vectorbt

Agentic coding skills for backtesting trading strategies using VectorBT. Supports Indian, US, and Crypto markets with realistic transaction cost modeling, TA-Lib indicators, QuantStats tearsheets, and 12 ready-made strategy templates.

A failure-aware harness for reproducible quantitative-strategy research, with typed agent workflows and inspectable experiment memory.

Backtest manager in VSCode Extension

VectorBT Backtesting

A strategy tester for MetaTrader5 in Python language

Web app for pandas-ta indicators

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.

A backtesting web app for walk-forward optimization with common indicator strategies.

Nine-agent AI framework automating quant research from hypothesis generation through leakage-aware backtesting

A-share AI quant research with PIT data, walk-forward validation, DeepSeek agents & continuous paper trading｜中国 A 股 AI 量化研究与持续模拟盘

GUI für VectorBT Pro und KI-gestützte Research-Werkbank: algorithmische Crypto-Trading-Strategien als JSON-Spec entwickeln, in Multiparameter-Läufen backtesten und über Test-Sets und Leaderboard vergleichen. Kein Live-Trading.

A professional, reproducible environment for researching, backtesting, and executing algorithmic trading strategies.

A comprehensive framework for developing and backtesting quantitative trading strategies.

Nous Ergon — signal quality analysis, portfolio simulation, and autonomous parameter optimization via sweep + VectorBT

Node-based drag-and-drop trading strategy builder with VectorBT backtesting

One residual GRU per stock for 1,726 Taiwan stocks, per-stock backtests and a screening rule. The README documents why the stored results are not realistic (look-ahead, zero costs, in-sample selection).

Local AI Investing Platform — v3.1 Master Spec. Hybrid AI-assisted quant. LangGraph + Temporal, DeepSeek R1 + Qwen 2.5, VectorBT + Nautilus, Next.js 16 PWA.

Everything you need to know for quantitative analysis using Python.

Multi-agent LLM portfolio management system with a 21-year walk-forward backtest (2005–2025). Diploma thesis, University of Thessaly.

This quantitative pipeline engineers and compares two models: a baseline using news sentiment (FinBERT) and an enhanced ensemble model adding options volatility. Both are validated with rigorous backtesting and Fama-French analysis to prove the superiority of the multi-factor ensemble strategy.