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

Simple btc trend following algorithm, using cbpro api.

Automated stock screener scanning 3,800+ stocks daily for confirmed Stage 2 uptrends passing 8 strict criteria. Features: smart caching (74% fewer API calls), market regime filtering, automated stop-loss calculation, and GitHub Actions automation. Follows Mark Minervini's Trend Template.

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.

Production-grade Freqtrade fork for algorithmic trading on Hyperliquid. Multi-bot OHLCV/pairlist caching, PlateauSampler hyperopt, walk-forward with CPCV, custom hyperopt losses, liquidation detection, 32+ enhancements. Includes showcase strategies. Maintained by Freqtrade France.

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.

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.

Testfolio-compatible synthetic leveraged-ETF return series for Python, with proper borrow-cost modeling. Calibrated against real TQQQ to within 5%.

📈 Python scanner for liquid exchange-listed coins: volume/momentum gates, uniformity scoring, and strategy backtests. Ships a public JSON snapshot to a GitHub Pages–style dashboard.

Research-backed time-series momentum backtester that tests 12-month trend-following signals, volatility targeting, transaction costs, parameter robustness, and walk-forward evaluation across ETFs.

The project explores trend-following techniques using L1 and L2 regularization.

Structured corpus of 448 long-form finance and investing articles from PicturePerfectPortfolios.com, with full article text, metadata, content hashes, CSV/JSONL exports, schema, citation, license, manifest, checksums, and llms files.

Algorithmic trading framework for QQQ: backtesting, live execution (IBKR + Alpaca), and dynamic risk management. 28% CAGR | -14% max drawdown (2009–2025).

Algorithmic trading files.

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".

🚀 VIPER — Autonomous algorithmic trading bot for Bybit Perpetual Futures. 5 strategies, LLM validation, WebSocket real-time feed, Sniper entry, ATR-based SL/TP, Telegram commands, 24/7 daemon.