Like many ML novices, I find it fascinating that it is possible to create a model that can make winning trades in any or all markets. Would you kindly explain why this is not possible or can be accomplished before devoting a significant amount of time to an unsuccessful endeavor?
In my experience, the signal to noise ratio is abysmal, especially when making trades solely based on time series data. I’ve experimented with various approaches using supervised and reinforcement learning, but the market’s constant fluctuations and high noise levels pose significant challenges. It’s incredibly difficult to avoid overfitting, even with large datasets. Perhaps the most promising approach would involve aggregating extensive news data from diverse sources and applying a BERT-like model. This approach demands more than just sentiment analysis; a nuanced understanding of the articles is crucial. However, achieving profitability remains highly uncertain. For instance, Renaissance Technologies, a leading quant hedge fund processing 30 TB of data daily, achieves a win rate of 50.25%, showcasing their advanced strategies beyond simple time series RNNs.
Hi, Aiden. According to my research, many traders have complained about the market’s rapid adjustments and excessive noise.
This is a notion that almost every amateur has, and those who actually pursue it will fail terribly and lose everything they have invested.
Essentially, it comes down to the reality that other traders exist. The markets are being deliberately manipulated; the patterns are not historical.
To obtain better data than what is readily available from various sources, a great deal of cutting-edge engineering work must be done, and it must all be done at a low level for speed. Have fun creating your Rust HTML parser and web scraper.
You’re not going to succeed in the long run, even with all that effort. Your six-month profits could be lost with just one poor trade. You might as well invest in an index fund if you diversify.
That being said, it’s a fantastic method to land a job at a hedge fund or prestigious bank and a project that will make everyone say, “Oh sh*t, that’s cool,” right away.
Basically, go there to have fun and don’t expect to make any money; approach it like a casino.
Creating a model that consistently makes winning trades in all markets is highly challenging due to several fundamental reasons. Financial markets are complex and influenced by numerous unpredictable factors, including economic events, market sentiment, and geopolitical developments. Additionally, markets are subject to high variability and noise, which makes it difficult for any model to achieve consistent success. Even sophisticated models and algorithms often struggle to outperform the market over the long term because they can’t always adapt to sudden changes or unforeseen events. Many models that have performed well in the past can experience a significant decline in performance when market conditions change. Furthermore, the efficient market hypothesis suggests that prices reflect all available information, making it hard to consistently gain an edge. As a result, while machine learning can assist in identifying patterns and trends, expecting a model to deliver guaranteed winning trades across all markets is unrealistic. It’s crucial to approach this endeavor with a realistic understanding of its limitations and the inherent risks involved.