Build an AI trading assistant with transparent rules in Coinrule. Automate entries, exits and risk controls across Binance, Kraken, OKX and Bybit from one dashboard.
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Instead of reacting to every candle, Coinrule helps you design an AI trading assistant that follows your plan with consistent, rules-based execution. Connect Binance, Kraken, Bybit or OKX, choose a template, and set clear triggers like RSI, moving averages and price moves. You stay in control of risk with position sizing, stop losses and timeouts.
Traders often want intelligent automation without handing decisions to an opaque model. Coinrule uses transparent if-this-then-that logic, so you can audit every entry, exit and alert. Combine indicators, time windows and portfolio rules, then backtest before going live.
Your assets remain on your exchange while the platform connects via API keys with limited permissions. Set separate rules per venue, for example spot on Coinbase and perps on Hyperliquid, while monitoring everything from one dashboard. Activity logs and notifications make it easy to verify what happened and why.
Control And Transparency
Build logic you can describe in one sentence, then iterate. Example: buy 2 percent of balance if price drops 5 percent in 4 hours and RSI(14) is below 35, then take profit at 3 percent and trail by 1.2 percent. Clone the rule, change one parameter, and compare results over the same market period.
Risk First Automation
Guardrails keep strategies from spiraling: max open positions, cooldown timers, and portfolio exposure caps. If volatility spikes, pause all rules, or reduce size by 50 percent when drawdown exceeds 6 percent. These controls help you trade systematically rather than emotionally.
Buy the Dips
Buys after a 3-7 percent pullback while a trend filter stays bullish. Scales in with two entries. Exits on a 4-6 percent rebound or a protective stop.
Grid Trading
Places a ladder of buys and sells inside a defined range to harvest chop. Uses 6-12 grid levels with fixed spacing. Stops if price breaks the range by a set percent.
Supertrend Momentum
Enters when Supertrend flips bullish on 1H or 4H candles and volume confirms. Exits on the first bearish flip. Adds a trailing stop to protect fast moves.
Stop Loss and Trailing Take Profit
Opens a position on a simple trigger, then manages risk automatically. Uses a fixed stop like 2.5 percent and a trailing take profit like 1.0 percent. Designed for hands-off exits.
Bollinger Band Mean Reversion
Buys when price closes below the lower band and RSI confirms oversold. Sells near the middle or upper band. Works best in range-bound conditions with tight risk limits.
Buy the Dips
Buys after a 3-7 percent pullback while a trend filter stays bullish. Scales in with two entries. Exits on a 4-6 percent rebound or a protective stop.
Grid Trading
Places a ladder of buys and sells inside a defined range to harvest chop. Uses 6-12 grid levels with fixed spacing. Stops if price breaks the range by a set percent.
Supertrend Momentum
Enters when Supertrend flips bullish on 1H or 4H candles and volume confirms. Exits on the first bearish flip. Adds a trailing stop to protect fast moves.
Stop Loss and Trailing Take Profit
Opens a position on a simple trigger, then manages risk automatically. Uses a fixed stop like 2.5 percent and a trailing take profit like 1.0 percent. Designed for hands-off exits.
Bollinger Band Mean Reversion
Buys when price closes below the lower band and RSI confirms oversold. Sells near the middle or upper band. Works best in range-bound conditions with tight risk limits.
MA Crossover with Volume Filter
Buys when MA20 crosses above MA50 and volume is above its 20-period average. Sells on the reverse cross. Helps avoid weak signals during low-liquidity periods.
RSI Divergence Hunter
Looks for bullish divergence where price makes a lower low while RSI makes a higher low. Enters on confirmation and exits at a predefined target. Adds a time stop if momentum fades.
Volatility Breakout
Enters when price breaks above a recent high and ATR expands beyond a threshold. Uses a tight initial stop and a trailing exit. Aims to catch trend days, not chop.
DCA Accumulator
Splits a monthly budget into scheduled buys, such as 500 dollars into four 125 dollar weekly entries. Adds an extra buy if price drops 4 percent in a day. Reduces timing risk with steady accumulation.
Portfolio Rebalance
Rebalances back to target weights when an asset drifts 3-5 percent from allocation. Runs weekly or daily depending on volatility. Helps maintain a consistent risk profile across holdings.
For fast experimentation, start with one market and one trigger, then add confirmation. A common setup is: enter when RSI(14) crosses below 30 on 1H candles, but only if MA200 is rising. Another is a breakout rule that buys after a close above the 20-day high, then adds on a retest within 1.5 percent. Because every condition is explicit, you can see which filter improved your results.
Every bot needs a failure plan before it needs a profit plan. Set a max daily loss, a max number of trades per day, and a cooldown like 45 minutes after a stop-out. You can also cap exposure, for example never allocate more than 20 percent of your portfolio to one token. These limits make automation survivable across different volatility regimes.
Once your rules are stable, deploy them across venues without rewriting logic. Run spot strategies on Binance, Kraken and OKX, hedge with perps on Hyperliquid, and keep a separate rule set for high-vol coins on Bybit. Use the AI trading assistant to keep configuration consistent while tuning position size per exchange and per asset. Review fills and slippage with alerts before scaling up.
Separate from CEX APIs, you can execute on-chain using smart accounts for self-custody. Session keys can limit permissions, and routing finds liquidity across pools when you trade. This setup is designed for controlled automation rather than giving up wallet access. See /automated-trading for the broader workflow.
Launch BotFAQ
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Start by writing your thesis as a checklist, then convert it into conditions and actions. Keep the first version simple, like one entry signal and one exit rule. After 30-50 trades, adjust only one variable, such as an RSI threshold from 30 to 33. This approach avoids overfitting and keeps improvements measurable.
Notifications are not just for emergencies, they are for learning. Set alerts when a stop triggers, when a rule is skipped due to a filter, and when exposure hits a cap. Reviewing these events helps you spot whether the logic or the market regime caused the outcome. You can then refine filters instead of guessing.
Most blowups come from sizing, not signals. Use fixed fractions like 0.5-2 percent per trade, and avoid increasing size after losses. If you want to scale, do it after a stable sample, for example increasing by 10 percent after a month of consistent behavior. Boring sizing keeps you in the game.
Do analysis when markets are calm, not during a spike. Build and edit rules in batches, then let them run without constant interference. If you must intervene, pause the rule first, document why, and only then change parameters. This separation improves discipline and makes results easier to interpret.
Add filters that keep you out of bad conditions. Examples include skipping trades when spread widens beyond a threshold, or when ATR doubles versus its 20-day average. You can also block trading during specific hours if liquidity is thin. Avoiding low-quality periods often matters more than finding new entries.
If you want systematic execution without coding, Coinrule gives you templates, a visual rule builder, and risk controls that work across exchanges and on-chain venues. Start with one strategy, measure outcomes, and iterate with data. When you are ready, scale to multiple assets and venues while keeping the same disciplined process.
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