Description
There are three main forms of securities trading: day trading, swing trading and long-term investment. I wanted to compare long term investment with swing trading for the same stock. For example, I believe in the product or service of some company XYZ and it shows stable growth with good potential to grow further. My question: can I get potentially higher return on investment(ROI) when I switch from long investment to swing trading, by buying low and selling high and repeat it multiple times for stock XYZ for long period. Also, will my risk change, with increasing reward?
Modeling & Results
To predict when to enter into the position, I used risk scorecard model, which based on the logistic regression. To get correct exit point, I applied trailing stop-loss. Due to the nature of the target, model picked up some local minima with order of less than 10 days, which created more false positives than I expected. However, with correct implementation of stop-loss, I minimized losses at real false positives entries.
I observed, that choosing stocks without prior screening still could be profitable, but cannot beat the long term investment return.
However, individual models were able to beat long term investment. Model works better in conjunction with balanced trading strategy.
On the example of S&P 500 index, I did backtest from 05-2017 till 05-2020. I got ROI of 1.5 using ML model compare to ROI of 1.22 when just doing long investment.
Conclusion
It is possible to get higher ROI using ML model, than just staying in the long position. Risks of loss could be controlled by proper strategy.
Future work
I would like to select initially high potential portfolio to feed my model and optimize exit strategy using resistance/support lines individually per each stock. As well, I want to explore how to get better ROI than popular trading algorithms.
Link to github repo.
Note: This is a concept check project, which I did as part of my educational goal to learn time-series in data science. This blog is not a financial advise and I am not a broker or professional investor. Stock investing is risky and can lead to loosing money. Please consult a licensed stock advisor before investing.
Top comments (5)
Your analysis highlights some fascinating aspects of combining machine learning with stock trading strategies! Comparing long-term investment to swing trading using ML models is a great way to explore how predictive analytics can impact ROI and risk management.
Your use of logistic regression for entry points and trailing stop-loss for exits is particularly insightful. It’s impressive that individual models outperformed long-term investments when combined with a balanced trading strategy. However, as your results suggest, stock screening is essential to enhance profitability and manage risks effectively.
If you're interested in deepening your understanding of trading strategies and leveraging machine learning for better financial outcomes, you might find learn.binany.com helpful. It's a great resource for exploring innovative techniques and expanding your knowledge in the trading domain.
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That's a fascinating question regarding the comparison between long-term investment and swing trading. When looking at stocks like XYZ, it’s important to note that swing trading offers the potential for quicker returns as you buy low and sell high multiple times over a set period. However, it can come with increased risk due to the frequency of trades. Tools like risk scorecards and stop-losses can certainly help mitigate these risks. You might also want to balance these strategies with your overall risk tolerance. For those interested in other forms of active trading, Pocket Option might be worth exploring. You can check out a detailed review of the platform at this link httрs://revieweek.com/review/pocke... which provides insights into the benefits of using it for binary options trading. In essence, swing trading can complement long-term investments, but finding the right tools is key to managing the potential risks and rewards—much like in any trading environment, including Pocket Option.
Congratulations Abzal on you final project and completing the program!
👍