Connect exchanges, build rule-based automation, and execute trades with clear logs, alerts, and risk controls - no coding required.
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Instead of wiring endpoints and maintaining servers, an AI trading api workflow can start with Coinrule rules and templates. Connect Binance, Kraken, OKX or Hyperliquid via API keys, then automate entries, exits and hedges with clear conditions. You keep custody on the venue while the platform runs your logic with logs and alerts.
Traders often want programmatic control without babysitting rate limits, retries and edge cases. Coinrule gives you a visual rule builder that behaves like an execution layer with triggers, actions, schedules and safeguards. Clone strategies, test parameters, and keep a clean audit trail for every order event.
When volatility spikes, the system should react consistently. Use indicators like RSI, moving averages and Supertrend, then add filters such as time windows, volume thresholds or trend confirmation. Risk controls include max position size, cooldown timers and portfolio exposure caps so one bad signal does not cascade.
Exchange Connections
On centralized venues you link read and trade permissions only, then test with small size. Start with a rule like: if BTC drops 5 percent in 4 hours, buy 2 percent of balance, then place a 3 percent stop loss. Review the event log, adjust, and scale gradually.
Execution Transparency
Every action is traceable: which condition fired, which order type was sent, and what filled. That transparency matters when you iterate, especially across multiple exchanges with different tick sizes and minimums.
Buy the Dips
Buys after a defined pullback, such as -3% from a 24h high. Adds a cooldown to avoid repeated entries. Exits on a rebound target or trailing take profit.
Grid Trading
Places staggered buys and sells inside a range to capture chop. Uses fixed spacing or percent steps. Pauses the grid if price breaks out beyond the range.
Optimised RSI and MA Strategy
Enters when RSI drops below 35 while price is above a long MA filter. Exits when RSI rises above 65 or the MA trend flips. Works well on 4h or daily candles.
Volatility Breakout
Buys when price closes above a recent high with volatility expansion. Adds a stop loss based on ATR distance. Takes profit in tiers as momentum continues.
DCA Accumulator
Splits a budget into scheduled buys, like $500 monthly as four $125 weekly orders. Adds an extra buy if price dips 4% from the last entry. Stops when an allocation cap is reached.
Buy the Dips
Buys after a defined pullback, such as -3% from a 24h high. Adds a cooldown to avoid repeated entries. Exits on a rebound target or trailing take profit.
Grid Trading
Places staggered buys and sells inside a range to capture chop. Uses fixed spacing or percent steps. Pauses the grid if price breaks out beyond the range.
Optimised RSI and MA Strategy
Enters when RSI drops below 35 while price is above a long MA filter. Exits when RSI rises above 65 or the MA trend flips. Works well on 4h or daily candles.
Volatility Breakout
Buys when price closes above a recent high with volatility expansion. Adds a stop loss based on ATR distance. Takes profit in tiers as momentum continues.
DCA Accumulator
Splits a budget into scheduled buys, like $500 monthly as four $125 weekly orders. Adds an extra buy if price dips 4% from the last entry. Stops when an allocation cap is reached.
Stop Loss and Trailing Take Profit
Sets an initial stop, then trails profits once price moves in your favor. Example: trail at 2% after a 6% gain. Helps protect upside while limiting downside.
Supertrend Momentum
Enters when Supertrend flips bullish on 1h or 4h charts. Exits on the first bearish flip or a time stop. Adds a filter to avoid trades during low volume.
MA Crossover with Volume Filter
Buys on a fast MA crossing above a slow MA only if volume is above its average. Sells on the cross back or a fixed take profit. Reduces whipsaws in choppy markets.
Bollinger Band Mean Reversion
Buys when price closes below the lower band and RSI confirms oversold. Sells near the middle band or upper band. Uses a tight stop to cap drawdowns.
Breakout + Retest
Waits for a breakout, then enters on a retest of the level. Confirms with higher-high structure before buying. Exits if the level fails or a target is reached.
Unlike a black-box model, Coinrule keeps your logic explicit and editable. You can express conditions such as: when RSI crosses below 30 on 1-hour candles and MA100 is rising, buy 1% of balance, then set a 2.5% stop. Add a second rule that sells 50% at +4% and trails the rest by 1.8%. This approach gives you repeatability and fast iteration without rewriting code.
For crypto automation, running the same strategy across venues can reduce single-exchange risk. Manage connections to Coinbase, Bybit, KuCoin and Bitget from one place, then decide which pairs and sizes each venue should use. You can keep separate allocations per exchange while monitoring total exposure in a unified portfolio view.
Once you want decentralized execution, Coinrule can run on-chain via smart accounts. Trades route through Li.fi to find efficient liquidity across pools, while smart sessions can limit permissions for safer automation. This is useful for rotating between tokens or rebalancing without manual swapping.
Risk-managed execution is a process, not a single setup. Use small sizing, review logs, and tighten rules after you see real fills and slippage. Alerts help you catch unusual behavior fast, like repeated triggers or missed conditions.
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Most traders do not need a mysterious model, they need a system they can verify. Coinrule shows exactly why an order fired and what happened next. That makes post-trade review practical, even when you run multiple strategies at once.
Set max daily trades, per-asset allocation limits, and cooldowns after a stop loss. Add time windows to avoid thin liquidity, like skipping the first 10 minutes after a major news release. These controls reduce the chance of runaway behavior.
If you share strategies with a team, explicit rules are easier to review than code snippets. Clone a template, change one variable, and document the intent in rule notes. You can standardize risk settings across bots.
Start with one market and one timeframe, then expand. For example, run a 4h trend strategy on ETH with 0.5% position size, then add a second rule that hedges when trend flips. Scale only after you see stable behavior over weeks, not days.
If you want deeper guidance on automation concepts and templates, see /automated-trading and /crypto-trading-bot. You will find examples for entries, exits, DCA and portfolio rebalancing. Use them as patterns for your own rules.
Pick a single objective, like buying dips with a hard stop, and deploy it with small size. Review fills, adjust thresholds, and add one improvement at a time. Consistency comes from iteration, not complexity.
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