feature-engineering

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---
name: feature-engineering
description: Feature construction from market data for ML trading models including price, volume, on-chain, and microstructure features
---

# Feature Engineering for Trading ML

Feature engineering is the single highest-leverage activity in building ML trading
models. Model selection (XGBoost vs. neural net vs. logistic regression) matters far
less than the quality and diversity of input features. A simple model on great
features will outperform a complex model on raw prices every time.

This skill covers constructing, validating, and selecting features from market data
for use in classification (signal-classification) and regression models targeting
crypto/Solana token trading.

## Why Features Beat Models

Raw OHLCV data is non-stationary, noisy, and high-dimensional. Models trained
directly on price series will overfit. Feature engineering transforms raw data into
stationary, informative signals that capture distinct aspects of market behavior:

- **Compression**: Reduce thousands of price bars to dozens of descriptive statistics
- **Stationarity**: Convert non-stationary prices into stationary returns and ratios
- **Domain knowledge**: Encode trader intuition (support/resistance, volume climax)
  as computable quantities
- **Regime awareness**: Features that behave differently in trending vs. ranging
  markets help models adapt

## Feature Categories

### 1. Price Features

Derived purely from OHLCV price columns. These capture trend, momentum, and
volatility from the price series itself.

| Feature | Formula | Lookback |
|---------|---------|----------|
| `log_return` | `ln(close_t / close_{t-1})` | 1 bar |
| `abs_return` | `abs(log_return)` | 1 bar |
| `return_volatility` | `std(log_return, N)` | 20 bars |
| `momentum_N` | `close_t / close_{t-N} - 1` | 5, 10, 20 |
| `acceleration` | `momentum_5 - momentum_5[5]` | 10 bars |
| `high_low_range` | `(high - low) / close` | 1 bar |
| `close_position` | `(close - low) / (high - low)` | 1 bar |
| `gap` | `open_t / close_{t-1} - 1` | 1 bar |
| `rolling_skew` | `skew(log_return, N)` | 20 bars |
| `rolling_kurtosis` | `kurtosis(log_return, N)` | 20 bars |

### 2. Volume Features

Volume confirms or contradicts price movements. Divergences between price and
volume are among the most reliable signals in short-term trading.

| Feature | Formula | Lookback |
|---------|---------|----------|
| `volume_ratio` | `volume_t / mean(volume, N)` | 20 bars |
| `volume_ma_ratio` | `sma(volume, 5) / sma(volume, 20)` | 20 bars |
| `obv_slope` | `slope(OBV, N)` | 10 bars |
| `vwap_deviation` | `(close - VWAP) / VWAP` | intraday |
| `volume_acceleration` | `volume_ratio_t - volume_ratio_{t-1}` | 21 bars |
| `buy_volume_ratio` | `buy_volume / total_volume` | 1 bar |
| `dollar_volume` | `close * volume` | 1 bar |
| `volume_cv` | `std(volume, N) / mean(volume, N)` | 20 bars |

### 3. Technical Features

Standard technical indicators computed via `pandas-ta`. Use the `pandas-ta` skill
for full parameter documentation.

| Feature | Source | Lookback |
|---------|--------|----------|
| `rsi` | RSI(14) | 14 bars |
| `macd_histogram` | MACD(12,26,9) histogram | 33 bars |
| `bb_position` | `(close - BB_lower) / (BB_upper - BB_lower)` | 20 bars |
| `bb_width` | `(BB_upper - BB_lower) / BB_mid` | 20 bars |
| `atr_ratio` | `ATR(14) / close` | 14 bars |
| `adx` | ADX(14) | 14 bars |
| `stoch_k` | Stochastic %K(14,3) | 14 bars |
| `cci` | CCI(20) | 20 bars |
| `mfi` | MFI(14) | 14 bars |
| `supertrend_direction` | Supertrend direction (+1/-1) | 10 bars |

### 4. Microstructure Features

Derived from trade-level data (individual swaps/transactions). Require on-chain
or DEX API data.

| Feature | Description |
|---------|-------------|
| `trade_count_ratio` | Trades this bar / avg trades per bar |
| `avg_trade_size` | Mean trade size in USD |
| `large_trade_pct` | % of volume from trades > $10k |
| `unique_traders` | Count of distinct wallet addresses |
| `buy_count_ratio` | Buy trade