Hi, Iām @penhsuanwang
Researching on streaming data techniques and relative ML application.
Designed architecture based on Kafka & Spark platform.
Iām interested in state-of-the-art ML model for incremental learning.
A simple project to do stock price analysis and train a LSTM model
License: MIT License
Hi, Iām @penhsuanwang
Researching on streaming data techniques and relative ML application.
Designed architecture based on Kafka & Spark platform.
Iām interested in state-of-the-art ML model for incremental learning.
The current implementation of data_fetcher.py
includes start_date
and end_date
in the constructors of BaseLoader
, YahooFinanceDataLoader
, and CsvLoader
classes. This design limits the flexibility of the module, as it requires the date range to be set at the time of object creation. The proposed refactor aims to enhance the flexibility by allowing the date range to be specified at the time of data retrieval.
Remove Date Initialization from BaseLoader
: The start_date
and end_date
initialization should be removed from the BaseLoader
class.
Update YahooFinanceDataLoader
and CsvLoader
Classes: These classes should no longer receive date parameters during initialization. Instead, the date parameters should be included in the methods responsible for data retrieval.
Modify Data Retrieval Methods: The methods in YahooFinanceDataLoader
and CsvLoader
for fetching data should be updated to accept start_date
and end_date
as parameters.
This is a significant change and might require careful consideration of backward compatibility and potential impacts on existing codebases using this module.
We need to enhance our existing stock market analysis project by integrating a module that can reliably identify and analyze various candlestick chart patterns. These patterns are crucial for understanding market sentiments and can greatly aid in making informed trading decisions.
The module should be capable of recognizing the following candlestick patterns:
These patterns often indicate a potential reversal from a downtrend to an upward trend:
These patterns typically suggest a reversal from an uptrend to a downward trend:
Patterns indicating market rest or indecision:
The objective of this module is to provide clear and accurate identification of these patterns to enhance our market trend analysis and trading strategy formulation.
This feature will be a significant step towards making our tool more robust for technical analysis. It's essential that the implementation is not only accurate but also efficient to maintain the overall performance of the system.
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