- Chart Pattern Image Recognition - Identify chart patterns using Convolutional Neural Networks
- Convolutional Neural Network 06-10-2022
- Build a chart pattern image recognition model and evaluate if chart patterns are useful to predict future prices in the market
- At this time the model is only able to classify the charts as bullish or bearish
- You should add images to the dataset if you wish to improve the model
- Live test added
- Live Classification added
- Candlestick chart added
- Importing pickle data
- Add layers
- Fit the model
- Test the model using your own Images
- Plot multiple charts
- Add more data to the dataset
- Add Automated Download dataset from google images
- Add more CATEGORIES such as Flags, Pennant, Cup and handle and so on.
- Add Auto Test Image from any Chart
- Plot Multiple Chart Symbols and Multiple Chart time frames
- Store prediction
- Store real market direction
- compare with different targets
- run loop to get best parameters and results
- Set Cross validation to be able to save parameters
- Set auto run
- Evaluate Results on multiple charts
- Save correct predictions and add it to training dataset (Auto Feed)
- Identify potential targets
- Add Break out as bullish with target
- Identify specific chart patterns such as breakouts
- Set Screener
- If there is any patterns useful it should identify and classify it
- Harrison Kinsley @Sentdex - Deep Learning with Python, TensorFlow, and Keras tutorial - https://pythonprogramming.net/introduction-deep-learning-python-tensorflow-keras/
- Shaan Shah - Identifying Candlestick Patterns using Deep Learning - https://towardsdatascience.com/identifying-candlestick-patterns-using-deep-learning-b7d706726874
- https://www.tensorflow.org/api_docs/python/tf/all_symbols
- https://keras.io/api/layers/