Transform equity research with AI stock analysis that processes market data, earnings reports and technical indicators automatically across major stock brokers.
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The platform processes earnings data, technical indicators and market sentiment automatically across Alpaca, Charles Schwab and Trading212. AI stock analysis scans thousands of stocks simultaneously while you focus on strategy rather than data collection. Machine learning models identify patterns in price action, volume and fundamental metrics that manual research might miss.
The platform runs continuous stock screens based on your criteria while adaptive algorithms refine results over time. You set parameters like P/E ratios below 15 or revenue growth above 20 percent and the system monitors thousands of equities. Smart filters learn from your selections to surface similar opportunities automatically.
Instead of manually reviewing quarterly reports the engine processes earnings releases, SEC filings and analyst updates within minutes. Machine learning extracts key metrics from financial statements while sentiment analysis evaluates management commentary. The system flags significant changes in guidance, margins or competitive positioning immediately.
Technical analysis powered by AI
Machine learning models identify chart patterns, support levels and momentum shifts across your watchlist simultaneously. The system recognizes head and shoulders formations, ascending triangles and breakout setups while calculating probability scores for each pattern.
Multi-timeframe analysis engine
Algorithms analyze stocks across daily, weekly and monthly timeframes providing context that single-period analysis misses while maintaining consistent risk parameters.
Moving Average Scalper
Buys when 20-day MA crosses above 50-day MA with volume 150% above average. Exits when momentum indicators show divergence. Effective for capturing short-term trends in liquid stocks.
RSI Oversold Rebound
Enters positions when RSI drops below 30 on daily charts with strong fundamentals. Exits at RSI 70 or 8% gain. Works well during market corrections with quality companies.
Earnings Drift Rider
Buys stocks showing positive earnings surprises with analyst upgrades. Holds for 5-10 days to capture post-earnings momentum. Targets companies with revenue beats and raised guidance.
Breakout Plus Retest
Enters on volume breakouts above resistance then adds on successful retests. Uses 2% stop loss with trailing exits. Focuses on stocks near 52-week highs with strong sectors.
Dividend ETF Seasonality
Accumulates dividend ETFs before ex-dividend dates in tax-advantaged periods. Rebalances quarterly based on yield spreads. Targets high-quality dividend aristocrats and REITs.
Moving Average Scalper
Buys when 20-day MA crosses above 50-day MA with volume 150% above average. Exits when momentum indicators show divergence. Effective for capturing short-term trends in liquid stocks.
RSI Oversold Rebound
Enters positions when RSI drops below 30 on daily charts with strong fundamentals. Exits at RSI 70 or 8% gain. Works well during market corrections with quality companies.
Earnings Drift Rider
Buys stocks showing positive earnings surprises with analyst upgrades. Holds for 5-10 days to capture post-earnings momentum. Targets companies with revenue beats and raised guidance.
Breakout Plus Retest
Enters on volume breakouts above resistance then adds on successful retests. Uses 2% stop loss with trailing exits. Focuses on stocks near 52-week highs with strong sectors.
Dividend ETF Seasonality
Accumulates dividend ETFs before ex-dividend dates in tax-advantaged periods. Rebalances quarterly based on yield spreads. Targets high-quality dividend aristocrats and REITs.
Momentum Pullback Entry
Buys strong stocks pulling back to 21-day EMA with intact uptrends. Exits on break below 50-day MA or 15% gain. Screens for stocks up 30% in 3 months with earnings growth.
Sector Rotation Strategy
Rotates between sector ETFs based on relative strength rankings. Holds top 3 performing sectors monthly. Includes technology, healthcare, financials and consumer discretionary rotation.
Support Resistance Bounce
Buys at established support levels with confirmation from volume and RSI. Targets resistance levels for exits. Uses 1.5% stops below support with 3:1 risk-reward ratios.
High Beta Growth Basket
Accumulates high-beta growth stocks during risk-on market environments. Monitors VIX levels and yield curve signals. Targets stocks with beta above 1.5 in trending markets.
Low Volatility Defense
Shifts to low-volatility stocks and defensive sectors during market stress. Includes utilities, consumer staples and healthcare. Uses VIX spikes above 25 as entry signals.
The engine tests strategies against years of historical data while accounting for survivorship bias and transaction costs. You see how rules would have performed during different market cycles including bear markets, recessions and sector rotations. The system adjusts for stock splits, dividends and delisting events to provide realistic performance estimates.
Machine learning optimizes position sizing and correlation analysis across your holdings automatically. The system identifies when positions become too concentrated in single sectors while suggesting rebalancing opportunities. Risk metrics update continuously as market conditions change keeping your portfolio aligned with target allocations.
Connect seamlessly with Alpaca, WeBull, E*TRADE, Robinhood and other major platforms through secure API connections. Your analysis flows directly into execution while maintaining non-custodial security. The system works with fractional shares, options and ETFs across different account types including IRAs and taxable accounts.
Build sophisticated stock screens combining fundamental metrics, technical indicators and machine learning scores. Set criteria like debt-to-equity ratios below 0.5, revenue growth above 15% and momentum scores in top quartile. The platform monitors thousands of stocks continuously updating results as new data becomes available.
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Create multi-layered screens combining growth metrics, value indicators and technical momentum simultaneously. The system processes criteria like revenue growth above 20%, P/E ratios below sector average and RSI between 40-60 across thousands of stocks. Custom scoring algorithms rank results while filtering out penny stocks and low-volume securities automatically.
Machine learning models calculate position sizes based on volatility, correlation and portfolio heat maps continuously. The system suggests stop-loss levels using Average True Range while monitoring sector concentration limits. Risk alerts trigger when individual positions exceed target allocations or when portfolio beta drifts outside acceptable ranges.
During quarterly reporting periods the platform processes hundreds of earnings releases simultaneously extracting key metrics and management guidance changes. Natural language processing identifies positive and negative sentiment shifts in conference calls while flagging revenue surprises and margin expansions. The system ranks earnings quality scores helping prioritize follow-up research.
Machine learning identifies which technical indicator combinations work best for different stock characteristics and market environments. The system tests thousands of parameter combinations across moving averages, oscillators and momentum indicators. Results show which setups produce highest win rates for growth stocks versus value plays during various volatility regimes.
Algorithms track relative strength across eleven market sectors identifying rotation patterns and leadership changes. The system monitors sector ETF performance, earnings revisions and economic indicators to predict shifts between growth and value styles. Rotation signals help time entries into emerging sector leaders while avoiding declining groups.
Machine learning models retrain on new market data improving pattern recognition and screening accuracy over time. The system adapts to changing market structures, new trading patterns and evolving fundamental relationships. Performance metrics track model effectiveness while user feedback helps refine screening algorithms and risk calculations.
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