As I continue to build out my algorithmic trading models, here is an ongoing list of features I find would be helpful in elucidating high probability trade setups. These may or may not be correct — but these are the features I am experimenting with.
- Analyzing tick data on a stock’s price
- Close, open, high, low prices
- Trade volume
- Volume profile distribution
- Generating real-time averages
- Simple Moving Average (SMA)
- Particularly a 20-day window
- Exponential Moving Average (EMA)
- Particularly an 8-day window
- Gauging any momentum
- Relative Strength Index (RSI)
- Specifically margins for x where x ≤ 30 or x ≥ 70
- Moving Average Convergence Divergence (MACD)
- Assessing volatility
- Historical volatility
- Implied volatility
- Baking in the news
- Earnings announcements
- Mergers and acquisitions
- Analyzing order books
- Focus on orders that were never executed
- Isolate any large trade volume outliers in an order book
- Determine whether the standard deviation of unexecuted order book trades exceeds a certain k-factor
- Assess for any skewness
- Apply kurtosis
- Bid-ask spread (seeking any imbalances)
- Order book depth
- Sentiment analysis (opinion mining)
- Correlation with other assets
- Correlation coefficient with relevant indices or assets
- Gauge the health of the economy
- Employment data
- Unemployment rates
- GDP growth
- Consumer spending
- Industrial production
- Inflation rates
- Use short-term and long-term interest rates to generate a unique score
- Find a temporal correlation with equity prices
- Time of day
- Day of the week
- Seasonal trends
- Incorporating and equity’s calculated risk factor
- Value at Risk (VaR)
- Conditional Value at Risk (CVaR)
Examples of features I’ve heard of but won’t use:
- Analyzing satellite data showing the parking lots of shopping malls or factories to analyze activity levels and extrapolate projections around business activity
- Analyzing the text of news reports
- Searching for corporate action announcements