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Python toolkit for quantitative finance: stock analysis, technical indicators, strategy backtesting, portfolio optimization, and financial modeling.

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.

Optimized Python fork of Backtrader for strategy backtesting and live trading, with vectorized and event-driven modes, multi-timeframe data, technical indicators, and portfolio analytics.

Trade backtesting engine for DCA (Dollar Cost Averaging) and Grid trading strategies with technical indicator support and historical market data simulation for

Composable Python toolkit for algorithmic trading on Polymarket crypto prediction markets. Plugin-based strategies, indicators, backtesting, and multi-source data feeds.

Select a supervised algorithm that can predict stock prices of historical data based on the predictors (statistical indicators). Accordingly formulate a trading strategy based on predicted values to generate orders on same historical training set to backtest how much portfolio would have increase... Read more

Backraded different trading strategies implemented using following indicators - SMA, EMA, WMA, RSI, MACD, MACD Histogram, Bollinger Bands

This project uses Python to create an optimally weighted stock portfolio by combining 7 common technical indicators, generating trading signals, backtesting the strategy, and aiming to outperform the standard buy-and-hold strategy of the SPY ETF.

End-to-end ML-enhanced Hierarchical Risk Parity (HRP) portfolio strategy for multi-crypto assets: from research and feature engineering (price normalization, technical indicators, on-chain metrics) to predictive modeling, backtesting, and live execution-ready allocation.

Built in C# & Winforms, QuantTrade provides a way to quickly build and backtest stock investment strategies using technical indicators such as the MACD, SMA, etc.