Over the last 6-7 months, I have created an algorithm that had an accuracy as high as 97 % over 80 stockmarket/commodity trades using CFD. During that time I managed to achieve 700% profit.
This was an amazing feat, and also a dangerous one. The accuracy rate was high such that the risks involved were masked.
These are highly leveraged (x10) stocks which means a fluctuation of 10% would mean that the stocks would hit stop-loss (SL) levels. Given the accuracy at that time, it was safe to say splitting the trades over 3 different volatile stocks would also provide adequate split, and x50 leverage would allow for larger profits. Unfortunately, all 3 stocks fluctuated too much, and stop losses were hit despite the predictions being correct. The issue was the volatility, not the predictions.
The project was still profitable overall (70-80% return), but I have decided to scrap the algorithm and rewrite the code altogether.
Take home messages for version 1:
- First I would create artificial fail data by using a virtual portfolio.
- I would then add the data of failed trades to the pool of successful trades.
- It will be best to achieve 50 % successful and 50 % failed trades in the generated data.
- It is not the predictions that matter but the ability to survive the tides of volatility. That has to be taken into account but I am debating whether to use AI or common sense.
- Pure TA is not good enough. When stocks such as oil are manipulated by production amount, it makes other investable commodities such as cryptocurrencies much more favourable.
- To those who are following me, be ready for version 2. Meanwhile, I will be trading cautiously using x 10 leverage. Perhaps with the new profits, I may decide to spread risk.
- No software lasts forever. That is why the human touch exists.
Trade data is available publicly. The software is only available for internal use.