How to use TA-Lib with Python for technical analysis trading
This video will highlight the need of using a hopeful reliable technical analysis indicator library. My video explains the history of how this library was built and used in other popular projects.
Correct technical analysis indicators
I could go on and on about technical analysis but I do find it useful for timing your positions including entry and exit. In past videos on testing visually various technical indicators with JForex, you will clearly see why I use this process. For instance, I thought linear regression would be useful. By checking visually on charts, it clearly showed clearly how this indicator was not right. It was too sensitive on potential market direction at low levels e.g. 1 minute timeframe.
Technical indicator lessons
Using the chosen indicators it clearly showed how they are not as sensitive/noisy nor lagging. This was an important eureka moment. It is unfortunate I spent too many weeks to figure this out. Logging your positions in full detail of the indicators results you want to use is very helpful. This is a critical process you need to understand instead of guessing where your trading positions results will end up. Many retail traders will never to get to this point. As a result, it does help to have this point of view when testing your trading ideas.
Python faster to develop with
Also, the difference between developing 100% in the JForex API is very time consuming. As you get more confident with Python. you will be able to bang out trading scripts much faster. You will n longer be bogged down by the limitation of a broker or their API. As hinted in my video, this is why I would prefer to have all the market and trading data in one central hub (eg. Redis or CSV). Once again, i cover this in my video.
Here is one benefit of using Python for my own development!
I now have the ability to fully backtest with the backtrader Python package as opposed to what I did in the past.
Here are the links from the video
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