Algorithmic trading strategy for long/short asset optimisation- with specific risk and return parameters.


Proprietary quantitative model specified by the investor profile, utilising deep learning and Q reinforcement learning agents. The model is employing: recurrent neural networks, sentiment analyzer, convolutional neural systems, multivariate+logistic regression, lasso+ridge regression, linear+quadratic discriminant analysis, decision trees, K neighbors, Naive Bayes, random forest, support vector machine, Adaptiveboost, GradientBoost, XGB, and portfolio optimization. Trading Bot optimises the mix of technologies used to maximise return and minimise volatility for various investor risk profiles- in an attempt to predict specific asset class forecasted prices through forex, equities, digital assets, bonds, futures, and Equity Traded Funds.


Long+short intraday algorithm utilising the Broker API and IB_Insync python library.



  • 3 Variations
  • 4 Scenarios
  • 5 Graphs
  • Model Documentation
  • Model Validation+Testing
  • Data Source Connectivity

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