lgbm-crypto-ev-entry

LGBM Crypto Expected-Value Entry Classifier

Overview

This model is a LightGBM-based binary classifier trained to identify high-probability long entry points in cryptocurrency markets based on engineered OHLCV features.

The model outputs a probability representing whether a trade has positive expected value over a fixed future horizon, given current market conditions.

It is designed as an entry signal component, not a full trading system.

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Intended Use

• Identifying high-confidence trade entry points
• Research into ML-driven alpha signals
• Use as a signal input for rule-based or reinforcement-learning trading systems
• Educational and experimental quantitative finance projects

Not intended for:

• Direct execution without risk management
• Standalone portfolio management
• Live trading without additional validation

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Data

• Assets: BTC_USDT, ETH_USDT (Binance spot)
• Frequency: 1-minute OHLCV bars
• Time period: Historical Binance data (multi-year)
• Source: Public Binance data via CryptoDataDownload

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Features (high-level)

The model uses engineered, asset-agnostic features including:

• Log returns over multiple horizons
• Rolling volatility estimates
• Moving averages and trend slopes
• ATR-based volatility
• Volume and trade-count z-scores

All features are computed using only past information (no leakage).

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Labels

The target label represents whether a hypothetical long trade achieves positive expected value over a fixed future horizon, accounting for transaction costs.

This is not a directional price prediction.

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Model Details

• Model type: LightGBM Gradient Boosted Trees
• Objective: Binary classification (expected value > 0)
• Loss: Binary log loss
• Training style: Time-based train/validation split
• Evaluation: AUC, log loss, walk-forward backtests

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Performance Summary

Typical validation metrics (varies by window):

• AUC: ~0.55
• Log loss: ~0.68

Despite modest AUC, the model demonstrates positive expectancy when thresholded, consistent with real-world trading signals.

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Usage Example

import joblib
import pandas as pd

bundle = joblib.load("lgbm_ev_classifier.joblib")
model = bundle["model"]
feature_cols = bundle["feature_cols"]

# df must already contain engineered features
df["prob"] = model.predict(df[feature_cols])