The combination of QuantConnect's data and backtester, Python, and Jupyter have far more flexibility and power than Tradingview. Especially since the code for any relevant features could be straightforwardly converted into modules for use in production algorithms, far more programming power and data would be available, and documenting one's findings come naturally with the use of said tool. I thought it would be far superior to use the included Jupyter research environment for exploratory feature research. Complicated algorithms cannot be researched and coded here adequately, but Pinescript does make visualizing certain features quick and relatively straightforward. To accomplish this I find it important to visualize what the code is doing to avoid errors and determine relevance of features, especially since many of the features I'm exploring-I or others-have identified visually through years of discretionary trading.Ī popular charting platform, Tradingview, allows one to code scripts and algorithms in "Pinescript"-an easy to use but not very powerful "programming language". I became interested in quantitative trading largely to "scientifically" explore the relevance of many technical charting principles I've used and come across (something quite time-consuming to do manually), build a library of materially useful modules, then combine those modules to make profitable automated trading strategies.
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