Task: Create a model to classify and identify asset prices rising and lowering utilizing Bollinger bands and Moving Average convergence divergence
ML Model: Decision Tree: Sci-Learn library Graphics via Matplotlib
Input and Target: Input: BB and MACD Output: Bullish or Bearish
Data Sampling Obtain data samples via quantconnect API add to dictionary and use Pandas to format into DataFrames Training Data: Data{ “PriceChange”: [] “Bollinger Band”:[] “MACD”:[] } Use PriceChange to test if bullish or bearish Real Data (model will be filled with live data): Data{ “Bollinger Band”:[] “MACD”:[] } Data: all data will be defined by pre-established periods (i.e. month, day, hour, minute, tick) Price Change-Price change within defined period of time Bollinger Band- STDEV (distribution value) MACD- First degree derivative of slope of Moving Average Convergence Divergence
Data Periods: Start with daily PriceChange BB and MACD obtained at noon (can tweak times later) And transition to hourly to every minute (as long as logical correlations exist and there’s accuracy)
Project targets 50+% Model accuracy (enough accuracy to establish correspondence to have a gaining purchasing effect)