{"repo":"dimssrmdn01/gold-price-forecast-ml","free":true,"listed":false,"github":"https://github.com/dimssrmdn01/gold-price-forecast-ml","clone":"git clone https://github.com/dimssrmdn01/gold-price-forecast-ml.git","description":"End-to-end algorithmic trading terminal featuring PyTorch LSTM forecasting, NLP market sentiment analysis, Lasso ML feature selection, and vectorbt backtesting. Built for multi-asset quantitative research.","language":"Python","stars":16,"topics":["algorithmic-trading-dotnet","backtesting","data-science","financial-analysis","python","quantitative-finance","streamlit","yfinance","backtesting-engine","deep-learning"],"license":null,"category":"trading","readme_excerpt":"Empowering financial decisions through hybrid machine learning, robust backtesting, and Agentic AI. An institutional-grade quantitative dashboard for analyzing and forecasting financial asset volatility (default: XAU/USD, extensible to any Yahoo Finance ticker). This system merges strict machine learning validation, walk-forward algorithmic backtesting, and an autonomous LLM agent capable of real-time tool calling for fundamental and technical market analysis. Key Architectural Upgrades - Agentic LLM Integration. Groq API (LLaMA-3.3-70B) with strict function-calling: the agent can pull live ML projections, backtest metrics, and real-time market news (via DuckDuckGo Search) on its own, with an explicit guardrail against fabricating numbers when a tool hasn't been run yet. - Walk-Forward Validation. The vectorbt backtest engine chunks 5 years of historical data into rolling 1-year windows, reporting win rate, return, and max drawdown per period so strategy robustness is checked across bull, bear, and sideways regimes, not just one lucky window. - Data Leakage Prevention. Both the Lasso and PyTorch LSTM pipelines use strict train/test splits the LSTM's MinMaxScaler is fit only on the training slice, never on the full dataset, eliminating forward-looking bias. - Confidence Intervals & Model Persistence. Lasso predictions ship with a 95% confidence interval (Z = 1.96) derived from test-set RMSE. Trained models are cached to disk via joblib to skip redundant retraining. - Live Sign","default_branch":null,"files":null,"tree":[],"storefront":"/r/dimssrmdn01","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/dimssrmdn01/gold-price-forecast-ml/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}