Harness machine learning for stock automation with the AI stock platform. Deploy intelligent strategies across brokers like Alpaca, Trading212 and WeBull with adaptive algorithms.
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traders using AI automation
1.2M+
ML-powered strategies executed
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Transform stock trading with an AI stock platform that applies machine learning to equity automation across brokers like Alpaca, Charles Schwab and Robinhood. Intelligent algorithms adapt to changing market conditions while executing systematic entries and exits. Your funds stay secure with brokers while ML models optimize timing and position sizing based on historical patterns.
The system processes vast amounts of equity data to identify patterns invisible to manual analysis. Machine learning models adjust strategy parameters based on volatility regimes, sector rotation and earnings cycles. Algorithms continuously refine entry conditions and risk parameters as market dynamics evolve.
Connect Trading212, WeBull, Public and Tastytrade through a unified interface that routes orders intelligently. The platform balances execution across brokers to optimize fills while maintaining consistent risk controls. Real-time synchronization ensures your ML strategies operate seamlessly across all connected accounts.
Templates optimized for equity markets
Access machine learning templates designed specifically for equity markets including earnings drift riders, sector momentum switchers and dividend capture strategies. Each template incorporates AI-driven risk controls and position sizing optimized for stock volatility patterns.
Intelligent risk management built-in
Every strategy includes adaptive stop losses that tighten based on volatility measurements and correlation analysis. The system monitors portfolio heat and sector concentration to prevent overexposure while maintaining diversification across your equity positions.
Earnings Drift Rider
Captures post-earnings momentum using ML sentiment analysis and options flow data. Enters positions after earnings beats with adaptive holding periods. Exits based on momentum decay signals and volatility normalization patterns.
Sector Momentum Switcher
Rotates between sector ETFs using relative strength algorithms and economic indicators. ML models identify sector leadership changes before they become obvious. Rebalances monthly with momentum confirmation filters.
High Beta Risk-On Basket
Builds baskets of high-beta stocks during risk-on periods identified by VIX levels and credit spreads. Uses machine learning to optimize basket composition. Exits when risk-off conditions emerge from macro indicators.
Low Vol Defensive Rotation
Switches to defensive stocks during high volatility periods using adaptive volatility thresholds. ML models identify regime changes before major drawdowns. Emphasizes dividend aristocrats and low-beta names during uncertainty.
Pairs Trading Cointegration
Identifies cointegrated stock pairs using statistical analysis and executes mean reversion trades. Machine learning optimizes pair selection and hedge ratios. Monitors correlation breakdown to exit positions early.
Earnings Drift Rider
Captures post-earnings momentum using ML sentiment analysis and options flow data. Enters positions after earnings beats with adaptive holding periods. Exits based on momentum decay signals and volatility normalization patterns.
Sector Momentum Switcher
Rotates between sector ETFs using relative strength algorithms and economic indicators. ML models identify sector leadership changes before they become obvious. Rebalances monthly with momentum confirmation filters.
High Beta Risk-On Basket
Builds baskets of high-beta stocks during risk-on periods identified by VIX levels and credit spreads. Uses machine learning to optimize basket composition. Exits when risk-off conditions emerge from macro indicators.
Low Vol Defensive Rotation
Switches to defensive stocks during high volatility periods using adaptive volatility thresholds. ML models identify regime changes before major drawdowns. Emphasizes dividend aristocrats and low-beta names during uncertainty.
Pairs Trading Cointegration
Identifies cointegrated stock pairs using statistical analysis and executes mean reversion trades. Machine learning optimizes pair selection and hedge ratios. Monitors correlation breakdown to exit positions early.
Beta Hedged Momentum
Captures momentum while hedging market beta exposure through ETF shorts. Uses ML to optimize hedge ratios based on rolling correlations. Maintains market-neutral exposure during momentum trades.
Minimum Variance Rebalance
Optimizes portfolio weights to minimize variance using machine learning risk models. Rebalances monthly based on changing correlation matrices. Incorporates transaction costs and liquidity constraints in optimization.
Sharpe Targeting Rebalance
Dynamically adjusts portfolio allocation to target specific Sharpe ratios using ML forecasting. Increases risk during favorable conditions and reduces exposure during uncertainty. Monitors realized vs expected Sharpe continuously.
Dividend ETF Seasonality
Captures seasonal patterns in dividend ETF performance using historical analysis and ML pattern recognition. Times entries around ex-dividend dates and distribution announcements. Optimizes holding periods for tax efficiency.
Tax-Loss Harvest Rebalance
Automatically harvests tax losses while maintaining portfolio exposure through similar securities. ML algorithms identify optimal replacement securities to avoid wash sale rules. Maximizes after-tax returns through systematic loss realization.
Machine learning algorithms calculate optimal position sizes based on volatility forecasts, correlation analysis and portfolio heat measurements. The system adjusts allocation dynamically as market conditions change while respecting maximum position limits. Risk budgets adapt to realized volatility ensuring consistent exposure across different market regimes.
AI models continuously scan equity markets for opportunities and risks sending intelligent alerts when conditions match your strategy parameters. The platform identifies unusual options flow, earnings surprises and sector rotation signals in real-time. Smart notifications filter noise to surface only actionable insights for your automated strategies.
Test strategies against historical data using walk-forward analysis and out-of-sample validation techniques. ML models prevent overfitting by validating performance across different market cycles and volatility regimes. Backtests incorporate realistic transaction costs, slippage and liquidity constraints for accurate performance estimates.
Optimize allocation across multiple brokerage accounts using modern portfolio theory enhanced with machine learning forecasts. The system considers account-specific constraints, tax implications and execution costs when rebalancing. Cross-broker coordination ensures optimal portfolio construction while maintaining diversification requirements.
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Begin your journey by connecting a brokerage account and exploring pre-built templates designed for equity markets. The platform walks you through strategy selection with risk profiling questionnaires and backtesting tools. Paper trading lets you validate approaches before committing real capital to automated execution.
As confidence grows expand automation across multiple brokers for diversification and execution optimization. The system coordinates strategies across accounts while maintaining overall portfolio balance. Cross-broker analytics provide unified performance tracking and risk monitoring.
Access institutional-grade tools including factor analysis, regime detection and alternative data integration. Machine learning models incorporate earnings estimates, analyst revisions and options flow data for enhanced signal generation. Custom model training lets experienced users build proprietary algorithms.
Comprehensive risk systems monitor portfolio heat, sector concentration and correlation exposure in real-time. Automated stop losses adapt to volatility while position sizing algorithms prevent overallocation to individual names. Emergency controls let you halt all strategies instantly if market conditions deteriorate.
Machine learning models retrain periodically using fresh market data to maintain performance as conditions evolve. The system identifies when strategies need parameter adjustments or complete replacement. Performance attribution helps you understand which signals drive returns over time.
Access comprehensive documentation, video tutorials and community forums to maximize your automation success. Regular webinars cover advanced topics like factor investing, risk parity and alternative data integration. Support teams help troubleshoot technical issues and optimize strategy performance.
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