crypto-15min-direction-classifier

Crypto 15-Minute Direction Classifier

A time-series classification model that predicts whether Bitcoin (BTC/USDT) price will move up or down over the next 15-minute interval using multivariate historical market data.

Model Overview

Performance

Note: 15-minute crypto price direction prediction is an extremely hard problem due to market efficiency at short timeframes. The model consistently edges above random chance (50%), demonstrating a non-trivial but small signal. This pipeline is valuable as a complete data engineering and feature extraction system for further research.

Data Sources

• WinkingFace/CryptoLM-Bitcoin-BTC-USDT - BTC 1-min OHLCV + 15 technical indicators
• WinkingFace/CryptoLM-Ethereum-ETH-USDT - ETH 1-min OHLCV + 15 technical indicators

Features (49 per timestep)

BTC & ETH (separately)

• Price: open, high, low, close
• Volume: volume
• Moving Averages: MA_20, MA_50, MA_200
• Momentum: RSI, %K, %D, ADX, ATR
• Trend: MACD, Signal, Histogram, Trendline
• Volatility: BL_Upper, BL_Lower, MN_Upper, MN_Lower

Cross-Asset Engineered

• eth_btc_ratio - ETH/BTC price ratio
• btc_ret_1m, eth_ret_1m - 1-minute returns
• btc_vol_ma20, eth_vol_ma20 - 20-period volume MA
• btc_range, eth_range - Normalized price range

Pipeline

1. Load & Merge BTC and ETH 1-minute datasets on timestamp
2. Engineer Features - Add returns, ratios, ranges, volume MAs
3. Create Windows - 60-minute lookback → predict next 15-minute direction
4. Clean - Drop NaN/Inf, standardize per-feature
5. Split - 70/15/15 temporal train/val/test (no data leakage)
6. Train - Logistic Regression + Random Forest baselines

Usage

import pickle
import numpy as np

# Load model
with open("model.pkl", "rb") as f:
    model = pickle.load(f)

# Load preprocessing artifacts
mean = np.load("feature_mean.npy")
std = np.load("feature_std.npy")
valid = np.load("valid_cols.npy")

# X shape: (samples, 60 minutes, 49 features)
X_flat = X.reshape(X.shape[0], -1)      # flatten to 2940 features
X_flat = X_flat[:, valid]               # keep valid columns
X_norm = (X_flat - mean) / std            # standardize

# Predict
preds = model.predict(X_norm)            # 0=down, 1=up
probs = model.predict_proba(X_norm)[:, 1]  # probability of up

Files

Limitations

• Market Efficiency: 15-min prediction is near-random walk; edge is small
• No Costs: Evaluation ignores fees, slippage, spread
• Historical Data: Trained on 2017-2020 data; may not generalize to current regimes
• Simple Models: Deep learning (Conv-LSTM, TCN, Transformer) may improve results

Future Work

1. Deep Learning: Conv-LSTM, Temporal CNN, or Transformer architectures
2. More Data: Order book, funding rates, on-chain metrics, sentiment
3. Multi-Scale: Combine 1-min, 5-min, 15-min, 1-hour features
4. Regime Detection: Train separate models for bull/bear/sideways markets
5. Cost-Aware Evaluation: Incorporate transaction costs in metric

License

MIT License

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