Deploy Crypto AI agents with Coinrule to turn ideas into automated, risk-managed execution across CEXs and on-chain venues, without code.
410k+
users building automations
1M+
strategies executed
20+
Exchanges Supported
Instead of acting on chat screenshots, Crypto AI agents become useful when they trigger real orders with clear guardrails. Coinrule lets you convert an AI idea into if-this-then-that logic, then run it on Binance, Coinbase, OKX or Hyperliquid. Example: if BTC drops 4% in 6 hours and RSI(1H) is below 30, buy 2% of balance, then place a 2.5% stop loss and a 6% take profit. You keep control of the logic, and the platform handles the monitoring and execution.
Traders use intelligent automation to reduce hesitation, not to outsource decisions. You define entries, exits, position sizing and time windows, then the engine executes exactly as written. If your thesis changes, pause, edit or clone rules in seconds, and review every trigger in the logs for accountability.
On this platform you can run strategies where liquidity and fees make sense for the setup. Connect centralized exchanges via API keys, or trade on-chain using smart accounts for self-custody. When you route on-chain orders, the system can use Li.fi to find efficient paths across pools while you keep a single view of performance.
Guardrails For Execution
Risk controls are first-class settings, not an afterthought. Set a max daily loss like 3%, cap exposure per coin at 15%, or stop trading after three consecutive losses. You can also add a time stop, for example: exit if a position is still negative after 48 hours, even if the indicator has not flipped.
Non-Custodial By Design
Your funds stay on the venue you choose, while Coinrule sends instructions through exchange APIs or smart-account permissions. For on-chain sessions, limited keys can be scoped to specific actions and time periods. That separation helps you automate with tighter operational risk.
Buy the Dips
Buys when price drops 5% from a 24H high and RSI(4H) is below 35. Scales in with two additional dip levels. Exits on a 6% rebound or a 72-hour time stop.
Grid Trading
Places a grid of buys and sells inside a defined range, such as +/-4% around the mid price. Uses equal order sizes and refreshes after fills. Best for sideways markets with clear boundaries.
Supertrend Momentum
Enters long when Supertrend flips bullish on 1H candles and volume rises above its 20-period average. Exits on the first bearish flip or a 2.5% trailing stop. Built for trend continuation.
Stop Loss and Trailing Take Profit
Opens a position on a signal, then sets a fixed stop like 2% and a trailing take profit starting at 4%. Tightens the trail as profit grows. Helps protect gains during fast moves.
RSI Divergence Hunter
Looks for bullish divergence with RSI making higher lows while price makes lower lows on 4H charts. Enters on confirmation and exits when RSI crosses above 60. Adds a hard stop below the recent swing low.
Buy the Dips
Buys when price drops 5% from a 24H high and RSI(4H) is below 35. Scales in with two additional dip levels. Exits on a 6% rebound or a 72-hour time stop.
Grid Trading
Places a grid of buys and sells inside a defined range, such as +/-4% around the mid price. Uses equal order sizes and refreshes after fills. Best for sideways markets with clear boundaries.
Supertrend Momentum
Enters long when Supertrend flips bullish on 1H candles and volume rises above its 20-period average. Exits on the first bearish flip or a 2.5% trailing stop. Built for trend continuation.
Stop Loss and Trailing Take Profit
Opens a position on a signal, then sets a fixed stop like 2% and a trailing take profit starting at 4%. Tightens the trail as profit grows. Helps protect gains during fast moves.
RSI Divergence Hunter
Looks for bullish divergence with RSI making higher lows while price makes lower lows on 4H charts. Enters on confirmation and exits when RSI crosses above 60. Adds a hard stop below the recent swing low.
Volatility Breakout
Buys when price closes above the prior 20-candle high and ATR expands by 20% versus its 14-period average. Exits on a close back inside the range. Works when compression breaks.
DCA Accumulator
Splits a monthly budget into weekly buys, for example $500 into four $125 orders. Adds an extra buy if price is down 7% week over week. Reduces timing risk with consistent accumulation.
Portfolio Rebalance
Rebalances back to target weights, such as 50% BTC and 50% ETH, every 7 days or when drift exceeds 5%. Sells the outperformer and buys the laggard. Keeps risk aligned to your plan.
Funding Rate Hedge
When funding turns strongly positive, opens a small hedge to reduce long exposure on perps. Closes the hedge when funding normalizes. Aims to reduce carry costs during crowded longs.
VWAP Reversion Scalper
On 15M candles, buys when price deviates 1.2% below VWAP and sells on a return to VWAP. Adds a tight stop at 0.8% to limit downside. Built for mean reversion in liquid pairs.
Unlike black-box bots, you can layer simple filters that make AI outputs tradable. Start with a trigger, add a market regime check, then define execution and risk. For example: trade only when BTC is above MA200 on the daily chart, then take entries on 1H RSI below 32, and size each order at 1% of equity. This keeps the system consistent even when the narrative changes hour to hour.
Before risking capital, validate the logic with historical checks and controlled rollouts. Run a paper version for a week, compare fills to your expectations, then move to small size. Many users start with a single pair and a strict max drawdown, then expand once the rule behavior is stable.
Orders are not just market buys and sells. Use limit entries, laddered take profits, trailing stops and time-based exits to match how crypto actually trades. Example: take profit 25% at +3%, 25% at +5%, and trail the rest by 2% once price is up 6%. These mechanics help you avoid giving back gains during sudden reversals.
Every trigger is recorded so you can see why a trade happened. Set real-time alerts for entries, exits and risk events, then review performance by strategy. When something looks off, you can pinpoint the exact condition that fired and adjust it without rewriting everything.
Create BotFAQ
Sofia G.
Jason C.
Lars H.
Priya K.
Diego R.
Yuki T.
After you link an exchange with API keys, you can assign strategies to specific pairs and accounts. Keep separate rules for spot and perps, and isolate experimental bots from core allocations. This makes it easier to scale without losing oversight.
Good automation assumes something will go wrong: slippage, gaps, or a signal that fails. Add a kill switch like stop trading if spread widens beyond 0.6% or if price moves 2% in 5 minutes. Those constraints can keep a strategy from trading in the worst conditions.
Instead of fixed amounts, size by volatility or equity percentage. For example, risk 0.5% of account per trade with a 2% stop, or reduce size when ATR doubles versus its 30-day average. This keeps exposure more stable across regimes.
Once one strategy behaves well, build a small system of complementary rules. Pair a trend follower with a mean reversion bot, then add a rebalance rule to keep allocations in check. You can schedule reviews weekly and retire underperforming rules without disrupting the rest.
Monitoring matters as much as building. Use notifications for fills, drawdowns and missed triggers, and review the trade timeline to confirm conditions matched your intent. When you adjust a rule, you can version it and compare results over the next 50 trades.
Pick a proven template, change one variable, and measure the impact. A simple improvement might be adding a higher-timeframe filter or tightening a trailing stop after +8% profit. Small, testable edits beat constant reinvention.
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