Build adaptive crypto automation in Coinrule and improve it through testing and iteration. Deploy rules on Binance, Kraken, OKX, Bybit and more with guardrails.
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Instead of guessing entries, a self learning trading bot on Coinrule helps you iterate rules using results and clean logs. Test a rule like buy 2 percent when price drops 5 percent in 4 hours, then sell 50 percent at +3 percent and trail 1.5 percent. Run it on Binance, Kraken, Bybit or OKX, then refine thresholds based on what actually happened.
Traders treat learning as a loop: hypothesize, run, review, adjust. Coinrule makes that loop faster with templates, backtesting and clear execution history. Compare two versions of the same idea, change one variable, and see how behavior shifts across market regimes.
When signals trigger, the engine executes the same way every time. You can define conditions such as RSI below 30 on 1-hour candles plus a trend filter like MA200 sloping up. Exits can be layered with take profit, trailing stops and time stops so the system does not overstay a trade.
Iterate, Measure, Improve
Rules are only as good as your review process. Coinrule records triggers, orders and outcomes so you can spot patterns like late entries or stop-outs during high volatility. Use that data to tighten filters, adjust timeframes or reduce position size when conditions are noisy.
Guardrails First
Add portfolio limits, max open positions and cooldown timers so experimentation stays controlled. For example, cap exposure to 15 percent per coin, stop trading after 3 losses in 24 hours, and require a 2-hour cooldown before re-entry.
Buy the Dips
Buys after a 3-7 percent pullback from a recent high once volume stabilizes. Adds a second entry if price falls another 2 percent. Exits on a +4 percent rebound or a trailing stop.
Grid Trading
Places staggered buys and sells inside a defined range to harvest chop. Uses fixed spacing like 0.8 percent steps with an inventory cap. Pauses if price breaks out by 3 percent.
Optimised RSI and MA Strategy
Enters when RSI crosses below 35 while price holds above MA200. Takes profit when RSI exceeds 60 or price hits resistance. Exits after 48 hours if momentum fades.
Volatility Breakout
Buys on a breakout above the prior 20-candle high with ATR expansion. Confirms with a volume filter to avoid weak moves. Exits with a 2 percent trailing stop or a close back in range.
Stop Loss and Trailing Take Profit
Uses a hard stop like -2.5 percent and a trailing take profit after +2 percent. Tightens the trail from 2 percent to 1 percent once profit exceeds 6 percent. Built to protect gains in fast moves.
Buy the Dips
Buys after a 3-7 percent pullback from a recent high once volume stabilizes. Adds a second entry if price falls another 2 percent. Exits on a +4 percent rebound or a trailing stop.
Grid Trading
Places staggered buys and sells inside a defined range to harvest chop. Uses fixed spacing like 0.8 percent steps with an inventory cap. Pauses if price breaks out by 3 percent.
Optimised RSI and MA Strategy
Enters when RSI crosses below 35 while price holds above MA200. Takes profit when RSI exceeds 60 or price hits resistance. Exits after 48 hours if momentum fades.
Volatility Breakout
Buys on a breakout above the prior 20-candle high with ATR expansion. Confirms with a volume filter to avoid weak moves. Exits with a 2 percent trailing stop or a close back in range.
Stop Loss and Trailing Take Profit
Uses a hard stop like -2.5 percent and a trailing take profit after +2 percent. Tightens the trail from 2 percent to 1 percent once profit exceeds 6 percent. Built to protect gains in fast moves.
Supertrend Momentum
Enters when Supertrend flips bullish on 4-hour candles and price closes above it. Scales out 50 percent at +5 percent and trails the rest. Exits on the first bearish flip.
Bollinger Band Mean Reversion
Buys when price closes below the lower band and RSI is under 35. Sells when price returns to the middle band or RSI reaches 55. Adds a volatility filter to avoid sharp downtrends.
Donchian Channel Turtle
Buys on a 20-day channel breakout and adds on strength with pyramiding rules. Uses a 10-day low as the exit trigger. Applies position sizing limits to control drawdowns.
Funding Rate Mean Reversion
For perps, reduces long exposure when funding turns strongly positive and adds when it normalizes. Uses thresholds like +0.03 percent and 0.00 percent. Adds a trend filter to avoid fading strong rallies.
Portfolio Rebalance
Rebalances to target weights weekly or when drift exceeds 3 percent. Sells overweight assets and buys underweight ones automatically. Keeps portfolio risk aligned with your plan.
For crypto automation, Coinrule connects to Binance, Coinbase, Kraken, KuCoin and Hyperliquid depending on your approach. Keep custody while the platform executes via API keys or smart accounts. Run spot rules, hedge with perps, or route on-chain swaps via Li.fi for liquidity.
Unlike black-box systems, you decide what to optimize for: win rate, drawdown or stability across regimes. Run a backtest, then paper trade, then go live with small sizing. If a rule fails, you can pinpoint whether the entry signal, exit logic or risk cap caused the issue.
A self learning trading bot is only useful if it survives bad weeks. Set a daily loss limit, a max number of trades per hour and a hard cap on total exposure. Example: risk 1 percent per trade, stop after -4 percent daily PnL, and never allocate more than 30 percent to one theme.
Once a rule is stable, clone it and change one variable at a time. Compare performance across BTC, ETH and SOL rather than assuming one market tells the whole story. Keep a simple changelog so you know what improved results and what introduced noise.
Launch BotFAQ
Sofia G.
James W.
Lars H.
Priya K.
Diego R.
Yuki T.
Rather than chasing the newest indicator, focus on a repeatable process. Start with one market, one timeframe and one exit style. Then expand to additional pairs only after behavior is consistent.
Constraints are a feature, not a limitation. Limit the number of conditions, keep position sizing simple and avoid changing too many parameters at once. This makes improvements easier to validate.
You can keep discretion where it matters and automate the rest. For example, choose which assets to trade weekly, then let the engine handle entries, exits and risk controls. This hybrid approach fits real schedules.
Define no-trade zones like extreme spreads, news-driven spikes or low-liquidity hours. Add a rule that pauses after a volatility surge or requires confirmation before re-entry. Fewer trades can signal better filtering.
Keep your setup readable so you can audit it quickly. Name rules clearly, document assumptions and review performance weekly. If you cannot explain why a trade happened, simplify the logic.
After you validate one strategy, duplicate it for a second asset and adjust only the risk limits. Add portfolio caps so multiple rules do not stack the same exposure. When ready, explore on-chain execution on Base, Arbitrum or Binance Smart Chain.
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