/smart-recommendation-feature

Smart Recommendation Feature ~ API based build with Machine Learning, Moving Average, Mirror Moving Average and VWAP

Primary LanguagePython

Smart Recommendation Feature

Smart recommendation to generate setting when user create robot in Robot Trading Dollar Cost-Averaging (DCA) (such as AiGate or RoyalQ or Ninebot) or it call Compounding Strategy based on Machine Learning (Polynomial Regression), Moving Average, Mirror Moving Average and VWAP

Source Data : Binance (https://api.binance.com/api/v3/klines?symbol=BTCUSDT&interval=1h)

Build in Python with Libraries:

  1. FastAPI
  2. pandas
  3. plotly
  4. numpy

Input

  • Market Symbol (ex: BTCUSDT)
  • Timeframe (ex: 1h)
  • Price (ex: 65230)

Output

  • Market Trend (Bearish or Bullish) from MA
  • Entry Price from MA
  • Take Profit from MA
  • Earning Callback from MA
  • Buy Back from MA
  • Buy In Callback from MA
  • Status (Recomended or Not Recomended) from ML
  • Image Represent Data

Sample Output

Hope this code can implement in AiGate or RoyalQ or Ninebot ecosystem