lightweight-charts VS connector-x

link https://github.com/tradingview/lightweight-charts

My quick guess is that they're using this library:https://github.com/tradingview/lightweight-charts

Charting library in HTML5: https://www.tradingview.com/lightweight-charts/ https://github.com/tradingview/lightweight-charts

Tradingview charting library is the best OHLC charting library I’ve found by far.

The open source charting library TV offers is also very cool. Played around with it recently and have plans to use it in a side project soon.

Actually, the TradingView charting library is free https://www.tradingview.com/HTML5-stock-forex-bitcoin-charting-library/ couple that with your brokers API and a technical analysis library, and you can reproduce exactly what you see in TradingView

Surprised they didn’t point you to the charting library: https://www.tradingview.com/HTML5-stock-forex-bitcoin-charting-library/

What I went with. Biggest thing I haven't gotten working yet (because it's not supported out of the box) is ability to have indicators that need their own panels (oscillators like RSI, for example). Some people have implemented their own here: https://github.com/tradingview/lightweight-charts/issues/50

This was originally a blocker, however, we managed to set up a multi-stage Docker build to build from source. Here is the Github issue where we, along with community members, managed to solve it.

There's packages like connector-x and polars that do a lot of what you're mentioning out of the box. I used these two to massively speed up an SQLalchemy + Pandas based ETL in the past as well.

Thanks for sharing connector-x, I will also start to use it. I wonder if there are a list of tools like that. I know Ruff, Polars, pydantic-core.

I was trying https://github.com/sfu-db/connector-x and hacking around with this https://github.com/spitz-dan-l/postgres-binary-parser but it turned out that a COPY to csv using asyncpg and then converting to parquet was the fastest.

if you store the OHLC data in a relational database, use connector-x to load the data into pandas dataframe

For SQL reading I'd really recommend connector-x, they do a great job preventing unneeded serialization and don't have to go through python.

Indeed, currently we do not support persistent connections among different queries. We target more on the bulk loading scenario where the bottleneck is caused by the data size and the connection construction overhead is negligible. However, one possible solution to the problem is to expose our connection pool object that we use inside Rust to users, so the next call could reuse the same pool. We do not plan for this yet, but happy to see whether this is a common need! Feel free to open an issue in our github repo: https://github.com/sfu-db/connector-x

Feel free to ask any questions here or open an issue in our github repo: https://github.com/sfu-db/connector-x . You can also join our discord community: https://discord.com/invite/xwbkFNk and ask question under connector channel!