hmmm… Looking more into it.
1. Data Cleaning and Preprocessing
a. Handle Missing Data
• Identify gaps in OHLC data using visualization or null checks.
• Techniques to fill gaps:
• Forward/Backward Fill: Fill with the nearest available data.
• Interpolation: Linear or spline methods for smoother filling.
• Drop Rows: If missing values are minimal and non-critical.
• Research:
• “A Comparison of Techniques for Handling Missing Data in Time Series” (Journal of Time Series Analysis).
b. Adjust for Corporate Actions
• Adjust prices for stock splits, dividends, and rights issues.
• Use adjustment factors from reliable sources like Bloomberg or Yahoo Finance.
c. Outlier Detection
• Identify price or volume spikes due to anomalies (e.g., data errors).
• Methods:
• Z-Score/Standard Deviation Filtering.
• Advanced: Isolation Forest or DBSCAN for density-based outlier detection.
d. Standardization
• Normalize features like price, returns, and volume to comparable scales.
• Standard Scaler (Z-Score) or Min-Max scaling based on clustering needs.
2. Feature Engineering
a. Basic Features
• Returns: Daily, weekly, and cumulative.
• Volatility: Standard deviation, Average True Range (ATR).
• Moving Averages: Simple, Exponential, and Weighted.
• Momentum Indicators: RSI, MACD, Stochastic Oscillators.
• Research:
• “The Use of Moving Averages in Technical Analysis” (Technical Analysis of Stocks & Commodities Journal).
b. Risk-Adjusted Metrics
• Sharpe Ratio: Reward-to-risk ratio.
• Sortino Ratio: Penalizes downside risk more heavily.
• Research:
• “Sharpe and Beyond: An Empirical Analysis of Risk-Adjusted Performance Metrics” (Financial Analysts Journal).
c. Market and Sector Features
• Compute beta values to market indices.
• Use sector/industry classification for grouping.
d. Derived Features
• Volume Indicators: On-Balance Volume, Chaikin Money Flow.
• Trend Strength: Average Directional Index (ADX).
e. Dimensionality Reduction for Clustering
• Use Principal Component Analysis (PCA) to reduce feature redundancy.
3. Pattern and Correlation Analysis
a. Distributions
• Visualize distributions of returns, volatility, etc. using histograms and KDE plots.
• Research:
• “Modeling Financial Returns: Insights from Fat-Tail Distributions” (Quantitative Finance).
b. Correlations
• Correlation Matrices: Visualize Pearson/Spearman correlations.
• Rolling Correlations: Capture time-varying relationships.
• Tools: Use heatmaps with Seaborn or D3.js for interactive exploration.
• Research:
• “Dynamic Correlation Models for Financial Time Series” (Journal of Econometrics).
c. Pattern Detection
• Identify recurring patterns using Time Series Motif Discovery.
• Research:
• “Efficient Discovery of Frequent Patterns in Time Series Data” (Knowledge and Information Systems).
d. Seasonality and Trends
• Use STL decomposition (Seasonal and Trend decomposition using LOESS).
• Research:
• “Detecting and Understanding Seasonal Patterns in Stock Returns” (Journal of Finance).
e. Anomaly Detection
• Methods:
• Z-Score, Bollinger Bands for deviations.
• Advanced: Autoencoders for unsupervised anomaly detection.
• Research:
• “Anomaly Detection in Financial Time Series Using Deep Learning Models” (arXiv).
4. Resources for Exploration
Quantitative Finance Stack Exchange
• Threads:
• “Best Practices for Feature Engineering in Quant Finance”.
• “Time Series Preprocessing for Financial Models”.
• Ask and explore discussions on advanced preprocessing.
Research Repositories
• arXiv: Search for papers on “OHLC preprocessing” or “feature extraction in finance.”
• SSRN: Database of finance-related academic papers.
Tools
• Python Libraries:
• tsfresh and pmdarima: For automated feature extraction.
• finta: For technical indicators.
• pyod: For anomaly detection.
Would you like me to find specific research papers or expand on any of these techniques?