In the late 1970s, when I discovered the amazing power and incredible accuracy of seasonality in the stock and futures markets, my life and my future as a trader changed forever and for the better! For over 30 years now the predictive ability of seasonality in stocks, futures, and Forex has amazed me time and time again. The wealth of information that can be obtained and used by traders using seasonality is phenomenal. Seasonals give us the winning edge!

No, seasonals are not perfect. Nothing is!

The best news about seasonality is that the rules used in finding, researching and implementing seasonal trades are 100% objective and never subject to interpretation.

Harnessing the power of Machine Learning
and Scientific Optimization

Even more amazing than seasonality is the tremendous ability of computers to analyze and learn from their "mistakes and failures." Over 15 years ago I began working with a group of software engineers and traders on the idea and application of artificial intelligence for trading. The road to our current state of knowledge in this unique combination of market generated information has been long and difficult, but finally extremely productive! Massive computer power is now available to all traders at exceptionally low prices.

Now we can integrate the learning abilities of machine intelligence and predictive analytics with the statistical reliability of seasonals to further improve their performance.

Using the Right Algorithms

But in order to effectively use this awesome combination, we need to feed computers the right information and combine it with the right instructions and codes. As I'm sure you know, computers have the ability to follow a set of algorithms (rules) perfectly. As human beings, however, we find it very difficult, if not impossible, to follow through on the rules consistently. Of course, if we ask the computer to employ algorithms that are not productive or not applicable then we will end up with the proverbial "garbage in, garbage out." After over 30 years experience with seasonals, I am very clear on the algorithms that enhance seasonal trades.

What my Computer Based Seasonal Research Shows

Although seasonals are tremendous, they are not 100% accurate. Nothing is. However, by feeding the computer all the rules, nuances and correlations that are the ingredients to successful seasonal trade discovery, selection, optimization, and implementation, we can elevate to a higher level of performance. We begin with a highly accurate pattern and we make it even better!

Here's the bottom line: using the learning ability and algorithmic ability of computers combined with the already high probability of seasonal patterns, I believe we can markedly improve the performance of seasonals.

INTRODUCING THE
JAKE BERNSTEIN
SEASONAL TRADERS BIBLE AI 2018
(That's right! trades for 2018 BEFORE they happen!)

For the first time ever I'm combining the exceptional accuracy of seasonality with the learning ability of computer-based models and trading research based on seasonality, using "knowledge" obtained from the behavior of previous seasonal tendencies of the same type. To put it simply, we are applying computer learning models to seasonal trades to determine, based on previous "experience" with those specific trades, whether they are likely to be profitable on the next occurrence.

Factors and Inputs

Long before a seasonal pattern occurs, a variety of predictive variables give advance notice of their probability. These variables include, but are not limited to, the following factors, ALL of which are part of the learning process used to find the best of the best seasonals - long-term cycles, intermediate-term cycles, commercials net positions, intermediate term trends, the 3-4 year business cycle, price ratios, long-term spread relationships, cross correlations, underlying currency trends and more.

Here's an Example

The seasonal trade shown below (generated by seasonaltrader.com) indicates a good but not outstanding history. Although it was bottom-line profitable, it had some periods of significant drawdown. Still, (without using predictive analytics) the trade was better than what most traders can achieve. However, by applying algorithmic learning models to the trade using external data not directly related to seasonals we were able to improve the historical result.

When selecting seasonal trades from seasonaltrader.com in S&P futures for the month of July, the following list came up as possibilities. As you can see the statistics are complete, showing entry date, exit date, stop loss and history. Seasonaltrader.com is programmed to put its idea of the "best trade" in the first position. In this case, however, rather than select the trade I have identified as A, my machine-based algorithms picked the trade marked B as the best choice this year based on the learning model I developed. Look at the chart to see what actually happened and compare trades A and B.

As you can see trade B got in sooner and got out later. Why? Because the algorithms indicated that trade B would likely be the better choice this year. Even though the % odds for trade B were LOWER, the model wisely picked B as best. No, it won't always be this good, but I believe it's much better than the old style.

Two versions: Stocks and Futures

There will be 2 volumes of SEASONAL TRADERS BIBLE AI 2018. The first volume of this ground-breaking report will be published on or about November 15, 2017. It will contain trades for January 2018 through June 2018. The 2nd volume will be emailed to you on or about 15 May 2018 containing trades for June 2018 through December 2018. Format will be an eBook complete with instructions and explanations. The stock version will contain 10-15 algorithmically selected stocks for each month and the futures version will contain 3-6 futures picks for each month based on the learning criteria. EACH TRADE WILL SHOW ALL STATISTICS, entry date, stop loss, target, and best computer choice selected with my new model. You can subscribe to one or both reports (discounted if you take both reports).

A New Era

Eventually most technical analysis in the capital markets will give way to computer-generated learning models. The process will take time. A leap of faith is required to trust computers in the decision-making process. However, it will come. Let's see what artificial intelligence and computer learning can do to help us make better trades. NO, this new and innovative research isn’t cheap. Great research costs money.

THE 25% DISCOUNT PRICES EXPIRE 25 NOVEMBER 2017

PS If you have any questions email me at jake@trade-futures.com

PPS At this time I'm not ready to reveal the proprietary model that I am using for this program, however, I have complete confidence that it will bring significant improvement over the already powerful seasonal strategies that I have developed.

Best of trading,

JAKE BERNSTEIN