Support Vector Machine Classifier eMini S&P500 Futures Trading Strategy Template

Description

Python Support Vector Machine Classifier eMini S&P500 Futures Trading Strategy Machine Learning Template

With this SVM Trading Strategy Template the research possibilities are endless:

1. All of the Python code is transparent and customizable. ** Available for IMMEDIATE Download. **
2. This SVM 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.
3. 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.
4. Available immediately as a Python Jupyter Notebook download!
5. This Support Vector Machine TEMPLATE is for trading strategy research use only and under no circumstances should be used for actual trading.
6. This SVM Strategy TEMPLATE displays results using the Continuous eMini S&P500 Futures contract.
7. You can modify it to use any other contract — stocks, futures, bonds, etc.
8. Create your own Feature Set and add a Technical Analysis library to create your unique SVM model.
9. Fine tune the Support Vector Machine Classifier Hyperparameters (c, gamma, kernel) to create your unique SVM model.
10. Enter your own Begin and End Train Dates and your own Begin and End Test Dates.
11. 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.
12. You can set up a demo account at Interactive Brokers to access historical or real-time market data, if you want to.
13. If you do not have IB, you can modify it to use your own historical data from a .csv file.
14. Or modify it to connect to a market data feed of your choice from the web.
15. You do NOT need the IB API.
16. You can download ib_insync at https://ib-insync.readthedocs.io/index.html
17. This notebook is NOT connected to a Live Trading platform.
18. The Support Vector Machine Classifier uses the Sci-Kit Learn Machine Learning Python library distributed with Anaconda.
19. The Anaconda Python Distribution includes the standard libraries — pandas, numpy, datetime, time, matplotlib
20. THERE ARE NO REFUNDS OR RETURNS ON DOWNLOADABLE PRODUCTS.

I, Sheikh Pancham of panchamAI, legally own this product/code and all intellectual property because I wrote it, and am therefore authorized to sell it. No other party is authorized to sell or distribute this product/code.

Disclaimer:
Any downloads, videos, tutorials, programs and information contained herein are presented solely for educational, informational, research and/or entertainment use and under no circumstances should be used for actual trading. We at panchamAI are not Broker/Dealers, Financial Advisers or Registered Analysts. No offer or solicitation to buy or sell securities, securities derivatives, futures products or off-exchange foreign currency (forex) transactions of any kind, or any type of trading or investment advice, recommendation or strategy, is made, given or in any manner endorsed by panchamAI . You are fully responsible for any investment or trading decisions you make. Futures and options trading has a large potential risk. You must be aware of the risks and be willing to accept them in order to invest in the futures and options markets. No representation is being made that any account will or is likely to achieve profits or losses similar to those discussed in this product. Past performance, whether actual or indicated by historical tests of strategies, is no guarantee of future performance or success. No warranties of profitability are being made or given. There is a possibility that you may sustain a loss equal to or greater than your entire investment regardless of which asset class you trade (equities, options, futures or forex); therefore, you should not invest or risk money that you cannot afford to lose. Hypothetical or simulated performance results have certain inherent limitations. Unlike an actual performance record, simulated results do not represent actual trading. Also, since the trades have not actually been executed, the results may have under- or over-compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated trading programs in general are also subject to the fact that they are designed with the benefit of hindsight. No representation is being made that any account will or is likely to achieve profits or losses similar to those shown. Screen shots on this site are examples and are not meant to imply that any of the programs or tutorials available in this product will generate profitable trades. panchamAI trading strategies, signals, studies included in or attached to this tutorial or program description are examples only, and have been included solely for educational, informational, and entertainment use. The use of any such trading strategies, signals, studies, indicators, functions and techniques does not guarantee that you will make profits, increase profits, or minimize losses. panchamAI assumes no liability for any damages, direct or otherwise, resulting from the use of this information and/or program(s) described, and no warranty is made regarding its accuracy or completeness. Use of this product including the information, tutorials and/or programs included on it is at your own risk. You shall indemnify and hold panchamAI harmless from any and all liability, loss, trading losses, costs, damage, or expense, including attorneys’ fees you or third party may suffer. This product contains or may contain references and links to other companies and/or their Web sites, none of which is under the control of panchamAI.
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