X-Trend: Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies
Automated Python implementation of a Mount Lucas Management (MLM) style trend-following strategy for futures using the IBKR API (ib_insync). Calculates 200-day MA signals on Continuous Futures, filters trades by volatility, and executes on front-month contracts.
Advanced trend detection and labelling for time series with Python
Modular Python scaffold for systematic trend-following/managed-futures research: validates multi-asset futures data, builds continuous contracts, runs configurable backtests via CLI/API, simulates trading costs and rolls, and delivers institutional-grade analytics and reports.
A Python-based framework for back testing, optimizing and identifying cryptocurrency trading strategies using historical data.
Do more than HODL. Simulate hedge-fund-like trend following strategies on popular crypto coins.
📈 Automated crypto trend scanner & backtester with Telegram alerts and 3Commas alignment.
The project explores trend-following techniques using L1 and L2 regularization.
Quant research: early-breakout momentum on SP500 — 9.4% CAGR vs SPY's 7.9% on 26-year survivorship-bias-free backtest (1,081 historical constituents). Automated Alpaca paper trading. 305 tests. Not financial advice.
Algorithmic trading framework for QQQ: backtesting, live execution (IBKR + Alpaca), and dynamic risk management. 28% CAGR | -14% max drawdown (2009–2025).
Event-driven Python trading engine implementing EMA-based signal generation and risk-controlled execution.
A real-time, automated trend-following dashboard for Hyperliquid perps built with Python
🔥 Top Crypto AI Trend Predictor 2026 – Free Automated Insights, Backtesting & Signals 🚀
Unified trend-following systems implemented in pure NumPy with comprehensive validation and testing. This implementation follows the theoretical framework presented in Sepp & Lucic (2025) "The Science and Practice of Trend-following Systems".
Python implementation of Risk Parity (ERC) optimization with tactical Trend-Following overlays. Empirical analysis based on Bhansali et al. (2024).
A Python tool for calculating the Hurst Exponent of financial time series.
📈 Develop and backtest systematic trend-following strategies with managed futures in Python, enabling efficient investment research and analysis.
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