Alternative Data Sets

• Momentum: Trading on trends or continuation of price movements.
• Mean Reversion: Identifying assets that have deviated significantly from their historical mean price and anticipating a return to the mean.
• Breakouts: Buying or selling when an asset's price moves outside defined support or resistance levels.

• Liquidity: Assessing volume and bid-ask spreads to ensure cost-effective execution.
• Unusual Volume Spikes: Capitalizing on significant volume increases as a precursor to price movements.

• Moving Averages: Simple (SMA), Exponential (EMA), and crossovers as signals.
• Relative Strength Index (RSI): Overbought or oversold conditions.
• Bollinger Bands: Price volatility and deviation from the mean.
• MACD (Moving Average Convergence Divergence): Momentum and trend direction.

• Earnings Reports: Trading on earnings announcements and revisions.
• Economic Indicators: GDP, employment data, inflation, etc.
• Valuation Metrics: Price-to-earnings (P/E), price-to-book (P/B), etc.

• Order Book Dynamics: Analyzing bid/ask levels, imbalances, and order flow.
• Latency Arbitrage: Exploiting microseconds of information lag.
• Quote Manipulations: Detecting spoofing or layering activity.

• Statistical Arbitrage: Trading pairs or baskets of correlated securities.
• Event Arbitrage: Trading on mergers, acquisitions, or other corporate events.
• Cross-Exchange Arbitrage: Exploiting price differences across different markets or exchanges.

• News Sentiment: Using NLP (Natural Language Processing) to interpret news or social media sentiment.
• Reddit and Forums: Monitoring retail sentiment on platforms like WallStreetBets.
• Earnings Call Sentiment: Parsing management tone during earnings calls.

• Satellite Data: Monitoring parking lots, crop conditions, or shipping activity.
• Web Traffic: Analyzing online metrics like search trends, e-commerce sales.
• Social media Trends: Trending topics and hashtags impacting market sentiment.

• Volatility: Using the VIX or other measures to adjust portfolio exposure.
• Beta Hedging: Managing exposure relative to market movements.
• Drawdown Limits: Algorithms pausing trading when certain thresholds are breached.

• Pattern Recognition: Identifying and exploiting recurring patterns in market data.
• Predictive Modeling: Using supervised or unsupervised machine learning for price prediction.
• Feature Engineering: Combining factors (like price, volume, and news) to create new predictive variables.

• Regulatory News: Trading based on new policies or regulations.
• Geopolitical Events: Analyzing impacts of wars, sanctions, elections, etc.
• Interest Rates: Central bank actions influencing forex and equity markets.

• Intraday Patterns: Taking advantage of typical opening or closing market behavior.
• Seasonality: Exploiting yearly cycles like the "Santa Claus Rally" or summer slowdowns.
• Options Expiry: Managing positions based on time decay and gamma exposure.

Algorithms leverage a mix of historical data, real-time signals, and advanced models to execute trades. The choice of factors depends on the algorithm's goals, market, and the sophistication of the underlying technology.