Here is the 2019 YTD performance of the major market indices (as of the close on 1/31/19):
Can the market correction can “officially” be declared over? It doesn’t really matter, because our alerts can help you stay profitable even if the market goes down.
In 2018 I have implemented multiple improvements for the stock alerting system: switched to a more reliable market data provider; developed, trained, and implemented more advanced Machine Learning methods for analyzing the data and generating more profitable alerts.
Last year also brought some challenges. In 2018 the number of subscribers became so high that it started to impact the speed of e-mail alerts delivery. It took some e-mail severs longer to process the messages. Some of the subscribers complained that the alerts arrive too late.
To overcome that challenge, I’ve decided to develop a mobile app, which will allow instant delivering of the alerts directly to my subscribers’ smart phones. The Android app is being tested now and will go-live in the end of Q1. The iOS app will follow.
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Supply and Demand are the key concepts of a market economy. Since market economy is based on exchange of goods and services for a value, for it to function there must be some goods and services to offer (supply) and people who are willing and able buy them (demand).
There is an important difference between classic Supply and Demand theory and Supply and Demand that applies to trading. In classic approach suppliers generally stay as suppliers in the process of exchange, however in trading we can’t identify certain participants as sellers or buyers. All participants in trading can be buyers or sellers at any point of time.
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Several subscribers and visitors have reached out to me asking to share the historical performance of the stock trading alerts which my system sends. The alerts indicate the entry points with a high probability of growth. Currently, the stock trading alerts system does not suggest specific target prices as well as stop losses. Due to that, it would have been misleading to post here a performance report of some hypothetical stock trading strategy. The system just sends the alert. You decide whether to trade or not and where to put your stops.
In this post, I’d like to share the charts for each alert between the beginning of January and end of September of 2017, so you’ll be able to see stock price behavior before and after the alert. I hope these charts will help you to analyze your stock trading approach and make it more profitable. If you need to do back testing of any particular trading strategy, please take a look at stockstrategytest.com. You can order backtesting or request customized alerts according to your own rules there.
We are constantly looking for opportunities to improve the day trading alerts we send, so our subscribers can make more profit. We keep accumulating all kinds of information about each trading opportunity identified. In this article, we’d like to share some results of our research based on statistics of last six months.
Our system identifies about twenty trading opportunities daily. However, in average, it sends just 2-3 trading alerts per day. The system analyses a number of market parameters, selects the opportunities with maximum probability of success, and sends instant unbiased trading alerts to all subscribers. Some of the parameters the system is tracking are: difference with the Simple Moving Average (SMA) and growth potential. Growth potential shows how the stock price grew since the demand zone was formed. Usually, we do not consider opportunities with growth potential below 0.5%. The table below shows a summary of hypothetic trading results over six months between March and August 2016. It demonstrates how number of winning and losing trades depends on the initial parameters of the trade – potential growth and difference with SMA (50 intervals). In this experiment, the gain target was +0.4% and stop loss -0.6%.
Using day trading alerts was very profitable in March. While S&P 500 was about 6.6% up, I was able to make more than 11%.
During past months, I’ve been asked several times to provide more guidance on how to use day trading alerts in the best way. Today I want to share the approach that brings consistent positive results. I don’t want to say this is the best practice. This a strategy that works for me so far.
Here are the rules I follow:
Here is the list of hypothetical trades in March with starting capital of $50,000. This table shows how much you could earn if you took each trade recommended by Stocks Buy Alerts. I’m sorry to post here a 172 line spreadsheet, but I think it’s a good way to show how the day trading alerts work and how you can apply them for your benefit. Scroll down to see the result!
My name is Alex and I use day trading alerts to grow my savings. I am not a professional trader. I have full time job not related to trading and can’t spend much time for trading. I found that with day trading alerts I can have moderate, but consistent gain spending (in average) not more than 1.5 hours per day for trading. I was able to make more than 30% in 2015 and I am on track to beat this number in 2016 regardless of the bearish start of the year. In fact, I found that with day trading alerts it is possible to make money even when markets go down..
The key to consistent growth is discipline. You need to be disciplined when your positions are growing, and you need to be particularly disciplined when you are losing. Yes, losses happen too, but the probability of a losing trade when you use day trading alerts is about 25%. It takes some time develop trading discipline and avoid holding a position more than you’ve planned.
In this blog I will share with you some of my trades (both good and bad) to help you understand how you can use day trading alerts. Meanwhile, if you still haven’t, go ahead and subscribe (while it’s still free). It’s simple and you will start receiving the alerts next day. You will be able to see that it’s really useful information before you start trading.
First, here is how the usual alert looks:
What does it mean? It menas that:
