backtesting trading strategies in python

I chose to backtest a strategy. On a periodic basis, the portfolio is rebalanced, resulting in the purchase and sale of portfolio holdings as required to align with the optimized weights. Supported order types include Market, Limit, Stop and StopLimit. To make it simple, the RSI is an index going from 0 to 100 that is supposed to indicate whether the product you are currently trading is overbought and oversold. Openinterest0 means the first column is the open interest column.

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All of them are described in Successful Algorithmic Trading by Michael.Halls-Moore (founder of QuantStart). Ohlc (Open High Low Close) candlesticks is pretty hard to find, even more if you are not willing to pay for it! Slope is row Beta against column as determinant. While most of the frameworks support US Equities data via YahooFinance, if a strategy incorporates derivatives, ETFs, or EM securities, the data needs to be importable or provided by the framework. Zipline Zipline is an algorithmic trading simulator with paper and live trading capabilities. Data feeds from csv/files, online sources or from pandas and blaze, filters for datas, like most successful stock trading strategies breaking a daily bar into chunks to simulate intraday or working with Renko bricks. Open source contributors are welcome.

Python, backtesting, libraries For Quant Backtrader/backtrader: Python, backtesting library for trading strategies Python, for Finance: Algorithmic, trading (article) - DataCamp