META: Implement working RL agent

[lucasfbn]

• Objectives

  • [ ] Realize working RL agent

• Steps

• Ideas

  • [x] #109
  • [x] #114
  • [ ] Since we don't scale the sentiment data daywise anymore we may need a feature that shows the price range of ticker
  • [x] #102
  • [ ] #117
  • [x] #104
  • [x] #86
  • [x] #76
  • [x] #110
  • [x] #116
  • [x] #111
  • [x] #112
  • [x] #113
  • [x] #95

• Paper read

  • Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy
  • PPO best overall result
  • Reward function is nothing fancy, they are using the change in the balance as reward
  • They trade several different stocks by combining them in a single matrices (but the number of stocks are fixed with this approach)
  • Implementation available
  • Market sentiment-aware deep reinforcement learning approach for stock portfolio allocation
  • Also use 30 stocks from DOW
  • Using DDPG as a base, but extending it
  • State is also: price, number of shares, balance, market sentiment for all 30 stocks
  • Action is continuous for each stock. They limit max buy/sells to 5 stocks per day. Positive value: buy, negative: sell, 0: hold, result is a vector of size 30. For instance: [0, 0, 3, ..., -5, 0] (maybe useful for later)
  • They use google new and twitter sentiment
  • Also using vader, total polarity score is average of all polarity scores
  • 300% over 3 years

• Paper to be read

  • Optimistic Bull or Pessimistic Bear: Adaptive Deep Reinforcement Learning for Stock Portfolio Allocation
  • Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization

[lucasfbn]

Closed by #119