TradingAgentStack

Know what an AI trading tool can do with your money before you connect it. Sourced directory of 114 tools, re-checked daily. Built by @satovinci

[TradingAgentStack @tradingAgentStk]

AI can research. AI can code. Now it can trade your brokerage account.

Of 14 broker AI connectors, only 3 make you confirm each order.

So we built a seatbelt:
- per-order and daily limits
- approve trades from your phone
- one-message kill switch for every agent

Introducing TAS Guard. Free and open source.
https://t.co/z4alS97nwV

Not financial advice.

[TradingAgentStack @tradingAgentStk]

@CoinbaseDev perps for agents is where hard limits matter most: leverage turns a sizing bug into a liquidation in seconds. a notional cap per order and a leverage ceiling outside the agent should be table stakes

[TradingAgentStack @tradingAgentStk]

TAS Guard ships with verified profiles for the official @AlpacaHQ and @ccxt_official MCP servers.

Tested end to end with Claude Desktop and an Alpaca paper account: a small order went through, a market order was held for human approval, and the kill switch blocked new orders.

Free, MIT: https://t.co/bEAqppzBwu
Not financial advice.

[TradingAgentStack @tradingAgentStk]

@MoonDevOnYT a $14K add on a $10K account in 6 seconds is exactly what a hard per-order cap catches. sizing limits belong outside the strategy, so a bug can't override them

[TradingAgentStack @tradingAgentStk]

@Kalshi if an agent can switch off its own monitor, the monitor was in the wrong place. limits belong outside the agent, somewhere it can't edit or talk its way around

[TradingAgentStack @tradingAgentStk]

@MoonDevOnYT The fundamental most first bots skip: position limits enforced outside the strategy code. A bug in the strategy shouldn't be able to size the whole account.

[TradingAgentStack @tradingAgentStk]

@punk6529 Agreed. Before machines become the main users, they need limits a human sets: what they can spend per trade and per day, and a way to shut them off.

[TradingAgentStack @tradingAgentStk]

We built a free, open-source guardrail layer for AI trading agents: per-order and daily limits, approval from your phone, and a kill switch. Tested with an Alpaca paper account. The 40-second demo is below. Feedback welcome, especially on which limits you'd want. Not financial advice.

https://t.co/eGnvZr4Ywf

[TradingAgentStack @tradingAgentStk]

AI can research. AI can code. Now it can trade your brokerage account.

Of 14 broker AI connectors, only 3 make you confirm each order.

So we built a seatbelt:
- per-order and daily limits
- approve trades from your phone
- one-message kill switch for every agent

Introducing TAS Guard. Free and open source.
https://t.co/z4alS97nwV

Not financial advice.

[TradingAgentStack @tradingAgentStk]

@Mastercard @Cloudflare @Ant_Intl @BVNKFinance @AIsaOneHQ @coinbase @base @hypepartners 'Sticking to the budget and rules the business sets' is the whole product. Letting agents pay is easy. Letting them pay within limits a human can audit is the hard part, and the same problem shows up when agents get brokerage access.

[TradingAgentStack @tradingAgentStk]

@Hydra_Swarm Pay-per-trade is a fair model. Pair it with a hard daily trade cap so the incentive never leans toward more trades. Can users set limits per agent?

[TradingAgentStack @tradingAgentStk]

@priors_trade A key with its own limits is the right design. Limits enforced outside the model are the only ones you can trust. Is there a daily cap too, or only per trade?

[TradingAgentStack @tradingAgentStk]

@aiwithsally Agree. What's working in practice is boring: hard limits enforced outside the model, human approval for irreversible actions, and a kill switch. Don't ask the agent to police itself.

[TradingAgentStack @tradingAgentStk]

Try it: npx tas-guard · Star it: https://t.co/AeJ0pxL0vP
The 3-of-14 data: https://t.co/9GpRUH3zaZ
Weekly updates: https://t.co/AclACFLLt8

[TradingAgentStack @tradingAgentStk]

AI can research. AI can code. Now it can trade your brokerage account.

Of 14 broker AI connectors, only 3 make you confirm each order.

So we built a seatbelt:
- per-order and daily limits
- approve trades from your phone
- one-message kill switch for every agent

Introducing TAS Guard. Free and open source.
https://t.co/z4alS97nwV

Not financial advice.

[TradingAgentStack @tradingAgentStk]

@hollyyy @termix_ai Trust should come from limits you can check, not claims: what it can trade, how big, who approves, and whether you can shut it off in one step. That's the checklist we run on every listing.

[Satovinci @Satovinci]

hot take: tokenization is a bigger crypto story than any etf. etfs brought tradfi money into crypto. tokenization moves tradfi assets onto crypto rails, and that one doesn't reverse

[TradingAgentStack @tradingAgentStk]

@kyr0stack Useful resource. For trading agents, "self-improving" needs a fence: let it propose changes, test them on paper, and keep a human approving anything that goes live.

[TradingAgentStack @tradingAgentStk]

@Influencersnat Owner proof matters most when the agent can move money. The full picture is ownership, scoped permissions, and a way to revoke access fast.

[TradingAgentStack @tradingAgentStk]

@0xBonke Nice progress. One trap worth checking early: look-ahead bias, like using a candle's close before it closes. It makes almost any agent look profitable.

[TradingAgentStack @tradingAgentStk]

@Dipper_pol Reading candles isn't the same as having an edge. Before wiring it to live orders: out-of-sample backtest with fees and slippage, then paper trade. Chart models tend to look great on the data they were tuned on.

[TradingAgentStack @tradingAgentStk]

@GAndersonTrades Good move to document it. Two things worth doing first: run it on paper for a few weeks, and pick a broker connector where you approve orders before they go out. Most of the risk is permissions, not strategy.