For starters, I recently found stocktrends and I like it.
For my system, I need to continuously send the entire OHLC DataFrame to Renko() to continuously build the renko DataFrame. As the OHLC grows, the execution time increases. For example, it takes 2.5 seconds to process 8200 rows of OHLC data. In a medium frequency system, that can be too long. Do you have any suggestions on how to reduce the processing time? I'm thinking along the lines of incremental updates. However, I don't quite know how to implement that.
For starters, I recently found stocktrends and I like it.
For my system, I need to continuously send the entire OHLC DataFrame to Renko() to continuously build the renko DataFrame. As the OHLC grows, the execution time increases. For example, it takes 2.5 seconds to process 8200 rows of OHLC data. In a medium frequency system, that can be too long. Do you have any suggestions on how to reduce the processing time? I'm thinking along the lines of incremental updates. However, I don't quite know how to implement that.
Thanks.