Securities Analysis Based AutoTrading

.env

API_KEY - API key from https://site.financialmodelingprep.com/developer/docs/dashboard
DEBUG - Debug printing
MarketRate - market rate of return
RiskFreeRate - risk free rate
DefaultEffectiveTaxRate - effective tax rate to use if one isn't reported

Gather up the fundamentals of a ton of companies (at least 1000)
Perform and document as many securities analysis functions as possible against the financial statements
Give each result a weight from -1.0 to 1.0, the final value will be result*weight
Organize each company in order by some aggregate value func(calculations*weights) and pick the top 10 companies to backtest
Identify the 'best' weights using a (genetic algorithm, simulated annealing, whatever) and multi-range backtesting
Add backtesting purchase/sale rules to make the algorithm more proactive / reactive
Add weights to each of the buy/sell indicators and repeat the algorithmic weight optimization process switching between optimizing weights for securities analysis using optimized buy/sell weights and weights for buy/sell signals with optimized security analysis weights
Optimization should be based on some function that works to maximize certain portfolio risk/reward values while minimizing others based on their definitions
Research news related to each company in the 10 company portfolio using biztoc and chatgpt
Perform sentiment analysis on news reports and compare it to sentiment provided by chatgpt
give sentiment analysis and chatgpt analysis points weights between -1.0 and 1.0
Perform algorithmic optimization on the weights, this part could require a ton of time

gather company fundamentals and calculations
create a weight system from -1.0 to 1.0 for each fundamentals item
- TODO: fix the full financial document and employee count retrievers and add the full financial document type to the final results struct type / weight type for weighting
- TODO: finalize fundamentals calculations with post-calculation meta/performance calculations
calculate a normalized weighted value that represents every value in all of the fundamentals / calculations * their field weights
set up a backtesting utility for backtesting a buy/sell strategy across multiple timelines, tickers, and portfolios
set up some function to rate the backtesting performance
create a genetic algorithm to identify optimized fundamentals analysis weights
add more complex portfolio backtesting strategies