----Workflow---
---------------

---New Ticker
   (1) Fetch historical data
      % Fetches exchange ticker data and save
      % in csv file
   FetchHistorical.py

---Training
   (1) Update ticker data in csv file
      UpdateHistorical.py
   (2) Load csv data in Matlab
      IngestCrypto.m
   (3) Normalize Price
      NormalizePrice.m
   (4) Calculate features (indicators)
      CalcIndicators.m
      GetIndsDtDdt.m
   (5) Calculate "Hotness"
      getOpenClose.m
   (6) Train Models
      BuildForest.m
      BuildcForest.m
      PredBuyIdx.m

---Optimize
   (1) Predictors 
      - calculate predictors with multiple paramter sets
      - get predictor importance
      - Recursive Feature elimination using Simulated profits as Performance indicator 
   (2) Optimize for trailing Stop Loss and Limit Orders threshold.
      - Profit?
      - Maximum drawdown?
   (3) Calculate risk VS % used funds
   (4) Simulate again with leverage and optimize for parameters.

---Trading
   (1) Check for new ticker Data
   (2) Run Model
   (3) Run Startegy
   (4) Fire signals through API
   (5) Save execution
   (6) Update plots to be updated online

---Notes
   - Training will require some optimization
   - Will need Validation and Test Set
   - Optimize on Validation and Evaluate on Test Set
   - Try rerunning a model that takes in running average + instant value + classification.