Group Members :

• Forrest Surles
  • Taiwan Semiconductor Manufacturing Company : TSM
  • Qualcomm : QCOM
  • Vale : VALE
  • Advanced Micro Devices : AMD
• Vishwanath Subramanian
  • BHP : BHP
  • Rio Tinto : RIO
  • Freeport McMoran : FCX
• John Weldon
  • Intel Corp : INTC
  • Microsoft : MSFT
  • 3D Systems Corp : DDD
• Ashley Guidot
  • TSM : TSM
  • Nvidia : NVDA
  • Tesla : TSLA

FINAL PORTFOLIO : - TSM : Taiwan Semiconductor Manufacturing Company - QCOM : Qualcomm - AMAT : Applied Materials - AMD : Advanced Micro Devices - NVDA : Nvidia - DDD : 3D Systems Corp - FCX : Freeport McMoran - RIO : Rio Tinto - TSLA : Tesla - F : Ford Motor Company - VALE : Vale - BHP : BHP - INTC : Intel Corporation - MSFT : Microsoft - VLKAF : Volkswagen

END GOAL: - Compare two or more ML models for solving a predictive task

1. CLI App
2. Feed a Portfolio/ X amount of capital / What is the ideal weighting?
3. Calculate those weights/ trade on the selected equities
4. Train the ML models w/ different strategies to make the most $$$
5. Profit?

USER INPUTS: - Risk Tolerance Level: - Conservative - Moderately Conservative (cut for time if necessary) - Moderate - Moderately Aggressive (cut for time if necessary) - Aggressive - List of Potential Portfolio Options - How many to choose - Amount of capital

DEVELOPMENT: - Test the trading Strategy - Testing & Optimization for which to implement - DMAC (Dual Moving Average Crossover) - Bollinger Bands - CLI Interface - Data Collection - Alpaca? - Check if any tickers are not available - Backtesting framework - Train the models

ACTION ITEMS: Reconvene Wednesday (2021-09-29) - Ashley: - Implementing Alpaca API: - Check if ticker available - Returning historical data from search - How much data can we gain access to? - Forrest: - User Stories -- Outline the CLI App/project - John: - Implementing Risk Tolerance selection into Trading Strategy - Vish: - Structure the outline for backtesting and training ML models

Project Proposal:

Group Name:

	Group 1 - Intelligent Derivations Group

Project Title:

	The CAT Project (CLI Algorithmic Trading)

Project Description:

	Build a CLI app to optimize a user's potential portfolio
	using neural network models to maximize portfolio profit.

Project Objective:

	To create from start to finish a practical application
	to thoroughly test historic stock data, and predict future
	performance applying lessons learned throughout recent modules.

Research Questions to Answer:

	Our primary question is to determine the optimal combination
	and weightings of a user's input portfolio and capital
	while respecting their risk tolerance.

Enter links or describe datasets to be used:

	Our primary datasets will be the historic performance for the list
	or a subset list of stocks provided by the user obtained via
	API from services like Alpaca.

Rough Breakdown of tasks:

	Forrest - Develop user stories and documentation
	Vish    - Establish back-testing framework and outline deep learning models
	John    - Implement risk tolerance and strategy selection logic
	Ashley  - Create API connections and collect stock data