crossover, a fixed stop-loss percentage, maybe an RSI threshold. It works for a week. Then volatility shifts, and your bot bleeds out because it's executing the same playbook in a completely different environment.

The core problem is context blindness. Your bot doesn't know if it's in a ranging market or a trending one. It doesn't know if the volatility just spiked 3x in the last hour. It just sees "condition met → execute."

When I built Ascent Terminal, I started with this question: what if the bot could actually evaluate whether its own signals still make sense?

That led to three design decisions:

1. Dynamic position sizing via ATR. Instead of fixed lot sizes, the terminal reads real-time Average True Range to compute position size, stop-loss, and take-profit on every trade. Higher volatility → smaller position, wider stops. Lower volatility → bigger position, tighter stops. Your risk stays consistent regardless of market conditions.

2. Multi-threaded execution. Most Python trading scripts run sequentially — scan, then evaluate, then execute. If you're watching 20 pairs, that delay kills you. Ascent Terminal runs on a thread-safe async queue. Scanning, evaluation, and execution happen simultaneously.

3. AI re-evaluation layer. This is the part I'm most proud of. Before the terminal acts on a signal, it feeds a real-time data snapshot to an AI model to re-evaluate the position. Is this signal still valid given the current market structure? Should we adjust the target? Skip entirely?

It's not a crystal ball. But it adds a layer of judgment that static bots simply don't have.

If you've been burned by rigid automation before, I built this for you.