Resources
Python : https://www.w3schools.com/python/
Finance : Leagure on Invstr app to get started with paper trading
Reinforcement Learning :
(MDPs) :
Part2 : https://towardsdatascience.com/reinforcement-learning-markov-decision-process-part-2-96837c936ec3
Part3(Policy Iteration and Value Iteration) : https://towardsdatascience.com/reinforcement-learning-solving-mdps-using-dynamic-programming-part-3-b53d32341540
Q-Learning :
Grid world problem : https://towardsdatascience.com/implement-grid-world-with-q-learning-51151747b455
Tic-Tac-Toe : https://towardsdatascience.com/how-to-play-tic-tac-toe-using-reinforcement-learning-9604130e56f6
--------- Complete upto here, and then we'll resume after endsems/midsems---------
Quant Trading :
Intro to financial python : https://www.quantconnect.com/tutorials/tutorial-series/introduction-to-financial-python
For coding and backtesting strategies : Blueshift(https://blueshift.quantinsti.com/) or Quantconnect(https://www.quantconnect.com)
Regression based Strategies : https://www.quantconnect.com/tutorials/strategy-library (First read up about mean reversion, momentum, pair trading and then others)
Sample blueshift strategies : https://github.com/QuantInsti/blueshift-demo-strategies/tree/master/equities (Check indicators.py and utils.py from https://github.com/QuantInsti/blueshift-demo-strategies/tree/master/library)