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language: en
license: mit
tags:
- finance
- trading
- cryptocurrency
- lightgbm
- tabular
- time-series
- quantitative-finance
---
# 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
```python
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])
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