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What I Learned From Studying & Creating Stock Market Algorithms
Sometimes the Answer is Right in Front of You
When I started my journey on Quantitative Finance and Stock Market Algorithms, I had big dreams of cracking the code, and creating something that could functionally print money for me.
I thought that the amount of time that I put into learning and coding, would have a direct correlation to my chances of hitting the algo-lottery.
Now, after almost a year of revising, planning, and coding numerous algorithms, I seem to have found a winner, and a solution that seems easy, almost too easy to be true.
You see, when anyone working on a stock market algorithm wants to measure their returns, they compare their historical returns with those of the market.
The goal of all hedge funds, banks, and fund managers have always been the same; try and beat the market. A good algorithm is one which beats the market, while a bad algorithm is anything else. This criteria is what makes these algorithms so tough to develop.
The reason why this can be so hard is because the tools/indicators which form the heart of an algorithm are all based on historical data, and our next moves are all “predictions” at best.
