Someone may want to trade using daily stock data, for finding the daily trends in stocks. This opposes the view of longer term traders/ investors who may choose weekly, or monthly data for the work.
Indeed, the advice that goes around in the lobbies of traders/ investors is that it may be better for someone to be an investor rather than a trader and also use longer term data to catch the long term trends of the stocks. For a trader who tries to trade using daily data, the randomness of daily data starts to become a problem. There is a difference in the way of trading stocks for short- term traders and longer- term investors in that that usually the short- term data can move erratically up, or down, i.e. that in a short amount of time, fortunes can be made or lost due to the speed that the stock may be moving. On the other hand, longer- term investors are happy with catching the longer term trends which move less randomly.
First of all, in order to trade short- term data, i.e. daily prices a good model may be needed. A good model in this respect is 3 indicators used together Plus the addition on protective stops since we are dealing with short- term data and can move erratically up or down.
The first indicator can be an oscillator, or ROC. This is also called speed, or velocity oscillator. This indicator will need optimization using backtesting software to find the best fit for it according to the stock being traded and the older data of the stock.
The second indicator can be a momentum indicator. Momentum oscillator is any convergence/ divergence indicator. Again this can be optimized using backtesting software.
The third indicator can be a bands indicator, i.e. either percentage bands above/ below a moving average, or simply the use of Bollinger bands. This indicator again will need to be backtested to find the best optimization curve to fit the stock data.
And lastly, as I mentioned, a type of protective stops can be placed to exit the market when the data become too volatile, or to protect the gains when the gains are running quickly.
This is a good plan for trading short- term stock market data (daily values of stocks). The entire model can be backtested as well with a more sophisticated backtesting data that can backtest 3, or 4 indicators together.
Furthermore, the need to run backtests again will be more meaningful if done once every month for example, so that the new data of the stock are incorporated into the system, while the older historical data are discarded.
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[Because I dont get paid for writing in my blogs, I would request perhaps any readers who may find the topics discussed here as interesting, then they may want to contribute some money so I have available resources to write more topics. If you can submit a small payment for example to my paypal account email chrisk144@gmail.com then I can continue writing my blogs without delay, or hesitations because of my busy time/ schedule, or lack of resources.
Thank you very much]
Indeed, the advice that goes around in the lobbies of traders/ investors is that it may be better for someone to be an investor rather than a trader and also use longer term data to catch the long term trends of the stocks. For a trader who tries to trade using daily data, the randomness of daily data starts to become a problem. There is a difference in the way of trading stocks for short- term traders and longer- term investors in that that usually the short- term data can move erratically up, or down, i.e. that in a short amount of time, fortunes can be made or lost due to the speed that the stock may be moving. On the other hand, longer- term investors are happy with catching the longer term trends which move less randomly.
First of all, in order to trade short- term data, i.e. daily prices a good model may be needed. A good model in this respect is 3 indicators used together Plus the addition on protective stops since we are dealing with short- term data and can move erratically up or down.
The first indicator can be an oscillator, or ROC. This is also called speed, or velocity oscillator. This indicator will need optimization using backtesting software to find the best fit for it according to the stock being traded and the older data of the stock.
The second indicator can be a momentum indicator. Momentum oscillator is any convergence/ divergence indicator. Again this can be optimized using backtesting software.
The third indicator can be a bands indicator, i.e. either percentage bands above/ below a moving average, or simply the use of Bollinger bands. This indicator again will need to be backtested to find the best optimization curve to fit the stock data.
And lastly, as I mentioned, a type of protective stops can be placed to exit the market when the data become too volatile, or to protect the gains when the gains are running quickly.
This is a good plan for trading short- term stock market data (daily values of stocks). The entire model can be backtested as well with a more sophisticated backtesting data that can backtest 3, or 4 indicators together.
Furthermore, the need to run backtests again will be more meaningful if done once every month for example, so that the new data of the stock are incorporated into the system, while the older historical data are discarded.
---------
[Because I dont get paid for writing in my blogs, I would request perhaps any readers who may find the topics discussed here as interesting, then they may want to contribute some money so I have available resources to write more topics. If you can submit a small payment for example to my paypal account email chrisk144@gmail.com then I can continue writing my blogs without delay, or hesitations because of my busy time/ schedule, or lack of resources.
Thank you very much]
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