Backtesting & live trading engine built for AI agents. Zipline rebuilt on Polars with a native MCP server for Claude, Cursor & Codex. Stocks, ETFs, futures.
MT5 Live Trend Direction Signal Hub 2026 – Real-Time Forex & CFD Alerts
X-Trend: Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies
The 10-line CTA: a trend-following strategy in ten lines of code, and four refinements measured in Sharpe ratio, kurtosis and trading costs
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.
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
Closed-form trend-following analytics, reference system implementations, and reproducible futures evidence in Python for quantitative researchers and practitioners
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.
Building blocks for trend-following CTA strategies: scale-free oscillators, volatility-adjusted returns, matrix shrinkage and a position engine
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.
Trend-following Expert Advisor for XAUUSD built in MQL5 — ATR-based risk management, FTMO-compliant circuit breakers, 4-year backtest
EMA-VWAP crossover trading strategy in Python
A Python-based framework for back testing, optimizing and identifying cryptocurrency trading strategies using historical data.
Algorithmic trading framework for QQQ: backtesting, live execution (IBKR + Alpaca), and dynamic risk management. 28% CAGR | -14% max drawdown (2009–2025).
Falsification-oriented multi-instrument backtest of a multi-timeframe Smart-Money-Concepts price-action strategy — tested three disjoint ways (walk-forward OOS −0.339R, multiple-testing-corrected 5-instrument replication 0/210, random-entry nulls), all negative. Zero-look-ahead engine, honest statistics.
Personalized frontier intelligence for discovering emerging AI & tech signals before they become obvious — from signal discovery to research and build directions.
Deterministic, drawdown-controlled ETF allocation framework (Convex Core) — reproducibility companion to the research paper: model engine, report code, tests, and computed result artifacts. Hypothetical/backtested; not investment advice.
To associate your repository with the trend-following topic, visit your repo's landing page and select "manage topics."