stock-price-predictor-xgboost

XGBoost 5-Day Stock Price Predictor for Swing Trading

Author: mohan170802
Models: mohan170802/stock-price-predictor-xgboost

A production-ready XGBoost ensemble trained to predict 5-day-ahead stock price movements for swing trading. Two models per ticker:

• Regression model: predicts log-return over next 5 trading days
• Classification model: predicts probability that price goes UP over next 5 trading days

Supported Tickers

Model Architecture

• Library: XGBoost 3.2.0
• Type: Gradient Boosted Decision Trees (native Booster API)
• Features: 66 engineered features per sample
• Training data: 15 years of daily OHLCV (2011–2026)
• Validation: Expanding-window walk-forward (5 folds, time-aware)

Feature Engineering

Performance Summary

Key insight: Exact price prediction (R² ≈ 0) is extremely hard — the directional signal at ~57% beats random chance (50%) and is actionable for swing trades when combined with risk management.

Per-Ticker Breakdown

Files in this Repository

summary.json              # Complete training metrics & feature list
TICKER_reg_5d.json        # XGBoost regression model (log-return target)
TICKER_clf_5d.json        # XGBoost classification model (direction target)
TICKER_reg_5d.pkl         # Pickled Booster (convenience)
TICKER_clf_5d.pkl         # Pickled Booster (convenience)
stock_predictor.py        # Training & feature engineering code
inference.py              # Simple inference script
README.md                 # This file

Usage

Quick inference (single ticker)

import xgboost as xgb
import yfinance as yf
import numpy as np
import pandas as pd

# 1. Load model from Hub
from huggingface_hub import hf_hub_download
model_path = hf_hub_download(
    repo_id="mohan170802/stock-price-predictor-xgboost",
    filename="SPY_reg_5d.json"
)
model = xgb.Booster()
model.load_model(model_path)

# 2. Fetch latest data & engineer features
# (see stock_predictor.py in source for full feature pipeline)
# ...

# 3. Predict
X = xgb.DMatrix(latest_features, feature_names=feature_names)
predicted_log_return = model.predict(X)[0]
predicted_price = current_price * np.exp(predicted_log_return)

Batch inference with the inference script

git clone https://huggingface.co/mohan170802/stock-price-predictor-xgboost
cd stock-price-predictor-xgboost
python inference.py --ticker SPY

Trading Notes

• This is NOT financial advice. Models have ~57% directional accuracy — use strict stop-losses and position sizing.
• Best used as one signal among many (combine with macro analysis, earnings calendar, options flow, etc.).
• Retrain monthly as market regimes evolve; expanding-window CV already simulates this.
• Regression model output is log-return; convert with price * exp(pred).

Training Details

• Data source: Yahoo Finance via yfinance
• Feature count: 66 (no leakage, all features computed from strictly past data)
• Validation: Expanding-window walk-forward (5 folds)
• Hyperparameters: shallow trees (max_depth=5), strong regularization (α=0.5, λ=1.0), learning_rate=0.03
• Hardware: CPU (no GPU required)
• Training time: ~5 minutes for all 10 tickers

License

MIT — use at your own risk for research & educational purposes.