Build rule-based stock and ETF bots with Coinrule, then automate entries, exits and risk controls across brokers like TradeStation, Alpaca and E*TRADE without coding.
410k+
users building rules
1.2M+
strategies created
20+
Venues Connected
Most traders start with a checklist, then miss fills when the market moves fast. With Coinrule, tradestation algo trading becomes a set of clear if-then rules you can run consistently, monitor in one dashboard, and adjust without redeploying code.
Link your stock and ETF accounts and run the same logic across multiple venues when needed. Use broker connections such as TradeStation, Alpaca, E*TRADE and Charles Schwab to route orders while keeping position sizing and limits centralized. The platform focuses on guardrails: allocation caps, max open positions and stop logic that is easy to audit.
Templates and a visual rule builder help you translate ideas into systematic execution. Set indicator triggers, time windows and order types, then review logs to see exactly why each trade fired. You can clone a rule, change one parameter, and compare behavior without rewriting anything.
Broker-ready automation
Create a core strategy once, then adapt it per account by changing position size, symbols or trading hours. For example, run a trend rule on SPY during regular hours, while a mean-reversion rule trades QQQ only in the first 90 minutes. This keeps your process consistent even when you diversify.
Risk controls you can verify
Every rule includes explicit exits and safety checks, so you can see the full lifecycle before going live. Add a hard stop, a trailing take profit and a time stop so positions do not linger when conditions change.
MA Crossover with Volume Filter
Buys when fast MA crosses above slow MA and volume is above its 20-period average. Exits on crossback or a 2.5% stop. Works well on 1H to daily charts for liquid ETFs.
RSI Oversold Rebound
Enters when RSI drops below 30 and price holds above a higher-timeframe MA. Takes profit when RSI returns above 55 or after 3 days. Adds a 1.8% stop to cap downside.
Opening Range Breakout
Defines the first 15 minutes as the range, then buys the break with a volatility filter. Sells on a close back inside the range or a trailing stop. Designed for active sessions.
Bollinger Band Mean Reversion
Buys when price closes below the lower band and reclaims the mid band within 2 candles. Exits at the upper band or after a time stop. Best for range-bound periods.
ATR Channel Breakout
Enters on a close above an ATR channel with ADX confirmation. Scales out at 1R and trails the remainder. Uses a fixed ATR-based stop for consistent risk per trade.
MA Crossover with Volume Filter
Buys when fast MA crosses above slow MA and volume is above its 20-period average. Exits on crossback or a 2.5% stop. Works well on 1H to daily charts for liquid ETFs.
RSI Oversold Rebound
Enters when RSI drops below 30 and price holds above a higher-timeframe MA. Takes profit when RSI returns above 55 or after 3 days. Adds a 1.8% stop to cap downside.
Opening Range Breakout
Defines the first 15 minutes as the range, then buys the break with a volatility filter. Sells on a close back inside the range or a trailing stop. Designed for active sessions.
Bollinger Band Mean Reversion
Buys when price closes below the lower band and reclaims the mid band within 2 candles. Exits at the upper band or after a time stop. Best for range-bound periods.
ATR Channel Breakout
Enters on a close above an ATR channel with ADX confirmation. Scales out at 1R and trails the remainder. Uses a fixed ATR-based stop for consistent risk per trade.
Support and Resistance Bounce
Buys near a defined support zone after a rejection candle and rising RSI. Exits at the next resistance level or on a break of support. Adds a time stop to avoid dead trades.
Volatility Regime Switcher
Runs trend rules when volatility is expanding and switches to mean reversion when volatility contracts. Uses simple thresholds like ATR percentile. Helps avoid forcing one style in all markets.
Portfolio Rebalance
Rebalances a basket back to target weights on a weekly schedule. Adds drift bands so small moves do not trigger trades. Useful for long-term ETF allocations.
Mean Reversion with Time Stop
Buys a pullback after a sharp move, then exits on a return to a moving average. If price does not revert within 10 bars, it closes the position. Keeps capital from getting stuck.
Stop Loss and Trailing Take Profit
Adds a hard stop plus a trailing take profit that tightens as gains grow. Example: start with 3% stop, then trail 1.5% once profit exceeds 6%. Works as a risk layer for many systems.
Rather than writing and maintaining scripts, you express conditions in plain rule blocks and keep them readable. If you are comparing tradestation algo trading approaches, this lets you prototype faster: for instance, buy when RSI(14) < 30 on 1H, sell when RSI > 55, and add a 2% stop loss. You can then clone the rule and test RSI < 25 versus < 35 to see sensitivity.
Start with historical checks to validate the idea and spot obvious failure modes. Next, run the same rules in a simulated environment to confirm order timing, session constraints and symbol behavior. Once results look stable, switch to live execution with smaller size and scale up gradually.
Risk is easiest to manage when it is explicit. You might cap any single position at 5% of equity, limit total exposure to 40%, and stop trading for the day after a 2% drawdown. Another common setup is a trailing stop that activates only after a 4% unrealized gain, so early noise does not shake you out. These controls are simple to audit because they live inside the rule.
Logs, alerts and rule status make it clear what is running and why. When a condition fails, you see the exact check that blocked the order. This reduces guesswork and helps you iterate with discipline.
Launch BotFAQ
Lars H.
Priya K.
Diego R.
Sakura O.
Amelia C.
Kofi A.
Equities behave differently across the open, midday and close. Schedule rules to trade only when liquidity is highest, or avoid the first 5 minutes if your setup is sensitive to spreads. Time filters often improve consistency more than new indicators.
If two bots can buy the same symbol, define precedence with mutual exclusions or allocation limits. Keep one rule responsible for entries and another for risk exits only when possible. This makes performance attribution much clearer.
Templates accelerate setup, but your edge comes from constraints and context. Adjust thresholds, add a volatility filter, and define when the rule should stand down. Small changes like a time stop or volume condition can materially change behavior.
Track win rate, average win to loss and maximum drawdown per rule. If drawdown exceeds your tolerance, reduce size or pause and retest. Consistent review beats constant tinkering.
Increase size only after stable results across different weeks. Many traders scale in steps, such as 0.5% risk per trade to 0.75%, then 1%, while keeping the same stop distance logic. This keeps growth controlled.
Pick a template, set your symbols, define risk limits and run a quick backtest. Then paper trade for a week to confirm fills and behavior. When you are ready, switch on live execution and let the system do the repetitive work.
Get Started
