Neural Network Classifier eMini S&P 500 Futures Trading Strategy Template
$149
- With this NN Trading Strategy Template the research possibilities are endless.
- All of the Python code is transparent and customizable. ** IMMEDIATE Download **
- This NN Notebook is designed to run out-of-the-box on the Anaconda Python Distribution which includes the Sci-Kit Machine Learning library and Jupyter Notebook.
- FREE: 3 Years of historical Daily data (OHLCV) for the eMini S&P Futures.
- Modify it to use any other contract — stocks, futures, bonds, etc.
See Full Product Description and Disclaimer below.
Description
Python Neural Network Classifier eMini S&P 500 Futures Trading Strategy Machine Learning Template
With this NN Trading Strategy Template the research possibilities are endless:
- All of the Python code is transparent and customizable. ** Available for IMMEDIATE Download. **
- This NN Notebook is designed to run out-of-the-box on the Anaconda Python Distribution which includes the Sci-Kit Machine Learning library and Jupyter Notebook.
- You get FREE: 3 Years of historical Daily data (OHLCV) for the eMini S&P Futures. Or you can use your own historical data from a .csv file.
- Available immediately as a Python Jupyter Notebook download!
- This Neural Network TEMPLATE is for trading strategy research use only and under no circumstances should be used for actual trading.
- This NN Strategy TEMPLATE displays results using the Continuous eMini S&P 500 Futures contract.
- You can modify it to use any other contract — stocks, futures, bonds, etc.
- Create your own Feature Set and add a Technical Analysis library to create your unique NN model.
- Fine tune the Neural Network Classifier Hyperparameters (hidden layer sizes, maximum iterations) to create your unique NN model.
- Enter your own Begin and End Train Dates and your own Begin and End Test Dates.
- This Python Jupyter Notebook includes a pre-setup to use, if desired, ib_insync to connect to Interactive Brokers to access historical or real-time market data.
- You can set up a demo account at Interactive Brokers to access historical or real-time market data, if you want to.
- If you do not have IB, you can modify it to use your own historical data from a .csv file.
- Or modify it to connect to a market data feed of your choice from the web.
- You do NOT need the IB API.
- You can download ib_insync at https://ib-insync.readthedocs.io/index.html
- This notebook is NOT connected to a Live Trading platform.
- The Neural Network Classifier uses the Sci-Kit Learn Machine Learning Python library distributed with Anaconda.
- The Anaconda Python Distribution includes the standard libraries — pandas, numpy, datetime, time, matplotlib
- THERE ARE NO REFUNDS OR RETURNS ON DOWNLOADABLE PRODUCTS.
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