Develop portfolio optimization approach

[lucasfbn]

Objective

The optimal portfolio strategy is defined by:

• The highest profit

with subject to the following constraints

• Balance must be >0 to execute a buy

Approaches

Supervised

Note that hold and sell signals do not have to be optimized as they are executed anyways

Idea No. 1

Somehow generate the best combination of buys and train a model based on these signals. Can use the feature extractor from stock-wise training (#131).

Problems

• Dataset can not be easily created when brute-forcing since the number of possible buy-combinations is 2**n_buy_signals.

Idea No. 2

Based on the backtracked rewards, generate a heuristic to create the dataset. E.g. always buy the first 3 signals or only buy signals that have backtracked rewards above a certain limit.

Problems

• Doesn't take balance into consideration
• Not really the optimum

Reinforcement Learning

Idea

Generate an env that processes multiple stocks in parallel (one giant vector). E.g. don't create an optimal dataset directly.

Problems

• No variable amount of stocks is possible as we have to fix the number of stocks per day
• Can't feed the raw state, will need to pre-process it prior to feeding into rl agent

Advantages

• Could even optimize amount of stocks bought

Use scipy.optimize

Idea

Use scipy.optimize to find optimal values.

Problems

• Have no clue how this works
• No clue about the runtime
• Optimum guaranteed?

[lucasfbn]

Closed by 1f117f8cef9a42f25c70b620fce38387d9ec0e57